Using road patrol data to identify factors associated with carnivore roadkill counts
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Date
Authors
Williams, Samual T.
Collinson, Wendy J.
Patterson-Abrolat, Claire
Marneweck, David G.
Swanepoel, Lourens H.
Journal Title
Journal ISSN
Volume Title
Publisher
Peer J
Abstract
As the global road network expands, roads pose an emerging threat to wildlife
populations. One way in which roads can affect wildlife is wildlife-vehicle collisions, which can be a significant cause of mortality through roadkill. In order to successfully mitigate these problems, it is vital to understand the factors that can explain the distribution of roadkill. Collecting the data required to enable this can be expensive and time consuming, but there is significant potential in partnering with organisations that conduct existing road patrols to obtain the necessary data. We assessed the feasibility of using roadkill data collected daily between 2014 and 2017 by road patrol staff from a private road agency on a 410 km length of the N3 road in South Africa. We modelled the relationship between a set of environmental and anthropogenic variables on the number of roadkill carcasses, using serval (Leptailurus serval) as a model species. We recorded 5.24 serval roadkill carcasses/100 km/year. The number of carcasses was related to season, the amount of wetland, and NDVI, but was not related to any of the anthropogenic variables we included. This suggests that roadkill patterns may differ greatly depending on the ecology of species of interest, but targeting
mitigation measures where roads pass through wetlands may help to reduce serval roadkill. Partnering with road agencies for data collection offers powerful opportunities to identify factors related to roadkill distribution and reduce the threats posed by roads to wildlife.
Description
Supplementary material: Figure S1: Histogram showing distribution of serval roadkill counts in relation to a range of distributions
Figure S2: Scatter plot of fitted vs residual values for the full generalized linear mixed model with negative binomial distribution
Figure S3: Mantel tests for the full generalized linear mixed model with negative binomial distribution
Figure S4: Variogram from for residuals for the full generalized linear mixed model with negative binomial distribution
Supplemental Information 1: Summary of full generalized linear mixed model with negative binomial distribution
Figure S2: Scatter plot of fitted vs residual values for the full generalized linear mixed model with negative binomial distribution
Figure S3: Mantel tests for the full generalized linear mixed model with negative binomial distribution
Figure S4: Variogram from for residuals for the full generalized linear mixed model with negative binomial distribution
Supplemental Information 1: Summary of full generalized linear mixed model with negative binomial distribution
Keywords
Road ecology, Human-wildlife conflict, Wildlife management, Wildlife-vehicle collision
Sustainable Development Goals
Citation
Williams, S.T., Collinson, W., Patterson-Abrolat, C. et al. Using road patrol data to identify factors associated with carnivore roadkill counts. Peer J 2019, 7:e6650. http://DOI.org/10.7717/peerj.6650.
