Ecology

Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).CAS 
PubMed 
Article 

Google Scholar 
Beech, E., Rivers, M., Oldfield, S. & Smith, P. P. GlobalTreeSearch: the first complete global database of tree species and country distributions. J. Sustain. 36, 454–489 (2017).Article 

Google Scholar 
ter Steege, H. et al. The discovery of the Amazonian tree flora with an updated checklist of all known tree taxa. Sci. Rep. 6, 29549 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).CAS 
PubMed 
Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
PubMed 
Article 

Google Scholar 
Maia, V. A. et al. The carbon sink of tropical seasonal forests in southeastern Brazil can be under threat. Sci. Adv. 6, eabd4548 (2020).CAS 
PubMed 
Article 

Google Scholar 
Malhi, Y. et al. The regional variation of aboveground live biomass in old‐growth Amazonian forests. Glob. Change Biol. 12, 1107–1138 (2006).Article 

Google Scholar 
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).CAS 
PubMed 
Article 

Google Scholar 
Malhi, Y. et al. Climate change, deforestation, and the fate of the Amazon. Science 319, 169–172 (2008).CAS 
PubMed 
Article 

Google Scholar 
Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595, 388–393 (2021).CAS 
PubMed 
Article 

Google Scholar 
Hisano, M., Searle, E. B. & Chen, H. Y. Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems. Biol. Rev. 93, 439–456 (2018).PubMed 
Article 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).PubMed 
Article 
CAS 

Google Scholar 
Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106, 20610–20615 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seager, R. et al. Climatology, variability, and trends in the US vapor pressure deficit, an important fire-related meteorological quantity. J. Appl. Meteorol. Climatol. 54, 1121–1141 (2015).Article 

Google Scholar 
Smith, M. N. et al. Empirical evidence for resilience of tropical forest photosynthesis in a warmer world. Nat. Plants 6, 1225–1230 (2020).CAS 
PubMed 
Article 

Google Scholar 
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Costa, F. R. C., Schietti, J., Stark, S. C. & Smith, M. N. The other side of tropical forest drought: do shallow water table regions of Amazonia act as large‐scale hydrological refugia from drought?. New Phytol. https://doi.org/10.1111/nph.17914 (2022).Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).CAS 
PubMed 
Article 

Google Scholar 
Allen, K. et al. Will seasonally dry tropical forests be sensitive or resistant to future changes in rainfall regimes? Environ. Res. Lett. 12, 023001 (2017).Article 

Google Scholar 
Esquivel‐Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25, 39–56 (2019).Article 

Google Scholar 
Aguirre‐Gutiérrez, J. et al. Drier tropical forests are susceptible to functional changes in response to a long‐term drought. Ecol. Lett. 22, 855–865 (2019).PubMed 
Article 

Google Scholar 
Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48.5, 1079–1087 (2011).Article 

Google Scholar 
Aguirre‐Gutiérrez, J. et al. Butterflies show different functional and species diversity in relationship to vegetation structure and land use. Glob. Ecol. Biogeogr. 26, 1126–1137 (2017).Article 

Google Scholar 
Arruda Almeida, B. et al. Comparing species richness, functional diversity and functional composition of waterbird communities along environmental gradients in the neotropics. PLoS ONE 13, e0200959 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Correia, D. L. P., Raulier, F., Bouchard, M. & Filotas, É. Response diversity, functional redundancy, and post‐logging productivity in northern temperate and boreal forests. Ecol. Appl. 28, 1282–1291 (2018).PubMed 
Article 

Google Scholar 
Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

Google Scholar 
Loreau, M. & de Mazancourt, C. Biodiversity and ecosystem stability: a synthesis of underlying mechanisms. Ecol. Lett. 16, 106–115 (2013).PubMed 
Article 

Google Scholar 
Petchey, O. L., Evans, K. L., Fishburn, I. S. & Gaston, K. J. Low functional diversity and no redundancy in British avian assemblages. J. Anim. Ecol. 76, 977–985 (2007).PubMed 
Article 

Google Scholar 
Jucker, T. et al. Stabilizing effects of diversity on aboveground wood production in forest ecosystems: linking patterns and processes. Ecol. Lett. 17, 1560–1569 (2014).PubMed 
Article 

Google Scholar 
Fonseca, C. R. & Ganade, G. Species functional redundancy, random extinctions and the stability of ecosystems. J. Ecol. 89, 118–125 (2001).Article 

Google Scholar 
Aguirre-Gutiérrez, J. et al. Long-term droughts may drive drier tropical forests towards increased functional, taxonomic and phylogenetic homogeneity. Nat. Commun. 11, 3346 (2020).PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
Laliberté, E. & Legendre, P. A distance‐based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 
Article 

Google Scholar 
Bauman, D. et al. Tropical tree growth sensitivity to climate is driven by species intrinsic growth rate and leaf traits. Glob. Change Biol. 28, 1414–1432 (2022).Article 

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

Google Scholar 
Bennett, A. C. et al. Resistance of African tropical forests to an extreme climate anomaly. Proc. Natl Acad. Sci. USA 118, e2003169118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (eds Shukla, P.R. et al.) (IPCC, 2019).Ashton, I. W., Miller, A. E., Bowman, W. D. & Suding, K. N. Niche complementarity due to plasticity in resource use: plant partitioning of chemical N forms. Ecology 91, 3252–3260 (2010).PubMed 
Article 

Google Scholar 
Petchey, O. L. On the statistical significance of functional diversity effects. Funct. Ecol. 18, 297–303 (2004).Article 

Google Scholar 
Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).Article 

Google Scholar 
ter Steege, H. et al. Continental-scale patterns of canopy tree composition and function across Amazonia. Nature 443, 444–447 (2006).PubMed 
Article 
CAS 

Google Scholar 
Raes, N. et al. Botanical richness and endemicity patterns of Borneo derived from species distribution models. Ecography 32, 180–192 (2009).Article 

Google Scholar 
Shenkin, A. et al. The influence of ecosystem and phylogeny on tropical tree crown size and shape. Front. For. Glob. Change 3, 501757 (2020).Article 

Google Scholar 
Harrison, S., Spasojevic, M. J. & Li, D. Climate and plant community diversity in space and time. Proc. Natl Acad. Sci. USA 117, 4464–4470 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Grossman, J. J., Cavender‐Bares, J., Hobbie, S. E., Reich, P. B. & Montgomery, R. A. Species richness and traits predict overyielding in stem growth in an early‐successional tree diversity experiment. Ecology 98, 2601–2614 (2017).PubMed 
Article 

Google Scholar 
Williams, L. J. et al. Remote spectral detection of biodiversity effects on forest biomass. Nat. Ecol. Evol. 5, 46–54 (2021).PubMed 
Article 

Google Scholar 
Hutchison, C., Gravel, D., Guichard, F. & Potvin, C. Effect of diversity on growth, mortality, and loss of resilience to extreme climate events in a tropical planted forest experiment. Sci. Rep. 8, 15443 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
González-M, R. et al. Diverging functional strategies but high sensitivity to an extreme drought in tropical dry forests. Ecol. Lett. 24, 451–463 (2021).PubMed 
Article 

Google Scholar 
Hoegh-Guldberg, O. et al. in IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) Ch. 3 (WMO, 2018).de la Riva, E. G. et al. The importance of functional diversity in the stability of Mediterranean shrubland communities after the impact of extreme climatic events. J. Plant Ecol. 10, 281–293 (2017).
Google Scholar 
Reich, P. B. The world-wide “fast-slow” plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).Article 

Google Scholar 
Oliveira, R. S. et al. Linking plant hydraulics and the fast–slow continuum to understand resilience to drought in tropical ecosystems. New Phytol. 230, 904–923 (2021).PubMed 
Article 

Google Scholar 
Anderegg, W. R. L. & Meinzer, F. C. in Functional and Ecological Xylem Anatomy (ed Hacke, U.) Ch. 9 (Springer, 2015).Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).PubMed 
Article 

Google Scholar 
Pratt, R., Jacobsen, A., Ewers, F. & Davis, S. Relationships among xylem transport, biomechanics and storage in stems and roots of nine Rhamnaceae species of the California chaparral. New Phytol. 174, 787–798 (2007).CAS 
PubMed 
Article 

Google Scholar 
Zanne, A. E. et al. Angiosperm wood structure: global patterns in vessel anatomy and their relation to wood density and potential conductivity. Am. J. Bot. 97, 207–215 (2010).PubMed 
Article 

Google Scholar 
Bucci, S. J. et al. The stem xylem of Patagonian shrubs operates far from the point of catastrophic dysfunction and is additionally protected from drought‐induced embolism by leaves and roots. Plant Cell Environ. 36, 2163–2174 (2013).CAS 
PubMed 
Article 

Google Scholar 
Meinzer, F. C. et al. Coordination of leaf and stem water transport properties in tropical forest trees. Oecologia 156, 31–41 (2008).PubMed 
Article 

Google Scholar 
Scholz F. G., Phillips N. G., Bucci S. J., Meinzer F. C. & Goldstein G. in Size- and Age-Related Changes in Tree Structure and Function (eds Meinzer F. C. C. et al.) 341–361 (Springer, 2011).Mitchell, P. J. et al. Using multiple trait associations to define hydraulic functional types in plant communities of south-western Australia. Oecologia 158, 385–397 (2008).PubMed 
Article 

Google Scholar 
Villagra, Mariana et al. Functional relationships between leaf hydraulics and leaf economic traits in response to nutrient addition in subtropical tree species. Tree Physiol. 33, 1308–1318 (2013).PubMed 
Article 

Google Scholar 
Ishida, Atsushi et al. Coordination between leaf and stem traits related to leaf carbon gain and hydraulics across 32 drought-tolerant angiosperms. Oecologia 156, 193–202 (2008).PubMed 
Article 

Google Scholar 
Malhi, Y. et al. The Global Ecosystems Monitoring network: monitoring ecosystem productivity and carbon cycling across the tropics. Biol. Conserv. 253, 108889 (2021).Article 

Google Scholar 
Martin, R. E. et al. Covariance of sun and shade leaf traits along a tropical forest elevation gradient. Front. Plant Sci. 10, 1810 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Enquist, B. J. et al. Assessing trait‐based scaling theory in tropical forests spanning a broad temperature gradient. Glob. Ecol. Biogeogr. 26, 1357–1373 (2017).Article 

Google Scholar 
Both, S. et al. Logging and soil nutrients independently explain plant trait expression in tropical forests. New Phytol. 221, 1853–1865 (2019).CAS 
PubMed 
Article 

Google Scholar 
Oliveras, I. et al. The influence of taxonomy and environment on leaf trait variation along tropical abiotic gradients. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2020.00018 (2020).Gvozdevaite, A. et al. Leaf-level photosynthetic capacity dynamics in relation to soil and foliar nutrients along forest–savanna boundaries in Ghana and Brazil. Tree Physiol. 38, 1912–1925 (2018).CAS 
PubMed 
Article 

Google Scholar 
Aguirre-Gutiérrez, J. et al. Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data. Remote Sens. Environ. 252, 112122 (2021).Article 

Google Scholar 
Pavoine, S. adiv: an R package to analyse biodiversity in ecology. Methods Ecol. Evol. 11, 1106–1112 (2020).Article 

Google Scholar 
Pavoine, S. & Ricotta, C. A simple translation from indices of species diversity to indices of phylogenetic diversity. Ecol. Ind. 101, 552–561 (2019).Article 

Google Scholar 
Ricotta, C. et al. Measuring the functional redundancy of biological communities: a quantitative guide. Methods Ecol. Evol. 7, 1386–1395 (2016).Article 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 
Article 
CAS 

Google Scholar 
van der Plas, F., Van Klink, R., Manning, P., Olff, H. & Fischer, M. Sensitivity of functional diversity metrics to sampling intensity. Methods Ecol. Evol. 8, 1072–1080 (2017).Article 

