The influence of prey consumption and demographic stochasticity on population growth rate of Isle Royale wolves Canis lupus

The relationship between the rates of prey capture and predator population growth is a fundamental aspect of predation, yet it is rarely measured for vertebrate predators. For the isolated wolf population on Isle Royale, annual variation in kill rate explains 22% of the variation in wolf population growth rate. From the slope of this relationship, we estimate that the production efficiency (ratio of production to respiration) of wolves is between 0.5% and 1.5%. More generally, we assess the relative extent to which wolf population growth rate is affected by density dependence, prey availability (moose, Alces alces ), winter weather, and demographic stochasticity. Prey availability explains the most variation in wolf growth rate (42%), but this is only recognized after accounting for the influence of a disease-induced population crash and age structure of the prey population (i.e. number of vulnerable moose, >9 years of age). Demographic stochasticity accounts for approximately 30% of the variation in wolf growth rate. This recognition is important, but not surprising, given that the average population size of Isle Royale wolves is 22. Previous work indicates that the effect of winter climate, as mediated through prey vulnerability and kill rates, is substantial. This work indicates that the direct effect of winter climate is weak, and explains only about 4% of the variation in wolf growth rate (P=0.10).     

Conservation of wildlife populations: factoring in incremental disturbance

Progressive anthropogenic disturbance can alter ecosystem organization potentially causing shifts from one stable state to another. This potential for ecosystem shifts must be considered when establishing targets and objectives for conservation. We ask whether a predator–prey system response to incremental anthropogenic disturbance might shift along a disturbance gradient and, if it does, whether any disturbance thresholds are evident for this system. Development of linear corridors in forested areas increases wolf predation effectiveness, while high density of development provides a safe‐haven for their prey. If wolves limit moose population growth, then wolves and moose should respond inversely to land cover disturbance. Using general linear model analysis, we test how the rate of change in moose (Alces alces) density and wolf (Canis lupus) harvest density are influenced by the rate of change in land cover and proportion of land cover disturbed within a 300,000 km² area in the boreal forest of Alberta, Canada. Using logistic regression, we test how the direction of change in moose density is influenced by measures of land cover change. In response to incremental land cover disturbance, moose declines occurred where <43% of land cover was disturbed; in such landscapes, there were high rates of increase in linear disturbance and wolf density increased. By contrast, moose increases occurred where >43% of land cover was disturbed and wolf density declined. Wolves and moose appeared to respond inversely to incremental disturbance with the balance between moose decline and wolf increase shifting at about 43% of land cover disturbed. Conservation decisions require quantification of disturbance rates and their relationships to predator–prey systems because ecosystem responses to anthropogenic disturbance shift across disturbance gradients.     

Summer kill rates and predation pattern in a wolf–moose system: can we rely on winter estimates?

So far the vast majority of studies on large carnivore predation, including kill rates and consumption, have been based on winter studies. Because large carnivores relying on ungulates as prey often show a preference for juveniles, kill rates may be both higher and more variable during the summer season than during the rest of the year leading to serious underestimates of the total annual predation rate. This study is the first to present detailed empirical data on kill rates and prey selection in a wolf–moose system during summer (June–September) as obtained by applying modern Global Positioning System-collar techniques on individual wolves (Canis lupus) in Scandinavia. Moose (Alces alces) was the dominant prey species both by number (74.4%) and biomass (95.6%); 89.9% of all moose killed were juveniles, representing 76.0% of the biomass consumed by wolves. Kill rate in terms of the kilogram biomass/kilogram wolf per day averaged 0.20 (range: 0.07–0.32) among wolf territories and was above, or well above, the daily minimum food requirements in most territories. The average number of days between moose kills across wolf territories and study periods was 1.71 days, but increased with time and size of growing moose calves during summer. Over the entire summer (June–September, 122 days), a group (from two to nine) of wolves killed a total of 66 (confidence interval 95%; 56–81) moose. Incorporation of body growth functions of moose calves and yearlings and wolf pups over the summer period showed that wolves adjusted their kill rate on moose, so the amount of biomass/kilogram wolf was relatively constant or increased. The kill rate was much higher (94–116%) than estimated from the winter period. As a consequence, projecting winter kill rates to obtain annual estimates of predation in similar predator–prey systems may result in a significant underestimation of the total number of prey killed. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Time‐to‐kill: measuring attack rates in a heterogenous landscape with multiple prey types

