IntroductionEvolutionary history and its relevance in understanding and conserving southern
African biodiversity
Presentée à la
Faculté de Biologie et Médicine de l’Université de Lausanne
Par
(University of Aberdeen and University of East Anglia)
Jury de Thèse:
Prof. Antoine Guisan, Directeur de thèse
Dr Nicolas Salamin, Co-directeur de thèse
Dr Richard Grenyer, Expert
Prof. Luca Fumagalli, Expert
Abstract Understanding how biodiversity is distributed is central to any conservation effort and has
traditionally been based on niche modeling and the causal relationship between spatial
distribution of organisms and their environment. More recently, the study of species’
evolutionary history and relatedness has permeated the fields of ecology and conservation
and, coupled with spatial predictions, provides useful insights to the origin of current
biodiversity patterns, community structuring and potential vulnerability to extinction.
This thesis explores several key ecological questions by combining the fields of niche
modeling and phylogenetics and using important components of southern African
biodiversity. The aims of this thesis are to provide comparisons of biodiversity measures, to
assess how climate change will affect evolutionary history loss, to ask whether there is a clear
link between evolutionary history and morphology and to investigate the potential role of
relatedness in macro-climatic niche structuring.
The first part of my thesis provides a fine scale comparison and spatial overlap quantification
of species richness and phylogenetic diversity predictions for one of the most diverse plant
families in the Cape Floristic Region (CFR), the Proteaceae. In several of the measures used,
patterns do not match sufficiently to argue that species relatedness information is implicit in
species richness patterns.
The second part of my thesis predicts how climate change may affect threat and potential
extinction of southern African animal and plant taxa. I compare present and future niche
models to assess whether predicted species extinction will result in higher or lower
phylogenetic diversity survival than what would be experienced under random extinction
processes. I find that predicted extinction will result in lower phylogenetic diversity survival
but that this non-random pattern will be detected only after a substantial proportion of the
taxa in each group has been lost.
The third part of my thesis explores the relationship between phylogenetic and
morphological distance in southern African bats to assess whether long evolutionary
histories correspond to equally high levels of morphological variation, as predicted by a
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neutral model of character evolution. I find no such evidence; on the contrary weak negative
trends are detected for this group, as well as in simulations of both neutral and convergent
character evolution.
Finally, I ask whether spatial and climatic niche occupancy in southern African bats is
influenced by evolutionary history or not. I relate divergence time between species pairs to
climatic niche and range overlap and find no evidence for clear phylogenetic structuring. I
argue that this may be due to particularly high levels of micro-niche partitioning.
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Résumé
Comprendre la distribution de la biodiversité représente un enjeu majeur pour la
conservation de la nature. Les analyses se basent le plus souvent sur la modélisation de la
niche écologique à travers l’étude des relations causales entre la distribution spatiale des
organismes et leur environnement. Depuis peu, l'étude de l'histoire évolutive des organismes
est également utilisée dans les domaines de l'écologie et de la conservation. En combinaison
avec la modélisation de la distribution spatiale des organismes, cette nouvelle approche
fournit des informations pertinentes pour mieux comprendre l'origine des patterns de
biodiversité actuels, de la structuration des communautés et des risques potentiels
d'extinction.
Cette thèse explore plusieurs grandes questions écologiques, en combinant les domaines de
la modélisation de la niche et de la phylogénétique. Elle s’applique aux composants
importants de la biodiversité de l'Afrique australe. Les objectifs de cette thèse ont été 1) de
comparer différentes mesures de la biodiversité, 2) d'évaluer l’impact des changements
climatiques à venir sur la perte de diversité phylogénétique, 3) d’analyser le lien potentiel
entre diversité phylogénétique et diversité morphologique et 4) d’étudier le rôle potentiel de
la phylogénie sur la structuration des niches macro-climatiques des espèces.
La première partie de cette thèse fournit une comparaison spatiale, et une quantification du
chevauchement, entre des prévisions de richesse spécifique et des prédictions de la diversité
phylogénétique pour l'une des familles de plantes les plus riches en espèces de la région
floristique du Cap (CFR), les Proteaceae. Il résulte des analyses que plusieurs mesures de
diversité phylogénétique montraient des distributions spatiales différentes de la richesse
spécifique, habituellement utilisée pour édicter des mesures de conservation.
La deuxième partie évalue les effets potentiels des changements climatiques attendus sur les
taux d’extinction d’animaux et de plantes de l'Afrique australe. Pour cela, des modèles de
distribution d’espèces actuels et futurs ont permis de déterminer si l'extinction des espèces se
traduira par une plus grande ou une plus petite perte de diversité phylogénétique en
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comparaison à un processus d'extinction aléatoire. Les résultats ont effectivement montré
que l'extinction des espèces liées aux changements climatiques pourrait entraîner une perte
plus grande de diversité phylogénétique. Cependant, cette perte ne serait plus grande que
celle liée à un processus d’extinction aléatoire qu’à partir d’une forte perte de taxons dans
chaque groupe.
La troisième partie de cette thèse explore la relation entre distances phylogénétiques et
morphologiques d’espèces de chauves-souris de l’Afrique australe. Il s’agit plus précisément
de déterminer si une longue histoire évolutive correspond également à des variations
morphologiques plus grandes dans ce groupe. Cette relation est en fait prédite par un modèle
neutre d'évolution de caractères. Aucune évidence de cette relation n’a émergé des analyses.
Au contraire, des tendances négatives ont été détectées, ce qui représenterait la conséquence
d'une évolution convergente entre clades et des niveaux élevés de cloisonnement pour
chaque clade.
Enfin, la dernière partie présente une étude sur la répartition de la niche climatique des
chauves-souris de l’Afrique australe. Dans cette étude je rapporte temps de divergence
évolutive (ou deux espèces ont divergé depuis un ancêtre commun) au niveau de
chevauchement de leurs niches climatiques. Les résultats n’ont pas pu mettre en évidence de
lien entre ces deux paramètres. Les résultats soutiennent plutôt l’idée que cela pourrait être
dû à des niveaux particulièrement élevés de répartition de la niche à échelle fine.
