Articles Woody Species as Landscape Modulators and Their

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Ecology focuses on the reciprocal relationshipsbetween organisms and their environment. Until re-

cently, however, greater attention was devoted to theoreticalwork concerning the impact of the environment on organ-isms than towork concerning the impact of organisms on theenvironment. Ecologists have developed theoretical modelsof how climate modulates the physiology, behavior, dis-tribution, and abundance of organisms (Porter et al. 2000,Helmuth et al. 2005). Similarly, they have studied how envi-ronmental disturbances, such as fire and drought, have mod-ulated organisms’ evolution (Tilman and El Haddi 1992,Barton et al. 2001). Ecological studies have also addressedthe effect of the biotic environment on organisms by usingmathematical models to explore competition and predationprocesses.

By comparison, modeling of environmental modulationby organisms has been limited. Ecosystem ecologists haveinvestigated the impact of organisms on nutrient fluxes in theenvironment (Vanni 2002,Hartley and Jones 2004). Forestershave examined how trees modify the abiotic and bioticenvironment (Perry 1998).However, only recently have ecol-ogists realized that understanding modulation of the envi-ronment by organisms requires a scientific framework thatencompasses concepts, theories,models, and methodologiesconcerned with organism-induced environmental changes(Jones et al. 1994, Odling-Smee et al. 2003). Ultimately, en-vironmental modulation by organisms may prove as impor-tant for biodiversity studies as the shaping of organisms bytheir environment (Jones and Lawton 1995,Day et al. 2003).

Moshe Shachak (e-mail: [email protected]) and Yael Lubin are ecology professors, and Elli Groner is an ecologist, in the Mitrani Department of Desert Ecology at

Ben-Gurion University of the Negev in Israel. Bertrand Boeken is a senior lecturer in the Wyler Department of Dryland Agriculture at the Jacob Blaustein Institutes

for Desert Research, Ben-Gurion University of the Negev. Ronen Kadmon is an associate professor in the Department of Evolution, Systematics and Ecology at the

Hebrew University of Jerusalem in Israel. Ehud Meron is a professor of physics in the Department of Solar Energy and Environmental Physics at the Jacob Blaustein

Institutes for Desert Research, Ben-Gurion University of the Negev, and in the physics department at the Marcus Family Campus of Ben-Gurion University, Beer-

Sheva, Israel. Gidi Ne’eman is an associate professor in the Department of Biology at the University of Haifa–Oranim in Tivon, Israel. Avi Perevolotsky is an adjunct

professor at Ben-Gurion University of the Negev. Eugene David Ungar is chief scientist, and Perevolotsky is senior scientist, at the Agricultural Research Organization,

Volcani Center, Bet Dagan, Israel. Yehoshua Shkedy is chief scientist of the Israel Nature and National Parks Protection Authority. © 2008 American Institute of

Biological Sciences.

Woody Species as LandscapeModulators and Their Effect onBiodiversity Patterns

MOSHE SHACHAK, BERTRAND BOEKEN, ELLI GRONER, RONEN KADMON, YAEL LUBIN, EHUD MERON,GIDI NE’EMAN, AVI PEREVOLOTSKY, YEHOSHUA SHKEDY, AND EUGENE UNGAR

Ecological research on organism-environment interactions has developed asymmetrically. Modulation of organisms by the environment has receivedmuch attention, while theoretical studies on the environmental impact of organisms have until recently been limited. We propose a theoreticalframework for studying the environmental impacts of woody plants in order to understand their effects on biodiversity.We adopt pattern formationtheory to discuss how woody plants organize ecological systems on the patch and landscape level through patch formation, and how organismpatchiness creates resource patchiness that affects biodiversity. We suggest an integrative model that links organisms as landscape modulatorsthrough resource distribution and species filtering from larger to smaller spatial scales. Our “biodiversity cycling hypothesis” states that in organism-modulated landscapes, disturbance enables the coexistence of different developmental stages of vegetation patches, thereby increasing biodiversity.This hypothesis emphasizes that species and landscape diversity vary with the development, renewal, maturation, and decay of biotically inducedpatches.

Keywords: assemblage similarity, biodiversity cycling, ecosystem engineer, pattern formation, resource contrast

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All plants and animals modulate the landscape to someextent.However, in terms of their effects on species richness,woody plants can be considered primary landscape modu-lators. By creating biomass patches in about 40% of terres-trial ecosystems (House et al. 2003), woody plants havesignificant environmental impacts (Perry 1998), changingthe distribution of resources in space and time and conse-quently the distribution and abundance of organisms. In thisarticle, we aim to develop a conceptual framework tobetter understand biodiversity processes in the context ofenvironmental modulation induced by woody plants. Specif-ically, we ask: (a) How do woody plants modulate their en-vironment? (b) How does modulation of the environmentaffect species richness? and (c) What drives biodiversitydynamics?

In addressing these questions, we will elaborate on theconcept of organisms as landscape modulators, use vegeta-tion pattern formation theory to relate landscapemodulationand landscape diversity, and provide a conceptual model forthe dynamics of species richness and the formation of speciesassemblages in a landscape modulated by woody vegetation.On the basis of those points, we have formulated a “bio-diversity cycling hypothesis,”which suggests that in bioticallymodulated landscapes, components of species and landscapediversity are not stable, but cycle through phases of devel-opment, maturation, decay, and renewal.

The concept of organism-modulated landscapesLife and environmental impacts are inseparable phenom-ena. Organisms make their impact felt through exploitationof resources and modulation of ecosystems and landscapes(figure 1). Theories on the environmental impacts of organ-isms, communities, and ecosystems are well developed.The-ories on environmental impacts resulting from ecosystem orlandscape modulation are less developed. The concept oforganisms as ecosystem engineers, which focuses on modu-lation processes, is relatively new and lacks a comprehensivetheoretical background (Wright and Jones 2006). Ecosys-tem engineers are defined as “organisms that directly or in-directlymodulate the availability of resources to other speciesby causing state changes in biotic and abiotic materials”(Jones et al. 1994).This definition introduced modulation byorganisms as a driver of environmental impact but did notexplicitly distinguish between ecosystem and landscapemod-ulation (figure 1).

We suggest that the division of ecosystem engineering intotwo pathways, ecosystem and landscape modulation, is crit-ical for understanding the role of modulation in controllingbiodiversity processes. Recognizing the two pathways is im-portant for analysis of the effects of modulation on landscapeand species diversity. Studies of the effect on biodiversity ofresource redistribution and concentration in ecosystems re-quire an approach that deals with the reciprocal relationships

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210 BioScience • March 2008 / Vol. 58 No. 3 www.biosciencemag.org

Figure 1. Biotically induced environmental impacts and biodiversity. The impacts affect populations, communities,ecosystems, and landscapes. Two basic processes drive environmental impact: (1) abiotic and biotic resource exploitation(dashed arrows) and (2) ecosystem and landscape modulation (solid arrows). We focus on woody vegetation and its effectson biodiversity through the landscape modulation pathway (the arrows on the right).


between species and materials (Jones and Lawton 1995). Theeffect of landscape modulation on biodiversity demandslandscape methodology that focuses on understanding thereciprocal interactions between spatial heterogeneity andecological processes. Dividing modulation into two path-ways makes it possible to integrate the processes of modula-tionwith existing paradigms of two ecological subdisciplines,ecosystem and landscape ecology.

Environmental impacts due to ecosystem modulation areusually associatedwith bioturbation caused by burrowing an-imals (Whitford and Kay 1999, Reichman and Seabloom2002). By mixing detritus with mineral soil, earthworms in-crease the rate of mineralization and redistribute nutrients(Darwin 1881, Meysman et al. 2006). By transporting salinesoil frombelow ground to the surface, desert isopods enhancedesalinization processes that redistribute the salt over theecosystem (Shachak et al. 1995).

Landscape modulation via patch formation is common innature. Plants form biomass patches that increase landscapediversity. Shrubs create water- and nutrient-enriched patchesinwater-limited systems (Ludwig andTongway 1995,Boekenand Orenstein 2001). Animal activities also modify land-scape mosaics. For example, bison (Bison bison) drive het-erogeneity patterns in North American prairies. Desertporcupines (Hystrix indica) introduce patches in the form ofpits that diversify the landscape.

The division of ecosystem engineering into ecosystem andlandscapemodulation is essential when referring to plants thatcover a high proportion of the terrestrial landscape and sub-stantiallymodulate the landscape by forming live biomass andlitter patches (Boeken and Orenstein 2001,Gabet et al. 2003,Peters and Havstad 2006). We propose that the processes ofpatch formation by vegetation, and especially by woodyplants that create distinct biomass patches, have majornonconsumptive impacts on the landscape.Woody biomasspatches differ from the surrounding patches in manyaspects, such as shade,water, and litter regimes. The contrastbetween the woody patch environment and its surroundingsaffects organism assemblages (Badano et al. 2006,Wright etal. 2006).

We define organisms that modify the landscape at thepatch and themosaic (patch configuration) scales as landscapemodulators. The concept of landscape modulation is em-bedded in the concept of organisms as ecosystem engineers(Jones et al. 1997), but our definition emphasizes the uniqueenvironmental impact of landscape modulators on commu-nity structure via patch formation.This definition links land-scape modulation with landscape ecology aimed at studyingthe relation between structure and function in ecologicalsystems (Turner 2005). Landscape-modulator studies couldbe viewed as a branch of landscape ecology that investigatesthe relation between biotically made structures and ecolog-ical processes.

In the following section,we elaborate on structure-functionrelationships in landscapes created by woody vegetation.Using ideas and models from pattern formation theory, we

develop the theoretical framework for linking structures andprocesses in biotically modulated landscapes. Next, wediscuss the relationships between landscape diversity andbiotic landscape formation by woody plants acting as land-scapemodulators. Finally,we integrate landscape diversity andspecies diversity to analyze the relationship between landscapemodulation and biodiversity.

Biomass patterns and landscape modulationUsing a landscape perspective to integrate plant biomasspatches made by a landscape modulator requires theoreticaltools that capture the mechanism of landscape formation byvegetation. The theory of vegetation pattern formation canhelp in understanding landscapemodulation bywoody plants(box 1).We define pattern formation as the creation of a land-scape state wherein the woody vegetation does not cover thewhole landscape, so that modulated and nonmodulatedpatches coexist. Theory suggests that vegetation patternsemerge as a consequence of interactions between plants andtheir environment through the consumption of resourcesand the modulation of their flows (Klausmeier 1999, Hille-RisLambers et al. 2001,Lejeune et al. 2004,Rietkerk et al. 2004).For example, models have shown that patterns of woodyvegetation form in water-limited systems when roots forageand compete for water (exploitation) and when the woodyplant modifies soil properties in the patch so as to increasewater infiltration rates (modulation) (Gilad et al. 2004,Meronet al. 2004).The modulation causes horizontal redistributionof water among patches,which is amain factor in pattern for-mation. These models have shown that plant biomass patch-iness derives from a positive feedback between biomass andresource utilization (Gilad et al. 2004, Meron et al. 2004).

