Soil predictors are crucial for modelling vegetation distribution and its responses to climate change

• Published in: The Science of the total environment Vol. 780; p. 146680
• Main Authors: Oliveira, Guilherme de Castro, Arruda, Daniel Meira, Fernandes Filho, Elpídio Inácio, Veloso, Gustavo Vieira, Francelino, Márcio Rocha, Schaefer, Carlos Ernesto Gonçalves Reynaud
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2021
• Subjects: Brazilian semiarid; Caatinga; Ecological niche modelling; Environmental policy; Machine learning; Seasonally Dry Tropical Forests; Brazilian semiarid; Seasonally Dry Tropical Forests; Caatinga; Environmental policy; Machine learning; Ecological niche modelling
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2021.146680

Bioclimatic envelope models have been extensively used to predict the vegetation dynamics in response to climate changes. However, they are prone to the uncertainties arising from General Circulation Models (GCMs), classification algorithms and predictors, with low-resolution results and little detail at the regional level. Novel research has focused on the improvement of these models through a combination of climate and soil predictors to enhance ecological consistency. In this framework, we aimed to apply a joint edaphoclimatic envelope to predict the current and future vegetation distribution in the semiarid region of Brazil, which encompasses several classes of vegetation in response to the significant environmental heterogeneity. We employed a variety of machine learning algorithms and GCMs under RCP 4.5 and 8.5 scenarios of Coupled Model Intercomparison Project Phase 5 (CMIP5), in 1 km resolution. The combination of climate and soil predictors resulted in higher detail at landscape-scale and better distinction of vegetations with overlapping climatic niches. In forecasts, soil predictors imposed a buffer effect on vegetation dynamics as they reduced shifts driven solely by climatic drift. Our results with the edaphoclimatic approach pointed to an expansion of the dry Caatinga vegetation, ranging from an average of 16% to 24% on RCP 4.5 and RCP8.5 scenarios, respectively. The shift in environmental suitability from forest to open and dry vegetation implies a major loss to biodiversity, as well as compromising the provision of ecosystem services important for maintaining the economy and livelihoods of the world's largest semiarid population. Predicting the most susceptible regions to future climate change is the first step in developing strategies to mitigate impacts in these areas. [Display omitted] •Impacts of climate change on the Brazilian semi-arid region are assessed.•Vegetation distribution models are improved with the use of soil predictors.•Uncertainties from General Circulation Models are reduced in forecasts.•Climate change scenarios led to major impacts on the vegetation distribution.•Results showed the expansion of Caatinga vegetation in the RCP 4.5 and 8.5 scenarios.