Philippa Kaur

Nearly two years into the United Nations Decade of Ocean Science, research, including some featured in this month’s issue, shows that there is still a wealth of scientific secrets to uncover in the ocean depths.
In many ways, considering the ocean as a single unit is overly broad. The global ocean covers 71% of the planet’s surface, reaches down to depths of over 10 kilometres, includes about 1.35 billion cubic kilometres of water and houses an approximated 2.2 million eukaryotic species. There are distinct regions, with distinct physical properties, and, in turn, there are distinct species. Yet, the world’s oceans do have a level of physical and thematic connectivity.
Credit: Daria Zaseda / DigitalVision Vectors / GettyPhysically, a large part of the connection is related to the presence of large rotating ocean currents that transfer heat across latitudes and contribute to ocean mixing (thermohaline circulation). Some of these currents are warming at alarming rates — up to three times faster than the rest of the ocean, leading to questions about the underlying mechanisms of the warming and expectations for change.Focusing on western boundary currents (WBCs) in the Southern Hemisphere, in an Article in this issue of Nature Climate Change, Li and colleagues answer a long-debated question on the mechanisms of change, showing that temperature-gradient-related instabilities, rather than flow-speed-related instabilities are behind the shifts. In another Article, focusing on the global future changes of eddies (including eddy-rich WBCs), Beech and colleagues report the development of a flexible method that maximizes local model resolution while minimizing computational costs, to reveal the long-term geographical specificities and nonlinear temperature increases expected to 2100 (see also the News and Views article by Yang on these papers).A recent paper1 has demonstrated the important role of large ocean currents in defining plankton biogeography and dynamics, and WBC warming has previously been linked to impacts such as fishery collapses. The tight link between physical processes and biological responses is an underscoring theme of climate change ecology, but is perhaps more apparent in the open ocean, where physical processes can be easily (if imperfectly) linked to primary productivity using remotely sensed phytoplankton pigment absorption, and where life is generally less impacted by geographical, political or disturbance-based boundaries compared with land and freshwater systems. These aspects may facilitate modelling of current and future communities, while also allowing broader assumptions to be made about biological movement and connectivity.Despite these benefits, understanding ocean change comes with its own difficulties. Biological sampling, while easy enough in the surface waters, becomes increasingly difficult at depth. Although future habitats for various organisms have been projected on the basis of their thermal limits in the ocean, these predictions often still rely on temperatures at the surface of the sea. Addressing this, Santana-Falcón and colleagues report in an Article the global mapping of ocean temperature changes to depths of 1,000 metres, and reveal the complex depth-dependent changes in thermal upper and lower bounds that marine organisms will soon be subjected to. In another Article, Ariza and colleagues neatly address the issue of directly monitoring deep-ocean change by compiling a large database of sound-based observations, and subsequently classifying the ocean’s ‘echobiomes’, defined as sound-scattering communities with comparable structural and functional properties (see also the accompanying News and Views article by Hazen). Sound-based methods are also increasingly being used on land2, and represent an exciting tool for monitoring change, particularly in hard-to-reach places such as deep forests, high mountaintops or underground. While the sound reflection method used in the study by Ariza and colleagues has limits in its ability to identify organisms at the individual or species levels, it does provide a community-level focus on change, which remains much needed in the field of global change ecology.At the other end of the spatial spectrum, research by Lee and colleagues reported in an Article also in this issue dives deep into the DNA of a keystone ocean organism (a copepod), to understand the mechanisms that may allow longer-term adaptation to warming and pH stress. The work reveals remarkable adaptation over just a few short generations, which is linked to epigenetic changes. As climate change impacts continue to escalate, the ability of organisms to invoke both shorter- and longer-term adaptations has become an increasingly relevant area of research. Epigenetics has previously been reported as a quick-response method to cope with environmental stress, and may be particularly relevant in defining the adaptation of short-lived animals such as insects and the resilience of the communities they uphold.The five research pieces linked to the oceans in this issue reveal just some of the diversity of topics, methods and scales relevant to understanding global change. Also increasingly relevant are works on ocean conservation3 and on the social and economic impacts of ocean change4,5. Like climate change science, the topic of ocean change is less of a field, and more of a cross-disciplinary theme. More

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).CAS 

Google Scholar 
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).CAS 

Google Scholar 
Choy, C., Wabnitz, C., Weijerman, M., Woodworth-Jefcoats, P. & Polovina, J. Finding the way to the top: how the composition of oceanic mid-trophic micronekton groups determines apex predator biomass in the central North Pacific. Mar. Ecol. Prog. Ser. 549, 9–25 (2016).
Google Scholar 
Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).Bertrand, A. et al. Broad impacts of fine-scale dynamics on seascape structure from zooplankton to seabirds. Nat. Commun. 5, 5239 (2014).CAS 

Google Scholar 
Brierley, A. S. Diel vertical migration. Curr. Biol. 24, R1074–R1076 (2014).CAS 

Google Scholar 
Behrenfeld, M. J. et al. Global satellite-observed daily vertical migrations of ocean animals. Nature 576, 257–261 (2019).CAS 

Google Scholar 
Angel, M. V. & de C. Baker, A. Vertical distribution of the standing crop of plankton and micronekton at three stations in the northeast Atlantic. Biol. Oceanogr. 2, 1–30 (1982).
Google Scholar 
Cook, A. B., Sutton, T. T., Galbraith, J. K. & Vecchione, M. Deep-pelagic (0–3000 m) fish assemblage structure over the Mid-Atlantic Ridge in the area of the Charlie-Gibbs Fracture Zone. Deep Sea Res. 2 98, 279–291 (2013).
Google Scholar 
Hidaka, K., Kawaguchi, K., Murakami, M. & Takahashi, M. Downward transport of organic carbon by diel migratory micronekton in the western equatorial Pacific: its quantitative and qualitative importance. Deep Sea Res. 1 48, 1923–1939 (2001).Ariza, A., Garijo, J. C., Landeira, J. M., Bordes, F. & Hernández-León, S. Migrant biomass and respiratory carbon flux by zooplankton and micronekton in the subtropical northeast Atlantic Ocean (Canary Islands). Prog. Oceanogr. 134, 330–342 (2015).
Google Scholar 
Saba, G. K. et al. Toward a better understanding of fish-based contribution to ocean carbon flux. Limnol. Oceanogr. 66, 1639–1664 (2021).CAS 

Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).
Google Scholar 
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).CAS 

Google Scholar 
Tittensor, D. P. et al. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0. Geosci. Model Dev. 11, 1421–1442 (2018).
Google Scholar 
Bryndum-Buchholz, A. et al. Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Glob. Change Biol. 25, 459–472 (2019).
Google Scholar 
Kwiatkowski, L., Aumont, O. & Bopp, L. Consistent trophic amplification of marine biomass declines under climate change. Glob. Change Biol. 25, 218–229 (2019).
Google Scholar 
Lotze, H. K. et al. Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change. Proc. Natl Acad. Sci. USA 116, 12907–12912 (2019).CAS 

Google Scholar 
Tittensor, D. P. et al. Next-generation ensemble projections reveal higher climate risks for marine ecosystems. Nat. Clim. Change 11, 973–981 (2021).
Google Scholar 
Heneghan, R. F. et al. Disentangling diverse responses to climate change among global marine ecosystem models. Prog. Oceanogr. 198, 102659 (2021).
Google Scholar 
Reid, S. B., Hirota, J., Young, R. E. & Hallacher, L. E. Mesopelagic-boundary community in Hawaii: micronekton at the interface between neritic and oceanic ecosystems. Mar. Biol. 109, 427–440 (1991).
Google Scholar 
Ben Mustapha, Z., Alvain, S., Jamet, C., Loisel, H. & Dessailly, D. Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote Sens. Environ. 146, 97–112 (2014).
Google Scholar 
Pakhomov, E. & Yamamura, O. Report of the Advisory Panel on Micronekton Sampling Inter-calibration Experiment. PICES Scientific Report 38 (North Pacific Marine Science Organization, 2010).Kaartvedt, S., Staby, A. & Aksnes, D. Efficient trawl avoidance by mesopelagic fishes causes large underestimation of their biomass. Mar. Ecol. Prog. Ser. 456, 1–6 (2012).
Google Scholar 
Gjøsaeter, J. & Kawaguchi, K. A Review of the World Resources of Mesopelagic Fish Fisheries Technical Paper 193 (FAO, 1980).Catul, V., Gauns, M. & Karuppasamy, P. K. A review on mesopelagic fishes belonging to family Myctophidae. Rev. Fish Biol. Fish. 21, 339–354 (2011).
Google Scholar 
Benoit-Bird, K. J. & Lawson, G. L. Ecological insights from pelagic habitats acquired using active acoustic techniques. Annu. Rev. Mar. Sci. 8, 463–490 (2016).
Google Scholar 
Annasawmy, P. et al. Micronekton diel migration, community composition and trophic position within two biogeochemical provinces of the south west Indian Ocean: insight from acoustics and stable isotopes. Deep Sea Res. 1 138, 85–97 (2018).CAS 

Google Scholar 
Haris, K. et al. Sounding out life in the deep using acoustic data from ships of opportunity. Sci. Data 8, 23 (2021).CAS 

Google Scholar 
Irigoien, X. et al. The Simrad EK60 echosounder dataset from the Malaspina circumnavigation. Sci. Data 8, 259 (2021).
Google Scholar 
Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).
Google Scholar 
Klevjer, T. A. et al. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6, 19873 (2016).CAS 

Google Scholar 
Proud, R., Cox, M., Le Guen, C. & Brierley, A. Fine-scale depth structure of pelagic communities throughout the global ocean based on acoustic sound scattering layers. Mar. Ecol. Prog. Ser. 598, 35–48 (2018).
Google Scholar 
Proud, R., Cox, M. J. & Brierley, A. S. Biogeography of the global ocean’s mesopelagic zone. Curr. Biol. 27, 113–119 (2017).CAS 

Google Scholar 
Ramsay, J. O. & Silverman, B. W. Functional Data Analysis (Springer, 2005).Moriarty, R. & O’Brien, T. D. Distribution of mesozooplankton biomass in the global ocean. Earth Syst. Sci. Data 5, 45–55 (2013).
Google Scholar 
Aksnes, D. L. et al. Light penetration structures the deep acoustic scattering layers in the global ocean. Sci. Adv. 3, e1602468 (2017).
Google Scholar 
Bertrand, A., Ballón, M. & Chaigneau, A. Acoustic observation of living organisms reveals the upper limit of the oxygen minimum zone. PLoS ONE 5, e10330 (2010).
Google Scholar 
Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K. A. S. & Stock, C. A. Intensification of open-ocean oxygen depletion by vertically migrating animals. Nat. Geosci. 6, 545–548 (2013).CAS 

