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Mammal Rev. 2012, Volume 42, No. 1, 55–77. Printed in Singapore. REVIEW Habitat heterogeneity and mammalian predator–prey interactions mam_189 55..77 Lucrezia GORINI* Hedmark University College, Department of Applied Ecology and Agricultural Sciences, Evenstad, NO-2480 Koppang, Norway, and Department of Animal and Human Biology, University of Rome ‘La Sapienza’, Viale dell’Università 32, 00185, Rome, Italy. E-mail: [email protected] John D. C. LINNELL Norwegian Institute for Nature Research, Tungasletta-2, NO-7485 Trondheim, Norway. E-mail: [email protected] Roel MAY Norwegian Institute for Nature Research, Tungasletta-2, NO-7485 Trondheim, Norway. E-mail: [email protected] Manuela PANZACCHI Norwegian Institute for Nature Research, Tungasletta-2, NO-7485 Trondheim, Norway. E-mail: [email protected] Luigi BOITANI Department of Animal and Human Biology, University of Rome ‘La Sapienza’, Viale dell’Università 32, 00185, Rome, Italy. E-mail: [email protected] Morten ODDEN Hedmark University College, Department of Forestry and Wildlife Research, Evenstad, NO-2480 Koppang, Norway. E-mail: [email protected] Erlend. B. NILSEN Norwegian Institute for Nature Research, Tungasletta – 2, NO-7485 Trondheim Norway. E-mail: [email protected] ABSTRACT 1. In predator–prey theory, habitat heterogeneity can affect the relationship between kill rates and prey or predator density through its effect on the predator’s ability to search for, encounter, kill and consume its prey. Many studies of predator– prey interactions include the effect of spatial heterogeneity, but these are mostly based on species with restricted mobility or conducted in experimental settings. 2. Here, we aim to identify the patterns through which spatial heterogeneity affects predator–prey dynamics and to review the literature on the effect of spatial heterogeneity on predator–prey interactions in terrestrial mammalian systems, i.e. in freely moving species with high mobility, in non-experimental settings. We also review current methodologies that allow the study of the predation process within a spatial context. 3. When the functional response includes the effect of spatial heterogeneity, it usually takes the form of predator-dependent or ratio-dependent models and has wide applicability. 4. The analysis of the predation process through its different stages may further contribute towards identifying the spatial scale of interest and the specific spatial mechanism affecting predator–prey interactions. 5. Analyzing the predation process based on the functional response theory, but separating the stages of predation and applying a multiscale approach, is likely to *Correspondence author. © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 56 L. Gorini et al. increase our insight into how spatial heterogeneity affects predator–prey dynamics. This may increase our ability to forecast the consequences of landscape transformations on predator–prey dynamics. Keywords: anti-predator behaviour, functional response, hunting behaviour, predation stage, spatial features Mammal Review (2012), 42, 55–77 doi: 10.1111/j.1365-2907.2011.00189.x INTRODUCTION The past 70 years have seen the development of a large body of theory in predation ecology that has centred on the concept of ‘functional response’ (Solomon 1949, Holling 1959). In its simplest form, the functional response relates predator kill rate, i.e. the number of prey items killed by a predator in a given time interval, to prey and/or predator density. Most of the predictions derived from this theoretical background find support in model systems and experiments, but they rest on a number of assumptions that are usually violated in heterogeneous biological systems (see Jeschke et al. 2002 for a review of the functional response). Spatial heterogeneity is one of the factors affecting the rate at which a predator can kill and consume its prey. The form of predator–prey interactions in heterogeneous and dynamic landscapes may deviate significantly from that of interactions in homogeneous and static systems such as the environments used in controlled experiments and model simulations. The spatially mediated mechanisms that affect kill rates can ultimately be related to the fact that spatial heterogeneity introduces a difference between the total prey population density and the proportion which the predator has the potential to kill and consume. If this difference is not taken into account when estimating the form of a functional response, the outcome may be biased, or the type of functional response may be mistaken (e.g. Nachman 2006). A large body of empirical and theoretical evidence, including that based on invertebrate systems (Gause 1934, Huffaker et al. 1963, Luckinbill 1974, Pacala et al. 1990, Denno et al. 2005, Bergström et al. 2006), suggests that spatial heterogeneity may affect predator–prey dynamics through a number of mechanisms (e.g. altered prey vulnerability or predator hunting success). Most studies of predator–prey interactions including the effect of spatial heterogeneity have so far been based on highly simplified experimental conditions (Sih 2005): either the predator or the prey species has reduced mobility (or is caged) or the predator’s presence is simulated (but see also Lima 2002). Studies of the spatial distribution and habitat selection of highly mobile terrestrial mammals as a response to predation risk and prey availability confirm, however, the importance of spatial heterogeneity in understanding observed kill rates (e.g. Hebblewhite et al. 2005, Hopcraft et al. 2005, Kauffman et al. 2007, Zub et al. 2008). For instance, traitmediated mechanisms (sensu Preisser et al. 2005) induced by perceived predation risk have the potential to affect prey population dynamics more strongly than kill rates, as these also involve the individuals that are not consumed (Preisser et al. 2005). Spatial heterogeneity may affect prey vulnerability, and therefore determine the relative importance of these induced behavioural responses on predator–prey interactions (Luttbeg et al. 2003). Alteration of kill rates associated with habitat-dependent prey vulnerability and predator hunting success in simple one prey/one predator systems © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 Predator–prey interactions in a spatial world 57 may have consequences on other trophic levels and the overall food web (Denno et al. 2005 and references therein). Changes in the landscape caused by human activity are among the most important drivers affecting species and ecosystems worldwide (Houghton 1994, Noss et al. 1997) and urge the understanding of the impact of spatial heterogeneity on predator–prey and community dynamics. In this review, we aim to explore how habitat spatial heterogeneity may affect predator–prey dynamics in terrestrial mammalian systems, i.e. in systems characterized by highly mobile species, and focus on predation by mammals on