Ecology

1.UNEP. Global Environment Outlook – GEO6: Healthy Planet, Healthy People (Cambridge Univ. Press, 2019); https://www.unep.org/resources/global-environment-outlook-62.Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).CAS 
Article 

Google Scholar 
3.Mori, A. S., Spies, T. A., Sudmeier-Rieux, K. & Andrade, A. Reframing ecosystem management in the era of climate change: issues and knowledge from forests. Biol. Conserv. 165, 115–127 (2013).Article 

Google Scholar 
4.Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5° C rather than 2° C. Science 360, 791–795 (2018).CAS 
Article 

Google Scholar 
5.Garcia, R. A., Cabeza, M., Rahbek, C. & Araujo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science 344, 1247579 (2014).Article 
CAS 

Google Scholar 
6.Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).CAS 
Article 

Google Scholar 
7.IPBES secretariat. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (eds. Diaz, S. et al.) (IPBES, 2019); https://ipbes.net/global-assessment8.Midgley, G. F. et al. Terrestrial carbon stocks and biodiversity: key knowledge gaps and some policy implications. Curr. Opin. Environ. Sustain. 2, 264–270 (2010).Article 

Google Scholar 
9.Jones, A. D., Calvin, K. V., Collins, W. D. & Edmonds, J. Accounting for radiative forcing from albedo change in future global land-use scenarios. Clim. Change 131, 691–703 (2015).Article 

Google Scholar 
10.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).CAS 
Article 

Google Scholar 
11.Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Change 9, 84–87 (2019).Article 

Google Scholar 
12.Morecroft, M. D. et al. Measuring the success of climate change adaptation and mitigation in terrestrial ecosystems. Science 366, eaaw9256 (2019).CAS 
Article 

Google Scholar 
13.Mori, A. S. Advancing nature-based approaches to address the biodiversity and climate emergency. Ecol. Lett. 23, 1729–1732 (2020).14.Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
Article 

Google Scholar 
15.Holl, K. D. & Brancalion, P. H. S. Tree planting is not a simple solution. Science 368, 580–581 (2020).CAS 
Article 

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

Google Scholar 
17.Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354, aaf8957 (2016).Article 
CAS 

Google Scholar 
18.Mori, A. S. Environmental controls on the causes and functional consequences of tree species diversity. J. Ecol. 106, 113–125 (2018).Article 

Google Scholar 
19.Hulvey, K. B. et al. Benefits of tree mixes in carbon plantings. Nat. Clim. Change 3, 869–874 (2013).CAS 
Article 

Google Scholar 
20.World Economic Forum. The Global Risks Report 2020 https://www.weforum.org/reports/the-global-risks-report-2020 (2020).21.Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Ann. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
22.Isbell, F., Tilman, D., Polasky, S. & Loreau, M. The biodiversity-dependent ecosystem service debt. Ecol. Lett. 18, 119–134 (2015).Article 

Google Scholar 
23.Gonzalez, A. et al. Scaling-up biodiversity-ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).Article 

Google Scholar 
24.Mokany, K. et al. Integrating modelling of biodiversity composition and ecosystem function. Oikos 125, 10–19 (2016).Article 

Google Scholar 
25.Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).CAS 
Article 

Google Scholar 
26.Running, S., Mu, Q., Zhao, M. & MODAPS-SIPS. MOD17A3 MODIS/Terra Gross Primary Productivity Yearly L4 Global 1km SIN Grid (NASA, 2015); https://doi.org/10.5067/MODIS/MOD17A3.00627.Fujimori, S., Hasegawa, T., Ito, A., Takahashi, K. & Masui, T. Gridded emissions and land-use data for 2005-2100 under diverse socioeconomic and climate mitigation scenarios. Sci. Data 5, 180210 (2018).Article 

Google Scholar 
28.Ohashi, H. et al. Biodiversity can benefit from climate stabilization despite adverse side effects of land-based mitigation. Nat. Commun. 10, 5240 (2019).Article 
CAS 

Google Scholar 
29.Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).Article 

Google Scholar 
30.Fadrique, B. et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature 564, 207–212 (2018).CAS 
Article 

Google Scholar 
31.Ammer, C. Diversity and forest productivity in a changing climate. New Phytol. 221, 50–66 (2019).Article 

Google Scholar 
32.Hasegawa, T. et al. Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Change 8, 699–703 (2018).Article 

Google Scholar 
33.Ricke, K., Drouet, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Change 8, 895–900 (2018).CAS 
Article 

Google Scholar 
34.Anderson, C. M. et al. Natural climate solutions are not enough. Science 363, 933–934 (2019).CAS 
Article 

Google Scholar 
35.Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).Article 

Google Scholar 
36.Mori, A. S., Lertzman, K. P. & Gustafsson, L. Biodiversity and ecosystem services in forest ecosystems: a research agenda for applied forest ecology. J. Appl. Ecol. 54, 12–27 (2017).Article 

Google Scholar 
37.Bastin, J. F. et al. The global tree restoration potential. Science 365, 76–79 (2019).CAS 
Article 

Google Scholar 
38.Quine, C. P., Bailey, S. A., Watts, K. & Hulme, P. Sustainable forest management in a time of ecosystem services frameworks: common ground and consequences. J. Appl. Ecol. 50, 863–867 (2013).Article 

Google Scholar 
39.Climate Change for Forest Policy-Makers: An Approach for Integrating Climate Change into National Forest Policy in Support of Sustainable Forest Management Version 2.0. FAO Forestry Paper No. 181 (FAO, 2018); http://www.fao.org/3/CA2309EN/ca2309en.pdf40.The Future We Want: Biodiversity and Ecosystems—Driving Sustainable Development. United Nations Development Programme Biodiversity and Ecosystems Global Framework 2012-2020 (UNDP, 2012); https://www.cbd.int/financial/mainstream/undp-globalframework2012-2020.pdf41.Thompson, I., Mackey, B., McNulty, S. & Mosseler, A. Forest Resilience, Biodiversity, and Climate Change. A Synthesis of the Biodiversity/Resilience/Stability Relationship in Forest Ecosystems. Technical Series No. 43 (Convention on Biological Diversity, 2009); https://www.cbd.int/doc/publications/cbd-ts-43-en.pdf42.CBD secretariat. Connecting Biodiversity and Climate Change Mitigation and Adaptation: Report of the Second ad hoc Technical Expert Group on Biodiversity and Climate Change. Technical Series No. 41 (Convention on Biological Diversity, 2009); https://www.cbd.int/doc/publications/cbd-ts-41-en.pdf43.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).CAS 
Article 

Google Scholar 
44.Dee, L. E. et al. When do ecosystem services depend on rare species? Trends Ecol. Evol. 34, 746–758 (2019).Article 

Google Scholar 
45.Fois, M., Cuena-Lombraña, A., Fenu, G. & Bacchetta, G. Using species distribution models at local scale to guide the search of poorly known species: review, methodological issues and future directions. Ecol. Model. 385, 124–132 (2018).Article 

Google Scholar 
46.Jordano, P. & Rees, M. What is long-distance dispersal? And a taxonomy of dispersal events. J. Ecol. 105, 75–84 (2017).Article 

Google Scholar 
47.Veldman, J. W. et al. Comment on ‘The global tree restoration potential’. Science 366, eaay7976 (2019).Article 

Google Scholar 
48.Naudts, K. et al. Europe’s forest management did not mitigate climate warming. Science 351, 597–600 (2016).CAS 
Article 

Google Scholar 
49.Luyssaert, S. et al. Trade-offs in using European forests to meet climate objectives. Nature 562, 259–262 (2018).CAS 
Article 

Google Scholar 
50.Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108 (2016).CAS 
Article 

Google Scholar 
51.Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).CAS 
Article 

Google Scholar 
52.Bellamy, R. & Osaka, S. Unnatural climate solutions? Nat. Clim. Change 10, 98–99 (2020).Article 

Google Scholar 
53.Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).Article 

Google Scholar 
54.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
55.Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011).Article 

Google Scholar 
56.Collins, W. J. et al. Development and evaluation of an Earth-System model – HadGEM2. Geosci. Model Dev. 4, 1051–1075 (2011).Article 

Google Scholar 
57.Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).Article 

Google Scholar 
58.Griffies, S. M. et al. The GFDL CM3 coupled climate model: characteristics of the ocean and sea ice simulations. J. Clim. 24, 3520–3544 (2011).Article 

Google Scholar 
59.Fujimori, S., Hasegawa, T. & Masui, T. In Post-2020 Climate Action (eds Fujimori, S., Kainuma, M. & Masui, T.) 305–328 (Springer, 2017).60.Hasegawa, T., Fujimori, S., Ito, A., Takahashi, K. & Masui, T. Global land-use allocation model linked to an integrated assessment model. Sci. Total Environ. 580, 787–796 (2017).CAS 
Article 

Google Scholar 
61.Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).Article 

Google Scholar 
62.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
63.Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).Article 

Google Scholar 
64.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).Article 

Google Scholar 
65.Pearson, R. G., Dawson, T. P. & Liu, C. Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography 27, 285–298 (2004).Article 

Google Scholar 
66.Tamme, R. et al. Predicting species’ maximum dispersal distances from simple plant traits. Ecology 95, 505–513 (2014).Article 

Google Scholar 
67.Engen, S., Lande, R., Walla, T. & DeVries, P. J. Analyzing spatial structure of communities using the two-dimensional Poisson lognormal species abundance model. Am. Nat. 160, 60–73 (2002).Article 

Google Scholar 
68.He, F. & Gaston, K. J. Occupancy, spatial variance, and the abundance of species. Am. Nat. 162, 366–375 (2003).Article 

Google Scholar 
69.Magurran, A. E. & McGill, B. J. Biological Diversity (Oxford Univ. Press, 2011).70.Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785–794 (KDD, 2016); https://doi.org/10.1145/2939672.293978571.He, F. & Hubbell, S. P. Species–area relationships always overestimate extinction rates from habitat loss. Nature 473, 368–371 (2011).CAS 
Article 

Google Scholar 
72.Neigel, J. E. Species–area relationships and marine conservation. Ecol. Appl 13, 138–145 (2003).Article 

Google Scholar 
73.Rogan, J. E. & Lacher, T. E. Impacts of Habitat Loss and Fragmentation on Terrestrial Biodiversity. in Earth Systems and Environmental Sciences. https://doi.org/10.1016/b978-0-12-409548-9.10913-3 (Elsevier, 2018).74.Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity–biodiversity relationship. Nature 416, 427–430 (2002).CAS 
Article 

Google Scholar 
75.Botanic Gardens Conservation International. Global Tree Search Database. Version 1.3 (Botanic Gardens Conservation International, 2019); https://tools.bgci.org/global_tree_search.php More

1.Harvell, D., Altizer, S., Cattadori, I. M., Harrington, L. & Weil, E. Climate change and wildlife diseases: when does the host matter the most? Ecology 90, 912–920 (2009).2.Wood, C. L. & Johnson, P. T. J. A world without parasites: exploring the hidden ecology of infection. Front. Ecol. Environ. 13, 425–434 (2015).3.Randall, C. J. & Van Woesik, R. Contemporary white-band disease in Caribbean corals driven by climate change. Nat. Clim. Chang. 5, 375–379 (2015).Article 

