Abstract
Reducing deforestation underpins global biodiversity conservation efforts. However, this focus on retaining forest cover overlooks the multitude of anthropogenic pressures that can degrade forest quality and imperil biodiversity. We use remotely sensed indices of tropical rainforest structural condition and associated human pressures to quantify the relative importance of forest cover, structural condition and integrity (the cumulative effect of condition and pressures) on vertebrate species extinction risk and population trends across the global humid tropics. We found that tropical rainforests of high integrity (structurally intact and under low pressures) were associated with lower likelihood of species being threatened and having declining populations, compared with forest cover alone (without consideration of condition and pressures). Further, species were more likely to be threatened or have declining populations if their geographic ranges contained high proportions of degraded forest than if their ranges contained lower proportions of forest cover but of high quality. Our work suggests that biodiversity conservation policies to preserve forest integrity are now urgently required alongside ongoing efforts to halt deforestation in the hyperdiverse humid tropics.
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Data availability
All datasets used in this paper are openly available via the citations identified in the Methods. Processed spreadsheets can be accessed at Zenodo https://doi.org/10.5281/zenodo.7036360.
Code availability
Python and R code to replicate geospatial and statistical analyses can also be accessed through the same Zenodo repository https://doi.org/10.5281/zenodo.7036360. Additional Python code to process species range maps before raster overlay and tabulation of area can be accessed at https://doi.org/10.5281/zenodo.5525586.
References
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).
Pillay, R. et al. Tropical forests are home to over half of the world’s vertebrate species. Front. Ecol. Environ. 20, 10–15 (2022).
Turubanova, S., Potapov, P. V., Tyukavina, A. & Hansen, M. C. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).
Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).
Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).
Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).
Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).
Hansen, A. et al. Global humid tropics forest structural condition and forest structural integrity maps. Sci. Data 6, 232 (2019).
Hansen, A. J. et al. A policy-driven framework for conserving the best of Earth’s remaining moist tropical forests. Nat. Ecol. Evol. 4, 1377–1384 (2020).
COP 11 Decision X/2. Strategic Plan for Biodiversity 2011–2020 (Convention on Biological Diversity, 2010).
New York Declaration on Forests (UN, 2014).
Transforming our World: The 2030 Agenda for Sustainable Development. A/RES/70/1 Resolution Adopted by the United Nations General Assembly (UN, 2015).
Adoption of the Paris Agreement. Proposal by the President. Draft Decision -/CP.21 (UNFCCC, 2015).
Hansen, A. J. et al. Toward monitoring forest ecosystem integrity within the post-2020 Global Biodiversity Framework. Conserv. Lett. 14, e12822 (2021).
Scholes, R. et al. (eds) Summary for Policymakers of the Assessment Report on Land Degradation and Restoration (IPBES, 2018).
First Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2021).
Williams, B. A. et al. Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth 3, 371–382 (2020).
The IUCN Red List of Threatened Species Version 2020–1 (IUCN, 2020).
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).
Ives, A. R. & Garland, T. Phylogenetic logistic regression for binary dependent variables. Syst. Biol. 59, 9–26 (2010).
Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).
Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).
Fletcher, R. & Fortin, M.-J. Spatial Ecology and Conservation Modeling: Applications with R (Springer, 2018). https://doi.org/10.1007/978-3-030-01989-1
Briant, G., Gond, V. & Laurance, S. G. W. Habitat fragmentation and the desiccation of forest canopies: a case study from eastern Amazonia. Biol. Conserv. 143, 2763–2769 (2010).
Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).
Pillay, R. et al. Using interview surveys and multispecies occupancy models to inform vertebrate conservation. Conserv. Biol. 36, e13832 (2022).
Agresti, A. Categorical Data Analysis (John Wiley and Sons, 2002).
Smith, A. C., Koper, N., Francis, C. M. & Fahrig, L. Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation. Landsc. Ecol. 24, 1271–1285 (2009).
Mittermeier, R. A. et al. Wilderness and biodiversity conservation. Proc. Natl Acad. Sci. USA 18, 10309–10313 (2003).