mammals with a few exceptions of avian predation on mammals. We provide evidence from the literature that the consequences of spatial heterogeneity on anti-predator behaviour, hunting strategy or risk perception in natural conditions are essential for improving our understanding of predator–prey dynamics. We emphasize the importance of separately investigating the effect of spatial heterogeneity on each of the four stages of predation described by Endler (1986), i.e. searching, encountering, killing and consuming prey. We will also touch on wider consequences: how changes in predator–prey interactions may influence the overall community. Lastly, we will review models and field methods that have the potential to take spatial heterogeneity into account and suggest directions for further research. EFFECTS OF SPATIAL HETEROGENEITY ON PREDATOR–PREY INTERACTIONS Spatial heterogeneity can modify the form of the functional response by introducing a difference between the total prey population density and the proportion that is actually available to the predators. Predator habitat selection can often best be explained by the hunting success associated with certain habitat types (e.g. Hopcraft et al. 2005), or by constraints such as territoriality and competition that violate the assumptions of the ideal-free distribution (e.g. Kauffman et al. 2007). Spatial heterogeneity can thus modify prey availability through several mechanisms that can be grouped based on the stage of the predation process, i.e. searching, encountering, killing and consuming prey (Endler 1986), which they affect (Tables 1 and 2). First and second stages: searching and encountering The distribution and detection probability of prey can influence the search efficiency of predators. The spatial distribution of predators and prey is largely the result of trade-off decisions between energy intake, risk of predation and intra-guild competition (Palomares & Caro 1999, Linnell & Strand 2000, Lima 2002) causing spatial variation in reproduction and mortality rates. Prey can respond to predator presence by avoiding areas or patches with high predator density or perceived risk (Lima & Dill 1990). This type of behavioural response may be particularly strong during certain phases of the individual’s life when its own survival or that of its offspring is critically dependent on access to safe structures such as nests and den sites (Olsson et al. 2008), and behavioural flexibility is necessary to maintain high fitness (Charnov 1976). Shifts in space use may also be responses to temporal variation in predation risk (Lima & Dill 1990), i.e. prey may use a given high-quality habitat, or may venture farther from a safe but poor habitat, depending on the presence or absence of predators at the fine temporal scale (e.g. Creel & Winnie 2005, van Dijk et al. 2008). Behavioural responses including alteration of fine-scale habitat use induced by risk perception alone may affect a larger number of prey individuals than is actually killed and consumed by a predator, and may therefore strongly affect predator–prey dynamics (Luttbeg & © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 Kill Space use alteration Search efficiency and encounter probability Refuges Detection probability Mechanism Stage of predation M. nivalis nivalis C. lupus V. vulpes P. pardus, P. leo C. lupus, P. concolor Vulpes vulpes Mortality mainly due to Panthera leo C. lupus C. lupus, L. lynx, Gulo gulo, Ursus arctos Mustela nivalis nivalis M. nivalis nivalis Small Mustelida M. nivalis nivalis, Mustela erminea C. lupus Panthera pardus Martes americana P. concolor, C. lupus C. lupus C. lupus C. lupus C. lupus Canis lupus C. lupus Human caused mortality C. lupus Puma concolor Canis latrans Predator Rodentia Rodentia Myodes glareolus Microtus agrestis, sibling vole Microtus rossiaemeridionalis Rodentia A. alces C. capreolus Papyo cynocephalus ursinus C. elaphus, O. hemionus Cervus elaphus C. elaphus Lynx lynx C. elaphus Ovis canadensis Odocoileus virginianus, Odocoileus hemionus C. elaphus, O. hemionus C. elaphus C. elaphus C. elaphus Rangifer tarandus caribou Migratory ungulates O. virginianus, C. elaphus, Alces alces 6 ungulate species Myodes gapperi (former Clethrionomys gapperi) Capreolus capreolus Acynonix jubatus C. elaphus Several prey species Prey Zub et al. (2008) Kunkel and Pletscher (2000) Panzacchi et al. (2010) Cowlishaw (1997) Atwood et al. (2007) Oksanen et al. (2001) Ylönen et al. (2003) Sundell and Norrdahl (2002) Norrdahl and Korpimäki (2005) Panzacchi et al. (2009, 2010) Laurenson (1993) Hebblewhite et al. (2005) May et al. (2008) Atwood et al. (2009) Mao et al. (2005) Creel et al. (2006) Creel et al. (2008) Wittmer et al. (2007) Fryxell et al. (1988) Kunkel et al. (2004) Balme et al. (2007) Andruskiw et al. (2008) Creel and Winnie (2005) Fortin et al. (2005b) Bunnefeld et al. (2006) Christianson and Creel (2008) Wehausen (1996) Lingle (2002) Reference Table 1. Evidence, from the literature, of the effect of habitat spatial heterogeneity on the predation process in terrestrial mammalian systems 58 L. Gorini et al. © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 Kittle et al. (2008) Telfer and Kelsall (1984) Landa et al. (1999) Husseman et al. (2003) Post et al. (1999) Helldin (2000) Huggard (1993) Owen-Smith and Mills (2008) Kunkel et al. (2004) Mills et al. (2004) Huggard (1993) Hopcraft et al. (2005) Wilmers et al. (2003) O. virginianus, C. elaphus, A. alces Large ungulates Rangifer tarandus C. elaphus, O. hemionus A. alces Rodentia C. elaphus 12 prey species O. virginianus, C. elaphus C. elaphus C. elaphus C. elaphus Mills et al. (2004) Pierce et al. (2004) Ockenfels (1994) Hopcraft et al. (2005) Kauffman et al. (2007) Andruskiw et al. (2008) Thibault and Ouellet (2005) Cresswell et al. (2003) Fitzgibbon (1994) Husseman et al. (2003) Pierce et al. (2004) Atwood et al. (2009) Fryxell et al. (2007) Panzacchi et al. (2009) Dussault et al. (2005) Different medium-sized herbivores O. hemionus Antilocapra americana C. elaphus C. elaphus M. gapperi Lepus americanus Several avian species Gazella thomsoni C. elaphus, O. hemionus O. hemionus C. elaphus, O. hemionus Connochaetes taurinus C. capreolus A. alces The evidence is grouped according to the stage of the predation process (see text and Endler 1986) and according to the ‘spatial’ mechanisms that each study is focused on. A few studies are included in more than one group, as the authors analyzed more than one stage of predation. Species are given by their scientific names to avoid confusion. Consumption Kill A. jubatus P. concolor P. concolor C. lupus C. lupus M. americana C. latrans Accipiter nisus Anti-predator A. jubatus tactic C. lupus, P. concolor P. concolor P. concolor, C. lupus P. leo V. vulpes Physical Two potential predators: C. lupus, features Ursus americanus (snow C. lupus depth, C. lupus, C. latrans, G. gulo rainfall) G. gulo C. lupus, P. concolor C. lupus Martes martes C. lupus P. leo C. lupus