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Aknowledgements First and foremost I would like to thank my two supervisors Antoine Guisan and Nicolas Salamin, for their guidance, their patience and their competence. Working with them was in more ways than one a privilege and I thank them for all that they have taught me in the last few years. Amongst other things, I greatly admire how they both balance very busy professional and private lives and still manage to be such thoroughly pleasant people. My gratitude goes to the two examiners Richard Grenyer and Luca Fumagalli who kindly agreed to participate in the reviewing process of this thesis and for all the constructive criticism they already provided during the intermediate evaluation. Amongst the people without whom this thesis would never have been written I would like to mention Robin Engler and Olivier Broennimann, I owe them a few hundred beers for their time, their patience, their infinite expertise and their British sense of humour. I would also like to thank Peter Pearman, Julien Pottier, Gwenaëlle Lelay, Christophe Randin, Luigi Maiorano and Blaise Petitpierre for their stimulating conversation, support and advice. Pascal Vittoz, Glenn Litsios, Patricio Pliscoff, Anne Dubuis, Loïc Pellissier, Maryam Zaheri, Charlotte Ndiribe and Anna Kostikova all contributed to making one of the best working environments anyone could hope for. My labwork would not have been possible without the expertise and help of Nadia Bruyndonckx, Nelly di Marco, Dessislava Savova Bianchi, Chloe Andrey, Sabrina Joye, Pascal-Antoine Christin and Guillaume Besnard. I would like to thank the students I had over the past couple of years for helping me discover the pleasure of teaching, for being keen learners and for challenging me. I would like to express my gratitude to all the external collaborators I had the pleasure of working with during my thesis. In particular, my thanks go to Ara Monadjem, Michael Curran and Mirjam Kopp who accompanied me on tough but fabulous bat catching trips to southern Africa. Amongst the many people whose friendliness softened the culture shock when I first moved to Switzerland, Christophe Randin, Daniel Croll, Philippe Christe, Nicole Galland and Luc Gigord definitely stand out. My thanks also go to France Pham, Virginie Cantamessa, Felicidad Jaquiéry, Giuseppina Rota, Marinette Donadeo and Corinne Bolle who tirelessly helped me navigate the local bureaucracy and FBM doctoral school requirements. Thanks to all of the Hotspots project students, for their energy, their drive, their eccentric ways and their quirky sense of humour, which made me feel comfortable from the start. I also thank the Hotspots Consortium for accepting me onto their programme and giving me this amazing opportunity. A big thanks goes to Tropical Biology Association (TBA) director Rosy Trevelyan, who organized two courses I was lucky enough to go on as part of the Hotspots applied conservation training programme. Her commitment to capacity building for conservation in developing countries, her infinite energy and indomitable character are a huge inspiration.
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Other people who have inspired me over the last few years are: Koen Meyers, Alfie Alexander, Siti Rachmania, Paul Racey, Frank Clarke and William Sutherland. Outside the University I would like to thank Carole Revelly, Marielle Fraser, Duncan Fraser, Christine de Luca, Anne-Laure Pernet of Yogaworks, and Sarah Zahno of Khatoon Dance, who have taught me so much and made life in Lausanne that much more enjoyable. Amongst the people who made my stay in Lausanne particularly special I would like to mention Sébastian Gay, Krister Swenson, Marie-Noëlle Wurm, Daniele Fraboulet, Cedric Wurm, Christopher Cianci, Carmen Cianfrani, Federica Sandrone, Paroma Basu and Rajat Mukherjee. Amongst my dearest friends here in Lausanne it would be impossible not to mention Karen Sanguinet. Thanks for sharing pain and joy for the past few years. Saya cinta anda. I would like to thank my parents, Anna and Julian and my two sisters, Miranda and Carolina, who have come to accept my restlessness over the past 12 years, I thank you for your encouragement, your faith, your patience and most of all for your unconditional and unwavering love. Finally, I thank Yannick for being so loving, understanding and for being my rock during these last few months.
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Chapter 1 - Spatial predictions of phylogenetic diversity challenge conservation decision making 27
Chapter 2 - Climate change effects on phylogentic diversity 50
Chapter 3 - Exploring the relationship between morphology and phylogenetic diversity 72
Chapter 4 - No macro-climatic niche conservatism in southern African bats 83
Conclusions 104
Annexes - A recent inventory of the bats of Mozambique with documentation of seven new 112 species to the country Bats of Borneo: diversity, distributions and representation in protected areas 149
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Introduction Studying how the components of diversity are related to each other and spatially distributed
is relevant to conservation for several reasons. Firstly, an understanding of the evolutionary
mechanisms which have generated and currently rule diversity patterns is essential if we are
to ensure their future through conservation. Secondly, knowledge of how particular lineages
have responded to challenges in the past may help us understand how they now respond or
will soon respond to environmental changes. Thirdly, the way diversity is spatially and
climatically distributed can tell us a lot about species requirements, community structuring
and potential vulnerability to such changes. This thesis explores the relationship between
spatial and phylogenetic patterns of several biodiversity components in southern Africa, a
region of high biogeographic and conservation interest.
In this introductory chapter I summarize some of the key findings on the origins of current
diversity patterns in the southern African region. I then describe some of our knowledge
about past and present biodiversity loss. Finally, I illustrate how the study of species
distributions has gradually merged with the study of evolutionary relationships to understand
why specific biodiversity patterns establish, why some taxa occupy the niches they do and
why certain species may go extinct before others.
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The southern African biodiversity hotspots Understanding the origins of diversity can assist in its protection by contrasting current and
historical patterns. Ultimately such understanding can help preserve the conditions required
for the establishment of diverse communities. Why are some areas so much more diverse
than others? What are the respective roles of environmental and historical factors in the
radiation of particularly diverse clades? We have few satisfactory answers to these and other
questions, but we do know that past climate change and refugia locations have had an
enormous impact on how diversity is distributed today (Moritz et al., 2005). Furthermore, we
know that lineages often differ in their evolutionary responses to the same environmental
history, thus complicating the use of one lineage as a surrogate model for another (Moritz et
al., 2005).
Southern Africa contains 4 out of 7 biodiversity hotspots identified on the African continent
(Myers et al., 2000). These are the coastal forests of eastern Africa, Mapotaland-Pondoland-
Albany, the Succulent Karoo and the Cape Floristic Region (CFR). All, by definition, display
very high levels of floral species richness, endemism and have lost over 70% of their original
extent due to human activities. The flora of the south-western tip of southern Africa is made
up of over 9,000 species in an area of 90,000 km2 and is much more speciose than would be
expected from its area or latitude (Goldblatt, 1978). Endemism levels of almost 70% are
comparable only to those found on islands (Linder, 2003) and most likely accounted for by
the ecological and geographical isolation of the CFR (Linder, 2003). Explanations for the
high species richness, resulting from extreme radiation of 33 Cape floral clades, however are
harder to find (Linder et al., 1992; Linder & Hardy, 2004; Linder, 2005; Linder, 2008;
Verboom et al., 2009; Valente et al., 2010).
The historical events underlying the origin of this diversity, as well as the time frame over
which it occurred, have been the subject of considerable debate in the literature (Levyns,
1964; Linder et al., 1992; Linder, 2003; Linder et al., 2005). A recent study using succulent
karoo- and fynbos-endemic lineages across 17 groups of plants, found that all succulent
karoo-endemic lineages are less than 17.5 My old, the majority being younger than 10 My
(Verboom et al., 2009). This is largely consistent with suggestions that this biome is the
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product of recent radiation in the late Miocene (Levyns, 1964). In contrast, the even richer
fynbos-endemic lineages were found to display a broader age distribution, with some
lineages originating in the Oligocene, but most being more recent (Verboom et al., 2009).