Google Scholar 
Godø, O. R., Patel, R. & Pedersen, G. Diel migration and swimbladder resonance of small fish: some implications for analyses of multifrequency echo data. ICES J. Mar. Sci. 66, 1143–1148 (2009).
Google Scholar 
Agersted, M. D. et al. Mass estimates of individual gas-bearing mesopelagic fish from in situ wideband acoustic measurements ground-truthed by biological net sampling. ICES J. Mar. Sci. 78, 3658–3673 (2021).
Google Scholar 
Backus, R. & Craddock, J. in Oceanic Sound Scattering Prediction (eds Anderson, N. R. & Zahuranec, B. J.) 529–547 (Springer, 1977).Longhurst, A. Ecological Geography of the Sea (Elsevier, 2010).Spalding, M. D., Agostini, V. N., Rice, J. & Grant, S. M. Pelagic provinces of the world: A biogeographic classification of the world’s surface pelagic waters. Ocean Coast. Manage. 60, 19–30 (2012).
Google Scholar 
Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. 1 126, 85–102 (2017).
Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Kooijman, B. & Kooijman, S. A. L. M. Dynamic Energy Budget Theory for Metabolic Organisation (Cambridge Univ. Press, 2010).Cheung, W. W. L., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).CAS 

Google Scholar 
Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677 (2015).
Google Scholar 
Proud, R., Handegard, N. O., Kloser, R. J., Cox, M. J. & Brierley, A. S. From siphonophores to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fish biomass. ICES J. Mar. Sci. 76, 718–733 (2019).
Google Scholar 
Chapman, R. P., Bluy, O. Z., Adlington, R. H. & Robison, A. E. Deep scattering layer spectra in the Atlantic and Pacific oceans and adjacent seas. J. Acoust. Soc. Am. 56, 1722–1734 (1974).
Google Scholar 
Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Swimbladder morphology masks Southern Ocean mesopelagic fish biomass. Proc. R. Soc. B 286, 20190353 (2019).
Google Scholar 
Escobar-Flores, P. C., O’Driscoll, R. L., Montgomery, J. C., Ladroit, Y. & Jendersie, S. Estimates of density of mesopelagic fish in the Southern Ocean derived from bulk acoustic data collected by ships of opportunity. Polar Biol. 43, 43–61 (2020).
Google Scholar 
Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Large mesopelagic fish biomass in the Southern Ocean resolved by acoustic properties. Proc. R. Soc. B 289, 20211781 (2022).
Google Scholar 
Reygondeau, G. et al. Climate change-induced emergence of novel biogeochemical provinces. Front. Mar. Sci. 7, 657 (2020).
Google Scholar 
Blanchard, J. L. et al. Linked sustainability challenges and trade-offs among fisheries, aquaculture and agriculture. Nat. Ecol. Evol. 1, 1240–1249 (2017).
Google Scholar 
Bianchi, D., Carozza, D. A., Galbraith, E. D., Guiet, J. & DeVries, T. Estimating global biomass and biogeochemical cycling of marine fish with and without fishing. Sci. Adv. 7, eabd7554 (2021).
Google Scholar 
Grimaldo, E. et al. Investigating the potential for a commercial fishery in the northeast Atlantic utilizing mesopelagic species. ICES J. Mar. Sci. 77, 2541–2556 (2020).
Google Scholar 
Olsen, R. E. et al. Can mesopelagic mixed layers be used as feed sources for salmon aquaculture? Deep Sea Res. 2 180, 104722 (2020).CAS 

Google Scholar 
De Robertis, A. & Higginbottom, I. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. ICES J. Mar. Sci. 64, 1282–1291 (2007).
Google Scholar 
Ryan, T. E., Downie, R. A., Kloser, R. J. & Keith, G. Reducing bias due to noise and attenuation in open-ocean echo integration data. ICES J. Mar. Sci. 72, 2482–2493 (2015).
Google Scholar 
Perrot, Y. et al. Matecho: an open-source tool for processing fisheries acoustics data. Acoust. Aust. 46, 241–248 (2018).
Google Scholar 
Stanton, T. Review and recommendations for the modelling of acoustic scattering by fluid-like elongated zooplankton: euphausiids and copepods. ICES J. Mar. Sci. 57, 793–807 (2000).
Google Scholar 
GEBCO: A Continuous Terrain Model of the Global Oceans and Land (British Oceanographic Data Centre, 2019).EchoPY v.1.1: Fisheries Acoustic Data Processing in Python (Python, 2020); https://pypi.org/project/echopyde Boor, C. A Practical Guide to Splines (Springer, 1978).Clustering (SciKit Learn, 2021); https://scikit-learn.org/stable/modules/clusteringEyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Google Scholar 
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Google Scholar 
Sonnewald, M., Dutkiewicz, S., Hill, C. & Forget, G. Elucidating ecological complexity: unsupervised learning determines global marine eco-provinces. Sci. Adv. 6, eaay4740 (2020).
Google Scholar 
Sonnewald, M. & Lguensat, R. Revealing the impact of global heating on North Atlantic circulation using transparent machine learning. J. Adv. Model. Earth Syst. 13, e2021MS002496 (2021).
Google Scholar 
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar 
Locarnini, R. et al. World Ocean Atlas 2018, Volume 1: Temperature NOAA Atlas NESDIS 81 (NOAA, 2018).García, H. et al. World Ocean Atlas 2018, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation NOAA Atlas NESDIS 83 (NOAA, 2018).Sathyendranath, S. et al. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 5.0 Data. NERC EDS Centre for Environmental Data Analysis, 19 May 2021; http://www.esa-oceancolour-cci.org More

Levin, L. A. et al. The function of marine critical transition zones and the importance of sediment biodiversity. Ecosystems 4, 430–451 (2001).CAS 

Google Scholar 
Wagner, G. M. & Sallema-Mtui, R. in Estuaries: A Lifeline of Ecosystem Services in the Western Indian Ocean Estuaries of the World (eds S. Diop, P. Scheren, & J. Machiwa) 183–207 (2016).Brown, C. J. et al. The assessment of fishery status depends on fish habitats. Fish Fish. 20, 1–14 (2019).CAS 

Google Scholar 
De La Morinière, E. C., Pollux, B., Nagelkerken, I. & Van der Velde, G. Post-settlement life cycle migration patterns and habitat preference of coral reef fish that use seagrass and mangrove habitats as nurseries. Estuar. Coast. Shelf Sci. 55, 309–321 (2002).ADS 

Google Scholar 
Branton, M. & Richardson, J. S. Assessing the value of the umbrella-species concept for conservation planning with meta-analysis. Conserv. Biol. 25, 9–20 (2011).PubMed 

Google Scholar 
Dudgeon, D. et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182 (2006).PubMed 

Google Scholar 
Zainal Abidin, D. H. et al. DNA-based taxonomy of a mangrove-associated community of fishes in Southeast Asia. Sci. Rep. 11, 1–15. https://doi.org/10.1038/s41598-021-97324-1 (2021).CAS 
Article 

Google Scholar 
Gauthier, G. et al. Long-term monitoring at multiple trophic levels suggests heterogeneity in responses to climate change in the Canadian Arctic tundra. Philos. Trans. Roy. Soc. B Biol. Sci. 368, 20120482 (2013).
Google Scholar 
Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).CAS 
PubMed 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Chong, V. C., Lee, P. K. & Lau, C. M. Diversity, extinction risk and conservation of Malaysian fishes. J. Fish Biol. 76, 2009–2066. https://doi.org/10.1111/j.1095-8649.2010.02685.x (2010).CAS 
Article 
PubMed 

Google Scholar 
Zainal Abidin, D. H. et al. Ichthyofauna of Sungai Merbok Mangrove Forest Reserve, northwest Peninsular Malaysia, and its adjacent marine waters. Check List 17, 601–631. https://doi.org/10.15560/17.2.601 (2021).Article 

Google Scholar 
Ong, J. et al. in Hutan paya laut Merbok, Kedah: Pengurusan hutan, persekitaran fizikal dan kepelbagaian flora. Vol. 23 Siri kepelbagaian biologi hutan (ed Ku Aman KA Abd Rahim AR, Abu Hassan MN, Abdullah M, Nor Hazliza MB, Latiff A) 21–33 (Jabatan Perhutanan Semenanjung Malaysia, 2015).Hookham, B., Shau-Hwai, A. T., Dayrat, B. & Hintz, W. A baseline measure of tree and gastropod biodiversity in replanted and natural mangrove stands in Malaysia: Langkawi Island and Sungai Merbok. Trop. Life Sci. Res. 25, 1 (2014).PubMed 
PubMed Central 

Google Scholar 
Jamaluddin, J. A. F. et al. DNA barcoding of shrimps from a mangrove biodiversity hotspot. Mitochondrial DNA Part A 30, 618–625. https://doi.org/10.1080/24701394.2019.1597073 (2019).CAS 
Article 

Google Scholar 
Mansor, M., Mohammad-Zafrizal, M., Nur-Fadhilah, M., Khairun, Y. & Wan-Maznah, W. Temporal and spatial variations in fish assemblage structures in relation to the physicochemical parameters of the Merbok estuary, Kedah. J. Nat. Sci. Res. 2, 110–127 (2012).
Google Scholar 
Alshari, N. F. M. A. H. et al. Metabarcoding of Fish Larvae in the Merbok River reveals species diversity and distribution along its mangrove environment. Zool. Stud. 60, 60–76. https://doi.org/10.6620/ZS.2021 (2021).Article 

Google Scholar 
Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 1–9 (2016).
Google Scholar 
Hupało, K. et al. An urban Blitz with a twist: Rapid biodiversity assessment using aquatic environmental DNA. Environ. DNA 3, 200–213 (2020).
Google Scholar 
Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358–367 (2014).PubMed 

Google Scholar 
Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).CAS 
PubMed 

Google Scholar 
Ahn, H. et al. Evaluation of fish biodiversity in estuaries using environmental DNA metabarcoding. PLoS ONE 15, e0231127 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Polanco, F. A. et al. Detecting aquatic and terrestrial biodiversity in a tropical estuary using environmental DNA. Biotropica 53, 1606–1619 (2021).
Google Scholar 
Zhang, H., Yoshizawa, S., Iwasaki, W. & Xian, W. Seasonal fish assemblage structure using environmental DNA in the Yangtze Estuary and its adjacent waters. Front. Mar. Sci. 6, 515. https://doi.org/10.3389/fmars.2019.00515 (2019).Article 