Kleptoparasitism A. jubatus C. lupus Scavenging C. lupus C. lupus Hunting strategy Predator–prey interactions in a spatial world 59 © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 60 L. Gorini et al. Table 2. Summary of the effect that each spatially mediated mechanism can have on the encounter, kill and consumption rate Mechanism Encounter rate Shift in space-use of the prey Use of ‘risky’ habitat types due to: low availability of safe habitat types, inter- and intra-specific competition Use of cover (sensu Mysterud & Østbye 1999) Occurrence of snow Kill rate Cover (sensu Mysterud & Østbye 1999) Snow Poor physiological condition of the prey associated with space use Consumption rate Caching in open areas Effect In most cases it decreases the encounter rate In most cases it increases the encounter rate It can increase or decrease the encounter rate depending on the species community composition It can increase or decrease the encounter rate depending on the specific hunting behaviour It decreases the kill rate It increases the kill rate It increases the kill rate It decreases the consumption rate due to kleptoparasitism The dominant effect (i.e. the most common one) based on the literature is shown. See the text for further discussion on each of the mechanisms. Kerby 2005). At a larger spatial scale, migratory movements may primarily represent an optimization of the use of food resources but may also render the individuals less vulnerable to predation if the predator is not capable of following its prey (Fryxell et al. 1988, Skogland 1991, but see Hofer & East 1993, Forbes & Theberge 1995, Walton et al. 2001). All these shifts in space use can strongly decrease prey availability and encounter rates, both temporally and spatially. Therefore, failure to consider space shifts in research may lead to an overestimation of prey availability, unless shifting habitat use increases vulnerability to another co-occurring predator (see below). However, when either the availability of high-quality habitats is low or when individuals are energetically stressed, prey may be forced to use patches in which predation risk is high (Lima & Dill 1990, Hugie & Dill 1994, Sinclair & Arcese 1995, Battin 2004) or may be forced to increase their inter-patch movements (Johannesen et al. 2003). Although other anti-predator responses may occur in these cases, such as increased vigilance, decreased foraging time or changes in group size (e.g. Laundré et al. 2001), the search efficiency of the predator probably also increases (e.g. Storaas et al. 1999). Hence, by increased chances of encountering prey due to increased inter-patch movements or use of risky habitats, the proportion of prey available to a given predator may increase. It would be valuable to investigate if and under which conditions the increase in inter-patch movements or use of risky habitats may lead to a shift from food-limited (i.e. bottom-up) to predator-driven (i.e. top-down) population dynamics. Variation in cover availability in different habitat types can greatly affect the predator’s search efficiency (Caro 2005). The term cover includes any structural or non-structural feature of the landscape which protects, hides or conceals (sensu Mysterud & Østbye 1999). Structural features are those provided by vegetation and topography, including canopy and ground cover, shelters and bed-sites. © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 Predator–prey interactions in a spatial world 61 Well-hidden dens, nests and dense vegetation associated with certain habitat types may decrease prey detection probability, particularly for specific age-classes (Fortin et al. 2005a, Hebblewhite et al. 2005, Panzacchi et al. 2009), thus decreasing the amount of available prey. Habitat types with dense vegetation cover may also be risky for the prey due to their reduced chances of detecting an approaching predator. Besides vegetation cover, snow can provide shelter for burrowing species but can also hinder movements, and hence result in increased use of corridors where the probability of encountering a predator is high (e.g. Post et al. 1999). Specialist predators such as small mustelids may benefit from a well-developed layer of snow, as they rely on the subnivean space when hunting microtines (Hansson & Henttonen 1988, Oksanen et al. 2001 and below). It has been suggested that a decreased amount of snow-cover in winter may on the other hand improve the hunting efficiency of generalist predators such as the red fox Vulpes vulpes at the expense of the specialist weasel Mustela nivalis nivalis, and thus alter the population dynamics of their microtine prey (Oksanen et al. 2001). In summary, the spatially mediated mechanisms discussed here can lead to either overestimation or underestimation of prey availability, depending on whether their effect on the encounter rate is positive or negative. Third and fourth stages: killing and consuming Provided that prey is encountered, a predator may still not be capable of capturing and killing it. There might be a danger associated with the prey (e.g. due to competing predator individuals or prey defence responses); the predator might be prevented from capturing or killing the prey by spatially mediated mechanisms such as habitat differences in availability of escape structures or presence of physical obstacles (a river, a fence), or the likelihood of capture and killing might be affected by habitat-dependent prey vulnerability and hunting success, conditions of the terrain (e.g. snow depth) or physiological condition of the prey (see Table 1 for references on each of these). Escape structures such as holes, burrows, cliffs or dense vegetation inaccessible to predators are regarded as refuges (Caro 2005) but are sometimes included in a more general definition of cover (see above, Mysterud & Østbye 1999). If the number of individuals that can use refuges is constant, and the availability of refuges is a limiting factor, there is a density dependent feedback on predation rates (Berryman & Hawkins 2006), and competition mechanisms arise for the limiting resource (see also the concept of a ‘doomed surplus’; Errington 1963). Both prey and predators are capable of adjusting their respective anti-predator or hunting strategy based on the habitat type they are using (e.g. Fitzgibbon 1994, Kunkel et al. 1999). For instance, prey can adjust group size, vigilance or activity patterns depending on the degree of visibility (Fitzgibbon 1994, Panzacchi et al. 2010). Similarly, a given predator can behave as an ambush stalker in wooded habitats and as a cursorial hunter in open habitats (e.g. Cresswell 1996). If these spatially mediated factors are not taken into account, prey availability may be overestimated in the first example or underestimated in the second one. Open habitats usually imply high visibility and high encounter rates but also cause prey to be more easily alerted, and thus affect the distance at which a hunter