Identifying wildlife road crossing mitigation sites using a multi-data approach - A case study from southwestern Costa Rica.

Roads are one of the most widespread structures that drive habitat loss and fragmentation. But they also restrict animal movement and drive landscape-level impacts on biodiversity. The South Pacific of Costa Rica is known for its high levels of biodiversity, but little has been done to reduce road impacts upon wildlife communities. To understand these impacts and advise on possible mitigation action, we used three key data approaches: 1. Camera traps, to survey wildlife activity along two major road sections that dissect the region's protected areas and biological corridors. Seventy-eight camera traps were deployed in secondary forest patches at different distances (between 200 m and 1 km) from the roads for six months and covariates were collected to explain the patterns found. 2. Citizen science data extracted from iNaturalist to identify roadkill "hotspots" along the roads. And 3. Circuitscape analysis, to assess how landscape structure could influence animal movement. Camera traps recorded 30 terrestrial species. Ocelots and agoutis displayed a negative effect of distance from protected area, while the Apex predators displayed a positive effect toward higher forest cover and vegetation density. Circuitscape analysis showed high connectivity throughout most of the area. Only a few locations showed higher flow (bottle neck locations), which coincided with roadkill "hotspots" identified through citizen science direct observations (70 observations of 21 species). Amalgamating data from the different analyses allow us to identify four key wildlife crossing locations (one of less priority) along the Inter-American Highway. We strongly recommend the placement of under/overpasses in these locations, with the aim to ensure wildlife safe movement and connectivity of wildlife populations in the region. Culvert modifications in the area could also be considered to incorporate wildlife underpasses at a reduced cost.