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Site Occupancy Models with Heterogeneous Detection Probabilities
Author(s) -
Royle J. Andrew
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00439.x
Subject(s) - occupancy , inference , population , statistics , mixture model , context (archaeology) , computer science , population size , negative binomial distribution , binomial distribution , econometrics , mathematics , ecology , artificial intelligence , poisson distribution , geography , biology , demography , archaeology , sociology
Summary Models for estimating the probability of occurrence of a species in the presence of imperfect detection are important in many ecological disciplines. In these “site occupancy” models, the possibility of heterogeneity in detection probabilities among sites must be considered because variation in abundance (and other factors) among sampled sites induces variation in detection probability ( p ). In this article, I develop occurrence probability models that allow for heterogeneous detection probabilities by considering several common classes of mixture distributions for p . For any mixing distribution, the likelihood has the general form of a zero‐inflated binomial mixture for which inference based upon integrated likelihood is straightforward. A recent paper by Link (2003, Biometrics 59, 1123–1130) demonstrates that in closed population models used for estimating population size, different classes of mixture distributions are indistinguishable from data, yet can produce very different inferences about population size. I demonstrate that this problem can also arise in models for estimating site occupancy in the presence of heterogeneous detection probabilities. The implications of this are discussed in the context of an application to avian survey data and the development of animal monitoring programs.