Google Scholar 
4.Bruno, J. F. et al. Thermal stress and coral cover as drivers of coral disease outbreaks. PLoS Biol. 5, 1220–1227 (2007).Article 
CAS 

Google Scholar 
5.Pollock, F. J. et al. Sediment and turbidity associated with offshore dredging increase coral disease prevalence on nearby reefs. PLoS ONE 9, e102498 (2014).6.Harvell, C. D. et al. Climate warming and disease risks for terrestrial and marine biota. Science 296, 2158–2162 (2002).7.Lafferty, K. D. & Kuris, A. M. Mass mortality of abalone Haliotis cracherodii on the California Channel Islands: tests of epidemiological hypotheses. Mar. Ecol. Prog. Ser. 96, 239–239 (1993).8.Miner, C. M. et al. Large-scale impacts of sea star wasting disease (SSWD) on intertidal sea stars and implications for recovery. PLoS ONE 13, e0192870 (2018).9.Patterson, K. L. et al. The etiology of white pox, a lethal disease of the Caribbean elkhorn coral, Acropora palmata. Proc. Natl Acad. Sci. USA 99, 8725–8730 (2002).10.Aronson, R. B. & Precht, W. F. White-band disease and the changing face of Caribbean coral reefs. Hydrobiologia. 460, 25–38 (2001).Article 

Google Scholar 
11.Weil, E., Croquer, A. & Urreiztieta, I. Temporal variability and impact of coral diseases and bleaching in La Parguera, Puerto Rico from 2003-2007. Caribb. J. Sci. 45, 221–246 (2009).12.Muller, E. et al. Coral disease following massive bleaching in 2005 causes 60% decline in coral cover on reefs in the US Virgin Islands. Coral Reefs. 28, 925–937 (2009).Article 

Google Scholar 
13.Jones, G. P., McCormick, M. I., Srinivasan, M. & Eagle, J. V. Coral decline threatens fish biodiversity in marine reserves. Proc. Natl Acad. Sci. USA 101, 8251–8253 (2004).14.Jones, D. O. B. et al. Global reductions in seafloor biomass in response to climate change. Glob Chang Biol. 20, 1861–1872 (2014).15.Descombes, P. et al. Forecasted coral reef decline in marine biodiversity hotspots under climate change. Glob. Chang Biol. 21, 2479–2487 (2015).16.Weil, E., Smith, G. & Gil-Agudelo, D. L. Status and progress in coral reef disease research. Dis. Aquatic Organ. 69, 1–7 (2006).17.Sutherland, K. P., Porter, J. W. & Torres, C. Disease and immunity in Caribbean and Indo-Pacific zooxanthellate corals. Marine Ecol. Progress Ser. 266, 273–302 (2004).18.Pollock, F. J., Morris, P. J., Willis, B. L. & Bourne, D. G. The urgent need for robust coral disease diagnostics. PLoS Pathog. 7, e1002183 (2011).19.Williams, L., Smith, T. B., Burge, C. A. & Brandt, M. E. Species-specific susceptibility to white plague disease in three common Caribbean corals. Coral Reefs 39, 27–31 (2020).20.Velthuis, A. G. J., Bouma, A., Katsma, W. E. A., Nodelijk, G. & De Jong, M. C. M. Design and analysis of small-scale transmission experiments with animals. Epidemiol. Infect. 135, 202–217 (2007).21.Richardson, L. L., Goldberg, W. M., Carlton, R. G. & Halas, J. C. Coral disease outbreak in the Florida keys: Plague type II. Rev. Biol. Trop. 46, 187–198 (1998).
Google Scholar 
22.Frias-Lopez, J., Klaus, J. S., Bonheyo, G. T. & Fouke, B. W. Bacterial community associated with black band disease in corals. Appl. Environ. Microbiol. 70, 5955–5962 (2004).23.Soffer, N., Brandt, M. E., Correa, A. M. S., Smith, T. B. & Thurber, R. V. Potential role of viruses in white plague coral disease. ISME J. 8, 271–283 (2014).24.Sweet, M. et al. Compositional homogeneity in the pathobiome of a new, slow-spreading coral disease. Microbiome 7, 1–14 (2019).25.Sweet, M. J. & Bulling, M. T. On the importance of the microbiome and pathobiome in coral health and disease. Front. Marine Sci. 4, 9 (2017).26.Egan, S. & Gardiner, M. Microbial dysbiosis: rethinking disease in marine ecosystems. Front. Microbiol. 7, 991 (2016).27.Ezzat, L. et al. Parrotfish predation drives distinct microbial communities in reef-building corals. Anim. Microbiome 2, 5 (2020).28.Ezzat, L. et al. Surgeonfish feces increase microbial opportunism in reef-building corals. Mar. Ecol. Prog. Ser. 631, 81–97 (2019).29.Meyer, J. L. et al. Microbial community shifts associated with the ongoing stony coral tissue loss disease outbreak on the Florida reef tract. Front. Microbiol. 10, 2244 (2019).30.Lima, L. F. O. et al. Modeling of the coral microbiome: the influence of temperature and microbial network. MBio. 11, e02691–19 (2020).31.Thurber, R. V. et al. Deciphering coral disease dynamics: integrating host, microbiome, and the changing environment. Front. Ecol. Evol. 8, 402 (2020).
Google Scholar 
32.Cárdenas, A., Rodriguez-R, L. M., Pizarro, V., Cadavid, L. F. & Arévalo-Ferro, C. Shifts in bacterial communities of two Caribbean reef-building coral species affected by white plague disease. ISME J. 6, 502–512 (2012).Article 
CAS 

Google Scholar 
33.Meyer, J. L., Gunasekera, S. P., Scott, R. M., Paul, V. J. & Teplitski M. Microbiome shifts and the inhibition of quorum sensing by Black Band Disease cyanobacteria. ISME J. 10, 1204–1216 (2016).34.Sweet M. J., Burian A., Bulling M. Corals as canaries in the coalmine: towards the incorporation of marine ecosystems into the ‘One Health’ concept. OSF Prepr. https://doi.org/10.31219/osf.io/gv6s7 (2020).35.Glasl, B. et al. Microbial indicators of environmental perturbations in coral reef ecosystems. Microbiome 7, 1–13 (2019).36.Zaneveld, J. R., McMinds, R. & Thurber, R. V. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat. Microbiol. 2, 1–8 (2017).37.Fan, L., Liu, M., Simister, R., Webster, N. S. & Thomas T. Marine microbial symbiosis heats up: the phylogenetic and functional response of a sponge holobiont to thermal stress. ISME J. 7, 991–1002 (2013).38.Darling, E. S., Alvarez-Filip, L., Oliver, T. A., Mcclanahan, T. R. & Côté, I. M. Evaluating life-history strategies of reef corals from species traits. Ecol. Lett. 15, 1378–1386 (2012).39.Calnan, J. M., Smith, T. B., Nemeth, R. S., Kadison, E. & Blondeau, J. Coral disease prevalence and host susceptibility on mid-depth and deep reefs in the United States Virgin Islands. Rev. Biol. Trop. 56, 223–224 (2008).40.Perry, C. T. et al. Regional-scale dominance of non-framework building corals on Caribbean reefs affects carbonate production and future reef growth. Glob Chang Biol. 21, 1153–1164 (2015).41.Okazaki, R. R. et al. Species-specific responses to climate change and community composition determine future calcification rates of Florida Keys reefs. Glob. Chang Biol. 23, 1023–1035 (2017).42.Green, D. H., Edmunds, P. J. & Carpenter, R. C. Increasing relative abundance of Porites astreoides on Caribbean reefs mediated by an overall decline in coral cover. Mar. Ecol. Prog. Ser. 359, 1–10 (2008).43.Pinzón, C. J. H., Beach-Letendre, J., Weil, E. & Mydlarz, L. D. Relationship between phylogeny and immunity suggests older caribbean coral lineages are more resistant to disease. PLoS ONE 9, e104787 (2014).44.Smith, T. B. et al. Convergent mortality responses of Caribbean coral species to seawater warming. Ecosphere 4, 1–40 (2013).45.Jolles, A. E., Sullivan, P., Alker, A. P., Harvell, C. D. Disease transmission of aspergillosis in sea fans: Inferring process from spatial pattern. Ecology 83, 2373–2378 (2002).46.Shore, A. & Caldwell, J. M. Modes of coral disease transmission: how do diseases spread between individuals and among populations? Mar. Biol. 166, 45 (2019).Article 

Google Scholar 
47.Glasl, B., Herndl, G. J., Frade, P. R. The microbiome of coral surface mucus has a key role in mediating holobiont health and survival upon disturbance. ISME J. 10, 2280–2292 (2016).48.Lesser, M. P., Bythell, J. C., Gates, R. D. & Johnstone, R. W., Hoegh-Guldberg, O. Are infectious diseases really killing corals? Alternative interpretations of the experimental and ecological data. J. Exp. Mar. Bio. Ecol. 346, 36–44 (2007).49.Muller, E. M. & Van Woesik, R. Caribbean coral diseases: primary transmission or secondary infection? Glob. Chang Biol. 18, 3529–3535 (2012).50.Fernandes, N. et al. Genomes and virulence factors of novel bacterial pathogens causing bleaching disease in the marine red alga Delisea pulchra. PLoS ONE 6, e27387 (2011).51.Vandecandelaere, I. et al. Nautella italica gen. nov., sp. nov., isolated from a marine electroactive biofilm. Int. J. Syst. Evol. Microbiol. 59, 811–817 (2009).52.Dang, H. & Lovell, C. R. Bacterial primary colonization and early succession on surfaces in marine waters as determined by amplified rRNA gene restriction analysis and sequence analysis of 16S rRNA genes. Appl. Environ. Microbiol. 66, 467–475 (2000).53.Kviatkovski, I. & Minz, D. A member of the Rhodobacteraceae promotes initial biofilm formation via the secretion of extracellular factor(s). Aquat. Microb. Ecol. 75, 155–167 (2015).54.Rosales, S. M., Clark, A. S., Huebner, L. K., Ruzicka, R. R. & Muller E. M. Rhodobacterales and Rhizobiales are associated with stony coral tissue loss disease and its suspected sources of transmission. Front. Microbiol. 11, 681 (2020).55.Campbell, A. H., Harder, T., Nielsen, S., Kjelleberg, S. & Steinberg, P. D. Climate change and disease: bleaching of a chemically defended seaweed. Glob. Chang. Biol. 17, 2958–2970 (2011).56.Kumar, V., Zozaya-Valdes, E., Kjelleberg, S., Thomas, T. & Egan, S. Multiple opportunistic pathogens can cause a bleaching disease in the red seaweed Delisea pulchra. Environ. Microbiol. 18, 3962–3975 (2016).57.Brandt, M. E. & Mcmanus, J. W. Disease incidence is related to bleaching extent in reef-building corals. Ecology 90, 2859–2867 (2009).58.Brandt, M. E., Smith, T. B., Correa, A. M. S. & Vega-Thurber, R. Disturbance driven colony fragmentation as a driver of a coral disease outbreak. PLoS ONE 8, e57164 (2013).59.Godwin, S., Bent, E., Borneman, J. & Pereg, L. The role of coral-associated bacterial communities in Australian subtropical white Syndrome of Turbinaria mesenterina. PLoS ONE 7, e44243 (2012).60.Ranson, H. J. et al. Draft Genome Sequence of the Putative Marine Pathogen Thalassobius sp. I31.1. Microbiol. Resour. Announc. 8, e01431–18 (2019).61.Miller A. W., Richardson L. L. Fine structure analysis of black band disease (BBD) infected coral and coral exposed to the BBD toxins microcystin and sulfide. J. Invertebr. Pathol. 109, 27–33 (2012).62.Geffen, Y., Ron, E. Z. & Rosenberg, E. Regulation of release of antibacterials from stressed scleractinian corals. FEMS Microbiol. Lett. 295, 103–109 (2009).63.Beurmann, S. et al. Pseudoalteromonas piratica strain OCN003 is a coral pathogen that causes a switch from chronic to acute Montipora white syndrome in Montipora capitata. PLoS ONE (2017).64.Apprill, A., Marlow, H. Q., Martindale, M. Q., Rappé, M. S. Specificity of associations between bacteria and the coral Pocillopora meandrina during early development. Appl. Environ. Microbiol. 78, 7467–7475 (2012).65.Shnit-Orland, M., Sivan, A. & Kushmaro, A. Antibacterial activity of Pseudoalteromonas in the Coral Holobiont. Microb. Ecol. 64, 851–859 (2012).66.Sunagawa, S. et al. Bacterial diversity and white Plague disease-associated community changes in the caribbean coral montastraea faveolata. ISME J. 3, 512–521 (2009).Article 
CAS 