Although spatial heterogeneity of prey and landscapes are known to contribute to variation around predator‐prey functional response models, few studies have quantified these effects. We illustrate a new approach using data from winter movement paths of GPS‐collared wolves in the Rocky Mountains of Canada and time‐to‐event models with competing risks for measuring the effect of prey and landscape characteristics on the time‐to‐kill, which is the reciprocal of attack rate (aN) in a Holling's functional response. We evaluated 13 a priori models representing hypothesized mechanisms influencing attack rates in a heterogeneous landscape with two prey types. Models ranged from variants on Holling's disc equation, including search rate and prey density, to a full model including prey density and patchiness, search rates, satiation, and landscape features, which were measured along the wolf's movement path. Movement rates of wolves while searching explained more of the variation in time‐to‐kill than prey densities. Wolves did not compensate for low prey density by increasing movement rates and there was little evidence that spatial aggregation of prey influenced attack rates in this multi‐prey system. The top model for predicting time‐to‐kill included only search rate and landscape features. Wolves killed prey more quickly in flat terrain, likely due to increased vulnerability from accumulated snow, whereas attack rates were lower when wolves hunted near human‐made features presumably due to human disturbance. Understanding the sources of variation in attack rates provides refinements to functional response models that can lead to more effective predator–prey management in human‐dominated landscapes.     

Mobility of moose—comparing the effects of wolf predation risk,reproductive status,and seasonality

In a predator–prey system, prey species may adapt to the presence of predators with behavioral changes such as increased vigilance, shifting habitats, or changes in their mobility. In North America, moose (Alces alces) have shown behavioral adaptations to presence of predators, but such antipredator behavioral responses have not yet been found in Scandinavian moose in response to the recolonization of wolves (Canis lupus). We studied travel speed and direction of movement of GPS‐collared female moose (n = 26) in relation to spatiotemporal differences in wolf predation risk, reproductive status, and time of year. Travel speed was highest during the calving (May–July) and postcalving (August–October) seasons and was lower for females with calves than females without calves. Similarly, time of year and reproductive status affected the direction of movement, as more concentrated movement was observed for females with calves at heel, during the calving season. We did not find support for that wolf predation risk was an important factor affecting moose travel speed or direction of movement. Likely causal factors for the weak effect of wolf predation risk on mobility of moose include high moose‐to‐wolf ratio and intensive hunter harvest of the moose population during the past century.     

Modeling female brown bear kill rates on moose calves using global positioning satellite data

The recent development in Global Positioning System (GPS) techniques has started a new era in predation studies. Estimates of kill rates based on animal movements and GPS relocation clusters have proven to be valid in several obligatory carnivores. The main focus has been to obtain accurate mean predation estimates for the management of wildlife populations. We present a model to estimate individual kill rates of moose calves by adult female brown bears in Sweden, based on spatiotemporal clustering of 30,889 bear GPS relocations and 71 moose calves verified killed during 714 field investigations in 2004–2006. In this virtually single-predator single large prey system, the omnivorous brown bear is an efficient predator on moose calves up to 4 weeks of age. The top model set only included models with cluster radii of 30 m or 50 m, indicating very high kill-site fidelity. The best model included a cluster radius of 30 m and number of periods of bear activity at the kill site as a single covariate. The mean estimated individual kill rate of 7.6 ± 0.71 (n = 18, ± SE) moose calves per calving season is comparable to the estimate of 6.8 from a previous study of radio-tracked moose in our study area, though at a lower moose/bear ratio. The mean annual kill rates varied from 6.1 to 9.4 calves per bear. The estimated individual kill rates ranged from 2 to 15 calves per season, indicating a large individual variation in hunting skills and possibly effort. Predation and livestock depredation represent a core conflict between humans and carnivores in rural Scandinavia. Accurate predation estimates represent an important step in quantifying costs of carnivores and reducing human–carnivore conflicts. Our technique may be applied in the exploration of predation mechanisms and predator–prey interactions, and contribute to the old and global debate of problem individuals in livestock depredation. © 2012 The Wildlife Society.     

