Biomass-dominant species shape the productivity-diversity relationship in two temperate forests

International audienceAbstractKey messageA negative productivity-diversity relationship was determined for biomass-dominant species at the community level. This study thus supports the hypothesis in which the effects of individual species on the productivity-diversity relationships at the community level are related to their biomass density, an important functional trait.ContextThe productivity-diversity relationships have been extensively studied in various forest ecosystems, but key mechanisms underlying the productivity-diversity relationships still remain controversial.AimsThe objective of this study is to explore the productivity-diversity relationships at the community level, and to investigate the roles of individual species in shaping the community-level relationships between productivity and diversity under different forest types.MethodsThe study was conducted in two fully stem-mapped temperate mixed forest plots in Northeastern China: a natural secondary forest plot, and an old-growth forest plot. An individual-based study framework was used to estimate the productivity-diversity relationships at both species and community levels. A homogeneous Thomas point process was used to evaluate the significance of productivity-diversity relationship deviating from the neutral.ResultsAt the species level, most of the studied species exhibit neutral productivity-diversity relationship in both forest plots. The percentage of species showing negative productivity-diversity relationship approaches linearly a peak value for very close neighborhoods (the secondary forest plot: r = 3 m, 38%; the old-growth forest plot: r = 4 m, 42%), and then decreases gradually with increasing spatial scale. Interestingly, only a few species displayed positive productivity-diversity relationship within their neighborhoods. Dominant species mainly exhibit negative productivity-diversity relationship while tree species with lower importance values exhibit neutral productivity-diversity relationship in both forests. At the community level, a consistent pattern of productivity-diversity relationship was observed in both forests, where tree productivity is significantly negatively associated with local species richness. Four biomass-dominant species (Juglans mandshurica Maxim., Acer mono Maxim.,Ulmus macrocarpa Hance and Acer mandshuricum Maxim.) determined a negative productivity-diversity relationship at the community level in the secondary forest plot, but only one species (Juglans mandshurica) in the old-growth forest plot.ConclusionThe productivity-diversity relationship is closely related to the dominance of individual species at the species level. Moreover, this analysis is the first to report the roles of biomass-dominant species in shaping the productivity-diversity relationship at the community level

A constructive review of the State Forest Inventory in the Russian Federation

The State Forest Inventory (SFI) in the Russian Federation is a relatively new project that is little known in the English-language scientific literature. Following the stipulations of the Forest Act of 2006, the first SFI sample plots in this vast territory were established in 2007. The 34 Russian forest regions were the basic geographical units for all statistical estimates and served as a first-level stratification, while a second level was based on old inventory data and remotely sensed data. The sampling design was to consist of a simple random sample of 84,700 circular 500m(2) sample plots over forest land. Each sample plot consists of three nested concentric circular subplots with radii of 12.62, 5.64 and 2.82m and additional subplots for assessing and describing undergrowth, regeneration and ground vegetation. In total, 117 variables were to be measured or assessed on each plot.Although field work has begun, the methodology has elicited some criticism. The simple random sampling design is less efficient than a systematic design featuring sample plot clusters and a mix of temporary and permanent plots. The second-level stratification is mostly ineffective for increasing precision. Qualitative variables, which are not always essential, are dominant, while important quantitative variables are under-represented. Because of very slow progress, in 2018 the original plan was adjusted by reducing the number of permanent sample plots from 84,700 to 68,287 so that the first SFI cycle could be completed by 2020.Peer reviewe

Analysis of the Global Warming Potential of Biogenic CO2 Emission in Life Cycle Assessments

Biomass is generally believed to be carbon neutral. However, recent studies have challenged the carbon neutrality hypothesis by introducing metric indicators to assess the global warming potential of biogenic CO2 (GWPbio). In this study we calculated the GWPbio factors using a forest growth model and radiative forcing effects with a time horizon of 100 years and applied the factors to five life cycle assessment (LCA) case studies of bioproducts. The forest carbon change was also accounted for in the LCA studies. GWPbio factors ranged from 0.13–0.32, indicating that biomass could be an attractive energy resource when compared with fossil fuels. As expected, short rotation and fast-growing biomass plantations produced low GWPbio. Long-lived wood products also allowed more regrowth of biomass to be accounted as absorption of the CO2 emission from biomass combustion. The LCA case studies showed that the total life cycle GHG emissions were closely related to GWPbio and energy conversion efficiency. By considering the GWPbio factors and the forest carbon change, the production of ethanol and bio-power appeared to have higher GHG emissions than petroleum-derived diesel at the highest GWPbio

Latitudinal gradients and ecological drivers of β-diversity vary across spatial scales in a temperate forest region

CITATION: Zhang, C. et al. 2020. Latitudinal gradients and ecological drivers of β-diversity vary across spatial scales in a temperate forest region. Global Ecology and Biogeography, 29(7): 1257– 1264. doi:10.1111/geb.13101The original publication is available at https://onlinelibrary.wiley.com/journal/14668238Aim: Our understanding of the mechanisms driving β-diversity is still rather rudimentary. This study evaluates the influences of environmental filtering versus spatial scale of regional communities on β-diversity across latitudes. Location: North-eastern China. Methods: The β-diversity was calculated in each regional community. The spatial extent of these “regional communities”, which included five or 10 plots, was ≤ 140 km. A random assembly null model was used to assess the effects of species abundance distribution on the β-diversity. Moreover, the deviation of observed β-diversity from a null model (called β-deviation) was also assessed. The variations of the β values were partitioned into environmental, latitudinal and their joint effects. Results: The observed β-diversity declined with increasing latitude, although the β-deviations showed a non-monotonic pattern as the latitude increased at two studied scales. All the regional communities consisting of five or 10 local plots exhibited significantly positive β-deviations. The total amount of variation in β-deviations explained by environmental and latitudinal variables increased dramatically with increasing scale. A significant pure environmental effect was observed at both scales, explaining 30% of the variation in β-deviation for regional communities consisting of five local plots and 58.7% for regional communities consisting of 10 local plots. The spatial variation in precipitation primarily accounted for the β-gradient. Main conclusions: This is one of the few multiscale analyses to investigate latitudinal patterns and driving mechanisms of tree β-diversity in temperate forests. The β-deviation showed a similar trend of change with latitude, but the variation of β-deviation explained by the environments and latitude was highly dependent on the scale of regional communities studied. Environmental filtering and the spatial scale of regional communities jointly accounted for the β-gradient, with environmental filtering appearing to determine the high variation of species turnover along the latitudinal gradient.https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13101Publishers versio

