Ecology

1.Gruber, N. Warming up, turning sour, losing breath: ocean biogeochemistry under global change. Philos. Trans. A Math. Phys. Eng. Sci. 369, 1980–1996 (2011). Identifies the potential synegistic threat to marine ecosystems resulting from ocean warming, deoxygenation and acidification.ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, aac4722 (2015).PubMed 
PubMed Central 

Google Scholar 
3.Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018).PubMed 
PubMed Central 

Google Scholar 
4.Cheng, L. et al. Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv. 3, e1601545 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
5.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 

Google Scholar 
6.Keeling, R. F., Kortzinger, A. & Gruber, N. Ocean deoxygenation in a warming world. Ann. Rev. Mar. Sci. 2, 199–229 (2010).PubMed 
PubMed Central 

Google Scholar 
7.Li, G. et al. Increasing ocean stratification over the past half-century. Nat. Clim. Change 10, 1116–1123 (2020).ADS 

Google Scholar 
8.Sallée, J. B. et al. Summertime increases in upper-ocean stratification and mixed-layer depth. Nature 591, 592–598 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
9.Sarmiento, J. L. & Gruber, N. Ocean Biogeochemical Dynamics (Princeton Univ. Press, 2006).10.Mikaloff Fletcher, S. E. et al. Inverse estimates of anthropogenic CO2 uptake, transport, and storage by the ocean. Glob. Biogeochem. Cycles 20, GB2002 (2006).ADS 

Google Scholar 
11.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).PubMed 
PubMed Central 

Google Scholar 
12.Doney, S. C., Busch, D. S., Cooley, S. R. & Kroeker, K. J. The impacts of ocean acidification on marine ecosystems and reliant human communities. Annu. Rev. Environ. Resour. 45, 83–112 (2020).
Google Scholar 
13.Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).PubMed 
PubMed Central 

Google Scholar 
14.Mollica, N. R. et al. Ocean acidification affects coral growth by reducing skeletal density. Proc. Natl Acad. Sci. USA 115, 1754–1759 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).ADS 

Google Scholar 
16.Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).ADS 
CAS 

Google Scholar 
17.Seneviratne, S. I. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (Cambridge Univ. Press, 2012).18.Lavell, A. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 25–64 (2012).19.Parmesan, C., Root, T. L. & Willig, M. R. Impacts of extreme weather and climate on terrestrial biota. Bull. Am. Meteor. Soc. 81, 443–450 (2000).ADS 

Google Scholar 
20.Smith, M. D. An ecological perspective on extreme climatic events: a synthetic definition and framework to guide future research. J. Ecol. 99, 656–663 (2011).
Google Scholar 
21.Oliver, E. C. J. et al. Marine heatwaves. Ann. Rev. Mar. Sci. 13, 313–342 (2021).PubMed 
PubMed Central 

Google Scholar 
22.Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018). Quantifies the future evolution of marine heatwaves under different climate scenarios and their attribution to climate change.ADS 
PubMed 
PubMed Central 

Google Scholar 
23.Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018). Highlights the strong increase in the occurrence and intensity of marine heatwaves in recent decades.ADS 
PubMed 
PubMed Central 

Google Scholar 
24.Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 734 (2019).
Google Scholar 
25.Benedetti-Cecchi, L. Complex networks of marine heatwaves reveal abrupt transitions in the global ocean. Sci. Rep. 11, 1739 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019). Assesses the potential for global ocean ecosystem impacts of marine heatwaves.ADS 

Google Scholar 
27.Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change 3, 78–82 (2012). Demonstrates marked ocean ecosystem changes in response to a heatwave.ADS 

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

Google Scholar 
29.Holbrook, N. J. et al. A global assessment of marine heatwaves and their drivers. Nat. Commun. 10, 2624 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
30.Huang, B. et al. Extended reconstructed sea surface temperature version 4 (ERSST.v4). Part I: upgrades and intercomparisons. J. Clim. 28, 911–930 (2015).ADS 

Google Scholar 
31.Gentemann, C. L., Fewings, M. R. & García-Reyes, M. Satellite sea surface temperatures along the west coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophys. Res. Lett. 44, 312–319 (2017).ADS 

Google Scholar 
32.Cavole, L. et al. Biological impacts of the 2013–2015 warm-water anomaly in the northeast Pacific: winners, losers, and the future. Oceanography 29, 273–285 (2016). A synthesis of the ecosystem impacts of the 2013–2015 Blob heatwave.33.Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 north Pacific marine heatwave. Nat. Clim. Change 6, 1042–1047 (2016).ADS 

Google Scholar 
34.Brodeur, R. D., Auth, T. D. & Phillips, A. J. Major shifts in pelagic micronekton and macrozooplankton community structure in an upwelling ecosystem related to an unprecedented marine heatwave. Front. Mar. Sci. 6, 212 (2019).
Google Scholar 
35.Cheung, W. W. L. & Frölicher, T. L. Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep. 10, 6678 (2020).ADS 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
37.Hauri, C., Gruber, N., McDonnell, A. M. P. & Vogt, M. The intensity, duration, and severity of low aragonite saturation state events on the California continental shelf. Geophys. Res. Lett. 40, 3424–3428 (2013). Models the evolution of ocean-acidification-related extremes in the California Current System.ADS 

Google Scholar 
38.Burger, F. A., John, J. G. & Frölicher, T. L. Increase in ocean acidity variability and extremes under increasing atmospheric CO2. Biogeosciences 17, 4633–4662 (2020).ADS 
CAS 

Google Scholar 
39.Leonard, M. et al. A compound event framework for understanding extreme impacts. Wiley Interdiscip. Rev. Clim. Change 5, 113–128 (2014).
Google Scholar 
40.Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).
Google Scholar 
41.Le Grix, N., Zscheischler, J., Laufkötter, C., Rousseaux, C. S. & Frölicher, T. L. Compound high-temperature and low-chlorophyll extremes in the ocean over the satellite period. Biogeosciences 18, 2119–2137 (2021).ADS 

Google Scholar 
42.Boyd, P. W. & Brown, C. J. Modes of interactions between environmental drivers and marine biota. Front. Mar. Sci. 2, 9 (2015).
Google Scholar 
43.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).44.Limburg, K. E., Breitburg, D., Swaney, D. P. & Jacinto, G. Ocean deoxygenation: a primer. One Earth 2, 24–29 (2020).
Google Scholar 
45.Dunne, J. P. et al. GFDL’s ESM2 global coupled climate-carbon earth system models. Part II: carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).ADS 

Google Scholar 
46.Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).ADS 

Google Scholar 
47.Allen, M. R. et al. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C Above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change (eds. Masson-Delmotte, V. et al.) 49–91 (IPCC, 2018).48.Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 650 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
49.Pilo, G. S., Holbrook, N. J., Kiss, A. E. & Hogg, A. M. C. sensitivity of marine heatwave metrics to ocean model resolution. Geophys. Res. Lett. 46, 14604–14612 (2019).ADS 

Google Scholar 
50.Schlegel, R. W., Oliver, E. C. J., Hobday, A. J. & Smit, A. J. Detecting marine heatwaves with sub-optimal data. Front. Mar. Sci. 6, 737 (2019).
Google Scholar 
51.Hobday, A. et al. Categorizing and naming marine heatwaves. Oceanography 31, 162–173 (2018).52.Alexander, M. A. et al. The atmospheric bridge: the influence of ENSO teleconnections on air–sea interaction over the global oceans. J. Clim. 15, 2205–2231 (2002).ADS 

Google Scholar 
53.Amaya, D. J., Miller, A. J., Xie, S. P. & Kosaka, Y. Physical drivers of the summer 2019 north Pacific marine heatwave. Nat. Commun. 11, 1903 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Negrete-García, G., Lovenduski, N. S., Hauri, C., Krumhardt, K. M. & Lauvset, S. K. Sudden emergence of a shallow aragonite saturation horizon in the Southern Ocean. Nat. Clim. Change 9, 313–317 (2019).ADS 

Google Scholar 
55.Schaeffer, A. & Roughan, M. Subsurface intensification of marine heatwaves off southeastern Australia: the role of stratification and local winds. Geophys. Res. Lett. 44, 5025–5033 (2017).ADS 

Google Scholar 
56.Jackson, J. M., Johnson, G. C., Dosser, H. V. & Ross, T. Warming from recent marine heatwave lingers in deep British Columbia fjord. Geophys. Res. Lett. 45, 9757–9764 (2018).ADS 

Google Scholar 
57.Scannell, H. A., Johnson, G. C., Thompson, L., Lyman, J. M. & Riser, S. C. Subsurface evolution and persistence of marine heatwaves in the northeast Pacific. Geophys. Res. Lett. 47, e2020GL090548 (2020).ADS 

Google Scholar 
58.Deutsch, C., Brix, H., Ito, T., Frenzel, H. & Thompson, L. Climate-forced variability of ocean hypoxia. Science 333, 336–339 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Frenger, I. et al. Biogeochemical role of subsurface coherent eddies in the ocean: tracer cannonballs, hypoxic storms, and microbial stewpots? Glob. Biogeochem. Cycles 32, 226–249 (2018).ADS 
CAS 

Google Scholar 
60.Schütte, F. et al. Characterization of ‘dead-zone’ eddies in the eastern tropical north Atlantic. Biogeosciences 13, 5865–5881 (2016).ADS 

Google Scholar 
61.Lauvset, S. K. et al. Processes driving global interior ocean pH distribution. Glob. Biogeochem. Cycles 34, e2019GB006229 (2020).ADS 
CAS 

Google Scholar 
62.Gaube, P., Chelton, D. B., Strutton, P. G. & Behrenfeld, M. J. Satellite observations of chlorophyll, phytoplankton biomass, and Ekman pumping in nonlinear mesoscale eddies. J. Geophys. Res. Oceans 118, 6349–6370 (2013).ADS 
CAS 

Google Scholar 
63.Frenger, I., Münnich, M., Gruber, N. & Knutti, R. Southern Ocean eddy phenomenology. J. Geophys. Res. Oceans 120, 7413–7449 (2015).ADS 

Google Scholar 
64.Hauss, H. et al. Dead zone or oasis in the open ocean? Zooplankton distribution and migration in low-oxygen modewater eddies. Biogeosciences 13, 1977–1989 (2016).ADS 
CAS 

Google Scholar 
65.Gruber, N. et al. Rapid progression of ocean acidification in the California Current System. Science 337, 220–223 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Santora, J. A. et al. Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat. Commun. 11, 536 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 42, 3414–3420 (2015).ADS 

Google Scholar 
68.Peterson, W. T., Bond, N. A. & Robert, M. The Blob (part three): going, going, gone? PICES Press 24, 46–48 (2016).
Google Scholar 
69.Frischknecht, M., Münnich, M. & Gruber, N. Local atmospheric forcing driving an unexpected California Current System response during the 2015–2016 El Niño. Geophys. Res. Lett. 44, 304–311 (2017).ADS 

Google Scholar 
70.Pörtner, H. O. & Knust, R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315, 95–97 (2007).ADS 
PubMed 
PubMed Central 

Google Scholar 
71.Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Temperature-dependent hypoxia explains biogeography and severity of end-Permian marine mass extinction. Science 362, eaat1327 (2018).72.Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Pörtner, H. O. Ecosystem effects of ocean acidification in times of ocean warming: a physiologist’s view. Mar. Ecol. Progr. Ser. 373, 203–217 (2008).ADS 

Google Scholar 
74.Stillman, J. H. Heat waves, the new normal: summertime temperature extremes will impact animals, ecosystems, and human communities. Physiology 34, 86–100 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Straub, S. C. et al. Resistance, extinction, and everything in between—the diverse responses of seaweeds to marine heatwaves. Front. Mar. Sci. 6, 763 (2019).
Google Scholar 
76.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018). Demonstrates the global-scale impact of marine heatwaves on warm-water corals.ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Donovan, M. K. et al. Local conditions magnify coral loss following marine heatwaves. Science 372, 977–980 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
78.Klein, S. G. et al. Projecting coral responses to intensifying marine heatwaves under ocean acidification. Glob. Change Biol. https://doi.org/10.1111/gcb.15818 (2021).79.McMahon, B. R. Physiological responses to oxygen depletion in intertidal animals. Am. Zool. 28, 39–53 (1988).
Google Scholar 
80.Kroeker, K. J. et al. Ecological change in dynamic environments: accounting for temporal environmental variability in studies of ocean change biology. Glob. Change Biol. 26, 54–67 (2020).ADS 

Google Scholar 
81.Hofmann, G. E. et al. High-frequency dynamics of ocean pH: a multi-ecosystem comparison. PLoS ONE 6, e28983 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
82.Spisla, C. et al. Extreme levels of ocean acidification restructure the plankton community and biogeochemistry of a temperate coastal ecosystem: a mesocosm study. Front. Mar. Sci. 7, 611157 (2021).
Google Scholar 
83.Engström-Öst, J. et al. Eco-physiological responses of copepods and pteropods to ocean warming and acidification. Sci. Rep. 9, 4748 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
84.Bednaršek, N. et al. El Niño-related thermal stress coupled with upwelling-related ocean acidification negatively impacts cellular to population-level responses in pteropods along the California current system with implications for increased bioenergetic costs. Front. Mar. Sci. 5, 486 (2018). Shows the impact of a compound event on pteropods, a keystone zooplankton species in many marine ecosystems.
Google Scholar 
85.Calderón-Liévanos, S. et al. Survival and respiration of green abalone (Haliotis fulgens) facing very short-term marine environmental extremes. Mar. Freshw. Behav. Physiol. 52, 1–15 (2019).
Google Scholar 
86.Mieszkowska, N., Burrows, M. T., Hawkins, S. J. & Sugden, H. Impacts of pervasive climate change and extreme events on rocky intertidal communities: evidence from long-term data. Front. Mar. Sci. 8, 642764 (2021).87.Nielsen, J. M. et al. Responses of ichthyoplankton assemblages to the recent marine heatwave and previous climate fluctuations in several northeast Pacific marine ecosystems. Glob. Chang. Biol. 27, 506–520 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
88.Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
89.Garrabou, J. et al. Mass mortality in northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).ADS 

Google Scholar 
90.Darling, E. S., McClanahan, T. R. & Côté, I. M. Life histories predict coral community disassembly under multiple stressors. Glob. Change Biol. 19, 1930–1940 (2013).ADS 

Google Scholar 
91.Ateweberhan, M., McClanahan, T. R., Graham, N. A. J. & Sheppard, C. R. C. Episodic heterogeneous decline and recovery of coral cover in the Indian Ocean. Coral Reefs 30, 739–752 (2011).ADS 

