Abstract
Tropical forests are some of the most biodiverse ecosystems in the world, yet their functioning is threatened by anthropogenic disturbances and climate change. Global actions to conserve tropical forests could be enhanced by having local knowledge on the forestsʼ functional diversity and functional redundancy as proxies for their capacity to respond to global environmental change. Here we create estimates of plant functional diversity and redundancy across the tropics by combining a dataset of 16 morphological, chemical and photosynthetic plant traits sampled from 2,461 individual trees from 74 sites distributed across four continents together with local climate data for the past half century. Our findings suggest a strong link between climate and functional diversity and redundancy with the three trait groups responding similarly across the tropics and climate gradient. We show that drier tropical forests are overall less functionally diverse than wetter forests and that functional redundancy declines with increasing soil water and vapour pressure deficits. Areas with high functional diversity and high functional redundancy tend to better maintain ecosystem functioning, such as aboveground biomass, after extreme weather events. Our predictions suggest that the lower functional diversity and lower functional redundancy of drier tropical forests, in comparison with wetter forests, may leave them more at risk of shifting towards alternative states in face of further declines in water availability across tropical regions.
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Data availability
The vegetation census and plant functional traits data that support the findings of this study are available from their sources (www.ForestPlots.net and gem.tropicalforests.ox.ac.uk/). To comply with the original data owners, the processed community-level data used in this study can be accessed through the corresponding author upon request.
Code availability
All relevant R functions and code used in this study are referred to in the Methods section and can be accessed at https://doi.org/10.5281/zenodo.6367982.
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Acknowledgements
This work is a product of the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk). J.A.-G. was funded by the Natural Environment Research Council (NERC; NE/T011084/1) and the Oxford University Jhon Fell Fund (10667). The traits field campaign was funded by a grant to Y.M. from the European Research Council (advanced grant GEM-TRAIT: 321131) under the European Union’s Seventh Framework Programme (FP7/2007–2013) with additional support from NERC grant NE/D014174/1 and NE/J022616/1 for traits work in Peru, NERC grant ECOFOR (NE/K016385/1) for traits work in Santarem, NERC grant BALI (NE/K016369/1) for plot and traits work in Malaysia and ERC advanced grant T-FORCES (291585) to O.L.P. for traits work in Australia. Plot setup in Ghana and Gabon was funded by a NERC grant NE/I014705/1 and by the Royal Society-Leverhulme Africa Capacity Building Programme. The Malaysia campaign was also funded by NERC grant NE/K016253/1. Plot inventories in Peru were supported by funding from the US National Science Foundation Long-Term Research in Environmental Biology program (LTREB; DEB 1754647) and the Gordon and Betty Moore Foundation Andes–Amazon Program. Plots inventories in Nova Xavantina (Brazil) were supported by the National Council for Scientific and Technological Development (CNPq), Long Term Ecological Research Program (PELD), process 441244/2016–5 and the Foundation of Research Support of Mato Grosso (FAPEMAT), Project ReFlor, process 589267/2016. During data collection, I.O.M. was supported by a Marie Curie Fellowship (FP7-PEOPLE-2012-IEF-327990). GEM trait data in Gabon were supported by the Gabon National Parks Agency. D.B. was funded by the Belgian American Educational Foundation (BAEF) and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 895799. W.D.K. acknowledges funding from the University of Amsterdam via a starting grant and through the Faculty Research Cluster ‘Global Ecology’. S.A.