Borda de Água, LuísAscensão, FernandoSapage, ManuelBarrientos Yuste, RafaelPereira, Henrique M.2025-12-092025-12-092019-02Borda-de-Água, L., Ascensão, F., Sapage, M., Barrientos, R., & Pereira, H. M. (2019). On the identification of mortality hotspots in linear infrastructures. Basic and Applied Ecology, 34, 25-35. https://doi.org/10.1016/J.BAAE.2018.11.0011439-179110.1016/j.baae.2018.11.001https://hdl.handle.net/20.500.14352/128564This article is a result of the project NORTE-01-0145- FEDER-000007, supported by Norte Portugal Regional Operational Programme (NORTE2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). LBA, FA and RB were funded by Infraestruturas de Portugal Biodiversity Chair. FA was also supported by a FCT postdoctoral grant (SFRH/BPD/115968/2016). MS was funded by a PhD grant from Fundacão para a Ciência e a Tecnologia (FCT), Portugal (ref. PD/BD/128349/2017). All sources of funding are acknowledged in the manuscript, and the authors declare no direct financial benefits from its publication. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.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.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/journal article1618-0089https://doi.org/10.1016/j.baae.2018.11.001https://www.sciencedirect.com/science/article/pii/S1439179118301798?via%3Dihubopen access591.5502.15502.22711.7357.087.1Bayesian hierarchical modelFalse discovery rate correctionFamilywise error rate correctionHotspotSpatial auto-correlationEcología (Biología)ZoologíaMedio ambiente naturalBiomatemáticas2401.06 Ecología Animal3105.12 Ordenación y Conservación de la Fauna Silvestre2404.01 Bioestadística3305.29 Construcción de Carreteras