Cross-continental differences in patterns of predation: will naive moose in Scandinavia ever learn?

Predation has been recognized as a major selective force in the evolution of behavioural characteristics of mammals. As a consequence of local predator extinction, prey may lose knowledge about natural predators but usually express behavioural adjustments after return of predators. Human harvest may replace natural predation but prey selection may differ from that of natural predators leading to a change in the behavioural response of prey. We show that hunting success (HS) of re-colonizing wolves (Canis lupus) on moose (Alces alces) in Scandinavia was higher than reported in North America, where moose have been continuously exposed to wolves and grizzly bears. We found no evidence that moose expressed behavioural adjustments that lowered the HS of wolves in territories that had been occupied by wolves for up to 21 years. Moose behaviour towards wolves and humans typically differs in Scandinavia compared to North America. We explain the differences found to be caused by variation in predation pressure by large carnivores and the rate, and mode, of human harvest during the twentieth century.     

Consequences of ratio-dependent predation by wolves for elk population dynamics

A growing number of studies suggest ratio-dependence may be common in many predator–prey systems, yet in large mammal systems, evidence is limited to wolves and their prey in Isle Royale and Yellowstone. More importantly, the consequences of ratio-dependent predation have not been empirically examined to understand the implications for prey. Wolves recolonized Banff National Park in the early 1980s, and recovery was correlated with significant elk declines. I used time-series data of wolf kill rates of elk, wolf and elk densities in winter from 1985–2007 to test for support for prey-, ratio-, or predator dependent functional and numeric responses of wolf killing rate to elk density. I then combined functional and numeric responses to estimate the total predation response to identify potential equilibrium states. Evidence suggests wolf predation on elk was best described by a type II ratio-dependent functional response and a type II numeric response that lead to inversely density-dependent predation rate on elk. Despite support for ratio-dependence, like other wolf-prey systems, there was considerable uncertainty amongst functional response models, especially at low prey densities. Consistent with predictions from ratio-dependent models, however, wolves contributed to elk population declines of over 80 % in our Banff system. Despite the statistical signature for ratio-dependence, the biological mechanism remains unknown and may be related to multi-prey dynamics in our system. Regardless, ratio-dependent models strike a parsimonious balance between theory and empiricism, and this study suggests that large mammal ecologists need to consider ratio-dependent models in predator–prey dynamics.     

Habitat heterogeneity and mammalian predator–prey interactions

• 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 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.

     

Predicting prey population dynamics from kill rate, predation rate and predator-prey ratios in three wolf-ungulate systems

1.?Predation rate (PR) and kill rate are both fundamental statistics for understanding predation. However, relatively little is known about how these statistics relate to one another and how they relate to prey population dynamics. We assess these relationships across three systems where wolf-prey dynamics have been observed for 41 years (Isle Royale), 19 years (Banff) and 12 years (Yellowstone). 2.?To provide context for this empirical assessment, we developed theoretical predictions of the relationship between kill rate and PR under a broad range of predator-prey models including predator-dependent, ratio-dependent and Lotka-Volterra dynamics. 3.?The theoretical predictions indicate that kill rate can be related to PR in a variety of diverse ways (e.g. positive, negative, unrelated) that depend on the nature of predator-prey dynamics (e.g. structure of the functional response). These simulations also suggested that the ratio of predator-to-prey is a good predictor of prey growth rate. That result motivated us to assess the empirical relationship between the ratio and prey growth rate for each of the three study sites. 4.?The empirical relationships indicate that PR is not well predicted by kill rate, but is better predicted by the ratio of predator-to-prey. Kill rate is also a poor predictor of prey growth rate. However, PR and ratio of predator-to-prey each explained significant portions of variation in prey growth rate for two of the three study sites. 5.?Our analyses offer two general insights. First, Isle Royale, Banff and Yellowstone are similar insomuch as they all include wolves preying on large ungulates. However, they also differ in species diversity of predator and prey communities, exploitation by humans and the role of dispersal. Even with the benefit of our analysis, it remains difficult to judge whether to be more impressed by the similarities or differences. This difficulty nicely illustrates a fundamental property of ecological communities. Second, kill rate is the primary statistic for many traditional models of predation. However, our work suggests that kill rate and PR are similarly important for understanding why predation is such a complex process.     