Google Scholar 
Stat, M. et al. Ecosystem biomonitoring with eDNA: Metabarcoding across the tree of life in a tropical marine environment. Sci. Rep. 7, 1–11 (2017).ADS 
CAS 

Google Scholar 
West, K. et al. Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia. Divers. Distrib. 27, 1942–1957 (2021).
Google Scholar 
Hallam, J., Clare, E. L., Jones, J. I. & Day, J. J. Biodiversity assessment across a dynamic riverine system: A comparison of eDNA metabarcoding versus traditional fish surveying methods. Environ. DNA 3, 1247–1266 (2021).
Google Scholar 
Seymour, M. et al. Environmental DNA provides higher resolution assessment of riverine biodiversity and ecosystem function via spatio-temporal nestedness and turnover partitioning. Commun. Biol. 4, 1–12 (2021).
Google Scholar 
Aglieri, G. et al. Environmental DNA effectively captures functional diversity of coastal fish communities. Mol. Ecol. 30, 3127–3139 (2021).PubMed 

Google Scholar 
Fujii, K. et al. Environmental DNA metabarcoding for fish community analysis in backwater lakes: A comparison of capture methods. PLoS ONE 14, e0210357 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lecaudey, L. A., Schletterer, M., Kuzovlev, V. V., Hahn, C. & Weiss, S. J. Fish diversity assessment in the headwaters of the Volga River using environmental DNA metabarcoding. Aquat. Conserv. Mar. Freshwat. Ecosyst. 29, 1785–1800 (2019).
Google Scholar 
Zou, K. et al. eDNA metabarcoding as a promising conservation tool for monitoring fish diversity in a coastal wetland of the Pearl River Estuary compared to bottom trawling. Sci. Total Environ. 702, 134704 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Klymus, K. E., Marshall, N. T. & Stepien, C. A. Environmental DNA (eDNA) metabarcoding assays to detect invasive invertebrate species in the Great Lakes. PLoS ONE 12, 24. https://doi.org/10.1371/journal.pone.0177643 (2017).CAS 
Article 

Google Scholar 
Wilson, C. et al. Tracking ghosts: Combined electrofishing and environmental DNA surveillance efforts for Asian carps in Ontario waters of Lake Erie. Manag. Biol. Invasion 5, 225–231. https://doi.org/10.3391/mbi.2014.5.3.05 (2014).Article 

Google Scholar 
Alexander, J. B. et al. Development of a multi-assay approach for monitoring coral diversity using eDNA metabarcoding. Coral Reefs 39, 159–171. https://doi.org/10.1007/s00338-019-01875-9 (2020).Article 

Google Scholar 
Port, J. A. et al. Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA. Mol. Ecol. 25, 527–541. https://doi.org/10.1111/mec.13481 (2016).CAS 
Article 
PubMed 

Google Scholar 
Fritts, A. K. et al. Development of a quantitative PCR method for screening ichthyoplankton samples for bigheaded carps. Biol. Invasions 21, 1143–1153 (2019).
Google Scholar 
Maruyama, A., Nakamura, K., Yamanaka, H., Kondoh, M. & Minamoto, T. The release rate of environmental DNA from juvenile and adult fish. PLoS ONE 9, e114639 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Amberg, J. J., Merkes, C. M., Stott, W., Rees, C. B. & Erickson, R. A. Environmental DNA as a tool to help inform zebra mussel, Dreissena polymorpha, management in inland lakes. Manag. Biol. Invasion 10, 96 (2019).
Google Scholar 
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812. https://doi.org/10.1093/bioinformatics/btu393 (2014).CAS 
Article 
PubMed 

Google Scholar 
Zainal Abidin, D. H. & Noor Adelyna, M. A. Environmental DNA (eDNA) Metabarcoding as a Sustainable Tool of Coastal Biodiversity Assessment in Universities as Living Labs for Sustainable Development 211–225 (Springer, 2020).Sard, N. M. et al. Comparison of fish detections, community diversity, and relative abundance using environmental DNA metabarcoding and traditional gears. Environ. DNA 1, 368–384 (2019).
Google Scholar 
Hoffman, J. C., Kelly, J. R., Trebitz, A. S., Peterson, G. S. & West, C. W. Effort and potential efficiencies for aquatic non-native species early detection. Can. J. Fish. Aquat. Sci. 68, 2064–2079 (2011).
Google Scholar 
Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 1–12 (2017).
Google Scholar 
Whitfield, A. K. Fish species in estuaries—From partial association to complete dependency. J. Fish Biol. 97, 1262–1264 (2020).PubMed 

Google Scholar 
Carpenter, K. & Niem, V. The living marine resources of the Western Central Pacific. Volume 5. Bony Fishes Part 3 (Menidae to Pomacentridae). Vol. 5, 2791–3380 (Food and Agriculture Organization of the United Nations, 2001).Carpenter, K. E. & Niem, V. FAO species identification guide for fishery purposes. The Living Marine Resources of the Western Central Pacific. Volume 6. Bony Fishes Part 4 (Labridae to Latimeriidae), Estuarine Crocodiles, Sea Turtles, Sea Snakes and Marine Mammals. Vol. 6, 3381–4218 (Food and Agriculture Organization of the United Nations, 2001).Carpenter, K. E. & Niem, V. H. The living marine resources of the Western Central Pacific: Batoid fishes, chimaera and bony fishes part 1 (Elopidae to Linophrynidae). Vol. 3, 1397–2068 (Food and Agriculture Organization of the United Nations, 1999).Carpenter, K. E. & Niem, V. H. The living marine resources of the Western Central Pacific. Volume 4. Bony Fishes Part 2 (Mugilidae to Carangidae). Vol. 4, 2069–2790 (Food and Agriculture Organization of the United Nations, 1999).Benson, D. A. et al. GenBank. Nucleic Acids Res. 46, D41–D47 (2018).CAS 
PubMed 

Google Scholar 
Pentinsaari, M., Ratnasingham, S., Miller, S. E. & Hebert, P. D. N. BOLD and GenBank revisited—Do identification errors arise in the lab or in the sequence libraries?. PLoS ONE 15, e0231814–e0231814. https://doi.org/10.1371/journal.pone.0231814 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ardura, A., Planes, S. & Garcia-Vazquez, E. Applications of DNA barcoding to fish landings: Authentication and diversity assessment. Zookeys 365, 49–65. https://doi.org/10.3897/zookeys.365.6409 (2013).Article 

Google Scholar 
ZainalAbidin, D. H. et al. Population genetics of the black scar oyster, Crassostrea iredalei: Repercussion of anthropogenic interference. Mitochondrial DNA Part A 27, 647–658 (2016).CAS 

Google Scholar 
Kelly, R. P. et al. Genetic and manual survey methods yield different and complementary views of an ecosystem. Front. Mar. Sci. 3, 283 (2017).
Google Scholar 
Ratnasingham, S. & Hebert, P. D. BOLD: The barcode of life data system (http://www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17. https://doi.org/10.1007/s10592-015-0775-4 (2016).CAS 
Article 

Google Scholar 
Vasconcelos, R. P. et al. Global patterns and predictors of fish species richness in estuaries. J. Anim. Ecol. 84, 1331–1341 (2015).PubMed 

Google Scholar 
Shah, A. S. R. M., Hashim, Z. H. & Sah, S. A. M. Freshwater fishes of Gunung Jerai, Kedah Darul Aman: A preliminary study. Trop. Life Sci. Res. 20, 59 (2009).PubMed 
PubMed Central 

Google Scholar 
Md. Zain, K. et al. Fish diversity along streams in Ulu Muda Forest Reserve, Kedah, Peninsular Malaysia. Malayan Nat. J. 73, 349–361 (2021).
Google Scholar 
Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).CAS 
PubMed 

Google Scholar 
Wang, S. et al. Methodology of fish eDNA and its applications in ecology and environment. Sci. Total Environ. 755, 142622. https://doi.org/10.1016/j.scitotenv.2020.142622 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 

Google Scholar 
Southeast Asian Fisheries Development Centre (SEAFDEC). Status and trends of sharks fisheries in South East Asia in Malaysia Shark Fisheries (Fisheries and Resources Monitoring System (FIRMS), Rome, 2004).Zhang, S., Zhao, J. & Yao, M. A comprehensive and comparative evaluation of primers for metabarcoding eDNA from fish. Methods Ecol. Evol. 11, 1609–1625 (2020).ADS 

Google Scholar 
Doi, H. et al. Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshw. Biol. 62, 30–39 (2017).CAS 

Google Scholar 
Hayami, K. et al. Effects of sampling seasons and locations on fish environmental DNA metabarcoding in dam reservoirs. Ecol. Evol. 10, 5354–5367 (2020).PubMed 
PubMed Central 

Google Scholar 
Collins, R. A. et al. Persistence of environmental DNA in marine systems. Commun. Biol. https://doi.org/10.1038/s42003-018-0192-6 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Morey, K. C., Bartley, T. J. & Hanner, R. H. Validating environmental DNA metabarcoding for marine fishes in diverse ecosystems using a public aquarium. Environ. DNA 2, 330–342 (2020).
Google Scholar 
Shaw, J. L. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Cons. 197, 131–138 (2016).
Google Scholar 
Siegenthaler, A. et al. Metabarcoding of shrimp stomach content: Harnessing a natural sampler for fish biodiversity monitoring. Mol. Ecol. Resour. 19, 206–220. https://doi.org/10.1111/1755-0998.12956 (2019).CAS 
Article 
PubMed 

Google Scholar 
Stoeckle, M. Y., Das Mishu, M. & Charlop-Powers, Z. Improved environmental DNA reference library detects overlooked marine fishes in New Jersey, United States. Front. Mar. Sci. 7, 226 (2020).
Google Scholar 
Collins, R. A. et al. Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods Ecol. Evol. 10, 1985–2001 (2019).
Google Scholar 
Hebert, P. D., Ratnasingham, S. & De Waard, J. R. Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc. Roy. Soc. Lond. Ser. B Biol. Sci. 270, S96–S99 (2003).CAS 

Google Scholar 
Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. Roy. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 

Google Scholar 
Mariani, S., Baillie, C., Colosimo, G. & Riesgo, A. Sponges as natural environmental DNA samplers. Curr. Biol. 29, R401–R402 (2019).CAS 
PubMed 

Google Scholar 
Bylemans, J., Gleeson, D. M., Duncan, R. P., Hardy, C. M. & Furlan, E. M. A performance evaluation of targeted eDNA and eDNA metabarcoding analyses for freshwater fishes. Environ. DNA 1, 402–414 (2019).
Google Scholar 
Chin, A. T. et al. Beta diversity changes in estuarine fish communities due to environmental change. Mar. Ecol. Prog. Ser. 603, 161–173 (2018).ADS 

