On the identification of mortality hotspots in linear infrastructures

Abstract

One of the main tasks when dealing with the impacts of infrastructures on wildlife is to identify hotspots of high mortality so one can devise and implement mitigation measures. A common strategy to identify hotspots is to divide an infrastructure into several segments and determine when the number of collisions in a segment is above a given threshold, reflecting a desired significance level that is obtained assuming a probability distribution for the number of collisions, which is often the Poisson distribution. The problem with this approach, when applied to each segment individually, is that the probability of identifying false hotspots (Type I error) is potentially high. The way to solve this problem is to recognize that it requires multiple testing corrections or a Bayesian approach. Here, we apply three different methods that implement the required corrections to the identification of hotspots: (i) the familywise error rate correction, (ii) the false discovery rate, and (iii) a Bayesian hierarchical procedure. We illustrate the application ofthese methods with data on two bird species collected on a road inBrazil. The proposed methods provide practitioners with procedures that are reliable and simple to use in real situations and, in addition, can reflect a practitioner’s concerns towards identifying false positive or missing true hotspots. Although one may argue that an overly cautionary approach (reducing the probability of type I error) may be beneficial from a biological conservation perspective, it may lead to a waste of resources and, probably worse, it may raise doubts about the methodology adopted and the credibility of those suggesting it.