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American Journal of Botany 98(3): 503–516. 2011.
THE DISENTANGLED BANK: HOW LOSS OF HABITAT
FRAGMENTS AND DISASSEMBLES ECOLOGICAL NETWORKS1
Andrew Gonzalez2, Bronwyn Rayfield, and Zoë Lindo
Department of Biology, McGill University, 1205 Docteur Penfield, Montréal, Québec, Canada, H3A 1B1
Habitat transformation is one of the leading causes of changes in biodiversity and the breakdown of ecosystem function and
services. The impacts of habitat transformation on biodiversity are complex and can be difficult to test and demonstrate. Network
approaches to biodiversity science have provided a powerful set of tools and models that are beginning to present new insight into
the structural and functional effects of habitat transformation on complex ecological systems. We propose a framework for studying the ways in which habitat loss and fragmentation jointly affect biodiversity by altering both habitat and ecological interaction
networks. That is, the explicit study of “networks of networks” is required to understand the impacts of habitat change on biodiversity. We conduct a broad review of network methods and results, with the aim of revealing the common approaches used by
landscape ecology and community ecology. We find that while a lot is known about the consequences of habitat transformation for
habitat network topology and for the structure and function of simple antagonistic and mutualistic interaction networks, few studies have evaluated the consequences for large interaction networks with complex and spatially explicit architectures. Moreover,
almost no studies have been focused on the continuous feedback between the spatial structure and dynamics of the habitat network
and the structure and dynamics of the interaction networks inhabiting the habitat network. We conclude that theory and experiments that tackle the ecology of networks of networks are needed to provide a deeper understanding of biodiversity change in
fragmented landscapes.
Key words: biodiversity; extinction; graph theory; habitat fragmentation; networks; spatial ecology.
One of the main scientific challenges of the 21st century is to
understand the extent of biodiversity change that results from
human expansion (Dirzo and Raven, 2003) and whether this
change is detrimental to the functioning of the biosphere and
the well-being of human society (Naeem et al., 2009). Our best
data and models indicate that biodiversity is being eroded
(Butchart et al., 2010). Land cover change is the primary cause
of biodiversity change, and the loss of habitat area is at the
core of estimates of global and regional extinction rates (e.g.,
Lawton and May, 1995; Pimm et al., 1995; Pimm and Raven,
2000). Other anthropogenic drivers, such as climate change and
invasive species, are of growing importance, but their effects on
biodiversity in the future will likely interact with habitat transformation to modify the rates of biodiversity loss (Darling and
Côté, 2008).
The impacts of habitat transformation on biodiversity are
complex, changing not only species richness and diversity, but
also the pattern of species interactions that link them in networks
and the functions that species perform (Morris, 2010). Habitat
transformations may follow many different trajectories involving a combination of loss, degradation, and fragmentation;
hence, teasing apart the individual contributions of different
types of transformations can be difficult to test and demonstrate
(Ewers and Didham, 2006). Habitat fragmentation, for example,
is a catch-all term for a multiscale (Keitt et al., 1997; Olff and
Ritchie, 2002) process that alters habitat isolation, quality, arrangement, and connectivity (Fahrig, 2003). Difficulties in eval1
uating the effects of habitat fragmentation have led to
considerable controversy surrounding the idea that habitat fragmentation per se is a significant cause of biodiversity loss
(Fahrig, 2003; Yaacobi et al., 2007). Indeed, Yaacobi et al.
(2007) argued that fragmentation has become a “reified doctrine” in ecology (sensu Slobodkin, 2001) because evidence for
fragmentation per se as a major factor driving biodiversity loss
is so weak. Recent reviews of the empirical literature (Debinski
and Holt, 2000; Ewers and Didham, 2006) suggest otherwise
and repeatedly find that fragmentation effects are numerous and
sometimes large, but are often associated with species’ responses
to other forms of environmental change that act in synergy with
fragmentation. Fragmentation effects are most pronounced
when the amount of habitat remaining is low (Andrén, 1994),
but theses effects may be offset by spatial arrangements of habitat that promote species’ movements to create a networked system of habitat fragments (Huxel and Hastings, 1999).
Network approaches (Bascompte, 2007) to biodiversity science have improved our understanding of the causes and consequences of biodiversity loss (Solé et al., 2005; Tylianakis et al.,
2008). Network-based representations of ecological systems
are powerful models that can describe biodiversity change
(McCann, 2007) and characterize structural (e.g., Rayfield
et al., in press) and functional consequences of fragmentation
(Stouffer and Bascompte, 2010). Habitat transformation causes
nonlinear changes in habitat network connectivity (Minor et al.,
2009; Urban et al., 2009), but it also modulates the strength and
timing of species interactions, that can cause cascading secondary extinctions in ecological community networks (Terborgh et
al., 2001; Solé and Montoya, 2006) over extended periods of
time (Helm et al., 2006; Vellend et al., 2006). Here we will review how network approaches can address key questions relating biodiversity change to the spatial structure of habitat
networks and the interaction structure of ecological community
networks. Steps toward a synthesis of network approaches have
Manuscript received 26 October 2010; revision accepted 14 January 2011.
A.G. is supported by the Canada Research Chair program and NSERC
Discovery and Strategic grants. B.R. and Z.L. are supported by NSERC
post-doctoral fellowships.
2 Author for correspondence (e-mail: [email protected])
doi:10.3732/ajb.1000424
American Journal of Botany 98(3): 503–516, 2011; http://www.amjbot.org/ © 2011 Botanical Society of America
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been made (Holt, 2002; Polis et al., 2004; McCann et al., 2005;
Amarasekare, 2008; Rooney et al., 2008). We build on these
efforts by identifying promising applications of network ecology to the problem of biodiversity loss due to habitat loss and
fragmentation from theoretical (Hassell et al., 1993; Bodin
et al., 2006; Fortuna and Bascompte, 2006; Solé and Montoya,
2006), empirical (Komonen et al., 2000; Terborgh et al., 2001;
Tylianakis et al., 2007; Bell et al., 2010), and experimental
(Holyoak, 2000; Staddon et al., 2010) approaches. We close
with some considerations for conservation and a framework to
guide future directions for research.
in fragmented landscapes that is thought to cause the extirpation of avian prey (Crooks and Soule, 1999). The fragmentation
of habitat networks further affects ecosystem function as a result of changes in the flow of information, resources, and energy. For example, the loss of insect–plant pollinator interactions
in fragmented landscapes can reduce the efficiency and increase
the variability of this important ecosystem function (e.g., Steffan-Dewenter et al., 2006). The feedback between spatial networks and interaction networks (mutualistic or antagonistic)
generates the complex dynamics typical of biodiversity change
in fragmented habitats.
DEFINING BIODIVERSITY AS INTERACTION
NETWORKS
DEFINING HABITAT FRAGMENTATION
Biodiversity is the variety of living organisms, the ecological
complexes in which they occur, and the ways in which they
interact with each other and the environment (Groves et al.,
2002). The impacts of habitat loss and fragmentation are commonly expressed through changes in species richness and the
relative abundance of ecological communities. But biodiversity
is also defined as variation in the number and type of interactions (e.g., predation, pollination) in which different species are
engaged (Dyer et al., 2010). Some species may disproportionately facilitate biodiversity through their interactions with other
species. In most ecosystems, species interactions form a vast
network whose nodes and links are variable in space and time
(Ings et al., 2009). For example, Petanidou et al. (2008) found
that plant–pollinator interactions were highly inconstant as a
result of high species turnover and changes in the degree of interaction specialization from year to year.
The interaction diversity of a network can be summarized by
simple measures such as link richness or more complex measures such as link asymmetry and compartmentalization. Figure
1 presents different properties of ecological network structure
that have been measured in a variety of systems. These properties are highly nonrandom (McCann, 2007; Bascompte, 2009)
and may be surprisingly invariant despite high species turnover
(Petanidou et al., 2008). Interactions seem also to be structured
in ways that may favor the robustness and resilience of the interaction networks to perturbations causing species extinction
(Fortuna and Bascompte, 2006; Srinivasan et al., 2007; Allesina
et al., 2009; Staniczenko et al., 2010). However, the problem of
how large networks disassemble and fragment has only recently
received theoretical attention (Krishna et al., 2008; Saavedra
et al., 2009; Campbell et al., 2011).
The fragmentation of formerly continuous habitat is expected
to cause considerable node (species) extinction and transform
the interaction topology of ecological networks (Fortuna and
Bascompte, 2006; Solé and Montoya, 2006). This occurs, in
part, because the habitat itself is converted into a network of
fragments with variable spatial connectivity and quality that
transforms the interaction network of species inhabiting the
landscape. Only recently has the mutual influence between spatial
and interaction networks been studied. A product of such a merger
is the study of “networks of networks” (or “graphs-of-graphs”
sensu Dale and Fortin, 2010) that emphasize the mutual influence
of spatial habitat networks and species interaction networks
Transformations of the interaction network also stem from
the dynamics of species loss that may induce cascading effects
mediated by indirect interactions. An example is mesopredator
release following the extinction of large mammalian predators
The conversion of vegetation for agriculture or urbanization
dramatically shifts the composition of habitat in the landscape.
Habitat loss is not spatially uniform and may sweep in a wave
across a landscape over a period of decades (Ritters et al., 2000;
Etter et al., 2006), leaving a discontinuous mosaic of remnant
habitat fragments, interspersed with a mixed matrix of disturbed
(e.g., intensely grazed) or regenerating vegetation. Crucially,
the resulting habitat fragmentation also involves profound alterations to the physical environment that generates new habitat
and great heterogeneity over multiple scales of space and time
(Saunders et al., 1991; Saunders, 1998). The spectrum of organisms, from microbes to mammals, that occupies heavily
fragmented landscapes experience these effects differently
depending upon their size and mobility. Following Franklin
et al. (2002), we define habitat fragmentation as human-induced
discontinuity in the spatial distribution of resources and environmental conditions present in an area, over at least one scale,
that affects the survival, reproduction (fitness), and mobility of
multiple interacting species (we ignore natural causes of fragmentation such as wind and fire). The emphasis here is on the
ecological response to disrupted species interactions because of
changes in the connectivity of the mosaic of interspersed habitats.