Google Scholar 
Rao, C. R. Diversity and dissimilarity coefficients: a unified approach. Theor. Popul. Biol. 21, 24–43 (1982).Article 

Google Scholar 
Simpson, E. H. Measurement of diversity. Nature https://doi.org/10.1038/163688a0 (1949).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Fan, Y. Groundwater in the earth’s critical zone: relevance to large-scale patterns and processes. Water Resour. Res. 51, 3052–3069 (2015).Article 

Google Scholar 
Moulatlet, G. M. et al. Using digital soil maps to infer edaphic affinities of plant species in Amazonia: problems and prospects. Ecol. Evol. 7, 8463–8477 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).Article 

Google Scholar 
Makowski, D., Ben-Shachar, M. S. & Lüdecke, D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).Article 

Google Scholar 
Kruschke, J. K. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (Academic Press, 2014). More

Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shi, Z. Gut microbiota: an important link between Western diet and chronic diseases. Nutrients 11, 2287 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).CAS 
PubMed 
Article 

Google Scholar 
Glowacki, R. W. P. & Martens, E. C. In sickness and health: effects of gut microbial metabolites on human physiology. PLoS Pathog. 16, e1008370 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nazaries, L. et al. Evidence of microbial regulation of biogeochemical cycles from a study on methane flux and land use change. Appl. Environ. Microbiol. 79, 4031–4040 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Konopka, A. What is microbial community ecology? ISME J. 3, 1223–1230 (2009).PubMed 
Article 

Google Scholar 
Flemming, H.-C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575 (2016).CAS 
PubMed 
Article 

Google Scholar 
Bauer, E., Zimmermann, J., Baldini, F., Thiele, I. & Kaleta, C. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput. Biol. 13, e1005544 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
van Hoek, M. J. A. & Merks, R. M. H. Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. BMC Syst. Biol. 11, 56 (2017).Article 
CAS 

Google Scholar 
Gorter, F. A., Manhart, M. & Ackermann, M. Understanding the evolution of interspecies interactions in microbial communities. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190256 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, J., Yoshinaga, M. & Rosen, B. P. The antibiotic action of methylarsenite is an emergent property of microbial communities. Mol. Microbiol. 111, 487–494 (2019).CAS 
PubMed 
Article 

Google Scholar 
Konstantinidis, D. et al. Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion. Mol. Syst. Biol. 17, e10189 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Park, H. et al. Artificial consortium demonstrates emergent properties of enhanced cellulosic-sugar degradation and biofuel synthesis. NPJ Biofilms Microbiomes 6, 59 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schwartzman, J. A. et al. Bacterial growth in multicellular aggregates leads to the emergence of complex lifecycles. Preprint at bioRxiv https://doi.org/10.1101/2021.11.01.466752 (2021).Levins, R. & Lewontin, R. The Dialectical Biologist (Harvard Univ. Press, 1985).Diaz, P. I. & Valm, A. M. Microbial interactions in oral communities mediate emergent biofilm properties. J. Dent. Res. 99, 18–25 (2020).CAS 
PubMed 
Article 

Google Scholar 
Buerger, A. N. et al. Gastrointestinal dysbiosis following diethylhexyl phthalate exposure in zebrafish (Danio rerio): altered microbial diversity, functionality, and network connectivity. Environ. Pollut. 265, 114496 (2020).CAS 
PubMed 
Article 

Google Scholar 
Kim, M. K., Ingremeau, F., Zhao, A., Bassler, B. L. & Stone, H. A. Local and global consequences of flow on bacterial quorum sensing. Nat. Microbiol. 1, 15005 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ebrahimi, A. & Or, D. Hydration and diffusion processes shape microbial community organization and function in model soil aggregates. Water Resour. Res. 51, 9804–9827 (2015).Article 

Google Scholar 
Falconer, R. E. et al. Microscale heterogeneity explains experimental variability and non-linearity in soil organic matter mineralisation. PLoS ONE 10, e0123774 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).CAS 
PubMed 
Article 

Google Scholar 
Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 160081 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Marino, S., Baxter, N. T., Huffnagle, G. B., Petrosino, J. F. & Schloss, P. D. Mathematical modeling of primary succession of murine intestinal microbiota. Proc. Natl Acad. Sci. USA 111, 439–444 (2014).CAS 
PubMed 
Article 

Google Scholar 
Cariboni, J., Gatelli, D., Liska, R. & Saltelli, A. The role of sensitivity analysis in ecological modelling. Ecol. Modell. 203, 167–182 (2007).Article 

Google Scholar 
Oreskes, N., Shrader-Frechette, K. & Belitz, K. Verification, validation, and confirmation of numerical models in the Earth sciences. Science 263, 641–646 (1994).CAS 
PubMed 
Article 

Google Scholar 
Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).CAS 
PubMed 
Article 

Google Scholar 
Hammarlund, S. P., Chacón, J. M. & Harcombe, W. R. A shared limiting resource leads to competitive exclusion in a cross-feeding system. Environ. Microbiol. 21, 759–771 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).CAS 
PubMed 
Article 

Google Scholar 
Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Current status and applications of genome-scale metabolic models. Genome Biol. 20, 121 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Brien, E. J., Monk, J. M. & Palsson, B. O. Using genome-scale models to predict biological capabilities. Cell 161, 971–987 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fang, X., Lloyd, C. J. & Palsson, B. O. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat. Rev. Microbiol. 18, 731–743 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Colarusso, A. V., Goodchild-Michelman, I., Rayle, M. & Zomorrodi, A. R. Computational modeling of metabolism in microbial communities on a genome-scale. Curr. Opin. Syst. Biol. 26, 46–57 (2021).Article 

Google Scholar 
García-Jiménez, B., Torres-Bacete, J. & Nogales, J. Metabolic modelling approaches for describing and engineering microbial communities. Comput. Struct. Biotechnol. J. 19, 226–246 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Frioux, C., Singh, D., Korcsmaros, T. & Hildebrand, F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes. Comput. Struct. Biotechnol. J. 18, 1722–1734 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chaffron, S., Rehrauer, H., Pernthaler, J. & von Mering, C. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 20, 947–959 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
PubMed 
Article 

Google Scholar 
Li, J. et al. Distinct mechanisms shape soil bacterial and fungal co-occurrence networks in a mountain ecosystem. FEMS Microbiol. Ecol. 96, fiaa030 (2020).CAS 
PubMed 
Article 

Google Scholar 
Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Stein, R. R. et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, e1003388 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Barbier, M., Arnoldi, J.-F., Bunin, G. & Loreau, M. Generic assembly patterns in complex ecological communities. Proc. Natl Acad. Sci. USA 115, 2156–2161 (2018).Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
PubMed 
Article 

Google Scholar 
Madeo, D., Comolli, L. R. & Mocenni, C. Emergence of microbial networks as response to hostile environments. Front. Microbiol. 5, 407 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Ratzke, C. & Gore, J. Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wang, B. & Allison, S. D. Emergent properties of organic matter decomposition by soil enzymes. Soil Biol. Biochem. 136, 107522 (2019).CAS 
Article 

Google Scholar 
Walsh, A. M. et al. Microbial succession and flavor production in the fermented dairy beverage kefir. mSystems 1, e00052-16 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Oliveira, N. M., Niehus, R. & Foster, K. R. Evolutionary limits to cooperation in microbial communities. Proc. Natl Acad. Sci. USA 111, 17941–17946 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leigh, E. R. in Some Mathematical Problems in Biology (ed. Gerstenhaber, M.) 1–61 (American Mathematical Society, 1968).Nedorezov, L. The dynamics of the lynx–hare system: an application of the Lotka–Volterra model. Biophys. 61, 149–154 (2016).CAS 
Article 

Google Scholar 
Mühlbauer, L. K., Schulze, M., Harpole, W. S. & Clark, A. T. gauseR: simple methods for fitting Lotka–Volterra models describing Gause’s “struggle for existence”. Ecol. Evol. 10, 13275–13283 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Belovsky, G. E. Moose and snowshoe hare competition and a mechanistic explanation from foraging theory. Oecologia 61, 150–159 (1984).CAS 
PubMed 
Article 

Google Scholar 
May, R. M. Limit cycles in predator–prey communities. Science 177, 900–902 (1972).CAS 
PubMed 
Article 

Google Scholar 
Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 109 (2017).PubMed 
Article 

Google Scholar 
Voit, E. O., Davis, J. D. & Olivença, D. V. Inference and validation of the structure of Lotka–Volterra models. Preprint at bioXriv https://doi.org/10.1101/2021.08.14.456346 (2021).Bucci, V. & Xavier, J. B. Towards predictive models of the human gut microbiome. J. Mol. Biol. 426, 3907–3916 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bucci, V. et al. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gao, X., Huynh, B.-T., Guillemot, D., Glaser, P. & Opatowski, L. Inference of significant microbial interactions from longitudinal metagenomics data. Front. Microbiol. 9, 2319 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Li, C. et al. An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data. Microbiome 7, 118 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Joseph, T. A., Shenhav, L., Xavier, J. B., Halperin, E. & Pe’er, I. Compositional Lotka–Volterra describes microbial dynamics in the simplex. PLoS Comput. Biol. 16, e1007917 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hosoda, S., Fukunaga, T. & Hamada, M. Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model. Bioinformatics 37, i16–i24 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Remien, C. H., Eckwright, M. J. & Ridenhour, B. J. Structural identifiability of the generalized Lotka–Volterra model for microbiome studies. R. Soc. Open Sci. 8, 201378 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
White, J. R. Novel Methods for Metagenomic Analysis. PhD thesis, Univ. of Maryland (2010).Sousa, A., Frazão, N., Ramiro, R. S. & Gordo, I. Evolution of commensal bacteria in the intestinal tract of mice. Curr. Opin. Microbiol. 38, 114–121 (2017).PubMed 
Article 

Google Scholar 
Mounier, J. et al. Microbial interactions within a cheese microbial community. Appl. Environ. Microbiol. 74, 172–181 (2008).CAS 
PubMed 
Article 

Google Scholar 
Momeni, B., Xie, L. & Shou, W. Lotka–Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. eLife 6, e25051 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Hoek, T. A. et al. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl Acad. Sci. USA 116, 15979–15984 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mai, T. S. N. Impact of Metabolic Plasticity on Microbial Community Diversity and Stability. MSc thesis, Univ. of Groningen (2021).Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F. & Sanchez, A. High-order interactions distort the functional landscape of microbial consortia. PLoS Biol. 17, e3000550 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mickalide, H. & Kuehn, S. Higher-order interaction between species inhibits bacterial invasion of a phototroph-predator microbial community. Cell Syst. 9, 521–533.e10 (2019).CAS 
PubMed 
Article 

Google Scholar 
Guo, X. & Boedicker, J. Q. The contribution of high-order metabolic interactions to the global activity of a four-species microbial community. PLoS Comput. Biol. 12, e1005079 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Meroz, N., Tovi, N., Sorokin, Y. & Friedman, J. Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales. Nat. Commun. 12, 2891 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zomorrodi, A. R. & Segrè, D. Synthetic ecology of microbes: mathematical models and applications. J. Mol. Biol. 428, 837–861 (2016).CAS 
PubMed 
Article 

Google Scholar 
Song, H. S., Cannon, W. R., Beliaev, A. S. & Konopka, A. Mathematical modeling of microbial community dynamics: a methodological review. Processes 2, 711–752 (2014).Article 

Google Scholar 
Descheemaeker, L., Grilli, J. & de Buyl, S. Heavy-tailed abundance distributions from stochastic Lotka–Volterra models. Phys. Rev. E 104, 034404 (2021).CAS 
PubMed 
Article 

Google Scholar 
Bairey, E., Kelsic, E. D. & Kishony, R. High-order species interactions shape ecosystem diversity. Nat. Commun. 7, 12285 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ji, B., Herrgård, M. J. & Nielsen, J. Microbial community dynamics revisited. Nat. Comput. Sci. 1, 640–641 (2021).Article 

