Grizzly bear habitat selection is scale dependent.

The purpose of our study is to show how ecologists' interpretation of habitat selection by grizzly bears (Ursus arctos) is altered by the scale of observation and also how management questions would be best addressed using predetermined scales of analysis. Using resource selection functions (RSF) we examined how variation in the spatial extent of availability affected our interpretation of habitat selection by grizzly bears inhabiting mountain and plateau landscapes. We estimated separate models for females and males using three spatial extents: within the study area, within the home range, and within predetermined movement buffers. We employed two methods for evaluating the effects of scale on our RSF designs. First, we chose a priori six candidate models, estimated at each scale, and ranked them using Akaike Information Criteria. Using this method, results changed among scales for males but not for females. For female bears, models that included the full suite of covariates predicted habitat use best at each scale. For male bears that resided in the mountains, models based on forest successional stages ranked highest at the study-wide and home range extents, whereas models containing covariates based on terrain features ranked highest at the buffer extent. For male bears on the plateau, each scale estimated a different highest-ranked model. Second, we examined differences among model coefficients across the three scales for one candidate model. We found that both the magnitude and direction of coefficients were dependent upon the scale examined; results varied between landscapes, scales, and sexes. Greenness, reflecting lush green vegetation, was a strong predictor of the presence of female bears in both landscapes and males that resided in the mountains. Male bears on the plateau were the only animals to select areas that exposed them to a high risk of mortality by humans. Our results show that grizzly bear habitat selection is scale dependent. Further, the selection of resources can be dependent upon the availability of a particular vegetation type on the landscape. From a management perspective, decisions should be based on a hierarchical process of habitat selection, recognizing that selection patterns vary across scales.     

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.     

The importance of incorporating functional habitats into conservation planning for highly mobile species in dynamic systems

The distribution of mobile species in dynamic systems can vary greatly over time and space. Estimating their population size and geographic range can be problematic and affect the accuracy of conservation assessments. Scarce data on mobile species and the resources they need can also limit the type of analytical approaches available to derive such estimates. We quantified change in availability and use of key ecological resources required for breeding for a critically endangered nomadic habitat specialist, the Swift Parrot (Lathamus discolor). We compared estimates of occupied habitat derived from dynamic presence‐background (i.e., presence‐only data) climatic models with estimates derived from dynamic occupancy models that included a direct measure of food availability. We then compared estimates that incorporate fine‐resolution spatial data on the availability of key ecological resources (i.e., functional habitats) with more common approaches that focus on broader climatic suitability or vegetation cover (due to the absence of fine‐resolution data). The occupancy models produced significantly (P < 0.001) smaller (up to an order of magnitude) and more spatially discrete estimates of the total occupied area than climate‐based models. The spatial location and extent of the total area occupied with the occupancy models was highly variable between years (131 and 1498 km²). Estimates accounting for the area of functional habitats were significantly smaller (2–58% [SD 16]) than estimates based only on the total area occupied. An increase or decrease in the area of one functional habitat (foraging or nesting) did not necessarily correspond to an increase or decrease in the other. Thus, an increase in the extent of occupied area may not equate to improved habitat quality or function. We argue these patterns are typical for mobile resource specialists but often go unnoticed because of limited data over relevant spatial and temporal scales and lack of spatial data on the availability of key resources. Understanding changes in the relative availability of functional habitats is crucial to informing conservation planning and accurately assessing extinction risk for mobile resource specialists.     

Habitat selection models to account for seasonal persistence in radio telemetry data

Models for the analysis of habitat selection data incorporate covariates in an independent multinomial selections model (McCracken et al. 1998) Ramsey and Usner 2003 and an extension of that model to include a persistence parameter (2003). In both cases, all parameters are assumed to be fixed through time. Radio telemetry data collected for habitat selection studies typically consist of animal relocations through time, suggesting the need for an extension to these models. We use a Bayesian approach that allows for the habitat selection probabilities, persistence parameter, or both, to change with season. These extensions are particularly important when movement patterns are expected to differ seasonally and/or when availabilities of habitats change throughout the study period due to weather or migration. We implement and compare the models using radio telemetry data for westslope cutthroat trout in two streams in eastern Oregon.     

