Ecology

Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).Article 
ADS 

Google Scholar 
Polyakov, I. V. et al. Borealization of the Arctic Ocean in response to anomalous advection from sub-Arctic seas. Front. Mar. Sci. 7, 983–992 (2020).Article 

Google Scholar 
Lannuzel, D. et al. The future of Arctic sea-ice biogeochemistry and ice-associated ecosystems. Nat. Clim. Change. 10, 983–992 (2020).Article 
ADS 

Google Scholar 
Macias-Fauria, M. & Post, E. Effects of sea ice on Arctic biota: An emerging crisis discipline. Biol. Lett. 14, 20170702 (2018).Article 

Google Scholar 
Kohlbach, D. et al. The importance of ice algae-produced carbon in the central Arctic Ocean ecosystem: Food web relationships revealed by lipid and stable isotope analyses. Limnol. Oceanogr. 61, 2027–2044 (2016).Article 
ADS 

Google Scholar 
Søreide, J. E. et al. Sympagic-pelagic-benthic coupling in Arctic and Atlantic waters around Svalbard revealed by stable isotopic and fatty acid tracers. Mar. Biol. Res. 9, 831–850 (2013).Article 

Google Scholar 
Slagstad, D., Wassmann, P. F. J. & Ellingsen, I. Physical constrains and productivity in the future Arctic Ocean. Front. Mar. Sci. 2015, 2 (2015).
Google Scholar 
FISCAO. Final Report of the Fifth Meeting of Scientific Experts on Fish Stocks in the Central Arctic Ocean. https://apps-afsc.fisheries.noaa.gov/documents/Arctic_fish_stocks_fifth_meeting/508_Documents/508_Final_report_of_the_505th_FiSCAO_meeting.pdf (2018).David, C. et al. Under-ice distribution of polar cod Boreogadus saida in the central Arctic Ocean and their association with sea-ice habitat properties. Polar Biol. 39, 981–994 (2016).Article 

Google Scholar 
Gradinger, R. Vertical fine structure of the biomass and composition of algal communities in Arctic pack ice. Mar. Biol. 133, 745–754 (1999).Article 

Google Scholar 
Kosobokova, K. N., Hopcroft, R. R. & Hirche, H.-J. Patterns of zooplankton diversity through the depths of the Arctic’s central basins. Mar Biodivers. 41, 29–50 (2011).Article 

Google Scholar 
Mumm, N. et al. Breaking the ice: Large-scale distribution of mesozooplankton after a decade of Arctic and transpolar cruises. Polar Biol. 20, 189–197 (1998).Article 

Google Scholar 
Snoeijs-Leijonmalm, P. et al. Unexpected fish and squid in the central Arctic deep scattering layer. Sci. Adv. 8, 7536 (2022).Article 

Google Scholar 
David, C., Lange, B., Rabe, B. & Flores, H. Community structure of under-ice fauna in the Eurasian central Arctic Ocean in relation to environmental properties of sea-ice habitats. Mar. Ecol. Prog. Ser. 522, 15–32 (2015).Article 
ADS 

Google Scholar 
Gosselin, M., Levasseur, M., Wheeler, P. A., Horner, R. A. & Booth, B. C. New measurements of phytoplankton and ice algal production in the Arctic Ocean. Deep-Sea Res. Part II(44), 1623–1644 (1997).Article 
ADS 

Google Scholar 
Ardyna, M. & Arrigo, K. R. Phytoplankton dynamics in a changing Arctic Ocean. Nat. Clim. Change. 10, 892–903 (2020).Article 
ADS 
CAS 

Google Scholar 
Hays, G. C. In Migrations and Dispersal of Marine Organisms. (eds Jones, M. B. et al.) 163–170 (Springer).Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).Article 
ADS 

Google Scholar 
Geoffroy, M. et al. Mesopelagic sound scattering layers of the high arctic: Seasonal variations in biomass, species assemblage, and trophic relationships. Front. Mar. Sci. 2019, 6 (2019).
Google Scholar 
Gjøsæter, H., Wiebe, P. H., Knutsen, T. & Ingvaldsen, R. B. Evidence of Diel vertical migration of mesopelagic sound-scattering organisms in the Arctic. Front. Mar. Sci. 2017, 4 (2017).
Google Scholar 
Knutsen, T., Wiebe, P. H., Gjøsæter, H., Ingvaldsen, R. B. & Lien, G. High latitude epipelagic and mesopelagic scattering layers—a reference for future arctic ecosystem change. Front. Mar. Sci. 2017, 4 (2017).
Google Scholar 
Priou, P. et al. Dense mesopelagic sound scattering layer and vertical segregation of pelagic organisms at the Arctic-Atlantic gateway during the midnight sun. Prog. Oceanogr. 196, 102611 (2021).Article 

Google Scholar 
Snoeijs-Leijonmalm, P. et al. A deep scattering layer under the North Pole pack ice. Prog. Oceanogr. 194, 102560 (2021).Article 

Google Scholar 
St-John, M. A. et al. A dark hole in our understanding of marine ecosystems and their services: Perspectives from the mesopelagic community. Front. Mar. Sci. 2016, 3 (2016).
Google Scholar 
Fransson, A. et al. Joint cruise 2-2 2021: Cruise report. The Nansen Legacy Report Series, 30/2022. https://doi.org/10.7557/nlrs.6413 (2022).Rudels, B. et al. Observations of water masses and circulation with focus on the Eurasian Basin of the Arctic Ocean from the 1990s to the late 2000s. Ocean Sci. 9, 147–169 (2013).Article 
ADS 

Google Scholar 
Krumpen, T. et al. Arctic warming interrupts the Transpolar Drift and affects long-range transport of sea ice and ice-rafted matter. Sci. Rep. 9, 5459 (2019).Article 
ADS 

Google Scholar 
Aagaard, K. A synthesis of the Arctic Ocean circulation. Rapp. P.-V. Rcun. Cons. int. Explor. Mer. 188, 11–22 (1989).
Google Scholar 
Perez-Hernandez, M. D. et al. The Atlantic Water boundary current north of Svalbard in late summer. J. Geophys. Res. 122, 2269–2290 (2017).Article 
ADS 

Google Scholar 
Våge, K. et al. The Atlantic Water boundary current in the Nansen Basin: Transport and mechanisms of lateral exchange. J. Geophys. Res. 121, 6946–6960 (2016).Article 
ADS 

Google Scholar 
Crews, L., Sundfjord, A., Albretsen, J. & Hattermann, T. Mesoscale Eddy Activity and Transport in the Atlantic Water Inflow Region North of Svalbard. J. Geophys. Res. 123, 201–215 (2018).Article 
ADS 

Google Scholar 
Kolås, E. H., Koenig, Z., Fer, I., Nilsen, F. & Marnela, M. Structure and Transport of Atlantic Water North of Svalbard From Observations in Summer and Fall 2018. J. Geophys. Res. 125, 6174 (2020).Article 

Google Scholar 
Basedow, S. L. et al. Seasonal variation in transport of zooplankton into the arctic basin through the atlantic gateway. Fram Strait. Front. Mar. Sci. 2018, 5 (2018).
Google Scholar 
Vernet, M., Carstensen, J., Reigstad, M. & Svensen, C. Editorial: Carbon bridge to the Arctic. Front. Mar. Sci. 2020, 7 (2020).
Google Scholar 
Wassmann, P. et al. The contiguous domains of Arctic Ocean advection: Trails of life and death. Prog. Oceanogr. 139, 42–65 (2015).Article 
ADS 

Google Scholar 
Wassmann, P., Slagstad, D. & Ellingsen, I. Advection of mesozooplankton into the northern svalbard shelf region. Front. Mar. Sci. 2019, 6 (2019).
Google Scholar 
Auel, H. Egg size and reproductive adaptations among Arctic deep-sea copepods (Calanoida, Paraeuchaeta). Helgol. Mar. Res. 58, 147–153 (2004).Article 
ADS 

Google Scholar 
Gluchowska, M. et al. Zooplankton in Svalbard fjords on the Atlantic-Arctic boundary. Polar Biol. 39, 1785–1802 (2016).Article 

Google Scholar 
Wang, Y.-G., Tseng, L.-C., Lin, M. & Hwang, J.-S. Vertical and geographic distribution of copepod communities at late summer in the Amerasian Basin. Arctic Ocean. Plos One. 14, e0219319 (2019).Article 
CAS 

Google Scholar 
Gislason, A. & Silva, T. Abundance, composition, and development of zooplankton in the Subarctic Iceland Sea in 2006, 2007, and 2008. ICES J. Mar. Sci. 69, 1263–1276 (2012).Article 

Google Scholar 
Zhukova, N. G., Nesterova, V. N., Prokopchuk, I. P. & Rudneva, G. B. Winter distribution of euphausiids (Euphausiacea) in the Barents Sea (2000–2005). Deep-Sea Res. Part II(56), 1959–1967 (2009).Article 
ADS 

Google Scholar 
Dalpadado, P. & Skjoldal, H. R. Abundance, maturity and growth of the krill species Thysanoessa inermis and T. longicaudata in the Barents Sea. Mar. Ecol. Prog. Ser. 144, 175–183 (1996).Article 
ADS 

Google Scholar 
Koszteyn, J., Timofeev, S., Węsławski, J. M. & Malinga, B. Size structure of Themisto abyssorum Boeck and Themisto libellula (Mandt) populations in European Arctic seas. Polar Biol. 15, 85–92 (1995).Article 

Google Scholar 
Dalpadado, P., Borkner, N., Bogstad, B. & Mehl, S. Distribution of Themisto (Amphipoda) spp. in the Barents Sea and predator-prey interactions. ICES J. Mar. Sci. 58, 876–895 (2001).Article 

Google Scholar 
Macnaughton, M. O., Thormar, J. & Berge, J. Sympagic amphipods in the Arctic pack ice: Redescriptions of Eusirus holmii Hansen, 1887 and Pleusymtes karstensi (Barnard, 1959). Polar Biol. 30, 1013–1025 (2007).Article 

Google Scholar 
Kraft, A., Graeve, M., Janssen, D., Greenacre, M. & Falk-Petersen, S. Arctic pelagic amphipods: Lipid dynamics and life strategy. J. Plankton Res. 37, 790–807 (2015).Article 
CAS 

Google Scholar 
Kreibich, T., Hagen, W. & Saborowski, R. Food utilization of two pelagic crustaceans in the Greenland Sea: Meganyctiphanes norvegica (Euphausiacea) and Hymenodora glacialis (Decapoda, Caridea). Mar. Ecol. Prog. Ser. 413, 105–115 (2010).Article 
ADS 
CAS 

Google Scholar 
Geoffroy, M. et al. Increased occurrence of the jellyfish Periphylla periphylla in the European high Arctic. Polar Biol. 41, 2615–2619 (2018).Article 

Google Scholar 
Grigor, J. J., Søreide, J. E. & Varpe, Ø. Seasonal ecology and life-history strategy of the high-latitude predatory zooplankter Parasagitta elegans. Mar. Ecol. Prog. Ser. 499, 77–88 (2014).Article 
ADS 

Google Scholar 
Maclennan, D. N., Fernandes, P. G. & Dalen, J. A consistent approach to definitions and symbols in fisheries acoustics. ICES J. Mar. Sci. 59, 365–369 (2002).Article 

Google Scholar 
Gjøsæter, H. & Ushakov, N. G. Acoustic estimates of the Barents Sea Arctic cod Stock (Boreogadus saida). Forage Fishes in Marine Ecosystems. Alaska Sea Grant Collage Program, University of Alaska Fairbanks 97:01, 485–504 (1997).Raskoff, K. A., Hopcroft, R. R., Kosobokova, K. N., Purcell, J. E. & Youngbluth, M. Jellies under ice: ROV observations from the Arctic 2005 hidden ocean expedition. Deep-Sea Res. Part II(57), 111–126 (2010).Article 
ADS 

Google Scholar 
Bluhm, B. A. et al. The Pan-Arctic continental slope: Sharp gradients of physical processes affect pelagic and benthic ecosystems. Front. Mar. Sci. 2020, 7 (2020).
Google Scholar 
Hop, H. et al. Pelagic ecosystem characteristics across the atlantic water boundary current from Rijpfjorden, Svalbard, to the Arctic Ocean During Summer (2010–2014). Front. Mar. Sci. 2019, 6 (2019).
Google Scholar 
Mumm, N. Composition and distribution of mesozooplankton in the Nansen Basin, Arctic Ocean, during summer. Polar Biol. 13, 451–461 (1993).Article 

Google Scholar 
Ona, E. & Nielsen, J. Acoustic detection of the Greenland shark (Somniosus microcephalus) using multifrequency split beam echosounder in Svalbard waters. Prog. Oceanogr. 206, 102842 (2022).Article 

Google Scholar 
Gjøsæter, H., Ingvaldsen, R. & Christiansen, J. S. Acoustic scattering layers reveal a faunal connection across the Fram Strait. Prog. Oceanogr. 185, 102348 (2020).Article 

Google Scholar 
Ingvaldsen, R. B., Gjosaeter, H., Ona, E. & Michalsen, K. Atlantic cod (Gadus morhua) feeding over deep water in the high Arctic. Polar Biol. 40, 2105–2111 (2017).Article 

Google Scholar 
Chawarski, J., Klevjer, T. A., Coté, D. & Geoffroy, M. Evidence of temperature control on mesopelagic fish and zooplankton communities at high latitudes. Front. Mar. Sci. 2022, 9 (2022).
Google Scholar 
Chernova, N. V. Catching of Greenland halibut Reinhardtius hippoglossoides (Pleuronectidae) on the shelf edge of the Laptev and East Siberian Seas. J. Ichthyol. 57, 219–227 (2017).Article 

Google Scholar 
Benzik, A. N., Budanova, L. K. & Orlov, A. M. Hard life in cold waters: Size distribution and gonads show that Greenland halibut temporarily inhabit the Siberian Arctic. Water Biol. Secur. 1, 100037 (2022).Article 

Google Scholar 
Olsen, L. M. et al. A red tide in the pack ice of the Arctic Ocean. Sci. Rep. 9, 9536 (2019).Article 
ADS 

Google Scholar 
Assmy, P. et al. Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice. Sci. Rep. 7, 40850 (2017).Article 
ADS 
CAS 

Google Scholar 
Leu, E., Søreide, J. E., Hessen, D. O., Falk-Petersen, S. & Berge, J. Consequences of changing sea-ice cover for primary and secondary producers in the European Arctic shelf seas: Timing, quantity, and quality. Prog. Oceanogr. 90, 18–32 (2011).Article 
ADS 

