Climate data source matters in species distribution modelling: the case of the Iberian Peninsula
- PDF / 1,505,682 Bytes
- 18 Pages / 439.37 x 666.142 pts Page_size
- 31 Downloads / 197 Views
(0123456789().,-volV)(0123456789().,-volV)
ORIGINAL PAPER
Climate data source matters in species distribution modelling: the case of the Iberian Peninsula Alberto Jime´nez-Valverde1
•
Marta Rodrı´guez-Rey1 • Pablo Pen˜a-Aguilera1,2
Received: 9 June 2020 / Revised: 24 September 2020 / Accepted: 31 October 2020 Ó Springer Nature B.V. 2020
Abstract 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 9 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. Keywords Iberian climate atlas Presence–absence models Precipitation Temperature Uncertainty WorldClim
Communicated by Daniel Sanchez Mata. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10531-02002075-6) contains supplementary material, which is available to authorized users. & Alberto Jime´nez-Valverde [email protected] Extended author information available on the last page of the article
123
Biodiversity and Conservation
Introduction Species distribution modelling (SDM) has experienced exponential growth since the beginning of the twenty-first century (Lobo et al. 2010). In SDM, species occurrence is modelled as a function of environmental variables using a variety of statistical techniques (see monographs by Franklin 2009; Peterson et al. 2011; Halvorsen 2012; Guisan et al. 2017). Once a distribution model is parameterised and
Data Loading...
