Eric Vander Wal

Considering ecological dynamics in resource selection functions.

1. Describing distribution and abundance is requisite to exploring interactions between organisms and their environment. Recently, the resource selection function (RSF) has emerged to replace many of the statistical procedures used to quantify resource selection by animals. 2. A RSF is defined by characteristics measured on resource units such that its value for a unit is proportional to the probability of that unit being used by an organism. It is solved using a variety of techniques, particularly the binomial generalized linear model. 3. Observing dynamics in a RSF - obtaining substantially different functions at different times or places for the same species - alerts us to the varying ecological processes that underlie resource selection. 4. We believe that there is a need for us to reacquaint ourselves with ecological theory when interpreting RSF models. We outline a suite of factors likely to govern ecologically based variation in a RSF. In particular, we draw attention to competition and density-dependent habitat selection, the role of predation, longitudinal changes in resource availability and functional responses in resource use. 5. How best to incorporate governing factors in a RSF is currently in a state of development; however, we see promise in the inclusion of random as well as fixed effects in resource selection models, and matched case-control logistic regression. 6. Investigating the basis of ecological dynamics in a RSF will allow us to develop more robust models when applied to forecasting the spatial distribution of animals. It may also further our understanding of the relative importance of ecological interactions on the distribution and abundance of species.

Increasing density leads to generalization in both coarse‐grained habitat selection and fine‐grained resource selection in a large mammal

Density is a fundamental driver of many ecological processes including habitat selection. Theory on density-dependent habitat selection predicts that animals should be distributed relative to profitability of habitat, resulting in reduced specialization in selection (i.e. generalization) as density increases and competition intensifies. Despite mounting empirical support for density-dependent habitat selection using isodars to describe coarse-grained (interhabitat) animal movements, we know little of how density affects fine-grained resource selection of animals within habitats [e.g. using resource selection functions (RSFs)]. Using isodars and RSFs, we tested whether density simultaneously modified habitat selection and within-habitat resource selection in a rapidly growing population of feral horses (Equus ferus caballus Linnaeus; Sable Island, Nova Scotia, Canada; 42% increase in population size from 2008 to 2012). Among three heterogeneous habitat zones on Sable Island describing population clusters distributed along a west-east resource gradient (west-central-east), isodars revealed that horses used available habitat in a density-dependent manner. Intercepts and slopes of isodars demonstrated a pattern of habitat selection that first favoured the west, which generalized to include central and east habitats with increasing population size consistent with our understanding of habitat quality on Sable Island. Resource selection functions revealed that horses selected for vegetation associations similarly at two scales of extent (total island and within-habitat zone). When densities were locally low, horses were able to select for sites of the most productive forage (grasslands) relative to those of poorer quality. However, as local carrying capacity was approached, selection for the best of available forage types weakened while selection for lower-quality vegetation increased (and eventually exceeded that of grasslands). Isodars can effectively describe coarse-grained habitat selection in large mammals. Our study also shows that the main predictions of density-dependent habitat selection are highly relevant to our interpretation of RSFs in space and time. At low but not necessarily high population size, density will be a leading indicator of habitat quality. Fitness maximization from specialist vs. generalist strategies of habitat and resource selection may well be apparent at multiple spatial extents and grains of resolution.

Influence of landscape and social interactions on transmission of disease in a social cervid

The mechanisms of pathogen transmission are often social behaviours. These occur at local scales and are affected by landscape‐scale population structure. Host populations frequently exist in patchy and isolated environments that create a continuum of genetic and social familiarity. Such variability has an important multispatial effect on pathogen spread. We assessed elk dispersal (i.e. likelihood of interdeme pathogen transmission) through spatially explicit genetic analyses. At a landscape scale, the elk population was composed of one cluster within a southeast‐to‐northwest cline spanning three spatially discrete subpopulations of elk across two protected areas in Manitoba (Canada). Genetic data are consistent with spatial variability in apparent prevalence of bovine tuberculosis (TB) in elk. Given the existing population structure, between‐subpopulation spread of disease because of elk dispersal is unlikely. Furthermore, to better understand the risk of spread and distribution of the TB, we used a combination of close‐contact logging biotelemetry and genetic data, which highlights how social intercourse may affect pathogen transmission. Our results indicate that close‐contact interaction rate and duration did not covary with genetic relatedness. Thus, direct elk‐to‐elk transmission of disease is unlikely to be constrained to related individuals. That social intercourse in elk is not limited to familial groups provides some evidence pathogen transmission may be density‐dependent. We show that the combination of landscape‐scale genetics, relatedness and local‐scale social behaviours is a promising approach to understand and predict landscape‐level pathogen transmission within our system and within all social ungulate systems affected by transmissible diseases.