In natural situations, loss of connectivity co-occurs with habitat loss, which complicates the study of habitat fragmentation
per se. Fahrig (2003) has called for greater rigor in the study of
habitat fragmentation; few studies have clearly decomposed the
contribution of changes in habitat isolation, quality, arrangement, and connectivity to biodiversity change. The effects of
fragmentation, independent of habitat loss, are thought to be
due in part to increased edge effects (Ewers and Didham, 2006),
which can both diminish and enhance habitat quality depending
upon the species (trophic) group and the spatial configuration of
the habitat mosaic. For example, Fig. 2 shows the effects of
spatial network structure and habitat quality on predator and
prey richness in experimental moss-based landscapes (see also
Chisholm et al., in press). In this experiment, habitat loss was
controlled while changes in habitat connectivity and habitat
patch quality were altered. The effects of spatial network form
on diversity were strongest in habitat patches of poor quality
within heterogeneous landscapes. Predator richness (mesostigmatid mites) only benefited in continuous landscapes, but suffered extinction when placed in fragmented landscapes (square
or linear networks). Prey richness (Collembola) benefited from
connectivity in all three network forms, although the effects of
connectivity were less pronounced in square networks. Experimental approaches such as these coupled to theory are a powerful means of revealing the effects of changing network structure
and quality on biodiversity.
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Gonzalez et al.—Biodiversity networks in fragmented habitats
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Fig. 1. Schematic representation of network structural properties that are relevant for biodiversity persistence. Examples of habitat, antagonistic, and
mutualistic networks are illustrated. Network nodes represent habitat patches (black polygons, column A), species in different trophic levels (resources, R;
consumers, C; and predators, P; column B), and mutualistic plant (black circles, column C) and pollinator (black squares, column C) species. Links represent potential movement routes (column A), feeding interactions (column B), and pollination interactions (column C). Link arrowheads indicate directional
movements or interactions and link thickness indicates the flow rate or strength of a given link.
HOW DOES HABITAT TRANSFORMATION ALTER
SPATIAL HABITAT NETWORKS?
indicate how to characterize the connectivity of habitat networks and quantify their network structure.
In this section, we describe how the habitat networks emerge
from the process of habitat loss and fragmentation. We then
Modeling fragmented habitat as a network— Network approaches founded on graph theory are now increasingly applied
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Fig. 2. The effects of network form on diversity were stronger in heterogeneous landscapes and differed among trophic groups. In this moss-based
experiment, habitat loss was controlled for while changes in habitat fragmentation and network arrangement (continuous, square [looped], linear networks),
and habitat quality (wet, heterogeneous, dry) were altered at the landscape level. The effects of connectivity are shown at the individual-patch level. Patches
1–4 were good quality “wet” patches in wet landscapes, patches 1–4 were poor quality “dry” patches in dry landscapes. No beneficial effects of connectivity were observed in these treatments (all values equal zero). In heterogeneous landscapes, patches 1 and 2 were good quality “wet” patches, while patches
3 and 4 were poor quality “dry” patches. (A) Predator richness (mesostigmatid mites) benefited from connectivity in heterogeneous continuous landscapes,
but suffered extinction when placed in discontinuous networks of either square or linear arrangements. (B) Prey richness (collembola) benefited from connectivity in heterogeneous networks of all three forms, although the effects of connectivity were less pronounced in square networks. Values are means ±
two standard errors (after Chisholm et al., in press).
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Gonzalez et al.—Biodiversity networks in fragmented habitats
to the problem of describing the static patterns and dynamic
changes due to habitat fragmentation (reviewed by Urban et al.,
2009). Habitat fragments can be represented as nodes in a spatial network connected via species dispersal to model connectivity at multiple spatial scales (Cumming et al., 2010; Dale and
Fortin, 2010). These spatial habitat networks integrate several
key spatial components of habitat fragmentation in their delineation and analysis: permeability of the matrix surrounding
habitat fragments (McRae et al., 2008), multiple scales of dispersal among fragments (Brooks et al., 2008), and spatial configuration of habitat fragments (Andersson and Bodin, 2009).
Nonlinear fragmentation in eroding habitat networks—Over
time, as the total area of original habitat declines, the pattern of
fragmentation in the landscape changes considerably, and metrics describing changes in the number and connectivity of remnant fragments change in a highly nonlinear manner (Forman,
1995; Trani and Giles, 1999). These observations are easily recovered by simulations based on percolation theory (Andrén,
1994; Bascompte and Solé, 1996), by which separating the role
of habitat loss from habitat fragmentation per se is more easily
done, and parameters that describe the change in structural
fragmentation can isolate the abrupt thresholds in habitat contiguity as habitat loss progresses (Bascompte and Solé, 1996).
However, critical thresholds in contiguity, predicted by random
models of habitat loss, are not as common in empirical studies
(Swift and Hannon, 2010), and percolation theory may be limited in its application to human-transformed landscapes (Keitt
et al., 1997).
Critical connectivity thresholds can be identified in models
by systematically removing nodes (e.g., Bascompte et al., 2006;
Bodin et al., 2006; Wilson et al., 2010) or links (e.g., Brooks,
2006; Lookingbill et al., 2010). Node-deletion sequences in
habitat networks have shown a dramatic scale-dependent effect
of habitat loss on landscape connectivity (Keitt et al., 1997).
Habitat networks disconnect abruptly when key patches are removed that linked large habitat components (Bodin et al., 2006;
Andersson and Bodin, 2009). Link-deletion sequences have
demonstrated threshold effects as species’ dispersal abilities are
decreased (Keitt et al., 1997). Habitat networks may suddenly
fragment at relatively short distances compared to the distribution of interpatch distances (Urban and Keitt, 2001), reducing
the potential for long-distance rescue within the network. In
summary, critical transitions in network connectivity can occur
over a narrow range of habitat loss or reduction in dispersal
distances.
Dispersal on habitat networks— Habitat networks have the
flexibility to represent a spectrum of biological and spatial detail required to define structural, potential, or functional connectivity (Calabrese and Fagan, 2004). Field-measured dispersal
rates among pairs of habitat patches should define the links in a
spatial habitat network. In the absence of these data, links are
defined based on knowledge of species’ movement ecology
(Fagan and Calabrese, 2006; Minor and Urban, 2008). When
permeability of the landscape matrix surrounding habitat patches
determines species’ movements, straight-line distances between
patches may no longer capture connectivity. Movement pathways may become nonlinear in which case Euclidean links and
distances between patches are replaced by least-cost links and
effective distances (O’Brien et al., 2006; Fall et al., 2007). Multiple links between pairs of habitat patches can be delineated
(Theobald, 2006; Pinto and Keitt, 2009) and analyzed (McRae
507
et al., 2008) to account for the presence of alternative dispersal
routes in fragmented landscapes.
Biodiversity implies a spectrum of responses to habitat fragmentation. The many scales at which species respond to fragmentation may be addressed a priori by delineating network
links based on species’ dispersal estimates (e.g., Bunn et al.,
2000), or they may be identified a posteriori through a hierarchical series of networks constructed with increasing link threshold distances (Keitt et al., 1997; Brooks, 2006; Dale and Fortin,
2010). This latter approach can identify link threshold distances
that correspond to abrupt changes in the connectivity pattern of
the habitat network (Urban and Keitt, 2001). These threshold
distances represent scales at which habitat fragments are minimally aggregated into subnets or network components (Brooks,
2006).
General network properties— Network theory has identified
several metrics that capture important properties of transformed
habitat (Fig. 1). The six properties shown in Fig. 1 are also emphasized in network analyses of ecological interaction networks. We briefly summarize here their implications for habitat
network connectivity and robustness.
Link richness—Link richness provides a rudimentary indication of network connectivity. In habitat networks, absolute
measures of link richness, such as the total number of links in
components (Marcot and Chinn, 1982; Pascual-Hortal and
Saura, 2006; Saura, 2008) or networks (Schick and Lindley,
2007; Treml et al., 2008; Saura and Torne, 2009), are common.
However, relative link-richness measures have also been reported
for habitat networks, most often in the form of the gamma index
(Forman, 1995; Ricotta et al., 2000; Jordán, 2001) and the average node degree (Bascompte et al., 2003). The number of links
in a component or network has been shown to respond to node
deletion (Kininmonth et al., 2010) and addition (Brooks et al.,
2008) sequences such that sparsely connected networks remain
when the weaker, longer-distance links are absent.
Disassortativity—Disassortative network structure results
from well-connected nodes (nodes with high degrees) joining
to a large number of less-well-connected nodes (nodes of low
degree). Nestedness refers to the presences of disassortativity in
bipartite networks (Sugihara and Ye, 2009). For example, the
potential habitat of the Mexican Spotted Owl was shown to display a “hub” and “spoke” topology that indicates spatial dissasortivity (Urban and Keitt, 2001).
Compartmentalization—Compartmentalization occurs when
connected subnetwork groupings (i.e., compartments or modules) that are internally well connected are only loosely connected to other subnetwork groupings. Resilience is enhanced
through compartmentalization because disturbances are confined within this network structure (Melián and Bascompte,
2002a). Minor and Urban (2008) specified that in their idealized reserve network highly connected nodes (“hubs”) should
be spatially separated to create compartmentalization that would
isolate disturbances but permit species movements across the
landscape.
