An integrated population model for bird monitoring in North America

Abstract

Integrated population models (IPMs) provide a unified framework for simultaneously analyzing data sets of different types to estimate vital rates, population size, and dynamics; assess contributions of demographic parameters to population changes; and assess population viability. Strengths of an IPM include the ability to estimate latent parameters and improve the precision of parameter estimates. We present a hierarchical IPM that combines two broad-scale avian monitoring data sets: count data from the North American Breeding Bird Survey (BBS) and capture-recapture data from the Monitoring Avian Productivity and Survivorship (MAPS) program. These data sets are characterized by large numbers of sample sites and observers, factors capable of inducing error in the sampling and observation processes. The IPM integrates the data sets by modeling the population abundance as a first-order autoregressive function of the previous year's population abundance and vital rates. BBS counts were modeled as a log-linear function of the annual index of population abundance, observation effects (observer identity and first survey year), and overdispersion. Vital rates modeled included adult apparent survival, estimated from a transient Cormack-Jolly-Seber model using MAPS data, and recruitment (surviving hatched birds from the previous season + dispersing adults) estimated as a latent parameter. An assessment of the IPM demonstrated it could recover true parameter values from 200 simulated data sets. The IPM was applied to data sets (1992-2008) of two bird species, Gray Catbird (Dumetella carolinensis) and Wood Thrush (Hylocichla mustelina) in the New England/Mid-Atlantic coastal Bird Conservation Region of the United States. The Gray Catbird population was relatively stable (trend +0.4% per yr), while the Wood Thrush population nearly halved (trend -4.5% per yr) over the 17-yr study period. IPM estimates of population growth rates, adult survival, and detection and residency probabilities were similar and as precise as estimates from the stand-alone BBS and CJS models. A benefit of using the IPM was its ability to estimate the latent recruitment parameter. Annual growth rates for both species correlated more with recruitment than survival, and the relationship for Wood Thrush was stronger than for Gray Catbird. The IPM's unified modeling framework facilitates integration of these important data sets.

Spatial and temporal drivers of avian population dynamics across the annual cycle

“… , b, Ahrestani et al. ). It also emphasizes that spatial variation in breeding habitat quality influences the contribution of vital rates to variance in λ.…”

“…Expanding this model to aggregate data sources over larger spatial and temporal scales (Ahrestani et al. ) will be especially important in understanding the causes of long‐term population declines (which may differ from the drivers of annual variation in abundance, e.g., Johnston et al. ) and for predicting the consequences of environmental change or management strategies.…”

The recent past and promising future for data integration methods to estimate species’ distributions

“…Integrated approaches with large‐scale efforts to collect multiple types of data (i.e. BBS count data and capture–recapture data from the Monitoring Avian Productivity and Survivorship [MAPS] program illustrated in Ahrestani, Saracco, Sauer, Pardieck, and Royle () could be used to evaluate changes in species distribution.…”

Using ring‐recovery and within‐season recapture data to estimate fecundity and population growth

“…To assess fecundity, population modelers have used age ratios at harvest from hunted species (Péron & Koons, ), fledgling counts from citizen‐scientist nest‐record programs (Robinson, Morrison, & Baillie, ), data from small‐scale nesting studies (Weegman, Arnold, Dawson, Winkler, & Clark, ) and reverse‐time mark–recapture models (which measure the product of fecundity and first‐year survival; Pradel, ; Saracco et al., ), but age ratios at capture could provide an alternative or complementary data stream to assess spatiotemporal variation in fecundity (Mazerolle et al., ; Ross et al., ; Specht & Arnold, ). In the absence of live recapture data, vulnerability to capture ( V ) could be estimated in an integrated population modeling (IPM) framework (Ahrestani et al., ), assuming that auxiliary population count data were available and that there were no confounding influences of immigration or emigration: …”