Google Scholar 
Sloterdijk, H. et al. Composition and structure of the larval fish community related to environmental parameters in a tropical estuary impacted by climate change. Estuar. Coast. Shelf Sci. 197, 10–26 (2017).ADS 

Google Scholar 
Malaysian Meteorological Department. Tinjauan Cuaca bagi Tempoh November 2017 hingga April 2018. National Climate Centre: Ministry of Science, Technology and Innovation. Retrieved on February 1st, 2018, from https://www.met.gov.my/iklim/ramalanbermusim/ (2017).Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
PubMed Central 

Google Scholar 
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
PubMed 

Google Scholar 
Illumina. 16S Metagenomic Sequencing Library Preparation. https://support.illumina.com/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf 1–28 (2013).Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. (Babraham Bioinformatics (Babraham Institute, 2010).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 

Google Scholar 
Andruszkiewicz, E. A. et al. Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding. PLoS ONE 12, e0176343 (2017).PubMed 
PubMed Central 

Google Scholar 
Goldberg, C. S. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7, 1299–1307 (2016).
Google Scholar 
Fricke, R., Eschmeyer, W. N. & Van der Laan, R. Eschmeyer’s Catalog of Fishes: Genera, species, references. http://www.calacademy.org/scientists/catalog-of-fishes-family-group-names/ (2021).Ebert, D. A. & Fowler, S. Sharks of the World (Princeton University Press, 2013).
Google Scholar 
R Core Team. RStudio: integrated development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com42, 14 (2015).McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. et al. Package ‘vegan’. Commun. Ecol. Pack. 2, 1–295 (2013).
Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar  More

Longhurst, A., Sathyendranath, S., Platt, T. & Caverhill, C. An estimate of global primary production in the ocean from satellite radiometer data. J. Plankton Res. 17, 1245–1271 (1995).
Google Scholar 
Karl, D. M. et al. Seasonal and interannual variability in primary production and particle flux at station ALOHA. Deep Res. Part II Top. Stud. Oceanogr. 43, 539–568 (1996).CAS 

Google Scholar 
Yang, B., Emerson, S. R. & Quay, P. D. The subtropical ocean’s biological carbon pump determined from O2 and DIC/DI13C tracers. Geophys. Res. Lett. 46, 5361–5368 (2019).
Google Scholar 
Nowicki, M., DeVries, T. & Siegel, D. A. Quantifying the carbon export and sequestration pathways of the ocean’s biological carbon pump. Glob. Biogeochem. Cycles 36, 1–22 (2022).
Google Scholar 
Chávez, F. P., Messié, M. & Pennington, J. T. Marine primary production in relation to climate variability and change. Annu. Rev. Mar. Sci. 3, 227–260 (2011).
Google Scholar 
Polovina, J. J., Howell, E. A. & Abecassis, M. Ocean’s least productive waters are expanding. Geophys. Res. Lett. 35, 2–6 (2008).
Google Scholar 
Irwin, A. J. & Oliver, M. J. Are ocean deserts getting larger? Geophys. Res. Lett. 36, 1–5 (2009).
Google Scholar 
Signorini, S. R., Franz, B. A. & McClain, C. R. Chlorophyll variability in the oligotrophic gyres: Mechanisms, seasonality and trends. Front. Mar. Sci. 2, 1–11 (2015).
Google Scholar 
Sarmiento, J. L., Hughes, T. M. C., Stouffer, R. J. & Manabe, S. Simulated response of the ocean carbon cycle to anthropogenic climate warming. Nature 393, 245–249 (1998).CAS 

Google Scholar 
Bopp, L. et al. Potential impact of climate change on marine export production. Glob. Biogeochem. Cycles 15, 81–99 (2001).CAS 

Google Scholar 
Taucher, J. & Oschlies, A. Can we predict the direction of marine primary production change under global warming? Geophys. Res. Lett. 38, 1–6 (2011).
Google Scholar 
Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. 13, 116–120 (2020).CAS 

Google Scholar 
Behrenfeld, M. Uncertain future for ocean algae. Nat. Clim. Chang. 1, 33–34 (2011).CAS 

Google Scholar 
Flombaum, P. & Martiny, A. C. Diverse but uncertain responses of picophytoplankton lineages to future climate change. Limnol. Oceanogr. 66, 4171–4181 (2021).
Google Scholar 
Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 10, 1063–1085 (1972).
Google Scholar 
Falkowski, P. G. & Oliver, M. J. Mix and max: how climate selects phytoplankton. Nature. Rev. Microbiol. 5, 813–819 (2007).CAS 

Google Scholar 
van de Waal, D. B. & Litchman, E. Multiple global change stressor effects on phytoplankton nutrient acquisition in a future ocean. Philos. Trans. R. Soc. B Biol. Sci. 375, 1–8 (2020).
Google Scholar 
Kremer, C. T., Thomas, M. K. & Litchman, E. Temperature- and size-scaling of phytoplankton population growth rates: reconciling the Eppley curve and the metabolic theory of ecology. Limnol. Oceanogr. 62, 1658–1670 (2017).
Google Scholar 
Cross, W. F., Hood, J. M., Benstead, J. P., Huryn, A. D. & Nelson, D. Interactions between temperature and nutrients across levels of ecological organization. Glob. Chang. Biol. 21, 1025–1040 (2015).PubMed 

Google Scholar 
Marañón, E., Lorenzo, M. P., Cermeño, P. & Mouriño-Carballido, B. Nutrient limitation suppresses the temperature dependence of phytoplankton metabolic rates. ISME J. 12, 1836–1845 (2018).PubMed 
PubMed Central 

Google Scholar 
Skau, L. F., Andersen, T., Thrane, J.-E. & Hessen, D. O. Growth, stoichiometry and cell size; temperature and nutrient responses in haptophytes. PeerJ 5, e3743 (2017).PubMed 
PubMed Central 

Google Scholar 
Fernández‐González, C. et al. Effects of temperature and nutrient supply on resource allocation, photosynthetic strategy and metabolic rates of Synechococcus sp. J. Phycol. 56, 818–829 (2020).PubMed 

Google Scholar 
O’Connor, M. I., Piehler, M. F., Leech, D. M., Anton, A. & Bruno, J. F. Warming and resource availability shift food web structure and metabolism. PLoS Biol. 7, e1000178 (2009).PubMed 
PubMed Central 

Google Scholar 
Liu, K., Suzuki, K., Chen, B. & Liu, H. Are temperature sensitivities of Prochlorococcus and Synechococcus impacted by nutrient availability in the subtropical northwest Pacific? Limnol. Oceanogr. 66, 639–651 (2020).
Google Scholar 
Hayashida, H., Matear, R. J. & Strutton, P. G. Background nutrient concentration determines phytoplankton bloom response to marine heatwaves. Glob. Chang. Biol. 26, 4800–4811 (2020).PubMed 

Google Scholar 
Davey, M. et al. Nutrient limitation of picophytoplankton photosynthesis and growth in the tropical North Atlantic. Limnol. Oceanogr. 53, 1722–1733 (2008).CAS 

Google Scholar 
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).CAS 

Google Scholar 
Browning, T. J. et al. Nutrient co-limitation at the boundary of an oceanic gyre. Nature 551, 242–246 (2017).CAS 
PubMed 

Google Scholar 
Ustick, L. J. et al. Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation. Science 372, 287–291 (2021).CAS 
PubMed 

Google Scholar 
Zubkov, M. V., Sleigh, M. A., Tarran, G. A., Burkill, P. H. & Leakey, R. J. G. Picoplanktonic community structure on an Atlantic transect from 50°N to 50°S. Deep Res. Part I Oceanogr. Res. Pap. 45, 1339–1355 (1998).
Google Scholar 
Marañón, E., Behrenfeld, M. J., González, N., Mouriño, B. & Zubkov, M. V. High variability of primary production in oligotrophic waters of the Atlantic Ocean: Uncoupling from phytoplankton biomass and size structure. Mar. Ecol. Prog. Ser. 257, 1–11 (2003).
Google Scholar 
Marañón, E. Cell size as a key determinant of phytoplankton metabolism and community structure. Annu. Rev. Mar. Sci. 7, 241–264 (2015).
Google Scholar 
Worden, A. Z., Nolan, J. K. & Palenik, B. Assessing the dynamics and ecology of marine picophytoplankton: the importance of the eukaryotic component. Limnol. Oceanogr. 49, 168–179 (2004).CAS 

Google Scholar 
Visintini, N., Martiny, A. C. & Flombaum, P. Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton abundances in the global ocean. Limnol. Oceanogr. Lett. 6, 207–215 (2021).
Google Scholar 
Chen, B., Liu, H., Huang, B. & Wang, J. Temperature effects on the growth rate of marine picoplankton. Mar. Ecol. Prog. Ser. 505, 37–47 (2014).
Google Scholar 
Stawiarski, B., Buitenhuis, E. T. & Le Quéré, C. The physiological response of picophytoplankton to temperature and its model representation. Front. Mar. Sci. 3, 1–13 (2016).
Google Scholar 
Marañón, E. et al. Unimodal size scaling of phytoplankton growth and the size dependence of nutrient uptake and use. Ecol. Lett. 16, 371–379 (2013).PubMed 

Google Scholar 
Duhamel, S., Kim, E., Sprung, B. & Anderson, O. R. Small pigmented eukaryotes play a major role in carbon cycling in the P-depleted western subtropical North Atlantic, which may be supported by mixotrophy. Limnol. Oceanogr. 64, 2424–2440 (2019).CAS 

Google Scholar 
Berthelot, H. et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 13, 651–662 (2019).CAS 
PubMed 

Google Scholar 
Berthelot, H., Duhamel, S., L’Helguen, S., Maguer, J. F. & Cassar, N. Inorganic and organic carbon and nitrogen uptake strategies of picoplankton groups in the northwestern Atlantic Ocean. Limnol. Oceanogr. 66, 3682–3696 (2021).CAS 

Google Scholar 
Marañón, E. et al. Degree of oligotrophy controls the response of microbial plankton to Saharan dust. Limnol. Oceanogr. 55, 2339–2352 (2010).
Google Scholar 
Mouriño-Carballido, B. et al. Nutrient supply controls picoplankton community structure during three contrasting seasons in the northwestern Mediterranean Sea. Mar. Ecol. Prog. Ser. 543, 1–19 (2016).
Google Scholar 
Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science 338, 1085–1088 (2012).CAS 
PubMed 

