Using worldwide edaphic data to model plant species niches: An assessment at a continental extent

• Published in: PloS one 2017-10, Vol.12 (10), p.e0186025-e0186025
• Main Authors: Velazco, Santiago José Elías, Galvão, Franklin, Villalobos, Fabricio, De Marco Júnior, Paulo
• Format: Article
• Language: English
• Subjects: Algorithms; Aluminum; Biology and Life Sciences; Climate; Climate change; Climate models; Climatic data; Computer and Information Sciences; Earth Sciences; Ecological monitoring; Ecological niches; Ecology; Ecology and Environmental Sciences; Ecosystem; Endangered & extinct species; Flowers & plants; Geographical distribution; Models, Biological; Niches; Niches (Ecology); Physical Sciences; Physiology; Plant ecology; Plant species; Plants - classification; Research and Analysis Methods; Soil properties; Species; Species Specificity; Studies; Terrestrial environments; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0186025

Ecological niche modeling (ENM) is a broadly used tool in different fields of plant ecology. Despite the importance of edaphic conditions in determining the niche of terrestrial plant species, edaphic data have rarely been included in ENMs of plant species perhaps because such data are not available for many regions. Recently, edaphic data has been made available at a global scale allowing its potential inclusion and evaluation on ENM performance for plant species. Here, we take advantage of such data and address the following main questions: What is the influence of distinct predictor variables (e.g. climatic vs edaphic) on different ENM algorithms? and what is the relationship between the performance of different predictors and geographic characteristics of species? We used 125 plant species distributed over the Neotropical region to explore the effect on ENMs of using edaphic data available from the SoilGrids database and its combination with climatic data from the CHELSA database. In addition, we related these different predictor variables to geographic characteristics of the target species and different ENM algorithms. The use of different predictors (climatic, edaphic, and both) significantly affected model performance and spatial complexity of the predictions. We showed that the use of global edaphic plus climatic variables generates ENMs with similar or better accuracy compared to those constructed only with climate variables. Moreover, the performance of models considering these different predictors, separately or jointly, was related to geographic properties of species records, such as number and distribution range. The large geographic extent, the variability of environments and the different species' geographical characteristics considered here allowed us to demonstrate that global edaphic data adds useful information for plant ENMs. This is particularly valuable for studies of species that are distributed in regions where more detailed information on soil properties is poor or does not even exist.