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The role of disease risk and life history in the immune function of larks in differentenvironmentsHorrocks, Nicholas Piers Christopher

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The role of disease risk andlife history in the immune functionof larks in different environments

The research presented in this thesis was carried out at the Animal Ecology Group, Centre forEcological and Evolutionary Studies (CEES) at the University of Groningen, The Netherlands.The research was financially supported by a VENI grant (863.04.023) from the NetherlandsOrganisation for Scientific Research (NWO) and a Rosalind Franklin Fellowship from theUniversity of Groningen, both awarded to B.I. Tieleman.The printing of this thesis was partly funded by the University of Groningen and the Faculty ofMathematics and Natural Sciences.

Layout and figures: Dick VisserCover design*: Nicholas P.C. HorrocksArtwork: Nicholas P.C. HorrocksPhoto: Rob VoestenPrinted by: Drukkerij Van Denderen BV, Groningen

ISBN: 978-90-367-5315-9ISBN: 978-90-367-5316-6 (electronic version)

* Cover image modified from an unattributed photograph available athttp://www.berkshirefinearts.com/uploadedImages/articles/1084_Lark-Ascending178230.jpg

RIJKSUNIVERSITEIT GRONINGEN

The role of disease risk andlife history in the immune functionof larks in different environments

PROEFSCHRIFT

ter verkrijging van het doctoraat in deWiskunde en Natuurwetenschappenaan de Rijksuniversiteit Groningen

op gezag van de Rector Magnificus, dr. E. Sterken,in het openbaar te verdedigen op

vrijdag 24 februari 2012om 16.15 uur

door

Nicholas Piers Christopher Horrocks

geboren op 21 januari 1979te Londen, Verenigd Koninkrijk

Promotor: Prof. dr. B.I. Tieleman

Copromotor: Dr. K.D. Matson

Beoordelingscommissie: Dr. A.L. GrahamProf. dr. H. RichnerProf. dr. J.D. van Elsas

Part I Introduction

Chapter 1 Introduction and synthesis 9

Chapter 2 Pathogen pressure puts immune defence into perspective 17

Part II Immune defence along a gradient of predicted disease risk

Chapter 3 Environmental disease risk proxies explain variation in 37immune investment better than pace-of-life indices

Chapter 4 Antimicrobial proteins in avian eggs: ovotransferrin increases 53but lysozyme decreases with environmental correlates oftrans-shell infection

Part III Environmental and seasonal variation in immune defenceand disease risk

Chapter 5 Immune defences are associated with microbial pressure 69rather than life history in larks from contrasting environments

Chapter 6 Seasonal patterns in immune indices reflect microbial loads 89on birds but not microbes in the wider environment

Part IV A contribution to the ecologists’ immunological toolbox

Chapter 7 A simple assay for measurement of ovotransferrin – a marker 109of inflammation and infection in birds

References 127

Nederlandse samenvatting (Dutch summary) 143

Acknowledgements 153

Addresses of co-authors 158

List of publications 159

Contents

Introduction

IPART

General introduction

1CHAPTER

Nicholas P.C. Horrocks

PrefaceThis thesis investigates the role of disease risk and life history in shaping theimmune function of larks (Alaudidae) in different environments. I use data col-lected from birds living in the mountains of Afghanistan, in the deserts of SaudiArabia, in remnant plateau grasslands in Kenya, and in a national park in theNetherlands. Birds in all these environments must overcome the universal chal-lenges associated with survival and reproduction. Different species have evolveddifferent means – that is, different life histories – to accomplish this. One essen-tial component of self-maintenance and survival is defence against the diverseand unrelenting threats of infection and disease to which all species are continu-ally exposed. It is primarily for this purpose that the immune system hasevolved. Within and among species, the form and function of the immune sys-tem varies, which might be due to different life histories or the disease land-scapes that different species inhabit. Investigating how these two possibilitiescontribute to immunological variation is a key aim of this thesis.

Ecological Immunology

All animals possess some form of immune system. This is the set of anatomical,chemical and physiological defences that together protect an animal from the for-eign organisms and substances, including its own abnormal cells, that might do itharm. This highly complex, multi-layered, and often redundant system is essen-tial to life and offers many benefits, including minimising the negative impacts onfitness exerted by infections and disease (Brown, Brown and Rannala 1995; Fitze,Tschirren and Richner 2004). However, immune systems are costly in terms ofthe energy and time required by their development, maintenance and usage(Schmid-Hempel and Ebert 2003; Klasing 2004) and because of the ‘collateraldamage’ to the body’s own healthy cells that may result from some immuneresponses (Råberg et al. 1998). This means that a maximal immune response isnot necessarily an optimal one (Viney, Riley and Buchanan 2005). Depending onthe levels of resource availability and disease threat, animals may respond differ-ently in terms of the type and the extent of immune response (Sheldon and Ver-hulst 1996). Understanding and explaining this type of immunological variationwithin and among species are central goals of ecological immunology, which usesimmunological measures to test ecological and evolutionary hypotheses (Sheldonand Verhulst 1996; Sadd and Schmid-Hempel 2009). Studies in ecologicalimmunology are typically conducted in non-domesticated animals, often in free-living conditions, rather than in traditional model species maintained under con-trolled, laboratory settings. Understanding the causes of immunological variation,rather than the particular molecular mechanisms, is the goal.

A question of costs and benefits

Since the outset, ecological immunology has used a cost-benefit framework as apowerful means to explaining why and how immune systems should vary. Oneapproach, taking a cost-based perspective, relies heavily on life history theory(Roff 1992; Stearns 1992) and considers immune function as a costly physiologi-cal trait of an individual that is involved in unavoidable trade-offs with otherphysiological processes such as growth or reproduction. As a trait that contributestowards self-maintenance, and hence survival and opportunities for future repro-duction, immune function is expected to have evolved alongside other life historytraits in order to maximise fitness. Ecological immunologists interpret trade-offsinvolving self-maintenance as one explanation for the variation in immune defenc-es observed among animals (Sheldon and Verhulst 1996; Norris and Evans 2000;Schmid-Hempel 2003). While this approach to explaining immune variation hasbeen productive, the conclusions may be incomplete. Indeed, many ecologicalimmunology studies fail to appreciate the benefits that immune defences convey.

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Thus, a second approach to understanding immunological variation considersthe benefits of the immune system. The main benefit, of course, is the protectionagainst infection by fitness-reducing foreign organisms such as parasites andpathogens (disease-causing agents). Given the costs associated with the immunesystem, immune defences are predicted to be higher or stronger when the risk ofinfection is greater (Lindström et al. 2004; Tschirren and Richner 2006; Horrocks,Matson and Tieleman 2011). High-risk environments might include the tropics(e.g. Møller 1998; Guernier, Hochberg and Guegan 2004) and freshwater (e.g.Piersma 1997; Mendes et al. 2005). A high degree of sociality, large group sizesor aggressive interactions between individuals are examples of behaviours thatcould increase the risk of disease transmission and infection (e.g. Semple,Cowlishaw and Bennett 2002; Snaith et al. 2008; Spottiswoode 2008). Despitethese examples, finding appropriate measures to assess the disease risk encoun-tered by an individual is not straightforward. For example, different species mayencounter different types of disease risks as well as different levels of exposure,and measures applicable to one environment might not be appropriate in anoth-er. Instead, researchers have often quantified immune defence levels and usedthese as justification for prior assumptions about how disease risk may vary.Alternatively, the abundance and diversity of parasites on a host are used asindices of disease risk. These measures are likely already confounded by currenthost defences, both behavioural (e.g. grooming, parasite avoidance) and immune(Moyer, Drown and Clayton 2002). In chapter 2, my co-authors and I addresssome of these issues head-on. We outline a framework within which immunefunction and the selective pressures exerted by pathogens can be considered andhighlight how a greater understanding of the threats posed by infectious agentscan help put variation in immune defences into perspective. We introduce theconcept of the immunobiome - all the living organisms that can live in or on ahost and with the potential to evolve in response to immune defences. Interac-tions between the immune system and the particular immunobiome of an animalshape its immune defences, both over evolutionary and ecological time-scales.We also tackle the practical problem of how to measure pathogens and broaderdisease risk in different environments, by suggesting molecular and other meth-ods that may be appropriate for use in ecological immunological studies.

A comparative approach using the lark family

Birds are often studied in ecology and ecological immunology because they aregenerally easy to observe and capture, and tend to occur in sufficient densitiesthat large numbers can be studied. Individual birds can be non-invasively markedwith colour rings, and life history variables such as clutch size can be relativelyeasily measured. In chapter 3 we first encounter the larks (Alaudidae), the family

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of birds that provide the study system used in this thesis. At first glance, larksmay appear an unusual choice of study species. These songbirds are relativelycryptic in appearance and conceal their nests on or close to the ground. Thesequalities might make larks less tractable to experimental manipulations than, forexample, box-nesting species. Nonetheless, the lark family provides an ideal studysystem for the comparative approach that I took in this thesis. The comparativeapproach is most powerful when comparing related species, avoiding complica-tions that arise from different evolutionary histories. All lark species eat similarfoods and behave in similar ways, meaning that comparisons among species arenot confounded by diet or behaviour. Despite these similarities and the universalpreference of larks for open grassland habitats, different species experience awide range of climatic and environmental conditions, ranging from hyper-arid tomesic and tropical. The behaviour, physiology and life history of several larkspecies living under these different conditions have already been intensively stud-ied (reviewed in Tieleman 2005). However, until now the immune systems with-in and among lark species have been poorly characterized. Since the diverseclimatic and environmental conditions experienced by larks are thought to reflectdifferences in terms of disease risk (i.e. hyper-arid habitats, low disease risk;mesic and tropical habitats, higher disease risk), larks provide a potent tool forstudying the ecology and evolution of immune function. Overall, the lark familyprovides a powerful study system where behavioural and physiological aspectsare well understood, where life histories and environmental disease risks amongspecies vary, and where understanding of the causes and consequences ofimmune system variation represents the next frontier.

Immune defence along a gradient of predicted disease risk

I use lark species to address a question that is central to both my thesis and toecological immunology: to what extent are environmental disease risk and lifehistory responsible for influencing immune investment and driving observed pat-terns of immunological variation? Both disease risk and life history have beenused previously to explain variation in immune responses among and withinspecies. However, the two often co-vary, making it difficult to determine whichfactor is more fundamentally associated with immune variation. Furthermore, incertain circumstances, predictions about immune investment based on thesefactors can diverge (chapter 2). In chapter 3, I relate multiple immune indices,measured in 23 populations of 12 lark species, to proxies of environmentaldisease risk and indices of life history. Unlike some comparative study systems inwhich predicted disease risk and life history are positively correlated, these poten-tial explanatory variables vary independently among lark populations. This inde-pendence provides an opportunity to disentangle their relative roles in shaping

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immune variation. I found that in larks, indices of innate immunity were stronglyand positively correlated with abiotic proxies of environmental disease risk. Incontrast, life history traits related to reproduction showed little relation to invest-ment in innate immunity.

I used the gradient of disease risk again in chapter 4 where, together with myco-authors, I explored variation in immune defence using similar analytical tech-niques, but from a slightly different perspective. I investigated patterns of theantimicrobial proteins ovotransferrin and lysozyme in the albumen of lark eggscollected along the gradient of disease risk. Eggs provide a simplified model ofthe immune system, characterised by fewer defence components and fewer infec-tion risks. Furthermore, comparing and contrasting the relationships between dis-ease risk and immune defences in birds and their eggs helps to identify theselective pressures that have shaped each type of defence. Declines in egg viabili-ty, microbial loads on eggshells, and trans-shell infection of eggs are higher inmore humid environments (Cook et al. 2003; Beissinger, Cook and Arendt 2005;Cook et al. 2005a; Cook et al. 2005b; Wang, Firestone and Beissinger 2011). Thisled us to predict that if egg antimicrobial defences have evolved to match the riskof microbial infection, then concentrations of antimicrobial proteins in eggsshould vary with environmental conditions. In that case, eggs from humid loca-tions should contain higher concentrations of ovotransferrin and lysozyme thaneggs from more arid environments. The results of chapter 4 show that ovotrans-ferrin concentrations matched our prediction but concentrations of lysozymeshowed opposite patterns and were highest in arid environments with low dis-ease risk. This raises interesting questions regarding the function of lysozyme inavian eggs as well as suggesting possible trade-offs between antimicrobial pro-teins in the albumen. The study also revealed that precipitation, one proxy ofenvironmental disease risk, had very little power to explain patterns of antimicro-bial variation, despite experimental evidence pointing to the importance of mois-ture for trans-shell infection (Cook et al. 2005a; Shawkey et al. 2009; D'Alba,Oborn and Shawkey 2010). Temperature was better than precipitation at predict-ing concentrations of antimicrobial proteins in eggs, but temperature was a poorpredictor of immune defences of larks along the disease risk gradient (chapter 3).This contrasting role of temperature perhaps reflects the ectothermic nature ofeggs and the endothermic capacities of birds.

Environmental and seasonal variation in immune defence anddisease risk

Both chapters 3 and 4 highlight the value of abiotic proxies for biotic variation indisease risk. Nonetheless, in part III of this thesis I followed up on a conclusionfrom chapter 2, that biotic measures of disease risk components are vital to

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understand more fully observed patterns of immunological variation. I intro-duced a novel air-sampling technique to quantify the abundance of microbes shedfrom birds and in ambient air. These methods provide both host-dependent andhost-independent measures of biotic disease risk. The de-coupling of hostdefences and potential host exposure represents a significant advancement in eco-logical immunology and provides a new avenue for future studies. We used theseair-sampling methods to assess environmental and bird-associated microbialassemblages in larks living in the Arabian Desert and in the temperate Nether-lands. We also measured indices of innate immunity and assessed these in light ofpredictions arising from disease risk and life history, which has been previouslywell studied. Supporting the results of chapter 3, environmental disease riskexplained more of the variation in immune defences than did life history. Temper-ate larks, which were exposed to higher concentrations of airborne microbes, andcarried denser microbial assemblages, also exhibited higher innate immuneindices than their desert-living counterparts did. In contrast, the life historyexplanation of immune variation, which predicted higher immune investment indesert-living larks, was not supported.

Variation in disease risk is observed between environments (chapter 5), butvariation in disease risk within an environment may also be a significant driver ofimmune variation. In chapter 6, I report that immune indices change seasonallyin larks living in the Arabian Desert. I also show that these changes are accompa-nied by parallel modulations in the microbial densities shed by birds and con-trasting modulations in the microbial concentrations in the wider environment.This study underscores the necessity of both host-dependent and host-independ-ent indices when quantifying disease risk and the results raise interesting ques-tions about the environmental scale at which animals respond immunologicallyto microbes.

A contribution to the ecologists’ immunological toolbox

Despite considerable progress in the last fifteen years, the desire of ecologicalimmunologists to study non-domesticated, free-living species is also their Achilles’heel. Reagents for non-model species are generally unavailable, and protocolssuitable for laboratory-controlled animals may be unsuitable for animals that areliving free. In the final part of this thesis, I contribute to the ecologists’ immuno-logical toolbox by validating a field-friendly assay for the measurement of ovo-transferrin (chapter 7). Ovotransferrin is an acute phase protein that increases inthe blood in response to inflammation and infection. The same antimicrobial pro-tein is also found in the albumen of avian eggs, as we report in chapter 4.

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Conclusion

In larks it is disease risk rather than life history that explains variation in invest-ment in innate immune defences (chapters 3 and 5). This result provides a count-er-point to earlier theoretical and empirical studies that emphasise theimportance of life history as an explanation for immunological variation (Sheldonand Verhulst 1996; Norris and Evans 2000; Lee 2006; Martin II, Hasselquist andWikelski 2006). This new insight has, in part, resulted from the development andapplication of a method for measuring host-dependent and host-independentaxes of disease risk (chapters 5 and 6). These direct measurement methodsrevealed among-individual and among-population differences in disease risk andhelped validate abiotic proxies for use when direct measurements are impossible.The direct measurement methods also revealed insights into and raised questionsabout the scale at which larks perceive threats and respond immunologically:data in this thesis suggest that this scale might be quite small (chapter 6). Abioticproxies for environmental disease risk were relied upon when assessing the rela-tive influence of disease risk and life history on immunological variation (chap-ters 3 and 4). Although these abiotic proxies are useful, well validated, logisticallyuncomplicated, and easy to understand, further application of direct measure-ment methodologies offers great promise for shaping our understanding of inter-actions between disease risk and immune investment. Molecular techniques inparticular have the potential to revolutionise the direct measurement ofpathogens and other immuno-reactive agents (chapter 2), particularly as theirapplication becomes easier and more practical in diverse field settings. In a simi-lar vein, new assays for measuring additional aspects of immunity (chapter 7)will allow for a more complete characterisation of the immune system. Since itsinception the field of ecological immunology has raised and addressed manythought-provoking questions about how and why the immune system is so vari-able in its structure and its responses. I hope that my thesis contributes to thaton-going process by answering some questions and posing many more.

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Pathogen pressure puts immune defenceinto perspective

2CHAPTER

AbstractThe extent to which organisms can protect themselves from disease depends onboth the immune defences they maintain and the pathogens they face. At thesame time, immune systems are shaped by the antigens they encounter, bothover ecological and evolutionary time. Ecological immunologists often recognisethese interactions, yet ecological immunology currently lacks major efforts tocharacterise the environmental, host-independent, antigenic pressures to whichall animals are exposed. Failure to quantify relevant diseases and pathogens instudies of ecological immunology leads to contradictory hypotheses. In contrast,including measures of environmental and host-derived commensals, pathogensand other immune-relevant organisms will strengthen the field of ecologicalimmunology. In this paper we examine how pathogens and other organismsshape immune defences and highlight why such information is essential for abetter understanding of the causes of variation in immune defences. We intro-duce the concept of ‘operative protection’ for understanding the role of immuno-logically-relevant organisms in shaping immune defence profiles, anddemonstrate how the evolutionary implications of immune function are bestunderstood in the context of the pressures that diseases and pathogens bring tobear on their hosts. We illustrate common mistakes in characterising theseimmune-selective pressures, and provide suggestions for the use of molecularand other methods for measuring immune-relevant organisms.

Nicholas P.C. Horrocks, Kevin D. Matson and B. Irene Tieleman

Integrative and Comparative Biology 51: 563-576 (2011)

Integrating immunology and ecology

The immune system bridges the divide between internal and external environ-ments, integrating an organism’s physiology and environment. In doing so, theimmune system acts as a barrier to infection and disease, identifying threats andcoordinating necessary responses. Despite its complexity, immunologists have elu-cidated many of the cellular processes and specific mechanisms that allow theimmune system to function. Yet our knowledge of how evolutionary pressuresshape immune systems is still incomplete.

Ecological immunology promotes the use of immunological measures to testecological and evolutionary hypotheses. The field arose from a desire to explainthe variation in immune function that is observed within and among individuals,populations and species, across environments and over time. Many factors influ-ence, and can generate variation in, immune responses: these include sex, nutri-tional status, social dominance, exercise, and seasonality, as well as trade-offs inresource allocation between the immune system and other physiological systemssuch as reproduction (Sadd and Schmid-Hempel 2009; Schulenburg et al. 2009).Over both ecological and evolutionary timescales however, the most enduringselective pressures on the immune system are the myriad challenges posed byeverything that immune systems encounter. Particularly important in terms ofevolution are interactions between the immune system and organisms with theability to live in, or on, a host and the potential to evolve in response to currentimmune defences. We refer to the specific suite of components that generatethese evolutionary and ecological selective forces on the immune system as the‘immunobiome’ and their ability to shape immune defences as ‘immunobioticpressure’ (Fig. 2.1). Understanding the interactions of the immune system withimmunobiomes is essential for helping to explain patterns of immunologicalvariation.

In light of immunological costs, animals should match their immune defences*to the threats that they face (Sheldon and Verhulst 1996; Tschirren and Richner2006). However, the nature of immunobiomes is poorly understood. For example,does their basic composition differ among environments? Which componentsmost strongly shape immune defences in which hosts? Understanding theseissues will be central to solving broader challenges in immunology such as theconsequences of emergent infectious diseases (Jones et al. 2008) or the conse-quences for health of altering commensal microbial communities (Blaser andFalkow 2009). We propose that to advance ecological immunology, measures ofthe immunobiome that are independent of immune indices should be developed

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* Any anatomical, chemical, physiological or behavioural barrier maintained by an animalthat inhibits or controls the establishment and reproduction of any element of the immuno-biome within or on the animal.

and incorporated into future studies. Much as data on availability of food arerequired for an understanding of diet selection (e.g. Belovsky 1981) and environ-mental temperature profiles are required for explaining heat balance (e.g. Tiele-man and Williams 2002), knowledge of immunobiomes and immune stimuli isessential when testing hypotheses in ecological immunology. Rather than beingrelegated to anonymous, yet highly relevant sources of variation, the diverse con-stituents of the immunobiome and the evolutionary pressures they exert must beseen as central to ecoimmunological studies (Bordes and Morand 2009; Sadd andSchmid-Hempel 2009; Graham et al. 2011; Pedersen and Babayan 2011).

Interactions with entire immunobiomes shape immune defences,but pathogens and commensals are particularly important

Animals live in diverse and variable environments and their immune systemsmust interact with and respond to equally diverse and variable immunobiomes.However, across immunobiomes, two categories of organisms that lie at all points

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'Immunobiome'

Pathogens

Commensals

Antigenic universe

Figure 2.1. A representation of the antigenic universe, which consists of all the possibleantigens that any immune system could ever encounter. This universe includes antigenic,immunogenic, inflammatory and toxic agents. Within the antigenic universe is theimmunobiome. The immunobiome contains all the living organisms that can live in or on ahost and with the potential to evolve in response to immune defences. The immunobiomedoes not include other immuno-reactive particles such as dust that cannot multiply. Twomajor components of the immunobiome, in terms of the evolution of the immune system,are commensals and pathogens. Since some commensals may be pathogenic under suitableconditions, these groups are not mutually exclusive. Immunobiome components that falloutside these two categories (light grey area) include environmental microbes such as‘pseudo-commensals’ (Rook 2009), which although regularly encountered by hosts, do notgain any benefit from their temporary association with a host, yet may still shape regulato-ry circuits of the immune system. Scaling of the different subsets is arbitrary.

along a continuum from benign, or even beneficial, to harmful, are expected tobe particularly important sources of immunobiotic pressure (Fig. 2.1). At thedetrimental end of this continuum are pathogens. These microparasites (viruses,bacteria, fungi and protists) and macroparasites (e.g. helminths, ticks, lice) canpotentially harm host tissues through their inherent ability to breach immunedefences that normally restrict other organisms. Pathogens seek to circumventhost immune defences and may disrupt normal immune processes (Tortorella etal. 2000; Finlay and McFadden 2006). This might include immunosuppression(Babu et al. 2006; Jackson et al. 2009) or shifting of the immune system towardsa specific mix of defences (Maizels and Yazdanbakhsh 2003).

Of equal importance to the evolution of the immune system, commensals nor-mally sit at the benign end of the pathogenicity spectrum. Benefiting from inti-mate associations with their host, commensals can also modulate immuneresponses and play an essential role in development of the immune system(Rakoff-Nahoum et al. 2004; Mazmanian et al. 2005; Rook 2009; Round andMazmanian 2009). Different commensal communities might offer distinct advan-tages or disadvantages in terms of immunomodulation (Jackson et al. 2009) andcolonization by pathogens (Stecher and Hardt 2008; Stecher et al. 2010). Undersome circumstances, organisms that normally behave commensally can becomepathogenic (sensu ‘amphibiosis’; Rosebury 1962). Commensals that escapeimmune controls by inappropriately breaching defensive barriers (e.g. the intes-tinal epithelium) may also become de facto pathogens (Blaser and Falkow 2009).Overall, interactions with the entire immunobiome, encompassing organismsassociated with the full range of the pathogenicity spectrum, shape both immunedefences and immunobiotic components themselves, and can have implicationsfor health and survival (Round and Mazmanian 2009).

Hosts might encounter immunobiomes that vary temporally (e.g. at differenttimes of the day, or across seasons) or spatially (e.g. small scale differences in useof habitat, or large-scale differences in biogeography). At ecological scalesimmunobiotic pressures mould individual responses and physiological condition.Immune function interacts with and is influenced by each antigen (pathogenic,commensal or otherwise) encountered over the lifetime of an individual, from inutero or in ovo (Boulinier and Staszewski 2008; Grindstaff 2008) to adulthood.Nonetheless, these relationships are ultimately governed by an immune systemthat has been shaped through evolution. At evolutionary scales, differences incomposition and function of immunobiomes and levels of exposure to them areexpected to lead to genetically-based changes in the organization of the immunesystem, with immunobiotic pressures shaping immuno-defensive architecture.

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Operative protection – balancing immune defences andimmunobiotic pressure

How can ecological immunologists best understand what immune indices repre-sent in terms of protection from infection and enhancement of fitness to an ani-mal in the wild? We advocate that levels of immune defence be consideredrelative to the immunobiotic pressures that are encountered by an organism,focusing on effectiveness of protection rather than upon magnitude of response(see also e.g. Viney, Riley and Buchanan 2005; Graham et al. 2011; Pedersen andBabayan 2011). We refer to this immunobiome-specific assessment of immunedefence as ‘operative protection’. Operative protection encompasses the fitness-enhancing protection against immunobiotic pressure (and immunopathology)afforded by the immune system, relative to the immunobiotic pressure underwhich an organism is placed (Fig. 2.2). Put more simply, operative protectiondescribes the goodness of fit between the immunobiotic pressure in a given envi-ronment and the immune defences of an animal in that environment. A mismatchbetween immunobiotic pressure and immune defences could result in three out-comes. Inappropriately low operative protection (i.e. immune defences inade-quate to match immunobiotic pressure) could lead to increased infection anddisease. Inappropriately high operative protection (i.e. immune defences exceed-ing the immunobiotic pressure of the current environment) could lead toimmunopathology and unnecessary expenditure of energetic and nutritionalresources on the immune system. This in turn might affect other physiologicalsystems (e.g. reproduction) that must compete with the immune system for allo-cation of resources. In both instances a reduction in fitness is expected. In somespecific instances, inappropriately high operative protection might indirectly cor-relate with increased fitness. For example, if an invasive species leaves behind theimmunobiome with which it co-evolved (Torchin et al. 2003), then that speciesmight encounter a less-threatening immunobiome that requires reduced invest-ment in immune defences. The positive effects on fitness of this enemy releasemight partially outweigh any negative fitness consequences associated withsuperfluous immunological investment. On balance, this excess investment stillhas the potential to reduce fitness (i.e. the gross fitness benefits of enemy releasemight be even greater than the net, realized fitness benefits). Therefore any mis-match in operative protection may be transient on an evolutionary timescale. Ingeneral however, immune responses that are evolutionarily advantageous in oneimmunobiome may not be advantageous in a different immunobiome. Operativeprotection might change, for example, over an annual cycle or among environ-ments, if immune defence or immunobiotic pressures also vary. Identifying andunderstanding such differences in operative protection would be an importantadvancement over simply identifying variation in immune indices. The magni-tude of an immune response need not relate directly to fitness, while knowledge

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Immunobiotic load Immune system

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Pathogen load

Immunobioticpressure

Immunobiome operativeprotection approach

Pathogenpressure

Single pathogenapproach

Physiologicalconditioning

Immunedefence

Immunobiome

Fitness / Life history

Physiologicalconditioning

Immunedefence

CostsCosts Costs?

Figure 2.2. Operative protection requires the consideration of immune defences in terms ofimmunobiotic pressures. Immune systems and immunobiomes are complex, and these mul-tivariate systems interact to shape immunobiotic load, fitness and life history. Immunobioticload (or pathogen load in studies of single pathogens or parasites) relates to intensity ofinfection after the deployment of immune defences. The broadly integrative and multidi-mensional nature of the ‘immunobiome operative protection approach’ (left side of figure)offers several advantages over a ‘single pathogen approach’ (right side of figure). Specificcomponents of the immunobiome may not be universal, making operative protection par-ticularly relevant in comparative studies. Furthermore, simultaneous measurement of multi-ple components of the immunobiome allows for the fact that the effects of immunobioticload (e.g. co-infections) on fitness may be interactive rather than simply additive. The costsinvolved in shaping fitness or life history trade-offs are also clearer when consideringimmunobiotic load than when considering the load of a single putative parasite orpathogen, which may impose negligible or even no fitness costs.

of changes in operative protection identifies when and where individuals, popula-tions, or species are most at risk from disease and infection.

Current hypotheses about pathogen pressure, a component ofimmunobiotic pressure, require additional data and more testing

Understanding operative protection requires insight both into immune defencesand immunobiotic pressure (Fig. 2.2). In terms of pressure, researchers have gen-erally focused their thinking on only pathogens and parasites, and not on othercomponents of an immunobiome such as commensals. We refer to this subset ofimmunobiotic pressure as ‘pathogen pressure’. Several hypotheses link differencesin pathogen pressure (e.g. abundance, diversity) to ecological or environmentalvariation (Table 2.1). These hypotheses have generally been tested using meas-ures of either host-associated pathogens (i.e. pathogen load) or immune defence(Table 2.1), but neither approach provides a complete and independent picture,or direct measures, of environmental pathogen pressure (Fig. 2.3).

i) Using pathogen load to test hypotheses about pathogen pressureIn one approach, pathogen load is used as an indicator of exposure to pathogensand hence as a proxy for pathogen pressure. Pathogen load is measured as theprevalence or intensity of infection by a single pathogen, or as parameters ofhost-associated pathogen guilds such as species richness (Table 2.1A). A greaterpathogen load is taken to indicate a stronger pathogen pressure. Yet pathogenload provides information about intensity of infection only after immunedefences and other behavioural counterstrategies have been deployed (Moyer,Drown and Clayton 2002). Intrinsic to these studies are assumptions thatimmune defence only involves eliminating or reducing pathogen load (i.e. resist-ance); processes that limit the damage caused by a given pathogen load withoutnecessarily reducing it (i.e. tolerance), are neglected (Råberg, Graham and Read2009). Resistance, tolerance, and pathogen pressure together dictate pathogenload. High pathogen loads could indicate high pathogen pressure, but also low(or compromised) immune defences, or tolerance to the measured entities (Fig.2.3). Pathogen load need not always equate to pathogen pressure.

ii) Using measures of immune function to test ideas about hypothesizedpathogen pressureIn another approach, differences in immunity among groups or locations areattributed to a priori assumptions regarding differences in pathogen pressure, butpathogen pressure is usually not directly measured (Table 2.1B). This approacharises from theoretical predictions that relate the inherent costs of immunedefences to the evolution of immune systems that optimally match pathogen

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pressure (Lochmiller and Deerenberg 2000; Bonneaud et al. 2003; Tschirren andRichner 2006). That is, when pathogen pressure is low, immune defences areexpected to be low, and vice versa. It is unclear over short timescales however, towhat extent assays of immune function reflect previous exposure to pathogens,current state of health or disease, or the degree of evolved protection (Matson2006; Bradley and Jackson 2008). Even immune assays that are seen as measur-ing fundamental attributes of individuals (i.e. are significantly repeatable;Buehler et al. 2008) exhibit large amounts of unexplained variation. Further com-plications can arise when immune measures are not clearly linked to knownaspects of the immunobiome. Even when identified, any such links are expectedto be highly specific and might not extend beyond the circumstances of a givenstudy system. Since later studies often continue to cite initial reports and poorlysubstantiated hypotheses as a basis for further predictions and new conclusions,the need for direct measures of immunobiotic pressure is real. Combined measures

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Table 2.1. Examples of studies testing predictions about pathogen pressure.