Google Scholar 
67.Bettarel, Y. et al. Corallivory and the microbial debacle in two branching scleractinians. ISME J. 12, 1109–1126 (2018).68.Ritchie, K. B. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar. Ecol. Prog. Ser. 322, 1–14 (2006).69.Bayer, T. et al. The microbiome of the red sea coral stylophora pistillata is dominated by tissue-associated endozoicomonas bacteria. Appl. Environ. Microbiol. 79, 4759–4762 (2013).70.Lesser, M. P. & Jarett, J. K. Culture-dependent and culture-independent analyses reveal no prokaryotic community shifts or recovery of Serratia marcescens in Acropora palmata with white pox disease. FEMS Microbiol. Ecol. 88, 457–467 (2014).Article 
CAS 

Google Scholar 
71.Morrow, K. M., Muller, E. & Lesser, M. P. How does the coral microbiome cause, respond to, or modulate the bleaching process? Coral Bleaching 233, 153–188 (2018).72.Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. 9, 1–13 (2018).73.Price, E. P. et al. Accurate and rapid identification of the Burkholderia pseudomallei near-neighbour, Burkholderia ubonensis, using real-time PCR. PLoS ONE 8, e71647 (2013).74.Price, E. P. et al. Phylogeographic, genomic, and meropenem susceptibility analysis of Burkholderia ubonensis. PLoS Negl. Trop. Dis. 11, e0005928 (2017).75.Therneau, T. Package Survival: A Package for Survival Analysis in R. R Package version 238. (2015).76.Oksanen, J. et al. Vegan: community ecology. R package version 2.2-1. (2015).77.Andres, B., David, O., Sebastien, V., Julien, De B. & Fabien, L. betapart: partitioning Beta Diversity into Turnover and Nestedness Components. R Packag. (1.5.1). https://cran.r-project.org/package=betapart (2018).78.nmacknight. nmacknight/16sCommunityAnalysis: First Release. 2021 Mar 24 [cited 2021 Mar 24]: https://doi.org/10.5281/zenodo.4635319#.YFvOI-zLDNs.mendeley. (2021). More

1.Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).Article 

Google Scholar 
2.Brown, J. H. & Kodric-Brown, A. Turnover rates in insular biogeography: Effect of immigration on extinction. Ecology 58, 445–449 (1977).Article 

Google Scholar 
3.Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).Article 

Google Scholar 
4.Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255–1262 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Presley, S. J., Higgins, C. L. & Willig, M. R. A comprehensive framework for the evaluation of metacommunity structure. Oikos 119, 908–917 (2010).Article 

Google Scholar 
6.Leibold, M. A. & Mikkelson, G. M. Coherence, species turnover, and boundary clumping: Elements of metacommunity structure. Oikos 97, 237–250 (2002).Article 

Google Scholar 
7.Clements, F. E. Plant Succession: An Analysis of the Development of Vegetation (Carnegie Institution of Washington, Washington, DC, 1916).Book 

Google Scholar 
8.Patterson, B. D. & Atmar, W. Nested subsets and the structure of insular mammalian faunas and archipelagos. Biol. J. Linn. Soc. 28, 65–82 (1986).Article 

Google Scholar 
9.Nekola, J. C. & White, P. S. The distance decay of similarity in biogeography and ecology. J. Biogeogr. 26, 867–878 (1999).Article 

Google Scholar 
10.Tornero, I. et al. Dispersal mode and spatial extent influence distance-decay patterns in pond metacommunities. PLOS ONE 13, e0203119. https://doi.org/10.1371/journal.pone.0203119 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Heino, J. The importance of metacommunity ecology for environmental assessment research in the freshwater realm. Biol. Rev. 88, 166–178 (2013).PubMed 
Article 

Google Scholar 
12.Walker, D. M. et al. Variability in snake skin microbial assemblages across spatial scales and disease states. ISME J. 13, 2209–2222 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Presley, S. J., Cisneros, L. M., Patterson, B. D. & Willig, M. R. Vertebrate metacommunity structure along an extensive elevational gradient in the tropics: A comparison of bats, rodents and birds. Glob. Ecol. Biogeogr. 21, 968–976 (2012).Article 

Google Scholar 
14.Heino, J. et al. Elements of metacommunity structure and community-environment relationships in stream organisms. Freshw. Biol. 60, 973–988 (2015).Article 

Google Scholar 
15.Hernández-Gómez, O., Hoverman, J. T. & Williams, R. N. Cutaneous microbial community variation across populations of eastern hellbenders (Cryptobranchus alleganiensis alleganiensis). Front. Microbiol. 8, 1379. https://doi.org/10.3389/fmicb.2017.01379 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
16.Wilber, M. Q., Jani, A. J., Mihaljevic, J. R. & Briggs, C. J. Fungal infection alters the selection, dispersal and drift processes structuring the amphibian skin microbiome. Ecol. Lett. 23, 88–98 (2020).PubMed 
Article 

Google Scholar 
17.Brown, J. J. et al. Metacommunity theory for transmission of heritable symbionts within insect communities. Ecol. Evol. 10, 1703–1721 (2020).PubMed 
Article 

Google Scholar 
18.Belden, L. K. & Harris, R. N. Infectious diseases in wildlife: The community ecology context. Front. Ecol. Environ. 5, 533–539 (2007).Article 

Google Scholar 
19.Grice, E. A. & Segre, J. A. The skin microbiome. Nat. Rev. Microbiol. 9, 244–253 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Frick, W. F., Puechmaille, S. J. & Willis, C. K. R. White-nose syndrome in bats. In Bats in the Anthropocene: Conservation of Bats in a Changing World (eds Voigt, C. C. & Kingston, T.) 245–262 (Springer, New York, 2016). https://doi.org/10.1007/978-3-319-25220-9_9
Google Scholar 
22.Langwig, K. E. et al. Resistance in persisting bat populations after white-nose syndrome invasion. Philos. Trans. R. Soc. B. 372, 20160044. (2017).Article 

Google Scholar 
23.Langwig, K. E. et al. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol. Lett. 15, 1050–1057 (2012).PubMed 
Article 

Google Scholar 
24.Grisnik, M. et al. The cutaneous microbiota of bats has in vitro antifungal activity against the white nose pathogen. FEMS Microbiol. Ecol. 96, fiz193. https://doi.org/10.1093/femsex/fitz193 (2020).CAS 
Article 
PubMed 

Google Scholar 
25.Wickham H. ggplot2: Elegant Graphics for Data Analysis. R package version 3.2.2. https://CRAN.R-project.org/package=ggplot2 (2020).26.Dallas, T. metacom: An R package for the analysis of metacommunity structure. Ecography 37, 402–405 (2014).Article 

Google Scholar 
27.Alves, A. T., Petsch, D. K. & Barros, F. Drivers of benthic metacommunity structure along tropical estuaries. Sci. Rep. 10, 1–12 (2020).Article 
CAS 

Google Scholar 
28.Risely, A. Applying the core microbiome to understand host–microbe systems. J Anim. Ecol. 89, 1549–1558 (2020).PubMed 
Article 

Google Scholar 
29.Harris, R. N. et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 3, 818–824 (2009).CAS 
PubMed 
Article 

Google Scholar 
30.Lemieux-Labonté, V., Simard, A., Willis, C. K. & Lapointe, F. J. Enrichment of beneficial bacteria in the skin microbiota of bats persisting with white-nose syndrome. Microbiome 5, 115 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
31.Buckley, D. H., Huangyutitham, V., Nelson, T. A., Rumberger, A. & Thies, J. E. Diversity of Planctomycetes in soil in relation to soil history and environmental heterogeneity. Appl Environ Microbiol 72, 4522–4531 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Zimmermann, J., Gonzalez, J. M., Saiz-Jimenez, C. & Ludwig, W. Detection and phylogenetic relationships of highly diverse uncultured acidobacterial communities in altamira cave using 23s rRNA sequence analysis. Geomicrobiol. J. 22, 379–388 (2005).CAS 
Article 

Google Scholar 
33.Wilder, A. P., Kunz, T. H. & Sorenson, M. D. Population genetic structure of a common host predicts the spread of white-nose syndrome, an emerging infectious disease in bats. Mol. Ecol. 24, 5495–5506 (2015).PubMed 
Article 

Google Scholar 
34.Martin, A. M. Historical Demography and Dispersal Patterns in the Eastern Pipistrelle Bat (Perimyotis subflavus). MS Thesis Grand Valley State University (2014).35.Kolodny, O. et al. Coordinated change at the colony level in fruit bat fur microbiomes through time. Nat Ecol. Evol. 3, 116–124 (2019).PubMed 
Article 

Google Scholar 
36.Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U.S.A. 103, 626–631 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Liu, L., Yang, J., Yu, Z. & Wilkinson, D. M. The biogeography of abundant and rare bacterioplankton in the lakes and reservoirs of China. ISME J. 9, 2068–2077 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Reche, I., Pulido-Villena, E., Morales-Baquero, R. & Casamayor, E. O. Does ecosystem size determine aquatic bacterial richness?. Ecology 86, 1715–1722 (2005).Article 

Google Scholar 
39.Hillebrand, H., Watermann, F., Karez, R. & Berninger, U. G. Differences in species richness patterns between unicellular and multicellular organisms. Oecologia 126, 114–124 (2001).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Avena, C. V. et al. Deconstructing the bat skin microbiome: Influences of the host and the environment. Front. Microbiol. 7, 1–14 (2016).MathSciNet 
Article 

Google Scholar 
41.Lemieux-Labonté, V., Tromas, N., Shapiro, B. J. & Lapointe, F. J. Environment and host species shape the skin microbiome of captive neotropical bats. PeerJ 4, e2430 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Goldenberg Vilar, A. et al. Eutrophication decreases distance decay of similarity in diatom communities. Freshw. Biol. 59, 1522–1531 (2014).Article 

