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Estimating population size with imperfect detection using a parametric bootstrap

November 19, 2019

We develop a novel method of estimating population size from imperfectly detected counts of individuals and a separate estimate of detection probability. Observed counts are separated into classes within which detection probability is assumed constant. Within a detection class, counts are modeled as a single binomial observation X with success probability p where the goal is to estimate index N. We use a Horvitz–Thompson‐like estimator for N and account for uncertainty in both sample data and estimated success probability via a parametric bootstrap. Unlike capture–recapture methods, our model does not require repeated sampling of the population. Our method is able to achieve good results, even with small X. We show in a factorial simulation study that the median of the bootstrapped sample has small bias relative to N and that coverage probabilities of confidence intervals for N are near nominal under a wide array of scenarios. Our methodology begins to break down when P(X=0)>0.1 but is still capable of obtaining reasonable confidence coverage. We illustrate the proposed technique by estimating (1) the size of a moose population in Alaska and (2) the number of bat fatalities at a wind power facility, both from samples with imperfect detection probabilities, estimated independently.

Publication Year 2020
Title Estimating population size with imperfect detection using a parametric bootstrap
DOI 10.1002/env.2603
Authors Lisa Madsen, Daniel Dalthorp, Manuela Huso, Andy Aderman
Publication Type Article
Publication Subtype Journal Article
Series Title Environmetrics
Index ID 70217797
Record Source USGS Publications Warehouse
USGS Organization Forest and Rangeland Ecosystem Science Center