A) Studies that use pathogen load only to test hypotheses about pathogen pressureVariable examined Reference

Cooperative breeding Poiani 1992

Sociality and group size Snaith et al. 2008

Migration Figuerola and Green 2000

Saline vs. freshwater environments Piersma 1997; Figuerola 1999; Mendes et al. 2005

Latitude Rohde and Heap 1998; Guernier, Hochberg andGuegan 2004; Nunn et al. 2005; Salkeld, Trivedi and Schwarzkopf 2008

B) Studies that use immune measures only to test hypotheses about pathogen pressureVariable examined Reference

Diet Blount et al. 2003

Sexual promiscuity Nunn 2002; Nunn, Gittleman and Antonovics 2003

Cooperative breeding Spottiswoode 2008

Population size, group size or sociality Nunn 2002; Semple, Cowlishaw and Bennett 2002;Nunn, Gittleman and Antonovics 2003; Wilson et al.2003; Stow et al. 2007

Migration Møller and Erritzoe 1998

Life history strategy Nunn 2002

Substrate use Nunn 2002; Nunn, Gittleman and Antonovics 2003

Risk of injury Semple, Cowlishaw and Bennett 2002

Tropical vs. temperate environments Møller 1998

Continental vs. insular environments Matson 2006

Saline vs. freshwater environments Mendes et al. 2006

of pathogen load and immune defences may be more instructive about broad pat-terns of potential pathogen pressure than either measure alone. Nonetheless, con-clusions regarding pathogen pressure drawn from just these combined data muststill be regarded as incomplete (Fig. 2.3).

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(a)low low both predictions

correct

pathogenload

= 1 pathogen unit = 1 immune defence unit

pathogenpressure

pathogen load &immune defence

immunedefences

pathogen pressureprediction correct?

pathogen pressurepredictions based upon:

(b)low high only with

immune function

(c)high low only with

pathogen load

(d)high high both predictions

correct

Figure 2.3. Many studies investigating immunobiotic pressure actually focus just onpathogen pressure. However, pathogen pressure is rarely measured directly. Insteadresearchers rely on pathogen load or immune function (Table 2.1 in the main text), withoutactually calibrating these indices with host-independent measures of pathogens. On theirown, neither type of index consistently predicts pathogen pressure correctly. In the leftmostcolumn, the birds (which could equally symbolize non-avian taxa) represent four host indi-viduals, populations or species. In each case, pathogen loads (indicated by the number ofpathogens in each bird) and immune defences (indicated by the number of antibodies ineach bird) have been measured independently of each other. More antibodies equate tostronger or more fitness-enhancing immune defences; more pathogens equate to higherpathogen loads. Pathogen pressure is usually unknown in ecological immunology studies,but to illustrate our point we provide hypothetical values of pathogen pressure in the sec-ond column. More pathogens equate to greater environmental pathogen pressures. In thethird and fourth columns we list predictions about pathogen pressure based on typicalassumptions: higher pathogen loads indicate a greater pathogen pressure and strongerimmune defences also indicate a greater pathogen pressure. In the rightmost column thesepredictions are evaluated in light of the hypothetical pathogen pressures.

An example: Immunobiotic and immune-defence perspectives lead todivergent predictionsThe study of immune defence as a life history correlate is an active area ofresearch in ecological immunology (Tella, Scheuerlein and Ricklefs 2002; Tiele-man et al. 2005; Martin II, Hasselquist and Wikelski 2006). For the purpose ofthese studies, related organisms from distinct environments and with different lifehistory strategies are compared (Martin II et al. 2004). However, life historystrategies tend to co-vary with environmental conditions (e.g. Tieleman, Williamsand Visser 2004) and immunobiotic pressure might also co-vary with these condi-tions. As a result, all three factors (life history strategy, immunobiotic pressure,and abiotic environment) are potentially correlated and are difficult to disentan-gle. Neglecting to explicitly measure immunobiotic pressure ignores the possibili-ty that immunological variation among different environments may relate directlyto differences in immunobiotic pressure (Buehler, Piersma and Tieleman 2008)and only indirectly to disparate life history strategies. Experimental studies ofindividual animals could involve similarly confounded factors; manipulationsaimed at directly altering immune defence could indirectly change either compo-sition of the immunobiome or levels of exposure (Moyer, Drown and Clayton2002). For example, experimental inflammation which makes animals moresedentary (Adelman et al. 2010) might increase exposure to vector-borne diseasesthrough reduced anti-vector behaviours (e.g. less grooming). Similarly, experi-mental removal of immunobiotic components (e.g. specific pathogens) can alterimmune phenotypes and might affect infections by other pathogens or membersof immunobiomes (Ezenwa et al. 2010).

Using birds as an example, we illustrate one instance of confounded factors ina comparative study. Specifically, we show how simplistic predictions aboutimmune parameters differ depending on whether they are derived from the per-spective of life history strategy or immunobiotic pressure. Long-lived species gen-erally have long development periods, which allow for diverse repertoires ofantibody-producing B-lymphocytes (Lee et al. 2008). Over a lifetime long-livedspecies potentially encounter the same immunobiotic components repeatedly, soantibody-mediated acquired immunity and immunological memory may be espe-cially valuable (Boots and Bowers 2004). Long-lived birds include those inhabit-ing the tropics (Wiersma et al. 2007), open oceans (Ricklefs 1990), and deserts(Tieleman, Williams and Visser 2004). However, these three environments arepredicted to differ in terms of immunobiomes. Marine (Piersma 1997; Mendeset al. 2005) and xeric (Moyer, Drown and Clayton 2002; Valera et al. 2003)environments are hypothesized to be relatively pathogen-free, while wet tropicalenvironments might harbour abundant and diverse pathogens (Møller 1998;Guernier, Hochberg and Guegan 2004). From a life history perspective, these‘slow-living’ birds are all predicted to invest similarly in immunity and self main-tenance (Lee 2006). Yet from the view point of immunobiotic (and more specifi-

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cally, pathogen) pressure, tropical land birds are predicted to invest differently inimmune defence than do oceanic or desert birds. In fact, the extent to whichenvironments truly differ in either pathogen pressure or broader immunobioticpressure is not at all clear.

Measuring immunobiotic pressure: an immunobiome-wideapproach

Veterinarians, parasitologists, ecologists and immunologists all think differentlyabout the role of disease. Often, single pathogens or diseases are identified oranalyzed outside of the contexts of ecology and evolution. In some field studies,one or more key pathogens may have been identified, and the effects of theseinfections on fitness might be clear. For the majority of wild hosts however, thefitness-reducing effects of most individual pathogens or parasites (or the fitness-enhancing effects of most commensals) are poorly understood. Studying the costsof infection of single pathogens is informative and should not be abandoned, butto understand immunobiotic pressure and operative protection we advocate abroader strategy. Increasingly, more attention is being devoted to understandingmultiple infections, better reflecting the ecological context of individual animals,populations or species and their diseases (e.g. Jolles et al. 2008; Behnke et al.2009; Ezenwa et al. 2010). Earlier authors have touched upon the importance ofincorporating ecological measures (i.e. immunobiomes) into ecological studies ofimmunology (e.g. Sadd and Schmid-Hempel 2009; Schulenburg et al. 2009; Ped-ersen and Babayan 2011), but these ideas require further development. Theassessment of operative protection necessitates descriptions of immune defenceprofiles that are protective against all (or at least a diverse range of) encounteredpathogens and other immunobiotic components. Immunobiomes are central play-ers, and an ‘immunobiome approach’ must treat them as such. We envisageresearchers examining the diverse immunobiotic selective pressures that animalsencounter, by exploring the immunobiomes relevant to their study systems(Alcaide et al. 2010). Although a daunting task, smart use of relevant technolo-gies will make it feasible. Such an integrative effort is comparable to the HumanMicrobiome Project (HMP; http://nihroadmap.nih.gov/hmp), which focuses onidentifying all human-associated microbes and analyzing the role of thesemicrobes in health and disease. Similar to the HMP, we expect that the microbialcomponent of the immunobiome will provide a particularly rich and diverse land-scape to describe, since for wild animals it remains unexplored.

To start this daunting task, we suggest focusing first on microbial pathogensand microbial pathogen pressure, before expanding to the rest of the microbialimmunobiome (Appendix 2.1). A range of methods, including microbiologicaland metagenomic techniques will be required for initial surveys and description.

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Subsequently, the most relevant pathogens can be identified, and connections tospecific components of immunity can be established, allowing the identificationof protective immune phenotypes (Pedersen and Babayan 2011). Eventually,other subsets of the immunobiome can also be evaluated (e.g. commensals, mul-ticellular pathogens). Then, a more complete picture can be drawn of how theimmunobiome shapes immune defences, and conclusions regarding operativeprotection can be made (Fig. 2.2).

Description is innovationFrom some perspectives (e.g. that of a parasitologist), attempting to measure allthe organisms associated with a host might not appear particularly novel. Fromthe viewpoint of ecological immunology, however, assembling detailed descrip-tions of host-associated and habitat-associated immunobiomes is highly novel(Appendix 2.1). The first step in analyzing any community is to identify who ispresent and in what numbers, and without this essential foundation progress inecological immunology will be stunted. Furthermore, it is essential for under-standing the selective pressures that immunobiomes exert on the immune system.While it may not be possible to measure all relevant factors, simultaneous meas-urement of multiple components of the immunobiome will always be desirablebecause the effects of immunobiotic load (e.g. co-infections) on fitness may beinteractive rather than simply additive (Jolles et al. 2008; Behnke et al. 2009;Ezenwa et al. 2010). As such, description represents the first level of innovationin the immunobiome approach that we promote, and will be a major advance-ment. By freeing researchers from the constraints of microbial culture techniques,molecular-genetic approaches dramatically increase the number of components ofthe immunobiome that can be surveyed. At the same time, the battery of indicesavailable in comparative immunology continues to diversify. Integrating theseindices of immunity with the newfound understanding of immunobiotic pressurewill lead to a second level of innovation. Linking broadly defined immunobioticpressures to evolution of the immune system and to operative protection remainsan ultimate goal.

Collaboration is keyA generalist approach to quantifying immunobiotic pressure is not without chal-lenges, and requires careful consideration of sampling schemes and laboratorytechniques. For example, samples should be collected with the interactionsbetween host and immunobiome in mind. Outside surfaces of hosts and locationswhere external environment meets internal physiology, such as mucous mem-branes, should be targeted. Environmental substrates that hosts commonly con-tact, such as water sources and sleeping areas should be evaluated. The mostprobable routes of infection, such as ingestion with food, must also be considered.Ideally, potential pathogens found in the wider environment, including micropar-

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asites and macroparasites, will be linked to hosts through ecology and habitat.In terms of methodology, adapting procedures already used for routine moni-

toring of microbial communities in other applications, such as in wastewatermanagement, air-monitoring and soil science, will be fruitful (Appendix 2.1).Likewise, reagents or methodologies already established in model and commer-cially-exploited animals may be suitable for studies of wild and non-model ani-mals (Abolins et al. 2011). Early pioneers of ecological immunology realized thatif broad comparisons were to be made, then the complexity of immune systemsdictates that multiple immune components must be measured (Norris and Evans2000). Similarly, multiple axes (e.g. diversity and abundance of archaea, bacteria,fungi and viruses) will be required to encapsulate the complexity of microbialimmunobiotic pressure. Available molecular techniques allow this to be done(Appendix 2.1, Table A2.1). Individual pathogenic strains can be identified andknown markers or genes of pathogens can be targeted. Of course, a sequencingapproach alone does not guarantee that all pathogens and virulence-factors willbe recognized. Moreover, sequence data has a limited capacity to predict theimpact that a given immunobiotic element has on a particular host. Pathogenicitymay vary depending on hosts’ characteristics at the individual, population andspecies levels. Collaboration among parasitologists, microbiologists, veterinariansand other wildlife-disease experts will be essential for overcoming these issuesrelating to methodology and interpretation.

A next step will then be to classify components of newly described immuno-biomes according to their mode of action. This will more easily allow individualcomponents of immunobiotic pressure to be related to the particular classes ofimmune responses that they elicit. At some levels this will be obvious (e.g. extra-cellular parasites should elicit extracellular immune responses), but identifyingwhich specific components of the immunobiome elicit which specific responsesmay be less clear. On one hand, pairing immune challenges and immune respons-es in this way is a nod to traditional immunology. On the other hand, the compar-ative nature and ecosystem-wide scope of this ‘immunobiome approach’ firmlyplants this research outside the bounds of immunology and anchors it within ecol-ogy. Joining these opposing factors will allow for the development of more target-ed immune assays. Eventually, specific knowledge of immunobiotic pressures willdirect choice of immune assay. A clearer understanding of which parameters ofimmunity are most important for hosts when exposed to immunobiomes with dif-ferent compositions (e.g. more or less diversity) will ultimately emerge.

Future directions and broader impacts

From the standpoint of ecological immunology, the necessity for measuring ele-ments of the immunobiome is to gain a better handle on the selective pressures

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that shape the immune systems and define the immune-strategies of animals liv-ing in the wild. In a wider context however, these novel evolutionary and ecologi-cal perspectives on immune systems also have much to offer, both to ecologistsinterested in the immune system and to immunologists who wish to placeimmunology in the context of a more ‘real’ world. For example, studying the evo-lution of immunological defences may provide insight into the rise of autoim-mune diseases (i.e. inappropriately high operative protection) and allergies inindustrialized societies (Bach 2002), which in turn might point towards therapeu-tic treatments (Fallon and Alcami 2006). Comparing related organisms from envi-ronments with different immunobiomes (Alcaide et al. 2010) may shed light onthe effects of artificially reduced antigenic diversity and increased sanitation incontemporary human environments (Blaser and Falkow 2009). Global transportand climate change can have potentially major repercussions in terms of addition-al anthropogenic alterations to the immunobiomes of humans and other organ-isms, e.g. birds (Van Riper III et al. 1986) and corals (Rosenberg and Ben-Haim2002). The emergence of new diseases and the spread of current ones can affectsocieties globally. Wild animals can serve powerfully both as reservoirs and sen-tinels of these diseases (Daszak, Cunningham and Hyatt 2000), and these func-tions further highlight the value of comparative studies of immunology, ecology,and disease.

AcknowledgementsWe thank Deborah Buehler, Stéphanie Grizard, Arne Hegemann, Chris Horrocks, Robert

Mauck, Jeroen Reneerkens, Joana Falcão Salles, Theunis Piersma, Joost Tinbergen and

Maaike Versteegh who all contributed to the ideas in this manuscript. Two anonymous ref-

erees also made comments on the manuscript. We are grateful to Susannah French, Denise

Dearing and Gregory Demas for organizing the symposium ‘Bridging the Gap Between

Ecoimmunology and Disease Ecology’.

FundingK.D.M. and B.I.T. were supported by the Netherlands Organisation for Scientific Research

(NWO; VENI grants 863.08.026 and 863.04.023, respectively). B.I.T. was also supported by

a Rosalind Franklin Fellowship from the University of Groningen. Funding for the sympo-

sium was provided by the SICB (divisions: DEC, DEE).

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Appendix 2.1. Methods for measuring pathogens.

Direct molecular techniquesMolecular techniques have transformed microbial ecology, and many of thesetools lend themselves to the development of indices of microbial immunobioticpressure. For example, ‘finger-printing’ methods (sequence-based metagenomics)allow the richness of microbial assemblages to be assessed. These metagenomictechniques use genetic material recovered directly from environmental samplescontaining mixed populations, to study naturally occurring microbial assem-blages. Function-based metagenomic approaches can provide additional informa-tion, such as the presence of markers of pathogenicity. Table A2.1 in thisappendix lists molecular techniques for assessing microbial immunobiotic pres-sure (with a focus on pathogens), along with the specific types of questions forwhich different techniques are most appropriate.

Many techniques have been developed for assaying pathogens in wastewater,sewage, soil or clinical samples (Gilbride, Lee and Beaudette 2006; Malik et al.2008). These methods can be easily applied both to environmentally-derivedsamples and those collected directly from study animals, such as faeces or surfaceswabs (e.g. skin, mucosa). Both ambient and exhaled air contains diverse microbialcommunities that can also be sampled and analyzed (West et al. 2008; Muscatello,Gilkerson and Browning 2009). Microbial concentrations in ambient air have pre-viously been linked to levels of immunological responses (Huttunen et al. 2010).

Other techniquesIndirect assessments of pathogen pressure can be a helpful first step for revealingbroad patterns potentially related to immunobiotic pressure. For example, disease-transmitting vectors might be a useful proxy for some subsets of pathogen pressurein some systems. However, indirect measures that rely on this type of relationshiprequire detailed knowledge of study systems to appreciate their limitations(Keesing et al. 2009) and pitfalls (Fig. 2.3) and ideally always should be combinedwith direct approaches to validate and calibrate actual immunobiotic pressure.

In addition to indices of immune function in adults, defences by eggs andmaternally transferred antibodies are promising avenues of research for ecoim-munologists interested in pathogen pressures. Avian eggs harbour microbes ontheir shells and under certain conditions some of these microbes can penetratethe eggshell, infect the egg’s contents, and reduce viability (Cook et al. 2003).Chemical barriers protecting the embryo include three quantifiable antibacterialproteins (avidin, lysozyme and ovotransferrin) in the albumen (Shawkey et al.2008). Combined with indices of microbes on the surface of the shell, compar-isons between matched species or populations of hosts may reveal the relativepathogenicity associated with different environments or nesting strategies(Godard et al. 2007). This approach could also be applicable to eggs from non-

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avian taxa. For example, lysozyme is present in invertebrate (Matsuura et al.2007) and fish (Yousif, Albright and Evelyn 1994) eggs, and avidin occurs inamphibian eggs (Korpela et al. 1981).

Through pre-natal and post-natal transfer of antibodies, mothers transmit theirexperience of the environment to offspring (Boulinier and Staszewski 2008).

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Tabl

e A

2.1.

Mol

ecul

ar a

ppro

ache

s to

mea

suri

ng p

atho

gens

.

Leve

l / Q

uest

ion

bein

g as

ked

Host-

path

ogen

inte

ract

ion:

•H

ow d

oes t

he p

rese

nce

of a

sing

le ty

pe o

fpa

thog

en c

orre

late

with

imm

une

func

tion?

Poss

ible

met

hods

for

mea

surin

g pa

thog

ens

•Pr

eval

ence

/abu

ndan

ceco

unts

•M

icro

scop

y (e

.g. b

lood

of sm

ears

, cop

rosc

opy)

•Cu

lture

-bas

ed te

chni

ques

•qP

CR w

ith p

atho

gen-

spec

ific

prim

ers

(Why

te et

al.

2002

)

Path

ogen

s app

licab

le to

•Ec

to- a

nd e

ndop

aras

ites

•<

1% m

icro

bial

spec

ies

•An

y m

icro

bial

spec

ies

Com

men

ts

Does

not

refle

ct th

e co

mpl

exity

of p

atho

gen

asse

mbl

ages

.Ig

nore

s pos

sibili

ty o

f int

erac

tions

am

ong

co-in

fect

ing

path

ogen

s.O

ften

uncl

ear w

hich

imm

une

para

met

er(s

) is/

are

mos

tap

prop

riate

to te

st. A

ny o

bser

ved

corr

elat

ion

may

be

betw

een

an u

nmea

sure

d pa

thog

en a

nd im

mun

e fu

nctio

n.Un

likel

y th

at si

ngle

pat

hoge

n m

easu

res a

re re

pres

enta

tive

of o

vera

ll pa

thog

en p

ress

ure.

The

link

betw

een

host

fitn

ess a

nd in

fect

ion

with

indi

vidu

al p

atho

gens

may

be

very

wea

k.

Inte

ract

ion

of h

ost w

ith th

epa

thog

en a

ssem

blag

e (d

escr

iptiv

e):

•H

ow d

oes t

he d

iver

sity

of th

e pa

thog

enas

sem

blag

e co

rrel

ate

with

imm

une

func

tion?

•H

ow d

oes t

he a

bund

ance

of

com

pone

nts o

f the

path

ogen

ass

embl

age

corr

elat

e w

ith im

mun

efu

nctio

n?

•Pr

eval

ence

/abu

ndan

ceco

unts

•M

icro

scop

y

•Cu

lture

-bas

ed te

chni

ques

•Se

quen

ce-b

ased

met

agen

omic

s (co

mm

unity

‘fing

erpr

intin

g’):

- DGG

E (K

lom

p et

al.

2008

- RIS

A (R

uiz-

Rodr

ígue

z et

al.

2009

)- T

RFLP

(Hac

kl et

al.

2004

)- G

ene

sequ

ence

libr

arie

s(C

orby

-Har

ris et

al.

2007

)- B

ar-c

oded

pyr

oseq

uenc

ing

(Cox

-Fos

ter e

t al.

2007

)- T

axon

omic

Mic

roar

rays

(Gen

try

et a

l.20

06)

•Al

l mic

robe

s, ei

ther

one

taxo

nom

ic g

roup

at a

time,

or s

imul

tane

ously

(i.e.

mic

roar

ray;

cont

aini

ng D

NA

sequ

ence

s fro

mca

ndid

ate

com

mun

itym

embe

rs o

f man

y ta

xae.

g. b

acte

ria,

arch

aea,

fung

i etc

.)

Prev

alen

ce/a

bund

ance

cou

nts a

re la

borio

us a

nd sa

mpl

esiz

e af

fect

s the

pat

hoge

n di

vers

ity re

cord

ed (W

alth

eret

al.

1995

). Al

ongs

ide

visu

al c

ount

s, di

agno

stic

ass

ays

dapt

ed fr

om th

e liv

esto

ck/p

et in

dust

ries c

ould

be

a us

edto

mea

sure

larg

e pa

thog

ens (

e.g.

end

opar

asite

s)(T

rave

rsa

and

Otr

anto

200

9).

Fing

erpr

intin

g m

etho

ds a

re u

ninf

orm

ativ

e ab

out t

heid

entit

y of

mem

bers

of t

he a

ssem

blag

es d

escr

ibed

; with

som

e te

chni

ques

(e.g

. DGG

E), f

ollo

w-u

p se

quen

cing

can

achi

eve

this.

Unre

pres

enta

tive

of tr

ue d

iver

sity;

onl

y th

e m

ost

abun

dant

mem

bers

are

repr

esen

ted.

The

exc

eptio

n is

bar-c

oded

pyr

oseq

uenc

ing,

whi

ch a

llow

s dee

per c

over

age

of m

icro

bial

div

ersit

y.Pr

ovid

es so

me

info

rmat

ion

abou

t whi

ch fe

atur

es o

f the

asse

mbl

age

mig

ht c

orre

late

with

imm

une

func

tion.

Uncl

ear a

s to

caus

e an

d ef

fect

; is d

iver

sity

of th

epa

thog

en a

ssem

blag

e dr

iven

by

imm

une

defe

nces

,or

vic

e ve

rsa?

(Bor

des a

nd M

oran

d 20

09).

Levels of antibodies in eggs are proportional to levels circulating in the mother(Grindstaff 2008), and recent or repeated exposure to pathogens probablyincreases the level of antibodies in the circulation. Specificity and amounts ofmaternally transmitted antibodies may therefore be informative about thosepathogens found in the local environment that young first encounter.

33

PA

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OG

EN

PR

ES

SU

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AN

D I

MM

UN

E D

EF

EN

CE

Leve

l / Q

uest

ion

bein

g as

ked

Inte

ract

ion

of h

ost w

ith th

epa

thog

en a

ssem

blag

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Immune defence along a gradient ofpredicted disease risk

IIPART

Environmental disease risk proxies explainvariation in immune investment better thanindices of pace-of-life

3CHAPTER

Nicholas P.C. Horrocks, Arne Hegemann, Stephane Ostrowski,Henry Ndithia, Mohammed Shobrak, Muchane Muchai,Joseph. B. Williams, Kevin D. Matson & B. Irene Tieleman

Unpublished manuscript

AbstractInvestment in immune defences is often predicted to co-vary with a variety ofecologically and evolutionarily relevant axes, with physiological pace-of-life andenvironmental disease risk being two notable examples. These axes may them-selves co-vary directly or inversely, and such relationships can sometimes lead toconflicting predictions regarding immune defences. We investigated the relativeinfluence of variation in pace-of-life and environmental disease risk on immuneinvestment using comparable species of larks (Alaudidae) with different life his-tories and risks of infection. We used number of eggs per clutch and number ofclutches per year as indicators of pace-of-life, and we used climatic variables,including aridity, as correlates of environmental disease risk. We quantifiedimmune investment by measuring concentrations of haptoglobin and ovotrans-ferrin and titres of agglutination and lysis, all indices of innate immunity. Ifpace-of-life shapes immune investment then we expected slow-living arid-zonelark species to invest more in immune defence than fast-living temperate andtropical species. Alternatively, if disease risk drives immune investment, then weexpected larks in high-risk temperate and tropical environments to exhibit high-er immune indices than larks from low-risk arid locations. We found that pace-of-life indices explained little of the variation in immune investment: onlyagglutination titre showed a significant correlation, although not in the predict-ed direction. Conversely, environmental disease risk proxies were highly predic-tive of immune function, and larks in high-risk environments had higherimmune indices than those living in arid, low-risk locations. Overall, our studysuggests that environmental variables that have strong ties to disease risk aremore powerful drivers of immunological variation than reproductive indicesrelated to the pace-of-life.

Introduction

Variation in immune investment is widely observed within and among species.Understanding the causes of this variation is one general goal of ecologicalimmunology (Sadd and Schmid-Hempel 2009). Two particular explanations ofimmunological variation are often looked to in order to make sense of patternsobserved in nature. One explanation takes a life history perspective (Roff 1992;Stearns 1992) and focuses on the role of pace-of-life, the costs associated withimmunity, and the presence of trade-offs between key physiological processes(Sheldon and Verhulst 1996; Norris and Evans 2000; Schmid-Hempel 2003). Theother explanation takes an environmental disease threat perspective (e.g. Mendeset al. 2006) and focuses more on the benefits of immune defence. In fact, pace-of-life, disease risk and immune defences might all co-vary. Consequently, formingclear predictions about immune investment can be difficult.

Life history theory states that because resources are finite, the time and energyinvested in, for example, reproduction, are not available to other essential func-tions such as growth or self-maintenance (Soler et al. 2003; Ardia 2005a; Ardia2005b). Comparative studies of birds and mammals reveal that a single axis dom-inates life history variation (Saether 1988; Promislow and Harvey 1990; Ricklefs2000). Combinations of life history traits lie along this slow-fast axis, known asthe pace-of-life: at the slow end low extrinsic mortality combines with low annualreproductive output; at the fast end high extrinsic mortality associates with highannual reproductive output. Since the immune system is integral to self-mainte-nance and hence to survival and opportunities for future reproduction, ecologicalimmunologists have invoked trade-offs between current and future reproductionand exploited differences in pace-of-life to explain variation in immune defence(Sheldon and Verhulst 1996; Norris and Evans 2000; Schmid-Hempel 2003).Fundamental to this explanation is the idea that the costs of development, main-tenance and usage differ among immune defence components and the immunesystems they constitute (Klasing and Leshchinsky 1999; Tieleman et al. 2005; Lee2006).

Environmental disease risk represents another axis that might explain immuneinvestment. Immune systems provide clear benefits in terms of protection againstan array of endogenously and exogenously originating threats, including fitness-reducing infections by micro- and macro-parasites. Thus, immune investmentmight be greater in situations where the risk of infection is higher (Tschirren andRichner 2006; Horrocks, Matson and Tieleman 2011), which could be associatedwith environment, time, and other ecological factors (e.g. Piersma 1997; Møller1998; Guernier, Hochberg and Guegan 2004; Guerra et al. 2010). For example,environmental moisture conditions appear to be important in shaping parasiticand microbial assemblages. Parasites tend to have low prevalence in arid environ-ments (Little and Earlé 1995; Moyer, Drown and Clayton 2002; Valera et al.

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2003; Jex et al. 2007; Froeschke et al. 2010), and microbial assemblages exhibitreduced abundance and diversity as environmental aridity increases (Tong andLighthart 1997; Guernier, Hochberg and Guegan 2004; Burrows et al. 2009; Tang2009; Bachar et al. 2010). If aridity is considered as a proxy for disease risk thenimmunological investment should be greater in environments that are cooler,wetter and more humid, suggesting a negative correlation between aridity andimmune function.

Disentangling the relative contributions of pace-of-life and disease risk toobserved variation in immune investment is difficult, particularly since pace-of-life and disease risk may themselves co-vary directly or inversely, depending onthe study system (Horrocks, Matson and Tieleman 2011). Where pace-of-life anddisease risk co-vary directly, predictions about immune investment should coin-cide, even if the causal factor responsible for immunological variation is not clear.For example, relative to temperate birds, those in the tropics might invest relative-ly more in immunity due to their slower pace-of-life (Martin II, Hasselquist andWikelski 2006; Wiersma et al. 2007), because of increased exposure to diseaserisk (Møller 1998; Guernier, Hochberg and Guegan 2004), or perhaps as a resultof both factors. Where pace-of-life and disease risk co-vary inversely, conflictingpredictions can arise. For example, in addition to tropical birds, birds living indeserts are also predicted to invest strongly in immunity due to their slow pace-of-life (Tieleman, Williams and Visser 2004), even though the tropics and desertsmay pose very different disease risks (Horrocks, Matson and Tieleman 2011).Investigating the drivers and correlates of immunological variation in diverseenvironments therefore requires careful consideration of study system characteris-tics. If the goal is to separate the contributions of pace-of-life and disease risk toimmunological variation, then these two factors must be as un-confounded aspossible.

An ideal study system to investigate the relative contributions of pace-of-lifeand disease risk to immune investment is a large-scale aridity gradient inhabitedby a group of closely-related larks (Alaudidae). Despite large geographical andenvironmental differences, lark species inhabiting this gradient exhibit similarecological traits: larks are ground-nesting passerines that consume similar diets,display similar behaviours and inhabit open grassland habitats (del Hoyo, Elliottand Christie 2004). Moreover, physiological, demographic and life history traitsof several lark species are well-documented (reviewed in Tieleman 2005). Alongthe gradient from hyper-arid to mesic, pace-of-life co-varies with environmentalaridity, a finding unaffected by phylogeny (Tieleman, Williams and Bloomer2003; Tieleman, Williams and Visser 2004). Slower-living arid-zone larks laysmaller and fewer clutches per year and have slower nestling growth rates thanfaster-living larks from more mesic environments (Tieleman, Williams and Visser2004). As defined, the environmental gradient varies predominantly in terms ofenvironmental moisture levels, but locations along the gradient differ in terms of

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related abiotic characteristics (e.g. temperature) as well. The net result of thisvariation in abiotic conditions is that the environments most associated withspecies exhibiting a slow pace-of-life (which may select for immune investment)are the same environments with the lowest presumed risks of disease (which mayselect against immune investment). This contrast is useful for comparing alterna-tive hypotheses to explain variation in immune defences.