Effects of density dependence in a temperate forest in northeastern China

CITATION: Yao, J., et al. 2016. Effects of density dependence in a temperate forest in northeastern China. Scientific Reports 6:32844, doi:10.1038/srep32844.The original publication is available at http://www.nature.com/srepENGLISH ABSTRACT: Negative density dependence may cause reduced clustering among individuals of the same species, and evidence is accumulating that conspecific density-dependent self-thinning is an important mechanism regulating the spatial structure of plant populations. This study evaluates that specific density dependence in three very large observational studies representing three successional stages in a temperate forest in northeastern China. The methods include standard spatial point pattern analysis and a heterogeneous Poisson process as the null model to eliminate the effects of habitat heterogeneity. The results show that most of the species exhibit conspecific density-dependent self-thinning. In the early successional stage 11 of the 16 species, in the intermediate successional stage 18 of the 21 species and in the old growth stage all 21 species exhibited density dependence after removing the effects of habitat heterogeneity. The prevalence of density dependence thus varies among the three successional stages and exhibits an increase with increasing successional stage. The proportion of species showing density dependence varied depending on whether habitat heterogeneity was removed or not. Furthermore, the strength of density dependence is closely related with species abundance. Abundant species with high conspecific aggregation tend to exhibit greater density dependence than rare species.https://www.nature.com/articles/srep32844Publisher's versio

Drivers of seedling survival in a temperate forest and their relative importance at three stages of succession

CITATION: Yan, Y. et al. 2015. Drivers of seedling survival in a temperate forest and their relative importance at three stages of succession. Ecology and Evolution, 5(19):4287–4299, doi:10.1002/ece3.1688.The original publication is available at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2045-7758Negative density dependence (NDD) and niche partitioning have been perceived as important mechanisms for the maintenance of species diversity. However, little is known about their relative contributions to seedling survival. We examined the effects of biotic and abiotic neighborhoods and the variations of biotic neighborhoods among species using survival data for 7503 seedlings belonging to 22 woody species over a period of 2 years in three different forest types, a half-mature forest (HF), a mature forest (MF), and an old-growth forest (OGF), each of these representing a specific successional stage in a temperate forest ecosystem in northeastern China. We found a convincing evidence for the existence of NDD in temperate forest ecosystems. The biotic and abiotic variables affecting seedlings survival change with successional stage, seedling size, and age. The strength of NDD for the smaller (<20 cm in height) and younger seedlings (1–2 years) as well as all seedlings combined varies significantly among species. We found no evidence that a community compensatory trend (CCT) existed in our study area. The results of this study demonstrate that the relative importance of NDD and habitat niche partitioning in driving seedling survival varies with seedling size and age and that the biotic and abiotic factors affecting seedlings survival change with successional stage.http://onlinelibrary.wiley.com/doi/10.1002/ece3.1688/fullPublisher's versio

Latitudinal patterns of forest ecosystem stability across spatial scales as affected by biodiversity and environmental heterogeneity

Our planet is facing a variety of serious threats from climate change that are unfolding unevenly across the globe. Uncovering the spatial patterns of ecosystem stability is important for predicting the responses of ecological processes and biodiversity patterns to climate change. However, the understanding of the latitudinal pattern of ecosystem stability across scales and of the underlying ecological drivers is still very limited. Accordingly, this study examines the latitudinal patterns of ecosystem stability at the local and regional spatial scale using a natural assembly of forest metacommunities that are distributed over a large temperate forest region, considering a range of potential environmental drivers. We found that the stability of regional communities (regional stability) and asynchronous dynamics among local communities (spatial asynchrony) both decreased with increasing latitude, whereas the stability of local communities (local stability) did not. We tested a series of hypotheses that potentially drive the spatial patterns of ecosystem stability, and found that although the ecological drivers of biodiversity, climatic history, resource conditions, climatic stability, and environmental heterogeneity varied with latitude, latitudinal patterns of ecosystem stability at multiple scales were affected by biodiversity and environmental heterogeneity. In particular, α diversity is positively associated with local stability, while β diversity is positively associated with spatial asynchrony, although both relationships are weak. Our study provides the first evidence that latitudinal patterns of the temporal stability of naturally assembled forest metacommunities across scales are driven by biodiversity and environmental heterogeneity. Our findings suggest that the preservation of plant biodiversity within and between forest communities and the maintenance of heterogeneous landscapes can be crucial to buffer forest ecosystems at higher latitudes from the faster and more intense negative impacts of climate change in the future

Co-limitation towards lower latitudes shapes global forest diversity gradients

Funding Information: 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. Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer Nature Limited.Peer reviewedPostprin

Native diversity buffers against severity of non-native tree invasions

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species$^{1,2}$. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies$^{3,4}$. Here, leveraging global tree databases$^{5,6,7}$, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions