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Simultaneous‐count models to estimate abundance from counts of unmarked individuals with imperfect detection
Author(s) -
Ryan Gerard Edward,
Nicholson Emily,
Eames Jonathan C.,
Gray Thomas N. E.,
Loveridge Robin,
Mahood Simon P.,
Sum Phearun,
McCarthy Michael A.
Publication year - 2019
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1111/cobi.13261
Subject(s) - abundance (ecology) , negative binomial distribution , distance sampling , sampling (signal processing) , statistics , abundance estimation , multinomial distribution , count data , population , population size , range (aeronautics) , geography , ecology , mathematics , biology , computer science , poisson distribution , demography , sociology , materials science , filter (signal processing) , computer vision , composite material
We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous‐count models to estimate abundance, the latter being equivalent to an N‐mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real‐world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.