Native plant diversity resists invasion at both low and high resource levels

Human modification of the environment is causing both loss of species and changes in resource availability. While studies have examined how species loss at the local level can influence invasion resistance, interactions between species loss and other components of environmental change remain poorly studied. In particular, the manner in which native diversity interacts with resource availability to influence invasion resistance is not well understood. We created experimental plant assemblages that varied in native species (1-16 species) and/or functional richness (defined by rooting morphology and phenology; one to five functional groups). We crossed these diversity treatments with resource (water) addition to determine their interactive effects on invasion resistance to spotted knapweed (Centaurea maculosa), a potent exotic invader in the intermountain West of the United States. We also determined how native diversity and resource addition influenced plant-available soil nitrogen, soil moisture, and light. Assemblages with lower species and functional diversity were more heavily invaded than assemblages with greater species and functional diversity. In uninvaded assemblages, experimental addition of water increased soil moisture and plant-available nitrogen and decreased light availability. The availability of these resources generally declined with increasing native plant diversity. Although water addition increased susceptibility to invasion, it did not fundamentally change the negative relationship between diversity and invasibility. Thus, native diversity provided strong invasion resistance even under high resource availability. These results suggest that the effects of local diversity can remain robust despite enhanced resource levels that are predicted under scenarios of global change.     

Weighted distributions and estimation of resource selection probability functions

Understanding how organisms selectively use resources is essential for designing wildlife management strategies. The probability that an individual uses a given resource, as characterized by environmental factors, can be quantified in terms of the resource selection probability function (RSPF). The present literature on the topic has claimed that, except when both used and unused sites are known, the RSPF is non-estimable and that only a function proportional to RSPF, namely, the resource selection function (RSF) can be estimated. This paper describes a close connection between the estimation of the RSPF and the estimation of the weight function in the theory of weighted distributions. This connection can be used to obtain fully efficient, maximum likelihood estimators of the resource selection probability function under commonly used survey designs in wildlife management. The method is illustrated using GPS collar data for mountain goats (Oreamnos americanus de Blainville 1816) in northwest British Columbia, Canada.     

Effects of rarity form on species’ responses to land use

Anthropogenic land-use change causes substantial changes in local and global biodiversity. Rare and common species can differ in sensitivity to land-use change; rare species are expected to be affected more negatively. Rarity may be defined in terms of geographic range size, population density, or breadth of habitat requirements. How these 3 forms of rarity interact in determining global responses to land use is yet to be assessed. Using global data representing 912 vertebrate species, we tested for differences in responses to land use of species characterized by different types of rarity. Land-use responses were fitted using generalized linear mixed-effects models, allowing responses to vary among groups of species with different forms of rarity. Species considered rare with respect to all 3 forms of rarity showed particularly strong declines in disturbed land uses (>40% of species and 30% of individuals in the most disturbed land uses). In contrast, species common both geographically and numerically and with broad habitat requirements showed strong increases (up to 90% increase in species and 40% in abundance in some land uses). Our results suggest that efforts to understand the vulnerability of species to environmental changes should account for different types of rarity where possible. Our results also have potentially important implications for ecosystem functioning, given that rare species may play unique roles within ecosystems.     

Transcending scale dependence in identifying habitat with resource selection functions

Multi-scale resource selection modeling is used to identify factors that limit species distributions across scales of space and time. This multi-scale nature of habitat suitability complicates the translation of inferences to single, spatial depictions of habitat required for conservation of species. We estimated resource selection functions (RSFs) across three scales for a threatened ungulate, woodland caribou (Rangifer tarandus caribou), with two objectives: (1) to infer the relative effects of two forms of anthropogenic disturbance (forestry and linear features) on woodland caribou distributions at multiple scales and (2) to estimate scale-integrated resource selection functions (SRSFs) that synthesize results across scales for management-oriented habitat suitability mapping. We found a previously undocumented scale-specific switch in woodland caribou response to two forms of anthropogenic disturbance. Caribou avoided forestry cut-blocks at broad scales according to first- and second-order RSFs and avoided linear features at fine scales according to third-order RSFs, corroborating predictions developed according to predator-mediated effects of each disturbance type. Additionally, a single SRSF validated as well as each of three single-scale RSFs when estimating habitat suitability across three different spatial scales of prediction. We demonstrate that a single SRSF can be applied to predict relative habitat suitability at both local and landscape scales in support of critical habitat identification and species recovery.     