-B. acknowledges funding from The Leverhulme Trust—Royal Society of the United Kingdom (A130026) under the Water Stress, Ecosystem Function and tree FD in tropical African forests project. C.A.J. acknowledges support from the Brazilian National Research Council/CNPq (PELD process 403710/2012–0), NERC and the State of São Paulo Research Foundation/FAPESP as part of the projects Functional Gradient, PELD/BIOTA and ECOFOR (processes 2003/12595-7, 2012/51509-8 and 2012/51872-5, within the BIOTA/FAPESP Program—The Biodiversity Virtual Institute (www.biota.org.br); COTEC/IF 002.766/2013 and 010.631/2013 permits. B.S.M. was supported by the CNPq/PELD projects (number 441244/2016-5 and number 441572/2020-0) and CAPES (number 136277/2017-0). D.F.R.P.B. thanks the financial support from NERC (NE/K016253/1) for trait data collection in Sabah Malaysia. M.S. acknowledges funding for Andes Biodiversity and Ecosystem Research Group (ABERG) plot network from the US National Science Foundation (NSF) Long-Term Research in Environmental Biology (LTREB) 1754647, the Gordon and Betty Moore Foundation’s Andes to Amazon Initiative and RAINFOR. E.B, J.B. and Y.M. acknowledge the support from NERC under projects NE/K016431/1 and NE/S01084X/1. R.M.E. acknowledges support from the Sime Darby Foundation. Measurements and analysis include support from NERC (‘AMAZONICAʼ, NE/F005806; ‘BIO-REDʼ, NE/N012542/1; ARBOLES, NE/S011811/1), the Moore Foundation and the AfriTRON and RAINFOR networks. Y.M. is supported by the Jackson Foundation.
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J.A.-G. conceived the study, designed and carried out the analysis and wrote the first draft of the paper. E.B. contributed to the main ideas and design of the study. Y.M. conceived and implemented the GEM Network, obtained funding for most of the GEM traits field campaigns and commented on earlier versions of the manuscript. J.A.-G., E.B., I.O.M., D.B., J.J.C.-R., M.G.N.-M., S.B., J.E.N., F.E.O., N.N.B., V.M., J.W.D., K.H., A.F., R.G.-M., N.N., A.B.H.-M., D.G., B.S.-N., S.M.R., M.M.M.S., W.F.-R., A.S., T.R., C.A.J.G., S.M., K.A., G.P.A., L.P.B., D.F.R.P.B., L.A.C., B.J.E., R.M.E., J.F., K.J.J., C.A.J., B.H.M.-J., R.E.M., P.S.M., O.L.P., A.C.B., S.L.L., C.A.Q., B.S.M., W.D.K., M.S., Y.A.T., L.J.T.W., N.S., D.A.C., J.B., S.A.-B. and Y.M. participated in or coordinated vegetation, trait data and/or soil data collection or processed field data and commented on and approved the manuscript.
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Extended data
Extended Data Fig. 1 Violin plots of the plant functional traits and their value ranges across the study area.
The plant functional traits used in the study with raw trait values are shown (corresponding to n = 2461 individual trees) but these data were log-transformed prior to further analysis. The colours correspond to the field sampling areas where in situ traits collection plots are located; the Y axis shows the raw data values for each functional trait. Photosynthetic traits are Amax: Light-saturated maximum rates of net photosynthesis at saturated CO2 (2000 ppm CO2); μmol m-2 s-1), Asat: light-saturated rates of net photosynthesis at ambient CO2 concentration (400 ppm CO2; μmol m−2 s−1), RDark: dark respiration (μmol m−2 s−1). Leaf nutrient concentration traits are, Ca: leaf calcium (%), K: leaf potassium (%), Mg: leaf magnesium (%), N: leaf nitrogen (%), P: leaf phosphorus (%). Leaf morphological and structural traits are, A: leaf area (cm²), DM: leaf dry mass (g), FM: leaf fresh mass (g), LDMC: leaf dry matter content (mg/g), LWC: leaf water content (%), SLA: specific leaf area (m2/g), T: leaf thickness (mm), WD: wood density (g/cm3). No traits were collected in Mexico and were thus assigned to the vegetation censuses from other locations as explained in the methods section. Brazil -ST: Brazil Santarem, Brazil -NX: Brazil Nova Xavantina. The horizontal lines within each boxplot represent the mean trait value and the vertical lines encompass the first (25th) and third (75th) quartiles of the data distribution for each trait.