Google Scholar 
Drivdal, M. et al. Connections to the deep: Deep vertical migrations, an important part of the life cycle of Apherusa glacialis, an arctic ice-associated amphipod. Front. Mar. Sci. 2021, 8 (2021).
Google Scholar 
Scoulding, B., Chu, D., Ona, E. & Fernandes, P. G. Target strengths of two abundant mesopelagic fish species. J. Acoust. Soc. Am. 137, 989–1000 (2015).Article 
ADS 

Google Scholar 
Popova, E. E., Yool, A., Aksenov, Y. & Coward, A. C. Role of advection in Arctic Ocean lower trophic dynamics: A modeling perspective. J. Geophys. Res. 118, 1571–1586 (2013).Article 
ADS 

Google Scholar 
Saunders, R. A., Ingvarsdóttir, A., Rasmussen, J., Hay, S. J. & Brierley, A. S. Regional variation in distribution pattern, population structure and growth rates of Meganyctiphanes norvegica and Thysanoessa longicaudata in the Irminger Sea, North Atlantic. Prog. Oceanogr. 72, 313–342 (2007).Article 
ADS 

Google Scholar 
Tarling, G. A. et al. Can a key boreal Calanus copepod species now complete its life-cycle in the Arctic? Evidence and implications for Arctic food-webs. Ambio 51, 333–344 (2022).Article 

Google Scholar 
Purcell, J. E., Juhl, A. R., Manko, M. K. & Aumack, C. F. Overwintering of gelatinous zooplankton in the coastal Arctic Ocean. Mar. Ecol. Prog. Ser. 591, 281–286 (2018).Article 
ADS 

Google Scholar 
Purcell, J. E., Hopcroft, R. R., Kosobokova, K. N. & Whitledge, T. E. Distribution, abundance, and predation effects of epipelagic ctenophores and jellyfish in the western Arctic Ocean. Deep-Sea Res. Part II(57), 127–135 (2010).Article 
ADS 

Google Scholar 
Solvang, H. K. et al. Distribution of rorquals and Atlantic cod in relation to their prey in the Norwegian high Arctic. Polar Biol. 44, 761–782 (2021).Article 

Google Scholar 
Ingvaldsen, R. B. et al. Physical manifestations and ecological implications of Arctic Atlantification. Nat. Rev. Earth Environ. 2, 874–889 (2021).Article 
ADS 

Google Scholar 
Flores, H. et al. Macrofauna under sea ice and in the open surface layer of the Lazarev Sea, Southern Ocean. Deep-Sea Res. Part II(58), 1948–1961 (2011).Article 
ADS 

Google Scholar 
Godø, O. R., Valdemarsen, J. W. & Engås, A. Comparison of efficiency of standard and experimental juvenile gadoid sampling trawls. ICES Mar. Sci. Symp. 196, 196–201 (1993).
Google Scholar 
Klevjer, T. et al. Micronekton biomass distribution, improved estimates across four north Atlantic basins. Deep-Sea Res. Part II. 180, 104691 (2020).Article 

Google Scholar 
Krafft, B. A. et al. Distribution and demography of Antarctic krill in the Southeast Atlantic sector of the Southern Ocean during the austral summer 2008. Polar Biol. 33, 957–968 (2010).Article 

Google Scholar 
Foote, K. G. Maintaining precision calibrations with optimal copper spheres. J. Acoust. Soc. Am. 73, 1054–1063 (1983).Article 
ADS 

Google Scholar 
Korneliussen, R. J. et al. Acoustic identification of marine species using a feature library. Methods Oceanogr. 17, 187–205 (2016).Article 

Google Scholar 
Lavergne, T. et al. Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records. Cryosphere. 13, 49–78 (2019).Article 
ADS 

Google Scholar 
Firing, E., Ramada, J. & Caldwell, P. Processing ADCP Data with the CODAS Software System Version 3.1. Joint Institute for Marine and Atmospheric Research University of Hawaii. http://currents.soest.hawaii.edu/docs/adcp_doc/index.html (1995).Padman, L. & Erofeeva, S. A barotropic inverse tidal model for the Arctic Ocean. Geophys. Res. Lett. 31, 256 (2004).Article 

Google Scholar  More

Taranu, Z. E. et al. Acceleration of cyanobacterial dominance in north temperate-subarctic lakes during the Anthropocene. Ecol. Lett. 18, 375–384 (2015).Article 

Google Scholar 
Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).Article 
CAS 

Google Scholar 
Monchamp, M. E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324 (2018).Article 

Google Scholar 
Chorus, I. & Bartram, J. Toxic Cyanobacteria in Water. A Guide to Their Public Health Consequences, Monitoring, and Management. In: World Health Organization (eds. Chorus I. & Bertram J.) (CRC Press, 1999).Rabalais, N. N. et al. Dynamics and distribution of natural and human-caused hypoxia. Biogeosciences 7, 585–619 (2010).Article 
CAS 

Google Scholar 
Carmichael, W. W. Health effects of toxin-producing cyanobacteria: “The CyanoHABs”. Hum. Ecol. Risk Assess. Int. J. 7, 1393–1407 (2001).Article 

Google Scholar 
Whitton, B. A. Ecology of Cyanobacteria II: Their Diversity in Space and Time (Springer, 2012).Smol, J. P., Birks, H. J. B. & Last, W. M. Tracking Environmental Change Using Lake Sediments. Volume 4: Zoological Indicators, Developments in Paleoenvironmental Research. (Springer, 2002).Domaizon, I., Winegardner, A., Capo, E., Gauthier, J. & Gregory-Eaves, I. DNA-based methods in paleolimnology: new opportunities for investigating long-term dynamics of lacustrine biodiversity. J. Paleolimnol. 52, 1–21 (2017).Article 

Google Scholar 
Livingstone, D. & Jaworski, G. H. M. The viability of akinetes of blue-green algae recovered from the sediments of rostherne mere. Br. Phycol. J. 15, 357–364 (1980).Article 

Google Scholar 
van Geel, B., Mur, L. R., Ralska-Jasiewiczowa, M. & Goslar, T. Fossil akinetes of Aphanizomenon and Anabaena as indicators for medieval phosphate-eutrophication of Lake Gosciaz (Central Poland). Rev. Palaeobot. Palynol. 83, 97–105 (1994).Article 

Google Scholar 
Hillbrand, M., van Geel, B., Hasenfratz, A., Hadorn, P. & Haas, J. N. Non-pollen palynomorphs show human- and livestock-induced eutrophication of Lake Nussbaumersee (Thurgau, Switzerland) since Neolithic times (3840 bc). Holocene 24, 559–568 (2014).Article 

Google Scholar 
Gosling, W. D. et al. Human occupation and ecosystem change on Upolu (Samoa) during the Holocene. J. Biogeogr. 47, 600–614 (2020).Article 

Google Scholar 
Hertzberg, S., Liaaen-Jensen, S. & Siegelman, H. W. The carotenoids of blue-green algae. Phytochemistry 10, 3121–3127 (1971).Article 
CAS 

Google Scholar 
Leavitt, P. R. & Findlay, D. L. Comparison of fossil pigments with 20 years of phytoplankton data from eutrophic Lake 227, Experimental Lakes Area, Ontario. Can. J. Fish. Aquat. Sci. 51, 2286–2299 (1994).Article 
CAS 

Google Scholar 
Kaiser, J., Ön, B., Arz, H. & Akçer-Ön, S. Sedimentary lipid biomarkers in the magnesium-rich and highly alkaline Lake Salda (south-western Anatolia). J. Limnol. 75, 581–596 (2016).
Google Scholar 
Bauersachs, T., Talbot, H. M., Sidgwick, F., Sivonen, K. & Schwark, L. Lipid biomarker signatures as tracers for harmful cyanobacterial blooms in the Baltic Sea. PLoS ONE 12, e0186360 (2017).Article 

Google Scholar 
Domaizon, I. et al. DNA from lake sediments reveals the long-term dynamics and diversity of Synechococcus assemblages. Biogeosci. Discuss. 10, 2515–2564 (2013).
Google Scholar 
Britton, G., Liaaen-Jensen, S. & Pfander, H. in Carotenoids (eds. Britton, G., Liaaen-Jensen, S., Pfander, H.). Vol. 4, 1–6 (Birkhäuser Press, 2008).Capo, E. et al. Lake sedimentary dna research on past terrestrial and aquatic biodiversity: overview and recommendations. Quaternary 4, 6 (2021).Article 

Google Scholar 
Monchamp, M. E., Walser, J. C., Pomati, F. & Spaak, P. Sedimentary DNA reveals cyanobacterial community diversity over 200 years in two perialpine lakes. Appl. Environ. Microbiol. 82, 6472–6482 (2016).Article 
CAS 

Google Scholar 
Nwosu, E. C. et al. Evaluating sedimentary DNA for tracing changes in cyanobacteria dynamics from sediments spanning the last 350 years of Lake Tiefer See, NE Germany. J. Paleolimnol. 66, 279–296 (2021).Article 

Google Scholar 
Zhang, J. et al. Pre-industrial cyanobacterial dominance in Lake Moon (NE China) revealed by sedimentary ancient DNA. Quat. Sci. Rev. 261, 106966 (2021).Article 

Google Scholar 
Brauer, A., Schwab, M. J., Brademann, B., Pinkerneil, S. & Theuerkauf, M. Tiefer See–a key site for lake sediment research in NE Germany. DEUQUA Spec. Publ. 2, 89–93 (2019).Article 

Google Scholar 
Dräger, N. et al. Varve microfacies and varve preservation record of climate change and human impact for the last 6000 years at Lake Tiefer See (NE Germany). Holocene 27, 450–464 (2017).Article 

Google Scholar 
Dräger, N. et al. Hypolimnetic oxygen conditions influence varve preservation and δ13C of sediment organic matter in Lake Tiefer See, NE Germany. J. Paleolimnol. 62, 181–194 (2019).Article 

Google Scholar 
Theuerkauf, M., Dräger, N., Kienel, U., Kuparinen, A. & Brauer, A. Effects of changes in land management practices on pollen productivity of open vegetation during the last century derived from varved lake sediments. Holocene 25, 733–744 (2015).Article 

Google Scholar 
Heinrich, I. et al. Interdisciplinary geo-ecological research across time scales in the Northeast German Lowland Observatory (TERENO-NE). Vadose Zone J. 17, 1–25 (2018).Article 

Google Scholar 
Roeser, P. et al. Advances in understanding calcite varve formation: new insights from a dual lake monitoring approach in the southern Baltic lowlands. Boreas 50, 419–440 (2021).Article 

Google Scholar 
Nwosu, E. C. et al. From water into sediment—tracing freshwater Cyanobacteria via DNA analyses. Microorganisms 9, 1778 (2021).Article 
CAS 

Google Scholar 
Schmidt, J. -P. Ein Fremdling im Nordischen Kreis Jungbronzezeitliche Funde aus dem Flachen See bei Sophienhof, Lkr. Mecklenburgische Seenplatte. In: D. Brandherm/B. Nessel (Hrsg.), Phasenübergänge und Umbrüche im bronzezeitlichen Europa. Beiträge zur Sitzung der Arbeitsgemeinschaft Bronzezeit auf der 80. Jahrestagung des Nordwestdeutschen Verbandes für Altertumskunde. Vol. 297, 271–281. (Universitätsforschungen zur Prähistorischen Archäologie, 2017).Raese, H. & Schmidt, J. -P. Zur Besiedlung Mecklenburg-Vorpommernswährend des Spätneolithikums und der frühenBronzezeit (2500–1500 v. Chr.). In: Siedlungsarchäologie des Endneolithikums und der frühen Bronzezeit. 11. Mitteldeutscher Archäologentag (eds. Meller, H., Friedderich, S., Küßner, M., Stäuble, H. & Risch, R.) 621–634 (2019).Kienel, U., Dulski, P., Ott, F., Lorenz, S. & Brauer, A. Recently induced anoxia leading to the preservation of seasonal laminae in two NE-German lakes. J. Paleolimnol. 50, 535–544 (2013).Article 

Google Scholar 
Callieri, C. & Stockner, J. Picocyanobacteria success in oligotrophic lakes: fact or fiction? J. Limnol. 59, 72–76 (2000).Article 

Google Scholar 
Sollai, M. et al. The Holocene sedimentary record of cyanobacterial glycolipids in the Baltic Sea: an evaluation of their application as tracers of past nitrogen fixation. Biogeosciences 14, 5789–5804 (2017).Article 

Google Scholar 
Mur, L. R., Skulberg, O. M. & Utkilen, H. In: Toxic Cyanobacteria in Water: A Guide to Their Public Health Consequences, Monitoring, and Management. (eds. Chorus, I. and Bartram, J.) 15–40 (St Edmundsbury Press, 1999).Schmidt, J.-P. Ein bronzenes Hallstattschwert der Periode VI aus dem Flachen See bei Sophienhof, Lkr. Mecklenburgische Seenplatte. Arch.äologische Ber. aus Mecklenbg.-Vorpommern 26, 26–34 (2019).
Google Scholar 
Schmidt, J.-P. “Aller guten Dinge sind drei!”–Ein weiteres bronzezeitliches Schwert aus dem Flachen See bei Lütgendorf, Lkr. Mecklenburgische Seenplatte. Arch.äologische Ber. aus Mecklenbg.-Vorpommern 27, 49–55 (2020).
Google Scholar 
Küster, M., Stöckmann, M., Fülling, A. & Weber, R. Kulturlandschaftselemente, Kolluvien und Flugsande als Archive der spätholozänen Landschaftsentwicklung im Bereich des Messtischblattes Thurow (Müritz-Nationalpark, Mecklenburg). In: Neue Beiträge zum Naturraum und zur Landschaftsgeschichte im Teilgebiet. (Geozon Science Media, 2015).Feeser, I., Dörfler, W., Kneisel, J., Hinz, M. & Dreibrodt, S. Human impact and population dynamics in the Neolithic and Bronze Age: Multi-proxy evidence from north-western Central Europe. Holocene 29, 1596–1606 (2019).Article 