The massive speciation in the Cape flora might be due to genetic isolation because of a
topographically and climatically heterogeneous landscape, availability of many pollinators, a
long flowering season, as well as a regular fire regime (Goldblatt, 1978; Linder & Ferguson,
1985; Linder, 1995; Bakker et al., 2005). Though all of these factors are likely to have played
a role, climate continues to be considered the main trigger for this radiation (Levyns, 1964;
Linder, 2003). Levyns (1964) was the first to suggest that the remarkable plant species
diversity of the western Cape was the result of elevated speciation following the onset of arid
climates in the area, which started around the end of the Miocene. This may have led to
widespread extinction, opening a variety of empty niches into which lineages which were
pre-adapted to survive summer aridity were able to diversify. However, more recent studies
have estimated the start of the origin and radiation of several Cape lineages to be well before
the late Miocene (Linder and Hardy, 2004; Bakker et al., 2005; Linder, 2005), when climates
were presumably moister than they are at present. It is possible that much radiation may
have happened in high-altitude environments which support the greatest fynbos plant
species richness as well as the highest concentrations of local endemics, a pattern that may
partly be a result of reduced extinction in the past (Cowling & Lombard, 2002). It is also in
these environments that most of the region’s palaeoendemic taxa occur (Linder et al., 1992).
Past and present biodiversity loss Both speciation and extinction are heavily affected by climate change (Erwin, 2001; Linder,
2003; Midgley et al., 2005; Barnosky, 2008; Erwin, 2009).
All of the five mass extinction events have been related to large scale climatic changes, such
as sea level fluctuations, which resulted from extensive global warming in the first mass
extinction and global cooling after bolide impacts in the second mass extinction (Erwin,
2001; Erwin, 2009). During the Late Permian, a combination of drop in atmospheric oxygen
and climate warming (supposedly caused by another bolide impact and subsequent volcanic
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activity) is thought to have induced hypoxic stress and compressed altitudinal ranges to near
sea level with consequent habitat fragmentation and population isolation effects (Huey &
Ward, 2005).
A period of climatic oscillations that began about 1 Mya, during the Pleistocene, was
characterized by glaciations alternating with episodes of glacial melting (Barnosky, 2008).
The current episode of global warming can be considered as an extreme and extended
interglacial period; however, most geologists treat this period as a separate epoch, the
Holocene, which began ~11,000 years ago at the end of the last glaciation. The Holocene
extinctions were greater than occurred in the Pleistocene, especially with respect to large
terrestrial vertebrates. These are also the only major extinctions that took place when
humans were on the planet and occurred during a global warming episode at a time when
human populations were rapidly expanding (Fig. 1). Around 20,000 years ago megafauna
biomass collapsed at the same time human biomass started increasing exponentially, reached
a new lower plateau ~10,000 years ago and has not recovered (Fig. 2). Recent studies suggest
that human impacts such as hunting and habitat alteration contributed in many places to
extinction events, and that climate change exacerbated them (Barnosky, 2008).
Figure 1: Number of non-human magafauna species that went extinct through time plotted against estimated population growth of humans (from Barnosky, 2008).
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Figure 2: Estimated biomass of humans plotted against the estimated biomass of non- human megafauna (from Barnosky, 2008).
The Holocene extinctions take on special significance in understanding the potential
outcomes of similar kinds of pressures on biodiversity today: the exponential growth of
human populations at the same time as the Earth is warming at unprecedented rates.
The possibility that a new mass extinction spasm is upon us has received much attention.
Many scientists argue that we are either entering or in the midst of the sixth great mass
extinction and that it may be largely triggered by human activities (Wilson, 1988; Leakey &
Lewin, 1995).
Causes of current biodiversity loss The latest update of the IUCN Red List of Threatened Species shows that 17,291 species out
of the 47,677 assessed species are threatened with extinction and that 875 are already extinct
or extinct in the wild (IUCN, 2009).
The well known causes of present biodiversity loss are multiple, but almost all inextricably
linked to poverty and human population growth in developing countries, as well as
disproportionately high per capita resource consumption and inadequate technological
advancement in wealthier countries. Together with the realisation that local actions anywhere
in the world have global repercussions for biodiversity and human survival, climate change
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has become more and more prevalent in the popular and scientific literature (Biello; Lewis,
2006; Ott et al., 2008; Levi, 2009; Pettorelli et al., 2009; Veron et al., 2009).
Climate change is a major cause of biodiversity loss in southern Africa, partly because it
exacerbates the effects of land use change and introductions of exotic species. Temperatures
have risen in this region by approximately 1 degree over the past 100 years, which is 0.3
degrees higher than the world average (IPCC, 2007). There is now evidence that many
species are disappearing from the northern parts of their ranges. In addition, there is
experimental evidence that the recorded expansion of woody invasions into grasslands and
savannas may be driven by rising global CO2 concentrations (Millennium Ecosystem
Assessment, 2005). The ability of native species to disperse and survive these pressures will
be hampered by a severely fragmented landscape (Bomhard et al., 2005; Midgley et al., 2006).
Major losses in many southern African mammal species are predicted in the next 40 to 70
years as a result of climate change, as well as an eastward shift of mammal diversity (Thuiller
et al., 2006). These results suggested that the effects of climate change on wildlife
communities may be most noticeable not only as substantial loss of species from their
current ranges, but also as a fundamental change in community structure, as species
associations shift with influxes of new colonisers (Thuiller et al., 2006). The Cape Floristic
Region and the Succulent Karoo are also predicted to lose more than 41% of endemic plant
species richness and undergo 39% range reduction by 2050 (Broennimann et al., 2006).
The effects of a warming climate are magnified by human landuse. Forests and woodlands
are converted to croplands and pastures at a very fast rate. Half of the southern African
region consists of drylands, where overgrazing is the main cause of desertification
(Millennium Ecosystem Assessment, 2005). The spread of oil palm in the upper limits of
southern Africa as well as South-East Asia is another example of landuse with strong effects
on local climates. African oil palm, Elaeis guineensis, is grown across more than 13.5 million ha
of tropical, low-lying areas, a zone naturally occupied by moist tropical forest, one of the
most biologically diverse terrestrial ecosystems on Earth (Corley & Tinker, 2003;
MillenniumEcosystemAssessment, 2005). Vegetable oils are among the most rapidly
16
expanding agricultural sectors (EC, 2006), and more palm oil is produced than any other
vegetable oil (Corley & Tinker, 2003). Global palm oil production increased by 55% between
2001 and 2006 (http://faostat.fao.org), prompted largely by expanding biofuel markets in
the European Union (MillenniumEcosystemAssessment, 2005) and by food demand globally
(EC, 2006). Some of the largest multinationals worldwide, including Nestlé, Unilever and
Dove, make abundant use of palm oil in their processed food and beauty products, as it is
far cheaper than any other oil on the market (Fitzherbert et al., 2008). In palm oil
plantations, 85% of the pre-existing vertebrate and invertebrate communities are unable to
persist and go locally extinct (Fitzherbert et al., 2008). The species lost include species with
the most specialised diets, those reliant on habitat features not found in plantations, those
with the smallest range sizes and those of highest conservation concern (Chung et al., 2000;
Corley & Tinker, 2003; Aratrakorn S. et al., 2006). Plantation assemblages are typically
dominated by a few abundant generalists, non-forest species (including alien invasives) and
pests (Chung et al., 2000; Corley & Tinker, 2003; Aratrakorn S. et al., 2006).