Google Scholar 
Abreu, C. I., Anderen Woltz, V. L., Friedman, J. & Gore, J. Microbial communities display alternative stable states in a fluctuating environment. PLoS Comput. Biol. 16, e1007934 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xu, L., Xu, X., Kong, D., Gu, H. & Kenney T. Stochastic generalized Lotka–Volterra model with an application to learning microbial community structures. Preprint at arXiv https://doi.org/10.48550/arXiv.2009.10922 (2020).Brunner, J. D. & Chia, N. Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species–species modelling. J. R. Soc. Interface 16, 20190423 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
MacArthur, R. Species packing and competitive equilibrium for many species. Theor. Popul. Biol. 1, 1–11 (1970).CAS 
PubMed 
Article 

Google Scholar 
Tilman, D. Resource competition and community structure. Monogr. Popul. Biol. 17, 1–296 (1982).CAS 
PubMed 

Google Scholar 
Chesson, P. MacArthur’s consumer-resource model. Theor. Popul. Biol. 37, 26–38 (1990).Article 

Google Scholar 
Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marsland, R.3rd et al. Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marsland, R.3rd, Cui, W. & Mehta, P. A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns. Sci. Rep. 10, 3308 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Estrela, S., Sanchez-Gorostiaga, A., Vila, J. C. & Sanchez, A. Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments. eLife 10, e65948 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cui, W., Marsland, R. & Mehta, P. Diverse communities behave like typical random ecosystems. Phys. Rev. E 104, 034416 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haygood, R. Coexistence in MacArthur-style consumer–resource models. Theor. Popul. Biol. 61, 215–223 (2002).PubMed 
Article 

Google Scholar 
Dubinkina, V., Fridman, Y., Pandey, P. P. & Maslov, S. Multistability and regime shifts in microbial communities explained by competition for essential nutrients. eLife 8, e49720 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial community responses to environmental complexity. Nat. Commun. 12, 2365 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crowther, T. W. et al. Untangling the fungal niche: the trait-based approach. Front. Microbiol. 5, 579 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Pacciani-Mori, L., Suweis, S., Maritan, A. & Giometto, A. Constrained proteome allocation affects coexistence in models of competitive microbial communities. ISME J. 15, 1458–1477 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marsland, R. et al. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Obadia, B. et al. Probabilistic invasion underlies natural gut microbiome stability. Curr. Biol. 27, 1999–2006.e8 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
D’Andrea, R., Gibbs, T. & O’Dwyer, J. P. Emergent neutrality in consumer-resource dynamics. PLoS Comput. Biol. 16, e1008102 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mancuso, C. P., Lee, H., Abreu, C. I., Gore, J. & Khalil, A. S. Environmental fluctuations reshape an unexpected diversity-disturbance relationship in a microbial community. eLife 10, e67175 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019).CAS 
PubMed 
Article 

Google Scholar 
Zakharova, L., Meyer, K. M. & Seifan, M. Trait-based modelling in ecology: a review of two decades of research. Ecol. Modell. 407, 108703 (2019).Article 

Google Scholar 
Merico, A., Brandt, G., Lan Smith, S. L. & Oliver, M. Sustaining diversity in trait-based models of phytoplankton communities. Front. Ecol. Evol. 2, 59 (2014).Grigoratou, M. et al. A trait-based modelling approach to planktonic foraminifera ecology. Biogeosciences 16, 1469–1492 (2019).Article 

Google Scholar 
Muscarella, M. E., Howey, X. M. & Lennon, J. T. Trait-based approach to bacterial growth efficiency. Environ. Microbiol. 22, 3494–3504 (2020).CAS 
PubMed 
Article 

Google Scholar 
Shao, P., Lynch, L., Xie, H., Bao, X. & Liang, C. Tradeoffs among microbial life history strategies influence the fate of microbial residues in subtropical forest soils. Soil Biol. Biochem. 153, 108112 (2021).CAS 
Article 

Google Scholar 
Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).CAS 
PubMed 
Article 

Google Scholar 
Le Roux, X. et al. Predicting the responses of soil nitrite-oxidizers to multi-factorial global change: a trait-based approach. Front. Microbiol. 7, 628 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Bouskill, N. J., Tang, J., Riley, W. J. & Brodie, E. L. Trait-based representation of biological nitrification: model development, testing, and predicted community composition. Front. Microbiol. 3, 364 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kyker-Snowman, E., Wieder, W. R., Frey, S. D. & Grandy, A. S. Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0). Geosci. Model Dev. 13, 4413–4434 (2020).CAS 
Article 

Google Scholar 
Kruk, C. et al. A trait-based approach predicting community assembly and dominance of microbial invasive species. Oikos 130, 571–586 (2021).Article 

Google Scholar 
Litchman, E., Ohman, M. D. & Kiørboe, T. Trait-based approaches to zooplankton communities. J. Plankton Res. 35, 473–484 (2013).Article 

Google Scholar 
Garcia, C. A. et al. Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190254 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moreno, A. R., Hagstrom, G. I., Primeau, F. W., Levin, S. A. & Martiny, A. C. Marine phytoplankton stoichiometry mediates nonlinear interactions between nutrient supply, temperature, and atmospheric CO2. Biogeosciences 15, 2761–2779 (2018).CAS 
Article 

Google Scholar 
Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).CAS 
PubMed 
Article 

Google Scholar 
Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).CAS 
PubMed 
Article 

Google Scholar 
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).PubMed 
Article 

Google Scholar 
Bradford, M. A. et al. Quantifying microbial control of soil organic matter dynamics at macrosystem scales. Biogeochemistry 156, 19–40 (2021).Article 

Google Scholar 
Ward, B. A., Dutkiewicz, S., Moore, C. M. & Follows, M. J. Iron, phosphorus, and nitrogen supply ratios define the biogeography of nitrogen fixation. Limnol. Oceanogr. 58, 2059–2075 (2013).CAS 
Article 

Google Scholar 
Zwart, J. A., Solomon, C. T. & Jones, S. E. Phytoplankton traits predict ecosystem function in a global set of lakes. Ecology 96, 2257–2264 (2015).PubMed 
Article 

Google Scholar 
Nemergut, D. R., Shade, A. & Violle, C. When, where and how does microbial community composition matter. Front. Microbiol. 5, 497 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Severin, I., Östman, Ö. & Lindström, E. S. Variable effects of dispersal on productivity of bacterial communities due to changes in functional trait composition. PLoS ONE 8, e80825 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Staley, C. et al. Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover. Front. Microbiol. 5, 414 (2014).PubMed 
PubMed Central 

Google Scholar 
Worden, L. Conservation of community functional structure across changes in composition in consumer-resource models. J. Theor. Biol. 493, 110239 (2020).PubMed 
Article 

Google Scholar 
van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 
Article 

Google Scholar 
Song, H.-S. et al. Regulation-structured dynamic metabolic model provides a potential mechanism for delayed enzyme response in denitrification process. Front. Microbiol. 8, 1866 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Hemelrijk, C. K. & Hildenbrandt, H. Schools of fish and flocks of birds: their shape and internal structure by self-organization. Interface Focus 2, 726–737 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Hellweger, F. L., Clegg, R. J., Clark, J. R., Plugge, C. M. & Kreft, J.-U. Advancing microbial sciences by individual-based modelling. Nat. Rev. Microbiol. 14, 461–471 (2016).CAS 
PubMed 
Article 

Google Scholar 
Griesemer, M. & Sindi, S. S. Rules of engagement: a guide to developing agent-based models. Methods Mol. Biol. 2349, 367–380 (2022).PubMed 
Article 

Google Scholar 
Jayathilake, P. G. et al. A mechanistic individual-based model of microbial communities. PLoS ONE 12, e0181965 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Clark, J. R., Daines, S. J., Lenton, T. M., Watson, A. J. & Williams, H. T. P. Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters. Ecol. Modell. 222, 3823–3837 (2011).Article 

Google Scholar 
Nadell, C. D. et al. Cutting through the complexity of cell collectives. Proc. Biol. Sci. 280, 20122770 (2013).PubMed 
PubMed Central 

Google Scholar 
Allen, B., Gore, J. & Nowak, M. A. Spatial dilemmas of diffusible public goods. eLife 2, e01169 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Abs, E., Leman, H. & Ferrière, R. A multi-scale eco-evolutionary model of cooperation reveals how microbial adaptation influences soil decomposition. Commun. Biol. 3, 520 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kreft, J.-U. et al. Mighty small: observing and modeling individual microbes becomes big science. Proc. Natl Acad. Sci. USA 110, 18027–18028 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parise, F., Lygeros, J. & Ruess, J. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Front. Environ. Sci. 3, https://doi.org/10.3389/fenvs.2015.00042 (2015).Allison, S. D. & Goulden, M. L. Consequences of drought tolerance traits for microbial decomposition in the DEMENT model. Soil Biol. Biochem. 107, 104–113 (2017).CAS 
Article 

Google Scholar 
Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012).CAS 
PubMed 
Article 

Google Scholar 
Doloman, A., Varghese, H., Miller, C. D. & Flann, N. S. Modeling de novo granulation of anaerobic sludge. BMC Syst. Biol. 11, 69 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gogulancea, V. et al. Individual based model links thermodynamics, chemical speciation and environmental conditions to microbial growth. Front. Microbiol. 10, 1871 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Gutierrez, M. & Rodriguez-Paton, A. Simulating multicell populations with an accelerated gro simulator. In Proc. ECAL 2017, Fourteenth European Conf. on Artificial Life, 186–188 (2017).Gutiérrez, M. et al. A new improved and extended version of the multicell bacterial simulator gro. ACS Synth. Biol. 6, 1496–1508 (2017).PubMed 
Article 
CAS 

Google Scholar 
Momeni, B., Waite, A. J. & Shou, W. Spatial self-organization favors heterotypic cooperation over cheating. eLife 2, e00960 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kreft, J.-U., Booth, G. & Wimpenny, J. W. T. BacSim, a simulator for individual-based modelling of bacterial colony growth. Microbiology 144, 3275–3287 (1998).CAS 
PubMed 
Article 

Google Scholar 
Picioreanu, C., Van Loosdrecht, M. C. & Heijnen, J. J. Effect of diffusive and convective substrate transport on biofilm structure formation: a two-dimensional modeling study. Biotechnol. Bioeng. 69, 504–515 (2000).CAS 
PubMed 
Article 

Google Scholar 
Lardon, L. A. et al. iDynoMiCS: next-generation individual-based modelling of biofilms. Environ. Microbiol. 13, 2416–2434 (2011).CAS 
PubMed 
Article 

Google Scholar 
Chacón, J. M., Möbius, W. & Harcombe, W. R. The spatial and metabolic basis of colony size variation. ISME J. 12, 669–680 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Oyebamiji, O. K. et al. Gaussian process emulation of an individual-based model simulation of microbial communities. J. Comput. Sci. 22, 69–84 (2017).Article 

Google Scholar 
Menon, R. & Korolev, K. S. Public good diffusion limits microbial mutualism. Phys. Rev. Lett. 114, 168102 (2015).PubMed 
Article 
CAS 

Google Scholar 
Dobay, A., Bagheri, H. C., Messina, A., Kümmerli, R. & Rankin, D. J. Interaction effects of cell diffusion, cell density and public goods properties on the evolution of cooperation in digital microbes. J. Evol. Biol. 27, 1869–1877 (2014).CAS 
PubMed 
Article 

Google Scholar 
Canzian, L., Zhao, K., Wong, G. C. L. & van der Schaar, M. A dynamic network formation model for understanding bacterial self-organization into micro-colonies. IEEE Trans. Mol. Biol. Multiscale Commun. 1, 76–89 (2015).Article 

