Can community science predict seasonal variation in the timing of human-snake conflict?

Maritz, Bryan

bryanmaritz@gmail.com

Department of Biodiversity and Conservation Biology                                                                      

University of the Western Cape                                                                                                             

Bellville, WC, South Africa

Efforts to reduce the burden of snakebite envenomation (SBE) globally are hindered, in part, by a paucity of data describing the timing and distribution of SBE, and by limited knowledge regarding the ecology of medically important snakes. Community science offers opportunities to gather ecological data on snakes at broad spatial and temporal scales but is constrained by issues around variation in sampling effort. Here, I explore the adoption of a time-for-space substitution approach to analysing community science data using n-mixture models. By treating months of the year as spatial “plots” and using consecutive years as repeat surveys, as well as including appropriate community science derived covariates describing changes in sampling effort across both dimensions, I aimed to make predictions of monthly variation in snake encounter rates. Analysis of simulated unimodal, bimodal, and randomly distributed monthly encounter rates revealed strong correlations between simulated and predicted seasonality. Next, I applied the approach to iNaturalist data for medically important snake species in regions where pitvipers dominate the regional SBE burden. I selected regions for which monthly SBE data were published, including northern, north-eastern, and south-eastern Brazil, and northern, central, and southern Mexico. Predicted relative monthly snakebite frequency correlated strongly with published SBE data in northern, central and southern Mexico, and south-eastern Brazil, but not northern or north-eastern Brazil. Critical evaluation of these findings suggests that the approach has the potential to (1) predict seasonal variation of SBE in parts of the world where such data do not exist, (2) identify ecological drivers of SBE, and (3) contribute to efforts to predict the distribution and timing of SBE worldwide.