Modeling current and potential distributions of mammal species using presence‐only data: A case study on British deer

Abstract

Aim Decisions on wildlife conservation, management, and epidemiological risk are best based on robust evidence. The continual improvement of species distributions, such that they can be relied upon in decision‐making, is important. Here we seek to refine aspects of a generic modelling approach and improve the utility of species distribution maps. Location Great Britain (GB). Methods We applied a modeling framework based on hierarchical Bayesian species distribution models exploiting opportunistic occurrence records from citizen science datasets to predict both current and potential distributions for each of the six deer species known to be present in GB. Using the resulting maps, we performed a simple analysis of the overlap between species to illustrate possible contact, which we interpret as the relative risk of potential disease spread given an introduction. Results Predicted distribution maps showed good agreement with the broader scale occurrence reported by a recent national deer survey with an average True Skill Statistics and AUC of 0.69 and 0.89, respectively. Aggregation of the maps for all species highlighted regions of central and eastern England as well as parts of Scotland where extensive areas of range overlap could result in interspecific contact with consequences for risk assessments for diseases of deer. However, if populations are allowed to expand to their predicted potential, then areas of overlap, and therefore disease interspecific transmission risk, will become extensive and widespread across all of mainland Britain. Main conclusions The generic modeling approach outlined performed well across all of the deer species tested, offering a robust and reliable tool through which current and potential animal distributions can be estimated and presented. Our application, intended to inform quantitative risk assessments, demonstrates the practical use of such outputs to generate the valuable evidence required to inform policy decisions on issues such as management strategy.

Observer‐oriented approach improves species distribution models from citizen science data

“…Specifically, accounting for surrogates of sampling effort led to an overall increase in predictive accuracy as shown by the higher values of validation statistics of the SDMs carried out with observer‐oriented pseudo‐absences than those of SDMs carried out considering random pseudo‐absences. Thus, our results proved the usefulness of large citizen science datasets to estimate species distributions not only considering target species locations but also those of other species collected by the same observers of the target species as pseudo‐absences, accounting for the unequal sampling effort that could occur in site selection, in agreement with previous studies suggesting that records of other species may provide a suitable proxy to estimate survey effort (Phillips et al, 2009; Croft et al., 2019; van Strien et al., 2013). Thus, we believe that our “observer‐oriented” approach represents a new methodological way to develop more robust and accurate SDMs than those developed using random pseudo‐absences, potentially useful and widely applicable to many ecological contexts.…”

“…While only citizen science projects can gather sufficient quantities of species locations, these data are inherently noisy and heterogeneous (Kelling et al., 2015). Moreover, citizen science datasets available on online platforms do not provide information on all sampling sites (even those were target species where absent) or on sampling effort, both of which are fundamental to distinguish evidence of true absence of the target species from merely insufficient effort to detect it (Croft et al., 2019).…”

Reeves’ muntjac populations continue to grow and spread across Great Britain and are invading continental Europe

“…We assumed that landcover in China was likely too different to that of Europe for its inclusion in our predictions of potential spread across Europe, although this assumption has yet to be tested. It is perhaps noteworthy that Croft et al (2019) predicted a greater area of favourability for muntjac in northern Britain than did Acevedo et al (2010), and that there were many subtle differences in predicted future distributions and likely maximum range edges between the two studies. The maximum geographical range extent of muntjac in Britain may be greater and distributed differently than currently predicted (Croft et al 2019).…”

“…It is perhaps noteworthy that Croft et al (2019) predicted a greater area of favourability for muntjac in northern Britain than did Acevedo et al (2010), and that there were many subtle differences in predicted future distributions and likely maximum range edges between the two studies. The maximum geographical range extent of muntjac in Britain may be greater and distributed differently than currently predicted (Croft et al 2019). The reliability of predictions of future range extent in Britain could be improved with more reliable distribution data than are currently available.…”

“…As muntjac have in-filled the core range in central England and reached the south and east coasts, the ability to expand their range has been limited to the remaining northerly and westerly directions. Moreover, the suitability of land for muntjac is not uniform across the country, potentially becoming less favourable in more northern and western regions (Croft et al 2019). However, predictions of land favourability for muntjac in Britain have necessarily been based on data recorded across their national range at the time of the study in question and included no information on landcover within their native range.…”