Philippa Kaur

Arneth, A. et al. Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci. 3, 525–532 (2010).Article 

Google Scholar 
Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCC, 2015).IPCC Special Report on Climate Change and Land (eds Shukla, P. R. et al.) (IPCC, 2019).Oertel, C., Matschullat, J., Zurba, K., Zimmermann, F. & Erasmi, S. Greenhouse gas emissions from soils—a review. Geochemistry 76, 327–352 (2016).Article 

Google Scholar 
Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry: An Analysis of Global Change 3rd edn (Elsevier, 2013).Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11, 234–240 (2021).Article 

Google Scholar 
Ackerman, D., Millet, D. B. & Chen, X. Global estimates of inorganic nitrogen deposition across four decades. Glob. Biogeochem. Cycles 33, 100–107 (2019).Article 

Google Scholar 
Du, E. Rise and fall of nitrogen deposition in the United States. Proc. Natl Acad. Sci. USA 113, E3594–E3595 (2016).Article 

Google Scholar 
Schmitz, A. et al. Responses of forest ecosystems in Europe to decreasing nitrogen deposition. Environ. Pollut. 244, 980–994 (2019).Article 

Google Scholar 
Hietz, P. et al. Long-term change in the nitrogen cycle of tropical forests. Science 334, 664–666 (2011).Article 

Google Scholar 
Fang, Y. T., Gundersen, P., Mo, J. M. & Zhu, W. X. Input and output of dissolved organic and inorganic nitrogen in subtropical forests of South China under high air pollution. Biogeosciences 5, 339–352 (2008).Article 

Google Scholar 
Yu, G. et al. Stabilization of atmospheric nitrogen deposition in China over the past decade. Nat. Geosci. 12, 424–429 (2019).Article 

Google Scholar 
Liu, L. L. & Greaver, T. L. A global perspective on belowground carbon dynamics under nitrogen enrichment. Ecol. Lett. 13, 819–828 (2010).Article 

Google Scholar 
LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).Article 

Google Scholar 
Reich, P. B. et al. Scaling of respiration to nitrogen in leaves, stems and roots of higher land plants. Ecol. Lett. 11, 793–801 (2008).Article 

Google Scholar 
Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).Article 

Google Scholar 
Mo, J. et al. Nitrogen addition reduces soil respiration in a mature tropical forest in southern China. Glob. Change Biol. 14, 403–412 (2008).Article 

Google Scholar 
Janssens, I. A. et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).Article 

Google Scholar 
Zhong, Y., Yan, W. & Shangguan, Z. The effects of nitrogen enrichment on soil CO2 fluxes depending on temperature and soil properties. Glob. Ecol. Biogeogr. 25, 475–488 (2016).Article 

Google Scholar 
Deng, L. et al. Soil GHG fluxes are altered by N deposition: new data indicate lower N stimulation of the N2O flux and greater stimulation of the calculated C pools. Glob. Change Biol. 26, 2613–2629 (2020).Article 

Google Scholar 
Hagedorn, F., Kammer, A., Schmidt, M. W. I. & Goodale, C. L. Nitrogen addition alters mineralization dynamics of 13C-depleted leaf and twig litter and reduces leaching of older DOC from mineral soil. Glob. Change Biol. 18, 1412–1427 (2012).Article 

Google Scholar 
Du, Y. et al. Different types of nitrogen deposition show variable effects on the soil carbon cycle process of temperate forests. Glob. Change Biol. 20, 3222–3228 (2014).Article 

Google Scholar 
Yan, T. et al. Negative effect of nitrogen addition on soil respiration dependent on stand age: evidence from a 7-year field study of larch plantations in northern China. Agr. For. Meteorol. 262, 24–33 (2018).Article 

Google Scholar 
Xing, A. et al. Nonlinear responses of ecosystem carbon fluxes to nitrogen deposition in an old-growth boreal forest. Ecol. Lett. 25, 77–78 (2021).Article 

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

Google Scholar 
Gao, Q. et al. Stimulation of soil respiration by elevated CO2 is enhanced under nitrogen limitation in a decade-long grassland study. Proc. Natl Acad. Sci. USA 117, 33317–33324 (2020).Article 

Google Scholar 
Liu, X. J. et al. Nitrogen deposition and its ecological impact in China: an overview. Environ. Pollut. 159, 2251–2264 (2011).Article 

Google Scholar 
Zhu, F. F., Yoh, M., Gilliam, F. S., Lu, X. K. & Mo, J. M. Nutrient limitation in three lowland tropical forests in southern China receiving high nitrogen deposition: insights from fine root responses to nutrient additions. PLoS ONE 8, e82661 (2013).Article 

Google Scholar 
Wang, C. et al. Responses of soil microbial community to continuous experimental nitrogen additions for 13 years in a nitrogen-rich tropical forest. Soil Biol. Biochem. 121, 103–112 (2018).Article 