Niche modeling meets phylogenetics Niche or species distribution modeling has traditionally been one of the most powerful tools
in conservation science (Vaughan et al., 2003; Rushton et al., 2004; Guisan & Thuiller, 2005).
These empirical models relate field observations to environmental predictor variables to
identify current and future species distributions (Guisan & Zimmermann, 2000; Guisan &
Thuiller, 2005). At the core of species distribution models is the concept of the “ecological
niche”, the theoretical framework to the quantification of the relationship between species
and their environment (Austin et al., 1990; Araujo & Guisan, 2006). The concept of niche as
used in niche models was formalized by Hutchinson (1957) as the ensemble of
environmental conditions under which populations of a species can maintain a positive
growth rate. At this time an important distinction between “fundamental” and “realized”
niches was made. In the “fundamental” niche abiotic factors only (such as climate and
topography) are taken into account whilst both biotic (such as competition and facilitation)
and abiotic factors make up the “realized” niche (Hutchinson, 1957). Since they are
calibrated from field observations of species that include the effects of biotic interactions,
niche models capture an approximate realized niche (Jimenez-Valverde et al., 2008).
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Some of the major applications of niche models (Guisan & Thuiller, 2005; Franklin, 2010)
include improving the likelihood of identifying the location of rare species (Engler et al.,
2004; Guisan et al., 2006; Le Lay et al., in review), predicting the susceptibility of a particular
area to invasive species (Thuiller et al., 2005c; Broennimann et al., 2007) and predicting how
species will shift their distributions as a result of climate change (Thuiller et al., 2005b;
Randin et al., 2009).
There have been substantial improvements to niche models in terms of accounting for
dispersal (Engler & Guisan, 2009; Engler et al., 2009) and increasingly for species
interactions (Araujo & Luoto, 2007). A major drawback of using niche models to predict
future distributions is that they generally assume either no dispersal at all or unlimited
dispersal (i.e. the species occupies all potentially suitable habitat; e.g. (Thomas et al., 2004;
Engler et al., 2009). Inevitably these two options provide unrealistic scenarios of plant
dispersal. Recently, models have started to account for a large number of parameters such as
seed dispersal, evolution of a population’s reproductive potential over time, stochastic long
distance dispersal events, barriers to dispersal, random population extinctions, vegetative and
seed-bank resilience to environmental change and differential dispersal along rivers or roads
(Engler & Guisan, 2009; Thuiller et al., 2009b).
Phylogenetics, traditionally used by systematists, is the science of species’ evolutionary
relationships and their reconstruction into phylogenetic trees. The explosion of molecular
phylogenetics in the last couple of decades has been triggered by the emergence of new
molecular methods and statistical techniques and has been used to address a wide array of
important evolutionary and ecological questions. Phylogenetics has been used to identify the
presence of cryptic species (Bode et al., 2010; Schonhofer & Martens, 2010), to test
speciation and biogeographic hypotheses (Moritz et al., 2005; Oliveros & Moyle, 2010;
Thinh et al., 2010) and to understand why some species may be better biological invaders
than others (Strauss et al., 2006). It has also been useful to retrace switches in evolutionary
history, the rise of key adaptations and whether these made single or parallel appearances
(Christin et al., 2007; Christin et al., 2008).
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The overlap between the science of species distributions and that of species evolutionary
relationships has taken several interesting directions. Firstly, niche models and phylogenies
are increasingly coupled to study speciation patterns (Hugall et al., 2002; Savolainen et al.,
2006; Carnaval et al., 2009; Malay & Paulay, 2009).
Secondly, species distribution data and phylogenies have been used to study the relationship
between biodiversity measures. Traditionally, the units in conservation biology have been
species, which provide an intuitive measure to compare biodiversity at different sites.
However, species are not equivalent in the amount of evolutionary history they contribute to
a community and it has been argued by many authors that they should not be considered as
equal conservation units. Phylogenetics made one of its first contributions to conservation
biology with the introduction of phylogenetic diversity (PD) (Faith, 1992a), a measure of
diversity which takes evolutionary relationships into account. The important question of
how species richness and phylogenetic diversity patterns compare (and thus whether most of
the past conservation efforts based on species richness have intrinsically incorporated
evolutionary history or not) has been examined by several authors with different methods.
Some studies found a tight relationship between patterns of SR and PD (Rodrigues &
Gaston, 2002), while others found significant discrepancies (Rissler et al., 2006; Forest et al.,
2007), but in general little attention has been paid to how these two measures overlap
spatially.
Thirdly, species distributions and phylogenies have been used to study niche evolution. A
large body of literature still disagrees as to whether closely related species strive to partition
resources by differentiating their ecological niches or whether they tend to conserve more
similar niches (Peterson et al., 1999; Losos et al., 2003; Graham et al., 2004; Knouft et al.,
2006; Losos, 2008a; Pearman et al., 2008). Because characters are assumed to evolve
following a neutral model, most studies expect close relatives to occupy similar niches
(Losos, 2008a). However, theory and practice do not always match and the evidence for this
pattern in nature is limited and controversial (Peterson et al., 1999; Losos & Glor, 2003; Rice
et al., 2003; Knouft et al., 2006).
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Together with life history traits, phylogenies have been related to the level of threat
experienced by many species (Purvis et al., 2000; Purvis et al., 2005; Davies et al., 2008; Fritz
et al., 2009). Considerable attention has been devoted to investigate rarity patterns and
whether extinctions within a particular clade or taxon are generally random or
phylogenetically clumped (Purvis et al., 2000; Sakai et al., 2002; Pilgrim et al., 2004; Sjostrom
& Gross, 2006; Vamosi & Vamosi, 2007; Vamosi & Wilson, 2008). If extinction risk were
indeed mostly phylogenetically clumped as argued for some bird, mammal and plant groups
(Purvis et al., 2000; Vamosi & Wilson, 2008) this could have very dramatic consequences on
evolutionary history loss, especially within hotspots of diversity. So far estimates have been
made for the present, but very little attention has been paid to what consequences climate
change may have on future loss of evolutionary histories. Only through spatially explicit
niche modeling will this be possible.
Finally, patterns of phylogenetic relatedness within communities have been widely used to
infer the importance of different ecological and evolutionary processes during community
assembly (Kembel, 2009) and are increasingly used in combination with niche modeling to
make powerful predictions in community ecology.
