Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change.

Over the past half century, migratory birds in North America have shown divergent population trends relative to resident species, with the former declining rapidly and the latter increasing. The role that climate change has played in these observed trends is not well understood, despite significant warming over this period. We used 43 y of monitoring data to fit dynamic species distribution models and quantify the rate of latitudinal range shifts in 32 species of birds native to eastern North America. Since the early 1970s, species that remain in North America throughout the year, including both resident and migratory species, appear to have responded to climate change through both colonization of suitable area at the northern leading edge of their breeding distributions and adaption in place at the southern trailing edges. Neotropical migrants, in contrast, have shown the opposite pattern: contraction at their southern trailing edges and no measurable shifts in their northern leading edges. As a result, the latitudinal distributions of temperate-wintering species have increased while the latitudinal distributions of neotropical migrants have decreased. These results raise important questions about the mechanisms that determine range boundaries of neotropical migrants and suggest that these species may be particularly vulnerable to future climate change. Our results highlight the potential importance of climate change during the nonbreeding season in constraining the response of migratory species to temperature changes at both the trailing and leading edges of their breeding distributions. Future research on the interactions between breeding and nonbreeding climate change is urgently needed.

Modeling spatially and temporally complex range dynamics when detection is imperfect.

Species distributions are determined by the interaction of multiple biotic and abiotic factors, which produces complex spatial and temporal patterns of occurrence. As habitats and climate change due to anthropogenic activities, there is a need to develop species distribution models that can quantify these complex range dynamics. In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. The model is flexible and can accommodate data from a range of sampling designs that provide information about both occupancy and detection probability. Output from the model can be used to create distribution maps and to estimate indices of temporal range dynamics. We demonstrate the utility of this approach by modeling long-term range dynamics of 10 eastern North American birds using data from the North American Breeding Bird Survey. We anticipate this framework will be particularly useful for modeling species' distributions over large spatial scales and for quantifying range dynamics over long temporal scales.

Estimating indices of range shifts in birds using dynamic models when detection is imperfect.

There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997-2013 study period. Near-term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection - a new extension to correlated detection occupancy models - were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.

Are songbirds at risk from lead at small arms ranges? An application of the spatially explicit exposure model.

Use of small arms during training is an important activity associated with the development and proficiency of soldiers. These weapons traditionally have used copper-jacketed lead projectiles; the copper facilitates the oxidation of the metallic lead resulting in more mobile oxides and carbonates. Consequently, many ranges at installations have high soil concentrations of lead. Many of these ranges are no longer used and have become habitat for wildlife. To address the potential for adverse effects from lead exposure in songbirds, we compared the outputs of traditional deterministic exposure models with a spatial model and compared the results of both with blood-lead levels from songbird species at two small arms range complexes. An integrative data collection procedure was used and incorporated into the spatially explicit exposure model (SEEM) for two small arms range sites. Site-specific data were used to refine model input parameters. These data included lead soil concentrations, analysis of lead concentrations in nestling food items, acid-insoluble ash content of feces (to estimate soil ingestion), location and mapping of singing males, and nest site location and characteristics. Territorial males also were spot-mapped to determine likelihood of breeding activity. Modeled estimates of risk were compared with blood and feather lead levels of adults and nestlings. Overall, edge species had higher blood-lead concentrations; however, most had concentrations below subclinical effect levels. Conventional deterministic methods produced risk estimates exceeding 10-fold the highest SEEM estimates. The spatially explicit exposure model provided good agreement with field observations and therefore produced more accurate risk estimates. The present study provides support for the application of spatial methods over conventional deterministic methods.