Google Scholar 
Nadell, C. D., Foster, K. R. & Xavier, J. B. Emergence of spatial structure in cell groups and the evolution of cooperation. PLoS Comput. Biol. 6, e1000716 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mendoza, S. N., Olivier, B. G., Molenaar, D. & Teusink, B. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol. 20, 158 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Feist, A. M. & Palsson, B. O. The biomass objective function. Curr. Opin. Microbiol. 13, 344–349 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Blasche, S. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat. Microbiol. 6, 196–208 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dukovski, I. et al. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nat. Protoc. 16, 5030–5082 (2021).CAS 
PubMed 
Article 

Google Scholar 
Varahan, S., Sinha, V., Walvekar, A., Krishna, S. & Laxman, S. Resource plasticity-driven carbon-nitrogen budgeting enables specialization and division of labor in a clonal community. eLife 9, e57609 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Angeles-Martinez, L. & Hatzimanikatis, V. Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities. PLoS Comput. Biol. 17, e1009140 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 22, 81 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zorrilla, F., Buric, F., Patil, K. R. & Zelezniak, A. metaGEM: reconstruction of genome scale metabolic models directly from metagenomes. Nucleic Acids Res. 49, e126 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ponomarova, O. et al. Yeast creates a niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345–357.e6 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).CAS 
PubMed 
Article 

Google Scholar 
Borer, B., Ataman, M., Hatzimanikatis, V. & Or, D. Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH). PLoS Comput. Biol. 15, e1007127 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Labhsetwar, P., Cole, J. A., Roberts, E., Price, N. D. & Luthey-Schulten, Z. A. Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proc. Natl Acad. Sci. USA 110, 14006–14011 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zmora, N. et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell 174, 1388–1405.e21 (2018).CAS 
PubMed 
Article 

Google Scholar 
Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hamilton, J. J. et al. Metabolic network analysis and metatranscriptomics reveal auxotrophies and nutrient sources of the cosmopolitan freshwater microbial lineage acI. mSystems 2, e00091-17 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
PubMed 
Article 

Google Scholar 
IPCC. Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (IPCC, 2020).Pacciani-Mori, L., Giometto, A., Suweis, S. & Maritan, A. Dynamic metabolic adaptation can promote species coexistence in competitive microbial communities. PLoS Comput. Biol. 16, e1007896 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Synthesis and data extractionData were collected using a literature search of Web of Science for peer-reviewed journal articles published between 1970 and November 2019. We conducted two sets of searches to capture grazing with discrete comparisons (e.g., grazed/ungrazed, moderate vs. heavy intensity grazing) and a range of grazing intensities. The search terms used for each were as follows 1) (graz* OR livestock) AND (exclosure* OR exclusion OR exclude* OR ungrazed OR retire* OR fallow* OR fence* OR paddock*), 2) (“grazing intensity” OR “grazing gradient” OR “stocking rate” OR “rotation*grazing”). Our synthesis includes domesticated and wild grazer species, with the latter defined as an undomesticated species naturally occurring in the study area during the study. Wild grazers are typically native species to the region (e.g., the American bison in Western North America) but can include non-native species that are naturalized in the area (e.g., feral horses on Sable Island).We excluded any study that did not test the effect of grazing animals. A grazer was defined using the definition provided by the authors of the respective study to account for the proportion of forage types in a herbivore’s diet that varies between seasons and habitats. For example, we included animals where their diet is assumed to come from all (e.g., cattle, sheep), most (e.g., wapiti, kangaroos), or some (e.g., deer species) grass species. However, within the included studies, these animals were classified as grazers as most of their diet was grass for the duration of the study. For added clarity about the herbivore composition in each study, we extracted a list of any herbivores listed in the paper regardless of foraging type or if any data was provided.We only included studies that measured animal diversity or abundance as a response variable and included data we could extract or contact the author to obtain9. We included any study with a grazing treatment and included observations within these studies of any grazed and ungrazed sites. All studies with grazing included a comparison to either ungrazed sites, different grazing practices (e.g., cattle vs. sheep), and/or differences in intensities (e.g., heavy/light, extensive/intensive). Studies that only measured plants or soil biota were excluded because syntheses of grazing effects on these groups have already been conducted7,11,12, and our goal was to provide a robust inventory of animal diversity. However, if a study included plants, lichens, or fungi in addition to animals, we included this data. Studies discussing marine grazing or aquatic systems were also excluded. From these preliminary filters, we identified 3,489 published manuscripts. We reviewed these 3,489 published articles and found 245 studies that surveyed animals in grazed sites. In total, we extracted 16,105 observations for over 1,200 species.We extracted 28 variables that focus on management systems, assemblages of grazer species, ecosystem characteristics, and survey type (Table 1). The latitudes, longitudes, and elevations of each study were included when provided for use with geospatial data. In addition, we included variables about the study site’s disturbance history, including last time grazed, if a flood event or fire had occurred, if fertilization was used, if the area was open or fenced off, and if the area was publicly or privately owned. Furthermore, the timeline for the study (i.e., the years the authors initiated and completed the study) was also provided. Study initiation was described by the authors and could include when the grazing treatment started, another treatment was applied, and/or animal surveys began. These timeline columns can be useful in identifying long-term studies and differentiating single grazing events or multi-year experiments. Finally, we generalized the characteristics of the ecosystem of the sites used in each study based on the climate and dominant vegetation.Table 1 The attributes and description of the metadata.csv file that lists the general characteristics of each study.Full size tableWithin the grazing data, we included information about the grazer when provided, including any measurement of the intensity of grazing (e.g., animals per hectare, the height of residual vegetation). We also provided two columns that detailed whether the study tested grazing effects using a discrete comparison or gradient of intensities (Table 2). The value for the target specimens extracted may represent either a single observation or a summarized statistic (e.g., mean animals per site). We identify unique observations as “count” and summarized statistics by the metric used, such as mean, median, standard deviation (column stat in grazingData.csv). When possible, we also included any record of other grazers that co-occurred with the observed grazer species. The data for these variables were extracted from the papers by a single researcher who read through each paper and filled in available data on the mentioned variables.Table 2 The attributes and description of the grazingData.csv file that has the extracted data from each study.Full size tableWe extracted information about the target specimen, site, year, experimental replicate, and response estimate (Table 2). We included multiple categorizations of the target species to assist future users in synthesizing similar taxa (Table 2). When a species name or genus was provided, we conducted a search query (see detailedTaxa.r) through the global biodiversity information facility (GBIF.org) to determine the taxonomic classification of the species, including kingdom, phylum, order, class, and family. When a species name was not included, we provided the lowest taxonomic resolution available. We also included a broader classification of ‘higherTaxon’ to distinguish plants, fungi, vertebrates, and invertebrates. These columns may help group similar species together for community-level analyses. Lastly, we included the characteristic of the plant community (i.e., planted or self-assembled, tilled, and its vegetation class) when plant data was reported.Patterns among studiesMost of the studies took place in the United States (26%), Australia (9%), and the United Kingdom (7%) (Fig. 1). As expected, most studies were conducted in grasslands (n = 206), followed by forests (n = 92) and shrublands (n = 82) (Fig. 2). We included publications from the entire range of years (i.e., 1970–2019), but most were published after 2000 (76%). The number of sites in a study and the study duration showed a bimodal distribution with a long tail (Fig. 3). Most studies included one to eight different sites, and few were conducted longer than five years (Fig. 3). A few studies were highly replicated, while many were limited in their replication (Fig. 3).Fig. 1The locations of studies that measured the response of animals to domestic or wild grazing.Full size imageFig. 2The number of grazing studies conducted in ecosystems around the world. We generalized the characteristics of the ecosystem of the sites used in each study based on the climate and dominant vegetation community. We separated grassland communities into those that were (a) semi-natural without recent cultivation or seeding (self-assembled), (b) recently cultivated or had supplemental seeding (planted/cultivated), and (c) a combination of both. In most grasslands, the cultivation history was unclear.Full size imageFig. 3The number of independent sites surveyed and the duration of each study. Most studies were conducted at either a single site or with some replication (e.g., 6–8 sites). Similarly, most studies were either conducted in one year ( >30%) or over a few years (e.g., 3–6 years). Very few studies (32) or lasted longer than 15 years.Full size imageSite and management data were not reported in all studies, as found in other reviews of grazing impacts on ecological processes10. Of the studies that mentioned the ownership status of the land used, 46% were on private land, 42% were on public land, and 12% had a history of both public and private ownership. Most studies included binary comparisons (56%) of grazed vs. ungrazed plots or sites, though some also included a discrete (22%) or a continuous estimate of grazing intensities (18%).Of the studies that reported plant community origin, 76% were self-assembled, 17% were planted communities, and the remaining included sites were a combination of the two. Domesticated grazers as the focal herbivore made up 67% of the studies, with 12% of the studies having wild grazers as the focal herbivore, and 21% having both present. Domesticated livestock were the most frequently surveyed grazers including cattle (n = 164), sheep (n = 83), and horses (n = 21), but studies are included that examined wild grazers, such as kangaroos (n = 6), elephants (n = 5), and pronghorn (n = 5) (Fig. 4).Fig. 4The frequency in which a study reported herbivores. We included any mention of herbivores regardless of being a grazer, browser, granivore, or other class. This list was obtained by the text within the manuscript and is different than the representation of species in the database (i.e., the measured species).Full size image More

Roff, D. A. The Evolution of Life Histories (Chapman & Hall, 1992).
Google Scholar 
Stearns, S. C. The Evolution of Life Histories (Oxford University Press, 1992).
Google Scholar 
Tibblin, P. et al. Evolutionary divergence of adult body size and juvenile growth in sympatric subpopulations of a top predator in aquatic ecosystems. Am. Nat. 186, 98–110 (2015).PubMed 

Google Scholar 
Voituron, Y., de Fraipont, M., Issartel, J., Guillaume, O. & Clobert, J. Extreme lifespan of the human fish (Proteus anguinus): A challenge for ageing mechanisms. Biol. Lett. 7, 105–107 (2011).PubMed 

Google Scholar 
Longhurst, A. Murphy’s law revisited: Longevity as a factor in recruitment to fish populations. Fish. Res. 56, 125–131 (2002).
Google Scholar 
Schaffer, W. M. Optimal reproductive effort in fluctuating environments. Am. Nat. 108, 783–790 (1974).
Google Scholar 
Beamish, R. J., McFarlane, G. A. & Benson, A. Longevity overfishing. Prog. Oceanogr. 68, 289–302 (2006).ADS 

Google Scholar 
Conti, B. Considerations on temperature, longevity and aging. Cell. Mol. Life Sci. 65, 1626–1630 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Inness, C. L. W. & Metcalfe, N. B. The impact of dietary restriction, intermittent feeding and compensatory growth on reproductive investment and lifespan in a short-lived fish. Proc. R. Soc. Lond. B Biol. Sci. 275, 1703–1708 (2008).
Google Scholar 
Liu, R. K. & Walford, R. L. Increased growth and life-span with lowered ambient temperature in the annual fish, Cynolebias adloffi. Nature 212, 1277–1278 (1966).ADS 

Google Scholar 
Trip, E. D., Clements, K. D., Raubenheimer, D. & Choat, J. H. Temperature-related variation in growth rate, size, maturation and life span in a marine herbivorous fish over a latitudinal gradient. J. Anim. Ecol. 83, 866–875 (2014).PubMed 

