Ecology

MacPherson, E. Ontogenetic shifts in habitat use and aggregation in juvenile sparid fishes. J. Exp. Mar. Bio. Ecol. 220, 127–150 (1998).Article 

Google Scholar 
Freitas, C., Olsen, E. M., Knutsen, H., Albretsen, J. & Moland, E. Temperature-associated habitat selection in a cold-water marine fish. J. Anim. Ecol. 85, 628–637 (2016).Article 
PubMed 

Google Scholar 
Michelot, C. et al. Seasonal variation in coastal marine habitat use by the European shag: Insights from fine scale habitat selection modeling and diet. Deep. Res. Part II Top. Stud. Oceanogr. 141, 224–236 (2017).Article 

Google Scholar 
Davoren, G. K., Montevecchi, W. A. & Anderson, J. T. Distributional patterns of a marine bird and its prey: Habitat selection based on prey and conspecific behaviour. Mar. Ecol. Prog. Ser. 256, 229–242 (2003).Article 

Google Scholar 
Chiarello, A. G. et al. A translocation experiment for the conservation of maned sloths, Bradypus torquatus (Xenarthra, Bradypodidae). Biol. Conserv. 118, 421–430 (2004).Article 

Google Scholar 
Fukuda, Y. et al. Environmental resistance and habitat quality influence dispersal of the saltwater crocodile. Mol. Ecol. 31, 1076–1092 (2022).Article 
PubMed 

Google Scholar 
O’Leary, S. J., Dunton, K. J., King, T. L., Frisk, M. G. & Chapman, D. D. Genetic diversity and effective size of Atlantic sturgeon, Acipenser oxyrhinchus oxyrhinchus river spawning populations estimated from the microsatellite genotypes of marine-captured juveniles. Conserv. Genet. 15, 1173–1181 (2014).Article 

Google Scholar 
Brüniche-Olsen, A. et al. Genetic data reveal mixed-stock aggregations of gray whales in the North Pacific Ocean. Biol. Lett. 14, 1–4 (2018).Article 

Google Scholar 
Carroll, E. L. et al. Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile-Peru wintering grounds and the South Georgia (Islas Georgias Del Sur) feeding ground. J. Hered. 111, 263–276 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Bowen, A. B. W. et al. Origin of hawksbill turtles in a Caribbean feeding area as indicated by genetic markers. Ecol. Appl. 6, 566–572 (1996).Article 

Google Scholar 
Paxton, K. L., Yau, M., Moore, F. R. & Irwin, D. E. Differential migratory timing of western populations of Wilson’s Warbler (Cardellina pusilla) revealed by mitochondrial DNA and stable isotopes. Auk 130, 689–698 (2013).Article 

Google Scholar 
Anderson, E. C., Waples, R. S. & Kalinowski, S. T. An improved method for predicting the accuracy of genetic stock identification. Can. J. Fish. Aquat. Sci. 65, 1475–1486 (2008).Article 

Google Scholar 
Debevec, E. M. SPAM (version 3.2): Statistics program for analyzing mixtures. J. Hered. 91, 509–511 (2000).Article 
PubMed 

Google Scholar 
Bolker, B. M., Okuyama, T., Bjorndal, K. A. & Bolten, A. B. Incorporating multiple mixed stocks in mixed stock analysis: ‘Many-to-many’ analyses. Mol. Ecol. 16, 685–695 (2007).Article 
PubMed 

Google Scholar 
Neaves, P. I., Wallace, C. G., Candy, J. R. & Beacham, T. D. CBayes: Computer Program for Mixed Stock Analysis of Allelic Data. Free Program Distributed by the Authors Over the Internet. at (2005).Pella, J. & Masuda, M. Bayesian methods for analysis of stock mixtures from genetic characters. Fish. Bull. 99, 151–167 (2001).
Google Scholar 
Bolker, B., Okuyama, T., Bjorndal, K. A. & Bolten, A. B. Sea turtle stock estimation using genetic markers: Accounting for sampling error of rare genotypes. Ecol. Appl. 13, 763–775 (2003).Article 

Google Scholar 
Okuyama, T. & Bolker, B. M. Combining genetic and ecological data to estimate sea turtle origins. Ecol. Appl. 15, 315–325 (2005).Article 

Google Scholar 
Nishizawa, H. et al. Composition of green turtle feeding aggregations along the Japanese archipelago: Implications for changes in composition with current flow. Mar. Biol. 160, 2671–2685 (2013).Article 

Google Scholar 
Naro-Maciel, E. et al. Predicting connectivity of green turtles at Palmyra Atoll, central Pacific: A focus on mtDNA and dispersal modelling. J. R. Soc. Interface 11, 20130888 (2014).Proietti, M. C. et al. Green turtle Chelonia mydas mixed stocks in the western South Atlantic, as revealed by mtDNA haplotypes and drifter trajectories. Mar. Ecol. Prog. Ser. 447, 195–209 (2012).Article 

Google Scholar 
van der Zee, J. P. et al. Population recovery changes population composition at a major southern Caribbean juvenile developmental habitat for the green turtle, Chelonia mydas. Sci. Rep. 9, 1–11 (2019).
Google Scholar 
Shamblin, B. M. et al. Mexican origins for the Texas green turtle foraging aggregation: A cautionary tale of incomplete baselines and poor marker resolution. J. Exp. Mar. Bio. Ecol. 488, 111–120 (2017).Article 

Google Scholar 
Seminoff, J. A. et al. Status Review of the Green Turtle (Chelonia mydas) Under the Endangered Species Act. (NOAA Technical Memorandum, NOAA-NMFS-SWFSC, 2015).Chaloupka, M. et al. Encouraging outlook for recovery of a once severely exploited marine megaherbivore. Glob. Ecol. Biogeogr. 17, 297–304 (2008).Article 

Google Scholar 
Bjorndal, K. A. & Bolten, A. B. Annual variation in source contributions to a mixed stock: Implications for quantifying connectivity. Mol. Ecol. 17, 2185–2193 (2008).Article 
PubMed 

Google Scholar 
Roland, J., Keyghobadi, N. & Fownes, S. Alpine Parnassius butterfly dispersal: Effects of landscape and population size. Ecology 81, 1642–1653 (2000).Article 

Google Scholar 
Vanschoenwinkel, B., De Vries, C., Seaman, M. & Brendonck, L. The role of metacommunity processes in shaping invertebrate rock pool communities along a dispersal gradient. Oikos 116, 1255–1266 (2007).Article 

Google Scholar 
Shamblin, B. M. et al. Mitogenomic sequences better resolve stock structure of southern Greater Caribbean green turtle rookeries. Mol. Ecol. 21, 2330–2340 (2012).Article 
PubMed 

Google Scholar 
Witherington, B., Hirama, S. & Hardy, R. Young sea turtles of the pelagic Sargassum-dominated drift community: Habitat use, population density, and threats. Mar. Ecol. Prog. Ser. 463, 1–22 (2012).Article 

Google Scholar 
Putman, N. F. & Mansfield, K. L. Direct evidence of swimming demonstrates active dispersal in the sea turtle ‘lost years’. Curr. Biol. 25, 1221–1227 (2015).Article 
PubMed 

Google Scholar 
Mansfield, K. L., Wyneken, J. & Luo, J. First Atlantic satellite tracks of ‘lost years’ green turtles support the importance of the Sargasso Sea as a sea turtle nursery. Proc. R. Soc. B Biol. Sci. 288, 20210057 (2021).Putman, N. F. et al. Predicted distributions and abundances of the sea turtle ‘lost years’ in the western North Atlantic Ocean. Ecography (Cop.) 43, 506–517 (2020).Article 

Google Scholar 
Putman, N. F. & Naro-Maciel, E. Finding the ‘lost years’ in green turtles: Insights from ocean circulation models and genetic analysis. Proc. R. Soc. B Biol. Sci. 280, 20131468 (2013).Naro-Maciel, E., Hart, K. M., Cruciata, R. & Putman, N. F. DNA and dispersal models highlight constrained connectivity in a migratory marine megavertebrate. Ecography (Cop.) 40, 586–597 (2017).Article 

Google Scholar 
Ehrhart, L. M., Redfoot, W. E. & Bagley, D. A. Marine turtles of the central region of the Indian River Lagoon system, Florida. Florida Sci. 70, 415–434 (2007).
Google Scholar 
Redfoot, W. & Ehrhart, L. Trends in size class distribution, recaptures, and abundance of juvenile green turtles (Chelonia mydas) utilizing a rock riprap lined embayment at Port Canaveral, Florida, USA, as developmental habitat. Chelonian Conserv. Biol. 12, 252–261 (2013).Article 

Google Scholar 
Ehrhart, L., Redfoot, W., Bagley, D. & Mansfield, K. Long-term trends in loggerhead (Caretta caretta) nesting and reproductive success at an important western Atlantic rookery. Chelonian Conserv. Biol. 13, 173–181 (2014).Article 

Google Scholar 
Bolten, A. B. Techniques for measuring sea turtles. in Research and Management Techniques for the Conservation of Sea Turtles. (eds. Eckert, K. L., Bjorndal, K. A., Abreu-Grobois, F. A. & Donnelly, M.). 1–5 (1999).Bagley, D. A. Characterizing Juvenile Green Turtles, (Chelonia mydas), from Three East Central Florida Developmental Habitats. (University of Central Florida, 2003).Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Faircloth, B. & Glenn, T. Preparation of an AMPure XP Substitute. AKA Serapure https://doi.org/10.6079/J9MW2F26 (2016).Article 

Google Scholar 
Abreu-Grobois, F. A. et al. New mtDNA Dloop primers which work for a variety of marine turtle species may increase the resolution of mixed stock analyses. in Proceedings of the 26th Annual Symposium on Sea Turtle Biology. 179 (International Sea Turtle Society, 2006).Kearse, M. et al. Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Leigh, J. W. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).Article 
PubMed 

Google Scholar 
Wright, S. Evolution and the Genetics of Populations. Vol. 4. Variability Within and Among Natural Populations. (University of Chicago Press, 1978).Hays, G. C. Ocean currents and marine life. Curr. Biol. 27, R470–R473 (2017).Article 
PubMed 

Google Scholar 
Engstrom, T. N., Meylan, P. A. & Meylan, A. B. Origin of juvenile loggerhead turtles (Caretta caretta) in a tropical developmental habitat in Caribbean Panamá. Anim. Conserv. 5, 125–133 (2002).Article 

Google Scholar 
Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research Institute, F. W. C. F. W. R. I. Index Nesting Beach Survey (INBS). (2021).Cuevas Flores, E. A., Guzmán Hernández, V., Guerra Santos, J. J. & Rivas Hernández, G. A. El uso del Conocimiento de las Tortugas Marinas Como Herramienta para la Restauración de sus Poblaciones y Hábitats Asociados. (Universidad Autónoma del Carmen, 2019).Pineda, O. G. & Rocha, A. R. B. Las Tortugas Marinas en México: Logros y Perspectivas para su Conservación. (CONANP, 2016).Varela, R. G., Quílez, G. Z. & Harrison, E. Report on the 2014 Green Turtle Program at Tortuguero, Costa Rica. (2015).Azanza Ricardo, J. et al. Nesting ecology of Chelonia mydas (Testudines: Cheloniidae) on the Guanahacabibes Peninsula. Cuba. Rev. Biol. Trop. 61, 1935–1945 (2013).PubMed 