Google Scholar 
43.Chase, J. M. Drought mediates the importance of stochastic community assembly. Proc. Natl. Acad. Sci. U.S.A. 104, 17430–17434 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Muletz-Wolz, C. R., Fleischer, R. C. & Lips, K. R. Fungal disease and temperature alter skin microbiome structure in an experimental salamander system. Mol. Ecol. 2, 2917–3293 (2019).
Google Scholar 
45.Minich, J. J. et al. Quantifying and understanding well-to-well contamination in microbiome research. MSystems 4, e00186-e219 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
47.Muller, L. K. et al. Bat white-nose syndrome: A real-time TaqMan polymerase chain reaction test targeting the intergenic spacer region of Geomyces destructans. Mycologia 105, 253–259 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Janicki, A. F. et al. Efficacy of visual surveys for white-nose syndrome at bat hibernacula. PLoS ONE 10, e01333902015 (2015).Article 
CAS 

Google Scholar 
49.Ellison, S. L., English, C. A., Burns, M. J. & Keer, J. T. Routes to improving the reliability of low level DNA analysis using real-time PCR. BMC Biotechnol. 6, 33 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2012).Article 
CAS 

Google Scholar 
52.Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
53.Schloss, P. D. & Westcott, S. L. Assessing and improving methods used in operational taxonomic unit-based approaches for 16S rRNA gene sequence analysis. Appl. Environ. Microbiol. 77, 3219–3226 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Glassman, S.I., & Martiny, J.B. Broadscale ecological patterns are robust to use of exact sequence variants versus operational taxonomic units. MSphere, 3, (2018).
55.Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
56.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2020).57.De Caceres, M., Jansen, F. & De Caceres, M.M. ‘indicspecies’. R package version 1.7.9. https://CRAN.R-project.org/package=indicspecies (2020).58.Bates, D., Sarkar, D., Bates, M.D. & Matrix, L. The lme4 package. R package version 1–1.26. https://CRAN.R-project.org/package=lme4 (2020).59.Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front Microbiol. 8, 2224. https://doi.org/10.3389/fmicb.2017.02224 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
60.Baselga, A. & Orme, C. D. L. betapart: An R package for the study of beta diversity. Methods Ecol. Evol. 3, 808–812 (2012).Article 

Google Scholar 
61.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5–2. https://CRAN.R-project.org/package=vegan (2019).62.Fox, J. et al. ‘car’. R package version 2.1-4. https://CRAN.R-project.org/package=car (2016).63.Anderson, M. J. & Walsh, D. C. PERMANOVA, ANOSIM, and the mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?. Ecol. Monogr. 83, 557–574 (2013).Article 

Google Scholar  More

Exp 1: chemo-tactile conditioning of the proboscis extension response (PER)Bee foragers may assess the quality of floral nectars through chemo-sensilla located on their antennae47. In this first experiment, we asked whether nectar-relevant concentrations of GABA, β-alanine, taurine, citrulline and ornithine can be detected by bees through their antennae. To this aim, we used a chemo-tactile differential conditioning of PER protocol48 in which different groups of bees were trained to discriminate one of the five NPAAs from water. Briefly, tethered bees experienced five pairings of a neutral stimulus (either NPAA-laced water or water) (CS+) with a 30% sucrose solution reinforcement (US) and five pairings (either water or NPAA-laced water) (CS−) with a saturated NaCl solution (US) used as punishment. The results showed that bees increased their response to both the rewarded (CS+) and the punished (CS−) stimuli over the ten conditioning trials (GLMM, trial: GABA: n = 76, χ2 = 65.75, df = 1, p  More

1.Lade, S. J., Wang-Erlandsson, L., Staal, A. & Rocha, J. C. Empirical pressure-response relations can benefit assessment of safe operating spaces. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01481-5 (2021).2.Hillebrand, H. et al. Thresholds for ecological responses to global change do not emerge from empirical data. Nat. Ecol. Evol. 4, 1502–1509 (2020).Article 

Google Scholar 
3.Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. in Introduction to Meta-Analysis (eds Borenstein M. et al.) 277–292 (John Wiley & Sons, 2009).4.Barto, E. K. & Rillig, M. C. Dissemination biases in ecology: effect sizes matter more than quality. Oikos 121, 228–235 (2012).Article 

Google Scholar 
5.Carpenter, G., Kleinjans, R., Villasante, S. & O’Leary, B. C. Landing the blame: the influence of EU Member States on quota setting. Mar. Policy 64, 9–15 (2016).Article 

Google Scholar 
6.Galland, G. R., Nickson, A. E. M., Hopkins, R. & Miller, S. K. On the importance of clarity in scientific advice for fisheries management. Mar. Policy 87, 250–254 (2018).Article 

Google Scholar 
7.Lechenet, M., Dessaint, F., Py, G., Makowski, D. & Munier-Jolain, N. Reducing pesticide use while preserving crop productivity and profitability on arable farms. Nat. Plants 3, 17008 (2017).Article 

Google Scholar 
8.Gaba, S., Gabriel, E., Chadœuf, J., Bonneu, F. & Bretagnolle, V. Herbicides do not ensure for higher wheat yield, but eliminate rare plant species. Sci. Rep. 6, 30112 (2016).CAS 
Article 

Google Scholar 
9.Hillebrand, H. & Kunze, C. Meta-analysis on pulse disturbances reveals differences in functional and compositional recovery across ecosystems. Ecol. Lett. 23, 575–585 (2020).Article 

Google Scholar 
10.Elahi, R. et al. Recent trends in local-scale marine biodiversity reflect community structure and human impacts. Curr. Biol. 25, 1938–1943 (2015).CAS 
Article 

Google Scholar 
11.Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).Article 

Google Scholar 
12.Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).CAS 
Article 

Google Scholar 
13.Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).CAS 
Article 

Google Scholar 
14.Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. Meta-analysis and the science of research synthesis. Nature 555, 175–182 (2018).CAS 
Article 

Google Scholar  More

1.Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).CAS 
Article 

Google Scholar 
2.Song, J. et al. A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat. Ecol. Evol. 3, 1309–1320 (2019).Article 

Google Scholar 
3.Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).CAS 
Article 

Google Scholar 
4.Houghton, R. A. The contemporary carbon cycle. Treatise Geochem. 8, 473–513 (2003).Article 

Google Scholar 
5.Paterson, E., Midwood, A. J. & Millard, P. Through the eye of the needle: a review of isotope approaches to quantify microbial processes mediating soil carbon balance. New Phytol. 184, 19–33 (2009).CAS 
Article 

Google Scholar 
6.Bader, M. K. F. & Körner, C. No overall stimulation of soil respiration under mature deciduous forest trees after 7 years of CO2 enrichment. Glob. Change Biol. 16, 2830–2843 (2010).Article 

Google Scholar 
7.Reynolds, L. L., Lajtha, K., Bowden, R. D., Johnson, B. R. & Bridgham, S. D. The carbon quality–temperature hypothesis does not consistently predict temperature sensitivity of soil organic matter mineralization in soils from two manipulative ecosystem experiments. Biogeochemistry 136, 249–260 (2017).CAS 
Article 

Google Scholar 
8.Knorr, W., Prentice, I. C., House, J. & Holland, E. Long-term sensitivity of soil carbon turnover to warming. Nature 433, 298–301 (2005).CAS 
Article 

Google Scholar 
9.Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil–carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).CAS 
Article 

Google Scholar 
10.Kirschbaum, M. U. F. The temperature dependence of organic-matter decomposition—still a topic of debate. Soil Biol. Biochem. 38, 2510–2518 (2006).CAS 
Article 

Google Scholar 
11.Feng, X., Simpson, A. J., Wilson, K. P., Williams, D. D. & Simpson, M. J. Increased cuticular carbon sequestration and lignin oxidation in response to soil warming. Nat. Geosci. 1, 836–839 (2008).CAS 
Article 

Google Scholar 
12.Pries, C. E. H., Castanha, C., Porras, R. & Torn, M. The whole-soil carbon flux in response to warming. Science 355, 1420–1423 (2017).Article 
CAS 

Google Scholar 
13.Li, J. et al. Reduced carbon use efficiency and increased microbial turnover with soil warming. Glob. Change Biol. 25, 900–910 (2019).Article 

Google Scholar 
14.Schaphoff, S. et al. Contribution of permafrost soils to the global carbon budget. Environ. Res. Lett. 8, 014026 (2013).CAS 
Article 

Google Scholar 
15.Nottingham, A. T., Meir, P., Velasquez, E. & Turner, B. L. Soil carbon loss by experimental warming in a tropical forest. Nature 584, 234–237 (2020).CAS 
Article 

Google Scholar 
16.Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).CAS 
Article 

Google Scholar 
17.Koven, C. D. et al. The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4. Biogeosciences 10, 7109–7131 (2013).CAS 
Article 

Google Scholar 
18.Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E. & Pacala, S. W. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Change 4, 1099–1102 (2014).CAS 
Article 

Google Scholar 
19.Schmidt, M. W. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).CAS 
Article 

Google Scholar 
20.Wieder, W. R. et al. Explicitly representing soil microbial processes in Earth system models. Glob. Biogeochem. Cycles 29, 1782–1800 (2015).CAS 
Article 

Google Scholar 
21.Gonzalez-Dominguez, B. et al. Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics. Sci. Rep. 9, 6422 (2019).CAS 
Article 

Google Scholar 
22.Blankinship, J. C. et al. Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models. Biogeochemistry 140 (2018).23.Koven, C. D. et al. Permafrost carbon–climate feedbacks accelerate global warming. Proc. Natl Acad. Sci. USA 108, 14769–14774 (2011).CAS 
Article 

Google Scholar 
24.Angst, G. et al. Soil organic carbon stocks in topsoil and subsoil controlled by parent material, carbon input in the rhizosphere, and microbial-derived compounds. Soil Biol. Biochem. 122, 19–30 (2018).CAS 
Article 

Google Scholar 
25.Abramoff, R. et al. The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century. Biogeochemistry 137, 51–71 (2017).Article 

Google Scholar 
26.Doetterl, S. et al. Links among warming, carbon and microbial dynamics mediated by soil mineral weathering. Nat. Geosci. 11, 589–593 (2018).CAS 
Article 

Google Scholar 
27.Hamdi, S., Moyano, F., Sall, S., Bernoux, M. & Chevallier, T. Synthesis analysis of the temperature sensitivity of soil respiration from laboratory studies in relation to incubation methods and soil conditions. Soil Biol. Biochem. 58, 115–126 (2013).CAS 
Article 

Google Scholar 
28.Hashimoto, S. et al. Global spatiotemporal distribution of soil respiration modeled using a global database. Biogeosciences 12, 4121–4132 (2015).Article 

Google Scholar 
29.Varney, R. M. et al. A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming. Nat. Commun. 11, 5544 (2020).CAS 
Article 

Google Scholar 
30.Wu, D., Piao, S., Liu, Y., Ciais, P. & Yao, Y. Evaluation of CMIP5 Earth System Models for the spatial patterns of biomass and soil carbon turnover times and their linkage with climate. J. Clim. 31, 5947–5960 (2018).Article 

Google Scholar 
31.Wieder, W. R. et al. Carbon cycle confidence and uncertainty: exploring variation among soil biogeochemical models. Glob. Change Biol. 24, 1563–1579 (2018).Article 

