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The stratified nature of tropical forest structure had been noted by early explorers, but until recent use of satellite-based LiDAR (GEDI, or Global Ecosystems Dynamics Investigation LiDAR), there has been no way to quantify stratification across all tropical forests. Understanding stratification is important because by some estimates, a majority of the world’s species inhabit tropical forest canopies. Stratification can modify vertical microenvironment, and thus can affect a species’ susceptibility to global warming. A better understanding of structure could also improve predictions of biomass across the tropics. Here we find that, based on analyzing each GEDI 25m diameter footprint in tropical forests (after screening for human impact), most footprints (60-90%) do not have multiple layers of vegetation. This result is highly scale dependent, but with a 25m footprint, the most common forest structure has a minimum plant area index (PAI) at ~40m followed by an increase in PAI until ~15m followed by a decline in PAI to the ground layer (described hereafter as a one peak footprint). However, there are large geographic patterns to forest structure within the Amazon basin (ranging between 60-90% one peak) and between the Amazon (79±9sd) and SE Asia or Africa (72±14 v 73±11). The number of canopy layers is significantly correlated with tree height (r2=0.12), forest biomass (r2=0.14), maximum temperature (Tmax) (r2=0.05), vapor pressure deficit (VPD) (r2=0.03) and soil fertility proxies (e.g. total cation exchange capacity -r2=0.01). Certain boundaries, like the Pebas Formation and Ecoregions, clearly delineate continental scale structural changes. More broadly, deviation from more ideal conditions (e.g. lower fertility or higher temperatures) leads to shorter, less stratified forests with lower biomass.

Semi-arid tropical rangelands substantially contribute to livelihoods of subsistence farmers, but are threatened by undesired vegetation shifts due to climate change and overgrazing. Grazing-induced shifts of the grass community composition are often associated with rangeland degradation. To identify sustainable management strategies, a process-based understanding of grass functional diversity and rangeland dynamics is required. We present a new scheme for aDGVM2, a dynamic vegetation model for tropical ecosystems, that distinguishes annual and perennial grasses based on trait trade-offs to improve the representation of rangeland communities. Additionally, the model includes a new scheme that describes selective grazing and grazing effects on grass-layer composition. We tested the new model version for various grazing intensities along a precipitation gradient in South Africa. Mean annual precipitation below 500 mm constrained rangeland productivity and carrying capacity. Increasing grazing intensity reduced rangeland productivity and increased annual grass abundance. Heavy grazing resulted in annual grass dominance. Livestock preferred perennial over annual grasses at low grazing intensities at all except the two driest sites; preference switched to annual grasses at intermediate intensities, and became non-discriminating at high grazing intensities. Rangeland recovery after removal of grazers required 2–15 years. We conclude that management intervention reducing or eliminating grazing pressure during and after stress years is crucial to allow rangeland recovery and avoid permanent degradation.

Objectives: This study aims to assess the validity, internal consistency, implementation, and feasibility of a sequence of tests, the Vitality Test Battery, designed to measure physical fitness, at a large scale in French older adults.Methods: A total of 528 volunteers (age ≥60 years) took the battery of 10 tests: 6-min walk, trunk strength, hand grip strength, medicine ball throwing, 30-s chair stand, flexibility, balance, plate tapping, ruler drop, and dual task.Results: Internal consistency was high, with the Cronbach alpha coefficients at around 0.77, explaining 64% of the variance. The test–retest correlations (0.3–0.6) between the items were acceptable and displayed an internal consistency property. Although five components explained 65% of the variance, all the items were kept because their eigenvalues were near to 0.9. External consistency was validated by a significant decrease in fitness scores (p < 0.001) with age and body mass index.Discussion: The Vitality Test Battery is a safe, valid tool for assessing physical fitness in persons aged over 60 years.

South Asian vegetation provides essential ecosystem services to the 1.7 billion inhabitants living in the region. However, biodiversity and ecosystem services are threatened by climate and land-use change. Understanding and assessing how ecosystems respond to simultaneous increases in atmospheric CO2 and future climate change is of vital importance to avoid undesired ecosystem change. Failed reaction to increasing CO2 and climate change will likely have severe consequences for biodiversity and humankind. Here, we used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation dynamics in South Asia under RCP4.5 and RCP8.5, and we explored how the presence or absence of CO2 fertilization influences vegetation responses to climate change. Simulated vegetation under both representative concentration pathways (RCPs) without CO2 fertilization effects showed a decrease in tree dominance and biomass, whereas simulations with CO2 fertilization showed an increase in biomass, canopy cover, and tree height and a decrease in biome-specific evapotranspiration by the end of the 21st century. The predicted changes in aboveground biomass and canopy cover triggered transition towards tree-dominated biomes. We found that savanna regions are at high risk of woody encroachment and transitioning into forest. We also found transitions of deciduous forest to evergreen forest in the mountain regions. Vegetation types using C3 photosynthetic pathway were not saturated at current CO2 concentrations, and the model simulated a strong CO2 fertilization effect with the rising CO2. Hence, vegetation in the region has the potential to remain a carbon sink. Projections showed that the bioclimatic envelopes of biomes need adjustments to account for shifts caused by climate change and elevated CO2. The results of our study help to understand the regional climate–vegetation interactions and can support the development of regional strategies to preserve ecosystem services and biodiversity under elevated CO2 and climate change.