Google Scholar 
Nalovic, M. A. et al. Sea Turtles in the North Atlantic & Wider Caribbean Region. (2020).Makowski, D., Ben-Shachar, M. & 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. https://doi.org/10.1016/B978-0-12-405888-0.09999-2 (Academic Press, 2015).Ruiz-Urquiola, A. et al. Population genetic structure of greater Caribbean green turtles (Chelonia mydas) based on mitochondrial DNA sequences, with an emphasis on rookeries from southwestern Cuba. Rev. Investig. Mar. 31, 33–52 (2010).
Google Scholar 
Long, C. A. et al. Incongruent long-term trends of a marine consumer and primary producers in a habitat affected by nutrient pollution. Ecosphere 12, e03553 (2021).Article 

Google Scholar 
Phillips, K. F., Stahelin, G. D., Chabot, R. M. & Mansfield, K. L. Long-term trends in marine turtle size at maturity at an important Atlantic rookery. Ecosphere 12, 7 (2021).Article 

Google Scholar 
Bjorndal, K. A., Bolten, A. B. & Chaloupka, M. Y. Evaluating trends in abundance of immature green turtles, Chelonia mydas, in the Greater Caribbean. Ecol. Appl. 15, 304–314 (2005).Article 

Google Scholar 
Naro-Maciel, E. et al. The interplay of homing and dispersal in green turtles: A focus on the southwestern atlantic. J. Hered. 103, 792–805 (2012).Article 
PubMed 

Google Scholar 
Monzón-Argüello, C. et al. Evidence from genetic and Lagrangian drifter data for transatlantic transport of small juvenile green turtles. J. Biogeogr. 37, 1752–1766 (2010).Article 

Google Scholar 
Luke, K., Horrocks, J. A., LeRoux, R. A. & Dutton, P. H. Origins of green turtle (Chelonia mydas) feeding aggregations around Barbados, West Indies. Mar. Biol. 144, 799–805 (2004).Article 

Google Scholar 
Bass, A. L., Epperly, S. P. & Braun-McNeill, J. Green turtle (Chelonia mydas) foraging and nesting aggregations in the Caribbean and Atlantic: Impact of currents and behavior on dispersal. J. Hered. 97, 346–354 (2006).Article 
PubMed 

Google Scholar 
Lahanas, P. N. et al. Genetic composition of a green turtle (Chelonia mydas) feeding ground population: Evidence for multiple origins. Mar. Biol. 130, 345–352 (1998).Article 

Google Scholar 
Foley, A. M. et al. Characteristics of a green turtle (Chelonia mydas) assemblage in northwestern Florida determined during a hypothermic stunning event. Gulf Mex. Sci. 25, 131–143 (2007).
Google Scholar 
Bass, A. L., Lagueux, C. J. & Bowen, B. W. Origin of green turtles, Chelonia mydas, at ‘Sleeping Rocks’ off the Northeast coast of Nicaragua. Copeia 1998, 1064 (1998).Article 

Google Scholar 
Bass, A. L. & Witzell, W. N. Demographic composition of immature green turtles (Chelonia mydas) from the East Central Florida Coast: Evidence from mtDNA markers. Herpetologica 56, 357–367 (2000).
Google Scholar 
Bjorndal, K. A., Parsons, J., Mustin, W. & Bolten, A. B. Threshold to maturity in a long-lived reptile: Interactions of age, size, and growth. Mar. Biol. 160, 607–616 (2013).Article 

Google Scholar 
Perrault, J. R. et al. Maternal health status correlates with nest success of leatherback sea turtles (Dermochelys coriacea) from Florida. PLoS ONE 7, e31841 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Montero, N. et al. Warmer and wetter conditions will reduce offspring production of hawksbill turtles in Brazil under climate change. PLoS ONE 13, 1–16 (2018).Article 

Google Scholar 
Shamblin, B. M. et al. Geographic patterns of genetic variation in a broadly distributed marine vertebrate: New insights into loggerhead turtle stock structure from expanded mitochondrial DNA sequences. PLoS ONE 9, 85956 (2014).Article 

Google Scholar 
Anderson, J. D., Shaver, D. J. & Karel, W. J. Genetic Diversity and Natal Origins of Green Turtles (Chelonia mydas) in the Western Gulf of Mexico. J. Herpetol. 47, 251–257 (2013).Article 

Google Scholar  More

Area-based sub-targetWe found that 13.2% of Asian terrestrial landscapes were covered by PAs by the target date for Aichi 11 based on our in-country sources. However, it was 17.4% lower based on WDPA data (10.9%). The average increase in coverage across Asia during the 2010s was 0.4% ± SE 0.1% per year. PA coverage at the level of individual countries increased from a mean 11.1% in 2010 (SE = 1.4%) to 14.1% by 2020 (SE = 1.8%) based on our in-country sources, which was 16.5% higher than WDPA data (12.1 ± SE 1.6%). However, these overall figures concealed considerable country-level and sub-regional heterogeneity.A total of 8,673,433 km2 across 10 countries, equaling 19.6% of Asian terrestrial landscapes was managed as hunting concessions, governed by governments, communities or private sectors, but these areas have not been included in the countries’ report to the Protected Planet Initiative databases. Most of these areas are locally important in terms of biodiversity conservation and local socioeconomic outcomes which may qualify them as examples of “other effective area-based conservation measures” (OECMs). The increase in area-based conservation coverage represented by these areas, above the current Protected Planet Initiative statistic, ranged from 0.2% (Iran) to 41.4% (Russia). With that update incorporated, a total of 32.9% of Asian terrestrial landscapes are under protection, either as protected areas or hunting concessions (potentially as one type of OECMs).We found that 40% of Asian countries met a target of 17% coverage for PAs by 2020 based on our in-country sources, mainly in East and some South Asia, whereas West and Central Asian countries had generally not achieved this target (Figs. 1 and 2). We did not find any statistically significant association between the proportions of highly at-risk (CR/EN) mammalian species range outside PAs and the % PA extent in 2020 (β = −0.22 ± SE 0.15, t = −1.51, P = 0.14 in a Generalized Linear Model). The highest proportions of the highly at-risk (CR/EN) mammalian species range outside PAs were seen in West (βCR/EN_outsidePA = 1.77 ± SE 0.46, t = 3.86, P 10%, but Kuwait lost area. In East Asia, all countries showed at least some PA expansion (South Korea and Japan by >10%) whereas in Central Asia, almost no change was seen. It is also noteworthy that between 2010 and 2015, agricultural lands increased by 2.0% across the continent, averaging 0.51 ± SE 0.03% per year at country level, although 18 counties (45.0%) had agricultural land loss, mainly in West and Central Asia (12 out of 18 countries with agricultural land loss; Fig. 2).In our attempt to model the variation in achievement of area-based target (% PA extent), we found a single model with a ΔAICc weight of 1.0 (R2adj = 0.66; Table 1). There was no evidence to reject the null hypothesis that the model fits well (P = 0.99). This model included the predictors % agricultural extent in 2015, % PA extent in 2010, and sub-region (Table 1). Specifically, the coefficients suggested that countries with greater PA extent in 2010 and a smaller percentage of agricultural lands in 2015 were more likely to achieve higher percentage of PA extent by 2020 (βPAExtent2020 = 0.58 ± SE 0.10, t = 5.74, P  0.05).Table 2 Results of generalized linear models testing different hypotheses on the association between the percentage of ecoregions protected by the PA network in 2020 and ecological and geopolitical factors in Asian countries.Full size tableFor the coverage of highly at-risk (CR/EN) mammalian species, a single statistical model was also selected, with non-significant deviance goodness of fit (P = 0.83), which included only the % PA extent by 2020 and Region as predictors (R2adj = 0. 27). Although there was no evidence for association between the % PA extent by 2020 and the coverage of threatened species (βPAExtent2020 = −0.23 ± SE 0.15, t = −1.57, P = 0.13). However, the coverage of threatened species varied geographically, with high intercept differences for East Asia (βEastAsia = −0.23 ± SE 0.15, t = −1.57, P = 0.13), implying the largest median of range of highly at-risk (CR/EN) mammalian species outside the current network of PAs within each country.PA management effectiveness sub-targetFor the level of PAME assessment, we found that out of 22781 PAs within the 40 studied Asian countries, only 7.0% have been assessed based on PAME criteria (n = 1599), averaging 17.4% ± of PAs per country (SE = 2.5%). Israel, Japan, Lao, Bahrain, Oman and Qatar had no PA assessed based on the PAME criteria while over 1/3 of PAs in Indonesia, Cambodia, Bhutan, Jordan, Nepal, Turkey, Singapore and the UAE were PAME assessed. When modeling the level of PAME assessment, three best supported models were averaged (Table 3), with the averaged model including GDP2019, % PA extent 2020 and the Region as predictors. The averaged model coefficients would be non-significant under a hypothesis-testing approach (βGDP2019 = −0.18 ± SE 0.12, t = 1.47, P = 0.14 and βPAExtent2020 = −0.15 ± SE 0.11, t = 1.31, P = 0.19). Similarly, there was no evidence for the association between the ratio of PAs with PAME and Asian regions (P  > 0.05).Table 3 Results of generalized linear models testing different hypotheses on the association between the ratio of PAs with management effectiveness (PAME) in 2020 and ecological and geopolitical factors in Asian countries.Full size table More

Millien, V. et al. Ecotypic variation in the context of global climate change: Revisiting the rules. Ecol. Lett. 9, 853–869 (2006).Article 
PubMed 

Google Scholar 
Calder, W. A. Size, Function and Life History (Harvard University Press, 1984).
Google Scholar 
Bergmann, C. Über die verhältnisse der wärmeökonomie der thiere zu ihrer grösse. Göttinger Stud. 3, 595–708 (1847).
Google Scholar 
Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).Article 

Google Scholar 
Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. PNAS 116, 21609–21615 (2019).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Foster, J. B. Evolution of mammals on islands. Nature 202, 234–235 (1964).Article 
ADS 

Google Scholar 
Lomolino, M. V. Body size evolution in insular vertebrates: Generality of the island rule. J. Biogeogr. 32, 1683–1699 (2005).Article 

Google Scholar 
Benítez-López, A. et al. The island rule explains consistent patterns of body size evolution in terrestrial vertebrates. Nat. Ecol. Evol. 5, 768–786 (2021).Article 
PubMed 

Google Scholar 
Meiri, S. & Dayan, T. On the validity of Bergmann’s rule. J. Biogeogr. 30, 331–351 (2003).Article 