Google Scholar 
32.Koven, C. D., Hugelius, G., Lawrence, D. M. & Wieder, W. R. Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nat. Clim. Change 7, 817–822 (2017).CAS 
Article 

Google Scholar 
33.Mahecha, M. D. et al. Global convergence in the temperature sensitivity of respiration at ecosystem level. Science 329, 838–840 (2010).CAS 
Article 

Google Scholar 
34.Foereid, B., Ward, D., Mahowald, N., Paterson, E. & Lehmann, J. The sensitivity of carbon turnover in the Community Land Model to modified assumptions about soil processes. Earth Syst. Dynam. 5, 211–221 (2014).Article 

Google Scholar 
35.Friedlingstein, P. et al. Climate–carbon cycle feedback analysis: results from the C4MIP model intercomparison. J. Clim. 19, 3337–3353 (2006).Article 

Google Scholar 
36.Post, H., Vrugt, J. A., Fox, A., Vereecken, H. & Hendricks Franssen, H. J. Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites. J. Geophys. Res. Biogeosci. 122, 661–689 (2017).CAS 
Article 

Google Scholar 
37.Luo, Y. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Glob. Biogeochem. Cycles 30, 40–56 (2016).CAS 
Article 

Google Scholar 
38.Bailey, V. L. et al. Soil carbon cycling proxies: understanding their critical role in predicting climate change feedbacks. Glob. Change Biol. 24, 895–905 (2018).Article 

Google Scholar 
39.Conant, R. T. et al. Temperature and soil organic matter decomposition rates—synthesis of current knowledge and a way forward. Glob. Change Biol. 17, 3392–3404 (2011).Article 

Google Scholar 
40.Meyer, N., Welp, G. & Amelung, W. The temperature sensitivity (Q10) of soil respiration: controlling factors and spatial prediction at regional scale based on environmental soil classes. Glob. Biogeochem. Cycles 32, 306–323 (2018).CAS 
Article 

Google Scholar 
41.Doetterl, S. et al. Soil carbon storage controlled by interactions between geochemistry and climate. Nat. Geosci. 8, 780–783 (2015).CAS 
Article 

Google Scholar 
42.Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–105 (2017).CAS 
Article 

Google Scholar 
43.Kramer, M. G. & Chadwick, O. A. Climate-driven thresholds in reactive mineral retention of soil carbon at the global scale. Nat. Clim. Change 8, 1104–1108 (2018).CAS 
Article 

Google Scholar 
44.Cusack, D. F. et al. Decadal-scale litter manipulation alters the biochemical and physical character of tropical forest soil carbon. Soil Biol. Biochem. 124, 199–209 (2018).CAS 
Article 

Google Scholar 
45.Wang, X. et al. Are ecological gradients in seasonal Q10 of soil respiration explained by climate or by vegetation seasonality? Soil Biol. Biochem. 42, 1728–1734 (2010).CAS 
Article 

Google Scholar 
46.Warner, D. L., Bond‐Lamberty, B., Jian, J., Stell, E. & Vargas, R. Spatial predictions and associated uncertainty of annual soil respiration at the global scale. Glob. Biogeochem. Cycles 33, 1733–1745 (2019).CAS 
Article 

Google Scholar 
47.Todd-Brown, K., Zheng, B. & Crowther, T. W. Field-warmed soil carbon changes imply high 21st-century modeling uncertainty. Biogeosciences 15, 3659–3671 (2018).CAS 
Article 

Google Scholar 
48.He, Y. et al. Radiocarbon constraints imply reduced carbon uptake by soils during the 21st century. Science 353, 1419–1424 (2016).CAS 
Article 

Google Scholar 
49.Haddix, M. L. et al. The role of soil characteristics on temperature sensitivity of soil organic matter. Soil Sci. Soc. Am. J. 75, 56–68 (2011).CAS 
Article 

Google Scholar 
50.Lara, M. J., Lin, D. H., Andresen, C., Lougheed, V. L. & Tweedie, C. E. Nutrient release from permafrost thaw enhances CH4 emissions from Arctic tundra wetlands. J. Geophys. Res. Biogeosci. 124, 1560–1573 (2019).CAS 
Article 

Google Scholar 
51.Prater, I. et al. From fibrous plant residues to mineral-associated organic carbon–the fate of organic matter in Arctic permafrost soils. Biogeosciences 17, 3367–3383 (2020).CAS 
Article 

Google Scholar 
52.Åkerman, H. J. & Johansson, M. Thawing permafrost and thicker active layers in sub‐arctic Sweden. Permafr. Periglac. Process. 19, 279–292 (2008).Article 

Google Scholar 
53.Jilling, A. et al. Minerals in the rhizosphere: overlooked mediators of soil nitrogen availability to plants and microbes. Biogeochemistry 139, 103–122 (2018).CAS 
Article 

Google Scholar 
54.Jones, M. C. et al. Rapid carbon loss and slow recovery following permafrost thaw in boreal peatlands. Glob. Change Biol. 23, 1109–1127 (2017).Article 

Google Scholar 
55.Korell, L., Auge, H., Chase, J. M., Harpole, W. S. & Knight, T. M. We need more realistic climate change experiments for understanding ecosystems of the future. Glob. Change Biol. 26, 325–327 (2019).Article 

Google Scholar 
56.Raich, J. W. & Schlesinger, W. H. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B 44, 81–99 (1992).Article 

Google Scholar 
57.Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).58.Crowther, T. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).59.R Core Team. C. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).60.Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–582 (2010).CAS 
Article 

Google Scholar 
61.Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).Article 

Google Scholar 
62.Conover, W. J., Johnson, M. E. & Johnson, M. M. A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics 23, 351–361 (1981).Article 

Google Scholar 
63.Chen, X., Zhao, P. L. & Zhang, J. A note on ANOVA assumptions and robust analysis for a cross‐over study. Stat. Med. 21, 1377–1386 (2002).Article 

Google Scholar 
64.McGuinness, K. A. Of rowing boats, ocean liners and tests of the ANOVA homogeneity of variance assumption. Austral. Ecol. 27, 681–688 (2002).Article 

Google Scholar 
65.Zimmerman, D. W. & Zumbo, B. D. Relative power of the Wilcoxon test, the Friedman test, and repeated-measures ANOVA on ranks. J. Exp. Educ. 62, 75–86 (1993).Article 

Google Scholar 
66.Tomczak, M. & Tomczak, E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends Sport Sci. 1, 19–25 (2014).
Google Scholar 
67.Thornley, J. & Cannell, M. Soil carbon storage response to temperature: an hypothesis. Ann. Bot. 87, 591–598 (2001).CAS 
Article 

Google Scholar 
68.Lloyd, J. & Taylor, J. On the temperature dependence of soil respiration. Funct. Ecol. 8, 315–323 (1994).69.Libohova, Z. et al. The anatomy of uncertainty for soil pH measurements and predictions: implications for modellers and practitioners. Eur. J. Soil Sci. 70, 185–199 (2019).Article 

Google Scholar 
70.Kirkby, C. A. et al. Carbon–nutrient stoichiometry to increase soil carbon sequestration. Soil Biol. Biochem. 60, 77–86 (2013).CAS 
Article 

Google Scholar 
71.Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005).CAS 
Article 

Google Scholar 
72.Beer, C. et al. Temporal and among‐site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 23, GB2018 (2009).Article 
CAS 

Google Scholar 
73.Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543 (2014).CAS 
Article 

Google Scholar 
74.Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. 4, 333 (2013).Article 

Google Scholar 
75.Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning Vol. 1 (Springer, 2001).76.Efron, B., Hastie, T., Johnstone, I. & Tibshirani, R. Least angle regression. Ann. Stat. 32, 407–499 (2004).Article 

Google Scholar 
77.Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301–320 (2005).Article 

Google Scholar 
78.Kuhn, M. & Johnson, K. Applied Predictive Modeling Vol. 26 (Springer, 2013).79.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
80.Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).81.Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).
Google Scholar 
82.Quinlan, J. R. Learning with Continuous Classes in Proceedings of the 5th Australian Joint Conference on Artificial Intelligence (eds Adams, A. & Sterling, L.) 343–348 (World Scientific, 1992).83.Boulesteix, A. L., Janitza, S., Kruppa, J. & König, I. R. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. WIRES Data Mining Knowl. Discov. 2, 493–507 (2012).Article 

Google Scholar 
84.Xu, Q.-S. & Liang, Y.-Z. Monte Carlo cross validation. Chemom. Intell. Lab. Syst. 56, 1–11 (2001).CAS 
Article 

Google Scholar 
85.Shcherbakov, M. V. et al. A survey of forecast error measures. World Appl. Sci. J. 24, 171–176 (2013).
Google Scholar 
86.James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).87.Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28 (2008).88.Grömping, U. Variable importance assessment in regression: linear regression versus random forest. Am. Statistician 63, 308–319 (2009).Article 

Google Scholar 
89.Wei, P., Lu, Z. & Song, J. Variable importance analysis: a comprehensive review. Reliab. Eng. Syst. Saf. 142, 399–432 (2015).Article 

Google Scholar 
90.Yang, R.-M. et al. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol. Indic. 60, 870–878 (2016).CAS 
Article 

Google Scholar 
91.Greenwell, B. M. pdp: an R package for constructing partial dependence plots. R J. 9, 421–436 (2017).Article 

Google Scholar 
92.Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
Article 

Google Scholar 
93.Land Cover CCI Product User Guide Version 2 (ESA, 2017); maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf94.Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 
CAS 

Google Scholar 
95.Moran, P. A. A test for the serial independence of residuals. Biometrika 37, 178–181 (1950).CAS 
Article 

Google Scholar 
96.Legendre, P. Spatial autocorrelation: trouble or new paradigm? Ecology 74, 1659–1673 (1993).Article 

Google Scholar  More

1.Fornace, K. M. et al. Exposure and infection to Plasmodium knowlesi in case study communities in Northern Sabah, Malaysia and Palawan, The Philippines. PLoS Negl. Trop. Dis. 12, e0006432 (2018).Article 

Google Scholar 
2.Singh, B. et al. A large focus of naturally acquired Plasmodium knowlesi infections in human beings. Lancet 363, 1017–1024 (2004).Article 

Google Scholar 
3.Chin, A. Z. et al. Malaria elimination in Malaysia and the rising threat of Plasmodium knowlesi. J. Physiol. Anthropol. https://doi.org/10.1186/s40101-020-00247-5 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Cooper, D. J. et al. Plasmodium knowlesi Malaria in Sabah, Malaysia, 2015–2017: Ongoing increase in incidence despite nearelimination of the human-only plasmodium species. Clin. Infect. Dis. 70, 361–367 (2020).Article 

Google Scholar 
5.William, T. et al. Increasing incidence of Plasmodium knowlesi malaria following control of P. falciparum and P. vivax malaria in Sabah, Malaysia. PLoS Negl. Trop. Dis. 7, e2026 (2013).Article 

Google Scholar 
6.Fornace, K. M. et al. Association between landscape factors and spatial patterns of Plasmodium knowlesi infections in Sabah, Malaysia. Emerg. Infect. Dis. 22, 201–208 (2016).CAS 
Article 

Google Scholar 
7.Gunggut, H., Saufi, D. S. N. S. A. M., Zaaba, Z. & Liu, M.S.-M. Where have all the forests gone? Deforestation in land below the wind. Procedia Soc. Behav. Sci. 153, 363–369 (2014).Article 