Heterogeneous link strengths—The heterogeneity of link
strengths among habitat patches has been based on movement
costs of underlying land cover types (e.g., Halpin and Bunn,
2000; O’Brien et al., 2006), dispersal probability between two
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nodes (Brooks et al., 2008; Treml et al., 2008), and the presence
of multiple movement routes (McRae et al., 2008; Pinto and
Keitt, 2009). Network connectivity assessments will be sensitive to least-cost definitions of links, especially in highly fragmented landscapes (Rayfield et al., 2010).
Asymmetry—Habitat networks are highly asymmetric when
movement between patches is directional (e.g., Treml et al.,
2008) and focused into one or a few habitat fragments in the
landscape. Strongly asymmetric movement patterns may arise
because habitat fragments act as “ecological traps” (Schlaepfer
et al., 2002), arising because of a mismatch between preference
and habitat quality (Delibes et al., 2001; Robertson and Hutto,
2006) or because of a decision to select lower quality habitat
(Gilroy and Sutherland, 2007). This affect may drive source–
sink dynamics. In asymmetric landscapes, the persistence of
populations living in sink habitats will depend strongly on their
connectivity to populations living in source habitats (Urban et al.,
2009).
HOW DOES HABITAT LOSS PER SE CAUSE THE
DISASSEMBLY OF INTERACTION NETWORKS?
The effect of habitat area loss on ecological networks has
been studied using scaling theory that quantifies the relationships between habitat area, species diversity, and food webs/
interaction networks. We begin with an overview of how species and link richness scale with area as a first step toward understanding how ecological networks are affected by spatial
patterns.
Spatial scaling—That species diversity accumulates with habitat area is a central tenet of ecology. The most common published species–area relationship (SAR) is a power function,
S cA z ,
varied substantially between species. This interspecific variation
was explained by the observation that animal-dispersed tree species were less vulnerable than wind-dispersed species.
However, we are particularly concerned with how ecological
networks and their properties might change with reduced habitat area. Spatial scaling theory now makes predictions for how
trophic rank, food chain length (Schoener, 1989; Cohen and
Newman, 1991; Holt et al., 1999; Holt, 2002; Dobson et al.,
2006), and the number of trophic links (Brose et al., 2004) scale
with area. Because z increases with increasing trophic level, we
expect higher trophic levels to be more sensitive to habitat loss
than lower trophic levels (Holt et al., 1999; Cagnolo et al.,
2009). Communities should disassemble from the top-down
leading to a trophic flattening of the food web in the smallest
habitats (Post et al., 2000b). Extinction risk is correlated with
high trophic position, larger body size, and smaller local population densities (Gilbert et al., 1998; Petchey et al., 1999). Habitat
loss is known to truncate interaction networks through the loss of
top predators (Terborgh et al., 2001) and parasitoids (Komonen
et al., 2000), but the evidence remains equivocal (Mikkelson,
1993; Holyoak, 2000; Gonzalez and Chaneton, 2002; Rantalainen
et al., 2005).
A simple network property within local communities describes the power-law scaling of link richness, L, with species
richness, S, as
L bS u ,
where b and u are positive constants. Empirical studies find that
u varies between 1 and 2; u = 1 suggests consumers have a finite
number of resource species that is independent of consumer diversity, while u = 2 suggests that species are connected to a
fixed fraction of other species and that links scale with the
square of species richness. Brose et al. (2004) combined Eqs. 1
and 2 to give a simple model of the scaling of links with area:
(1)
where S is the number of species, A is area, c is a constant, and z
is the exponent describing the rate at which species accumulate
with area (recent study questions the generality of the power
SAR, Guilhaumon et al., 2008). The SAR approach has been
used to evaluate the effects of habitat fragmentation on biodiversity over and above the effects of habitat loss (Gonzalez, 2000;
Yaacobi et al., 2007). If the original habitat area, Ao, is reduced
to a smaller area, An, we do not simply expect the original number of species to decline to Sn, but rather to Sn estimated with a
new higher value of z. The method involves increasing the exponent, z, of the power-relation to account for the disproportionate
loss of species from small areas of habitat, whether because of
small population size, reduced immigration or intense edge
effects. Of course, the method is a bare bones approach and
ignores a great deal of detail. Recent improvements to the quantitative framework now allow consideration of species ranges
(Ney-Nifle and Mangel, 2000) and variable matrix affinity in
fragmented vegetation mosaics (Pereira and Daily, 2006). Empirical estimates of biodiversity change due to habitat loss have
been conducted in field studies and experiments (Spencer and
Warren, 1996; Post et al., 2000a; Steffan-Dewenter et al., 2002;
Steffan-Dewenter, 2003; Hoyle and Gilbert, 2004). For example,
Montoya (2008) found that decreased forest cover had a strong
negative effect on the occurrence of 34 common tree species.
The loss of forest cover reduced tree diversity, but the response
(2)
L bc u Auz ,
(3)
All else equal, larger areas should sustain communities with
more links, but that relationship holds independently of S. This
assumption holds for communities in which consumer and
resource species co-occur spatially; however, in metacommunities or fragmented landscapes, species may be spatially separated from potential consumers or resources and thus prevent
some links from occurring. Therefore, Eq. 3 cannot predict how
this network property will change during broad-scale habitat
loss. Brose et al. (2004) provide a unified spatial scaling relation
for L that does not assume consumer-resource co-occurrence,
L
K
θ (A)S u
ck cr
(4)
u 1n(Sk Sr ) 1n(S ) ,
where K is a constant, and the subscripts k and r account for
consumers and resources respectively. This relation allows for
different z for each trophic level, and the term θ(A) represents
the likelihood of the consumer occurring in patches with their
resources, compared with random patches. This scaling relation
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Gonzalez et al.—Biodiversity networks in fragmented habitats
is better adapted to explain changes in link richness in fragmented landscapes, but a formal theoretical analysis of this
problem has not been conducted to date.
Ultimately, because space and interactions are treated implicitly, this approach cannot address how species networks are
altered by habitat loss on spatial networks. Metacommunity
theory has taken the step to evaluate how interaction networks
disassemble under habitat loss for implicit space.
Metacommunity theory— Metapopulation theory and patchdynamic models were first introduced by Levins (1969) to explain the persistence of a single species in a spatially subdivided
habitat. Since then, they have been used extensively as a spatially implicit framework for studying the persistence of single
species populations (Hanski and Ovaskainen, 2002), and the
role of space in inducing coexistence among a large number of
competitors (Levins and Culver, 1971; Hastings, 1980; May
and Nowak, 1994). Despite the use of patch-dynamic models
for competitive interactions, their application to trophic interactions has been more limited (Zeigler, 1977; Crowley and
Murdoch, 1986).
The first applications of metacommunity theory to the problem of habitat loss includes May and Nowak’s (1994) study of
the effects of habitat loss on predator and prey persistence, and
Tilman et al.’s (1994) elucidation of the extinction debt. These
revealed that incorporating species interactions can alter our
understanding of the effects of habitat loss on species persistence. The latter study predicted plant species extinctions because the destruction of habitat lowered colonization rates in
fragments. Assuming that strong plant competitors are also
weak colonizers, this model predicted the biased extinction of
competitive dominants that can take many generations to occur.
Extending this patch-dynamic model to additional trophic levels exacerbates species loss and indicates that higher trophic
levels are more sensitive to habitat destruction (Wennergren
et al., 1995). Melián and Bascompte (2002b) extended this metacommunity model to include simple networks in which food
web modules (ranging from linear chains to apparent competition) composed of three trophic levels were studied. They found
that network structure simplified from the top down in response
to habitat destruction (see also Solé and Montoya, 2006), but
that the degree of destruction required to eliminate the top
trophic level varied between modules. Trophically mediated indirect interactions, in particular omnivory, conferred greater
persistence for the top trophic level.
In a recent development of the patch-dynamics framework,
Pillai et al. (2010) showed that the emergence of complex food
webs in metacommunities can be studied by tracking the changing patch occupancy of the various species interactions rather
than patch occupancy of individual species. This general framework allows the study of arbitrarily complex networks in metacommunities undergoing habitat destruction. This framework
can also incorporate mutualist interactions occurring in space
(Amarasekare, 2004). Fortuna and Bascompte (2006) applied a
network approach to much larger and more realistic mutualist
networks and showed that the topology of mutualistic networks
may make them robust to habitat loss. When compared to randomized null networks, realistic networks persisted at high levels of habitat destruction, even if they started to erode earlier
than null networks. Results from patch-dynamic metacommunity models thus confirm species-area scaling theory, but also
incorporate additional network complexity.
509
Metacommunity models of habitat loss have generally ignored explicit representation of space and hence spatial processes related to habitat arrangement. This is a significant gap,
although exceptions exist (e.g., Hassell et al., 1993), they have
focused on questions of population stability in simple host–
parasitoid networks occupying landscapes undergoing habitat
loss. So we are left acknowledging that much more theory is
needed to understand how complex interaction networks assemble and disassemble in spatially explicit landscapes. New
frameworks are now in place to study the spatial emergence of
interaction network complexity (Lafferty and Dunne, 2010; Pillai
et al., 2010).
HOW DOES HABITAT FRAGMENTATION PER SE ALTER
INTERACTION NETWORKS?
The habitat area–scaling relation suggests link richness, a
simple network measure, will change in landscapes as habitat
area decreases. Here we summarize what is known about how
link richness and other more complex interaction network structures are expected to change in fragmented landscapes. We restrict our attention to the structural properties shown in Fig. 1
and compare trophic and mutualist networks where possible. In
general, we find that relatively little is known about how habitat
fragmentation affects these aspects of network structure; much
more research is needed before general patterns become apparent and a general theory can be tested.
Link richness— The number of interactions in a food web is
known to be affected by variation in the physical environment
(Arim and Jaksic, 2005), but evidence for link richness responding to habitat fragmentation comes from pollinator networks.
Sabatino et al. (2010) found direct effects of sierra habitat area
on link richness in pollination networks persisting in transformed agricultural landscapes of the Argentine Pampas. They
found that link richness increased 2-fold faster than species
richness with area in pollination networks. Habitat proximity,
an inverse measure of isolation, had a marginally significant influence on link but not on species richness.
Disassortativity/nestedness— Two definitions of nestedness
are used in the community ecology literature: (1) community
nestedness and (2) interaction nestedness (i.e., disassortativity
in bipartite networks). Community nestedness is the degree to
which a set of communities forms different-sized subsets from
the same ordered composition of species (Atmar and Patterson,
1993). Strong nestedness has been associated with communities
ordered by extinction on land-bridge islands and habitat fragments of differing size (Wright et al., 2007), but this research
has not examined interaction nestedness. The expectation that
nestedness increases, and only increases, in communities undergoing extinction, arises from the hypothesis that species have
different and predictable extinction probabilities. This hypothesis extends to changes in nestedness in interaction networks.
Interaction networks are nested when species interacting with
specialists are a proper subset of species interacting with generalists (Tylianakis, 2009). Specialist species, that interact with
few other species, can be more sensitive to habitat fragmentation than generalists (Henle et al., 2004), and theory has shown
that generalist mutualists are the least likely to suffer extinction
in network simulations of habitat loss (Fortuna and Bascompte,
2006).
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American Journal of Botany
Comparmentalization/modularity— Compartmentalization
has long been recognized as an important stabilizing property in
food webs (Pimm and Lawton, 1980). Just as in spatial networks, compartmentalization in interaction networks occurs
when internally highly connected subnetworks (i.e., compartments or modules) of species are poorly connected by none,
one, or a few connector species to other subnetworks. Recent
studies have detected compartments in food webs (Krause et al.,
2003) and pollinator networks (Olesen et al., 2007). Bellisario
et al. (2010) found three distinct compartments in detritus-based
macroinvertebrate communities inhabiting a fragmented network of salt ponds. Community compartments were associated
with ponds in closer spatial proximity. In this case, compartmentalization was due to species sorting among the salt ponds
at different points along a salinity gradient. Habitat fragmentation can also increase compartmentalization by causing the extinction of top predators, an event that may disconnect spatially
segregated interaction networks. However, no evidence for this
was found within host–parasitoid webs in human-modified
landscapes (Tylianakis et al., 2007). Conversely, the invasion of
hypergeneralist species may connect distinct compartments and
reduce compartmentalization in fragmented landscapes (Aizen
et al., 2008). Surprisingly few studies have addressed the effects
of compartmentalization induced by habitat fragmentation, although as we discuss below, the implications for the stability of
networks dynamics are being studied (McCann et al., 2005).
Interaction strength— Ecological networks may show considerable variance in the distribution of link interaction strengths
(i.e., the effect of one species on the population growth rate of
another). Does habitat fragmentation cause the differential extinction of species connected by strong interaction strengths?
Does differential extinction of strong interactors reduce the average network interaction strength? These important questions
are also not well studied despite a strong appeal by conservation science to protect highly interactive species (Soulé et al.,
2003, 2005). Duffy (2003) concluded that biodiversity loss is
biased toward strong interactors among animals but weak interactors among plants. Consistent with this, an extensive literature review by Tylianakis et al. (2008) suggests that interaction
strengths in mutualist networks are reduced by environmental
change, including habitat loss. In trophic networks, some evidence exists for increased variability in species abundances
near fragment edges (Ewers and Didham, 2006). This variability may arise because interaction strengths are increased and/or
because species interactions are exposed to more variable abiotic conditions (edge effects). Experiments directly measuring
interaction strength in fragmented networks are now needed.
Asymmetry— Symmetry in mutualist plant–animal networks
occurs when specialist plants are pollinated by a few specialist
pollinators and generalist plants are pollinated by different generalist pollinators. This symmetry does not appear to be the rule
(Bascompte et al., 2003; Vázquez and Aizen, 2003); for example, specialist plants may interact with a several generalist pollinators (Vázquez and Simberloff, 2003). This asymmetry is
essential to understand if we are to predict plant vulnerability to
habitat fragmentation. Ashworth et al. (2004) argued that network asymmetry may explain why specialist and generalist
plant species appear to respond similarly to habitat loss. They
suggested that the biased loss of specialist pollinators in fragmented landscapes leaves both specialist and generalist plants
with a similar reliance on the remaining generalist pollinators—
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the decline in the remaining generalist pollinators would cause
similar declines in specialist and generalist plants. This perspective requires a network approach to the study of biodiversity loss in fragmented landscapes.
HOW DO INTERACTION NETWORK DYNAMICS
DEPEND UPON HABITAT NETWORK STRUCTURE?
The effects of habitat loss on the dynamics of interaction
webs have predominantly focused on colonization–extinction
stability (see section How does habitat loss per se cause the
disassembly of interaction networks?). This approach has revealed interaction network fragility to habitat loss and predicted
the existence of delayed extinction dynamics (e.g., extinction
debts), but patch-dynamic models ignore population dynamics,
and additional insights are gained by studying the stability of
populations embedded in interaction networks undergoing extinction (Petchey et al., 2008).
The effects of habitat fragmentation are destabilizing for
populations embedded in interaction networks (Hassell et al.,
1993; McCann et al., 2005). These destabilizing effects are
manifested as increased population variability, longer return
times following disturbance, and extinction cascades that take
many generations to unfold. However, spatial subdivision can stabilize highly unstable consumer–resource interactions, by introducing spatial refuges and rescue effects (Briggs and Hoopes,
2004). The effect of fragmentation per se can thus be both stabilizing and destabilizing. Rather than review this large literature here (Holt and Hoopes, 2005), we focus on how habitat
loss destabilizes interaction networks by reducing the capacity
of the landscape to sustain the interaction network.
Spatial compression and food web instability— Mobile consumers at high trophic levels can couple distinct interaction networks in connected habitats and stabilize food web dynamics
by dampening bottom up variability in resource dynamics
(McCann et al., 2005; Gouhier et al., 2010). These mobile consumers persist and are themselves stabilized by averaging over
asynchronous resource dynamics. A possible outcome of habitat
loss is the confinement of consumer–resource networks within
fragments. If habitat fragments become isolated, then the
spatially extended feeding interactions of high trophic level consumers become compressed into a reduced area (McCann et al.,
2005). Spatial compression generates stronger top-down trophic
cascades and can cause high amplitude consumer-resource oscillations and instability. In a tritrophic network, the destabilized
predator may become omnivorous by feeding on both intermediate and basal resource species (as depicted in Fig. 3). This
behavioral switch to omnivory in an isolated habitat is predicted
to be stabilizing, although the amount of omnivory required for
stability may increase with increasing spatial compression
(McCann et al., 2005). These effects point to the importance of
behavioral flexibility (Abrams, 2007) as a means of dynamically
altering the topology of the food web for persistence in fragmented landscapes.
Habitat loss triggers instability of plant–pollinator networks— The progressive loss of habitat is known to reduce the
capacity of spatial habitat networks to sustain metapopulations
(Hanski and Ovaskainen, 2002). In some cases, clear thresholds
of metapopulation persistence may be crossed when a critical
fraction of habitat is removed. Keitt (2009) demonstrated the
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Gonzalez et al.—Biodiversity networks in fragmented habitats
existence of a critical habitat threshold for mutualist interaction
networks between plants and pollinators. Mutualist interactions
are disproportionately sensitive to habitat destruction because
of inherent positive feedbacks in the form of Allee effects (see
also Amarasekare 2004). Thus, scenarios involving 50–60%
habitat destruction rendered the landscape incapable of sustaining the mutualist network. Spatially explicit analysis that allowed for localized interactions and movement, revealed cases
of extremely slow collapse of the network; many hundreds of
seasons were required to detect discernable decline even though
the network was globally unstable and committed to extinction.
Different patterns of habitat fragmentation (from regular to random and autocorrelated) altered persistence thresholds, with
autocorrelated habitat structure favoring persistence and pollination services compared to landscapes with uncorrelated habitat distribution.
WHAT ARE THE CONSERVATION IMPLICATIONS OF A
NETWORK APPROACH?
The interaction between habitat transformation and climate
change is expected to increase the loss rate of plant (Giam et al.,
2010) and animal (Jetz et al., 2007) biodiversity. Maintaining
or restoring biodiversity in the face of these changes necessitates landscape-level conservation strategies that create networks of protected areas while managing the landscape matrix
surrounding those reserves (Brudvig et al., 2009). A network
approach is well suited to evaluate, model, and propose conservation paradigms. One of the founding paradigms in conservation biology (island biogeography theory [IBT]; MacArthur
and Wilson, 1967) was that local species richness on islands (or
habitat patches) is held in equilibrium not only by the resources
present but also by a balance between immigration and emigration (or local extinctions). While network approaches can be
applied to island models of movement among discrete, static
patches of habitat, they can further incorporate new conservation paradigms pertaining to heterogeneous landscapes comprised of dynamic habitat patches embedded within a mosaic of
other dynamic land cover types of varying qualities. Ultimately,
these network approaches will produce future conservation
paradigms about the relationship between the dynamic structural properties of networks and their functioning in terms of
biodiversity maintenance and ecosystem services (May, 2006;
Newman et al., 2006).
