Integrating broad-scale data to assess demographic and climatic contributions to population change in a declining songbird.

James F Saracco,Madeleine Rubenstein

doi:10.1002/ece3.5975

Abstract

Climate variation and trends affect species distribution and abundance across large spatial extents. However, most studies that predict species response to climate are implemented at small spatial scales or are based on occurrence‐environment relationships that lack mechanistic detail. Here, we develop an integrated population model (IPM) for multi‐site count and capture‐recapture data for a declining migratory songbird, Wilson's warbler (Cardellina pusilla), in three genetically distinct breeding populations in western North America. We include climate covariates of vital rates, including spring temperatures on the breeding grounds, drought on the wintering range in northwest Mexico, and wind conditions during spring migration. Spring temperatures were positively related to productivity in Sierra Nevada and Pacific Northwest genetic groups, and annual changes in productivity were important predictors of changes in growth rate in these populations. Drought condition on the wintering grounds was a strong predictor of adult survival for coastal California and Sierra Nevada populations; however, adult survival played a relatively minor role in explaining annual variation in population change. A latent parameter representing a mixture of first‐year survival and immigration was the largest contributor to variation in population change; however, this parameter was estimated imprecisely, and its importance likely reflects, in part, differences in spatio‐temporal distribution of samples between count and capture‐recapture data sets. Our modeling approach represents a novel and flexible framework for linking broad‐scale multi‐site monitoring data sets. Our results highlight both the potential of the approach for extension to additional species and systems, as well as needs for additional data and/or model development.

Highlights

We used transient life table response experiments (LTREs) to decompose variation in population growth rates among vital rate and demographic structure components and to examine how these demographic contributions depended on climate covariates (Koons, Arnold, & Schaub, 2017; Koons, Iles, Schaub, & Caswell, 2016)
This parameterization provides a natural extension of integrated population model (IPM) for continuous data, whereby population dynamics are described by a shape parameter representing the previous year's population state and vital rate parameters representing net demographic losses and gains
The IPM presented here provides a flexible framework for these broad-scale multi-site applications by modeling the survival and recruitment processes as functions of continuous random variables, rather than as functions of binomial and poisson processes typical of most IPM applications

Summary

| INTRODUCTION

Widespread population declines, range retractions, and extinctions highlight an urgent need to better understand drivers of wildlife population dynamics (Ceballos, Ehrlich, & Dirzo, 2017; Tittensor et al, 2014). Most studies that relate climate to populations across large spatial extents are based on occurrence or count data (Dawson, Jackson, House, Prentice, & Mace, 2011; Pacifici et al, 2015) These data types have become relatively common and available across large spatial extents (Sullivan et al, 2009) and can be used to model both population state and demographic rate parameters (Dail & Madsen, 2011; Royle, 2004). We develop an IPM for data from the North American Breeding Bird Survey (BBS; Pardieck, Ziolkowski, Hudson, & Campbell, 2016) and the Monitoring Avian Productivity and Survivorship program (MAPS; DeSante & Kaschube, 2009) to assess potential climate impacts on demographic rates and population dynamics of a migratory songbird species, Wilson's warbler (Cardellina pusilla), within three distinct genetic regions of the western United States (Ruegg et al, 2014). We used transient life table response experiments (LTREs) to decompose variation in population growth rates among vital rate and demographic structure components and to examine how these demographic contributions depended on climate covariates (Koons, Arnold, & Schaub, 2017; Koons, Iles, Schaub, & Caswell, 2016)

| METHODS

Findings

| DISCUSSION