Scale for resource selection functions

Resource selection functions (RSFs) are statistical models defined to be proportional to the probability of use of a resource unit. My objective with this review is to identify how RSFs can be used to unravel the influence of scale in habitat selection. In wildlife habitat studies, including radiotelemetry, RSFs can be estimated using a variety of statistical methods, all of which can be used to explore the role of scale. All RSFs are bounded by the resolution of data and the spatial extent of the study area, but also allow predictor covariates to be measured at a variety of scales. Conditional logistic regression permits designs (e.g. matched case) that relate the process of habitat selection to a limited domain of resource units that might better characterize what is truly ‘available’ to the animal. Scale influences the process of habitat selection, e.g. food resources are often selected at fine spatial scales, whereas landscape patterns at much larger scales typically influence the location of home ranges. Scale also influences appropriate sampling in many ways: (1) heterogeneity might be obliterated (transmutation) if resolution or grain size is too large, (2) variance of habitat characteristics might be undersampled if extent or domain is too small, (3) timing and duration of observations can influence RSF models, and (d) both spatial and temporal autocorrelations can vary directly with the intensity of sampling. Using RSFs, researchers can examine habitat selection at multiple scales, and predictive models that bridge scales can be estimated. Using Geographical Information Systems, predictor covariates in RSF models can be measured at different scales easily so that the predictive ability of models at alternative spatial and temporal domains can be explored by the investigator. Identification of the scale that best explains the data can be evaluated by comparing alternative models using information‐theoretic metrics such as Akaike Information Criteria, and predictive capability of the models can be assessed using k‐fold cross validation.     

A Comparison of Two Modeling Approaches for Evaluating Wildlife-Habitat Relationships

ABSTRACT Studies of resource selection form the basis for much of our understanding of wildlife habitat requirements, and resource selection functions (RSFs), which predict relative probability of use, have been proposed as a unifying concept for analysis and interpretation of wildlife habitat data. Logistic regression that contrasts used and available or unused resource units is one of the most common analyses for developing RSFs. Recently, resource utilization functions (RUFs) have been developed, which also predict probability of use. Unlike RSFs, however, RUFs are based on a continuous metric of space use summarized by a utilization distribution. Although both RSFs and RUFs predict space use, a direct comparison of these 2 modeling approaches is lacking. We compared performance of RSFs and RUFs by applying both approaches to location data for 75 Rocky Mountain elk (Cervus elaphus) and 39 mule deer (Odocoileus hemionus) collected at the Starkey Experimental Forest and Range in northeastern Oregon, USA. We evaluated differences in maps of predicted probability of use, relative ranking of habitat variables, and predictive power between the 2 models. For elk, 3 habitat variables were statistically significant (P < 0.05) in the RSF, whereas 7 variables were significant in the RUF. Maps of predicted probability of use differed substantially between the 2 models for elk, as did the relative ranking of habitat variables. For mule deer, 4 variables were significant in the RSF, whereas 6 were significant in the RUF, and maps of predicted probability of use were similar between models. In addition, distance to water was the top-ranked variable in both models for mule deer. Although space use by both species was predicted most accurately by the RSF based on cross-validation, differences in predictive power between models were more substantial for elk than mule deer. To maximize accuracy and utility of predictive wildlife-habitat models, managers must be aware of the relative strengths and weaknesses of different modeling techniques. We conclude that although RUFs represent a substantial advance in resource selection theory, techniques available for generating RUFs remain underdeveloped and, as a result, RUFs sometimes predict less accurately than models derived using more conventional techniques.     

SCALE-DEPENDENT SUMMER RESOURCE SELECTION BY REINTRODUCED ELK IN WISCONSIN,USA

Abstract: Identifying how habitat use is influenced by environmental heterogeneity at different scales is central to understanding ungulate population dynamics on complex landscapes. We used resource selection functions (RSF) to study summer habitat use in a reintroduced and expanding elk (Cervus elaphus nelsoni) population in the Chequamegon National Forest, Wisconsin, USA. Factors were examined that influenced where elk established home ranges and that influenced habitat use within established home ranges. We also determined grain sizes over which elk responded to environmental heterogeneity and the number of categories of habitat selection from low to high that the elk distinguished. At a large spatial extent, elk home-range establishment was largely explained by the spatial distribution of wolf (Canis lupus) territories. Forage abundance was also influential but was relatively more important at a small spatial extent when elk moved within established home ranges. Areas near roads were avoided when establishing a home-range, but areas near roads were selected for use within the established home range. Elk distinguished among 4 different categories of habitat selection when establishing and moving within home ranges. Spatial and temporal cross validation demonstrated that to improve the predictive strength of habitat models in areas of low inter-annual variability in the environment, it is better to follow more individuals across diverse environmental conditions than to follow the same individuals over a longer time period. Last, our results show that the effects of environmental variables on habitat use were scale-dependent and reemphasize the necessity of analyzing habitat use at multiple scales that are fit to address specific research questions.     