Google Scholar 
92.Weitzman, B. et al. Changes in rocky intertidal community structure during a marine heatwave in the Northern Gulf of Alaska. Front. Mar. Sci. 8, 556820 (2021).93.Samuels, T., Rynearson, T. A. & Collins, S. Surviving heatwaves: thermal experience predicts life and death in a Southern Ocean Diatom. Front. Mar. Sci. 8, 600343 (2021).94.Vajedsamiei, J., Wahl, M., Schmidt, A. L., Yazdanpanahan, M. & Pansch, C. The higher the needs, the lower the tolerance: extreme events may select ectotherm recruits with lower metabolic demand and heat sensitivity. Front. Mar. Sci. 8, 660427 (2021).
Google Scholar 
95.Bindoff, N. L. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds. Pörtner, H.-O. et al.) Ch. 5 (IPCC, 2021).96. IPCC. Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) Ch. 6 (Cambridge Univ. Press, 2014).97.Harvey, B. P., Gwynn-Jones, D. & Moore, P. J. Meta-analysis reveals complex marine biological responses to the interactive effects of ocean acidification and warming. Ecol. Evol. 3, 1016–1030 (2013).PubMed 
PubMed Central 

Google Scholar 
98.Gunderson, A. R., Armstrong, E. J. & Stillman, J. H. Multiple stressors in a changing world: the need for an improved perspective on physiological responses to the dynamic marine environment. Ann. Rev. Mar. Sci. 8, 357–378 (2016).PubMed 
PubMed Central 

Google Scholar 
99.Seifert, M., Rost, B., Trimborn, S. & Hauck, J. Meta-analysis of multiple driver effects on marine phytoplankton highlights modulating role of pCO2. Glob. Change Biol. 26, 6787–6804 (2020).ADS 

Google Scholar 
100.Sampaio, E. et al. Impacts of hypoxic events surpass those of future ocean warming and acidification. Nat. Ecol. Evol. 5, 311–321 (2021).PubMed 
PubMed Central 

Google Scholar 
101.Bernhardt, J. R., O’Connor, M. I., Sunday, J. M. & Gonzalez, A. Life in fluctuating environments: adaptation to changing environments. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190454 (2020).
Google Scholar 
102.Somero, G. N. The cellular stress response and temperature: function, regulation, and evolution. J. Exp. Zool. A Ecol. Integr. Physiol. 333, 379–397 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
103.Fordyce, A. J., Ainsworth, T. D., Heron, S. F. & Leggat, W. Marine heatwave hotspots in coral reef environments: physical drivers, ecophysiological outcomes and impact upon structural complexity. Front. Mar. Sci. 6, 498 (2019).
Google Scholar 
104.Krueger, T. et al. Antioxidant plasticity and thermal sensitivity in four types of Symbiodinium sp. J. Phycol. 50, 1035–1047 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
105.Reusch, T. B. H. & Boyd, P. W. Experimental evolution meets marine phytoplankton. Evolution 67, 1849–1859 (2013).PubMed 
PubMed Central 

Google Scholar 
106.Schlüter, L., Lohbeck, K. T., Gröger, J. P., Riebesell, U. & Reusch, T. B. H. Long-term dynamics of adaptive evolution in a globally important phytoplankton species to ocean acidification. Sci. Adv. 2, e1501660 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
107.Schlüter, L. et al. Adaptation of a globally important coccolithophore to ocean warming and acidification. Nat. Clim. Change 4, 1024–1030 (2014).ADS 

Google Scholar 
108.Aranguren-Gassis, M., Kremer, C. T., Klausmeier, C. A. & Litchman, E. Nitrogen limitation inhibits marine diatom adaptation to high temperatures. Ecol. Lett. 22, 1860–1869 (2019).PubMed 
PubMed Central 

Google Scholar 
109.Dam, H. G. Evolutionary adaptation of marine zooplankton to global change. Ann. Rev. Mar. Sci. 5, 349–370 (2013).PubMed 
PubMed Central 

Google Scholar 
110.Hinder, S. L. et al. Multi-decadal range changes vs. thermal adaptation for north east Atlantic oceanic copepods in the face of climate change. Glob. Change Biol. 20, 140–146 (2014).ADS 

Google Scholar 
111.Antell, G. S., Fenton, I. S., Valdes, P. J. & Saupe, E. E. Thermal niches of planktonic foraminifera are static throughout glacial-interglacial climate change. Proc. Natl Acad. Sci. USA 118, e2017105118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
112.Lonhart, S. I., Jeppesen, R., Beas-Luna, R., Crooks, J. A. & Lorda, J. Shifts in the distribution and abundance of coastal marine species along the eastern Pacific Ocean during marine heatwaves from 2013 to 2018. Mar. Biodivers. Rec. 12, 13 (2019).
Google Scholar 
113.Pinsky, M. L., Selden, R. L. & Kitchel, Z. J. Climate-driven shifts in marine species ranges: scaling from organisms to communities. Ann. Rev. Mar. Sci. 12, 153–179 (2020).PubMed 
PubMed Central 

Google Scholar 
114.Thurman, L. L. et al. Persist in place or shift in space? Evaluating the adaptive capacity of species to climate change. Front. Ecol. Environ. 18, 520–528 (2020).
Google Scholar 
115.Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).PubMed 
PubMed Central 

Google Scholar 
116.Dutkiewicz, S., Boyd, P. W. & Riebesell, U. Exploring biogeochemical and ecological redundancy in phytoplankton communities in the global ocean. Glob. Change Biol. 27, 1196–1213 (2021).ADS 

Google Scholar 
117.Bernhardt, J. R. & Leslie, H. M. Resilience to climate change in coastal marine ecosystems. Ann. Rev. Mar. Sci. 5, 371–392 (2013).PubMed 
PubMed Central 

Google Scholar 
118.Grant, P. R. et al. Evolution caused by extreme events. Philos. Trans. R. Soc. B Biol. Sci. 372, 5–8 (2017).ADS 

Google Scholar 
119.Gonzalez, A. & Loreau, M. The causes and consequences of compensatory dynamics in ecological communities. Annu. Rev. Ecol. Evol. Syst. 40, 393–414 (2009).
Google Scholar 
120.Vallina, S. M. & Le Quéré, C. Stability of complex food webs: resilience, resistance and the average interaction strength. J. Theor. Biol. 272, 160–173 (2011).ADS 
MathSciNet 
PubMed 
PubMed Central 
MATH 

Google Scholar 
121.Neutel, A. M. et al. Reconciling complexity with stability in naturally assembling food webs. Nature 449, 599–602 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
122.Ives, A. R. & Cardinale, B. J. Food-web interactions govern the resistance of communities after non-random extinctions. Nature 429, 174–177 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
123.Nagelkerken, I., Goldenber, S. U., Ferreir, C. M., Ullah, H. & Conne, S. D. Trophic pyramids reorganize when food web architecture fails to adjust to ocean change. Science 369, 829–832 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
124.Carpenter, S. R. et al. Cascading trophic interactions and lake productivity. Bioscience 35, 634–639 (1985).
Google Scholar 
125.Bideault, A. et al. Thermal mismatches in biological rates determine trophic control and biomass distribution under warming. Glob. Change Biol. 27, 257–269 (2021).ADS 

Google Scholar 
126.Dee, L. E., Okamtoto, D., Gårdmark, A., Montoya, J. M. & Miller, S. J. Temperature variability alters the stability and thresholds for collapse of interacting species: species interactions facing variability. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190457 (2020).
Google Scholar 
127.Adjeroud, M. et al. Recovery of coral assemblages despite acute and recurrent disturbances on a south central Pacific reef. Sci. Rep. 8, 9680 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
128.Hughes, T. P. et al. Ecological memory modifies the cumulative impact of recurrent climate extremes. Nat. Clim. Change 9, 40–43 (2019).ADS 

Google Scholar 
129.Boyd, P. W. et al. Biological responses to environmental heterogeneity under future ocean conditions. Glob. Change Biol. 22, 2633–2650 (2016).ADS 

Google Scholar 
130.Ainsworth, T. D., Hurd, C. L., Gates, R. D. & Boyd, P. W. How do we overcome abrupt degradation of marine ecosystems and meet the challenge of heat waves and climate extremes? Glob. Change Biol. 26, 343–354 (2020).ADS 

Google Scholar 
131.Pörtner, H.-O. Oxygen- and capacity-limitation of thermal tolerance: a matrix for integrating climate-related stressor effects in marine ecosystems. J. Exp. Biol. 213, 881–893 (2010). Develops the concept of how other stressors can interact with each other in marine ectotherms.PubMed 
PubMed Central 

Google Scholar 
132.Deutsch, C., Ferrel, A., Seibel, B., Portner, H.-O. & Huey, R. B. Climate change tightens a metabolic constraint on marine habitats. Science 348, 1132–1135 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
133.Deutsch, C., Penn, J. L. & Seibel, B. Metabolic trait diversity shapes marine biogeography. Nature 585, 557–562 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
134.Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).ADS 

Google Scholar 
135.Bertolini, C. & Pastres, R. Tolerance landscapes can be used to predict species-specific responses to climate change beyond the marine heatwave concept: using tolerance landscape models for an ecologically meaningful classification of extreme climate events. Estuar. Coast. Shelf Sci. 252, 107284 (2021).
Google Scholar 
136.Le Gland, G., Vallina, S. M., Smith, S. L. & Cermeño, P. SPEAD 1.0—simulating plankton evolution with adaptive dynamics in a two-trait continuous fitness landscape applied to the Sargasso Sea. Geosci. Model Dev. 14, 1949–1985 (2021).ADS 

Google Scholar 
137.Merico, A., Bruggeman, J. & Wirtz, K. A trait-based approach for downscaling complexity in plankton ecosystem models. Ecol. Modell. 220, 3001–3010 (2009).CAS 

Google Scholar 
138.Walworth, N. G., Zakem, E. J., Dunne, J. P., Collins, S. & Levine, N. M. Microbial evolutionary strategies in a dynamic ocean. Proc. Natl Acad. Sci. USA 117, 5943–5948 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
139.Toseland, A. et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nat. Clim. Change 3, 979–984 (2013).ADS 
CAS 

Google Scholar 
140.Collins, M. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds. Pörtner, H.-O. et al.) Ch. 6 (IPCC, 2021).141.Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
142.Cheung, W. W. L., Reygondeau, G. & Frölicher, T. L. Large benefits to marine fisheries of meeting the 1.5 °C global warming target. Science 354, 1591–1594 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
143.Rashid Sumaila, U. et al. Benefits of the Paris Agreement to ocean life, economies, and people. Sci. Adv. 5, eaau3855 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
144.Tilbrook, B. et al. An enhanced ocean acidification observing network: from people to technology to data synthesis and information exchange. Front. Mar. Sci. 6, 337 (2019).
Google Scholar 
145.Claustre, H., Johnson, K. S. & Takeshita, Y. Observing the global ocean with Biogeochemical-Argo. Ann. Rev. Mar. Sci. 12, 23–48 (2020).PubMed 
PubMed Central 

Google Scholar 
146.Chai, F. et al. Monitoring ocean biogeochemistry with autonomous platforms. Nat. Rev. Earth Environ. 1, 315–326 (2020).ADS 

Google Scholar 
147.Fennel, K. et al. Advancing marine biogeochemical and ecosystem reanalyses and forecasts as tools for monitoring and managing ecosystem health. Front. Mar. Sci. 6, 89 (2019).
Google Scholar 
148.Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
149.Sen Gupta, A. et al. Drivers and impacts of the most extreme marine heatwaves events. Sci. Rep. 10, 19359 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
150.Holbrook, N. J. et al. Keeping pace with marine heatwaves. Nat. Rev. Earth Environ. 1, 482–493 (2020).ADS 

Google Scholar 
151.Boyd, P. & Hutchins, D. Understanding the responses of ocean biota to a complex matrix of cumulative anthropogenic change. Mar. Ecol. Prog. Ser. 470, 125–135 (2012).ADS 

Google Scholar 
152.Thomas, M. K. et al. Temperature–nutrient interactions exacerbate sensitivity to warming in phytoplankton. Glob. Change Biol. 23, 3269–3280 (2017).ADS 

Google Scholar 
153.Clark, J. R., Daines, S. J., Lenton, T. M., Watson, A. J. & Williams, H. T. P. Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters. Ecol. Modell. 222, 3823–3837 (2011).
Google Scholar 
154.Bruggeman, J. & Kooijman, S. A. L. M. A biodiversity-inspired approach to aquatic ecosystem modeling. Limnol. Oceanogr. 52, 1533–1544 (2007).ADS 

Google Scholar 
155.Small-Lorenz, S. L., Culp, L. A., Ryder, T. B., Will, T. C. & Marra, P. P. A blind spot in climate change vulnerability assessments. Nat. Clim. Change 3, 91–93 (2013).ADS 

Google Scholar 
156.Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
157.Halpern, B. S. et al. An index to assess the health and benefits of the global ocean. Nature 488, 615–620 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
158.Suryan, R. M. et al. Ecosystem response persists after a prolonged marine heatwave. Sci. Rep. 11, 6235 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
159.Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).
Google Scholar 
160.Liquete, C. et al. Current status and future prospects for the assessment of marine and coastal ecosystem services: a systematic review. PLoS ONE 8, e67737 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
161.Glynn, P. W. & D’Croz, L. Experimental evidence for high temperature stress as the cause of El Nino-coincident coral mortality. Coral Reefs 8, 181–191 (1990).ADS 

Google Scholar 
162.Eakin, C. M. et al. Caribbean corals in crisis: record thermal stress, bleaching, and mortality in 2005. PLoS ONE 5, e13969 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
163.Gardner, J., Manno, C., Bakker, D. C. E., Peck, V. L. & Tarling, G. A. Southern Ocean pteropods at risk from ocean warming and acidification. Mar. Biol. 165, 8 (2018).PubMed 
PubMed Central 

