Can we model distribution of population abundance from wildlife–vehicles collision data?

Abstract

Reliable estimates of the distribution of species abundance are a key element in wildlife studies, but such information is usually difficult to obtain for large spatial or long temporal scales. Wildlife–vehicle collision (WVC) data is systematically registered in many countries and could be used as a proxy of population abundance if the number of WVC in each territory increase with the population abundance. However, factors such as road density or human population should be controlled to obtain accurate abundance estimations from WVC data. Here, we propose a hierarchical modeling approach using the Royle–Nichols model for detection–non‐detection data to obtain population abundance indices from WVC. Relative abundance and individual detectability were modeled for two species, wild boar Sus scrofa and roe deer Capreolus capreolus at 10 × 10 km cells in mainland Spain from WVC data using environmental, anthropological and temporal covariates. For each cell, a detection was annotated if at least one WVC was recorded at each month (used as survey occasion). The predicted abundance indices were compared with raw hunting statistics at region level to assess the performance of the modeling approach. Site specific covariates such as road density or administrative region and the month of the year, affected individual detectability, with higher WVC probability between October and December for wild boar and between April and July for roe deer. Wild boar and roe deer abundance can be explained by both, bioclimatic and land cover covariates. Abundance indices obtained from WVC data were significantly positively correlated with regional raw hunting yields for both species. We presented empirical evidence supporting that accurate wildlife abundance indices at fine spatial resolution can be generated from WVC data when individual detectability is considered in the modeling process.

The distribution of vertebrate roadkill varies by season, surrounding environment, and animal class

“…We found that roadkill mortality varies throughout the year, with different patterns for each vertebrate class, the peak of collisions coinciding with suitable environmental conditions for each group, when they are most active (Garriga et al 2017;Fernández-López et al 2022). Of the 413 carcasses recorded, mammals were the most run over, with 189 animals killed on the road (45.76% of the total).…”

“…To analyse whether the number of accidents depended on road tortuosity and the presence of fencing (i.e. characteristics of the road), we fitted an N-mixture hierarchical model (Royle and Nichols 2003;Fernández-López et al 2022) using the unmarked package (Fiske and Chandler 2011). In the N-mixture model, we considered the repeated monthly sampling (12 months) of the 10 road sections, the effect of the presence of fence (fenced vs unfenced) on the animal abundance, and the additive effects of tortuosity (scaled values of this continuous variable) and presence of fence as covariates modelling the detectability of roadkill events.…”

“…Santos et al 2018). Although animal abundance depends on habitat type, the use of occupancydetection models (as the N-mixture models used here) can decouple abundance from detectability (roadkill in our case; see Santos et al 2018 andFernández-López et al 2022).…”

“…Two important factors related to the species are motility (D'Amico et al 2015) and the ability to avoid or escape from vehicles, with animals that run less oftensuch as amphibians-being more likely to be run over than others with a greater capacity of reaction to a vehicle-such as birds or some mammals (Mazerolle et al 2005). Within an animal group, the risk of being hit will depend on the time of year, either because the species has different requirements depending on it, as is the case of the dependence of water sources in some amphibians and birds (Gibbs and Shriver 2005), or due to the variation of the activity periods, as may be the case of some reptiles with brumation, some mammals with hibernation, or amphibians with aestivation (Garriga et al 2017;Fernández-López et al 2022). In the latter, the probability of being run over increases in rainy periods and, particularly, after sunset, when they reach their maximum activity (Gibbs and Shriver 2005).…”

Recommendations and technical specifications for sustainable surveillance of zoonotic pathogens where wildlife is implicated

“… Passive surveillance can be enhanced: for instance, by increasing effort to detect and collect road kills (Grilo et al 2020;Fernández-López et al 2022), which can be risk based (e.g., attending to areas of more likely introduction of pathogens).…”