Eigenanalysis of selection ratios from animal radio-tracking data

The development of methods to analyze habitat selection when resources are defined by several categories (e.g., vegetation types) is a topical issue in radio-tracking studies. The White and Garrott statistic, an extension of the widely used test of Neu et al., can be used to determine whether habitat selection is significant. As well, Manly's selection ratio, a particularly useful measure of resource selectivity by resource users, allows detection of the most strongly selected habitat types. However, when both the number of animals and types of habitat are large, the biologist often has to deal with an excessively large number of measures. In this paper we present a new method, the eigenanalysis of selection ratios, that generalizes these two common methods within the framework of eigenanalyses. This method undertakes an additive linear partitioning of the White and Garrott statistic, so that the difference between habitat use and availability is maximized on the first factorial axes. The eigenanalysis of selection ratios is therefore optimal in habitat selection studies. Although we primarily consider the case where the habitat availability is the same for all animals (design II), we also extend this analysis to the case where the habitat availability varies from one animal to another (design III). An application of this method is provided using radio-tracking data collected on 17 squirrels in five habitat types. The results indicate variability in habitat selection, with two groups of animals displaying two patterns of preference. This difference between the two groups is explained by the patch structure of the study area. Because this method is mainly exploratory, and therefore does not rely on any distributional assumption, we recommend its use in studies of habitat selection.     

Flooding impacts on responses of a riparian consumer to cross-ecosystem subsidies

Landscape-driven processes impact the magnitude and direction of cross-ecosystem resource subsidies, but they may also control consumers' numerical and functional responses by altering habitat availability. We investigated effects of the interaction between habitat availability and subsidy level on populations of a riparian fishing spider, Dolomedes aquaticus, using a flood disturbance gradient in the Waimakariri River catchment, New Zealand. D. aquaticus predominantly eat aquatic prey as they hunt from the water surface. However, D. aquaticus biomass peaked at rivers with intermediate flood disturbance, rather than at less flood-prone rivers where the biomass of aquatic insect prey was markedly higher. Flooding positively influenced spider habitat quality, and an experimental manipulation at stable rivers indicated that unembedded cobbles, preferred D. aquaticus habitat, were a limiting factor, preventing response to the increased prey resource at stable sites. Potential terrestrial prey abundance was low, did not vary across the disturbance gradient, and is likely to have been a much smaller component of the fishing spiders' diet than aquatic insect prey. Thus landscape-driven factors not only controlled the magnitude of resource subsidies, but also influenced the ability of consumers to respond to them by altering the physical nature of the ecosystem boundary.     

Transferability of Species Distribution Models: a Functional Habitat Approach for Two Regionally Threatened Butterflies

Abstract:  Numerous models for predicting species distribution have been developed for conservation purposes. Most of them make use of environmental data (e.g., climate, topography, land use) at a coarse grid resolution (often kilometres). Such approaches are useful for conservation policy issues including reserve-network selection. The efficiency of predictive models for species distribution is usually tested on the area for which they were developed. Although highly interesting from the point of view of conservation efficiency, transferability of such models to independent areas is still under debate. We tested the transferability of habitat-based predictive distribution models for two regionally threatened butterflies, the green hairstreak ( Callophrys rubi ) and the grayling ( Hipparchia semele ), within and among three nature reserves in northeastern Belgium. We built predictive models based on spatially detailed maps of area-wide distribution and density of ecological resources. We used resources directly related to ecological functions (host plants, nectar sources, shelter, microclimate) rather than environmental surrogate variables. We obtained models that performed well with few resource variables. All models were transferable—although to different degrees—among the independent areas within the same broad geographical region. We argue that habitat models based on essential functional resources could transfer better in space than models that use indirect environmental variables. Because functional variables can easily be interpreted and even be directly affected by terrain managers, these models can be useful tools to guide species-adapted reserve management.     

