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Incorporating individual variability into mark–recapture models
Author(s) -
Ford Jessica H.,
Bravington Mark V.,
Robbins Jooke
Publication year - 2012
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/j.2041-210x.2012.00243.x
Subject(s) - mark and recapture , statistics , population , econometrics , variation (astronomy) , population size , ecology , estimation , random effects model , population model , geography , computer science , mathematics , biology , demography , engineering , sociology , astrophysics , medicine , meta analysis , physics , systems engineering
SummaryUnderstanding individual variation is a key challenge in ecology. Inherent individual differences in movement and behaviour pose fundamental problems in the analysis of mark–recapture data as unmodelled individual differences can bias estimates of population size and survival rates. Multi‐state mark–recapture models have been the focus of much recent research but have yet to explicitly incorporate individual variability. We use a multi‐state mark–recapture model with individual‐level random effects, built in admb‐re , a software tool that automatically provides an accurate analytical approximation of the likelihood which is otherwise intractable. We tested the model using simulation studies and applied the model to data from N orth A tlantic humpback whales in the S tellwagen B ank N ational M arine S anctuary where heterogeneity is apparent in both sighting probability and site preference. Simulation studies demonstrated accurate estimation of true parameter values with random effects models but bias sometimes resulted from fitting simpler models. In application to data from the N orth A tlantic humpback whales, we were able to estimate both annual variation in the local population and three measures of individual‐level variation. Results indicate considerable heterogeneity within this population in both sighting probability and site preference. Ignoring random effects led to bias in estimates of proportion of time within a marine reserve.

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