Modeling spatial variation in avian survival and residency probabilities

Abstract

Abstract. The importance of understanding spatial variation in processes driving animal population dynamics is widely recognized. Yet little attention has been paid to spatial modeling of vital rates. Here we describe a hierarchical spatial autoregressive model to provide spatially explicit year-specific estimates of apparent survival (/) and residency (p) probabilities from capture-recapture data. We apply the model to data collected on a declining bird species, Wood Thrush (Hylocichla mustelina), as part of a broad-scale bird-banding network, the Monitoring Avian Productivity and Survivorship (MAPS) program. The Wood Thrush analysis showed variability in both / and p among years and across space. Spatial heterogeneity in residency probability was particularly striking, suggesting the importance of understanding the role of transients in local populations. We found broad-scale spatial patterning in Wood Thrush / and p that lend insight into population trends and can direct conservation and research. The spatial model developed here represents a significant advance over approaches to investigating spatial pattern in vital rates that aggregate data at coarse spatial scales and do not explicitly incorporate spatial information in the model. Further development and application of hierarchical capture-recapture models offers the opportunity to more fully investigate spatiotemporal variation in the processes that drive population changes.

Spatial modeling of survival and residency and application to the Monitoring Avian Productivity and Survivorship program

“…Recently, CAR models have been extended to capture-recapture data under a Bayesian hierarchical framework, and have been applied to bird-ringing data collected as part of the Monitoring Avian Productivity and Survivorship (MAPS) program in North America (Royle and Dorazio 2008, Ch. 11;Saracco et al 2010). The Bayesian hierarchical approach, with Markov chain Monte Carlo (MCMC) implementation, affords several advantages over classical approaches, including (1) flexibility in modeling heterogeneity in responses at a variety of levels from individuals to groups using fixed or random effects and (2) ease of handling and modeling missing data (both response and predictor variables).…”

“…Here, we implement the hierarchical CAR model described in Saracco et al (2010) to provide spatially and temporally specific estimates of adult apparent survival (hereafter 'survival') and residency probabilities for a bird species commonly captured as part of the MAPS program, Common Yellowthroat Geothlypis trichas. We interpret 'residents' here to be (at least attempted) local breeders, distinguishing them from 'floaters', dispersing birds, or passage migrants.…”

“…We acknowledge that the residency parameter in our model is conditioned on first capture (it is based on the 'transient' Cormack-Jolly-Seber, CJS, model; Pradel et al 1997) and thus applies to a subset of the larger population of interest; however, we believe that spatial modeling of this parameter can lend insight into demographic processes. We adapt the model of Saracco et al (2010) to highlight how the basic modeling approach can accommodate simple random effects (here heterogeneity in capture probability, p) and missing covariate data (here an indicator variable for 'sex').…”

Persistent impacts of West Nile virus on North American bird populations

“…However, these studies used animal count data that likely underestimate impacts because, even in the face of high mortality, recruitment and immigration may mask population declines (11). WNV is hypothesized to influence bird populations through reductions in survival, which can be influenced by various biotic and abiotic factors, including age (12), climate (13,14), and regional environment heterogeneity (15). In addition, a number of studies suggest that the impact of WNV on bird populations increases with human land use (16,17).…”

A new approach to the “apparent survival” problem: estimating true survival rates from mark–recapture studies

“…While relatively crude, we believe our approach represents a step in the right direction towards a better integration of survival and dispersal within empirical demographic models. We anticipate that further advances will be made via extensions to our approach, particularly if dispersal-kernel estimation can be directly incorporated within a spatially explicit CJS model (e.g., Saracco et al 2010). We encourage researchers to make maximum use of the information at hand within capture-history data, and take advantage of the highly flexible range of tools available for demographic modeling within the Bayesian framework.…”