Google Scholar 
8.Brock, P. M. et al. Predictive analysis across spatial scales links zoonotic malaria to deforestation. Proc. R. Soc. B Biol. Sci. 286, 20182913 (2019).Article 

Google Scholar 
9.World Health Organization. WHO|Larval Source Management: A Supplementary Measure for Malaria Vector Control (WHO, 2013).
Google Scholar 
10.Wong, M. L. et al. Incrimination of Anopheles balabacensis as the vector for simian malaria in Kudat Division, Sabah, Malaysia. J. Microbiol. Immunol. Infect. 48, S47–S48 (2015).Article 

Google Scholar 
11.Vythilingam, I. & Hii, J. Simian malaria parasites: Special emphasis on Plasmodium knowlesi and their anopheles vectors in Southeast Asia. in Anopheles mosquitoes: New insights into malaria vectors (InTech, 2013). https://doi.org/10.5772/54491.Article 

Google Scholar 
12.Loh, E., Murray, K., Nava, K., Aguirre, A. & Daszak, A. Evaluating the links between biodiversity, land-use change, and infectious disease emergence. in Tropical Conservation (eds. Aguirre, A. & Sukumar, R.) 79–88. (Oxford, 2016).
Google Scholar 
13.Brant, H. L. et al. Vertical stratification of adult mosquitoes (Diptera: Culicidae) within a tropical rainforest in Sabah, Malaysia. Malar. J. 15, 1–10 (2016).Article 

Google Scholar 
14.Chua, T. H., Manin, B. O., Vythilingam, I., Fornace, K. & Drakeley, C. J. Effect of different habitat types on abundance and biting times of Anopheles balabacensis Baisas (Diptera: Culicidae) in Kudat district of Sabah, Malaysia. Parasit. Vectors 12, 364 (2019).Article 

Google Scholar 
15.Wong, M. L. et al. Seasonal and spatial dynamics of the primary vector of Plasmodium knowlesi within a major transmission focus in Sabah, Malaysia. PLoS Negl. Trop. Dis. 9, e0004153 (2015).Article 

Google Scholar 
16.Brown, R. et al. Human exposure to zoonotic malaria vectors in village, farm and forest habitats in Sabah, Malaysian Borneo. PLoS Negl. Trop. Dis. 14, 1–18 (2020).Article 

Google Scholar 
17.Yasuoka, J. & Levins, R. Impact of deforestation and agricultural development on anopheline ecology and malaria epidemiology. Am. J. Trop. Med. Hyg. 76, 450–460 (2007).Article 

Google Scholar 
18.Manin, B. O. et al. Investigating the contribution of peri-domestic transmission to risk of zoonotic malaria infection in humans. PLoS Negl. Trop. Dis. 10, e0000506 (2016).Article 

Google Scholar 
19.Rohani, A. et al. Characterization of the larval breeding sites of Anopheles balabacensis (Baisas), in Kudat, Sabah Malaysia. Southeast Asian. J. Trop. Med. Public Health 49, 566–579 (2018).
Google Scholar 
20.Ageep, T. B. et al. Spatial and temporal distribution of the malaria mosquito Anopheles arabiensis in northern Sudan: Influence of environmental factors and implications for vector control. Malar. J. 8, 123 (2009).Article 

Google Scholar 
21.Roleček, J., Chytrý, M., Hájek, M., Lvončík, S. & Tichý, L. Sampling design in large-scale vegetation studies: Do not sacrifice ecological thinking to statistical purism!. Folia Geobot. 42, 199–208 (2007).Article 

Google Scholar 
22.Bellier, E., Monestiez, P., Durbec, J.-P. & Candau, J.-N. Identifying spatial relationships at multiple scales: Principal coordinates of neighbour matrices (PCNM) and geostatistical approaches. Ecography 30, 385–399 (2007).Article 

Google Scholar 
23.Brock, P. M. et al. Plasmodium knowlesi transmission: Integrating quantitative approaches from epidemiology and ecology to understand malaria as a zoonosis. Parasitology 143, 389–400 (2016).CAS 
Article 

Google Scholar 
24.Fornace, K. M., Drakeley, C. J., William, T., Espino, F. & Cox, J. Mapping infectious disease landscapes: Unmanned aerial vehicles and epidemiology. Trends Parasitol. 30, 514–519 (2014).Article 

Google Scholar 
25.GES DISC. Tropical Rainfall Measurement Mission (TRMM). TRMM (TMPA) Rainfall Estimate L3 3 hour 0.25 degree x 0.25 degree V7, Greenbelt. https://doi.org/10.5067/TRMM/TMPA/3H/7 (2011).Article 

Google Scholar 
26.Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 NASA EOSDIS Land Processes DAAC. USGS 5, 2002–2015 (2015).
Google Scholar 
27.Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. NASA EOSDIS Land Processes DAAC. 5, 2002–2015. https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).Article 

Google Scholar 
28.NASA/METI/AIST/Japan Spacesystems, and U. S. /Japa. A. S. T. ASTER Global Digital Elevation Model V003. NASA EOSDIS Land Processes DAAC. https://lpdaac.usgs.gov/products/astgtmv003 (2019).29.Fornace, K. M. et al. Environmental risk factors and exposure to the zoonotic malaria parasite Plasmodium knowlesi across northern Sabah, Malaysia: A population-based cross-sectional survey. Lancet Planet. Heal. 3, e179–e186 (2019).Article 

Google Scholar 
30.Stark, D. J. et al. Long-tailed macaque response to deforestation in a plasmodium knowlesi-endemic area. EcoHealth 16, 638–646 (2019).Article 

Google Scholar 
31.Davidson, G., Chua, T. H., Cook, A., Speldewinde, P. & Weinstein, P. Defining the ecological and evolutionary drivers of Plasmodium knowlesi transmission within a multi-scale framework. Malar. J. 18, 1–13 (2019).Article 

Google Scholar 
32.Diuk-Wasser, M. A. et al. Effect of rice cultivation patterns on malaria vector abundance in rice-growing villages in Mali. Am. J. Trop. Med. Hyg. 76, 869–874 (2007).Article 

Google Scholar 
33.Stefani, A., Roux, E., Fotsing, J. M. & Carme, B. Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations. Int. J. Health Geogr. 10, 65 (2011).Article 

Google Scholar 
34.Wang, X., Blanchet, F. G. & Koper, N. Measuring habitat fragmentation: An evaluation of landscape pattern metrics. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.12198 (2014).Article 

Google Scholar 
35.McGarigal, K., Cushman, S. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html. https://doi.org/10.1049/oap-cired.2017.1227 (2012).Book 

Google Scholar 
36.TuckerLima, J. M., Vittor, A., Rifai, S. & Valle, D. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philos. Trans. R. Soc. B. 372, 20160125 (2017).Article 

Google Scholar 
37.Sallum, M. A. M., Peyton, E. L. & Wilkerson, R. C. Six new species of the Anopheles leucosphyrus group, reinterpretation of An. elegans and vector implications. Med. Vet. Entomol. 19, 158–199 (2005).CAS 
Article 

Google Scholar 
38.Stoops, C. A. et al. Remotely-sensed land use patterns and the presence of Anopheles larvae (Diptera: Culicidae) in Sukabumi, West Java, Indonesia. J. Vector Ecol. 33, 30–39 (2008).Article 

Google Scholar 
39.Singh, J. & Tham, A. S. Case history on malaria vector control through the application of environmental management in Malaysia. World Health Org. 88, 1–70 (1988).
Google Scholar 
40.Tangena, J. A. A., Thammavong, P., Wilson, A. L., Brey, P. T. & Lindsay, S. W. Risk and control of mosquito-borne diseases in southeast asian rubber plantations. Trends Parasitol. 32, 402–415 (2016).Article 

Google Scholar 
41.Kaewwaen, W. & Bhumiratana, A. Landscape ecology and epidemiology of malaria associated with rubber plantations in Thailand: Integrated approaches to malaria ecotoping. Interdiscipl. Perspect. Infect. Dis. 2015, 1–15 (2015).Article 

Google Scholar 
42.Foley, D. H., Torres, E. P. & Mueller, I. Stream-bank shade and larval distribution of the Philippine malaria vector Anopheles flavirostris. Med. Vet. Entomol. 16, 347–355 (2002).CAS 
Article 

Google Scholar 
43.Service, M. W. & Service, M. W. Sampling the Larval Population. in Mosquito Ecology 75–209 (Springer, 1993). https://doi.org/10.1007/978-94-015-8113-4_2.Article 
MATH 

Google Scholar 
44.Sallum, M. A. M., Peyton, E. L., Harrison, B. A. & Wilkerson, R. C. Revision of the Leucosphyrus group of Anopheles (Cellia) (Diptera, Culicidae). Rev. Bras. Entomol. 49, 1–152 (2005).Article 

Google Scholar 
45.Rattanarithikul, R., Harrison, B. A., Harbach, R. E., Panthusiri, P. & Coleman, R. E. Illustrated keys to the mosquitoes of Thailand IV. Anopheles. J. Trop. Med. Public Health 37, 1–26 (2006).
Google Scholar 
46.R Core Team. R: The R Project for Statistical Computing. https://www.r-project.org/ (2020).47.Borremans, B., Faust, C., Manlove, K. R., Sokolow, S. H. & Lloyd-Smith, J. O. Cross-species pathogen spillover across ecosystem boundaries: Mechanisms and theory. Philos. Trans. R. Soc. B https://doi.org/10.1098/rstb.2018.0344 (2019).Article 

Google Scholar  More

1.Norse, E. A. et al. Sustainability of deep-sea fisheries. Mar. Policy 36, 307–320 (2012).Article 

Google Scholar 
2.Simpfendorfer, C. A. & Kyne, P. M. Limited potential to recover from overfishing raises concerns for deep-sea sharks, rays and chimaeras. Environ. Conserv. 36, 97–103 (2009).Article 

Google Scholar 
3.Kyne, P. & Simpfendorfer, C. In Sharks and Their Relatives II: Biodiversity, Physiology, and Conservation (eds Carrier, J. C. et al.) 37–113 (CRC Press, 2010).
Google Scholar 
4.Dunn, M. R., Szabo, A., McVeagh, M. S. & Smith, P. J. The diet of deepwater sharks and the benefits of using DNA identification of prey. Deep Sea Res. Part I 57, 923–930 (2010).CAS 
Article 

Google Scholar 
5.Mauchline, J. & Gordon, J. Diets of the sharks and chimaeroids of the Rockall Trough, northeastern Atlantic Ocean. Mar. Biol. 75, 269–278 (1983).Article 

Google Scholar 
6.Cortes, E. Standardized diet compositions and trophic levels in sharks. ICES J. Mar. Sci. 56, 707–717 (1999).Article 

Google Scholar 
7.Peterson, B. J. & Fry, B. Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18, 293–320 (1987).Article 

Google Scholar 
8.Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
9.Estrada, J. A., Rice, A. N., Lutcavage, M. E. & Skomall, G. B. Predicting trophic position in sharks of the north-west Atlantic Ocean using stable isotope analysis. J. Mar. Biol. Assoc. UK 83, 1347–1350 (2003).CAS 
Article 

Google Scholar 
10.Hussey, N. E. et al. Stable isotopes and elasmobranchs: Tissue types, methods, applications and assumptions. J. Fish. Biol. 20, 1449–1484 (2012).Article 
CAS 

Google Scholar 
11.Meyer, L., Pethybridge, H., Nichols, P. D., Beckmann, C. & Huveneers, C. Abiotic and biotic drivers of fatty acid tracers in ecology: A global analysis of chondrichthyan profiles. Funct. Ecol. 20, 20 (2019).
Google Scholar 
12.Munroe, S., Meyer, L. & Heithaus, M. Dietary biomarkers in shark foraging and movement ecology. Shark Res. Emerg. Technol. Appl. Field Lab. 20, 20 (2018).

