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 8,959 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 (six 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

“…Predictions of terrestrial PNV changes at high latitudes in the Northern Hemisphere under a sharper temperature increase scenario are consistent with recent predicted vegetation changes in North America (Flanagan et al, 2016) and Europe (Gang et al, 2017;Kicklighter et al, 2014;Shiyatov et al, 2005). 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

“…At high latitudes and altitudes, climate change is expected to reduce persistent snow cover 48 and temperature limitations on growth 49 , both of which would reduce the albedo offset from restoring tree cover. However, the negative climate impacts in drylands will persist, or even grow if the extent of dryland biomes increases and tree growth is further 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 .…”

Microbial co-occurrence network demonstrates spatial and climatic trends for global soil diversity

“…These results may have repercussions for our comprehension of the composition and operation of microbial communities in various temperate zones, as well as their potential responses to environmental disturbances or modifications 31 . They also emphasise the significance of considering the intricate relationships that exist between microbial species and their surroundings, since these relationships may eventually have an impact on ecosystem processes and human health 32 – 34 . Together these findings suggest that microbial interactions may have a more substantial impact on soil functions than species diversity, underscoring the significance of comprehending the mechanisms that shape microbial communities in distinct regions and climates.…”