Danger under wheels: mammal roadkills in the threaten lowland Atlantic Forest in southeast Brazil / Perigo sob rodas: atropelamentos de mamíferos na ameaçada Mata Atlântica de baixada no sudeste do Brasil

Understanding the ecological impacts of roads on mammals requires periodic monitoring of roads, and identification of both temporal and spatial distribution of roadkills (i.e., roadkill hotspots). The main aim of the study was to identify the most roadkilled mammals and evaluate the temporal and spatial distribution of roadkills in the RJ-122, a highway that crosses the threaten lowland Atlantic Forest in the state of Rio de Janeiro, southeast Brazil. Between October 2017 and January 2020, an intense monitoring study was conducted, with the carcasses of the roadkilled mammals being collected three times a week. Overall, we recorded 295 roadkilled mammals belonging to 22 species, resulting in 11 roadkills per month for the RJ-122 highway. The black-eared opossum, Didelphis aurita (N=149, 51%) stood out as the most roadkilled mammal, followed by porcupine, Coendou spinosus (N= 24, 8%), crab-eating fox, Cerdocyon thous (N= 23, 8), nine-banded armadillo, Dasypus novemcinctus (N= 23, 8%), and white-tufted marmoset, Callithrix jacchus (N= 20, 7%). Roadkills on the RJ-122 varied throughout the year, being more frequent in the rainy season (N=180) than in the dry season (N=115), and were concentrated in two hotspots, indicating some critical points with high roadkill frequency. Hotspots were associated with areas with dense natural vegetation, which can function as forest corridors in this fragmented landscape. Based on our results, several mitigation measures are recommended for the RJ-122 highway.(AU)

Compreender os impactos ecológicos das estradas sobre a fauna de mamíferos requer o monitoramento periódico das estradas e a identificação da distribuição temporal e espacial dos atropelamentos (ou seja, hotspots de atropelamentos). O objetivo principal do estudo foi identificar os mamíferos mais atropelados e avaliar a distribuição temporal e espacial dos atropelamentos na RJ-122, uma rodovia que atravessa a ameaçada Mata Atlântica de baixada no estado do Rio de Janeiro, sudeste do Brasil. Entre outubro de 2017 e janeiro de 2020, foi realizado um intenso estudo de monitoramento, com a coleta três vezes por semana das carcaças dos mamíferos atropelados. Ao todo, registramos 295 mamíferos atropelados pertencentes a 22 espécies, resultando em 11 atropelamentos por mês para a rodovia RJ-122. O gambá-de-orelha-preta, Didelphis aurita (N=149, 51%) destacou-se como o mamífero mais atropelado, seguido pelo ouriço-cacheiro, Coendou spinosus (N= 24, 8%), cachorro-do-mato, Cerdocyon thous (N= 23, 8%), tatu-galinha, Dasypus novemcinctus (N= 23, 8%) e sagui-de-tufo-branco, Callithrix jacchus (N= 20, 7%). Os atropelamentos na RJ-122 variaram ao longo do ano, sendo mais frequentes na estação chuvosa (N=180) do que na estação seca (N=115), e se concentraram em duas áreas, indicando alguns pontos críticos com alta frequência de atropelamentos. Os hotspots foram associados a áreas com vegetação natural densa, que podem funcionar como corredores florestais nesta paisagem fragmentada. Com base em nossos resultados, várias medidas de mitigação são recomendadas para a rodovia RJ-122.(AU)

Forecasting seasonal peaks in roadkill patterns for improving road management.

For several species, roadkill is not spatially aggregated on hotspots, having instead a more diffuse pattern along the roads. For such species, management measures such as road passages may be insufficient for effective mitigation, since a large part of the road crossings is likely to occur outside the influence of those structures. One complementary approach could be to implement temporary mitigation actions, such as traffic calming. This requires understanding when roadkill peaks may occur. We tested the feasibility of predicting seasonal peaks of roadkill using data from a 3-year systematic monitoring (78 surveys over ca. 960 km of roads) from eight non-flying vertebrate species from Mato Grosso do Sul, Brazil, with different body size and life history traits (ca. 6400 records from focal species). We modelled the time-series of the roadkill of these species at large scale (state level) using generalized additive mixed models (GAMMs). We used the data of the first 2 years as training datasets, and the information from the third year of surveys as testing datasets to evaluate the prediction performance of models. Overall, the models of species feed with a higher number of records were able to follow reasonably well the variations of roadkill over time, although they were not able to correctly predict the number of collisions. For species with fewer observations, the models presented a poorer goodness-of-fit and prediction ability. Our results suggest that, at least for those species with higher roadkill rates, it can be possible to forecast periods of higher probability of occurring hot-moments of mortality. Such models can provide valuable information to implement seasonal management actions.