Main aims and thesis structure The general aim of this thesis is to answer several questions relating to diversity patterns and
evolutionary history of southern African animal and plant taxa. More specifically, my aims
are to provide the first spatial comparison of species richness and phylogenetic diversity
predictions, to assess how much phylogenetic diversity may be lost in the future, to ask
whether there is a clear link between evolutionary history and morphology and to investigate
the structure and stability of climatic niches. The thesis structure is as follows:
Chapter 1: Spatial predictions of phylogenetic diversity challenge conservation decision
making
I quantify spatial overlap of species richness and phylogenetic diversity predictions in an
extremely diversified plant family found in the Cape region: the Proteaceae.
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Chapter 2: Climate change effects on phylogenetic diversity
I compare present and future predictions for several animal and plant taxa to assess how
species extinctions will affect evolutionary history loss.
Chapter 3: Exploring the relationship between phylogenetic diversity and
morphology
I compare phylogenetic diversity measures to morphological disparity in a diverse bat
community to evaluate whether phylogenetic and morphological distances can be thought of
as interchangeable.
Chapter 4: Are climatic niches conserved?
I present the first species level phylogeny for southern African bats and employ it to
determine the extent to which spatial and climatic partitioning is influenced by evolutionary
relationships.
Conclusions
In this section, I recapitulate the main findings of each chapter and discuss some of the
limitations, as well as how an understanding of evolutionary history may best contribute to
conservation in the future.
Annexes
I include two studies to which I contributed during my doctorate.
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decision making Dorothea V. Pio1,2, Olivier Broennimann1, Timothy G. Barraclough3, , Gail Reeves4,5,
Anthony G. Rebelo5, Wilfried Thuiller6, Antoine Guisan1*, and Nicolas Salamin1,2
1Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne,
Switzerland
2Swiss Institute of Bioinformatics, University of Lausanne, 1015 Lausanne, Switzerland
3Division of Biology and NERC Centre for Population Biology, Imperial College London,
Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK
4Jodrell Laboratory, Royal Botanic Gardens, Kew, TW9 3DS, UK
5Protea Atlas Project, South African National Biodiversity Institute, P/Bag X7, Claremont
7735, Cape Town, South Africa
6Laboratoire d'Ecologie Alpine, CNRS, Université Joseph Fourier, BP 53, 38041 Grenoble
Cedex 9, France
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Abstract The inclusion of a measure of evolutionary history and relatedness (phylogenetic diversity) in
conservation has long been argued as an important step towards preserving biodiversity in a
more meaningful and comprehensive way. Some of the studies that have addressed this issue
find that phylogenetic diversity patterns do not differ enough from those of species richness
to justify their inclusion in conservation planning. This conclusion, however, is often
reached by correlating these two measures across a series of sites without paying much
attention to their spatial patterns. Here, we compared fine-scale species richness and
phylogenetic diversity predictions of a diverse plant family, the Cape Proteaceae, obtained
through individual species distribution models and ten different phylogenetic diversity
indices. We examined their correlations, spatial patterns of overlap and performance in a
complementarity algorithm. Overlap was found to vary enormously among phylogenetic
indices, but discrepancies existed for most measures when considering realistic amounts of
land set aside for conservation. Climate explained in part the segregation of particularly
species rich versus phylogenetically rich areas. In view of our results, the gradual breakdown
in the species concept and an increased availability of molecular data, we encourage
conservation prioritization to take advantage of the additional information provided by
phylogenetic diversity.
Keywords: phylogenetic diversity, species richness, Proteaceae, spatial overlap, South Africa, conservation planning, predictive modeling, Angiosperms.
Contribution to the project: I carried out the analyses in collaboration with O.B. and N.S., produced figures and wrote the paper. This paper is currently in review
29
Introduction
Allocation of funds for nature conservation relies heavily on prioritisation exercises. The
budgets in most environmental organizations are very limited, making the use of the most
meaningful criteria a number one priority in the design of protected area networks (Bottrill
et al., 2008). In an effort to provide more realistic and comprehensive examples for
conservation practice, several authors have argued for the inclusion of costs, ecosystem
services, potential human-wildlife conflicts and other socio-economic factors (Moore et al.,
2004; Eigenbrod et al., 2009). However, the primary purpose of these prioritization exercises
is the identification of the most biologically rich and unique areas still existing today. In this
regard, the inclusion of evolutionary history in conservation, through the calculation of
phylogenetic diversity has long been argued as an important step towards preserving
biodiversity in a more meaningful and comprehensive way (Faith, 1992a). Species are not
equal in the amount of evolutionary history they bring to their community and should not, as
such, be considered equivalent conservation units. Biodiversity hotspots (Myers et al., 2000)
for example contain a higher proportion of species characterized by exceptionally long and
unique evolutionary histories (Sechrest et al., 2002). If phylogenetic diversity patterns were
found to match those of species richness, there would be no reason to use phylogenetic
diversity measures in conservation prioritization, as species richness will always be easier,
cheaper and quicker to measure. Some studies have found a tight relationship between
patterns of species richness and phylogenetic diversity (Rodrigues & Gaston, 2002; Schipper
et al., 2008), while others have found significant discrepancies (Rissler et al., 2006; Forest et
al., 2007). However, as a general rule it seems that species richness is a bad surrogate for
phylogenetic diversity only when species restricted to species poor areas correspond to the
ancient branches of an “unbalanced” tree (i.e. containing long ancient branches which
account for a disproportionate amount of phylogenetic diversity; Rodrigues & Gaston,
2002).
Phylogenetic methods have mostly been applied across a limited number of systems and
spatial scales, and often at the genus rather than species levels (Rodrigues & Gaston, 2002;
Forest et al., 2007; Proches et al., 2009) with notable exceptions (Winter et al., 2009). Many
30
global studies use incomplete and coarse data to identify areas for conservation. While these
exercises may be useful for resource allocation at a country level, the conclusions they reach
and their use at a finer geographical scale are limited. If we are to incorporate phylogenetic
diversity information into practical conservation prioritization efforts, it is of prime
importance that we test whether phylogenetic diversity measures are congruent with species
richness, using appropriate spatially-explicit species level data. The most recent and one of
the most thorough studies examining the relationship between species richness and
phylogenetic diversity found a decoupling of taxon richness and phylogenetic diversity for
plant genera in the Cape Floristic Region (Forest et al. 2007). Furthermore, by means of a
complementarity algorithm, this study illustrated that within a conservation planning context,
gains in phylogenetic diversity are poorly matched by gains in taxon richness (Forest et al.
2007).