Google Scholar 
Alsleben, A. In How’s Life? Living Conditions in the 2nd and 1st Millennia BCE. Scales of Transformation in Prehistoric and Archaic Societies (eds. Dal Corso, M. et al.) 85–102 (Sidestone Press, 2019).Kneisel, J., Bork, H.-R. & Czebreszuk, J. In Defensive Structures from Central Europe to the Aegean in the 3rd and 2nd Millennia bc (eds. Czebreszuk, J., Kadrow, S. & Müller, J.) 155–170 (Habelt, 2008).Haas, J. N. & Wahlmüller, N. Floren-, Vegetations- und Milieuveränderungen im Zuge der bronzezeitlichen Besiedlung von Bruszczewo (Polen) und der landwirtschaftlichen Nutzung der umliegenden Gebiete. In: Ausgrabungen und Forschungen in einer prähistorischen Siedlungskammer Großpolens. (eds. Müller, J., Czebreszuk, J. & Kneisel, J.) Studien zur Archäologie in Ostmitteleuropa Vol. 6.1, 50–81 (Bonn, 2010).Theuerkauf, M. et al. Holocene lake-level evolution of Lake Tiefer See, NE Germany, caused by climate and land cover changes. Boreas 51, 299–316 (2021).Article 

Google Scholar 
Büntgen, U. et al. 2500 years of European climate variability and human susceptibility. Science 331, 578–582 (2011).Article 

Google Scholar 
Büntgen, U. et al. Cooling and societal change during the Late Antique Little Ice Age from 536 to around 660 AD. Nat. Geosci. 9, 231–236 (2016).Article 

Google Scholar 
Kienel, U. et al. Effects of spring warming and mixing duration on diatom deposition in deep Tiefer See, NE Germany. J. Paleolimnol. 57, 37–49 (2017).Article 

Google Scholar 
Monchamp, M. E., Spaak, P. & Pomati, F. High dispersal levels and lake warming are emergent drivers of cyanobacterial community assembly in peri-Alpine lakes. Sci. Rep. 9, 7366 (2019).Article 

Google Scholar 
Erratt, K. et al. Paleolimnological evidence reveals climate-related preeminence of cyanobacteria in a temperate meromictic lake. Can. J. Fish. Aquat. Sci. 79, 558–565 (2021).Article 

Google Scholar 
Schmidt, J.-P. ders., Kein Ende in Sicht? Neue Untersuchungen auf dem Feuerstellenplatz von Naschendorf, Lkr. Nordwestmecklenburg. Arch.äologische Ber. aus Mecklenbg.-Vorpommern 19, 26–46 (2012).
Google Scholar 
Marcott, S. A., Shakun, J. D., Clark, P. U. & Mix, A. C. A reconstruction of regional and global temperature for the past 11,300 years. Science 339, 1198–1201 (2013).Article 
CAS 

Google Scholar 
Wanner, H. et al. Holocene climate variability and change; a data-based review. J. Geol. Soc. Lond. 172, 254–263 (2015).Article 

Google Scholar 
Rigosi, A., Carey, C. C., Ibelings, B. W. & Brookes, J. D. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnol. Oceanogr. 59, 99–114 (2014).Article 

Google Scholar 
Dittmann, E., Fewer, D. P. & Neilan, B. A. Cyanobacterial toxins: Biosynthetic routes and evolutionary roots. FEMS Microbiol. Rev. 37, 23–43 (2013).Article 
CAS 

Google Scholar 
Dolman, A. M. et al. Cyanobacteria and cyanotoxins: the influence of nitrogen versus phosphorus. PLoS ONE 7, e38757 (2012).Article 
CAS 

Google Scholar 
Kurmayer, R., Christiansen, G., Fastner, J. & Börner, T. Abundance of active and inactive microcystin genotypes in populations of the toxic cyanobacterium Planktothrix spp. Environ. Microbiol. 6, 831–841 (2004).Article 
CAS 

Google Scholar 
Liu, A., Zhu, T., Lu, X. & Song, L. Hydrocarbon profiles and phylogenetic analyses of diversified cyanobacterial species. Appl. Energy 11, 383–393 (2013).Article 

Google Scholar 
Coates, R. C. et al. Characterization of cyanobacterial hydrocarbon composition and distribution of biosynthetic pathways. PLoS ONE 9, e85140 (2014).Article 

Google Scholar 
Marciniak, S. et al. Ancient human genomics: the methodology behind reconstructing evolutionary pathways. J. Hum. Evol. 79, 21–34 (2015).Article 

Google Scholar 
Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. MapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. in. Bioinformatics 29, 1682–1684 (2013).Article 

Google Scholar 
Borry, M., Hübner, A., Rohrlach, A. B. & Warinner, C. PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly. PeerJ 9, e11845 (2021).Article 

Google Scholar 
Murchie, T. J. et al. Optimizing extraction and targeted capture of ancient environmental DNA for reconstructing past environments using the PalaeoChip Arctic-1.0 bait-set. Quat. Res. (U. S.) 99, 305–328 (2021).Article 
CAS 

Google Scholar 
Armbrecht, L., Hallegraeff, G., Bolch, C. J. S., Woodward, C. & Cooper, A. Hybridisation capture allows DNA damage analysis of ancient marine eukaryotes. Sci. Rep. 11, 3220 (2021).Article 
CAS 

Google Scholar 
Wulf, S. et al. Holocene tephrostratigraphy of varved sediment records from Lakes Tiefer See (NE Germany) and Czechowskie (N Poland). Quat. Sci. Rev. 132, 1–14 (2016).Article 

Google Scholar 
Sugita, S. Theory of quantitative reconstruction of vegetation I: Pollen from large sites REVEALS regional vegetation composition. Holocene 17, 2 (2007).Article 

Google Scholar 
Epp, L. S., Zimmermann, H. H. & Stoof-Leichsenring, K. R. In: Ancient DNA. Methods in Molecular Biology (eds. Shapiro B., Barlow A., Heintzman P., Hofreiter M., Paijmans J., Soares A.) Vol. 1963, 31–44 (Humana Press, 2019).Janse, I., Meima, M., Kardinaal, W. E. A. & Zwart, G. High-resolution differentiation of Cyanobacteria by using rRNA-internal transcribed spacer denaturing gradient gel electrophoresis. Appl. Environ. Microbiol. 69, 6634–6643 (2003).Article 
CAS 

Google Scholar 
Nwosu, E. C. et al. Species-level spatio-temporal dynamics of cyanobacteria in a hard-water temperate lake in the Southern Baltics. Front. Microbiol. 12, https://doi.org/10.3389/fmicb.2021.761259 (2021).Savichtcheva, O. et al. Quantitative PCR enumeration of total/toxic Planktothrix rubescens and total cyanobacteria in preserved DNA isolated from lake sediments. Appl. Environ. Microbiol. 77, 8744–8753 (2011).Article 
CAS 

Google Scholar 
Coolen, M. J. L. et al. Ancient DNA derived from alkenone-biosynthesizing haptophytes and other algae in Holocene sediments from the Black Sea. Paleoceanography 21, PA1005 (2006).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina 7 amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Kieser, S., Brown, J., Zdobnov, E. M., Trajkovski, M. & McCue, L. A. ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinformat. 21, 257 (2020).Article 

Google Scholar 
Yilmaz, P. et al. The SILVA and ‘all-species Living Tree Project (LTP)’ taxonomic frameworks. Nucleic Acids Res. 42, 643–648 (2014).Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).Article 
CAS 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformat. 11, 119–119 (2010).Article 

Google Scholar 
Huerta-Cepas, J. et al. EggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).Article 
CAS 

Google Scholar 
Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msab293 (2021).Shen, W. & Ren, H. TaxonKit: a practical and efficient NCBI taxonomy toolkit. J. Genet. Genomics. 48, 844–850 (2021).Article 

Google Scholar 
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 29, 471–482 (2001).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R Package Version 2.5-2. Cran R (2019).Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).CAS 

Google Scholar 
Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).Article 

Google Scholar  More

Guillou, L. et al. Widespread occurrence and genetic diversity of marine parasitoids belonging to Syndiniales (Alveolata). Environ. Microbiol. 10, 3349–3365 (2008).Article 
CAS 

Google Scholar 
Massana, R. Eukaryotic picoplankton in surface oceans. Annu. Rev. Microbiol. 65, 91–110 (2011).Article 
CAS 

Google Scholar 
Rocke, E., Pachiadaki, M. G., Cobban, A., Kujawinski, E. B. & Edgcomb, V. P. Protist community grazing on prokaryotic prey in deep ocean water masses. PLoS ONE 10, e0124505 (2015).Article 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).Article 

Google Scholar 
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097 (2019).Article 
CAS 

Google Scholar 
Cordier, T. et al. Patterns of eukaryotic diversity from the surface to the deep-ocean sediment. Sci. Adv. 8, https://doi.org/10.1126/sciadv.abj9309 (2022).Giner, C. R. et al. Marked changes in diversity and relative activity of picoeukaryotes with depth in the world ocean. ISME J. 14, 437–449 (2020).Article 

Google Scholar 
Obiol, A. et al. A metagenomic assessment of microbial eukaryotic diversity in the global ocean. Mol. Ecol. Resour. 20, 718–731 (2020).Article 
CAS 

Google Scholar 
Pernice, M. C. et al. Large variability of bathypelagic microbial eukaryotic communities across the world’s oceans. ISME J. 10, 945–958 (2016).Article 

Google Scholar 
Santoferrara, L. et al. Perspectives from ten years of protist studies by high‐throughput metabarcoding. J. Eukaryot. Microbiol. 67, 612–622 (2020).Article 

Google Scholar 
Schoenle, A. et al. High and specific diversity of protists in the deep-sea basins dominated by diplonemids, kinetoplastids, ciliates and foraminiferans. Commun. Biol. 4, 1–10 (2021).Article 

Google Scholar 
Sommeria-Klein, G. et al. Global drivers of eukaryotic plankton biogeography in the sunlit ocean. Science 374, 594–599 (2021).Article 
CAS 

Google Scholar 
Tremblay, J. É. et al. Global and regional drivers of nutrient supply, primary production and CO2 drawdown in the changing Arctic Ocean. Prog. Oceanogr. 139, 171–196 (2015).Article 

Google Scholar 
Zoccarato, L., Pallavicini, A., Cerino, F., Umani, S. F. & Celussi, M. Water mass dynamics shape Ross Sea protist communities in mesopelagic and bathypelagic layers. Prog. Oceanogr. 149, 16–26 (2016).Article 

Google Scholar 
Biggs, T. E. G., Huisman, J. & Brussaard, C. P. D. Viral lysis modifies seasonal phytoplankton dynamics and carbon flow in the Southern Ocean. ISME J. 15, 3615–3622 (2021).Article 
CAS 

Google Scholar 
Clarke, L. J., Bestley, S., Bissett, A. & Deagle, B. E. A globally distributed Syndiniales parasite dominates the Southern Ocean micro-eukaryote community near the sea-ice edge. ISME J. 13, 734–737 (2019).Article 
CAS 

Google Scholar 
Gast, R. J., Fay, S. A. & Sanders, R. W. Mixotrophic activity and diversity of Antarctic marine protists in austral summer. Front. Mar. Sci. 5, 13 (2018).Article 

Google Scholar 
Grattepanche, J. D., Jeffrey, W. H., Gast, R. J. & Sanders, R. W. Diversity of microbial eukaryotes along the West Antarctic Peninsula in austral spring. Front. Microbiol. 13, 844856 (2022).Article 

Google Scholar 
Hamilton, M. et al. Spatiotemporal variations in Antarctic protistan communities highlight phytoplankton diversity and seasonal dominance by a novel cryptophyte lineage. mBio 12, e0297321 (2021).Article 

Google Scholar 
Lin, Y. et al. Decline in plankton diversity and carbon flux with reduced sea ice extent along the Western Antarctic Peninsula. Nat. Commun. 12, 4948 (2021).Article 
CAS 

Google Scholar 
Martin, K. et al. The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole. Nat. Commun. 12, 5483 (2021).Article 
CAS 

Google Scholar 
Vernet, M. et al. The Weddell Gyre, Southern Ocean: present knowledge and future challenges. Rev. Geophysics 57, 623–708 (2019).Article 

Google Scholar 
Callahan, J. E. The structure and circulation of deep water in the Antarctic. Deep‐Sea Res. 19, 563–575 (1972).
Google Scholar 
Janout, M. A. et al. FRIS revisited in 2018: on the circulation and water masses at the Filchner and Ronne ice shelves in the southern Weddell Sea. J. Geophys. Res.: Oceans 126, e2021JC017269 (2021).Article 

Google Scholar 
Orsi, A. H., Smethie, W. M. & Bullister, J. L. On the total input of Antarctic waters to the deep ocean: a preliminary estimate from chlorofluorocarbon measurements. J. Geophys. Res. 107, 3122 (2002).Article 

Google Scholar 
Hoppema, M., Fahrbach, E. & Schröder, M. On the total carbon dioxide and oxygen signature of the circumpolar deep water in the Weddell Gyre. Oceanol. Acta 20, 783–798 (1997).CAS 

Google Scholar 
Karstensen, J. & Tomczak, M. Age determination of mixed water masses using CFC and oxygen data. J. Geophys. Res. 103, 18599–18609 (1998).Article 
CAS 

Google Scholar 
De Cáceres, M. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology 90, 3566–3574 (2009).Article 

Google Scholar 
De Cáceres, M., Legendre, P. & Moretti, M. Improving indicator species analysis by combining groups of sites. Oikos 119, 1674–1684 (2010).Article 

Google Scholar 
Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 
Agogué, H., Lamy, D., Neal, P. R., Sogin, M. L. & Herndl, G. J. Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol. Ecol. 20, 258–274 (2011).Article 

Google Scholar 
Celussi, M., Bergamasco, A., Cataletto, B., Umani, S. F. & Del Negro, P. Water masses bacterial community structure and microbial activities in the Ross Sea, Antarctica. Antarct. Sci. 22, 361–370 (2010).Article 

Google Scholar 
Galand, P. E., Potvin, M., Casamayor, E. O. & Lovejoy, C. Hydrography shapes bacterial biogeography of the deep Arctic Ocean. ISME J. 4, 564–576 (2010).Article 

Google Scholar 
Hamdan, L. J. Ocean currents shape the microbiome of Arctic marine sediments. ISME J. 7, 685–696 (2013).Article 
CAS 

Google Scholar 
Wilkins, D., van Sebille, E., Rintoul, S. R., Lauro, F. M. & Cavicchioli, R. Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects. Nat. Commun. 4, 2457 (2013).Article 

Google Scholar 
Flegontova, O. et al. Extreme diversity of diplonemid eukaryotes in the ocean. Curr. Biol. 26, 3060–3065 (2016).Article 
CAS 