Turner, I. M. & Corlett, R. T. The conservation value of small, isolated fragments of lowland tropical rain forest. Trends Ecol. Evol. 11, 330–333 (1996).
Tulloch, A. I. T., Barnes, M. D., Ringma, J., Fuller, R. A. & Watson, J. E. M. Understanding the importance of small patches of habitat for conservation. J. Appl. Ecol. 53, 418–429 (2016).
Wintle, B. A. et al. Global synthesis of conservation studies reveals the importance of small habitat patches for biodiversity. Proc. Natl Acad. Sci. USA 116, 909–914 (2019).
Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).
Prugh, L. R., Hodges, K. E., Sinclair, A. R. E. & Brashares, J. S. Effect of habitat area and isolation on fragmented animal populations. Proc. Natl Acad. Sci. USA 105, 20770–20775 (2008).
Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).
Beyer, H. L., Venter, O., Grantham, H. S. & Watson, J. E. M. Substantial losses in ecoregion intactness highlight urgency of globally coordinated action. Conserv. Lett. 13, e12692 (2020).
Ehbrecht, M. et al. Global patterns and climatic controls of forest structural complexity. Nat. Commun. 12, 519 (2021).
França, F. et al. Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles. J. Appl. Ecol. 53, 1098–1105 (2016).
Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinction risk. Nat. Commun. 9, 4621 (2018).
Betts, M. G. et al. Forest degradation drives widespread avian habitat and population declines. Nat. Ecol. Evol. 6, 709–719 (2022).
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
Basset, Y. et al. Arthropod diversity in a tropical forest. Science 338, 1481–1484 (2012).
Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).
Newbold, T. et al. Ecological traits affect the response of tropical forest bird species to land-use intensity. Proc. R. Soc. B 280, 20122131 (2013).
Maron, M., Simmonds, J. S. & Watson, J. E. M. Bold nature retention targets are essential for the global environment agenda. Nat. Ecol. Evol. 2, 1194–1195 (2018).
Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411–413 (2020).
Bird Species Distribution Maps of the World Version 2018.1 (BirdLife International, accessed 16 August 2019).
Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).
González-del-Pliego, P. et al. Phylogenetic and trait-based prediction of extinction risk for data-deficient amphibians. Curr. Biol. 29, 1557–1563 (2019).
IUCN Habitats Classification Scheme Version 3.1 (IUCN, 2012).
Böhm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385 (2013).
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
Hansen, M. C. et al. Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data. Remote Sens. Environ. 185, 221–232 (2016).
Sanderson, E. W. et al. The human footprint and the last of the wild. Bioscience 52, 891–904 (2002).
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).
Di Marco, M., Watson, J. E. M., Possingham, H. P. & Venter, O. Limitations and trade-offs in the use of species distribution maps for protected area planning. J. Appl. Ecol. 54, 402–411 (2017).
Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2603–E2610 (2013).
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).
Sexton, J. O. et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digit. Earth 6, 427–448 (2013).
Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).
Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204, 23–31 (2016).
Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).
Ho, L. S. T. & Ané, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Verhoeven, K. J. F., Simonsen, K. L. & McIntyre, L. M. Implementing false discovery rate control: increasing your power. Oikos 108, 643–647 (2005).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Bivand, R. et al. spdep: Spatial dependence: weighting schemes, statistics and models. R package version 0.7-4 (2017).
Bjornstad, O. N. ncf: Spatial covariance functions. R package version 1.2-1 (2018).
Acknowledgements
This work was funded by the NASA Biodiversity and Ecological Forecasting Program under the 2016 ECO4CAST solicitation through grant NNX17AG51G to A.J.H., J.E., S.J.G, P.A.J., J.E.M.W. and O.V., the NASA Global Ecosystem Dynamics Investigation (NNL15AA03 to S.J.G.) and the NASA GEO solicitation (80NSSC18K0338 to P.A.J.).