Google Scholar 
Munch, S. B. & Salinas, S. Latitudinal variation in lifespan within species is explained by the metabolic theory of ecology. Proc. Natl. Acad. Sci. U.S.A. 106, 13860–13864 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Britton, J. R., Pegg, J., Sedgwick, R. & Page, R. Investigating the catch returns and growth rate of wels catfish, Silurus glanis, using mark-recapture. Fish. Man. Ecol. 14, 263–268 (2007).
Google Scholar 
Hamel, M. J. et al. Range-wide age and growth characteristics of shovelnose sturgeon from mark–recapture data: Implications for conservation and management. Can. J. Fish. Aquat. Sci. 72, 71–82 (2015).
Google Scholar 
Hamel, M. J. et al. Using mark–recapture information to validate and assess age and growth of long-lived fish species. Can. J. Fish. Aquat. Sci. 71, 559–566 (2014).
Google Scholar 
Casale, P., Mazaris, A. D., Freggi, D., Vallini, C. & Argano, R. Growth rates and age at adult size of loggerhead sea turtles (Caretta caretta) in the Mediterranean Sea, estimated through capture-mark-recapture records. Sci. Mar. 73, 589–595 (2009).
Google Scholar 
IUCN (International Union for Conservation of Nature) 2008. Siluris glanis. The IUCN Red List of Threatened Species. Version 2021-3 (2010). https://www.iucnredlist.org. (Accessed 25 February 2021).Copp, G. H. et al. Voracious invader or benign feline? A review of the environmental biology of European catfish Silurus glanis in its native and introduced ranges. Fish. Fish. 10, 252–282 (2009).
Google Scholar 
Palm, S., Vinterstare, J., Nathanson, J. E., Triantafyllidis, A. & Petersson, E. Reduced genetic diversity and low effective size in peripheral northern European catfish Silurus glanis populations. J. Fish. Biol. 95, 1407–1421 (2019).PubMed 

Google Scholar 
Jensen, A., Lillie, M., Bergstrom, K., Larsson, P. & Hoglund, J. Whole genome sequencing reveals high differentiation, low levels of genetic diversity and short runs of homozygosity among Swedish wels catfish. Heredity 127, 79–91 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cucherousset, J. et al. Ecology, behaviour and management of the European catfish. Rev. Fish. Biol. Fish. 28, 177–190 (2017).
Google Scholar 
Kuzishchin, K. V., Gruzdeva, M. A. & Pavlov, D. S. Traits of biology of European Wels Catfish Silurus glanis from the Volga-Ahtuba water system, the Lower Volga. J. Ichthyol. 58, 833–844 (2019).
Google Scholar 
Alp, A., Kara, C., Üçkardeş, F., Carol, J. & García-Berthou, E. Age and growth of the European catfish (Silurus glanis) in a Turkish Reservoir and comparison with introduced populations. Rev. Fish. Biol. Fish. 21, 283–294 (2010).
Google Scholar 
Carol, J., Benejam, L. B. & García-Berthou, E. Growth and diet of European catfish (Silurus glanis) in early and late invasion stages. Fund. Appl. Limnol. 174, 317–328 (2009).
Google Scholar 
Severov, Y. A. Size–age structure, growth rate, and fishery of European Catfish Silurus glanis in the lower Kama Reservoir. J. Ichthyol. 60, 118–121 (2020).
Google Scholar 
Lessmark, O. Malprovfiske i Möckeln 2006. Länsstyrelsens rapportserie (2006).Lessmark, O. Malprovfiske i Möckeln 2007. Länsstyrelsens rapportserie (2007).Harka, A. Studies on the growth of the sheatfish (Silurus glanis L.) in River Tisza. Aquac. Hung. (Szarvas) 4, 135–144 (1984).
Google Scholar 
Edwards, J. E. et al. Advancing research for the management of long-lived species: A case study on the Greenland shark. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00087 (2019).Article 

Google Scholar 
Pikitch, E. K., Doukakis, P., Lauck, L., Chakrabarty, P. & Erickson, D. L. Status, trends and management of sturgeon and paddlefish fisheries. Fish. Fish. 6, 233–265 (2005).
Google Scholar 
Pironon, S. et al. Geographic variation in genetic and demographic performance: New insights from an old biogeographical paradigm. Biol. Rev. 92, 1877–1909 (2017).PubMed 

Google Scholar 
Antonovics, J., McKane, A. J. & Newman, T. J. Spatiotemporal dynamics in marginal populations. Am. Nat. 167, 16–27 (2006).CAS 
PubMed 

Google Scholar 
Alp, A., Kara, C. & Büyükcapar, H. M. Reproductive biology in a Native European Catfish, Siluris glanis L., 1758, population in Menzelet Resevoir. Turk. J. Vet. Ani. Sci. 28, 613 (2004).
Google Scholar 
Boulêtreau, S. & Santoul, F. The end of the mythical giant catfish. Ecosphere 7(11), e01606. https://doi.org/10.1002/ecs2.1606 (2016).Article 

Google Scholar 
Bergmann, C. Ober die verhaltnisse der warmeokonomie der thiere zu ihrer grosse. Gottinger Studien 3, 595–708 (1847).
Google Scholar 
Blanck, A. & Lamouroux, N. Large-scale intraspecific variation in life-history traits of European freshwater fish. J. Biogeogr. 34, 862–875 (2007).
Google Scholar 
Charnov, E. L., Turner, T. F. & Winemiller, K. O. Reproductive constraints and the evolution of life histories with indeterminate growth. Proc. Natl. Acad. Sci. U.S.A. 98, 9460–9464 (2001).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ricklefs, R. E. Embryo development and ageing in birds and mammals. Proc. R. Soc. B 273, 2077–2082 (2006).PubMed 
PubMed Central 

Google Scholar 
Lee, W. S., Monaghan, P. & Metcalfe, N. B. Experimental demonstration of the growth rate-lifespan trade-off. Proc. R. Soc. B 280, 20122370 (2013).PubMed 
PubMed Central 

Google Scholar 
Rennie, M. D., Kraft, C., Sprules, W. G. & Johnson, T. B. Factors affecting the growth and condition of lake whitefish (Coregonus clupeaformis). Can. J. Fish. Aquat. Sci. 66, 2096–2108 (2009).
Google Scholar 
Prats, J., Val, R., Armengol, J. & Dolz, J. Temporal variability in the thermal regime of the lower Ebro River (Spain) and alteration due to anthropogenic factors. J. Hydrol. 387, 105–118 (2010).ADS 

Google Scholar 
Kale, S. & Sönmez, A. Y. Climate change effects on annual streamflow of Filyos River (Turkey). J. Water Clim. Change 11, 420–433 (2020).
Google Scholar 
Britton, J. R., Cucherousset, J., Davies, G. D., Godard, M. J. & Copp, G. H. Non-native fishes and climate change: Predicting species responses to warming temperatures in a temperate region. Freshw. Biol. 55, 1130–1141 (2010).
Google Scholar 
Garcia, V. B., Lucifora, L. O. & Myers, R. A. The importance of habitat and life history to extinction risk in sharks, skates, rays and chimaeras. Proc. R. Soc. B 275, 83–89 (2008).PubMed 

Google Scholar 
Jackson, J. B. C. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
PubMed 

Google Scholar 
Kuparinen, A. & Merilä, J. Detecting and managing fisheries-induced evolution. TREE 22, 652–659 (2007).PubMed 

Google Scholar 
Swedish University of Agricultural Sciences (SLU). National Data Host Lakes and Watercourses, and National Data Host Agricultural Land (Swedish University of Agricultural Sciences, 2021).
Google Scholar 
Emåförbundet. Vattenflöden och Nivåer (n.d.). http://www.eman.se/sv/vattenhushallning/vattenfloden-och-nivaer/historik/. (Accessed 12 May 2021)Fabens, A. J. Properties and fitting of the Von Bertalanffy growth curve. Growth 29, 265–289 (1965).CAS 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/. (Accessed 13 April 2021)Bokor, Z. et al. Survival and growth rates of wels catfish (Siluris glanis Linnaeus, 1758) larvae originating from fertilization with cryopreserved or fresh sperm. J. Appl. Ichthyol. 31, 164–168 (2015).
Google Scholar 
du Sert, N. P. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18, e3000410 (2020).
Google Scholar 
Horoszewicz, L. & Backiel, T. Growth of Wels (Silurus glanis L.) in the Vistula river and the Zegrzyñski reservoir. Arch. Polish Fish. 11, 115–121 (2003).
Google Scholar  More

MacArthur, R. H. & Wilson, E. O. The theory of island biogeography (Princeton University Press, 1967).
Google Scholar 
MacArthur, R. H. & Wilson, E. O. An equilibrium theory of insular zoogeography. Evolution 17, 373–387 (1968).
Google Scholar 
Caraballo, H. Emergency department management of mosquito-borne illness: malaria, dengue, and west nile virus. Emerg. Med. Pract. 16(5), 1–2 (2014).MathSciNet 
PubMed 

Google Scholar 
Rejmánková, E., Grieco, J., Achee, N., Roberts, DR. Ecology of larval habitats. In: Manguin S, editor. Anopheles mosquitoes: new insights into malaria vectors 9th. InTech; Rijeka: pp. 397–446. (2013).Sharma, M., Quader, S., Guttal, V. & Isvaran, K. The enemy of my enemy: multiple interacting selection pressures lead to unexpected anti-predator responses. Oecologia 192(1), 1–12 (2020).ADS 
PubMed 

Google Scholar 
Yee, D. A., Kesavaraju, B. & Juliano, S. A. Interspecific differences in feeding behavior and survival under food-limited conditions for larval Aedes albopictus and Aedes aegypti (Diptera: Culicidae). Ann. Entomol. Soc. Am. 97, 720–728 (2006).
Google Scholar 
Messina, J. P. et al. The current and future global distribution and population at risk of dengue. Nat. Microbiol. 4, 1508–1515 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rose, N. H. et al. Climate and urbanization drive mosquito preference for humans. Curr. Biol. 30, 3570-3579.e6 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Day, J. F. Mosquito oviposition behavior and vector control. Insects 7(4), 65 (2016).PubMed Central 

Google Scholar 
McBride, C. S. Genes and odors underlying the recent evolution of mosquito preference for humans. Curr. Biol. 26, R41–R46 (2016).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Southerst, R. W. Global change and human vulnerability to vector-borne diseases. Clin. Microbiol. Rev. 17, 136–173 (2004).
Google Scholar 
Vitousek, P. M. Nutrient cycling and limitation: Hawai‘i as a model system (Princeton University Press, 2004).
Google Scholar 
Grant, P. R. & Grant, B. R. How and why species multiply: the radiation of darwin’s finches (Princeton University Press, 2011).
Google Scholar 
Cliff, A. D. & Haggett, P. The epidemiological significance of islands. Health Place. 1, 199–209 (1995).
Google Scholar 
Arrhenius, O. Species and area. J. Ecol. 9(1), 95–99 (1921).
Google Scholar 
Preston, F. W. Time and space and the variation of species. Ecology 41(4), 611–627 (1960).
Google Scholar 
Rosenzweig, M. L. Species diversity in space and time (Cambridge University Press, 1995).
Google Scholar 
Drakare, S. et al. The imprint of the geographical, evolutionary and ecological context on species-area relationships. Ecol. Lett. 9: 215 227. (2006).Kotiaho, J., Kaitala, V., Komonen, A. & Päivinen, J. Predicting the risk of extinction from shared ecological characteristics. Proc. Natl. Acad. Sci. USA 102, 1963–1967 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bataille, A. et al. Natural colonization and adaptation of a mosquito species in Galápagos and its implications for disease threats to endemic wildlife. Proc. Nat. Acad. Sci. 106(25), 10230–10235 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sinka, M. E. et al. A new malaria vector in Africa: predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk. Proc. Nat. Acad. Sci. 117(40), 24900–24908 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Powell, J.R. Genetic variation in insect vectors: death of typology? Insects. 11;9(4):139. (2018).Whittaker, R. H. Communities and ecosystems (Macmillan, 1975).
Google Scholar 
Nekola, J. C. & White, P. S. The distance decay of similarity in biogeography and ecology. J. Biogeogr. 26, 867–878 (1999).
Google Scholar 
Green, J. L. et al. Spatial scaling of microbial eukaryote diversity. Nature 432, 747–750 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Horner-Devine, M. C., Lage, M. & Hughes, J. B. Bohannan BJ A taxa-area relationship for bacteria. Nature 432, 750–753 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Martiny, J, B. H., Eisen, J.A., Penn, K., Allison, S.D., Horner-Devine, M.C. Drivers of bacterial beta-diversity depend on spatial scale. Proc. Natl. Acad. Sci. USA 108(19):7850−4. (2011).Segre, H., Ron, R., de Malach, N., Henkin, Z., Mandel, M., Kadmon, R. Competitive exclusion, beta diversity, and deterministic vs. stochastic drivers of community assembly. Ecol. Lett., 17(11):1400−8. (2014).Ishtiaq, F. et al. Biogeographical patterns of blood parasite lineage diversity in avian hosts from southern Melanesian islands. J. Biogeogr. 37, 120–132 (2010).
Google Scholar 
Barrera, R., Amador, M. & MacKay, A. J. Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan. Puerto Rico. PLoS Negl. Trop. Dis. 5(12), e1378. https://doi.org/10.1371/journal.pntd.0001378 (2011).Article 
PubMed 