We studied larks from arid, semi-arid and mesic locations, which have beenstudied previously (Tieleman 2005). We also incorporated species from cold-desert and tropical locations that could further help us to tease apart the roles oflife history and environmental disease risk in shaping immune investment. Cold-desert larks have clutch sizes more typical of a fast pace-of-life yet live in a pre-dicted low disease risk environment. Tropical larks display life history traitsconsistent with a slow pace-of-life yet live in potentially high disease risk settings.We used clutch size and total number of eggs per year as indicators of pace-of-life(Saether 1988; Ricklefs 2000), and we used aridity, and the climatic variablesthat influence aridity as proxies for environmental disease risk. To assess immuneinvestment we focused on four indices of innate immunity, which represent theinitial circulating defences encountered by pathogens that have breached physicaldefensive barriers (Janeway et al. 2004). The acute phase proteins haptoglobinand ovotransferrin respond to inflammation or infection by increasing in concen-tration (van de Crommenacker et al. 2010; Horrocks, Tieleman and Matson2011) in order to limit microbial growth (Cray, Zaias and Altman 2009). Naturalantibodies (measured as agglutination titres) opsonize invading microorganismsto facilitate phagocytosis and activate the complement system, which leads to celllysis (measured as lysis titres; Ochsenbein & Zinkernagel 2000).

We predicted that if immunological investment is driven by pace-of-life andlife history trade-offs, then slow-living, arid-zone and tropical larks should investrelatively more in immune defence than fast-living species from temperate andcold-arid environments. However, if disease risk is more important for determin-ing investment in the immune system, then we predicted that immune indicesshould be lowest in lark populations from arid locations and be higher in temper-ate and tropical larks living in environments with greater risk of disease.

Methods

BirdsWe captured individuals of 12 species of larks in 23 locations during the breedingseason and in winter from 2006 to 2009. Details of species, sample sizes and geo-graphic locations are provided in Table 3.1. Upon capture of birds, we collected200-300 µl of blood from the brachial vein and stored it on ice until processing bycentrifugation to separate plasma and cellular fractions. Plasma was then frozen

40

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Tabl

e 3.

1.Sa

mpl

e si

ze (

n),

sam

plin

g pe

riod

(br

eedi

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B),

non-

bree

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(N

B),

or s

ampl

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n bo

th p

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ds (

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)),

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raph

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f lar

k. T

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limat

ic v

aria

bles

are

mea

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val

ues

for

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(P),

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ture

(T)

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entia

l eva

potr

ansp

irat

ion

(PET

), a

nd tw

o in

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: AU

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PET

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d A

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titud

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ngitu

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(mm

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(°C)

PET

(mm

)A U

NEP

A M

aho

opoe

lark

Ala

emon

ala

udip

es4

B19

°53’

N16

°18’

W49

.90

24.5

520

81.1

20.

021.

44b

61bo

th22

°20’

N41

°44’

E82

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41°4

4’ E

82.0

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0.03

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crow

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finch

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mop

teri

x ni

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eps

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th22

°20’

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°44’

E82

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824

27.5

40.

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5’ N

40°4

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201.

2621

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2165

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fcr

este

d la

rk G

aler

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E82

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32g

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5’ N

40°4

2’ E

201.

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h2

NB

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2’ N

41°4

4’ E

208.

216

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1571

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0.13

7.96

iD

unn’

s la

rk E

rem

alau

da d

unni

35bo

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°20’

N41

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E82

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27.5

40.

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t-toe

d la

rk C

alan

drel

la b

rach

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2N

B22

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°45’

E82

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824

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acul

ated

lark

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ha b

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NB

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2’ N

41°4

4’ E

208.

216

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0.13

7.96

l6

NB

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4’ N

67°1

1’ E

199.

8816

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0.14

7.41

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5.92

15.6

113

69.2

80.

168.

82n

14N

B34

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N66

°53’

E35

9.48

4.48

1159

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0.31

24.8

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lark

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anoc

oryp

ha ca

land

ra11

NB

34°2

2’ N

41°4

4’ E

208.

216

.14

1571

.63

0.13

7.96

p3

NB

36°5

4’ N

67°1

1’ E

199.

8816

.98

1409

.99

0.14

7.41

q6

NB

34°5

4’ N

66°5

3’E

359.

484.

4811

59.6

10.

3124

.83

rre

d-ca

pped

lark

Cal

andr

ella

cine

rea

5B

0°52

’ S36

°23’

E57

0.48

20.2

514

63.9

50.

3918

.86

s8

B0°

34’ S

36°2

8’ E

806.

7715

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1043

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7731

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tru

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ed la

rk M

iraf

ra a

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ana

4B

0°52

’ S36

°23’

E57

0.48

20.2

514

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u2

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36°2

8’ E

806.

7715

.39

1043

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7731

.78

vsk

ylar

k Al

auda

arv

ensis

144

both

52°5

6’ N

6°18

’ E77

0.11

9.19

557.

821.

3840

.13

ww

oodl

ark

Lullu

la a

rbor

ea61

both

52°5

6’ N

6°18

’ E77

0.11

9.19

557.

821.

3840

.13

and stored at -20°C until use in immune assays. We gathered data on pace-of-lifeindicators (mean clutch size and number of clutches per year; Table 3.2) directlyfrom our own study populations and from Tieleman, Williams and Visser (2004),Cramp (1988) and del Hoyo, Elliott and Christie (2004).

Climatic variables and aridity indicesWe obtained high-resolution (0.5 × 0.5 degree) gridded data on climatic variablesfor the period 1901–2009 from http://badc.nerc.ac.uk/view/badc.nerc.ac.uk_ATOM_dataent_1256223773328276. This dataset is described in detail by

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Table 3.2. Mean clutch size and number (year-1), concentrations of acute phase proteinshaptoglobin and ovotransferrin, and agglutination and lysis titres for 23 populations of 12lark species. Values for life history variables are from this study and from the literature(data source column).

# species clutch clutches hapto- ovotrans- agglu- lysis data size year-1 globin ferrin tination (titres) source*

(mg ml-1) (mg ml-1) (titres)

a hoopoe lark 2.88 1 0.25 7.41 4.50 1.63 3

b 2.99 1 0.28 7.47 5.85 0.86 2

c bar-tailed desert lark 3.24 1 0.29 9.08 6.13 0.38 2

d black-crowned finchlark 2.00 1 0.27 9.11 5.83 0.58 3, 4

e 2.57 1 0.49 5.43 7.03 1.52 3, 4

f crested lark 4.15 2 0.25 5.18 6.24 0.53 2

g 4.15 2 0.25 15.28 6.31 1.94 2

h 4.75 2 0.07 11.20 5.25 0.00 2, 3

i Dunn’s lark 2.88 1 0.49 9.76 6.65 1.63 2

j short-toed lark 3.50 2 0.37 9.18 11.00 1.00 2

k bimaculated lark 3.96 1.5 0.11 12.72 4.21 0.21 3

l 3.96 1.5 0.19 - 5.17 2.08 3

m 3.96 1.5 0.17 - 7.63 4.88 3

n 3.96 1.5 0.33 14.53 4.90 2.63 3

o calandra lark 4.20 2 0.07 9.78 5.90 1.25 2

p 4.20 2 0.08 - 7.25 3.58 2

q 4.20 2 0.06 6.01 6.46 1.75 2

r red-capped lark 1.83 2 0.15 9.10 4.50 0.13 1

s 1.89 2 0.57 7.25 5.17 2.42 1

t rufous-naped lark 2.11 1 0.74 9.08 6.31 3.69 1, 4

u 2.00 1 0.19 10.20 5.63 3.63 1, 4

v skylark 3.56 3.5 0.48 - 7.82 2.26 1

w woodlark 4.02 2.5 0.46 9.41 7.20 2.21 1

* 1 Own data; 2 Tieleman, Williams and Visser (2004); 3 Cramp (1988);4 del Hoyo, Elliott and Christie (2004).

Mitchell and Jones (2005). For each bird-sampling location we extracted meanannual values for precipitation (P; mm), temperature (T; °C), and potential evap-otranspiration (PET; mm). PET is a derived reference measurement of the amountof water lost to the atmosphere through the combined processes of evaporationand plant transpiration. PET is dependent on a range of climatic and environ-mental factors but tends to be low in cool and humid environments and high inarid locations such as deserts. We used the three climatic variables to calculatetwo alternative indices of aridity: the United Nations Environment Programmearidity index (AUNEP: P / PET; UNEP 1992) and de Martonne’s aridity index (AM:P / T + 10; de Martonne 1926). Climatic variables and aridity indices for eachlark population are shown in Table 3.1.

Immune assaysWe determined haptoglobin concentrations (mg ml-1) using a functional assaythat measures the haem-binding capacity of plasma (TP801; Tri-Delta Diagnos-tics, NJ, USA), following the ‘manual method’ instructions provided by the manu-facturer and with incubation at 30°C for 5 minutes. We measured ovotransferrinconcentrations (mg ml-1) according to Horrocks, Tieleman and Matson (2011),but not all populations were measured due to blood volume limitations and logis-tical reasons (Table 3.2). We quantified natural antibody-mediated agglutinationtitres and complement-mediated lysis titres against rabbit red blood cells (B-0009D, Harlan, UK), according to the assay of Matson, Ricklefs & Klasing (2005).

Statistical analysesOur dataset consisted of values at the individual level (immune measures) andvalues at the population- or species-level (all other variables). We took a conser-vative approach and calculated mean values per population (i.e. per species perlocation) of each dependent variable for which we had measurements on individ-uals. We treated species and geographically distinct populations as independentpoints. First, we tested whether populations differed in their immune responses:haptoglobin F22, 444 = 2.89, P < 0.001; ovotransferrin F18, 106 = 0.89, P = 0.594;agglutination F22, 409 = 2.21, P = 0.001; lysis F22, 412 = 5.30, P < 0.001. Thenwe used linear models to investigate relationships between immune indices andclimatic and life history variables. Because sample size varied among species andpopulations (Table 3.1), we weighted regression models by the square root of thenumber of individuals sampled in each population (Sokal and Rohlf 1995). Sincesome species or populations were only sampled during one period (breeding ornon-breeding; Table 3.1) we ran analyses using restricted datasets containing val-ues per period, as well as with the entire dataset of all values. The results of theseanalyses were qualitatively similar and so we only present results based on theentire dataset. We performed all analyses using R 2.13.0 (R Development CoreTeam 2009).

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Results

To disentangle the roles of pace-of-life and environmental disease risk in directingimmune investment, these factors should not be positively correlated. We con-firmed that this was the case in our dataset by examining correlations betweenpace-of-life indicators (clutch size and number of eggs per year) and our primaryproxies of environmental disease risk (aridity indices AUNEP and AM). Whenrestricting analyses to previously-studied species by excluding tropical and cold-desert larks (where pace-of-life and environmental disease risk do not correlate),clutch size and number of eggs per year correlated negatively with environmentaldisease risk (Table 3.3), as previously shown (Tieleman, Williams and Visser2004). These correlations were not significant for clutch size but were significantfor total number of eggs per year (Table 3.3). When including tropical and cold-desert species, correlations generally remained negative overall, but were muchweaker and were all non-significant (Table 3.3).

Relationships between immune indices and pace-of-life parameters did not dis-play consistent patterns, and with the exception of agglutination titres, werealways non-significant and weak (excluding agglutination, all r2 ≤0.12; Table3.4). Haptoglobin and ovotransferrin concentrations showed weak but oppositetrends with respect to clutch size (Figs 3.1A, 3.1C) but not with number of eggsper year. (Figs 3.1B, 3.1D). Agglutination titres were positively associated withboth life history variables (Figs 3.1E–F), and significantly associated with numberof eggs per year (Fig. 3.1F; Table 3.4). Lysis titre showed no relationship withmean clutch size (Fig. 3.1G) and a very weak but positive relationship with num-ber of eggs per year (Fig. 3.1H; Table 3.4).

Lark populations consistently exhibited lower indices of immunity in environ-ments with reduced proxies of disease risk (Fig. 3.2), regardless of which aridityindex was used. These negative correlations were significant for haptoglobin con-centrations and agglutination and lysis titres (Fig. 3.2; Table 3.4). In line with

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Table 3.3. Correlations between pace-of-life indicators (clutch size and number of eggs laidper year) and proxies of environmental disease risk (aridity indices AUNEP and AM – seeMethods for details) for 23 populations of 12 lark species.

correlation Excluding tropical and All speciescold-desert species

t P r t P r

clutch size vs. AUNEP -1.24 0.245 -0.38 0.53 0.603 0.11

total eggs year-1 vs. AUNEP -3.51 0.007 -0.76 -1.89 0.073 -0.38

clutch size vs. AM -1.28 0.238 -0.39 0.60 0.554 0.13

total eggs year-1 vs. AM -3.59 0.006 -0.77 -1.50 0.149 -0.31

this, haptoglobin concentration and agglutination and lysis titres were also posi-tively and significantly correlated with mean annual precipitation (Fig. 3.3; Table3.4). Ovotransferrin concentration showed no relationship with aridity or precipi-tation (Figs 3.2B and 3.3D). Mean annual temperature correlated negatively withimmune indices (Fig. 3.3), but only lysis showed a significant relationship withthis climatic variable (Fig. 3.3K; Table 3.4). Mean annual PET also showed nega-tive relationships with immune indices (Fig. 3.3): these relationships were only

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Table 3.4. Results (F tests and P values) of linear models examining relationships betweenimmune indices of 23 populations of 12 lark species in relation to pace-of-life indicators(mean clutch size and total number of eggs laid per year) and to climatic proxies of envi-ronmental disease risk. Significant P values are shown in bold.

response variable explanatory variable r2 F P

haptoglobin (mg ml-1) mean clutch size 0.12 F1, 21 = 2.86 0.105total eggs year-1 0.01 0.20 0.657aridity index AUNEP 0.27 7.55 0.001aridity index AM 0.21 5.48 0.029mean annual precipitation (mm) 0.24 6.49 0.019mean annual temperature (°C) 0.02 0.34 0.569mean annual PET (mm) 0.07 1.61 0.218

ovotransferrin (mg ml-1) mean clutch size 0.06 F1, 17 = 1.02 0.327total eggs year-1 0.02 0.41 0.530aridity index AUNEP 0.02 0.38 0.545aridity index AM 0.05 0.97 0.339mean annual precipitation (mm) 0.03 0.59 0.450mean annual temperature (°C) 0.15 3.10 0.096mean annual PET (mm) 0.10 1.92 0.184

agglutination (titre) mean clutch size 0.05 F1, 21 = 1.17 0.292total eggs year-1 0.34 10.58 0.004aridity index AUNEP 0.37 12.39 0.002aridity index AM 0.25 6.96 0.015mean annual precipitation (mm) 0.20 5.39 0.030mean annual temperature (°C) 0.12 2.73 0.113mean annual PET (mm) 0.19 4.87 0.039

lysis (titre) mean clutch size 0.01 F1, 21 = 0.23 0.637total eggs year-1 0.07 1.59 0.222aridity index AUNEP 0.17 4.15 0.055aridity index AM 0.23 6.30 0.020mean annual precipitation (mm) 0.24 6.68 0.017mean annual temperature (°C) 0.28 8.32 0.009mean annual PET (mm) 0.34 10.62 0.004

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1

0

1

2

3

4

5

lysi

s (ti

tre)

2 3 4 5mean clutch size

Jr2 = 0.01

0 8 12 164total eggs year-1

H

r2 = 0.07

4

5

6

7

8

9

aggl

utin

atio

n (ti

tre)

E

r2 = 0.05

F

r2 = 0.34

0

12

4

8

ovot

rans

ferr

in c

onc.

(mg

ml-1

)

C

r2 = 0.06

D

r2 = 0.02

hapt

pglo

bin

conc

. (m

g m

l-1) A

r2 = 0.12

B

r2 = 0.01

16

0.0

0.6

0.2

0.4

0.8

Figure 3.1. Haptoglobin (A-B) and ovotransferrin (C-D) concentrations and agglutination(E-F) and lysis (G-H) titres as a function of mean clutch size and mean total number ofeggs year-1 in 23 populations of 12 lark species. The size of each data point is proportionalto the number of records (number of individuals) contributing to the value. Hot-desertspecies are in light grey, cold-desert species in black, temperate species in dark grey andtropical lark species are in white.

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-1

0

1

2

3

4

5

lysi

s (ti

tre)

540 1 2 3UNEP aridity index

D

r2 = 0.17

4

5

6

7

8

9

aggl

utin

atio

n (ti

tre)

C

r2 = 0.37

0

12

4

8

ovot

rans

ferr

in c

onc.

(mg

ml-1

)B

r2 = 0.02

hapt

pglo

bin

conc

. (m

g m

l-1) A

r2 = 0.27

16

0.0

0.6

0.2

0.4

0.8

CLIMATE, ARIDITYcooler, wetter, more humid hotter, drier, more arid

DISEASE RISKhigher lower

Figure 3.2. Haptoglobin (A) and ovotransferrin (B) concentrations and agglutination (C)and lysis (D) titres as a function of environmental aridity in 23 populations of 12 larkspecies. Aridity is plotted on a natural log scale and so that the aridity index increases withincreasing aridity of the environment. The size of each data point is proportional to thenumber of records (number of individuals) contributing to the value. Hot-desert species arein light grey, cold-desert species in black, temperate species in dark grey and tropical larkspecies are in white.

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0

0

1

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3

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lysi

s (ti

tre)

1000200 400 600 800mean annual precipitation (mm)

J

r2 = 0.24

0 3010 20mean annual temperature (°C)

K

r2 = 0.28

0 1000mean annual PET (mm)

L

r2 = 0.34

2000 3000

4

5

6

7

8

9

aggl

utin

atio

n (ti

tre)

G

r2 = 0.20

H

r2 = 0.12

I

r2 = 0.19

0

12

4

8

ovot

rans

ferr

in c

onc.

(mg

ml-1

)

D

r2 = 0.03

E

r2 = 0.15

F

r2 = 0.10

hapt

pglo

bin

conc

. (m

g m

l-1) A

r2 = 0.24

B

r2 = 0.02

C

r2 = 0.07

16

0.0

0.6

0.2

0.4

0.8

Figure 3.3. Haptoglobin (A-C) and ovotransferrin (D-F) concentrations and agglutination(G-I) and lysis (J-L) titres as a function of mean annual precipitation, mean annual temper-ature and mean annual potential evapotranspiration (PET) in 23 populations of 12 larkspecies measured along an environmental aridity gradient. The size of each data point isproportional to the number of records (number of individuals) contributing to the value.Hot-desert species are in light grey, cold-desert species in black, temperate species in darkgrey and tropical lark species are in white.

significant for agglutination (Fig. 3.3I) and lysis (Fig. 3.3L) titres (Table 3.4).Thus, patterns of immune variation with precipitation, temperature and PET allsupported the trend for decreasing immune indices with increasing predicted dis-ease risk.

Discussion

Pace-of-life and environmental disease risk represent two ecological axes withwhich immune investment may co-vary. Disentangling the relative contributionsof these two factors can be complicated in some study systems (Horrocks, Matsonand Tieleman 2011), particularly when pace-of-life and environmental diseaserisk are positively correlated. We studied lark populations living in diverse envi-ronments where pace-of-life and disease risk were not positively correlated. Wefound that pace-of-life explained very little of the variation in immune indices. Incontrast, environmental correlates of disease risk were much better at explainingvariation in immune investment. Our data provide an interesting counterpoint tostudies suggesting a role for pace-of-life in shaping immune defences (e.g. Tiele-man et al. 2005; Lee 2006; Sparkman and Palacios 2009) and underscore thevalue of incorporating indices of disease risk into ecological immunology studies.

In agreement with our second hypothesis, immune indices matched predic-tions based on disease risk. Measures of innate immunity were highest in popula-tions of larks from humid locations where disease risk is expected to be higher,and these immune indices decreased with increasing aridity. In line with this, wealso found significant positive associations between immune indices and precipi-tation and negative correlations between some immune indices and temperatureand PET. Thus, with aridity as our a priori proxy for environmental disease risk,investment in innate immunity can be seen as increasing in tandem with this risk.Previous authors have linked disease risk and abiotic environmental variables(e.g. salinity exposure; Piersma 1997; Figuerola 1999; Mendes et al. 2005) andparticularly disease prevalence and climatic factors (Guernier, Hochberg and Gue-gan 2004; Gage et al. 2008). These more general links and the specific linksbetween aridity and parasitic (e.g. Jex et al. 2007; Froeschke et al. 2010) andmicrobial assemblages (e.g. Burrows et al. 2009; Tang 2009; Bachar et al. 2010;chapter 5) suggest that biotic disease risk variation is reflected by abiotic environ-mental variation. Directly quantifying and understanding biotic disease riskparameters is challenging, particularly among species and across environments,since parasitic and microbial assemblages may not be directly comparable, anduniversal measurement methods may not exist (Horrocks, Matson and Tieleman2011). Abiotic environmental proxies can therefore provide a useful alternativewhile conceptual and methodological issues are resolved. Nevertheless, futurework involving direct measurements of disease risk will no doubt shed additional

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light on the strong associations we have identified between disease risk andimmune defence.

In contrast to environmental disease risk, correlations between immune indicesand two pace-of-life parameters were generally mixed and very weak (r2 ≤ 0.12,with one exception). Only one relationship, between agglutination and total eggsper year, was significant. Importantly, this significant correlation was positive,suggesting that lark species with a faster pace-of-life had higher agglutinationability. Thus, this finding contradicts our first hypothesis that larks with a slowpace-of-life should invest more in immunity than fast-living species. It also con-tradicts earlier work conducted on a range of tropical bird species that foundhigher natural antibody levels (i.e. greater agglutination ability) in birds withlonger development times, indicative of a slower pace-of-life (Lee et al. 2008). Inthis context, populations in our dataset that are exceptions to the predominantinverse relationship between pace-of-life and environmental disease risk deservefurther consideration. These exceptions can provide particular insight into thedrivers of immune investment. For example, both tropical larks in our dataset(red-capped lark and rufous-naped lark; white spheres in figures) have smallclutches and lay few clutches per year (Table 3.2) indicative of a slow pace-of-life.While their pace-of-life is similar to arid-zone larks (light grey spheres in figures),the disease risk experienced by tropical larks (based on aridity values; Table 3.1)is high and more similar to the temperate populations (dark grey spheres in fig-ures) in our dataset. If pace-of-life strongly influenced immune investment, val-ues of immune indices for red-capped larks and rufous-naped larks should clusterwith values for arid-zone larks. In contrast, lysis titres in these two tropical larkswere more similar to values for temperate larks (Fig. 3.1), which reasserts thatimmune investment is more likely related to disease risk. No obvious groupingswere apparent with the other three immune indices. Conversely, cold-desert larks(bimaculated lark and calandra lark; black spheres in figures), which have clutchsizes indicative of a fast pace-of-life, inhabit low disease risk environments. In thiscase, if pace-of-life strongly influenced immune investment, values of immuneindices for these larks should cluster with values for temperate lark populations.This was not the case, particularly for agglutination titres and haptoglobin con-centrations (Fig. 3.1), which grouped more closely with arid-zone (hot-desert)species that are predicted to experience similar disease risks to cold-desert larks.Thus, this result also reinforces the idea that immune investment relates to dis-ease risk rather than pace-of-life.

Natural antibody level (measured as agglutination titres) was the notableexception where there was a significant correlation with one measure of pace-of-life. This positive relationship between natural antibodies and pace-of-life mightsignify preferential investment in non-specific antibodies by short-lived species.Despite the otherwise weak relationships between pace-of-life and immuneindices, this single correlation hints at a link between immune defence and life

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history strategy (Lee 2006). Fast-living species are predicted to favour innate overadaptive immune defences, since adaptive immunity requires longer developmen-tal times that are incompatible with the fast growth associated with a fast pace-of-life (Lee 2006; Lee et al. 2008). While this prediction is supported by theresults of several studies (Martin II, Hasselquist and Wikelski 2006; Sparkmanand Palacios 2009; although see Tieleman et al. 2005, conducted in a single envi-ronment), overall our data provide only limited support. Natural antibodies,although generally considered an innate immune defence, straddle the boundarybetween innate and adaptive immunity. For example, natural antibodies titrescorrelate positively with adaptive antibody responses (Parmentier et al. 2004)and provide links between the innate and adaptive arms of the immune system(Caroll and Prodeus 1998; Ochsenbein and Zinkernagel 2000). Nonetheless,measures of purely adaptive immune defences (e.g. induction of specific antibodyresponses) are necessary to draw conclusions about trade-offs within the immunesystem and overall protection.

Comparative studies, both within and among species and environments, repre-sent a powerful approach for disentangling the roles of pace-of-life and environ-mental disease risk in shaping immune defences and for understanding immunedefence variation in more general ecological and evolutionary terms. By employ-ing a study system in which pace-of-life variation is uncoupled from environmen-tal disease risk variation, we demonstrated that investment in innate immunedefences is related more to environmental disease risk than to pace-of-life. Anobvious next step is a comparative study that integrates indices of host-depend-ent and host-independent disease pressures. Adding, in essence, an axis that rep-resents biotic environmental disease risk opens up an exciting frontier that willstimulate research and advance our understanding of immunological variation.

AcknowledgementsWe thank the following people and organisations for logistical support, and for permission

to work with wild birds: HH Prince Bandar bin Saud, Secretary General of the Saudi

Wildlife Commission, Mr Ahmad Al Bouq, Director of the National Wildlife Research Center

and staff at Taif and Mahazat as-Sayd, Saudi Arabia; Staatsbosbeheer and volunteers on the

Aekingerzand Lark Project, Netherlands; National Museums Kenya, Friends of Kinangop

Plateau, and Sarah Higgins, Kenya. Sample collection in Afghanistan was made possible by

the generous support of the American people through the United States Agency for Interna-

tional Development (USAID) and its collaborative grantee the Wildlife Conservation Soci-

ety (WCS). Financial support came from the Schure-Beijerinck-Poppings Fonds (to

N.P.C.H), BirdLife Netherlands (to A.H. and B.I.T.), VENI grants from the Netherlands

Organisation for Scientific Research (863.08.026 and 863.04.023, to K.D.M. and B.I.T.,

respectively), and a Rosalind Franklin Fellowship from the University of Groningen (to

B.I.T.).

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Antimicrobial proteins in avian eggs:ovotransferrin increases but lysozymedecreases with environmental correlates oftrans-shell infection

4CHAPTER

Nicholas P.C. Horrocks, Kathryn Hine, Arne Hegemann, Henry Ndithia,Mohammed Shobrak, Stephane Ostrowski, Joseph B. Williams,Kevin D. Matson & B. Irene Tieleman

Unpublished manuscript

AbstractUnderstanding how immune investment relates to disease risk could be onemeans of explaining the variation in immune defences observed among animalsin different environments. A useful way to study this is to use eggs, since theyrepresent a simplified version of the immune system, with a reduced suite ofimmune defences to measure, and fewer potential sources of infection to be con-sidered. The albumen of avian eggs contains antimicrobial proteins. These pro-teins protect the embryo from microbes, which can penetrate the eggshell andcause loss of egg viability. Microbial loads on eggs, microbial penetration of theeggshell and declines in egg viability increase with environmental humidity,while eggs that are kept dry experience little microbial contamination. Usinghumidity as a proxy for risk of trans-shell infection, we tested whether concen-trations of antimicrobial proteins were higher in eggs laid in conditions that aremore humid. We collected eggs of larks (Alaudidae) that live along a humiditygradient, gathered climatic data, and measured concentrations of two antimicro-bial proteins, ovotransferrin and lysozyme. We also measured pH of the albu-men, since this could influence the microbicidal ability of the albumen. Asexpected, concentrations of ovotransferrin were highest in eggs collected fromenvironments that are more humid. Contrary to expectations, lysozyme concen-trations decreased in eggs from increasingly humid environments and correlatednegatively with ovotransferrin levels. Albumen pH was not significantly relatedto humidity. Temperature explained more of the variation in egg defences thanprecipitation, a result inconsistent with studies stressing the importance of waterfor trans-shell infection. Our study raises interesting questions about the func-tional role of albumen defence proteins and the potential trade-offs betweenthem, as well as highlighting the usefulness of eggs as a simplified model forstudying the evolution of immune defences in different environments.

Introduction

Viability of avian eggs exposed to ambient temperatures above physiological zerodecreases over time (Beissinger, Cook and Arendt 2005). One cause of reducedviability is trans-shell microbial infection (Cook et al. 2003; Cook et al. 2005a;Cook et al. 2005b). To minimise infection by microbes, eggs possess physical bar-riers in the form of the shell, membranes and viscous albumen, and chemical bar-riers that include antimicrobial proteins in the albumen and the pH of thealbumen (Board and Fuller 1974). Two such antimicrobial proteins are ovotrans-ferrin, which has bactericidal properties and binds iron to make it unavailable forbacterial growth (Superti et al. 2007), and lysozyme, which catalyses the lysis ofcell wells of gram-positive bacteria (Callewaert and Michiels 2010). In some birdspecies these proteins show patterns related to ambient temperature, or to layorder of eggs in a clutch, but these patterns are not universal (Saino et al. 2002;Saino et al. 2004; Shawkey et al. 2008; Cucco et al. 2009; Bonisoli-Alquati et al.2010; D'Alba et al. 2010). The antimicrobial activity of ovotransferrin andlysozyme is influenced by pH of the albumen (Tranter and Board 1984), whichchanges during embryonic development (Romanoff 1944). Albumen at high pH(9-10) is bactericidal, while albumen with lower pH (6-8) displays only bacterio-static properties (Tranter and Board 1984).

Concentrations of ovotransferrin and lysozyme, like other defensive propertiesof the egg, are set by the mother during egg formation. It has been shown thatfor antibodies in the yolk, concentrations are proportional to levels circulating inthe mother (Grindstaff et al. 2006; Grindstaff 2008) which are likely influencedby recent or repeated exposure to pathogens. Similarly, the amount of lysozymedeposited in the albumen may relate to plasma levels in the mother (Saino et al.2002), and both plasma lysozyme and ovotransferrin levels are related to infec-tion status (Xie et al. 2002a; Millet et al. 2007). Thus, mothers can transmit theirexperience of the wider environment to their offspring (Gasparini et al. 2001;Boulinier and Staszewski 2008) thereby influencing offspring phenotype and sur-vival. However, aside from vertical transmission of microbes from mother to egg,the risks of infection faced specifically by eggs are primarily associated with themicroenvironment of the egg. Likely sources of potential contamination includethe substrates that eggs contact, such as nest materials (Singleton and Harper1998; Berger, Disko and Gwinner 2003), and the skin and feathers of incubatingparents (Shawkey et al. 2005; Ruiz-de-Castañeda et al. 2011a; Ruiz-de-Castañedaet al. 2011b; but see below for effects of incubation). This makes eggs a simpli-fied yet useful model for studying the evolution of immune defences in differentenvironments.

Egg qualities differ among environments. For example, declines in egg viabilityare greater and occur more rapidly in the humid tropics than in temperate ecosys-tems, and microbial loads on eggshells and trans-shell infection rates are also

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highest in the tropics (Cook et al. 2003; Beissinger, Cook and Arendt 2005; Cooket al. 2005a; Cook et al. 2005b; Messens, Grijspeerdt and Herman 2005; Wang,Firestone and Beissinger 2011). Rates of trans-shell infection and levels of eggviability in arid environments have not been studied. However, eggs that are keptdry, either experimentally (D'Alba, Oborn and Shawkey 2010) or through incuba-tion (Cook et al. 2005a; Shawkey et al. 2009), have reduced microbial loads anddiminished opportunities for trans-shell infection, highlighting the apparentimportance of moisture in mediating this process (Bruce and Drysdale 1994).Temperature affects avian egg viability independently of microbial infectionthrough its effect on embryonic development (Webb 1987). Temperature alsoinfluences microbial infection of eggs, either by affecting the growth of microbeson eggshells (Ayres and Taylor 1956) or through the potentiation of antimicrobialproteins, which work optimally at physiological temperatures (Tranter & Board1984).