may start the chase attempt (Mills et al. 2004). The chance of a successful hunt for a cursorial predator depends more on the type of terrain through which the pursuit takes place © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 62 L. Gorini et al. than on the habitat features where it detects its prey. On the contrary, an ambush predator’s success depends more on the cover present where prey is detected, as this affects the distance at which the chase can start (Husseman et al. 2003, see below). The amount of cover can also affect the prey’s capacity to exploit refuges, and hence its chances of escaping an attack (Sundell et al. 2008). Variation in snow depth and consistency associated with different habitat types can affect the pursuit success of a cursorial predator. Certain prey species may become accessible to a predator (e.g. Landa et al. 1999), or a specialist predator may be capable of exploiting its limited niche (e.g. Oksanen et al. 2001), only when specific snow conditions are present. Within a prey community, however, the effect of snow depth may be stronger on some prey species than on others, and may thus affect prey mortality patterns for different species differentially (Kittle et al. 2008). The capacity of a prey to escape an attack may also depend on its physiological conditions which, in turn, can be a consequence of space use. For instance, avoidance of high-quality habitats as an anti-predator response or because of temporal variation in the physical conditions of the habitat (e.g. yearly variation in amount of snow) may have costs for the physiological state of individuals and, in turn, alter their vulnerability to predation (Post et al. 1999, Abramski et al. 2002, Creel et al. 2006, Christianson & Creel 2008) or their reproductive output (Hik 1995). All these mechanisms can affect the chances of a predator successfully killing its prey, and may therefore lead to either an overestimation or an underestimation of prey availability. Once a kill is made, further spatially mediated mechanisms can affect the consumption rate. During the period of time in which a carcass is consumed, it might be scavenged or kleptoparasitized by other predatory or scavenger species (e.g. Kaczensky et al. 2005). If a kill is made in an open habitat, it might be exploited by scavengers or kleptoparasitized by dominant predators more rapidly because of the higher visibility (Mills et al. 2004). Caching behaviour may decrease the accessibility of carcasses to scavengers, and thereby affect the consumption rates of both scavenger and killer. Even when a carcass is cached, however, the chances of other species scavenging on the carcass are affected by its location. Snow depth or dense undergrowth, for instance, may reduce the chances of finding a carcass by affecting the movement rates and distances travelled (Huggard 1993, Wilmers et al. 2003). However, when carcasses of individuals dying due to starvation or diseases are scavenged, their location is often associated with high prey density areas (Hopcraft et al. 2005). Thus, spatial heterogeneity can lead to an increase or a decrease in kill rates depending on the specific behavioural characteristics of the species considered (e.g. cursorial and ambush stalker, see below) and the community composition. Differences in hunting behaviour or alternative prey availability may explain the switch from a type 2 to a type 3 functional response for the same predator in two different areas (Keith et al. 1977, O’Donoghue et al. 1998). Particularly in the case of habitat fragmentation and loss, the effect of spatial heterogeneity on generalist and specialist species may lead to very different consequences both for predators and for prey populations (Ryall & Fahrig 2006). Generally, mechanisms of interference between species in a community that are mediated by spatial heterogeneity, such as predator interference (Preisser et al. 2005, Berec 2010), are very common, and the potential for consequences on community dynamics deserves careful investigation. © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 Predator–prey interactions in a spatial world 63 SPATIAL HETEROGENEITY AND THE IMPACTS OF COMMUNITY STRUCTURE ON PREDATOR–PREY INTERACTIONS Predators can affect the population dynamics of their prey directly through the number of prey individuals killed, and indirectly through trait-mediated or indirect density-mediated mechanisms, such as behavioural responses to predation risk (Preisser et al. 2005) or apparent competition (Holt & Lawton 1994). For instance, when prey face predation from multiple predator species, their behavioural responses may be complex: shifting habitat use to reduce the probability of being detected by one predator species may simultaneously increase the chances of encountering another one. Hence, by shifting to a densely forested area, prey may decrease their vulnerability to a cursorial predator while simultaneously increasing it to a stalker (e.g. Atwood et al. 2009). Similarly, use of a certain habitat by a given prey species may decrease its exposure to an avian predator and increase its exposure to a terrestrial predator (e.g. Korpimäki et al. 1995). According to Preisser et al. (2005), ‘predatorpredator facilitation’ accounts for ca. 50% of the total trait-mediated effects of predation on prey survival, which suggests that it may be a relatively common mechanism. On the other hand, intra-guild competition is widespread among mammals (Palomares & Caro 1999) and may result in spatial avoidance (e.g. Helldin et al. 2006) or reduced scavenging opportunities. Carcasses can be a valuable resource for predators within the same guild but can also be very risky places (e.g. Linnell & Strand 2002, van Dijk et al. 2008). Intra-specific competition among predators may also limit the use of habitats that provide good ‘hunting grounds’ (Kauffman et al. 2007), and hence affect hunting success. Similarly, sympatric prey species may compete for access to refuges and safe dens (Koivunen et al. 1998), thus affecting their relative vulnerability to predation. The use of different hunting strategies in different habitats may reduce potential conflicts within a community, at least when the densities of the competing species are not very high and habitat availability allows it (May et al. 2008). Similarly, the hunting strategy of a predator may be more successful on one of two sympatric prey species, if the different vulnerability of the prey species is associated with different habitat features (Lingle 2002, Sundell et al. 2008). Cursorial predators tend to select weak individuals and have a relatively low hunting success (Kunkel et al. 1999) compared with stalking predators. Difference in prey selection affects the demography, and thus population dynamics of the prey population. It has been suggested that differences in environmental sensitivity among functionally similar species (e.g. coyote Canis latrans and wolf Canis lupus) favour