Google Scholar 
Campbell, K. M., Lin, C. D., Iamsirithaworn, S. & Scott, T. W. The complex relationship between weather and dengue virus transmission in Thailand. Am. J. Trop. Med. Hyg. 89, 1066–1080. https://doi.org/10.4269/ajtmh.13-0321 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Evans, M. V. et al. Microclimate and larval habitat density predict adult Aedes albopictus abundance in Urban Areas. Am. J. Trop. Med. Hyg. 101(2), 362–370 (2019).PubMed 
PubMed Central 

Google Scholar 
Mustak, M. S. et al. The peopling of Lakshadweep Archipelago. Sci. Rep. 9, 6968 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sharma, S. K. & Hamzakoya, K. K. Geographical spread of Anopheles stephensi, vector of urban malaria, Aedes aegypti vector of Dengue/DHF, in the Arabian sea islands of Lakshadweep. India. Dengue Bull. 25, 88–91 (2001).
Google Scholar 
Sharma RS, Ali, MKS, Dhillon GPS. Epidemiological and entomological aspects of an outbreak of chikungunya in Lakshadweep islands, India, during 2007. Dengue Bull., 178–185 (2008).Subramaniam, H., Ramoo, H. & Sumanam, S. D. Filariasis survey in the Laccadive, minicoy and amindivi Islands. Madras state. Indian J. Malariol. 12, 115–127 (1958).CAS 
PubMed 

Google Scholar 
Roy, R. G., Joy, C. T., Hussain, C. M. & Mohamed, I. K. Malaria in Lakshadweep Islands. Indian J. Med. Res. 67, 924–925 (1978).CAS 
PubMed 

Google Scholar 
Ali, S. M. K. et al. Study on the ecoepidemiology of chikungunya in UT of Lakshadweep. J. Commun. Dis. 41(2), 81–92 (2009).
Google Scholar 
Samuel, P. P., Krishnamoorthi, R., Hamzakoya, K. K. & Aggarwal, C. S. Entomo-epidemiological investigations on chikungunya outbreak in the Lakshadweep Islands. Indian Ocean. Indian J. Med. Res. 129(4), 442–445 (2009).PubMed 

Google Scholar 
Jayalakshmi, K. & Mathiarasan, L. Prevalence of disease vectors in Lakshadweep Islands during post-monsoon season. J. Vector Borne Dis. 55, 189–196 (2018).
Google Scholar 
Su, C. L. et al. Molecular epidemiology of Japanese encephalitis virus in mosquitoes in Taiwan during 2005–2012. PLoS Negl. Trop. Dis. 8, e3122 (2014).PubMed 
PubMed Central 

Google Scholar 
Muslim, A. et al. Armigeres subalbatus incriminated as a vector of zoonotic Brugia pahangi filariasis in suburban Kuala Lumpur. Peninsular Malaysia. Parasites Vectors 6, 219 (2013).PubMed 

Google Scholar 
Wilke, A. B. B. et al. Community composition and year-round abundance of vector species of mosquitoes make Miami-Dade County, Florida a receptive gateway for arbovirus entry to the United States. Sci. Rep. 9, 8732 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Medeiros-Sousa, A. R., Fernandes, A., Ceretti-Junior, W., Wilke, A. B. B. & Marrelli, M. T. Mosquitoes in urban green spaces: using an island biogeographic approach to identify drivers of species richness and composition. Sci. Rep. 7, 17826 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Lum, J. K., Kaneko, A., Taleo, G., Amos, M. & Reiff, D. M. Genetic diversity and gene flow of humans, Plasmodium falciparum, and Anopheles farauti s.s. of Vanuatu. inferred malaria dispersal and implications for malaria control. Acta Trop. 103, 102–107 (2007).CAS 
PubMed 

Google Scholar 
Marques, T. C. et al. Mosquito (Diptera: Culicidae) assemblages associated with Nidularium and Vriesea bromeliads in Serra do Mar, Atlantic Forest, Brazil. Parasites Vectors 5, 41 (2012).PubMed 
PubMed Central 

Google Scholar 
Laporta, G. Z. & Sallum, M. A. M. Coexistence mechanisms at multiple scales in mosquito assemblages. BMC Ecol. 14, 30 (2014).PubMed 
PubMed Central 

Google Scholar 
Koenraadt, C. J. & Takken, W. Cannibalism and predation among larvae of the Anopheles gambiae complex. Med. Vet. Entomol. 17(1), 61–66 (2003).CAS 
PubMed 

Google Scholar 
Chathuranga, W. G. D., Karunaratne, S. H. P. P., Priyanka, W. A. & De Silva, P. Predator–prey interactions and the cannibalism of larvae of Armigeres subalbatus (Diptera: Culicidae). J. Asia-Pac. Entomol. 23, 124–131 (2020).
Google Scholar 
Focks, D. A. & Chadee, D. D. Pupal survey: an epidemiologically significant surveillance method for Aedes aegypti: an example using data from Trinidad. Am. J. Trop. Med. Hyg. 56(2), 159–167 (1997).CAS 
PubMed 

Google Scholar 
Lounibos, L. P., Bargielowski, I., Carrasquilla, M. C. & Nishimura, N. Coexistence of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in Peninsular Florida two decades after competitive displacements. J. Med. Entomol. 53, 1385–1390 (2016).PubMed 

Google Scholar 
Juliano, S. A. Species interactions among larval mosquitoes: context dependence across habitat gradients. Annu. Rev. Entomol. 54, 37–56 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bargielowski, I.E., Lounibos, L.P., Carrasquilla, M.C. Evolution of resistance to satyrization through reproductive character displacement in populations of invasive dengue vectors. Proc. Natl. Acad. Sci. 19:110(8):2888–92. (2013).Chadee, D. D. Dengue cases and Aedes aegypti indices in Trinidad. West Indies. Acta Trop. 112(2), 174–180 (2009).CAS 
PubMed 

Google Scholar 
XX. https://www.census2011.co.in/census/state/lakshadweep.htmlChristophers, S. R. The fauna of British India, including Ceylon and Burma; Diptera: Family Culicidae; Tribe Anophelini Vol. 4 (Taylor & Francis, 1933).
Google Scholar 
Barraud, P.J. The fauna of British India, including Ceylon and Burma. Diptera V. Family Culicidae. Tribes Megarhinini and Culicini. London: Taylor and Francis p. 463. (1934).Walther, B. A., Cotgreave, P., Price, R. D., Gregory, R. D. & Clayton, D. H. Sampling effort and parasite species richness. Parasitol. Today 11, 306–310 (1995).CAS 
PubMed 

Google Scholar 
Chao, A. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11, 265–270 (1984).
Google Scholar 
Oksanen, J. et al. Vegan: community ecology package. R Package Version 2(10), 2013 (2015).
Google Scholar 
R Core Team. R Development Core Team. R A Lang. Environ. Stat. Comput. 55, 275–286 (2016).McFadden, D. Conditional logit analysis of qualitative choice behavior. Front. Econ. 1, 105–142 (1974).
Google Scholar 
Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).
Google Scholar 
Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monograph. 27, 325–349 (1957).
Google Scholar 
Sokal, R. R. & Rohlf, F. J. Biometry: the principles and practice of statistics in biological research 3rd edn. (Freeman, 1995).MATH 

Google Scholar 
Fortin, M. J. & Dale, M. R. T. Spatial analysis: a guide for ecologists 1–30 (Cambridge University Press, 2005).
Google Scholar 
Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. http://florianhartig.github.io/DHARMa/. (2019).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
World Health Organization, Guidelines for dengue surveillance and mosquito control. Western Pacific Education in Action Series No.8 (WHO, Geneva, 1995) More