Google Scholar  More

Quantification of changes in floristic similarityTo quantify changes in floristic similarity by naturalized flowering plant species, we extracted regional lists of alien species from the Global Naturalized Alien Flora (GloNAF) database45 and regional lists of native species from the Global Inventory of Floras and Traits (GIFT) database46. The GloNAF database contains lists of naturalized vascular plant taxa for 861 regions (countries or subnational administrative units), ranging in size from 0.03 to 6,864,961 km2 (median size is 15,152 km2) and covering >80% of the terrestrial ice-free surface globally47. GloNAF includes 13,803 plant taxa that, according to the original data sources, are alien plants and have established self-sustaining wild populations in the respective regions (i.e., are naturalized5). The GIFT database is a compilation of floras and checklists of predominantly native vascular plant species with an indication of their floristic status for more than 300,000 species across nearly 3000 regions with near global coverage46. We first selected regions that matched perfectly between GloNAF and GIFT. Additionally, we merged some GloNAF regions to match a larger GIFT region, and vice versa, by comparing the overlapping area of nested regions using the R package ‘sf’ (version 0.8-0)48.To ensure the highest data quality, and to be on the conservative side, we restricted our analysis to regions with complete or nearly complete checklists of both native and naturalized alien species. For GloNAF, we only included regions for which there was at least one species list judged to include more than 50% of the naturalized taxa for that region45. Although the judgment of species-list completeness is coarse and for most lists made by the GloNAF curators, it allows the exclusion of regions for which the data are obviously poor. For GIFT, we included a region only if at least one species list aimed to represent its entire native angiosperm flora. Our strict selection criteria resulted in a dataset including native and naturalized species for 658 non-overlapping regions, including 154 island regions, 503 mainland regions and one region including both islands and mainland areas (Chile). These regions covered all continents, except Antarctica, but there was low coverage for parts of Africa and Asia (Fig. 4).We restricted our analyses to flowering plants (angiosperms), which had the most complete species lists, and to species with accepted names in The Plant List24 (http://www.theplantlist.org/). We excluded species with an uncertain native/alien status or with a conflicting status, i.e., being native to a region according to GIFT but being alien to the same region according to GloNAF. Furthermore, since the native/alien status of many infraspecific taxa and hybrid taxa are less clear, we restricted our analyses to the species level (i.e., infraspecific taxa were assigned to the binomial species name), and we excluded hybrids. Our final dataset included 1,139,254 native species-by-region records for 189,110 species and 141,762 naturalized species-by-region records for 10,130 species.For all 216,153 possible pairwise combinations of the 658 regions, we quantified the taxonomic and phylogenetic similarities between their native floras (SimTaxnative, SimPhylnative), and between their floras including both native and naturalized alien species (SimTaxnative+naturalized, SimPhylnative+naturalized). As the regions vary largely in species richness (ranging from 11 to 13,720 species with a median of 1704), we used the Simpson similarity index for taxonomic similarity (Eq. 1)49, which is largely insensitive to species richness:50$${SimTax}=1-frac{{{min }}left(b,cright)}{a+{{min }}left(b,cright)}$$
(1)
Here a is the number of species common to both regions, b is the number of species that occur in the first region but not in the second and c is the number of species that occur in the second region but not in the first51. Likewise, we calculated the Simpson phylogenetic similarity index as phylogenetic similarity (Eq. 2) as implemented in the R package ‘betapart’ (version 1.5.1)52:$${SimPhyl}=1-frac{{{min }}left(B,Cright)}{A+{{min }}left(B,Cright)}$$
(2)
Here A is the total length of the phylogenetic branches in the phylogenetic tree that are shared by the species of both regions, B is the total length of the phylogenetic branches that are shared only by the first region and C is the total length of the phylogenetic branches that are shared only by the second region51. To quantify changes in similarity due to naturalization of alien species, we calculated the degree of homogenization H (or differentiation, see below) for each pair of regions as$$H={ln}frac{{{Sim}}_{{native}+{naturalized}}+0.001}{{{Sim}}_{{native}}+0.001}$$
(3)
A small value of 0.001 was added to both similarities to avoid infinite values. A positive log-response ratio indicates homogenization (i.e., increased floristic similarity between two regions), and a negative one indicates differentiation (i.e., decreased floristic similarity). As an alternative to the Simpson similarity index, we also calculate the Sørensen similarity index, which additionally takes into consideration the nestedness of the floras in the paired regions51. As the results were not sensitive to the choice of similarity indices (Supplementary Fig. 14), we focused our analyses on the Simpson similarity index.To quantify phylogenetic similarity, we used a phylogenetic tree including all angiosperms with accepted names in The Plant List (Supplementary Fig. 2). The tree was developed based on the mega phylogeny of Smith and Brown53. We added missing species (n = 71,124, of which 733 are naturalized in other regions) with their accepted names in The Plant List to the root of their genus or families. For details on the development of the phylogenetic tree, see ref. 47.Quantification of geographic distances and climatic distancesWe calculated the pairwise geographic distance between regions as the distance between their geographic centroids using the R package ‘geosphere’ (version 1.5-10)54. We also calculated the nearest distance between the geographic borders of regions. However, since the distances between geographic centroids are highly correlated with distances between region borders (n = 216,153, r = 0.996, P  More

1.Griffiths, J. R. et al. The importance of benthic-pelagic coupling for marine ecosystem functioning in a changing world. Glob. Chang. Biol. 23, 2179–2196 (2017).ADS 
PubMed 

Google Scholar 
2.Graf, G., Bengtsson, W., Diesner, U., Schulz, R. & Theede, H. Benthic response to sedimentation of a spring phytoplankton bloom: Process and budget. Mar. Biol. 67, 201–208 (1982).
Google Scholar 
3.Campanyà-llovet, N., Snelgrove, P. V. R. & Parrish, C. C. Rethinking the importance of food quality in marine benthic food webs. Prog. Oceanogr. 156, 240–251 (2017).
Google Scholar 
4.Blomqvist, S. & Heiskanen, A.-S. The challenge of sedimentation in the Baltic Sea. In A Systems Analysis of the Baltic Sea. Ecological Studies (Analysis and Synthesis) Vol. 148 (eds Wulff, F. V. et al.) 211–227 (Springer, Berlin, 2001).
Google Scholar 
5.Elmgren, R. Trophic dynamics in the enclosed, brackish Baltic Sea. Rapp. P.-V. Réun. Cons. int. Explor. Mer. 183, 152–169 (1984).
Google Scholar 
6.Kahru, M., Elmgren, R., Di Lorenzo, E. & Savchuk, O. Unexplained interannual oscillations of cyanobacterial blooms in the Baltic Sea. Sci. Rep. 8, 6–10 (2018).ADS 

Google Scholar 
7.BACC II Author Team. Second Assessment of Climate Change for the Baltic Sea Basin. (SpringerOpen, 2015) https://doi.org/10.1007/978-3-319-16006-1.8.Spilling, K. & Lindström, M. Phytoplankton life cycle transformations lead to species-specific effects on sediment processes in the Baltic Sea. Cont. Shelf Res. 28, 2488–2495 (2008).ADS 

Google Scholar 
9.Suikkanen, S. et al. Climate change and eutrophication induced shifts in northern summer plankton communities. PLoS ONE 8, e66475 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Tamelander, T., Spilling, K. & Winder, M. Organic matter export to the seafloor in the Baltic Sea: Drivers of change and future projections. Ambio 46, 842–851 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Giere, O. Meiobenthology: The Microscopic Motile Fauna of Aquatic Sediments (Springer, 2009).
Google Scholar 
12.Schratzberger, M. & Ingels, J. Meiofauna matters: The roles of meiofauna in benthic ecosystems. J. Exp. Mar. Biol. Ecol. 502, 12–25 (2018).
Google Scholar 
13.Bonaglia, S., Nascimento, F. J. A., Bartoli, M., Klawonn, I. & Brüchert, V. Meiofauna increases bacterial denitrification in marine sediments. Nat. Commun. 5, 5133 (2014).ADS 
CAS 
PubMed 

Google Scholar 
14.Nascimento, F. J. A., Näslund, J. & Elmgren, R. Meiofauna enhances organic matter mineralization in soft sediment ecosystems. Limnol. Oceanogr. 57, 338–346 (2012).ADS 
CAS 

Google Scholar 
15.Nealson, K. H. Sediment bacteria: Who’s there, what are they doing, and what’s new?. Annu. Rev. Earth Planet Sci. 25, 403–434 (1997).ADS 
CAS 
PubMed 

Google Scholar 
16.Meyer-Reil, L.-A. Seasonal and spatial distribution of extracellular enzymatic activities and microbial incorporation of dissolved organic substrates in marine sediments. Appl. Environ. Microbiol. 53, 1748–1755 (1987).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Ólafsson, E. & Elmgren, R. Seasonal dynamics of sublittoral meiobenthos in relation to phytoplankton sedimentation in the Baltic Sea. Estuar. Coast. Shelf Sci. 45, 149–164 (1997).ADS 

Google Scholar 
18.Pfannkuche, O. Benthic response to the sedimentation of particulate organic matter at the BIOTRANS station, 47°N, 20°W. Deep. Res. Part II 40, 135–149 (1993).
Google Scholar 
19.Hoffmann, K., Hassenrück, C., Salman-Carvalho, V., Holtappels, M. & Bienhold, C. Response of bacterial communities to different detritus compositions in Arctic deep-sea sediments. Front. Microbiol. 8, 266 (2017).PubMed 
PubMed Central 

Google Scholar 
20.Stoeck, T., Kochems, R., Forster, D., Lejzerowicz, F. & Pawlowski, J. Metabarcoding of benthic ciliate communities shows high potential for environmental monitoring in salmon aquaculture. Ecol. Indic. 85, 153–164 (2018).
Google Scholar 
21.Rudnick, D. T. Time lags between the deposition and meiobenthic assimilation of phytodetritus. Mar. Ecol. Prog. Ser. 50, 231–240 (1989).ADS 

Google Scholar 
22.van der Heijden, L. H. et al. How do food sources drive meiofauna community structure in soft-bottom coastal food webs?. Mar. Biol. 165, 166 (2018).
Google Scholar 
23.Schratzberger, M., Forster, R. M., Goodsir, F. & Jennings, S. Nematode community dynamics over an annual production cycle in the central North Sea. Mar. Environ. Res. 66, 508–519 (2008).CAS 
PubMed 

Google Scholar 
24.Wieser, W. Die beziehung zwischen mundhöhlengestalt, ernährungsweise und vorkommen bei freilebenden marinen nematoden. Ark Zool 2, 439–484 (1953).
Google Scholar 
25.Moens, T., Van Gansbeke, D. & Vincx, M. Linking estuarine nematodes to their suspected food. A case study from the Westerschelde Estuary (south-west Netherlands). J. Mar. Biol. Assoc. UK 79, 1017–1027 (1999).
Google Scholar 
26.Nascimento, F. J. A., Karlson, A. M. L. & Elmgren, R. Settling blooms of filamentous cyanobacteria as food for meiofauna assemblages. Limnol. Oceanogr. 53, 2636–2643 (2008).ADS 

Google Scholar 
27.Nascimento, F. J. A., Karlson, A. M. L., Näslund, J. & Gorokhova, E. Settling cyanobacterial blooms do not improve growth conditions for soft bottom meiofauna. J. Exp. Mar. Biol. Ecol. 368, 138–146 (2009).
Google Scholar 
28.Groendahl, S. & Fink, P. High dietary quality of non-toxic cyanobacteria for a benthic grazer and its implications for the control of cyanobacterial biofilms. BMC Ecol. 17, 20 (2017).PubMed 
PubMed Central 

Google Scholar 
29.Broman, E. et al. Spring and late summer phytoplankton biomass impact on the coastal sediment microbial community structure. Microb. Ecol. 77, 288–303 (2019).CAS 
PubMed 

Google Scholar 
30.Fagervold, S. K. et al. River organic matter shapes microbial communities in the sediment of the Rhône prodelta. ISME J. 8, 2327–2338 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Reed, H. E. & Martiny, J. B. H. Microbial composition affects the functioning of estuarine sediments. ISME J. 7, 868–879 (2013).CAS 
PubMed 

Google Scholar 
32.Tuominen, L. et al. Nutrient fluxes, porewater profiles and denitrification in sediment influenced by algal sedimentation and bioturbation by Monoporeia affinis. Estuar. Coast. Shelf Sci. 49, 83–97 (1999).ADS 
CAS 

Google Scholar 
33.Zilius, M., De Wit, R. & Bartoli, M. Response of sedimentary processes to cyanobacteria loading. J. Limnol. 75, 236–247 (2016).
Google Scholar 
34.Blazewicz, S. J., Barnard, R. L., Daly, R. A. & Firestone, M. K. Evaluating rRNA as an indicator of microbial activity in environmental communities: Limitations and uses. ISME J. 7, 2061–2068 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Guardiola, M. et al. Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA. PeerJ 4, e2807 (2016).PubMed 
PubMed Central 

Google Scholar 
36.Soto, E., Quiroga, E., Ganga, B. & Alarcón, G. Influence of organic matter inputs and grain size on soft-bottom macrobenthic biodiversity in the upwelling ecosystem of central Chile. Mar. Biodivers. 47, 433–450 (2017).
Google Scholar 
37.Broman, E., Bonaglia, S., Norkko, A., Creer, S. & Nascimento, F. J. A. High throughput shotgun sequencing of eRNA reveals taxonomic and derived functional shifts across a benthic productivity gradient. Mol. Ecol. 00, 1–17 (2020).CAS 

Google Scholar 
38.Ingels, J., Tchesunov, A. V. & Vanreusel, A. Meiofauna in the Gollum Channels and the Whittard Canyon, Celtic Margin—How local environmental conditions shape nematode structure and function. PLoS ONE 6, e20094 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Albert, S. et al. Influence of settling organic matter quantity and quality on benthic nitrogen cycling. Limnol. Oceanogr. 66, 1882–1895 (2021).ADS 
CAS 

Google Scholar 
40.Modig, H. & Ólafsson, E. Responses of Baltic benthic invertebrates to hypoxic events. J. Exp. Mar. Biol. Ecol. 229, 133–148 (1998).
Google Scholar 
41.Ankar, S. Annual dynamics of a Northern Baltic Soft Bottom. In Cyclic Phenomena in Marine Plants and Animals (eds Naylor, E. & Hartnoll, R. G.) 29–36 (Pergamon Press, 1979). https://doi.org/10.1016/b978-0-08-023217-1.50011-4.Chapter 

Google Scholar 
42.Karlson, A. M. L., Nascimento, F. J. A. & Elmgren, R. Incorporation and burial of carbon from settling cyanobacterial blooms by deposit-feeding macrofauna. Limnol. Oceanogr. 53, 2754–2758 (2008).ADS 

Google Scholar 
43.Hedberg, P., Albert, S., Nascimento, F. J. A. & Winder, M. Effects of changing phytoplankton species composition on carbon and nitrogen uptake in benthic invertebrates. Limnol. Oceanogr. 66, 469–480 (2021).ADS 
CAS 

Google Scholar 
44.Ólafsson, E., Modig, H. & van de Bund, W. J. Species specific uptake of radio-labelled phytodetritus by benthic meiofauna from the Baltic Sea. Mar. Ecol. Prog. Ser. 177, 63–72 (1999).ADS 

Google Scholar 
45.Guden, R. M., Vafeiadou, A., De Meester, N., Derycke, S. & Moens, T. Living apart-together: Microhabitat differentiation of cryptic nematode species in a saltmarsh habitat. PLoS ONE 13, e0204750 (2018).PubMed 
PubMed Central 

Google Scholar 
46.Rudnick, D. T. & Oviatt, C. A. Seasonal lags between organic carbon deposition and mineralization in marine sediments. J. Mar. Res. 44, 815–837 (1986).CAS 

Google Scholar 
47.Moens, T. et al. Diatom feeding across trophic guilds in tidal flat nematodes, and the importance of diatom cell size. J. Sea Res. 92, 125–133 (2014).ADS 

Google Scholar 
48.Schuelke, T., Pereira, T. J., Hardy, S. M. & Bik, H. M. Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats. Mol. Ecol. 27, 1930–1951 (2018).PubMed 

