Aim Temporal transferability is an important issue when habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of habitat monitoring. While the combination of remote sensing technology and habitat modelling provides a useful tool for habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite-derived variables affect temporal transferability of habitat models and their usefulness for habitat monitoring.Location Wolong Nature Reserve, Sichuan Province, China.Methods We modelled giant panda habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single-year or multi-year (i.e. 3-year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda habitat within and across time periods by using threshold-independent and threshold-dependent evaluation methods and five indices of temporal transferability. ResultsOur results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda habitat. However, the models developed using multi-year data exhibited significantly higher temporal transferability than those developed using single-year data. In addition, models developed with phenology metrics, especially when using multi-year data, exhibited significantly higher temporal transferability than those developed with the time series.Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with habitat modelling constitutes a suitable tool for characterizing wildlife habitat and monitoring its temporal dynamics. Using multi-year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by interannual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for habitat monitoring through the integration of remote sensing technology and habitat modelling, which may be useful for the conservation of the giant panda and many other species.

Despite the near universal recognition that roads negatively affect wildlife, the mechanisms that elicit animal responses to roads are often ambiguous or poorly understood. We conducted a multi-year, multi-season study to assess the relative influence of roads on elk (Cervus elaphus) in a human-dominated landscape in South Dakota. We evaluated the effects of habitat covariates including security cover, forage quality, distance to roads (primary, secondary, and tertiary), and visibility from roads at the home range scale. We radio-collared 28 elk (21 adult females and 7 adult males) and calculated seasonal (winter, spring, summer, and autumn) utilization distributions (UDs). We assigned habitat covariates to use percentiles within the UDs (1% increments; from 1 to 98 percentiles) and used spatially explicit mixed linear regression to model the relationship between use percentile and habitat covariates. For each season and sex, we evaluated 15 candidate models and used Akaike's Information Criterion weights (v i ) to identify top-ranking models. We plotted influential coefficients from these models with 95% confidence intervals to examine the magnitude of effects. Our analysis revealed fundamental differences in response to roads, by road type, between sexes, and across seasons. Male elk established home ranges near roads devoid of vehicle traffic in winter, spring, and autumn. In summer, coinciding with peak vehicle traffic levels, male elk reduced their use of habitat that was both visible from and close to primary roads. Female elk subherds similarly responded to primary roads in spring and autumn, during times of year when they were calving and mating, respectively. In spring and summer, female elk subherds selected habitat near roads that were closed to vehicle traffic. Forage quality and security cover were influential in the periphery (>50th use percentile) of elk home ranges, whereas road covariates were more influential towards the core of elk home ranges. This analysis further demonstrates the utility of visibility from road metrics and suggests that the retention of vegetation structures that screen visibility potential from roads could be important components of elk management strategies. ß 2012 The Wildlife Society.

Telemetry data have been widely used to quantify wildlife habitat relationships despite the fact that these data are inherently imprecise. All telemetry data have positional error, and failure to account for that error can lead to incorrect predictions of wildlife resource use. Several techniques have been used to account for positional error in wildlife studies. These techniques have been described in the literature, but their ability to accurately characterize wildlife resource use has never been tested. We evaluated the performance of techniques commonly used for incorporating telemetry error into studies of wildlife resource use. Our evaluation was based on imprecise telemetry data (mean telemetry error = 174 m, SD = 130 m) typical of field‐based studies. We tested 5 techniques in 10 virtual environments and in one real‐world environment for categorical (i.e., habitat types) and continuous (i.e., distances or elevations) rasters. Technique accuracy varied by patch size for the categorical rasters, with higher accuracy as patch size increased. At the smallest patch size (1 ha), the technique that ignores error performed best on categorical data (0.31 and 0.30 accuracy for virtual and real data, respectively); however, as patch size increased the bivariate‐weighted technique performed better (0.56 accuracy at patch sizes >31 ha) and achieved complete accuracy (i.e., 1.00 accuracy) at smaller patch sizes (472 ha and 1,522 ha for virtual and real data, respectively) than any other technique. We quantified the accuracy of the continuous covariates using the mean absolute difference (MAD) in covariate value between true and estimated locations. We found that average MAD varied between 104 m (ignore telemetry error) and 140 m (rescale the covariate data) for our continuous covariate surfaces across virtual and real data sets. Techniques that rescale continuous covariate data or use a zonal mean on values within a telemetry error polygon were significantly less accurate than other techniques. Although the technique that ignored telemetry error performed best on categorical rasters with smaller average patch sizes (i.e., ≤31 ha) and on continuous rasters in our study, accuracy was so low that the utility of using point‐based approaches for quantifying resource use is questionable when telemetry data are imprecise, particularly for small‐patch habitat relationships.