The activity of antimicrobial proteins increases with protein concentration(Salton 1957; Horrocks, Tieleman and Matson 2011, but see Wilcox and Daniel1954; Friedberg and Avigad 1966 for effects in lysozyme). If antimicrobialdefences have evolved to match the risk of microbial infection (Wellman-Labadie,Picman and Hincke 2008; Horrocks, Matson and Tieleman 2011), then concen-trations of antimicrobial proteins in eggs should vary with environmental condi-tions. To test this hypothesis we collected eggs from larks (Alaudidae) living indifferent locations along a gradient of environmental humidity that ranges fromhumid (tropical and temperate) to hyper-arid (desert; Tieleman, Williams andBloomer 2003; Tieleman, Williams and Visser 2004; Tieleman 2005). We focusedon humidity as a proxy for the risk of trans-shell infection because microbialinfection of eggs is correlated with humidity (Cook et al. 2003; Beissinger, Cookand Arendt 2005; Cook et al. 2005a; Cook et al. 2005b). We measured concentra-tions of ovotransferrin and lysozyme in the albumen of these eggs and, since itmay contribute to the antimicrobial properties of the albumen (Tranter and Board1984), we recorded the pH of the albumen.

Larks are an ideal system of comparable species for studying geographic differ-ences in egg defences because different species inhabit a variety of environmentswith different macroclimates and show a range of well-documented physiologicaland life history traits associated with these environmental differences (Tieleman,Williams and Bloomer 2003; Tieleman, Williams and Visser 2004). Furthermore,all lark species build open-cup nests on or close to the ground in open habitats.This leaves the eggs relatively unprotected from environmental conditions andmight make them more vulnerable to microbial contamination than eggs ofspecies that nest off the ground or in cavities (Godard et al. 2007). We predictedthat the concentrations of ovotransferrin and lysozyme and the pH of the albu-men would be lowest in eggs from hot and dry environments and higher in eggsfrom cooler and wetter, more humid locations.

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Materials and methods

Antimicrobial protein assaysWe collected 125 eggs from nine lark species in 14 locations (Table 4.1). We dis-sected eggs into constituent parts, recorded the pH of the albumen, and if pres-ent, we weighed the mass of any embryonic material. We used the quotient ofembryo mass over total egg mass as a proxy for egg age. Of the 125 eggs, 76were collected on the day of laying and only 13 were estimated to be more thanfour days old based on embryo mass (maximum estimated age 7 days for oneegg). The incubation period in all lark species is normally 12 days (del Hoyo,Elliott and Christie 2004). Ovotransferrin concentration (mg ml-1) was measuredas described in Horrocks, Tieleman and Matson (2011), using 10 µl of albumeninstead of plasma. Lysozyme concentration was measured by recording the rate ofoptical density change (OD, 450 nm) following addition of 200 µl of a one mgml-1 solution of Micrococcus lysodeikticus (M3770) in potassium phosphate buffer(pH 7.0, 100 mM) to microplate wells containing 50 µl sample. OD was recordedevery ten seconds for 60 minutes at 25°C on a spectrophotometric microplatereader (VersaMax, Molecular Devices, Sunnyvale, CA, USA). Samples were run attwo dilutions (Appendix 4.1). Standards (over the range 0.04–0.004 mg ml-1) of50 µl purified chicken egg white lysozyme (L6876) were run in duplicate. Thetime at which OD had decreased to 75% of the OD of a negative control (potassi-um phosphate buffer only) was recorded (T75). A standard curve relating T75 tolysozyme concentration of the standards was used to calculate mean lysozymeconcentrations (mg ml-1) of the two sample dilutions. All chemicals were pur-chased from Sigma-Aldrich (St Louis, MO, USA).

Climatic data and indices of environmental humidityWe obtained high-resolution (0.5 × 0.5 degree) gridded data on climatic variablesfor the period 1901–2009 from http://badc.nerc.ac.uk/view/badc.nerc.ac.uk_ATOM_dataent_1256223773328276. This dataset is described in more detail byMitchell & Jones (2005). For each egg-collection location, we extracted meanannual values for precipitation (P; mm), temperature (T; °C) and potential evapo-transpiration (PET; mm). PET is a derived reference measurement of how muchwater could be lost to the atmosphere through the combined processes of evapo-ration and plant transpiration. PET is dependent on a range of climatic and envi-ronmental factors including solar radiation, cloud cover and vegetation cover buttends to be low in cool and wet environments and high in arid locations such asdeserts. We used the three climatic variables to calculate two alternative indicesof humidity: the United Nations Environment Programme index AUNEP (P / PET;UNEP 1992) and de Martonne’s index AM (P / T + 10; de Martonne 1926). Orig-inally created as a means to define the drylands of the world, these indices maybe considered to define either humidity or aridity, depending on perspective (e.g.

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Tabl

e 4.

1.N

umbe

r of

egg

s (n

), n

umbe

r of

nes

ts (

nest

n),

geo

grap

hic

orig

in a

nd c

limat

ic v

aria

bles

for

nin

e sp

ecie

s of

lark

. The

clim

atic

var

iabl

es a

rem

ean

annu

al v

alue

s fo

r pr

ecip

itatio

n (P

), te

mpe

ratu

re (

T), p

oten

tial e

vapo

tran

spir

atio

n (P

ET),

and

two

indi

ces

of h

umid

ity: A

UN

EP(P

/ P

ET)

and

AM

(P /

T +

10)

.

spec

ies

nne

st n

latit

ude

long

itude

P (m

m)

T (°

C)PE

T (m

m)

A UN

EPA M

hoop

oe la

rk A

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Hulme, Marsh and Jones 1992) and are alternately referred to as humidity oraridity indices. For both indices, low values are associated with arid conditionsand increasing values indicate increasing humidity.

Statistical analysesDuring embryonic development, the pH and water content of albumen decreases(Deeming 2002). Therefore, we first explored the relationships between egg ageand antimicrobial concentrations and pH, before relating these to the environ-mental variables. We corrected antimicrobial concentrations and pH values foregg age using two approaches: i) correction based on the differences in groupmeans between fresh and incubated eggs (i.e. eggs with no embryonic develop-ment vs. eggs with any embryonic development); ii) correction based on a regres-sion against amount of embryonic development. Neither correction approachqualitatively changed the outcome of any analyses, so we present all results basedon analyses of uncorrected values. We took a conservative approach and calculat-ed mean values per population (i.e. per species per location) of each dependentvariable. To do this, we first calculated mean values per nest for each population,and used these values to calculate mean values per population. We treatedspecies and geographically distinct populations as independent points and usedlinear models to investigate relationships between antimicrobial concentrations,pH and climate. Because sample size varied among species (Table 4.1), regressionmodels were weighted by the square root of the number of nests sampled in eachpopulation (Sokal and Rohlf 1995). We performed all statistical analyses using R2.13.0 (R Development Core Team 2009).

Results

Ovotransferrin, lysozyme and pH related to climatic variables and humidityindices in different ways (Fig. 4.1). Ovotransferrin correlated negatively with pre-cipitation, temperature and PET (Figs 4.1A-C), but temperature was the only cli-matic variable where the relationship was significant (Table 4.2). Therefore,ovotransferrin concentrations matched predictions regarding temperature, butnot regarding precipitation. However, there was a positive but non-significanttrend (Table 4.2) for ovotransferrin concentrations to increase with increasing

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Figure 4.1. (Right) Concentrations of ovotransferrin and lysozyme, and the pH of the albu-men of eggs from nine lark species, in relation to three climatic variables and two indices ofhumidity, the UNEP humidity index AUNEP (P/PET) and de Martonne’s index AM. (P / T +10). Humidity indices are plotted such that humidity increases along the x-axis. The size ofeach data point is proportional to the number of records (number of nests) contributing tothe value.

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humidity according to de Martonne’s index AM (Fig. 4.1E). The UNEP humidityindex AUNEP showed no relationship with ovotransferrin (Fig. 4.1D).

Lysozyme also showed a non-significant negative relationship with precipita-tion (Fig. 4.1F), but correlated positively and highly significantly with tempera-ture and PET (Figs 4.1G, H; Table 4.2). This resulted in a negative correlationbetween lysozyme and both humidity indices (Figs 4.1I, J) that was highly signifi-cant in the case of AM but not in the case of AUNEP (Table 4.2). Thus, for allclimatic variables and humidity indices, lysozyme concentrations displayed pat-terns contrary to our predictions.

There were no significant relationships between any climatic variables and pH(Figs 4.1K-O), although the positive relationship with temperature (Fig. 4.1l) wasonly marginally non-significant (Table 4.2). There was also no relationshipbetween AUNEP and the pH of the albumen (Fig. 4.1N), but a trend for pH of thealbumen to be higher in eggs from less humid environments according to AM

(Fig. 4.1O).Lysozyme and ovotransferrin concentrations were negatively correlated with

each other, but this relationship was not significant either when testing at thelevel of individual eggs (Spearman’s rank correlation S = 3.6 x105, P = 0.090,rho = –0.15) or at the level of populations (S = 622, P = 0.1973, rho = –0.37).

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Table 4.2. Results (F tests and P values) of linear models examining relationships betweenconcentrations of ovotransferrin and lysozyme and the pH of the albumen of eggs fromnine lark species, in relation to mean annual precipitation, temperature and potential evapo-transpiration (PET) and two indices of humidity, AUNEP and AM. Nominator and denomina-tor degrees of freedom are 1 and 12 for each model. Significant P values are shown in bold.

response variable explanatory variable F P

ovotransferrin precipitation 0.67 0.430temperature 4.59 0.053PET 0.83 0.380AUNEP 0.04 0.844AM 3.67 0.079

lysozyme precipitation 2.08 0.174temperature 16.46 < 0.001PET 15.95 0.002AUNEP 3.35 0.092AM 19.18 < 0.001

pH precipitation 0.94 0.350temperature 3.91 0.072PET 0.54 0.476AUNEP 0.25 0.626AM 1.80 0.204

Discussion

One prediction related to the immunological variation that has been documentedamong individuals, populations, and species is that immune defences mightmatch the risk of infection or disease (Horrocks, Matson and Tieleman 2011).This study provides a novel insight into this prediction by investigating the non-specific chemical defences of eggs collected along a gradient of presumed infec-tion risk, as indexed by environmental variation in humidity. The main finding ofour study is that, when applied to the antimicrobial defences of eggs, support forthis prediction is mixed. In general, concentrations of ovotransferrin supportedour hypothesis that chemical defences should be highest in eggs from morehumid environments where the risk of trans-shell infection is also highest. In con-trast, concentrations of lysozyme were lower in eggs from wetter environments,were positively correlated with environmental temperature and PET, and weresignificantly and negatively correlated with humidity. Thus, lysozyme showedpatterns directly contrary to our predictions. pH, a third quality of the albumen,showed qualitatively similar trends to those of lysozyme in relation to climaticvariables, although these trends were never significant.

Concentrations of ovotransferrin and lysozyme showed opposing patterns inrelation to humidity, our proxy for risk of trans-shell infection. One explanationfor contrasting patterns might be the existence of a trade-off between these pro-teins. In fact, concentrations of ovotransferrin and lysozyme were negatively cor-related, although not significantly. Eggs containing high concentrations ofovotransferrin – and that presumably are well protected – might require lesslysozyme, and vice versa. One testable possibility is that the microbial assem-blages on eggshells in different environments differ in composition and not justdensity (Cook et al. 2003). In that case, one antimicrobial protein may be moreeffective than another. For example, on its own, lysozyme is only effective againstgram-positive bacteria, while the iron-binding function of ovotransferrin makes itmore broadly effective. Interestingly however, when combined, ovotransferrinand lysozyme may display synergistic effects. In the presence of lactoferrin, a pro-tein similar to ovotransferrin, lysozyme becomes bactericidal against gram-nega-tive bacteria (Ellison and Giehl 1991) increasing its range of antimicrobialactivity. Lysozyme may also potentiate the activity of ovotransferrin (Ko et al.2009). A balance between the concentrations of ovotransferrin and lysozymemight maximise antimicrobial defence of the egg while minimising the amountsof protein required. This could be important if production of antimicrobial pro-teins was costly (but see Shawkey et al. 2008), or if increasing the concentrationof these proteins in the egg came at the expense of other albumen componentsthat are required for embryonic development. Nonetheless, the existence of sucha trade-off in protein levels cannot explain why the balance between ovotransfer-rin and lysozyme shifts with respect to predicted trans-shell infection risk and fur-

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ther points to the need to evaluate egg microbial assemblages.An explanation for why lysozyme does not correlate with predicted infection

risk could be that the primary function of lysozyme in egg albumen is not as adefensive protein. This argument has been proposed previously (Board and Fuller1974), but largely ignored in subsequent investigations of egg antimicrobial prop-erties. Aside from antimicrobial properties, lysozyme in albumen possesses addi-tional functions, some of which might relate to environmental factors such astemperature. For example, the viscosity of the albumen is determined by thebinding of lysozyme to another albumen protein called ovomucin (Burley andVadehra 1989), and albumen viscosity correlates strongly with lysozyme activity(Trziska and Clostermann 1993). In commercial chicken lines, albumen viscositymay relate to egg hatchability, (Hurnik, Reinhart and Hurnik 1978), and albumenviscosity degrades faster at higher temperatures (Reijrink et al. 2008). The physic-ochemical properties of lysozyme may therefore make it more valuable in eggslaid in hot and dry environments, where the risk of trans-shell infection may alsobe lower. In cooler and wetter environments reductions in hatchability due totemperature-induced albumen degradation may be less relevant. In these envi-ronments, if microbes are the dominant selection pressure, then the broader-spec-trum antimicrobial activity of ovotransferrin may be more valuable. This scenariocould further support the idea of a trade-off between concentrations of ovotrans-ferrin and lysozyme. Nonetheless, given the ubiquitous function of lysozyme asan antimicrobial agent in other biological systems (reviewed in Callewaert andMichiels, 2010), a complete lack of any antimicrobial function of lysozyme in eggalbumen seems unlikely.

We found no significant correlations between albumen pH and any climaticvariables. We suggest that this is because physiological constraints related toembryonic development limit the extent to which the pH of an egg can bealtered. Upon laying, eggs lose carbon dioxide, which results in an increase in pHof the albumen from about pH 7.6 to pH 9.5 (Sharp and Powell 1931), changingthe albumen from bacteriostatic (pH 6-8) to bactericidal (pH 9-10; Tranter andBoard 1984). This pH burst, which occurs during the first days after laying, couldeffectively sterilise the albumen of any microbial contamination, both by makingthe albumen inhospitable to microbial growth, and by potentiating the activity ofantimicrobial proteins (Tranter and Board 1984; Reijrink et al. 2008). Yet, albu-men with a pH above pH 8.2 is detrimental to embryonic development (Reijrinket al. 2008). This implies that beyond the initial short-term increase in pH, birdsare limited in their ability to rely on high albumen pH as an antimicrobialdefence.

Temperature explained the highest amount of variation in ovotransferrin andlysozyme concentrations and also in albumen pH. Our data are in line with anearlier study showing that eggs contain more lysozyme when ambient tempera-ture during egg formation is higher (Saino et al. 2004), which might relate to

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albumen viscosity and protein trade-offs, as discussed already. The relative impor-tance of temperature to egg defences also provides an interesting contrast to arelated study of immune function in adult larks measured along a similar gradi-ent of climatic variation and disease risk (chapter 3). In that study, immuneindices of birds declined with disease risk, as predicted by environmental humidi-ty. Contrary to the findings of the current study however, variation in the immuneindices of birds (including plasma concentrations of ovotransferrin) wasexplained by precipitation and not by temperature. We suggest that the impor-tance of temperature in explaining variation in immune defences of eggs but notof birds relates to the ectothermic nature of eggs and the endothermic abilities ofbirds. Although the enzymatic activity of antimicrobial proteins is believed to betemperature-dependent (Tranter and Board 1984) it is interesting to note that inour study ovotransferrin showed a negative (although very weak and non-signifi-cant) trend with temperature while lysozyme showed a positive relationship.

Despite the apparent necessity of water for trans-shell infection (D'Alba, Obornand Shawkey 2010), mean annual precipitation explained little to no variation inany albumen defence component. This result could be explained if the negativeeffects of water on trans-shell infection are countered by incubation (Cook et al.2005a; Shawkey et al. 2009; D'Alba, Oborn and Shawkey 2010). With the excep-tion of lysozyme concentration, PET was also poor at explaining variation inalbumen defences. Most likely for that reason, and because of the strong correla-tions with temperature, albumen defences correlated much more strongly withhumidity index AM (based on precipitation and temperature) than with humidityindex AUNEP (calculated from precipitation and PET). Humidity indices morespecifically, and environmental variation in general, may still be useful proxies forrisk of microbial infection, as evidenced by the decrease in soil microbial diversityand abundance associated with decreasing annual precipitation (and presumablyhumidity; Bachar et al. 2010). However, describing and quantifying the microbialassemblages of eggs and the surrounding microenvironment, such as nest materi-al, is essential to developing a better understanding of the risks of trans-shellinfection in different environments (Cook et al. 2005b; Wang, Firestone andBeissinger 2011). Ultimately, studies that combine these measurements withquantification of albumen defences and assessment of egg hatchability will beinformative in understanding how immune protection evolves to match the riskof infection. In the meantime, we have uncovered interesting inverse relation-ships between ovotransferrin and lysozyme and environmental parameters. Theserelationships provide new insight regarding the functional role of albumendefence proteins, the potential trade-offs between proteins, and the value of abi-otic environmental variables as proxies for the variation in biotic infection risk.

AcknowledgementsWe thank the following people and organisations for logistical support, and for permission

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to collect eggs and work with wild birds: HH Prince Bandar bin Saud, Secretary General of

the Saudi Wildlife Commission and Mr Ahmad Al Bouq, Director of the National Wildlife

Research Center, Saudi Arabia; Henk-Jan Ottens and Staatsbosbeheer, Netherlands;

Muchane Muchai and National Museums Kenya. Sample collection in Afghanistan was

made possible by the generous support of the American people through the United States

Agency for International Development (USAID) and its collaborative grantee the Wildlife

Conservation Society (WCS). Financial support came from the Schure-Beijerinck-Poppings

Fonds (to N.P.C.H), BirdLife Netherlands (to B.I.T.), VENI grants from the Netherlands

Organisation for Scientific Research (863.08.026 and 863.04.023, to K.D.M. and B.I.T.,

respectively), and a Rosalind Franklin Fellowship from the University of Groningen (to

B.I.T.).

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Environmental and seasonal variation inimmune defence and disease risk

IIIPART

Immune defences are associated withmicrobial pressure rather than life historyin larks from contrasting environments

5CHAPTER

Nicholas P.C. Horrocks, Arne Hegemann, Kevin D. Matson,Kathryn Hine, Sophie Jaquier, Mohammed Shobrak,Joseph B. Williams, Joost M. Tinbergen & B. Irene Tieleman

Unpublished manuscript

AbstractVariation in immune defence is hypothesised to arise either through parallelvariation in disease risk or due to trade-offs with other life history traits encom-passed in the ‘pace-of-life’ concept. We studied lark species (Alaudidae) in theArabian Desert and temperate Netherlands to test contrasting predictions fromthese two hypotheses. Based on expected lower exposure to disease risk, desertspecies are predicted to have relatively weaker immune defences, while basedon their slower pace-of-life, desert-living larks are predicted to have relativelystronger immune defences than temperate larks. We developed and applied anovel technique to quantify host-independent and host-dependent measures ofdisease risk by assaying the abundance of culturable microbes in ambient airand on the surface of birds. We also measured four indices of constitutive innateimmunity. Desert-living larks were exposed to significantly lower concentrationsof airborne microbes than temperate larks. Densities of some classes of bird-associated microbes were also lower in desert species, although compared withairborne microbes, differences between environments in bird-associated densi-ties were less pronounced. Compared to their temperate relatives, desert-livinglarks exhibited significantly lower levels in two of four immune indices. Varia-tion in microbial abundance and variation in immune defence among specieswas less in the desert than in the temperate zone. Because immune system lev-els broadly matched with microbial exposure but not with pace-of-life, we con-cluded that, in our study system, disease risk is a more important modulator ofimmune defences than life history. The use of both host-independent and host-dependent measures of disease risk, including assessment of microbial assem-blages, provides novel insight into the mechanisms underlying immunologicalvariation.

Introduction

Investment in immune defences is hypothesised to be shaped by disease risk andby trade-offs associated with life history. If immune defences are related to thethreats posed by disease risk, for example the abundance of pathogens and/orparasites (e.g. Piersma 1997; Møller 1998; Blount et al. 2003; Guernier,Hochberg and Guegan 2004; Mendes et al. 2005; Matson 2006; Spottiswoode2008), then environments with numerous pathogens should favour selection forrobust immune systems. Furthermore, if as predicted, immune systems consumelimited resources (e.g. energy; Sheldon and Verhulst 1996; Lochmiller andDeerenberg 2000; Norris and Evans 2000), then environments with fewerpathogens should favour selection for relatively weaker immune systems (Lind-ström et al. 2004; Tschirren and Richner 2006). Another hypothesis, also basedon consumption of limited resources by the immune system, predicts trade-offsbetween immune function and other resource-demanding activities such as repro-duction (Ilmonen, Taarna and Hasselquist 2000). Life history theory (Roff 1992;Stearns 1992) predicts that species with low reproductive rates and long lifespans (slow ‘pace-of-life’) should invest more in (certain) immune defences thanspecies with high reproductive rates and short life spans (fast ‘pace-of-life’; Rick-lefs and Wikelski 2002; Tieleman et al. 2005; Lee 2006). Interestingly, whenapplying these hypotheses to species with a slow pace-of-life that live in environ-ments with predicted low disease risk (e.g. deserts), opposing predictions abouttheir immune defences arise (Horrocks, Matson and Tieleman 2011).

Microbes represent a component of disease risk encountered by all animals.Vectors are typically not required for transmission (Kulkarni and Heeb 2007), andthe presence of specific microbes may vary among environments (Corby-Harriset al. 2007). Variation in microbial pressure has physiological implications: higherconcentrations of airborne microbes produce greater inflammation and havehigher toxicity in vitro (Huttunen et al. 2010); increased microbial diversity andabundance correlates with greater immunological investment in vivo (Alcaideet al. 2010).

We introduced a novel technique to quantify two measures of broad, non-spe-cific microbial pressures: host-independent microbial concentrations present inambient air and host-dependent densities of microbes on the surface of birds.Host-dependent measures (e.g. load or prevalence of specific parasites) are moretypically used, but such measures are affected by host immune defences and maynot reflect the disease risk associated with an environment (Horrocks, Matsonand Tieleman 2011).

General microbial challenges are first countered by the constitutive innate armof the immune system, which provides a non-specific defence against infection(Janeway et al. 2004). We measured three aspects of constitutive innate immunity.The ability of whole blood to limit bacterial growth integrates humoural and cell-

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mediated aspects of constitutive innate immunity (Tieleman et al. 2005; Millet etal. 2007). The ability of plasma to agglutinate and lyse foreign cells (Matson,Ricklefs and Klasing 2005) involves natural antibodies and the complement sys-tem (Ochsenbein and Zinkernagel 2000). Lastly, concentrations of haptoglobin,an acute phase protein, increase in concentration in response to inflammation orinfection (Dobryszycka 1997; van de Crommenacker et al. 2010).

To investigate how disease risk and life history may be associated with immu-nity, we compared microbial exposure and immune defence in seven species oflarks (Alaudidae) from the Arabian Desert and the mesic temperate Netherlands.Well-characterised life histories, behaviours and physiologies (reviewed by Tiele-man 2005) illustrate that the pace-of-life is slower in desert-living larks than tem-perate larks (Tieleman, Williams and Visser 2004). A slow pace-of-life may resultin higher immune investment by larks in the desert (Tieleman et al. 2005; Lee2006). However, relative to other environments, deserts may pose a lower risk ofinfection by endo- (Little and Earlé 1995; Valera et al. 2003; Jex et al. 2007;Froeschke et al. 2010) and ecto-parasites, (Moyer, Drown and Clayton 2002; butsee Carrillo et al. 2007). The combined effects of low primary productivity, highambient air temperature, minimal precipitation and humidity and high solar radi-ation likely limit growth rates, abundance and diversity of microbial assemblagesin xeric environments (Tong and Lighthart 1997; Burrows et al. 2009; Tang 2009;Bachar et al. 2010). Thus, compared with temperate larks, desert larks mightencounter fewer microbes and a relatively lower microbial pressure, a circum-stance that may lead to lower investment in immune defence. In this study weexamined differences between desert and temperate environments in concentra-tions of microbes in ambient air, densities of bird-associated microbes andimmune indices. Because little is known of other sources of variation that mayaffect our interpretation of differences between environments, we also exploredyear-to-year variation in immune indices within each environment. In addition,we investigated variation among species, also within environments, in densitiesof bird-associated microbes and immune indices.

Methods

Study species, study locations and bird handlingWe studied seven lark species during the breeding season. Arid-zone larks(hoopoe lark Alaemon alaudipes, n = 37; Dunn’s lark Eremalauda dunni, n = 29;bar-tailed desert lark Ammomanes cincture, n = 11; black-crowned finchlark Ere-mopterix nigriceps, n = 27; crested lark Galerida cristata, n = 13) were capturedin May and June 2006 and 2007 at two locations in central Saudi Arabia. In 2006all five species were captured at Mahazat as-Sayd (henceforth Mahazat), areserve in the Arabian Desert (N 22°15’ E 41°50’). In 2007 the nomadic species –

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black-crowned finchlark and crested lark – were absent from Mahazat and wereinstead captured about 170 km away at the National Wildlife Research Center,Taif (N 21°15’ E 40°42’). Mahazat is characterised by gravel plains, sparse vegeta-tion, and annual mean rainfall of 96 ± 71 mm (± SD). Spring conditions are hotand dry, with mean air temperatures of at least 30°C (Tieleman and Williams2002). Throughout the year, environmental conditions at Taif are wetter andcooler than at Mahazat (Tieleman, Williams and Bloomer 2003).

Temperate larks (woodlark Lullula arborea, n = 58; skylark Alauda arvensis, n =123) were captured in the Aekingerzand, northern Netherlands (N 52°56’ E 06°18’)between April and July in 2006–2008. The Aekingerzand consists of heath andgrazed meadowland, surrounded by agricultural fields and mixed woodland. Meanannual rainfall is 853 ± 160 mm (SD). Mean daily air temperature during thebreeding season is 13.8°C (Koninklijk Nederlands Meteorologisch Instituut).

Birds were captured using mist nets and clap traps, sometimes with the addi-tion of playback calls or bait (bird seed and mealworms). Upon capture, we sam-pled microbial loads from birds (desert n = 36, 2007 only; temperate n = 36,2008 only) using an air-sampling method (bird air-sampling; see below). We thenbled (45-60 minutes after capture), weighed (± 0.1 g) and measured (wing ±0.1 cm; tarsus ± 0.01 cm) all birds. A further 226 birds (desert n = 81, temper-ate n = 145) were bled within 10 minutes of capture and measured but were notsampled for microbes. Between 200–300 µl of blood were collected from thebrachial vein and stored on ice until processing later the same day. Samples werecentrifuged to separate plasma from red blood cells. Plasma was frozen andstored at –20°C until use in immune assays. All birds were sexed by body meas-urements and behavioural observations. Permission to work with wild birds inSaudi Arabia was obtained from the National Wildlife Research Centre. Proce-dures in the Netherlands were conducted under licence from the Animal Experi-mentation Committee of the University of Groningen (DEC 5219, 5219A).

Air samplingWe used a battery-powered portable air sampler (Burkard, Rickmansworth, UK)to determine concentrations of microbes in ambient air and densities of microbeson birds. The air sampler drew air at a constant rate through a perforated metalplate, filtering microbial particles that collect onto an agar-filled Petri dish below.Following incubation of the agar plate, the number of colony-forming units(CFUs) was counted to obtain an index of the concentration of culturablemicrobes. Culture-dependent methods remain useful for measuring microbialexposure (e.g. Haas et al. 2010) despite the unculturability of many microorgan-isms (Rappé and Giovannoni 2003). For both environmental air-sampling andbird air-sampling we cultured three different microbial groups. Generalist aerobicbacteria were cultured on non-selective Tryptic soy agar; gram-negative bacteriawere cultured on MacConkey agar with crystal violet sodium, chloride and 0.15%

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bile salts (Sigma-Aldrich, St Louis, MO, USA); fungi were cultured on Sabouraud4% Glucose agar (Sigma-Aldrich), with 50 mg litre-1 Gentamicin antibiotic(Invitrogen, Breda, The Netherlands) to inhibit bacterial growth. Plates wereincubated at 30°C and numbers of CFUs were counted after 24 hours (generalistbacteria,), 72 hours (fungi) and 96 hours (gram-negative bacteria), due to differ-ent growth rates. The collection efficiency of impaction air samplers varies withparticle size: smaller particles are collected less efficiently than larger ones. If par-ticles are cross-contaminated, for example fungal spores with bacteria on theirsurface, then CFU counts might be correlated among agar types. To account forthe possibility that more than one culturable particle might be collected through asampling hole but produce only a single countable colony we applied a correctionto all CFU counts (Andersen 1958).

Environmental air-samplingSampling of ambient air for microbes took place over 20 days in the desert in2007 and over 37 days in the Netherlands in 2008. We sampled at 16 sites in thedesert and at 12 sites in the temperate zone, all of which were micro-habitatswithin our study areas where birds commonly occurred. Environmental air sam-ples do not correspond with specific bird air-sampling events, however. Sampleswere collected throughout daylight hours to account for potential diurnal varia-tion in microbial aerosol loads (Tong and Lighthart 1999). Sampling durationwas optimised for both environments to avoid the situation where colonies weretoo numerous to count: air was sampled for 15–30 minutes per agar plate at thedesert sites and for five minutes per agar plate at the temperate site. Samplingeffort was standardised by multiplying the duration of sampling by the air flowrate (20 litres minute-1) and expressing the data as CFU m-3 of air.

Bird air-samplingWe sampled bird-associated microbes by using the air sampler to collect themicrobes shed from the surface of a bird as air passed over it. Birds wererestrained inside a mesh tube and were placed, always facing the same way,inside a plastic box (33 × 22 × 16 cm) with fitted lid. The head of the air samplerwas fitted through a hole in the side of the box so that air re-circulated back intothe box. The order of agar types varied among birds, but duplicate plates wererun for each agar type and for 5 minutes per agar plate. Handling of birds priorto being air-sampled was kept to a minimum. We cleaned our hands with antibac-terial hand wash and used single-use bird bags to avoid cross-contaminationamong birds. All equipment was sterilised with ethanol before and after use.Since we sampled species of varying body size, we determined body surface area(Walsberg and King 1978) and expressed counts per bird as CFU cm-2 of bodysurface area. Bird air-sampling took place in the desert in 2007 and in the tem-perate zone in 2008.