Google Scholar 
49.Fenchel, T. & Jansson, B.-O. On the vertical distribution of the microfauna in the sediments of a brackish-water beach. Ophelia 3, 161–177 (1966).
Google Scholar 
50.Fenchel, T. The ecology of marine microbenthos II. The food of marine benthic ciliates. Ophelia 5, 73–121 (1968).
Google Scholar 
51.Shimeta, J., Starczak, V. R., Ashiru, O. M. & Zimmer, C. A. Influences of benthic boundary-layer flow on feeding rates of ciliates and flagellates at the sediment-water interface. Limnol. Oceanogr. 46, 1709–1719 (2001).ADS 

Google Scholar 
52.Nagata, T. Organic matter–bacteria interactions in seawater. In Microbial Ecology of the Oceans 2nd edn (ed. Kirchman, D. L.) 207–241 (Wiley, 2008).
Google Scholar 
53.De Mesel, I. et al. Top-down impact of bacterivorous nematodes on the bacterial community structure: A microcosm study. Environ. Microbiol. 6, 733–744 (2004).PubMed 

Google Scholar 
54.Landa, M. et al. Phylogenetic and structural response of heterotrophic bacteria to dissolved organic matter of different chemical composition in a continuous culture study. Environ. Microbiol. 16, 1668–1681 (2014).CAS 
PubMed 

Google Scholar 
55.Izabel-Shen, D., Albert, S., Winder, M., Farnelid, H. & Nascimento, F. J. A. Quality of phytoplankton deposition structures bacterial communities at the water-sediment interface. Mol. Ecol. 30, 3515–3529 (2021).CAS 
PubMed 

Google Scholar 
56.Bowen, J. L., Babbin, A. R., Kearns, P. J. & Ward, B. B. Connecting the dots: Linking nitrogen cycle gene expression to nitrogen fluxes in marine sediment mesocosms. Front. Microbiol. 5, 429 (2014).PubMed 
PubMed Central 

Google Scholar 
57.Broman, E. et al. Denitrification responses to increasing cadmium exposure in Baltic Sea sediments. Aquat. Toxicol. 217, 105328 (2019).CAS 
PubMed 

Google Scholar 
58.van der Loos, L. M. & Nijland, R. Biases in bulk: DNA metabarcoding of marine communities and the methodology involved. Mol. Ecol. 30, 3270–3288 (2021).PubMed 

Google Scholar 
59.Zinger, L. et al. DNA metabarcoding—Need for robust experimental designs to draw sound ecological conclusions. Mol. Ecol. 28, 1857–1862 (2019).PubMed 

Google Scholar 
60.Prokopowich, C. D., Gregory, T. R. & Crease, T. J. The correlation between rDNA copy number and genome size in eukaryotes. Genome 46, 48–50 (2003).CAS 

Google Scholar 
61.Nascimento, F. J. A., Lallias, D., Bik, H. M. & Creer, S. Sample size effects on the assessment of eukaryotic diversity and community structure in aquatic sediments using high-throughput sequencing. Sci. Rep. 8, 11737 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
62.Brannock, P. M. & Halanych, K. M. Meiofaunal community analysis by high-throughput sequencing: Comparison of extraction, quality filtering, and clustering methods. Mar. Genomics 23, 67–75 (2015).PubMed 

Google Scholar 
63.Wallenstein, M. D., Myrold, D. D., Firestone, M. & Voytek, M. Environmental controls on denitrifying communities and denitrification rates: Insights from molecular methods. Ecol. Appl. 16, 2143–2152 (2006).PubMed 

Google Scholar 
64.Höglander, H., Larsson, U. & Hajdu, S. Vertical distribution and settling of spring phytoplankton in the offshore NW Baltic Sea proper. Mar. Ecol. Prog. Ser. 283, 15–27 (2004).ADS 

Google Scholar 
65.Walsby, A. E. Gas vesicles. Annu. Rev. Plant Physiol. 26, 427–439 (1975).CAS 

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

Google Scholar 
67.Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, 36–42 (2013).
Google Scholar 
68.Huson, D. H. et al. MEGAN community edition—Interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957 (2016).PubMed 
PubMed Central 

Google Scholar 
69.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).
Google Scholar 
70.Murali, A., Bhargava, A. & Wright, E. S. IDTAXA: A novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 1–14 (2018).
Google Scholar 
71.Urban-Malinga, B., Warzocha, J. & Zalewski, M. Effects of the invasive polychaete Marenzelleria spp. on benthic processes and meiobenthos of a species-poor brackish system. J. Sea Res. 80, 25–34 (2013).ADS 

Google Scholar 
72.McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Oksanen, J. et al. Vegan: Community ecology package. version 2.5-7, 1–298 (2020).74.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
75.Alberdi, A., Aizpurua, O., Gilbert, M. T. P. & Bohmann, K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol. Evol. 9, 134–147 (2017).
Google Scholar 
76.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
Google Scholar  More

AffiliationsDepartment of Integrative Biology, Oregon State University, Corvallis, OR, USABenjamin D. Dalziel & Mark NovakDepartment of Mathematics, Oregon State University, Corvallis, OR, USABenjamin D. DalzielCollege of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR, USAJames R. WatsonDepartment of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USAStephen P. EllnerAuthorsBenjamin D. DalzielMark NovakJames R. WatsonStephen P. EllnerCorresponding authorCorrespondence to
Benjamin D. Dalziel. More

1.Manel, S., Schwartz, M. K., Luikart, G. & Taberlet, P. Landscape genetics: Combining landscape ecology and population genetics. Trends Ecol. Evol. 18, 189–197. https://doi.org/10.1016/S0169-5347(03)00008-9 (2003).Article 

Google Scholar 
2.Storfer, A., Murphy, M. A., Spear, S. F., Holderegger, R. & Waits, L. P. Landscape genetics: Where are we now?. Mol. Ecol. 19, 3496–3514. https://doi.org/10.1111/j.1365-294X.2010.04691.x (2010).Article 
PubMed 

Google Scholar 
3.Alp, M., Keller, I., Westram, A. M. & Robinson, C. T. How river structure and biological traits influence gene flow: A population genetic study of two stream invertebrates with differing dispersal abilities. Freshw. Biol. 57, 969–981. https://doi.org/10.1111/j.1365-2427.2012.02758.x (2012).Article 

Google Scholar 
4.Mamos, T., Wattier, R., Majda, A., Sket, B. & Grabowski, M. Morphological vs. molecular delineation of taxa across montane regions in Europe: The case study of Gammarus balcanicus Schäferna, 1922 (Crustacea: Amphipoda). J. Zool. Syst. Evol. Res. 52, 237–248. https://doi.org/10.1111/jzs.12062 (2014).Article 

Google Scholar 
5.Mamos, T., Wattier, R., Burzýnski, A. & Grabowski, M. The legacy of a vanished sea: A high level of diversification within a European freshwater amphipod species complex driven by 15 My of Paratethys regression. Mol. Ecol. 25, 795–810. https://doi.org/10.1111/mec.13499 (2016).Article 
PubMed 

Google Scholar 
6.Grabowski, M., Mamos, T., Bacela-Spychalska, K., Rewicz, T. & Wattier, R. A. Neogene paleogeography provides context for understanding the origin and spatial distribution of cryptic diversity in a widespread balkan freshwater amphipod. PeerJ 5, e3016. https://doi.org/10.7717/peerj.3016 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Copilaş-Ciocianu, D., Zimţa, A. A., Grabowski, M. & Petrusek, A. Survival in northern microrefugia in an endemic Carpathian gammarid (Crustacea: Amphipoda). Zool. Scr. 47, 357–372. https://doi.org/10.1111/zsc.12285 (2018).Article 

Google Scholar 
8.Copilaș-Ciocianu, D., Zimța, A. & Petrusek, A. Integrative taxonomy reveals a new Gammarus species (Crustacea, Amphipoda) surviving in a previously unknown southeast European glacial refugium. J. Zool. Syst. Evol. Res. 57, 272–297. https://doi.org/10.1111/jzs.12248 (2019).Article 

Google Scholar 
9.Wattier, R. et al. Continental-scale patterns of hyper-cryptic diversity within the freshwater model taxon Gammarus fossarum (Crustacea, Amphipoda). Sci. Rep. 10, 16536. https://doi.org/10.1111/j.1365-2699.2012.02793.x (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Neumann, K. et al. Genetic spatial structure of European common hamsters (Cricetus cricetus)—A result of repeated range expansion and demographic bottlenecks. Mol. Ecol. 14, 1473–1483. https://doi.org/10.1111/j.1365-294X.2005.02519.x (2005).CAS 
Article 
PubMed 

Google Scholar 
11.Kotlík, P. et al. A northern glacial refugium for bank voles (Clethrionomys glareolus). PNAS 103, 14860–14864. https://doi.org/10.1073/pnas.0603237103 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Theissinger, K. et al. Glacial survival and post-glacial recolonization of an arctic-alpine freshwater insect (Arcynopteryx dichroa, Plecoptera, Perlodidae) in Europe. J. Biogeogr. 40, 236–248. https://doi.org/10.1111/j.1365-2699.2012.02793.x (2012).Article 

Google Scholar 
13.Vörös, J., Mikulíček, P., Major, Á., Recuero, E. & Arntzen, J. W. Phylogeographic analysis reveals northern refugia for the riverine amphibian Triturus dobrogicus (Caudata: Salamandridae). Biol. J. Linn. Soc. 119, 974–991. https://doi.org/10.1111/bij.12866 (2016).Article 

Google Scholar 
14.Copilaș-Ciocianu, D., Rutová, T., Pařil, P. & Petrusek, A. Epigean gammarids survived millions of years of severe climatic fluctuations in high latitude refugia throughout the Western Carpathians. Mol. Phylogenet. Evol. 112, 218–229. https://doi.org/10.1016/j.ympev.2017.04.027 (2017).Article 

Google Scholar 
15.Juřičková, L. et al. Early postglacial recolonisation, refugial dynamics the origin of a major biodiversity hotspot. A case study from the Malá Fatra mountains, Western Carpathians, Slovakia. Holocene 28(4), 583–594. https://doi.org/10.1177/0959683617735592 (2017).ADS 
Article 

Google Scholar 
16.Mamos, T., Jażdżewski, K., Čiamporová-Zaťovičová, Z., Čiampor, F. & Grabowski, M. Fuzzy species borders of glacial survivalists in the Carpathian biodiversity hotspot revealed using a multimarker approach. Sci. Rep. 11, 21629. https://doi.org/10.1038/s41598-021-00320-8 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Pinceel, J., Jordaens, K., Pfenninger, M. & Backeljau, T. Rangewide phylogeography of a terrestrial slug in Europe: Evidence for Alpine refugia rapid colonization after the Pleistocene glaciations. Mol. Ecol. 14, 1133–1150. https://doi.org/10.1111/j.1365-294X.2005.02479.x (2005).CAS 
Article 
PubMed 

Google Scholar 
18.Magri, D. et al. A new scenario for the Quaternary history of European beech populations: Palaeobotanical evidence genetic consequences. New Phytol. 171, 199–221. https://doi.org/10.1111/j.1469-8137.2006.01740.x (2006).CAS 
Article 
PubMed 

Google Scholar 
19.Jamrichová, E., Potůčková, A. & Horsák, M. Landscape history, calcareous fen development historical events in the Slovak Eastern Carpathians. Veg. Hist. Archaeobot. 23, 497–513. https://doi.org/10.1007/s00334-013-0416-0 (2014).Article 

Google Scholar 
20.Jamrichová, E., Petr, L. & Jiménez-Alfaro, B. Pollen-inferred millennial changes in landscape patterns at a major biogeographical interface within Europe. J. Biogeogr. 44, 2386–2397 (2017).Article 

Google Scholar 
21.Wielstra, B., Babik, W. & Arntzen, J. W. The crested newt Triturus cristatus recolonized temperate Eurasia from an extra-Mediterranean glacial refugium. Biol. J. Linn. Soc. 114, 574–587. https://doi.org/10.1111/bij.12446 (2015).Article 

Google Scholar 
22.Mráz, P. & Ronikier, M. Biogeography of the Carpathians: Evolutionary spatial facets of biodiversity. Biol. J. Linn. Soc. 119, 528–559. https://doi.org/10.1111/bij.12918 (2016).Article 

Google Scholar 
23.Pauls, S. U., Lumbsch, H. A. T. & Haase, P. Phylogeography of the montane caddisfly Drusus discolor: Evidence for multiple refugia and periglacial survival. Mol. Ecol. 15(8), 2153–2169. https://doi.org/10.1111/j.1365-294X.2006.02916.x (2006).CAS 
Article 
PubMed 

Google Scholar 
24.Pauls, S. U., Theissinger, K., Ujvarosi, L., Bálint, M. & Haase, P. Patterns of population structure in two closely related, partially sympatric caddisflies in eastern Europe: Historic introgression, limited dispersal, and cryptic diversity. J. N. Am. Benthol. Soc. 28, 517–536. https://doi.org/10.1899/08-100.1 (2009).Article 

Google Scholar 
25.Lehrian, S., Pauls, S. U. & Haase, P. Contrasting patterns of population structure in the montane caddisflies Hydropsyche tenuis and Drusus discolor in the Central European highlands. Freshw. Biol. 54, 283–295. https://doi.org/10.1111/j.1365-2427.2008.02107.x (2009).Article 

Google Scholar 
26.Lande, R. & Shannon, S. The role of genetic variation in adaptation and population persistence in a changing environment. Evolution 216, 434–437 (1996).Article 

Google Scholar 
27.Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to Conservation Genetics (Cambridge University Press, 2002).Book 

Google Scholar 
28.Robert, S. & Curtean-Bănăduc, A. Aspects concerning Târnava Mare and Târnava Mică rivers (Transylvania, Romania) caddisfly (Insecta, Trichoptera) larvae communities. Transylv. Rev. Syst. Ecol. Res. 2, 89–98 (2005).
Google Scholar 
29.Bálint, M., Ujvárosi, L., Dénes, A. L. & Octavian, P. European phylogeography of Rhyacophila tristis Pictet (Trichoptera: Rhyacophilidae): Preliminary results. Zoosymposia 5, 11–18. https://doi.org/10.11646/zoosymposia.5.1.1 (2011).Article 

Google Scholar 
30.Bielik, M. Geophysical features of the Slovak Western Carpathians. Geol. Q. 43, 251–262. https://doi.org/10.1016/j.quascirev.2008.08.019 (1999).Article 

Google Scholar 
31.Céréghino, R., Cugny, P. & Lavandier, P. Influence of intermittent hydropeaking on the longitudinal zonation patterns of benthic invertebrates in a mountain stream. Int. Rev. Hydrobiol. 87, 47–60. https://doi.org/10.1002/1522-2632(200201)87:1%3c47::AID-IROH47%3e3.0.CO;2-9 (2002).Article 

Google Scholar 
32.Sworobowicz, L., Mamos, T., Grabowski, M. & Wysocka, A. Lasting through the ice age: The role of the proglacial refugia in the maintenance of genetic diversity, population growth, and high dispersal rate in a widespread freshwater crustacean. Freshw. Biol. 65, 1028–1046. https://doi.org/10.1111/fwb.13487 (2020).CAS 
Article 