An ecological model of the habitat mosaic in estuarine nursery areas: Part I—Interaction of dispersal theory and habitat variability in describing juvenile fish distributions

Identification of critical habitat in estuarine nursery areas is an important conservation and management objective. Habitat can be viewed as a mosaic of both temporally variable environmental features and spatially variable structural features that combine to define optimal habitat. Effective models of juvenile distributions should account for individual movement, as well as the full suite of habitat variability including both spatial and temporal components. We have extended a terrestrial model of small-scale movement patterns to describe habitat choices of an index juvenile fish in an estuarine nursery system. Movement of small juvenile fishes was found to be influenced by both spatial and temporal patterns in habitat quality, and it was a balanced mix of both that resulted in an optimal distribution. Fishes that perceive habitat on a scale much smaller than the scale of spatial heterogeneity may respond to temporal change as a movement cue allowing for more deterministic outcomes at larger scales despite perceptual limitations. These model outcomes suggest a hierarchical approach is best for describing habitat choice in juvenile fishes and this approach will be used in the future to explore individual and population responses to predictable habitat change.     

Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.     

Nest selection by snow petrels Pagodroma nivea in East Antarctica: Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas.From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability.We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.     

Disturbance and resource availability act differently on the same suite of plant traits: revisiting assembly hypotheses

Understanding the mechanisms of trait selection at the scale of plant communities is a crucial step toward predicting community assembly. Although it is commonly assumed that disturbance and resource availability constrain separate suites of traits, representing the regenerative and established phases, respectively, a quantification and test of this accepted hypothesis is still lacking due to limitations of traditional statistical techniques. In this paper we quantify, using structural equation modeling (SEM), the relative contributions of disturbance and resource availability to the selection of suites of traits at the community scale. Our model specifies and reflects previously obtained ecological insights, taking disturbance and nutrient availability as central drivers affecting leaf, allometric, seed, and phenology traits in 156 (semi-) natural plant communities throughout The Netherlands. The common hypothesis positing that disturbance and resource availability each affect a set of mutually independent traits was not consistent with the data. Instead, our final model shows that most traits are strongly affected by both drivers. In addition, trait-trait constraints are more important in community assembly than environmental drivers in half of the cases. Both aspects of trait selection are crucial for correctly predicting ecosystem processes and community assembly, and they provide new insights into hitherto underappreciated ecological interactions.     

A dynamic host selection model for mountain pine beetle,Dendroctonus ponderosae Hopkins

The link between individual habitat selection decisions (i.e., mechanism) and the resulting population distributions of dispersing organisms (i.e., outcome) has been little-studied in behavioural ecology. Here we consider density-dependent habitat (i.e., host) selection for an energy- and time-limited forager: the mountain pine beetle (Dendroctonus ponderosae Hopkins). We present a dynamic state variable model of individual beetle host selection behaviour, based on an individual’s energy state. Field data are incorporated into model parameterization which allows us to determine the effects of host availability (with respect to host size, quality, and vigour) on individuals’ decisions. Beetles choose larger trees with thicker phloem across a larger proportion of the state-space than smaller trees with thinner phloem, but accept lower quality trees more readily at low energy- and time-states. In addition, beetles make habitat selection decisions based on host availability, conspecific attack densities, and beetle distributions within a forest stand. This model provides a framework for the development of a spatial game model to examine the implications of these results for attack dynamics of beetle populations.     

