Abstract
Tropical cloud forests (TCFs) are one of the worldâs most species- and endemism-rich terrestrial ecosystems. TCFs are threatened by direct human pressures and climate change, yet the fate of these extraordinary ecosystems remains insufficiently quantified. With discussions of the post-2020 biodiversity framework underway, TCFs are a defining test case of the success and promise of recent policy targets and their associated mechanisms to avert the global biodiversity crisis. Here we present a global assessment of the recent status and trends of TCFs and their biodiversity and evaluate the efficacy of current protection measures. We find that cloud forests occupied 0.4% of the global land surface in 2001 and harboured ~3,700 species of birds, mammal, amphibians and tree ferns (~15% of the global diversity of those groups), with half of those species entirely restricted to cloud forests. Worldwide, ~2.4% of cloud forests (in some regions, more than 8%) were lost between 2001 and 2018, especially in readily accessible places. While protected areas have slowed this decline, a large proportion of loss in TCF cover is still occurring despite formal protection. Increased conservation efforts are needed to avert the impending regional or global demise of TCFs and their unique biodiversity.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 /Â 30Â days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Biodiversity data related to this study are available at Map of Life (www.mol.org). Cloud forest predictions are available at http://www.earthenv.org/cloudforest.
Code availability
Codes are available at: https://gitlabext.wsl.ch/karger/cloudforests.
References
Bruijnzeel, L. A., Scatena, F. N. & Hamilton, L. S. (eds) Tropical Montane Cloud Forests: Science for Conservation and Management (Cambridge Univ. Press, 2011); https://doi.org/10.1017/CBO9780511778384
Mulligan, M. in Tropical Montane Cloud Forests: Science for Conservation and Management (eds Bruijnzeel, L. A. et al.) 14â38 (Cambridge Univ. Press, 2011); https://doi.org/10.1017/CBO9780511778384.004
Doumenge, C., Gilmour, D., Pérez, M. R. & Blockhus, J. in Tropical Montane Cloud Forests (eds Hamilton, L. S. et al.) 24â37 (Springer-Verlag, 1995).
Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079â1087 (2011).
Bruijnzeel, L. A., Mulligan, M. & Scatena, F. N. Hydrometeorology of tropical montane cloud forests: emerging patterns. Hydrol. Process. 25, 465â498 (2011).
Gentry, A. H. Tropical forest biodiversity: distributional patterns and their conservational significance. Oikos 63, 19â28 (1992).
Foster, P. The potential negative impacts of global climate change on tropical montane cloud forests. Earth-Sci. Rev. 55, 73â106 (2001).
Hamilton, L. S., Juvik, J. O. & Scatena, F. N. in Tropical Montane Cloud Forests (eds Hamilton, L. S. et al.) 1â18 (Springer-Verlag, 1995).
Ponce-Reyes, R. et al. Vulnerability of cloud forest reserves in Mexico to climate change. Nat. Clim. Change 2, 448â452 (2012).
Swenson, J. J. et al. Plant and animal endemism in the eastern Andean slope: challenges to conservation. BMC Ecol. 12, 1 (2012).
Gould, W. A., González, G. & Rivera, G. C. Structure and composition of vegetation along an elevational gradient in Puerto Rico. J. Veg. Sci. 17, 653â664 (2006).
Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441â444 (2017).
Paulsen, J. & Körner, C. A climate-based model to predict potential treeline position around the globe. Alp. Bot. 124, 1â12 (2014).
Jarvis, A. & Mulligan, M. The climate of cloud forests. Hydrol. Process. 25, 327â343 (2011).
Scatena, F. N., Bruijnzeel, L. A., Bubb, P. & Das, S. in Tropical Montane Cloud Forests: Science for Conservation and Management (eds Bruijnzeel, L. A. et al.) 3â13 (Cambridge Univ. Press, 2011); https://doi.org/10.1017/CBO9780511778384.003
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850â853 (2013).
Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1â15 (2017).
Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151â159 (2012).
Gillespie, R. G. et al. Long-distance dispersal: a framework for hypothesis testing. Trends Ecol. Evol. 27, 47â56 (2012).
