Hespanhol Helena, Cezón Katia, Felicísimo Ángel M, Muñoz Jesús, Mateo Rubén G
CIBIO/InBio Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto Campus Agrário de Vairão 4485-661 Vairão Portugal.
Real Jardín Botánico (CSIC) Plaza de Murillo 2 28014 Madrid Spain.
Ecol Evol. 2015 Oct 28;5(23):5443-55. doi: 10.1002/ece3.1796. eCollection 2015 Dec.
A large amount of data for inconspicuous taxa is stored in natural history collections; however, this information is often neglected for biodiversity patterns studies. Here, we evaluate the performance of direct interpolation of museum collections data, equivalent to the traditional approach used in bryophyte conservation planning, and stacked species distribution models (S-SDMs) to produce reliable reconstructions of species richness patterns, given that differences between these methods have been insufficiently evaluated for inconspicuous taxa. Our objective was to contrast if species distribution models produce better inferences of diversity richness than simply selecting areas with the higher species numbers. As model species, we selected Iberian species of the genus Grimmia (Bryophyta), and we used four well-collected areas to compare and validate the following models: 1) four Maxent richness models, each generated without the data from one of the four areas, and a reference model created using all of the data and 2) four richness models obtained through direct spatial interpolation, each generated without the data from one area, and a reference model created with all of the data. The correlations between the partial and reference Maxent models were higher in all cases (0.45 to 0.99), whereas the correlations between the spatial interpolation models were negative and weak (-0.3 to -0.06). Our results demonstrate for the first time that S-SDMs offer a useful tool for identifying detailed richness patterns for inconspicuous taxa such as bryophytes and improving incomplete distributions by assessing the potential richness of under-surveyed areas, filling major gaps in the available data. In addition, the proposed strategy would enhance the value of the vast number of specimens housed in biological collections.
大量关于不显眼分类群的数据存储在自然历史收藏中；然而，在生物多样性模式研究中，这些信息常常被忽视。在这里，鉴于对于不显眼分类群，这些方法之间的差异尚未得到充分评估，我们评估了博物馆收藏数据直接插值（等同于苔藓植物保护规划中使用的传统方法）和叠加物种分布模型（S-SDMs）的性能，以生成可靠的物种丰富度模式重建。我们的目标是对比物种分布模型是否比简单选择物种数量较多的区域能更好地推断多样性丰富度。作为模型物种，我们选择了藓纲 Grimmia 属的伊比利亚物种，并使用四个收集良好的区域来比较和验证以下模型：1）四个最大熵丰富度模型，每个模型在生成时不使用四个区域之一的数据，以及一个使用所有数据创建的参考模型；2）四个通过直接空间插值获得的丰富度模型，每个模型在生成时不使用一个区域的数据，以及一个使用所有数据创建的参考模型。在所有情况下，部分最大熵模型与参考模型之间的相关性都更高（0.45至0.99），而空间插值模型之间的相关性为负且较弱（-0.3至-0.06）。我们的结果首次表明，S-SDMs为识别苔藓植物等不显眼分类群的详细丰富度模式以及通过评估调查不足区域的潜在丰富度来改善不完整分布提供了一个有用的工具，填补了现有数据中的主要空白。此外，所提出的策略将提高保存在生物收藏中的大量标本的价值。