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Immune assaysWe measured haptoglobin concentrations (mg ml-1) with an assay that quantifiesthe haem-binding capacity of plasma (TP801; Tri-Delta Diagnostics, NJ, USA). Wequantified natural antibody-mediated agglutination titres and complement-medi-ated lysis titres by incubating red blood cells from rabbits (B-0009D, Harlan, UK)with serially diluted plasma samples from birds, according to Matson, Ricklefsand Klasing (2005). We performed the microbial-killing assay under sterile condi-tions following Tieleman et al. (2005) and Matson, Tieleman and Klasing (2006).Fresh blood samples, taken immediately upon capture (from non-air sampledbirds only) were transported directly to the laboratory, always arriving within 30minutes. Blood was diluted with CO2-independent media (Invitrogen) and subdi-vided for incubation at 41°C (bird body temperature) with different microbialstrains. Microbicidal ability of blood was tested against three organisms, chosento minimise effects of exposure histories of birds: Escherichia coli (ATCC 8739),Candida albicans (ATCC 10231), and Staphylococcus aureus (ATCC 6538; MicroBi-oLogics, St. Cloud, MN, USA). Lyophilised microbial pellets were reconstitutedaccording to manufacturer’s instructions and then incubated with the blood-media mixture under strain-specific conditions: E. coli was diluted 1:10 withblood-media mixture and incubated for 30 minutes; C. albicans and S. aureuswere diluted 1:9 with blood-media mixture and incubated for 180 minutes. Afterincubation 75 µl of the diluted blood-media-bacteria mixture were plated induplicate on agar plates. Control plates were also inoculated with 75 µl ofmicroorganisms, diluted with media to the same final concentration as the blood-media-bacteria mixes, but not incubated at 41°C. These control plates reflectedthe number of microorganisms that the bird blood first encountered (i.e. the inoc-ulating dose). All plates were incubated at 30°C and the number of CFUs wascounted 24 hours later. We calculated killing ability as one minus the quotient ofCFUs on the blood plates divided by the number of CFUs on control plates.

Statistical analysesWe used Mann-Whitney U tests and Fisher’s F tests to examine environmentalvariation in mean values and variance of airborne microbial concentrations. Weused generalised and linear mixed models to examine environmental variation inbird-associated microbes and immune indices. For each response variable weanalysed data from all years and a restricted dataset containing only values col-lected from both environments in the same year(s). Results were qualitativelysimilar whichever dataset was used; we only report results obtained using thelarger dataset. Full models contained the categorical factors environment (desertor temperate), sex, year and the interaction environment*sex. We controlled forspecies differences and repeated measures by including species and individualnested within species as random effects. To examine species differences withinand among environments, we refitted models with species as a main effect and

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without the term environment and its interaction. When the term species was sig-nificant, we used Tukey post-hoc tests to identify significant species differences.Full starting models to examine inter-annual variation in immune indiceswithin environments contained the terms species, sex, year and the interactionspecies*year. We simplified models using stepwise backward elimination, basedon log-likelihood ratio tests and P > 0.05. Residuals of models were checked fornormality and homogeneity of variance by graphical examination. Analyses wereperformed using R version 2.10.01 (R Development Core Team 2009).

We included time of day as a covariate in analyses of bird-associated microbesto account for potential temporal fluctuations in concentrations of airbornemicrobes (Tong and Lighthart 1999). We grouped bird-derived data for our twodesert sites since immune indices and bird-associated microbial densities did notdiffer between these two sites during the breeding season (Horrocks et al. unpub-lished data). We also combined data on haptoglobin concentration and agglutina-tion and lysis titres for air-sampled and non-air sampled birds. There were nodifferences between these groups (all P > 0.08), despite differences in time ofblood collection after capture (45–60 minutes vs. <10 minutes).

For the microbicidal assay, incubation with blood from some individuals pro-moted growth of bacteria, rather than killing, resulting in negative values (E. coli21/90 individuals; C. albicans 14/79; S. aureus 65/83). Therefore, we followedthe approach of Buehler et al. (2008) and present the results of analyses using anormal distribution to describe the data. Microbicidal ability might depend on theinoculation concentration, so we always included the mean control plate colonycount as a covariate.

To investigate the effect of general microbial pressure on immunity, we exam-ined correlations between immune indices – haptoglobin concentrations, aggluti-nation and lysis titres – and total microbial density, which was the sum of allbacterial and fungal densities per bird. We calculated species means of totalmicrobial density and individual deviations from these means to allow us to dis-tinguish the contributions of within- and among-species variation (van de Pol andWright 2009).

Results

Concentrations of environmental airborne microbesMean airborne concentrations of all three microbial groups were significantlylower in desert locations than in the temperate environment (Figs 5.1, 5.4A).Comparing the two desert locations, concentrations of generalist bacteria andfungi were significantly lower at Mahazat than at Taif (Fig. 5.1). Variance in theconcentration of microbes within an environment was also lower in the desertthan in the temperate environment (Figs 5.1, 5.4A). When combining data for

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Mahazat and Taif, the difference in variance was significant for all three microbialgroups: generalist bacteria (F11, 12 = 7.66, P = 0.001); gram-negative bacteria(F11, 13 = 11.34, P < 0.001); fungi (F10, 10 = 109.40, P < 0.001). Gram-negativebacteria were the least numerous microbial group in both environments (Fig.5.1).

Densities of bird-associated microbesDensities of generalist bacteria (z = 0.97, P = 0.334) and gram-negative bacteria(z = –1.42, P = 0.156) associated with birds did not differ significantly betweenlocations (Figs 5.2, 5.4B), but significantly lower densities of fungi were shedfrom larks living in the desert compared with temperate larks (z = –3.74, P <0.001; Figs 5.2, 5.4B). Excluding three outliers (> two standard deviations fromthe mean) from the fungi data did not change the result. There was a significanteffect of time of day on densities of bird-associated generalist bacteria (z = 2.11,P = 0.035) and fungi (z = 2.35, P = 0.019): birds sampled later in the day shedsignificantly more microbes, although the size of this effect was exceedingly small(an increase of approximately one CFU cm-2 per 12 hours, in each case). Femalelarks shed more of all microbe types than males, but this difference was signifi-cant only for fungi (z = –2.01, P = 0.045; generalist bacteria, z = –1.91, P =0.056; gram-negative microbes, z = –0.25, P = 0.799).

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Figure 5.1. Concentrations of airborne microbes (generalist bacteria, gram-negative bacte-ria and fungi) measured at two sites in the Arabian Desert (Mahazat as-Sayd and Taif) andin the temperature Netherlands, during spring. Boxes encompass all data points betweenthe 25th and 75th percentiles (interquartile range, IQR). Thick bars inside boxes indicate themedian data value. ‘Whiskers’ indicate either the minimum or maximum data value, or 1.5times the IQR (approximately two standard deviations), whichever is smaller. Data pointsoutside this range (‘outliers’) are plotted individually as black dots.

Excluding environment, and comparing among species, we found a significanteffect of species on densities of bird-associated fungi (F6, 61 = 3.16, P = 0.009)and a non-significant trend in generalist bacteria (F6, 64 = 1.99, P = 0.080). Forfungi, a post-hoc test could not resolve the source of the significance, while forgeneralist bacteria the near-significant species effect was driven by a singlespecies, crested lark, which was significantly different from all other species (Fig.5.2). There was no effect of species on densities of gram-negative bacteria (F6, 64

= 1.58, P = 0.167).

Immune indicesTaking into account species and variation among years, concentrations of hapto-globin (F1, 5 = 8.45, P = 0.034) and titres of lysis (F1, 5 = 30.25, P = 0.003)were significantly lower in arid-zone larks than in the two lark species measuredat the temperate site (Table 5.1A; Figs 5.3, 5.4C). Agglutination titres (F1, 5 =3.98, P = 0.102) and microbicidal ability against E. coli (F1, 5 = 0.03, P = 0.865),C. albicans (F1, 5 = 0.06, P = 0.816) and S. aureus (F1, 5 = 0.66, P = 0.452) allshowed no significant differences between environments (Table 5.1a; Figs 5.3,5.4B, 5.4C).

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Figure 5.2. Densities of bird-associated microbes (generalist bacteria, gram-negative bacte-ria and fungi) air-sampled from seven lark species in the Arabian Desert (n = 5 species)and the temperate Netherlands (n = 2 species, light grey bars) in spring. Numbers abovebars are sample size. HL = hoopoe lark; DuL = Dunn’s lark; BTDL = bar-tailed desert lark;BCFL = black-crowned finchlark; CL = crested lark; WL = woodlark; SL = skylark. Boxesencompass all data points between the 25th and 75th percentiles (interquartile range, IQR).Thick bars inside boxes indicate the median data value. ‘Whiskers’ indicate either the mini-mum or maximum data value, or 1.5 times the IQR (approximately two standard devia-tions), whichever is smaller. Data points outside this range (‘outliers’) are plottedindividually as black dots.

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Table 5.1. Mean values (intercept), estimate sizes (difference) and 95% confidence inter-vals around the difference (95% C.I.) for environmental and inter-annual variation inindices of immunity measured in larks in the Arabian Desert (n = 5 species) and temperateNetherlands (n = 2) in spring. Desert larks were measured in 2006 and 2007 and temper-ate larks were measured in 2006-2008. Intercepts and differences are taken from final sta-tistical models examining the role of environmental and inter-annual effects on eachparameter. For estimates of environmental variation ‘desert’ is the reference category anddifferences are given relative to this. Estimates of inter-annual variation are given for eachconsecutive year (i.e. 2006 to 2007 and 2007 to 2008).

source of variance immune index mean (intercept) difference 95% C.I.

(A) environmentaldesert vs. temperate haptoglobin (mg ml-1) 0.37 0.21 0.02 – 0.40

agglutination (titre) 5.58 1.03 -0.30 – 2.36lysis (titre) 0.15 1.15 0.61 – 1.67E. coli (% killed) 0.73 -0.04 -0.58 – 0.51C. albicans (% killed) 0.33 0.03 -0.28 – 0.34S. aureus (% killed) -0.13 0.13 -0.27 – 0.53

(B) inter-annual desert2006 vs. 2007 haptoglobin (mg ml-1) 0.30 0.19 0.07 – 0.32

agglutination (titre) 5.86 1.13 0.24 – 2.02lysis (titre) 0.37 1.27 0.82 – 1.73

(C) inter-annual temperate2006 vs. 2007 haptoglobin (mg ml-1) 0.60 -0.04 -0.15 – 0.072007 vs. 2008 0.55 -0.39 -0.52 – -0.262006 vs. 2007 agglutination (titre) 6.73 2.05 1.07 – 3.032007 vs. 2008 8.78 -0.72 -1.82 – 0.372006 vs. 2007 lysis (titre) 1.25 1.23 0.64 – 1.822007 vs. 2008 2.48 -0.94 -1.52 – -0.362006 vs. 2007 E. coli (% killed) 0.95 -0.35 -0.70 – -0.012007 vs. 2008 0.60 0.55 0.25 – 0.842006 vs. 2007 C. albicans (% killed) 0.61 -0.22 -0.60 – 0.172007 vs. 2008 0.39 0.18 -0.14 – 0.512006 vs. 2007 S. aureus (% killed) -0.06 0.05 -0.40 – 0.502007 vs. 2008 -0.10 0.34 -0.31 – 1.00

Figure 5.3. (right). Patterns of species variation, inter-annual variation and environmentalvariation in indices of constitutive innate immunity (haptoglobin concentration, agglutina-tion and lysis titres, and microbicidal ability (proportion killed) of whole blood against E.coli, C. albicans and S. aureus) measured in seven lark species in the Arabian Desert (n = 5species; black symbols) and the temperate Netherlands (n = 2 species; white symbols) dur-ing spring. Desert larks were measured in two consecutive years in either Mahazat as-Sayd(black circles) or Taif (black triangles). Temperate larks were measured over three consecu-tive years. Data are plotted as mean values ± S.D. Numbers above bars are sample size. HL= hoopoe lark; DuL = Dunn’s lark; BTDL = bar-tailed desert lark; BCFL = black-crownedfinchlark; CL = crested lark; WL = woodlark; SL = skylark.

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Excluding environment as a factor and focusing on inter-specific differences,all measures of immunity exhibited variation at the species level that was signifi-cant or close to significant at the P = 0.05 level. Post-hoc tests revealed that forsome measures the effect of species was driven by members of a single speciespair that were significantly different from each other (Table A5.1). Desert-livinglark species were never significantly different from each other but the two tem-perate lark species sometimes were (Table A5.1).

Correlations between immune indices and total microbial loadLysis titre was the only immune index that showed any relationship with total

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microbial load (Table A5.2). Among species, there was no correlation betweenlysis titre and total microbial load (F1, 5 =0.34, P = 0.587). Within species, birdswith higher total microbial loads had greater lysis ability, but this finding did notreach significance (F1, 57 = 3.21, P = 0.079). Analysing data for each environ-ment separately revealed that the within-species relationship was strongly drivenby desert birds (F1, 27 = 5.61, P = 0.025) and was much weaker in temperatelarks (F1, 29 = 0.09, P = 0.761).

Inter-annual variation in immune function within environmentsIn desert-living larks, haptoglobin concentration (F1, 108 = 9.05, P = 0.003),agglutination titre (F1, 93 = 6.40, P = 0.013) and lysis titre (F1, 95 = 31.13, P <0.001) were all significantly higher in 2007 than in 2006 (Fig. 5.3, Table 5.1B).The interaction species*year was never significant, suggesting that all five desertlark species responded to the effect of year in a similar manner.

In the two temperate species, the interaction between species and year wasnot significant for lysis, but years differed (F2, 60 = 11.85, P < 0.001; Fig. 5.3,Table 5.1C). The interaction between species and year was significant for aggluti-nation titre (F2, 57 = 6.46, P = 0.003) and for haptoglobin concentration (F2, 59

= 10.81, P < 0.001) suggesting that woodlarks and skylarks differed in how theseimmune indices varied over years (Fig. 5.3). Analysing these variables separatelyfor each species revealed that woodlarks demonstrated significant inter-annualvariation in haptoglobin concentration (F2, 9 = 32.89, P < 0.001) but not aggluti-nation titre (F2, 9 = 1.17, P = 0.355; Fig. 5.3). In skylarks the pattern wasreversed; inter-annual variation in haptoglobin concentration was not significant(F2, 50 = 1.34, P = 0.270) whereas inter-annual variation in agglutination titrewas significant (F2, 48 = 15.99, P < 0.001; Fig. 5.3). The two temperate speciesexhibited similar significant inter-annual differences in microbicidal abilityagainst E. coli (F2, 41 = 7.30, P = 0.002) and both exhibited no inter-annual dif-ferences in microbicidal ability against C. albicans (F2, 31 = 0.96, P = 0.393) orS. aureus (F2, 32 = 1.85, P = 0.174; Fig. 5.4, Table 5.1C). After accounting forspecies differences, inter-annual variation in immune indices within an environ-ment could be of greater magnitude than variation in immune indices betweendesert and temperate environments (Fig. 5.3, Table 5.1A vs. Table 5.1B, C).

Discussion

Explanations for variation in immune defence focus on the role of either diseaserisk or life history but these two hypotheses can lead to opposing predictions(Horrocks, Matson and Tieleman 2011). We explored these opposing predictionssimultaneously by contrasting the differences in disease risk and life historybetween desert and temperate environments. We found that desert-living larks –

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with a slow pace-of-life (Tieleman, Williams and Visser 2004) – exhibited similaror significantly lower values of constitutive innate immunity than ‘fast-living’temperate larks, Moreover, we found that host-independent and some-host-dependent measures of microbial pressure, a proxy for disease risk, were signifi-cantly lower for desert-living larks than for temperate larks. Thus, our data areconsistent with the ideas that disease risks differ among environments and thatincreased disease risk is associated with stronger immune defences. In contrast,our data provide limited support for a relationship between pace-of-life andimmune investment. Desert-living larks exhibited the same or reduced levels ofimmune indices compared with temperate-living larks. These results offer limitedsupport to existing hypotheses regarding which types of immune defence mightbe limited by slow-living species (Lee 2006). However, we found no evidencethat a slow pace-of-life is associated with overall increases in immune defence.

Our novel air-sampling technique allowed us to quantify host-independent andhost-dependent measures of disease risk, providing greater insight into how hostdefences – both behavioural and immune – and disease risk may interact. The useof non-specific microbial assemblages provides an easily accessible and widelyapplicable index of disease risk. The emerging links between immune defenceand microbial abundance (Alcaide et al. 2010; this study; Horrocks et al. unpub-lished data) suggest that microbial assemblages will be a fruitful avenue of fur-ther ecoimmunology research. Culture-independent techniques offer furtherpossibilities to enhance and increase the amount of information about microbialassemblages that can be gained, including identification of pathogenic and otherfunctions (Horrocks, Matson and Tieleman 2011).

We identified macro-scale variation in microbial abundance, since desert ambi-ent air contained lower concentrations of microbes than air from the temperatezone. Low concentrations of airborne microbes in the desert likely relate to thefact that deserts are areas of low primary productivity (Field et al. 1998). Thisecological characteristic and the associated low rainfall and humidity, but greatlyelevated temperatures and solar radiation likely make deserts inhospitable formicrobial growth and could reduce microbial abundance (Tong and Lighthart1997; Burrows et al. 2009; Tang 2009). In fact, soil microbial abundance decreas-es with increasing aridity (Bachar et al. 2010) and fungal abundance increaseswith increasing precipitation (Talley, Coley and Kursar 2002). Within the desert,microbial concentrations were highest at Taif, which receives more annual rainfalland experiences lower average temperatures than does Mahazat (Tieleman,Williams and Bloomer 2003), further implicating a role for climatic conditions ininfluencing airborne microbial loads. Previous studies have examined environ-mental and habitat-related differences in antigens and pathogen pressure (e.g.Figuerola 1999; Mendes et al. 2005; Salkeld, Trivedi and Schwarzkopf 2008) butthese studies have relied on host-dependent measures of disease risk, which aswe have shown may differ from host-independent measures.

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Compared with concentrations of airborne microbes, environmental differ-ences in densities of microbes sampled from birds were small. Airborne and bird-associated microbes displayed similar relative abundances (generalist bacteria >fungi > gram-negative bacteria) in desert and temperate areas, but fungi werethe only bird-associated microbial group that was significantly lower on desert-living larks compared with temperate larks. Gram-negative bacteria showed asimilar but non-significant trend. Densities of microbes shed from birds are likelyimpacted by feather maintenance activities such as preening (Kulkarni and Heeb2007). These activities, while perhaps limited in all larks during reproduction(Tieleman, Williams and Visser 2003; Tieleman, van Noordwijk and Williams2008) might be particularly restricted in desert-living larks due to additional con-straints on activity budgets imposed by high temperatures (Tieleman andWilliams 2002). Habitat-specific behaviours might also influence densities ofbird-associated microbes. For example, the use of burrows and soil scrapes asthermal refuges by larks in the desert (Williams, Tieleman and Shobrak 1999)might increase exposure to soil microbes (Shawkey et al. 2005). Thus, desert-liv-ing larks might carry higher densities of microbes than expected based on air-borne concentrations. Sex-differentiated roles during the breeding season may beresponsible for higher fungal and generalist bacterial densities on female birdsthan males. By spending more time incubating eggs than males, female larksincrease their exposure to nest material and perhaps also to associated microbes(Berger, Disko and Gwinner 2003; Goodenough and Stallwood 2010). Femalesmight also be constrained in the time they have available for feather care com-pared with males.

We found no evidence that the slow pace-of-life in desert-living larks was asso-ciated with increased investments in immune function. However, we measuredonly constitutive innate immune indices which we thought were particularly rele-vant in the context of microbial pressure. A more refined hypothesis of the rela-tionship between pace-of-life and immune function states that slow-living speciesshould rely more on developmentally-expensive adaptive immunity and less onenergetically-costly constitutive innate immunity compared with fast-livingspecies (Lee 2006). Two of four constitutive innate immune indices were higherin fast-living temperate larks, but information on adaptive immunity in desert-living and temperate lark species is currently unavailable. Interestingly, the acutephase protein haptoglobin, and complement-triggered cell lysis, the two indicesthat were greater in temperate larks, both relate to inflammatory responses. Ingeneral a greater reliance on inflammation might be associated with fast-livingspecies (Lee 2006). The lack of correlation that we found between microbicidalability of whole blood and life history in larks contrasts with other studies. Slow-living bird species exhibited relatively higher microbicidal ability in a study thatfocused on life history variation in a single (tropical) environment (Tielemanet al. 2005; Matson, Tieleman and Klasing 2006). However, comparing across

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environments, Sparkman and Palacios (2009) found fast-living ecotypes of gartersnakes Thamnophis elegans exhibited greater microbicidal ability than their slow-living counterparts. Comparisons among studies are complicated when packagesof co-varying life history traits and environmental variation differ. Furthermore,the microbicidal ability assay is highly integrative, suggesting that similar end-points might be reached by employing strategies that differ in terms of cost ofefficiency. Overall, relationships between innate immunity and life history remainunclear.

An important additional finding of our study was that the variation in immuneindices between consecutive breeding seasons could sometimes be of similar orgreater magnitude to the immunological variation explained by environmentaldifferences. The extent of inter-annual variation also differed between environ-ments and among species for temperate larks, but not for desert-living larks.Although we do not exclude the possibility that small sample sizes for somedesert species meant that significant species*year interactions could not bedetected, it is noteworthy that the five desert lark species (almost without excep-tion) showed similar patterns of inter-annual variation in haptoglobin concentra-tion and agglutination and lysis titres. We also detected no significant speciesdifferences among desert-living larks in any immune indices when accounting forthe effect of year. In contrast, the two temperate lark species showed little consis-tency in immune indices over years, suggesting that different factors may be driv-ing patterns of inter-annual variation for these species, despite them inhabitingthe same mesic environment. This lack of consistency might be related to species-specific characteristics (e.g. greater phenotypic flexibility; Tieleman et al. 2003)or could indicate greater variability of conditions within the temperate environ-ment, as suggested by the data on airborne microbial concentrations. Inter-annu-al variation in immune function within an environment or season can beexpected if local conditions (e.g. resource availability, temperature, exposure topathogens) are not fixed from year to year, and if these conditions influence theoptimal investment in immune defence versus other fitness-enhancing traits(Hegemann et al. unpublished data). The importance of local conditions is illus-trated by the clear seasonal patterns in airborne and bird-associated microbes andimmune indices that we identified in desert-living larks sampled in spring andwinter (Horrocks et al. unpublished data). These findings illustrate the relevanceof changes in local conditions to immune function and highlight the importanceof considering such factors when trying to understand patterns of immunologicalvariation.

In conclusion, weaker immune defences in desert-living larks compared withtemperate larks were associated with microbial pressures and not life history. Theuse of both host-independent and host-dependent measures of disease risk,including assessment of microbial assemblages, offers a promising approach tounderstanding underlying causes in patterns of immunological variation.

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AcknowledgementsWe are grateful to HH Prince Bandar bin Saud, Secretary General of the Saudi Wildlife

Commission, and Mr Ahmad Al Bouq, Director of the National Wildlife Research Center

(NWRC) for their support during this study. We also thank the staff at the NWRC Taif and

Mahazat as-Sayd, for logistical support. Staatsbosbeheer gave permission to work in the

Aekingerzand. Several volunteers contributed greatly to the Aekingerzand Lark Project, par-

ticularly Rob Voesten. Maaike Versteegh provided advice on statistical analyses. Financial

support for this study was provided to N.P.C.H. by the Schure-Beijerinck-Poppings Fonds, to

A.H. and B.I.T. by BirdLife Netherlands, and to K.D.M. and B.I.T. by VENI grants

(863.08.026 and 863.04.023, respectively) from the Netherlands Organisation for Scientific

Research (NWO). B.I.T. also received a Rosalind Franklin Fellowship from the University of

Groningen.

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Appendix 5.1

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Table A5.1. Results (F tests and P values) of general linear models examining species varia-tion in immune indices of seven species of desert (n = 5 species) and temperate (n = 2species) larks. The final column shows those species that differed significantly from eachother (at the P = 0.05 level) in pair-wise post-hoc Tukey tests. HL = hoopoe lark; DuL =Dunn’s lark; BTDL = bar-tailed desert lark; BCFL = black-crowned finchlark; CL = crestedlark; WL = woodlark; SL = skylark.

immune measure F P species pairs

haptoglobin (mg ml-1) F6, 263 = 4.56 < 0.001 WL - HLWL - BCFLWL - CLSL - HLSL - CL

agglutination(titre) F6, 245 = 3.83 0.001 SL - DuL

lysis (titre) F6, 247 = 0.70 < 0.001 WL - HLWL - BTDLWL - BCFLSL - HLSL - BTDLSL - BCFL

% E. coli killed F6, 80 = 4.39 < 0.001 WL - HLWL - CLWL - SLSL - BTDL

% C. albicans killed F6, 65 = 2.05 0.071 WL - SL

% S. aureus killed F6, 71 = 2.19 0.054 SL - DuL

Table A5.2. Correlations at the among-species and within-species level between totalmicrobial density shed from birds and immune indices measured in five arid-zone and twotemperate lark species in Saudi Arabia and the Netherlands. Within-species centreing wasused to partition the contribution of among- and within-species variation to any relation-ship.

among-species within-species

immune measure estimate ± SE F P estimate ± SE F P

haptoglobin (mg ml-1) -0.07 ± 0.06 F1, 5 = 1.22 0.320 0.01 ± 0.02 F1, 59 = 0.15 0.702

agglutination (titre) -0.31 ± 0.40 F1, 5 = 0.58 0.480 0.11 ± 0.17 F1, 55 = 0.40 0.530

lysis (titre) 0.11 ± 0.19 F1, 5 = 0.34 0.587 0.13 ± 0.08 F1, 57 = 3.21 0.079

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Seasonal patterns in immune indicesreflect microbial loads on birds but notmicrobes in the wider environment

6CHAPTER

Nicholas P.C. Horrocks, Kevin D. Matson, Mohammed Shobrak, JoostM. Tinbergen and B. Irene Tieleman

Ecosphere, in press

AbstractDocumenting patterns in immune function is a first step to understandingimmune variation, but to comprehend causes and consequences, antigen andparasite exposure that may drive such variation must be determined. We meas-ured host-independent microbial exposure in five species of larks (Alaudidae) inthe Arabian Desert by sampling ambient air for culturable microbes during latespring and winter, two periods with contrasting environmental conditions. Wedeveloped a novel technique to assay densities of microbes shed from birds, andwe quantified four indices of constitutive innate immunity. Birds shed signifi-cantly more microbes during spring than winter, and all immune indices exceptone were also significantly higher during spring. In contrast, concentrations ofairborne environmental microbes were higher in winter. Among all birds in bothseasons, lysis titres were positively correlated with total densities of microbesshed from birds, suggesting that immune defences are directed towards themicrobes that birds carry, rather than microbes in the wider environment. Ourfindings highlight the relevance of quantifying non-specific immune challengesin ecological immunology studies, and reinforce the importance of both host-dependent and host-independent measures of antigenic pressure for understand-ing immune variation.

Introduction

Explanations for immunological variation originate from two different, but relat-ed, viewpoints. Following life history theory (Roff 1992; Stearns 1992), theimmune system competes for limited resources with other physiological and eco-logical processes (Sheldon and Verhulst 1996; Norris and Evans 2000; Schmid-Hempel 2003). Investment in resource-demanding activities such as reproduction(Ilmonen, Taarna and Hasselquist 2000) or migration (Buehler, Tieleman andPiersma 2010) might result in decreased investment in immune function. Byshifting focus to infections and other threats to fitness, immune system variationcan instead be examined through its benefits rather than its costs. Since parasitesand pathogens reduce host fitness (Brown, Brown and Rannala 1995; Fitze,Tschirren and Richner 2004), evolutionary theory suggests that investment indefences should be higher when the negative consequences of parasites andpathogens are greater (Tschirren and Richner 2006). Differences in diseasethreats that are a function of environment, time, or other ecological factors (e.g.Piersma 1997; Guernier, Hochberg and Guegan 2004), might therefore lead toparallel differences in immune function.

These two perspectives and the processes underlying them are not mutuallyexclusive. For example, seasonality, one type of environmental variation, coulddrive immune variation through either or both of the processes described above.Seasonal variation in immunity might arise if resource trade-offs change over theannual cycle (Buehler et al. 2008; Martin, Weil and Nelson 2008), for examplewhen thermoregulatory demands or food availability vary (Nelson et al. 2002).Alternatively, seasonal variation in immune defence may occur if disease riskchanges over the annual cycle (Nelson et al. 2002). Seasonal differences in theabundance of disease-transmitting vectors (Franklin and Whelan 2009) or in thefrequency of interactions between conspecifics might alter the risk of parasitetransmission (Sheldon 1993), injury or infection (Zuk and Johnsen 1998). Varia-tion in weather may affect microbial exposure: heat and humidity promote con-tact with fungal spores (Talley, Coley and Kursar 2002); wind increasesencounters with airborne microbes (Jones and Harrison 2004).

To understand how changes in disease risk shape immune variation over theannual cycle requires measuring host-dependent and host-independent parasites,pathogens and microbial assemblages (Horrocks, Matson and Tieleman 2011).Microbial assemblages, in particular, can offer an informative and expansive viewinto the antigenic pressures faced by wild animals. Microbes are found in essen-tially all environments and can be encountered by all individuals, since co-evolved vectors are not required for their transmission. Higher concentrations ofairborne microbes lead to increased inflammation and more dead macrophages invitro (Huttunen et al. 2010). Host-associated microbial assemblages of wild ani-mals may show connections to local environmental characteristics (Klomp et al.

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2008) and relate directly to immunological investment (Alcaide et al. 2010).We developed and implemented a novel air-sampling method to measure con-

centrations of culturable microbes in ambient air and densities of microbes shedfrom the surface of birds. This dual approach advances ecological immunologyand builds upon previous studies by assessing components of disease pressure(i.e. microbial abundances) that are strictly independent of host defences. Suchhost defences include constitutive innate immunity, which may be particularlyrelevant for defence against the continual antigenic pressure posed by airborneand bird-associated microbes because it is always active, and is not specific toparticular pathogens (Janeway et al. 2004). Important elements of constitutiveinnate immunity include the acute phase proteins, natural antibodies, and lyticenzymes (Janeway et al. 2004). Acute phase proteins, such as haptoglobin andovotransferrin, increase in concentration during inflammation or infection (vande Crommenacker et al. 2010; Horrocks, Tieleman and Matson 2011), and limitmicrobial growth (Cray, Zaias and Altman 2009). Natural antibodies recogniseforeign particles such as invading microorganisms and mark them for phagocyto-sis. These non-specific antibodies also activate the complement system, anenzyme cascade that leads to cell lysis (Ochsenbein and Zinkernagel 2000).

The aims of this study were to assess seasonal differences in microbial abun-dance in ambient air and on birds and to investigate how these differences relatedto immunity. We studied three resident and two nomadic lark species (Alaudidae)in the Arabian Desert, a well-studied system (reviewed in Tieleman 2005), whereenvironmental conditions differ markedly between seasons. We compared birdsduring late spring, when they breed, and in winter, when birds are reproductivelyinactive. During late spring high levels of solar radiation and temperatures mayseverely limit microbial growth and survival (Tong and Lighthart 1997; Talley,Coley and Kursar 2002; Burrows et al. 2009; Tang 2009). Simultaneously, hightemperatures may affect immune defences through resource trade-offs: restrictedfood availability and activity budgets negatively impact activities such as preen-ing (Tieleman and Williams 2002) that might otherwise reduce microbial loads,while reduced basal metabolic rates (Tieleman et al. 2003) might also have reper-cussions for immune functioning (Tieleman et al. 2005). In contrast, lower tem-peratures in winter may relax time and food constraints, and allow for enhancedself-maintenance, but microbial pressures could increase. Thus, immune indicescould match patterns in airborne and bird-associated microbes, or could reflectbroader seasonal differences in avian physiology. To further explore the potentialimpact of environmental variation on immune function, we compared residentlarks with nomadic larks, which ostensibly move location to keep their ecologicalsetting more constant, and we analysed inter-annual variation in immunedefences between consecutive springs.