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R.P. conceived this study with O.V., J.E.M.W. and A.J.H. providing major inputs. J.A.O. and R.P. developed the Python code for geospatial analyses. P.A.J. simulated potential error in mapping canopy cover and height for SCI and FSII data. N.P.R. processed change in SCI and forest cover loss data. P.G.D.P. provided amphibian range maps not available in the IUCN Red List. R.P. performed all geospatial and statistical analyses and wrote the manuscript. O.V., J.E.M.W., A.J.H., S.J.G., P.A.J., P.B., C.S., D.A., B.A.W., P.G.D.P., J.A.O., N.P.R., S.C.A., J.E. and A.L.S.V. reviewed and edited manuscript drafts.
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Extended data
Extended Data Fig. 1 The relative importance of forest integrity, structural condition, the human footprint or pressure, and forest cover on the odds of mammals, birds, reptiles, and amphibians being threatened and having declining population trends (for sample sizes, see Supplementary Table 1a).
Structural condition and human pressures considered together (that is, FSII) tended to be associated with lower odds of species extinction risk and declining population trends than either structural condition or human pressures individually. Point estimates represent median standardized odds ratios of species being threatened (circles) or having a declining population (squares), generated by exponentiating standardized coefficients (log odds) of 100 phylogenetic logistic regressions (Supplementary Table 3). The vertical dotted line represents an odds ratio of 1, denoting statistical non-significance. Error bars represent median 95% confidence intervals generated with 2,000 parametric bootstraps in each regression. Each regression was performed with one phylogenetic tree randomly drawn from 10,000 available trees for each taxonomic group. Separate models were parameterized for rainforest-obligate and rainforest-associated species for each response variable. Illustration credits: Steven Traver, Ferran Sayol, Birgit Szabo, and Jose Carlos Arenas-Monroy.
Extended Data Fig. 2 Predicted probabilities of tropical rainforest-obligate and associated mammal, bird, reptile, and amphibian species being threatened and having declining population trends as a function of two-way interactions between forest structural condition and integrity.
Species tended to be at higher risk of being threatened and having declining populations when high proportions of forest cover within their ranges were structurally intact but of low integrity (that is, under high human pressure) than when their ranges contained low proportions of forest cover in high structural condition and low human pressure. Median predicted probabilities were generated from 100 phylogenetic logistic regressions. See Supplementary Tables 1a and 6 for sample sizes and model estimates, respectively. Illustration credits: Steven Traver, Ferran Sayol, Birgit Szabo, and Jose Carlos Arenas-Monroy.
Extended Data Fig. 3 Predicted probabilities of rainforest-associated mammals, birds, reptiles, and amphibians being threatened and having declining population trends across the four biogeographic realms within the tropical rainforest biome.
The bar plots show the baseline probabilities in each realm estimated without consideration of either forest cover or integrity. The adjacent line plots show the probability of being threatened and having a declining population with increasing forest integrity after statistically controlling for the effects of forest cover (that is, among species with average area of forest cover within their ranges). Data points (1, threatened/declining; 0, not threatened/not declining) are vertically and horizontally jittered to reduce overlap. The bars and lines represent median predicted probabilities from 100 phylogenetic logistic regressions. Each regression was performed with one phylogenetic tree randomly drawn from 10,000 available trees for each taxonomic group. Error bars and the shaded areas of the lines represent median 95% confidence intervals generated with 2,000 parametric bootstraps in each regression. These results were mirrored in rainforest-obligate vertebrates (Fig. 3). See Supplementary Table 1b and Table 7 for sample sizes and model estimates, respectively. Illustration credits: Steven Traver, Ferran Sayol, Birgit Szabo, and Jose Carlos Arenas-Monroy.
Extended Data Fig. 4 Original and reclassified SCI and FSII datasets.