Study sitesThe sample site Tei Tong Tsai is located within the Island District (112°5’ E, 22°5’ N Hong Kong, China) and connected to Lantau Country Park. The rich woods in Tei Tong Tsai provide a suitable environment for insects to survive, with rich biodiversity. Weather records (Supplement 1) for May 2019 show that the highefst temperature was 27.2 °C, the lowest was 15.7 °C, the average was 21.7 °C; and the annual average rainfall was 297.8 mm. The suitable temperature and rainfall have created a suitable ecological environment and high biodiversity, establishing Tei Tong Tsai as a prime location for studying beetle diversity. In May 2019, a 13 sample sites were selected for beetle collection (Fig. 1). All latitude and longitude formats were converted to degrees, minutes, and seconds.Fig. 1Sampling points for the three passive acquisition methods used in the Tei Tong Tsai sampling site (indicated by red dots).Full size imageExperimental protocolIn this study, three passive collection methods were used for beetle collection. FIT is an efficient collecting method for insects with strong flying abilities and was first developed and used abroad14. MT and PT collect insects that are not strong flyers and live on the surface. A flight interception trap, a malaise trap, and 10 pitfall traps were set up to collect beetles in each sample site. Samples were selected to cover ecological environments at different longitudes, latitudes, altitudes, and distances from water sources. Reasonable sampling distances (depending on the terrain, with an interval between 100 and 200 m) were set up between sample sites to fully cover Tei Tong Tsai’s habitats. Due to the topography, the distance between the 10th and 11th sample points was about 350 m. The distance between two other close sample points were in the range of 100–200 m. All three traps were based on the original device to maximize the advantages and achieve better collection results.Collection devices. The flight interception trap (Fig. 2a) mainly comprises an interceptor screen (plastic net, PVC plastic glass, or plexiglas) and an insect specimen receiver (PVC), which is an efficient collection device for intercepting and collecting insects with strong flight ability. The detailed installation steps include the following: Firstly, punch two holes on the long side of the PVC screen with a hole puncher spaced about 30 cm apart; then, fix the screen to a bamboo pole with silk, install the specimen receiver, fix all three, bolt the rope, and fix it in the air with a thick rope (the sink is about 0.5–1 m from the ground). After installation, relevant drugs were placed inside the specimen receiver to poison the insects. The drugs used depend on the purpose of the study. For morphological studies, saline (5 mmol/L NaCl solution) or water with detergent is used. By contrast, DNA molecular studies use a mixture of 2% SDS (sodium dodecyl sulfate) and EDTA (ethylene diamine tetraacetic acid, 0.1 mol/L, PH = 8) or highly concentrated alcohol, which effectively controls the degradation of DNA. Currently, high-concentration alcohol, SDS and EDTA mixtures are commonly used. The device is widely applicable and can be installed in almost any habitat; however, it is best installed along the insects’ flight paths, including roads, rivers, or creeks between valleys. In this experiment, we improved this device by increasing the size of the water trough considering the actual situation of the sample site. Also, to properly conduct the molecular experiments, the reagents we used were a mixture of SDS and EDTA. Therefore, the improved device was more suitable for diverse habitats, and the insect species collected were abundant, reflecting good collection practices14.Fig. 2Three passive acquisition methods: (a) flight interception trap; (b) malaise trap; (c) pitfall trap.Full size imageMalaise traps (Fig. 2b) are large tent-like structures constructed from thin mesh. They are among the most commonly used static non-attractant insect traps and insect collection devices. Invented by Malaise (1937) and later improved upon by Townes and Sharkey, these traps are important tools for insect collection and monitoring worldwide15. The malaise trap used at the Tei Tong Tsai Country Park was the Townes type, which is generally set up in forest areas with rich habitats and relatively stable ground. The material is usually meshed mosquito netting fabricated into a tent-shaped insect interception field. The insects hit the net vertically, continue to fly upward, and are gradually led into the trap by the tilted top. The drug in the trap is usually anhydrous ethanol, which intercepts beetles with weak flying abilities16,17.The pitfall trap (Fig. 2c) is an effective method for capturing surface beetles; it is simple to use, easy to carry, and a common device for collection in the wild. The PT is created by digging a pit into the ground with the same depth as a wide-mouth plastic cup (20 cm high, 10 cm in diameter); The upper edge of the cup must be flushed with the soil surface, and a mixture of absolute ethanol is poured inside to collect flightless beetles14. About one-quarter of the way from the top, small holes are punched above the wide-mouth cup to prevent the loss of specimens from rainwater filling the cups. The 10 sets of traps in this experiment were not evenly distributed, but they were all in suitable habitats.Specimen samplingThe sampling site for this study was Tei Tong Tsai, and the sampling period was from 1st May to 28th May (2019). FIT, and PTs were collected once every two days. Due to the small number of beetles collected by MT, mt was collected only once. All beetles were picked out and arranged separately after collection, added to anhydrous ethanol, preserved, and labeled. The beetles collected by the three passive acquisition methods were picked according to morphological species.Specimen identificationThe taxonomic status for the family level of all samples was determined based on the relevant literature18,19,20,21. Relevant experts completed further identification (Supplement 2).All the specimens collected in this study are currently in the zoological museum of the Institute of Zoology, Chinese Academy of Sciences (Beijing, China).Specimen photographyBeetles were poured from the bottle and arranged separately according to the general species. Firstly, we used tweezers or a brush to place the beetles on unbreakable and unwrinkled paper (as far as possible with the backside upwards to keep them tight and neat, reducing the space left, and considering the label in the photograph). Simultaneously, we captured multiple photos according to the size and species of insect for the large specimens in the tube, adjusted the light near them to brighten the background, placed graph paper next to the beetles as a reference scale, then adjusted our Olympus camera settings to the appropriate photographing parameters. Finally, we inserted the photographed beetles and matching labels back into the tube and added anhydrous ethanol for preservation (Fig. 3). The labels were set in the photos as 2019 DTZ-FIT/MT/PTX-5XX-5XX (-N), in which 2019 represents the collection time, DTZ represents Tei Tong Tsai, FIT/MT/PT signifies the collection method, X represents the number of sampling points, 5XX-5XX represents sampling time, and N represents the photo number. If a sample site had many insects on the same date and required more than one photo, n was used to represent the number of photos. See the Supplement 3 for the complete document.Fig. 3Examples of beetles collected from three passive acquisition methods: overall photos of beetles collected by (a) FIT, (b) PT, and (c) MT. On the bottom right corner shows scale in each photo.Full size imageAfter the morphological data of the samples were collected, their Latin name and collection information were recorded in a table. Each passive acquisition method corresponded to a table, and each table was divided into 13 sheets according to 13 sampling points. The collection time was listed horizontally on each sheet, and the beetles’ species names were listed vertically (were named in the morphological species order as 1, 2, 3, …, N). The number of beetles was recorded in the corresponding position and the Supplement 4 file.Finally, data show the beetles’ biodiversity collected from each sampling site. Firstly, we summarized the data from each sampling point after completing the data statistics. Afterward, we counted the number of beetle individuals collected under the different passive acquisition methods at different points (Fig. 4). In Fig. 4, red, blue, and green represent the number of beetle individuals collected by MT, PT, and FIT, respectively. Fig. 4 shows that MT collected fewer beetles than FIT and PT. Secondly, the data of 13 sampling points in each collecting method were summarized to obtain the total number of families and species collected by each method (Fig. 5). A graph created in Excel 2016 displays the collection method as the horizontal coordinate and the number as the vertical coordinate. In the graph, red represents the number of families, and blue represents the number of species. Fig. 5 shows that FIT collected more beetle species and individuals than PT and MT, and MT collected the least. Thirdly, all data from the 13 sampling points and the three collection methods were summarized. The number of species collected in all families was counted. Families with more than ten species were selected (a total of 11 families) for data presentation (Fig. 6). Finally, a graphic was drawn in Excel 2016. Fig. 6 shows that the number of species in Staphylinidae, Curculionidae, and Chrysomelidae accounted for a large number, and the diversity was relatively high.Fig. 4Data table of numbers of individual beetles collected by different methods at 13 sampling points. The red, blue, and green columns represent the number of beetles collected by MT, PT, and FIT, respectively.Full size imageFig. 5The number of beetles collected by different passive acquisition methods. Horizontal coordinates represent collection methods. The red column and blue column represent the number of beetles collected on the family level and species level, respectively.Full size imageFig. 6Families with more than ten species (a total of 11 families) were selected for presentation. The sample sizes of each groups were also shown.Full size image More

The difficulties inherent in ranking global threats are due to them being context-dependent, which result from conditions and the nature of the threats themselves differing among locations, habitats, and taxa (Fig. 1). Current high-risk hotspots from habitat loss and overexploitation are primarily located in the tropics, whereas Europe is documented as a threat hotspot for pollution6. On islands, biological invasions mainly threaten biodiversity in the Pacific and Atlantic Oceans, while islands in the Indian Ocean and near the coasts of Asia are mostly threatened by overexploitation and agriculture3. Climate change affects species more at higher latitudes and altitudes because species are constrained by the physical environment (geographic barriers and mountain tops) to follow their optimal isotherms.Fig. 1: Divergence of global threat rankings across different references and international agencies.IPBES, WWF, and IUCN established global rankings of the five threats responsible for the current biodiversity crisis (B: central, yellow panel). However, the relative importance of each threat depends on the taxon, system, species’ characteristics, time, and/or the metric considered, resulting in divergences. Global biodiversity threats are represented by colors and symbols, given in the top panel. This figure encapsulates results combined from different studies detailed in Supplementary Table 1 with their associated references.Full size imageThe relative importance of threats also depends on the taxon considered. At the global scale, vertebrates are primarily threatened by habitat loss, overexploitation, and then biological invasions. But even within the vertebrates rankings differ — birds and mammals are mainly affected by overexploitation, while amphibians have a higher probability of succumbing to habitat loss6. Because of species-specific traits and adaptations, some species are likely to respond differently to global threats even within a clade. Large-bodied vertebrates are more likely to be threatened by overexploitation, whereas small-bodied vertebrates are more prone to habitat loss or pollution (Fig. 1). Threat ranking also depends on the habitat under consideration. Marine mammals are more threatened by overexploitation and pollution than terrestrial mammals for which habitat loss is the primary threat (Fig. 1). On islands, habitat loss is secondary to the pressures of biological invasions in freshwater systems, but the former is more important for terrestrial vertebrates and plants3. Another source of uncertainty is that most studies examining threats are based on well-studied taxa such as terrestrial vertebrates, which only represent a small subset of the tree of life. For instance, only 0.2% of fungi, 1.7% of invertebrates, and 10% of described plants are assessed in the IUCN update of 20197, potentially underestimating the intensity of some threats and biasing conservation priorities for these groups. Similarly, there is a bias of research effort towards regions with high-income countries, while research from low or middle-income countries is generally underrepresented8. This may give the false impression of absence of threats in some regions of the world.Likewise, period-specific global threat ranks are subject to the vagaries of temporal dynamics (Fig. 1). However, distinguishing past, current, and future threats is essential for current or future conservation interventions. Historically, overexploitation caused most of the Pleistocene megafauna extinctions, likely exacerbated by climate change. As agricultural practices intensified, habitat loss played a major role in extinctions. As humans later colonized islands, biological invasions caused the extinction of hundreds of species worldwide3. In contrast, climate change is only predicted to become major in the near future9. In fact, the effects of recent threats might be masked by delayed species’ responses, especially in under-studied regions, resulting in a large extinction debt. For instance, the severity of biological invasions often causes native species to decline rapidly to local extinction, while other threats such as habitat loss might affect species more slowly. In both cases, the eventual extinctions are ultimately if similar magnitude. More

Cox, P. & Jones, C. Climate change – Illuminating the modern dance of climate and CO2. Science 321, 1642–1644 (2008).CAS 
PubMed 
Article 

Google Scholar 
Gilmanov, T. G. et al. Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements. Glob. Biogeochem. Cycle 17, 1071 (2003).ADS 
Article 
CAS 

Google Scholar 
Running, S. W. Climate change – Ecosystem disturbance, carbon, and climate. Science 321, 652–653 (2008).CAS 
PubMed 
Article 

Google Scholar 
Sun, Z. et al. Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982–2015. Ecol. Inform. 46, 156–165 (2018).CAS 
Article 

Google Scholar 
Running, S. W. et al. A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens. Environ. 70, 108–127 (1999).ADS 
Article 

Google Scholar 
Madani, N. et al. The Impacts of Climate and Wildfire on Ecosystem Gross Primary Productivity in Alaska. J. Geophys. Res.-Biogeosci. 126, e2020JG006078 (2021).ADS 
Article 

Google Scholar 
Morales, P. et al. Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes. Glob. Change Biol. 11, 2211–2233 (2005).ADS 
Article 

Google Scholar 
Tramontana, G., Ichii, K., Camps-Valls, G., Tomelleri, E. & Papale, D. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data. Remote Sens. Environ. 168, 360–373 (2015).ADS 
Article 

Google Scholar 
Canadell, J. G. et al. Carbon metabolism of the terrestrial biosphere: A multitechnique approach for improved understanding. Ecosystems 3, 115–130 (2000).CAS 
Article 

Google Scholar 
Fletcher, B. J. et al. Photosynthesis and productivity in heterogeneous arctic tundra: consequences for ecosystem function of mixing vegetation types at stand edges. J. Ecol. 100, 441–451 (2012).CAS 
Article 

Google Scholar 
Liu, L., Guan, L. & Liu, X. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence. Agr. Forest Meteorol. 232, 1–9 (2017).ADS 
Article 

Google Scholar 
Xu, X. et al. Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data. J. Environ. Manage. 246, 605–616 (2019).PubMed 
Article 

Google Scholar 
Baldocchi, D. D. How eddy covariance flux measurements have contributed to our understanding of Global Change Biology. Glob. Change Biol. 26, 242–260 (2020).ADS 
Article 

Google Scholar 
He, L., Chen, J. M., Liu, J., Belair, S. & Luo, X. Assessment of SMAP soil moisture for global simulation of gross primary production. J. Geophys. Res.-Biogeosci. 122, 1549–1563 (2017).Article 

Google Scholar 
Wang, S., Ibrom, A., Bauer-Gottwein, P. & Garcia, M. Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest. Agr. Forest Meteorol. 248, 479–493 (2018).ADS 
Article 

Google Scholar 
Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yu, G., Fu, Y., Sun, X., Wen, X. & Zhang, L. Recent progress and future directions of ChinaFLUX. Sci. China Ser. D-Earth Sci. 49, 1–23 (2006).ADS 
Article 

Google Scholar 
McCallum, I. et al. Improved light and temperature responses for light-use-efficiency-based GPP models. Biogeosciences 10, 6577–6590 (2013).ADS 
Article 

Google Scholar 
Stocker, B. D. et al. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nature Geoscience 12, 264‐+ (2019).ADS 
Article 
CAS 

Google Scholar 
Cheng, S. J. et al. Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems. Agr. Forest Meteorol. 201, 98–110 (2015).ADS 
Article 

Google Scholar 
Zhang, M. et al. Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China. Agr. Forest Meteorol. 151, 803–816 (2011).ADS 
Article 

Google Scholar 
Oliphant, A. J. et al. The role of sky conditions on gross primary production in a mixed deciduous forest. Agr. Forest Meteorol. 151, 781–791 (2011).ADS 
Article 