Habitat selection predicts genetic relatedness in an alpine ungulate

Landscape heterogeneity plays an integral role in shaping ecological and evolutionary processes. Despite links between the two disciplines, ecologists and population geneticists have taken different approaches to evaluating habitat selection, animal movement, and gene flow across the landscape. Ecologists commonly use statistical models such as resource selection functions (RSFs) to identify habitat features disproportionately selected by animals, whereas population genetic approaches model genetic differentiation according to the distribution of habitat variables. We combined ecological and genetic approaches by using RSFs to predict genetic relatedness across a heterogeneous landscape. We constructed sex- and season-specific resistance surfaces based on RSFs estimated using data from 102 GPS (global positioning system) radio-collared mountain goats (Oreamnos americanus) in southeast Alaska, USA. Based on mountain goat ecology, we hypothesized that summer and male surfaces would be the best predictors of relatedness. All individuals were genotyped at 22 microsatellite loci, which we used to estimate genetic relatedness. Summer resistance surfaces derived from RSFs were the best predictors of genetic relatedness, and winter models the poorest. Mountain goats generally selected for areas close to escape terrain and with a high heat load (a metric related to vegetative productivity and snow depth), while avoiding valleys. Male- and female-specific surfaces were similar, except for winter, for which male habitat selection better predicted genetic relatedness. The null models of isolation-by-distance and barrier only outperformed the winter models. This study merges high-resolution individual locations through GPS telemetry and genetic data, that can be used to validate and parameterize landscape genetics models, and further elucidates the relationship between landscape heterogeneity and genetic differentiation.     

A general approach to the analysis of habitat selection

The investigation of animal habitat selection aims at the detection of selective usage of habitat types and the identification of covariates influencing their selection. The results not only allow for a better understanding of the habitat selection process but are also intended to help improve the conservation of animals. Usually, habitat selection by larger animals is assessed by radio-tracking or visual observation studies, where the chosen habitat is determined for some animals at a set of specific points in time. Hence the resulting data often have the following structure: a categorical variable indicating the habitat type selected by an animal at a specific point in time is repeatedly observed and will be explained by covariates. These may either describe properties of the habitat types currently available and/or properties of the animal. In this paper, we present a general approach to the analysis of such data in a categorical regression setup. The proposed model generalizes and improves upon several of the approaches previously discussed in the literature. In particular, it accounts for changing habitat availability due to the movement of animals within the observation area. It incorporates both habitat- and animal-specific covariates, and includes individual-specific random effects to account for correlations introduced by the repeated measurements on single animals. Furthermore, the assumption that the effects are linear can be dropped by including the effects in nonparametric manner based on a penalized spline approach. The methodology is implemented in a freely available software package. We demonstrate the general applicability and the potential of the proposed approach in two case studies: The analysis of a songbird community in South-America and a study on brown bears in Central Europe.     

Effects of anthropogenic disturbance on primate density at the landscape scale

Accurate estimations of the abundance of threatened animal populations are required for assessment of species’ status and vulnerability and conservation planning. However, density estimation is usually difficult and resource demanding, so researchers often collect data at local scales. However, anthropogenic pressures most often have landscape-level effects, for example, through habitat loss and fragmentation. We applied hierarchical distance sampling (HDS) to transect count data to determine the effect of habitat and anthropogenic factors on the density of 3 arboreal primate species inhabiting 5 distinct tropical forests across a landscape of 19,000 km² in the Udzungwa Mountains of Tanzania. We developed a novel, multiregion extension of HDS that allowed us to model density and detectability jointly across forests without losing site-specific information. For all species, the effect of anthropogenic disturbance on density was overwhelmingly negative among metapopulations: −0.63 Angolan colobus (Colobus angolensis palliatus) (95% Bayesian CI −1.03 to −0.27), −0.54 Udzungwa red colobus (Procolobus gordonorum) (−0.89 to −0.22), and −0.33 Sykes' monkey (Cercopithecus mitis monoides) (−0.63 to −0.07). Some responses to habitat factors were shared, notably the negative effect of elevation and the positive effect of climber coverage. These results are important for conservation science and practice because: the among-populations negative responses to anthropogenic disturbance provides a foundation for development of conservation plans that hold at the landscape scale, which is a comprehensive and cost-efficient approach; the among-species consistency in responses suggests conservation measures may be generalized at the guild level, which is especially relevant given the functional importance of primates in tropical rainforests; and the greater primate densities in areas at low elevation, which are closer to human settlements, point to specific management recommendations, such as the creation of buffer zones and prioritization of areas for protection.