Google Scholar 
Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, 1819–1827 (2014).Article 
CAS 

Google Scholar 
Jeong, H. J. et al. Growth, feeding and ecological roles of the mixotrophic and heterotrophic dinoflagellates in marine planktonic food webs. Ocean Sci. 45, 65–91 (2010).Article 
CAS 

Google Scholar 
Stoecker, D. K., Hansen, P. J., Caron, D. A. & Mitra, A. Mixotrophy in the marine Plankton. Ann. Rev. Mar. Sci. 9, 311–335 (2016).Article 

Google Scholar 
Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proc. Natl Acad. Sci. USA 116, 11824–11832 (2019).Article 
CAS 

Google Scholar 
Gutierrez-Rodriguez, A. et al. High contribution of Rhizaria (Radiolaria) to vertical export in the California Current Ecosystem revealed by DNA metabarcoding. ISME J. 13, 964–976 (2019).Article 
CAS 

Google Scholar 
Lampitt, R. S., Salter, I. & Johns, D. Radiolaria: major exporters of organic carbon to the deep ocean. Glob. Biogeochem. Cycles 23, GB1010 (2009).Article 

Google Scholar 
Suzuki, N. & Not, F. In Marine Protists: Diversity and Dynamics 179–222 (Springer Japan, 2015).Decelle, J. et al. Diversity, ecology and biogeochemistry of cyst-forming Acantharia (Radiolaria) in the oceans. PLoS ONE 8, e53598 (2013).Article 
CAS 

Google Scholar 
Tashyreva, D. et al. Diplonemids—a review on “new“ flagellates on the oceanic block. Protist 173, 125868 (2022).Article 
CAS 

Google Scholar 
Flegontova, O. et al. Environmental determinants of the distribution of planktonic diplonemids and kinetoplastids in the oceans. Environ. Microbiol 22, 4014–4031 (2020).Article 
CAS 

Google Scholar 
Xu, D. et al. Microbial eukaryote diversity and activity in the water column of the South China sea based on DNA and RNA high throughput sequencing. Front. Microbiol. 8, 1121 (2017).Article 

Google Scholar 
Bråte, J. et al. Radiolaria associated with large diversity of marine alveolates. Protist 163, 767–777 (2012).Article 

Google Scholar 
Strassert, J. F. H. et al. Single cell genomics of uncultured marine alveolates shows paraphyly of basal dinoflagellates. ISME J. 12, 304–308 (2017).Article 

Google Scholar 
Yabuki, A. & Tame, A. Phylogeny and reclassification of Hemistasia phaeocysticola (Scherffel) Elbrächter & Schnepf, 1996. J. Eukaryot. Microbiol. 62, 426–429 (2015).Article 

Google Scholar 
Larsen, J. & Patterson, J. Some flagellates (Protista) from tropical marine sediments. J. Nat. Hist. 24, 801–937 (1990).Article 

Google Scholar 
Prokopchuk, G. et al. Trophic flexibility of marine diplonemids – switching from osmotrophy to bacterivory. ISME J. 16, 1409–1419 (2022).Article 
CAS 

Google Scholar 
Arístegui, J. & Gasol, J. Microbial oceanography of the dark ocean’s pelagic realm. Limnol. Oceanogr. 54, 1501–1529 (2009).Article 

Google Scholar 
Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS ONE 4, e6372 (2009).Article 

Google Scholar 
Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm v2: highly-scalable and high-resolution amplicon clustering. PeerJ 3, e1420 (2015).Article 

Google Scholar 
Kolisko, M. et al. EukRef-excavates: seven curated SSU ribosomal RNA gene databases. Database 2020, baaa080 (2020).
Google Scholar 
Adl, S. M. et al. Revisions to the classification, nomenclature, and diversity of eukaryotes. J. Eukaryot. Microbiol. 66, 4–119 (2019).Article 

Google Scholar 
Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083 (2019).Article 
CAS 

Google Scholar  More

Chapman, A. It’s okay to call them drones. J. Unmanned Veh. Syst. 2, iii–v (2014).Article 

Google Scholar 
Chabot, D., Hodgson, A. J., Hodgson, J. C. & Anderson, K. ‘Drone’: Technically correct, popularly accepted, socially acceptable. Drone Syst. Appl. 10, 399–405 (2022).Article 

Google Scholar 
Chabot, D. & Bird, D. M. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?. J. Unmanned Veh. Syst. 3, 137–155 (2015).Article 

Google Scholar 
Christie, K. S., Gilbert, S. L., Brown, C. L., Hatfield, M. & Hanson, L. Unmanned aircraft systems in wildlife research: Current and future applications of a transformative technology. Front. Ecol. Environ. 14, 241–251 (2016).Article 

Google Scholar 
Whitehead, K. & Hugenholtz, C. H. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: A review of progress and challenges. J. Unmanned Veh. Syst. 2, 69–85 (2014).Article 

Google Scholar 
Barnas, A. et al. Evaluating behavioral responses of nesting lesser snow geese to unmanned aircraft surveys. Ecol. Evol. 8, 1328–1338 (2018).Article 

Google Scholar 
Mulero-Pázmány, M. et al. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PLoS ONE 12, e0178448 (2017).Article 

Google Scholar 
Linchant, J., Lisein, J., Semeki, J., Lejeune, P. & Vermeulen, C. Are unmanned aircraft systems (UAS s) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Rev. 45, 239–252 (2015).Article 

Google Scholar 
Whitehead, K. et al. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: Scientific and commercial applications. J. Unmanned Veh. Syst. 2, 86–102 (2014).Article 

Google Scholar 
Barasona, J. A. et al. Unmanned aircraft systems for studying spatial abundance of ungulates: Relevance to spatial epidemiology. PLoS ONE 9, e115608 (2014).Article 
ADS 

Google Scholar 
Chrétien, L. P., Théau, J. & Ménard, P. Wildlife multispecies remote sensing using visible and thermal infrared imagery acquired from an unmanned aerial vehicle (UAV). Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40, 241 (2015).Article 

Google Scholar 
Guo, X. et al. Application of UAV remote sensing for a population census of large wild herbivores—Taking the headwater region of the yellow river as an example. Remote Sens. 10, 1041 (2018).Article 
ADS 

Google Scholar 
Hu, J., Wu, X. & Dai, M. Estimating the population size of migrating Tibetan antelopes Pantholops hodgsonii with unmanned aerial vehicles. Oryx 54, 101–109 (2020).Article 

Google Scholar 
Mulero-Pázmány, M., Stolper, R., Van Essen, L. D., Negro, J. J. & Sassen, T. Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS ONE 9, e83873 (2014).Article 
ADS 

Google Scholar 
Rey, N., Volpi, M., Joost, S. & Tuia, D. Detecting animals in African Savanna with UAVs and the crowds. Remote Sens. Environ. 200, 341–351 (2017).Article 
ADS 

Google Scholar 
Schroeder, N. M., Panebianco, A., Gonzalez Musso, R. & Carmanchahi, P. An experimental approach to evaluate the potential of drones in terrestrial mammal research: A gregarious ungulate as a study model. R. Soc. Open Sci. 7, 191482 (2020).Article 
ADS 

Google Scholar 
Su, X. et al. Using an unmanned aerial vehicle (UAV) to study wild yak in the highest desert in the world. Int. J. Remote Sens. 39, 5490–5503 (2018).Article 

Google Scholar 
Vermeulen, C., Lejeune, P., Lisein, J., Sawadogo, P. & Bouché, P. Unmanned aerial survey of elephants. PLoS ONE 8, e54700 (2013).Article 
ADS 
CAS 

Google Scholar 
Mallory, M. L. et al. Financial costs of conducting science in the Arctic: Examples from seabird research. Arct. Sci. 4, 624–633 (2018).Article 

Google Scholar 
Sasse, D. B. Job-related mortality of wildlife workers in the United States, 1937–2000. Wildl. Soc. Bull. 31, 1015–1020 (2003).
Google Scholar 
Loarie, S. R., Joppa, L. N. & Pimm, S. L. Satellites miss environmental priorities. Trends Ecol. Evol. 22, 630–632 (2007).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. IUCN Red List of Threatened Species https://www.iucnredlist.org/en (2021).Mech, L. D. & Barber, S. M. A critique of wildlife radio-tracking and its use in National Parks: a report to the National Park Service. (2002).Patterson, C., Koski, W., Pace, P., McLuckie, B. & Bird, D. M. Evaluation of an unmanned aircraft system for detecting surrogate caribou targets in Labrador. J. Unmanned Veh. Syst. 4, 53–69 (2015).Article 

Google Scholar 
Hodgson, J. C. et al. Drones count wildlife more accurately and precisely than humans. Methods Ecol. Evol. 9, 1160–1167 (2018).Article 

Google Scholar 
Seymour, A. C., Dale, J., Hammill, M., Halpin, P. N. & Johnston, D. W. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery. Sci. Rep. 7, 1–10 (2017).Article 

Google Scholar 
COSEWIC. COSEWIC assessment and status report on the caribou (Rangifer tarandus) eastern migratory population, Torngat mountain population in Canada. (COSEWIC, Committee on the Status of Endangered Wildlife in Canada, 2017).Albawi, S., Mohammed, T. A. & Al-Zawi, S. Understanding of a convolutional neural network. in 2017 international conference on engineering and technology (ICET) 1–6 (IEEE, 2017).Gu, J. et al. Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018).Article 
ADS 

Google Scholar 
Teuwen, J. & Moriakov, N. Convolutional neural networks. in Handbook of medical image computing and computer assisted intervention 481–501 (Elsevier, 2020).Corcoran, E., Winsen, M., Sudholz, A. & Hamilton, G. Automated detection of wildlife using drones: Synthesis, opportunities and constraints. Methods Ecol. Evol. 12, 1103–1114 (2021).Article 

Google Scholar 
Corcoran, E., Denman, S., Hanger, J., Wilson, B. & Hamilton, G. Automated detection of koalas using low-level aerial surveillance and machine learning. Sci. Rep. 9, 3208 (2019).Article 
ADS 

Google Scholar 
Gray, P. C. et al. Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry. Methods Ecol. Evol. 10, 1490–1500 (2019).Article 

Google Scholar 
Gray, P. C. et al. A convolutional neural network for detecting sea turtles in drone imagery. Methods Ecol. Evol. 10, 345–355 (2019).Article 

Google Scholar 
Peng, J. et al. Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau. ISPRS J. Photogramm. Remote Sens. 169, 364–376 (2020).Article 
ADS 

Google Scholar 
Borowicz, A. et al. Multi-modal survey of Adélie penguin mega-colonies reveals the Danger Islands as a seabird hotspot. Sci. Rep. 8, 3926 (2018).Article 
ADS 

Google Scholar 
Francis, R. J., Lyons, M. B., Kingsford, R. T. & Brandis, K. J. Counting mixed breeding aggregations of animal species using drones: Lessons from waterbirds on semi-automation. Remote Sens. 12, 1185 (2020).Article 
ADS 

Google Scholar 
Santangeli, A. et al. Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land. Sci. Rep. 10, 1–8 (2020).Article 

Google Scholar 
Bowley, C., Mattingly, M., Barnas, A., Ellis-Felege, S. & Desell, T. An analysis of altitude, citizen science and a convolutional neural network feedback loop on object detection in unmanned aerial systems. J. Comput. Sci. 34, 102–116 (2019).Article 

Google Scholar 
Bowley, C., Mattingly, M., Barnas, A., Ellis-Felege, S. & Desell, T. Detecting wildlife in unmanned aerial systems imagery using convolutional neural networks trained with an automated feedback loop. in International Conference on Computational Science 69–82 (Springer, 2018).Delplanque, A., Foucher, S., Lejeune, P., Linchant, J. & Théau, J. Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks. Remote Sens. Ecol. Conserv. 8, 166–179 (2021).Article 

Google Scholar 
Eikelboom, J. A. J. et al. Improving the precision and accuracy of animal population estimates with aerial image object detection. Methods Ecol. Evol. 10, 1875–1887 (2019).Article 

Google Scholar 
Kellenberger, B., Marcos, D. & Tuia, D. Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning. Remote Sens. Environ. 216, 139–153 (2018).Article 
ADS 

Google Scholar 
Hooge, I. T. C., Niehorster, D. C., Nyström, M., Andersson, R. & Hessels, R. S. Is human classification by experienced untrained observers a gold standard in fixation detection?. Behav. Res. Methods 50, 1864–1881 (2018).Article 

Google Scholar 
Barnas, A. F., Darby, B. J., Vandeberg, G. S., Rockwell, R. F. & Ellis-Felege, S. N. A comparison of drone imagery and ground-based methods for estimating the extent of habitat destruction by lesser snow geese (Anser caerulescens caerulescens) in La Pérouse Bay. PLoS ONE 14, e0217049 (2019).Article 
CAS 

Google Scholar 
Brook, R. K. & Kenkel, N. C. A multivariate approach to vegetation mapping of Manitoba’s Hudson Bay Lowlands. Int. J. Remote Sens. 23, 4761–4776 (2002).Article 

Google Scholar 
Shilts, W. W., Aylsworth, J. M., Kaszycki, C. A., Klassen, R. A. & Graf, W. L. Canadian shield. in Geomorphic Systems of North America vol. 2 119–161 (Geological Society of America Boulder, Colorado, 1987).Barnas, A. F., Felege, C. J., Rockwell, R. F. & Ellis-Felege, S. N. A pilot (less) study on the use of an unmanned aircraft system for studying polar bears (Ursus maritimus). Polar Biol. 41, 1055–1062 (2018).Article 

Google Scholar 
Ellis-Felege, S. N. et al. Nesting common eiders (Somateria mollissima) show little behavioral response to fixed-wing drone surveys. J. Unmanned Veh. Syst. 10, 1–4 (2021).
Google Scholar 
Barnas, A. F. et al. A standardized protocol for reporting methods when using drones for wildlife research. J. Unmanned Veh. Syst. 8, 89–98 (2020).Article 

Google Scholar 
Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91–99 (2016).
Google Scholar 
Chen, T., Xu, B., Zhang, C. & Guestrin, C. Training Deep Nets with Sublinear Memory Cost. ArXiv160406174 Cs (2016).Pinckaers, H. & Litjens, G. Training convolutional neural networks with megapixel images. ArXiv180405712 Cs (2018).Abadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous systems. (2015).Janocha, K. & Czarnecki, W. M. On loss functions for deep neural networks in classification. ArXiv Prepr. ArXiv170205659. (2017).Murata, N., Yoshizawa, S. & Amari, S. Learning curves, model selection and complexity of neural networks. Adv. Neural Inf. Process. Syst. 5, 607–614 (1992).
Google Scholar 
Hänsch, R. & Hellwich, O. The truth about ground truth: Label noise in human-generated reference data. in IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium 5594–5597 (IEEE, 2019).Bowler, E., Fretwell, P. T., French, G. & Mackiewicz, M. Using deep learning to count albatrosses from space: Assessing results in light of ground truth uncertainty. Remote Sens. 12, 2026 (2020).Article 
ADS 

Google Scholar 
Brack, I. V., Kindel, A. & Oliveira, L. F. B. Detection errors in wildlife abundance estimates from Unmanned Aerial Systems (UAS) surveys: Synthesis, solutions, and challenges. Methods Ecol. Evol. 9, 1864–1873 (2018).Article 

Google Scholar 
Jagielski, P. M. et al. The utility of drones for studying polar bear behaviour in the Canadian Arctic: Opportunities and recommendations. Drone Syst. Appl. 10, 97–110 (2022).Article 

Google Scholar 
Williams, P. J., Hooten, M. B., Womble, J. N. & Bower, M. R. Estimating occupancy and abundance using aerial images with imperfect detection. Methods Ecol. Evol. 8, 1679–1689 (2017).Article 

Google Scholar 
Link, W. A., Schofield, M. R., Barker, R. J. & Sauer, J. R. On the robustness of N-mixture models. Ecology 99, 1547–1551 (2018).Article 

Google Scholar 
Horvitz, D. G. & Thompson, D. J. A generalization of sampling without replacement from a finite universe. J. Am. Stat. Assoc. 47, 663–685 (1952).Article 
MATH 

Google Scholar 
Corcoran, E., Denman, S. & Hamilton, G. New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys. Ecol. Evol. 10, 8176–8185 (2020).Article 

Google Scholar 
Lunga, D., Arndt, J., Gerrand, J. & Stewart, R. ReSFlow: A remote sensing imagery data-flow for improved model generalization. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 10468–10483 (2021).Article 
ADS 

Google Scholar 
Fromm, M., Schubert, M., Castilla, G., Linke, J. & McDermid, G. Automated detection of conifer seedlings in drone imagery using convolutional neural networks. Remote Sens. 11, 2585 (2019).Article 
ADS 

Google Scholar 
Velumani, K. et al. Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: Impact of the spatial resolution. Plant Phenomics 2021, 9824843 (2021).Article 
CAS 

Google Scholar 
Hodgson, A., Peel, D. & Kelly, N. Unmanned aerial vehicles for surveying marine fauna: Assessing detection probability. Ecol. Appl. 27, 1253–1267 (2017).Article 

Google Scholar 
Ferguson, M. C. et al. Performance of manned and unmanned aerial surveys to collect visual data and imagery for estimating arctic cetacean density and associated uncertainty. J. Unmanned Veh. Syst. 6, 128–154 (2018).Article 

Google Scholar 
Zmarz, A. et al. Application of UAV BVLOS remote sensing data for multi-faceted analysis of Antarctic ecosystem. Remote Sens. Environ. 217, 375–388 (2018).Article 
ADS 

Google Scholar 
Lyons, M. B. et al. Monitoring large and complex wildlife aggregations with drones. Methods Ecol. Evol. 10, 1024–1035 (2019).Article 

Google Scholar  More

Possingham, H. P., Wilson, K. A., Andelman, S. J. & Vynne, C. H. Protected areas. Goals, limitations, and design. In Principles of Conservation Biology (eds Groom, M. J. et al.) 507–549 (Sinauer Associates Inc, 2006).
Google Scholar 
Marquet, P. A., Lessmann, J. & Shaw, M. R. Protected-area management and climate change. In Biodiversity and Climate Change: Transforming the Biosphere (eds Lovejoy, T. E. & Hannah, L.) 283–293 (Yale University Press, 2019).Chapter 

Google Scholar 
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. PNAS https://doi.org/10.1073/pnas.1908221116 (2019).Article 

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

Google Scholar 
Cazalis, V. et al. Effectiveness of protected areas in conserving tropical forest birds. Nat. Commun. 11, 4461 (2020).Article 
ADS 
CAS 

Google Scholar 
Dudley, N., Mansourian, S., Stolton, S. & Suksuwan, S. Do protected areas contribute to poverty reduction?. Biodiversity 11, 5–7 (2010).Article 

Google Scholar 
Dudley, N. & Stolton, S. Arguments for Protected Areas (Earthscan, 2010).Book 

Google Scholar 
CBD. Strategic Plan for Biodiversity 2011–2020, Including Aichi Biodiversity Targets. http://www.cbd.int/sp/ and http://www.cbd.int/decision/cop/?id=12268 (2010).UNEP-WCMC & IUCN. Protected Planet: The World Database on Protected Areas (WDPA). www.protectedplanet.net. Accessed October 2022 (2022).Watson, J. E. M. et al. Persistent disparities between recent rates of habitat conversion and protection and implications for future global conservation targets. Conserv. Lett. 9, 413–421 (2016).Article 

Google Scholar 
Díaz, S. et al. Summary for Policymakers of the IPBES Global Assessment Report on Biodiversity and Ecosystem Services. (2019).Barnes, M. D., Glew, L., Wyborn, C. & Craigie, I. D. Prevent perverse outcomes from global protected area policy. Nat. Ecol. Evol. 2, 759–762 (2018).Article 

Google Scholar 
Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).Article 
ADS 
CAS 

Google Scholar 
Kukkala, A. S. & Moilanen, A. Core concepts of spatial prioritisation in systematic conservation planning. Biol. Rev. 88, 443–464 (2013).Article 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and Far: Biases in the location of protected areas. PLoS ONE 4, e8273 (2009).Article 
ADS 

Google Scholar 
Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).Article 
ADS 
CAS 

Google Scholar 
CBD. CoP 7 Decision VII/30. Strategic Plan: Future Evaluation of progress. 12 https://www.cbd.int/doc/decisions/cop-07/cop-07-dec-30-en.pdf (2004).Venter, O. et al. Bias in protected-area location and its effects on long-term aspirations of biodiversity conventions. Conserv. Biol. 32, 127–134 (2017).Article 

Google Scholar 
Kuempel, C. D., Chauvenet, A. L. M. & Possingham, H. P. Equitable representation of ecoregions is slowly improving despite strategic planning shortfalls. Conserv. Lett. 9, 422–428 (2016).Article 

Google Scholar 
Barr, L. M., Watson, J. E. M., Possingham, H. P., Iwamura, T. & Fuller, R. A. Progress in improving the protection of species and habitats in Australia. Biol. Conserv. 200, 184–191 (2016).Article 

Google Scholar 
Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 1–10 (2019).Article 

Google Scholar 
Hannah, L. Protected areas and climate change. Ann. N. Y. Acad. Sci. 1134, 201–212 (2008).Article 
ADS 

Google Scholar 
Thomas, C. D. & Gillingham, P. K. The performance of protected areas for biodiversity under climate change. Biol. J. Lin. Soc. 115, 718–730 (2015).Article 

Google Scholar 
Ramirez-Villegas, J. et al. Using species distributions models for designing conservation strategies of Tropical Andean biodiversity under climate change. J. Nat. Conserv. 22, 391–404 (2014).Article 

Google Scholar 
Bax, V. & Francesconi, W. Conservation gaps and priorities in the Tropical Andes biodiversity hotspot: Implications for the expansion of protected areas. J. Environ. Manage. 232, 387–396 (2019).Article 

Google Scholar 
Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. PNAS 110, E2602–E2610 (2013).Article 
ADS 
CAS 

Google Scholar 
Rodrigues, A. S. L. et al. Global gap analysis: Priority regions for expanding the global protected-area network. Bioscience 54, 1092–1100 (2004).Article 

Google Scholar 
Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modeling. (2015).Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 
MATH 

Google Scholar 
Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).Article 
MATH 

Google Scholar 
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893 (2017).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. (2017).Gotelli, N. J. & Graves, G. R. Null Models in Ecology. (1996).Araújo, M. B. & Pearson, R. G. Equilibrium of species’ distributions with climate. Ecography 28, 693–695 (2005).Article 

Google Scholar 
Watson, J. E. M., Grantham, H. S., Wilson, K. A. & Possingham, H. P. Systematic conservation planning: Past, present and future. In Conservation Biogeography (eds Ladle, R. J. & Whittaker, R. J.) (Wiley, 2011).
Google Scholar 
Bevilacqua, M. Áreas protegidas y conservación de la diversidad biológica. Biodivers. Venezuela 2, 922–943 (2003).
Google Scholar 
Franco, P., Saavedra-Rodríguez, C. A. & Kattan, G. H. Bird species diversity captured by protected areas in the Andes of Colombia: A gap analysis. Oryx 41, 57–63 (2007).Article 

Google Scholar 
Barzetti, V. Parks and Progress: Protected Areas and Economic Development in Latin America and the Caribbean. (1993).Schulman, L. et al. Amazonian biodiversity and protected areas: Do they meet?. Biodivers. Conserv. 16, 3011–3051 (2007).Article 

Google Scholar 
Dourojeanni, M. J. Áreas naturales protegidas e investigación científica en el Perú. Rev. For. Perú 33, 91–101 (2018).
Google Scholar 
Rodriguez, L. & Young, K. Biological diversity of peru: Determining priority areas for conservation. Ambio 29, 329–337 (2000).Article 

Google Scholar 
Ministerio del Ambiente & SERNANP. Plan Director de las Áreas Naturales Protegidas (Estrategia Nacional) (2009).Cuesta-Camacho, F. et al. Identificación de Vacíos y Prioridades de Conservación Para la Biodiversidad Terrestre en el Ecuador Continental. http://protectedareas.info/upload/document/ecuador_terrestrial_gap_analysis.pdf (2006).Naveda, J. A. Evaluación del grado de representatividad ecológica y geográfica del sistema de parques nacionales de Venezuela al norte del Orinoco: Anteproyecto. Rev. Geog. Venez. 38, 193–208 (1997).
Google Scholar 
Araujo, N., Müller, R., Nowicki, C. & Ibisch, P. L. Prioridades de conservación de la biodiversidad de Bolivia (editorial FAN, 2010)Arango, N. et al. Vacíos de Conservación del Sistema de Parques Nacionales Naturales de Colombia desde una Perspectiva Ecorregional. https://wwflac.awsassets.panda.org/downloads/vacios_de_conservacion.pdf (2003).Margules, C. R. & Pressey, R. L. Systematic conservation planning. Nature 405, 243–253 (2000).Article 
CAS 

Google Scholar 
Sarkar, S., Sánchez-Cordero, V., Londoño, M. C. & Fuller, T. Systematic conservation assessment for the Mesoamerica, Chocó, and Tropical Andes biodiversity hotspots: A preliminary analysis. Biodivers. Conserv. 18, 1793–1828 (2009).Article 

Google Scholar 
Lessmann, J., Muñoz, J. & Bonaccorso, E. Maximizing species conservation in continental Ecuador: A case of systematic conservation planning for biodiverse regions. Ecol. Evol. 4, 2410–2422 (2014).Article 

Google Scholar 
Young, B. E. et al. Using spatial models to predict areas of endemism and gaps in the protection of Andean slope birds. Auk 126, 554–565 (2009).Article 

Google Scholar 
Fajardo, J., Lessmann, J., Bonaccorso, E., Devenish, C. & Muñoz, J. Combined use of systematic conservation planning, species distribution modelling, and connectivity analysis reveals severe conservation gaps in a megadiverse country (Peru). PLoS ONE 9, 1–23 (2014).Article 

Google Scholar 
Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 6187 (2014).Article 

Google Scholar 
Swenson, J. J. et al. Plant and animal endemism in the eastern Andean slope: Challenges to conservation. BMC Ecol. 12, 1 (2012).Article 

Google Scholar 
Lessmann, J., Fajardo, J., Bonaccorso, E. & Bruner, A. Cost-effective protection of biodiversity in the western Amazon. Biol. Conserv. 235, 250–259 (2019).Article 

Google Scholar 
Rodrigues, A. S. L. & Gaston, K. J. How large do reserve networks need to be?. Ecol. Lett. 4, 602–609 (2001).Article 

Google Scholar 
Reyes-Puig, C. Diversity, threat, and conservation of reptiles from continental Ecuador. Amphib. Reptile Conserv. 11, 8 (2017).
Google Scholar 
Shanee, S. et al. Protected area coverage of threatened vertebrates and ecoregions in Peru: Comparison of communal, private and state reserves. J. Environ. Manage. 202, 12–20 (2017).Article 

Google Scholar 
Kujala, H., Moilanen, A., Araújo, M. B. & Cabeza, M. Conservation planning with uncertain climate change projections. PLoS ONE 8, e53315 (2013).Article 
ADS 
CAS 

Google Scholar 
Hannah, L. et al. 30% land conservation and climate action reduces tropical extinction risk by more than 50%. Ecography 43, 1–11 (2020).Article 

Google Scholar 
Velásquez-Tibatá, J., Salaman, P. & Graham, C. H. Effects of climate change on species distribution, community structure, and conservation of birds in protected areas in Colombia. Reg. Environ. Change 13, 235–248 (2013).Article 

Google Scholar 
del Avalos, V. R. & Hernández, J. Projected distribution shifts and protected area coverage of range-restricted Andean birds under climate change. Glob. Ecol. Conserv. 4, 459–469 (2015).Article 

Google Scholar 
Warren, R. et al. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat. Clim. Change 3, 678–682 (2013).Article 
ADS 

Google Scholar 
Golden Kroner, R. et al. COVID-era policies and economic recovery plans: Are governments building back better for protected and conserved areas?. PARKS 27, 135–148 (2021).Article 

Google Scholar 
IPCC Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge University Press, 2013).
Google Scholar 
Chevalier, M., Zarzo-Arias, A., Guélat, J., Mateo, R. G. & Guisan, A. Accounting for niche truncation to improve spatial and temporal predictions of species distributions. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2022.944116 (2022).Article 

Google Scholar 
Watson, J. E. M. et al. Bolder science needed now for protected areas. Conserv. Biol. 30, 243–248 (2016).Article 

Google Scholar 
CBD. Kunming-Montreal Global Biodiversity Framework, Draft Decision Submitted by the PRESIDENT. (2022). CBD/COP/15/L.25. https://www.cbd.int/doc/c/e6d3/cd1d/daf663719a03902a9b116c34/cop-15-l-25-en.pdfCBD. Report of the Expert Workshop on the Monitoring Framework for the Post-2020 Global Biodiversity Framework (CBD, 2022).
Google Scholar 
Chaplin-Kramer, R. et al. Mapping the planet’s critical natural assets. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01934-5 (2022).Article 

Google Scholar 
Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).Article 

Google Scholar 
Elbers, J. Las Áreas Protegidas de América Latina: Situación Actual y Perspectivas PARA el Futuro (2011).Miller, D. C. & Nakamura, K. S. Protected areas and the sustainable governance of forest resources. Curr. Opin. Environ. Sustain. 32, 96–103 (2018).Article 

Google Scholar 
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186 (2000).Article 

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

Google Scholar 
van Proosdij, A. S. J., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).Article 

Google Scholar 
Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6, 1210–1218 (2015).Article 

Google Scholar 
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).Article 

Google Scholar 
Thornhill, A. H. et al. Spatial phylogenetics of the native California flora. BMC Biol 15, 96 (2017).Article 

Google Scholar 
Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: Complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643 (2014).Article 

Google Scholar 
Kershaw, F. et al. Informing conservation units: Barriers to dispersal for the yellow anaconda. Divers. Distrib. 19, 1164–1174 (2013).Article 

Google Scholar 
Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).Article 

Google Scholar 
Gaston, K. J. The Structure and Dynamics of Geographic Ranges (Oxford University Press, 2003).
Google Scholar 
Yin, L., Fu, R., Shevliakova, E. & Dickinson, R. E. How well can CMIP5 simulate precipitation and its controlling processes over tropical South America?. Clim. Dyn. 41, 3127–3143 (2013).Article 

Google Scholar  More

Research areaThe YREB covers Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Guizhou, and Yunnan. It includes the Yangtze River Delta urban agglomerations (YRDUA), Yangtze River midstream urban agglomeration (YRMUA), and Chengdu-Chongqing urban agglomeration (CCUA). With a regional area of 2.05 million km2, the YREB runs through the eastern, central and western regions in China32. In 2019, the total GDP of YREB is 45.8 trillion yuan, accounting for 46.2% of the national GDP. The YREB plays a pivotal strategic support and leading role in the overall situation of stable economic growth in China33. At the same time, the contradiction between the shortage of land resources and economic growth in the YREB is very prominent. Therefore, this paper selects 107 cities in YREB as the research sample. The specific geographic locations are shown in Fig. 2. This article uses ARCGIS 10.2 version to draw the map. The URL link is http://demo.domain.com:6080/arcgis/services.Figure 2The geographic location of the YREB in China.Full size imageResearch methodsGlobal Malmquist–Luenberger indexUGLUE refers to the effective utilization degree of land elements under certain input of other elements. The green utilization of urban land mainly comes from three aspects: first, improve the utilization intensity of the existing actual input land, that is, increase the input intensity of other elements of the unit land area. Second, reduce the input of land in the production process to avoid excessive waste of land. Third, promote the optimal allocation of land elements among production units. Technical efficiency refers to the maximum degree that all factor inputs need to expand or shrink in equal proportion when all production units reach the production frontier. However, for production units with high technical efficiency, the factor allocation structure may not be reasonable. The land factors may still have the problem of under-input or over-input, resulting in the reduction of UGLUE.Pastor and Lovell34 proposed a global index, which uses all the inspection periods of each decision-making unit as a benchmark to construct the production frontier. According to the current benchmark construction period t, the production possibility set reference set is defined as follows:$$P_{C}^{t} (x^{t} ) = left{ {left. {(y^{t} ,b^{t} )} right|x^{t} {kern 1pt} can{kern 1pt} , produce{kern 1pt} , b^{t} ,y^{t} } right}$$
(1)
The global benchmark is defined as: (P_{G} = P_{C}^{1} , cup ,P_{C}^{2} , cup , cdots ,P_{C}^{t}), The subscripts C and G represent the current benchmark and the global benchmark respectively. The ML index of decision-making unit i is calculated based on the current reference benchmark:$$ML^{S} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{C}^{S} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{C}^{S} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$
(2)
Among them, the superscript S indicates two adjacent periods, t period and t + 1 period. The subscript C indicates the current benchmark, which is a simplified directional distance function. (ML^{s} > 1), indicates that the productivity increases. (ML^{s} < 1), indicates that the productivity decreases.According to Hofmann et al.35, the GMLI is defined as follows:$$GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$ (3) Among them, (D_{G}^{T} (x,y,b) = max left{ {alpha |(y - alpha y,b - alpha b) in P_{G} (x)} right}). (GMLI^{t,t + 1} > 1) indicates that the productivity has increased. (GMLI^{t,t + 1} < 1) indicates that the productivity decreases. The GMLI is further broken down as follows:$$begin{aligned} & GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} \ & quad = frac{{1 + D_{G}^{t} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{t + 1} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} times left[ {frac{{(1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} ))/(1 + D_{C}^{T} (x^{t} ,y^{t} ,b^{t} ))}}{{(1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))/(1 + D_{C}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))}}} right] \ & quad = frac{{TE^{t + 1} }}{{TE^{t} }} times left( {frac{{BPG_{t + 1}^{t + 1} }}{{BPG_{t}^{t + 1} }}} right) = EC_{t}^{t + 1} times BPC_{t}^{t + 1} \ end{aligned}$$ (4) Among them, TE is the change of technological progress. EC is the change of technological efficiency. The change of technological progress reflects the change of the highest technical level. The improvement of the highest technical level often requires the introduction and innovation of advanced technology, which often requires a large amount of investment. The change of technical efficiency reflects the gap with the highest technical level. Narrowing the gap with the highest technical level often requires improvements in internal management and governance structures. (BPG_{t}^{t + 1}) is the “best practitioner gap” between the current period and overall technological frontier. (BPC_{t}^{t + 1}) measures the changes in the “best practitioner gap” between two periods (technological changes). (BPC_{t}^{t + 1} , > , 1 ,) indicates technological progress. (BPC_{t}^{t + 1} < 1) indicates technology regress.Econometric techniques of industrial agglomeration on UGLUEIn recent years, many scholars used the traditional SPM for empirical analysis, which is a basic measurement model suitable for panel data. Therefore, this article firstly uses the traditional SPM to analyze the impact of industrial agglomeration on UGLUE. The formula is:$$begin{aligned} ln UGLUE_{it} & = alpha_{0} + alpha_{1} ln RZI_{it} + alpha_{2} ln RZI_{it} *ln RZI_{it} + alpha_{3} ln RDI_{it} + alpha_{4} ln EC_{it} \ & quad + alpha_{5} ln GDP_{it} + alpha_{6} ln TEC_{it} + alpha_{7} ln ROAD_{it} + alpha_{8} ln GOV_{it} + varepsilon_{it} \ end{aligned}$$ (5) Among them, ε is the disturbance term. i represents the city, i in this paper involves 107 cities in YREB. t represents the time, and the range of t in this paper is from 2007 to 2016. UGLUE is the explained variable, which represents the UGLUE. RZI and RDI are explanatory variables, representing industrial specialization agglomeration and industrial diversification agglomeration. EC is the industrial structure. GDP is the level of economic development. TEC is the level of technology. ROAD is the level of infrastructure. GOV is the degree of government intervention. (alpha_{1}) to (alpha_{8}) is the coefficient of each variable.Formula (5) assumes that the UGLUE changes with the changes of various influencing factors in the current period. That is, there is no time lag effect. But in reality, land use often has a time lag effect. The previous level has a non-negligible impact on the current results. Therefore, this paper selects the dynamic panel model for empirical analysis. However, there is often a two-way causal relationship between industrial agglomeration and UGLUE, which may cause endogenous bias. For example, cities with higher UGLUE levels tend to have higher levels of economic development, which promotes industrial agglomeration in this city. Therefore, this paper adopts the method of system GMM for regression analysis of dynamic panel model. Compared with mixed OLS, system GMM can make full use of sample information, select appropriate lag terms as instrumental variables36. It can effectively solve the endogeneity problem between industrial agglomeration and UGLUE. Based on the above analysis, this paper introduces the first-order lag term of UGLUE on the basis of formula (5). The DPM is as follows:$$begin{aligned} ln UGLUE_{it} & = beta_{0} + tau ln UGLUE_{i(t - 1)} + beta_{1} ln RZI_{it} + beta_{2} ln RZI_{it} times ln RZI_{it} + beta_{3} ln RDI_{it} \ & quad + beta_{4} ln EC_{it} + beta_{5} ln GDP_{it} + beta_{6} ln TEC_{it} + beta_{7} ln ROAD_{it} + beta_{8} ln GOV_{it} + varepsilon_{{{text{it}}}} \ end{aligned}$$ (6) Among them, (tau) is the first-order lag coefficient of UGLUE, reflecting the time lag effect of UGLUE.Variable descriptionExplained variableThe GMLI is used to measure the UGLUE of 107 cities in YREB. According to existing research37, the following core evaluation index of UGLUE are selected (see Table 1). Regarding input indicators, we mainly choose land element input M, labor element input L, and capital element input K as input indicators. Regarding output indicators, we choose the added value of the secondary and tertiary industries in the municipal area as the expected output, and use the GDP deflator to convert it into a comparable value. At the same time, pollution indicators such as industrial wastewater emissions, industrial sulfur dioxide emissions, and industrial smoke (dust) emissions are selected as undesired output. Since the GMLI reflects the growth rate of UGLUE, this paper assumes that the GMLI in 2006 is 1, and then multiplies the calculated GMLI year by year to obtain the development level of UGLUE in each city from 2007 to 2016.Table 1 Input and output index.Full size tableExplanatory variablesIndustrial specialization index ZI is usually used to measure the specialization level of urban industries. The specialization index is represented by the share of the employment of the industry in the total employment of the city:$$ZI_{i} = Max_{j} (S_{ij} )$$ (7) Nextly, we use the relative specialization index to make a horizontal comparison of the specialization level between different cities:$$RZI_{i} = Max(S_{ij} /S_{j} )$$ (8) The most common measure of the level of industrial diversification is the Herfindahl–Hirschman Index (HHI). For city i, the HHI is the sum of the square sum of employment shares of all industries in the city. The diversification index is the reciprocal of the HHI:$$DZ_{i} = frac{1}{{sumlimits_{j} {S_{ij}^{2} } }}$$ (9) The expression of relative diversification index is as follows:$$RDI_{i} = {1 mathord{left/ {vphantom {1 {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}} right. kern-0pt} {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}$$ (10) Among them, Sij is the employment proportion of j industry in city i, and Sj is the proportion of the total employment of the national j industry. The greater value of RZI and RDI, the higher level of industrial specialization and diversification.Control variablesRegarding control variables, we choose the following variables as control variables.Industrial structure (EC): The continuous optimization of industrial structure promotes the improvement of UGLUE through three aspects: saving land, increasing land income and promoting the optimal allocation of land resources. This paper selects the added value of the tertiary industry as a percentage of GDP (take the logarithm) to express.Technological level (TEC): The higher the technological innovation level of a city is, the more it promotes the use of input elements and the transformation of innovation results, thereby improving the UGLUE. This paper selects the proportion of science and technology expenditure to fiscal expenditure (take the logarithm) to represent.Economic development level (GDP): The continuous economic development promote the rational allocation of various production factors and increase the level of urban land output, thereby improving the UGLUE. This paper selects GDP per capita (take the logarithm) to express.Road infrastructure level (ROAD): The continuous improvement of infrastructure reduces transportation costs and transaction costs, and promotes communication externalities between producers, consumers, and between producers and consumers. This paper selects the average road area per capita (take the logarithm) to express.Government behavior (GOV): Fiscal expenditure is an important means for the government to carry out macro-control. Appropriate fiscal expenditure makes up for market shortages, improves factor flow and resource allocation efficiency, and realizes positive economic externalities. This paper selects the proportion of fiscal expenditure to GDP (take the logarithm) to express. We can see the meaning of specific variables from Table 2.Table 2 The descriptive statistics of variables.Full size tableData sourceThe object of this thesis is the 107 cities in YREB from 2007 to 2016. The urban construction land area data comes from the "China Urban Construction Statistical Yearbook", and the rest of the index data all come from the "China City Statistical Yearbook". The URL link is https://www.cnki.net/. In order to maintain the integrity of the data, this article uses the average method to fill in the missing values. In addition, because Chaohu City began to be under the jurisdiction of Hefei City in 2011, Bijie City and Tongren City in Guizhou Province only became prefecture-level cities in 2011. The three cities and Pu'er City are taken from the sample to maintain the continuity of data. More

Steffen, W. Introducing the Anthropocene: The human epoch. Ambio 50, 1784–1787 (2021).Article 

Google Scholar 
Keys, P. W. et al. Anthropocene risk. Nat. Sustain. 2, 667–673 (2019).Article 

Google Scholar 
Bell, G. Evolutionary rescue and the limits of adaptation. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120080 (2013).Article 

Google Scholar 
Byrne, M. & Przeslawski, R. Multistressor impacts of warming and acidification of the ocean on marine invertebrates’ life histories. Integr. Comp. Biol. 53, 582–596 (2013).Article 
CAS 

Google Scholar 
Feely, R. A. et al. Impact of anthropogenic CO2 on the CaCO3 system in the oceans. Science 305, 362–366 (2004).Article 
CAS 

Google Scholar 
Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: the other CO2 problem. Ann. Rev. Mar. Sci. 1, 169–192 (2009).Article 

Google Scholar 
Hill, T. S. & Hoogenboom, M. O. The indirect effects of ocean acidification on corals and coral communities. Coral Reefs https://doi.org/10.1007/s00338-022-02286-z (2022).Biagi, E. et al. Patterns in microbiome composition differ with ocean acidification in anatomic compartments of the Mediterranean coral Astroides calycularis living at CO2 vents. Sci. Total Environ. 724, 138048 (2020).Article 
CAS 

Google Scholar 
Chen, B. et al. Microbiome community and complexity indicate environmental gradient acclimatisation and potential microbial interaction of endemic coral holobionts in the South China Sea. Sci. Total Environ. 765, 142690 (2021).Article 
CAS 

Google Scholar 
Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014).Article 
CAS 

Google Scholar 
Wood, R. The ecological evolution of reefs. Annu. Rev. Ecol. Syst. 29, 179–206 (1998).Article 

