Presence–absence data are used widely in analysis of wildlife–habitat relationships. Failure to detect a species’ presence in an occupied habitat patch is a common sampling problem when the population size is small, individuals are difficult to sample, or sampling effort is limited. In this paper, the influence of non-detection of occurrence on parameter estimates of logistic regression models of wildlife–habitat relationships was assessed using analytical analysis and simulations. Two patterns of non-detection were investigated: (1) a random distribution of non-detection among occupied patches; and (2) a non-random distribution of non-detection in which the probability of detecting a species in an occupied patch covaried with measurable habitat variables. Our results showed that logistic regression models of wildlife–habitat relationships were sensitive to even low levels of non-detection in occupancy data. Both analytic and simulation studies show that non-detection yields bias in parameter estimation of logistic regression models. More importantly, the direction of bias was affected by the underlying pattern of non-detection and whether the habitat variable was positively or negatively related to occupancy. For a positive habitat coefficient, a random distribution of non-detection yielded negative bias in estimation, whereas linkage of the probability of non-detection to habitat covariates produced positive bias. For a negative habitat coefficient, the pattern was reversed, with a random distribution of non-detection leading to positive bias in estimation. A release–recapture livetrapping study of small mammals in central Indiana, USA, was used to illustrate the magnitude of non-detection in a typical field sampling protocol with varying levels of sampling intensity. Estimates of non-detection error ranged from 0 to 23\% for seven species after 5 days of sampling. We suggest that for many sampling situations, relationships between probability of detection and habitat covariates need to be established to correctly interpret results of wildlife–habitat models.
%0 Journal Article
%1 gu_absent_2004
%A Gu, Weidong
%A Swihart, Robert K
%D 2004
%J Biological Conservation
%K Logistic Misclassification, Patch capture-recapture, data, detectability, ecological model model, modelling, niche occupancy
%N 2
%P 195--203
%R 10.1016/S0006-3207(03)00190-3
%T Absent or undetected? Effects of non-detection of species occurrence on wildlife–habitat models
%U http://www.sciencedirect.com/science/article/pii/S0006320703001903
%V 116
%X Presence–absence data are used widely in analysis of wildlife–habitat relationships. Failure to detect a species’ presence in an occupied habitat patch is a common sampling problem when the population size is small, individuals are difficult to sample, or sampling effort is limited. In this paper, the influence of non-detection of occurrence on parameter estimates of logistic regression models of wildlife–habitat relationships was assessed using analytical analysis and simulations. Two patterns of non-detection were investigated: (1) a random distribution of non-detection among occupied patches; and (2) a non-random distribution of non-detection in which the probability of detecting a species in an occupied patch covaried with measurable habitat variables. Our results showed that logistic regression models of wildlife–habitat relationships were sensitive to even low levels of non-detection in occupancy data. Both analytic and simulation studies show that non-detection yields bias in parameter estimation of logistic regression models. More importantly, the direction of bias was affected by the underlying pattern of non-detection and whether the habitat variable was positively or negatively related to occupancy. For a positive habitat coefficient, a random distribution of non-detection yielded negative bias in estimation, whereas linkage of the probability of non-detection to habitat covariates produced positive bias. For a negative habitat coefficient, the pattern was reversed, with a random distribution of non-detection leading to positive bias in estimation. A release–recapture livetrapping study of small mammals in central Indiana, USA, was used to illustrate the magnitude of non-detection in a typical field sampling protocol with varying levels of sampling intensity. Estimates of non-detection error ranged from 0 to 23\% for seven species after 5 days of sampling. We suggest that for many sampling situations, relationships between probability of detection and habitat covariates need to be established to correctly interpret results of wildlife–habitat models.
@article{gu_absent_2004,
abstract = {Presence–absence data are used widely in analysis of wildlife–habitat relationships. Failure to detect a species’ presence in an occupied habitat patch is a common sampling problem when the population size is small, individuals are difficult to sample, or sampling effort is limited. In this paper, the influence of non-detection of occurrence on parameter estimates of logistic regression models of wildlife–habitat relationships was assessed using analytical analysis and simulations. Two patterns of non-detection were investigated: (1) a random distribution of non-detection among occupied patches; and (2) a non-random distribution of non-detection in which the probability of detecting a species in an occupied patch covaried with measurable habitat variables. Our results showed that logistic regression models of wildlife–habitat relationships were sensitive to even low levels of non-detection in occupancy data. Both analytic and simulation studies show that non-detection yields bias in parameter estimation of logistic regression models. More importantly, the direction of bias was affected by the underlying pattern of non-detection and whether the habitat variable was positively or negatively related to occupancy. For a positive habitat coefficient, a random distribution of non-detection yielded negative bias in estimation, whereas linkage of the probability of non-detection to habitat covariates produced positive bias. For a negative habitat coefficient, the pattern was reversed, with a random distribution of non-detection leading to positive bias in estimation. A release–recapture livetrapping study of small mammals in central Indiana, USA, was used to illustrate the magnitude of non-detection in a typical field sampling protocol with varying levels of sampling intensity. Estimates of non-detection error ranged from 0 to 23\% for seven species after 5 days of sampling. We suggest that for many sampling situations, relationships between probability of detection and habitat covariates need to be established to correctly interpret results of wildlife–habitat models.},
added-at = {2017-01-09T13:57:26.000+0100},
author = {Gu, Weidong and Swihart, Robert K},
biburl = {https://www.bibsonomy.org/bibtex/20e542f563a61d5bfaac6abaa23086aed/yourwelcome},
doi = {10.1016/S0006-3207(03)00190-3},
interhash = {177d7985b7e9e22860a43b5a69d77038},
intrahash = {0e542f563a61d5bfaac6abaa23086aed},
issn = {0006-3207},
journal = {Biological Conservation},
keywords = {Logistic Misclassification, Patch capture-recapture, data, detectability, ecological model model, modelling, niche occupancy},
month = apr,
number = 2,
pages = {195--203},
shorttitle = {Absent or undetected?},
timestamp = {2017-01-09T14:01:11.000+0100},
title = {Absent or undetected? {Effects} of non-detection of species occurrence on wildlife–habitat models},
url = {http://www.sciencedirect.com/science/article/pii/S0006320703001903},
urldate = {2012-05-22},
volume = 116,
year = 2004
}