Mapping resource selection functions in wildlife studies: Concerns and recommendations

Abstract

Predicting the spatial distribution of animals is an important and widely used tool with applications in wildlife management, conservation, and population health. Wildlife telemetry technology coupled with the availability of spatial data and GIS software have facilitated advancements in species distribution modeling. There are also challenges related to these advancements including the accurate and appropriate implementation of species distribution modeling methodology. Resource Selection Function (RSF) modeling is a commonly used approach for understanding species distributions and habitat usage, and mapping the RSF results can enhance study findings and make them more accessible to researchers and wildlife managers. Currently, there is no consensus in the literature on the most appropriate method for mapping RSF results, methods are frequently not described, and mapping approaches are not always related to accuracy metrics. We conducted a systematic review of the RSF literature to summarize the methods used to map RSF outputs, discuss the relationship between mapping approaches and accuracy metrics, performed a case study on the implications of employing different mapping methods, and provide recommendations as to appropriate mapping techniques for RSF studies. We found extensive variability in methodology for mapping RSF results. Our case study revealed that the most commonly used approaches for mapping RSF results led to notable differences in the visual interpretation of RSF results, and there is a concerning disconnect between accuracy metrics and mapping methods. We make 5 recommendations for researchers mapping the results of RSF studies, which are focused on carefully selecting and describing the method used to map RSF studies, and relating mapping approaches to accuracy metrics.

Interspecific prey neighborhoods shape risk of predation in a savanna ecosystem

“…Then, we projected the RSF in each 30 9 30 m grid cell across our study area. We rescaled each RSF to create continuous estimates ranging between 0 (strongest avoidance) and 1 (strongest selection), which we split into five bins of equal width following Morris et al (2016). Finally, we combined species 9 survey-specific densities with species 9 survey-specific RSFs to create spatially explicit density estimates for each species of lion prey at each survey.…”

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

“…Methods Thomas et al, 2003Lu et al, 2004Ma et al, 2017Maulik and Chakraborty, 2017Guisan and Thuiller, 2005Elith and Leathwick, 2009Franklin, 2010Li and Wang, 2013Morris et al, 2016Ashraf et al, 2017de Rivera and López-Quílez, 2017 can be approached from two perspectives: the data collection methods and the map production methods. Developments in data collection techniques in the last few decades have increased the types, amount and quality of data that can be collected for marine environmental characterization, particularly in terms of remotely sensed data (Brown et al, 2011;Kachelriess et al, 2014;Lecours et al, 2016b).…”

Improving spatial predictions of animal resource selection to guide conservation decision making

“…We plot results by sample size when considering all RSF predictions together and for the low‐intensity results we also binned the true RSF values into quartile groups of low to high selection (0–25%, 25–50%, 50–75%, 75–100%) and calculated , R 2 , and MAE with their corresponding RSF predictions. Binning predictions is commonly done when creating resource selection maps for managers (Morris et al ) and clarifies which values are most difficult to predict. Last, we investigated the inferential reliability in interpreting estimated coefficients as selection and avoidance by evaluating the proportion of coefficients with the correct sign (+, 0, −) across simulations within each strategy.…”

“…However, the practical utility of an RSF for many resource managers and conservationists is the spatially mapped predictions produced from these models (Morris et al 2016), which can influence on-the-ground management decisions. Resource selection predictions are used for land-use planning (Coates et al 2016), managing populations (Hebblewhite et al 2011, Northrup et al 2016, and more (Morris et al 2016). Because RSF predictions are widely relied upon in conservation and management decision making, it is of paramount importance to obtain accurate predictions.…”