Applying evolutionary concepts to wildlife disease ecology and management.

Existing and emerging infectious diseases are among the most pressing global threats to biodiversity, food safety and human health. The complex interplay between host, pathogen and environment creates a challenge for conserving species, communities and ecosystem functions, while mediating the many known ecological and socio‐economic negative effects of disease. Despite the clear ecological and evolutionary contexts of host–pathogen dynamics, approaches to managing wildlife disease remain predominantly reactionary, focusing on surveillance and some attempts at eradication. A few exceptional studies have heeded recent calls for better integration of ecological concepts in the study and management of wildlife disease; however, evolutionary concepts remain underused. Applied evolution consists of four principles: evolutionary history, genetic and phenotypic variation, selection and eco‐evolutionary dynamics. In this article, we first update a classical framework for understanding wildlife disease to integrate better these principles. Within this framework, we explore the evolutionary implications of environment–disease interactions. Subsequently, we synthesize areas where applied evolution can be employed in wildlife disease management. Finally, we discuss some future directions and challenges. Here, we underscore that despite some evolutionary principles currently playing an important role in our understanding of disease in wild animals, considerable opportunities remain for fostering the practice of evolutionarily enlightened wildlife disease management.

Designating Seasonality Using Rate of Movement

Abstract Traditionally, seasons for animals have been designated based on single external variables such as climate or plant phenology, rather than an animals response to the dynamic environments within which it lives. By interpreting a rate of movement function of cumulative movement through time we established a method that distinguishes transitions between behaviors limited by winter habitat conditions from those present during summer. Identification of these time periods provides temporal definition to subsequent home-range analyses and use–availability comparisons. We used location data from 32 Global Positioning System–collared female moose (Alces alces) to demonstrate the method. We used model selection (Akaikes Information Criterion) to differentiate between candidate rate of movement response curves. Of 32 moose, 29 clearly conformed to an annual movement pattern described by a logistic curve, with increased rates of movement in summer compared to winter. Conversely, 3 aberrant individuals did not alter their movement rate through the year and were best fit with a linear response curve. The seasonal rate of movement model we developed suggests an average summer period of 122 days (median  =  119 days, range  =  96–173 days) for moose in northwestern Ontario, Canada. The rate of movement model we applied to individuals indicated 1 May as the median date for the winter–summer transition (range  =  2 Apr–24 May), and the median transition from summer to winter was 25 August (range  =  1 Aug–23 Oct). Wide variation in timing and duration of summer and winter seasons among individuals demonstrates potential failure of the single external variable approach to capture the suite of factors potentially influencing animal behaviors. By plotting cumulative distance moved throughout the year, we elucidated individual variation in response to known and unknown variables that affect animal movement. Accounting for variability among individuals in designation of biologically significant temporal boundaries is critical to delineation of seasonally important habitats for conservation and sustainability of healthy wildlife populations.

Factors driving variation in movement rate and seasonality of sympatric ungulates

Abstract Defining biologically relevant seasons is a critical issue in the interpretation of animal space-use studies. Moreover, understanding the effects of extrinsic (e.g., predation risk) and intrinsic (e.g., age and sex) factors on individual differences in seasonal transition dates will deepen our understanding of the mechanisms driving animal movement and potentially population dynamics. We used nonlinear modeling of movement rate over time using global positioning system–collared nonmigratory elk (Cervus elaphus manitobensis) and white-tailed deer (Odocoileus virginianus) in southern Manitoba, Canada, to derive species- and sex-specific seasonal transition dates. In addition, we used variables known to influence timing of migration in migratory populations to explain individual differences in seasonal transition dates. We found ecologically important differences in start and length of seasons between male and female elk and white-tailed deer. Individual differences in seasonal transition dates were large, and could be explained by a combination of intrinsic and extrinsic factors. Age-class of the individual animal and elevation influenced timing of winter, spring, and date of parturition, whereas predation risk from wolves (Canis lupus) influenced onset of spring, summer, and autumn. Our findings suggest that similar extrinsic and intrinsic factors can influence both large- (i.e., migratory) and small-scale movement patterns and can be used effectively to empirically define biologically relevant seasons for sympatric large herbivores.

