Abstract
Current methods to correct for detection error require multiple visits
to the same survey location. Many historical datasets exist that were
collected using only a single visit, and logistical/cost considerations
prevent many current research programs from collecting multiple visit
data. In this paper, we explore what can be done with single visit count
data when there is detection error. We show that when appropriate
covariates that affect both detection and abundance are available,
conditional likelihood can be used to estimate the regression parameters
of a binomial–zero-inflated Poisson (ZIP) mixture model and correct for
detection error. We use observed counts of Ovenbirds (Seiurus
aurocapilla) to illustrate the estimation of the parameters for the
binomial–zero-inflated Poisson mixture model using a subset of data from
one of the largest and longest ecological time series datasets that
only has single visits. Our single visit method has the following
characteristics: (i) it does not require the assumptions of a closed
population or adjustments caused by movement or migration; (ii) it is
cost effective, enabling ecologists to cover a larger geographical
region than possible when having to return to sites; and (iii) its
resultant estimators appear to be statistically and computationally
highly efficient.
Keywords
closed populations, conditional likelihood, ecological monitoring, mixture models, open populations, pseudo-likelihood
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