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The climate over Europe has been recorded to be hotter, drier, and more fire-prone over the last decade than ever before, leading to concerns about how climate change will alter fire weather in the future. A typical measure to estimate fire weather severity based on climate is the Canadian fire weather index (FWI). In this study, we used high-resolution, bias-corrected climate model output (∼9 km) from six CMIP6 climate models and four shared socio-economic pathway projections (SSPs) to calculate consistent and comparable daily FWI datasets for Europe from 1950 to 2080. Our study aims to identify regional and large-scale shifts in fire weather severity and its predictability over time to support adaptive planning. We show that irrespective of the future SSP, fire weather will become more severe, but the increase is much stronger under high greenhouse gas emissions. This leads to new areas being exposed to severe fire weather, such as central Europe and rapidly warming mountainous areas. Already fire-prone regions in southern Europe will experience more extreme conditions. We conclude that only the low-emission SSP1-2.6 pathway can prevent strong increases in fire weather beyond the 2050s. Fire surveillance and management will become more important, even in areas and in seasons where they have not been in the focus so far.

Grasslands contribute to global biogeochemical cycles and can host a high number of plant species. Both-species dynamics and biogeochemical fluxes-are influenced by abiotic and biotic environmental factors, management and natural disturbances. In order to understand and project grassland dynamics under global change, vegetation models which explicitly capture all relevant processes and drivers are required. However, the parameterization of such models is often challenging. Here, we report on testing an individual- and process-based model for simulating the dynamics and structure of a grassland experiment in temperate Europe. We parameterized the model for three species and confront simulated grassland dynamics with empirical observations of their monocultures and one two-species mixture. The model reproduces general trends of vegetation patterns (vegetation cover and height, aboveground biomass and leaf area index) for the monocultures and two-species community. For example, the model simulates well an average annual grassland cover of 70% in the species mixture (observed cover of 77%), but also shows mismatches with specific observation values (e.g. for aboveground biomass). By a sensitivity analysis of the applied inverse model parameterization method, we demonstrate that multiple vegetation attributes are important for a successful parameterization while leaf area index revealed to be of highest relevance. Results of our study pinpoint to the need of improved grassland measurements (esp. of temporally higher resolution) in close combination with advanced modelling approaches.

Oases are azonal, highly productive, densely vegetated areas within drylands, often converted to agriculture, and characterized by significant biocultural diversity. Despite their importance, comprehensive information on the global distribution and biocultural diversity of oases has been lacking. To address this gap, a detailed bibliographic search and random forest modeling were combined to create a global map of oases, with a focus on Asia and North Africa (ANA). In the ANA region, oases cover 1.5% of the dryland area and are populated by 150 million people, with an additional 268 million people living nearby and most likely being dependent on them. Globally, oases contain more than 8,200 vertebrate species, of which 13% are classified as threatened. However, less than 0.5% of their total area is currently under protection, making oases one of the least conserved ecosystems worldwide. These findings highlight the distinct biocultural, ecological, and geopolitical importance of oases, which are increasingly threatened by climate change and direct human impacts. Despite their significance, oases remain undervalued, emphasizing an urgent need for developing adaptative strategies to sustainably manage these pivotal ecosystems.

Tree regeneration is a key process in forest dynamics, particularly in the context of forest resilience and climate change. Models are pivotal for assessing long‐term forest dynamics, and they have been in use for more than 50 years. However, there is a need to evaluate their capacity to accurately represent tree regeneration. We assess how well current models capture the overall abundance, species composition, and mortality of tree regeneration. Using 15 models built to capture long‐term forest dynamics at the stand, landscape, and global levels, we simulate tree regeneration at 200 sites representing large environmental gradients across Central Europe. The results are evaluated against extensive data from unmanaged forests. Most of the models overestimate recruitment levels, which is compensated only in some models by high simulated mortality rates in the early stages of individual‐tree dynamics. Simulated species diversity of recruitment generally matches observed ranges. Models simulating higher stand‐level species diversity do not feature higher species diversity in the recruitment layer. The effect of light availability on recruitment levels is captured better than the effects of temperature and soil moisture, but patterns are not consistent across models. Increasing complexity in the tree regeneration modules is not related to higher accuracy of simulated tree recruitment. Furthermore, individual model design is more important than scale (stand, landscape, and global) and approach (empirical and process‐based) for accurately capturing tree regeneration. Despite the mismatches between simulation results and data, it is remarkable that most models capture the essential features of the highly complex process of tree regeneration, while not having been parameterized with such data. We conclude that much can be gained by evaluating and refining the modeling of tree regeneration processes. This has the potential to render long‐term projections of forest dynamics under changing environmental conditions much more robust.

