Search USGS⌕ Search
Home/Archive/USGS

USGS · 5224045

Combining band recovery data and Pollock's robust design to model temporary and permanent emigration

M. S. Lindberg·W. L. Kendall·J.E. Hines·Michael G. Anderson
View original source ↗Export metadata ↓

Abstract

Capture-recapture models are widely used to estimate demographic parameters of marked populations. Recently, this statistical theory has been extended to modeling dispersal of open populations. Multistate models can be used to estimate movement probabilities among subdivided populations if multiple sites are sampled. Frequently, however, sampling is limited to a single site, Models described by Burnham (1993, in Marked Individuals in the Study of Bird Populations , 199–213), which combined open population capture-recapture and band-recovery models, can be used to estimate permanent emigration when sampling is limited to a single population. Similarly, Kendall, Nichols, and Hines (1997, Ecology 51 , 563–578) developed models to estimate temporary emigration under Pollock's (1982, Journal of Wildlife Management 46 , 757–760) robust design. We describe a likelihood-based approach to simultaneously estimate temporary and permanent emigration when sampling is limited to a single population. We use a sampling design that combines the robust design and recoveries of individuals obtained immediately following each sampling period. We present a general form for our model where temporary emigration is a first-order Markov process, and we discuss more restrictive models. We illustrate these models with analysis of data on marked Canvasback ducks. Our analysis indicates that probability of permanent emigration for adult female Canvasbacks was 0.193 ( ) and that birds that were present at the study area in year i — 1 had a higher probability of presence in year i than birds that were not present in year i — 1.

Explore related subjects

BiometricsArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M. S. Lindberg, W. L. Kendall, J.E. Hines, Michael G. Anderson. 2004-05-24. Combining band recovery data and Pollock's robust design to model temporary and permanent emigration. https://doi.org/10.1111/j.0006-341x.2001.00273.x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2025

A flexible framework for N-mixture occupancy models: Applications to breeding bird surveys

Estimating species abundance under imperfect detection is a key challenge in biodiversity conservation. The N -mixture model, widely recognized for its ability to distinguish between abundance and individual detection probability without marking individuals, is constrained by its stringent closure assumption, which leads to biased estimates when violated in real-world settings. To address this limitation, we propose an extended framework based on a development of the mixed Gamma-Poisson model, incorporating a community parameter that represents the proportion of individuals consistently present throughout the survey period. This flexible framework generalizes both the zero-inflated type occupancy model and the standard N -mixture model as special cases, corresponding to community parameter values of 0 and 1, respectively. The model’s effectiveness is validated through simulations and applications to real-world datasets, specifically with 5 species from the North American Breeding Bird Survey and 46 species from the Swiss Breeding Bird Survey, demonstrating its improved accuracy and adaptability in settings where strict closure may not hold.

Biometrics↗
USGS2021

Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity

Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses in robust assemblages. But, patterns and mechanisms of sensitivity and robustness are not yet well understood, largely due to a lack of long-term measurements of climate and vegetation. Fortunately, observations are sometimes available across a broad spatial extent. We develop a novel statistical model for a multivariate response based on unknown cluster-specific effects and covariances, where cluster labels correspond to sensitivity and robustness. Our approach utilizes a prototype model for cluster membership that offers flexibility while enforcing smoothness in cluster probabilities across sites with similar characteristics. We demonstrate our approach with an application to vegetation abundance in Alaska, USA, in which we leverage the broad spatial extent of the study area as a proxy for unrecorded historical observations. In the context of the application, our approach yields interpretable site-level cluster labels associated with assemblage-level sensitivity and robustness without requiring strong a priori assumptions about the drivers of climate sensitivity.

Alaska↗
USGS2019

A temporally stratified extension of space‐for‐time Cormack–Jolly–Seber for migratory animals

Understanding drivers of temporal variation in demographic parameters is a central goal of mark‐recapture analysis. To estimate the survival of migrating animal populations in migration corridors, space‐for‐time mark–recapture models employ discrete sampling locations in space to monitor marked populations as they move past monitoring sites, rather than the standard practice of using fixed sampling points in time. Because these models focus on estimating survival over discrete spatial segments, model parameters are implicitly integrated over the temporal dimension. Furthermore, modeling the effect of time‐varying covariates on model parameters is complicated by unknown passage times for individuals that are not detected at monitoring sites. To overcome these limitations, we extended the Cormack–Jolly–Seber (CJS) framework to estimate temporally stratified survival and capture probabilities by including a discretized arrival time process in a Bayesian framework. We allow for flexibility in the model form by including temporally stratified covariates and hierarchical structures. In addition, we provide tools for assessing model fit and comparing among alternative structural models for the parameters. We demonstrate our framework by fitting three competing models to estimate daily survival, capture, and arrival probabilities at four hydroelectric dams for over 200 000 individually tagged migratory juvenile salmon released into the Snake River, USA.

Biometrics↗