(a) The original SCI and FSII raster datasets from Hansen et al. 2019, 2020 and used in the main analyses presented here. The tropics lie between 23.5° N and 23.5° S latitudes (indicated by the dotted lines) but the tropical rainforest or humid tropical biome extends into the subtropics in some areas. (b) A reclassified SCI raster generated by simulating a + 20% error in canopy cover and height derived from multispectral satellite imagery (left). This +20% error reduced the number of pixels classified as high SCI (values 14–18), effectively simulating underestimates of canopy cover and height. (c) A reclassified SCI raster simulating a -20% error in canopy cover and height measurements (left). This -20% error increased the number of high SCI pixels, effectively simulating overestimates of canopy cover and height. See Supplementary Table 7 for original and reclassified thresholds of canopy cover and height. As with the original SCI, the Human Footprint was overlaid on both simulated SCI rasters to generate corresponding FSII rasters incorporating the assumed ±20% errors (b, c: right). All raster data were resampled from the original 30 m pixel resolution to 1 km (Methods). See Fig. 1 for model results estimated with the original datasets in Extended Data Fig. 4a, and Extended Data Figs. 5a, b for results estimated with the simulated datasets in Extended Data Figs. 4b, c.
Extended Data Fig. 5 Propagating simulated errors in mapping canopy cover and height with the SCI and FSII datasets to statistical models.
The relative importance of forest integrity, structural condition, and forest cover on the odds of mammals, birds, reptiles, and amphibians being threatened and having declining population trends. The underlying structural condition and integrity data for these analyses are a reclassified SCI raster generated by (a) simulating a + 20% error in canopy cover and height derived from multispectral satellite imagery. This +20% error reduced the number of pixels classified as high SCI (values 14–18), effectively simulating underestimates of canopy cover and height (Extended Data Fig. 4b) and (b) simulating a -20% error in canopy cover and height measurements. This -20% error increased the number of high SCI pixels, effectively simulating overestimates of canopy cover and height (Extended Data Fig. 4c). As with the original SCI, the Human Footprint was overlaid on both simulated SCI rasters to generate FSII rasters incorporating the assumed ±20% errors. Our overall conclusions remained robust to this simulated range of potential error in mapping canopy cover and height in the SCI and FSII datasets. Point estimates represent median standardized odds ratios of species being threatened (circles) or having a declining population (squares) generated by exponentiating standardized coefficients (log odds) of 100 phylogenetic logistic regressions. The vertical dotted line represents an odds ratio of 1, denoting statistical non-significance. Error bars represent median 95% confidence intervals generated with 2,000 parametric bootstraps in each regression. Each regression was performed with one phylogenetic tree randomly drawn from 10,000 available trees for each taxonomic group. Separate models were parameterized for rainforest-obligate and associated species for each response variable. See Supplementary Table 1a for sample sizes, Supplementary Table 8 for original and reclassified thresholds of canopy cover and height, and Supplementary Tables 9–10 for model estimates. Illustration credits: Steven Traver, Ferran Sayol, Birgit Szabo, and Jose Carlos Arenas-Monroy.
Extended Data Fig. 6 Pooling moderate structural condition and integrity forests with structurally intact and high-integrity forests.
The relative importance of forest integrity, structural condition, and forest cover on the odds of mammals, birds, reptiles, and amphibians being threatened and having declining population trends. In the main text, we calculated the area (km2) of structurally intact and high-integrity forests (SCI and FSII values 14–18), relative to the area of structurally degraded and low-integrity forests (SCI values 2–5 and FSII values 1–5) within species humid tropical ranges (Methods). Here, we conducted an additional analysis pooling the area of moderate structural condition and integrity forests (SCI and FSII values 6–13) with structurally intact and high-integrity forests and thereafter parameterizing an identical set of models as the main analyses. We were thus able to consider the entire gradient of forest quality when examining its effects on species extinction risk and declining populations. Our overall conclusions remained consistent, suggesting forests of moderate structural condition and integrity can have value for biodiversity conservation. Point estimates represent median standardized odds ratios of species being threatened (circles) or having a declining population (squares) generated by exponentiating standardized coefficients (log odds) of 100 phylogenetic logistic regressions. The vertical dotted line represents an odds ratio of 1, denoting statistical non-significance. Error bars represent median 95% confidence intervals generated with 2,000 parametric bootstraps in each regression. Each regression was performed with one phylogenetic tree randomly drawn from 10,000 available trees for each taxonomic group. Separate models were parameterized for rainforest-obligate and associated species for each response variable. See Supplementary Tables 1a and 11 for sample sizes and model estimates, respectively. Illustration credits: Steven Traver, Ferran Sayol, Birgit Szabo, and Jose Carlos Arenas-Monroy.