Google Scholar 
Doney, S. C. et al. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci. 4, 11–37 (2012).
Google Scholar 
Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).PubMed 

Google Scholar 
Gruber, N., Boyd, P. W., Frölicher, T. L. & Vogt, M. Biogeochemical extremes and compound events in the ocean. Nature 600, 395–407 (2021).CAS 
PubMed 

Google Scholar 
Babin, S. M., Carton, J. A., Dickey, T. D. & Wiggert, J. D. Satellite evidence of hurricane-induced phytoplankton blooms in an oceanic desert. J. Geophys. Res. Oceans 109, 1–21 (2004).
Google Scholar 
Walker, N. D., Leben, R. R. & Balasubramanian, S. Hurricane-forced upwelling and chlorophyll a enhancement within cold-core cyclones in the Gulf of Mexico. Geophys. Res. Lett. 32, 1–5 (2005).
Google Scholar 
Boyd, P. W. et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—a review. Glob. Chang. Biol. 24, 2239–2261 (2018).PubMed 

Google Scholar 
Mills, M. M., Ridame, C., Davey, M., La Roche, J. & Geider, R. J. Iron and phosphorus co-limit nitrogen fixation in the eastern tropical North Atlantic. Nature 429, 292–294 (2004).CAS 
PubMed 

Google Scholar 
Marañón, E. Phytoplankton growth rates in the Atlantic subtropical gyres. Limnol. Oceanogr. 50, 299–310 (2005).
Google Scholar 
Halsey, K. H. & Jones, B. M. Phytoplankton strategies for photosynthetic energy allocation. Annu. Rev. Mar. Sci. 7, 265–297 (2015).
Google Scholar 
Quevedo, M. & Anadón, R. Protist control of phytoplankton growth in the subtropical north-east Atlantic. Mar. Ecol. Prog. Ser. 221, 29–38 (2001).
Google Scholar 
Schmoker, C., Hernández-León, S. & Calbet, A. Microzooplankton grazing in the oceans: Impacts, data variability, knowledge gaps and future directions. J. Plankton Res. 35, 691–706 (2013).
Google Scholar 
Landry, M. R. & Hassett, R. P. Estimating the grazing impact of marine micro-zooplankton. Mar. Biol. 67, 283–288 (1982).
Google Scholar 
Kiørboe, T. Turbulence, phytoplankton cell size, and the structure of pelagic food webs. Adv. Mar. Biol. 29, 1–72 (1993).
Google Scholar 
Cermeño, P. et al. Marine primary productivity is driven by a selection effect. Front. Mar. Sci. 3, 1–10 (2016).Browning, T. J. et al. Nutrient co-limitation in the subtropical Northwest Pacific. Limnol. Oceanogr. Lett. 7, 52–61 (2022).
Google Scholar 
Klausmeier, C. A., Litchman, E. & Levin, S. A. Phytoplankton growth and stoichiometry under multiple nutrient limitation. Limnol. Oceanogr. 49, 1463–1470 (2004).
Google Scholar 
Behrenfeld, M. J. & Milligan, A. J. Photophysiological expressions of iron stress in phytoplankton. Annu. Rev. Mar. Sci. 5, 217–246 (2013).
Google Scholar 
Geider, R. J. Light and temperature dependence of the carnon to chlorophyll a ratio in microalgae and cyanobacteria: implications for physiology and growth of phytoplankton. N. Phytol. 106, 1–34 (1987).CAS 

Google Scholar 
Maxwell, D. P., Laudenbach, D. E. & Huner, N. P. Redox regulation of light-harvesting complex II and cab mRNA abundance in Dunaliella salina. Plant Physiol. 109, 787–795 (1995).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ye, H. J., Sui, Y., Tang, D. L. & Afanasyev, Y. D. A subsurface chlorophyll a bloom induced by typhoon in the South China Sea. J. Mar. Syst. 128, 138–145 (2013).
Google Scholar 
Zhang, H., He, H., Zhang, W. Z. & Tian, D. Upper ocean response to tropical cyclones: a review. Geosci. Lett. 8, 1–12 (2021).
Google Scholar 
Lin, I. et al. New evidence for enhanced ocean primary production triggered by tropical cyclone. Geophys. Res. Lett. 30, 1–4 (2003).Chai, F. et al. A limited effect of sub-tropical typhoons on phytoplankton dynamics. Biogeosciences 18, 849–859 (2021).
Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).CAS 
PubMed 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Google Scholar 
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).CAS 
PubMed 

Google Scholar 
Somero, G. N. Adaptation of enzymes to temperature: Searching for basic ‘strategies’. Comp. Biochem. Physiol.—B Biochem. Mol. Biol. 139, 321–333 (2004).PubMed 

Google Scholar 
Rose, J. M. & Caron, D. A. Does low temperature constrain the growth rates of heterotrophic protists? Evidence and implications for algal blooms in cold waters. Limnol. Oceanogr. 52, 886–895 (2007).
Google Scholar 
Harvey, B. P., Marshall, K. E., Harley, C. D. G. & Russell, B. D. Predicting responses to marine heatwaves using functional traits. Trends Ecol. Evol. 37, 20–29 (2022).PubMed 

Google Scholar 
Staehr, P. A. & Birkeland, M. J. Temperature acclimation of growth, photosynthesis and respiration in two mesophilic phytoplankton species. Phycologia 45, 648–656 (2006).
Google Scholar 
Morán, X. A. G., Calvo-Díaz, A., Arandia-Gorostidi, N. & Huete-Stauffer, T. M. Temperature sensitivities of microbial plankton net growth rates are seasonally coherent and linked to nutrient availability. Environ. Microbiol. 20, 3798–3810 (2018).PubMed 

Google Scholar 
Courboulès, J. et al. Effects of experimental warming on small phytoplankton, bacteria and viruses in autumn in the Mediterranean coastal Thau Lagoon. Aquat. Ecol. 55, 647–666 (2021).
Google Scholar 
López-Sandoval, D. C., Duarte, C. M. & Agustí, S. Nutrient and temperature constraints on primary production and net phytoplankton growth in a tropical ecosystem. Limnol. Oceanogr. 66, 2923–2935 (2021).
Google Scholar 
Landry, M. R., Selph, K. E., Hood, R. R., Davies, C. H. & Beckley, L. E. Low temperature sensitivity of picophytoplankton P:B ratios and growth rates across a natural 10 °C temperature gradient in the oligotrophic Indian Ocean. Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2.10224 (2021)Martiny, A. C. et al. Strong latitudinal patterns in the elemental ratios of marine plankton and organic matter. Nat. Geosci. 6, 279–283 (2013).CAS 

Google Scholar 
Fernández-González, C. & Marañón, E. Effect of temperature on the unimodal size scaling of phytoplankton growth. Sci. Rep. 11, 1–9 (2021).
Google Scholar 
Marañón, E. et al. Patterns of phytoplankton size structure and productivity in contrasting open-ocean environments. Mar. Ecol. Prog. Ser. 216, 43–56 (2001).
Google Scholar 
Tarran, G. A., Heywood, J. L. & Zubkov, M. V. Latitudinal changes in the standing stocks of nano- and picoeukaryotic phytoplankton in the Atlantic Ocean. Deep Res. Part II Top. Stud. Oceanogr. 53, 1516–1529 (2006).
Google Scholar 
Hillebrand, H. et al. Cell size as driver and sentinel of phytoplankton community structure and functioning. Funct. Ecol. 1–18 https://doi.org/10.1111/1365-2435.13986 (2021).Partensky, F. & Garczarek, L. Prochlorococcus: advantages and limits of minimalism. Annu. Rev. Mar. Sci. 2, 305–331 (2010).
Google Scholar 
Landry, M. R. et al. Biological response to iron fertilization in the eastern equatorial Pacific (IronEx II). I. Microplankton community abundances and biomass. Mar. Ecol. Prog. Ser. 201, 27–42 (2000).CAS 

Google Scholar 
Morel, A. et al. Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach. Remote Sens. Environ. 111, 69–88 (2007).
Google Scholar 
Fofonoff, N. P. & Millard, R. C. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. Mar. Sci. 44, 1–53 (1983).
Google Scholar 
Becker, S. et al. GO-SHIP repeat hydrography nutrient manual: the precise and accurate determination of dissolved inorganic nutrients in seawater, using continuous flow analysis methods. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.581790 (2020).Marañón, E. et al. Resource supply overrides temperature as a controlling factor of marine phytoplankton growth. PLoS ONE 9, 20–23 (2014).
Google Scholar 
Schuback, N. et al. Single-turnover variable chlorophyll fluorescence as a tool for assessing phytoplankton photosynthesis and primary productivity: opportunities, caveats and recommendations. Front. Mar. Sci. 8, 1–24 (2021).Piggott, J. J., Townsend, C. R. & Matthaei, C. D. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5, 1538–1547 (2015).PubMed 
PubMed Central 

