Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation

Abstract

The global potential distribution of biomes (natural vegetation) was modelled using 8959 training points from the BIOME 6000 dataset and a stack of 72 environmental covariates representing terrain and the current climatic conditions based on historical long term averages (1979–2013). An ensemble machine learning model based on stacked regularization was used, with multinomial logistic regression as the meta-learner and spatial blocking (100 km) to deal with spatial autocorrelation of the training points. Results of spatial cross-validation for the BIOME 6000 classes show an overall accuracy of 0.67 and R2logloss of 0.61, with ”tropical evergreen broadleaf forest” being the class with highest gain in predictive performances (R2logloss = 0.74) and ”prostrate dwarf shrub tundra” the class with the lowest (R2logloss = -0.09) compared to the baseline. Temperature-related covariates were the most important predictors, with the mean diurnal range (BIO2) being shared by all the base-learners (i.e. random forest, gradient boosted trees and generalized linear models). The model was next used to predict the distribution of future biomes for the periods 2040–2060 and 2061–2080 under three climate change scenarios (RCP 2.6, 4.5 and 8.5). Comparisons of predictions for the three epochs (present, 2040–2060 and 2061–2080) show that increasing aridity and higher temperatures will likely result in significant shifts in natural vegetation in the tropical area (shifts from tropical forests to savannas up to 1.7 × 105 km2 by 2080) and around the Arctic Circle (shifts from tundra to boreal forests up to 2.4 × 105 km2 by 2080). Projected global maps at 1 km spatial resolution are provided as probability and hard classes maps for BIOME 6000 classes and as hard classes maps for the IUCN classes (6 aggregated classes). Uncertainty maps (prediction error) are also provided and should be used for careful interpretation of the future projections.

Natural Vegetation Succession Under Climate Change and the Combined Effects on Net Primary Productivity

“…Bonannella et al. (2023) revealed a trend in which the polar/alpine biome trends to shift toward temperate‐boreal forest biome, as indicted by integrated machine learning model simulations, this finding is consistent with the results of our study. By the conclusion of the 21st century, temperate forest is forecast to replace some of the original tundra and alpine steppe, together with temperate humid grassland, on a large scale (Figure S2 in Supporting Information S1).…”

“…In tropical forest regions, the projected sharp temperature increases have the potential to intensify droughts and increase the risk of wildfires (Gloor et al., 2013; Schwalm et al., 2017), for example, in South America, certain parts of tropical forest are expected to be replaced by savanna under the pressure of temperature increase, prolonged drought, and fire, among others. Predictions of tropical forest and savanna through integrated machine learning (Bonannella et al., 2023) and generalized linear (Anadon et al., 2014) models show a similar transition from tropical forests to Savanna. As the climate has warmed, decreased annual precipitation and increased solar radiation along the Mediterranean coast have led to increase in area of warm desert vegetation in the northern part of the Sahara and western coast of the Mediterranean, replacing the original savanna (Boko et al., 2007).…”

Accounting for albedo change to identify climate-positive tree cover restoration

“…diminished as the climate warms 50 . Beyond climate change, future timber demands will likely alter the extent, management (e.g., rotation lengths), and types of forest (e.g., when an evergreen timber plantation replaces a deciduous native forest) 51 .…”

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

“…The concept of "Potential Natural Vegetation" (PNV) has first been defined by Tüxen (1956) as the vegetation that would be encountered in the absence of human intervention and of any significant changes to the current climatic conditions. The PNV concept has previously been applied in various studies by modeling the potential distribution of natural biomes (Bonannella, Hengl, Parente, & de Bruin, 2023;Hengl et al, 2018;Levavasseur, Vrac, Roche, & Paillard, 2012), and by mapping the potential biomass stocks of forests (Erb et al, 2018;Roebroek, Duveiller, Seneviratne, Davin, & Cescatti, 2023). Hengl et al (2018), specifically, proposed a data-driven framework using a machine learning approach to estimate potential primary productivity.…”