Extended Data Fig. 7 Alternative definitions of IUCN Threatened Status.
The relative importance of forest integrity, structural condition, and forest cover on the odds of mammals, birds, reptiles, and amphibians being threatened. In the main analyses, we considered a species to be threatened if it was classified in any one of the IUCN Red List categories Critically Endangered (CR), Endangered (EN) or Vulnerable (VU). Here, we performed additional analyses considering a species as threatened only if it was classified as (a) CR and (b) either CR or EN. For all analyses, we classified species in the Near Threatened and Least Concern categories as non-threatened. This allowed us to maintain the binary classification (threatened/non-threatened) of the response variable, which was necessary for the logistic regression analyses used in this paper. Our overall conclusions remained consistent across these different degrees of threat. Point estimates represent median standardized odds ratios of species being threatened, generated by exponentiating standardized coefficients (log odds) of 100 phylogenetic logistic regressions. The vertical dotted line represents an odds ratio of 1, denoting statistical non-significance. Error bars represent median 95% confidence intervals generated with 2,000 parametric bootstraps in each regression. Each regression was performed with one phylogenetic tree randomly drawn from 10,000 available trees for each taxonomic group. Separate models were parameterized for rainforest-obligate and associated species. See Supplementary Tables 13–14 for sample sizes and model estimates, respectively. Illustration credits: Steven Traver, Ferran Sayol, Birgit Szabo, and Jose Carlos Arenas-Monroy.
Extended Data Fig. 8 Excluding species designated as threatened under IUCN criteria A and B.
The relative importance of forest integrity, structural condition, and forest cover on the odds of mammals, birds, reptiles, and amphibians being threatened. These analyses were performed after excluding (a) 2,751 species listed as threatened in criterion B of the IUCN Red List of Threatened Species, and (b) 3,745 species listed as threatened in both criteria A and B of the IUCN Red List. We did not include declining population data in this analysis of potential circularity because the IUCN Red List criteria are not used for determining overall population trends. Point estimates represent median standardized odds ratios of species being threatened, generated by exponentiating standardized coefficients (log odds) of 100 phylogenetic logistic regressions. The vertical dotted line represents an odds ratio of 1, denoting statistical non-significance. Error bars represent median 95% confidence intervals generated with 2,000 parametric bootstraps in each regression. Each regression was performed with one phylogenetic tree randomly drawn from 10,000 available trees for each taxonomic group. Separate models were parameterized for rainforest-obligate and associated species. See Supplementary Tables 15–17 for sample sizes and model estimates. Illustration credits: Steven Traver, Ferran Sayol, Birgit Szabo, and Jose Carlos Arenas-Monroy.
Extended Data Fig. 9 Change in forest structural condition and forest cover loss.
The relative importance of change (degradation) in tropical rainforest structural condition and forest cover loss between 2012 and 2018 on the odds of mammal, bird, reptile, and amphibian species being threatened and having declining population trends. Point estimates represent median standardized odds ratios of species being threatened (circles) or having a declining population (squares) generated by exponentiating standardized coefficients (log odds) of 100 phylogenetic logistic regressions. The vertical dotted line represents an odds ratio of 1, denoting statistical non-significance. Error bars represent median 95% confidence intervals generated with 2,000 parametric bootstraps in each regression. Each regression was performed with one phylogenetic tree randomly drawn from 10,000 available trees for each taxonomic group. Separate models were parameterized for rainforest-obligate and associated species for each response variable. See Supplementary Tables 1a and 19 for sample sizes and model estimates, respectively. Illustration credits: Steven Traver, Ferran Sayol, Birgit Szabo, and Jose Carlos Arenas-Monroy.
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Pillay, R., Watson, J.E.M., Hansen, A.J. et al. Humid tropical vertebrates are at lower risk of extinction and population decline in forests with higher structural integrity. Nat Ecol Evol 6, 1840–1849 (2022). https://doi.org/10.1038/s41559-022-01915-8
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DOI: https://doi.org/10.1038/s41559-022-01915-8
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