Google Scholar  More

The overall magnitude of changes in SOC, TN, and C:N in response to chemical nitrogen fertilizers reductionThe results showed that chemical nitrogen fertilizers reduction significantly decreased SOC and TN by 2.76% and 4.19% respectively, while increased C:N by 6.11% across all database (Fig. 1). SOC mainly derives from crop residues and secretions which closely related to crops growths, and crops growths were affected by fertilization, especially nitrogen fertilization20,21. The reduction of chemical nitrogen fertilizer led to poor crop growth, which reduced the amount of crop residues return, and then decreased SOC. Similarly, TN from crops was reduced due to poor crop growth. In addition, the reduction of chemical nitrogen fertilizers directly reduced the input of soil nitrogen. The increase of C:N was the result of the decrease of TN being greater than that of SOC. The responses of C:N to chemical nitrogen fertilizers reduction enhanced the comprehension of the couple relationship between SOC and TN, which was beneficial to the evolution of the C-N coupling models. Moreover, the accuracy of C-N coupling models depends on the precise quantification of the responses of SOC and TN to nitrogen fertilization. And our results accurately quantified the difference responses of SOC and TN to different nitrogen fertilization regimes, thus optimizing the C-N coupling models.Figure 1The weighted response ratio (RR++) for the responses to chemical nitrogen fertilizers of soil organic carbon (SOC, a), total nitrogen (TN, b), and their ratios (C:N, c). Bars denote the overall mean response ratio RR++ and 95% confidence intervals (CI). The star (*) indicates significance when the 95% CI that do not go across the zero line. The vertical lines are drawn at lnRR = 0. The value represents independent sample size.Full size imageResponses of SOC, TN and C:N to chemical nitrogen fertilizers reduction magnitudeWhen grouped by chemical nitrogen fertilizers reduction magnitude, SOC showed a significant increase by 6.9% in medium magnitude, while SOC was significantly decreased by 3.10% and 7.26% in high and total magnitude respectively (Fig. 1a). Liu and Greaver22 also stated the reduction of medium nitrogen fertilizer increased the average microbial biomass from 15 to 20%, thereby increasing the SOC content. Previous studies had reported that there were strong positive correlations between soil organic matter and soil microbial biomass in both the agricultural ecosystem and natural ecosystem23,24. Numerous researchers have demonstrated the significance of nitrogen availability in soil for the plant biomass across most ecosystems25,26. Moreover, nitrogen deficient would inhibit the activity of extracellular enzymes and root activities27. Generally, soil degradation caused by continuous rising chemical nitrogen fertilizers application may inhibit the growth of crops and ultimately reduce the SOC28.TN showed no significant difference in low and medium chemical nitrogen fertilizers reduction magnitude (p  > 0.05), while TN in high magnitude and total chemical nitrogen fertilizers reduction magnitude exhibited a decrease with 3.10% and 9.37% respectively (Fig. 1b). Numerous studies described that the amount of nitrogen fertilizers used in China was higher than the demand of N for crop, which caused serious N leaching and runoff29,30. Chemical nitrogen fertilizers in low and medium magnitude would not decrease the TN of soil by reducing N leaching and runoff. However, the residual nitrogen in soil cannot meet the requirement for the sustainable growth of plant with litter or without exogenous nitrogen supplement, which resulted in the decrease of TN in high and total chemical nitrogen fertilizers magnitude. Consequently, optimal nitrogen fertilizers application rates will take into account crops yield and environment friendliness.Additionally, C:N had a significant increase with ranging from 1.82% to 8.98% under the four chemical nitrogen fertilizers reduction magnitude (Fig. 1c), suggesting the relative increase of SOC compared to TN. Previous studies have revealed that C:N had significantly influence on the soil bacterial community structures31. And there were also considerable studies indicated that chemical nitrogen fertilizers have impact on the soil bacterial communities32,33. We may speculate that the change of C:N would bring about the variations of soil bacteria communities under the chemical nitrogen fertilizers regimes.Responses of SOC, TN, and C:N to chemical nitrogen fertilizers reduction durationNegative response of SOC to short-term chemical nitrogen fertilizers reduction was observed in our study, which was consistent with the study of Gong, et al.34 that chemical nitrogen fertilizers reduction decreased SOC by reducing crop-derived carbon by one year. However, SOC was significantly increased by 1.06% and 4.65% at mid-term and long-term chemical nitrogen fertilizers reduction respectively, which was similar with the findings of Ning, et al.11 that SOC was significantly increased under more than 5 years of chemical nitrogen fertilizers reduction treatment. TN was significantly decreased by 1.96% at short-term chemical nitrogen fertilizers reduction duration, while the results converted at mid-term chemical nitrogen fertilizers reduction duration. The effect of long-term chemical nitrogen fertilizers reduction on TN was not significant (p  > 0.05). The divergent response of TN to different chemical nitrogen fertilizers duration was mainly caused by the various treatments. In terms of C:N, a greater positive response was observed at short-term chemical nitrogen fertilizers duration (9.06%) than mid-term and long-term duration (1.99%). Moreover, with the prolongation of the chemical reduction time of nitrogen, the response ratio tends to zero, suggesting that the effect of chemical fertilizers gradually decrease. This may be ascribed to the buffer capacity of soil to resist the changes from external environment, including nutrients, pollutants, and redox substances35.Responses of SOC, TN, and C:N to different chemical nitrogen fertilizers reduction patternsUnder the pattern of chemical nitrogen fertilizers reduction without organic fertilizers supplement, SOC and TN significantly decreased by 3.83% and 11.46% respectively, however, chemical nitrogen fertilizers reduction with organic fertilizers supplement significantly increased SOC and TN by 4.92% and 8.33% respectively. Moreover, C:N significantly increased under the two chemical nitrogen fertilizers patterns (p  0.05), but there was a negative effect on SOC in high and total magnitude (p  0.05). The no significant decrease at mid-term duration might result from the limited information reported in original studies of this meta-analysis36. TN showed no significant response to chemical nitrogen fertilizers without organic fertilizers supplement in the low and medium magnitude (p  > 0.05). However, TN was significantly decreased by 8.62% and 16.7% respectively in the high and total magnitude. When regarding to chemical nitrogen fertilizers reduction duration, TN was significantly reduced at all of the categories, ranging from 3.13% to 13.4% (Fig. 2c). In the pattern of chemical nitrogen fertilizers reduction with organic fertilizers supplement, chemical nitrogen fertilizers reduction at medium, high, and total magnitudes significantly increased SOC by 13.85%, 13.03%, and 5.46%respectively, however, the response of SOC in the low chemical nitrogen fertilizers magnitude was not significant. Chemical nitrogen fertilizers reduction duration significantly increased SOC by 7.01%, 1.71%, and 22.02% in the short-term, mid-term, and long-term respectively. Comparatively, TN showed a significantly increase in most chemical nitrogen fertilizers categories expect for the long-term chemical nitrogen fertilizers duration, with an increasing from 4.90% to 14.69% (Fig. 2d).Figure 2The weighted response ratio (RR++) for the responses to chemical nitrogen fertilizers of soil organic carbon (SOC, a), total nitrogen (TN, b), and their ratios (C:N, c) under the two patterns (with organic fertilizers ; without organic fertilizers). Bars denote the overall mean response ratio RR++ and 95% confidence intervals (CI). The star (*) indicates significance when the 95% CI that do not go across the zero line. The vertical lines are drawn at lnRR = 0. The values represent independent sample size.Full size imageOrganic fertilizers were mainly derived from animal manure or crops straws, which contained large amount of organic matter and nitrogen elements37,38. The application of organic fertilizers increased the input of SOC and TN directly. Moreover, organic fertilizer could promote the growth of crops by releasing phenols, vitamins, enzymes, auxins and other substances during the decomposition process, thus the SOC derived from crops would be increased37,39. In addition, organic fertilizers provide various nutrients for microbial reproduction, which increase the microbial population and organic carbon and total nitrogen content37. More importantly, the application of organic fertilizers could improve organic carbon sequestration and maintain its stability in aggregates, thereby reducing losses of SOC and TN40.C:N showed an increase under all of the chemical nitrogen fertilizers reduction with organic fertilizer supplement. The positive response of C:N to organic fertilizer supplement may be related to the higher C:N of organic fertilizer than soil. The average values of C:N of the commonly used organic fertilizers including animal manure, crop straws and biochar were 14, 60 and 30 respectively, while the C:N of soil was lower than 10 in average according to extensive literature researches41. Therefore, organic fertilizers would be a favorable alternative of chemical fertilizers for the sustainable development of agriculture.The correlation between the response of SOC, TN, and C:N and environmental variablesThe analysis of linear regression was conducted to analyze the environmental variables including mean annual temperature (MAT), mean annual precipitation (MAP), accumulated temperature above 10 °C (MATA), which may exert influence on SOC, TN and C:N. No significant correlation among the lnRR of SOC, TN, C:N and environmental variables were observed among the whole database (p  > 0.05; Fig. S1). Rule out the interference of organic fertilizers supplement, we analyzed the relationship between lnRR of SOC, TN, C:N and environmental variables as the Figures showed in Figs. 3 and 4 respectively. Under chemical nitrogen fertilizers without organic fertilizers supplement, there was a significant negative correlation between lnRR of SOC and MAT (p  More