Google Scholar 
Priess, J. & Fölster, H. Microbial properties and soil respiration in submontane forests of Venezuelian Guyana: characteristics and response to fertilizer treatments. Soil Biol. Biochem. 33, 503–509 (2001).Article 

Google Scholar 
He, T., Wang, Q., Wang, S. & Zhang, F. Nitrogen addition altered the effect of belowground C allocation on soil respiration in a subtropical forest. PLoS ONE 11, e0155881 (2016).Article 

Google Scholar 
Fan, H. et al. Nitrogen deposition promotes ecosystem carbon accumulation by reducing soil carbon emission in a subtropical forest. Plant Soil 379, 361–371 (2014).Article 

Google Scholar 
Zheng, M. et al. Effects of nitrogen and phosphorus additions on nitrous oxide emission in a nitrogen-rich and two nitrogen-limited tropical forests. Biogeosciences 13, 3503–3517 (2016).Article 

Google Scholar 
Lu, X. et al. Nitrogen deposition accelerates soil carbon sequestration in tropical forests. Proc. Natl Acad. Sci. USA 118, e2020790118 (2021).Article 

Google Scholar 
Zhou, G. Y. et al. Old-growth forests can accumulate carbon in soils. Science 314, 1417–1417 (2006).Article 

Google Scholar 
Tian, J. et al. Long-term nitrogen addition modifies microbial composition and functions for slow carbon cycling and increased sequestration in tropical forest soil. Glob. Change Biol. 25, 3267–3281 (2019).Article 

Google Scholar 
Huang, N. et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv. 6, eabb8508 (2020).Article 

Google Scholar 
Lu, X. K. et al. Effect of simulated N deposition on soil exchangeable cations in three forest types of subtropical China. Pedosphere 19, 189–198 (2009).Article 

Google Scholar 
Fang, Y., Gundersen, P., Mo, J. & Zhu, W. Nitrogen leaching in response to increased nitrogen inputs in subtropical monsoon forests in southern China. For. Ecol. Manage. 257, 332–342 (2009).Article 

Google Scholar 
Chen, X. M. et al. Effects of nitrogen deposition on soil organic carbon fractions in the subtropical forest ecosystems of S. China. J. Plant Nutr. Soil Sci. 175, 947–953 (2012).Article 

Google Scholar 
Fang, H. J. et al. 13C abundance, water-soluble and microbial biomass carbon as potential indicators of soil organic carbon dynamics in subtropical forests at different successional stages and subject to different nitrogen loads. Plant Soil 320, 243–254 (2009).Article 

Google Scholar 
Liu, L. et al. Effects of nitrogen and phosphorus additions on soil microbial biomass and community structure in two reforested tropical forests. Sci. Rep. 5, 14378–14378 (2014).Article 

Google Scholar 
Chen, H. et al. Nitrogen saturation in humid tropical forests after 6 years of nitrogen and phosphorus addition: hypothesis testing. Funct. Ecol. 30, 305–313 (2015).Article 

Google Scholar 
Lu, X., Mao, Q., Gilliam, F. S., Luo, Y. & Mo, J. Nitrogen deposition contributes to soil acidification in tropical ecosystems. Glob. Change Biol. 20, 3790–3801 (2014).Article 

Google Scholar 
Mao, Q. G. Impacts of Long-Term Nitrogen and Phosphorus Addition on Understory Plant Diversity in Subtropical Forests in Southern China. Doctoral Thesis, Univ. Chinese Academy of Sciences (2017).Xing, A. J. et al. High-level nitrogen additions accelerate soil respiration reduction over time in a boreal forest. Ecol. Lett. https://doi.org/10.1111/ele.14065 (2022).Cao, J. et al. Plant–bacteria–soil response to frequency of simulated nitrogen deposition has implications for global ecosystem change. Funct. Ecol. 34, 723–734 (2020).Article 

Google Scholar 
Mo, J. M., Brown, S., Peng, S. L. & Kong, G. H. Nitrogen availability in disturbed, rehabilitated and mature forests of tropical China. For. Ecol. Manage. 175, 573–583 (2003).Article 

Google Scholar 
Huang, Z. L., Ding, M. M., Zhang, Z. P. & Yi, W. M. The hydrological processes and nitrogen dynamics in a monsoon evergreen broad-leafed forest of Dinghushan. Acta Phytoecol. Sin. 18, 194–199 (1994).
Google Scholar 
Wright, R. F. & Rasmussen, L. Introduction to the NITREX and EXMAN projects. For. Ecol. Manage. 101, 1–7 (1998).Article 

Google Scholar 
Gundersen, P. et al. Impact of nitrogen deposition on nitrogen cycling in forests: a synthesis of NITREX data. For. Ecol. Manage. 101, 37–55 (1998).Article 