Density-Dependent Effects on Group Size Are Sex-Specific in a Gregarious Ungulate

Density dependence can have marked effects on social behaviors such as group size. We tested whether changes in population density of a large herbivore (elk, Cervus canadensis) affected sex-specific group size and whether the response was density- or frequency-dependent. We quantified the probability and strength of changes in group sizes and dispersion as population density changed for each sex. We used group size data from a population of elk in Manitoba, Canada, that was experimentally reduced from 1.20 to 0.67 elk/km2 between 2002 and 2009. Our results indicated that functional responses of group size to population density are sex-specific. Females showed a positive density-dependent response in group size at population densities ≥0.70 elk/km2 and we found evidence for a minimum group size at population density ≤0.70 elk/km2. Changes in male group size were also density-dependent; however, the strength of the relationship was lower than for females. Density dependence in male group size was predominantly a result of fusion of solitary males into larger groups, rather than fusion among existing groups. Our study revealed that density affects group size of a large herbivore differently between males and females, which has important implications for the benefits e.g., alleviating predation risk, and costs of social behaviors e.g., competition for resources and mates, and intra-specific pathogen transmission.

Spatial and Temporal Factors Influencing Sightability of Elk

ABSTRACT Few tracking studies consider seasonal changes in ability to re-sight wildlife, despite potential for biases in sightability to mislead our interpretation of models of movement and abundance. We developed seasonal sightability models based on visual observations of radio-collared elk (Cervus elaphus) in Manitoba, Canada, through 6 seasons. We located 377 elk 8,862 times using aerial telemetry from 2002 to 2009. We tested the hypothesis that sites where we were able to visually observe radio-collared elk during aerial telemetry differed from sites where collared elk were known to be present but could not be sighted. Relationships varied with season and elk sightability was influenced by forest type, habitat openness, distance to edge, and time of day. Our results confirm that observers have the highest probability of detecting elk in early and late winter. However, factors such as day length, which increases by 64% during this period, suggest that fewer impediments to detection exist in late winter. Our findings reinforce the need to account for seasonal as well as spatial changes in habitat-specific sightability models.

Juxtaposition between host population structures: implications for disease transmission in a sympatric cervid community

Sympatric populations of phylogenetically related species are often vulnerable to similar communicable diseases. Although some host populations may exhibit spatial structure, other hosts within the community may have unstructured populations. Thus, individuals from unstructured host populations may act as interspecific vectors among discrete subpopulations of sympatric alternate hosts. We used a cervid‐bovine tuberculosis (Mycobacterium bovis) system to investigate the landscape‐scale potential for bovine tuberculosis transmission within a nonmigratory white‐tailed deer (Odocoileus virginianus) and elk (Cervus canadensis) community. Using landscape population genetics, we tested for genetic and spatial structure in white‐tailed deer. We then compared these findings with the sympatric elk population that is structured and which has structure that correlates spatially and genetically to physiognomic landscape features. Despite genetic structure that indicates the white‐tailed deer population forms three sympatric clusters, the absence of spatial structure suggested that intraspecific pathogen transmission is not likely to be limited by physiognomic landscape features. The potential for intraspecific transmission among subpopulations of elk is low due to spatial population structure. Given that white‐tailed deer are abundant, widely distributed, and exhibit a distinct lack of spatial population structure, white‐tailed deer likely pose a greater threat as bovine tuberculosis vectors among elk subpopulations than elk.

Targeting hunter distribution based on host resource selection and kill sites to manage disease risk

Endemic and emerging diseases are rarely uniform in their spatial distribution or prevalence among cohorts of wildlife. Spatial models that quantify risk-driven differences in resource selection and hunter mortality of animals at fine spatial scales can assist disease management by identifying high-risk areas and individuals. We used resource selection functions (RSFs) and selection ratios (SRs) to quantify sex- and age-specific resource selection patterns of collared (n = 67) and hunter-killed (n = 796) nonmigratory elk (Cervus canadensis manitobensis) during the hunting season between 2002 and 2012, in southwestern Manitoba, Canada. Distance to protected area was the most important covariate influencing resource selection and hunter-kill sites of elk (AICw = 1.00). Collared adult males (which are most likely to be infected with bovine tuberculosis (Mycobacterium bovis) and chronic wasting disease) rarely selected for sites outside of parks during the hunting season in contrast to adult females and juvenile males. The RSFs showed selection by adult females and juvenile males to be negatively associated with landscape-level forest cover, high road density, and water cover, whereas hunter-kill sites of these cohorts were positively associated with landscape-level forest cover and increasing distance to streams and negatively associated with high road density. Local-level forest was positively associated with collared animal locations and hunter-kill sites; however, selection was stronger for collared juvenile males and hunter-killed adult females. In instances where disease infects a metapopulation and eradication is infeasible, a principle goal of management is to limit the spread of disease among infected animals. We map high-risk areas that are regularly used by potentially infectious hosts but currently underrepresented in the distribution of kill sites. We present a novel application of widely available data to target hunter distribution based on host resource selection and kill sites as a promising tool for applying selective hunting to the management of transmissible diseases in a game species.