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Evaluating range-wide habitat use by a target species requires information on species occurrence over broad geographic regions, a process made difficult by species rarity, large spatiotemporal sampling domains, and imperfect detection. We address these challenges in an assessment of habitat use for jaguars (Panthera onca) outside protected areas in Central America. Occurrence records were acquired within 12 putative corridors using interviews with knowledgeable corridor residents. We developed a Bayesian hierarchical occupancy model to gain robust inference, allowing for heterogeneity introduced in the sampling process over space and time, using records of jaguar occurrence prone to false positives and false negatives. Probability of false detection of jaguars increased with the number of interviews conducted per unit (from 5.42% to 7.74% given

Summary Atlas data provide biodiversity information at a relatively fine spatial grain over a broad spatial extent and, increasingly, at multiple points in time, which make them invaluable for understanding processes that affect species distributions over time. The effect of survey effort on species detection has long been appreciated and Atlases typically include survey standards and records of effort, but challenges remain in analysing Atlas data that have not been collected using a repeated sampling protocol designed to correct for imperfect detection. We developed a single-visit dynamic occupancy model to quantify the effects of climatic and land-use drivers on local species extinction and colonization while accounting for imperfect detection using repeat Atlas data. We evaluated model stability using data simulated under alternative scenarios and, ultimately, applied the model to empirical data for Canada warbler Cardellina canadensis, a wide-spread species exhibiting a long-term population decline. At sample sizes that are realistic for many Atlases (n = 1000–10 000 independent survey blocks), our models produced unbiased estimates of detection, occupancy, colonization and extinction parameters. Slope estimates for explanatory covariates were somewhat less stable than overall occupancy, colonization and extinction rates, with covariate effects being sensitive to the total number of, and relationships among, explanatory variables. In comparison to other analyses of Canada warbler distributions that indicated minor changes over time, our approach identified a widespread decline in occupancy probability across New York, consistent with the broader population trend, particularly in the areas where it was initially more likely to occur. Synthesis and applications. A single-visit dynamic occupancy model is a novel method for analysing common, ecologically valuable datasets, such as Atlases, that lack repeated sampling necessary to correct for imperfect detection using alternative multi-season occupancy modelling approaches. As a result, using this method can improve understanding of species distributions and factors that shape them over time, thereby providing more accurate information to guide conservation and management.

Summary 1. The ecological footprint of a road may extend for several kilometres with overlapping effects from neighbouring roads causing a nonlinear accumulation of road effects in the landscape. Availability of preferred habitat, spatial dependencies between roads and habitat types, and fidelity to traditionally used areas further confound our ability to predict population-level responses of animals to growing road networks. 2. To isolate these effects, we developed an individually based movement model using elk Cervus elaphus L. as a model system. Empirically derived movement rules redistributed elk under different amounts of preferred habitat (clearcuts), road densities, and road development schemes. We tracked potential mortality risk (given time spent near roads) and emigration rates (given declining accessibility of foraging habitat). 3. Design of the road network accounted for up to 30‐55% difference in mortality risk and emigration rates, with the largest differences occurring at intermediate road densities (1‐1·5 km km ‐2 ) when road effects began to saturate the landscape. Maintaining roads in association with clearcuts caused a decline in habitat accessibility equivalent to replacing 50‐75% of these foraging patches with conifer forest. A nine-fold difference in potential emigration was observed after varying elk tolerance for declining habitat accessibility despite holding local movement biases constant. 4. Elk responses to growing road networks were non-linear, exposing thresholds for road density that were reflected in the home range occupancy patterns of a large sample of elk in the region. 5. Synthesis and applications . Our approach provides a means of scaling-up complex movement decisions to population-level redistribution, separating the confounding effects of landscape context from road effects, and exposing thresholds in connectivity and mortality risks for wildlife caused by infrastructure growth. Our model indicated that road densities ≤ 0·5 km km ‐2 yielded the highest probability of elk occurrence where elk were hunted (and sensitive to roads), but disassociating roads from foraging habitats or managing human access to roads may maintain effective elk habitat at substantially higher road densities.

Summary 1. Compared to traditional radio-collars, global positioning system (GPS) collars provide finer spatial resolution and collect locations across a broader range of spatial and temporal conditions. However, data from GPS collars are biased because vegetation and terrain interfere with the satellite signals necessary to acquire a location. Analyses of habitat selection generally proceed without correcting for this known sampling bias. We documented the effects of bias in resource selection functions (RSF) and compared the effectiveness of two bias-correction techniques. 2. The effects of environmental conditions on the probability of a GPS collar collecting a location were modelled for three brands of collar using data collected in 24-h trials at 194 test locations. The best-supported model was used to create GPS-biased data from unbiased animal locations. These data were used to assess the effects of bias given data losses in the range of 10‐40% at both 1- and 6-h sampling intensities. We compared the sign, value and significance of coefficients derived using biased and unbiased data. 3. With 6-h locations we observed type II error rates of 30‐40% given as little as a 10% data loss. Biased data also produced coefficients that were significantly more negative than unbiased estimates. Increasing the sampling intensity from 6- to 1-h locations eliminated type II errors but increased the magnitude of coefficient bias. No type I errors or changes in sign were observed. 4. We applied sample weighting and iterative simulation given a 30% data loss. For a biased vegetation type, simulation reduced more type II errors than weighting, most probably because the original sample size was re-established. However, selection for areas near trails, which was influenced by a biased vegetation type, showed fewer type II errors after weighting existing animal locations than after simulation. Both techniques corrected 100% and ≥ 80% of the biased coefficients at the 6- and 1-h sampling intensities, respectively. 5. Synthesis and applications. This study demonstrates that GPS error is predictable and biases the coefficients of resource selection models dependant upon the GPS sampling intensity and the level of data loss. We provide effective alternatives for correcting bias and discuss applying corrections under different sampling designs.