Johnson Bethany A, Pinilla-Buitrago Gonzalo E, Anderson Robert P
Department of Biology, City College of New York City University of New York New York New York USA.
Center for Biodiversity and Conservation American Museum of Natural History New York New York USA.
Ecol Evol. 2025 Jun 27;15(7):e71631. doi: 10.1002/ece3.71631. eCollection 2025 Jul.
Species distribution modeling can be used to predict environmental suitability, and removing areas currently lacking appropriate vegetation can refine range estimates for conservation assessments. However, the uncertainty around geographic coordinates can exceed the fine resolution of remotely sensed habitat data. Here, we present a novel methodological approach to reflect this reality by processing habitat data to maintain its fine resolution, but with new values characterizing a larger surrounding area (the "neighborhood"). We implement its use for a forest-dwelling species () considered threatened by the IUCN. We determined deforestation tolerance threshold values by matching occurrence records with forest cover data using two methods: (1) extracting the exact pixel value where a record fell; and (2) using the neighborhood value (more likely to characterize conditions within the radius of actual sampling). We removed regions below these thresholds from the climatic suitability prediction, identifying areas of inferred habitat loss. We calculated Extent of Occurrence (EOO) and Area of Occupancy (AOO), two metrics used by the IUCN for threat level categorization. The values estimated here suggest removing the species from threatened categories. However, the results highlight spatial patterns of loss throughout the range not reflected in these metrics, illustrating drawbacks of EOO and showing how localized losses largely disappeared when resampling to the 2 × 2 km grid required for AOO. The neighborhood approach can be applied to various data sources (NDVI, soils, marine, etc.) to calculate trends over time and should prove useful to many terrestrial and aquatic species. It is particularly useful for species having high coordinate uncertainty in regions of low spatial autocorrelation (where small georeferencing errors can lead to great differences in habitat, misguiding conservation assessments used in policy decisions). More generally, this study illustrates and enhances the practicality of using habitat-refined distribution maps for biogeography and conservation.
物种分布模型可用于预测环境适宜性，去除当前缺乏适宜植被的区域能够优化用于保护评估的分布范围估计。然而，地理坐标的不确定性可能超过遥感栖息地数据的精细分辨率。在此，我们提出一种新颖的方法，通过处理栖息地数据来反映这一现实，以保持其精细分辨率，但赋予更大周边区域（“邻域”）新的值。我们将其应用于一种被国际自然保护联盟（IUCN）视为受威胁的森林栖息物种。我们通过两种方法将出现记录与森林覆盖数据相匹配来确定森林砍伐耐受阈值：（1）提取记录所在的确切像素值；（2）使用邻域值（更有可能表征实际采样半径内的状况）。我们从气候适宜性预测中去除低于这些阈值的区域，识别出推断的栖息地丧失区域。我们计算了IUCN用于威胁等级分类的两个指标，即分布范围（EOO）和占有面积（AOO）。此处估计的值表明应将该物种从受威胁类别中移除。然而，结果突出了整个分布范围内未在这些指标中体现的丧失空间模式，说明了EOO的缺点，并展示了在重新采样到AOO所需的2×2千米网格时，局部丧失情况如何基本消失。邻域方法可应用于各种数据源（归一化植被指数、土壤、海洋等）以计算随时间的趋势，并且对许多陆生和水生物种都应是有用的。它对于在低空间自相关性区域具有高坐标不确定性的物种尤其有用（在这些区域，小的地理参考误差可能导致栖息地的巨大差异，误导用于政策决策的保护评估）。更普遍地说，本研究说明了并增强了使用经过栖息地细化的分布图进行生物地理学和保护研究的实用性。