community stability, whereas differences in sensitivity among functionally different species (e.g. ungulate prey vs. large carnivores) make communities more vulnerable to change (Chapin et al. 1997, Muradian 2001, Hooper et al. 2005). More complex interactions can further affect predator and prey population dynamics among trophic levels. If one prey species is relatively abundant, it may support a large predator population which in turn can exert high predation pressure on a rarer sympatric prey species (e.g. Storaas et al. 1999, Wittmer et al. 2007, Kojola et al. 2009, Wegge et al. 2009). In community dynamics, this type of mechanism has received increasing attention, as it has been suggested to be in some cases enhanced by habitat fragmentation and loss, thus relating kill rates to spatial heterogeneity (e.g. Storch et al. 2005, Wittmer et al. 2007). Certain predator species may in fact be capable of affecting populations of different prey species not only in their usual habitat but also in neighbouring areas through spill-over predation (Schneider 2001). © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 64 L. Gorini et al. In fragmented landscapes, the presence of predators in adjacent areas may negatively affect prey species population dynamics, even if 70% of the prey species’ habitat is still intact (Schneider 2001). Generalist consumers, in particular, appear to benefit more than specialists from heterogeneous landscapes with high edge to area ratios (Tattersall et al. 2002). This is supported both by theoretical predictions (Melian & Bascompte 2002) and by strong empirical evidence showing an increase in prey vulnerability parallel to the abundance and diversity of generalist predators associated with ecotones (Angelstam 1986, Andrén & Angelstam 1988, Andrén 1994). Hence, increased landscape heterogeneity and mechanisms of apparent competition (sensu Holt 1977) may have combined effects on the kill rates within a community (e.g. Oliver et al. 2009). Apparent competition has been used to explain the dramatic decline in woodland caribou Rangifer tarandus caribou in North America and Europe. Initially, increased timber harvesting negatively affected the demography of the population, as woodland caribou are tightly connected to old serial forest stages for foraging (Terry et al. 2000). However, the increase in timber harvest also benefited the moose Alces alces population, which has increased in abundance. This increase in the prey base has in turn subsidized a denser wolf population, which has in turn further contributed to the woodland caribou decline (Seip 1992, Wittmer et al. 2007, Kojola et al. 2009). Much emphasis has been placed on predicting species-specific responses to habitat fragmentation and loss in the past years, as these are considered to be among the greatest threats to ecosystems worldwide (Soulé 1991). However, most of the studies so far have been based on short-term and small-scale field investigations, or are flawed in other ways (see Mortelliti et al. 2010 for a review on the consequences of habitat loss and fragmentation). In addition, few researchers have investigated in detail the relationship between habitat loss and the structure of the community or food web (Melian & Bascompte 2002). Including the effects of spatial heterogeneity in community studies is challenging because of the increasing number of indirect effects linking trophic levels; these include mechanisms of predator interference, saturation and apparent competition, the effect of the scale of observation, and species variation in hunting and anti-predator behaviour. MODELLING PREDATOR–PREY INTERACTIONS IN SPATIALLY HETEROGENEOUS LANDSCAPES At the core of predator–prey theory is the concept of functional and numerical responses (Solomon 1949). To include the effect of spatial heterogeneity on kill rates, the classic prey-dependent functional response may be made more complex by accounting for predator interference (Beddington 1975, Arditi & Akçakaya 1990) or aggregated spatial distributions (Cosner et al. 1999). The challenges that still remain derive from the fact that measures of kill rates and habitat features vary with the scale of observation (Jost et al. 2005, Bergström et al. 2006) and that the effect of habitat structure on individual species may depend on their life-history traits (e.g. Fahrig 2003). Alternative approaches such as individual-based models (e.g. NabeNielsen et al. 2010) based on simulations are beyond the scope of this review and are not discussed here. Functional response Despite variation in spatially mediated mechanisms, which may affect kill rates, simple prey-dependent functional responses are used in a remarkably large number © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 Predator–prey interactions in a spatial world 65 of studies (O’Donoghue et al. 1998, Jeschke et al. 2002 and references. therein). This is because the main predictions of the functional response, i.e. kill rates reach an asymptote at increasing prey density, and predators may switch to an alternative prey if the density of the main prey species is below a certain threshold, can be caused by several mechanisms. According to theory, for instance, saturation at high prey density explains the asymptote effect and may be associated with constraints in predators’ handling (and digesting) time or with local prey clumping. Among the spatially mediated mechanisms that can cause prey switching are changes in vulnerability of the prey due to changes in physical condition or habitat use (e.g. Wolff 1980, McNamara & Houston 1987, Hik 1995). In general, in the forms of functional response, which include spatial heterogeneity (e.g. non-random encounters between predator and prey, heterogeneous distribution of the prey and/or the predator), the effect of predator density on kill rates is explicitly added, and takes the form of either predator dependency (Hassell & Varley 1969, Beddington 1975, Cosner et al. 1999, Jost et al. 2005) or ratio dependency (e.g. Arditi & Ginzburg 1989, Arditi & Akçakaya 1990, Jost et al. 2005). The main prediction of these types of functional responses is that per capita kill rates decrease at increasing predator density because of direct aggressive interactions among searching predators (McCarthy et al. 1995, van Gils & Piersma 2004, Kratina et al. 2009); they may describe well predator dependence at low predator densities as well (Kratina et al. 2009). Group hunting predators may increase (e.g. by positive interference, Abrams & Ginzburg 2000) or decrease (e.g. Fryxell et al. 2007) their per capita kill rate with increasing group size. This provides an example of how predator interference may have both negative and positive effects on kill rates. Furthermore, Fryxell et al. (2007) demonstrated how prey grouping had as large an effect on per capita kill rate as predator grouping. Thus, the functional response has the great merit of describing patterns of predation that can