Density‐independent predation affects migrants and residents equally in a declining partially migratory elk population

Migration is expected to benefit individuals through exposure to higher quality forage and reducing predation rates more than non‐migratory conspecifics. Previous studies of partially migratory ungulates (with migrant and resident individuals) have focused on bottom–up factors regulating resident and migrant segments, yet differential predation between strategies could also be a density‐dependent regulatory mechanism. Our study tested for density‐dependence in mortality, as well as mechanisms of ­bottom–up or top–down regulation in the resident and migrant portions of the partially migratory Ya Ha Tinda elk population. We tested for density dependence in adult female and juvenile survival rates, and then discriminated between predator‐ and food‐regulation hypotheses by testing for density‐dependence amongst mortality causes for adult female elk. Notably, the population declined almost 70% from near previously published estimates of carrying capacity over 10 years, providing ideal conditions to test for density dependence. In contrast to predictions, we found only weak support for density dependence in adult survival and juvenile survival. We also found few differences between migrant and resident elk in adult or juvenile survival, though juvenile survival differences were biologically significant. Predation by humans and grizzly bears was density dependent, but similar between migratory strategies. Predation by wolves was the leading known cause of mortality, yet remained constant with declining elk density equally for both migrant and resident elk, indicating wolf predation was density‐independent. Instead of being strongly regulated by food or predation, we found adult female survival was driven by density‐independent predation and climatic factors. The few differences between migratory strategies suggest equivalent fitness payoffs for migrants and residents. This population is being limited by density‐independent predation leading to declines of both migratory strategies. Our results challenge classical predator–prey theory, and call for better integration between predator–prey and migration theory.     

Encounter frequencies between GPS-collared wolves (Canis lupus) and moose (Alces alces) in a Scandinavian wolf territory

Over 6,000 GPS fixes from two wolves (Canis lupus) and 30,000 GPS fixes from five moose (Alces alces) in a wolf territory in southern Scandinavia were used to assess the static and dynamic interactions between predator and prey individuals. Our results showed that wolves were closer to some of the moose when inside their home ranges than expected if they had moved independently of each other, and we also found a higher number of close encounters (<500 m) than expected. This suggests that the wolves were actively seeking the individual moose within their territory. Furthermore, the wolves showed a preference for moving on gravel forest roads, which may be used as convenient travel routes when patrolling the territory and seeking areas where the moose are. However, due to the particularly large size of the wolf territory combined with relatively high moose densities, the wolves generally spent a very small proportion of their time inside the home range of each individual moose, and the frequency of encounters between the wolves and any particular moose was very low. We suggest that the high moose:wolf ratio in this large Scandinavian wolf territory compared to that typically occurring in North America, results in a relatively low encounter frequency and a low predation risk for individual moose, as the predation pressure is spread over a high number of prey individuals.     

Seasonal patterns of predation for gray wolves in the multi-prey system of Yellowstone National Park

1.?For large predators living in seasonal environments, patterns of predation are likely to vary among seasons because of related changes in prey vulnerability. Variation in prey vulnerability underlies the influence of predators on prey populations and the response of predators to seasonal variation in rates of biomass acquisition. Despite its importance, seasonal variation in predation is poorly understood. 2.?We assessed seasonal variation in prey composition and kill rate for wolves Canis lupus living on the Northern Range (NR) of Yellowstone National Park. Our assessment was based on data collected over 14 winters (1995-2009) and five spring-summers between 2004 and 2009. 3.?The species composition of wolf-killed prey and the age and sex composition of wolf-killed elk Cervus elaphus (the primary prey for NR wolves) varied among seasons. 4.?One's understanding of predation depends critically on the metric used to quantify kill rate. For example, kill rate was greatest in summer when quantified as the number of ungulates acquired per wolf per day, and least during summer when kill rate was quantified as the biomass acquired per wolf per day. This finding contradicts previous research that suggests that rates of biomass acquisition for large terrestrial carnivores tend not to vary among seasons. 5.?Kill rates were not well correlated among seasons. For example, knowing that early-winter kill rate is higher than average (compared with other early winters) provides little basis for anticipating whether kill rates a few months later during late winter will be higher or lower than average (compared with other late winters). This observation indicates how observing, for example, higher-than-average kill rates throughout any particular season is an unreliable basis for inferring that the year-round average kill rate would be higher than average. 6.?Our work shows how a large carnivore living in a seasonal environment displays marked seasonal variation in predation because of changes in prey vulnerability. Patterns of wolf predation were influenced by the nutritional condition of adult elk and the availability of smaller prey (i.e. elk calves, deer). We discuss how these patterns affect our overall understanding of predator and prey population dynamics.     

Metrics of predation: perils of predator-prey ratios

We developed an original modeling approach using program Stella® to investigate the usefulness of predator–prey ratios (PPRs) for interpreting top-down and bottom-up forcing on moose Alces alces. We included density-dependent feedbacks for the moose population, allowed K to vary based on amount and quality of available forage for moose, integrated effects of compensatory mortality, and added time lags in wolves Canis lupus tracking the moose population. Modeling scenarios we developed included bottom-up and top-down regulation as predetermined outcomes. We then evaluated whether PPRs would reflect the various combinations of trajectories of predator and prey populations under top-down versus bottom-up regulation. The resulting patterns of PPRs were impossible to disentangle from one another, and did not provide reliable insights into whether top-down or bottom-forcing occurred, especially over short time spans where critical decisions related to management of moose and wolves might be necessary. Only under top-down regulation did PPRs reflect the degree of predation experienced by moose, but in that instance, knowledge of top-down regulation must be known a priori to correctly interpret PPRs. Potential problems with interpreting PPRs include their double-variable nature, which resulted in the failure to reflect patterns of increase and decrease for predators and prey. We suggest that confidence intervals for PPRs be calculated from a binomial, similar to that proposed for sex and age ratios, which should help discourage the inappropriate use of this metric. We caution that the temptation to use PPRs often is irresistible, but their reliability is highly questionable. We provide an alternative method to using PPRs or other predation metrics for determining whether top-down or bottom-up forcing is occurring by adopting an approach based on the physical condition and life-history characteristics of prey.     

Predicting the potential demographic impact of predators on their prey: a comparative analysis of two carnivore-ungulate systems in Scandinavia

1. Understanding the role of predation in shaping the dynamics of animal communities is a fundamental issue in ecological research. Nevertheless, the complex nature of predator-prey interactions often prevents researchers from modelling them explicitly. 2. By using periodic Leslie-Usher matrices and a simulation approach together with parameters obtained from long-term field projects, we reconstructed the underlying mechanisms of predator-prey demographic interactions and compared the dynamics of the roe deer-red fox-Eurasian lynx-human harvest system with those of the moose-brown bear-gray wolf-human harvest system in the boreal forest ecosystem of the southern Scandinavian Peninsula. 3. The functional relationship of both roe deer and moose λ to changes in predation rates from the four predators was remarkably different. Lynx had the strongest impact among the four predators, whereas predation rates by wolves, red foxes, or brown bears generated minor variations in prey population λ. Elasticity values of lynx, wolf, fox and bear predation rates were -0·157, -0·056, -0·031 and -0·006, respectively, but varied with both predator and prey densities. 4. Differences in predation impact were only partially related to differences in kill or predation rates, but were rather a result of different distribution of predation events among prey age classes. Therefore, the age composition of killed individuals emerged as the main underlying factor determining the overall per capita impact of predation. 5. Our results confirm the complex nature of predator-prey interactions in large terrestrial mammals, by showing that different carnivores preying on the same prey species can exert a dramatically different demographic impact, even in the same ecological context, as a direct consequence of their predation patterns. Similar applications of this analytical framework in other geographical and ecological contexts are needed, but a more general evaluation of the subject is also required, aimed to assess, on a broader systematic and ecological range, what specific traits of a carnivore are most related to its potential impact on prey species.     