Google Scholar 
Meiri, S., Cooper, N. & Purvis, A. The island rule: Made to be broken?. Proc. R. Soc. B. 275, 141–148 (2008).Article 
PubMed 

Google Scholar 
Millien, V. Relative effects of climate change, isolation and competition on body-size evolution in the Japanese field mouse, Apodemus argenteus. J. Biogeogr. 31, 1267–1276 (2004).Article 

Google Scholar 
Millien, V. & Damuth, J. Climate change and size evolution in an island rodent species: New perspectives on the island rule. Evolution 58, 1353–1360 (2004).Article 
PubMed 

Google Scholar 
Lomolino, M. V., Sax, D. F., Riddle, B. R. & Brown, J. H. The island rule and a research agenda for studying ecogeographical patterns. J. Biogeogr. 33, 1503–1510 (2006).Article 

Google Scholar 
Sargis, E. J., Millien, V., Woodman, N. & Olson, L. E. Rule reversal: Ecogeographical patterns of body size variation in the common treeshrew (Mammalia, Scandentia). Ecol. Evol. 8, 1634–1645 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Barnosky, A. D., Hadly, E. A. & Bell, C. J. Mammalian response to global warming on varied temporal scales. J. Mammal. 84, 354–368 (2003).Article 

Google Scholar 
Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).Article 
ADS 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: A third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).Article 
PubMed 

Google Scholar 
Teplitsky, C., Mills, J. A., Alho, J. S., Yarrall, J. W. & Merilä, J. Bergmann’s rule and climate change revisited: Disentangling environmental and genetic responses in a wild bird population. PNAS 105, 13492–13496 (2008).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Teplitsky, C. & Millien, V. Climate warming and Bergmann’s rule through time: Is there any evidence?. Evol. Appl. 7, 156–168 (2014).Article 
PubMed 

Google Scholar 
James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51, 385–390 (1970).Article 

Google Scholar 
Wigginton, J. D. & Dobson, F. S. Environmental influences on geographic variation in body size of western bobcats. Can. J. Zool. 77, 802–813 (1999).Article 

Google Scholar 
Yom-Tov, Y. & Geffen, E. Geographic variation in body size: The effects of ambient temperature and precipitation. Oecologia 148, 213–218 (2006).Article 
PubMed 
ADS 

Google Scholar 
Wagner, J. A. Schreber’s saugthiere, supplementband, 2. Abtheilung 1841(37–44), 553 (1841).
Google Scholar 
Hawkins, M. T. Family Tupaiidae (treeshrews). In Handbook of the Mammals of the World, Volume 8 Insectivores, Sloths and Colugos (eds Wilson, D. E. & Mittermeier, R. A.) (Lynx Edicions, 2018).
Google Scholar 
Roberts, T. E., Lanier, H. C., Sargis, E. J. & Olson, L. E. Molecular phylogeny of treeshrews (Mammalia: Scandentia) and the timescale of diversification in Southeast Asia. Mol. Phylogenet. Evol. 60, 358–372 (2011).Article 
PubMed 

Google Scholar 
Zhang, L., Yang, F., Wang, Z. K. & Zhu, W. L. Role of thermal physiology and bioenergetics on adaptation in tree shrew (Tupaia belangeri): The experiment test. Sci. Rep. 7, 41352 (2017).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Zhu, W., Zhang, H. & Wang, Z. Seasonal changes in body mass and thermogenesis in tree shrews (Tupaia belangeri): The roles of photoperiod and cold. J. Therm. Biol. 37, 479–484 (2012).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 
MATH 

Google Scholar 
South, A. rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0 (2017).Dunnington, D. ggspatial: Spatial Data Framework for ggplot2. R package version 1.1.4 (2020).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2018).Helgen, K. M. Order Scandentia. In Mammal Species of the World: A Taxonomic and Geographic Reference 3rd edn (eds Wilson, D. E. & Reeder, D. M.) (Johns Hopkins University Press, 2005).
Google Scholar 
Collins, P. M. & Tsang, W. N. Growth and reproductive development in the male tree shrew (Tupaia belangeri) from birth to sexual maturity. Biol. Reprod. 37, 261–267 (1987).Article 
CAS 
PubMed 

Google Scholar 
Heaney, L. R. Island area and body size of insular mammals: Evidence from the tri-colored squirrel (Callosciurus prevosti) of Southeast Asia. Evolution 32, 29–44 (1978).PubMed 

Google Scholar 
Husson, L., Boucher, F. C., Sarr, A. C., Sepulchre, P. & Cahyarini, S. Y. Evidence of Sundaland’s subsidence requires revisiting its biogeography. J. Biogeogr. 47, 843–853 (2020).Article 

Google Scholar 
Juman, M. M., Woodman, N., Olson, L. E. & Sargis, E. J. Ecogeographic variation and taxonomic boundaries in Large Treeshrews (Scandentia, Tupaiidae: Tupaia tana Raffles, 1821) from Southeast Asia. J. Mammal. 102, 1054–1066 (2021).Article 

Google Scholar 
Hinckley, A. et al. Challenging ecogeographical rules: Phenotypic variation in the Mountain Treeshrew (Tupaia montana) along tropical elevational gradients. PLoS ONE 17, e0268213 (2022).Article 
CAS 
PubMed 

Google Scholar 
Lomolino, M. V., Sax, D. F., Palombo, M. R. & van der Geer, A. A. Of mice and mammoths: evaluations of causal explanations for body size evolution in insular mammals. J. Biogeogr. 39, 842–854 (2011).Article 

Google Scholar 
Teta, P., de la Sancha, N. U., D’Elía, G. & Patterson, B. D. Andean rain shadow effect drives phenotypic variation in a widely distributed Austral rodent. J. Biogeogr. 49, 1767–1778 (2022).Article 

Google Scholar 
Yom-Tov, Y. & Yom-Tov, S. Climatic change and body size in two species of Japanese rodents. Biol. J. Linn. Soc. 82, 263–267 (2004).Article 

Google Scholar 
Yom-Tov, Y. & Yom-Tov, J. Global warming, Bergmann’s rule and body size in the masked shrew Sorex cinereus in Alaska. J. Anim. Ecol. 74, 803–808 (2005).Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. PNAS 105, 6668–6672 (2008).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 4, 1630–1638 (2020).Article 
PubMed 

Google Scholar 
Cronk, Q. C. B. Islands: stability, diversity, conservation. Biodivers. Conserv. 6, 477–493 (1997).Article 

Google Scholar 
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. PNAS 106, 9322–9327 (2009).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Yom-Tov, Y. & Geffen, E. Recent spatial and temporal changes in body size of terrestrial vertebrates: Probable causes and pitfalls. Biol. Rev. 86, 531–541 (2011).Article 
PubMed 

Google Scholar 
Theriot, M. K., Lanier, H. C. & Olson, L. E. Harnessing natural history collections to detect trends in body-size change as a response to warming: A critique and review of best practices. Methods Ecol. Evol. (2022).Rohwer, V. G., Rohwer, Y. & Dillman, C. B. Declining growth of natural history collections fails future generations. PLoS Biol. 20, e3001613 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Reese, A. T. & Olson, L. E. Morphological distinctiveness of Javan Tupaia hypochrysa (Scandentia, Tupaiidae). J. Mammal. 94, 938–947 (2013).Article 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Reese, A. T. & Olson, L. E. Island history affects faunal composition: The treeshrews (Mammalia: Scandentia: Tupaiidae) from the Mentawai and Batu Islands, Indonesia. Biol. J. Linn. Soc. 111, 290–304 (2014).Article 

Google Scholar 
Sargis, E. J., Campbell, K. K. & Olson, L. E. Taxonomic boundaries and craniometric variation in the treeshrews (Scandentia, Tupaiidae) from the Palawan faunal region. J. Mamm. Evol. 21, 111–123 (2014).Article 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Bell, T. N. & Olson, L. E. Skeletal variation and taxonomic boundaries among mainland and island populations of the common treeshrew (Mammalia: Scandentia: Tupaiidae). Biol. J. Linn. Soc. 120, 286–312 (2017).
Google Scholar 
Juman, M. M., Olson, L. E. & Sargis, E. J. Skeletal variation and taxonomic boundaries in the Pen-tailed Treeshrew (Scandentia, Ptilocercidae: Ptilocercus lowii Gray, 1848). J. Mamm. Evol. 28, 1193–1203 (2021).Article 

Google Scholar 
Juman, M. M., Woodman, N., Miller-Murthy, A., Olson, L. E. & Sargis, E. J. Taxonomic boundaries in Lesser Treeshrews (Scandentia, Tupaiidae: Tupaia minor Günther, 1876). J. Mammal. https://doi.org/10.1093/jmammal/gyac080 (2022).Article 

Google Scholar 
Woodman, N., Miller-Murthy, A., Olson, L. E. & Sargis, E. J. Coming of age: Morphometric variation in the hand skeletons of juvenile and adult Lesser Treeshrews (Scandentia: Tupaiidae: Tupaia minor Günther, 1876). J. Mammal. 101, 1151–1164 (2020).Article 

Google Scholar 
Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L. & Ram, K. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.7.2, https://CRAN.R-project.org/package=rgbif.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data. 7, 109 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Meiyappan, P. & Jain, A. K. Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Front. Earth Sci. 6, 122–139 (2012).Article 
ADS 

Google Scholar 
Ryan, W. B. F. et al. Global multi-resolution topography synthesis. Geochem. Geophys. 10, Q03014 (2009).
Google Scholar 
van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).Article 

Google Scholar 
Clavel, J., Merceron, G. & Escarguel, G. Missing data estimation in morphometrics: How much is too much? Syst. Biol. 63, 203–218 (2014).Article 
PubMed 

Google Scholar 
Nally, R. M. & Walsh, C. J. Hierarchical partitioning public-domain software. Biodivers. Conserv. 13, 659–660 (2004).Article 

Google Scholar 
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn. (Springer, 2013).Book 
MATH 