The models of pattern formation reproduce the diversityof vegetation patterns observed in nature, such as holes,labyrinths, stripes, and spots (box 1, figure 2; Lefever et al. 2000,Okayasu and Aizawa 2001, von Hardenberg et al. 2001, Ri-etkerk et al. 2002), and predict how they change in relationto water consumption and modulation of soil moisturepatchiness. The models also define the rainfall ranges,withinwhich different vegetation patterns are expected to coexist.Models of vegetation pattern formation capture the essenceof landscape modulation by plants. Most of the modelsrefer to water-limited systems but are also applicable tonutrient-limited systems and to areas where herbivory causesvegetation patterns.

We propose that without external interference,woody veg-etation as a landscape modulator creates the basic landscapestructures as suggested by the models. Pattern formationtheory enables us to reproduce biotic landscape structuresbased on the two ecological processes, exploitation and mod-ulation. It provides the foundation for further developmentof an integrated framework combining vegetation as a land-scape modulator with biodiversity. This requires under-standing the effects of biomass patterns on landscape andspecies processes, as described below.

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Biomass patterns and landscape diversityLandscape diversity refers to the number and types of patches,and to the differences among them in relation to resourcedensity, patch size and shape, and spatial distribution (Boekenet al. 2005).The process of landscape modulation by biomasspattern formation, as described in the previous section, affectsall of these components and produces higher landscapediversity than without the landscape modulator.

To demonstrate the relationship between biomass patternand landscape diversity,we use the example of a spot patternin which patches of woody vegetation are islands in an openarea (figure 3). This two-dimensional space-biomass rela-tionship captures the basic components of landscape diver-sity induced by the landscape modulator. The number ofbiomass peaks indicates the number of landscape-modulatorpatches (N), the width of the biomass peaks portrays thespace occupied by the biomass patches (A), and the height ofthe biomass peaks depicts the difference in biomass densitybetween landscape-modulator patches and unmodifiedpatches (C). The distance, or unmodified space, betweenbiomass patches (S) determines spatial organization.

The environmental impact of a landscapemodulator in re-lation to resource patch formation is complex. As indicatedin figure 3, the presence of a landscape modulator is associ-ated with two coupled patch types: the biomass of the land-scape modulator itself and the multilayered resourcepatchiness that it engenders, which together define its over-all impact on landscape diversity. Landscape diversity, in ad-dition to direct biomass diversity, is also a function of the

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Figure 2. Vegetation patterns along environmentalgradients. The model predicts a sequence of biomasspatterns—(a) spots, (b) stripes, and (c) gaps—along arainfall gradient. (d) Model simulations show the rangesof precipitation values (p1 to p2) where biomass patternformation by landscape modulation occurs. Solid linesrepresent stable solutions. From 0 to p1 on the precipita-tion gradient, bare soil persists. From p2 on, vegetationcover is uniform. The dotted line E represents the biomassof the modulated patches with the associated pattern(a, b, or c). The dashed line B represents unstable baresoil. Reprinted from Gilad and colleagues (2007). © 2007,used with permission from Elsevier.

A recent mathematical model demonstrated the utility of pattern formation theory for understanding the mechanisms of landscape

modulation by vegetation (Gilad et al. 2004, 2007). The model demonstrates landscape changes by landscape modulators, which

generate plant biomass and soil moisture patches. The model contains three dynamic variables: (1) biomass density, representing the

aboveground plant biomass; (2) soil water density, describing the amount of soil water available to the plants per unit area of ground

surface; and (3) surface water, describing the height of a thin water layer above ground level during rain events. At large spatial scales,

the model produces a sequence of biomass patterns of gaps, stripes, and spots for decreasing precipitation values (figure 2a, 2b, 2c). This

sequence has also been found in other models of vegetation pattern formation (Lefever et al. 2000, von Hardenberg et al. 2001, Okayasu

and Aizawa 2001, Rietkerk et al. 2002). The model predicts ranges of precipitation that allow coexistence of two patterns. The

coexistence of different patterns increases landscape diversity.

This model also shows that small-scale multilayered patchiness is the product of landscape modulators’ resource modulation due to

change of soil properties, and exploitation due to root water uptake. Changes of soil properties are depicted by “infiltration feedback”;

that is, as vegetation patches grow, their infiltration rate increases, and as a consequence they consume more water and grow faster.

Changes in soil properties create infiltration contrast between these vegetation patches and unmodulated patches. Model simulations

show that the properties of the modulated biomass and soil moisture patches depend on the strength of the water infiltration and

uptake feedbacks.

The general implications of the model simulations are presented in figure 4. Under the same amount of rainfall, landscape modulation

varied depending on landscape-modulator traits relating to the modulation and exploitation of environmental resources. The

modulation traits entail the ability of the modulator to create a contrast in resources between the modulated and unmodulated patches.

The exploitation traits are related to root growth potential and the plants’ ability to consume water.

