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Climatic and edaphic controls over tropical forest diversity and vegetation carbon storage

  • 1.

    Dirzo, R. & Raven, P. H. Global state of biodiversity and loss. Annu. Rev. Environ. Resour. 28, 137–167 (2003).

    • Article
    • Google Scholar
  • 2.

    Bonan, G. B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).

  • 3.

    Poorter, L. et al. Diversity enhances carbon storage in tropical forests. Glob. Ecol. Biogeogr. 24, 1314–1328 (2015).

    • Article
    • Google Scholar
  • 4.

    Chave, J. Neutral theory and community ecology. Ecology Letters 7, 241–253 (2004).

  • 5.

    Jarzyna, M. A. & Jetz, W. Taxonomic and functional diversity change is scale dependent. Nat. Commun. 9, 1–6 (2018).

  • 6.

    Sullivan, M. J. P. et al. Diversity and carbon storage across the tropical forest biome. Sci. Rep. 7, 1–12 (2017).

  • 7.

    Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354 (2016).

  • 8.

    Ferreira, J. et al. Carbon-focused conservation may fail to protect the most biodiverse tropical forests. Nat. Clim. Chang. 8, 744–749 (2018).

  • 9.

    Tilman, D. The ecological consequences of changes in biodiversity: A search for general principles. Ecology 80, 1455–1474 (1999).

    • Google Scholar
  • 10.

    Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Syst. 45, 471–493 (2014).

    • Article
    • Google Scholar
  • 11.

    Fauset, S. et al. Hyperdominance in Amazonian forest carbon cycling. Nat. Commun. 6, 1–2 (2015).

  • 12.

    Ter Steege, H. et al. Hyperdominance in the Amazonian tree flora. Science 342, 1243092 (2013).

  • 13.

    Safi, K. et al. Understanding global patterns of mammalian functional and phylogenetic diversity. Philos. Trans. R. Soc. B Biol. Sci. 366, 2536–2544 (2011).

    • Article
    • Google Scholar
  • 14.

    Bunker, D. E. Species Loss and Aboveground Carbon Storage in a Tropical Forest. Science 310, 1029–1031 (2005).

  • 15.

    Figueiredo, F. O. G. et al. Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species. J. Biogeogr. 45, 190–200 (2018).

    • Article
    • Google Scholar
  • 16.

    Prada, C. M. et al. Soils and rainfall drive landscape-scale changes in the diversity and functional composition of tree communities in premontane tropical forest. J. Veg. Sci. 28, 859–870 (2017).

    • Article
    • Google Scholar
  • 17.

    Fayolle, A. et al. Geological substrates shape tree species and trait distributions in African moist forests. Plos One 7, e42381 (2012).

  • 18.

    Reich, P. B. The world-wide ‘fast-slow’ plant economics spectrum: A traits manifesto. J. Ecol. 102, 275–301 (2014).

    • Article
    • Google Scholar
  • 19.

    Quesada, C. A. & Lloyd, J. Soil–Vegetation Interactions in Amazonia. In: Interactions Between Biosphere, Atmosphere and Human Land Use in the Amazon Basin. (eds. Nagy, L., Forsberg, B. & Artaxo, P.) 267–299 (Springer, Berlin, Heidelberg, 2016).

  • 20.

    Bloom, A. J., Chapin, F. S. & Mooney, H. A. Resource limitation in plants – an economic analogy. Annu. Rev. Ecol. Syst. 363–392 (1985).

  • 21.

    Rowland, L. et al. The response of tropical rainforest dead wood respiration to seasonal drought. Ecosystems 16, 1294–1309 (2013).

  • 22.

    Fauset, S. et al. Drought-induced shifts in the floristic and functional composition of tropical forests in Ghana. Ecol. Lett. 15, 1120–1129 (2012).

  • 23.

    Quesada, C. A. et al. Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences 9, 2203–2246 (2012).

  • 24.

    Phillips, O. L. et al. Drought sensitivity of the amazon rainforest. Science 323, 1344–1347 (2009).

  • 25.

    Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).

  • 26.

    Johnson, M. O. et al. Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models. Glob. Chang. Biol. 22, 3996–4013 (2016).

  • 27.

    Ter Steege, H. et al. Continental-scale patterns of canopy tree composition and function across Amazonia. Nature 443, 444–447 (2006).

  • 28.

    Galbraith, D. et al. Residence times of woody biomass in tropical forests. Plant Ecology and Diversity 6, 139–157 (2013).

    • Article
    • Google Scholar
  • 29.

    Taylor, P. et al. Landscape-scale controls on aboveground forest carbon stocks on the Osa Peninsula, Costa Rica. Plos One 10, e0126748 (2015).

  • 30.

    Hofhansl, F. et al. Sensitivity of tropical forest aboveground productivity to climate anomalies in SW Costa Rica. Global Biogeochem. Cycles 28, 1437–1454 (2014).

  • 31.

    Slik, J. W. et al. Phylogenetic classification of the world’s tropical forests. Proc. Natl. Acad. Sci. USA 115, 1837–1842 (2018).

