TY  - JOUR
PY  - 2019//
TI  - Predicting spatiotemporal patterns of road mortality for medium-large mammals
JO  - Journal of environmental management
A1  - Ascensão, Fernando
A1  - Yogui, Débora
A1  - Alves, Mario
A1  - Medici, Emília Patrícia
A1  - Desbiez, Arnaud
SP  - e109320
EP  - e109320
VL  - 248
IS  - 
N2  - We modelled the spatiotemporal patterns of road mortality for seven medium-large mammals, using a roadkill dataset from Mato Grosso do Sul, Brazil (800 km of roads surveyed every two weeks, for two years). We related roadkill presence-absence along the road sections (1000 m) and across the survey dates with a collection of environmental variables, including land cover, forest cover, distance to rivers, temperature, precipitation and vegetation productivity. We further included two variables aiming to reflect the intrinsic spatial and temporal roadkill risk. Environmental variables were obtained through remote sensing and weather stations, allowing the estimate of the roadkill risk for the entire surveyed roads and survey periods. Overall, the models could explain a small fraction of the spatiotemporal patterns of roadkills (<0.23), probably due to species being habitat generalists, but still had reasonable discrimination power (AUC averaging 0.70 ± 0.07). The intrinsic spatial and temporal roadkill risk were the most important variables, followed by land cover, climate and NDVI. We show that identifying spatiotemporal roadkill patterns may provide valuable information to define specific management actions focused on road sections and time periods, in complement to permanent road mitigation measures. Our approach thus offers a new insight into the understanding of road effects and how to plan and strategize monitoring and mitigation.<br><br>Copyright © 2019 Elsevier Ltd. All rights reserved.<p />  <p>Language: en</p>
LA  - en
SN  - 0301-4797
UR  - http://dx.doi.org/10.1016/j.jenvman.2019.109320
ID  - ref1
ER  -