In this study, our aim was to assess the relationship between spatial predictions of species
richness and several phylogenetic diversity indices by investigating how correlated they are
and by examining their spatial patterns of overlap. Since there is considerable variation in the
way evolutionary history is measured and we wanted this analysis to be as comprehensive as
possible, we employed all ten phylogenetic diversity measures recently listed by Schweiger et
al (2008). Our aim was also to conduct the first species level analysis (as conservation still
mostly operates on this scale) and to relate potential discrepancies between species richness
and phylogenetic diversity patterns to environmental gradients present in the study area. As a
model group, we use the Proteaceae, an ancient Gondwanan plant family with fossils
attributed to extant genera from the mid-Cretaceous (Drinnan et al., 1994; Dettmann &
Jarzen, 1996). This group is found in South-Africa’s Cape Floristic Region, a biodiversity
hotspot containing one of the highest levels of species richness and endemism of any known
tropical or temperate area (Myers et al., 2000; Linder, 2003). These extremely diverse, low-
growing shrubs and trees include over 330 species (Cowling & Lamont, 1998), and present a
wide variety of pollination and fire survival strategies (Rebelo, 2001). Of the 13 genera
occurring in mainland Africa, ten are almost entirely endemic to the fynbos vegetation of the
south-western Cape (Barker, 2002). The Cape Floristic Region contrasts with other high-
diversity areas, such as tropical forests as it is made up of dissimilar local communities, in
31
which most species are relatively abundant and very few are rare (Latimer et al., 2005). This
pattern can be explained by examining migration rates in the fynbos, which are two orders of
magnitude lower than in tropical forests, and speciation rates of this vegetation type, which
are higher than in any previously studied plant system (Latimer et al., 2005). The interesting
evolutionary history, high diversity and excellent quality of both genetic and occurrence data
available for Proteaceae in South Africa make this group an ideal model for the study of
spatial patterns of overlap between phylogenetic diversity measures and species richness.
Materials and Methods
Predicting species distributions
Niche modeling was employed to obtain all-inclusive and wide-ranging predictions of likely
species distributions at a fine scale in the Cape Floristic Region. Though the occurrence data
for this group is extensive and of excellent quality, its coverage does not include 100% of the
regions of the Cape Floristic Region. Niche modeling was therefore necessary to provide a
probability distribution of the occurrence of each Proteaceae species over the whole Cape
Floristic Region. We built species distribution models at a resolution of 1’ × 1’ (~1.6 × 1.6
km at this latitude) for 168 endemic or near endemic Proteaceae species (the availability of
both occurrence and genetic data was necessary in order to include species in the study) and
occurring in more than 20 mapping cells. Species distribution data were taken from the
Protea Atlas Project (PAP) database, comprising field-determined species presence and
absence observations at more than 40,000 geo-referenced locations. Generalised Additive
Models (Hastie & Tibshirani, 1990) were calibrated in the Splus-based BIOMOD application
(Thuiller, 2003) using seven bioclimatic variables. These variables were derived from the
Worldclim database for the Cape region and included annual evapo-transpiration,
evapotranspiration of the wettest quarter, annual precipitation, precipitation of the wettest
quarter (May to August), precipitation of the driest quarter (November to February), annual
temperature and temperature of the coldest quarter (May to August). A random sample of
the initial data (70%) and a stepwise selection methodology (forwards and backwards) were
employed to identify the best model using the Akaike information criterion (AIC) as a
selection criterion. The predictive power of each model was evaluated on the remaining 30%
32
of the initial dataset using the values obtained for the area under the curve (AUC) of a
receiver operating characteristic (ROC) plot (Fielding & Bell, 1997).
The probabilities of occurrence were filtered with a measure of anthropogenic disturbance,
the “human footprint”, considered as a regionally consistent way to represent land
transformation on a global scale (Sanderson et al., 2002).
Predictions for individual species distribution models were summed at each site to obtain
species richness predictions, which were in turn used to calculate corresponding values for
various phylogenetic diversity indices. Modeled distributions were therefore the basis for
both species richness and phylogenetic diversity predictions used throughout this study.
Calculation of phylogenetic diversity indices:
A calibrated phylogenetic tree for the Proteaceae family based on 23 genes was assembled
from pre-existing data (ITS, Reeves, Barraclough et al, unpublished data) and all other
available sequences for the South African (and some Australian) Proteaceae in GenBank
(McMahon & Sanderson, 2006). The tree comprising 284 species was built using MrBayes
3.1.2 (Huelsenbeck et al., 2001). Two runs of four Markov chain Monte Carlo chains were
run for 10 mio generations using the GTR+Gamma model of DNA evolution (as
determined by likelihood ratio tests) and default priors. The convergence of the two runs
was assessed using Tracer (Drummond & Rambaut, 2007). The tree with the highest
posterior probability was then dated with a penalized likelihood method (Sanderson, 2002)
as implemented in the ape package (Paradis et al., 2004) in R using previously described
fossils (Sauquet et al., 2009). To check the consistency of the date estimates, we also ran
penalized likelihood on 100 randomly sampled trees from the posterior distribution given by
MrBayes.
Phylogenetic diversity values for each of the grid cells on the map of the study area were
calculated using each of the measures listed in Schweiger et al. (2008). Calculations were
carried out with scripts in R based on the ape package (Paradis et al., 2004). These measures
include topology indices, which are based on node information only (W and Q) and pairwise-
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distance (J, F, AvTD, TTD and Dd) as well as minimum-spanning-path indices (PDroot,
PDnode, AvPD), which are based on both branch length and node information. Moreover,
indices used in this study, can be subdivided into total indices (Q, W, PDnode, PDroot, F,
TTD, Dd), which add the evolutionary history of all species present in an area and averaged
measures (AvTD, J, AvTD), where total evolutionary history is divided by the number of
species present. Details on the mathematical properties of each of these measures can be
found in a summary table in Schweiger et al (2008).
Discrepancy values
Species richness and phylogenetic diversity indices were first normalized. This consisted for
each of the two measures in subtracting the mean value and then dividing it by the standard
deviation calculated from each grid cell. Species richness was then subtracted from
phylogenetic diversity to obtain discrepancy values. Where these values were above zero,
phylogenetic diversity was greater than species richness and where they were below zero,
phylogenetic diversity was smaller than species richness.
Comparison between species richness and phylogenetic diversity by correlation, spatial overlap and
complementarity algorithm:
In order to describe the relationship between species richness and phylogenetic diversity in
the study area, a Spearman correlation was used for each phylogenetic diversity measure. In
addition, we ran the complementarity algorithm developed by Forest et al. (2007), a
traditional approach in reserve selection. This algorithm chooses the most diverse grid cell
first and sequentially adds grid cells with the highest complementary diversity (gain) until all
diversity is represented. This analysis investigates how gains in phylogenetic diversity or
species richness may change as a function of which measure is maximized and whether sites
selected by maximizing phylogenetic diversity or species richness overlap spatially. Finally,
we quantified the spatial overlap between phylogenetic diversity and species richness
measures when considering increasing amounts of land set aside for conservation. For
increasing percentages of land considered, the richest grid cells as measured by species
richness and phylogenetic diversity indices were identified. The spatial overlap was then
calculated as the percent of common grid cells among those identified by both measures,
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paying particular attention to the values obtained for average amounts of land set aside for
conservation (UNEP-WCMC, 2008).