Google Scholar 
13.Hobson, K. A., Barnett-Johnson, R. & Cerling, T. E. In Isoscapes: Understanding Movement, Pattern, and Process on Earth Through Isotope Mapping (eds West, J. B. et al.) 273–298 (Springer, 2010).
Google Scholar 
14.Michener, R. H. & Kaufman, L. In Stable Isotopes in Ecology and Environmental Science (eds Michener, R. & Lajtha, K.) 238–282 (Blackwell, 2007).
Google Scholar 
15.West, J. B., Bowen, G. J., Cerling, T. E. & Ehleringer, J. R. Stable isotopes as one of nature’s ecological recorders. Trends Ecol. Evol. 21, 408–414 (2006).PubMed 
Article 

Google Scholar 
16.DeNiro, M. J. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45, 341–345 (1981).ADS 
CAS 
Article 

Google Scholar 
17.DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506 (1978).ADS 
CAS 
Article 

Google Scholar 
18.Tieszen, L. L., Boutton, T. W., Tesdahl, K. G. & Slade, N. A. Fractionation and turnover of stable carbon isotopes in animal tissues: Implications for δ13C analysis of diet. Oecologia 57, 32–37 (1983).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.MacNeil, M. A., Skomal, G. B. & Fisk, A. T. Stable isotopes from multiple tissues reveal diet switching in sharks. Mar. Ecol. Prog. Ser. 302, 199–206 (2005).ADS 
Article 

Google Scholar 
20.Kim, S. L., Martinez del Rio, C., Casper, D. & Koch, P. L. Isotopic incorporation rates for shark tissues from a long-term captive feeding study. J Exp Biol 215, 2495–2500 (2012).21.Madigan, D. J. et al. Tissue turnover rates and isotopic trophic discrimination factors in the endothermic teleost, Pacific bluefin tuna (Thunnus orientalis). PLoS One 7, e49220 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Carlisle, A. B. et al. Using stable isotope analysis to understand the migration and trophic ecology of northeastern Pacific white sharks (Carcharodon carcharias). PLoS One 7, 30492 (2012).ADS 
Article 
CAS 

Google Scholar 
23.Madigan, D. J. et al. Reconstructing transoceanic migration patterns of Pacific bluefin tuna using a chemical tracer toolbox. Ecology 95, 1674–1683 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Ackman, R.G. & Macpherson, E.J. Coincidence of cis-and trans-monoethylenic fatty acids simplifies the open-tubular gas-liquid chromatography of butyl esters of butter fatty acids. Food chem. 50(1), 45–52 (1994).25.Sargent, J., Bell, G., McEvoy, L., Tocher, D. & Estevez, A. Recent developments in the essential fatty acid nutrition of fish. Aquaculture., 177(1–4), 191–199 (1999).26.Tocher, D.R. Metabolism and functions of lipids and fatty acids in teleost fish. Rev. Fish. Sci. 11(2), 107–184 (2003).27.McMeans, B. C. et al. The role of Greenland sharks (Somniosus microcephalus) in an Arctic ecosystem: Assessed via stable isotopes and fatty acids. Mar. Biol. 160, 1223–1238. https://doi.org/10.1007/s00227-013-2174-z (2013).Article 

Google Scholar 
28.Pethybridge, H. R., Nichols, P. D., Virtue, P. & Jackson, G. D. The foraging ecology of an oceanic squid, Todarodes filippovae: The use of signature lipid profiling to monitor ecosystem change. Deep Sea Res. Part II 95, 119–128 (2013).CAS 
Article 

Google Scholar 
29.Pethybridge, H. et al. Lipid and mercury profiles of 61 mid-trophic species collected off south-eastern Australia. Mar. Freshw. Res. 61, 1092–1108 (2010).CAS 
Article 

Google Scholar 
30.Beckmann, C. L., Mitchell, J. G., Stone, D. A. & Huveneers, C. A controlled feeding experiment investigating the effects of a dietary switch on muscle and liver fatty acid profiles in Port Jackson sharks Heterodontus portusjacksoni. J. Exp. Mar. Biol. Ecol. 448, 10–18 (2013).CAS 
Article 

Google Scholar 
31.Pethybridge, H. R., Choy, C. A., Polovina, J. J. & Fulton, E. A. Improving marine ecosystem models with biochemical tracers. Ann. Rev. Mar. Sci. 10, 199–228 (2018).PubMed 
Article 

Google Scholar 
32.Belicka, L. L., Matich, P., Jaffé, R. & Heithaus, M. R. Fatty acids and stable isotopes as indicators of early-life feeding and potential maternal resource dependency in the bull shark Carcharhinus leucas. Mar. Ecol. Prog. Ser. 455, 245–256 (2012).ADS 
CAS 
Article 

Google Scholar 
33.Every, S. L., Fulton, C. J., Pethybridge, H. R., Kyne, P. M. & Crook, D. A. A seasonally dynamic estuarine ecosystem provides a diverse prey base for Elasmobranchs. Estuar. Coasts 42, 580–595 (2019).CAS 
Article 

Google Scholar 
34.Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 20, e00547 (2019).Article 

Google Scholar 
35.Soininen, E. M. et al. Shedding new light on the diet of Norwegian lemmings: DNA metabarcoding of stomach content. Polar Biol 36, 1069–1076 (2013).Article 

Google Scholar 
36.De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14, 306–323 (2014).Article 
CAS 

Google Scholar 
37.Deagle, B. E., Kirkwood, R. & Jarman, S. N. Analysis of Australian fur seal diet by pyrosequencing prey DNA in faeces. Mol. Ecol. 18, 2022–2038 (2009).CAS 
PubMed 
Article 

Google Scholar 
38.Bade, L. M., Balakrishnan, C. N., Pilgrim, E. M., McRae, S. B. & Luczkovich, J. J. A genetic technique to identify the diet of cownose rays, Rhinoptera bonasus: Analysis of shellfish prey items from North Carolina and Virginia. Environ. Biol. Fishes 97, 999–1012 (2014).Article 

Google Scholar 
39.Jensen, M. R., Knudsen, S. W., Munk, P., Thomsen, P. F. & Møller, P. R. Tracing European eel in the diet of mesopelagic fishes from the Sargasso Sea using DNA from fish stomachs. Mar. Biol. 165, 130 (2018).Article 
CAS 

Google Scholar 
40.Compagno, L. FAO species catalogue. Vol. 4. Sharks of the world. An annotated and illustrated catalogue of sharks species known to date. Part 1. Hexanchiformes to Lammiformes. FAO Fish. Synop. 20, 1–249 (1984).
Google Scholar 
41.Jahn, A. & Haedrich, R. Notes on the pelagic squaloid shark Isistius brasiliensis. Biol. Oceanogr. 5, 297–309 (1988).
Google Scholar 
42.Nakano, H. & Tabuchi, M. Occurrence of the cookiecutter shark Isistius brasiliensis in surface waters of the North Pacific Ocean. Jpn. J. Ichthyol. 37, 60–63 (1990).
Google Scholar 
43.Hubbs, C. L., Iwai, T. & Matsubara, K. External and internal characters, horizontal and vertical distributions, luminescence, and food of the dwarf pelagic shark, Euprotomicrus bispinatus. (1967).44.Papastamatiou, Y. P., Wetherbee, B. M., O’Sullivan, J., Goodmanlowe, G. D. & Lowe, C. G. Foraging ecology of cookiecutter sharks (Isistius brasiliensis) on pelagic fishes in Hawaii, inferred from prey bite wounds. Environ. Biol. Fishes 88, 361–368 (2010).Article 

Google Scholar 
45.Feunteun, A. et al. First evaluation of the cookie-cutter sharks (Isistius sp.) predation pattern on different cetacean species in Martinique. Environ. Biol. Fishes 20, 1–11 (2018).
Google Scholar 
46.Jones, E. Isistius brasiliensis, a squaloid shark, probable cause of crater wounds on fishes and cetaceans. Fish Bull. 69, 791–798 (1971).
Google Scholar 
47.Strasburg, D. W. The diet and dentition of Isistius brasiliensis, with remarks on tooth replacement in other sharks. Copeia 20, 33–40 (1963).Article 

Google Scholar 
48.Widder, E. A. A predatory use of counter illumination by the squaloid shark, Isistius brasiliensis. Environ. Biol. Fishes 53, 267–273 (1998).Article 

Google Scholar 
49.Moore, M., Steiner, L. & Jann, B. Cetacean surveys in the Cape Verde Islands and the use of cookiecutter shark bite lesions as a population marker for fin whales. Aquat. Mamm. 29, 383–389 (2003).Article 

Google Scholar 
50.Muñoz-Chápuli, R., Salgado, J. R. & de La Serna, J. Biogeography of Isistius brasiliensis in the north-eastern Atlantic, inferred from crater wounds on swordfish (Xiphias gladius). J. Mar. Biol. Assoc. U K 68, 315–321 (1988).Article 

Google Scholar 
51.Murakami, C., Yoshida, H. & Yonezaki, S. Cookie-cutter shark Isistius brasiliensis eats Bryde’s whale Balaenoptera brydei. Ichthyol. Res. 65, 398–404 (2018).Article 

Google Scholar 
52.Castro, J., Anllo, T., Mejuto, J. & García, B. Ichnology applied to the study of Cookiecutter shark (Isistius brasiliensis) biogeography in the Gulf of Guinea. Environ. Biol. Fishes 101, 579–588 (2018).Article 

Google Scholar 
53.Kim, S. L. et al. Carbon and nitrogen discrimination factors for elasmobranch soft tissues based on a long-term controlled feeding study. Environ. Biol. Fishes 95, 37–52 (2012).Article 

Google Scholar 
54.Le Boeuf, B., McCosker, J. & Hewitt, J. Crater wounds on northern elephant seals: The Cookiecutter Shark strikes again. Fish Bull. 85, 20 (1987).
Google Scholar 
55.Niella, Y. et al. Cookie-cutter shark Isistius spp. predation upon different tuna species from the south-western Atlantic Ocean. J. Fish. Biol. 92, 1082–1089 (2018).CAS 
PubMed 
Article 

Google Scholar 
56.Manlick, P. J., Petersen, S. M., Moriarty, K. M. & Pauli, J. N. Stable isotopes reveal limited Eltonian niche conservatism across carnivore populations. Funct. Ecol. 33, 335–345 (2019).Article 