derive from a large number of mechanisms. However, to be useful in practical management, the focus should be on both the form that the functional response takes and the values of the parameters that make up the model structure. While saturation affects the consumption rate, the effect of interference is on the encounter rate and their impacts may counteract each other (Bergström et al. 2006). A classic prey-dependent computation of the functional response may therefore fail to detect these factors if they have opposite effects, e.g. predator interference may decrease kill rates thus preventing the functional response from reaching the saturation threshold. A modification of the functional response based on moment approximation may, on the other hand, allow such distinction by using statistical moments (e.g. variance, covariance) to estimate the effect of prey and predator–prey density on the functional response as proposed by Bergström et al. (2006). These authors applied this statistical method to a predator–prey system with invertebrate species and obtained explicit estimates of variance and covariance effects. Because the estimated covariance is related to predator and prey density, its effect may be explained by an alteration of the encounter rate. On the other hand, the estimated effect of spatial variance in prey density may be explained by local predator saturation (Bergström et al. 2006). This difference might be important in changing landscapes where alterations in predator–prey systems induced by habitat fragmentation could remain undetected at first, when the focus is on overall kill rates. Hence, spatial heterogeneity may affect the predation process even if the form of the functional response does not change. According to theoretical models, the effect of predator © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 66 L. Gorini et al. facilitation may also vary depending on the stage of the predation process which is altered. It may destabilize predator–prey dynamics if it affects the encounter rate, while it may have a stabilizing effect if it affects the consumption rate (Berec 2010). Empirical tests of such effects would be extremely valuable as they may improve the predictive power of the functional response associated with changes in spatial heterogeneity. The impact of spatially mediated effects on kill rates also depends on the spatial scale of observation (Jost et al. 2005). Jost et al. (2005) found that predator dependency and saturation were included in the best performing functional response models at all spatial scales and suggested that kill rates in the moose–wolf system on Isle Royale, Michigan, USA should be measured per single pack as a function of population densities over the whole island (i.e. at a mixed scale). At this spatial scale, Jost et al. (2005) found strong evidence for a ratio-dependent functional response and suggested that this was due to increased complexity in interference mechanisms. However, such long-term field data are seldom available from mammalian systems (Jost et al. 2005). In addition, and perhaps most importantly, the identification of the precise mechanisms of predator dependency (e.g. interference or saturation) taking place may not be possible. Models based on moment approximation have the potential to solve this latter problem by estimating separately variance and covariance effect but are also sensitive to the scale of observation (Bergström et al. 2006). Another study from the Isle Royale system shows that variation in the availability of vulnerable prey (i.e. moose over 9 years old) accounts for a larger portion of variation in wolf population growth rate than other abiotic factors (i.e. stochasticity) or total prey density (Vucetich & Peterson 2004). Determining the availability of vulnerable prey as a function of spatial features might be a promising extension of this approach. Spatial scale and predation stage The effect of spatial heterogeneity on predation, as predicted by the functional response, is also dependent on the specific predation stage. Such an effect can be either positive or negative: high levels of habitat fragmentation may increase the searching efficiency of a generalist predator (Storaas et al. 1999), while lack of snow cover may hinder the killing efficiency of a specialist (Oksanen et al. 2001). This might explain the pattern that shows a correlation between kill rates and prey density at the landscape scale (Hopcraft et al. 2005, Zub et al. 2008) and supports the theory of prey density-dependent kill rates (Palomares et al. 2001, Spong 2002). However, spatially mediated mechanisms are necessary to explain the observed predation patterns, and the functional response may change form not only at the local scale (Hopcraft et al. 2005, Jost et al. 2005, Zub et al. 2008) but also at the landscape scale when predator density dependency is taken into account (e.g. Abrams & Ginzburg 2000, Jost et al. 2005). Ryall and Fahrig (2006) observed in their review that while the majority of theoretical models fit the landscape scale, most empirical studies are conducted at the patch scale. It is therefore important that theoretical predictions of the effects of spatial heterogeneity on the predation process are tested empirically at the appropriate spatial scale (Bergström et al. 2006, Ryall & Fahrig 2006). The effect of spatial heterogeneity on kill rates is therefore sensitive to the scale of observation, and the specific predation stage on which habitat structure has its strongest impact depends, among other factors, on behavioural differences between species. For instance, in cursorial predators, such as many canids, the most © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 Predator–prey interactions in a spatial world 67 Table 3. Summary of the advantages and disadvantages or potential biases of the functional response in describing predator–prey interactions Advantages Disadvantages/potential biases Predictions have wide applicability Can easily be modified to take more parameters into account Tested on a large number of studies and with different species Sensitive to the spatial scale of observation Does not distinguish explicitly between the spatially mediated mechanisms which affect final consumption rates (but see moment approximation, in Bergström et al. 2006) critical stage of the predation process which ultimately determines the success or the failure of the hunt is the pursuit (Kunkel et al. 1999). The habitat type in which the prey is encountered is therefore less important than the terrain over which the chase takes place. On the other hand, solitary stalk–ambush predators, such as many felids, rely more heavily on good cover to approach their prey (Schaller 1972, Beier et al. 1995, Balme et al. 2007). Even for very fast predators like cheetahs Acynonix jubatu, the distance from the prey at which the chase starts can be crucial (Purchase & du Toit 2000, Mills et al. 2004). Hunting success for