Box 1. Vegetation and landscape modulation: A modeling approach.


number of water, nutrient, and litter patches; the differencesamong them; and their structural and functional organiza-tion. The multilayered patchiness controls the flow of re-sources (Schlesinger et al. 1990, Ludwig and Tongway 1995)and organisms (Soons et al. 2005) across the landscape,and thus the contrast of resources between the landscape-mod-ulator patches and the unmodulated area (Boeken andShachak 1994). The distribution and the multilayered patch-iness of resources in the landscape increase heterogeneityand thereby provide opportunities for more species to colo-nize the landscape (Tongway et al. 2001). The model of veg-etation pattern formation (box 1) provides insight into themechanisms controlling multilayer resource patchiness bywoody plants (Gilad et al. 2004, 2007). It shows that resourceflowmodulation and resource consumption feedbacks are themain controllers for the creation of multilayered patchiness.Gilad and colleagues (2004, 2007) demonstrated that themodulation of water infiltration rate by plants creates dif-ferences in infiltration rates between thewoody vegetation bio-mass patch and the unmodulated area. The infiltrationcontrast, along with the root growth rate of the landscapemodulator, determines water consumption and controls theformation of the double-layered patchiness of biomass andwater (box 1, figure 4).

In summary,models of vegetation pattern formation sug-gest that positive and negative feedbacks between biomass andresources form a diversemultilayer patchwork.Themultilayerpatchwork is a property of heterogeneous landscapes. For

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Figure 3. A simplified representation of multiple-layerlandscape diversity induced by landscape-modulator (LM)species. (a) An unmodulated area (brown) with a spottedpattern of modulated biomass patches (green) and associ-ated resource patches (red). (b) Biomass pattern diversity,including the number of patches (three), the differences inbiomass betweenmodulated and unmodulated patches(differences in y-value), and their structural organization(the space between two patches). (c) Resource pattern di-versity, including the number of patches (three), the differ-ences in resources betweenmodulated and unmodulatedpatches, and their structural organization.

Figure 4. Landscape modulation by the formation of multiple layers of biomass and water patches. Model simulations showthe effect of landscape-modulator (LM) traits (soil modulation and root water exploitation) on biomass and soil moisturefor the same amount of rainfall. At high levels of exploitation and at all levels of modulation, LM biomass is high and formsa water-deprived patch (a, d). At intermediate expoitaton levels, LM biomass is intermediate, with a water-enriched patchat high modulation (b) or a water-deprived patch at low modulation (e). At low levels of exploitation, high modulationcreates a water-enriched patch ©, while at low modulation, the LM cannot persist (f). Reprinted from Gilad and colleagues(2004). © 2007 by the American Physical Society.


example, in arid areas, a landscape composed of rock and soilpatches (physical patchiness) creates soil moisture patchi-ness due to runoff water generated in the rocky patches thatflows and infiltrates into the soil patches.Creating amultilayerpatchwork through physical processes differs fundamentallyfrom the creation of multilayer patchiness throughlandscape modulation. Physical creation of multilayerpatchiness is due to redistribution of resources, whereaslandscape-modulator-generated multilayer patchiness is theproduct of modulation and exploitation. Multilayer patchi-ness as a factor in controlling biodiversity will be elaboratedin the next section.

Landscape modulators and species diversityThus far we have explored the development of landscapediversity that emerges from complex interactions amonglandscape modulators and their resources. To understandhow landscape diversity affects species richness, we have in-corporated into our landscape-formation scheme the conceptof species filtering.This concept was developed to capture therelationships between the number of species at local and atregional scales, in order to understand the factors controllingthe establishment of local species assemblages from a largerregional species pool (Keddy 1992, Pärtel et al. 1996, Zobel1997). We use the concept in a more general way to under-stand the factors controlling the flow of species from a largerspatial scale to a smaller one. If the landscape acts as a sievethat controls species flow to and from the landscape,we viewthe process of landscapemodulation as a change in its species-filtering ability.

To illustrate the relationships between the processes oflandscape modulation and filtering, let us consider speciescolonization by landscape-modulor and nonmodulatorspecies. At first, both types of species disperse from thelarger-scale species pool into the small-scale site. Theirestablishment depends on the local ecological conditions ofthe unmodulated landscape,which could include rainfall, soilproperties, and radiation fluxes.Through time, the landscape-modulator species creates landscape patterns with associatedmultilayered resource patchiness, which constitutes a mod-ification of local ecological conditions. The modified con-ditions enable colonization by new species that are adaptedto them. For example, shrubs modulate the landscape byforming soil mounds under their canopies, and the moundscreate water-enriched patches by intercepting runoff. As aresult, species with highwater requirements,which previouslycould not occupy the area, can now colonize the mounds(Breshears et al. 2003, Wright et al. 2006).

The process of changing local ecological conditions throughlandscapemodulation facilitates the filtering in of new species,but at the same time, the new landscape may cause the ex-tinction of other species.We elaborate later in this section onthe outcome of local colonization and extinction in terms ofspecies richness at the landscape level.