The integration of current network approaches in landscape
ecology and community ecology promises to provide a muchneeded acceleration in both our understanding of biodiversity
loss and our ability to mitigate it. This integration will be mutually beneficial and will strengthen the arguments reviewed in
this paper for the conservation of both spatial and interaction
network structures (Rayfield et al., 2009; Tylianakis et al.,
2010). The network tools to needed to manage and engineer
landscapes for habitat connectivity are developing (McRae
et al., 2008; Shah and McRae, 2008), but they do not incorporate
species interaction networks. A recent application of network
approaches to conservation is the design of ecosystem networks
for sustainable landscapes (Opdam et al., 2006; Vos et al.,
2008). Ecosystem networks are composed of mixed ecosystem
types (e.g., forest, wetland, grassland etc.) linked into a spatially coherent network through the movement of organisms
and resources through the landscape matrix within which it is
embedded (Opdam et al., 2006). Ecosystem networks move be-
511
yond an island paradigm and combine elements of metacommunity/metaecosystem theory (Holyoak et al., 2005) with the
patch-corridor and matrix landscape model of landscape ecology (Forman, 1995). It explicitly acknowledges the value of a
network perspective for the management of highly fragmented
landscapes for biodiversity and associated ecosystem services.
Examples might include the management of a network of forest
patches to sustain pollinator networks for pollination services
and complex communities of natural enemies for pest control in
agricultural landscapes. The empirical basis for ecosystem networks is founded upon a growing body of experimental research
(Tewksbury et al., 2002; Brudvig et al., 2009; Staddon et al.,
2010; Chisholm et al., in press), but to our knowledge no network has been designed and implemented with a network of
species interactions in mind (network of networks, see below).
Finally, the creation of ecosystem networks that are robust to
climate change (Opdam and Wascher, 2004; Vos et al., 2008)
will require dynamic network models that incorporate dynamic
habitat and dynamic connectivity to account for the change in
the species distribution, abundance, phenology, and interactions (e.g., Memmot et al., 2007) as the climate shifts in space
and time (Vos et al., 2008; Williams et al., 2008).
FUTURE DIRECTIONS
We argued that improvement in our understanding of the effects of habitat loss and fragmentation on biodiversity will be
gained if we combine the insights made using the networkbased approaches of landscape ecology (e.g., Urban et al.,
2009), with those used in community ecology to capture the
complex characteristics of interaction networks (Bascompte,
2009). In both fields, emphasis is placed on how the structure
and strength of the links in a network affects its dynamics and
functional properties. But only recently has the mutual influence between spatial and interaction networks been studied.
The network properties identified and emphasized across ecological networks (Fig. 1) represent key structures hypothesized
to influence network dynamics. Empirical validation of these
hypotheses is required prior to usage in applications and to further develop a comprehensive theory of ecological networks.
The key insight of this perspective is that there is continuous
feedback between the structure and dynamics of the habitat network and the interaction networks occupying the habitat network (Fig. 3). For example, the exclusion of an apex predator
(e.g., wolves) from an isolated compartment of the habitat network may release control over the herbivore (e.g., white tailed
deer), initiating a trophic cascade with marked effects on lower
trophic levels (vegetation biomass, species floral and faunal
composition) and ecosystem services such as soil stability
(Rooney and Waller, 2003; Ripple et al., 2009). This mutual
influence between spatial and interaction networks is not frequently studied and is poorly understood, but its study is required if we are to clearly perceive the effects of habitat
fragmentation on biodiversity.
At this time, most studies have focused on the bottom-up effects of changing habitat network structure (via area or connectivity effects) on the interaction network, while the feedback
involving how changes in the interaction network affect the
spatial network has remained almost unexplored. Figure 3 suggests that biodiversity change in fragmented landscapes is best
understood as a dynamic feedback between the topology and
dynamics of habitat networks and interaction networks. We
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American Journal of Botany
Fig. 3. Visual representation of a spatial metacommunity network. A six-species interaction network (three resources, two consumers, and one predator) is spatially structured across a landscape with 10 habitat patches that are of low (white), medium (gray), or high (black) quality. Links in the habitat
network represent potential movements for all species and links in the interaction networks represent feeding interactions. Shades of species’ nodes indicate
the percentage of time spent in each patch, and the relative sizes of nodes indicate relative abundance. (A) The habitat network undergoes topological
changes based on landscape modifications, and these spatial changes in turn constrain the topology of the global interaction network distributed across
patches. Note that the illustration is for a subset of the global interaction network. Changes in the global interaction network topology influence community
dynamics within habitat patches. (B) Community dynamics within a given habitat node influence the topology of the local interaction network here. Two
possible outcomes of community dynamics are illustrated: omnivory and trophic cascades. Changes in the local interaction networks will in turn redefine
the spatial habitat network by altering species’ movement patterns, creating a positive feedback loop (Gross and Blasius, 2008).
know of no theoretical or empirical study that has studied repeated cycles around this diagram. Habitat-disturbance–succession metacommunity models (e.g., Guichard, 2005) should be
easily applied to study habitat loss. These models have contributed to our understanding of self-organized spatial patterns of
species distribution and abundance from local to regional scales
(Pascual and Guichard, 2005). Just as in Fig. 3, these models
emphasize the reciprocal interaction between pattern and process: spatial patterns of abundance feedback to influence local
dynamics, which in turn influence habitat pattern.
Conclusion—Two largely separate bodies of literature are
currently based on the same network methods to study the impacts of habitat change on biodiversity loss. The results of this
network approach clearly show that the structure and function of
habitat networks and ecological interaction networks are impacted by habitat loss. Common network measures have revealed
important differences in the topological robustness of antagonistic and mutualistic networks in response to habitat loss. Metacommunity models have been used to study network dynamics and
in particular the disassembly and stability of interaction networks
that results from habitat destruction. In general, the disassembly
of ecological networks is not equivalent to their assembly, and
more research is needed to understand this asymmetry (Bascompte and Stouffer, 2009). The convergence of concepts and
the application of common metrics and models identified in this
paper suggests that a synthesis based on the study of “networks
of networks” will be achieved and will likely bring massive gains
in our understanding of the complex process of biodiversity loss
in human-transformed landscapes and a concomitant improvement in our capacity to mitigate the impacts of habitat loss over
the coming century.
LITERATURE CITED
Abrams, P. A. 2007. Habitat choice in predator–prey systems: Spatial instability due to interacting adaptive movements. American Naturalist
169: 581–594.
Aizen, M. A., C. L. Morales, and J. M. Morales. 2008. Invasive mutualists erode native pollination webs. PLoS Biology 6: e31.
Allesina, S., A. Bodini, and M. Pascual. 2009. Functional links and robustness in food webs. Philosophical Transactions of the Royal Society,
B, Biological Sciences 364: 1701–1709.
March 2011]
Gonzalez et al.—Biodiversity networks in fragmented habitats
Amarasekare, P. 2004. Spatial dynamics of mutualistic interactions.
Journal of Animal Ecology 73: 128–142.
Amarasekare, P. 2008. Spatial dynamics of food webs. Annual Review of
Ecology, Evolution, and Systematics 39: 479–500.
Andersson, E., and O. Bodin. 2009. Practical tool for landscape planning? An empirical investigation of network based models of habitat
fragmentation. Ecography 32: 123–132.
Andrén, H. 1994. Effects of habitat fragmentation on birds and mammals
in landscapes with different proportions of suitable habitat: A review.
Oikos 71: 355–366.
Arim, M., and F. M. Jaksic. 2005. Productivity and food web structure:
Association between productivity and link richness among top predators. Journal of Animal Ecology 74: 31–40.
Ashworth, L., R. Aguilar, L. Galetto, and M. A. Aizen. 2004. Why
do pollination generalist and specialist plant species show similar reproductive susceptibility to habitat fragmentation? Journal of Ecology
92: 717–719.
Atmar, W., and B. Patterson. 1993. The measure of order and disorder in the distribution of species in fragmented habitat. Oecologia 96:
373–382.
Bascompte, J. 2007. Networks in ecology. Basic and Applied Ecology 8:
485–490.
Bascompte, J. 2009. Disentangling the web of life. Science 325: 416–419.
Bascompte, J., P. Jordano, C. J. Melián, and J. M. Olesen. 2003. The
nested assembly of plant–animal mutualistic networks. Proceedings
of the National Academy of Sciences, USA 100: 9383–9387.
Bascompte, J., P. Jordano, and J. M. Olesen. 2006. Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science
312: 431–433.
Bascompte, J., and R. V. Solé. 1996. Habitat fragmentation and extinction thresholds in spatially explicit models. Journal of Animal
Ecology 65: 465–473.
Bascompte, J., and D. B. Stouffer. 2009. The assembly and disassembly of ecological networks. Philosophical Transactions of the Royal
Society, B, Biological Sciences 364: 1781–1787.
Bell, J. R., R. A. King, D. A. Bohan, and W. O. C. Symondson.
2010. Spatial co-occurrence networks predict the feeding histories
of polyphagous arthropod predators at field scales. Ecography 33:
64–72.
Bellisario, B., F. Cerfolli, and G. Nascetti. 2010. Spatial network
structure and robustness of detritus-based communities in a patchy
environment. Ecological Research 25: 813–821.
Bodin, O., M. Tengo, A. Norman, J. Lundberg, and T. Elmqvist.
2006. The value of small size: Loss of forest patches and ecological thresholds in southern Madagascar. Ecological Applications 16:
440–451.
Briggs, C. J., and M. F. Hoopes. 2004. Stabilizing effects in spatial
parasitoid-host and predator–prey models: A review. Theoretical
Population Biology 65: 299–315.
Brooks, C. P. 2006. Quantifying population substructure: Extending the
graph-theoretic approach. Ecology 87: 864–872.
Brooks, C. P., A. Antonovics, and T. Keitt. 2008. Spatial and temporal heterogeneity explain disease dynamics in a spatially explicit
network model. American Naturalist 172: 149–159.
Brose, U., A. Ostling, K. Harrison, and N. D. Martinez. 2004.
Unified spatial scaling of species and their trophic interactions. Nature
428: 167–171.
Brudvig, L. A., E. I. Damschen, J. J. Tewksbury, N. M. Haddad, and
D. J. Levey. 2009. Landscape connectivity promotes plant biodiversity spillover into non-target habitats. Proceedings of the National
Academy of Sciences, USA 106: 9328–9332.
Bunn, A. G., D. L. Urban, and T. H. Keitt. 2000. Landscape connectivity: A conservation application of graph theory. Journal of
Environmental Management 59: 265–278.
Butchart, S. H. M., M. Walpole, B. Collen, A. van Strien, J. P. W.
Scharlemann, R. E. A. Almond, J. E. M. Baillie, et al. 2010. Global
biodiversity: Indicators of recent declines. Science 328: 1164–1168.
Cagnolo, L., G. Valladares, A. Salvo, M. Cabido, and M. Zak.
2009. Habitat fragmentation and species loss across three inter-
513
acting trophic levels: Effects of life-history and food-web traits.
Conservation Biology 23: 1167–1175.
Calabrese, J. M., and W. F. Fagan. 2004. A comparison-shopper’s guide to connectivity metrics. Frontiers in Ecology and the
Environment 2: 529–536.
Campbell, C., S. Yang, R. Albert, and K. Shea. 2011. A network
model for plant–pollinator community assembly. Proceedings of the
National Academy of Sciences, USA 108: 197–202.
Chisholm, C., Z. Lindo, and A. Gonzalez. In press. Metacommunity
diversity depends on network connectivity and arrangement in heterogeneous landscapes. Ecography [DOI: 10.1111/j.1600-0587.
2010.06588.x].
Cohen, J. E., and C. M. Newman. 1991. Community area and food-chain
length: Theoretical predictions. American Naturalist 138: 1542–1554.
Crooks, K. R., and M. E. Soule. 1999. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400: 563–566.
Crowley, P. L., and W. W. Murdoch. 1986. Predator-mediated coexistence: An equilibrium interpretation. Journal of Theoretical Biology
80: 129–144.
Cumming, G. S., O. Bodin, H. Ernstson, and T. Elmqvist. 2010.
Network analysis in conservation biogeography: Challenges and opportunities. Diversity & Distributions 16: 414–425.
Dale, M. R. T., and M.-J. Fortin. 2010. From graphs to spatial graphs.
Annual Review of Ecology Evolution and Systematics 41: 21–38.
Darling, E. S., and I. M. Côté. 2008. Quantifying the evidence for
ecological synergies. Ecology Letters 11: 1278–1286.
Debinski, D. M., and R. D. Holt. 2000. A survey and overview of habitat fragmentation experiments. Conservation Biology 14: 342–355.
Delibes, M., P. Gaona, and P. Ferreras. 2001. Effects of an attractive
sink leading into maladaptive habitat selection. American Naturalist
158: 277–285.
Dirzo, R., and P. H. Raven. 2003. Global state of biodiversity and loss.
Annual Review of Environment and Resources 28: 137–167.
Dobson, A., D. Lodge, J. Alder, G. S. Cumming, J. Keymer, J.
McGlade, H. Mooney, et al. 2006. Habitat loss, trophic collapse,
and the decline of ecosystem services. Ecology 87: 1915–1924.
Duffy, J. E. 2003. Biodiversity loss, trophic skew and ecosystem functioning. Ecology Letters 6: 680–687.
Dyer, L. A., T. R. Walla, H. F. Greeney, J. O. Stireman III, and
R. F. Hazen. 2010. Diversity of interactions: A metric for studies of
biodiversity. Biotropica 42: 281–289.
Etter, A., C. McAlpine, S. Phinn, D. Pullar, and H. Possingham.
2006. Characterizing a tropical deforestation wave: A dynamic spatial analysis of a deforestation hotspot in the Colombian Amazon.
Global Change Biology 12: 1409–1420.
Ewers, R. M., and R. K. Didham. 2006. Continuous response functions for quantifying the strength of edge effects. Journal of Applied
Ecology 43: 527–536.
Fagan, W. F., and J. M. Calabrese. 2006. Quantifying connectivity: Balancing metric performance with data requirements. In K. R.
Crooks and M. Sanjayan [eds.], Connectivity conservation, 297–317.
Cambridge University Press, Cambridge, UK.
Fahrig, L. 2003. Effects of habitat fragmentation on biodiversity. Annual
Review of Ecology Evolution and Systematics 34: 487–515.
Fall, A., M.-J. Fortin, M. Manseau, and D. O’Brien. 2007. Spatial
graphs: Principles and applications for habitat connectivity. Ecosystems
10: 448–461.
Forman, R. T. T. 1995. Land mosaics: The ecology of landscapes and
regions. Cambridge University Press, Cambridge, UK.
Fortuna, M. A., and J. Bascompte. 2006. Habitat loss and the
structure of plant–animal mutualistic networks. Ecology Letters 9:
281–286.
Franklin, A. B., B. R. Noon, and T. L. George. 2002. What is habitat
fragmentation? Studies in Avian Biology 25: 20–29.
Giam, X., C. J. A. Bradshaw, H. T. W. Tan, and N. S. Sodhi. 2010.
Future habitat loss and the conservation of plant biodiversity. Biological
Conservation 143: 1594–1602.
Gilbert, F., A. Gonzalez, and I. Evans-Freke. 1998. Corridors maintain species richness in the fragmented landscapes of a microecosystem.
514
American Journal of Botany
Proceedings of the Royal Society of London, B, Biological Sciences
265: 577–582.
Gilroy, J. J., and W. J. Sutherland. 2007. Beyond ecological traps:
Perceptual errors and undervalued resources. Trends in Ecology &
Evolution 22: 351–356.
Gonzalez, A. 2000. Community relaxation in fragmented landscapes:
The relation between species richness, area and age. Ecology Letters
3: 441–448.
Gonzalez, A., and E. J. Chaneton. 2002. Heterotroph species
extinction, abundance and biomass dynamics in an experimentally fragmented microecosystem. Journal of Animal Ecology 71:
594–602.
Gouhier, T. C., F. Guichard, and A. Gonzalez. 2010. Synchrony and
stability of food webs in metacommunities. American Naturalist 175:
E16–E34.
Gross, T., and B. Blasius. 2008. Adaptive coevolutionary networks: A
review. Journal of the Royal Society, Interface 5: 259–271.
Groves, C. R., D. B. Jensen, L. L. Valutis, K. H. Redford, M. L.
Shaffer, J. M. Scott, J. V. Baumgartner, et al. 2002. Planning
for biodiversity conservation: Putting conservation science into practice. BioScience 52: 499–512.
Guichard, F. 2005. Interaction strength and extinction risk in a metacommunity. Proceedings of the Royal Society of London, B, Biological
Sciences 272: 1571–1576.
Guilhaumon, F., O. Gimenez, K. J. Gaston, and D. Mouillot. 2008.
Taxonomic and regional uncertainty in species–area relationships and
the identification of richness hotspots. Proceedings of the National
Academy of Sciences, USA 105: 15458–15463.
Halpin, P. N., and A. G. Bunn. 2000. Using GIS to compute a least-cost
distance matrix: A comparison of terrestrial and marine ecological applications. In Proceedings of the 20th annual ESRI User Conference,
1–19, 2000, San Diego, California, USA. Esri, Redlands, California,
USA
Hanski, I., and O. Ovaskainen. 2002. Extinction debt at extinction
threshold. Conservation Biology 16: 666–673.
Hassell, M. P., H. N. C. Godfray, and H. N. Coinins. 1993. Effects of
global change on the dynamics of insect host–parasitoid interactions.
In P. Kareiva and J. Kingsolver [eds.], Biotic interactions and global
change, 402–423. Sinauer, Sunderland, Massachusetts, USA.
Hastings, A. 1980. Disturbance, coexistence, history, and competition
for space. Theoretical Population Biology 18: 363–373.
Helm, A., I. Hanski, and M. Pärtel. 2006. Slow response of plant
species richness to habitat loss and fragmentation. Ecology Letters
9: 72–77.
Henle, K., K. F. Davies, M. Kleyer, C. Margules, and J. Settele.
2004. Predictors of species sensitivity to fragmentation. Biodiversity
and Conservation 13: 207–251.
Holt, R. D. 2002. Food webs in space: On the interplay of dynamic instability and spatial processes. Ecological Research 17: 261–273.
Holt, R. D., and M. F. Hoopes. 2005. Food web dynamics in a metacommunity context: Modules and beyond. In M. Holyoak and M. A. Leibold
[eds.], Metacommunities: Spatial dynamics and ecological communities, 68-93. University of Chicago Press, Chicago, Illinois, USA.
Holt, R. D., J. H. Lawton, G. A. Polis, and N. D. Martinez.
1999. Trophic rank and the species–area relationship. Ecology 80:
1495–1504.
Holyoak, M. 2000. Habitat patch arrangement and metapopulation persistence of predators and prey. American Naturalist 156:
378–389.
Holyoak, M., M. A. Leibold, and R. D. Holt. 2005. Metacommunities:
Spatial dynamics and ecological communities. University of Chicago
Press, Chicago, Illinois, USA.
Hoyle, M., and F. Gilbert. 2004. Species richness of moss landscapes
unaffected by short-term fragmentation. Oikos 105: 359–367.
Huxel, G. R., and A. Hastings. 1999. Habitat loss, fragmentation, and
restoration. Restoration Ecology 7: 309–315.
Ings, T. C., J. M. Montoya, J. Bascompte, N. Bluthgen, L. Brown,
C. F. Dormann, F. Edwards, et al. 2009. Ecological networks—
Beyond food webs. Journal of Animal Ecology 78: 253–269.
[Vol. 98
Jetz, W., D. S. Wilcove, and A. P. Dobson. 2007. Predicted impacts
of climate change and land-use change on the global biodiversity of
birds. PLoS Biology 5: e157.
Jordán, F. 2001. Adding function to structure—Comments on Palmarola
landscape connectivity. Community Ecology 2: 133–135.
Keitt, T. H. 2009. Habitat conversion, extinction thresholds, and pollination
services in agroecosystems. Ecological Applications 19: 1561–1573.
