Global Mammal Assessment Program, Department of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy.
BirdLife International, Cambridge, UK.
Conserv Biol. 2022 Jun;36(3):e13851. doi: 10.1111/cobi.13851. Epub 2021 Nov 29.
Area of habitat (AOH) is defined as the "habitat available to a species, that is, habitat within its range" and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.
生境面积（AOH）定义为“物种可用的生境，即在其分布范围内的生境”，通过从分布范围中减去不适宜的土地覆盖和海拔高度来计算。国际自然保护联盟（IUCN）生境分类方案提供了有关物种生境关联的信息，通常使用未经验证的专家意见将生境与土地覆盖类别相匹配，这在 AOH 地图中产生了不确定性的来源。我们开发了一种基于 6986 种陆地哺乳动物、鸟类、两栖动物和爬行动物的点位置数据将 IUCN 生境类别转换为土地覆盖的基于数据的方法。我们从每个点位置提取土地覆盖类别，并将其与分配给在那里出现的每个物种的 IUCN 生境类别或类别相匹配。然后，我们使用逻辑回归模型将每个土地覆盖类别建模为与 IUCN 生境的函数（使用 SSG）。由此产生的优势比用于评估每个生境与土地覆盖类别的关联强度。然后，我们将我们基于数据的模型与基于专家知识的已发表翻译表的性能进行了比较。我们将生境类别的关联作为连续变量进行计算，但为了将 AOH 映射为存在或不存在的二进制值，有必要应用关联的阈值。用户可以根据需要在遗漏和委员会错误之间取得平衡来选择此阈值。一些生境（例如森林和沙漠）比其他生境（例如湿地和人工）更有信心地分配给土地覆盖类别。基于数据的翻译模型和专家知识的表现同样出色，但该模型提供了更大的标准化、客观性和可重复性。此外，我们的方法允许在使用结果和量化不确定性方面更加灵活。我们的模型可以针对区域检查和不同的分类群进行修改。
