Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities

Building useful models of species distributions requires attention to several important issues, one being imperfect detection of species. Data sets of species detections are likely to suffer from false absence records. Depending on the type of survey, false positive records can also be a problem. Disregarding these observation errors may lead to important biases in model estimation as well as overconfidence about precision. The severity of the problem depends on the intensity of these errors and how they correlate with environmental characteristics (e.g. where species detectability strongly depends on habitat features). A powerful modelling framework that accounts for imperfect detection in the modelling of species distributions has developed in the last 10-15 yr. Fundamental to this framework is that data must be collected in a way that is informative about the observation process. For instance, such data can be in the form of multiple detection/non-detection records obtained from several visits/observers/detection methods at (at least) some of the sites, or from data on times to detection within a survey visit. The framework can extend to studying species' range dynamics and the modelling of communities, as well as approaches for analysing data on abundance and multiple occupancy states (rather than binary presence/absence). This paper summarizes these modelling advances, discusses evidence about effects of imperfect detection and the difficulties of working with it, and concludes with the current outlook for future research and application of these methods.