Google Scholar  More

Differences in environmental factorsEnvironmental factors showed value differences between forest types, while the significance of differences differed among variables, which were both found with corrected values and original measurements (Fig. 1).Figure 1The differences in environmental factors between naturally regenerating forests (NF) and planted forests (PF) in China. The environmental factors include three annual climatic factors (a–c), three seasonal temperature factors (d–f), three seasonal precipitation factors (g–i), three biotic factors (j–l), and two soil factors (m,n). Three annual climatic factors include mean annual air temperature (MAT, a), mean annual precipitation (MAP, b), and aridity index (AI, c) defined as the ratio of MAP to annual potential evapotranspiration. Three seasonal temperature factors include the temperature of the warmest month (Tw, d), the temperature of the coldest month (Tc, e), temperature annual range (TR, f). Three seasonal precipitation factors include precipitation of the wettest month (Pw, g), precipitation of the driest month (Pd, h), and precipitation seasonality (Ps, i) defined as the standard deviation of monthly precipitation during the measuring year. Three biological factors include the mean annual leaf area index (LAI, j), the maximum leaf area index (MLAI, k), and stand age (SA, l). Two soil factors include soil organic carbon content (SOC, m) and soil total nitrogen content (STN, n). The differences are tested for each variable with one-way analysis of variance (ANOVA), where * and ** indicate significant differences between forest types at significance levels of α = 0.05 and α = 0.01, respectively. The corrected values are mean values during 2003–2019 after correcting the original measurements with the interannual trend (See methods), which are listed in each panel, while original measurements are mean values during the measuring period of each ecosystem, which are not shown in each panel.Full size imageFor annual climatic factors, the significant difference between NF and PF only appeared in MAT (Fig. 1a). The mean MAT of NF was 10.50 ± 7.81 °C, which was significantly lower than that of PF (15.65 ± 6.23 °C) (p  0.05) (Fig. 2c). Even considering the significant effects of MAT on ER, ANCOVA results obtained by fixing MAT as a covariant also suggested that ER values did not significantly differ between forest types (F = 0.01, p  > 0.05). Fixing other variables as a covariant also drew a similar result.Therefore, NF showed a lower NEP resulting from the lower GPP than PF, while their differences were not statistically significant (Fig. 2).Differences in NEP latitudinal patternsCarbon fluxes showed divergent latitudinal patterns between NF and PF, while their latitudinal patterns varied among carbon fluxes, which were both found with corrected values and original measurements (Fig. 3).Figure 3The latitudinal patterns of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  0.05).The ER of NF showed a significant decreasing latitudinal pattern (Fig. 3e), while that of PF exhibited no significant latitudinal pattern (Fig. 3f). The increasing latitude caused the ER of NF to significantly decrease. Each unit increase in latitude led to a 28.71 gC m−2 year−1 decrease in ER, with an R2 of 0.31. However, the increasing latitude contributed little to the ER spatial variation of PF (p  > 0.05).In addition, the latitudinal patterns of carbon fluxes and their differences between forest types were also obtained with the original measurements (Fig. 3, grey points). The latitudinal patterns of random error adding carbon fluxes were comparable to those of our corrected carbon fluxes (Fig. 3), which confirmed that the latitudinal patterns of carbon fluxes and their differences between forest types would not be affected by the uncertainties in generating the corrected carbon fluxes.Therefore, among NFs, the similar decreasing latitudinal patterns of GPP and ER meant that NEP showed no significant latitudinal pattern, while the significant decreasing latitudinal pattern of GPP and no significant latitudinal pattern of ER caused NEP to show a decreasing latitudinal pattern among PFs.Differences in the environmental effects on NEP spatial variationsEnvironmental factors, including the annual climatic factors, seasonal temperature factors, seasonal precipitation factors, biological factors, and soil factors, exerted divergent effects on the spatial variations of NEP and its components, which also differed between forest types (Table 1). No factor was found to affect that the spatial variation of NEP among NFs, while most annual and seasonal climatic factors were found to affect that among PFs. The spatial variations of GPP and ER among NFs were both affected by most annual and seasonal climatic factors and LAI, while those among PFs were primarily shaped by most annual and seasonal climatic factors. Though LAI showed no significant effect on GPP and ER spatial variations among PFs, SA exerted a significant negative effect. In addition, the spatial variations of soil variables contributed little to the spatial variations of carbon fluxes. Therefore, among NFs, most annual and seasonal climatic factors and LAI were found to affect GPP and ER spatial variations, while no factor was found to significantly influent the NEP spatial variation. However, among PFs, most annual and seasonal climatic factors were found to affect the spatial variations of NEP and its components, while LAI showed no significant effect. Using the original measurements also generated the similar correlation coefficients (Supplementary Table S1).Table 1 Correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF).Full size tableGiven the high correlations among annual climatic factors and seasonal climatic factors (Supplementary Table S2), the partial correlation analysis was applied to determine which factors should be employed to reveal the mechanisms underlying the spatial variations of NEP. Partial correlation analysis showed that MAT and MAP exerted the most important roles in spatial variations of NEP and its components (Table 2). After controlling MAT (or MAP), other factors seldom showed significant correlation with carbon fluxes, especially fixing MAT (Table 2). In addition, MAT and MAP exerted similar effects on the spatial variations of NEP and its components (Table 1). Using the original measurements also generated the similar partial correlation coefficients (Supplementary Table S3). Therefore, we only presented the effects of MAT on carbon flux spatial variations and their differences between forest types in detail.Table 2 Partial correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF) with fixing mean annual air temperature (MAT) or mean annual precipitation (MAP).Full size tableThe increasing MAT increased carbon fluxes, while the increasing rates differed between forest types (Fig. 4). The increasing MAT contributed little to the NEP spatial variation of NF but raised the NEP of PF (Fig. 4a,b). Each unit increase in MAT caused the NEP of PF to increase at a rate of 27.77 gC m−2 year−1, with an R2 of 0.31 (Fig. 4b). The increasing MAT significantly raised GPP in NF and PF (Fig. 4c,d). For NF, each unit increase in MAT increased GPP at a rate of 43.76 gC m−2 year−1, with an R2 of 0.49 (Fig. 4c), while each unit increase in MAT increased the GPP of PF at a rate of 69.18 gC m−2 year−1, with an R2 of 0.57 (Fig. 4d). The GPP increasing rates did not significantly differ between NF and PF (F = 1.52, p  > 0.05). The increasing MAT also raised ER in both NF and PF (Fig. 4e,f), whose increasing rates were 38.97 gC m−2 year−1 (Fig. 4e) and 36.79 gC m−2 year−1 (Fig. 4f), respectively, while their differences were not statistically significant (F = 0.01, p  > 0.05). In addition, using the original measurements also generated the similar spatial variations and their differences between forest types (Fig. 4). Furthermore, the random error adding carbon fluxes responded similarly to those of our correcting carbon fluxes (Fig. 4), indicating that the effects of MAT on carbon fluxes would not be affected by the uncertainties in our correcting carbon fluxes. Therefore, the similar responses of GPP and ER to MAT made MAT contribute little to NEP spatial variations among NFs, while GPP and ER showed divergent response rates to MAT, which made NEP increase with MAT among PFs.Figure 4The effects of mean annual air temperature (MAT) on the spatial variations of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  More

Alpha diversity differences among communitiesNematode gut microbiomes were assigned into their respective species categories of E. antarcticus and P. murrayi based on 18S host data that was consistent with morphology (see Methods “Microinvertebrate haplotypes”). In contrast, due to recovery of three undiscernible 18S tardigrade haplotypes, the gut microbiomes were assigned to Tardigrada. Mat bacterial communities were significantly (Tukey’s HSD, P  0.65, χ2(1)  0.38, χ2(3)  More

As humans warm the planet, biodiversity is plummeting. These two global crises are connected in multiple ways. But the details of the intricate feedback loops between biodiversity decline and climate change are astonishingly under-studied.It is well known that climate extremes such as droughts and heatwaves can have devastating impacts on ecosystems and, in turn, that degraded ecosystems have a reduced capacity to protect humanity against the social and physical impacts of such events. Yet only a few such relationships have been probed in detail. Even less well known is whether biodiversity-depleted ecosystems will also have a negative effect on climate, provoking or exacerbating weather extremes.For us, a group of researchers living and working mainly in Central Europe, the wake-up call was the sequence of heatwaves of 2018, 2019 and 2022. It felt unreal to watch a floodplain forest suffer drought stress in Leipzig, Germany. Across Germany, more than 380,000 hectares of trees have now been damaged (see go.nature.com/3etrrnp; in German), and the forestry sector is struggling with how to plan restoration activities over the coming decades1. What could have protected these ecosystems against such extremes? And how will the resultant damage further impact our climate?
Nature-based solutions can help cool the planet — if we act now
In June 2021, the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) published their first joint report2, acknowledging the need for more collaborative work between these two domains. And some good policy moves are afoot: the new EU Forest Strategy for 2030, released in July 2021, and other high-level policy initiatives by the European Commission, formally recognize the multifunctional value of forests, including their role in regulating atmospheric processes and climate. But much more remains to be done.To thoroughly quantify the risk that lies ahead, ecologists, climate scientists, remote-sensing experts, modellers and data scientists need to work together. The upcoming meeting of the United Nations Convention on Biological Diversity in Montreal, Canada, in December is a good opportunity to catalyse such collaboration.Buffers and responsesWhen lamenting the decline in biodiversity, most people think first about the tragedy of species driven to extinction. There are more subtle changes under way, too.For instance, a study across Germany showed that over the past century, most plant species have declined in cover, with only a few increasing in abundance3. Also affected is species functionality4 — genetic diversity, and the diversity of form and structure that can make communities more or less efficient at taking up nutrients, resisting heat or surviving pathogen attacks.When entire ecosystems are transformed, their functionality is often degraded. They are left with less capacity to absorb pollution, store carbon dioxide, soak up water, regulate temperature and support vital functions for other organisms, including humans5. Conversely, higher levels of functional biodiversity increase the odds of an ecosystem coping with unexpected events, including climate extremes. This is known as the insurance effect6.The effect is well documented in field experiments and modelling studies. And there is mounting evidence of it in ecosystem responses to natural events. A global synthesis of various drought conditions showed, for instance, that forests were more resilient when trees with a greater diversity of strategies for using and transporting water lived together7.

Dead trees near Iserlohn, Germany, in April 2020 (left) and after felling in June 2021 (right).Credit: Ina Fassbender/AFP via Getty