Extended Data Fig. 2 Density plots of the climatic and soil conditions that encompass each field sampling location where plant functional traits and vegetation censuses were collected.
The top density graph of each climatic and soil variable shows the values found across the tropical and subtropical dry and moist broadleaf forests. VPD: vapour pressure deficit, MCWD: maximum climatic water deficit, CEC: cation exchange capacity, Δ: change.
Extended Data Fig. 3 Spatial distribution of climatic and soil conditions across the tropical and subtropical dry and moist broadleaf forests.
MCWD: maximum climatic water deficit, VPD: vapour pressure deficit, CEC: soil cation exchange capacity, Clay: soil clay content. Δ: change.
Extended Data Fig. 4 Principal component analysis of the distribution of the plot locations in environmental space.
The PCA in (a) shows the distribution of plots in climatic and (b) in soil space. MCWD: average Maximum Climatic Water Deficit and VPD: average Vapour Pressure Deficit, ΔMCWD and ΔVPD: change in MCWD and VPD respectively between the 1958–1987 and 1988–2017 period. MCWD and VPD represent the full-term climatic conditions (1958–2017 period). CEC: cation exchange capacity and soil pH are highly correlated and only CEC is used for further analysis. Clay and sand are highly correlated and only clay is used for further analysis. Coloured ellipsoids in a) and b) encompass 95% of the distribution of the vegetation plots from each field sampling location.
Extended Data Fig. 5 Spatial predictions of functional diversity (FD) depicting the locations of vegetation plots (blue crosses) that were used to fit the statistical models.
The spatial predictions of morphological/structural (top panel), nutrients (middle panel) and photosynthetic (bottom panel) traits are shown. For details about the plots, their location and climatic and soil conditions see Supplementary Table S1).
Extended Data Fig. 6 Spatial predictions of functional redundancy (FRed) depicting the locations of vegetation plots (blue crosses) that were used to fit the statistical models.
The spatial predictions of morphological/structural (top panel), nutrients (middle panel) and photosynthetic (bottom panel) traits are shown. For details about the plots, their location and climatic and soil conditions see Supplementary Table S1).
Extended Data Fig. 7 Bivariate maps combining the functional diversity (FD) and redundancy (FRed) for the morphological traits.
Each map shows the predictions obtained using the full dataset (full model, top panel) and the changes that occur by leaving the plots from each continent out of the model. The second panel shows the spatial predictions when leaving the records from the Americas out of model fitting, the third panel when leaving records from Africa out and the bottom panel when leaving the records from Asia and Australia out from model fitting.
Extended Data Fig. 8 Bivariate maps combining the functional diversity (FD) and redundancy (FRed) for the nutrients traits.
Each map shows the predictions obtained using the full dataset (Full model) and the changes that occur by leaving the plots from each continent out of the model. The second panel shows the spatial predictions when leaving the records from the Americas out of model fitting, the third panel when leaving records from Africa out and the bottom panel when leaving the records from Asia and Australia out from model fitting.
Extended Data Fig. 9 Bivariate maps combining the functional diversity (FD) and redundancy (FRed) for the photosynthesis traits.
Each map shows the predictions obtained using the full dataset (Full model) and the changes that occur by leaving the plots from each continent out of the model. The second panel shows the spatial predictions when leaving the records from the Americas out of model fitting, the third panel when leaving records from Africa out and the bottom panel when leaving the records from Asia and Australia out from model fitting.
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Aguirre‐Gutiérrez, J., Berenguer, E., Oliveras Menor, I. et al. Functional susceptibility of tropical forests to climate change. Nat Ecol Evol 6, 878–889 (2022). https://doi.org/10.1038/s41559-022-01747-6
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DOI: https://doi.org/10.1038/s41559-022-01747-6
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