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Methods

Study species and study locationWe sampled larks in late spring 2006 (May 11th – June 12th), late spring 2007(May 4th – June 6th) and in winter 2007 (November 24th – December 16th). Inspring 2006 and winter 2007 hoopoe larks (Alaemon alaudipes), Dunn’s larks(Eremalauda dunni), bar-tailed desert larks (Ammomanes cincture), black-crownedfinchlarks (Eremopterix nigriceps) and crested larks (Galerida cristata) were cap-tured at Mahazat as-Sayd (‘Mahazat’), a reserve in central Saudi Arabia (N 22°15’E 41°50’). Mahazat is characterised by sparsely-vegetated gravel plains. Annualmean rainfall is 90 ± 76 mm (± SD). Spring conditions are hot and dry (Appen-dix 6.1, Table A6.1; Tieleman and Williams 2002). If there has been sufficientrainfall in the preceding months, then some green vegetation is available, provid-ing invertebrate and plant food for birds. In winter, temperatures are lower andrainfall is higher. If environmental conditions are extreme in spring and summer,then the two nomadic species, black-crowned finchlark and crested lark, leaveMahazat (B.I. Tieleman, personal observation). This was the case in spring 2007,when we caught both species ~170 km away at the National Wildlife ResearchCenter, Taif (N 21°15’ E 40°42’) where conditions are more benign (Tieleman,Williams and Bloomer 2003). We trapped birds with mist nets or clap traps. Per-mission was granted by the National Wildlife Research Center, Saudi Arabia.

Environmental and bird samplingWe sampled the environment (2007 only; see below) and birds (2006 and 2007)independently of each other. In spring and winter 2007, immediately upon cap-ture, birds (n = 77) were air-sampled to measure microbial densities (see below)and then bled (45-60 minutes after capture), weighed (± 0.1 g) and measured(wing ± 0.1 cm; tarsus ± 0.01 cm). A further 67 birds in 2007, and all birds inspring 2006 (n = 62), were only bled (<10 minutes after capture), weighed andmeasured. Blood was collected from the brachial vein (200-300 µl) and stored onice for 30 minutes to four hours until being centrifuged to separate plasma andcellular fractions. Plasma was frozen and stored at –20°C until use in immuneassays. Birds were sexed by body measurements and by behavioural observations.

Air samplingWe used a portable impaction air sampler for agar plates (Burkard, Rick-mansworth, UK), which draws air at a constant rate over a metal ‘sieve plate’ per-forated with 1.0 mm holes. Under the sieve plate, an agar-filled Petri dish collectsmicrobial particles passing through the holes (Fig. A6.1A). The number of colony-forming units (CFUs; Fig. A6.1B) that grow during incubation of the plate pro-vides an index of the concentration of culturable airborne microbial particles.Culture-based data from air-sampling devices are useful indexes of overall micro-

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bial concentrations (e.g. Haas et al. 2010), since they provide an easily under-standable measure of antigenic exposure, despite issues pertaining to culturability(Rappé and Giovannoni 2003).

We collected two types of data using the air sampler: concentrations of cultur-able microbes in ambient air and densities of culturable microbes shed by birds.In both cases we used three agars to culture different microbial assemblages: i)generalist aerobic bacteria (Tryptic soy agar; Sigma-Aldrich, St Louis, MO, USA);ii) gram-negative bacteria (MacConkey agar with crystal violet, sodium chloride,and 0.15% bile salts; Sigma-Aldrich), and iii) fungi (Sabouraud 4% Glucose agar(Sigma-Aldrich), with 50 mg litre-1 Gentamicin antibiotic (Invitrogen, Breda, TheNetherlands)). Plates were incubated at 30°C, but colonies grew at different rates,so numbers of CFUs were counted after 24 hours (generalist bacteria), 72 hours(fungi) and 96 hours (gram-negative bacteria). We applied a correction, calculat-ed for a 100-hole air sampler (Andersen 1958), to all CFU counts. This correctionadjusts for the possibility that multiple culturable particles pass through a sam-pling hole in the sieve plate and produce only one countable colony.

Environmental air-samplingEnvironmental air samples were collected at different times of the day to accountfor potential diurnal variation (Tong and Lighthart 1999), and over a series ofdays (spring n = 20 days, winter n = 16 days) that spanned each fieldwork peri-od. Sampling sites were micro-habitats used by birds, but environmental air sam-ples do not correspond with specific bird air-sampling events. We sampled at tensites at Mahazat in both seasons in 2007, but five sites at Taif were sampled onlyin spring 2007, when we also caught birds there. Environmental air was sampledfor 15–30 minutes per agar plate. We standardised sampling effort by multiplyingthe number of minutes by the air flow rate (20 litres minute-1) and expresseddata as concentrations (CFU m-3 of air).

Bird air-samplingWe sampled bird-associated microbes by isolating a bird in a sterile box and usingthe air sampler to collect the microbial particles shed from the bird as air passedover it. Birds were air-sampled immediately after capture. Handling was kept to aminimum, and before touching a bird we cleaned our hands with antibacterialhand wash. Single-use paper bags were used as handling bags to avoid cross-con-tamination among birds. Birds were restrained inside a soft mesh tube that wassterilised with ethanol and placed, always oriented the same way relative to theair sampler, inside a sterilised plastic box (33 × 22 × 16 cm) with fitted lid. Thesterilised head of the air sampler was fitted through a hole in the side of the boxso that it circulated air back into the box during sampling (Fig. A6.1C). The orderof the three agar types varied among birds, but duplicate plates were run for eachagar. Birds were always sampled for five minutes per plate. Since species differed

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in body size, we determined body surface area (Walsberg and King 1978) andexpressed counts as densities (CFU cm-2 of body surface area). Because a stan-dard sampling time was always used and since air in the sampling chamber wasrecycled, these data are not based on air volume.

Immune assaysWe determined haptoglobin concentrations (mg ml-1) using a functional assaythat measures the haem-binding capacity of plasma (TP801; Tri-Delta Diagnos-tics, NJ, USA; ‘manual method’). Ovotransferrin concentrations (mg ml-1) weremeasured according to Horrocks, Tieleman and Matson (2011) with the excep-tion of bar-tailed desert larks and black-crowned finchlarks, for which plasmavolumes were insufficient. We measured natural antibody-mediated agglutinationtitres and complement-mediated lysis titres using rabbit red blood cells (B-0009D,Harlan, UK), using the assay of Matson, Ricklefs and Klasing (2005).

Statistical analysesWe analysed concentrations of airborne microbial particles with Mann-Whitney Utests because data were not normally distributed. We analysed bird-associatedmicrobial densities with generalised linear models with Poisson (or quasi-Poissonin cases of over-dispersion) errors. Full models initially contained the fixed termsspecies, season, sex, and the interactions species*season and species*sex. Weincluded time of day as a covariate because concentrations of airborne microbescan fluctuate diurnally (Tong and Lighthart 1999).

Before examining seasonal and inter-annual differences in immune indices weexplored the effects on immunity of time delay between capture and blood collec-tion. This differed between air-sampled (45–60 minutes) and non-air-sampledbirds (<10 minutes) but had no effect on immune indices (all P > 0.1), so wecombined data in further analyses. We also explored the effects of nomadic statusand location (spring 2006 and winter 2007: residents and nomads at Mahazat;spring 2007: residents at Mahazat, nomads at Taif). The interaction nomadic sta-tus*season (all P > 0.08) and the term nomadic status (all P > 0.3) were non-sig-nificant for all measures of bird-associated microbes and immunity. Comparingimmune indices of birds sampled in spring 2006 and spring 2007, the interactionnomadic status*year (all P > 0.2) and the term nomadic status (all P > 0.3) werealso non-significant. Therefore we combined data from Mahazat and Taif andfrom resident and nomadic species in analyses of seasonal and inter-annual varia-tion. We then used analysis of variance to examine the effects of season or yearfor each immune variable. Full models contained the fixed terms season or year,species, sex, species*sex and, when relevant, species*season.

We examined correlations between total microbial density (the sum of general-ist, gram-negative and fungal densities per bird) and each immune index with gen-eralised linear mixed models. We included species means of total microbial density

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and individual deviations from these means as explanatory variables, to distinguishbetween within- and among-species variation (van de Pol and Wright 2009). Addi-tionally, initial models contained the fixed effects season, the random effectspecies, and the interactions species-mean*season and individual deviation*season.

Terms were dropped sequentially when P > 0.05. We report interaction termsonly when significant. Differences among levels were examined using post-hocTukey tests. Residuals of statistical models were examined graphically for normal-ity and homogeneity of variance and met these assumptions. Analyses were per-formed using R 2.10.1 (R Development Core Team 2009).

Results

Seasonal patterns in environmental airborne microbesConcentrations of culturable airborne microbes at Mahazat were significantlyhigher in winter than in spring for all three groups of microbes (Figs 6.1 and 6.2).In both seasons, concentrations of generalist bacteria were highest and gram-neg-ative bacteria were lowest. Spring concentrations of airborne microbes at Taifwere higher than spring concentrations at Mahazat and were not significantly dif-ferent from winter concentrations at Mahazat (Fig. 6.1).

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Figure 6.1. Concentrations of airborne microbes (generalist and gram-negative bacteria,and fungi) measured in the Arabian Desert at Mahazat as-Sayd and Taif in spring and atMahazat as-Sayd in winter. Boxes encompass all data between the 25th and 75th percentiles(interquartile range, IQR). Thick bars inside boxes indicate the median value. ‘Whiskers’indicate either the minimum or maximum value, or 1.5 times the IQR (approximately twostandard deviations), whichever is smaller. Data outside this range (‘outliers’) are plottedindividually as black dots.

Seasonal patterns in bird-associated microbesBirds shed significantly higher densities of generalist and gram-negative bacteriain spring than in winter (generalist bacteria, F1, 71 = 7.90, P = 0.006; gram-neg-ative bacteria, F1, 72 = 4.78, P = 0.013; Fig. 6.2; Appendix 6.1, Fig. A6.2), whenaccounting for differences among species. Densities of fungal CFUs sampled frombirds did not differ significantly between the two seasons (F1, 67 = 1.37, P =0.247; Figs 6.2 and A6.2). In both seasons, densities of generalist bacteria werehighest, and densities of gram-negative bacteria were lowest, matching patternsexhibited in ambient air. Densities of microbes shed by males and females did notdiffer (all P > 0.2), and there was no effect of the time of day when birds weresampled (all P > 0.1). When accounting for the effect of season, significant differ-ences existed among species for densities of generalist bacteria (F4, 71 = 2.93,P = 0.027) and gram-negative bacteria (F4, 72 = 2.99, P = 0.024) but not fordensities of fungi (F4, 64 = 1.38, P = 0.252). A post-hoc test revealed that crestedlarks shed significantly more generalist bacteria than the four other species,which otherwise did not differ (Fig. A6.2). A post-hoc test could not resolve anysignificant differences among species for densities of gram-negative bacteria,despite the significance of species in the main model.

Seasonal patterns in immune indicesHaptoglobin concentrations (F1, 133 = 19.21, P < 0.001) and agglutination(F1, 133 = 19.21, P = 0.041) and lysis (F1, 129 = 5.10, P = 0.026) titres were sig-nificantly higher in spring than in winter (Fig. 6.2; Appendix 6.1, Fig. A6.3). Ovo-transferrin concentrations showed a similar but non-significant trend (F1, 39 =0.92, P = 0.343; Figs 6.2 and A6.3). Males had significantly higher haptoglobinconcentrations than females (F1, 133 = 5.33, P = 0.023). Other immune indicesdid not differ between sexes. Lysis was the only immune parameter where speciesdiffered significantly (F4, 129 = 3.37, P = 0.012; Fig. A6.3). A post-hoc testrevealed the significance of this term was driven by a single species pair (Dunn’slark > bar-tailed desert lark).

Inter-annual variation in immune indicesHaptoglobin concentrations (F1, 108 = 9.05, P = 0.003) and agglutination (F1, 93

= 6.40, P = 0.013) and lysis (F1, 95 = 31.16, P < 0.001) titres were significantlyhigher in spring 2007 than in spring 2006. Ovotransferrin concentrations showeda similar trend but were not significantly different between years (F1, 41 = 3.27,P = 0.078). Relative to the mean, annual variation in all immune indices wasgreater than seasonal variation in the same indices (Appendix 6.1, Table A6.2).

Correlations between total microbial density and immune indicesLysis titres correlated positively and significantly with total densities of microbesshed by birds (F1, 65 = 4.09, P = 0.047; Fig. A6.4), after controlling for season

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(F1, 65 = 1.35, P = 0.249) and among-species variation (F1, 3 = 0.57, P =0.506). Haptoglobin and ovotransferrin concentrations and agglutination titresshowed no significant relationships with total microbial load, either at theamong-species level (all P > 0.4) or at the within-species level (all P > 0.1).

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Figure 6.2. Differences between springand winter in concentrations of airbornemicrobes, densities of bird-associatedmicrobes and indices of constitutiveinnate immunity measured in five larkspecies in the Arabian Desert. Data aremean values and error bars indicate stan-dard deviations. In the lower two panelsdata are plotted as standard residualsextracted from the final statistical modelfor each variable. Non-significant differ-ences between seasons for a particularvariable are represented by open symbols.

Discussion

We found strong seasonal patterns in both immune indices and environmentaland bird-associated microbial assemblages. Densities of bird-associated microbesand immune indices were higher in spring and lower in winter, while the reversepattern was observed for concentrations of airborne microbes (Fig. 6.2). There-fore, microbial loads carried by birds might shape immunity more than concentra-tions of microbes in the wider environment. This conclusion is strengthened byobservations on the two nomadic species, crested lark and black-crowned finch-lark: in 2007 these species encountered similar concentrations of airbornemicrobes at Taif during the spring and at Mahazat during the winter (Fig. A6.2),but exhibited different densities of bird-associated microbes (Fig. 6.2) andimmune defences between seasons (Fig. A6.3). We conclude that explanations forseasonal variation in immune function should incorporate antigenic pressure andnot be restricted to resource-allocation trade-offs between immunity and otherphysiological functions (Horrocks, Matson and Tieleman 2011). Quantifying bothhost-dependent and host-independent microbial loads to map ‘threat landscapes’is possible with new techniques, including the novel air-sampling approach usedin this study.

Airborne and bird-associated microbes followed opposite spring-winterpatternsLevels of airborne microbes were consistently and significantly higher in winter,with concentrations of gram-negative bacteria and fungi at least double thosemeasured in spring (Fig. 6.2). Previously-reported seasonal patterns of concentra-tions of airborne microbes match the patterns we found for fungi (Abdel-Hafez1984; Al-Suwaine, Bahkali and Hasnain 1999; Al-Suwaine, Hasnain and Bahkali1999), but not for bacteria (Burrows et al. 2009). However, only a single studyreports seasonal patterns of bacterial concentrations in a desert environment,with inconsistent patterns: bacterial concentrations are higher in spring at half ofthe sampling sites, but higher in winter or not seasonally variable at the remain-der (Mahdy and El-Sehrawi 1997). Perhaps counter to other non-desert locations,conditions for microbial growth at Mahazat might actually be more favourable inwinter than spring, due to reductions in temperature and solar radiation, andincreases in rainfall and relative humidity (Tong and Lighthart 1997; Talley, Coleyand Kursar 2002; Burrows et al. 2009; Tang 2009).

Given the strong seasonal pattern in concentrations of airborne microbes, it isparticularly intriguing to find such a clear pattern in the opposite direction for thedensities of microbes shed by birds (Fig. 6.2). Birds in our study population moultin summer. Thus, feathers of spring-sampled birds were older than those of win-ter-sampled birds. Older feathers may harbour more microbes, due to increasedtime for colonisation to occur (Burtt and Ichida 1999; Bisson et al. 2007;

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although see Giraudeau et al. 2010). Additionally, birds in spring might put lesseffort into feather maintenance if preening carries less value for old feathers, ifthey are harder to clean, or if time-budgets are restricted (Tieleman, Williams andVisser 2003; Lucas et al. 2005; Tieleman, van Noordwijk and Williams 2008).High spring temperatures restrict preening time in hoopoe larks, which spendonly 2.4% of the day preening (Tieleman and Williams 2002), compared to anaverage of 9.4% for other bird species (Cotgreave and Clayton 1994). Otherbehavioural differences between spring and winter may also affect bird-associat-ed microbial densities. To escape the midday heat during spring and summer,desert-living larks lay down in shallow scrapes in shade-spots or in lizard burrowsto conduct away heat (Williams et al. 1999). Such behaviour might increaseexposure to dust and soil microbes (Shawkey et al. 2005; but see Bisson et al.2007).

Despite opposing seasonal patterns, the relative abundance of the three micro-bial groups was the same in the air, and on birds (generalist bacteria > fungi >gram-negative bacteria). Thus, factors shaping relative abundance of these micro-bial groups in the wider aerial environment (e.g. temperature, humidity) mightalso be important for shaping microbial assemblages on the plumage of birds(Bisson et al. 2007). Interspecific variation in densities of bird-associatedmicrobes might arise from differences in exposure or in feather characteristicssuch as preen-wax composition (Shawkey, Pillai and Hill 2003; Reneerkens et al.2008). Crested larks had the highest densities of generalist bacteria and gram-negative bacteria (Fig. A6.2). The most cosmopolitan of the five study species,crested larks occupy the widest variety of habitats (del Hoyo, Elliott and Christie2004), including areas of human habitation, where concentrations of airbornemicrobes are hypothesised to be higher than in natural surroundings (Burrows etal. 2009). Therefore, the higher densities of microbes on crested larks may reflectgreater exposure.

Seasonal and inter-annual variation in constitutive innate immunityImmune indices were consistently higher in spring than in winter, matching pat-terns of microbial densities shed by birds, but not those of airborne microbes (Fig.6.2). Explanations for seasonal variation in immune function are divergent: someauthors predict reduced immunity during a challenging season due to resourcetrade-offs (e.g. Buehler et al. 2008; Martin, Weil and Nelson 2008); others predictincreased immunity during hard times, possibly in anticipation of an increasedneed for protection (Nelson and Demas 1996; Nelson et al. 2002). These aremostly studies of temperate environments, where winter is often considered thechallenging season because short days limit foraging time, food availability isreduced, and thermoregulatory requirements elevate energy demands. Our studycontrasts such temperate studies because, in deserts, spring and summer maypose challenges for survival. High temperatures restrict foraging time (Tieleman

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and Williams 2002), food availability is low, and evaporative cooling require-ments increase demands for water (Tieleman, Williams and Visser 2003), whichmust primarily be obtained from food, since free-standing water is scarce. Thus,our data support the second prediction: constitutive innate immunity was higherwhen conditions were more demanding, and when the need for protection, asjudged by densities of bird-associated microbes, was greater.

Across all species, regardless of nomadic habits, immune indices were consis-tently higher in spring 2007 than spring 2006. Body masses were also higher inbirds sampled in spring 2007 (Table A6.2). Larks in the desert skip breeding dur-ing lean years, favouring self-maintenance activities over reproduction duringsuch times (Tieleman and Williams 2002; Tieleman, Williams and Visser 2003,2004). In spring 2007, decreased food availability in Mahazat reduced breedingactivities among residents and increased dispersal of nomads (B.I. Tieleman, per-sonal observation). Apparently, in this lean year, larks allocated more resources tobody mass and immune defences, perhaps to increase chances of survival untilfuture breeding attempts.

Inter-annual variation was greater than seasonal variation in constitutiveinnate immune function (Table A6.2). Similar results have been observed in sky-larks (Alauda arvensis) living in a mesic environment (A. Hegemann, unpublisheddata). Despite differing in multiple characteristics (e.g. temperature, aridity, andprimary productivity), desert and temperate environments both appear to displayan inter-annual unpredictability that may be important for shaping the immunefunction of their inhabitants. These findings emphasise the value of multi-yearstudies for understanding immunological variation.

Lysis titres correlated with total microbial densities shed from birdsThe positive correlation between lysis titre and total microbial load shed by birds(Fig. A6.4) indicates that also at the individual level, bird-associated microbesmay be important for shaping immune function. Lytic mechanisms might be par-ticularly important for dealing with environmental microbes since lysing foreigncells is a critical first step in their neutralisation and removal (Janeway et al.2004). Huttunen et al. (2010) show that in vitro cell cultures release inflammato-ry cytokines when exposed to suspensions of microbial particles collected fromambient air and that more concentrated suspensions lead to greater release ofthese immune markers. These results, which are in line with our more ecological-ly-oriented results, suggest a positive relationship between exposure to environ-mental microbes and immune responses. Future studies should take advantage ofculture-independent techniques, which will further broaden our understanding ofhost-dependent and host-independent microbial pressures and their relation toimmune defences.

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AcknowledgementsWe are grateful to HH Prince Bandar bin Saud, Secretary General of the Saudi Wildlife

Commission, and Mr Ahmad Al Bouq, Director of the National Wildlife Research Center

(NWRC). We thank the staff at NWRC and Mahazat as-Sayd, and Joe Williams and Rob

Voesten for logistical support. Maaike Versteegh provided statistical advice. Financial sup-

port was provided by the Schure-Beijerinck-Poppings Fonds (N.P.C.H.), by a Rosalind

Franklin Fellowship from the University of Groningen (B.I.T.) and by VENI grants

(863.04.023 and 863.08.026, to B.I.T. and K.D.M. respectively) from the Netherlands

Organisation for Scientific Research (NWO).

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Appendix 6.1

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Table A6.1. Weather conditions at Mahazat as-Sayd, Saudi Arabia, based on historic data(National Wildlife Research Center, unpublished data). Data are for spring (May and June)and winter (November and December) and represent the monthly mean value. In the caseof relative humidity and solar radiation the mean of the absolute maximum values aregiven.

season rainfall (mm) temperature (°C) relative humidity (%) solar radiation (Wm-2)

mean ± SD mean (max) mean max ± SD mean max ± SD

spring 4.1 ± 6.1 29.3 (36.9) 44.9 ± 17.4 896.3 ± 139.4

winter 11.5 ± 18.3 18.4 (24.2) 70.2 ± 19.3 692.3 ± 109.9

Table A6.2. Mean values (intercept), estimate sizes (difference) and 95% confidence inter-vals (95% C.I.) around these estimates for four indices of constitutive innate immunity andbody mass from five species of larks in the Arabian Desert in spring and winter 2006 andspring 2007. The intercepts and estimates are taken from final statistical models examiningthe role of (A) seasonal and (B) annual effects on each parameter. The intercept indicatesthe mean value of the reference category (‘spring’ for seasonal variation, ‘2006’ for annualvariation) and estimates are given for the other category (‘winter’ and ‘2007’) relative tothis.

source of variance parameter mean (intercept) difference 95% C.I.

(A)seasonal

spring vs. winter haptoglobin (mg ml-1) 0.42 -0.25 -0.36 – -0.14

ovotransferrin (mg ml-1) 10.15 -2.29 -7.12 – 2.54

agglutination (titre) 6.99 -1.10 -2.16 – -0.04

lysis (titre) 1.46 -0.52 -0.98 – -0.06

mass (g) 15.10 -1.35 -2.44 – -0.26

(B) inter-annual

2006 vs. 2007 haptoglobin (mg ml-1) 0.30 0.19 0.07 – 0.32

ovotransferrin (mg ml-1) 6.35 3.59 -0.42 – 7.60

agglutination (titre) 5.86 1.13 0.24 – 2.02

lysis (titre) 0.37 1.27 0.82 – 1.73

mass (g) 13.46 1.67 0.48 – 2.85

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Figure A6.1. Air sampler and set-up for air-sampling of birds. (A) Air sampler with sieveplate removed to show the agar plate in place. (B) An agar plate after incubation. (C) Thebird air-sampling experimental set-up: (i) pre-sterilised, darkened plastic box; (ii) air sam-pler; (iii) bird restrained in soft mesh sterilised tube. Air passes over the bird and onto theagar plate (top arrows) and is then recycled back to the box through the air vents of thesampler (bottom arrows).

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Figure A6.2. Densities of bird-associated microbes (generalist and gram-negative bacteria,and fungi) air-sampled from five species of lark in the Arabian Desert in spring (dark greybars) and winter 2007. Numbers above bars are sample size. HL = hoopoe lark; DuL =Dunn’s lark; BTDL = bar-tailed desert lark; BCFL = black-crowned finchlark; CL = crestedlark. Boxes encompass all data between the 25th and 75th percentiles (interquartile range,IQR). Thick bars inside boxes indicate the median value. ‘Whiskers’ indicate either the mini-mum or maximum value, or 1.5 times the IQR (approximately two standard deviations),whichever is smaller. Data outside this range (‘outliers’) are plotted individually as black dots.

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Figure A6.3. Haptoglobin and ovotransferrin concentrations, and agglutination and lysistitres measured in five species of lark in the Arabian Desert in spring (dark grey bars) andwinter 2007. Numbers above bars are sample sizes. HL = hoopoe lark; DuL = Dunn’s lark;BTDL = bar-tailed desert lark; BCFL = black-crowned finchlark; CL = crested lark. Ovo-transferrin concentrations are missing for BTDL and BCFL because insufficient sample vol-umes were available to measure this protein in these species. Boxes encompass all databetween the 25th and 75th percentiles (interquartile range, IQR). Thick bars inside boxesindicate the median value. ‘Whiskers’ indicate either the minimum or maximum value, or1.5 times the IQR (approximately two standard deviations), whichever is smaller. Data out-side this range (‘outliers’) are plotted individually as black dots.

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A contribution to the ecologists’immunological toolbox

IVPART

A simple assay for measurement ofovotransferrin – a marker of inflammationand infection in birds

7CHAPTER

Nicholas P.C. Horrocks, B. Irene Tieleman & Kevin D. Matson

AbstractOvotransferrin is an acute phase protein with iron-binding and immunomodula-tory functions. In poultry, ovotransferrin levels increase in response to inflamma-tion or infection, but little is known about responses in wild bird species. Wepresent a simple assay for the determination of ovotransferrin-like activity in theplasma of wild birds. The assay uses very small sample volumes, works with pre-viously-frozen plasma, is inexpensive to run, and requires only standard labora-tory equipment and a spectrophotometer. Importantly, the assay does notrequire species-specific antibodies, making it applicable to a wide variety ofspecies and particularly useful in comparative studies of immune function. Wedetected significant variation in ovotransferrin concentrations among 22 birdspecies. Ovotransferrin concentrations were significantly repeatable among indi-viduals, and concentrations increased significantly in response to a lipopolysac-charide challenge. Within but not among species, concentrations ofovotransferrin were significantly and positively correlated with concentrations ofhaptoglobin, another acute phase protein that also binds iron. Differences inconcentrations of acute phase proteins might reflect broader differences inimmune strategies and responses to infection. Measuring ovotransferrin in addi-tion to haptoglobin therefore provides fresh insights into differences in immuno-logical defences among populations and species. This assay will serve as a usefuladdition to the existing arsenal of field-friendly assays that have been developedfor addressing questions in ecological immunology.

Methods in Ecology and Evolution 2: 518-526 (2011)

Introduction

For ecologists interested in studying the immune function of free-living animals, akey advancement in methodology was the introduction of field-friendly assaysthat could be carried out using small volumes of blood or plasma and that did notrequire species-specific antibodies or reagents (Matson, Ricklefs and Klasing2005; Tieleman et al. 2005; Matson 2006; Millet et al. 2007). These assays enableresearchers to study the immune function of individuals that may be capturedonly once and about which little or nothing may be known of their current healthstatus. Here we present a new addition to the immunoecologists’ tool kit: anassay for measuring plasma concentrations of ovotransferrin, an acute phase pro-tein in birds.

The transferrins are a group of iron-binding glycoproteins present in verte-brates and invertebrates (Lambert et al. 2005). In birds transferrin proteins occuras two forms, both called ovotransferrin, that are products of the same geneexpressed in different tissues (Thibodeau, Lee and Palmiter 1978). Serum ovo-transferrin is made in the liver and circulated in the blood. Egg-white ovotrans-ferrin (formerly conalbumin) is synthesised in the oviduct (Superti et al. 2007)and deposited in the albumen of eggs. In eggs ovotransferrin constitutes a majorcomponent of albumen (Burley and Vadehra 1989) and aids in antimicrobialdefence of the developing embryo. As products of the same gene, both forms ofovotransferrin have the same amino acid sequence and protein structure. Theydiffer only in the composition of attached carbohydrate side groups (Lee, McK-night and Palmiter 1980; Jacquinot et al. 1994).

Transferrins can sequester iron with high affinity; each molecule has the abilityto bind two Fe3+ ions (Aisen 1998). This property underlies their important func-tion as iron transport proteins. It also helps explain the defensive role that ovo-transferrin plays in birds. By binding free iron, an essential nutrient for bacterialgrowth (Skaar 2010), ovotransferrin limits infection by both gram-positive andgram-negative bacteria (Valenti et al. 1983; Abdallah and Chahine 1999; Supertiet al. 2007). Ovotransferrin also contains a bactericidal domain that functionsindependently of the protein’s iron-binding properties (Ibrahim et al. 1998). Thisdomain causes selective ion efflux through bacterial membranes, which can alsolead to bacteriostasis (Ibrahim, Sugimoto and Aoki 2000; Aguilera, Quiros andFierro 2003). The protein exhibits antiviral (Giansanti et al. 2002; Giansanti et al.2007) and antifungal activities (Valenti et al. 1985) and ovotransferrin also hasan immunomodulatory role in birds (Xie et al. 2002b).

In some organisms, ovotransferrin is known to be an acute phase protein(APP). Often produced in the liver, APPs increase (i.e. positive APP) or decrease(i.e. negative APP) in concentration during acute phase responses (Gruys et al.2005). Because these changes can stem from inflammation, infection, poor nutri-tion or disease, APPs can be used as non-specific markers of these processes but

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not as indicators of specific diseases. In fact, several APPs, including transferrins,are currently used as markers of health in humans and animals (Ritchie et al.1999; Cray, Zaias and Altman 2009). In chickens serum ovotransferrin is a posi-tive APP (Hallquist and Klasing 1994; Tohjo et al. 1995; Chamanza et al. 1999;Xie et al. 2002a; Rath et al. 2009), while in mammals transferrins are classed asnegative APPs (Gruys et al. 2005). Chicken ovotransferrin is a moderate APP(one- to ten-fold increase in circulating concentration in response to a trigger;Cray, Zaias and Altman 2009), and remains elevated as long as inflammation per-sists (Rath et al. 2009; Xie et al. 2009). Moderate APPs generally show prolongedincreases and relatively slow declines, and may be particularly associated withchronic inflammatory processes (Cray, Zaias and Altman 2009).

In this paper we describe a simple assay for quantifying ovotransferrin in avianplasma samples. We explored the applicability of the assay by analysing samplesfrom 22 species of birds. We also tested the ability of the assay to detect changesin ovotransferrin concentrations in response to inflammation by comparing plas-ma samples that were collected before and after lipopolysaccharide injection.Finally, we examined the correlation between concentrations of ovotransferrin andhaptoglobin, another iron-binding positive APP in birds. Variation in the responseof similar-functioning proteins to infection may provide new insights into differingstrategies of immune defence among species, and highlight otherwise hidden dif-ferences in how infections are dealt with. Overall, our study identifies the utilityof measuring ovotransferrin for answering questions in ecological immunology.