PCA of environmental variables and quadratic regression
A PCA of the environmental variables used to predict individual species distributions was
calculated in the R package ade4 (Franquet et al., 1995). The scores corresponding to higher
normalized phylogenetic diversity or species richness values were highlighted in different
colors to identify possible spatial segregation between the two groups of scores. Following
this analysis we conducted a polynomial quadratic regression to describe the relationship
between altitude and discrepancy values.
Results Correlations, complementarity analysis and discrepancy values
Species distribution model accuracy was consistently excellent with an average AUC of 0.98
over all species (range: 0.88-0.99). Spearman rho coefficients of correlations between species
richness and phylogenetic diversity varied greatly between phylogenetic diversity indices.
Topology measures scored high in their correlation to species richness (0.98 to 0.99
Spearman rho for W and Q respectively), while methods using both node and branch length
information showed considerable differences and ranged from -0.75 to 0.94 for minimum-
spanning-path methods (0.92, 0.94 and -0.72 for PDnode, PDroot and AvPD respectively) and
from -0.06 to 0.99 for pairwise distance methods (-0.06, -0.03, 0.99, 0.98, and 0.7 for AvTD,
J, F, TTD and Dd respectively). Graphical checks (data not shown) of these relationships
indicated that two of these correlations were linear (W and TTD). Others were upward
sloping asymptotically (PDnode, PDroot and Dd), downward sloping asymptotically (W and F)
and some showed no correlation to species richness (AvPD, AvTD and J).
The complementarity algorithm showed that gains in different phylogenetic diversity
measures did not match each other closely when complementarity in pixels added was
maximized for species richness (Fig. 1).
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Figure 1 – Complementarity analysis. Gains in species richness and several phylogenetic diversity indices when a complementarity algorithm is run maximizing species richness. The gains for the sites selected are normalized for each measure.
This was also not the case when complementarity was maximized for phylogenetic diversity
indices (data not shown). Moreover, when maximizing gains for each measure separately and
plotting the sites selected for each measure on a map of the Cape Floristic Region, no
overlap existed between species richness and phylogenetic diversity sites (data not shown).
Gains in Q (a topology measure) were the only ones to match species richness gains closely.
Those for a minimum-spanning-path (PDroot) were poorly predicted by species richness.
One of the pairwise-distance indices (Dd) followed an even more unpredictable trend, with a
decrease in values when the second and third cells were added. Finally, the two averaged
methods showed completely conflicting patterns with species richness with their values
decreasing as the number of cells were added (Fig. 1). Normalizing and subtracting species
richness from phylogenetic diversity revealed areas of discrepancy. The spatial patterns of
discrepancy between species richness and phylogenetic diversity varied considerably
depending on the index used (Fig. 2).
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Figure 2 - Discrepancy maps between species richeness and different phylogenetic diversity indices. In red are all the areas where phylogenetic diversity is greater than species richness, whilst areas in blue are areas where species richness is greater than phylogenetic diversity (both are normalized). The Spearman correlation (Rho) between species richness and phylogenetic diversity and the percent of grid cells where phylogenetic diversity is greater than species richness are indicated.
37
Both of the topology indices (W and Q) and the pairwise distance measure F showed the
areas harboring unexpectedly high phylogenetic diversity values to be congruent with the
most significant species richness hotspot (c in Fig.3). On the other hand, most of the
minimum spanning path and pairwise distance indices (e.g. PDroot, AvPD, J, AvTD, and Dd)
identified more peripheral areas to the main species richness hotspot as having higher than
expected levels of evolutionary history. These peripheral areas included the Koebeeberge
mountains in the northern portion of the Cederberg range (Fig. 3a) and the more low-lying
areas between Knysna and Port-Elizabeth (Fig. 3d) in particular, as well as the area
comprising parts of the Cederberg, KoueBokkeveld and Groot Winterhoek mountains (Fig.
3b.
Figure 3 - Predicted species richness for the Proteaceae of the Cape Floristic Region. Areas of special interest which are identified and discussed throughout this study are: the Koebeeberge mountains, in the northern portion of the Cederberg range (a), parts of the Cederberg, KoueBokkeveld and Groot Winterhoek mountains (b), the Hawekwas, Hottentots Holland and Kogelberg Mountains, the Cape Peninsula and the Agulhas plain (c), the areas between Knysna and Port Elizabeth (d).
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The overlap between species richness and various measures of evolutionary history varied
with the amount of surface area considered, but it did not increase linearly with it (Fig. 4).
The highest level of overlap when selecting the average amount of land set aside for
conservation, for example, was experienced with the use of the two topology methods, Q
and W (93% and 89% respectively) and two of the pairwise distance methods, F and TTD
(95% and 93% respectively). Intermediate levels of overlap with species richness patterns
were experienced in one of the pairwise distance methods, Dd (53%) and in two of the
minimum spanning distance methods, PDroot and PDnode (79% and 78% respectively), and
no overlap at all was experienced with two pairwise distance methods and the remaining
minimum spanning method (J, AvTD and AvPD respectively).
Figure 4 - Percentage of overlap between species richness and different evolutionary history patterns against amount of land cover considered. The change in overlap between predicted species richness and phylogenetic diversity indices when different percentages of the landscape are set aside for conservation. The overlap values obtained for realistic percentages of the “richest” land cover to be set aside for conservation are highlighted within the black box.
PCA of environmental variables and quadratic regression
39
The PCA-based gradient analysis of the six environmental variables employed to predict
species distributions (and consequently species richness and evolutionary history) was used
to investigate the differences in climatic features between areas where phylogenetic diversity
was higher than species richness and vice-versa (Fig. 5). The scores corresponding to these
grid cells are climatically separated along the axes of the PCA (Fig. 5a,b,c). Segregation
between these points was more evident in phylogenetic diversity measures which correlate
poorly with species richness (e.g. AvTD, AvPD and J, Fig. 5c), and less so in phylogenetic
diversity indices which correlate highly with species richness (e.g. TTD, F, W and Q, Fig 5a).
In general however scores corresponding to grid cells with higher than expected
evolutionary history were associated with higher temperatures and evapo-transpiration and
lower rates of precipitation (Fig 5). Altitude explained 31%, 11% and 9% of the variation in
the discrepancies between TTD (t(27,243)=-76.82, p<0.001, Fig.6a), PDroot (t(27,243)=-
40.32, p<0.001, Fig.6b) and AvTD (t(27,243)=-28.12, p<0.001, Fig.6c) respectively.
40
Figure 5 - Principal component analysis of the environmental variables used to predict patterns of species richness and phylogenetic diversity. The seven environmental variables in the analysis are: average annual evapo-transpiration (Evtr0112), average evapo-transpiration between May and August (Evtr0508), average annual temperature (Temp0112), average temperature between May and August (Temp0508), average annual precipitation (Prec0112), average precipitation between June and August (Prec0508) and average precipitation between November and February (Prec1102) (5d). Principal components 1, 2 and 3 explain respectively 74, 17 and 8% of the variability. Scores corresponding to grid cells where phylogenetic diversity is greater than species richness are highlighted in red, whilst those where species richness is greater than phylogenetic diversity are in blue.