Google Scholar 
33.Rudolph, K., Coleman, C. O., Mamos, T. & Grabowski, M. Description and post-glacial demography of Gammarus jazdzewskii sp. nov. (Crustacea: Amphipoda) from Central Europe. Syst. Biodivers. 16, 587–603. https://doi.org/10.1080/14772000.2018.1470118 (2018).Article 

Google Scholar 
34.Bozáňová, J., Čiamporová-Zaťovičová, Z., Čiampor, F. Jr., Mamos, T. & Grabowski, M. The tale of springs and streams: How different aquatic ecosystems impacted the mtDNA population structure of two riffle beetles in the Western Carpathians. PeerJ 8, e10039. https://doi.org/10.7717/peerj.10039 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Jedlička, L., Kúdela, M., Szemes, T. & Celec, P. Population genetic structure of Simulium degrangei (Diptera: Simuliidae) from Western Carpathians. Biologia 67, 777–787. https://doi.org/10.2478/s11756-012-0057-2 (2012).Article 

Google Scholar 
36.Hughes, J. M., Bunn, S. E., Hurwood, D. A. & Cleary, C. Dispersal and recruitment of Tasiagma ciliata (Trichoptera: Tasmiidae) in rainforest streams, south-east Queensland, Australia. Freshw. Biol. 41, 1–10 (1998).
Google Scholar 
37.Finn, D. S., Theobald, D. M., Black, W. C. & Poff, N. L. Spatial population genetic structure and limited dispersal in a Rocky Mountain alpine stream insect. Mol. Ecol. 15, 3553–3566 (2006).CAS 
Article 

Google Scholar 
38.Vuataz, L., Rutschmann, S., Monaghan, M. T. & Sartori, M. Molecular phylogeny and timing of diversification in Alpine Rhithrogena (Ephemeroptera: Heptageniidae). BMC Evol. Biol. 16, 194. https://doi.org/10.1186/s12862-016-0758-1 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Schiffers, K., Bourne, E. C., Lavergne, S., Thuiller, W. & Travis, J. M. J. Limited evolutionary rescue of locally adapted populations facing climate change. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120083. https://doi.org/10.1098/rstb.2012.0083 (2013).Article 

Google Scholar 
40.Spielman, D., Brook, B. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. Proc. Natl. Acad. Sci. 101, 15261–15264 (2004).ADS 
CAS 
Article 

Google Scholar 
41.Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).Article 

Google Scholar 
42.Bunn, S. E. & Hughes, J. M. Dispersal and recruitment in streams: Evidence from genetic studies. J. N. Am. Benthol. Soc. 16, 338–346. https://doi.org/10.2307/1468022 (1997).Article 

Google Scholar 
43.Barron, E. & Pollard, D. High-resolution climate simulations of oxygen isotope stage 3 in Europe. Quat. Res. 28, 296–309. https://doi.org/10.1006/qres.2002.2374 (2002).Article 

Google Scholar 
44.Bennet, K. & Provan, J. What do we mean by “refugia”? Quat. Sci. Rev. 27, 2449–2455 (2008).ADS 
Article 

Google Scholar 
45.Kondracki, J. Karpaty. Wydanie drugie i poprawione [The Carpathians. Ed. 2].—Wydawnictwa Szkolne i Pedagogiczne, Warszawa (1989).46.Grecula, P. (ed.). Geological evolution of the Western Carpathians. Monograph: Mineralia Slovaca (1997).47.Lukniš, M. The course of the last glaciation of the Western Carpathians in the relation to the Alps, to the glaciation of northern Europe, and to the division of the central European Wurm into periods. Geografický Časopis 16, 127–142 (1964).
Google Scholar 
48.Lindner, L., Dzierzek, J., Marciniak, B. & Nitychoruk, J. Outline of Quaternary glaciations in the Tatra Mts.: Their development, age and limits. Geol. Q. 47, 269–280 (2003).
Google Scholar 
49.Frost, S. Evaluation of kicking technique for sampling stream bottom fauna. Can. J. Zool. 49, 161–173. https://doi.org/10.1016/j.biocon.2005.05.002 (1971).Article 

Google Scholar 
50.Sedlák, E. Řád Chrostíci—Trichoptera. In Klíč vodních larev hmyzu (ed. Rozkošný, R.) 163–220 (ČSAV, 1980).
Google Scholar 
51.Waringer, J. & Graf, W. Atlas of Central European Trichoptera Larvae: Atlas der Mitteleuropäischen Köcherfliegenlarven (Erik Mauch, 2011).
Google Scholar 
52.Casquet, J., Thebaud, C. & Gillespie, R. G. Chelex without boiling, a rapid and easy technique to obtain stable amplifiable DNA from small amounts of ethanol-stored spiders. Mol. Ecol. Resour. 12(1), 136–141. https://doi.org/10.1111/j.1755-0998.2011.03073.x (2012).CAS 
Article 
PubMed 

Google Scholar 
53.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3(5), 294–299 (1994).CAS 
PubMed 

Google Scholar 
54.Bálint, M., Botoşaneanu, L., Ujvárosi, L. & Popescu, O. Taxonomic revision of Rhyacophila aquitanica (Trichoptera: Rhyacophilidae), based on molecular and morphological evidence and change of taxon status of Rhyacophila aquitanica ssp. carpathica to Rhyacophila carpathica stat. n. Zootaxa 2148, 39–48. https://doi.org/10.11646/zootaxa.2148.1.3 (2009).Article 

Google Scholar 
55.Simon, C. et al. Evolution, weighting and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers. Ann. Entomol. Soc. Am. 87, 651–701 (1994).CAS 
Article 

Google Scholar 
56.Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797. https://doi.org/10.1093/nar/gkh340 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. 33, 1870–1874. https://doi.org/10.1093/molbev/msw054 (2016).CAS 
Article 

Google Scholar 
58.Ratnasingham, S. & Hebert, P. D. N. The barcode of life data system. Mol. Ecol. Notes 7, 355–364. https://doi.org/10.1111/j.1471-8286.2007.01678.x (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
59.Puillandre, N., Brouillet, S. & Achaz, G. ASAP: Assemble species by automatic partitioning. Mol. Ecol. Resour. 21(2), 609–620. https://doi.org/10.1111/1755-0998.13281 (2021).Article 
PubMed 

Google Scholar 
60.Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25(11), 1451–1452. https://doi.org/10.1093/bioinformatics/btp187 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116. https://doi.org/10.1111/2041-210X.12410 (2015).Article 

Google Scholar 
62.Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15(4), e1006650. https://doi.org/10.1371/journal.pcbi.1006650 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
63.Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17(42), 1–11. https://doi.org/10.1186/s12862-017-0890-6 (2017).Article 

Google Scholar 
64.Brower, A. V. Z. Rapid morphological radiation and convergence among races of the butterfly Heliconius erato inferred from patterns of mitochondrial DNA evolution. PNAS 91(14), 6491–6495. https://doi.org/10.1073/pnas.91.14.6491 (1994).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
65.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarisation in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67(5), 901–904. https://doi.org/10.1093/sysbio/syy032 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
66.Miller, M. P. Alleles In Space (AIS): Computer software for the joint analysis of interindividual spatial and genetic information. J. Hered. 96, 722–724. https://doi.org/10.1093/jhered/esi119 (2005).CAS 
Article 
PubMed 

Google Scholar 
67.Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220 (1967).CAS 
PubMed 

Google Scholar 
68.Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 
PubMed 

Google Scholar 
69.Tajima, F. The effect of change in population size on DNA polymorphism. Genetics 123(3), 597–601 (1989).CAS 
Article 

Google Scholar 
70.Fu, Y. X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147(2), 915–925 (1997).CAS 
Article 

Google Scholar 
71.Fu, Y. X. & Li, W. H. Statistical tests of neutrality of mutations. Genetics 14, 693–709 (1993).Article 