Materials and methods

Assay principleWe used a modified version of the assay described and verified by Yamanishi et al.(2002). This assay measures total iron-binding capacity – the maximum amountof iron necessary to saturate all the ovotransferrin in a sample. This correlatesvery well with ovotransferrin concentration, as determined by comparison withimmunological measurement of serum transferrin (Gambino et al. 1997; Yaman-ishi et al. 2002). A similar version of the assay has been used previously to meas-ure concentrations of ovotransferrin in egg albumen (Shawkey et al. 2008; D'Albaet al. 2010). The assay consists of three reaction steps. First, ovotransferrin in thesample is saturated with ferric iron (Fe3+) under alkaline conditions. Then, theunbound excess iron is reduced to Fe2+ by addition of ascorbic acid, and thisFe2+ becomes inactivated by forming coloured complexes with the chromogenFerroZine. Finally, the ovotransferrin-bound Fe3+ is dissociated under acidic con-ditions. This newly released Fe3+ allows further formation of the coloured Fe2+-FerroZine complex. The associated increase in absorbance of the reaction mixturedue to this additional formation of the coloured complex is monitored over time.

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Assay procedureEach well of a 96-well microplate was filled with 50 µl of reagent one (300 mMTris, 150 mM sodium hydrogen carbonate, 4.2 g l-1 Triton X-100, pH 8.4) con-taining a 1:250 dilution of iron-standard solution (1000 mg l-1). Ten microlitresof plasma samples or ovotransferrin standards (conalbumin from chicken eggwhite, C0755, Sigma-Aldrich, St Louis, Missouri, USA) were added to duplicatewells in the plate. The standards were prepared in reagent one (without iron-standard solution) for a standard curve ranging from 1.0–20.0 mg ml-1. The assayplate was placed in a spectrophotometric microplate reader (VersaMax, MolecularDevices, Sunnyvale, California, US). In the reader, the plate was shaken for 10seconds to mix the well contents and then incubated for 5 minutes at 36°C. Fol-lowing incubation, initial ‘pre-read’ absorbances were recorded at 570 nm (pri-mary wavelength) and 660 nm (reference wavelength) to later account for anydifferences among the plasma samples and between the coloured plasma samplesand the colourless standards. Ten microlitres of reagent two (50 mM Tris, 32.6mM L-ascorbic acid, 10 mM FerroZine, pH 4.0) were then added to each well.The contents were mixed again for 10 s and left to incubate for 5 minutes at 36°Cin the plate reader. Finally, 20 µl of reagent three (600 mM citric acid, 25.6 mMthiourea) were added to each well, the contents were mixed for 3 seconds, andabsorbances were first recorded immediately after mixing (t = 0). Absorbance at570 and 660 nm were recorded again at six minutes (t = 6). The microplate read-er and all reagents were warmed to 36°C prior to use in the assay. All chemicalswere purchased from Sigma-Aldrich (St Louis, Missouri, USA).

Absorbance values were used to calculate ovotransferrin concentrations. First,we corrected for initial differences in absorbance values among samples and thestandards. We subtracted well-specific ‘pre-read’ absorbances at 570 and 660 nmfrom both the t = 0 and the t = 6 read at the corresponding wavelength. Then,we normalised all absorbance values by subtracting the reference wavelength(600 nm) absorbances from the primary wavelength (570 nm) absorbances atboth time points. Finally, we determined the change in absorbance (∆A) due torelease of Fe3+ from the ovotransferrin and additional formation of the colouredFe2+-FerroZine complex. For each well we subtracted the normalised absorbancevalue at the start of the assay (t = 0) from the normalised absorbance value atthe end of the assay (t = 6; i.e. ∆A = A570-660end - A570-660start). A standardcurve, which related ∆A and ovotransferrin concentration of the standards, wasplotted. This curve was used to calculate ovotransferrin concentration of the sam-ples (in mg ml-1) based on their ∆A. If sample volume was sufficient, then a samplewas run in duplicate, and the mean concentration was used in further analyses.

To account for potential among-assay variation (i.e. plate-to-plate differences),we included a standard sample in duplicate on all plates. Logistical limitations atour lab prevented us from using a plasma standard as intended. Instead the inter-plate standard was made from equal parts by weight of the albumen from three

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chicken eggs. We calculated the mean ovotransferrin concentration of this stan-dard over all assay plates. Then we determined a correction factor for each plateby dividing the among-plate mean by the within-plate mean. Ovotransferrin con-centrations of each sample on a plate were multiplied by the plate-specific correc-tion factor to give a plate-corrected concentration. We report plate-correctedvalues, unless otherwise stated.

Experimental samplesWe assayed five sets of plasma samples. The first set, which was used to examineassay variation, comprised 40 samples collected from adult homing pigeons(Columba livia, 8 males, 12 females) that were each sampled twice, four monthsapart (June and September 2008; 2 samples per bird). All of these samples wererun in duplicate.

The second set consisted of plasma from 16 of these same pigeons (8 males, 8females) and was used to estimate the repeatability of ovotransferrin concentra-tion as an individual trait. The samples were collected on a monthly basisbetween November 2007 and February 2008. We considered this to be a physio-logically quiescent period since the birds were not breeding or moulting (see Ver-steegh et al. (2008), for a similar approach to testing repeatability of physiologicaltraits). When sampled, none of the birds showed signs of disease or were receiv-ing medical or experimental treatment.

The third set was used to investigate the effect of sample storage duration onovotransferrin concentration and also to examine species variation in ovotransfer-rin concentration. It comprised 222 plasma samples from 22 bird species (Table7.1) that were collected for various projects within the authors’ research group.All samples came from adult birds that were either captured using standard tech-niques (mist-netting or clap traps) or maintained in captivity. None had experi-enced any experimental manipulations and all were apparently healthy anddisease-free at the time of sampling.

The fourth set of samples was used to test the effect of age class on ovotrans-ferrin concentration. Plasma samples from 10 adults and 20 chicks from 12 broodswere collected from Woodlarks (Lullula arborea).

The fifth and final set consisted of plasma from 8 pigeons (4 males, 4 females)and 5 red knots (Calidris canutus islandica, 2 males, 3 females) that received anintraperitoneal injection of lipopolysaccharide (LPS; L7261; Sigma-Aldrich). Weused these samples to test the effect of the LPS injection, which simulates a bacte-rial challenge, on ovotransferrin concentration. Apart from the LPS injection,these birds were from un-manipulated control groups in other experiments (seeBuehler et al. (2009) and van de Crommenacker et al. (2010), for full details).Samples were collected from the pigeons immediately before, and 18 hours afterLPS injection. For the red knots the post-LPS sample was collected one week afterthe baseline sample, and 17 hours post-injection.

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In every case plasma was separated from whole blood by centrifugation withinone hour of collection, and all samples were stored at –20°C or –80°C prior tobeing assayed. None of the samples showed signs of gross haemolysis (i.e. anobvious deep-red colour indicating contamination with free haem). Since theovotransferrin assay relies on the binding of iron, excess haem from lysed ery-throcytes can interfere with the assay, leading to abnormally high readings.

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Table 7.1. List of species assayed for plasma ovotransferrin concentration

Order and Species Sexes n OriginFamily

CharadriformesScolopacidae Ruff Philomachus pugnax 1 F, 16 M 17 Netherlands

Red Knot Calidris canutus islandica Hp 6 F, 4 M 10 Netherlands C

Glareolidae Cream-coloured Courser Cursorius cursor Hp 1 M, 4 NA1 5 Saudi Arabia

ColumbiformesColumbidae Namaqua Dove Oena capensis Hp 1 F, 2 M 3 Saudi Arabia

Homing pigeon Columba livia2 Hp 8 F, 8 M 16 Netherlands C

PasseriformeLaniidae Great Grey Shrike Lanius excubitor Hp 3 F, 4 M, 3 NA 10 Saudi Arabia

Alaudidae Rufous-naped Lark Mirafra africana Hp 3 F, 1 M 4 KenyaBar-tailed Desert Lark Ammomanes cincturus Hp 5 F, 12 M 17 Saudi ArabiaHoopoe Lark Alaemon alaudipes Hp 16 F, 19 M 35 Saudi ArabiaBimaculated Lark Melanocorypha bimaculata Hp 10 F, 1 M 11 AfghanistanCalandra Lark Melanocorypha calandra Hp 4 F, 4 M 8 AfghanistanRed-capped Lark Calandrella cinerea Hp 3 F, 4 M 7 KenyaDunn's Lark Eremalauda dunni Hp 2 F, 11 M 13 Saudi ArabiaCrested Lark Galerida cristata Hp 9 F, 6 M 15 Saudi ArabiaWoodlark Lullula arborea Hp 3 F, 7 M 10 Netherlands

Muscicapidae European Stonechat Saxicola rubicola 1 F, 2 M 3 Austria C

African Stonechat Saxicola torquata 3 F, 2 M 5 Kenya C

Desert Wheatear Oenanthe deserti Hp 3 F, 2 M 5 Saudi ArabiaIsabelline Wheatear Oenanthe isabellina Hp NA 4 Saudi Arabia

Turdidae Eastern Bluebird Sialia sialis Hp 5 F, 8 M 13 USA

Sturnidae European Starling Sturnus vulgaris Hp NA 7 Netherlands

Passeridae House Sparrow Passer domesticus Hp 3 F, 1 M 4 Saudi Arabia

1 NA – sex unknown; 2 Mean of November 2007-February 2008 values (sample set two, see Materials andMethods); C Maintained in captivity; Hp Used in the analysis of the correlation between ovotransferrin andhaptoglobin.

HaptoglobinLike the transferrins, haptoglobin and its functional equivalents are haem-bindingAPPs found in many taxa, including birds (Delers, Strecker and Engler 1988; Mat-son et al. 2006; Matson 2006). Generally, these proteins are absent or circulate atlow levels in the blood, but concentrations increase during the acute phaseresponse. Elevated levels can therefore indicate an immune response (Dobryszycka1997; Quaye 2008). We determined haptoglobin concentrations using a commer-cially-available functional assay that measures the haem-binding capacity ofplasma (TP801; Tri-Delta Diagnostics, NJ, USA). We followed the manufacturer’sinstructions for the ‘manual method’.

Statistical analysesWe used linear models to test the effects of various factors on ovotransferrin con-centration. Where repeated measures were involved (repeated measures per indi-vidual or per species) we used general linear mixed models (glmm) with therelevant term included as a random effect. To test the correlation between ovo-transferrin and haptoglobin we used within-subject centreing (van de Pol andWright 2009). This allowed us to distinguish between the contributions of within-species and among-species variation. We calculated haptoglobin species means (toaccount for among species variation) and individual deviations from these means(to account for within-species variation). Then, we ran a mixed model withspecies as a random effect, haptoglobin species means and individual deviationsas fixed terms, and ovotransferrin concentration as the response variable. Theresiduals of statistical models were examined graphically for normality and homo-geneity of variance and met these assumptions in every case. Statistical analyseswere performed using R, version 2.10.1 (R Development Core Team 2009).

Assay variationWithin- and among-assay variation was calculated using ovotransferrin concen-trations (not plate-corrected) of 40 pigeon samples (sample set one) spread over16 plates. Each sample was run in duplicate on two of the 16 different plates.That is, each sample was run four times in total over two plates.

In the case of three samples, one of the within-plate duplicates did not work,leaving 97 instances where two values were available for the same sample withina plate. Within-assay variation was calculated from the mean and standard devia-tion of each of these 97 intra-plate duplicates. We summarise the within-assayvariation by reporting the mean, minimum and maximum values of these stan-dard deviations (SD) and of the associated standard errors (SE, n = 2 wells) andcoefficients of variation (CV).

Among-assay variation was determined by first calculating plate-specific meanovotransferrin concentrations for each sample (i.e. mean of the duplicates on aplate). Inter-plate means and SDs were then calculated based on pairs of plate-

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specific means that originated from identical samples. We summarise among-assay variation by reporting the mean, minimum and maximum values of theinter-plate SD, SE (n = 2 plates) and CV.

Individual repeatabilityWe used sample set two to estimate the repeatability of ovotransferrin concentra-tion as an individual trait. We calculated repeatability according to Lessells andBoag (1987; repeatability = among-individual variance / within-individual vari-ance + among-individual variance). Since samples were run on 13 different assayplates without regard to sample month, we used plate-corrected values toaccount for among-plate variation. Within- and among-individual variances wereobtained from a mixed model containing only the intercept and with bird identityas a random effect. For comparison we also tested mixed models containing thefixed terms sex, month, and all their permutations. Statistical significance wasevaluated using a log-likelihood ratio test. Standard errors for repeatability valueswere calculated according to Becker (1984). Within-individual CV was calculatedas the mean of all the individual CVs, ascertained by first calculating means andSDs per individual (n = 4 monthly samples per individual). Among-individual CVwas determined by using the mean ovotransferrin concentration per individual,and then deriving CV from the overall mean and SD of these individual means.Thus, both within- and among-individual variation subsumed within- and among-assay variation.

Results

Assay variation, individual repeatability, and effects of sample storagedurationWithin- and among-assay variation (sample set one) is shown in Table 7.2. Meanwithin-assay CV equalled 0.15 and mean among-assay CV was 0.24. The inter-plate repeats (used to calculate among-assay variation) were run in two batchesapproximately one month apart, but with most of the same reagents, so there aremany possible sources of variation. This might account for why among-assay vari-ation is higher than within-assay variation. Nonetheless, with practice and whenplates are analyzed in short succession, variation can be reduced to almost zero,as the minimum CV values in Table 7.2 demonstrate. For comparison, Table 7.2also shows the assay variation calculated using the chicken albumen pool used tocorrect for plate-to-plate variation.

Individual repeatability, based on four measures per bird (sample set two) andwith a mixed model containing only the intercept and no fixed effects, equalled0.32 and was highly significant (P = 0.007; Table 7.3). Thus, ovotransferrin con-centration can be considered a distinctive trait of individuals, at least during the

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Tabl

e 7.

2.W

ithin

-and

am

ong-

assa

y va

riat

ion

Varia

tion

Sam

ple

nM

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SDSE

CV

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max

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200.

550.

003.

680.

150.

001.

14

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50 p

late

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70

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18 re

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0.96

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0.00

0.24

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1.59

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winter. Sex significantly affected ovotransferrin concentration, with males havinghigher concentrations than females (mean ovotransferrin concentration: males5.05 mg ml-1; females 3.69 mg ml-1; F1, 14 = 10.03, P = 0.007). The interactionof sex by month was not significant (F3, 42 = 0.08, P = 0.969), and there was noeffect of month on ovotransferrin concentration (F3, 45 = 0.55, P = 0.650).Including sex in the model decreased the repeatability (by decreasing amongindividual variation) and made it no longer significant (R = 0.16, P = 0.160).

To examine the effect of sample storage on ovotransferrin concentration weused 206 samples of 21 species from sample set one. These samples had beenstored frozen (at least –20°C) for between 431 and 2174 days (approximately 1–6years) before being assayed. The results of a mixed model containing species as arandom effect and sample age as fixed effect indicated that there was no effect ofstorage time on ovotransferrin concentration (sample age F1, 184 = 2.16, P =0.143).

Effects of species, sex, and age classSpecies variation in ovotransferrin concentrations was tested using values from22 species (sample set one), which encompassed nine families in three orders(Table 7.1). Since we were initially uncertain about the importance of accountingfor variation in ovotransferrin concentrations due to sex, we first tested this effectusing all individuals of known sex. A mixed model with species as a random fac-tor showed no significant differences in ovotransferrin concentrations betweenmales and females (F1, 183 = 0.89, P = 0.347, n = 204 individuals from 20species). Ovotransferrin concentrations varied significantly among species,regardless of whether sex and the species by sex interaction were included(species F19, 183 = 2.74, P < 0.001; sex F1, 183 = 0.49, P = 0.484; species*sexF18, 165 = 13.77, P = 0.915, n = 204 individuals from 20 species) or excluded(F21, 200 = 2.62, P < 0.001 n = 222 individuals from 22 species; Fig. 7.1).

To test the effect of age class on ovotransferrin concentration we used samplescollected from Woodlarks (Lullula arborea; sample set two). Because some indi-viduals were related (i.e. chicks or adults from the same nest), we used a mixedmodel with age class as a fixed effect and nest identity as a random effect. Themean ovotransferrin concentration of chicks (9.80 mg ml-1) was slightly higherthan that of adults (9.42 mg ml-1), but this difference was not significant (F1, 9 =0.025, P = 0.877).

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Table 7.3. Repeatability of ovotransferrin as an individual trait

Repeatability r SE χ2 P Within-individual CV Among-individual CV

0.32 0.14 7.28 <0.001 0.25 0.25

Effect of simulated bacterial infectionThe effect of a simulated bacterial infection on ovotransferrin concentration wastested in homing pigeons and red knots that were injected with LPS (sample setfive). Specifically, we examined the within-individual changes in ovotransferrinconcentration between baseline and LPS-response samples. We ran a mixedmodel containing the terms treatment (baseline or LPS response), species, theirinteraction, and the random factor of individual identity to account for therepeated measures design. The interaction term was non-significant (treatment*species, F1, 11 = 0.70, P = 0.420) and the two species also did not differ in theirresponse to LPS (species, F1, 11 = 0.12, P = 0.731). The effect of LPS injection onovotransferrin concentration was highly significant (treatment, F1, 12 = 9.73, P =0.009): response samples had higher ovotransferrin concentrations than baselinesamples (Fig. 7.2).

Correlation between ovotransferrin and haptoglobinWe tested the correlation between ovotransferrin and haptoglobin, another iron-binding APP, using within-subject centreing (van de Pol and Wright 2009).Analysing values of 192 individuals from 19 species (sample set one; Table 7.1),

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ovot

rans

ferr

in c

onc.

(m

g m

l-1)

0

10

20

30

40

Ruff

Red kn

ot

Cream-co

loured

cours

er

Namaq

ua do

ve

Homing

pige

on

Great g

rey sh

rike

Rufous

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Bar-tai

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Bimac

ulated

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Caland

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Red-ca

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Crested

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Europe

an st

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African

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Deser

t whe

atear

Isabe

lline w

heate

ar

Easter

n blue

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an st

arling

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sparr

ow

Figure 7.1. Box plot illustrating species variation in ovotransferrin concentration. Boxesencompass all data points between the 25th and 75th percentiles (interquartile range, IQR).Thick bars in boxes indicate the median data value. ‘Whiskers’ indicate either the minimumor maximum data value, or 1.5 times the IQR (approximately two standard deviations),whichever is smaller. Data points outside this range (‘outliers’) are plotted individually asblack dots.

we found a positive relationship between concentrations of haptoglobin andovotransferrin. At the within-species level the correlation was highly significant(F1, 172 = 11.42, P < 0.001) while at the among-species level this was not thecase (F1, 17 = 1.22, P = 0.284).

We also used the pigeon and red knot data (sample set five) to assess whetherthe changes in ovotransferrin concentration following LPS injection werematched by changes in haptoglobin concentration. As previously reported, bothpigeons (van de Crommenacker et al. 2010) and red knots (Buehler et al. 2009)significantly increase haptoglobin concentration in response to LPS injection (Fig.7.2). We ran a linear model containing the terms haptoglobin change (i.e. theintra-individual change in haptoglobin concentration between baseline and post-LPS samples), species, and the interaction of these two terms. The intra-individ-ual change in ovotransferrin concentration between baseline and post-LPSsamples was used as the response variable. All terms were non-significant (allP > 0.132), suggesting that haptoglobin response to LPS cannot be used to pre-dict ovotransferrin response to LPS.

Discussion

The method we describe for assaying ovotransferrin concentrations in avian plas-ma samples is a valuable new tool for addressing questions about the ecology andevolution of immune function. It will also be useful for monitoring animal health

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baseline post-LPS0

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20

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ovot

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25

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hapt

oglo

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. (m

g m

l-1)

red knots (n = 5)pigeons (n = 8)

15

0.1

0.4

0.2

0.5

0.3

Figure 7.2. Effect of LPS injection on ovotransferrin (left) and haptoglobin (right) plasmaconcentrations, in homing pigeons and red knots. The relative scaling of the y-axis in eachgraph is the same, to highlight that the magnitude of the change in haptoglobin concentra-tion in response to LPS is greater than that of ovotransferrin. Error bars indicate 2x stan-dard errors.

and welfare. Specifically, our study identified five important attributes of ovo-transferrin and its quantification that highlight the assay’s value. First, withinpopulations, ovotransferrin concentrations are repeatable. Second, concentrationsdiffer significantly among species (Fig. 7.1). Third, ovotransferrin concentrationincreases in response to a simulated bacterial challenge (Fig. 7.2). Fourth, withinspecies ovotransferrin concentrations are correlated with concentrations of anoth-er APP haptoglobin, but among species these two indices appear to be independ-ent of each other. Fifth, the assay has practical and cost advantages that make itapplicable to a wide variety of studies. The combination of these attributes resultsin a valuable new immune index in birds that provides useful information aboutdifferences in immune defences among species.

Ovotransferrin concentrations were repeatableIn our flock of pigeons ovotransferrin concentrations were significantly repeat-able. As such, ovotransferrin concentration appears to reflect a consistent aspectof an individual’s physiology, at least during the winter, and can be considered anindividual-bound trait. Individual-bound traits are interesting to ecologistsbecause they can be meaningfully correlated among individuals. Moreover,repeatability represents a maximum limit for heritability (Falconer and Mackay1996). By definition, APP concentrations vary in response to inflammation orinfection, so the significant repeatability of ovotransferrin during winter supportsthe notion that this season might be a physiologically quiescent period. However,this must be confirmed by comparing repeatabilities among seasons.

Accounting for differences between males and females decreased among-indi-vidual variation, which led to a reduced and non-significant repeatability. Differ-ences in health, which were not quantified in this study, could also affectrepeatability. Non-synchronous changes (i.e. those affecting one or a few individ-uals, but not all, on a particular sampling day) would lead to a deflated repeata-bility by increasing within-individual variance. Synchronous changes in health(i.e. those affecting the whole flock) were controlled for however, by includingthe term ‘month’ in the model. If health effects are left unaccounted, as will likelybe the case in many studies of wild birds, the calculated repeatability should like-ly be viewed as a low estimate. Other differences, like season or age, which didnot vary within or among our pigeons, can affect immune indices (Nelson andDemas 1996; Cichon, Sendecka and Gustafsson 2003; Lozano and Lank 2003;Buehler et al. 2008) and might affect ovotransferrin concentrations. These effectsshould also be considered when calculating repeatability.

Compared with other immunological traits measured over a similar time frame(i.e. months rather than days or weeks), ovotransferrin repeatability is high.Repeatability of serum proteins, including APPs, was 0.26 in captive greenfinches(Carduelis chloris) that were sampled twice over four months (Hõrak et al. 2002).In red knots sampled over an entire annual cycle, repeatabilities of several

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leukocyte and plasma parameters were mostly below 0.20 (Buehler et al. 2008).It should be noted however, that repeatabilities are population measures; we can-not discount the possibility that ovotransferrin repeatability may differ amongpopulations or species.

Ovotransferrin concentrations differed among speciesOvotransferrin concentrations differed significantly among species (Fig. 7.1), andthis effect could not be attributed to differences in sample storage time. This find-ing makes measurement of ovotransferrin particularly useful in comparative stud-ies among species, for example those investigating links between immunefunction and life history, species distribution, or other ‘species-bound’ traits (e.g.Tieleman et al. 2005; Matson 2006; Buehler, Tieleman and Piersma 2009).

In contrast to the differences among species, we found no overall differencesin ovotransferrin concentrations between the sexes. When tested using a differ-ent, single-species dataset however (sample set one, instead of sample set three),male pigeons had significantly higher ovotransferrin concentrations than females.Combined, these results hint that the effect of sex may act in opposite directionsin different groups (e.g. seasons, populations). The interaction between speciesand sex was not significant, but other interactions (e.g. sex by season) remainunexplored. We also found no difference in ovotransferrin concentration betweenage classes, but whether this finding is particular to the species we tested orapplies more generally is uncertain. Data on age-related changes in APPs in non-human species is limited, and further research in this area is warranted.

Ovotransferrin concentration increased after an immune challengeOvotransferrin concentration increased significantly in response to a simulatedbacterial challenge in two unrelated species (Fig. 7.2). This result confirmed that,in contrast to mammals, ovotransferrin is a positive APP in birds (Hallquist andKlasing 1994; Tohjo et al. 1995; Chamanza et al. 1999; Xie et al. 2002a; Rath etal. 2009). LPS-injected chickens also exhibit elevated levels of serum ovotransfer-rin and liver mRNA for the protein (Hallquist and Klasing 1994). Interestingly,these same LPS-injected chickens exhibit reduced levels of egg white ovotransfer-rin mRNA in the oviduct. These results allude to a potential trade-off between cir-culating ovotransferrin and the ovotransferrin deposited in eggs.

In pigeons and red knots, both ovotransferrin and haptoglobin increased sig-nificantly in response to LPS (Fig. 7.2), but among individuals the changes werenot significantly correlated. In terms of percent increase, the ovotransferrinresponse to LPS was smaller than the haptoglobin response in both species (Fig.7.2). Differences in ovotransferrin and haptoglobin responses to infection havebeen previously reported. For example, chickens infected with Escherichia colishow significant increases in ovotransferrin and haptoglobin concentration, butchickens infected with the gastrointestinal protozoan Eimeria tenella show only a

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significant increase in ovotransferrin (Rath et al. 2009; Georgieva et al. 2010).One study even contradicts the post-challenge increases in ovotransferrin that weand others observed: chickens infected with fowl typhoid (Salmonella gallinarum)show increased haptoglobin but decreased ovotransferrin (Garcia et al. 2009).This contradiction is particularly intriguing given that the LPS we used originatedfrom the outer membrane of a strain of Salmonella (S. enterica serotype typhi-murium). Regardless, greater or lesser reliance on a particular protein may reflectthe nature of immune challenge or could indicate differences in how free iron ismanaged.

Correlations between iron-binding acute phase proteinsIn a larger analysis of birds that were not experimentally challenged, we foundthat haptoglobin and ovotransferrin correlated at the individual level but not atthe species level. This suggests that for comparisons among species, measuringconcentrations of both ovotransferrin and haptoglobin provides more informationthan measuring only one of the proteins. Differences among species in the rela-tionship between these two proteins might relate to broader immunological dif-ferences among species. For example, the extent to which a species relies onovotransferrin, haptoglobin or both might provide insight into the relative impor-tance of induced responses in general or of limiting free iron more specifically.Moreover, the reliance on one protein versus the other might have downstreamimmunological ramifications, as APPs can provide feedback to the inflammatoryprocess (Janeway et al. 2004).

Within species, baseline concentrations of ovotransferrin and haptoglobinwere significantly correlated. Thus, in studies of unchallenged individuals of asingle species, measuring baseline concentrations of both proteins may be redun-dant. However, since inter-specific responses of different APPs can be challenge-specific (see above; Rath et al. 2009; Georgieva et al. 2010), we think thatmeasuring both ovotransferrin and haptoglobin remains a useful endeavour. Any‘positive redundancy’ identified by measuring both proteins can be advantageouswhen interpreting results. Veterinarians have highlighted the importance of usingmultiple APPs to monitor disease processes, and they suggest that multiple posi-tive and negative APPs should be measured (Cerón et al. 2008). This suggestioncomplements the conclusions of ecoimmunologists regarding the importance ofmeasuring multiple immune parameters when assessing immune status (Adamo2004; Matson et al. 2006).

Advantages of measuring ovotransferrinThe ovotransferrin assay has several practical advantages that make it applicableto a wide variety of species and useful in a range of settings. The assay requiresonly a small volume of plasma (10 µl), meaning the assay can be safely appliedto the smallest of birds. The assay does not require species-specific antibodies or

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reagents, thereby avoiding problems with lack of cross-reactivity, which otherresearchers have encountered when using antibody-based methods and multiplespecies (Rath et al. 2009). In combination with the inter-specific variation in ovo-transferrin concentration that we observed (Fig. 7.1), these qualities make theassay particularly suited to comparative studies of immune function amongspecies. Furthermore, although we tested plasma from birds, we see no reasonwhy the assay could not be used or adapted for different taxonomic classes. Ifdoing so, care must be taken to understand whether ovotransferrin levels increaseor decrease during the acute phase response, given the differences between mam-mals and birds (Cray, Zaias and Altman 2009).

The assay is also inexpensive to run. While it costs approximately 4 euro persample for the reagents to measure haptoglobin concentration, the cost ofreagents to measure ovotransferrin is less than 0.05 euro per sample. Given thatboth assays require similar equipment (pipettes, 96-well microplates, spectropho-tometer) and processing time, this cost-saving can represent a considerableadvantage of ovotransferrin quantification over the measurement of haptoglobin.

In conclusion, the assay described here provides a simple, reliable, and inex-pensive means for measuring ovotransferrin concentrations in blood plasma. Theassay detects significant species variation in ovotransferrin concentrations, and itcan be used to assess changes in response to infection or inflammation. Withinpopulations, ovotransferrin concentration is a repeatable, individual-bound trait.Within, but not among species, ovotransferrin concentrations correlate signifi-cantly with concentrations of another iron-binding APP, haptoglobin. The assay isideally suited to comparative studies of immune function and health status. Incombination with its field-friendly nature, these attributes make this ovotransfer-rin assay a new and valuable addition to the ecoimmunologists’ toolkit.

AcknowledgementsPlasma samples were provided by Luisa Mendes, Yvonne Verkuil, Theunis Piersma, Maaike

Versteegh, Debbie Buehler and Stephane Ostrowski. We thank them all for their generosity.

Samples from Saudi Arabia were collected with permission of the National Wildlife

Research Centre Taif, and with the assistance of Dr Mohammed Shobrak and the staff at

Mahazat as-Sayd nature reserve. Samples in Kenya were collected with permission of the

National Museums of Kenya and the help of Henry Ndithia, Dominic Kimani, Samuel

Bakari and Dr Muchai Muchane. Wouter de Vleiger and Staatsbosbeheer gave permission to

work in the Aekingerzand, where samples in the Netherlands were collected. Bluebird sam-

ples were collected around Kenyon College, Ohio with the assistance of Bob Mauck and

staff at the Brown Family Environmental Center. Blood sampling was conducted under per-

mit numbers: 4338B, 5095 and 5219A (University of Groningen Animal Experimentation

Committee); NIOZ.07.01 (Animal Experimentation Committee of the Royal Netherlands

Academy of Sciences); 11-95 (Ohio Department of Natural Resources, Division of Wildlife);

MB064522-0 (USFWS).

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K.D. Matson and B.I. Tieleman were supported by VENI grants (863.08.026 and

863.04.023, respectively) from the Netherlands Organisation for Scientific Research

(NWO). B.I. Tieleman was also a recipient of a Rosalind Franklin Fellowship from the Uni-

versity of Groningen. Maaike Versteegh, Debbie Buehler, Jeroen Renerkeens and three

anonymous reviewers all made useful comments on earlier versions of the manuscript.

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Nederlandse samenvatting (Dutch summary)

Dit proefschrift onderzoekt de rol van ziekterisico en ontwikkelingsgeschiedenisop de vorming van de immuunfunctie van leeuweriken (Alaudidae) in verschil-lende omgevingen. Daartoe heb ik data verzameld van vogels die leven in de ber-gen van Afghanistan, in de woestijnen van Saoedi-Arabië, in de graslanden inKenya en in een nationaal park in Nederland. In al deze verschillende omgevin-gen moeten de vogels de universele uitdagingen die zijn geassocieerd met overle-ven en voortplanting aangaan. Verschillende soorten hebben verschillendemanieren ontwikkeld - dat wil zeggen verschillende ontwikkelingsgeschiedenis-sen - om dit te bereiken. Een essentieel onderdeel van zelfbehoud en overleving isde verdediging tegen de diverse en meedogenloze bedreigingen van infectie enziekte, waar alle soorten continu aan worden blootgesteld. Speciaal voor dit doelheeft het immuunsysteem zich ontwikkeld. De vorm en functie van het immuun-systeem verschillen binnen en tussen de verschillende soorten, wat het gevolgkan zijn van verschillende ontwikkelingsgeschiedenissen of van de ziekte-omge-vingen waarin de verschillende soorten zich bevinden. Een belangrijk doel vandit proefschrift is te onderzoeken op welke manier deze twee mogelijkheden bij-dragen aan de immunologische variatie.

