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生态位模型作为一种预测淡水生物分布的有效工具:以 Sabaleta Brycon henni(Eigenmann,1913)为例。

Ecological niche modeling as an effective tool to predict the distribution of freshwater organisms: The case of the Sabaleta Brycon henni (Eigenmann, 1913).

机构信息

Departamento de Biología, Grupo de ictiología de la Universidad de Antioquia, Universidad de Antioquia, Medellín, Antioquia, Colombia.

The Nature Conservancy, Bogotá, Cundinamarca, Colombia.

出版信息

PLoS One. 2021 Mar 3;16(3):e0247876. doi: 10.1371/journal.pone.0247876. eCollection 2021.

DOI:10.1371/journal.pone.0247876
PMID:33657168
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7928524/
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 (ENMs) 的目的是再现物种与它们所处环境之间的关系,使我们能够识别出这些物种可能存在的地理上未被探索的区域。这些模型已成功应用于陆地生物,但在水生生物中的应用仍然很少。由于特定于水生系统的物种出现和环境信息的可用性的最新进展,这些模型得以评估。本研究旨在使用 ENMs 来预测哥伦比亚安第斯山脉特有鱼类 Sabaleta Brycon henni Eigenmann 1913 在 Magdalena 流域的地理分布,从而对其生态位进行特征描述。为此,我们使用了一组特定于淡水系统的环境变量,除了常用的生物气候变量外,还使用了物种出现数据,以最大熵算法 (MaxEnt) 对其潜在分布进行建模。我们评估了这两组变量之间的相对重要性、模型的性能及其与 IUCN 地图的地理重叠。在模型中,包括现场变量(年降水量、最冷月最低温度)和上游变量(开阔水域、最冷月平均温度和平均降水季节性),这些变量具有最高的预测准确性。曲线下面积为 90%,99%的物种出现和 68%的缺失被正确预测,我们的结果支持 ENMs 预测 Sabaleta 潜在分布的良好性能,以及该工具在国家层面的保护和决策中的实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2975/7928524/b35ae9529be3/pone.0247876.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2975/7928524/c5e96c96c51a/pone.0247876.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2975/7928524/2399ad941b49/pone.0247876.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2975/7928524/615c0616abdc/pone.0247876.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2975/7928524/b35ae9529be3/pone.0247876.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2975/7928524/c5e96c96c51a/pone.0247876.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2975/7928524/2399ad941b49/pone.0247876.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2975/7928524/615c0616abdc/pone.0247876.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2975/7928524/b35ae9529be3/pone.0247876.g004.jpg

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