However, biodiversity cannot protect all ecosystems against all kinds of impacts. In a study this year across plots in the United States and Canada, for example, mortality was shown to be higher in diverse forest ecosystems8. The proposed explanation for this unexpected result was that greater biodiversity could also foster more competition for resources. When extreme events induce stress, resources can become scarce in areas with high biomass and competition can suddenly drive mortality, overwhelming the benefits of cohabitation. Whether or not higher biodiversity protects an ecosystem from an extreme is highly site-specific.Some plants respond to drought by reducing photosynthesis and transpiration immediately; others can maintain business as usual for much longer, stabilizing the response of the ecosystem as a whole. So the exact response of ecosystems to extremes depends on interactions between the type of event, plant strategies, vegetation composition and structure.Which plant strategies will prevail is hard to predict and highly dependent on the duration and severity of the climatic extreme, and on previous extremes9. Researchers cannot fully explain why some forests, tree species or individual plants survive in certain regions hit by extreme climate conditions, whereas entire stands disappear elsewhere10. One study of beech trees in Germany showed that survival chances had a genomic basis11, yet it is not clear whether the genetic variability present in forests will be sufficient to cope with future conditions.And it can take years for ecosystem impacts to play out. The effects of the two consecutive hot drought years, 2018 and 2019, were an eye-opener for many of us. In Leipzig, tree growth declined, pathogens proliferated and ash and maple trees died. The double blow, interrupted by a mild winter, on top of the long-term loss of soil moisture, led to trees dying at 4–20 times the usual rate throughout Germany, depending on the species (see go.nature.com/3etrrnp; in German). The devastation peaked in 2020.Ecosystem changes can also affect atmospheric conditions and climate. Notably, land-use change can alter the brightness (albedo) of the planet’s surface and its capacity for heat exchange. But there are more-complex mechanisms of influence.Vegetation can be a source or sink for atmospheric substances. A study published in 2020 showed that vegetation under stress is less capable of removing ozone than are unstressed plants, leading to higher levels of air pollution12. Pollen and other biogenic particles emitted from certain plants can induce the freezing of supercooled cloud droplets, allowing ice in clouds to form at much warmer temperatures13, with consequences for rainfall14. Changes to species composition and stress can alter the dynamics of these particle emissions. Plant stress also modifies the emission of biogenic volatile organic gases, which can form secondary particles. Wildfires — enhanced by drought and monocultures — affect clouds, weather and climate through the emission of greenhouse gases and smoke particles. Satellite data show that afforestation can boost the formation of low-level, cooling cloud cover15 by enhancing the supply of water to the atmosphere.Research prioritiesAn important question is whether there is a feedback loop: will more intense, and more frequent, extremes accelerate the degradation and homogenization of ecosystems, which then, in turn, promote further climate extremes? So far, we don’t know.One reason for this lack of knowledge is that research has so far been selective: most studies have focused on the impacts of droughts and heatwaves on ecosystems. Relatively little is known about the impacts of other kinds of extremes, such as a ‘false spring’ caused by an early-season bout of warm weather, a late spring frost, heavy rainfall events, ozone maxima, or exposure to high levels of solar radiation during dry, cloudless weather.Researchers have no overview, much less a global catalogue, of how each dimension of biodiversity interacts with the full breadth of climate extremes in different combinations and at multiple scales. In an ideal world, scientists would know, for example, how the variation in canopy density, vegetation age, and species diversity protects against storm damage; and whether and how the diversity of canopy structures controls atmospheric processes such as cloud formation in the wake of extremes. Researchers need to link spatiotemporal patterns of biodiversity with the responses of ecosystem processes to climate extremes.
Biodiversity needs every tool in the box: use OECMs
Creating such a catalogue is a huge challenge, particularly given the more frequent occurrence of extremes with little or no precedent16. Scientists will also need to account for the increasing likelihood of pile-ups of climate stressors. The ways in which ecosystems respond to compound events17 could be quite different. Researchers will have to study which facets of biodiversity (genetic, physiological, structural) are required to stabilize ecosystems and their functions against these onslaughts.There is at least one piece of good news: tools for data collection and analysis are improving fast, with huge advances over the past decade in satellite-based observations for both climate and biodiversity monitoring. The European Copernicus Earth-observation programme, for example — which includes the Sentinel 1 and 2 satellite fleet, and other recently launched missions that cover the most important wavelengths of the electromagnetic spectrum — offer metre-scale resolution observations of the biochemical status of plants and canopy structure. Atmospheric states are recorded in unprecedented detail, vertically and in time.Scientists must now make these data interoperable and integrate them with in situ observations. The latter is challenging. On the ground, a new generation of data are being collected by researchers and by citizen scientists18. For example, unique insights into plant responses to stress are coming from time-lapse photography of leaf orientation; accelerometer measures of movement patterns of stems have been shown to provide proxies for the drought stress of trees19.High-quality models are needed to turn these data into predictions. The development of functional ‘digital twins’ of the climate system is now in reach. These models replicate hydrometeorological processes at the metre scale, and are fast enough to allow for rapid scenario development and testing20. The analogous models for ecosystems are still in a more conceptual phase. Artificial-intelligence methods will be key here, to study links between climate extremes and biodiversity.Researchers can no longer afford to track global transformations of the Earth system in disciplinary silos. Instead, ecologists and climate scientists need to establish a joint agenda, so that humanity is properly forewarned: of the risks of removing biodiversity buffers against climate extremes, and of the risk of thereby amplifying these extremes. More

Zimmer, H. D. et al. Memory for Action: A Distinct Form of Episodic Memory? (Oxford University Press, 2001).
Google Scholar 
Goswami, U. The Wiley-Blackwell Handbook of Childhood Cognitive Development (Wiley, 2013).
Google Scholar 
Fujita, K., Morisaki, A., Takaoka, A., Maeda, T. & Hori, Y. Incidental memory in dogs (Canis familiaris): Adaptive behavioral solution at an unexpected memory test. Anim. Cogn. 15, 1055–1063 (2012).Article 
PubMed 

Google Scholar 
Lind, J., Enquist, M. & Ghirlanda, S. Animal memory: A review of delayed matching-to-sample data. Behav. Processes 117, 52–58 (2015).Article 
PubMed 

Google Scholar 
Kuczaj, S. A. II. & Eskelinen, H. C. (2014) The “creative dolphin” revisited: What do dolphins do when asked to vary their behavior. Anim. Behav. Cogn. 1, 66–77 (2014).Article 

Google Scholar 
Tulving, E. Episodic and semantic memory. Organ. Mem. 1, 381–403 (1972).
Google Scholar 
Tulving, E. How many memory systems are there?. Am. Psychol. 40, 385 (1985).Article 

Google Scholar 
Fugazza, C., Pongrácz, P., Pogány, Á., Lenkei, R. & Miklósi, Á. Mental representation and episodic-like memory of own actions in dogs. Sci. Rep. 10, 1–8 (2020).Article 

Google Scholar 
Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: What is it, who has it, and how did it evolve?. Science 298, 1569–1579 (2002).Article 
PubMed 

Google Scholar 
Conway, M. A. Memory and the self. J. Mem. Lang. 53, 594–628 (2005).Article 

Google Scholar 
Scagel, A. & Mercado, E. III. Do that again! Memory for self-performed actions in dogs (Canis familiaris). J. Comp. Psychol. 20, 25 (2022).
Google Scholar 
Mercado, E., Murray, S. O., Uyeyama, R. K., Pack, A. A. & Herman, L. M. Memory for recent actions in the bottlenosed dolphin (Tursiops truncatus): Repetition of arbitrary behaviors using an abstract rule. Learn. Behav. 26, 210–218 (1998).Article 

Google Scholar 
Paukner, A., Anderson, J. R., Donaldson, D. I. & Ferrari, P. F. Cued repetition of self-directed behaviors in macaques (Macaca nemestrina). J. Exp. Psychol. Anim. Behav. Process. 33, 139 (2007).Article 
PubMed 

Google Scholar 
Smeele, S. Q. et al. Memory for own behaviour in pinnipeds. Anim. Cogn. 20, 1–12 (2019).
Google Scholar 
Clayton, N. S. Episodic-like memory and mental time travel in animals. (2017).Clayton, N. S., Griffiths, D. P. & Dickinson, A. Declarative and episodic-like memory in animals: Personal musings of a Scrub Jay (2000).Clayton, N. S. & Dickinson, A. Episodic-like memory during cache recovery by scrub jays. Nature 395, 272–274 (1998).Article 
PubMed 

Google Scholar 
Tulving, E. Episodic memory and autonoesis: Uniquely human. Missing Link Cogn. Orig. Self-Reflect. Conscious 20, 3–56 (2005).
Google Scholar 
Suddendorf, T. & Corballis, M. C. Mental time travel and the evolution of the human mind. Genet. Soc. Gen. Psychol. Monogr. 123, 133–167 (1997).PubMed 

Google Scholar 
Suddendorf, T. & Corballis, M. C. The evolution of foresight: What is mental time travel, and is it unique to humans?. Behav. Brain Sci. 30, 299–313 (2007).Article 
PubMed 

Google Scholar 
Crystal, J. D. Evaluating evidence from animal models of episodic memory. J. Exp. Psychol. Anim. Learn. Cogn. 47, 337 (2021).Article 
PubMed 

Google Scholar 
Mercado, E. III., Uyeyama, R. K., Pack, A. A. & Herman, L. M. Memory for action events in the bottlenosed dolphin. Anim. Cogn. 2, 17–25 (1999).Article 

Google Scholar 
Zentall, T. R. Coding of stimuli by animals: Retrospection, prospection, episodic memory and future planning. Learn. Motiv. 41, 225–240 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Fugazza, C., Pogány, Á. & Miklósi, Á. Recall of others’ actions after incidental encoding reveals episodic-like memory in dogs. Curr. Biol. 26, 3209–3213 (2016).Article 
PubMed 

Google Scholar 
Lambert, M. L., Jacobs, I., Osvath, M. & von Bayern, A. M. Birds of a feather? Parrot and corvid cognition compared. Behaviour 156, 505–594 (2019).Article 

Google Scholar 
Emery, N. J. Cognitive ornithology: The evolution of avian intelligence. Philos. Trans. R. Soc. B Biol. Sci. 361, 23–43 (2006).Article 

Google Scholar 
Olkowicz, S. et al. Birds have primate-like numbers of neurons in the forebrain. Proc. Natl. Acad. Sci. 113, 7255–7260 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Emery, N. J. & Clayton, N. S. Evolution of the avian brain and intelligence. Curr. Biol. 15, R946–R950 (2005).Article 
PubMed 

Google Scholar 
Bradbury, J. W. & Balsby, T. J. The functions of vocal learning in parrots. Behav. Ecol. Sociobiol. 70, 293–312 (2016).Article 

Google Scholar 
Baciadonna, L., Cornero, F. M., Emery, N. J. & Clayton, N. S. Convergent evolution of complex cognition: Insights from the field of avian cognition into the study of self-awareness. Learn. Behav. 49, 9–22 (2021).Article 
PubMed 

Google Scholar 
Osvath, M., Kabadayi, C. & Jacobs, I. Independent evolution of similar complex cognitive skills (2014).Zentall, T. R., Clement, T. S., Bhatt, R. S. & Allen, J. Episodic-like memory in pigeons. Psychon. Bull. Rev. 8, 685–690 (2001).Article 
PubMed 

Google Scholar 
Zentall, T. R., Singer, R. A. & Stagner, J. P. Episodic-like memory: Pigeons can report location pecked when unexpectedly asked. Behav. Processes 79, 93–98 (2008).Article 
PubMed 

Google Scholar 
Healy, S. D. & Hurly, T. A. Spatial learning and memory in birds. Brain. Behav. Evol. 63, 211–220 (2004).Article 
PubMed 

Google Scholar 
Taylor, A. H. Corvid cognition. Wiley Interdiscip. Rev. Cogn. Sci. 5, 361–372 (2014).Article 
PubMed 

Google Scholar 
Boeckle, M. & Bugnyar, T. Long-term memory for affiliates in ravens. Curr. Biol. 22, 801–806 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Marzluff, J. M., Walls, J., Cornell, H. N., Withey, J. C. & Craig, D. P. Lasting recognition of threatening people by wild American crows. Anim. Behav. 79, 699–707 (2010).Article 

Google Scholar 
Pepperberg, I. M. & Pepperberg, I. M. The Alex Studies: Cognitive and Communicative Abilities of Grey Parrots (Harvard University Press, 2009).Book 

Google Scholar 
Emery, N. J. & Clayton, N. S. Effects of experience and social context on prospective caching strategies by scrub jays. Nature 414, 443–446 (2001).Article 
PubMed 

Google Scholar 
Herzog, S. K. et al. First systematic sampling approach to estimating the global population size of the Critically Endangered Blue-throated Macaw Ara glaucogularis. Bird Conserv. Int. 31, 293–311 (2021).Article 

Google Scholar 
Auersperg, A. M. & von Bayern, A. M. Who’sa clever bird—now? A brief history of parrot cognition. Behaviour 156, 391–407 (2019).Article 

Google Scholar 
Tassin de Montaigu, C., Durdevic, K., Brucks, D., Krasheninnikova, A. & von Bayern, A. Blue-throated macaws (Ara glaucogularis) succeed in a cooperative task without coordinating their actions. Ethology 126, 267–277 (2020).Article 

Google Scholar 
Auersperg, A. M. et al. Social transmission of tool use and tool manufacture in Goffin cockatoos (Cacatua goffini). Proc. R. Soc. B Biol. Sci. 281, 20140972 (2014).Article 

Google Scholar 
Brucks, D. & von Bayern, A. M. Parrots voluntarily help each other to obtain food rewards. Curr. Biol. 30, 292–297 (2020).Article 
PubMed 

Google Scholar 
Krasheninnikova, A., Höner, F., O’Neill, L., Penna, E. & von Bayern, A. M. Economic decision-making in parrots. Sci. Rep. 8, 1–10 (2018).Article 

Google Scholar 
Jarvis, E. D. et al. Avian brains and a new understanding of vertebrate brain evolution. Nat. Rev. Neurosci. 6, 151–159 (2005).Article 
PubMed 

Google Scholar 
Gutiérrez-Ibáñez, C., Iwaniuk, A. N. & Wylie, D. R. Parrots have evolved a primate-like telencephalic-midbrain-cerebellar circuit. Sci. Rep. 8, 1–11 (2018).Article 

Google Scholar 
Smeele, S. Q. et al. Coevolution of relative brain size and life expectancy in parrots. Proc. R. Soc. B 289, 20212397 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Kirsch, J. A., Güntürkün, O. & Rose, J. Insight without cortex: Lessons from the avian brain. Conscious. Cogn. 17, 475–483 (2008).Article 
PubMed 

Google Scholar 
Dunbar, R. I. & Shultz, S. Evolution in the social brain. Science 317, 1344–1347 (2007).Article 
PubMed 

Google Scholar 
Wright, A. A. & Katz, J. S. Mechanisms of same/different concept learning in primates and avians. Behav. Processes 72, 234–254 (2006).Article 
PubMed 

Google Scholar 
Smirnova, A. A., Obozova, T. A., Zorina, Z. A. & Wasserman, E. A. How do crows and parrots come to spontaneously perceive relations-between-relations?. Curr. Opin. Behav. Sci. 37, 109–117 (2021).Article 

Google Scholar 
Schusterman, R. J. & Kastak, D. A California sea lion (Zalophus californianus) is capable of forming equivalence relations. Psychol. Rec. 43, 823–839 (1993).Article 

Google Scholar 
Kastak, D. & Schusterman, R. J. Transfer of visual identity matching-to-sample in two California sea lions (Zalophus californianus). Anim. Learn. Behav. 22, 427–435 (1994).Article 

Google Scholar 
Zentall, T. R., Wasserman, E. A., Lazareva, O. F., Thompson, R. K. & Rattermann, M. J. Concept learning in animals. Comp. Cogn. Behav. Rev. 20, 25 (2008).
Google Scholar 
Marino, L. Convergence of complex cognitive abilities in cetaceans and primates. Brain. Behav. Evol. 59, 21–32 (2002).Article 
PubMed 

Google Scholar 
Huber, L., Range, F. & Virányi, Z. Dog imitation and its possible origins. In Domestic dog Cognition and Behavior 79–100 (Springer, 2014).Chapter 

Google Scholar 
Schmidjell, T., Range, F., Huber, L. & Virányi, Z. Do owners have a Clever Hans effect on dogs? Results of a pointing study. Front. Psychol. 3, 558 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Hare, B., Brown, M., Williamson, C. & Tomasello, M. The domestication of social cognition in dogs. Science 298, 1634–1636 (2002).Article 
PubMed 

Google Scholar 
Lindqvist, C. & Jensen, P. Domestication and stress effects on contrafreeloading and spatial learning performance in red jungle fowl (Gallus gallus) and White Leghorn layers. Behav. Processes 81, 80–84 (2009).Article 
PubMed 

Google Scholar 
Pack, A. A., Herman, L. M. & Roitblat, H. L. Generalization of visual matching and delayed matching by a California sea lion (Zalophus californianus). Anim. Learn. Behav. 19, 37–48 (1991).Article 

Google Scholar 
Bennett, M. S. Five breakthroughs: A first approximation of brain evolution from early bilaterians to humans. Front. Neuroanat. 15, 25 (2021).Article 

Google Scholar 
Cisek, P. Resynthesizing behavior through phylogenetic refinement. Atten. Percept. Psychophys. 81, 2265–2287 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Toft, C. A. & Wright, T. F. Parrots of the wild. Nat. Hist. World’s Most Captiv. Birds 20, 25 (2015).
Google Scholar 
Merkle, J. A., Sigaud, M. & Fortin, D. To follow or not? How animals in fusion–fission societies handle conflicting information during group decision-making. Ecol. Lett. 18, 799–806 (2015).Article 
PubMed 

Google Scholar 
Stevens, J. R. & Gilby, I. C. A conceptual framework for nonkin food sharing: Timing and currency of benefits. Anim. Behav. 67, 603–614 (2004).Article 

Google Scholar 
Kamil, A. C. & Roitblat, H. L. The ecology of foraging behavior—Implications for animal learning and memory. Annu. Rev. Psychol. 36, 141–169 (1985).Article 
PubMed 

Google Scholar 
Ortiz, S. T., Castro, A. C., Balsby, T. J. S. & Larsen, O. N. Problem-solving in a cooperative task in peach-fronted conures (Eupsittula aurea). Anim. Cogn. 23, 265–275 (2020).Article 

Google Scholar 
Krasheninnikova, A., Brucks, D., Blanc, S. & von Bayern, A. M. Assessing African grey parrots’ prosocial tendencies in a token choice paradigm. R. Soc. Open Sci. 6, 190696 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Krasheninnikova, A. et al. Parrots do not show inequity aversion. Sci. Rep. 9, 1–12 (2019).Article 

Google Scholar 
Clayton, N. S., Griffiths, D. P., Emery, N. J. & Dickinson, A. Elements of episodic–like memory in animals. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356, 1483–1491 (2001).Article 
PubMed 
PubMed Central 

Google Scholar 
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall, 2020).Book 

Google Scholar  More

Pitcher, T. J. et al. Seamounts: Ecology, Fisheries & Conservation (Blackwell Publishing, 2007).Book 

Google Scholar 
Harris, P. T., Macmillan-Lawler, M., Rupp, J. & Baker, E. K. Geomorphology of the oceans. Mar. Geol. 352, 4–24 (2014).Article 

Google Scholar 
Wessel, P., Sandwell, D. T. & Kim, S.-S. The global seamount census. Oceanography 23, 24–33 (2010).Article 

Google Scholar 
Etnoyer, P. J. et al. BOX 12|How large is the seamount biome?. Oceanography 23, 206–209 (2010).Article 

Google Scholar 
De Forges, B. R., Koslow, J. A. & Pooro, G. C. B. Diversity and endemism of the benthic seamount fauna in the southwest Pacific. Nature 405, 944–947 (2000).Article 
PubMed 

Google Scholar 
Rowden, A. A., Dower, J. F., Schlacher, T. A., Consalvey, M. & Clark, M. R. Paradigms in seamount ecology: Fact, fiction and future. Mar. Ecol. 31, 226–241 (2010).Article 

Google Scholar 
Pinheiro, H. T. et al. Fish biodiversity of the Vitória-Trindade seamount chain, southwestern Atlantic: An updated database. PLoS ONE 10, 1–17 (2015).Article 

Google Scholar 
Morato, T., Hoyle, S. D., Allain, V. & Nicol, S. J. Seamounts are hotspots of pelagic biodiversity in the open ocean. PNAS 107, 9711 (2010).Article 

Google Scholar 
Rowden, A. A. et al. A test of the seamount oasis hypothesis: Seamounts support higher epibenthic megafaunal biomass than adjacent slopes. Mar. Ecol. 31, 95–106 (2010).Article 

Google Scholar 
Busch, K. et al. On giant shoulders: How a seamount affects the microbial community composition of seawater and sponges. Biogeosciences 17, 3471–3486 (2020).Article 
CAS 

Google Scholar 
Zhao, Y. et al. Virioplankton distribution in the tropical western Pacific Ocean in the vicinity of a seamount. Microbiol Open 9, e1031 (2020).Article 

Google Scholar 
Arístegui, J. et al. Plankton metabolic balance at two North Atlantic seamounts. Deep-Sea Res. II 56, 2646–2655 (2009).Article 

Google Scholar 
Dower, J. F. & Mackast, D. L. “Seamount effects” in the zooplankton community near Cobb Seamount. Deep-Sea Res. I 43, 837–858 (1996).Article 

Google Scholar 
O’Hara, T. D., Rowden, A. A. & Bax, N. J. A Southern Hemisphere bathyal fauna is distributed in latitudinal bands. Curr. Biol. 21, 226–230 (2011).Article 
PubMed 

Google Scholar 
Williams, A., Althaus, F., Clark, M. R. & Gowlett-Holmes, K. Composition and distribution of deep-sea benthic invertebrate megafauna on the Lord Howe Rise and Norfolk Ridge, southwest Pacific Ocean. Deep-Sea Res. II 58, 948–958 (2011).Article 
CAS 

Google Scholar 
Bridges, A. E. H., Barnes, D. K. A., Bell, J. B., Ross, R. E. & Howell, K. L. Benthic assemblage composition of South Atlantic seamounts. Front. Mar. Sci. 8, 660648 (2021).Article 

Google Scholar 
Lapointe, A. E., Watling, L., France, S. C. & Auster, P. J. Megabenthic assemblages in the lower bathyal (700–3000 m) on the New England and corner rise seamounts Northwest Atlantic. Deep-Sea Res. I 165, 103366 (2020).Article 

Google Scholar 
Clark, M. R. & Bowden, D. A. Seamount biodiversity: High variability both within and between seamounts in the Ross Sea region of Antarctica. Hydrobiologia 761, 161–180 (2015).Article 
CAS 

Google Scholar 
McClain, C. R., Lundsten, L., Barry, J. & DeVogelaere, A. Assemblage structure, but not diversity or density, change with depth on a northeast Pacific seamount. Mar. Ecol. 31, 14–25 (2010).Article 

Google Scholar 
Long, D. J. & Baco, A. R. Rapid change with depth in megabenthic structure-forming communities of the Makapu’u deep-sea coral bed. Deep-Sea Res. II 99, 158–168 (2014).Article 

Google Scholar 
Thresher, R. et al. Strong septh-related zonation of megabenthos on a rocky continental margin (∼ 700–4000 m) off southern Tasmania Australia. PLoS ONE 9, e85872 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
O’Hara, T. D., Consalvey, M., Lavrado, H. P. & Stocks, K. I. Environmental predictors and turnover of biota along a seamount chain. Mar. Ecol. 31, 84–94 (2010).Article 

Google Scholar 
Boschen, R. E. et al. Megabenthic assemblage structure on three New Zealand seamounts: Implications for seafloor massive sulfide mining. Mar. Ecol. Prog. Ser. 523, 1–14 (2015).Article 

Google Scholar 
Caratori Tontini, F. et al. Crustal magnetization of brothers volcano, New Zealand, measured by autonomous underwater vehicles: Geophysical expression of a submarine hydrothermal system. Econ. Geol. 107, 1571–1581 (2012).Article 

Google Scholar 
Rex, M. A., Etter, R. J., Clain, A. J. & Hill, M. S. Bathymetric patterns of body size in deep-sea gastropods. Evolution (N Y) 53, 1298–1301 (1999).
Google Scholar 
O’Hara, T. D. Seamounts: Centres of endemism or species richness for ophiuroids?. Glob. Ecol. Biogeogr. 16, 720–732 (2007).Article 

Google Scholar 
Clark, M. R. et al. The ecology of seamounts: Structure, function, and human impacts. Ann. Rev. Mar. Sci. 2, 253–278 (2010).Article 
PubMed 

Google Scholar 
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).Article 
PubMed 

Google Scholar 
Levin, L. A. & Thomas, C. L. The influence of hydrodynamic regime on infaunal assemblages inhabiting carbonate sediments on central Pacific seamounts. Deep Sea Res. A 36, 1897–1915 (1989).Article 

Google Scholar 
Puerta, P. et al. Variability of deep-sea megabenthic assemblages along the western pathway of the Mediterranean outflow water. Deep-Sea Res. I 185, 103791 (2022).Article 

Google Scholar 
Tapia-Guerra, J. M. et al. First description of deep benthic habitats and communities of oceanic islands and seamounts of the Nazca Desventuradas Marine Park Chile. Sci. Rep. 11, 6209 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morgan, N. B., Goode, S., Roark, E. B. & Baco, A. R. Fine scale assemblage structure of benthic invertebrate megafauna on the North Pacific seamount Mokumanamana. Front. Mar. Sci. 6, 715 (2019).Article 

Google Scholar 
Perez, J. A. A., Kitazato, H., Sumida, P. Y. G., Sant’Ana, R. & Mastella, A. M. Benthopelagic megafauna assemblages of the Rio Grande Rise (SW Atlantic). Deep-Sea Res. I 134, 1–11 (2018).Article 

Google Scholar 
Poore, G. C. B. et al. Invertebrate diversity of the unexplored marine western margin of Australia: Taxonomy and implications for global biodiversity. Mar. Biodivers. 45, 271–286 (2015).Article 

Google Scholar 
Henry, L. A., Moreno Navas, J. & Roberts, J. M. Multi-scale interactions between local hydrography, seabed topography, and community assembly on cold-water coral reefs. Biogeosciences 10, 2737–2746 (2013).Article 

Google Scholar 
Meyer, K. S. et al. Rocky islands in a sea of mud: Biotic and abiotic factors structuring deep-sea dropstone communities. Mar. Ecol. Prog. Ser. 556, 45–57 (2016).Article 

Google Scholar 
Stratmann, T., Soetaert, K., Kersken, D. & van Oevelen, D. Polymetallic nodules are essential for food-web integrity of a prospective deep-seabed mining area in Pacific abyssal plains. Sci. Rep. 11, 12238 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Genin, A., Dayton, P. K., Lonsdale, P. F. & Spiess, F. N. Corals on seamount peaks provide evidence of current acceleration over deep-sea topography. Nature 322, 59–61 (1986).Article 

Google Scholar 
Roberts, J. M., Wheeler, A. J. & Freiwald, A. Reefs of the deep: The biology and geology of cold-water coral ecosystems. Science 1979(312), 543–547 (2006).Article 

Google Scholar 
Kutti, T., Bannister, R. J. & Fosså, J. H. Community structure and ecological function of deep-water sponge grounds in the Traenadypet MPA-Northern Norwegian continental shelf. Cont. Shelf Res. 69, 21–30 (2013).Article 

Google Scholar 
Beazley, L., Kenchington, E. L., Murillo, F. J. & Sacau, M. D. M. Deep-sea sponge grounds enhance diversity and abundance of epibenthic megafauna in the Northwest Atlantic. ICES J. Mar. Sci. 70, 1471–1490 (2013).Article 

Google Scholar 
Buhl-Mortensen, L. et al. Biological structures as a source of habitat heterogeneity and biodiversity on the deep ocean margins. Mar. Ecol. 31, 21–50 (2010).Article 

Google Scholar 
Victorero, L., Robert, K., Robinson, L. F., Taylor, M. L. & Huvenne, V. A. I. Species replacement dominates megabenthos beta diversity in a remote seamount setting. Sci. Rep. 8, 1–11 (2018).Article 
CAS 

Google Scholar 
Yesson, C., Clark, M. R., Taylor, M. L. & Rogers, A. D. The global distribution of seamounts based on 30 arc seconds bathymetry data. Deep-Sea Res. I 58, 442–453 (2011).Article 

Google Scholar 
ICES. Report of the ICES-NAFO Working Group on Deep-Water Ecology (WGDEC), 9–13 March 2009, ICES CM2009ACOM:23. 2009.Cárdenas, P. & Rapp, H. T. Demosponges from the Northern mid-Atlantic ridge shed more light on the diversity and biogeography of North Atlantic deep-sea sponges. J. Mar. Biol. Assoc. U.K. 95, 1475–1516 (2015).Article 

Google Scholar 
Cárdenas, P. et al. Taxonomy, biogeography and DNA barcodes of Geodia species (Porifera, Demospongiae, Tetractinellida) in the Atlantic boreo-arctic region. Zool. J. Linn. Soc. 169, 251–311 (2013).Article 

Google Scholar 
Roberts, E. M. et al. Oceanographic setting and short-timescale environmental variability at an Arctic seamount sponge ground. Deep-Sea Res. I 138, 98–113 (2018).Article 

Google Scholar 
Roberts, E. et al. Water masses constrain the distribution of deep-sea sponges in the North Atlantic Ocean and Nordic seas. Mar. Ecol. Prog. Ser. 659, 75–96 (2021).Article 

Google Scholar 
Morganti, T. M. et al. Giant sponge grounds of central Arctic seamounts are associated with extinct seep life. Nat. Commun. 13, 638 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morganti, T. M. et al. In situ observation of sponge trails suggests common sponge locomotion in the deep central Arctic. Curr. Biol. 31, R368–R370 (2021).Article 
CAS 
PubMed 

Google Scholar 
Meyer, H. K., Roberts, E. M., Rapp, H. T. & Davies, A. J. Spatial patterns of arctic sponge ground fauna and demersal fish are detectable in autonomous underwater vehicle (AUV) imagery. Deep-Sea Res. I 153, 103137 (2019).Article 

Google Scholar 
McIntyre, F. D., Drewery, J., Eerkes-Medrano, D. & Neat, F. C. Distribution and diversity of deep-sea sponge grounds on the Rosemary bank seamount NE Atlantic. Mar. Biol. 163, 143 (2016).Article 

Google Scholar 
Buhl-Mortensen, P. & Buhl-Mortensen, L. Diverse and vulnerable deep-water biotopes in the Hardangerfjord. Mar. Biol. Res. 10, 253–267 (2014).Article 

Google Scholar 
de Clippele, L. H. et al. The effect of local hydrodynamics on the spatial extent and morphology of cold-water coral habitats at Tisler Reef Norway. Coral Reefs 37, 253–266 (2018).Article 
PubMed 

Google Scholar 
Dunlop, K., Harendza, A., Plassen, L. & Keeley, N. Epifaunal habitat Associations on mixed and hard bottom substrates in coastal waters of Northern Norway. Front. Mar. Sci. 7, 568802 (2020).Article 

Google Scholar 
Fiore, C. L. & Cox Jutte, P. Characterization of macrofaunal assemblages associated with sponges and tunicates collected off the southeastern United States. Biology 129, 105–120 (2010).
Google Scholar 
Murillo, F. J. et al. Deep-sea sponge grounds of the Flemish Cap, Flemish Pass and the Grand Banks of Newfoundland (Northwest Atlantic Ocean): Distribution and species composition. Mar. Biol. Res. 8, 842–854 (2012).Article 

Google Scholar 
Purser, A. et al. Local variation in the distribution of benthic megafauna species associated with cold-water coral reefs on the Norwegian margin. Cont. Shelf Res. 54, 37–51 (2013).Article 

Google Scholar 
Klitgaard, A. B. & Tendal, O. S. Distribution and species composition of mass occurrences of large-sized sponges in the northeast Atlantic. Prog. Oceanogr. 61, 57–98 (2004).Article 

Google Scholar 
Klitgaard, A. B. The fauna associated with outer shelf and upper slope sponges (porifera, demospongiae) at the faroe islands, northeastern Atlantic. Sarsia 80, 1–22 (1995).Article 

Google Scholar 
Cárdenas, P. & Moore, J. A. First records of Geodia demosponges from the New England seamounts, an opportunity to test the use of DNA mini-barcodes on museum specimens. Mar. Biodivers. 49, 163–174 (2019).Article 

Google Scholar 
Schejter, L., Chiesa, I. L., Doti, B. L. & Bremec, C. Mycale (Aegogropila) magellanica (Porifera: Demospongiae) in the southwestern Atlantic Ocean: Endobiotic fauna and new distributional information. Sci. Mar. 76, 753–761 (2012).
Google Scholar 
Beaulieu, S. E. Life on glass houses: Sponge stalk communities in the deep sea. Mar. Biol. 138, 803–817 (2001).Article 

Google Scholar 
Goren, L., Idan, T., Shefer, S. & Ilan, M. Macrofauna inhabiting massive demosponges from shallow and mesophotic habitats along the Israeli Mediterranean coast. Front. Mar. Sci. 7, 612779 (2021).Article 

Google Scholar 
Kersken, D. et al. The infauna of three widely distributed sponge species (Hexactinellida and Demospongiae) from the deep Ekström Shelf in the Weddell Sea Antarctica. Deep-Sea Res. II 108, 101–112 (2014).Article 

Google Scholar 
Meyer, H. K., Roberts, E. M., Rapp, H. T. & Davies, A. J. Spatial patterns of arctic sponge ground fauna and demersal fish are detectable in autonomous underwater vehicle (AUV) imagery. Deep Sea Res. 1 Oceanogr. Res. Pap. 153, 103137 (2019).Article 