  • 32.

    Fricker, G. A., Wolf, J. A., Saatchi, S. S. & Gillespie, T. W. Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing. Ecol. Appl. 25, 1776–1789 (2015).

  • 33.

    Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. USA 108, 9899–9904 (2011).

  • 34.

    Rödig, E., Cuntz, M., Heinke, J., Rammig, A. & Huth, A. Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory. Glob. Ecol. Biogeogr. 26, 1292–1302 (2017).

    • Article
    • Google Scholar
  • 35.

    Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecology Letters 21, 989–1000 (2018).

  • 36.

    Werner, F. A. & Homeier, J. Is tropical montane forest heterogeneity promoted by a resource-driven feedback cycle? Evidence from nutrient relations, herbivory and litter decomposition along a topographical gradient. Funct. Ecol. 29, 430–440 (2015).

    • Article
    • Google Scholar
  • 37.

    Gray, M. Geodiversity: developing the paradigm. Proc. Geol. Assoc. 119, 287–298 (2008).

    • Article
    • Google Scholar
  • 38.

    Prada, C. M. & Stevenson, P. R. Plant composition associated with environmental gradients in tropical montane forests (Cueva de Los Guacharos National Park, Huila, Colombia). Biotropica 48, 568–576 (2016).

    • Article
    • Google Scholar
  • 39.

    Arellano, G., Cala, V. & Macía, M. J. Niche breadth of oligarchic species in Amazonian and Andean rain forests. J. Veg. Sci. 25, 1355–1366 (2014).

    • Article
    • Google Scholar
  • 40.

    Fayolle, A. et al. Patterns of tree species composition across tropical African forests. J. Biogeogr. 41, 2320–2331 (2014).

    • Article
    • Google Scholar
  • 41.

    Grau, O. et al. Nutrient-cycling mechanisms other than the direct absorption from soil may control forest structure and dynamics in poor Amazonian soils. Sci. Rep. 7, 45017 (2017).

  • 42.

    Soong, J. L. et al. Soil properties explain tree growth and mortality, but not biomass, across phosphorus-depleted tropical forests. Sci. Rep. 10, 2302, https://doi.org/10.1038/s41598-020-58913-8 (2020).

  • 43.

    Kraft, N. J. B., Metz, M. R., Condit, R. S. & Chave, J. The relationship between wood density and mortality in a global tropical forest data set. New Phytol. 188, 1124–1136 (2010).

  • 44.

    Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).

  • 45.

    Homeier, J., Breckle, S. W., Günter, S., Rollenbeck, R. T. & Leuschner, C. Tree diversity, forest structure and productivity along altitudinal and topographical gradients in a species-rich Ecuadorian montane rain forest. Biotropica 42, 140–148 (2010).

    • Article
    • Google Scholar
  • 46.

    Levine, N. M. et al. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. Proc. Natl. Acad. Sci. USA 113, 793–797 (2016).

  • 47.

    Sakschewski, B. et al. Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model. Glob. Chang. Biol. 21, 2711–2725 (2015).

  • 48.

    Muelbert, A. E. et al. Compositional response of Amazon forests to climate change. Global Change Biology 25, 39–56, https://doi.org/10.1111/gcb.14413 (2019).

  • 49.

    Falster, D. S., Brännström, Å., Westoby, M. & Dieckmann, U. Multitrait successional forest dynamics enable diverse competitive coexistence. Proc. Natl. Acad. Sci. USA 114, E2719–E2728 (2017).

  • 50.

    Gilbert, L. E. et al. The Southern Pacific Lowland Evergreen Moist Forest of the Osa Region. In Costa Rican Ecosystems (ed. Kappelle, M.) 360–411 (University Chicago Press, 2016).

  • 51.

    Quesada, F. J., Jiménez-Madrigal, Q., Zamora-Villalobos, N., Aguilar-Fernández, R. & González-Ramírez, J. Árboles de la Península de Osa. (Instituto Nacional de Biodiversidad, 1997).

  • 52.

    Weissenhofer, A. et al. Ecosystem diversity in the Piedras Blancas National Park and adjacent areas (Costa Rica), with the first vegetation map of the area. In: Natural and cultural history of the Golfo Dulce region, Costa Rica. Stapfia 88, zugleich Kataloge der Oberösterreichischen Landesmuseen NS 80 (2008).

  • 53.

    Lobo, J. et al. Effects of selective logging on the abundance, regeneration and short-term survival of Caryocar costaricense (Caryocaceae) and Peltogyne purpurea (Caesalpinaceae), two endemic timber species of southern Central America. For. Ecol. Manage. 245, 88–95 (2007).

    • Article
    • Google Scholar
  • 54.

    Pérez, S., Alvarado, A. & Ramírez, E. Manual Descriptivo del Mapa de Asociaciones de Subgrupos de Suelos de Costa Rica. San José, Costa Rica: Oficina de Planificación Sectorial Agropecuario, IGN/MAG/FAO. Escala 1:200,000. (1978).

