Climate extremes are on the rise. Impacts of extreme climate and weather events on ecosystem services and ultimately human well-being can be partially attenuated by the organismic, structural, and functional diversity of the affected land surface. However, the ongoing transformation of terrestrial ecosystems through intensified exploitation and management may put this buffering capacity at risk. Here, we summarise the evidence that reductions in biodiversity can destabilise the functioning of ecosystems facing climate extremes. We then explore if impaired ecosystem functioning could, in turn, exacerbate climate extremes. We argue that only a comprehensive approach, incorporating both ecological and hydrometeorological perspectives, enables to understand and predict the entire feedback system between altered biodiversity and climate extremes. This ambition, however, requires a reformulation of current research priorities to emphasise the bidirectional effects that link ecology and atmospheric processes.

The impact of land use on ecosystems has reached critical levels, jeopardizing biosphere integrity. A key indicator that quantifies, monitors, and analyses such impacts is the Human Appropriation of Net Primary Production (HANPP). Assessing HANPP requires integrating data from sources such as remote sensing and census statistics, as well as modelled data like potential Net Primary Production (NPP), which reflects NPP without land use. Although the availability of global land cover data at high spatial detail from remote sensing has improved, with resolutions reaching 30 arcseconds (about 1 km) and higher, global NPP results from Dynamic Global Vegetation Models (DGVMs) are still unavailable at this resolution. This spatial mismatch causes uncertainties, as simple interpolation methods fail to capture fine-scaled productivity patterns. We here present a parsimonious method to downscale NPP, using the Miami NPP model with temperature and precipitation data as readily available auxiliary information at high spatial resolution. Our method uses a moving window approach with Gaussian convolution to minimize downscaling artefacts. We demonstrate this Smooth Auxiliary Data (SAD) downscaling approach by downscaling potential NPP results from the LPJ-GUESS-DGVM model for the year 2010 from 30arcmin to 30arcsec resolution. This approach, requiring low computational cost, generates fine-scaled productivity patterns and aligns with alternative models for smaller geographic units, offering a solution until high-resolution DGVM results become feasible.

jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Herbivory is one of the main biotic processes modulating plant diversity and productivity. In tropical forests, insects may remove up to 30% of total leaf biomass, but the effects on vegetation structure/productivity and biodiversity are poorly understood. Insect herbivory might promote or suppress plant growth, first reducing the photosynthetic area but also providing a rather direct path from nutrients in leaves to the plant-available soil pool. In this study, we used a trait-based Dynamic Global Vegetation Model (LPJ-GUESS-NTD), parameterized with unique field data from a tropical mountain forest gradient in southern Ecuador, to analyze how observed leaf-trait dependent insect herbivory influences the functional diversity and productivity of vegetation. According to the model, insect herbivory decreases net primary production by 6% and vegetation carbon storage by 26%. Herbivory also causes a vegetation community trait shift related to the leaf and wood economic spectrum, since with it specific leaf area (SLA) is reduced by 34% and wood specific gravity (WSG) increases by 10% respectively. This herbivory-induced change implies a shift towards a vegetation community with more conservative growth strategies, with negative effects on litter quality and nutrient availability. Accordingly, and in contrast to our expectations, herbivory reduces nutrient availability in the model. Finally, the inclusion of herbivory re-enforces gradients in nutrient availability and increases the community trait dissimilarity across altitudes (beta diversity). Our results suggest that insect herbivory has profound negative impacts on vegetation productivity and biomass in our study area, partly driven through feedbacks between soil processes and changes in plant traits. Furthermore, insect herbivory might be an important factor in shaping vegetation functional diversity.

Lignocellulose is a major component of plant biomass. Its decomposition is crucial for the terrestrial carbon cycle. Microorganisms are considered as primary decomposers and evidence increases that some invertebrates may also decompose lignocellulose. We investigated the taxonomic distribution and evolutionary origins of GH45 cellulases in a collection of soil invertebrate genomes and found that these genes are common in springtails and oribatid mites. Phylogenetic analysis revealed that cellulase genes were acquired early in the evolutionary history of these groups. Domain architectures and predicted 3D enzyme structures indicate that these cellulases are functional. Patterns of presence and absence of these genes across different lineages prompt further investigation into their evolutionary and ecological benefits. The ubiquity of cellulase genes suggests that soil invertebrates may play a role in lignocellulose decomposition, independently from microorganisms. Understanding the ecological and evolutionary implications might be crucial for understanding soil food webs and the carbon cycle.

Forecasts of forest responses to climate variability are governed by climate exposure and ecosystem sensitivity, but ecosystem model projections and process representations are under-constrained by data at multidecadal and longer timescales. Here, we assess ecosystem sensitivity to centennial-scale hydroclimate variability, by comparing dendroclimatic and pollen-inferred reconstructions of drought, forest composition and biomass for the last millennium with five ecosystem model simulations. In both observations and models, spatial patterns in ecosystem responses to hydroclimate variability are strongly governed by ecosystem sensitivity rather than climate exposure. Ecosystem sensitivity was highest in simpler models and higher than observations, suggesting that interactions among biodiversity, demography, and ecophysiology processes dampen the sensitivity of forest composition and biomass to climate variability and change. By integrating ecosystem models with observations from timescales extending beyond the instrumental record, we can better understand and forecast the mechanisms regulating forest sensitivity to climate variability in a complex and changing world.