The relation between species diversity and ecosystem functioning is one of the most frequently discussed topics in ecology. Experiments often revealed an increase of productivity in species‐rich ecosystems. But large variations in these relationships, both on a local scale and in comparisons of sites along environmental gradients, still challenge our understanding of the role of species (with specific traits) and their interactions in ecosystems. In this study, we explored the role of species traits for ecosystem functioning. We used an individual‐based mechanistic grassland model which captures intra‐ and interspecific competition between plants for light and soil resources. We explored how the dynamics and productivity of grasslands are influenced by species traits and analyzed in a simulation study two species, which differ only in one particular trait. Our focus was on traits that determine how species can cope with resource limitations, for which we identified their relative importance for (1) individual plant growth, (2) monoculture dynamics, and (3) species mixture dynamics. We observed diverse relationships between species traits and different vegetation attributes for the different ecosystem levels. Most traits showed positive but saturating trends of increasing trait values but the variability in these relations increased in monocultures with intraspecific plant interactions and even more pronounced in mixtures with interspecific interactions. Using a process‐based grassland model, we were able to simulate overyielding even though it was not correlated with trait values or trait differences between both species. Correlations were also not found in terms of stability of vegetation dynamics. In contrast, for some traits already small differences supported the dominance of a species in the mixture in which species dynamics generally followed trade‐offs. The here presented simulation study demonstrates the use of process‐based models for analyzing trait‐productivity relationships in grasslands. Such models can complement previous approaches in empirical and theoretical biodiversity research and can help to move closer to understanding the mechanisms governing grassland dynamics.

Tropical forests play an important role in the global carbon cycle as they store a large amount of carbon in their biomass. To estimate the mean biomass of a forested landscape, sample plots are often used, assuming that the biomass of these plots represents the biomass of the surrounding forest. In this study, we investigated the conditions under which a limited number of sample plots conform to this assumption. Therefore, the minimum number of sample sizes for predicting the mean biomass of tropical forest landscapes was determined by combining statistical methods with simulations of sampling strategies. We examined forest biomass maps of Barro Colorado Island (50 ha), Panama (50 000 km2), and South America, Africa, and Southeast Asia (3 × 106–11 × 106 km2). The results showed that around 100 plots (1–25 ha each) are necessary for continent-wide biomass estimations if the sampled plots are randomly distributed. However, locations of current inventory plots often do not meet this requirement, for example, as their sampling design is based on spatial transects among climatic gradients. We show that these nonrandom locations lead to a much higher sampling intensity being required (up to 54 000 plots for accurate biomass estimates for South America). The number of sample plots needed can be reduced using large distances (5 km) between the plots within transects. We also applied novel point pattern reconstruction methods to account for aggregation of inventory plots in known forest plot networks. The results implied that current plot networks can have clustered structures that reduce the accuracy of large-scale estimates of forest biomass if no further statistical approach is applied. To establish more reliable biomass predictions across South American tropical forests, we recommend more spatially randomly distributed inventory plots (minimum: 100 plots) and ensuring that the analyses of inventory plot data consider their spatial characteristics. The precision of forest attribute estimates depends on the sampling intensity and strategy.

Fire interacts with many parts of the Earth system. However, its drivers are myriad and complex, interacting differently in different regions depending on prevailing climate regimes, vegetation types, socioeconomic development, and land use and management. Europe is facing strong increases in projected fire weather danger as a consequence of climate change and has experienced extreme fire seasons and events in recent years. Here, we focus on understanding and simulating burnt area across a European study domain using remote sensing data and generalised linear models (GLMs). We first examined fire occurrence across land cover types and found that all non-cropland vegetation (NCV) types (comprising 26 % of burnt area) burnt with similar spatial and temporal patterns, which were very distinct from those in croplands (74 % of burnt area). We then used GLMs to predict cropland and NCV burnt area at ∼9×9 km and monthly spatial and temporal resolution, respectively, which together we termed BASE (Burnt Area Simulator for Europe). Compared to satellite burnt area products, BASE effectively captured the general spatial and temporal patterns of burning, explaining 32 % (NCV) and 36 % (cropland) of the deviance, and performed similarly to state-of-the-art global fire models. The most important drivers were fire weather and monthly indices derived from gross primary productivity followed by coarse socioeconomic indicators and vegetation properties. Crucially, we found that the drivers of cropland and NCV burning were very different, highlighting the importance of simulating burning in different land cover types separately. Through the choice of predictor variables, BASE was designed for coupling with dynamic vegetation and Earth system models and thus enabling future projections. The strong model skill of BASE when reproducing seasonal and interannual dynamics of NCV burning and the novel inclusion of cropland burning indicate that BASE is well suited for integration in land surface models. In addition to this, the BASE framework may serve as a basis for further studies using additional predictors to further elucidate drivers of fire in Europe. Through these applications, we suggest BASE may be a useful tool for understanding, and therefore adapting to, the increasing fire risk in Europe.

