Zhao Yipei, Liu Jianfeng, Wang Qi, Huang Ruizhi, Nie Wen, Yang Shaowei, Cheng Xiangfen, Li Maihe
State Key Laboratory of Efficient Production of Forest Resources, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration Research Institute of Forestry, Chinese Academy of Forestry Beijing China.
Ecology and Nature Conservation Institute Chinese Academy of Forestry Beijing China.
Ecol Evol. 2025 May 8;15(5):e71390. doi: 10.1002/ece3.71390. eCollection 2025 May.
Climate change is anticipated to escalate the frequency and severity of global natural disasters over the next few decades, thereby significantly reshaping species distributions and populations. Species distribution models (SDMs), as essential tools in biogeography and biodiversity conservation, are pivotal for evaluating the impacts of climate change on species and forecasting their distribution ranges under different climate change scenarios over various periods. However, the absence of necessary background knowledge for model construction significantly affects the accuracy of these models, with the selection of different occurrence data sources being a key factor that constrains the accuracy of model predictions. In this study, using as a case study, which has diverse ecological, economic, and cultural values, we employed the Biomod2 ensemble modeling platform to comparatively analyze disparities between two different occurrence data sources (i.e., online specimen and scientific survey data) in the species distribution prediction accuracy, relative contribution of major environmental variables, and predicted distribution ranges. Furthermore, we examined potential discrepancies between these two data sources in the migration distance and direction of the species distribution centroid under different future climate scenarios over various periods. Our results indicated substantial differences in the simulation outcomes of SDMs derived from various occurrence data sources. SDMs based on scientific survey data had higher predictive accuracy (AUC = 0.9720, TSS = 0.8370), with the simulated species distribution ranges not only closely matching the actual distributions but also showing more pronounced changes in suitable habitat areas and centroid migration trends under future climate scenarios. In comparison, models based on online specimen data predicted a wider species distribution range, yet exhibited less pronounced trends in suitable area changes and centroid migration under future climate scenarios. Additionally, although the main environmental variables affecting the simulation outcomes from different occurrence data sources were essentially identical, they varied in their contributions and order of importance. Among them, human activity had a relatively stronger contribution for the online specimen data (17.76%), while topographic variables had a stronger impact for the scientific survey data, such as elevation (17.79%). Therefore, the choice of occurrence data sources have a significant impact on SDMs modeling results; this study provides insights and guidance for selecting optimal occurrence data sources to enhance the reliability of SDMs simulations.
预计在未来几十年里，气候变化将加剧全球自然灾害的频率和严重程度，从而显著重塑物种分布和种群。物种分布模型（SDMs）作为生物地理学和生物多样性保护的重要工具，对于评估气候变化对物种的影响以及预测它们在不同气候变化情景下不同时期的分布范围至关重要。然而，模型构建所需背景知识的缺失严重影响了这些模型的准确性，不同出现数据来源的选择是制约模型预测准确性的关键因素。在本研究中，以具有多样生态、经济和文化价值的[具体案例未给出]为案例研究，我们使用Biomod2集成建模平台，比较分析了两种不同出现数据来源（即在线标本和科学调查数据）在物种分布预测准确性、主要环境变量的相对贡献以及预测分布范围方面的差异。此外，我们研究了在不同未来气候情景下不同时期，这两种数据来源在物种分布中心的迁移距离和方向上的潜在差异。我们的结果表明，源自不同出现数据来源的物种分布模型的模拟结果存在显著差异。基于科学调查数据的物种分布模型具有更高的预测准确性（AUC = 0.9720，TSS = 0.8370），其模拟的物种分布范围不仅与实际分布紧密匹配，而且在未来气候情景下适宜栖息地面积和中心迁移趋势的变化更为明显。相比之下，基于在线标本数据的模型预测的物种分布范围更广，但在未来气候情景下适宜面积变化和中心迁移趋势不太明显。此外，尽管影响不同出现数据来源模拟结果的主要环境变量基本相同，但它们的贡献和重要性顺序有所不同。其中，人类活动对在线标本数据的贡献相对较大（17.76%），而地形变量对科学调查数据的影响更强，如海拔（17.79%）。因此，出现数据来源的选择对物种分布模型的建模结果有重大影响；本研究为选择最佳出现数据来源以提高物种分布模型模拟的可靠性提供了见解和指导。