Google Scholar 
57.McMeans, B.C., Arts, M.T. & Fisk, A.T. Similarity between predator and prey fatty acid profiles is tissue dependent in Greenland sharks (Somniosus microcephalus): Implications for diet reconstruction. J. Exp. Mar. Biol. Ecol. 429, 55–63 (2012).58.Waugh, C.A., Nichols, P.D., Schlabach, M., Noad, M. & Nash, S.B. Vertical distribution of lipids, fatty acids and organochlorine contaminants in the blubber of southern hemisphere humpback whales (Megaptera novaeangliae). Mar. Environ. Res. 94, 24–31 (2014).59.Sigler, M. F. et al. Diet of Pacific sleeper shark, a potential Steller sea lion predator, in the north-east Pacific Ocean. J. Fish. Biol. 69, 392–405 (2006).Article 

Google Scholar 
60.Leclerc, L.-M. et al. Greenland sharks (Somniosus microcephalus) scavenge offal from minke (Balaenoptera acutorostrata) whaling operations in Svalbard (Norway). Polar. Res. 30, 7342 (2011).Article 

Google Scholar 
61.Yano, K., Stevens, J. & Compagno, L. Distribution, reproduction and feeding of the Greenland shark Somniosus (Somniosus) microcephalus, with notes on two other sleeper sharks, Somniosus (Somniosus) pacificus and Somniosus (Somniosus) antarcticus. J. Fish. Biol. 70, 374–390 (2007).Article 

Google Scholar 
62.Preti, A. et al. Comparative feeding ecology of shortfin mako, blue and thresher sharks in the California current. Environ. Biol. Fishes https://doi.org/10.1007/s10641-10012-19980-x (2012).Article 

Google Scholar 
63.Phillips, D. L. et al. Best practices for use of stable isotope mixing models in food-web studies. Can. J. Zool. 92, 823–835 (2014).Article 

Google Scholar 
64.Smith, J. A., Mazumder, D., Suthers, I. M. & Taylor, M. D. To fit or not to fit: Evaluating stable isotope mixing models using simulated mixing polygons. Methods Ecol. Evol. 4, 612–618 (2013).Article 

Google Scholar 
65.Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. https://doi.org/10.1111/ele.12226 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Childress, J. J. & Nygaard, M. H. Deep Sea Research and Oceanographic Abstracts 1093–1109 (Elsevier, 1973).
Google Scholar 
67.Childress, J., Price, M., Favuzzi, J. & Cowles, D. Chemical composition of midwater fishes as a function of depth of occurrence off the Hawaiian Islands: Food availability as a selective factor?. Mar. Biol. 105, 235–246 (1990).Article 

Google Scholar 
68.Choy, C. A., Popp, B. N., Hannides, C. C. & Drazen, J. C. Trophic structure and food resources of epipelagic and mesopelagic fishes in the North Pacific Subtropical Gyre ecosystem inferred from nitrogen isotopic compositions. Limnol. Oceanogr. 60, 1156–1171 (2015).ADS 
Article 

Google Scholar 
69.Gloeckler, K. et al. Stable isotope analysis of micronekton around Hawaii reveals suspended particles are an important nutritional source in the lower mesopelagic and upper bathypelagic zones. Limnol. Oceanogr. 63, 1168–1180 (2018).ADS 
CAS 
Article 

Google Scholar 
70.Hannides, C. C., Popp, B. N., Choy, C. A. & Drazen, J. C. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: A stable isotope perspective. Limnol. Oceanogr. 58, 1931–1946 (2013).ADS 
CAS 
Article 

Google Scholar 
71.Dunstan, G.A., Sinclair, A.J., O’Dea, K. & Naughton, J.M. The lipid content and fatty acid composition of various marine species from southern Australian coastal waters. Comp. Biochem. Physiol. B: Comp. Biochem. 91(1), 165–169 (1988).72.Semeniuk, C.A., Speers-Roesch, B. & Rothley, K.D. Using fatty-acid profile analysis as an ecologic indicator in the management of tourist impacts on marine wildlife: a case of stingray-feeding in the Caribbean. Environ. Manag. 40(4), 665–677 (2007).73.Wai, T.C., Leung, K.M., Sin, S.Y., Cornish, A., Dudgeon, D. & Williams, G.A. Spatial, seasonal, and ontogenetic variations in the significance of detrital pathways and terrestrial carbon for a benthic shark, Chiloscyllium plagiosum (Hemiscylliidae), in a tropical estuary. Limnol. Oceanogr. 56(3), 1035–1053 (2011).74.Ebert, D. A., Fowler, S. L., Compagno, L. J. & Dando, M. Sharks of the World: A Fully Illustrated Guide (Wild Nature Press, 2013).
Google Scholar 
75.Vaudo, J. J., Matich, P. & Heithaus, M. R. Mother-offspring isotope fractionation in two species of placentatrophic sharks. J. Fish. Biol. 77, 1724–1727 (2010).CAS 
PubMed 
Article 

Google Scholar 
76.Olin, J. A. et al. Maternal meddling in neonatal sharks: Implications for interpreting stable isotopes in young animals. Rapid Commun. Mass Spectrom. 25, 1008–1016 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
77.Grubbs, R. D. In Sharks and Their Relatives II: Biodiversity, Physiology, and Conservation (eds Carrier, J. C. et al.) 319–350 (CRC Press, 2010).
Google Scholar 
78.Yano, K. & Tanaka, S. Size at maturity, reproductive cycle, fecundity, and depth segregation of the deep sea squaloid sharks Centroscymnus owstoni and C. coelolepis in Suruga Bay Japan. Nippon Suisan Gakkaishi 54, 20 (1988).
Google Scholar 
79.Yano, K. & Tanaka, S. Review of the deep sea squaloid shark genus Scymnodon of Japan, with a description of a new species. Jpn. J. Ichthyol. 30, 341–360 (1984).
Google Scholar 
80.Munoz-Chapuli, R. Ethologie de la reproduction chez quelques requins de l’Atlantique Nord-Est. Cybium 8, 1–14 (1984).
Google Scholar 
81.Jakobsdóttir, K. B. Biological aspects of two deep-water squalid sharks: Centroscyllium fabricii (Reinhardt, 1825) and Etmopterus princeps (Collett, 1904) in Icelandic waters. Fish Res. 51, 247–265 (2001).Article 

Google Scholar 
82.Wetherbee, B. M. Distribution and reproduction of the southern lantern shark from New Zealand. J. Fish. Biol. 49, 1186–1196. https://doi.org/10.1111/j.1095-8649.1996.tb01788.x (1996).Article 

Google Scholar 
83.MacNeil, M. A., Drouillard, K. G. & Fisk, A. T. Variable uptake and elimination of stable nitrogen isotopes between tissues in fish. Can. J. Fish. Aquat. Sci. 63, 345–353 (2006).CAS 
Article 

Google Scholar 
84.Logan, J. M. & Lutcavage, M. Stable isotope dynamics in elasmobranch fishes. Hydrobiologia 644, 231–244 (2010).CAS 
Article 

Google Scholar 
85.Weidel, B. C., Carpenter, S. R., Kitchell, J. F. & Vander Zanden, M. J. Rates and components of carbon turnover in fish muscle: Insights from bioenergetics models and a whole-lake 13C addition. Can. J. Fish. Aquat. Sci. 68, 387–399 (2011).CAS 
Article 

Google Scholar 
86.Carlisle, A. B. et al. Interactive effects of urea and lipid content confound stable isotope analysis in elasmobranch fishes. Can. J. Fish. Aquat. Sci. 74, 419–428 (2016).Article 
CAS 

Google Scholar 
87.Kim, S. L. & Koch, P. L. Methods to collect, preserve, and prepare elasmobranch tissues for stable isotope analysis. Environ. Biol. Fishes 95, 53–63 (2012).Article 

Google Scholar 
88.Witteveen, B. H., Worthy, G. A. J. & Roth, J. D. Tracing migratory movements of breeding North Pacific humpback whales using stable isotope analysis. Mar. Ecol. Prog. Ser. 393, 173–183. https://doi.org/10.3354/meps08231 (2009).ADS 
Article 

Google Scholar 
89.Parry, M. P. The trophic ecology of two ommastrephid squid species, Ommastrephes bartamii and Sthenoteuthis oualaniensis, in the North Pacific sub-tropical gyre Ph.D. thesis, University of Hawaii, (2003).90.Parry, M. P. Trophic variation with length in two ommastrephid squids, Ommastrephes bartramiii and Sthenoteuthis oualaniensis. Mar. Biol. 153, 249–256 (2008).Article 

Google Scholar 
91.Graham, B. S. Trophic dynamics and movements of tuna in tropical Pacific Ocean inferred from stable isotope analyses Ph. D. thesis thesis, University of Hawaii, (2007).92.Graham, B. S., Grubbs, D., Holland, K. & Popp, B. N. A rapid ontogenetic shift in the diet of juvenile yellowfin tuna from Hawaii. Mar. Biol. 150, 647–658 (2007).Article 

Google Scholar 
93.Carlisle, A. B. et al. Stable isotope analysis of vertebrae reveals ontogenetic changes in habitat in an endothermic pelagic shark. Proc. R. Soc. B-Biol. Sci. 282, 20141446. https://doi.org/10.1098/rspb.2014.1446 (2015).CAS 
Article 

Google Scholar 
94.Stock, B. C. & Semmens, B. X. MixSIAR GUI user manual, version 1.0. http://conserver.iugo-cafe.org/user/brice.semmens/MixSIAR (2013).95.Folch, J., Lees, M. & Stanley, G.S. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 226(1), 497–509 (1957).96.Kartikasari, L.R., Hughes, R.J., Geier, M.S., Makrides, M. & Gibson, R.A. Dietary alpha-linolenic acid enhances omega-3 long chain polyunsaturated fatty acid levels in
chicken tissues. Prostaglandins Leukot. Essent. Fatty Acids. 87(4–5), 103–109 (2012).97.Froese, R. & D. Pauly. Editors. 2021. FishBase. World Wide Web electronic publication. https://www.fishbase.org, version (02/2021).98.Clarke, K. & Gorley, R. (PRIMER-E: Plymouth, 2006).
99.Riaz, T. et al. ecoPrimers: Inference of new DNA barcode markers from whole genome sequence analysis. Nucleic Acids Res. 39, e145–e145 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
100.Kelly, R. P., Port, J. A., Yamahara, K. M. & Crowder, L. B. Using environmental DNA to census marine fishes in a large mesocosm. PLoS One 9, e86175 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
101.Stoeckle, M. Y., Soboleva, L. & Charlop-Powers, Z. Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary. PLoS ONE 12, e0175186 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
102.Port, J. A. et al. Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA. Mol. Ecol. 25, 527–541 (2016).CAS 
PubMed 
Article 

Google Scholar 
103.Shehzad, W. et al. Carnivore diet analysis based on next-generation sequencing: Application to the leopard cat (Prionailurus bengalensis) in Pakistan. Mol. Ecol. 21, 1951–1965 (2012).CAS 
PubMed 
Article 

Google Scholar 
104.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
Article 

Google Scholar 
105.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
106.Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: A fast and accurate illumina paired-end reAd mergeR. Bioinformatics 30, 614–620 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
107.Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated-reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15, 1289–1303 (2015).CAS 
PubMed 
Article 

Google Scholar 
108.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421 (2009).Article 
CAS 

Google Scholar 
109.Oksanen, J. et al. Vegan: Community ecology package. R package version 1.17–4. http://cran.r-project.org. Acesso em 23, 2010 (2010). More