PDF(Pubmed)
Abstract：
Ecological niche models (ENMs) aim to recreate the relationships between species and the environments where they occur and allow us to identify unexplored areas in geography where these species might be present. These models have been successfully used in terrestrial organisms but their application in aquatic organisms is still scarce. Recent advances in the availability of species occurrences and environmental information particular to aquatic systems allow the evaluation of these models. This study aims to characterize the niche of the Sabaleta Brycon henni Eigenmann 1913, an endemic fish of the Colombian Andes, using ENMs to predict its geographical distribution across the Magdalena Basin. For this purpose, we used a set of environmental variables specific to freshwater systems in addition to the customary bioclimatic variables, and species\' occurrence data to model its potential distribution using the Maximum Entropy algorithm (MaxEnt). We evaluate the relative importance between these two sets of variables, the model\'s performance, and its geographic overlap with the IUCN map. Both on-site (annual precipitation, minimum temperature of coldest month) and upstream variables (open waters, average minimum temperature of the coldest month and average precipitation seasonality) were included in the models with the highest predictive accuracy. With an area under the curve of 90%, 99% of the species occurrences and 68% of absences correctly predicted, our results support the good performance of ENMs to predict the potential distribution of the Sabaleta and the utility of this tool in conservation and decision-making at the national level.
摘要：
生态位模型（ENM）旨在重建物种与其发生的环境之间的关系，并使我们能够确定可能存在这些物种的地理区域。这些模型已成功用于陆地生物，但在水生生物中的应用仍然很少。物种发生的可用性和水生系统特有的环境信息的最新进展允许对这些模型进行评估。这项研究旨在表征哥伦比亚安第斯山脉特有鱼类SabaletaBryconhenniEigenmann1913的生态位，使用ENM预测其在马格达莱纳盆地的地理分布。为此,除了常规的生物气候变量之外，我们还使用了一组特定于淡水系统的环境变量，和物种发生数据，使用最大熵算法(MaxEnt)对其潜在分布进行建模。我们评估这两组变量之间的相对重要性，模型的性能，以及它与自然保护联盟地图的地理重叠。都是现场(年降水量，最冷月份的最低温度)和上游变量(开阔水域，最冷月份的平均最低温度和平均降水季节性）包含在具有最高预测准确性的模型中。曲线下面积为90%，99％的物种发生和68％的缺席正确预测，我们的结果支持ENM在预测Sabaleta的潜在分布方面的良好性能，以及该工具在国家一级的保护和决策中的实用性。
