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Millions of nocturnally migrating birds die each year from collisions with built structures, especially brightly illuminated buildings and communication towers. Reducing this source of mortality requires knowledge of important behavioral, meteorological, and anthropogenic factors, yet we lack an understanding of the interacting roles of migration, artificial lighting, and weather conditions in causing fatal bird collisions. Using two decades of collision surveys and concurrent weather and migration measures, we model numbers of collisions occurring at a large urban building in Chicago. We find that the magnitude of nocturnal bird migration, building light output, and wind conditions are the most important predictors of fatal collisions. The greatest mortality occurred when the building was brightly lit during large nocturnal migration events and when winds concentrated birds along the Chicago lakeshore. We estimate that halving lighted window area decreases collision counts by 11× in spring and 6× in fall. Bird mortality could be reduced by ∼60% at this site by decreasing lighted window area to minimum levels historically recorded. Our study provides strong support for a relationship between nocturnal migration magnitude and urban bird mortality, mediated by light pollution and local atmospheric conditions. Although our research focuses on a single site, our findings have global implications for reducing or eliminating a critically important cause of bird mortality.

Individual variation in behavior, particularly consistent among-individual differences (i.e., personality), has important ecological and evolutionary implications for population and community dynamics, trait divergence, and patterns of speciation. Nevertheless, individual variation in spatial behaviors, such as home range behavior, movement characteristics, or habitat use has yet to be incorporated into the concepts or methodologies of ecology and evolutionary biology. To evaluate evidence for the existence of consistent among-individual differences in spatial behavior – which we refer to as “spatial personality” – we performed a meta-analysis of 200 repeatability estimates of home range size, movement metrics, and habitat use. We found that the existence of spatial personality is a general phenomenon, with consistently high repeatability (r) across classes of spatial behavior (r = 0.67–0.82), taxa (r = 0.31–0.79), and time between repeated measurements (r = 0.54–0.74). These results suggest: 1) repeatable spatial behavior may either be a cause or consequence of the environment experienced and lead to spatial personalities that may limit the ability of individuals to behaviorally adapt to changing landscapes; 2) interactions between spatial phenotypes and environmental conditions could result in differential reproduction, survival, and dispersal, suggesting that among-individual variation may facilitate population-level adaptation; 3) spatial patterns of species' distributions and spatial population dynamics may be better understood by shifting from a mean field analytical approach towards methods that account for spatial personalities and their associated fitness and ecological dynamics.

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multiscale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.

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Aim The importance of framing investigations of organism–environment relationships to interpret patterns at relevant spatial scales is increasingly recognized. However, most research related to environmental relationships is single‐scaled, implicitly or explicitly assuming that a “species characteristic selection scale” exists. We tested the premise that a single characteristic scale exists to understand species–environment relationships within species by asking (a) what are the characteristic scales of species’ relationships with environmental predictors, and (b) is within‐species, cross‐predictor consistency in characteristic scales a general phenomenon. Location Nebraska, USA. Time period 2016. Major taxa studied Birds. Methods We used data from 86 species at > 500 locations to build hierarchical N‐mixture models relating species abundance to land cover variables. By incorporating Bayesian latent indicator scale selection, we identified the spatial scales that best explain species–environment relationships with each land cover predictor. We quantified the extent of cross‐predictor consistency in characteristic scales, and contrasted this to the expectation given a single species’ characteristic scale. Results We found no evidence for a characteristic spatial scale explaining all abundance–environment relationships within species, rather we found substantial variation in scale‐dependence across multiple environmental attributes. Furthermore, 33% of species displayed evidence of multiple important spatial scales within environmental attributes. Major conclusions Within species there is little evidence for a single characteristic scale of environmental relationships and considerable variation in species’ scale dependencies. Because species may respond to multiple environmental attributes at different spatial scales, or single environmental attributes at multiple scales, we caution against any unoptimized single‐scale studies. Our results demonstrate that until a framework is developed to predict the scales at which species respond to environmental characteristics, multi‐scale investigations must be performed to identify and account for multi‐scale dependencies. Natural selection acting on species’ response to distinct environmental attributes, rather than natural selection acting on species’ perception of spatial scales per se, may have shaped patterns of scale dependency and is an area ripe for investigation.