Prey: predator ratio dependence in the functional response of a freshwater amphipod

1. First known for their shredding activity, freshwater amphipods also behave as active predators with consequences for prey population regulation and amphipod coexistence in the context of biological invasions. 2. A way to quantify predation is to determine the average consumption rate per predator, also known as its functional response (FR). 3. Although amphipods are gregarious and can display social interactions that can alter per capita consumption rates, previous studies using the FR approach to investigate amphipod predation ignored such potential mutual interference because they did not consider variations in predator density. 4. We investigated the FR of Echinogammarus berilloni feeding on dipteran larvae with joint variations in prey and predator densities. This bivariate experimental design allowed us to estimate interference and to compare the fits of the three main classes of theoretical FR models, in which the predation rate is a function of prey density alone (prey‐dependent models), of both prey and predator densities (predator‐dependent models) or of the prey‐to‐predator ratio (ratio‐dependent models). 5. The Arditi–Ginzburg ratio‐dependent FR model provided the best representation of the FR of E. berilloni, whose predation rate showed a decelerating rise to a horizontal asymptote as prey abundance increased. 6. Ratio dependence means that mutual interference between amphipods leads to prey sharing. Mutual interference is likely to vary between amphipod species, depending on their level of aggressiveness.     

Optimal predator management for mountain sheep conservation depends on the strength of mesopredator release

Large predators often suppress ungulate population growth, but they may also suppress the abundance of smaller predators that prey on neonatal ungulates. Antagonistic interactions among predators may therefore need to be integrated into predator–prey models to effectively manage ungulate–predator systems. We present a modeling framework that examines the net impact of interacting predators on the population growth rate of shared prey, using interactions among wolves Canis lupus, coyotes Canis latrans and Dall sheep Ovis dalli dalli as a case study. Wolf control is currently employed on approximately 16 million ha in Alaska to increase the abundance of ungulates for human harvest. We hypothesized that the positive effects of wolf control on Dall sheep population growth could be counteracted by increased levels of predation by coyotes. Coyotes and Dall sheep adult females (ewes) and lambs were radiocollared in the Alaska Range from 1999–2005 to estimate fecundity, age‐specific survival rates, and causes of mortality in an area without wolf control. We used stage‐structured population models to simulate the net effect of wolf control on Dall sheep population growth (λ). Our models accounted for stage‐specific predation rates by wolves and coyotes, compensatory mortality, and the potential release of coyote populations due to wolf control. Wolves were the main predators of ewes, coyotes were the main predators of lambs, and wolves were the main source of mortality for coyotes. Population models predicted that wolf control could increase sheep λ by 4% per year in the absence of mesopredator release. However, if wolf control released coyote populations, our models predicted that sheep λ could decrease by up to 3% per year. These results highlight the importance of integrating antagonistic interactions among predators into predator–prey models, because the net effect of predator management on shared prey can depend critically on the strength of mesopredator release.     

Comparative Patterns of Predation by Cougars and Recolonizing Wolves in Montana's Madison Range