We have integrated the processes of landscape modulationand species filtering into a conceptual model (figure 5). This

model depicts the relationships between the dynamics ofspecies assemblages and the formation of biomass patterns andmultilayered resource patchiness in landscapes structuredby landscape modulators. In this framework, two types of fil-ter determine local species richness. The first is controlled bythe ecological conditions of unmodulated patches (F1,F2, andF3 in figure 5). The filters specify the properties of the envi-ronment without modulation. From the large species pool,only species with physiological and ecological traits that fit theenvironmental constraints establish locally.The ecological con-ditions of unmodulated patches are determined by climatevariables, soil properties, and ecological interactions such ascompetition and predation. The second type of filter is con-trolled by landscape modulators that modify local ecologicalconditions on a small scale (F4 and F5 in figure 5.) This fil-ter represents the transition frommacro- tomicroclimate andfrom large-scale to small-scale soil properties. For example,trees in a grove in the Negev Desert reduced global radiationfrom 23.8 to 11.8 calories per square centimeter per hour.Wind velocity was reduced from 4.9 meters per second in theunmodulated area to 2.4 meters per second in the modulatedarea (Schiller 2001).

The two types of filter identify four groups of species bytheir associations with patches: landscape-modulator species,independent species, unmodulated-dependent species, andmodulator-dependent species (figure 5). Landscape modu-lator species are those that create multilayered patchiness.Local environmental conditions (F1 in figure 5) are the maincontroller of their number and composition. Independentspecies are those that fit into modulated and unmodulatedpatches, also controlled by local environmental conditions(F2 in figure 5).Unmodulated-dependent species are specificto unmodulated patches and are controlled by local andmodified ecological conditions (F3 and F4, respectively, infigure 5).

The conceptual model of figure 5 can help formulate theexpected relationships between (a) the development of land-scape diversity and (b) species richness and composition atthe landscape level. The richness and composition of inde-pendent species vary insignificantly during the early stages oflandscape development. Landscape-level species richness iscontrolled mainly by the balance between extinction of localunmodulated-dependent species and colonization oflandscape-modulator-dependent species. At early stages oflandscape development,when landscape-modulator patchesare small and spotted, they do not affect the species richnessof unmodulated-dependent species. At this stage, the filter-ing of landscape-modulator-dependent species can onlyincrease, not decrease, species richness. Thus, a positive rela-tionship exists between pattern development and speciesnumber (figure 6). The landscape species richness continuesto increase until themodulated landscape begins to reduce thenumber of species in the unmodulated area (figure 6,region A). Reduction and fragmentation of the unmodu-lated area could decrease the number of species in the un-modulated area as a result of increasing landscape-modulator

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biomass cover and changes in its pattern. For example, inthe early stages, when the landscape-modulator biomassoccurs as spots, the connectivity of the open, unmodulatedspace is high. When a pattern of landscape modulationdevelops and holes appear, the open space is surrounded bylandscape-modulator biomass and is fragmented.

The overall relationship of landscape species richness withlandscape-modulator development is either hump-shapedor linear positive, related to three trajectories (figure 6). Thefirst trajectory implies an increase in species richness withincreasing biomass cover of landscape modulators (figure 6,top line).This occurswhen the colonization rate of landscape-modulator-dependent species is higher than the extinction rateof unmodulated-dependent species. This transpires in semi-arid systemswhere the growth of herbaceous vegetation is fa-cilitated by shrub patches (Wright et al. 2006).

When the colonization rates of landscape-modulator-dependent species are lower than the extinction rates ofunmodulated-dependent species, then the number of speciesin the landscape decreases with landscape-modulator biomassand with the development of its pattern (figure 6, region B).When the landscape is totally covered by landscape-modulatorbiomass, the number of species in the landscape can be higheror lower than when the area was unmodulated (figure 6, re-gion B middle and lower lines). The changes in species num-ber due to biomass pattern formation are associated withchanges in species composition (figure 6, line C).

Landscape modulation and formationof species assemblagesIn the previous section, we explored the relationships be-tween (a) the spatial organization of bioticallymodulated land-scape and (b) species number and composition at thelandscape level. However, species-richness patterns at thelandscape level emerge from species assemblage formation atthe patch level.We now broaden our view to include the as-sociation between (a) multilayered patchiness created by thelandscape modulator and (b) species assemblages. Multi-

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Figure 5. The role of landscape-modulator (LM) species in species-filtering processes. In unmodulated landscapes, localecological conditions (F2 and F3) filter only two types of species from the species pool. Filtering of LM species by localecological conditions (F1) causes landscape modulation that modifies local conditions. These affect unmodulated-dependentspecies negatively (F4) and LM-dependent species positively (F5).

Figure 6. Species richness as a function of area covered bylandscape-modulator (LM) species, often coupled withunmodulated patch fragmentation.


layer patchiness represents landscape diversity,whereas speciesassemblages depict species diversity. Linking patchiness andassemblages implies linking landscape and species diversityinto one biodiversity framework (Shachak et al. 2005).Wepre-sent a graphical scheme that links patches and assemblages andshow its utility by generating a novel hypothesis on biodiversitydynamics in a biotically modulated landscape.

The patch-assemblage association occurs at the scale whereinteractions among individuals of various species and theirimmediate environment take place. The interactions at thisscale are the mechanisms that create landscape biodiversitypatterns. Multilayered patchiness creates a contrast in re-source density between landscape-modulator patches andunmodulated patches (figure 3). The resource contrast atthe patch level determines the contrast in species assem-blages, that is, the degree of similarity in species compositionbetween modulated and unmodulated patches.At low levelsof resource contrast, a large number of species occupy bothmodulated and unmodulated patches, and the similaritybetween species assemblages in the two patch types is high.As multilayer patchiness develops through time, resourcecontrast between the modulated and unmodulated patchesincreases and assemblage similarity decreases.