Kreft, H., Jetz, W., Mutke, J. & Barthlott, W. Contrasting environmental and regional effects on global pteridophyte and seed plant diversity. Ecography 33, 408â419 (2010).
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).
Venter, Z. S., Cramer, M. D. & Hawkins, H.-J. Drivers of woody plant encroachment over Africa. Nat. Commun. 9, 2272 (2018).
Lawton, R. O., Nair, U. S., Pielke, R. A. & Welch, R. M. Climatic impact of tropical lowland deforestation on nearby montane cloud forests. Science 294, 584â587 (2001).
Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).
Guo, W.-Y. et al. Half of the worldâs tree biodiversity is unprotected and is increasingly threatened by human activities. Preprint at bioRxiv https://doi.org/10.1101/2020/04.21.052464 (2020).
Helmer, E. H. et al. Neotropical cloud forests and páramo to contract and dry from declines in cloud immersion and frost. PLoS ONE 14, e0213155 (2019).
Peters, M. K. et al. Climateâland-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88â92 (2019).
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108â1111 (2018).
Seneviratne, S. I. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109â230 (Cambridge Univ. Press, 2012).
Foley, J. A. et al. Global consequences of land use. Science 309, 570â574 (2005).
Beusekom, A. E. V., González, G. & Scholl, M. A. Analyzing cloud base at local and regional scales to understand tropical montane cloud forest vulnerability to climate change. Atmos. Chem. Phys. 17, 7245â7259 (2017).
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788â791 (2018).
Gross, J. E., Goetz, S. J. & Cihlar, J. Application of remote sensing to parks and protected area monitoring: introduction to the special issue. Remote Sens. Environ. 113, 1343â1345 (2009).
Visconti, P. et al. Protected area targets post-2020. Science 364, 239â241 (2019).
Di Minin, E. & Toivonen, T. Global protected area expansion: creating more than paper parks. BioScience 65, 637â638 (2015).
Wetzel, F. T., Beissmann, H., Penn, D. J. & Jetz, W. Vulnerability of terrestrial island vertebrates to projected sea-level rise. Glob. Change Biol. 19, 2058â2070 (2013).
Keil, P., Storch, D. & Jetz, W. On the decline of biodiversity due to area loss. Nat. Commun. 6, 8837 (2015).
Rybicki, J. & Hanski, I. Speciesâarea relationships and extinctions caused by habitat loss and fragmentation. Ecol. Lett. 16, 27â38 (2013).
Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827â832 (2015).
Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270â275 (2017).
Wilson, E. O. Half-Earth: Our Planetâs Fight for Life (WW Norton & Company, 2016).
Liu, J. et al. Complexity of coupled human and natural systems. Science 317, 1513â1516 (2007).
Schulze, K., Malek, Ž. & Verburg, P. H. Towards better mapping of forest management patterns: a global allocation approach. For. Ecol. Manage. 432, 776â785 (2019).
Curtis, C. A., Pasquarella, V. J. & Bradley, B. A. Landscape characteristics of non-native pine plantations and invasions in southern Chile. Austral Ecol. 44, 1213â1224 (2019).
Aldrich, M., Billington, C., Edwards, M. & Laidlaw, R. A Global Directory of Tropical Montane Cloud Forests (WCMC, 1997).
Karger, D. N. et al. Climatologies at high resolution for the earthâs land surface areas. Sci. Data 4, 170122 (2017).
Karger, D. N. et al. Data from: Climatologies at high resolution for the earthâs land surface areas. Dryad https://doi.org/10.5061/dryad.kd1d4 (2017).
Danielson, J. J. & Gesch, D. B. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) Open-File Report No. 2011-1073 (USGS, 2011).
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147â186 (2000).
Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993â1009 (2005).
Karmalkar, A. V., Bradley, R. S. & Diaz, H. F. Climate Change scenario for Costa Rican montane forests. Geophys. Res. Lett. 35, L11702 (2008).
Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).
Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N. & Zimmermann, N. E. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 10, 1446 (2019).
Heikkinen, R. K. et al. Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog. Phys. Geogr. 30, 751â777 (2006).
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43â57 (2011).
Fithian, W. & Hastie, T. Finite-sample equivalence in statistical models for presence-only data. Ann. Appl. Stat. 7, 1917â1939 (2013).
