%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 }