4.6 Article

Climate data source matters in species distribution modelling: the case of the Iberian Peninsula

期刊

BIODIVERSITY AND CONSERVATION
卷 30, 期 1, 页码 67-84

出版社

SPRINGER
DOI: 10.1007/s10531-020-02075-6

关键词

Iberian climate atlas; Presence– absence models; Precipitation; Temperature; Uncertainty; WorldClim

资金

  1. Spanish Ministry of Science, Innovation and Universities [CGL2017-9 89000-P, RYC-2013-14441]

向作者/读者索取更多资源

This study compared global WorldClim v.2 database (WC) with regional Iberian Climate Atlas (ICA) in the geographical context of the Iberian Peninsula, focusing on differences in climatic variables and their impact on woody plant distribution models. Significant discrepancies were found in precipitation values between the two databases, while temperature values also showed noticeable differences, especially in high elevation areas. The source of climate data influenced estimated suitability values, discrimination capacity, and the importance of variables in the distribution models. Additionally, the rarity of species was associated with increased uncertainty related to the climate data source.
Differences between climatic databases have been reported to alter the spatial predictions of species distribution models (SDM). In the present study, the global WorldClim v.2 database (WC) and the regional Iberian Climate Atlas (ICA) were compared in the geographical context of the Iberian Peninsula. Six climatic variables were considered: BIO1, BIO5 and BIO6 (temperature-related variables) and BIO12, BIO13 and BIO14 (precipitation-related variables). We performed regression analyses between values for each pair of homologous variables and generated quantile-quantile plots to compare the distribution of ranges within 10 x 10 grid cells. Pearson correlations were used to determine whether absolute differences between homologue variables were related to elevation. We modelled the occurrence of 48 woody plant species using either WC or ICA variables, and tested for differences in the estimated suitability values, discrimination power and importance of variables. Precipitation values varied considerably between databases, with WC variables reaching lower maximum and less variable values than ICA. Regarding temperature values, BIO1 had the highest correlation value between both datasets, whereas we observed substantial differences in the case of BIO5, which showed consistently lower values in WC than in ICA. Higher discrepancies between datasets, especially for temperature variables, were found in high elevation areas. As regards distribution models, the climate data source affected estimated suitability values, discrimination capacity and estimated variable importance. In addition, the rarer the species, the higher the uncertainty associated with the climate source. Climate data source is another uncertainty factor to add to all those that have already been highlighted in SDM.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据