Google Scholar 
Bart, M. C., Hudspith, M., Rapp, H. T., Verdonschot, P. F. M. & de Goeij, J. M. A Deep-Sea Sponge Loop? Sponges transfer dissolved and particulate organic carbon and nitrogen to associated fauna. Front. Mar. Sci. 8, 604879 (2021).Article 

Google Scholar 
de Goeij, J. M. et al. Surviving in a marine desert: The sponge loop retains resources within coral reefs. Science 1979(342), 108–110 (2013).Article 

Google Scholar 
Pawlik, J. R. & Mcmurray, S. E. The emerging ecological and biogeochemical importance of sponges on coral reefs. (2019) https://doi.org/10.1146/annurev-marine-010419Wassmann, P., Slagstad, D. & Ellingsen, I. Primary production and climatic variability in the European sector of the Arctic Ocean prior to 2007: Preliminary results. Polar Biol. 33, 1641–1650 (2010).Article 

Google Scholar 
Arrigo, K. R., van Dijken, G. & Pabi, S. Impact of a shrinking Arctic ice cover on marine primary production. Geophys. Res. Lett. 35, L19603 (2008).Article 

Google Scholar 
Dunne, J. P., Sarmiento, J. L. & Gnanadesikan, A. A synthesis of global particle export from the surface ocean and cycling through the ocean interior and on the seafloor. Glob. Biogeochem. Cycles 21, GB4006 (2007).Article 

Google Scholar 
Wei, C.-L. et al. Global patterns and predictions of seafloor biomass using random forests. PLoS ONE 5, e15323 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stratmann, T. et al. The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities. Sci. Data 7, 206 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McClain, C. R., Lundsten, L., Ream, M., Barry, J. & DeVogelaere, A. Endemicity, biogeography, composition, and community structure on a Northeast Pacific seamount. PLoS ONE 4, e4141 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Walter, M., Köhler, J., Myriel, H., Steinmacher, B. & Wisotzki, A. Physical oceanography measured on water bottle samples during POLARSTERN cruise PS101 (ARK-XXX/3). PANGAEA https://doi.org/10.1594/PANGAEA.871927 (2017).van Appen, W.-J., Latarius, K. & Kanzow, T. Physical oceanography and current meter data from mooring F6–17. PANGAEA https://doi.org/10.1594/PANGAEA.870845 (2017).Ruhl, H. A. & Smith, K. L. Shifts in deep-sea community structure linked to climate and food supply. Science 1979(305), 513–515 (2004).Article 

Google Scholar 
Boetius, A. et al. Export of algal biomass from the melting Arctic sea ice. Science 1979(339), 1430–1432 (2013).Article 

Google Scholar 
Rybakova, E., Kremenetskaia, A., Vedenin, A., Boetius, A. & Gebruk, A. Deep-sea megabenthos communities of the Eurasian Central Arctic are influenced by ice-cover and sea-ice algal falls. PLoS ONE 14, e0211009 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhulay, I., Bluhm, B. A., Renaud, P. E., Degen, R. & Iken, K. Functional pattern of benthic epifauna in the Chukchi borderland Arctic deep sea. Front. Mar. Sci. 8, 609956 (2021).Article 

Google Scholar 
Boetius, A. & Purser, A. The expedition PS101 of the research vessel Polarstern to the Arctic Ocean in 2016. Berichte zur Polar-und Meeresforschung = Rep Polar Mar Res https://doi.org/10.2312/BzPM_0706_2017 (2017).Article 

Google Scholar 
Simon-Lledó, E. et al. Multi-scale variations in invertebrate and fish megafauna in the mid-eastern Clarion Clipperton Zone. Prog. Oceanogr. 187, 102405 (2020).Article 

Google Scholar 
Simon-Lledó, E. et al. Preliminary observations of the abyssal megafauna of Kiribati. Front. Mar. Sci. 6, 1–13 (2019).Article 

Google Scholar 
Zhulay, I., Iken, K., Renaud, P. E. & Bluhm, B. A. Epifaunal communities across marine landscapes of the deep Chukchi Borderland (Pacific Arctic). Deep Sea Res. 1 Oceanogr. Res. Pap. 151, 103065 (2019).Article 

Google Scholar 
Åström, E. K. L., Sen, A., Carroll, M. L. & Carroll, J. L. Cold seeps in a warming Arctic: Insights for benthic ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00244 (2020).Article 

Google Scholar 
Pedersen, R. B. et al. Discovery of a black smoker vent field and vent fauna at the Arctic Mid-Ocean Ridge. Nat. Commun. 1, 1–6 (2010).Article 
CAS 

Google Scholar 
Åström, E. K. L. et al. Methane cold seeps as biological oases in the high-Arctic deep sea. Limnol. Oceanogr. 63, S209–S231 (2018).Article 

Google Scholar 
Rybakova Goroslavskaya, E., Galkin, S., Bergmann, M., Soltwedel, T. & Gebruk, A. Density and distribution of megafauna at the Håkon Mosby mud volcano (the Barents Sea) based on image analysis. Biogeosciences 10, 3359–3374 (2013).Article 

Google Scholar 
Sweetman, A. K., Levin, L. A., Rapp, H. T. & Schander, C. Faunal trophic structure at hydrothermal vents on the southern mohn’s ridge, arctic ocean. Mar. Ecol. Prog. Ser. 473, 115–131 (2013).Article 

Google Scholar 
Decker, C. & Olu, K. Does macrofaunal nutrition vary among habitats at the Hakon Mosby mud volcano?. Cah. Biol. Mar. 51, 361–367 (2010).
Google Scholar 
Macdonald, I. R., Bluhm, B. A., Iken, K., Gagaev, S. & Strong, S. Benthic macrofauna and megafauna assemblages in the Arctic deep-sea Canada Basin. Deep-Sea Res. II 57, 136–152 (2010).Article 

Google Scholar 
Taylor, J., Krumpen, T., Soltwedel, T., Gutt, J. & Bergmann, M. Dynamic benthic megafaunal communities: Assessing temporal variations in structure, composition and diversity at the Arctic deep-sea observatory HAUSGARTEN between 2004 and 2015. Deep Sea Res. 1 Oceanogr. Res. Pap. 122, 81–94 (2017).Article 

Google Scholar 
Vedenin, A. A. et al. Uniform bathymetric zonation of marine benthos on a Pan-Arctic scale. Prog. Oceanogr. 202, 102764 (2022).Article 

Google Scholar 
Bart, M. C. et al. A deep-sea sponge loop? Sponges transfer dissolved and particulate organic carbon and nitrogen to associated fauna. Front. Mar. Sci. 8, 604879 (2021).Article 

Google Scholar 
Guihen, D., White, M. & Lundälv, T. Temperature shocks and ecological implications at a cold-water coral reef. ANZIAM J. https://doi.org/10.1017/S1755267212000413 (2014).Article 

Google Scholar 
Strand, R. et al. The response of a boreal deep-sea sponge holobiont to acute thermal stress. Sci. Rep. 7, 1660 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hanz, U. et al. The important role of sponges in carbon and nitrogen cycling in a deep-sea biological hotspot. Funct. Ecol. 36, 2188–2199 (2022).Article 
CAS 

Google Scholar 
Maier, S. R. et al. Reef communities associated with ‘dead’ cold-water coral framework drive resource retention and recycling in the deep sea. Deep-Sea Res. I 175, 103574 (2021).Article 
CAS 

Google Scholar 
Bart, M. C. et al. Dissolved organic carbon (DOC) is essential to balance the metabolic demands of four dominant North-Atlantic deep-sea sponges. Limnol. Oceanogr. https://doi.org/10.1002/lno.11652 (2020).Article 

Google Scholar 
Bart, M. C. et al. Differential processing of dissolved and particulate organic matter by deep-sea sponges and their microbial symbionts. Sci. Rep. 10, 1–13 (2020).Article 

Google Scholar 
Maier, S. R. et al. Recycling pathways in cold-water coral reefs: Use of dissolved organic matter and bacteria by key suspension feeding taxa. Sci. Rep. 10, 9942 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
International Hydrographic Bureau. 16th meeting of the GEBCO sub-committee on undersea feature names (SCUFN). Preprint at (2003).Torres-Valdés, S., Morische, A. & Wischnewski, L. Revision of nutrient data from Polarstern expedition PS101 (ARK-XXX/3). PANGAEA https://doi.org/10.1594/PANGAEA.908179 (2019).Purser, A. et al. Ocean floor observation and bathymetry system (OFOBS): A new towed camera/sonar system for deep-sea habitat surveys. IEEE J. Ocean. Eng. 44, 87–99 (2019).Article 

Google Scholar 
Marcon, Y. & Purser, A. PAPARA(ZZ)I : An open-source software interface for annotating photographs of the deep-sea. SoftwareX 6, 69–80 (2017).Article 

Google Scholar 
Greene, H. G., Bizzarro, J. J., O’Connell, V. M. & Brylinsky, C. K. Construction of digital potential marine benthic habitat maps using a coded classification scheme and its application. Spec. Pap.: Geol. Assoc. Canada 47, 141–155 (2007).
Google Scholar 
Horton, T. et al. Recommendations for the standardisation of open taxonomic nomenclature for image-based identifications. Front. Mar. Sci. 8, 620702 (2021).Article 

Google Scholar 
Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Application (Cambridge University Press, 1997).Book 
MATH 

Google Scholar 
Rodgers, J. L. The bootstrap, the jackknife, and the randomization test: A sampling taxonomy. Multivar. Behav. Res. 34, 441–456 (1999).Article 
CAS 

Google Scholar 
Crowley, P. H. Resampling methods for computation-intensive data analysis in ecology and evolution. Annu. Rev. Ecol. Syst. 23, 405–447 (1992).Article 

Google Scholar 
Simon-Lledó, E. et al. Ecology of a polymetallic nodule occurrence gradient: Implications for deep-sea mining. Limnol. Oceanogr. 64, 1883–1894 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).Article 

Google Scholar 
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

Google Scholar 
R-Core Team. R: A language and environment for statistical computing. Preprint at https://www.r-project.org/ (2017).Oksanen, J. et al. vegan: Community ecology package. Preprint at (2017).Veech, J. A. A probabilistic model for analysing species co-occurrence. Glob. Ecol. Biogeogr. 22, 252–260 (2013).Article 

Google Scholar 
Griffith, D. M., Veech, J. A. & Marsh, C. J. Cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. 69, 1–17 (2016).Article 

Google Scholar 
Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).Article 
CAS 
PubMed 

Google Scholar 
de Kluijver, A. Fatty acid analysis sponges. protocols.io 1, 1–14. https://doi.org/10.17504/protocols.io.bhnpj5dn (2021).Article 

Google Scholar 
de Kluijver, A. et al. Bacterial precursors and unsaturated long-chain fatty acids are biomarkers of North-Atlantic deep-sea demosponges. PLoS ONE 16, e0241095 (2021).Article 
PubMed 
PubMed Central 

Google Scholar  More