Soil factors improve predictions of plant species distribution in a mountain environment

Details

Ressource 1Request a copy Sous embargo indéterminé.
State: Public
Version: author
License: Not specified
Serval ID
serval:BIB_02068C98C90E
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Soil factors improve predictions of plant species distribution in a mountain environment
Journal
Progress in Physical Geography
Author(s)
Buri A., Cianfrani C., Adatte T., Pinto-Figueroa E., Spangenberg J.E ., Yashiro E., Verrecchia E., Guisan A., Pradervand J.-N.
ISSN
0309-1333
Publication state
Published
Issued date
31/10/2017
Peer-reviewed
Oui
Volume
41
Pages
703–722
Language
english
Abstract
Explanatory studies suggest that using very high resolution (VHR) topo-climatic predictors may improve the predictive power of plant species distribution models (SDMs). However, the use of topo-climatic VHR data alone was recently shown not to significantly improve SDM predictions. This suggests new VHR variables based on more direct field measurements are needed. Non topo-climatic variables, such as soil parameters, have important effects on plants. In this study, we investigated the effects of adding VHR predictors at a 5m resolution, including field measurements of temperature, carbon isotope composition of soil organic matter (δ<sup>13</sup>C<sub>SOM</sub> values) and soil pH, in addition to topo-climatic predictors, in SDMs for the Swiss Alps. We used data from temperature loggers to construct temperature maps, and we modelled the geographic variation in δ<sup>13</sup>C<sub>SOM</sub> and soil pH values. Then, we tested the effect of adding these VHR mapped variables as predictors into plant SDMs and assessed the improvement in spatial predictions across the study area. Our results demonstrated that the use of VHR predictors based on more proximal field measurements, particularly soil parameters, significantly increased the predictive power of models. Soil pH was the second most important predictor after temperature, followed by slope, δ<sup>13</sup>C<sub>SOM</sub>, radiation and curvature. The greatest increase in model performance was for species found at high elevation (i.e., 1500-2000 m a.s.l.). Addition of soil factors leads to better capture the plant species distribution in our models. It reflects the fact that edaphic properties, especially the soil pH, can influence vegetation growth and distribution directly or indirectly in a different way than topo-climatic variables do. Thus, taking into account these missing dimensions allow refining the potential habitat predicted for our alpine plant species. Modelling techniques to generalize edaphic information in space and then predict plant species distributions revealed a great potential in complex landscapes such as the mountain region considered in this study.
Keywords
Soil pH., very high resolution (VHR), mountain flora, Swiss Alps, species distribution models (SDMs), carbon isotope composition of soil organic
Publisher's website
Create date
29/09/2017 0:51
Last modification date
05/07/2020 7:08
Usage data