Humans alter habitat selection of birds on ocean‐exposed sandy beaches

Aim Resource‐selection functions (RSFs) can quantify and predict the density of animal populations across heterogeneous landscapes and are important conservation tools in areas subject to human disturbance. Sandy beach ecosystems have comparatively low habitat heterogeneity and structural relief in the intertidal zone, but intense human use. We aimed to develop predictive RSFs for birds on ocean‐exposed sandy beaches at two spatial scales, 25 ha (local scale) and 250 ha (landscape scale), and to test whether habitat selection of birds that commonly use the surf–beach–dune interface is influenced by the rates of human activities. Location Moreton and North Stradbroke Island, eastern Australia. Methods Avifauna and human activities were mapped on three sandy beaches covering 79 km of coastline for 15 months. Habitat characteristics of the surf–beach–dune interface were derived from remote sensing and ground surveys. RSFs were developed for 12 species of birds at two spatial scales: 25 ha (local scale) and 250 ha (landscape scale). Results  At local (25 ha) and landscape scales (250 ha), dune dimensions and the extent and type of vegetation structure were important predictors of bird density. Adding the frequency of human activities improved the predictive power of RSFs, suggesting that habitat selection of birds on beaches is modified by human use of these environments. Human activities occurred mostly in the mid‐ to lower intertidal zone of the beach, overlapping closely with the preferred habitats of Silver Gulls (Larus novaehollandiae), Pied Oystercatchers (Haematopus longirostris), Red‐capped Plovers (Charadrius ruficapillus) and endangered Little Terns (Sternula albifrons). Main conclusions In addition to demonstrating the appropriateness of RSFs to the surf–beach–dune interface, our results stress the need for systematic conservation planning for these ecosystems, where ecological values have traditionally been subsidiary to the maintenance of sand budgets and erosion control.     

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

Many publications make use of opportunistic data, such as citizen science observation data, to infer large‐scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS‐telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat‐use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat‐use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat‐use studies.     

Seasonal shifts in habitat selection of a large herbivore and the influence of human activity

Failure to recognize factors contributing to variation in habitat models like resource selection functions (RSFs) can affect their application for projecting probabilities of occurrence, and thereby limit their relevance for conservation and management. We compared seasonal RSFs (2006–2008) for 16 adult female moose (Alces alces) with home ranges located in western Algonquin Provincial Park (APP), Ontario, Canada, to those of 14 adult females located in provincial Wildlife Management Unit (WMU) 49, 40 km west of the protected area. Wildlife and habitat management practices differed between regions: hunting was higher in WMU 49 compared to APP, and APP preserved large tracts of old growth forest rarely found in WMU 49. Seasonal RSFs projected expected similarities in moose resource use between regions (e.g., responses to wetlands and stands of eastern hemlock, Tsuga canadensis [in winter]); however, we also observed differences consistent with the hypothesis that animals, through effects of hunting, would shift habitat use seasonally and in response to roads. We further observed evidence of functional responses in habitat selection due to underlying differences in forestry practices (e.g., responses to stands of old-growth hemlock forest). Given the close proximity and shared biogeographic region between study areas, we believe that observed spatial dynamics in RSFs were ultimately reflective of divergent management strategies between areas and ensuing differences in predation and hunting mortality risk, and functional habitat.     

