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A Bayesian optimal escape model reveals bird species differ in their capacity to habituate to humans

Cite this dataset

Sutton, Nicholas et al. (2021). A Bayesian optimal escape model reveals bird species differ in their capacity to habituate to humans [Dataset]. Dryad.


The capacity to habituate to, or tolerate, the close proximity of humans varies among wildlife species and may mediate population and species viability. Some species readily habituate to human proximity while others remain sensitive. These differences are important for predicting human impact on wildlife, but can be difficult to quantify given wildlife responses are highly idiosyncratic and are often context-dependent. A general method for assimilating multiple sources of information and variation in individual responses is needed to extract meaningful descriptors of population- and species-level behavior. We apply a previously verified Bayesian optimal escape model to quantify differences in the perceived risk of seven species of waterbird, and generate a metric for species-specific sensitivity by estimating the relative change in risk perception of each species across an environmental gradient from low to high prevailing human activity level. We found that, in general, birds are the least habituated (evidently perceived the highest level of risk from humans) in sites with low human activity and the most habituated (evidently perceived lower levels of risk from humans) in sites with high human activity. Species varied in the degree of these differences, with some insensitive to human activity level, while others were highly sensitive. Aside from improving our ability to study the habituation of wild populations, this method for quantifying risk perception at population and species scales has broad applications in the management and monitoring of wildlife, and may aid in environmental impact assessments and identifying populations/species susceptible to disturbance.

Usage notes

Please see the associated README file for steps to run the included code and analyses using this data.


United States Department of Agriculture, Award: ILLU 875-952

Simons Foundation, Award: 376199

McDonnell Foundation, Award: 220020439

McDonnell Foundation, Award: 220020439