Absent or undetected? Effects of non-detection of species occurrence on wildlife–habitat models Original Research Article

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.