Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species

Abstract

Aim: To evaluate the relative importance of climatic versus soil data when predicting species distributions for Amazonian plants and to gain understanding of potential range shifts under climate change.Location: Amazon rain forest. Methods:We produced species distribution models (SDM) at 5-km spatial resolution for 42 plant species (trees, palms, lianas, monocot herbs and ferns) using species occurrence data from herbarium records and plot-based inventories. We modelled species distribution with Bayesian logistic regression using either climate data only, soil data only or climate and soil data together to estimate their relative predictive powers. For areas defined as unsuitable to species occurrence, we mapped the difference between the suitability predictions obtained with climateonly versus soil-only models to identify regions where climate and soil might restrict species ranges independently or jointly.Results: For 40 out of the 42 species, the best models included both climate and soil predictors. The models including only soil predictors performed better than the models including only climate predictors, but we still detected a drought-sensitive response for most of the species. Edaphic conditions were predicted to restrict species occurrence in the centre, the north-west and in the north-east of Amazonia, while the climatic conditions were identified as the restricting factor in the eastern Amazonia, at the border of Roraima and Venezuela and in the Andean foothills.Main conclusions: Our results revealed that soil data are a more important predictor than climate of plant species range in Amazonia. The strong control of species ranges by edaphic features might reduce species' abilities to track suitable climate conditions under a drought-increase scenario. Future challenges are to improve the quality of soil data and couple them with process-based models to better predict species range dynamics under climate change. K E Y W O R D SAmazon rain forest, Bayesian logistic regression, cation exchange capacity, climate change, ecological niche models, soil factors, SoilGrids, species distribution models, species range, tropical soils Fittkau, Junk, Klinge, & Sioli, 1975;Higgins et al., 2011;Sombroek, 2000;Tuomisto & Poulsen, 1996). However, studies focused on the Amazonian rain forests and others also found in other biomes). Species occurrence records were obtained from two sources: plot-based inventories and herbarium records. To ensure data consistency, we targeted species that are easy to identify in the field. We included only species that had more than 20 presence records (see further details: Table S1, Appendix S1 in Supporting Information). 28 km at the equator) into two climatic variables: annual precipitation and dry season length, defined as the maximum consecutive number of months with <100 mm of precipitation. | Environmental dataWe used four remote sensing variables that describe terrain and forest structure properties: elevation, percentage tree cover, per- | Modelling frameworkTo evalu...

Climatic and edaphic controls over tropical forest diversity and vegetation carbon storage

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Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

“…The range of elevations was too small to be related to climate variation both in our study and in the one by Figueiredo et al (2015), but elevation can also be related to geological substrate and thereby soil nutrient status in Amazonia (Vormisto et al 2004, Costa et al 2005, Higgins et al 2011. Soils are a relevant factor for SDM (Figueiredo et al 2018), but accurate enough soil data are not available for our study area. There are soil maps at a nominal 1-km spatial resolution, but they have been found to contain major inaccuracies (Moulatlet et al 2017).…”

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