Abstract
Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.
Highlights
Data arising from vehicle-wildlife collisions are viewed as a potentially informative source of inference on trends in wildlife abundance
Three parallel Markov Chain Monte Carlo (MCMC) chains were run with different starting values for β0 and β1
The model was fitted to the mainland data, with the Tasmanian fox runway data point treated as missing
Summary
Data arising from vehicle-wildlife collisions (termed ‘‘roadkill’’ for road vehicles, ‘‘wildlife strike’’ for aircraft, and hereafter ‘‘wildlife collision’’) are viewed as a potentially informative source of inference on trends in wildlife abundance. A key challenge when making inference from wildlife collision data is that it is essentially presence-only data. We typically cannot resolve the incidence rate over the temporal and areal extent of the study from the raw data alone as we do not have information about the vehicle movement (traffic) factors (e.g., speed, How to cite this article Caley et al (2017), Making inference from wildlife collision data: inferring predator absence from prey strikes. Finder, Roseberry & Woolf (1999) give an example of landscape factors influencing the collision rate of vehicles with wildlife, D’Amico et al (2015) show that higher abundance leads to a higher collision rate, and Hobday & Minstrell (2008) show that vehicle speed influences the probability of a vehicle-wildlife collision
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