Morris Lillian R, Proffitt Kelly M, Blackburn Jason K
Spatial Epidemiology and Ecology Research Laboratory, Department of Geography, 3141 Turlington Hall, University of Florida, Gainesville, FL 32611.
Emerging Pathogens Institute, 2055 Mowry Road, University of Florida, Gainesville, FL 32611.
Appl Geogr. 2016 Nov;76:173-183. doi: 10.1016/j.apgeog.2016.09.025. Epub 2016 Sep 28.
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.
预测动物的空间分布是一种重要且广泛应用的工具，在野生动物管理、保护及种群健康方面都有应用。野生动物遥测技术与空间数据及地理信息系统（GIS）软件的可得性推动了物种分布建模的进展。这些进展也带来了一些挑战，包括物种分布建模方法的准确和恰当实施。资源选择函数（RSF）建模是理解物种分布和栖息地利用情况以及绘制RSF结果图的常用方法，这可以增强研究结果，并使研究人员和野生动物管理者更容易获取这些结果。目前，文献中对于绘制RSF结果的最合适方法尚无共识，方法常常未被描述，且绘图方法并不总是与准确性指标相关。我们对RSF文献进行了系统综述，以总结用于绘制RSF输出结果的方法，讨论绘图方法与准确性指标之间的关系，就采用不同绘图方法的影响进行了案例研究，并为RSF研究提供合适绘图技术的建议。我们发现绘制RSF结果的方法存在很大差异。我们的案例研究表明，绘制RSF结果最常用的方法导致RSF结果的视觉解读存在显著差异，而且准确性指标与绘图方法之间存在令人担忧的脱节。我们为绘制RSF研究结果的研究人员提出了5条建议，重点是仔细选择和描述用于绘制RSF研究的方法，并将绘图方法与准确性指标联系起来。