The attraction of the known: the importance of spatial familiarity in habitat selection in wapiti Cervus elaphus

Most studies of habitat selection by large herbivores focus on the resource availability and interactions with other species, but neglect the importance of an animal being familiar with an area due to past use. Yet, studies of the establishment and retention of territories, home ranges, birth sites, and feeding site choices in experimental settings have shown the importance of spatial familiarity at these scales. We used GPS locations of translocated wapiti Cervus elaphus , resource selection functions (RSF), and time-to-return to examine whether previous site use was important for selection of sites by wapiti in west-central Alberta. To construct RSFs, we used logistic regression that included spatial familiarity (presence of a previous GPS location within a 50-m radius) as well as estimates of herbaceous and shrub biomass, elevation, aspect, slope, and predation risk to wapiti from wolf predation, as dependent variables. We found that previous use had a strong positive relationship with subsequent site use, indicating that wapiti were not avoiding previously visited locations, as would be expected if memory of forage depletion (which we did not measure) determined response to familiar locations. Revisited sites were of higher quality, i.e. had more moderate terrain, higher forage, and lower predation risk, than sites that were not revisited, indicating that the selection of familiar locations was likely not the result of avoidance of unfamiliar locations. Finally, animals demonstrated preference for familiar locations that it had visited most recently, indicating that memory (which would decline with time) of higher site quality, rather than high quality alone, influenced selection for familiar locations. We conclude that spatial familiarity is important not only for large scale processes such as selection of home range and territory, but for smaller scale habitat selection and foraging as well.     

A machine‐learning approach for extending classical wildlife resource selection analyses

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.     

Can niche‐based distribution models outperform spatial interpolation?

Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms. Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada. Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species. Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R² in predicting artificial ranges. Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.     

Relationships between survival and habitat suitability of semi‐aquatic mammals

Spatial distribution and habitat selection are integral to the study of animal ecology. Habitat selection may optimize the fitness of individuals. Hutchinsonian niche theory posits the fundamental niche of species would support the persistence or growth of populations. Although niche‐based species distribution models (SDMs) and habitat suitability models (HSMs) such as maximum entropy (Maxent) have demonstrated fair to excellent predictive power, few studies have linked the prediction of HSMs to demographic rates. We aimed to test the prediction of Hutchinsonian niche theory that habitat suitability (i.e., likelihood of occurrence) would be positively related to survival of American beaver (Castor canadensis), a North American semi‐aquatic, herbivorous, habitat generalist. We also tested the prediction of ideal free distribution that animal fitness, or its surrogate, is independent of habitat suitability at the equilibrium. We estimated beaver monthly survival probability using the Barker model and radio telemetry data collected in northern Alabama, United States from January 2011 to April 2012. A habitat suitability map was generated with Maxent for the entire study site using landscape variables derived from the 2011 National Land Cover Database (30‐m resolution). We found an inverse relationship between habitat suitability index and beaver survival, contradicting the predictions of niche theory and ideal free distribution. Furthermore, four landscape variables selected by American beaver did not predict survival. The beaver population on our study site has been established for 20 or more years and, subsequently, may be approaching or have reached the carrying capacity. Maxent‐predicted increases in habitat use and subsequent intraspecific competition may have reduced beaver survival. Habitat suitability‐fitness relationships may be complex and, in part, contingent upon local animal abundance. Future studies of mechanistic SDMs incorporating local abundance and demographic rates are needed.     

A Hierarchical Analysis of Habitat Selection by Raccoons in Northern Indiana

Abstract: Although numerous studies have examined habitat use by raccoons (Procyon lotor), information regarding seasonal habitat selection related to resource availability in agricultural landscapes is lacking for this species. Additionally, few studies using radiotelemetry have investigated habitat selection at multiple spatial scales or core-use areas by raccoons. We examined seasonal habitat selection of 55 (31 M, 24 F) adult raccoons at 3 hierarchical orders defined by the movement behavior of this species (second-order home range, second-order core-use area, and third-order home range) in northern Indiana, USA, from May 2003 to June 2005. Using compositional analysis, we assessed whether habitat selection differed from random and ranked habitat types in order of selection during the crop growing period (season 1) and corn maturation period (season 2), which represented substantial shifts in resource availability to raccoons. Habitat rankings differed across hierarchical orders, between seasons within hierarchical orders, and between sexes within seasons; however, seasonal and intersexual patterns of habitat selection were not consistent across hierarchical orders of spatial scale. When nonrandom utilization was detected, both sexes consistently selected forest cover over other available habitats. Seasonal differences in habitat selection were most evident at the core-area scale, where raccoon selection of agricultural lands was highest during the maturation season when corn was available as a direct food source. Habitat use did not differ from availability for either sex in either season at the third-order scale. The selection of forest cover across both seasons and all spatial orders suggested that raccoon distribution and abundance in fragmented landscapes is likely dependent on the availability and distribution of forest cover, or habitats associated with forest (i.e., water), within the landscape. The lack of consistency in habitat selection across hierarchical scales further exemplifies the need to examine multiple biological scales in habitat-selection studies.     

