Timothy R. Baker

Accurate mapping of woody biomass and its temporal change is critical for understanding the global carbon balance, informing restoration and supporting evidence‐based policy on climate mitigation and livelihoods. The vegetation of the dry tropics, comprised largely of dry forests and savannas, holds substantial woody carbon stocks, but both stocks and change over time remain uncertain. Where Earth Observation (EO) data have been used to estimate change over time, different products give conflicting results on the direction, magnitude and location of change. We present pan‐dry‐tropical maps of aboveground carbon density (AGCD) and AGCD change at 100 m resolution for the years 2015–2024, developed using ALOS ScanSAR satellite radar observations calibrated with in situ measurements ( n = 237). These maps suggest an aboveground woody carbon pool (AGC) in the dry tropics of 25.3 2.0 PgC, ~13.0% of global AGC. Net AGC has increased by 0.43 0.07 PgC yr ⁻¹ , ~20.0% of the global net land sink. The overall net increase (0.43 PgC yr ⁻¹ ) is comprised of gross losses from deforestation and degradation (0.27 0.05 PgC yr ⁻¹ ) and gains due to densification and extensification of 0.59 0.1 PgC yr ⁻¹ , with the balance accounted for by small changes (change < 20% of AGCD; 0.11 PgC yr ⁻¹ ). Losses are equally split between degradation and deforestation, while gains are divided roughly 2:1 between the densification of woody ecosystems and expansion into previously grass‐dominated areas. These new maps, created with a dry‐tropics‐specific EO method, highlight the importance of tropical dry forests and savannas in the global carbon cycle. The prominent role of degradation‐related losses suggests that climate change mitigation will need to focus much more on reducing losses from overharvesting than at present. The widespread increases in carbon density and the increase in tree biomass into open systems present major challenges for biodiversity conservation, restoration and livelihoods.

Reliable monitoring of leaf phenology across environmental gradients is essential for understanding ecosystem functioning and climate responses, yet existing approaches in African savannas are constrained by either sparse ground-based observations or the limited resolution of satellite products. We evaluated the ability of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), and Sentinel-2-derived vegetation indices (Normalised-Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Green Chromatic Coordinate (GCC)) to track phenocam-derived phenology across 10 plots along a water-availability gradient in Africa. Satellite vegetation index time series were smoothed using generalized additive models and compared with phenocam-derived GCC, including estimates of transition dates. All satellite datasets estimated longer growing seasons than phenocams, associated with earlier start-of-season (SOS) and later end-of-season (EOS) estimates. MODIS NDVI and VIIRS NDVI most accurately estimated transition dates (SOS root-mean-squared error (RMSE), a standard measure of model accuracy, of 10 and 11 days, respectively; EOS RMSE of 27 days in both datasets). MODIS GCC and VIIRS EVI followed with high estimated transition date accuracy (SOS RMSE of 11 days; EOS RMSE of 32 days, in both datasets). VIIRS EVI and Sentinel-2 (S2) GCC most accurately tracked phenocam temporal fluctuations (r = 0.83 ± 0.08 and 0.83 ± 0.10, respectively), while MODIS GCC and MODIS NDVI also showed high correlations (r = 0.78 ± 0.12 and 0.78 ± 0.15). These MODIS products tracked intraseasonal fluctuations more closely than the VIIRS and S2 datasets. We consider MODIS NDVI the most accurate dataset overall, given its accuracy in estimating SOS and EOS, visible tracking of intraseasonal fluctuations, and high temporal correlation with phenocam phenology. These results support using MODIS and VIIRS for large-scale phenology monitoring and upscaling phenocam observations in African savannas.