Nelder, J. A. & Wedderburn, R. W. M. Generalized linear models. J. R. Stat. Soc. Ser. A 135, 370â384 (1972).
Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics and Applied Probability, 1990).
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3, 327â338 (2012) .
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223â1232 (2006).
Aide, T. M. et al. Deforestation and reforestation of Latin America and the Caribbean (2001â2010). Biotropica 45, 262â271 (2013).
Aide, T. M., Ruiz-Jaen, M. C. & Grau, H. R. in Tropical Montane Cloud Forests: Science for Conservation and Management (eds Bruijnzeel, L. A. et al.) 101â109 (Cambridge Univ. Press, 2011).
Schwartz, N. B., Aide, T. M., Graesser, J., Grau, H. R. & Uriarte, M. Reversals of reforestation across Latin America limit climate mitigation potential of tropical forests. Front. For. Glob. Change 3, 85 (2020).
Bubb, P. et al. Cloud Forest Agenda (UNEP-WCMC, 2004); https://www.unep-wcmc.org/cloud-forest-agenda
Bockor, I. Analyse von Baumartenzusammensetzung und Bestandes-struckturen eines andinen Wolkenwaldes in Westvenezuela als Grundlagezur Wald-typengliederung. PhD thesis, Univ. Göttingen (1979).
The State of the Worldâs Forests 2020: Forests, Biodiversity and People (FAO & UNEP, 2020); https://doi.org/10.4060/ca8642en
Ribas, L. G., dos, S., Pressey, R. L., Loyola, R. & Bini, L. M. A global comparative analysis of impact evaluation methods in estimating the effectiveness of protected areas. Biol. Conserv. 246, 108595 (2020).
Schleicher, J. et al. Statistical matching for conservation science. Conserv. Biol. 34, 538â549 (2020).
Khandker, S., B. Koolwal, G. & Samad, H. Handbook on Impact Evaluation: Quantitative Methods and Practices (World Bank, 2009).
Barber, C. P., Cochrane, M. A., Souza, C. M. & Laurance, W. F. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biol. Conserv. 177, 203â209 (2014).
Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl Acad. Sci. USA 105, 16089â16094 (2008).
Laurance, W. F. et al. Predictors of deforestation in the Brazilian Amazon. J. Biogeogr. 29, 737â748 (2002).
Etter, A., McAlpine, C., Wilson, K., Phinn, S. & Possingham, H. Regional patterns of agricultural land use and deforestation in Colombia. Agric. Ecosyst. Environ. 114, 369â386 (2006).
Geist, H. J. & Lambin, E. F. What drives tropical deforestation? LUCC Report Series No. 4 (LUCC, 2001).
Nelson, A. et al. A suite of global accessibility indicators. Sci. Data 6, 266 (2019).
Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).
Körner, C., Paulsen, J. & Spehn, E. M. A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. Alp. Bot. 121, 73 (2011).
The IUCN Red List of Threatened Species version 2016.1 (IUCN, 2016); http://www.iucnredlist.org
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444â448 (2012).
Storch, D., Keil, P. & Jetz, W. Universal speciesâarea and endemicsâarea relationships at continental scales. Nature 488, 78â81 (2012).
Drakare, S., Lennon, J. J. & Hillebrand, H. The imprint of the geographical, evolutionary and ecological context on speciesâarea relationships. Ecol. Lett. 9, 215â227 (2006).
Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246â250 (2018).
Acknowledgements
We are grateful to A. Ranipeta and J. Wilshire for help with web visualizations. D.N.K. acknowledges funding from the Swiss Federal Research Institute for Forest, Snow and Landscape Research internal grant exCHELSA, ClimEx, the Joint BiodivERsA COFUND Call on âBiodiversity and Climate Changeâ (project âFeedBaCksâ) with the national funder Swiss National Foundation (20BD21_193907), the ERA-NET BiodivERsAâBelmont Forum with the national funder Swiss National Foundation (20BD21_184131) (part of the 2018 Joint call BiodivERsAâBelmont Forum call; project âFutureWebâ), and the Swiss Data Science Projects SPEEDMIND and COMECO. W.J. acknowledges support from NSF grant DEB-1441737, NASA grants 80NSSC17K0282 and 80NSSC18K0435, and the EO Wilson Biodiversity Foundation.