Google Scholar  More

Table S1 lists the data used in this paper, the instruments that it is based on, the data repositories, and in which figures the data are used.Global data setsBathymetryBathymetric data was taken from the International Bathymetric Chart of the Arctic Ocean (IBCAO 30 sec V3)64 available at https://www.ngdc.noaa.gov/mgg/bathymetry/arctic/grids/version3_0/.Sea ice concentrationWe use data derived from the Advanced Microwave Scanning Radiometer sensor AMSR-2 for the years 2013–18 processed in accordance with65 and downloaded from https://seaice.uni-bremen.de/sea-ice-concentration-amsr-eamsr2/66. At each grid point the sum of days during all April/May/June of 2013–2018 when the sea ice concentration at the grid point was >20% was divided by the total number of days with data in those months to obtain the percentage of days with ice concentration >20% (Fig. 1). For separate 7-day periods in April/May/June 2017 and 2018 the mean ice concentration over those 7 days was calculated and the 20% contour of this mean was plotted separately for each of those 7-day periods. For each mooring and each day, the ice concentration at the grid cell closest to the mooring was calculated (Fig. 4a and S1a), and if the ice concentration at the mooring was below 20%, the shortest distance to grid cells where the ice concentration exceeded 20% was calculated (Fig. 4a and S1a). If the ice concentration at the mooring exceeded 20%, the shortest distance to grid cells where the ice concentration was below 20% was calculated and the distance was defined as negative.Sea ice velocity and sea ice area exportIce area flux estimates in Fig. 2a are calculated using CERSAT (Center for Satellite Exploitation and Research, France) motion estimates together with CERSAT ice concentration information67. Fluxes are estimated along a zonal gate positioned at 82°N between 12°W and 20°E and a meridional gate at 20°E between 80.5°N and 82°N (Fig. 1) for the period 1994–2020 (January–May). The ice area flux at the gate is the integral of the product between the meridional and zonal ice drift and ice concentration. For a more detailed description we refer to ref. 68. Arctic-wide sea ice velocity anomalies (Fig. 2b, c) were computed from the OSI-405-c motion product provided by the Ocean and Sea Ice Satellite Application 635 Facility (OSISAF)69.Satellite chlorophyllSurface chlorophyll concentrations measured with the Sentinel 3 A OLCI (Ocean and Land Colour Instrument) were downloaded from https://earth.esa.int/web/sentinel/sentinel-data-access. The 8-day satellite data were averaged for the time series over grid points within boxes of 60 km by 60 km around the moorings.Atmospheric reanalysisERA-Interim reanalysis70 data at the surface on a 0.25° latitude by 0.25° longitude grid at 12 hourly resolution was downloaded from https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/. Incoming shortwave radiation (ssr) and outgoing longwave radiation (str), sensible heat flux (sshf), and latent heat flux (slhf) were extracted and averaged to daily values.Physical numerical modelsFESOMIn this study, we used model data from the Finite-Element Sea ice-Ocean Model (FESOM) version 1.471. FESOM is a sea ice-ocean model that solves the hydrostatic primitive equations for the ocean and comprises a finite element sea ice component. It uses triangular surface meshes for spatial discretization, allowing for a refined mesh in regions of interest, while keeping a coarser mesh elsewhere. In the model configuration used here, a mesh resolution of nominally 1° was applied in the global oceans. The mesh was refined to 25 km north of 40°N, and to 4.5 km in the Nordic Seas and Arctic Ocean. In the wider Fram Strait (20°W-20°E/76°N-82°30′N), the mesh was further refined to 1 km. In this region, the simulation can be considered as eddy-resolving, as the local internal Rossby radius of deformation is about 2–6 km72,73. In the vertical, the model used 47 z-levels with a resolution of 10 m in the upper 100 m, and coarser resolution with depth (with a resolution of ~100 m at 800 m depth). For bottom topography, the RTopo-2 data set was used74. The model simulation covers the period 2010–2018 and has daily model output. It was forced with atmospheric reanalysis data from Era-Interim70, and was initialized with model fields from the simulation described in ref. 75. River runoff (except for Greenland) was taken from the JRA-55 data set76, and Greenland ice-sheet runoff was taken from ref. 77. Tides were not taken into account in this simulation. Here we studied the model data of 2016 to 2018 in Fram Strait for comparison with our observations.1-dimensional mixed layer depth modelThe PWP78 1-dimensional mixed layer model simulates the response of the ocean to surface fluxes. It ignores horizontal gradients and horizontal advection. This allows to judge whether certain surface flux conditions can on their own explain observed conditions. We ran the PWP model (as implemented for Matlab by http://www.po.gso.uri.edu/rafos/research/pwp/) with four different scenarios (Fig. S6: P17-M17, P17-M18, P18-M17, P18-M18) where: P17: An idealized initial profile based on the observed profiles (Fig. 3) representing the conditions in 2017: constant temperature of 2 °C in the vertical, linear salinity gradient from 30.5 at the surface to 35 at 50 m and another linear salinity gradient from 35 at 50 m to 35.1 at 200 m. P18: An idealized initial profile based on the observed profiles (Fig. 3) representing the conditions in 2018: Same as P17 except that the surface to 50 m salinity gradient is from 34.8 to 35. M17: A time series of the the meteorological forcing (10 m wind velocity, heat fluxes, and evaporation minus precipitation) from the ERA-Interim reanalysis (Fig. 4b) at the grid point closest to mooring HG-IV for the period 15-May-2017 to 01-Aug-2017. M18: Same as M17 but for the period 15-May-2018 to 01-Aug-2018. M17 and M18 are provided in Supplementary Data 1.Shipboard CTD dataShipboard CTD casts of a standard dual sensor Seabird 911+ CTD-rosette were occupied in spatial and temporal vicinity to the moored observations (Tab. S2) on three cruises: PS107 in 2017 (https://doi.org/10.1594/PANGAEA.894189), PS114 in 2018 (https://doi.org/10.1594/PANGAEA.898694) of RV Polarstern, and JR17005 in 2018 (https://doi.org/10.5285/84988765-5fc2-5bba-e053-6c86abc05d53) of RRS James Clark Ross. The data were processed according to standard routine79. Additionally, we use underway CTD data from an OceanScience underway CTD collected during PS107 in 2017 (https://doi.org/10.1594/PANGAEA.886146) and processed according to ref. 21.Mooring dataThe mooring data discussed in this paper is from two mooring clusters in the central and eastern Fram Strait (named “HG-IV” at ~79°N 4°20’E and “F4” at ~79°N 7°E) where moorings were located as close to each other as possible (the horizontal separation was equal to the water depth) in order to enable more measurements than could be fit physically onto a single mooring. Tab. S2/S3 list the deployment and recovery details of the moorings including the exact latitudes/longitudes as well as the individual instruments on the moorings. Note that all data shown in this paper from ~30 m depth and the temperature/salinity/oxygen data from ~55 m is from the HG-IV-S-* and F4-S-* moorings, while all other data is from the HG-IV-FEVI-* and F4-* moorings. The AZFP data is from F5-17 located roughly half way between the two clusters. All sensor based mooring raw data (except for the ASL AZFP data) is available at ref. 80.It is known that conversion factors for biogeochemical sensors (e.g., chlorophyll fluorescence) change over the seasons, depths, and regions81,82. In order to make as few assumptions as possible, we used the following approach: we could have determined the conversion factors from the instance when the ship was there with the CTD-rosette, but these conversion factors might not be appropriate for the majority of the time series. Hence, simply using the manufacturers’ calibrations, as we do here, introduces fewer uncertainties. Where we have different estimates of the same parameter, we present them together and demonstrate that they agree qualitatively and also mostly quantitatively (e.g., Fig. 5b). In particular the timing of events is robust.At some locations, the target variables were not measured the whole time or the measurements failed, hence we present what is available. The vertical location of the instruments (Fig. 4c and S1c) varied substantially (intermittently up to 200 m) as a result of mooring blow downs caused by strong intermittent ocean currents. Time series have not been corrected for this vertical motion, but data are not used during blow downs in order not to bias the time series interpretation by temporal changes introduced by instruments traversing through vertical property gradients.Physical sensor measurementsThe physical sensors (for pressure, temperature, conductivity, and oxygen) were pre-cruise manufacturer calibrated and processed similar to ref. 83; the processed data is also available at ref. 80.Mixed layer depth (MLD)Since there are no autonomous vertically profiling measurements available, we can only determine the minimum value of the mixed layer depth. At each hourly time step, the potential density difference (Δσ) between the uppermost (~30 m) temperature/salinity recorder and the underlying temperature/salinity recorders is calculated. The 0.5th percentile of each Δσ time series is added to the Δσ time series for the different deployments. This fixes slight offsets in the temperature and/or conductivity calibrations which result in too negative or too positive density differences. The minimum estimate of the mixed layer depth at hourly resolution is then determined as the depth of the deepest instrument where Δσ  0.05 kg m−3 for all depths at a time step, then the minimum mixed layer depth can only be determined as 0 for that time step. Daily values of the MLD were defined as the depth at which three hourly realizations of MLD were shallower within a 24 h time span and at which the remaining 21 MLD realizations were deeper. This biases the daily MLD estimate towards situations where phytoplankton is kept in the surface ocean rather than also being mixed down for some amount of time.Stratification estimated between 30 m and 55 mBased on the temperature and salinity time series observed at ~30 m and ~55 m, we estimate the buoyancy frequency as ({N}^{2}=frac{-g}{{rho }_{0}}frac{Delta rho }{Delta z}) where g is the acceleration due to gravity, Δσ is the potential density difference over the vertical distance of Δz = 25 m, and ρ0 is the average density. The contributions to stratification due to temperature (N2T) and salinity (N2S) are estimated as ({N}_{T}^{2}=g*alpha frac{Delta T}{Delta z}) and ({N}_{S}^{2}=-g*beta frac{Delta S}{Delta z}), respectively, where ΔT/ΔS are the temperature/salinity differences and α/β are the thermal expansion/haline contraction coefficients estimated from the average temperature/salinity at the two measurement depths.Apparent oxygen utilization (AOU)Oxygen concentration from the microcats was calculated using the pre-cruise manufacturer calibrations. AOU was calculated as the atmospherically equilibrated oxygen concentration (calculated from measured pressure, temperature, and salinity with sw_satO2 from the Seawater toolbox available at http://www.cmar.csiro.au/datacentre/ext_docs/seawater.htm) minus the measured oxygen concentration.LightPolar night/polar dayThe length of day (hours per 24 h that the sun is above the horizon) was calculated from the sunrise equation as implemented for Matlab by https://de.mathworks.com/matlabcentral/fileexchange/55509-sunrise-sunset.Photosynthetically available radiation (PAR)The WetLabs Eco PAR measured PAR for 5 (in 2016–2017) or 10 (in 2017–2018) individual measurements 1 s apart from each other before it slept for 1 h before repeating the measurement cycle. These 5 or 10 individual measurements are averaged linearly to obtain hourly values at ~30 m depth (Fig. 5a blue). Values below the detection limit are set to a constant of 10−1.32 μmol m−2 s−1. Hourly values are linearly averaged to daily values (Fig. 5a black). The incoming solar shortwave radiation varies as a function of season and latitude as well as cloud cover as represented in the ERA-Interim reanalysis (parameters ssr). Its unit of W m−2 is converted to PAR assuming a constant spectral distribution as 1 W m−2 = 2.1 μmol m−2 s−184. In order to compare the PAR measured at a depth of approximately 30 m to the surface values, we approximate a spectrally averaged diffuse attenuation coefficient for PAR in clear water using the values of85 as kd = 0.02 m−1 and apply it to calculate a constant exponential extinction applied to the reanalysis surface values (Fig. 5a yellow). The average PAR available (PARavailable) to phytoplankton being moved around in the clear water mixed layer of depth MLD was calculated as the depth averaged vertical integral of the clear water extinguished PAR at the surface (PARsurf from the shortwave radiation of ERA-Interim): ({{PAR}}_{{available}}=frac{1}{{MLD}}*{int }_{z=0}^{z={MLD}}{{PAR}}_{{surf}}*{e}^{-{k}_{d}z}{dz}) (Fig. 5a red).Chlorophyll concentration and optical backscatteringChlorophyll fluorescenceThe WetLabs ECO Triplet measures fluorescence at a “chlorophyll wavelength” and at a “CDOM wavelength” as well as optical scattering at 700 nm. The conversion from fluorescence to chlorophyll a concentration (in μg l−1) follows a manufacturer determined conversion determined for a mono-culture of phytoplankton (Thalassiosira weissflogii), which typically overestimates the chlorophyll concentration. Hence, we applied the community-established calibration bias of 2 for the WetLabs ECO-series fluorometer to these in situ fluorometric chlorophyll values81. This conversion factor may be different in ocean waters of Fram Strait, but it still gives reasonable agreement with independent estimates.Optical backscatteringThe EcoTriplet measured 8 individual measurements 1 s apart from each other before it slept for 1 h before repeating the measurement cycle. For the chlorophyll fluorescence, the individual measurements are averaged to hourly values. For the scattering, times when individual 1-second measurements exceed 0.002 m−1 sr−1 are indicative of strong optical backscattering not due to small particles in the water column, but rather to larger potentially aggregated particles. The times of strong backscattering are marked individually (Fig. 5b red).NutrientsNitrate (SUNA sensor)Prior to deployment (11 and 15 days for sensors deployed at HG-IV and F4, respectively), the reference spectrum of the sensors were updated as per manufacturer specifications. We first let the sensors cool down for 24 h at 0 °C in a temperature controlled laboratory. Next, the reference spectrum update was achieved by measuring Milli-Q water (i.e., no nitrate present). To verify if this update was successful, solutions with three different nitrate concentrations (3, 7, and 14 μmol l−1) were then measured, with the output being monitored live (expected to be within ±2 μmol l−1 of each concentration). A measuring time of 20 s yields stable results and was thus applied during the deployments with an interval of 6 h. Upon recovery, SUNA data were processed using the SeaBird UCI software package version 1.2.1. Here, temperature and salinity data were used to remove the spectrum of bromide and compensate for temperature dependent absorption using an algorithm developed by ref. 86. This step yields the spectrum of nitrate only, at a precision of ±0.3 μmol l−1. The sensor is characterized by a drift of 0.3 μmol l−1 per hour lamp time. Given the deployment settings, a total operational time of about 8 h was accumulated. Therefore, a linear drift correction of 2.4 μmol l−1 (365 days)−1 was applied. Up to this point, however, accuracy remains at 2 μmol l−1 as per manufacturer specifications. Therefore, an offset correction is then applied based on the in situ concentrations observed at the beginning of the deployment as well as with the RAS (see below) where available, with outliers excluded.Inorganic nutrients from Remote Access Samplers (RAS)McLane RAS were programmed to draw two 500 ml samples (1 h apart, starting at noon) approximately every other week. Samples within the RAS were collected in sterile plastic bags and fixed with 700 μl of 50% mercuric chloride solution. Upon recovery, two samples from a given sampling date were combined to yield a volume of 1 l, required for bacterial and phytoplankton genetic analyses (see below), and a 50-ml aliquot destined for the measurement of dissolved inorganic nutrients. Aliquots for nutrient analysis were collected in PE bottles, which were then stored frozen (−20 °C) until analysis on land. Analyses for inorganic nutrients were carried out using a QuAAtro Seal Analytical segmented continuous flow autoanalyser following standard colorimetric techniques. The accuracy of the analysis was evaluated through the measurement of KANSO LTD Japan Certified Reference Materials and corrections were applied accordingly. Finally, we evaluated pressure, temperature, and salinity data from the CTD (SBE37-SMP-ODO) attached to the RAS to determine whether the two samples taken one hour apart on a given date drew water from the same depth and with consistent properties.Carbonate system
pCO
2 and pH
The calibration of SAMI pH and SAMI CO2 sensors was carried out by the manufacturer, approximately 2 months prior to deployment. The calibration certificates specify accuracy and precision of ±0.003/±0.001 pH units and ±3/ More

1.Brewer TE, Aronson EL, Arogyaswamy K, Billings SA, Botthoff JK, Campbell AN, et al. Ecological and genomic attributes of novel bacterial taxa that thrive in subsurface soil horizons. mBio. 2019;10:e01318–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Brubaker SC, Jones AJ, Lewis DT, Frank K. Soil properties associated with landscape position. Soil Sci Soc Am J. 1993;57:235–9.
Google Scholar 
3.Richter DD, Markewitz D. How deep is soil? BioScience. 1995;45:600–9.
Google Scholar 
4.Rumpel C, Kögel-Knabner I. Deep soil organic matter—a key but poorly understood component of terrestrial C cycle. Plant Soil. 2010;338:143–58.
Google Scholar 
5.Dove NC, Arogyaswamy K, Billings SA, Botthoff JK, Carey CJ, Cisco C, et al. Continental-scale patterns of extracellular enzyme activity in the subsoil: an overlooked reservoir of microbial activity. Environ Res Lett. 2020;15:1040a1.CAS 

Google Scholar 
6.Sinsabaugh RL, Lauber CL, Weintraub MN, Ahmed B, Allison SD, Crenshaw C, et al. Stoichiometry of soil enzyme activity at global scale. Ecol Lett. 2008;11:1252–64.PubMed 

Google Scholar 
7.Tedersoo L, Bahram M, Põlme S, Kõljalg U, Yorou NS, Wijesundera R, et al. Global diversity and geography of soil fungi. Science. 2014;346:6213.
Google Scholar 
8.Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Jobbágy EG, Jackson RB. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol Appl. 2000;10:423–36.
Google Scholar 
10.Chapin FS, Matson PA, Vitousek P. Principles of terrestrial ecosystem ecology. New York: Springer Science & Business Media; 2011.
Google Scholar 
11.Bárcenas‐Moreno G, Gómez‐Brandón M, Rousk J, Bååth E. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob Change Biol. 2009;15:2950–7.
Google Scholar 
12.Wallenstein M, Allison SD, Ernakovich J, Steinweg JM, Sinsabaugh R. Controls on the temperature sensitivity of soil enzymes: a key driver of in situ enzyme activity rates. In: Shukla G, Varma A, editors. Soil enzymology. Berlin, Heidelberg: Springer; 2011. p. 245–58.13.German DP, Marcelo KRB, Stone MM, Allison SD. The Michaelis–Menten kinetics of soil extracellular enzymes in response to temperature: a cross-latitudinal study. Glob Change Biol. 2012;18:1468–79.
Google Scholar 
14.Oliverio AM, Bradford MA, Fierer N. Identifying the microbial taxa that consistently respond to soil warming across time and space. Glob Change Biol. 2017;23:2117–29.
Google Scholar 
15.Jenny H. Factors of soil formation. New York: McGraw-Hill; 1941.
Google Scholar 
16.Parton WJ, Scurlock JMO, Ojima DS, Schimel DS, Hall DO. Impact of climate change on grassland production and soil carbon worldwide. Glob Change Biol. 1995;1:13–22.
Google Scholar 
17.Jenny H. The soil resource: origin and behavior. Berlin: Springer Science & Business Media; 1980.18.Fierer N, Jackson RB. The diversity and biogeography of soil bacterial communities. PNAS. 2006;103:626–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Slessarev EW, Lin Y, Bingham NL, Johnson JE, Dai Y, Schimel JP, et al. Water balance creates a threshold in soil pH at the global scale. Nature. 2016;540:567–9.CAS 
PubMed 

Google Scholar 
21.Brovkin V. Climate-vegetation interaction. J Phys IV France. 2002;12:57–72.
Google Scholar 
22.Aerts R. Climate, leaf litter chemistry and leaf litter decomposition in terrestrial ecosystems: a triangular relationship. Oikos. 1997;79:439–49.
Google Scholar 
23.Djukic I, Kepfer-Rojas S, Schmidt IK, Larsen KS, Beier C, Berg B, et al. Early stage litter decomposition across biomes. Sci Total Environ. 2018;628–9:1369–94.
Google Scholar 
24.Shiozawa S, Campbell GS. Soil thermal conductivity. Remote Sens Rev. 1990;5:301–10.
Google Scholar 
25.Verhoef A, Fernández-Gálvez J, Diaz-Espejo A, Main BE, El-Bishti M. The diurnal course of soil moisture as measured by various dielectric sensors: effects of soil temperature and the implications for evaporation estimates. J Hydrol. 2006;321:147–62.
Google Scholar 
26.Dove NC, Torn MS, Hart SC, Taş N. Metabolic capabilities mute positive response to direct and indirect impacts of warming throughout the soil profile. Nat Commun. 2021;12:2089.CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Bai W, Wang G, Xi J, Liu Y, Yin P. Short-term responses of ecosystem respiration to warming and nitrogen addition in an alpine swamp meadow. Eur J Soil Biol. 2019;92:16–23.CAS 

Google Scholar 
28.Yost JL, Hartemink AE. How deep is the soil studied—an analysis of four soil science journals. Plant Soil. 2020;425:5–18.
Google Scholar 
29.Hicks Pries CE, Castanha C, Porras RC, Torn MS. The whole-soil carbon flux in response to warming. Science. 2017;355:1420–3.CAS 
PubMed 

Google Scholar 
30.Jones DL, Magthab EA, Gleeson DB, Hill PW, Sánchez-Rodríguez AR, Roberts P, et al. Microbial competition for nitrogen and carbon is as intense in the subsoil as in the topsoil. Soil Biol Biochem. 2018;117:72–82.CAS 

Google Scholar 
31.Ofiti NOE, Zosso CU, Soong JL, Solly EF, Torn MS, Wiesenberg GLB, et al. Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter. Soil Biol Biochem. 2021;156:108185.CAS 

Google Scholar 
32.Soong JL, Castanha C, Pries CEH, Ofiti N, Porras RC, Riley WJ, et al. Five years of whole-soil warming led to loss of subsoil carbon stocks and increased CO2 efflux. Sci Adv. 2021;7:eabd1343.CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Nottingham AT, Fierer N, Turner BL, Whitaker J, Ostle NJ, McNamara NP, et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology. 2018;99:2455–66.PubMed 