Google Scholar 
Drake, J. L. et al. How corals made rocks through the ages. Glob. Chang. Biol. 26, 31–53 (2020).Article 

Google Scholar 
Stanley, G. D. Photosymbiosis and the evolution of modern coral reefs. Science 312, 857–858 (2006).Article 
CAS 

Google Scholar 
Kitahara, M. V., Cairns, S. D., Stolarski, J., Blair, D. & Miller, D. J. A comprehensive phylogenetic analysis of the scleractinia (Cnidaria, Anthozoa) based on mitochondrial CO1 sequence data. PLoS One. 5, e11490 (2010).Article 

Google Scholar 
Dubinsky, Z. & Jokiel, P. Ratio of energy and nutrient fluxes regulates symbiosis between zooxanthellae and corals. Pac. Sci. 48, 313–324 (1994).
Google Scholar 
Falkowski, P. G., Dubinsky, Z., Muscatine, L. & Porter, J. W. Light and the bioenergetics of a symbiotic coral. Bioscience 34, 705–709 (1984).Article 
CAS 

Google Scholar 
Frankowiak, K., Roniewicz, E. & Stolarski, J. Photosymbiosis in Late Triassic scleractinian corals from the Italian Dolomites. PeerJ 9, e11062 (2021).Article 

Google Scholar 
Davy, S. K., Allemand, D. & Weis, V. M. Cell biology of cnidarian-dinoflagellate symbiosis. Microbiol. Mol. Biol. Rev. 76, 229–261 (2012).Article 
CAS 

Google Scholar 
Kremer, P. Ingestion and elemental budgets for Linuche unguiculata, a scyphomedusa with zooxanthellae. J. Mar. Biol. Assoc. UK. 85, 613–625 (2005).Article 

Google Scholar 
Welsh, D. T., Dunn, R. J. K. & Meziane, T. Oxygen and nutrient dynamics of the upside down jellyfish (Cassiopea sp.) and its influence on benthic nutrient exchanges and primary production. Hydrobiologia 635, 351–362 (2009).Article 
CAS 

Google Scholar 
Muscatine, L., McCloskey, L. R. & Marian, R. E. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611 (1981).Article 
CAS 

Google Scholar 
Ferrier‐Pagès, C. & Leal, M. C. Stable isotopes as tracers of trophic interactions in marine mutualistic symbioses. Ecol. Evol. 9, 723–740 (2019).Article 

Google Scholar 
Teixidó, N. et al. Ocean acidification causes variable trait shifts in a coral species. Glob. Chang. Biol. 26, 6813–6830 (2020).Article 

Google Scholar 
Fantazzini, P. et al. Gains and losses of coral skeletal porosity changes with ocean acidification acclimation. Nat. Commun. 6, 7785 (2015).Article 
CAS 

Google Scholar 
Prada, F. et al. Coral micro- and macro-morphological skeletal properties in response to life-long acclimatization at CO2 vents in Papua New Guinea. Sci. Rep. 11, 19927 (2021).Article 
CAS 

Google Scholar 
Kerrison, P., Hall-Spencer, J. M., Suggett, D. J., Hepburn, L. J. & Steinke, M. Assessment of pH variability at a coastal CO2 vent for ocean acidification studies. Estuar. Coast. Shelf Sci. 94, 129–137 (2011).Article 
CAS 

Google Scholar 
Johnson, V. R., Russell, B. D., Fabricius, K. E., Brownlee, C. & Hall-Spencer, J. M. Temperate and tropical brown macroalgae thrive, despite decalcification, along natural CO2 gradients. Glob. Chang. Biol. 18, 2792–2803 (2012).Article 

Google Scholar 
Caroselli, E. et al. Low and variable pH decreases recruitment efficiency in populations of a temperate coral naturally present at a CO2 vent. Limnol. Oceanogr. 64, 1059–1069 (2019).Article 
CAS 

Google Scholar 
González-Delgado, S. & Hernández, J. C. The importance of natural acidified systems in the study of ocean acidification: what have we learned? Adv. Mar. Biol. 80, 57–99 (2018).Article 

Google Scholar 
Capaccioni, B., Tassi, F., Vaselli, O., Tedesco, D. & Poreda, R. Submarine gas burst at Panarea Island (southern Italy) on 3 November 2002: A magmatic versus hydrothermal episode. J. Geophys. Res. 112, B05201 (2007).
Google Scholar 
Reggi, M. et al. Biomineralization in mediterranean corals: The role of the intraskeletal organic matrix. Cryst. Growth Des. 14, 4310–4320 (2014).Article 
CAS 

Google Scholar 
Prada, F. et al. Ocean warming and acidification synergistically increase coral mortality. Sci. Rep. 7, 1–10 (2017).Article 

Google Scholar 
Goffredo, S. et al. Biomineralization control related to population density under ocean acidification. Nat. Clim. Chang. 4, 593–597 (2014).Article 
CAS 

Google Scholar 
Wall, M. et al. Linking internal carbonate chemistry regulation and calcification in corals growing at a Mediterranean CO2 vent. Front. Mar. Sci. 6, 699 (2019).Article 

Google Scholar 
Zohary, T., Erez, J., Gophen, M., Berman-Frank, I. & Stiller, M. Seasonality of stable carbon isotopes within the pelagic food web of Lake Kinneret. Limnol. Oceanogr. 39, 1030–1043 (1994).Article 
CAS 

Google Scholar 
Xu, S. et al. Spatial variations in the trophic status of Favia palauensis corals in the South China Sea: Insights into their different adaptabilities under contrasting environmental conditions. Sci. China Earth Sci. 64, 839–852 (2021).Article 

Google Scholar 
Horwitz, R., Borell, E. M., Yam, R., Shemesh, A. & Fine, M. Natural high pCO2 increases autotrophy in Anemonia viridis (Anthozoa) as revealed from stable isotope (C, N) analysis. Sci. Rep. 5, 1–9 (2015).Article 

Google Scholar 
Chen, B., Zou, D., Zhu, M. & Yang, Y. Effects of CO2 levels and light intensities on growth and amino acid contents in red seaweed Gracilaria lemaneiformis. Aquac. Res. 48, 2683–2690 (2017).Article 
CAS 

Google Scholar 
Winters, G., Beer, S., Zvi, B., Brickner, I. & Loya, Y. Spatial and temporal photoacclimation of Stylophora pistillata: zooxanthella size, pigmentation, location and clade. Mar. Ecol. Prog. Ser. 384, 107–119 (2009).Article 

Google Scholar 
Fitt, W. K., McFarland, F. K., Warner, M. E. & Chilcoat, G. C. Seasonal patterns of tissue biomass and densities of symbiotic dinoflagellates in reef corals and relation to coral bleaching. Limnol. Oceanogr. 45, 677–685 (2000).Article 
CAS 

Google Scholar 
Wangpraseurt, D., Larkum, A. W. D., Ralph, P. J. & Kühl, M. Light gradients and optical microniches in coral tissues. Front. Microbiol. 3, 1–9 (2012).Article 

Google Scholar 
Krief, S. et al. Physiological and isotopic responses of scleractinian corals to ocean acidification. Geochim. Cosmochim. Acta. 74, 4988–5001 (2010).Article 
CAS 

Google Scholar 
Scucchia, F., Malik, A., Zaslansky, P., Putnam, H. M. & Mass, T. Combined responses of primary coral polyps and their algal endosymbionts to decreasing seawater pH. Proc. R. Soc. B Biol. Sci. 288, 20210328 (2021).Article 
CAS 

Google Scholar 
Anthony, K. R. N., Connolly, S. R. & Willis, B. L. Comparative analysis of energy allocation to tissue and skeletal growth in corals. Limnol. Oceanogr. 47, 1417–1429 (2002).Article 

Google Scholar 
LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580.e6 (2018).Article 
CAS 

Google Scholar 
Howells, E. J. et al. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim. Chang. 2, 116–120 (2012).Article 

Google Scholar 
Brading, P. et al. Differential effects of ocean acidification on growth and photosynthesis among phylotypes of Symbiodinium (Dinophyceae). Limnol. Oceanogr. 56, 927–938 (2011).Article 
CAS 

Google Scholar 
Takahashi, T., Broecker, W. S. & Langer, S. Redfield ratio based on chemical data from isopycnal surfaces. J. Geophys. Res. 90, 6907 (1985).Article 
CAS 

Google Scholar 
Xu, Z. et al. Changes of carbon to nitrogen ratio in particulate organic matter in the marine mesopelagic zone: A case from the South China Sea. Mar. Chem. 231, 103930 (2021).Article 
CAS 

Google Scholar 
Crawford, D. W. et al. Low particulate carbon to nitrogen ratios in marine surface waters of the Arctic. Glob. Biogeochem. Cycles. 29, 2021–2033 (2015).Article 
CAS 

Google Scholar 
Kikumoto, R. et al. Nitrogen isotope chemostratigraphy of the Ediacaran and Early Cambrian platform sequence at Three Gorges, South China. Gondwana Res. 25, 1057–1069 (2014).Article 
CAS 

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

Google Scholar 
Benavides, M., Bednarz, V. N. & Ferrier-Pagès, C. Diazotrophs: Overlooked key players within the coral symbiosis and tropical reef ecosystems? Front. Mar. Sci. 4, 10 (2017).Article 

Google Scholar 
Wannicke, N., Frey, C., Law, C. S. & Voss, M. The response of the marine nitrogen cycle to ocean acidification. Glob. Chang. Biol. 24, 5031–5043 (2018).Article 

Google Scholar 
Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).Article 
CAS 

Google Scholar 
Palladino, G. et al. Metagenomic shifts in mucus, tissue and skeleton of the coral Balanophyllia europaea living along a natural CO2 gradient. ISME Commun. 2, 65 (2022).Article 

Google Scholar 
Muscatine, L. et al. Stable isotopes (δ13C and δ15N) of organic matrix from coral skeleton. Proc. Natl Acad. Sci. 102, 1525–1530 (2005).Article 
CAS 

Google Scholar 
Lesser, M. P. et al. Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar. Ecol. Prog. Ser. 346, 143–152 (2007).Article 
CAS 

Google Scholar 
Alamaru, A., Loya, Y., Brokovich, E., Yam, R. & Shemesh, A. Carbon and nitrogen utilization in two species of Red Sea corals along a depth gradient: Insights from stable isotope analysis of total organic material and lipids. Geochim. Cosmochim. Acta. 73, 5333–5342 (2009).Article 
CAS 

Google Scholar 
Lesser, M. P., Mazel, C. H., Gorbunov, M. Y. & Falkowski, P. G. Discovery of symbiotic nitrogen-fixing cyanobacteria in corals. Science 305, 997–1000 (2004).Article 
CAS 

Google Scholar 
Lesser, M. P., Morrow, K. M., Pankey, S. M. & Noonan, S. H. C. Diazotroph diversity and nitrogen fixation in the coral Stylophora pistillata from the Great Barrier Reef. ISME J. 12, 813–824 (2018).Article 
CAS 

Google Scholar 
Marcelino, V. R., Morrow, K. M., Oppen, M. J. H., Bourne, D. G. & Verbruggen, H. Diversity and stability of coral endolithic microbial communities at a naturally high pCO2 reef. Mol. Ecol. 26, 5344–5357 (2017).Article 
CAS 

Google Scholar 
Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 23, 490–497 (2015).Article 

Google Scholar 
Santos, H. F. et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 8, 2272–2279 (2014).Article 

Google Scholar 
Olson, N. D., Ainsworth, T. D., Gates, R. D. & Takabayashi, M. Diazotrophic bacteria associated with Hawaiian Montipora corals: Diversity and abundance in correlation with symbiotic dinoflagellates. J. Exp. Mar. Bio. Ecol. 371, 140–146 (2009).Article 
CAS 

Google Scholar 
Zheng, X. et al. Effects of ocean acidification on carbon and nitrogen fixation in the hermatypic coral Galaxea fascicularis. Front. Mar. Sci. 8, 644965 (2021).Article 

Google Scholar 
Lewis, E. & Wallace, D. Program developed for CO2 system calculations. Ornl/Cdiac-105 1–21 (1998).Dickson, A. G. & Millero, F. J. A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media. Deep Sea Res. Part A. Oceanogr. Res. Pap. 34, 1733–1743 (1987).Article 
CAS 

Google Scholar 
Dickson, A. G. Thermodynamics of the dissociation of boric acid in potassium chloride solutions from 273.15 to 318.15 K. J. Chem. Eng. Data. 35, 253–257 (1990).Article 
CAS 

Google Scholar 
Mehrbach, C., Culberson, C. H., Hawley, J. E. & Pytkowicx, R. M. Measurement of the apparent dissociation constants of carbonic acid in seawater at atmospheric pressure. Limnol. Oceanogr. 18, 897–907 (1973).Article 
CAS 

Google Scholar 
Ivancic, I. & Degobbis, D. An optimal manual procedure for ammonia analysis in natural waters by the indophenol blue method. Water Res. 18, 1143–1147 (1984).Article 
CAS 

Google Scholar 
Parson, T. R., Maita, Y. & Llli, C. M. A manual of chemical & biological methods for seawater analysis. (Elsevier, 1984). https://doi.org/10.1016/C2009-0-07774-5Schreiber, U. Pulse-Amplitude-Modulation (PAM) fluorometry and saturation pulse method: an overview. in Chlorophyll a Fluorescence 1367, 279–319 (Springer Netherlands, 2004).Grover, R., Maguer, J. F., Reynaud-Vaganay, S. & Ferrier-Pagès, C. Uptake of ammonium by the scleractinian coral Stylophora pistillata: Effect of feeding, light, and ammonium concentrations. Limnol. Oceanogr. 47, 782–790 (2002).Article 

Google Scholar 
Tremblay, P., Grover, R., Maguer, J. F., Hoogenboom, M. & Ferrier-Pagès, C. Carbon translocation from symbiont to host depends on irradiance and food availability in the tropical coral Stylophora pistillata. Coral Reefs. 33, 1–13 (2014).Article 

Google Scholar 
Pupier, C. A. et al. Productivity and carbon fluxes depend on species and symbiont density in soft coral symbioses. Sci. Rep. 9, 17819 (2019).Article 

Google Scholar 
Ritchie, R. J. Universal chlorophyll equations for estimating chlorophylls a, b, c, and d and total chlorophylls in natural assemblages of photosynthetic organisms using acetone, methanol, or ethanol solvents. Photosynthetica 46, 115–126 (2008).Article 
CAS 