Google Scholar 
Urban, O. et al. Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparison of the response in direct vs. diffuse solar radiation. Glob. Change Biol. 13, 157–168 (2007).ADS 
Article 

Google Scholar 
Zhou, H. et al. Large contributions of diffuse radiation to global gross primary productivity during 1981–2015. Glob. Biogeochem. Cycle 35, e2021GB006957 (2021).ADS 
CAS 
Article 

Google Scholar 
Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236–251 (2012).ADS 
Article 

Google Scholar 
Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. USA 111, E1327–E1333 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, L. & Cheng, Z. Detection of vegetation light-use efficiency based on solar-induced chlorophyll fluorescence separated from canopy radiance spectrum. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 3, 306–312 (2010).ADS 
Article 

Google Scholar 
MacBean, N. et al. Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data (vol 8, 1973, 2018). Sci. Rep. 8, 10420 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Meroni, M. et al. Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sens. Environ. 113, 2037–2051 (2009).ADS 
Article 

Google Scholar 
Zheng, T. & Chen, J. M. Photochemical reflectance ratio for tracking light use efficiency for sunlit leaves in two forest types. ISPRS-J. Photogramm. Remote Sens. 123, 47–61 (2017).ADS 
Article 

Google Scholar 
Damm, A. et al. Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP). Glob. Change Biol. 16, 171–186 (2010).ADS 
Article 

Google Scholar 
Lee, J. E. et al. Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4. Glob. Change Biol. 21, 3469–3477 (2015).ADS 
Article 

Google Scholar 
Pinto, F. et al. Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ. 39, 1500–1512 (2016).CAS 
PubMed 
Article 

Google Scholar 
Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065–4095 (2014).CAS 
PubMed 
Article 

Google Scholar 
Xie, X., Li, A., Jin, H., Yin, G. & Nan, X. Derivation of temporally continuous leaf maximum carboxylation rate (V-cmax) from the sunlit leaf gross photosynthesis productivity through combining BEPS model with light response curve at tower flux sites. Agr. Forest Meteorol. 259, 82–94 (2018).ADS 
Article 

Google Scholar 
Chen, J. M., Liu, J., Leblanc, S. G., Lacaze, R. & Roujean, J. L. Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sens. Environ. 84, 516–525 (2003).ADS 
Article 

Google Scholar 
Chen, J. M. et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Glob. Biogeochem. Cycle 26, GB1019 (2012).ADS 
Article 
CAS 

Google Scholar 
Running, S. W., Thornton, P. E., Nemani, R. & Glassy, J. M. in Methods in Ecosystem Science. Ch.3 (Springer, New York, NY. Press, 2000).Wu, C., Munger, J. W., Niu, Z. & Kuang, D. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sens. Environ. 114, 2925–2939 (2010).ADS 
Article 

Google Scholar 
Makela, A. et al. Developing an empirical model of stand GPP with the LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe. Glob. Change Biol. 14, 92–108 (2008).ADS 
Article 

Google Scholar 
McCallum, I. et al. Satellite-based terrestrial production efficiency modeling. Carbon Balanc. Manag. 4, 8–8 (2009).Article 

Google Scholar 
Wang, H. et al. Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sens. Environ 114, 2248–2258 (2010).ADS 
Article 

Google Scholar 
Yu, R. An improved estimation of net primary productivity of grassland in the Qinghai-Tibet region using light use efficiency with vegetation photosynthesis model. Ecol. Model. 431, 109121 (2020).Article 

Google Scholar 
Yuan, W. et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agr. Forest Meteorol. 143, 189–207 (2007).ADS 
Article 

Google Scholar 
Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

Google Scholar 
Zhang, Y. et al. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agr. Forest Meteorol. 223, 116–131 (2016).ADS 
Article 

Google Scholar 
He, M. et al. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agr. Forest Meteorol. 173, 28–39 (2013).ADS 
Article 

Google Scholar 
Zhou, Y. et al. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites. J. Geophys. Res.-Biogeosci. 121, 1045–1072 (2016).Article 

Google Scholar 
Friedlingstein, P. et al. Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks. J. Clim. 27, 511–526 (2014).ADS 
Article 

Google Scholar 
Raich, J. W. et al. Potential net primary productivity in South-America – application of a global-model. Ecol. Appl. 1, 399–429 (1991).CAS 
PubMed 
Article 

Google Scholar 
Li, J. et al. An algorithm differentiating sunlit and shaded leaves for improving canopy conductance and vapotranspiration estimates. J. Geophys. Res.-Biogeosci. 124, 807–824 (2019).ADS 
Article 

Google Scholar 
Chen, J. M., Liu, J., Cihlar, J. & Goulden, M. L. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119 (1999).CAS 
Article 

Google Scholar 
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).PubMed 
Article 

Google Scholar 
Korson, L., Drosthan, W. & Millero, F. J. Viscosity of water at various temperatures. J. Phys. Chem. 73, 34–39 (1969).CAS 
Article 

Google Scholar 
Olofsson, P., Van Laake, P. E. & Eklundh, L. Estimation of absorbed PAR across Scandinavia from satellite measurements Part I: Incident PAR. Remote Sens. Environ. 110, 252–261 (2007).ADS 
Article 

Google Scholar 
González, J. A. & Calbó, J. Modelled and measured ratio of PAR to global radiation under cloudless skies. Agr. Forest Meteorol. 110, 319–325 (2002).ADS 
Article 

Google Scholar 
Zhang, X., Zhang, Y. & Zhoub, Y. Measuring and modelling photosynthetically active radiation in Tibet Plateau during April–October. Agr. Forest Meteorol. 102, 207–212 (2000).ADS 
Article 

Google Scholar 
Yang, Y., Xiao, P., Feng, X. & Li, H. Accuracy assessment of seven global land cover datasets over China. ISPRS-J. Photogramm. Remote Sens. 125, 156–173 (2017).ADS 
Article 

Google Scholar 
Liu, Y., Liu, R. & Chen, J. M. GLOBMAP global Leaf Area Index since 1981. Zenodo https://doi.org/10.5281/zenodo.4700264 (2019).Vermote, E. MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD09A1.006 (2015).Deng, F., Chen, J. M., Plummer, S., Chen, M. & Pisek, J. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans. Geosci. Remote Sens. 44, 2219–2229 (2006).ADS 
Article 

Google Scholar 
Vermote, E. NOAA CDR Program. NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5. LAI. NOAA National Centers for Environmental Information https://doi.org/10.7289/V5TT4P69 (2019).He, L., Chen, J. M., Pisek, J., Schaaf, C. & Strahler, A. Global clumping index map derived from the MODIS BRDF product. Remote Sens. Environ. 119, 118–130 (2012).ADS 
Article 

Google Scholar 
Liu, R. G. & Liu, Y. Generation of new cloud masks from MODIS land surface reflectance products. Remote Sens. Environ. 133, 21–37 (2013).ADS 
CAS 
Article 

Google Scholar 
Chen, J. M., Deng, F. & Chen, M. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter. IEEE Trans. Geosci. Remote Sens. 44, 2230–2238 (2006).ADS 
Article 

Google Scholar 
Harris, I.C. CRU JRA: Collection of CRU JRA forcing datasets of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data. Centre for Environmental Data Analysis http://catalogue.ceda.ac.uk/uuid/863a47a6d8414b6982e1396c69a9efe8 (2019).Li, X., Liang, H. & Cheng, W. Evaluation and comparison of light use efficiency models for their sensitivity to the diffuse PAR fraction and aerosol loading in China. Int. J. Appl. Earth Obs. Geoinf. 95, 102269 (2021).
Google Scholar 
Duan, Q. Y., Sorooshian, S. & Gupta, V. Effective and efficient global optimization for conceptual rain full-runoff models. Water Resour. Res. 28, 1015–1031 (1992).ADS 
Article 

Google Scholar 
Gu, L. H. et al. Advantages of diffuse radiation for terrestrial ecosystem productivity. J. Geophys. Res.-Atmos. 107, 4050 (2002).ADS 

Google Scholar 
Bi, W. & Zhou, Y. A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020). Dryad https://doi.org/10.5061/dryad.dfn2z352k (2022).Ogutu, B. O. & Dash, J. Assessing the capacity of three production efficiency models in simulating gross carbon uptake across multiple biomes in conterminous USA. Agr. Forest Meteorol. 174, 158–169 (2013).ADS 
Article 

Google Scholar 
Cai, W. et al. Large differences in terrestrial vegetation production derived from satellite-based light use efficiency models. Remote Sens. 6, 8945–8965 (2014).ADS 
Article 

Google Scholar 
Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785–818 (2015).ADS 
Article 

Google Scholar 
Li, X. & Xiao, J. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: A global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sens. 11, 2563 (2019).ADS 
Article 

Google Scholar 
Alemohammad, S. H. et al. Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. Biogeosciences 14, 4101–4124 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).ADS 
Article 

Google Scholar 
Wang, S., Zhang, Y., Ju, W., Qiu, B. & Zhang, Z. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Sci. Total Environ. 755, 142569 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zheng, Y. et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data 12, 2725–2746 (2020).ADS 
Article 

Google Scholar 
Running, S., Mu, Q. & Zhao, M. MOD17A2H MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD17A2H.006 (2015).Ciais, P. et al. A three-dimensional synthesis study of delta O-18 in atmospheric CO2 .1. Surface fluxes. J. Geophys. Res.-Atmos. 102, 5857–5872 (1997).ADS 
CAS 
Article 

Google Scholar 
Zhang, Y., Joiner, J., Gentine, P. & Zhou, S. Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts. Glob. Change Biol. 24, 2229–2230 (2018).ADS 
Article 

Google Scholar 
Xie, X. et al. Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models. Sci. Total Environ. 690, 1120–1130 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fang, H., Wei, S., Jiang, C. & Scipal, K. Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method. Remote Sens. Environ. 124, 610–621 (2012).ADS 
Article 

Google Scholar 
Camacho, F., Cemicharo, J., Lacaze, R., Baret, F. & Weiss, M. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sens. Environ. 137, 310–329 (2013).ADS 
Article 

Google Scholar 
Prince, S. D. & Goward, S. N. Global primary production: A remote sensing approach. J. Biogeogr. 22, 815–835 (1995).Article 

Google Scholar 
Verma, S. B. et al. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agr. Forest Meteorol. 131, 77–96 (2005).ADS 
Article 

Google Scholar 
Yan, H. et al. Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants. Ecol. Model. 297, 42–59 (2015).CAS 
Article 

Google Scholar 
Jiang, S. et al. Comparison of satellite-based models for estimating gross primary productivity in agroecosystems. Agr. Forest Meteorol. 297, 108253 (2021).ADS 
Article 

Google Scholar 
Yang, X. et al. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).ADS 
CAS 
Article 

Google Scholar 
Zhou, H. et al. Responses of gross primary productivity to diffuse radiation at global FLUXNET sites. Atmos. Environ. 244, 117905 (2021).CAS 
Article 

Google Scholar 
Han, J. et al. Effects of diffuse photosynthetically active radiation on gross primary productivity in a subtropical coniferous plantation vary in different timescales. Ecol. Indic. 115, 106403 (2020).Article 

Google Scholar 
Grant, I. F., Prata, A. J. & Cechet, R. P. The impact of the diurnal variation of albedo on the remote sensing of the daily mean albedo of grassland. J. Appl. Meteorol. 39, 231–244 (2000).ADS 
Article 

Google Scholar 
Singarayer, J. S., Ridgwell, A. & Irvine, P. Assessing the benefits of crop albedo bio-geoengineering. Environ. Res. Lett. 4, 045110 (2009).ADS 
Article 

Google Scholar 
Tang, S. et al. LAI inversion algorithm based on directional reflectance kernels. J. Environ. Manage. 85, 638–648 (2007).CAS 
PubMed 
Article 

Google Scholar  More