Plant functional composition and diversity are key dimensions of biodiversity that determine ecosystem functioning. While spaceborne remote sensing has enabled predicting functional composition, the prediction accuracy is still limited, particularly in tropical forests where persistent cloud cover, signal saturation and limited field data constrain conventional approaches, and large-scale spatial patterns of functional diversity remain understudied. Recent advances in self-supervised learning have produced representative embeddings from unlabeled satellite data, offering the potential to overcome such challenges. Here, we model and map community-weighted mean (CWM) and standard deviation (CWSD) of plant traits, representing functional composition and diversity, respectively, across the Amazon at 1 km resolution using >1,300 field plots of tree species composition and nine leaf, stem, structural and reproductive traits. We evaluate AlphaEarth, TESSERA, and SatCLIP embeddings, representing spatiotemporal features of multimodal information, temporal dynamics and geolocation characteristics, respectively, against conventional Sentinel-1/2 predictors. We show that self-supervised embeddings consistently outperform conventional satellite predictors. AlphaEarth combined with environmental variables provided the best balance in accuracy, resolution, and dimensionality, achieving mean R2 of 0.3-0.6 for CWM traits and 0.1-0.5 for CWSD traits. Variable importance analyses identified the combined influence of climate variability, soil fertility and satellite embeddings, with their importance varying across traits. The output maps reveal spatial patterns of functional composition and diversity that markedly differ from species diversity distributions, highlighting areas of high conservation value and/or functional vulnerability. These findings demonstrate new opportunities with satellite embeddings for large-scale mapping, monitoring and understanding of functional biodiversity in tropical forests.

1. The concept of nature’s contributions to people (NCP) is useful when exploring how the natural world underpins human well-being. Emphasising the importance of diverse worldviews and knowledge sy...

Forests are vital for regulating climate and sustaining biodiversity, but climate change threatens their ability to do so, especially in the tropics. Our knowledge of how tropical forests and their constituent trees will respond to changes in climate is largely based on functional trait studies; however, few previous studies have investigated trait changes within individual tropical trees across decades, limiting our ability to predict the future of these forests. In this study, we leveraged historical and contemporary botanical specimens collected from the same individual trees in the southern Peruvian Amazon, measured a suite of leaf traits to test for individual‐level trait changes over nearly 40 years, and then related these changes to concurrent changes in local climate. We hypothesized that trees have acclimated their functional traits in response to increasing air temperatures and drought intensification and that this acclimation should help to maintain stable leaf temperatures through time. In accord with our hypothesis, we found significant decreases in measured leaf traits, including size and shape metrics and stomatal traits, within individuals through time. We used these measured traits to model leaf temperatures through time, which increased faster than would be expected based on changes in air temperature alone. This accelerated warming of leaves was due to decreased stomatal conductance, a potential acclimation of trees to dry season intensification and rising [CO ₂ ], thus limiting leaf transpirational cooling. In other words, trees have decreased abilities to cool their leaves, and consequently they may be approaching critical thermal thresholds faster than they would in the absence of water limitation. Our study provides evidence that while individual trees are acclimating to climate change, tropical forests are undergoing increasing thermal stress and that intensifying drought may be elevating this risk.

Amazon rainforests face intensifying water stress due to increases in vapour pressure deficit and changing hydrological regimes. Embolism resistance (Ψ ₅₀ ) is a critical metric of tree survival under drought conditions, it is defined as a plant’s capacity to resist disruption of xylem water flow due to air bubble formation from water stress. However, measurements of Ψ ₅₀ are only available for a limited number of Amazon locations and species. Conversely, data on forest taxonomic composition are abundant across Amazonia, and if Ψ ₅₀ is conserved phylogenetically, these data could provide a way to scale-up drought resistance patterns. Here we evaluate Ψ ₅₀ measurements across non-flooded Amazonian tree taxa and reveal a moderate phylogenetic signal, with phylogenetic conservatism evident at the family-level. Notably, Fabaceae is amongst the most embolism-resistant tree families in Amazonia. Leveraging the phylogenetic signal we use species composition and tree size data from 448 forest plots across Amazonia to produce a macroecological assessment of Amazonian vulnerability to embolism. The resulting estimate spatial pattern reveals that forests in the Brazilian and Guiana Shield regions, where Fabaceae abundance is high, show strong resistance to embolism. In contrast, tree communities in Western Amazonia appear more vulnerable to embolism, suggesting a reduced capacity to withstand future drought conditions.