Eastern Hog-Nosed Snake Habitat Selection at Multiple Spatial Scales in Ontario,Canada

Habitat loss is the greatest contributor to the decline of species globally. To prioritize protection of imperiled species, it is important to examine habitat use at multiple spatial scales because the availability of different resources and habitat features is scale dependent. We conducted a radio-telemetry study in the Long Point region of Ontario, Canada, in 2009 and 2010 to examine habitat selection at multiple spatial scales by eastern hog-nosed snakes (Heterodon platirhinos), a species at risk in Canada. We documented the habitat composition of home ranges compared to the surrounding landscape, the selection of locations within home ranges based on classified satellite imagery, and the use of microhabitat features based on site characterization in the field. At the scale of the home ranges, hog-nosed snakes avoided areas of agriculture and selected sand barrens. Within home ranges, hog-nosed snakes selectively used areas altered by humans (e.g., residential sites, openings in tree plantations). Microhabitats used by hog-nosed snakes had more woody debris, logs, and lower vegetative coverage than adjoining random sites. Because hog-nosed snakes prefer open areas and require sandy soils for nesting, management efforts should focus on the conservation and maintenance of sand barrens and patches of early successional forest. © 2021 The Wildlife Society.     

Evaluating habitat selection models to predict critical habitat for mountain goats in northwest British Columbia

Anthropogenic activity imposes increasing pressure on wildlife populations globally; these pressures can affect habitat suitability and function, modify wildlife space use, and influence population viability. Native mountain goat (Oreamnos americanus) populations can be negatively affected by anthropogenic disturbance and modify their space use in response to land development and recreational activity. From 2018 to 2020, we studied space use of mountain goats northeast of Smithers, British Columbia, Canada, an area that is subject to increasing anthropogenic development and yearlong recreational activities. We aimed to generate models that would improve our ability to identify habitat for mountain goats relative to existing survey data and established ungulate winter ranges. Using resource selection function (RSF) analyses generated from global positioning system (GPS) collar data, we identified influential habitat covariates and compared these covariates and RSF values to existing habitat models. Additionally, we compared the extent to which our models were congruent with existing resource selection probability functions, were congruent with aerial survey data, and overlapped existing ungulate winter ranges previously derived from predictive models inside and outside of the study area. Overall, our models noted higher RSF values among GPS data relative to aerial survey data for winter months, while results for summer habitats were comparable. In extending our RSFs outside of the study area and evaluating the overlap with ungulate winter ranges in adjacent areas, values were similar, albeit lower, as is expected given that the models were developed elsewhere. Ultimately, these models, combined with existing methods, improve the accuracy and reliability of identified, important areas of habitat for mountain goats. We recommend that the RSF models generated here be used in conjunction with aerial survey data and existing methods to delineate ungulate winter ranges for mountain goats in similar eco-regions in British Columbia. The models developed here support existing methods that have been used to delineate or validate ungulate winter ranges for mountain goats in British Columbia and help facilitate mitigation measures to support the continued use of important winter habitat and significant landscape features that play a role in ensuring population viability and resilience through time.     

Crucial nesting habitat for gunnison sage-grouse: A spatially explicit hierarchical approach

Gunnison sage-grouse (Centrocercus minimus) is a species of special concern and is currently considered a candidate species under Endangered Species Act. Careful management is therefore required to ensure that suitable habitat is maintained, particularly because much of the species' current distribution is faced with exurban development pressures. We assessed hierarchical nest site selection patterns of Gunnison sage-grouse inhabiting the western portion of the Gunnison Basin, Colorado, USA, at multiple spatial scales, using logistic regression-based resource selection functions. Models were selected using Akaike Information Criterion corrected for small sample sizes (AIC_c) and predictive surfaces were generated using model averaged relative probabilities. Landscape-scale factors that had the most influence on nest site selection included the proportion of sagebrush cover >5%, mean productivity, and density of 2 wheel-drive roads. The landscape-scale predictive surface captured 97% of known Gunnison sage-grouse nests within the top 5 of 10 prediction bins, implicating 57% of the basin as crucial nesting habitat. Crucial habitat identified by the landscape model was used to define the extent for patch-scale modeling efforts. Patch-scale variables that had the greatest influence on nest site selection were the proportion of big sagebrush cover >10%, distance to residential development, distance to high volume paved roads, and mean productivity. This model accurately predicted independent nest locations. The unique hierarchical structure of our models more accurately captures the nested nature of habitat selection, and allowed for increased discrimination within larger landscapes of suitable habitat. We extrapolated the landscape-scale model to the entire Gunnison Basin because of conservation concerns for this species. We believe this predictive surface is a valuable tool which can be incorporated into land use and conservation planning as well the assessment of future land-use scenarios. © 2011 The Wildlife Society.     