Google Scholar 
34.O‘Geen A (Toby), Safeeq M, Wagenbrenner J, Stacy E, Hartsough P, Devine S., et al. Southern Sierra Critical Zone Observatory and Kings River Experimental Watersheds: a synthesis of measurements, new insights, and future directions. Vadose Zone J. 2018;17:180081.35.Frisbie JA. Soil organic carbon storage and aggregate stability in an arid mountain range, White Mountains, CA. UC Riverside Master’s Thesis. 2014. https://escholarship.org/uc/item/4rn6j9rq.36.Marchand DE. Soil contamination in the White Mountains, Eastern California. GSA Bull. 1970;81:2497–506.
Google Scholar 
37.Aciego SM, Riebe CS, Hart SC, Blakowski MA, Carey CJ, Aarons SM, et al. Dust outpaces bedrock in nutrient supply to montane forest ecosystems. Nat Commun. 2017;8:14800.CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Dove NC, Safford HD, Bohlman GN, Estes BL, Hart SC. High-severity wildfire leads to multi-decadal impacts on soil biogeochemistry in mixed-conifer forests. Ecol Appl. 2020;30:e02072.PubMed 

Google Scholar 
39.USDA-NRCS. Soil taxonomy: a basic system of soil classification for making and interpreting soil surveys. U.S. Department of Agriculture Handbook 436. 2nd edition. Washington D.C.: Natural Resources Conservation Service; 1999.40.Lajtha K, Driscoll CT, Jarrell WM, Elliot ET. Phosphorus characterization and total element analysis. In: Robertson GP, Coleman DC, Bledsoe CS, Sollins P, editors. Standard soil methods for long-term ecological research. New York: Oxford University Press; 1999. p. 115–42.41.Harris D, Horwáth WR, vanKessel C. Acid fumigation of soils to remove carbonates prior to total organic carbon or CARBON-13 isotopic analysis. Soil Sci Soc Am J. 2001;65:1853–6.CAS 

Google Scholar 
42.Thomas GW, Soil pH and soil acidity. In: Sparks DL, Page AL, Helmke PA, Loeppert RH, Soltanpour PN, Tabatabai MA, et al., editors. Methods of soil analysis, part 3: chemical methods. Madison, WI, USA: Soil Science Society of America, American Society of Agronomy; 1996, p. 475–90.43.Vance ED, Brookes PC, Jenkinson DS. An extraction method for measuring soil microbial biomass C. Soil Biol Biochem. 1987;19:703–7.CAS 

Google Scholar 
44.Hart SC, Firestone MK. Forest floor-mineral soil interactions in the internal nitrogen cycle of an old-growth forest. Biogeochemistry. 1991;12:103–27.CAS 

Google Scholar 
45.Haubensak KA, Hart SC, Stark JM. Influences of chloroform exposure time and soil water content on C and N release in forest soils. Soil Biol Biochem. 2002;34:1549–62.CAS 

Google Scholar 
46.Stenberg B, Johansson M, Pell M, Sjödahl-Svensson K, Stenström J, Torstensson L. Microbial biomass and activities in soil as affected by frozen and cold storage. Soil Biol Biochem. 1998;30:393–402.CAS 

Google Scholar 
47.Bell CW, Fricks BE, Rocca JD, Steinweg JM, McMahon SK, Wallenstein MD. High-throughput fluorometric measurement of potential soil extracellular enzyme activities. J Vis Exp. 2013;81:e50961.
Google Scholar 
48.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Ihrmark K, Bödeker ITM, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, et al. New primers to amplify the fungal ITS2 region – evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol. 2012;82:666–77.CAS 

Google Scholar 
50.Smith DP, Peay KG. Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PLoS ONE. 2014;9:e90234.PubMed 
PubMed Central 

Google Scholar 
51.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596.CAS 
PubMed 

Google Scholar 
54.Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421.
Google Scholar 
55.Abarenkov K, Nilsson RH, Larsson K-H, Alexander IJ, Eberhardt U, Erland S, et al. The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist. 2010;186:281–5.
Google Scholar 
56.Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016;20:241–8.
Google Scholar 
57.R Development Core Team. R: a language and environment for statistical computing. 2008. Vienna, Australia: R Foundation for Statistical Computing. http://www.R-project.org.58.Cribari-Neto F, Zeileis A. Beta regression in R. J Statl Softw. 2010;34:1–24.
Google Scholar 
59.Fox J, Weisberg S. An {R} companion to applied regression, Second. 2011. Thousand Oaks, CA: Sage.60.Bates D, Maechler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.
Google Scholar 
61.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Oksanen J, Blanchet FG, Kindt R, Legendre P, Simpson GL, Minchin PR, et al. vegan: community Ecology Package. 2013 http://CRAN.R-project.org/package=vegan.63.Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
Google Scholar 
64.Anderson MJ, Ellingsen KE, McArdle BH. Multivariate dispersion as a measure of beta diversity. Ecol Lett. 2006;9:683–93.PubMed 

Google Scholar 
65.Legendre P, Cáceres MD. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol Lett. 2013;16:951–63.PubMed 

Google Scholar 
66.Legendre P, Anderson MJ. Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecol Monogr. 1999;69:1–24.
Google Scholar 
67.Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:3514.CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.CAS 
PubMed 

Google Scholar 
69.Kanzaki Y, Takemoto K. Diversity of dominant soil bacteria increases with warming velocity at the global scale. Diversity. 2021;13:120.
Google Scholar 
70.Russell NJ, Harrisson P, Johnston IA, Jaenicke R, Zuber M, Franks F, et al. Cold adaptation of microorganisms. Philos Trans R Soc Lond B Biol Sci. 1990;326:595–611.CAS 
PubMed 

Google Scholar 
71.Chanal A, Chapon V, Benzerara K, Barakat M, Christen R, Achouak W, et al. The desert of Tataouine: an extreme environment that hosts a wide diversity of microorganisms and radiotolerant bacteria. Environ Microbiol. 2006;8:514–25.CAS 
PubMed 

Google Scholar 
72.Jobbágy EG, Jackson RB. The distribution of soil nutrients with depth: global patterns and the imprint of plants. Biogeochemistry. 2001;53:51–77.
Google Scholar 
73.Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.PubMed 

Google Scholar 
74.Offre P, Spang A, Schleper C. Archaea in biogeochemical cycles. Ann Rev Microbiol. 2013;67:437–57.CAS 

Google Scholar 
75.Stegen JC, Bottos EM, Jansson JK. A unified conceptual framework for prediction and control of microbiomes. Curr Opin in Microbiol. 2018;44:20–27.
Google Scholar 
76.Xiong J, Sun H, Peng F, Zhang H, Xue X, Gibbons SM, et al. Characterizing changes in soil bacterial community structure in response to short-term warming. FEMS Microbiol Ecol. 2014;89:281–92.CAS 
PubMed 

Google Scholar 
77.DeAngelis KM, Pold G, Topçuoğlu BD, van Diepen LTA, Varney RM, Blanchard JL, et al. Long-term forest soil warming alters microbial communities in temperate forest soils. Front Microbiol. 2015;6:104.PubMed 
PubMed Central 

Google Scholar 
78.Hayden HL, Mele PM, Bougoure DS, Allan CY, Norng S, Piceno YM, et al. Changes in the microbial community structure of bacteria, archaea and fungi in response to elevated CO2 and warming in an Australian native grassland soil. Environ Microbiol. 2012;14:3081–96.CAS 
PubMed 

Google Scholar 
79.Johnston ER, Hatt JK, He Z, Wu L, Guo X, Luo Y, et al. Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths. PNAS. 2019;116:15096–105.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Větrovský T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE. 2013;8:e57923.PubMed 
PubMed Central 

Google Scholar 
81.Sarkar JM, Leonowicz A, Bollag J-M. Immobilization of enzymes on clays and soils. Soil Biol Biochem. 1989;21:223–30.CAS 

Google Scholar 
82.Burns RG, DeForest JL, Marxsen J, Sinsabaugh RL, Stromberger ME, Wallenstein MD, et al. Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biol Biochem. 2013;58:216–34.CAS 

Google Scholar 
83.Eilers KG, Debenport S, Anderson S, Fierer N. Digging deeper to find unique microbial communities: The strong effect of depth on the structure of bacterial and archaeal communities in soil. Soil Biol Biochem. 2012;50:58–65.CAS 

Google Scholar 
84.Kellogg CA, Griffin DW. Aerobiology and the global transport of desert dust. Trends Ecol Evol. 2006;21:638–44.PubMed 

Google Scholar 
85.Martiny JBH, Bohannan BJM, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102–12.CAS 
PubMed 

Google Scholar 
86.Du X, Deng Y, Li S, Escalas A, Feng K, He Q, et al. Steeper spatial scaling patterns of subsoil microbiota are shaped by deterministic assembly process. Mol Ecol. 2021;30:1072–85.CAS 
PubMed 

Google Scholar 
87.Fanning DS, Fanning MCB. Soil morphology, genesis and classification. New York: John Wiley & Sons; 1989.88.IPCC. Climate Change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 2014. Geneva, Switzerland: IPCC.89.Bradford MA, Davies CA, Frey SD, Maddox TR, Melillo JM, Mohan JE, et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol Lett. 2008;11:1316–27.PubMed 

Google Scholar  More

1.Frank, S. A. Models of parasite virulence. Q. Rev. Biol.  https://doi.org/10.1086/419267 (1996).Article 
PubMed 

Google Scholar 
2.Dobson, A. P. The population dynamics of competition between parasites. Parasitology https://doi.org/10.1017/S0031182000057401 (1985).Article 
PubMed 

Google Scholar 
3.Haelewaters, D. et al. Mortality of native and invasive ladybirds co-infected by ectoparasitic and entomopathogenic fungi. PeerJ https://doi.org/10.7717/peerj.10110 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Shapiro-Ilan, D. I., Bruck, D. J. & Lacey, L. A. Principles of Epizootiology and Microbial Control. In Insect Pathology 29–72 (Elsevier, 2012). https://doi.org/10.1016/B978-0-12-384984-7.00003-8.5.Renkema, J. M. & Cuthbertson, A. G. S. Impact of multiple natural enemies on immature Drosophila suzukii in strawberries and blueberries. Biocontrol https://doi.org/10.1007/s10526-018-9874-8 (2018).Article 

Google Scholar 
6.Furlong, M. & Pell, J. Interactions between entomopathogenic fungi and other arthropods natural enemies. In Insect-Fungal Associations, Ecology and Evolution (eds Vega, F. & Blackwell, M.) 51–73 (Oxford University Press, 2005).
Google Scholar 
7.Lafferty, K. D. Interacting parasites. Science https://doi.org/10.1126/science.1196915 (2010).Article 
PubMed 

Google Scholar 
8.Price, S. L. et al. Recent findings in fungus-growing ants: evolution, ecology, and behavior of a complex microbial symbiosis. In Genes, Behaviors and Evolution of Social Insects (eds Azuma, N. & Higashi, S.) 255–280 (Hokkaido University Press, 2003).
Google Scholar 
9.Telfer, S. et al. Species interactions in a parasite community drive infection risk in a wildlife population. Science https://doi.org/10.1126/science.1190333 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Carlson, C. J. et al. A global parasite conservation plan. Biol. Conserv. https://doi.org/10.1016/j.biocon.2020.108596 (2020).Article 

Google Scholar 
11.Colwell, R. K., Dunn, R. R. & Harris, N. C. Coextinction and persistence of dependent species in a changing world. Annu. Rev. Ecol. Evol. Syst. https://doi.org/10.1146/annurev-ecolsys-110411-160304 (2012).Article 

Google Scholar 
12.Gagne, R. B. et al. Parasites as conservation tools. Conserv. Biol. https://doi.org/10.1111/cobi.13719 (2021).Article 
PubMed 

Google Scholar 
13.Csősz, S. & Majoros, G. Ontogenetic origin of mermithogenic Myrmica phenotypes (Hymenoptera, Formicidae). Insectes Soc.  https://doi.org/10.1007/s00040-008-1040-3 (2009).Article 

Google Scholar 
14.Csata, E. et al. Lock-picks: fungal infection facilitates the intrusion of strangers into ant colonies. Sci. Rep. https://doi.org/10.1038/srep46323 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Pearson, B. & Raybould, A. F. The effects of antibiotics on the development of larvae and the possible role of bacterial load in caste determination and diapause in Myrmica rubra (Hymenoptera: Formicidae). Sociobiology 31, 77–90 (1998).
Google Scholar 
16.Schmid Hempel, P. Evolutionary Parasitology—The Integrated Study of Infections, Immunology, Ecology, and Genetics (Oxford University Press, 2011).
Google Scholar 
17.Donisthorpe, J. K. The Guests of British Ants—Their Habits and Life Histories (George Routledge And Sons, Limited, 1927).
Google Scholar 
18.Hölldobler, B. E. & Wilson, E. O. The Ants (The Belknap Press of Harvard University Press, 1990).Book 

Google Scholar 
19.Buschinger, A. Social parasitism among ants: A review (Hymenoptera: Formicidae). Myrmecol. News 12, 219–235 (2009).
Google Scholar 
20.Quevillon, L. E. The Ecology, Epidemiology, and Evolution of Parasites Infecting Ants (Hymenoptera: Formicidae) (Pennsylvania State University, 2018).
Google Scholar 
21.Quevillon, L. E. & Hughes, D. P. Pathogens, parasites, and parasitoids of ants: a synthesis of parasite biodiversity and epide-miological traits. BioRxiv https://doi.org/10.1101/384495 (2018).Article 

Google Scholar 
22.Di Salvo, M. et al. The microbiome of the Maculinea-Myrmica host-parasite interaction. Sci. Rep. https://doi.org/10.1038/s41598-019-44514-7 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
23.Witek, M., Barbero, F. & Markó, B. Myrmica ants host highly diverse parasitic communities: from social parasites to microbes. Insectes Soc. https://doi.org/10.1007/s00040-014-0362-6 (2014).Article 

Google Scholar 
24.Witek, M. et al. Interspecific relationships in co-occurring populations of social parasites and their host ants. Biol. J. Linn. Soc. https://doi.org/10.1111/bij.12074 (2013).Article 

Google Scholar 
25.Tartally, A. et al. Patterns of host use by brood parasitic Maculinea butterflies across Europe. Philos. Trans. R Soc. B Biol. Sci. https://doi.org/10.1098/rstb.2018.0202 (2019).Article 

Google Scholar 
26.Wardlaw, J. C., Thomas, J. A. & Elmes, G. W. Do Maculinea rebeli caterpillars provide vestigial mutualistic benefits to ants when living as social parasites inside Myrmica ant nests? Entomol. Exp. Appl. https://doi.org/10.1046/j.1570-7458.2000.00646.x (2000).Article 

Google Scholar 
27.Thomas, J. A. & Wardlaw, J. C. The capacity of a Myrmica ant nest to support a predacious species of Maculinea butterfly. Oecologia https://doi.org/10.1007/BF00317247 (1992).Article 
PubMed 

