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Increasing pressure on land resources necessitates landscape management strategies that simultaneously deliver multiple benefits to numerous stakeholder groups with competing interests. Accordingly, we developed an approach that combines ecological data on all types of ecosystem services with information describing the ecosystem service priorities of multiple stakeholder groups. We identified landscape scenarios that maximize the overall ecosystem service supply relative to demand (multifunctionality) for the whole stakeholder community, while maintaining equitable distribution of ecosystem benefits across groups. For rural Germany, we show that the current landscape composition is close to optimal, and that most scenarios that maximize one or a few services increase inequities. This indicates that most major land-use changes proposed for Europe (for example, large-scale tree planting or agricultural intensification) could lead to social conflicts and reduced multifunctionality. However, moderate gains in multifunctionality (4%) and equity (1%) can be achieved by expanding and diversifying forests and de-intensifying grasslands. More broadly, our approach provides a tool for quantifying the social impact of land-use changes and could be applied widely to identify sustainable land-use transformations., Nature Sustainability, 6 (4), ISSN:2398-9629

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The biodiversity-ecosystem functioning concept asserts that processes in ecosystems are markedly influenced by species richness and other facets of biodiversity. However, biodiversity-ecosystem functioning studies have been largely restricted to single ecosystems, ignoring the importance of functional links - such as the exchange of matter, energy, and organisms - between coupled ecosystems. Here we present a basic concept and outline three pathways of cross-boundary biodiversity effects on ecosystem processes and propose an agenda to assess such effects, focusing on terrestrial-aquatic linkages to illustrate the case. This cross-boundary perspective of biodiversity-ecosystem functioning relationships presents a promising frontier for biodiversity and ecosystem science with repercussions for the conservation, restoration, and management of biodiversity and ecosystems from local to landscape scales.

Decomposition of dead fine roots contributes significantly to nutrient cycling and soil organic matter stabilization. Most knowledge of tree fine-root decomposition stems from studies in monospecific stands or single-species litter, although most forests are mixed. Therefore, we assessed how tree species mixing affects fine-root litter mass loss and which role initial litter quality and environmental factors play. For this purpose, we determined fine-root decomposition of 13 common tree species in four European forest types ranging from boreal to Mediterranean climates. Litter incubations in 315 tree neighborhoods allowed for separating the effects of litter species from environmental influences and litter mixing (direct) from tree diversity (indirect). On average, mass loss of mixed-species litter was higher than those of single-species litter in monospecific neighborhoods. This was mainly attributable to indirect diversity effects, that is, alterations in microenvironmental conditions as a result of tree species mixing, rather than direct diversity effects, that is, litter mixing itself. Tree species mixing effects were relatively weak, and initial litter quality and environmental conditions were more important predictors of fine-root litter mass loss than tree diversity. We showed that tree species mixing can alter fine-root litter mass loss across large environmental gradients, but these effects are context-dependent and of moderate importance compared to environmental influences. Interactions between species identity and site conditions need to be considered to explain diversity effects on fine-root decomposition.

International audience

Strong recent challenges of the long-standing paradigm that macroclimate predominantly controls decomposition questions the accuracy of climate change predictions. With a novel approach combining three experiments at continental scale, using 104 litter types in 194 plots in six major European forests, we show that the confusion around the macroclimate control on decomposition is mostly an experimental artefact. The relative role of decomposition drivers was incorrect when disrupting the natural context of locally-produced litter decomposing locally, with either a focus on litter characteristics neglecting microenvironmental context, or on environmental drivers neglecting local litter characteristics. Our data reaffirm macroclimate and its interaction with litter characteristics as predominant decomposition drivers. Conversely, standard litter types overrated microenvironmental control and failed to predict local decomposition of plot-specific litter. Our findings provide support for a strong macroclimate component in predictive decomposition models and call for cautious interpretation of data from experiments using standard litter types.

The impact of local biodiversity loss on ecosystem functioning is well established, but the role of larger-scale biodiversity dynamics in the delivery of ecosystem services remains poorly understood. Here we address this gap using a comprehensive dataset describing the supply of 16 cultural, regulating and provisioning ecosystem services in 150 European agricultural grassland plots, and detailed multi-scale data on land use and plant diversity. After controlling for land-use and abiotic factors, we show that both plot-level and surrounding plant diversity play an important role in the supply of cultural and aboveground regulating ecosystem services. In contrast, provisioning and belowground regulating ecosystem services are more strongly driven by field-level management and abiotic factors. Structural equation models revealed that surrounding plant diversity promotes ecosystem services both directly, probably by fostering the spill-over of ecosystem service providers from surrounding areas, and indirectly, by maintaining plot-level diversity. By influencing the ecosystem services that local stakeholders prioritized, biodiversity at different scales was also shown to positively influence a wide range of stakeholder groups. These results provide a comprehensive picture of which ecosystem services rely most strongly on biodiversity, and the respective scales of biodiversity that drive these services. This key information is required for the upscaling of biodiversity–ecosystem service relationships, and the informed management of biodiversity within agricultural landscapes.

