'How to adapt forests?'-Exploring the role of leaf trait diversity for long-term forest biomass under new climate normals.

Forests, critical components of global ecosystems, face unprecedented challenges due to climate change. This study investigates the influence of functional diversity-as a component of biodiversity-to enhance long-term biomass of European forests in the context of changing climatic conditions. Using the next-generation flexible trait-based vegetation model, LPJmL-FIT, we explored the impact of functional diversity on long-term forest biomass under three different climate change scenarios (video abstract: https://www.pik-potsdam.de/~billing/video/2023/video_abstract_billing_et_al_LPJmLFIT.mp4). Four model set-ups were tested with varying degrees of functional diversity and best-suited functional traits. Our results show that functional diversity positively influences long-term forest biomass, particularly when climate warming is low (RCP2.6). Under these conditions, high-diversity simulations led to an approximately 18.2% increase in biomass compared to low-diversity experiments. However, as climate change intensity increased, the benefits of functional diversity diminished (RCP8.5). A Bayesian multilevel analysis revealed that both full leaf trait diversity and diversity of plant functional types contributed significantly to biomass enhancement under low warming scenarios in our model simulations. Under strong climate change, the presence of a mixture of different functional groups (e.g. summergreen and evergreen broad-leaved trees) was found more beneficial than the diversity of leaf traits within a functional group (e.g. broad-leaved summergreen trees). Ultimately, this research challenges the notion that planting only the most productive and climate-suited trees guarantees the highest future biomass and carbon sequestration. We underscore the importance of high functional diversity and the potential benefits of fostering a mixture of tree functional types to enhance long-term forest biomass in the face of climate change.

Large-scale impact of climate change vs. land-use change on future biome shifts in Latin America.

Climate change and land-use change are two major drivers of biome shifts causing habitat and biodiversity loss. What is missing is a continental-scale future projection of the estimated relative impacts of both drivers on biome shifts over the course of this century. Here, we provide such a projection for the biodiverse region of Latin America under four socio-economic development scenarios. We find that across all scenarios 5-6% of the total area will undergo biome shifts that can be attributed to climate change until 2099. The relative impact of climate change on biome shifts may overtake land-use change even under an optimistic climate scenario, if land-use expansion is halted by the mid-century. We suggest that constraining land-use change and preserving the remaining natural vegetation early during this century creates opportunities to mitigate climate-change impacts during the second half of this century. Our results may guide the evaluation of socio-economic scenarios in terms of their potential for biome conservation under global change.

Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model.

Functional diversity is critical for ecosystem dynamics, stability and productivity. However, dynamic global vegetation models (DGVMs) which are increasingly used to simulate ecosystem functions under global change, condense functional diversity to plant functional types (PFTs) with constant parameters. Here, we develop an individual- and trait-based version of the DGVM LPJmL (Lund-Potsdam-Jena managed Land) called LPJmL- flexible individual traits (LPJmL-FIT) with flexible individual traits) which we apply to generate plant trait maps for the Amazon basin. LPJmL-FIT incorporates empirical ranges of five traits of tropical trees extracted from the TRY global plant trait database, namely specific leaf area (SLA), leaf longevity (LL), leaf nitrogen content (Narea ), the maximum carboxylation rate of Rubisco per leaf area (vcmaxarea), and wood density (WD). To scale the individual growth performance of trees, the leaf traits are linked by trade-offs based on the leaf economics spectrum, whereas wood density is linked to tree mortality. No preselection of growth strategies is taking place, because individuals with unique trait combinations are uniformly distributed at tree establishment. We validate the modeled trait distributions by empirical trait data and the modeled biomass by a remote sensing product along a climatic gradient. Including trait variability and trade-offs successfully predicts natural trait distributions and achieves a more realistic representation of functional diversity at the local to regional scale. As sites of high climatic variability, the fringes of the Amazon promote trait divergence and the coexistence of multiple tree growth strategies, while lower plant trait diversity is found in the species-rich center of the region with relatively low climatic variability. LPJmL-FIT enables to test hypotheses on the effects of functional biodiversity on ecosystem functioning and to apply the DGVM to current challenges in ecosystem management from local to global scales, that is, deforestation and climate change effects.

Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model.

Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero. Our results indicate a nonlinear but reversible relationship between vegetation Above Ground Biomass (AGB) and Mean Annual Precipitation (MAP), suggesting a threshold at a critical MAP value, below which vegetation biomass decline accelerates with decreasing MAP. We find that approaching this critical threshold is accompanied by critical slowing down, which can hence be expected to warn of accelerating biomass decline with decreasing rainfall. The critical precipitation threshold is lowest in the northwestern Amazon, whereas the eastern and southern regions may already be below their critical MAP thresholds. Overall, we identify the seasonality of precipitation and the potential evapotranspiration (PET) as the most important parameters determining the threshold value. While vegetation fires show little effect on the critical threshold and the biomass pattern in general, the ability of trees to adapt to water stress by investing in deep roots leads to increased biomass and a lower critical threshold in some areas in the eastern and southern Amazon where seasonality and PET are high. Our findings underscore the risk of Amazon forest degradation due to changes in the water cycle, and imply that regions that are currently characterized by higher water availability may exhibit heightened vulnerability to future drying.

