دانلود رایگان مقاله توابع انتخاب منابع نقشه برداری در مطالعات حیات وحش

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.

5. Conclusion

We make the following recommendations for RSF studies with a mapping component: 1. Methods employed to create RSF maps and binning classifications should be clearly and explicitly described. We suggest that vague language, such as “equal bins”, leads to challenges in interpretation, and advocate the use of detailed descriptions, such as “equal area” or “equal interval”. 2. Map legends should be informative and enhance the interpretation of RSF results. Predictions shown as low to high, and categorical classifications without further description of methods do not provide a meaningful or accurate representation of RSF results. 3. We recommend avoiding the display of continuous RSF surfaces, as there is not an established method in the RSF literature for testing the predictive accuracy of a continuous RSF surface. 4. The classification for mapped RSF bins should be in line with the classification employed for accuracy metrics. For example, if 10 equal bins are used to test the predictive accuracy of an RSF model, 10 equal area bins should also be used to map the predicted resource selection. 5. The bin classification technique should be selected based on the method with the highest predictive accuracy. Johnson et al. (2006) recommend trying a different binning technique if predictive accuracy is low, and we suggest that exploring the accuracy associated with multiple binning classifications is an important component to producing accurate maps. RSF binning classifications have been described in multiple studies (Boyce et al., 2002; Hirzel et al., 2006; Wiens et al., 2008).