Ecologische Immunologie

Alle dieren bezitten een bepaalde vorm van een immuunsysteem. Dit is de groepvan anatomische, chemische en fysiologische verdedigingsmechanismen die sameneen dier beschermen tegen vreemde organismen en stoffen, inclusief zijn eigenabnormale cellen, die potentieel gevaarlijk zijn voor het dier. Dit zeer complexe enuit meerdere niveaus bestaande systeem is van essentieel belang voor het leven enbrengt veel voordelen, waaronder het minimaliseren van de negatieve invloedenvan infecties en ziekte op een organisme. Immuunsystemen zijn echter kostbaar watbetreft de energie en tijd die nodig zijn om ze te ontwikkelen, te onderhouden en tegebruiken en vanwege de 'collateral damage' die sommige immunologische reactieshebben op de lichaamseigen gezonde cellen. Dit betekent dat een maximaleimmuunreactie niet noodzakelijkerwijs de meest optimale reactie is. Afhankelijk vande mate van beschikbaarheid van bronnen en de ziektedreiging kunnen dieren opeen verschillende manier reageren wat betreft het type en de omvang van deimmuunreactie. Het begrijpen en verklaren van dit type immunologische variatiebinnen en tussen soorten zijn centrale doelstellingen van de ecologische immunolo-gie, een wetenschap die gebruik maakt van immunologische metingen om ecologi-sche en evolutionaire hypotheses te toetsen. Studies in de ecologische immunologieworden doorgaans uitgevoerd op niet-gedomesticeerde dieren, die vaak in het wildleven, en niet op de gebruikelijke proefdieren die worden gehouden in gecontroleer-de laboratoriumcondities. Het doel is het begrijpen van de oorzaken van de immu-nologische variatie en niet zozeer de specifieke moleculaire mechanismen.

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Een kosten en baten analyse

Vanaf het begin heeft de ecologische immunologie gebruik gemaakt van eenkosten-baten kader als een krachtig middel om te verklaren waarom en op welkemanier immuunsystemen zouden moeten variëren. Een van de benaderingen,gezien vanuit een kostenperspectief, erkent dat immuunsystemen kostbaar zijn,omdat ze bronnen (energie en tijd) vereisen voor hun ontwikkeling, onderhouden gebruik. Omdat de bronnen beperkt zijn, gaat de investering in het immuun-systeem ten koste van andere fysiologische processen zoals groei en voortplan-ting, en vice versa. De 'ontwikkelingsgeschiedenis' van een soort beschrijft decombinatie van deze processen, die wordt gevormd door natuurlijke selectie omde hoeveelheid overlevend nageslacht te maximaliseren (dat wil zeggen om devitaliteit te maximaliseren). Omdat het een eigenschap is die bijdraagt aan zelfbe-houd, en daarmee aan overleving en mogelijkheden voor toekomstige voortplan-ting, verwacht men dat de immuunfunctie zich naast andere voor deontwikkelingsgeschiedenis kenmerkende eigenschappen heeft ontwikkeld met alsdoel de vitaliteit te maximaliseren. Dus sommige soorten hebben de investeringin het immuunsysteem ondergeschikt gemaakt aan voortplanting, daarbij zelfbe-houd en overleving opofferend ten gunste van het produceren van nageslacht opdat moment. Andere soorten investeren meer in zelfbehoud, langer leven endaarmee toekomstige mogelijkheden voor voortplanting, maar produceren min-der nageslacht per voortplantingsmoment. Immunologen die zich bezighoudenmet de ecologie interpreteren de afwegingen aangaande zelfbehoud als een ver-klaring voor de variatie in immuunreacties die wordt gevonden tussen dieren.Hoewel deze benadering in het verklaren van immunologische variatie vrucht-baar is gebleken, zijn de conclusies mogelijk niet compleet. Immers, veel studiesin de ecologische immunologie onderschatten de voordelen die immuunreactiesmet zich meebrengen.

Een tweede benadering in het leren begrijpen van de immunologische variatiebeschouwt de voordelen van het immuunsysteem. Het voornaamste voordeel isuiteraard de verdediging tegen infectie met vitaliteitverminderende vreemdeorganismen, zoals parasieten en pathogenen (ziekte veroorzakende stoffen).Gezien de kosten die geassocieerd zijn met het immuunsysteem, wordt gedachtdat verdedigingsmechanismen groter of sterker zijn wanneer het infectierisicogroter is. Voorbeelden van hoogrisico omgevingen zijn de tropen en zoetwater,omgevingen waarvan in sommige studies gesuggereerd wordt dat de blootstellingaan potentieel ziekteveroorzakende parasieten en pathogenen er zeer hoog is.Een hoge mate van socialiteit, grote groepen of agressieve interacties tussen indi-viduen zijn voorbeelden van gedrag dat het risico op transmissie en infectie kanvergroten. Ondanks deze voorbeelden is het vinden van adequate metingen voorhet beoordelen van het ziekterisico dat een individu treft niet vanzelfsprekend.Verschillende soorten kunnen bijvoorbeeld verschillende typen ziekterisico's en

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verschillende mate van blootstelling treffen, en metingen die toepasbaar zijn inde ene omgeving zijn mogelijk niet geschikt in een andere omgeving. Onderzoe-kers hebben in plaats daarvan vaak niveaus van de immunologische verdediginggekwantificeerd en deze gebruikt als rechtvaardiging voor eerdere veronderstel-lingen over de manier waarop ziekterisico kan variëren. Als een alternatief wor-den de overvloed en diversiteit van parasieten op een gastheer gebruikt alsaanduidingen van ziekterisico. Deze metingen worden waarschijnlijk al beïnvloeddoor de huidige immuunreacties van de gastheer, zowel door gedrag (zoals ver-zorging en de vermijding van parasieten) als op immunologische wijze. In hoofd-stuk 2, brengen mijn co-auteurs en ik deze onderwerpen onder de aandacht. Wecreëren een kader waarin immuunfunctie en selectieve druk door pathogenenkunnen worden beschouwd en belichten op welke manier een beter begrip vande bedreigingen door infecties kan helpen om variatie in immunologische verde-digingsmechanismen in perspectief te zetten. We introduceren het concept vanhet ‘immunobiome’ - alle levende organismen die leven in of op een gastheer ende mogelijkheid hebben zich te ontwikkelen in reactie op immunologische verde-digingsmechanismen. De interacties tussen het immuunsysteem en het specifiekeimmunobiome van een dier vormen zijn immunologische verdedigingsmechanis-men, zowel op evolutionaire als ecologische tijdschalen. We pakken ook het prak-tische probleem aan van het meten van pathogenen en verruimen vanziekterisico in verschillende omgevingen, door moleculaire en andere methodesvoor te stellen die geschikt kunnen zijn voor het gebruik in studies in de ecologi-sche immunologie.

Een vergelijkend onderzoek met gebruik van leeuweriken

In de ecologie en ecologische immunologie wordt vaak gebruik gemaakt vanvogels omdat ze over het algemeen goed zijn te observeren en te vangen enmeestal in voldoende aantallen voorkomen zodat grote groepen kunnen wordenbestudeerd. Individuele vogels kunnen op een niet-invasieve manier wordengemarkeerd met gekleurde ringen en variabelen in de levensgeschiedenis zoalslegselgrootte kunnen relatief gemakkelijk worden gemeten. In hoofdstuk 3 treffenwe voor het eerst de leeuweriken (Alaudidae), de familie van vogels die in ditproefschrift als proefsysteem wordt gebruikt. De keuze voor leeuweriken als stu-die-object is op het eerste gezicht wellicht een ongebruikelijke keuze. Deze zang-vogels laten zich relatief moeilijk ontdekken en verstoppen hun nesten op ofdichtbij de grond. Deze eigenschappen zouden leeuweriken minder geschiktmaken voor experimentele handelingen dan bijvoorbeeld soorten die gebruikmaken van nestkastjes. Toch bieden leeuweriken een ideaal studiesysteem voorhet vergelijkend onderzoek dat ik heb uitgevoerd voor dit proefschrift. De bena-dering die gebruik maakt van een vergelijkend onderzoek is het sterkst wanneer

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aan elkaar gerelateerde soorten worden vergeleken, waarbij complicerende facto-ren die ontstaan vanuit verschillende evolutionaire geschiedenissen worden ver-meden. Alle leeuwerikensoorten eten vergelijkbaar voedsel en gedragen zich opeen vergelijkbare manier, waardoor vergelijkingen tussen soorten niet wordenbeïnvloed door dieet of gedrag. Ondanks deze overeenkomsten en de universelevoorkeur van leeuweriken voor een open grasland omgeving, ervaren verschillen-de soorten een breed scala aan klimaats- en omgevingsinvloeden, variërend vanextreem droog tot vochtig en tropisch. Het gedrag, de fysiologie en de levensge-schiedenis van verscheidene leeuwerikensoorten levend onder deze verschillendeomstandigheden zijn reeds uitgebreid bestudeerd. Het immuunsysteem binnen entussen leeuwerikensoorten is echter nog nauwelijks gekarakteriseerd. Omdatwordt gedacht dat de verschillende klimaats- en omgevingsomstandigheden diede leeuweriken treffen een weerspiegeling geven van de verschillen in ziekterisi-co (dat wil zeggen een extreem droog leefgebied, laag ziekterisico; een vochtig entropisch leefgebied, hoger ziekterisico), bieden leeuweriken een waardevollemethode voor het bestuderen van de ecologie en de evolutie van de immuniteit.De leeuweriken familie biedt een waardevol studiesysteem waarin gedrags- enfysiologische aspecten goed duidelijk zijn, waarin ontwikkelingsgeschiedenissenen omgevingsgerelateerde ziekterisico's tussen soorten variëren en waarin hetbegrip van de oorzaken en gevolgen van de variatie in het immuunsysteem eenvolgende stap vormt.

Immunologische verdediging in verschillende gradaties vanvoorspeld ziekterisico

Ik maak gebruik van leeuwerikensoorten om een vraag te beantwoorden die cen-traal staat in zowel mijn proefschrift als in de ecologische immunologie: in welkemate beïnvloeden omgevingsgerelateerd ziekterisico en ontwikkelingsgeschiede-nis de investering in immuniteit en liggen ze ten grondslag aan de vastgesteldepatronen in immunologische variatie? Zowel ziekterisico als levensgeschiedeniszijn eerder gebruikt in het verklaren van variatie in immuunreacties binnen entussen soorten. Deze twee factoren vertonen echter co-variatie, wat het moeilijkmaakt om vast te stellen welke factor meer geassocieerd is met de variatie inimmuniteit. Bovendien kunnen voorspellingen over de investering in immuniteitop basis van deze factoren in sommige omstandigheden uiteenlopen (hoofdstuk2). In hoofdstuk 3 relateer ik meerdere immunologische indices, gemeten in 23populaties van 12 soorten leeuweriken, aan indicatoren van omgevingsgerela-teerd ziekterisico en indices van ontwikkelingsgeschiedenis. In tegenstelling totsommige vergelijkende studiesystemen waarin het voorspelde ziekterisico en ont-wikkelingsgeschiedenis positief gecorreleerd zijn, variëren deze potentieel voor-spellende variabelen onafhankelijk van elkaar tussen populaties leeuweriken.

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Door deze onafhankelijkheid is er de mogelijkheid om hun relatieve rol in de vor-ming van de immunologische variatie te ontrafelen. Ik heb gevonden dat indicesvan de aangeboren immuniteit sterk positief correleren met abiotische indicatorenvan omgevingsgerelateerd ziekterisico. Ontwikkelingsfactoren gerelateerd aanvoortplanting vertonen daarentegen weinig relatie met de investering in de aan-geboren immuniteit.

Ik heb de gradaties in ziekterisico weer gebruikt in hoofdstuk 4, waar ik samenmet mijn mede-auteurs de variatie in immunologische verdedigingsmechanismenonderzoek, gebruikmakend van vergelijkbare analytische technieken, maar vanuiteen iets ander perspectief bekeken. Ik heb de patronen van de antimicrobiotischeeiwitten ovotransferrine en lysozym in het eiwit van de eieren van leeuwerikendie zijn verzameld onderzocht in verschillende gradaties in ziekterisico. Eierenbieden een gesimplificeerd model van het immuunsysteem, gekarakteriseerd doorminder verdedigingscomponenten en minder infectierisico's. Bovendien helpt hetvergelijken en tegenover elkaar stellen van de relaties tussen ziekterisico enimmunologische verdedigingsmechanismen in vogels en hun eieren in het identi-ficeren van de selectiedruk die elk type verdedigingsmechanisme heeft gevormd.Afname van de vitaliteit van de eieren, de microbiologische belasting op de eier-schaal en infectie van eieren door de eierschaal heen komen meer voor in vochti-ger milieus. Dit bracht ons tot de voorspelling dat wanneer de antimicrobiolo-gische verdedigingsmechanismen van eieren zich ontwikkelen om zich aan te pas-sen aan het risico op microbiologische infectie, de concentraties van antimicrobio-logische eiwitten in de eieren zouden moeten variëren op basis van milieu-omstandigheden. In dat geval zouden eieren uit vochtige gebieden hogere con-centraties ovotransferrine en lysozym moeten bevatten dan eieren uit drogeremilieus. De resultaten van hoofdstuk 4 laten zien dat de concentraties ovotrans-ferrine overeenkomen met onze voorspelling maar dat de concentraties lysozymtegenovergestelde patronen vertonen en het hoogst waren in droge milieus meteen laag ziekterisico. Dit roept interessante vragen op over de functie van lyso-zym in vogeleieren en suggereert dat er mogelijk een uitwisseling bestaat tussenantimicrobiotische eiwitten in het eiwit. Uit de studie kwam ook naar voren datneerslag, een onderdeel van het milieugebonden ziekterisico, de patronen vanantimicrobiologische variatie niet goed kon verklaren, ondanks experimenteleaanwijzingen dat vochtigheid van belang is voor het optreden van infectie doorde eierschaal heen. Temperatuur was een betere voorspellende factor dan neer-slag voor de concentraties van antimicrobiologische eiwitten in de eieren, maartemperatuur was geen goede voorspellende factor voor de immunologische ver-dedigingsmechanismen van leeuweriken in verschillende gradaties van ziekterisi-co (hoofdstuk 3). Deze tegenstrijdige invloed van temperatuur weerspiegeltwellicht de ectothermische aard van eieren en de endothermische eigenschappenvan vogels.

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Milieu- en seizoensvariatie inimmunologische verdedigingsmechanismen en ziekterisico

Beide hoofdstukken 3 en 4 belichten de waarde van abiotische indicatoren voorde biotische variatie in ziekterisico. Toch ga ik in deel III van dit proefschrift ver-der met de conclusie van hoofdstuk 2, dat biotische metingen van componentenvan het ziekterisico essentieel zijn voor het verder begrijpen van de waargeno-men patronen in immunologische variatie. Ik introduceer een nieuwe techniekmet luchtmonsters om de uitgebreidheid van microben die zijn afgescheiden vanvogels en in de omringende lucht te kwantificeren. Deze methodes bieden zowelgastheerafhankelijke als gastheeronafhankelijke metingen van het biotisch ziekte-risico. De ontkoppeling van de verdedigingsmechanismen van de gastheer en demogelijke blootstelling van de gastheer vertegenwoordigt een belangrijke vooruit-gang in de ecologische immunologie en biedt een nieuwe ingang voor toekomsti-ge studies. We hebben de methode met luchtmonsters gebruikt om de milieu- envogelgerelateerde microbiologische verzamelingen te beoordelen van leeuwerikendie leven in de Arabische Woestijn en het gematigde Nederland. We hebben ookde indices van de aangeboren immuniteit gemeten en deze beoordeeld in hetlicht van voorspellingen die zijn ontstaan vanuit het ziekterisico en de ontwikke-lingsgeschiedenis, factoren die al eerder goed onderzocht zijn. Milieugebondenziekterisico was beter in staat de variatie in immunologische verdedigingsmecha-nismen te verklaren dan de ontwikkelingsgeschiedenis, een vinding die de resul-taten van hoofdstuk 3 onderbouwt. Leeuweriken in een gematigd milieu, diewerden blootgesteld aan hogere concentraties microben in de lucht en dichteremicrobiologische verzamelingen met zich meedroegen, vertoonden ook sterkereimmunologische indices dan hun gelijken die in de woestijn leven. De verklaringvan de immunologische variatie op basis van de ontwikkelingsgeschiedenis, dievoorspelt dat leeuweriken die leven in de woestijn meer investeren in hetimmuunsysteem, werd daarentegen niet onderbouwd.

Tussen milieus wordt variatie gezien in ziekterisico's (hoofdstuk 5), maar vari-atie in ziekterisico binnen een milieu kan ook een belangrijke onderliggendefactor zijn in de immunologische variatie. In hoofdstuk 6 beschrijf ik datimmunologische indices seizoensgewijs veranderen in leeuweriken die leven inde Arabische Woestijn. Ik laat ook zien dat deze veranderingen gepaard gaan metparallelle aanpassingen in de microbiologische dichtheden die worden afgeschei-den door vogels en met tegengestelde aanpassingen in de microbiologische con-centraties in het wijdere milieu. Deze studie onderstreept de noodzaak van zowelgastheerafhankelijke als gastheeronafhankelijke indices wanneer men het ziekte-risico kwantificeert en de resultaten roepen interessante vragen op over hetomgevingsniveau waarop dieren immunologisch reageren op microben.

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Een bijdrage aan de immunologische hulpmiddelen van de ecoloog

Ondanks het feit dat er de laatste vijftien jaar aanzienlijke vooruitgang isgeboekt, blijft de wens van immunologen in de ecologie om niet-gedomesticeer-de, in het wild levende soorten te bestuderen ook hun Achilleshiel. Reagentiavoor niet-proefdieren zijn over het algemeen niet verkrijgbaar en protocollen diegeschikt zijn voor dieren die worden gehouden in laboratoria kunnen ongeschiktzijn voor wilde dieren. In het laatste deel van dit proefschrift lever ik een bijdrageaan de immunologische hulpmiddelen van de ecoloog door het valideren van eenvoor veldwerk geschikte proefopzet voor het meten van ovotransferrine(hoofdstuk 7). Ovotransferrine is een acute fase eiwit waarvan de waarde in hetbloed toeneemt in reactie op ontsteking en infectie. Hetzelfde antimicrobiotischeeiwit wordt ook gevonden in het eiwit van vogeleieren, zoals we hebben beschre-ven in hoofdstuk 4.

Conclusie

In leeuweriken wordt de variatie in investering in aangeboren immunologischeverdedigingsmechanismen verklaard door het ziekterisico en niet zozeer door deontwikkelingsgeschiedenis (hoofdstukken 3 en 5). Dit resultaat biedt een tegen-pool voor eerdere theoretische en empirische studies die de nadruk leggen op hetbelang van de ontwikkelingsgeschiedenis in het verklaren van immunologischevariatie. Dit nieuwe inzicht is, deels, het resultaat van de ontwikkeling en toepas-sing van een methode voor het meten van gastheerafhankelijke en gastheeronaf-hankelijke factoren van ziekterisico (hoofdstukken 5 en 6). Deze methodes metdirecte metingen brachten verschillen in ziekterisico tussen individuen en tussenpopulaties aan het licht en hielpen in het valideren van abiotische indicatoren omte gebruiken wanneer directe metingen onmogelijk zijn. De methodes met directemetingen gaven ook inzichten in en riepen vragen op over de schaal waaropleeuweriken bedreigingen ondervinden en immunologisch reageren: de gegevensin dit proefschrift suggereren dat dit op vrij kleine schaal plaatsvindt (hoofdstuk6). Er werd vertrouwd op abiotische indicatoren voor milieugebonden ziekterisicobij het beoordelen van de relatieve invloed van ziekterisico en ontwikkelingsge-schiedenis op de immunologische variatie (hoofdstukken 3 en 4). Hoewel dezeabiotische indicatoren nuttig, goed gevalideerd, logistiek ongecompliceerd engemakkelijk te begrijpen zijn, is verdere toepassing van methodes met directemetingen veelbelovend voor de vorming van ons begrip van de interacties tussenziekterisico en de investering in immuniteit. Het zijn specifiek de moleculairetechnieken die de potentie hebben de directe meting van pathogenen en andereimmuunreactieve stoffen drastisch te wijzigen (hoofdstuk 2), met name wanneerhun toepassing gemakkelijker en praktischer wordt in de verscheidenheid aan

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omstandigheden in het veldwerk. In dezelfde geest zullen nieuwe proefopzettin-gen voor het meten van additionele aspecten van de immuniteit (hoofdstuk 7)een completere karakterisering van het immuunsysteem mogelijk maken. Vanafhet begin heeft men in het veld van de ecologische immunologie uitdagende vra-gen opgeworpen en geprobeerd te beantwoorden over hoe en waarom hetimmuunsysteem zo variabel is in zijn structuur en zijn reacties. Ik hoop dat mijnproefschrift bijdraagt aan dit voortschrijdende proces door het beantwoorden vansommige en het opwerpen van veel meer vragen.

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Acknowledgements

If you approach a new thesis book like me, then you will have probably reachedthis point by flicking through the pages to quickly look at the pictures. Maybe youchecked how many chapters there are, or how many manuscripts I have alreadymanaged to get published. Perhaps, if you are close to the end of your own thesis,you actually bothered to read the introduction. Forget the science though; in alllikelihood this is probably the first place that you have actually started reading inany detail. Well, so be it. In a world where most of the copies that I print willnever be looked at again (let’s be honest), my first acknowledgement is to saythanks for sticking with it even this far. Now let’s get on with thanking the reallyimportant people.

In almost all acknowledgements people thank their partners last. I want to changethat. After all, if I get this one wrong, I’ll never be allowed to forget it! Sarah, thankyou for all your support and help, both emotional and practical, throughout ourtime in Groningen. Thank you for moving to the Netherlands and for being under-standing when I disappeared to Saudi Arabia for six weeks as soon as you arrived.Thank you for your help with designing assays and for listening to my complaints.Our Dutch adventure may have come to an end, but I anticipate many more excit-ing adventures together in the future, although perhaps with fewer bicycles andnot so many bitterballen. Thanks also to my family and friends back in the UK,for putting up with the flying visits, and for your continual love and support.

I next want to acknowledge my supervisors, starting with Irene Tieleman. Eventsconspired against you for quite a period, with repercussions for all of us in theTieleman group. Nonetheless, it has truly been an education learning with youand I think that is the greatest aim a supervisor can hope to achieve. I hope thatyou have gained from the experience as much as I have. I also realise the greatdebt that my own studies owe to the fantastic groundwork that you, Joe Williamsand other collaborators laid down in your earlier studies on larks.

My second supervisor was Kevin Matson. Almost from day one we workedtogether, and it seems to have stayed that way throughout my entire time inGroningen, much to my benefit. Working so closely with someone whom I con-sider a real friend has been an added bonus and always made things more enjoy-able, particularly when times were tough. This thesis owes much to the supportand supervision that you provided and I am very grateful to you for that.

Last, but by no means least, Joost Tinbergen stepped in at a crucial time andreally helped push me over the last hurdles towards completion. I always foundour discussions stimulating and meeting with you and Kevin helped open myeyes to new ways of thinking about my work. Simply, it was a pleasure to havethe opportunity to work with you.

Apart from my supervisors, the other members of the Tieleman group haveprovided invaluable scientific, practical and emotional support. In particular,

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Arne Hegemann and Maaike Versteegh have shared the PhD journey along all itsups and downs. Arne, thanks for keeping our shared offices so green and tendingthe plants so well. Your fieldwork skills and ability to find nests are a wonder toobserve, and contributed much to this thesis. Good luck with finishing up. I’msure you’ll get there soon.

Maaike, apart from kicking my legs under the desk, it has always been goodfun working with you. Sharing the last few months of thesis stress with you madethings go that much easier and I’m very pleased that we will also be sharing ourdefence day. You have always been willing to help, whether that be with statis-tics, the Belastingdienst or just emptying some beer glasses in De Sleutel. Thanksso much.

Debbie Buehler, Liz Kleynhans, and now Stéphanie Grizard and Henry Ndithia,you have all contributed towards the atmosphere, both intellectual and social,within the Tieleman group and helped make it a generally fun and productiveplace to be.

As someone who collected samples for their PhD in Europe, Africa and Arabia, Ican appreciate the value of good collaborators. Fortunately I was lucky enough tofind such people in all the places I visited. In Saudi Arabia, Mohammed Shobrakand the staff at the National Wildlife Research Center and at Mahazat as-Saydwere unfailing in their welcome and assistance. Joe Williams taught me about thepracticalities of conducting research in Saudi Arabia and the usefulness of phrasessuch as ‘Ma fi mushkila’. Shukran to you and Irene for sharing your patch ofdesert with me.

Towards the end of the fieldwork component of my PhD, the opportunity tocollect samples in Kenya was a real treat and introduced me to new friends.Ndithia, Dominic Kimani, Samuel Bakari and other Friends of Kinangop Plateauturned a trip that was initially meant for scoping out potential new field sites intoa valuable data collection exercise and integral component of my thesis. I hopefor more such visits in the future as Ndithia progresses in his own PhD so success-fully. And Bakari, I am very pleased that Irene and I have been able to help you tocomplete your studies.

Another person who helped me with sample collection was Stéphane Ostrows-ki, along with unnamed local Afghan assistants. Your efforts in Afghanistan addedyet another element to my thesis and I can only imagine the logistical challenges(donkeys and all) that had to be overcome in order to get samples back to Europesafely. Meeting you by platform 4 at Gare du Nord in Paris to collect some ofthose samples was like something out of a spy thriller. I hope we have the oppor-tunity to meet in less fleeting circumstances in the future.

Back in the Netherlands, a whole host of volunteers and students contributedtowards fieldwork. Rob Voesten stands out in particular for his enthusiasm anddedication, and I am very pleased that he was able to join me on my second trip

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to Saudi Arabia where he contributed so much. Mauro Varaschin also made a bigimpact (especially with old ladies), but the efforts of all the volunteers are muchappreciated. As Masters students, Sophie Jaquier and Katie Hine both left theirmark on the group and contributed much to the working atmosphere in the field.Katie, as my first MSc student, thanks for all your hard work and for putting upwith all my supervisory failings. I don’t think that I made you cry too many times,so I count that as some kind of success.

I am indebted to the members of my reading committee, Andrea Graham, HeinzRichner and Dick van Elsas for taking the time to read my thesis, and approve it!You are now (hopefully) part of that select and no doubt small group of peoplethat may have actually read my thesis from cover to cover. Another person whodelved between the covers was Dick Visser, who prepared the layout of this thesisand turned my text files and figures into something recognisable as a book. Manythanks. Carolien Woolthuis also read at least part of my thesis, doing an excellentjob as translator and preparing the Nederlandse samenvatting. Haartelijkbedankt. Suus Bakker-Geluk and Joyce Rietveld made the administrative aspectsof university life go smoothly and kept me on the straight and narrow with theuniversity bureaucracy – no mean feat. And Marco van de Velde was always morethan willing to help with all matters lab-related. I wonder if you quite realise howmany items I ‘borrowed’ from your lab over the years.

I thank Claudia Burger and Tim Broesamle for agreeing to be my paranymphsand for all the good companionship throughout my time in Groningen. I look for-ward to being there to help you celebrate when you both complete your doctoraljourneys in due course.

Finally, I thank all those members of Animal Ecology and other groups whomade both the scientific and the social times in Groningen so enjoyable. Peoplelike Marion Nicolaus welcomed me into the group when I first arrived and got meinvolved in the social aspects of life in the old Biologisch Centrum Haren. It wasan honour to be your paranymph. Jeroen Kuipers, Marco Gottelt, Bruno Tesson,Tim Fawcett, Marion and others – thanks for the squash games, and for lettingme think that sometimes I could win. I could write many names, but what a the-sis and its acknowledgements can never fully convey are the friendships forgedand the good times shared. Lists of names just don’t do it justice. So, if we sharedthose good times then you know who you are, and thank you for your contribu-tion, however indirect, to this work. Now, flip back to the first page and startreading this thesis from the beginning, like a book should be read. You neverknow, you might just learn something and maybe all those printed copies won’tbe a complete waste…

Nicholas Horrocks, Cambridge, 22nd December 2011.

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Addresses of co-authors

Arne Hegemann1

Kathryn Hine1, 2

Sophie Jaquier1

Kevin D. Matson1

Muchane Muchai3

Henry K. Ndithia1, 3

Stephane Ostrowski4

Mohammed Shobrak5

B. Irene Tieleman1

Joost M. Tinbergen1

Joseph B. Williams6

1Animal Ecology Group, Centre for Evolutionary & Ecological Studies, Universityof Groningen, P.O. Box 11103, 9700 CC, Groningen, The Netherlands

2Department of Ecology and Evolution, UNIL Sorge, Le Biophore, CH – 1-15 Lau-sanne, Switzerland

3Ornithology Section, Zoology Department, National Museums of Kenya, P.O. Box40658, GPO 00100, Nairobi, Kenya

4Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, NY 10460, USA

5Biology Department, Science College, Taif University, P.O. Box 888, Taif 21974,Saudi Arabia

6Department of Evolution, Ecology & Organismal Biology, Ohio State University,Columbus, OH 43210, USA

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Publication List

Matson K.D., Horrocks N.P.C., Versteegh M.A., Tieleman B.I. Baseline haptoglo-bin concentrations are repeatable and predictive of certain aspects of a subse-quent experimentally-induced inflammatory response. Submitted.

Horrocks N.P.C., Hegemann A., Matson K.D., Hine K., Jaquier S., Tinbergen,J.M., Shobrak M., Williams J.B., & Tieleman B.I. Immune defences are associ-ated with microbial pressure rather than life-history in larks from differentenvironments. Submitted.

Horrocks N.P.C., Matson K.D., Shobrak M., & Tieleman B.I. Seasonal patterns inimmune indices reflect microbial loads on birds but not microbes in the widerenvironment. Ecosphere: in press.

Horrocks N.P.C., Matson K.D., Tieleman B.I., 2011. Pathogen pressure putsimmune defense into perspective. Integrative and Comparative Biology 51:563–576.

Horrocks N.P.C., Tieleman B.I., Matson K.D., 2011. A simple assay for measure-ment of ovotransferrin - a marker of inflammation and infection in birds.Methods in Ecology and Evolution 2: 518–523.

van de Crommenacker J., Horrocks N.P.C., Versteegh M.A., Komdeur J., Tiele-man B.I., Matson K.D., 2010. Effects of immune supplementation and immunechallenge on oxidative status and physiology in a model bird: Implications forecologists. Journal of Experimental Biology 213: 3527–3535.

Horrocks N., Perrins C., Charmantier A., 2009. Seasonal changes in male andfemale bill knob size in the Mute swan Cygnus olor. Journal of Avian Biology40: 511–519.

Horrocks N., Pounder R., 2006. Who’s for five nine-hour shifts a week? ClinicalMedicine 6: 440–442.

Horrocks N., Pounder R., RCP Working Group, 2006. Working the night shift:preparation, survival and recovery – a guide for junior doctors. Clinical Medi-cine 6: 61–67.

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