Earth beyond six of nine planetary boundaries.

This planetary boundaries framework update finds that six of the nine boundaries are transgressed, suggesting that Earth is now well outside of the safe operating space for humanity. Ocean acidification is close to being breached, while aerosol loading regionally exceeds the boundary. Stratospheric ozone levels have slightly recovered. The transgression level has increased for all boundaries earlier identified as overstepped. As primary production drives Earth system biosphere functions, human appropriation of net primary production is proposed as a control variable for functional biosphere integrity. This boundary is also transgressed. Earth system modeling of different levels of the transgression of the climate and land system change boundaries illustrates that these anthropogenic impacts on Earth system must be considered in a systemic context.

Future tree survival in European forests depends on understorey tree diversity.

Climate change heavily threatens forest ecosystems worldwide and there is urgent need to understand what controls tree survival and forests stability. There is evidence that biodiversity can enhance ecosystem stability (Loreau and de Mazancourt in Ecol Lett 16:106-115, 2013; McCann in Nature 405:228-233, 2000), however it remains largely unclear whether this also holds for climate change and what aspects of biodiversity might be most important. Here we apply machine learning to outputs of a flexible-trait Dynamic Global Vegetation Model to unravel the effects of enhanced functional tree trait diversity and its sub-components on climate-change resistance of temperate forests ( http://www.pik-potsdam.de/~billing/video/Forest_Resistance_LPJmLFIT.mp4 ). We find that functional tree trait diversity enhances forest resistance. We explain this with 1. stronger complementarity effects (~ 25% importance) especially improving the survival of trees in the understorey of up to + 16.8% (± 1.6%) and 2. environmental and competitive filtering of trees better adapted to future climate (40-87% importance). We conclude that forests containing functionally diverse trees better resist and adapt to future conditions. In this context, we especially highlight the role of functionally diverse understorey trees as they provide the fundament for better survival of young trees and filtering of resistant tree individuals in the future.

Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations.

The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. This uncertainty originates from how dynamic global vegetation models (DGVMs) simulate climate impacts on changes in vegetation distribution, productivity, biomass allocation, and carbon turnover. The present-day availability of a multitude of satellite observations can potentially help to constrain DGVM simulations within model-data integration frameworks. Here, we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle and of temporal dynamics in FAPAR, SIF and gross primary production. However, the optimized model reproduces better the observed spatial patterns of biomass, tree cover, and regional forest carbon turnover. Using a machine learning approach, we found that remaining errors in simulated forest carbon turnover can be explained with bioclimatic variables. This demonstrates the need to improve model formulations for climate effects on vegetation turnover and mortality despite the apparent successful constraint of simulated vegetation dynamics with multiple satellite observations.

How rare is complex life in the Milky Way?

An integrated Earth system model was applied to calculate the number of habitable Earth-analog planets that are likely to have developed primitive (unicellular) and complex (multicellular) life in extrasolar planetary systems. The model is based on the global carbon cycle mediated by life and driven by increasing stellar luminosity and plate tectonics. We assumed that the hypothetical primitive and complex life forms differed in their temperature limits and CO(2) tolerances. Though complex life would be more vulnerable to environmental stress, its presence would amplify weathering processes on a terrestrial planet. The model allowed us to calculate the average number of Earth-analog planets that may harbor such life by using the formation rate of Earth-like planets in the Milky Way as well as the size of a habitable zone that could support primitive and complex life forms. The number of planets predicted to bear complex life was found to be approximately 2 orders of magnitude lower than the number predicted for primitive life forms. Our model predicted a maximum abundance of such planets around 1.8 Ga ago and allowed us to calculate the average distance between potentially habitable planets in the Milky Way. If the model predictions are accurate, the future missions DARWIN (up to a probability of 65%) and TPF (up to 20%) are likely to detect at least one planet with a biosphere composed of complex life.

Maximum number of habitable planets at the time of Earth's origin: new hints for panspermia?

New discoveries have fuelled the ongoing discussion of panspermia, i.e. the transport of life from one planet to another within the solar system (interplanetary panspermia) or even between different planetary systems (interstellar panspermia). The main factor for the probability of interstellar panspermia is the average density of stellar systems containing habitable planets. The combination of recent results for the formation rate of Earth-like planets with our estimations of extrasolar habitable zones allows us to determine the number of habitable planets in the Milky Way over cosmological time scales. We find that there was a maximum number of habitable planets around the time of Earth's origin. If at all, interstellar panspermia was most probable at that time and may have kick-started life on our planet.