One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness., Proceedings of the National Academy of Sciences of the United States of America, 119 (6), ISSN:0027-8424, ISSN:1091-6490

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

Understanding drivers and monitoring changes of biodiversity forms the basis for evidence-based management and policy recommendations that aim to reduce biodiversity loss and to ensure the delivery of ecosystem services on which we rely. Ecoacoustic monitoring can be applied across large spatial and temporal scales, offering the potential for less resource-intensive ecosystem monitoring. Indices of acoustic diversity have been shown to correlate with species richness of various taxa, as well as vegetation and landscape structure. In order to apply ecoacoustic monitoring at large scales we need to improve our understanding of the effects of local and regional land-use on acoustic patterns, and assess how temporal aspects of acoustic patterns can be integrated in these analyses. We hypothesized that land-use intensity and landscape structure affect species richness and composition of birds and orthopteran, and therefore indirectly affect acoustic diversity and composition. To test this, we set up autonomous recording systems in grassland plots along land-use intensity gradients in three regions in Germany. We applied structural equation modelling to analyze the direct and indirect drivers of acoustic diversity and composition, including mowing and grazing intensities, landscape diversity, distance to the nearest road, vascular plant species richness as well as bird and orthopteran species richness and composition. Single indices of acoustic diversity did not reflect changes in bird or orthopteran species richness, making them poor predictors for local land-use related changes in species richness in grassland plots. Acoustic composition (non-metric multidimensional scaling combination of mean monthly acoustic indices at different day phases), however, did relate to differences in landscape structure, land-use intensity, vocalizing species composition and orthopteran species richness. We propose the developed acoustic composition metric as suitable proxy to detect ecosystem changes in grasslands.

Litter decomposition is a key ecosystem function in forests and varies in response to a range of climatic, edaphic, and local stand characteristics. Disentangling the relative contribution of these factors is challenging, especially along large environmental gradients. In particular, knowledge of the effect of management options, such as tree planting density and species composition, on litter decomposition would be highly valuable in forestry. In this study, we made use of 15 tree diversity experiments spread over eight countries and three continents within the global TreeDivNet network. We evaluated the effects of overstory composition (tree identity, species/mixture composition and species richness), plantation conditions (density and age), and climate (temperature and precipitation) on mass loss (after 3 months and 1 year) of two standardized litters: high-quality green tea and low-quality rooibos tea. Across continents, we found that early-stage decomposition of the low-quality rooibos tea was influenced locally by overstory tree identity. Mass loss of rooibos litter was higher under young gymnosperm overstories compared to angiosperm overstories, but this trend reversed with age of the experiment. Tree species richness did not influence decomposition and explained almost no variation in our multi-continent dataset. Hence, in the young plantations of our study, overstory composition effects on decomposition were mainly driven by tree species identity on decomposer communities and forest microclimates. After 12 months of incubation, mass loss of the high-quality green tea litter was mainly influenced by temperature whereas the low-quality rooibos tea litter decomposition showed stronger relationships with overstory composition and stand age. Our findings highlight that decomposition dynamics are not only affected by climate but also by management options, via litter quality of the identity of planted trees but also by overstory composition and structure.

Unprecedented tree dieback across Central Europe caused by recent global change-type drought events highlights the need for a better mechanistic understanding of drought-induced tree mortality. Although numerous physiological risk factors have been identified, the importance of two principal mechanisms, hydraulic failure and carbon starvation, is still debated. It further remains largely unresolved how the local neighborhood composition affects individual mortality risk. We studied 9435 young trees of 12 temperate species planted in a diversity experiment in 2013 to assess how hydraulic traits, carbon dynamics, pest infestation, tree height and neighborhood competition influence individual mortality risk. Following the most extreme global change-type drought since record in 2018, one third of these trees died. Across species, hydraulic safety margins (HSMs) were negatively and a shift towards a higher sugar fraction in the non-structural carbohydrate (NSC) pool positively associated with mortality risk. Moreover, trees infested by bark beetles had a higher mortality risk, and taller trees a lower mortality risk. Most neighborhood interactions were beneficial, although neighborhood effects were highly species-specific. Species that suffered more from drought, especially Larix spp. and Betula spp., tended to increase the survival probability of their neighbors and vice versa. While severe tissue dehydration marks the final stage of drought-induced tree mortality, we show that hydraulic failure is interrelated with a series of other, mutually inclusive processes. These include shifts in NSC pools driven by osmotic adjustment and/or starch depletion as well as pest infestation and are modulated by the size and species identity of a tree and its neighbors. A more holistic view that accounts for multiple causes of drought-induced tree mortality is required to improve predictions of trends in global forest dynamics and to identify mutually beneficial species combinations.

Aims The stability of hillslopes is an essential ecosystem service, especially in alpine regions with soils prone to erosion. One key variable controlling hillslope stability is soil aggregate stability. We aimed at identifying dominant controls of vegetation parameters on aggregate stability and analysed their importance for soil aggregate stability during landscape development. Methods We quantified the aggregate stability coefficient (ASC) and measured plant cover, diversity, root mass and root length, density (RMD, RLD) along two chronosequences with contrasting bedrocks (siliceous, calcareous) in the Swiss Alps. Results We found that ASC developed slower along the calcareous chronosequence. Furthermore, we observed a significant positive effect of vegetation cover and diversity on ASC that was mediated via root density. These relationships developed in a time-depended manner: At young terrain ages, vegetation parameters had a strong effect on aggregate stability compared to older stages. Moreover, RLD was the most powerful predictor of ASC on young terrain, whereas on older moraines RMD became more important. Conclusions We highlight that root density plays a major role in governing ASC for soils differing in moraine ages. The changing importances of RLD and RMD for ASC development suggest different mechanistic linkages between vegetation and hillsope stability during landscape development.