MaterialsWe collected from the literature and available databases a dataset of radiocarbon dates from Europe (West of 60°E and North of 37°N) either obtained from remains of the four analyzed species or from archaeological layers where they have been observed. However, we only considered observations dated between 7500 and 47,000 cal BP: their scarcity before this period may bias the GAMs, and after it, domesticated cattle, pigs and (later) horses arrived in Europe, making it difficult to differentiate them from their wild forms.We excluded any record fitting one or more of the following conditions: unreliable; not in accord with the expected chronology of their archaeological layer; without a reported standard error; available only as terminus ante/post quem.All dates were calibrated with OxCal5 version 4.4 using the IntCal20 curve51, and we further excluded any record for which calibration resulted in an error, resulting in the number of points presented in Table 1 as “Original dataset” (available at the link https://doi.org/10.6084/m9.figshare.20510364).Table 1 Number of observations for each species.Full size tableSDMs based on GAMs need presence/background data, not frequencies; moreover, multiple observations (i.e., presence in different archaeological layers) from the same site and time slice are likely to introduce stronger sample biases linked to chrono-geographically differential sampling efforts. For this reason, we collapsed our observations by keeping only one point per grid cell per time slice for each species, leaving the number of observations reported in Table 1 as “Collapsed datasets”, used for all the analyses presented in this work.To perform all analyses, we used the R package pastclim v. 1.042 to couple each observation from the collapsed datasets to paleoclimatic reconstructions published in8 by setting dataset = “Beyer2020”. These are based on the Hadley CM3 model, include 14 different bioclimatic variables at a spatial resolution of 0.5°, and are available for the whole world every 1000 years until 22 kya and every 2000 years before that date (referred to in the manuscript as “time slices”). Specifically, each observation was associated with the relevant bioclimatic reconstruction based on its average age and spatial coordinates.As already mentioned, the four species analyzed show different preferences regarding temperature, habitat, and altitude. Therefore, for the Species Distribution Modelling, we choose five environmental variables that should be able to capture such differences: two measures of temperature (BIO5, maximum temperature of the warmest month, and BIO6, minimum temperature of the coldest month); two variables to help capture habitat differentiation (BIO12, total annual precipitation, and Net Primary Productivity, NPP), and one measure of topography (rugosity42).High collinearity can be problematic in SDMs; we confirmed that all our variables had a correlation below 0.7, a threshold commonly adopted for this kind of analysis52,53.Whilst the GAMs predicted all time points; we visualized our results by creating an average estimate for the following periods: pre-LGM (from the beginning of the time range analyzed, i.e., 47 kya to 27 kya), LGM (from 27 to 18 kya), Late Glacial (from 18 to 11.7 kya), Holocene (from 11.7 kya to the end of the time range analyzed, i.e., 7.5 kya).MethodsWe generated 25 sets of background points for each species to adequately represent the existing climatic space in our SDMs. Each set was generated by sampling, for each observation, 50 random locations matched by time. This resulted in n = 25 datasets (“repetitions”) of background points and presences (observations) for each species, which we used to repeat our analyses to account for the stochastic sampling of the background. For each dataset, we used GAMs to fit two possible models: a “constant niche” model, which included only the environmental variables as covariates, and a “changing niche” model, that also included interactions of each environmental variable with time (fitted as tensor products).In GAMs, the effect of a given continuous predictor on the response variable (in our case, the logit transformed probability of a presence) is represented by a smooth function; this smooth function can be linear or non-linear and can become highly complex in shape depending on the number of knots selected by the GAM fitting algorithm. The interaction between two covariates is modelled by tensor products54; this approach is equivalent to an interaction term in a linear model but with the added complexity of the smooth function. In our models, we confine tensor products to the interaction between an environmental variable and time; a simple way to think about such a tensor product is that it allows the smooth representation of the relationship between the variable and the probability of a presence to change progressively over time.GAMs were fitted using the mgcv package in R54 using thin plate regression splines (TPNR; bs = “tp”, default in mgcv) for environmental variables and their tensor products with time in the “niche changing” models. The GAM algorithm automatically selects the complexity of the smooth most appropriate to the data that are being fitted; as GAM can have issues with overfitting, we added an additional penalty against overly complex smooths (gamma = 1.4) and used Restricted Maximum Likelihood (REML = TRUE), as recommended by54. It is possible that even with these settings, the complexity of the smooth is not sufficient; we used mgcv::gam.check() to check this, and increased the basis dimension of the smooth, k, to make sure that k-1 was larger than the estimated degrees of freedom (edf). We found the best maximum thresholds for k to be 16 for bio06 and 10 for all other variables.We checked for non-linear correlation among variables using the mgcv::collinearity function and checked the values of estimated concurvity. All estimates were below the threshold of 0.8 in all models, runs and variables except for a few instances for time (Supplementary Figs. 5–8). We consider this not to be worrying: this is most likely a result of sample bias, and GAM is known to be robust to correlation/concurvity55,56.We verified the model assumptions by inspecting the residuals using the R package DHARMa57. Standard tests for deviations from the expected distribution and dispersion were non-significant for all repetitions for all species, as were the tests for outliers. Furthermore, we tested for spatial autocorrelation among residuals by computing Moran’s I; all tests were either non-significant or, when significance was detected, the estimate of Moran’s I was very close to zero, revealing a trivial deviation from the assumptions which should not impact the results (Supplementary Tables 1–4).We performed model choice (Supplementary Tables 5–8) by comparing the constant- and changing-niche models for each combination of species and repetition using the Akaike Information Criterion (AIC). AIC strongly supported the changing-niche model in all species and repetitions, an inference supported by the higher Nagelkerke R2 and expected deviance for those models than for the constant-niche ones (Supplementary Tables 5–8).The model fit for each of the changing niche GAMs was evaluated with the Boyce Continuous Index25,26, designed to be used with presence-only data58,59. We set a threshold of Pearson’s correlation coefficient  >  0.8 to define acceptable models25 (Supplementary Table 9).The relative importance of each environmental variable was quantified for all the models above the BCI threshold of 0.8 in two different ways. Firstly, we computed the total deviance explained by each variable by simply fitting a GAM with only that variable. We then estimated the unique deviance explained by each variable by comparing the full model with one for which that variable was excluded (i.e., we computed the explained deviance lost by dropping that predictor). The difference between the two values represents the deviance explained by a variable which can also be accounted for by other variables (i.e., the deviance in common with other variables).To achieve more robust predictions60, we averaged in two different ensembles the repetitions for the changing niche GAMs with BCI  > 0.8: by mean and median. This step is intended to reduce the weight of models that are highly sensitive to the random sampling of the background60. Then, for each species, we selected the ensemble (either based on mean or median) with the higher BCI as the most supported and used it to perform all further analyses.The effect of different variables through time was visualized by plotting the interactions of the GAMs. For each model with a BCI  > 0.8, we used the R package gratia27 to generate a surface with time as the x-axis, the environmental variable as the y-axis, and the effect size as the z-axis (visualized as colour shades). We then plotted the mean surface for each species, which captures the signal consistent across all randomized background sets.To visualize the prediction for each species, we then transformed the predicted probabilities of occurrence from the ensemble into binary presence/absences by using the threshold needed to get a minimum predicted area encompassing 99% of our presences (function ecospat.mpa() from the ecospat R package61). The binary predictions were then visualized using the mean over the time steps within each major climatic period.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Clutton-Brock, T. & Sheldon, B. C. Individuals and populations: The role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends Ecol. Evol. 25, 562–573 (2010).PubMed 
Article 

Google Scholar 
Keuling, O., Lauterbach, K., Stier, N. & Roth, M. Hunter feedback of individually marked wild boar Sus scrofa L.: Dispersal and efficiency of hunting in northeastern Germany. Eur. J. Wildl. Res. 56, 159–167 (2010).Article 

Google Scholar 
Trondrud, L. M. et al. Fat storage influences fasting endurance more than body size in an ungulate. Funct. Ecol. 35, 1470–1480 (2021).CAS 
Article 

Google Scholar 
Wilmers, C. C. et al. The golden age of bio-logging: How animal-borne sensors are advancing the frontiers of ecology. Ecology 96, 1741–1753 (2015).PubMed 
Article 

Google Scholar 
Kukalová, M., Gazárková, A. & Adamík, P. Should i stay or should i go? The influence of handling by researchers on den use in an arboreal nocturnal rodent. Ethology 119, 848–859 (2013).Article 

Google Scholar 
Holt, R. D. et al. Estimating duration of short-term acute effects of capture handling and radiomarking. J. Wildl. Manag. 73, 989–995 (2009).Article 

Google Scholar 
Marco, I., Viñas, L., Velarde, R., Pastor, J. & Lavin, S. Effects of capture and transport on blood parameters in free-ranging mouflon (Ovis ammon). J. Zoo Wildl. Med. 28, 428–433 (1997).CAS 
PubMed 

Google Scholar 
Cattet, M., Boulanger, J., Stenhouse, G., Powell, R. A. & Reynolds-Hogland, M. J. An evaluation of long-term capture effects in ursids: Implications for wildlife welfare and research. J. Mammal. 89, 973–990 (2008).Article 

Google Scholar 
Mortensen, R. M. & Rosell, F. Long-term capture and handling effects on body condition, reproduction and survival in a semi-aquatic mammal. Sci. Rep. 10, 1–16 (2020).Article 

Google Scholar 
Soulsbury, C. D. et al. The welfare and ethics of research involving wild animals: A primer. Methods Ecol. Evol. 11, 1164–1181 (2020).Article 

Google Scholar 
Herman, J. P. et al. Regulation of the hypothalamic-pituitary- adrenocortical stress response. Compr. Physiol. 6, 603–621 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Sapolsky, R. M., Romero, L. M. & Munck, A. U. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr. Rev. 21, 55–89 (2000).CAS 
PubMed 

Google Scholar 
Sjaastad, V. Ø., Hove, K. & Sand, O. Physiology of Domestic Animals (Scandinavian Veterinary Press, 2016).
Google Scholar 
Omsjø, E. H. et al. Evaluating capture stress and its effects on reproductive success in Svalbard reindeer. Can. J. Zool. 87, 73–85 (2009).Article 

Google Scholar 
Marco, I., Viñas, L., Velarde, R., Pastor, J. & Lavin, S. The stress response to repeated capture in mouflon (Ovis ammon): Physiological, haematological and biochemical parameters. J. Vet. Med. Ser. A Physiol. Pathol. Clin. Med. 45, 243–253 (1998).CAS 
Article 

Google Scholar 
Hattingh, J., Pitts, N. I. & Ganhao, M. F. Immediate response to repeated capture and handling of wild impala. J. Exp. Zool. 248, 109–112 (1988).CAS 
PubMed 
Article 

Google Scholar 
Ortega, A. C. et al. Effectiveness of partial sedation to reduce stress in captured mule deer. J. Wildl. Manag. 84, 1445–1456 (2020).Article 

Google Scholar 
Arnemo, J. M. & Caulkett, N. Stress. In Zoo Animal and Wildlife Anesthesia and Immobilization (eds West, G. et al.) 103–109 (Blackwell Publications, 2007).
Google Scholar 
Sinclair, M. D. A review of the physiological effects of α2-agonists related to the clinical use of medetomidine in small animal practice. Can. Vet. J. 44, 885–897 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ranheim, B. et al. The effects of medetomidine and its reversal with atipamezole on plasma glucose, cortisol and noradrenaline in cattle and sheep. J. Vet. Pharmacol. Ther. 23, 379–387 (2000).CAS 
PubMed 
Article 

Google Scholar 
Carroll, G. L. et al. Effect of medetomidine and its antagonism with atipamezole on stress-related hormones, metabolites, physiologic responses, sedation, and mechanical threshold in goats. Vet. Anaesth. Analg. 32, 147–157 (2005).CAS 
PubMed 
Article 

Google Scholar 
Rode, K. D. et al. Effects of capturing and collaring on polar bears: finDings from long-term research on the southern Beaufort Sea population. Wildl. Res. 41, 311–322 (2014).Article 

Google Scholar 
Sakamoto, H., Misumi, K., Nakama, M. & Aoki, Y. The effects of xylazine on intrauterine pressure, uterine blood flow, maternal and fetal cardiovascular and pulmonary function in pregnant goats. J. Vet. Med. Sci. 58, 211–217 (1996).CAS 
PubMed 
Article 

Google Scholar 
Katila, T. & Oijala, M. The effect of detomidine (Domosedan) on the maintenance of equine pregnancy and foetal development: ten cases. Equine Vet. J. 20, 323–326 (1988).CAS 
PubMed 
Article 

Google Scholar 
Larsen, D. G. & Gauthier, D. A. Effects of capturing pregnant moose and calves on calf survivorship. J. Wildl. Manag. 53, 564 (1989).Article 

Google Scholar 
Côté, S. D., Festa-Bianchet, M. & Fournier, F. Life-history effects of chemical immobilization and radiocollars on mountain goats. J. Wildl. Manage. 62, 745–752 (1998).Article 

Google Scholar 
DelGiudice, G. D., Mech, L. D., Paul, W. J. & Karns, P. D. Effects on fawn survival of multiple immobilizations of captive pregnant white-tailed deer. J. Wildl. Dis. 22, 245–248 (1986).CAS 
PubMed 
Article 

Google Scholar 
Brivio, F., Grignolio, S., Sica, N., Cerise, S. & Bassano, B. Assessing the impact of capture on wild animals: The case study of chemical immobilisation on alpine ibex. PLoS ONE 10, e0130957 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Wingfield, J. C. et al. Ecological bases of hormone-behavior interactions: The ‘emergency life history stage’. Am. Zool. 38, 191–206 (1998).CAS 
Article 

