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墨西哥灵长类动物的潜在分布:运用最大熵算法对生态位进行建模

Potential distribution of Mexican primates: modeling the ecological niche with the maximum entropy algorithm.

作者信息

Vidal-García Francisca, Serio-Silva Juan Carlos

机构信息

División de Posgrado, Instituto de Ecología A. C., Xalapa, Veracruz, Mexico.

出版信息

Primates. 2011 Jul;52(3):261-70. doi: 10.1007/s10329-011-0246-6. Epub 2011 Mar 16.

DOI:10.1007/s10329-011-0246-6
PMID:21404094
Abstract

We developed a potential distribution model for the tropical rain forest species of primates of southern Mexico: the black howler monkey (Alouatta pigra), the mantled howler monkey (Alouatta palliata), and the spider monkey (Ateles geoffroyi). To do so, we applied the maximum entropy algorithm from the ecological niche modeling program MaxEnt. For each species, we used occurrence records from scientific collections, and published and unpublished sources, and we also used the 19 environmental coverage variables related to precipitation and temperature from WorldClim to develop the models. The predicted distribution of A. pigra was strongly associated with the mean temperature of the warmest quarter (23.6%), whereas the potential distributions of A. palliata and A. geoffroyi were strongly associated with precipitation during the coldest quarter (52.2 and 34.3% respectively). The potential distribution of A. geoffroyi is broader than that of the Alouatta spp. The areas with the greatest probability of presence of A. pigra and A. palliata are strongly associated with riparian vegetation, whereas the presence of A. geoffroyi is more strongly associated with the presence of rain forest. Our most significant contribution is the identification of areas with a high probability of the presence of these primate species, which is information that can be applied to planning future studies and then establishing criteria for the creation of areas to primate conservation in Mexico.

摘要

我们为墨西哥南部热带雨林中的灵长类物种开发了一个潜在分布模型,这些物种包括:黑吼猴(Alouatta pigra)、长毛吼猴(Alouatta palliata)和蜘蛛猴(Ateles geoffroyi)。为此,我们应用了生态位建模程序MaxEnt中的最大熵算法。对于每个物种,我们使用了来自科学标本馆、已发表和未发表来源的出现记录,并且还使用了与WorldClim中降水和温度相关的19个环境覆盖变量来开发模型。黑吼猴的预测分布与最暖季度的平均温度密切相关(占23.6%),而长毛吼猴和蜘蛛猴的潜在分布与最冷月的降水密切相关(分别占52.2%和34.3%)。蜘蛛猴的潜在分布比吼猴属物种的分布范围更广。黑吼猴和长毛吼猴出现概率最高的区域与河岸植被密切相关,而蜘蛛猴的出现则与雨林的存在更为密切相关。我们最显著的贡献是确定了这些灵长类物种出现概率高的区域,这些信息可用于规划未来的研究,进而为墨西哥灵长类动物保护区的创建制定标准。

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