In figure 7a and 7b, we introduce a two-dimensional rep-resentation demonstrating the coupling between the twocomponents of landscape and species diversity (resourcecontrast and species assemblage similarity) and their trajec-tories over time.The x-axis shows the differences in resources,while the y-axis describes the similarity in species composi-tion between the modulated and unmodulated patches. Apoint in the plane represents resource and assemblage con-trast betweenmodulated and unmodulated patches at a giventime.A set of points describes changes in patchiness contrastand the associated changes in species assemblage similarityover time.The x-axis could be quantified by a single resourcecontrast, such as a difference in soilmoisture between the patchtypes, or an index that combines several resources. Speciesassemblage similarity could be quantified by comparingspecies composition between the two patch types, as, for ex-ample, the percentage of species common to the landscape-modulator patches and unmodulated patches, or any othersimilarity index (Whittaker 1975).

In real landscapes created by modulator species, resourcecontrast and species assemblages are constantly changingbecause of the increase and decrease in landscape-modulatorbiomass cover caused by natural and anthropogenic drivers.The increase in landscape-modulator biomass is associatedwith landscape development, where the landscape modula-tor disperses in space and creates landscape patterns. Thedecrease is connected with landscape-modulator mortalityor biomass removal by natural and human-induced distur-bances. The increase in biomass represents landscape devel-opment through time and is characterized by the expansionof landscape modulators and the formation of multilayeredpatchiness. At this stage, the resource contrast betweenlandscape-modulator patches and the unmodulated patches

increases,while the similarity between landscape-modulator-dependent and unmodulated-dependent species assemblagesdecreases. It is reasonable to assume that the complex rela-tionship between resource contrast and species assemblagesis not linear (figure 7a).At the onset of landscapemodulation,a time lag is expected between the progression of resourcecontrast and the organization of species into assemblages.Asmodulation progresses, the rate of species response to resourcemodulation is accelerated. This results in a convex trajec-tory of biodiversity dynamics during the stage of landscapedevelopment (figure 7a).

Decrease in biomass represents a different trajectory inpatch-assemblage dynamics (figure 7b). It represents de-gradation of the multilayered patchiness that accompanies

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Figure 7. Dynamics of landscape-modulator (LM)patches and biodiversity cycling. (a) The trajectory (withtime steps t1 to t5) of resource contrast and assemblagesimilarity during the development of an LM patch. Re-source contrast refers to the differences in resource densitybetween the modulated and unmodulated patches. Simi-larity of assemblages refers to the similarity between thespecies assemblages in the modulated and unmodulatedpatches. (b) The trajectory of resource contrast and simi-larity of assemblages during the decay of an LM patch.(c) Biodiversity cycling loop demonstrating the changesin resource contrast and species assemblage similarity asa result of the development, renewal, maturation, anddecay of LM patches.


partial or complete destruction of the landscape pattern bybiomass removal. Biomass patterns are prone to decay as a re-sult of senescence due to successional processes or natural andhuman-induced disturbances of the landscape modulator.These factors can modify the multilayered structure and, asa consequence, affect the relationships between resource con-trast and species assemblages.The main natural disturbancesthatmodify vegetation patterns are fire, herbivores, pathogens,insect damage, and climatic disasters (Sousa 1984). Humandisturbances include fire,mechanical removal of biomass, andlivestock herbivory.Disturbed landscape-modulator patchesdiffer from modulated and unmodulated patches in relationto biomass cover and resource concentration. For example,after canopy removal of a modulated patch, contrasts inwater and light levels decrease between the disturbed mod-ulated patch and the unmodulated patches. However, thereare still differences between unmodulated and disturbedmodulated patches, due to litter and roots that remain inthe disturbed patch.

The trajectory in the plane representing the relationshipbetween resource contrast and species assemblage similarityduring disturbance depicts the regression in landscape mod-ulation that represents decay of the multilayered patchinessformed by landscape modulators (figure 7b). Immediatelyafter disturbance, resource contrasts sharply decrease, whilespecies responses lag behind. The decay process continuesafter the disturbance event, since disturbance is usuallyassociated with a positive feedback that intensifies its effect(Suding et al. 2004). For example, after defoliation due toinsect attack, resource contrast in relation to radiation and soilmoisture is reduced.This decay process can be intensified byphysical factors such as rainfall and wind, which removelitter or sediment at higher rates without the protection of thecanopy. The decay process traces a trajectory in the resourcecontrast and species similarity plane, representing a processof decreasing resource contrast and increasing speciesassemblage similarity (figure 7b). The shape of the trajectoryand the rate of movement from high contrast and low simi-larity to low contrast and high similarity depends on thespecific resources affected by the disturbance and the time lagof species responses. The overall effect of the decay processon species assemblages is controlled by the intensity of the ini-tial disturbance, the strength of positive feedbacks followingdisturbance, and the extinction rate of landscape-modulator-dependent species. We suggest that the slow response ofspecies at the onset of the decay process and the acceleratedresponse to the positive feedback result in a concave trajec-tory (figure 7b).

The graphical presentation (figure 7a, 7b) of landscape-species interactions captures the basic difference betweenphysical and modulator-induced landscape mosaics. On anecological timescale, a physical landscape mosaic is relativelystable, whereas a biotically modulated landscape is prone tofrequent changes due to biomass growth and removal.