Abstract: Numerous studies have documented how prey may use antipredator strategies to reduce the risk of predation from a single predator. However, when a recolonizing predator enters an already complex predator—prey system, specific antipredator behaviors may conflict and avoidance of one predator may enhance vulnerability to another. We studied the patterns of prey selection by recolonizing wolves (Canis lupus) and cougars (Puma concolor) in response to prey resource selection in the northern Madison Range, Montana, USA. Elk (Cervus elaphus) were the primary prey for wolves, and mule deer (Odocoileus hemionus) were the primary prey for cougars, but elk made up an increasingly greater proportion of cougar kills annually. Although both predators preyed disproportionately on male elk, wolves were most likely to prey on males in poor physical condition. Although we found that the predators partitioned hunting habitats, structural complexity at wolf kill sites increased over time, whereas complexity of cougar kill sites decreased. We concluded that shifts by prey to structurally complex refugia were attempts by formerly naïve prey to lessen predation risk from wolves; nevertheless, shifting to more structurally complex refugia might have made prey more vulnerable to cougars. After a change in predator exposure, use of refugia may represent a compromise to minimize overall risk. As agencies formulate management strategies relative to wolf recolonization, the potential for interactive predation effects (i.e., facilitation or antagonism) should be considered.     

Habitat-dependent foraging in a classic predator–prey system: a fable from snowshoe hares

Current research contrasting prey habitat use has documented, with virtual unanimity, habitat differences in predation risk. Relatively few studies have considered, either in theory or in practice, simultaneous patterns in prey density. Linear predator–prey models predict that prey habitat preferences should switch toward the safer habitat with increasing prey and predator densities. The density‐dependent preference can be revealed by regression of prey density in safe habitat versus that in the riskier one (the isodar). But at this scale, the predation risk can be revealed only with simultaneous estimates of the number of predators, or with their experimental removal. Theories of optimal foraging demonstrate that we can measure predation risk by giving‐up densities of resource in foraging patches. The foraging theory cannot yet predict the expected pattern as predator and prey populations covary. Both problems are solved by measuring isodars and giving‐up densities in the same predator–prey system. I applied the two approaches to the classic predator–prey dynamics of snowshoe hares in northwestern Ontario, Canada. Hares occupied regenerating cutovers and adjacent mature‐forest habitat equally, and in a manner consistent with density‐dependent habitat selection. Independent measures of predation risk based on experimental, as well as natural, giving‐up densities agreed generally with the equal preference between habitats revealed by the isodar. There was no apparent difference in predation risk between habitats despite obvious differences in physical structure. Complementary studies contrasting a pair of habitats with more extreme differences confirmed that hares do alter their giving‐up densities when one habitat is clearly superior to another. The results are thereby consistent with theories of adaptive behaviour. But the results also demonstrate, when evaluating differences in habitat, that it is crucial to let the organisms we study define their own habitat preference.     

Moose movement rates are altered by wolf presence in two ecosystems

Predators directly impact prey populations through lethal encounters, but understanding nonlethal, indirect effects is also critical because foraging animals often face trade‐offs between predator avoidance and energy intake. Quantifying these indirect effects can be difficult even when it is possible to monitor individuals that regularly interact. Our goal was to understand how movement and resource selection of a predator (wolves; Canis lupus) influence the movement behavior of a prey species (moose; Alces alces). We tested whether moose avoided areas with high predicted wolf resource use in two study areas with differing prey compositions, whether avoidance patterns varied seasonally, and whether daily activity budgets of moose and wolves aligned temporally. We deployed GPS collars on both species at two sites in northern Minnesota. We created seasonal resource selection functions (RSF) for wolves and modeled the relationship between moose first‐passage time (FPT), a method that discerns alterations in movement rates, and wolf RSF values. Larger FPT values suggest rest/foraging, whereas shorter FPT values indicate travel/fleeing. We found that the movements of moose and wolves peaked at similar times of day in both study areas. Moose FPTs were 45% lower in areas most selected for by wolves relative to those avoided. The relationship between wolf RSF and moose FPT was nonlinear and varied seasonally. Differences in FPT between low and high RSF values were greatest in winter (?82.1%) and spring (?57.6%) in northeastern Minnesota and similar for all seasons in the Voyageurs National Park ecosystem. In northeastern Minnesota, where moose comprise a larger percentage of wolf diet, the relationship between moose FPT and wolf RSF was more pronounced (ave. across seasons: ?60.1%) than the Voyageurs National Park ecosystem (?30.4%). These findings highlight the role wolves can play in determining moose behavior, whereby moose spend less time in areas with higher predicted likelihood of wolf resource selection.