Literature DB >> 24649642

Modeling structured population dynamics using data from unmarked individuals.

Elise F Zipkin, James T Thorson, Kevin See, Heather J Lynch, Evan H Campbell Grant, Yoichiro Kanno, Richard B Chandler, Benjamin H Letcher, J Andrew Royle.   

Abstract

The study of population dynamics requires unbiased, precise estimates of abundance and vital rates that account for the demographic structure inherent in all wildlife and plant populations. Traditionally, these estimates have only been available through approaches that rely on intensive mark-recapture data. We extended recently developed N-mixture models to demonstrate how demographic parameters and abundance can be estimated for structured populations using only stage-structured count data. Our modeling framework can be used to make reliable inferences on abundance as well as recruitment, immigration, stage-specific survival, and detection rates during sampling. We present a range of simulations to illustrate the data requirements, including the number of years and locations necessary for accurate and precise parameter estimates. We apply our modeling framework to a population of northern dusky salamanders (Desmognathus fuscus) in the mid-Atlantic region (USA) and find that the population is unexpectedly declining. Our approach represents a valuable advance in the estimation of population dynamics using multistate data from unmarked individuals and should additionally be useful in the development of integrated models that combine data from intensive (e.g., mark-recapture) and extensive (e.g., counts) data sources.

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Year:  2014        PMID: 24649642     DOI: 10.1890/13-1131.1

Source DB:  PubMed          Journal:  Ecology        ISSN: 0012-9658            Impact factor:   5.499


  8 in total

1.  Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference.

Authors:  Thomas V Riecke; Dan Gibson; Marc Kéry; Michael Schaub
Journal:  Ecol Evol       Date:  2021-12-07       Impact factor: 2.912

2.  Inferences about population dynamics from count data using multistate models: a comparison to capture-recapture approaches.

Authors:  Elise F Zipkin; T Scott Sillett; Evan H Campbell Grant; Richard B Chandler; J Andrew Royle
Journal:  Ecol Evol       Date:  2014-01-20       Impact factor: 2.912

3.  Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity.

Authors:  Joshua H Schmidt; Tammy L Wilson; William L Thompson; Joel H Reynolds
Journal:  Ecol Evol       Date:  2017-05-25       Impact factor: 2.912

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

Authors:  James F Saracco; Madeleine Rubenstein
Journal:  Ecol Evol       Date:  2020-02-11       Impact factor: 2.912

5.  Spatial patterns in the contribution of biotic and abiotic factors to the population dynamics of three freshwater fish species.

Authors:  Mathieu Chevalier; Pablo Tedesco; Gael Grenouillet
Journal:  PeerJ       Date:  2022-02-23       Impact factor: 2.984

6.  N-mixture models provide informative crocodile (Crocodylus moreletii) abundance estimates in dynamic environments.

Authors:  José António Lemos Barão-Nóbrega; Mauricio González-Jaurégui; Robert Jehle
Journal:  PeerJ       Date:  2022-03-21       Impact factor: 2.984

7.  Open removal models with temporary emigration and population dynamics to inform invasive animal management.

Authors:  Bradley Udell; Julien Martin; Christina Romagosa; Hardin Waddle; Fred Johnson; Bryan Falk; Amy Yackel Adams; Sarah Funck; Jennifer Ketterlin; Eric Suarez; Frank Mazzotti
Journal:  Ecol Evol       Date:  2022-08-17       Impact factor: 3.167

8.  Simple statistical models can be sufficient for testing hypotheses with population time-series data.

Authors:  Seth J Wenger; Edward S Stowe; Keith B Gido; Mary C Freeman; Yoichiro Kanno; Nathan R Franssen; Julian D Olden; N LeRoy Poff; Annika W Walters; Phillip M Bumpers; Meryl C Mims; Mevin B Hooten; Xinyi Lu
Journal:  Ecol Evol       Date:  2022-09-27       Impact factor: 3.167

  8 in total

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