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Figure 6 – The relationship between altitude and discrepancy values – A quadratic regression using altitude as the explanatory variable and the discrepancy between species richness and three phylogenetic diversity indices (TTD in a, PDroot in b and AvTD in c) as response variables.
Discussion We found considerable variation in how different phylogenetic diversity indices correlated
with species richness. The investigation of spatial patterns, both from a complementarity
algorithm perspective and a spatial overlap approach revealed that only topology-based
phylogenetic diversity indices can be truly considered interchangeable with species richness.
Moreover, we find that regions highlighted preferentially by species richness or phylogenetic
diversity are, to some extent, segregated climatically and spatially. Given that the Proteaeceae
are part of a unique system with low migration rates and high beta-diversity, our results are
of particular relevance to this and other similar biodiversity hotspots.
Correlations
Highly significant correlations between species richness and phylogenetic diversity were
often but not always matched by an equally significant level of overlap in spatial patterns.
An important distinction exists amongst functions which calculate the evolutionary history
of species living in a particular area. Non-averaged phylogenetic indices sum the evolutionary
history across all species, whilst averaged phylogenetic diversity measures divide the sum of
evolutionary paths by a function of the number of species present, ultimately representing
the mean evolutionary history brought by each species. This property removes the
relationship between species richness and these phylogenetic diversity indices (Schweiger et
al. 2008). Because species richness is somehow included in non-averaged phylogenetic
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diversity measures, a positive relationship is to be expected, but the exact nature of this
relationship is likely to vary between different classes of non-averaged indices. Although
Rodrigues and Gaston (2002) found a linear relationship between genera richness and
phylogenetic diversity (using PDroot), our results showed that this may not always be the
case. Tree shape, a very influential feature in phylogenetic analyses (Mooers & Heard, 2002)
has also been shown to have an effect on the correlation between taxon richness and
phylogenetic diversity (Rodrigues et al., 2005) and the addition of a single species from a
heavily imbalanced tree, for example, will have a disproportionate effect on phylogenetic
diversity. It is also likely that diversification rates among lineages will have a large influence
on the correlation with species richness. For example, areas including clades which
originated from recent diversification bursts (and thus characterized by many closely related
taxa with shorter branches) should show lower expected phylogenetic diversity compared to
areas inhabited by a relatively higher proportion of older monotypic clades.
Discrepancies – the roles of climate and space
Soil characteristics, seasonal fire and climate regimes and pollinator specificity are amongst
some of the favorite candidates used to explain the unusual diversity found in the Cape
Floristic Region (Goldblatt & Manning, 2002; Linder, 2003). Colonization and climatic
history are regarded as some of the major factors explaining current distribution of diversity
within this exceptional region (Engler, 1904; Hedberg, 1965; Hedberg, 1970; Bergh &
Linder, 2009). In this study found that areas harboring higher than expected species richness
were characterized by higher precipitation and lower rates of evapotranspiration (Fig. 5).
These results were strengthened by the observation that higher elevations (normally
associated with higher precipitation and lower evapotranspiration regimes) supported
unusually high numbers of species (Fig.6).
Mountains in the Cape region have long been described as hotspots of diversity (Myers et al.,
2000; Linder, 2003; Linder & Hardy, 2004; Linder, 2005). Cowling and Lombard (2002) for
example attributed the high levels of species richness and endemism to reduced summer
aridity of these environments (Cowling & Lombard, 2002). Several authors attributed their
high diversity to a variety of repeated dispersal and colonization events from regions such as
43
the Mediterranean, the Northern and Southern Hemispheric temperate regions and the Cape
Floristic Region itself (Engler, 1904; Hedberg, 1965; Hedberg, 1970). It may be possible to
explain these unexpectedly high levels of species richness in mountain areas with the fact
that they are characterized by allopatric speciation processes, which took place in particularly
stable climates and which underwent very low extinction rates compared to lower lying
regions (Dynesius & Jansson, 2000; Lawes et al., 2000). Moreover, mountains provide a
sharp environmental gradient, where species are able to take advantage of the shorter
migration distances to re-colonize suitable habitats and survive climatic fluctuations (Loarie
et al., 2009). Therefore, relatively stable mountain climates (yet diverse from a micro-climate
perspective) may have allowed species to persist and evolve through time undisturbed,
displaying today most of the original members in each clade. Lower lying regions on the
other hand may have been subject to much higher extinction rates, and may contain today
only the surviving members of once much more diverse clades, thus accounting for higher
amounts of unique evolutionary history.
Spatial patterns of overlap
When considering portions of the landscape which may realistically be set aside for
conservation there was considerable difference in levels of spatial overlap, with topology
indices always scoring high and averaged indices always scoring low. Non-averaged
(minimum-spanning-path and pairwise-distance) indices mostly displayed intermediate levels
of overlap. The kind of index used can therefore influence greatly both the areas considered
to represent large amounts of evolutionary history and our decision to include such measures
in conservation planning altogether or to simply resort to species richness.
How to select the right index?
We agree with Schweiger et al. (2008) that there is no overall best phylogenetic diversity
index, but simply different situations to which certain indices may be better than others.
What kinds of factors should we take into account when selecting a phylogenetic diversity
index? And what may the advantages and disadvantages be of a high correlation with species
richness? Averaged indices were consistently found to have the lowest correlations with
species richness and lowest levels of spatial overlap. This was not particularly surprising as
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their very aim is to eliminate the effect of taxon richness (Schweiger et al. 2008). In theory, a
low correlation with species richness is a very desirable property for a phylogenetic diversity
measure, as it will maximize the effect of phylogeny and therefore provide an unbiased
measure of evolutionary history. From a practical point of view however, if phylogenetic
diversity information is to be included in a complementarity algorithm we discourage
conservation practitioners from using averaged measures. Our results showed (Fig. 1) that
the increase in phylogenetic diversity gained by adding a new site is heavily counter-weighted
by the number of new species that will be added ultimately causing a reduction in averaged
phylogenetic diversity estimated. The mathematical properties of averaged methods are such
that maximising diversity and representing all parts of the phylogenetic tree is impossible
through basic complementarity algorithms.
Ultimately, the choice of index and whether to include a phylogenetic diversity index at all
depends on what conservation efforts are trying to prioritise. Presently, a large complement
of inter-specific genetic diversity as option value for the future is both still a desirable and
sensible feature to preserve (Mooers et al., 2005b; Forest et al., 2007; Cadotte et al., 2009).
Though feature diversity of very old monotypic lineages is always likely to be higher, it may
very well also be more susceptible to going extinct (Purvis et al., 2000) and depending on
what is driving extinction, impossible to retain in the longterm. On the other hand, we have
evidence that evolutionary rich communities have higher levels of productivity in terms of
biomass (Cadotte et al., 2009) suggesting that high phylogenetic diversity not only preserves