  • 55.

    Herrera, W. Climate of Costa Rica. In: Costa Rican Ecosystems. (eds. Maarten Kappelle, M. & Lobo, R. G.) The University of Chicago Press, Chicago 60637. ISBN-13: 978-0-226-12164-2 (e-book), https://doi.org/10.7208/chicago/9780226121642.001.0001 (2016).

  • 56.

    Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).

  • 57.

    Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Chang. Biol. 20, 3177–3190 (2014).

  • 58.

    ASTER. Global Digital Elevation Map, https://doi.org/10.5067/ASTER/ASTGTM.002 (2009).

  • 59.

    Clark, D. B. & Clark, D. A. Landscape-scale variation in forest structure and biomass in a tropical rain forest. For. Ecol. Manage. 137, 185–198 (2000).

    • Article
    • Google Scholar
  • 60.

    Alder, D. & Synnott, T. Permanent sample plot techniques for mixed tropical forest. (University of Oxford, 1992).

  • 61.

    Malhi, Y. et al. An international network to monitor the structure, composition and dynamics of Amazonian forests (RAINFOR). J. Veg. Sci. 13, 439–450 (2002).

    • Article
    • Google Scholar
  • 62.

    Peacock, J., Baker, T. R., Lewis, S. L., Lopez‐Gonzalez, G. & Phillips, O. L. The RAINFOR database: monitoring forest biomass and dynamics. J. Veg. Sci. 18, 535–542 (2007).

    • Article
    • Google Scholar
  • 63.

    Zanne, A. E. et al. Data from: Towards a worldwide wood economics spectrum. Dryad Digital Repository. Dryad 235, 33 (2009).

    • Google Scholar
  • 64.

    Réjou-Méchain, M., Tanguy, A., Piponiot, C., Chave, J. & Hérault, B. Biomass: an r package for estimating above-ground biomass and its uncertainty in tropical forests. Methods Ecol. Evol. 8, 1163–1167 (2017).

    • Article
    • Google Scholar
  • 65.

    Martin, A. R. & Thomas, S. C. A reassessment of carbon content in tropical trees. Plos One 6, e23533 (2011).

  • 66.

    Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. J. Veg. Sci. 22, 610–613 (2011).

    • Article
    • Google Scholar
  • 67.

    Brundrett, M. C. Mycorrhizal associations and other means of nutrition of vascular plants: Understanding the global diversity of host plants by resolving conflicting information and developing reliable means of diagnosis. Plant and Soil 320, 37–77 (2009).

  • 68.

    Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis. (Academic Press, 2008).

  • 69.

    Valverde-Barrantes, O. J., Freschet, G. T., Roumet, C. & Blackwood, C. B. A worldview of root traits: the influence of ancestry, growth form, climate and mycorrhizal association on the functional trait variation of fine-root tissues in seed plants. New Phytol. 215, 1562–1573 (2017).

  • 70.

    Evett, S. R., Schwartz, R. C., Tolk, J. A. & Howell, T. A. Soil profile water content determination: spatiotemporal variability of electromagnetic and neutron probe sensors in access tubes. Vadose Zo. J. 8, 926–941 (2009).

  • 71.

    Hood-Nowotny, R., Umana, N. H.-N., Inselbacher, E., Oswald- Lachouani, P. & Wanek, W. Alternative methods for measuring inorganic, organic, and total dissolved nitrogen in soil. Soil Sci. Soc. Am. J. 74, 1018–1027 (2010).

  • 72.

    Peña, E. A. & Slate, E. H. Global Validation of Linear Model Assumptions. Journal of the American Statistical Association 101, 341–354 (2006).

  • 73.

    Fox, J. Teacher’s Corner: structural equation modeling with the sem package in R. Struct. Equ. Model. 13, 465–486 (2006).

  • 74.

    Lefcheck, J. S. PiecewiseSEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol Evol. 7, 573–579, https://doi.org/10.1111/2041-210X.12512 (2015).

    • Article
    • Google Scholar
  • 75.

    Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behavioral Ecology and Sociobiology 65, 23–35 (2011).

    • Article
    • Google Scholar
  • 76.

    Colman, B. P. & Schimel, J. P. Drivers of microbial respiration and net N mineralization at the continental scale. Soil Biol. Biochem. 60, 65–76 (2013).

  • 77.

    R Development Core Team. R: A Language and Environment for Statistical Computing. (2017).

  • 78.

    Schepaschenko, D. et al. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass. Sci. data 6, 198 (2019).

  • 79.

    Buchs, D. M. et al. Late Cretaceous to miocene seamount accretion and mélange formation in the osa and burica peninsulas (Southern Costa Rica): Episodic growth of a convergent margin. Geol. Soc. Spec. Publ. 328, 411–456 (2009).

  • 80.

    Open Source Geospatial Foundation. QGIS Geographic Information System Open Source. (2008).

  • 81.

    Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed‐effects models. Methods in Ecology and Evolution 4(2), 133–142 (2013).

    • Article
    • Google Scholar

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