ambush predators is therefore associated with a high degree of structural complexity, and the habitat features of the encounter location can have a strong impact on the outcome of the hunt (Mills et al. 2004). Hence, differences between areas selected for hunting by different predator species can be explained by their different hunting strategies (Balme et al. 2007). Similarly, differences in habitat use by prey species facing the same predator may be due to differences in their anti-predator behaviour (e.g. Lingle 2002). For instance, white-tailed deer Odocoileus virginianus are fast and adopt a fleeing tactic against coyotes. Mule-deer Odocoileus hemionus, on the other hand, are slower and retreat towards higher altitudes and rugged terrain to avoid predation (Lingle 2002). The anti-predator behaviour of white-tailed deer reduces their chances of being killed once detected, while mule deer reduce their risk of encountering a predator. The functional response has great potential to describe predator–prey dynamics, taking into account spatial heterogeneity, and can take advantage of a large body of theory. The analysis of each stage of the predation process separately may, in addition, contribute to the identification of the spatial scale of relevance and the specific spatial mechanism affecting predator–prey interactions. Predator- or ratiodependent computations of the functional response may successfully take into account the effect of spatial heterogeneity based on final consumption rates directly (e.g. Jost et al. 2005) but are sensitive to the scale of analyses and in most cases do not explicitly distinguish between the specific spatially mediated mechanisms affecting the interaction (Table 3). Hence, the separation between each predation stage may contribute to our understanding of the predation process and potentially increase our ability to forecast the consequences of landscape transformation on long-term population dynamics. Spatially explicit kill rates Most empirical tests of predator–prey models including spatial heterogeneity were conducted in experimental settings (e.g. Arditi & Ginzburg 1989, Blaine & DeAngelis © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 68 L. Gorini et al. 1997, Cosner et al. 1999, Arditi et al. 2001, Bergström et al. 2006, Hammond et al. 2007). Furthermore, simulation models often rest on a number of assumptions that are not realistic in biological systems under natural conditions: e.g. identical patches (Nachman 2006), equal deterministic dynamics for all the patches after a consumer visit (Nisbet et al. 1998) and equal predation pressure in all patches (deRoos et al. 1998). A considerable effort in field data collection is necessary in order to analyze each predation stage separately in free-ranging highly mobile species in heterogeneous landscapes. The most common methods linking predation and spatial heterogeneity to habitat-related measures affecting prey vulnerability are summarized in Table 4. Most of these methods allow the distinction between at least some of the different stages of predation. The so-called giving-up density, ‘the density of resources within a patch at which an individual ceases foraging’ (Brown 1988) separates the effects of resource availability and perceived predation risk on prey space use (Andruskiw et al. 2008, Searle et al. 2008) and can be combined with information on predator movements and hunting behaviour to relate habitat heterogeneity to kill rates. Although the method is semi-experimental and applies more easily to herbivores than to other mammals, it has potential for wider application (see Andruskiw et al. 2008). Unless predator presence or absence is known at any given time, however, this method fails to take into account the effect of short-term variation in predation risk (i.e. direct predator encounters) as opposed to long-term effects (perceived predation risk in specific habitats). Kittle et al. (2008) showed the importance of this distinction in a wolf–ungulate system. Data type Hunting and anti-predator behaviour Giving up density Kill locations Snow tracking Radio tracking Field observations Capture–mark–recapture Habitat features Cover measures Visibility measures Distances to refuges Stages of predation Table 4. Field data used to relate kill rates to spatial heterogeneity Kill Consumption Kill Consumption Search Kill Consumption Search Kill Consumption Search Kill Consumption Kill Search Kill Search Kill Search Kill The table shows which stages of predation can be analyzed separately through the use of certain data types. © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 Predator–prey interactions in a spatial world 69 Studying kill and carcass locations (e.g. Hopcraft et al. 2005, Balme et al. 2007, Kauffman et al. 2007) represents the final part of the predation process but, if done in isolation, does not allow distinction between the different predation stages, nor does the method explicitly take into account failed hunting attempts. Hence, the locations can be used only to derive a relative measure of hunting success in relation to spatial heterogeneity. However, in systems where kleptoparasitism and scavenging are important, analysis of kill locations is the most useful tool for investigating the effect of habitat on the last stage of predation (e.g. Mills et al. 2004). Data based on snow tracking have great potential, as they can provide information on searching, failed hunting attempts and consumption rates. The major sources of bias are in the sampling regime (e.g. the process leading to the start of the track) and in the high variability of snow conditions, which may not permit enough of the track to be followed. These data should therefore be combined with data from radiotelemetry (Hebblewhite & Pletscher 2002, Andruskiw et al. 2008), which allows the track to be calibrated against time. Also, this method is obviously temporally and spatially constrained as appropriate snow cover is available only in certain regions and only for a certain amount of time during the year. Direct observation of encounters and field observations of hunting behaviour (Fanshaw & Fitzgibbon 1993, Cresswell & Quinn 2004) can also provide information on all stages of predation. However, complete observations are seldom available and may be biased by the differential visibility afforded by different habitats. Data on movements and behaviour of the predator obtained through Global Positioning System-telemetry can in some cases be used to interpret hunting behaviour: clusters of positions observed in wolf or lynx Lynx lynx, for instance, preying on large ungulates such as moose or roe deer Capreolus capreolus, may be interpreted as kill sites (Sand et al. 2005, Zimmermann et al. 2007, Nilsen et al. 2009b). Caution is needed as this method is sensitive to the radius chosen to define a cluster (Sand et al. 2005) and cannot be used for smaller prey that are consumed faster and in their entirety. Also, it does not provide information on the encounter rates or on failed hunts. However, it provides a large amount of data in a relatively short time, thanks to technological improvements in radio-collar equipment (but see Hebblewhite et al. 2007) and may reduce the effort required in fieldwork (Zimmermann et al. 2007). A combination of GPS-telemetry and observations of kill sites, together with observations of hunting behaviour from snow tracking or direct observations for validation purposes, may, however, provide the insight into the predation process. Measures of cover, visibility and distance to refuges can then be related to each stage of predation and can therefore provide good insight into how habitat features can affect kill rates (Fortin et al. 2005b, Hebblewhite et al. 2005, May et al. 2008). Use of capture–mark–recapture data (CMR, Lebreton et al. 1992) allows the estimation of prey survival as a function of predator abundance and/or alternative prey presence or absence, which can then be used to derive the functional response (Miller et al. 2006). CMR methods have greatly advanced in recent times, making possible the estimation of crucial demographic parameters (e.g. survival, recruitment and reproduction probability) as a function of environmental variation and predation (e.g. White & Burnham 1999, MacKenzie et al. 2006, Miller et al. 2006). Similar information on prey mortality rates can be obtained through telemetry studies (Boutin 1992), although Marshall and Boutin (1999) have also shown that to be able to distinguish correctly between different types of functional responses, a large data set is necessary, © 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 70 L. Gorini et al. especially when a high number of parameters are estimated. As observing the predation process throughout all the different stages poses practical challenges in highly mobile species, CMR or telemetry data providing information on prey mortality rates may represent a good option (Marshall & Boutin 1999, Miller et al. 2006, Nilsen et al. 2009a). Information on changes in demographic parameters of the prey population so obtained could also help us understand the relative roles played by the trait-mediated mechanisms of predation (e.g. Luttbeg & Kerby 2005, Kittle et al. 2008). DISCUSSION AND SYNTHESIS Habitat heterogeneity introduces a difference between the total prey population density and the proportion available to the predator. The density of vulnerable prey may account for a larger variation in predators’ growth rates (i.e. numerical response) than total prey density or stochasticity (e.g. Vucetich & Peterson 2004), suggesting that the difference between total and available prey could have important consequences for the population dynamics of both prey and predators. When the functional response is calculated including the effect of spatial heterogeneity, it generally takes the form of predator- or ratio-dependency in kill rates (e.g. Abrams & Ginzburg 2000). As spatially mediated factors have the potential to counteract each other under certain conditions and affect individual predation stages (e.g. prey aggregation and predator interference), studying kill rates only might not always reveal the underlying mechanisms. Theoretical advances in predator–prey dynamics should be complemented by empirical analysis of (i) kill rates at different spatial scales; (ii) the proportion of vulnerable prey within the population; and (iii) separation between each stage of predation. The first point, analysis of kill rates at different spatial scales, allows the identification of the scale of observation at which habitat structure becomes relevant, which is well defined by Englund & Cooper (2003) as ‘the smallest scale of spatial variation in a driving variable which affects the outcome of the process’. An evaluation of the relevant spatial scale of observation could be attempted either by computing the functional response at different spatial scales, as done by Jost et al. (2005), or by using statistical approaches as indicated by Bergström et al. (2006) and references therein. The second point, quantifying the proportion of the prey population which is vulnerable, should be attempted following the example of Vucetich and Peterson (2004) but also by assessing availability of vulnerable prey as a function of features of the habitat. For instance, CMR or telemetry data can be used to model survival rates in the prey population as functions of direct and indirect predation, alternative prey and spatial distribution. Spatial analyses of foraging behaviour based on telemetry data and investigated with resource selection-based models may also help us to understand the relationship between individuals’ trade-off decisions and habitat structure (May et al. 2010). The third point, making a distinction in habitat dependency between predation stages, requires, where available, knowledge of the behavioural characteristics of both the predator and the prey species examined (e.g. cursorial – pursuit; ambush–stalker – encounter rate; generalist – specialist). This knowledge will not only help us to identify the most relevant landscape properties but may also shed light on which spatially mediated mechanisms affect the predation rate, i.e. on the distinction between saturation and interference for instance. The dominating effect of spatial heterogeneity appears to be the dampening of oscilla© 2011 The Authors. Mammal Review © 2011 Mammal Society, Mammal Review, 42, 55–77 Predator–prey interactions in a spatial world 71 tion dynamics (Huffaker et al. 1963, Arditi & Ginzburg 1989, Oksanen et al. 1999, Pascual et al. 2001, Melian & Bascompte 2002, but see Bergström et al. 2006 for an example of the opposite effect), and a similar dampening effect is predicted for generalist predators (Hanski et al. 1991), as they can switch to an alternative prey if their preferred prey density falls below a certain threshold (Kjellander & Nordström 2003) or according to variation in prey vulnerability (Owen-Smith & Mills 2008). However, it has also been shown that in highly fragmented landscapes, generalists are capable of exerting high kill rates on different prey species through spill-over predation. This ‘trophic generalization’ (Melian & Bascompte 2002) is mediated by transformations of the landscape, which lead to altered kill rates, and the consequences on population dynamics differ greatly depending on the species considered (Ryall & Fahrig 2006). As more interactions are taken into account (i.e. in community analyses), a larger number of indirect effects appear and affect these interactions. However, as space-use itself is the result of multiple mechanisms (e.g. predation risk, resource availability, intra-guild interactions), insight into predation ecology through a spatial approach may strongly increase our understanding of predator–prey interactions in heterogeneous landscapes. 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