Modelling and applying multivariate distributions is an important topic in ecology. In particular in plant ecology, the multidimensional nature of plant traits comes with challenges such as wide ranges in observations as well as correlations between several characteristics. In other disciplines (e.g., finances and economics), copulas have been proven as a valuable tool for modelling multivariate distributions. However, applications in ecology are still rarely used. Here, we present a copula-based methodology of fitting multivariate distributions to ecological data. We used product copula models to fit multidimensional plant traits, on example of observations from the global trait database TRY. The fitting procedure is split into two parts: fitting the marginal distributions and fitting the copula. We found that product copulas are well suited to model ecological data as they have the advantage of being asymmetric (similar to the observed data). Challenges in the fitting were mainly addressed to limited amount of data. In view of growing global databases, we conclude that copula modelling provides a great potential for ecological modelling.

Since its development in 2010, the SPITFIRE global fire model has had a substantial impact on the field of fire modelling using dynamic global vegetation models. It includes process-based representations of fire dynamics, including ignitions, fire spread, and fire effects, resulting in a holistic representation of fire on a global scale. Previously, work had been undertaken to understand the strengths and weaknesses of SPITFIRE and similar models by comparing their outputs against remotely sensed data. We seek to augment this work with new validation methods and extend it by completing a thorough review of the theory underlying the SPITFIRE model to better identify and understand sources of modelling uncertainty. We find several points of improvement in the model, the most impactful being an incorrect implementation of the Rothermel fire spread model that results in large positive biases in fire rate of spread and a live grass moisture parametrization that results in unrealistically dry grasses. The combination of these issues leads to excessively large and intense fires, particularly on the dry modelled grasslands. Because of the tall flames present in these intense fires, which can cause substantial damage to tree crowns, these issues bias SPITFIRE toward high tree mortality. We resolve these issues by correcting the implementation of the Rothermel model and implementing a new live grass moisture parametrization, in addition to several other improvements, including a multi-day fire spread algorithm, and evaluate these changes in the European domain. Our model developments allow SPITFIRE to incorporate more realistic live grass moisture content and result in more accurate burnt area on grasslands and reduced tree mortality. This work provides a crucial improvement to the theoretical basis of the SPITFIRE model and a foundation upon which future model improvements may be built. In addition, this work further supports these model developments by highlighting areas in the model where high amounts of uncertainty remain, based on new analysis and existing knowledge about the SPITFIRE model, and by identifying potential means of mitigating them to a greater extent.

This review of recent advances in biosphere research aims to provide information on eight selected themes related to changes in biodiversity, ecosystem functioning, social and economic interactions with ecosystems, and the impacts of climate change on the biosphere. An interdisciplinary panel of experts selected these eight themes from a public survey based on relevance and scientific evidence that have the potential to guide future actions as well as inspire future research questions. Our focus is on the interactions between climate, biosphere, and society and on strategies to sustain, restore, or promote ecosystems and their services. The themes focus on innovative opportunities for coastal habitats, forest linkages to droughts, and increasing fire risks. We further discuss nature-based carbon dioxide removal (CDR) implementation risks and the share of (semi-)natural habitats in the landscape. Finally, we highlight the importance of comprehensive international policy packages and the social–economic value of ecosystems in the future and present the idea of convivial conservation. Based on an analysis of these eight topics, we have synthesized four overarching insights: (i) improve mechanisms of inclusive decision-making, (ii) establish and strengthen incentives for sustainable practices, (iii) measure and share regional features, and finally (iv) adopt long-lasting holistic landscape management strategies. This review emphasizes that the interlinked challenges for ecosystems, including the socio-economic dimensions, require interdisciplinary and integrative approaches to develop effective and sustainable solutions.