Google Scholar 
Huber, S., Palme, R. & Arnold, W. Effects of season, sex, and sample collection on concentrations of fecal cortisol metabolites in red deer (Cervus elaphus). Gen. Comp. Endocrinol. 130, 48–54 (2003).CAS 
PubMed 
Article 

Google Scholar 
Morellet, N. et al. The effect of capture on ranging behaviour and activity of the European roe deer Capreolus capreolus. Wildlife Biol. 15, 278–287 (2009).Article 

Google Scholar 
Tarlow, E. M. & Blumstein, D. T. Evaluating methods to quantify anthropogenic stressors on wild animals. Appl. Anim. Behav. Sci. 102, 429–451 (2007).Article 

Google Scholar 
Hik, D. S. Does risk of predation influence the cyclic decline of snowshoe hares. Wildl. Res. 22, 115–129 (1995).Article 

Google Scholar 
Ordiz, A. et al. Lasting behavioural responses of brown bears to experimental encounters with humans. J. Appl. Ecol. 50, 306–314 (2013).Article 

Google Scholar 
Dechen Quinn, A. C., Williams, D. M. & Porter, W. F. Postcapture movement rates can inform data-censoring protocols for GPS-collared animals. J. Mammal. 93, 456–463 (2012).Article 

Google Scholar 
Cattet, M. R. L. Falling through the cracks: Shortcomings in the collaboration between biologists and veterinarians and their consequences for wildlife. ILAR J. 54, 33–40 (2013).CAS 
PubMed 
Article 

Google Scholar 
Albon, S. D. et al. Contrasting effects of summer and winter warming on body mass explain population dynamics in a food-limited Arctic herbivore. Glob. Change Biol. 23, 1374–1389 (2017).ADS 
Article 

Google Scholar 
Ovejero, R. et al. Do cortisol and corticosterone play the same role in coping with stressors? Measuring glucocorticoid serum in free-ranging guanacos (Lama guanicoe). J. Exp. Zool. Part A Ecol. Genet. Physiol. 319, 539–547 (2013).CAS 
Article 

Google Scholar 
Bonacic, C., Feber, R. E. & Macdonald, D. W. Capture of the vicuña (Vicugna vicugna) for sustainable use: Animal welfare implications. Biol. Conserv. 129, 543–550 (2006).Article 

Google Scholar 
Romero, L. M. & Beattie, U. K. Common myths of glucocorticoid function in ecology and conservation. J. Exp. Zool. Part A Ecol. Integr. Physiol. 337, 7–14 (2022).CAS 
Article 

Google Scholar 
Sire, J. E. et al. The effect of blood sampling on plasma cortisol in female reindeer (Rangifer tarandus tarandus L). Acta Vet. Scand. 36, 583–587 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Harlow, H. J., Thorne, E. T., Williams, E. S., Belden, E. L. & Gern, W. A. Adrenal responsiveness in domestic sheep ( Ovis aries ) to acute and chronic stressors as predicted by remote monitoring of cardiac frequency. Can. J. Zool. 65, 2021–2027 (1987).Article 

Google Scholar 
Pottinger, T. G. & Moran, T. A. Differences in plasma cortisol and cortisone dynamics during stress in two strains of rainbow trout (Oncorhynchus mykiss). J. Fish Biol. 43, 121–130 (1993).CAS 
Article 

Google Scholar 
Arnemo, J. M. & Ranheim, B. Effects of medetomidine and atipamezole on serum glucose and cortisol levels in captive reindeer (Rangifer tarandus tarandus). Rangifer 19, 85–89 (1999).Article 

Google Scholar 
Mentaberre, G. et al. Effects of azaperone and haloperidol on the stress response of drive-net captured Iberian ibexes (Capra pyrenaica). Eur. J. Wildl. Res. 56, 757–764 (2010).Article 

Google Scholar 
Northrup, J. M., Anderson, C. R. & Wittemyer, G. Effects of helicopter capture and handling on movement behavior of mule deer. J. Wildl. Manag. 78, 731–738 (2014).Article 

Google Scholar 
Jung, T. S. et al. Short-term effect of helicopter-based capture on movements of a social ungulate. J. Wildl. Manag. 83, 830–837 (2019).Article 

Google Scholar 
Nurmi, H., Laaksonen, S., Raekallio, M. & Hänninen, L. Wintertime pharmacokinetics of intravenously and orally administered meloxicam in semi-domesticated reindeer (Rangifer tarandus tarandus). Vet. Anaesth. Analg. 49, 423–428 (2022).CAS 
PubMed 
Article 

Google Scholar 
Chapple, R. S., English, A. W., Mulley, R. C. & Lepherd, E. E. Haematology and serum biochemistry of captive unsedated chital deer (Axis axis) in Australia. J. Wildl. Dis. 27, 396–406 (1991).CAS 
PubMed 
Article 

Google Scholar 
Brosh, A. Heart rate measurements as an index of energy expenditure and energy balance in ruminants: A review1. J. Anim. Sci. 85, 1213–1227 (2007).CAS 
PubMed 
Article 

Google Scholar 
Suazo, A. A., Delong, A. T., Bard, A. A. & Oddy, D. M. Repeated capture of beach mice (Peromyscus polionotus phasma and P. P. niveiventris) reduces body mass. J. Mammal. 86, 520–523 (2005).Article 

Google Scholar 
Hoyle, S. D., Horsup, A. B., Johnson, C. N., Crossman, D. G. & McCallum, H. Live-trapping of the northern hairy-nosed wombat (Lasiorhinus krefftii): Population-size estimates and effects on individuals. Wildl. Res. 22, 741–755 (1995).Article 

Google Scholar 
Estruelas, N. F. Short- and long-term physiological effects of capture and handling on free-ranging brown bears (Ursus arctos). PhD Thesis. (Inland Norway University of Applied Sciences, 2017).Veiberg, V. et al. Maternal winter body mass and not spring phenology determine annual calf production in an Arctic herbivore. Oikos 126, 980–987 (2017).Article 

Google Scholar 
Loe, L. E. et al. The neglected season: Warmer autumns counteract harsher winters and promote population growth in Arctic reindeer. Glob. Change Biol. 27, 993–1002 (2021).ADS 
CAS 
Article 

Google Scholar 
Larsen, T. S., Nilsson, N. & Blix, A. S. Seasonal changes in lipogenesis and lipolysis in isolated adipocytes from Svalbard and Norwegian reindeer. Acta Physiol. Scand. 123, 97–104 (1985).CAS 
PubMed 
Article 

Google Scholar 
Colman, J. E., Jacobsen, B. W. & Reimers, E. Summer response distances of Svalbard reindeer (Rangifer tarandus platyrhynchus) to provocation by humans on foot. Wildlife Biol. 7, 275–283 (2001).Article 

Google Scholar 
Trondrud, L. M. et al. Determinants of heart rate in Svalbard reindeer reveal mechanisms of seasonal energy management. Philos. Trans. R. Soc. B Biol. Sci. 376, 20200215 (2021).Article 

Google Scholar 
Pigeon, G. et al. Context-dependent fitness costs of reproduction despite stable body mass costs in an Arctic herbivore. J. Anim. Ecol. 91, 61–73 (2022).PubMed 
Article 

Google Scholar 
Peeters, B., Pedersen, Å., Veiberg, V. & Hansen, B. Hunting quotas, selectivity and stochastic population dynamics challenge the management of wild reindeer. Clim. Res. https://doi.org/10.3354/cr01668 (2021).Article 

Google Scholar 
Loe, L. E. et al. Activity pattern of arctic reindeer in a predator-free environment: No need to keep a daily rhythm. Oecologia 152, 617–624 (2007).ADS 
PubMed 
Article 

Google Scholar 
Dahl, S. R. et al. Assay of steroids by liquid chromatography–tandem mass spectrometry in monitoring 21-hydroxylase deficiency. Endocr. Connect. 7, 1542–1550 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Loe, L. E. et al. Testing five hypotheses of sexual segregation in an arctic ungulate. J. Anim. Ecol. 75, 485–496 (2006).PubMed 
Article 

Google Scholar 
Reimers, E., Lund, S. & Ergon, T. Vigilance and fright behaviour in the insular Svalbard reindeer (Rangifer tarandus platyrhynchus). Can. J. Zool. 89, 753–764 (2011).Article 

Google Scholar 
The R Core Team. R: A language and environment for statistical computing (2021).Burnham, K. P. & Anderson, D. R. in Model selection and multimodel inference. A Practical Information-Theoretic Approach. Ecological Modelling (Springer, 2002).Blanchet, F. G., Tikhonov, G. & Norberg, A. HMSC: Hierarchical modelling of species community. R package version 2.2-0 (2019).Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).PubMed 
Article 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology (Elsevier Science BV, 2012).MATH 

Google Scholar 
Diggle, P. J., Heagerty, P., Liang, K.-Y. & Zeger, S. L. Analysis of Longitudinal Data (Oxford University Press, 2013).MATH 

Google Scholar  More

Irigoien, X. et al. Nat. Commun. 5, 3271 (2014).Article 

Google Scholar 
Ariza, A. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01479-2 (2022).Article 

Google Scholar 
Klevjer, T. A. et al. Sci. Rep. 6, 19873 (2016).CAS 
Article 

Google Scholar 
Braun, C. D. et al. Annu. Rev. Mar. Sci. 14, 129–159 (2022).Article 

Google Scholar 
Heneghan, R. F. et al. Prog. Oceanogr. 198, 102659 (2021).Article 

Google Scholar 
Polovina, J. J., Dunne, J. P., Woodworth, P. A. & Howell, E. A. ICES J. Mar. Sci. 68, 986–995 (2011).Article 

Google Scholar 
Cheung, W. W. L. et al. Fish Fish. 10, 235–251 (2009).Article 

Google Scholar 
Hazen, E. L. et al. Nat. Clim. Change 3, 234–238 (2013).Article 

Google Scholar 
Powers, R. P. & Jetz, W. Nat. Clim. Change 9, 323–329 (2019).Article 

Google Scholar 
Purves, D. et al. Nature 493, 295–297 (2013).CAS 
Article 

Google Scholar 
Hobday, A. J., Spillman, C. M., Paige Eveson, J. & Hartog, J. R. Fish. Oceanogr. 25, 45–56 (2016).Article 

Google Scholar 
Pons, M. et al. Proc. Natl Acad. Sci. USA 119, e2114508119 (2022).Article 

Google Scholar  More