Google Scholar 
Goffredo, S., Arnone, S. & Zaccanti, F. Sexual reproduction in the Mediterranean solitary coral Balanophyllia europaea (Scleractinia, Dendrophylliidae). Mar. Ecol. Prog. Ser. 229, 83–94 (2002).Article 

Google Scholar 
Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl Acad. Sci. 110, 1387–1392 (2013).Article 
CAS 

Google Scholar 
Moore, R. B. Highly organized structure in the non-coding region of the psbA minicircle from clade C Symbiodinium. Int. J. Syst. Evol. Microbiol. 53, 1725–1734 (2003).Article 
CAS 

Google Scholar 
LaJeunesse, T. C. & Thornhill, D. J. Improved resolution of reef-coral endosymbiont (Symbiodinium) species diversity, ecology, and evolution through psbA non-coding region genotyping. PLoS One. 6, e29013 (2011).Article 
CAS 

Google Scholar 
LaJeunesse, T. C. et al. Revival of Philozoon Geddes for host-specialized dinoflagellates, ‘zooxanthellae’, in animals from coastal temperate zones of northern and southern hemispheres. Eur. J. Phycol. 57, 166–180 (2022).Article 

Google Scholar 
Anderson, M. J. PERMANOVA: a FORTRAN computer program for permutational multivariate analysis of variance. Wiley StatsRef: Statistics Reference Online (2005). More

Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).Article 
ADS 

Google Scholar 
Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).Article 
ADS 
CAS 

Google Scholar 
Harris, R. M. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).Article 
ADS 

Google Scholar 
Till, A., Rypel, A. L., Bray, A. & Fey, S. B. Fish die-offs are concurrent with thermal extremes in north temperate lakes. Nat. Clim. Change 9, 637–641 (2019).Article 
ADS 

Google Scholar 
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).Article 
ADS 

Google Scholar 
Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B 281, 20132612 (2014).Article 

Google Scholar 
Ma, G., Rudolf, V. H. & Ma, C. Extreme temperature events alter demographic rates, relative fitness, and community structure. Glob. Change Biol. 21, 1794–1808 (2015).Article 
ADS 

Google Scholar 
Vázquez, D. P., Gianoli, E., Morris, W. F. & Bozinovic, F. Ecological and evolutionary impacts of changing climatic variability. Biol. Rev. 92, 22–42 (2017).Article 

Google Scholar 
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).Article 
CAS 

Google Scholar 
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).Article 
ADS 
CAS 

Google Scholar 
Power, S. B. & Delage, F. P. Setting and smashing extreme temperature records over the coming century. Nat. Clim. Change 9, 529–534 (2019).Article 
ADS 

Google Scholar 
Fischer, E. M., Sippel, S. & Knutti, R. Increasing probability of record-shattering climate extremes. Nat. Clim. Change 11, 689–695 (2021).Article 
ADS 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).Article 
ADS 

Google Scholar 
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).Article 
ADS 
CAS 

Google Scholar 
McKechnie, A. E. & Wolf, B. O. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biol. Lett. 6, 253–256 (2010).Article 

Google Scholar 
Maxwell, S. L. et al. Conservation implications of ecological responses to extreme weather and climate events. Divers. Distrib. 25, 613–625 (2019).Article 

Google Scholar 
Seneviratne, S. I. et al. in 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.) Ch. 11, 1571–1759 (Cambridge Univ. Press, 2021).Mora, C. et al. Global risk of deadly heat. Nat. Clim. Change 7, 501–506 (2017).Article 
ADS 

Google Scholar 
Battisti, D. S. & Naylor, R. L. Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240–244 (2009).Article 
CAS 

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

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).Article 
ADS 
CAS 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).Article 
ADS 
CAS 

Google Scholar 
Ma, G., Hoffmann, A. A. & Ma, C.-S. Daily temperature extremes play an important role in predicting thermal effects. J. Exp. Biol. 218, 2289–2296 (2015).
Google Scholar 
Paaijmans, K. P. et al. Temperature variation makes ectotherms more sensitive to climate change. Glob. Change Biol. 19, 2373–2380 (2013).Article 
ADS 

Google Scholar 
Bütikofer, L. et al. The problem of scale in predicting biological responses to climate. Glob. Change Biol. 26, 6657–6666 (2020).Article 
ADS 

Google Scholar 
Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R. & Wilby, R. L. Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529, 477–483 (2016).Article 
ADS 
CAS 

Google Scholar 
Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Change Biol. 22, 3829–3842 (2016).Article 
ADS 

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

Google Scholar 
Vogel, M. M. et al. Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks. Geophys. Res. Lett. 44, 1511–1519 (2017).Article 
ADS 

Google Scholar 
Tamarin-Brodsky, T., Hodges, K., Hoskins, B. J. & Shepherd, T. G. Changes in Northern Hemisphere temperature variability shaped by regional warming patterns. Nat. Geosci. 13, 414–421 (2020).Article 
ADS 
CAS 

Google Scholar 
Schär, C. et al. The role of increasing temperature variability in European summer heatwaves. Nature 427, 332–336 (2004).Article 
ADS 

Google Scholar 
Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).Article 
ADS 
CAS 

Google Scholar 
Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).Article 
ADS 
CAS 

Google Scholar 
Perkins, S. E. & Alexander, L. V. On the measurement of heat waves. J. Clim. 26, 4500–4517 (2013).Article 
ADS 

Google Scholar 
Sunday, J. et al. Thermal tolerance patterns across latitude and elevation. Philos. Trans. R. Soc. B 374, 20190036 (2019).Article 

Google Scholar 
Hoffmann, A. A. Physiological climatic limits in Drosophila: patterns and implications. J. Exp. Biol. 213, 870–880 (2010).Article 
CAS 

Google Scholar 
Buckley, L. B. & Huey, R. B. How extreme temperatures impact organisms and the evolution of their thermal tolerance. Integr. Comp. Biol. 56, 98–109 (2016).Article 

Google Scholar 
Cohen, J. M., Fink, D. & Zuckerberg, B. Avian responses to extreme weather across functional traits and temporal scales. Glob. Change Biol. 26, 4240–4250 (2020).Article 
ADS 

Google Scholar 
Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl Acad. Sci. USA 117, 19656–19657 (2020).Article 
ADS 
CAS 

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

Google Scholar 
Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).Article 

Google Scholar 
Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).Article 
ADS 
CAS 

Google Scholar 
Welbergen, J. A., Klose, S. M., Markus, N. & Eby, P. Climate change and the effects of temperature extremes on Australian flying-foxes. Proc. R. Soc. B 275, 419–425 (2008).Article 

Google Scholar 
McKechnie, A. E., Rushworth, I. A., Myburgh, F. & Cunningham, S. J. Mortality among birds and bats during an extreme heat event in eastern South Africa. Austral Ecol. 46, 687–691 (2021).Article 

Google Scholar 
Thompson, R. M., Beardall, J., Beringer, J., Grace, M. & Sardina, P. Means and extremes: building variability into community-level climate change experiments. Ecol. Lett. 16, 799–806 (2013).Article 

Google Scholar 
Perez, T. M., Stroud, J. T. & Feeley, K. J. Thermal trouble in the tropics. Science 351, 1392–1393 (2016).Article 
ADS 
CAS 

Google Scholar 
Huey, R. B. et al. Why tropical forest lizards are vulnerable to climate warming. Proc. R. Soc. B 276, 1939–1948 (2009).Article 

Google Scholar 
Kingsolver, J. G., Diamond, S. E. & Buckley, L. B. Heat stress and the fitness consequences of climate change for terrestrial ectotherms. Funct. Ecol. 27, 1415–1423 (2013).Article 

Google Scholar 
R. Kearney, M. Activity restriction and the mechanistic basis for extinctions under climate warming. Ecol. Lett. 16, 1470–1479 (2013).Article 

Google Scholar 
Rezende, E. L., Bozinovic, F., Szilágyi, A. & Santos, M. Predicting temperature mortality and selection in natural Drosophila populations. Science 369, 1242–1245 (2020).Article 
ADS 
CAS 
MATH 

Google Scholar 
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).Article 
ADS 
CAS 

Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).Article 
ADS 

Google Scholar 
Levy, O., Dayan, T., Porter, W. P. & Kronfeld-Schor, N. Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecol. Monogr. 89, e01334 (2019).Article 

Google Scholar 
Faurby, S. & Araújo, M. B. Anthropogenic range contractions bias species climate change forecasts. Nat. Clim. Change 8, 252–256 (2018).Article 
ADS 

Google Scholar 
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).Article 
ADS 
CAS 

Google Scholar 
Scheffers, B. R., Edwards, D. P., Diesmos, A., Williams, S. E. & Evans, T. A. Microhabitats reduce animal’s exposure to climate extremes. Glob. Change Biol. 20, 495–503 (2014).Article 
ADS 

Google Scholar 
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. B 367, 1665–1679 (2012).Article 

Google Scholar 
Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer “cold-blooded” animals against climate warming. Proc. Natl Acad. Sci. USA 106, 3835–3840 (2009).Article 
ADS 
CAS 

Google Scholar 
Morley, S. A., Peck, L. S., Sunday, J. M., Heiser, S. & Bates, A. E. Physiological acclimation and persistence of ectothermic species under extreme heat events. Glob. Ecol. Biogeogr. 28, 1018–1037 (2019).Article 

Google Scholar 
Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).Article 

Google Scholar 
Lewis, F. et al. in 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.) 147–1926 (Cambridge Univ. Press, 2021).Thakur, M. P., Bakker, E. S., Veen, G. C. & Harvey, J. A. Climate extremes, rewilding, and the role of microhabitats. One Earth 2, 506–509 (2020).Article 
ADS 

Google Scholar 
Albright, T. P. et al. Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration. Proc. Natl Acad. Sci. USA 114, 2283–2288 (2017).Article 
ADS 
CAS 

Google Scholar 
Thrasher, B. et al. NASA Global daily downscaled projections, CMIP6. Sci. Data 9, 262 (2022).Article 

Google Scholar 
Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci. 16, 3309–3314 (2012).Article 
ADS 

Google Scholar 
Jin, Z. et al. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Glob. Change Biol. 22, 3112–3126 (2016).Article 
ADS 

Google Scholar 
Zhang, L., Yang, B., Li, S., Hou, Y. & Huang, D. Potential rice exposure to heat stress along the Yangtze River in China under RCP8.5 scenario. Agric. Forest Meteorol. 248, 185–196 (2018).Article 
ADS 

Google Scholar 
Al-Bakri, J. et al. Assessment of climate changes and their impact on barley yield in Mediterranean environment using NEX-GDDP downscaled GCMs and DSSAT. Earth Syst. Environ. 5, 751–766 (2021).Semakula, H. M. et al. Prediction of future malaria hotspots under climate change in sub-Saharan Africa. Clim. Change 143, 415–428 (2017).Article 
ADS 
CAS 

Google Scholar 
Iwamura, T., Guzman-Holst, A. & Murray, K. A. Accelerating invasion potential of disease vector Aedes aegypti under climate change. Nat. Commun. 11, 2130 (2020).Article 
ADS 
CAS 

Google Scholar 
Jones, A. E. et al. Bluetongue risk under future climates. Nat. Clim. Change 9, 153–157 (2019).Article 
ADS 

Google Scholar 
Obradovich, N. & Fowler, J. H. Climate change may alter human physical activity patterns. Nat. Hum. Behav. 1, 0097 (2017).Article 

Google Scholar 
Obradovich, N., Migliorini, R., Mednick, S. C. & Fowler, J. H. Nighttime temperature and human sleep loss in a changing climate. Sci. Adv. 3, e1601555 (2017).Article 
ADS 

Google Scholar 
Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6, eaba1981 (2020).Article 
ADS 

Google Scholar 
Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W. & Zelinka, M. Climate simulations: recognize the ‘hot model’ problem. Nature 605, 26–29 (2022).O’Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).Article 
ADS 

Google Scholar 
IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).IUCN Red List of Threatened Species Version 2017, 3 (IUCN, 2017).Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677 (2017).Article 

Google Scholar 
Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. USA 104, 13384–13389 (2007).Article 
ADS 
CAS 

Google Scholar 
Maclean, I. M. Predicting future climate at high spatial and temporal resolution. Glob. Change Biol. 26, 1003–1011 (2020).Article 
ADS 

Google Scholar 
Warren, R. et al. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat. Clim. Change 3, 678–682 (2013).Article 
ADS 

Google Scholar 
Jiguet, F. et al. Thermal range predicts bird population resilience to extreme high temperatures. Ecol. Lett. 9, 1321–1330 (2006).Article 

Google Scholar 
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).Article 
ADS 

Google Scholar 
Laufkötter, C., Zscheischler, J. & Frölicher, T. L. High-impact marine heatwaves attributable to human-induced global warming. Science 369, 1621–1625 (2020).Article 
ADS 

Google Scholar 
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).Article 
ADS 

Google Scholar 
Oliver, E. C. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).Article 
ADS 

Google Scholar 
Field, C. B., Barros, V., Stocker, T. F. & Dahe, Q. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2012).Woolway, R. I. et al. Lake heatwaves under climate change. Nature 589, 402–407 (2021).Article 
ADS 
CAS 

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).Article 
ADS 
CAS 

Google Scholar 
Cahill, A. E. et al. Causes of warm-edge range limits: systematic review, proximate factors and implications for climate change. J. Biogeogr. 41, 429–442 (2014).Article 

Google Scholar 
Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).Article 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).Article 

Google Scholar 
Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).Article 
ADS 
CAS 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 
ADS 

Google Scholar 
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).Article 

Google Scholar 
Louthan, A. M., Doak, D. F. & Angert, A. L. Where and when do species interactions set range limits? Trends Ecol. Evol. 30, 780–792 (2015).Article 

Google Scholar 
Barbarossa, V. et al. Threats of global warming to the world’s freshwater fishes. Nat. Commun. 12, 1701 (2021).Article 
ADS 
CAS 

Google Scholar 
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).Article 

Google Scholar 
Qu, Y.-F. & Wiens, J. J. Higher temperatures lower rates of physiological and niche evolution. Proc. R. Soc. B 287, 20200823 (2020).Article 

Google Scholar 
Pither, J. Climate tolerance and interspecific variation in geographic range size. Proc. R. Soc. Lond. B 270, 475–481 (2003).Article 

Google Scholar 
Bennett, J. M. et al. GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).Article 

Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); http://www.R-project.org/Chen, H., Sun, J., Lin, W. & Xu, H. Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Sci. Bull. 65, 1415–1418 (2020).Article 

Google Scholar  More