Using species traits to predict detectability of animals on aerial surveys.

Abstract

In animal surveys, detectability can vary widely across species. We hypothesized that detectability of animals should be a function of species traits such as mass, color, and mean herd size. We also hypothesized that models of detectability based on species traits can be used to predict detectability for new species not in the original data set, leading to substantial benefits for ecology and conservation. We tested these hypotheses with double-observer aerial surveys of 10 mammal species in northern Botswana. We combined all 10 species and modeled their detectability with species traits (mass, mean herd size, color) as predictors while controlling for observer effects, vegetation, and herd size. We found support for effects of mass and an interaction between herd size and mean herd size on detectability. This model accurately predicted the ratio of herds detected by two observers vs. one observer for 8 of 10 species. To test whether a model based on species traits could be applied to a new species, we serially deleted each species from the data set, fit a trait-based model to the remaining nine species, and used this model to predict detectability for the deleted species. The model was able to reproduce the species-trait model for seven species and accurately predicted the ratio of detections by one or two observers for a different set of seven species; the model was successful by both measures for five species. To our knowledge, this represents the first time that a mechanistic model for detectability of animals has been used to predict detectability for new species. Prediction failed for species with extreme values of traits, suggesting that predicting detectability is not possible near or beyond the boundaries of one's data set. The approach taken in this paper can potentially be used with a variety of taxa and may provide new opportunities to apply detectability corrections where they have not been possible before.