Although the role of biota in creating heterogeneity has beenrecognized, the basic differences between the effects of phys-

ical and modulator-induced landscape mosaics on biodiver-sity have not been explicitly formulated (Turner 2005).We em-phasize the differences by formulating a biodiversity cyclinghypothesis that depicts the regularity of biodiversity changesin a biotically modulated landscape.

Landscape modulation and biodiversity cyclingCombining the development and decay trajectories generatesa model that sums up the essence of biodiversity dynamics inbiotically modulated landscapes. The model suggests thatthe dynamics of resource contrast and species assemblagesimilarity create a loop trajectory (figure 7c). We define thecoupled movement of landscape and species diversity com-ponents through time as biodiversity cycling.We regard thebiodiversity cycle model as a hypothesis, since the modelemerges from theoretical reasoning that assumes nonlinear-ity and time lags (Norgaard and Baer 2005). An alternativehypothesis is that development and decay follow the sametrajectory.

To capture more stages in biodiversity cycling, we addmaturation and recovery stages to the development anddecay stages. Maturation implies that the landscape patternhas reached an asymptotic stage at which the resource con-trast is at a maximum, and similarity between unmodulated-dependent assemblages and landscape-modulator speciesassemblages is minimal.Recovery occurs when the landscapemodulator produces new biomass after the disturbance. Thegeneral pattern of recovery is similar to the patch developmentprocesses: an increase in resource contrast and a decrease inassemblage similarity between landscape-modulator patchesand unmodulated patches. According to the biodiversitycycling hypothesis, biodiversity dynamics constitute a hysteresisphenomenon, that is, the effect lags behind its cause.The for-mation of species assemblages lags behind resource contrastcreation. Hysteresis also implies that decay and recoverypathways are different, that is, under similar resource contrasts,two sets of assemblageswith different degrees of similarity cancoexist. According to this model, under the same resourcecontrast, higher similarity between landscape-modulatorpatches and unmodulated patches is expected in the devel-opment than in the decay phase.

Testing the biodiversity cycling hypothesis is vital, becauseif it accurately describes processes in nature, then we will beable to predict the trajectory of biodiversity changes overtime on the basis of initial conditions. Initial conditions iden-tify the direction of the trajectory in the plane representingresource contrast and assemblage similarity. For example,at a given resource contrast after disturbance, the trajectoryis toward lower contrast and higher similarity. However, ifthe initial contrast is in the recovery phase, the trajectory istoward higher contrast and lower similarity.

This is important when considering changes caused byhuman-induced disturbances. Studying biodiversity in mod-ulated landscapes addresses the drivers and changes in thenumber and organization of species and patches (Boeken etal. 2005). If the hypothesis describes the actual situation of

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many landscapes, which are constantly changing because ofrecovery after repeated disturbances, then our current viewon biodiversity-ecosystem relationships should be extended(Loreau et al. 2001). This implies that ecosystem processes ofbioticallymodulated landscapes are cycling because of changesin the assemblages that carry them out. The biodiversity cy-cling hypothesis suggests that species and ecosystem functions,such as productivity and nutrient fluxes, are linked throughlandscape modulation.

The biodiversity cycling hypothesis raises questions inrelation to conditions in which biodiversity cycling does notoccur, and to the evolutionary consequences of biodiversitycycling. Under low frequency and intensity of disturbancesto landscape modulators, and high rates of landscape mod-ulator recovery, there may not be sufficient time for thedecay process to occur.Therefore, under these conditions, thetrajectories before and after disturbance are similar. As forevolutionary responses, according to Silver and Di Paolo(2006) and Boogert and colleagues (2006), spatial effectssuch as those exhibited by landscape modulators may favorthe evolution of niche construction. If this is true, then bio-diversity cycling should reinforce itself through evolution-ary processes.

Testing the biodiversity cycling hypothesis requires long-term experiments that track the trajectories discussed above(figure 7a, 7b). In Israel,we designed a network of experimentsto test the biodiversity cycling hypothesis (box 2). Theseexperiments examine the changes in resource contrast andspecies assemblages of herbaceous vegetation and varioustaxa of invertebrates along a modulation gradient of woodyvegetation (figure 8). The gradient includes landscape-modulator cover, biomass density, and spatial patterns, rang-ing from arid shrubland with spotted, sparse cover of woodyvegetation to mesic Mediterranean woodland with densetree cover. The research focuses on the maturation anddecay phases, where decay is driven by aboveground bio-mass removal.

Conclusions: Landscapemodulation and biodiversityEcological entities such as genes, species, or patches havethree basic diversity properties: numbers, differences, andorganization (Gaston 1996, Shachak et al. 2005). In a land-scape created by woody vegetation, the number and densityof biomass patches, the differences in resource density amongpatches, and the structural organization of the patches into

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The biodiversity cycling hypothesis captures the essence of biodiversity dynamics in landscapes modulated by woody plants. It

encompasses landscape formation through vegetation and species filtering. Thus, the hypothesis can be viewed as an umbrella for

testing concepts, models, and hypotheses related to the framework of woody plants as landscape modulators. In Israel, we designed a

network of experiments to test the biodiversity cycling hypothesis and related hypotheses. The study is carried out at five long-term

ecological research sites representing a gradient of rainfall and woody vegetation cover and patterns (figure 8). Currently, we focus on

the maturation and decay phases of biodiversity cycling. The decay studies are experimentally based on removal of aboveground

biomass of the woody landscape modulators and partial defoliation by domestic livestock. The study examines the long-term changes in

resource contrast and species assemblage similarity between landscape-modulator patches and unmodulated patches for herbaceous

plants and various taxa of invertebrates. In relation to maturation and decay, we test (a) whether the filtering strength of the landscape

modulator increases, decreases, or is hump-shaped along the gradient; (b) whether the filtering effect of the landscape modulator on

species assemblages of various taxa and trophic levels depends on resource contrast or organism traits; (c) whether filtering depends on

woody vegetation patterns; and (d) whether changes in species filtering and assemblage formation during the decay process depend on

the degree of disturbance to biomass patches. In addition, we study whether different resources, taxonomic groups, food-web functional

groups, and disturbance agents affect the shape, size, and duration of the cycle.

