Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere.

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species-season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.

A spatially explicit hierarchical model to characterize population viability.

Many of the processes that govern the viability of animal populations vary spatially, yet population viability analyses (PVAs) that account explicitly for spatial variation are rare. We develop a PVA model that incorporates autocorrelation into the analysis of local demographic information to produce spatially explicit estimates of demography and viability at relatively fine spatial scales across a large spatial extent. We use a hierarchical, spatial, autoregressive model for capture-recapture data from multiple locations to obtain spatially explicit estimates of adult survival (ϕad ), juvenile survival (ϕjuv ), and juvenile-to-adult transition rates (ψ), and a spatial autoregressive model for recruitment data from multiple locations to obtain spatially explicit estimates of recruitment (R). We combine local estimates of demographic rates in stage-structured population models to estimate the rate of population change (λ), then use estimates of λ (and its uncertainty) to forecast changes in local abundance and produce spatially explicit estimates of viability (probability of extirpation, Pex ). We apply the model to demographic data for the Sonoran desert tortoise (Gopherus morafkai) collected across its geographic range in Arizona. There was modest spatial variation in λ^ (0.94-1.03), which reflected spatial variation in ϕ^ad (0.85-0.95), ϕ^juv (0.70-0.89), and ψ^ (0.07-0.13). Recruitment data were too sparse for spatially explicit estimates; therefore, we used a range-wide estimate ( R^  = 0.32 1-yr-old females per female per year). Spatial patterns in demographic rates were complex, but ϕ^ad , ϕ^juv , and λ^ tended to be lower and ψ^ higher in the northwestern portion of the range. Spatial patterns in Pex varied with local abundance. For local abundances >500, Pex was near zero (<0.05) across most of the range after 100 yr; as abundances decreased, however, Pex approached one in the northwestern portion of the range and remained low elsewhere. When local abundances were <50, western and southern populations were vulnerable (Pex  > 0.25). This approach to PVA offers the potential to reveal spatial patterns in demography and viability that can inform conservation and management at multiple spatial scales, provide insight into scale-related investigations in population ecology, and improve basic ecological knowledge of landscape-level phenomena.

A strategy for prioritizing threats and recovery actions for at-risk species.

Ensuring the persistence of at-risk species depends on implementing conservation actions that ameliorate threats. We developed and implemented a method to quantify the relative importance of threats and to prioritize recovery actions based on their potential to affect risk to Mojave desert tortoises (Gopherus agassizii). We used assessments of threat importance and elasticities of demographic rates from population matrix models to estimate the relative contributions of threats to overall increase in risk to the population. We found that urbanization, human access, military operations, disease, and illegal use of off highway vehicles are the most serious threats to the desert tortoise range-wide. These results suggest that, overall, recovery actions that decrease habitat loss, predation, and crushing will be most effective for recovery; specifically, we found that habitat restoration, topic-specific environmental education, and land acquisition are most likely to result in the greatest decrease in risk to the desert tortoise across its range. In addition, we have developed an application that manages the conceptual model and all supporting information and calculates threat severity and potential effectiveness of recovery actions. Our analytical approach provides an objective process for quantifying threats, prioritizing recovery actions, and developing monitoring metrics for those actions for adaptive management of any at-risk species.

Long-term effects of group-selection timber harvesting on abundance of forest birds.

Relatively few studies have examined the ecological effects of group-selection timber harvesting, and nearly all have been short-term and have lacked experimental manipulations that allow pre- and posttreatment comparisons. We have been documenting the effects of a group-selection timber harvest on bird abundance in a Maine forest for 24 years (preharvest, 1983-1987; postharvest, 1988-2006). Here we characterized the trends in bird abundance over the first 20 years of the study in the managed and control halves of the 40-ha study area. Species responses to the group-selection harvest were idiosyncratic, but in general the mature-forest bird community was retained and species dependent on early successional habitat temporarily (