Google Scholar 
28.Csata, E., Billen, J., Bernadou, A., Heinze, J. & Markó, B. Infection-related variation in cuticle thickness in the ant Myrmica scabrinodis (Hymenoptera: Formicidae). Insectes Soc. https://doi.org/10.1007/s00040-018-0628-5 (2018).Article 

Google Scholar 
29.Csősz, S., Rádai, Z., Tartally, A., Ballai, L. E. & Báthori, F. Ectoparasitic fungi Rickia wasmannii infection is associated with smaller body size in Myrmica ants. Sci. Rep. https://doi.org/10.1038/s41598-021-93583-0 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Csata, E., Erős, K. & Markó, B. Effects of the ectoparasitic fungus Rickia wasmannii on its ant host Myrmica scabrinodis: Changes in host mortality and behavior. Insectes Soc. https://doi.org/10.1007/s00040-014-0349-3 (2014).Article 

Google Scholar 
31.Báthori, F., Rádai, Z. & Tartally, A. The effect of Rickia wasmannii (Ascomycota, Laboulbeniales) on the aggression and boldness of Myrmica scabrinodis (Hymenoptera, Formicidae). J. Hymenopt. Res. https://doi.org/10.3897/jhr.58.13253 (2017).Article 

Google Scholar 
32.Báthori, F., Csata, E. & Tartally, A. Rickia wasmannii increases the need for water in Myrmica scabrinodis (Ascomycota: Laboulbeniales; Hymenoptera: Formicidae). J. Invertebr. Pathol. https://doi.org/10.1016/j.jip.2015.01.005 (2015).Article 
PubMed 

Google Scholar 
33.Tartally, A. Myrmecophily of Maculinea Butterflies in the Carpathian Basin (Lepidoptera: Lycaenidae), PhD thesis, https://dea.lib.unideb.hu/dea/handle/2437/78921 (University of Debrecen, Hungary, 2008)
Google Scholar 
34.Elmes, G. W., Wardlaw, J. C., Schönrogge, K., Thomas, J. A. & Clarke, R. T. Food stress causes differential survival of socially parasitic caterpillars of Maculinea rebeli integrated in colonies of host and non-host Myrmica ant species. Entomol. Exp. Appl. https://doi.org/10.1111/j.0013-8703.2004.00121.x (2004).Article 

Google Scholar 
35.Nash, D. R., Als, T. D. & Boomsma, J. J. Survival and growth of parasitic Maculinea alcon caterpillars (Lepidoptera, Lycaenidae) in laboratory nests of three Myrmica ant species. Insectes Soc. https://doi.org/10.1007/s00040-011-0157-y (2011).Article 

Google Scholar 
36.Wilson, K., Grenfell, B. T. & Shaw, D. J. Analysis of aggregated parasite distributions: a comparison of methods. Funct. Ecol. https://doi.org/10.2307/2390169 (1996).Article 

Google Scholar 
37.Tartally, A., Nash, D. R., Varga, Z. & Lengyel, S. Changes in host ant communities of Alcon Blue butterflies in abandoned mountain hay meadows. Insect Conserv. Divers. https://doi.org/10.1111/icad.12369 (2019).Article 

Google Scholar 
38.Csata, E., Bernadou, A., Rákosy-Tican, E., Heinze, J. & Markó, B. The effects of fungal infection and physiological condition on the locomotory behaviour of the ant Myrmica scabrinodis. J. Insect Physiol. https://doi.org/10.1016/j.jinsphys.2017.01.004 (2017).Article 
PubMed 

Google Scholar 
39.Baylis, M. & Pierce, N. E. Lack of compensation by final instar larvae of the myrmecophilous lycaenid butterfly, Jalmenus evagoras, for the loss of nutrients to ants. Physiol. Entomol. https://doi.org/10.1111/j.1365-3032.1992.tb01186.x (1992).Article 

Google Scholar 
40.Elgar, M. A. & Pierce, N. E. Mating success and fecundity in an ant-tended lycaenid butterfly. In Reproductive Success: Studies of Individual Variation in Contrasting Breeding Systems 59–75 (Chicago University Press, 1988).41.Thomas, J. A., Elmes, G. W. & Wardlaw, J. C. Contest competition among Maculinea rebeli butterfly larvae in ant nests. Ecol. Entomol. https://doi.org/10.1111/j.1365-2311.1993.tb01082.x (1993).Article 

Google Scholar 
42.Nash, D. R., Als, T. D., Maile, R., Jones, G. R. & Boomsma, J. J. A mosaic of chemical coevolution in a large blue butterfly. Science https://doi.org/10.1126/science.1149180 (2008).Article 
PubMed 

Google Scholar 
43.Schlick-Steiner, B. C. et al. A butterfly’s chemical key to various ant forts: intersection-odour or aggregate-odour multi-host mimicry? Naturwissenschaften https://doi.org/10.1007/s00114-004-0518-8 (2004).Article 
PubMed 

Google Scholar 
44.Schönrogge, K. et al. Changes in chemical signature and host specificity from larval retrieval to full social integration in the myrmecophilous butterfly Maculinea rebeli. J. Chem. Ecol.  https://doi.org/10.1023/B:JOEC.0000013184.18176.a9 (2004).Article 
PubMed 

Google Scholar 
45.Als, T. D., Nash, D. R. & Boomsma, J. J. Geographical variation in host-ant specificity of the parasitic butterfly Maculinea alcon in Denmark. Ecol. Entomol. https://doi.org/10.1046/j.1365-2311.2002.00427.x (2002).Article 

Google Scholar 
46.Als, T. D., Nash, D. R. & Boomsma, J. J. Adoption of parasitic Maculinea alcon caterpillars (Lepidoptera: Lycaenidae) by three Myrmica ant species. Anim. Behav. https://doi.org/10.1006/anbe.2001.1716 (2001).Article 

Google Scholar 
47.Tartally, A., Somogyi, A. Á., Révész, T. & Nash, D. R. Host ant change of a socially parasitic butterfly (Phengaris alcon) through host nest take-over. Insects https://doi.org/10.3390/insects11090556 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Thomas, J. A., Elmes, G. W., Schönrogge, K., Simcox, D. J. & Settele, J. Primary hosts, secondary hosts and ‘non-hosts’: common confusions in the interpretation of host specificity in Maculinea butterflies and other social parasites of ants. In Studies on the Ecology and Conservation of Butterflies in Europe (eds. Settele, J., Kühn, E. & Thomas, J. A.) vol. 2 99–104 (Pensoft, 2005).49.Thomas, J. A. et al. Mimetic host shifts in an endangered social parasite of ants. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2012.2336 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Fürst, M. A., Durey, M. & Nash, D. R. Testing the adjustable threshold model for intruder recognition on Myrmica ants in the context of a social parasite. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2011.0581 (2012).Article 

Google Scholar 
51.Maák, I. et al. Habitat features and colony characteristics influencing ant personality and its fitness consequences. Behav. Ecol. https://doi.org/10.1093/beheco/araa112 (2021).Article 
PubMed 

Google Scholar 
52.Chapman, B. B., Thain, H., Coughlin, J. & Hughes, W. O. H. Behavioural syndromes at multiple scales in Myrmica ants. Anim. Behav. https://doi.org/10.1016/j.anbehav.2011.05.019 (2011).Article 

Google Scholar 
53.Martin, S. J., Helanterä, H. & Drijfhout, F. P. Is parasite pressure a driver of chemical cue diversity in ants? Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2010.1047 (2011).Article 

Google Scholar 
54.Nehring, V., Evison, S. E. F., Santorelli, L. A., D’Ettorre, P. & Hughes, W. O. H. Kin-informative recognition cues in ants. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2010.2295 (2011).Article 

Google Scholar 
55.Van Zweden, J. S. et al. Blending of heritable recognition cues among ant nestmates creates distinct colony gestalt odours but prevents within-colony nepotism. J. Evol. Biol. https://doi.org/10.1111/j.1420-9101.2010.02020.x (2010).Article 
PubMed 

Google Scholar 
56.Nash, D. R. & Andersen, A. Maculinea-sommerfugle og stikmyrer på danske heder—coevolution i tid og rum. Flora og Fauna 121, 133–141 (2015).
Google Scholar 
57.Haelewaters, D., Boer, P., Gort, G. & Noordijk, J. Studies of Laboulbeniales (Fungi, Ascomycota) on Myrmica ants (II): variation of infection by Rickia wasmannii over habitats and time. Anim. Biol. https://doi.org/10.1163/15707563-00002472 (2015).Article 

Google Scholar 
58.Dallas, T. A., Laine, A.-L. & Ovaskainen, O. Detecting parasite associations within multi-species host and parasite communities. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2019.1109 (2019).Article 

Google Scholar 
59.Herczeg, D., Ujszegi, J., Kásler, A., Holly, D. & Hettyey, A. Host–multiparasite interactions in amphibians: a review. Parasit. Vectors https://doi.org/10.1186/s13071-021-04796-1 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
60.Bronstein, J. L. Conditional outcomes in mutualistic interactions. Trends Ecol. Evol. https://doi.org/10.1016/0169-5347(94)90246-1 (1994).Article 
PubMed 

Google Scholar 
61.Zhang, Z., Yan, C. & Zhang, H. Mutualism between antagonists: Its ecological and evolutionary implications. Integr. Zool. https://doi.org/10.1111/1749-4877.12487 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
62.Rogalski, M. A., Stewart Merrill, T., Gowler, C. D., Cáceres, C. E. & Duffy, M. A. Context-dependent host-symbiont interactions: Shifts along the parasitism-mutualism continuum. Am. Nat. https://doi.org/10.1086/716635 (2021).Article 
PubMed 

Google Scholar 
63.Pfliegler, W. P., Báthori, F., Haelewaters, D. & Tartally, A. Studies of Laboulbeniales on Myrmica ants (III): myrmecophilous arthropods as alternative hosts of Rickia wasmannii. Parasite https://doi.org/10.1051/parasite/2016060 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Chouvenc, T., Efstathion, C. A., Elliott, M. L. & Su, N.-Y. Resource competition between two fungal parasites in subterranean termites. Naturwissenschaften https://doi.org/10.1007/s00114-012-0977-2 (2012).Article 
PubMed 

Google Scholar 
65.Lawton, J. H. & Hassell, M. P. Asymmetrical competition in insects. Nature https://doi.org/10.1038/289793a0 (1981).Article 

Google Scholar 
66.Price, P. W. Evolutionary Biology of Parasites (Princeton University Press, 1980).
Google Scholar 
67.Nash, D. R. & Boomsma, J. J. Communication between hosts and social parasites. In Sociobiology of Communication (eds D’Ettorre, P. & Hughes, D. P.) 55–80 (Oxford University Press, 2008).Chapter 

Google Scholar 
68.Tartally, A., Szűcs, B. & Ebsen, J. R. The first records of Rickia wasmannii Cavara, 1899, a myrmecophilous fungus, and its Myrmica Latreille, 1804 host ants in Hungary and Romania (Ascomycetes: Laboulbeniales; Hymenoptera: Formicidae). Myrmecol. News 10, 123 (2007).
Google Scholar 
69.Radchenko, A. G. & Elmes, G. W. Myrmica (Hymenoptera: Formicidae) ants of the Old World. vol. 6 (Fauna Mundi 3, 2010).70.Tragust, S., Tartally, A., Espadaler, X. & Billen, J. Histopathology of Laboulbeniales (Ascomycota: Laboulbeniales): ectoparasitic fungi on ants (Hymenoptera: Formicidae). Myrmecol. News 23, 81–89 (2016).
Google Scholar 
71.Haelewaters, D., Boer, P. & Noordijk, J. Studies of Laboulbeniales (Fungi, Ascomycota) on Myrmica ants: Rickia wasmannii in the Netherlands. J. Hymenopt. Res. https://doi.org/10.3897/JHR.44.4951 (2015).Article 

Google Scholar 
72.Espadaler, X. & Santamaria, S. Ecto- and endoparasitic fungi on ants from the Holarctic Region. Psyche, 2012, 168478. https://doi.org/10.1155/2012/168478 (2012).Article 

Google Scholar 
73.Báthori, F., Pfliegler, W. P., Zimmerman, C.-U. & Tartally, A. Online image databases as multi-purpose resources: discovery of a new host ant of Rickia wasmannii Cavara (Ascomycota, Laboulbeniales) by screening AntWeb.org. J. Hymenopt. Res, 61, 85-94. https://doi.org/10.3897/jhr.61.20255 (2017).Article 

Google Scholar 
74.Riddick, E. W. Ectoparasitic mite and fungus on an invasive lady beetle: parasite coexistence and influence on host survival. Bull. Insectol. 63, 13–20 (2010).
Google Scholar 
75.Konrad, M., Grasse, A. V, Tragust, S. & Cremer, S. Anti-pathogen protection versus survival costs mediated by an ectosymbiont in an ant host. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2014.197620141976 (2015).76.De Kesel, A., Haelewaters, D. & Dekoninck, W. Myrmecophilous Laboulbeniales Ascomycota in Belgium. Sterbeeckia 34, 3–6 (2016).
Google Scholar 
77.Haelewaters, D. The first record of Laboulbeniales (Fungi, Ascomycota) on Ants (Hymenoptera, Formicidae) in The Netherlands. Ascomycete.org 4, 65-69 (2012).78.van Swaay, C. et al. European Red List of Butterflies (Publications Office of the European Union, 2010).
Google Scholar 
79.Gergely, P. & Hudák, T. Revision of threatened butterfly species in Hungary (Lepidoptera: Rhopalocera). Lepidopterol. Hungarica https://doi.org/10.24386/lephung.2021.17.1.27 (2021).Article 

Google Scholar 
80.Wallis de Vries, M. Code rood voor het gentiaanblauwtje. Vlinders 4, 5–8 (2017).
Google Scholar 
81.Barbero, F., Thomas, J. A., Bonelli, S., Balletto, E. & Schönrogge, K. Queen ants make distinctive sounds that are mimicked by a butterfly social parasite. Science https://doi.org/10.1126/science.1163583 (2009).Article 
PubMed 

Google Scholar 
82.Thomas, J. A., Elmes, G. W., Wardlaw, J. C. & Woyciechowski, M. Host specificity among Maculinea butterflies in Myrmica ant nests. Oecologia https://doi.org/10.1007/BF00378660 (1989).Article 
PubMed 

Google Scholar 
83.Elmes, G. W. et al. The ecology of Myrmica ants in relation to the conservation of Maculinea butterflies. J. Insect Conserv. https://doi.org/10.1023/A:1009696823965 (1998).Article 

Google Scholar 
84.Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods https://doi.org/10.1038/nmeth.2019 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
85.Cammaerts-Tricot, M.-C. Ontogenesis of the defence reactions in the workers of Myrmica rubra L. (Hymenoptera: Formicidae). Anim. Behav. https://doi.org/10.1016/0003-3472(75)90058-5 (1975).Article 

Google Scholar 
86.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015). More