Keitt, T. H., D. L. Urban, and B. T. Milne. 1997. Detecting critical scales in fragmented landscapes [online]. Ecology and Society 1:
http://www.consecol.org/vol1/iss1/art4/.
Kininmonth, S. J., G. De’ath, and H. P. Possingham. 2010. Graph theoretic topology of the Great but small Barrier Reef world. Theoretical
Ecology 3: 75–88.
Komonen, A., R. Penttilä, M. Lindgren, and I. Hanski. 2000. Forest
fragmentation truncates a food chain based on an old-growth forest
bracket fungus. Oikos 90: 119–126.
Krause, A. E., K. A. Frank, D. M. Mason, R. E. Ulanowicz, and
W. W. Taylor. 2003. Compartments revealed in food-web structure. Nature 426: 282–285.
Krishna, A., P. R. Guimaraes, P. Jordano, and J. Bascompte. 2008.
A neutral-niche theory of nestedness in mutualistic networks. Oikos
117: 1609–1618.
Lafferty, K. D., and J. A. Dunne. 2010. Stochastic ecological network
occupancy (SENO) models: A new tool for modeling ecological networks across spatial scales. Theoretical Ecology 3: 123–135.
Lawton, J. H., and R. M. May. 1995. Extinction rates. Oxford University
Press, Oxford, UK.
Levins, R. 1969. Some demographic and genetic consequences of environmental heterogeneity for biological control. Bulletin of the
Entomological Society of America 15: 237–240.
Levins, R., and D. Culver. 1971. Regional coexistence of species
and competition between rare species. Proceedings of the National
Academy of Sciences, USA 68: 1246–1248.
Lookingbill, T. R., R. H. Gardner, J. R. Ferrari, and C. E. Keller.
2010. Combining a dispersal model with network theory to assess
habitat connectivity. Ecological Applications 20: 427–441.
MacArthur, R. H., and E. O. Wilson. 1967. The theory of island
biogeography. Princeton University Press, Princeton, New Jersey,
USA.
Marcot, B. G., and P. Z. Chinn. 1982. Use of graph theory measures
for assessing diversity of wildlife habitat. In R. Lamberson [ed.],
Mathematical models of renewable resources, Proceedings of the First
Pacific Coast Conference on Mathematical Models of Renewable
Resources. Humboldt State University, Arcata, California, USA.
May, R. M. 2006. Network structure and the biology of populations.
Trends in Ecology & Evolution 21: 394–399.
May, R. M., and M. A. Nowak. 1994. Superinfection, metapopulation dynamics and the evolution of diversity. Journal of Theoretical
Biology 170: 95–114.
McCann, K. S. 2007. Protecting biostructure. Nature 446: 29.
McCann, K. S., J. B. Rasmussen, and J. Umbanhowar. 2005. The dynamics of spatially coupled food webs. Ecology Letters 8: 513–523.
McRae, B. H., B. G. Dickson, T. H. Keitt, and V. B. Shah. 2008.
Using circuit theory to model connectivity in ecology, evolution, and
conservation. Ecology 89: 2712–2724.
Melián, C. J., and J. Bascompte. 2002a. Complex networks: Two ways
to be robust? Ecology Letters 5: 705–708.
Melián, C. J., and J. Bascompte. 2002b. Food web structure and habitat
loss. Ecology Letters 5: 37–46.
Memmott, J., P. G. Craze, N. M. Waser, and M. V. Price. 2007.
Global warming and the disruption of plant–pollinator interactions.
Ecology Letters 10: 710–717.
Mikkelson, G. M. 1993. How do food webs fall apart—A study of
changes in trophic structure during relaxation on habitat fragments.
Oikos 67: 539–547.
Minor, E. S., S. M. Tessel, K. A. M. Engelhardt, and T. R.
Lookingbill. 2009. The role of landscape connectivity in assembling exotic plant communities: A network analysis. Ecology 90:
1802–1809.
March 2011]
Gonzalez et al.—Biodiversity networks in fragmented habitats
Minor, E. S., and D. L. Urban. 2008. A graph-theory frarmework
for evaluating landscape connectivity and conservation planning.
Conservation Biology 22: 297–307.
Montoya, D. 2008. Habitat loss, dispersal, and the probability of extinction of tree species. Communicative & Integrative Biology 1: 146–147.
Morris, R. J. 2010. Anthropogenic impacts on tropical forest biodiversity: A network structure and ecosystem functioning perspective. Philosophical Transactions of the Royal Society, B, Biological
Sciences 365: 3709–3718.
Naeem, S., D. E. Bunker, A. Hector, M. Loreau, and C. Perrings. 2009.
Biodiversity, ecosystem functioning, and human wellbeing: An ecological and economic perspective. Oxford University Press, Oxford, UK.
Newman, M., A.-L. Barabasi, and D. Watts. 2006. The structure and
dynamics of networks. Princeton University Press, Princeton, New
Jersey, USA.
Ney-Nifle, M., and M. Mangel. 2000. Habitat loss and changes in the
species–area relationship. Conservation Biology 14: 893–898.
O’Brien, D., M. Manseau, A. Fall, and M.-J. Fortin. 2006. Testing
the importance of spatial configuration of winter habitat for woodland caribou: An application of graph theory. Biological Conservation
130: 70–83.
Olesen, J. M., J. Bascompte, Y. L. Dupont, and P. Jordano. 2007.
The modularity of pollination networks. Proceedings of the National
Academy of Sciences, USA 104: 19891–19896.
Olff, H., and M. E. Ritchie. 2002. Fragmented nature: Consequences
for biodiversity. Landscape and Urban Planning 58: 83–92.
Opdam, P., E. Steingröver, and S. van Rooij. 2006. Ecological networks: A spatial concept for multi-actor planning of sustainable landscapes. Landscape and Urban Planning 75: 322–332.
Opdam, P., and D. Wascher. 2004. Climate change meets habitat fragmentation: Linking landscape and biogeographical scale level in research and conservation. Biological Conservation 117: 285–297.
Pascual, M., and F. Guichard. 2005. Criticality and disturbance in spatial ecological systems. Trends in Ecology & Evolution 20: 88–95.
Pascual-Hortal, L., and S. Saura. 2006. Comparison and development of new graph-based landscape connectivity indices: Towards
the priorization of habitat patches and corridors for conservation.
Landscape Ecology 21: 959–967.
Pereira, H. M., and G. C. Daily. 2006. Modeling biodiversity dynamics in countryside landscapes. Ecology 87: 1877–1885.
Petanidou, T., A. S. Kallimanis, J. Tzanopoulos, S. P. Sgardelis,
and J. D. Pantis. 2008. Long-term observation of a pollination
network: Fluctuation in species and interactions, relative invariance
of network structure and implications for estimates of specialization.
Ecology Letters 11: 564–575.
Petchey, O. L., A. P. Beckerman, J. O. Riede, and P. H. Warren.
2008. Size, foraging, and food web structure. Proceedings of the
National Academy of Sciences, USA 105: 4191–4196.
Petchey, O. L., P. T. McPhearson, T. M. Casey, and P. J. Morin.
1999. Environmental warming alters food-web structure and ecosystem function. Nature 402: 69–72.
Pillai, P., M. Loreau, and A. Gonzalez. 2010. A patch-dynamic
framework for food web metacommunities. Theoretical Ecology 3:
223–237.
Pimm, S. L., and J. H. Lawton. 1980. Are food webs divided into compartments? Journal of Animal Ecology 49: 879–898.
Pimm, S. L., and P. H. Raven. 2000. Biodiversity: Extinction by numbers. Nature 403: 843–845.
Pimm, S. L., G. J. Russell, J. L. Gittleman, and T. M. Brooks. 1995.
The future of biodiversity. Science 269: 347–350.
Pinto, N., and T. Keitt. 2009. Beyond the least-cost path: Evaluating
corridor redundancy using a graph-theoretic approach. Landscape
Ecology 24: 253–266.
Polis, G. A., M. E. Power, and G. R. Huxel. 2004. Food webs at
the landscape level. University of Chicago Press, Chicago, Illinois,
USA.
Post, D. M., M. E. Conners, and D. S. Goldberg. 2000a. Prey preference by a top predator and the stability of linked food chains. Ecology
81: 8–14.
515
Post, D. M., M. L. Pace, and N. G. Hairston. 2000b. Ecosystem size
determines food-chain length in lakes. Nature 405: 1047–1049.
Rantalainen, M. L., H. Fritze, J. Haimi, T. Pennanen, and H. Setälä.
2005. Species richness and food web structure of soil decomposer
community as affected by the size of habitat fragment and habitat corridors. Global Change Biology 11: 1614–1627.
Rayfield, B., M.-J. Fortin, and A. Fall. 2010. The sensitivity of leastcost habitat graphs to relative cost surface values. Landscape Ecology
25: 519–532.
Rayfield, B., M.-J. Fortin, and A. Fall. In press. Connectivity for conservation: A framework to classify network measures. Ecology doi:
10.1890/09-2190.1
Rayfield, B., A. Moilanen, and M.-J. Fortin. 2009. Incorporating
consumer–resource spatial interactions in reserve design. Ecological
Modelling 220: 725–733.
Ricotta, C., A. Stanisci, G. Avena, and C. Blasi. 2000. Quantifying
the network connectivity of landscape mosaics: A graph-theoretical
approach. Community Ecology 1: 89–94.
Ripple, W. J., T. P. Rooney, and R. L. Beschta. 2009. Large predators,
deer, and trophic cascades in boreal and temperate ecosystems. In J.
Terborgh and J. Estes [eds.], Trophic cascades: Predators, prey, and
the changing dynamics of nature, 141–161. Island Press, Washington,
D.C., USA.
Ritters, K., J. Wicksham, O. Neill, B. R. Jones, and E. Smith. 2000.