Species richness, area and climate correlates

Aim Species richness–area theory predicts that more species should be found if one samples a larger area. To avoid biases from comparing species richness in areas of very different sizes, area is often controlled by counting the numbers of co‐occupying species in near‐equal area grid cells. The assumption is that variation in grid cell size accrued from working in a three‐dimensional world is negligible. Here we provide a first test of this idea. We measure the surface area of c. 50 × 50 km and c. 220 × 220 km grid cells across western Europe. We then ask how variation in the area of grid cells affects: (1) the selection of climate variables entering a species richness model; and (2) the accuracy of models in predicting species richness in unsampled grid cells. Location Western Europe. Methods Models are developed for European plant, breeding bird, mammal and herptile species richness using seven climate variables. Generalized additive models are used to relate species richness, climate and area. Results We found that variation in the grid cell area was large (50 × 50 km: 8–3311 km²; 220 × 220: 193–55,100 km²), but this did not affect the selection of variables in the models. Similarly, the predictive accuracy was affected only marginally by exclusion of area within models developed at the c. 50 × 50 km grid cells, although predictive accuracy suffered greater reductions when area was not included as a covariate in models developed for c. 220 × 220 km grid cells. Main conclusions Our results support the assumption that variation in near‐equal area cells may be of second‐order importance for models explaining or predicting species richness in relation to climate, although there is a possibility that drops in accuracy might increase with grid cell size. The results are, however, contingent on this particular data set, grain and extent of the analyses, and more empirical work is required.     

Living on the edge: Multiscale habitat selection by cheetahs in a human‐wildlife landscape

Animals select habitats that will ultimately optimize their fitness through access to favorable resources, such as food, mates, and breeding sites. However, access to these resources may be limited by bottom‐up effects, such as availability, and top‐down effects, such as risk avoidance and competition, including that with humans. Competition between wildlife and people over resources, specifically over space, has played a significant role in the worldwide decrease in large carnivores. The goal of this study was to determine the habitat selection of cheetahs (Acinonyx jubatus) in a human‐wildlife landscape at multiple spatial scales. Cheetahs are a wide‐ranging, large carnivore, whose significant decline is largely attributed to habitat loss and fragmentation. It is believed that 77% of the global cheetah population ranges outside protected areas, yet little is known about cheetahs’ resource use in areas where they co‐occur with people. The selection, or avoidance, of three anthropogenic variables (human footprint density, distance to main roads and wildlife areas) and five environmental variables (open habitat, semiclosed habitat, edge density, patch density and slope), at multiple spatial scales, was determined by analyzing collar data from six cheetahs. Cheetahs selected variables at different scales; anthropogenic variables were selected at broader scales (720–1440 m) than environmental variables (90–180 m), suggesting that anthropogenic pressures affect habitat selection at a home‐range level, whilst environmental variables influence site‐level habitat selection. Cheetah presence was best explained by human presence, wildlife areas, semiclosed habitat, edge density and slope. Cheetahs showed avoidance for humans and steep slopes and selected for wildlife areas and areas with high proportions of semiclosed habitat and edge density. Understanding a species’ resource requirements, and how these might be affected by humans, is crucial for conservation. Using a multiscale approach, we provide new insights into the habitat selection of a large carnivore living in a human‐wildlife landscape.     

Evidence that implicit assumptions of ‘no evolution’ of disease vectors in changing environments can be violated on a rapid timescale

Projected impacts of climate change on vector-borne disease dynamics must consider many variables relevant to hosts, vectors and pathogens, including how altered environmental characteristics might affect the spatial distributions of vector species. However, many predictive models for vector distributions consider their habitat requirements to be fixed over relevant time-scales, when they may actually be capable of rapid evolutionary change and even adaptation. We examine the genetic signature of a spatial expansion by an invasive vector into locations with novel temperature conditions compared to its native range as a proxy for how existing vector populations may respond to temporally changing habitat. Specifically, we compare invasions into different climate ranges and characterize the importance of selection from the invaded habitat. We demonstrate that vector species can exhibit evolutionary responses (altered allelic frequencies) to a temperature gradient in as little as 7–10 years even in the presence of high gene flow, and further, that this response varies depending on the strength of selection. We interpret these findings in the context of climate change predictions for vector populations and emphasize the importance of incorporating vector evolution into models of future vector-borne disease dynamics.     

Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks

1. We used stream fish and decapod spatial occurrence data extracted from a national database and recent surveys with geospatial landuse data, geomorphologic, climatic, and spatial data in a geographical information system (GIS) to model fish and decapod occurrence in the Wellington Region, New Zealand. 2. To predict the occurrence of each species at a site from a common set of predictor variables we used a multi‐response, artificial neural network (ANN), to produce a single model that predicted the entire fish and decapod assemblage in one procedure. 3. The predictions from the ANN using this landscape scale data proved very accurate based on evaluation metrics that are independent of species abundance or probability thresholds. The important variables contributing to the predictions included the latitudinal and elevational position of the site reach, catchment area, average air temperature, the vegetation type, landuse proportions of the catchment, and catchment geology. 4. Geospatial data available for the entire regional river network were then used to create a habitat‐suitability map for all 14 species over the regional river network using a GIS. This prediction map has many potential uses including: monitoring and predicting temporal changes in fish communities caused by human activities and shifts in climate, identifying areas in need of protection, biodiversity hotspots, and areas suitable for the reintroduction of endangered or rare species.     

Spatial heterogeneity of climate and land-cover constraints on distributions of tick-borne pathogens

Aim To compare the geographical distributions of two tick‐borne pathogens vectored by different tick species, to examine the relative importance of climate, land cover and host density in structuring these distributions, and to assess the spatial variability of these environmental constraints across the species ranges. Location South‐central and south‐eastern North America. Methods Presence/absence data for two tick‐borne pathogens, Ehrlichia chaffeensis and Anaplasma phagocytophilum, were obtained for 567 counties from a regional data base based on white‐tailed deer (Odocoileus virginianus) serology. Environmental variables describing climate, land cover and deer density were calculated for these counties. Global logistic regression analysis was used to screen the environmental variables and select a parsimonious subset of predictors. Local analysis was carried out using geographically weighted regression (GWR) to explore spatial variability in the parameters of the regression models. Cluster analysis was applied to the GWR output to identify zones with distinctive species–habitat relationships. Results Global habitat models for E. chaffeensis and A. phagocytophilum included temperature, humidity, precipitation and forest cover as explanatory variables. The E. chaffeensis model also included forest fragmentation, whereas the A. phagocytophilum model included deer density. Local analyses revealed that climate was the primary correlate of pathogen presence in the eastern portion of the study area, whereas forest cover and fragmentation constrained the western range boundaries. Habitat relationships for all variables were weak in and around the Mississippi Delta. Main conclusions Efforts to model pathogen and disease ranges, and to predict shifts in response to global change should consider future scenarios of land‐cover change as well as climate change, and should address the possibility of spatial heterogeneity in species–habitat relationships. The methods presented here outline an approach for objectively delineating geographical zones with similar species–environment relationships, which can then be used to stratify landscapes for the purposes of further explanatory and predictive modelling.     

Seasonal patterns of habitat selection in the insectivorous bat‐eared fox

Understanding how animals utilize their habitat provides insights about their ecological needs and is of importance for both theoretical and applied ecology. As changing seasons impact prey habitat selection and vegetation itself, it is important to understand how seasonality impacts microhabitat choice in optimal foragers and their prey. We followed habituated bat‐eared foxes (Otocyon megalotis) in the Kalahari, South Africa, to study their seasonal habitat selection patterns and relate them to the habitat preferences of their main prey, termites (Hodotermes mossambicus). We used Resource Selection Functions (RSFs) to study bat‐eared foxes’ 3rd‐ and 4th‐order habitat selection by comparing used locations to random ones within their home ranges. Third‐order habitat selection for habitat type and composition was weak and varied little between seasons. We found that patterns of fox habitat selection did not mirror habitat selection of Hodotermes (quantified using RSFs), even when feeding on them (4th‐order). Taken together, these results might indicate that bat‐eared foxes’ food resources are homogenously distributed across habitats and that prey other than Hodotermes play an important role in bat‐eared foxes’ space use.