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Spatial and species‐level predictions of road mortality risk using trait data
Author(s) -
GonzálezSuárez Manuela,
Zanchetta Ferreira Flávio,
Grilo Clara
Publication year - 2018
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1111/geb.12769
Subject(s) - ecology , geography , spatial ecology , amazon rainforest , trait , wildlife , mortality rate , biology , demography , sociology , computer science , programming language
Aim Collisions between wildlife and vehicles are recognized as one of the major causes of mortality for many species. Empirical estimates of road mortality show that some species are more likely to be killed than others, but to what extent this variation can be explained and predicted using intrinsic species characteristics remains poorly understood. This study aims to identify general macroecological patterns associated with road mortality and generate spatial and species‐level predictions of risks. Location Brazil. Time period 2001–2014. Major taxa Birds and mammals. Methods We fitted trait‐based random forest regression models (controlling for survey characteristics) to explain 783 empirical road mortality rates from Brazil, representing 170 bird and 73 mammalian species. Fitted models were then used to make spatial and species‐level predictions of road mortality risk in Brazil, considering 1,775 birds and 623 mammals that occur within the continental boundaries of the country. Results Survey frequency and geographical location were key predictors of observed rates, but mortality was also explained by the body size, reproductive speed and ecological specialization of the species. Spatial predictions revealed a high potential standardized (per kilometre of road) mortality risk in Amazonia for birds and mammals and, additionally, a high risk in Southern Brazil for mammals. Given the existing road network, these predictions mean that >8 million birds and >2 million mammals could be killed per year on Brazilian roads. Furthermore, predicted rates for all Brazilian endotherms uncovered potential vulnerability to road mortality of several understudied species that are currently listed as threatened by the International Union for Conservation of Nature. Conclusion With a rapidly expanding global road network, there is an urgent need to develop improved approaches to assess and predict road‐related impacts. This study illustrates the potential of trait‐based models as assessment tools to gain a better understanding of the correlates of vulnerability to road mortality across species, and as predictive tools for difficult‐to‐sample or understudied species and areas.