Using species traits to predict detectability of animals on aerial surveys

Abstract

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.

Global abundance estimates for 9,700 bird species

“…Sólymos et al (81) and Johnston et al (82) found that body size was a significant predictor of bird species' detectability. There is also support that group size can influence detectability of animals (83). Although difficult to quantify, it is thought that the coloration of an organism influences its detectability (84).…”

“…1 A ) is likely to be influenced by a suite of species’ life history traits. Previous work has shown that species traits (e.g., body size, color, and group size) can influence the detectability of a species ( 79 – 84 ), and thus in turn, the likelihood a species is recorded in a citizen science dataset ( 85 ). Sólymos et al ( 81 ) and Johnston et al ( 82 ) found that body size was a significant predictor of bird species’ detectability.…”

Cameras replace human observers in multi‐species aerial counts in Murchison Falls, Uganda

“…. RSO detection of 'available' animals depends on a range of 'environmental factors' such as animal size, group size, vegetation cover, species coloration, reaction to the aircraft, occurrence in multi-species assemblages; 'survey factors' such as flying height, counting strip-width and sun angle; and 'observer factors' such as experience and level of fatigue (Caughley et al 1976;Anderson and Lindzey 1996;Jachmann 2002;Melville et al 2008;McConville et al 2009;Wal et al 2011;Ransom 2012;Griffin et al 2013;Jacques et al 2014;Strobel and Butler 2014;Lubow and Ransom 2016;Schlossberg et al 2016;Schlossberg et al 2017).…”

Trait-based modelling in ecology: lessons from two decades of research

“…Trait-based models of population dynamics investigated the responses of populations to environmental changes (Santini et al, 2016) and to perturbations (Ozgul et al, 2012). Using an approach similar to that of the time-todetection studies mentioned in section 1.2 (Trait-based models of species distributions), Schlossberg et al (2018) modelled detectability for ten mammal species. This model was based on species traits such as body mass, mean herd size and color and employed a statistical approach based on conditional likelihoods.…”