Summary 1.Habitat use is widely known to be influenced by abiotic and biotic factors, such as climate, population density, foraging opportunity and predation risk. The influence of the life-history state of an individual organism on habitat use is less well understood, especially for terrestrial mammals.2. There is good reason to expect that life-history state would affect habitat use. For example, organisms exhibiting poor condition associated with senescence have an increased vulnerability to predation and that vulnerability is known to alter habitat use strategies. 3. We assessed the influence of life-history stage on habitat use for 732 moose (Alces alces) killed by wolves (Canis lupus) over a 50-year period in Isle Royale National Park, an island ecosystem in Lake Superior, USA. We developed regression models to assess how location of death was associated with a moose's life-history stage (prime-aged or senescent), presence or absence of senescent-associated pathology (osteoarthritis and jaw necrosis), and annual variation in winter severity, moose density and ratio of moose to wolves, which is an index of predation risk. 4. Compared to senescent moose, prime-aged moose tend to make greater use of habitat farther from the shoreline of Isle Royale. That result is ecologically relevant because shoreline habitat on Isle Royale tends to provide better foraging opportunities for moose but is also associated with increased predation risk. During severe winters prime-aged moose tend to make greater use of habitat that is closer to shore in relation to senescent-aged moose. Furthermore, moose of both age classes were more likely to die in riskier, shoreline habitat during years when predation risk was lower in the preceding year. 5. Our results highlight a complicated connection between life history, age-structured population dynamics and habitat-related behaviour. Our analysis also illustrates why intraspecific competition should not be the presumed mechanism underlying density-dependent habitat use, if predation risk is related to density, as it is expected to be in many systems.

Global Positioning System (GPS) and very high frequency (VHF) telemetry data redefined the examination of wildlife resource use. Researchers collar animals, relocate those animals over time, and utilize the estimated locations to infer resource use and build predictive models. Precision of these estimated wildlife locations, however, influences the reliability of point‐based models with accuracy depending on the interaction between mean telemetry error and how habitat characteristics are mapped (categorical raster resolution and patch size). Telemetry data often foster the assumption that locational error can be ignored without biasing study results. We evaluated the effects of mean telemetry error and categorical raster resolution on the correct characterization of patch use when locational error is ignored. We found that our ability to accurately attribute patch type to an estimated telemetry location improved nonlinearly as patch size increased and mean telemetry error decreased. Furthermore, the exact shape of these relationships was directly influenced by categorical raster resolution. Accuracy ranged from 100% (200‐ha patch size, 1‐ to 5‐m telemetry error) to 46% (0.5‐ha patch size, 56‐ to 60‐m telemetry error) for 10 m resolution rasters. Accuracy ranged from 99% (200‐ha patch size, 1‐ to 5‐m telemetry error) to 57% (0.5‐ha patch size, 56‐ to 60‐m telemetry error) for 30‐m resolution rasters. When covariate rasters were less resolute (30 m vs. 10 m) estimates for the ignore technique were more accurate at smaller patch sizes. Hence, both fine resolution (10 m) covariate rasters and small patch sizes increased probability of patch misidentification. Our results help frame the scope of ecological inference made from point‐based wildlife resource use models. For instance, to make ecological inferences with 90% accuracy at small patch sizes (≤5 ha) mean telemetry error ≤5 m is required for 10‐m resolution categorical rasters. To achieve the same inference on 30‐m resolution categorical rasters, mean telemetry error ≤10 m is required. We encourage wildlife professionals creating point‐based models to assess whether reasonable estimates of resource use can be expected given their telemetry error, covariate raster resolution, and range of patch sizes. © 2011 The Wildlife Society.

Carnivore communities face unprecedented threats from humans. Yet, management regimes have variable effects on carnivores, where species may persist or decline in response to direct or indirect changes to the ecosystem. Using a hierarchical multispecies modeling approach, we examined the effects of alternative management regimes (i.e., active vs. passive enforcement of regulations) on carnivore abundances and group sizes at both species and community levels in the Masai Mara National Reserve, Kenya. Alternative management regimes have created a dichotomy in ecosystem conditions within the Reserve, where active enforcement of regulations maintains low levels of human disturbance in the Mara Triangle and passive enforcement of regulations in the Talek region permits multiple forms of human disturbance. Our results demonstrate that these alternative management regimes have variable effects on 11 observed carnivore species. As predicted, some species, such as African lions and bat‐eared foxes, have higher population densities in the Mara Triangle, where regulations are actively enforced. Yet, other species, including black‐backed jackals and spotted hyenas, have higher population densities in the Talek region where enforcement is passive. Multiple underlying mechanisms, including behavioral plasticity and competitive release, are likely causing higher black‐backed jackals and spotted hyena densities in the disturbed Talek region. Our multispecies modeling framework reveals that carnivores do not react to management regimes uniformly, shaping carnivore communities by differentially producing winning and losing species. Some carnivore species require active enforcement of regulations for effective conservation, while others more readily adapt (and in some instances thrive in response) to lax management enforcement and resulting anthropogenic disturbance. Yet, high levels of human disturbance appear to be negatively affecting the majority of carnivores, with potential consequences that may permeate throughout the rest of the ecosystem. Community approaches to monitoring carnivores should be adopted as single species monitoring may overlook important intra‐community variability.

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