Author information
Authors and Affiliations
Contributions
D.N.K. and W.J. conceived of and planned the study, W.J. prepared the biodiversity data, D.N.K. prepared the climatic layers and ran the models and analyses, M.L. and M.K. contributed the tree-fern data and D.N.K. and W.J. wrote the paper.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Nature Ecology & Evolution thanks Reuben Clements, Johanna Eklund and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisherâs note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Exact TCF region delineation polygons for which TCF areas and biodiversity data has been extracted.
Numbers indicate the ID of the respective TCF region. For TCF region names see Extended Data Fig. 1.
Extended Data Fig. 2 Probability of cloud forests occurrence P(TCF) with respect to elevation based on a random sample of 55,000 1âkm2 raster cells.
a, Elevational distribution of TCF loss with elevation. b, In both graphs, red lines are visual characterizations of the overall elevational trend, based on lowess regression.
Extended Data Fig. 3 Uniqueness in species composition of strictly TCF confined species among TCF regions for the four groups assessed by using the minimum Jaccard dissimilarity index for a given region compared to all other regions.
High values indicate that a TCF region is unique in their assemblage, while low values indicate that it shares a large amount of species with at least one other region. Numbers indicate the ID of the TCF region. For TCF region names see Fig. S4. For turnover between TCF associated species, see Fig. 3.
Extended Data Fig. 4 TCF area of within a region, compared to the number of species which are associated, or strictly confined to TCFs in said region separately for each group.
Yellow = Mammals, blue = Birds, green = Tree ferns, red = Amphibians. Numbers indicate the TCF region ID.
Extended Data Fig. 5 Comparison of tropical cloud forest (TCF) loss before the establishment of a protected area (PA) and after its establishment for 483 PAs that got established between 2002 and 2018.
The color indicates the year in which a shift in designation happened according to the World Databank of Protected Areas (WDPA).
Extended Data Fig. 6 Percentage change in TCF area in protected areas based on their respective IUCN category for the years 2001 â 2018.
Letters indicate significantly different groups based on a Tukey post-hoc testing for differences between IUCN categories. Colors indicate different groups. NAâ=âNot assigned, NAp = Not applicable, NRâ=âNot reported.
Extended Data Fig. 7 Variation among TCF regions in overall 2001 area (bars), relative protection (dark part = protected fraction, white part = unprotected fraction), 2001-18 area loss and corresponding expected region-level species loss.
TCF regions are organized by their expected species loss (from top = highest loss, to bottom lowest loss). TCF region names are given with their IDs (see Fig. 1) and colored by major continent (see bottom legend). On the left, labels provide the names (ISO3166 country codes51) and the relative (percentage) area countries hold of each TCF region (i.e their respective TCF region stewardship). On the right, species loss expectations are based on an EAR z value of 0.5 (violet boxes). The violet shading indicates the range of all possible outcomes of the EAR when species loss for all key TCF groups analyzed (birds, mammals, amphibians, treeferns) using a range of zâ=â0.1 to zâ=â0.9.
Supplementary information
Rights and permissions
About this article
Cite this article
Karger, D.N., Kessler, M., Lehnert, M. et al. Limited protection and ongoing loss of tropical cloud forest biodiversity and ecosystems worldwide. Nat Ecol Evol 5, 854â862 (2021). https://doi.org/10.1038/s41559-021-01450-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41559-021-01450-y
This article is cited by
-
Conserving Southeast Asian trees requires mitigating both climate and land-use change
Nature Sustainability (2024)
-
Deforestation amplifies climate change effects on warming and cloud level rise in African montane forests
Nature Communications (2024)
-
Network analyses show horizontal and vertical distribution of vascular epiphytes on their hosts in a fragment of cloud forest in Central Mexico
Journal of Plant Research (2024)
-
Mountains host significantly more data deficient and threatened bat species than lowlands
Biodiversity and Conservation (2024)
-
Refuting the hypothesis of Centinelan extinction at its place of origin
Nature Plants (2024)