Figure 9a is an example of preliminary results related to the changes in annual plant species assemblages during the decay process. The

figure is a principal component analysis ordination plot for herbaceous plant assemblages in three patch types (unmodulated patches,

modulated patches, and disturbed modulated patches) on north- and south-facing slopes in a Mediterranean woodland. Modulated

patches are similar and unmodulated patches are dissimilar to each other, while both patch types are segregated. The very small

variability of the modulated patches illustrates the overriding effect of multilayered patch formation on species composition. Canopy

removal of modulated patches increased similarity with unmodulated patches.

In figure 9b, the similarity (D) between the modulated and unmodulated patches during maturation is low (calculated as D = 1 – d,

from the Bray-Curtis distance d = Σ nik – njk / (nik + njk) for k species in samples i and j). After disturbance of the modulated patch

by canopy removal (decay), species assemblage similarity between the disturbed modulated and unmodulated patches increased. This

supports a basic premise of the biodiversity cycling hypothesis, that a reduction in resource contrast during landscape-modulator patch

decay increases assemblage similarity (see figure 7b).

Box 2. Experimental test of the biodiversity cycling hypothesis.


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Figure 8. Long-term ecological research sites (with annual rainfall) along a rainfall gradient in Israel. The sites have the sameexperimental design in order to test the biodiversity cycling hypothesis. Pictures show typical vegetation patterns of the sites.Satellite image courtesy of National Aeronautics and Space Administration. Photographs: Moshe Shachak and BertrandBoeken. Abbreviations: km, kilometer; mm,millimeter; yr, year.

Figure 9. Changes in (a) herbaceousspecies assemblages and (b) similarity ofthree patch types during the decay phasecaused by canopy removal. (a) Principalcomponent analysis (PCA) ordination ofherbaceous plants in Adulam duringspring 2005. Black symbols represent un-modulated patches; open symbols,patches modulated by trees; gray symbols,modulated patches disturbed by canopyremoval; squares, north-facing slope (N);and circles, south-facing slope (S). (b)Trajectories of annual plant species as-semblage in a phase plane of resourcecontrast and species similarity, due to thediminishing contrast of global radiation.


patterns define landscape diversity.For each landscape diversitycomponent, there is an analog in species diversity. The ana-log of number of patches is number of species. The resourcedifferences are analogous to the differences among speciesassemblages associated with each patch type. The analog tostructural organization is species composition and speciesabundance in the assemblages.The three diversity componentsof modulator-induced landscapes are interdependent andlinked through the biomass pattern.

The dynamic process of biomass pattern formation re-sults in a spatial configuration that incorporates the number,size, and spatial arrangement of the patches and the distrib-ution of resources. Species distribution and abundance in thelandscape-modulator-induced landscape are linked to biomassand resource patterns by species filtering processes. Filteringprocesses are controlled by species availability from the largerregional species pool; the ability of these species to disperseinto themodulator-induced landscape; and the traits that en-able them to establish, grow, and reproduce in the variety ofpatch types in the landscape (Zobel 1997).The landscape-levelproducts of species filtering are a variety of species assemblagesassociated with the landscape-modulator patches and theunmodulated area. Each assemblage encompasses speciesrichness and compositional organization.Therefore, patternformation and filtering processes, by their net effects on re-sources and species contrast, are the two core processes thatlink species and patches in a biotically induced landscape.

We suggest the biodiversity cycling hypothesis (figure 7c)that integrates resource contrast and assemblage similarityas a tool for future studies of biodiversity dynamics inmodulator-induced landscapes. We propose that the bio-diversity cycling framework (figure 7) can generate specifichypotheses concerning the shape, dimensions, and durationof the cycles in relation to different resources, taxonomicalgroups, food-web functional groups, and disturbance agents.Assemblage similarity and resource contrast can be mea-sured in the field, and therefore hypotheses can be tested. Inaddition, themechanisms that drive and connect the changesin resource and species contrasts can be investigated.

The landscape-modulator and biodiversity cycling conceptsrequire rethinking of the processes controlling biodiversity atthe landscape scale.This is because landscape-scale biodiversityprocesses have theoretical implications that are crucial for theconservation, restoration, and management of biodiversity(Steiner andKohler 2003,Waldhart 2003).These conceptsmayalso be useful in confronting environmental problems suchas global climate change and habitat fragmentation anddegradation.

AcknowledgmentsThis studywas partially supported by grants from theNationalCenter for Ecological Analysis and Synthesis to M. S. as aCenter Fellow; from the McDonell Foundation to E.M. andM. S.; from the Israel Science Foundation to Y. L., A. P., andG.N.; and from the Eshkol Foundation of the Israeli Science

Ministry to all the authors.We also thank three anonymousreviewers for their insights.

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