Global scale patterns of forest fragmentation. Conservation Ecology
4: 3.
Robertson, B. A., and R. L. Hutto. 2006. A framework for understanding ecological traps and an evaluation of existing evidence.
Ecology 87: 1075–1085.
Rooney, N., K. S. McCann, and J. C. Moore. 2008. A landscape theory for food web architecture. Ecology Letters 11: 867–881.
Rooney, T. P., and D. M. Waller. 2003. Direct and indirect effects of white-tailed deer in forest ecosystems. Forest Ecology and
Management 181: 165–176.
Saavedra, S., F. Reed-Tsochas, and B. Uzzi. 2009. A simple model
of bipartite cooperation for ecological and organizational networks.
Nature 457: 463–466.
Sabatino, M., N. Maceira, and M. A. Aizen. 2010. Direct effects of
habitat area on interaction diversity in pollination webs. Ecological
Applications 20: 1491–1497.
Saunders, D. A. 1998. The importance of networks for communication and application of ecological research. Surrey, Beatty & Sons,
Chipping Norton, North South Wales, Australia.
Saunders, D. A., R. J. Hobbs, and C. R. Margules. 1991. Biological
consequences of ecosystem fragmentation: A review. Conservation
Biology 5: 18–32.
Saura, S. 2008. Evaluating forest landscape connectivity through
Conefor Sensinode 2.2. In R. Lafortezza, J. Chen, G. Sanesi, and T. R.
Crow [eds.], Patterns and processes in forest landscapes: Multiple use
and sustainable management, 403–422. Springer Verlag, New York,
New York, USA.
Saura, S., and J. Torne. 2009. Conefor Sensinode 2.2: A software package for quantifying the importance of habitat patches for landscape
connectivity. Environmental Modelling & Software 24: 135–139.
Schick, R. S., and S. T. Lindley. 2007. Directed connectivity among
fish populations in a riverine network. Journal of Applied Ecology 44:
1116–1126.
Schlaepfer, M. A., M. C. Runge, and P. W. Sherman. 2002.
Ecological and evolutionary traps. Trends in Ecology & Evolution
17: 474–480.
Schoener, T. W. 1989. Food webs from the small to the large: The
Robert H. MacArthur Award Lecture. Ecology 70: 1559–1589.
Shah, V. B., and B. H. McRae. 2008. Circuitscape: A tool for landscape ecology. In G. Varoquaux, T. Vaught, and J. Millman [eds.],
Proceedings of the 7th Python in Science Conference, 62–66,
Pasadena, California, USA, 2008. SciPy2008, website http:http://conference.scipy.org/proceedings/SciPy2008/.
Slobodkin, L. B. 2001. The good, the bad and the reified. Evolutionary
Ecology Research 3: 1–13.
516
American Journal of Botany
Solé, R. V., F. Bartumeus, and J. G. P. Gamarra. 2005. Gap percolation in rainforests. Oikos 110: 177–185.
Solé, R. V., and J. M. Montoya. 2006. Ecological network meltdown from
habitat loss and fragmentation. In J. A. Dunne and M. Pascual [eds.],
Ecological networks, 305–323. Oxford University Press, Oxford, UK.
Soulé, M. E., J. A. Estes, J. Berger, and C. M. Del Rio. 2003.
Ecological effectiveness: Conservation goals for interactive species.
Conservation Biology 17: 1238–1250.
Soulé, M. E., J. A. Estes, B. Miller, and D. L. Honnold. 2005.
Strongly interacting species: Conservation policy, management, and
ethics. BioScience 55: 168–176.
Spencer, M., and P. H. Warren. 1996. The effects of habitat size and
productivity on food web structure in small aquatic microcosms.
Oikos 75: 419–430.
Srinivasan, U. T., J. A. Dunne, J. Harte, and N. D. Martinez. 2007.
Response of compex food webs to realistic extinction sequences.
Ecology 88: 671–682.
Staddon, P., Z. Lindo, P. Crittenden, F. Gilbert, and A. Gonzalez.
2010. Connectivity, non-random extinction, and ecosystem function
in experimental metacommunities. Ecology Letters 13: 543–552.
Staniczenko, P. P. A., O. T. Lewis, N. S. Jones, and F. Reed-Tsochas.
2010. Structural dynamics and robustness of food webs. Ecology
Letters 13: 891–899.
Steffan-Dewenter, I. 2003. Importance of habitat area and landscape
context for species richness of bees and wasps in fragmented orchard
meadows. Conservation Biology 17: 1036–1044.
Steffan-Dewenter, I., A.-M. Klein, V. Gaebelle, T. Alfert, and T.
Tschamtke. 2006. Bee diversity and plant–pollinator interactions in
fragmented landscapes. In N. M. Waser and J. Ollerton [eds.], Plant–
pollinator interactions: From specialization to generalization, 387–407.
University of Chicago Press, Chicago, Illinois, USA.
Steffan-Dewenter, I., U. Munzenberg, C. Burger, C. Thies, and T.
Tscharntke. 2002. Scale-dependent effects of landscape context on
three pollinator guilds. Ecology 83: 1421–1432.
Stouffer, D. B., and J. Bascompte. 2010. Understanding food-web persistence from local to global scales. Ecology Letters 13: 154–161.
Sugihara, G., and H. Ye. 2009. Cooperative network dynamics. Nature
458: 979–980.
Swift, T. L., and S. J. Hannon. 2010. Critical thresholds associated
with habitat loss: A review of the concepts, evidence, and applications.
Biological Reviews of the Cambridge Philosophical Society 85: 35–53.
Terborgh, J., L. Lopez, P. V. Nuñez, M. Rao, G. Shahabuddin, G.
Orihuela, M. Riveros, et al. 2001. Ecological meltdown in predator-free forest fragments. Science 294: 1923–1926.
Tewksbury, J. J., D. J. Levey, N. M. Haddad, S. Sargent, J. L. Orrock,
A. Weldon, B. J. Danielson, et al. 2002. Corridors affect plants,
animals, and their interactions in fragmented landscapes. Proceedings
of the National Academy of Sciences, USA 99: 12923–12926.
Theobald, D. M. 2006. Exploring the functional connectivity of landscapes using landscape networks. In K. R. Crooks and M. Sanjayan
[eds.], Connectivity conservation, 416–443. Cambridge University
Press, Cambridge, UK.
Tilman, D., R. M. May, C. L. Lehman, and M. A. Nowak. 1994. Habitat
destruction and the extinction debt. Nature 371: 65–66.
Trani, M., and R. Giles. 1999. An analysis of deforestation: Metrics
used to describe pattern change. Forest Ecology and Management 114:
459–470.
Treml, E. A., P. N. Halpin, D. L. Urban, and L. F. Pratson. 2008.
Modeling population connectivity by ocean currents, a graph-theoretic
approach for marine conservation. Landscape Ecology 23: 19–36.
Tylianakis, J. M. 2009. Warming up food webs. Science 323:
1300–1301.
Tylianakis, J. M., R. K. Didham, J. Bascompte, and D. A. Wardle.
2008. Global change and species interactions in terrestrial ecosystems. Ecology Letters 11: 1351–1363.
Tylianakis, J. M., E. Laliberte, A. Nielsen, and J. Bascompte. 2010.
Conservation of species interaction networks. Biological Conservation 143: 2270–2279.
Tylianakis, J. M., T. Tscharntke, and O. T. Lewis. 2007. Habitat
modification alters the structure of tropical host–parasitoid food
webs. Nature 445: 202–205.
Urban, D., and T. Keitt. 2001. Landscape connectivity: A graph-theoretic perspective. Ecology 82: 1205–1218.
Urban, D. L., E. S. Minor, E. A. Treml, and R. S. Schick. 2009. Graph
models of habitat mosaics. Ecology Letters 12: 260–273.
Vázquez, D. P., and M. A. Aizen. 2003. Null model analyses of specialization in plant–pollinator interactions. Ecology 84: 2493–2501.
Vázquez, D. P., and D. Simberloff. 2003. Changes in interaction
biodiversity induced by an introduced ungulate. Ecology Letters 6:
1077–1083.
Vellend, M., K. Verheyen, H. Jacquemyn, A. Kolb, H. Van Claster,
G. Peterken, and M. Hermy. 2006. Extinction debt of forest plants
persists for more than a century following habitat fragmentation.
Ecology 87: 542–548.
Vos, C., P. Berry, P. Opdam, H. Baveco, B. Nijhof, J. O’Hanley, C.
Bell, et al. 2008. Adapting landscapes to climate change: Examples
of climate-proof ecosystem networks and priority adaptation zones.
Journal of Applied Ecology 45: 1722–1731.
Wennergren, U., M. Ruckelshaus, and P. Kareiva. 1995. The promise and limitations of spatial models in conservation biology. Oikos
74: 349–356.
Williams, S. E., L. P. Shoo, J. L. Isaac, A. A. Hoffmann, and G.
Langham. 2008. Towards an integrated framework for assessing the vulnerability of species to climate change. PLoS Biology
6: e325.
Wilson, K. A., E. Meijaard, S. Drummond, H. S. Grantham, L.
Boitani, G. Catullo, L. Christie, et al. 2010. Conserving
biodiversity in production landscapes. Ecological Applications 20:
1721–1732.
Wright, D. H., A. Gonzalez, and D. C. Coleman. 2007. Changes
in nestedness in experimental communities of soil fauna undergoing
extinction. Pedobiologia 50: 497–503.
Yaacobi, G., Y. Ziv, and M. L. Rosenzweig. 2007. Habitat fragmentation may not matter to species diversity. Proceedings of the Royal
Society of London, B, Biological Sciences 274: 2409–2412.
Zeigler, B. P. 1977. Persistence and patchiness of predator–prey systems
induced by discrete event population exchange mechanisms. Journal
of Theoretical Biology 67: 687–713.