Ecological specialization and rarity indices estimated for a large number of plant species in France

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES Data in Brief Pub Date : 2015-06-01 DOI:10.1016/j.dib.2015.02.015
Samira Mobaied, Nathalie Machon, Emmanuelle Porcher
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引用次数: 6

Abstract

The biological diversity of the Earth is being rapidly depleted due to the direct and indirect consequences of human activities. Specialist or rare species are generally thought to be more extinction prone than generalist or common species. Testing this assumption however requires that the rarity and ecological specialization of the species are quantified. Many indices have been developed to classify species as generalists vs. specialists or as rare vs. common, but large data sets are needed to calculate these indices.

Here, we present a list of specialization and rarity values for more than 2800 plant species of continental France, which were computed from the large botanical and ecological dataset SOPHY. Three specialization indices were calculated using species co-occurrence data. All three indices are based on (dis)similarity among plant communities containing a focal species, quantified either as beta diversity in an additive (Fridley et al., 2007 [6]) or multiplicative (Zeleny, 2008 [15]) partitioning of diversity or as the multiple site similarity of Baselga et al. (2007) [1]. Species rarity was calculated as the inverse of a species occurrence.

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法国大量植物物种的生态专门化和稀有度指数估算
由于人类活动的直接和间接后果,地球的生物多样性正在迅速枯竭。专家或稀有物种通常被认为比通才或普通物种更容易灭绝。然而,要验证这一假设,需要对物种的稀有性和生态专门化进行量化。已经开发了许多指数来将物种分类为通才与专才或稀有与常见,但需要大量数据集来计算这些指数。本文以法国大陆2800多种植物为研究对象,利用大型植物与生态数据集SOPHY对其进行了分类分析。利用物种共现数据计算了3个特化指数。所有这三个指数都基于含有焦点物种的植物群落之间的(非)相似性,量化为多样性的加法划分(Fridley等,2007[6])或乘法划分(Zeleny, 2008[15])中的beta多样性,或Baselga等(2007)[1]的多站点相似性。物种稀有度是用物种出现的倒数来计算的。
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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