Vegetation in tropical Asia is highly diverse due to large environmental gradients and heterogeneity of landscapes. This biodiversity is threatened by intense land use and climate change. However, despite the rich biodiversity and the dense human population, tropical Asia is often underrepresented in global biodiversity assessments. Understanding how climate change influences the remaining areas of natural vegetation is therefore highly important for conservation planning. Here, we used the adaptive Dynamic Global Vegetation Model version 2 (aDGVM2) to simulate impacts of climate change and elevated CO2 on vegetation formations in tropical Asia for an ensemble of climate change scenarios. We used climate forcing from five different climate models for representative concentration pathways RCP4.5 and RCP8.5. We found that vegetation in tropical Asia will remain a carbon sink until 2099, and that vegetation biomass increases of up to 28% by 2099 are associated with transitions from small to tall woody vegetation and from deciduous to evergreen vegetation. Patterns of phenology were less responsive to climate change and elevated CO2 than biomes and biomass, indicating that the selection of variables and methods used to detect vegetation changes is crucial. Model simulations revealed substantial variation within the ensemble, both in biomass increases and in distributions of different biome types. Our results have important implications for management policy, because they suggest that large ensembles of climate models and scenarios are required to assess a wide range of potential future trajectories of vegetation change and to develop robust management plans. Furthermore, our results highlight open ecosystems with low tree cover as most threatened by climate change, indicating potential conflicts of interest between biodiversity conservation in open ecosystems and active afforestation to enhance carbon sequestration.

South Asia is one of the world’s most vulnerable regions to climate change and provides a home to approximately 1.7 billion people. South Asian vegetation is essential for ecosystem services, biodiversity and carbon storage in the region. Vegetation distribution and biome niches are likely to be severely altered by future climate change and rising atmospheric CO2 concentration. Assessing how ecosystems will respond to these changes is of vital importance. We used the aDGVM2 to simulate vegetation patterns of South Asia under RCP4.5 and RCP8.5. We found good agreement between observed and simulated biomass, height and potential vegetation maps. Model results show that large areas are susceptible to biome shift by the end of the 21st century. Woody encroachment is predicted in open savanna regions which are at high risk of transitioning into forest. We simulated vegetation under both scenarios with fixed CO2 concentration and found decreased tree dominance and biomass. Simulations under elevated CO2 concentrations predicted an increase in biomass, canopy cover, tree height and decrease in evapotranspiration. Changes in above ground biomass and canopy cover trigger biome shifts toward trees dominated the system. C3 vegetation is not saturated at current CO2 concentrations as the model simulated strong CO2 fertilization effect which will increase further with the rising CO2. Although there is considerable uncertainty in the biome projections, the geographic patterns of biomes are generally consistent across the RCP4.5 and RCP8.5 scenarios. The results provide potential future trajectories of the response of South Asian vegetation to the climate change. The results will help to understand the regional climate-vegetation interaction and to develop regional strategies for biodiversity conservation to cope with climate change.

South Africa's biomes are characterized by their exceptional biodiversity and they provide important ecosystem services such as food, livestock production, medical plants or fuel wood to people. However, during recent decades, vegetation in South Africa experienced substantial changes and loss of biodiversity due to habitat loss, intensification of land use and climate change. The development of sustainable management policies requires an understanding of interactions between vegetation, climate change as well as land use and an identification of the areas most vulnerable to vegetation change. Here, we use the aDGVM, a dynamic vegetation model for tropical ecosystems, to investigate the risk of biome shifts in South Africa's Limpopo province under a set of IPCC climate change trajectories. The Limpopo province exemplifies an area highly susceptible to climate and land use change, where people in rural areas heavily rely on natural resources. We found a general trend towards more tree-dominated ecosystems and a particularly high risk of vegetation shift in more open grassland and savanna areas. The rate of biome shift is strongly linked to the IPCC scenario applied with the highest risk of biome shifts in the RCP 8.5 scenario. We conclude that, irrespective of future climate trajectories, management and conservation initiatives should particularly focus on these more open grassland and savanna ecosystems.

Savannas cover large areas of tropical Asia. Yet, these ecosystems are threatened by intense land-use and governmental afforestation initiatives. They are vulnerable to woody encroachment due to fire suppression and climate change. Despite their ancient origins, Asian savannas have been misinterpreted as degraded forest since the colonial period. The consequences of this misinterpretation and climate change on ecosystem functions and diversity of savannas are highly uncertain. We used a dynamic vegetation model, the aDGVM2 to simulate vegetation state under different climate change scenarios to assess how different interpretations of simulated vegetation influence biome patterns in South Asia. Our results show that large areas in South Asia can be interpreted as woodland or degraded forest if we ignore the grassy component and as savanna if the grassy component is considered. The model projects woody encroachment in open savannas due to CO2-fertilization of woody plants and associated biome transitions towards forest by 2099. Our analysis shows that 23.7%–76.6% of the protected areas in the study region are at risk of change. Misclassifying grassy savannas as areas that are suitable for afforestation would lead to a 35–40% loss of these unique ecosystems. A grass-centric biome classification accounting for the grass component in addition to the woody component is necessary to correctly identify Asian savannas. We conclude that there is an urgent need for a correct interpretation of Asian savannas to allow sustainable management and conservation of biodiversity, which is already strongly threatened due to woody encroachment caused by climate change.