Google Scholar 
Aber, J. D. et al. Plant and soil responses to chronic nitrogen additions at the Harvard Forest, Massachusetts. Ecol. Appl. 3, 156–166 (1993).Article 

Google Scholar 
Cleveland, C. C. & Townsend, A. R. Nutrient additions to a tropical rain forest drive substantial soil carbon dioxide losses to the atmosphere. Proc. Natl Acad. Sci. USA 103, 10316–10321 (2006).Article 

Google Scholar 
Song, X. et al. Nitrogen addition increased CO2 uptake more than non-CO2 greenhouse gases emissions in a Moso bamboo forest. Sci. Adv. 6, eaaw5790 (2020).Article 

Google Scholar 
Lu, X. et al. Long-term nitrogen addition decreases carbon leaching in nitrogen-rich forest ecosystems. Biogeosciences 10, 3931–3941 (2013).Article 

Google Scholar 
Ackerman, D., Millet, D. B. & Chen, X. Global estimates of inorganic nitrogen deposition across four decades. Glob. Biogeochem. Cycles 33, 100–107 (2019).Article 

Google Scholar 
Tang, X., Liu, S., Zhou, G., Zhang, D. & Zhou, C. Soil–atmospheric exchange of CO2, CH4, and N2O in three subtropical forest ecosystems in southern China. Glob. Change Biol. 12, 546–560 (2006).Article 

Google Scholar 
Lei, J. et al. Temporal changes in global soil respiration since 1987. Nat. Commun. 12, 403 (2021).Article 

Google Scholar  More

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

Google Scholar 
Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).Article 
CAS 

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

Google Scholar 
Hughes, A. R. et al. Predicting the sensitivity of marine populations to rising temperatures. Front. Ecol. Environ. 17, 17–24 (2019).Article 

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

Google Scholar 
Bennett, S., Duarte, C. M., Marbà, N. & Wernberg, T. Integrating within-species variation in thermal physiology into climate change ecology. Philos. Trans. R. Soc. B 374, 20180550 (2019).Article 

Google Scholar 
Sasaki, M. C. & Dam, H. G. Integrating patterns of thermal tolerance and phenotypic plasticity with population genetics to improve understanding of vulnerability to warming in a widespread copepod. Glob. Change Biol. 25, 4147–4164 (2019).Article 

Google Scholar 
Kelly, M. W., Sanford, E. & Grosberg, R. K. Limited potential for adaptation to climate change in a broadly distributed marine crustacean. Proc. R. Soc. B 279, 349–356 (2012).Article 

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

Google Scholar 
Moran, E. V., Hartig, F. & Bell, D. M. Intraspecific trait variation across scales: implications for understanding global change responses. Glob. Change Biol. 22, 137–150 (2016).Article 

Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).Article 
CAS 

Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).Article 

Google Scholar 
Somero, G. N. The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. J. Exp. Biol. 213, 912–920 (2010).Article 
CAS 

Google Scholar 
Gunderson, A. R. & Stillman, J. H. Plasticity in thermal tolerance has limited potential to buffer ectotherms from global warming. Proc. R. Soc. B 282, 20150401 (2015).Article 

Google Scholar 
Barley, J. M. et al. Limited plasticity in thermally tolerant ectotherm populations: evidence for a trade-off. Proc. R. Soc. B 288, 202110765 (2021).Article 

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

Google Scholar 
Grummer, J. A. et al. Aquatic landscape genomics and environmental effects on genetic variation. Trends Ecol. Evol. 34, 641–654 (2019).Article 

Google Scholar 
Kinlan, B. P. & Gaines, S. D. Propagule dispersal in marine and terrestrial environments: a community perspective. Ecology 84, 2007–2020 (2003).Article 

Google Scholar 
Lester, S. E., Ruttenberg, B. I., Gaines, S. D. & Kinlan, B. P. The relationship between dispersal ability and geographic range size. Ecol. Lett. 10, 745–758 (2007).Article 

Google Scholar 
Kinlan, B. P., Gaines, S. D. & Lester, S. E. Propagule dispersal and the scales of marine community process. Diversity Distrib. 11, 139–148 (2005).Article 

Google Scholar 
Mayr, E. Animal Species and Evolution (Harvard Univ. Press, 2014).Haldane, J. B. S. The relation between density regulation and natural selection. Proc. R. Soc. Lond. B 145, 306–308 (1956).Article 
CAS 

Google Scholar 
Marshall, D. J., Monro, K., Bode, M., Keough, M. J. & Swearer, S. Phenotype–environment mismatches reduce connectivity in the sea. Ecol. Lett. 13, 128–140 (2010).Article 
CAS 

Google Scholar 
Burgess, S. C., Treml, E. A. & Marshall, D. J. How do dispersal costs and habitat selection influence realized population connectivity? Ecology 93, 1378–1387 (2012).Article 

Google Scholar 
Sanford, E. & Kelly, M. W. Local adaptation in marine invertebrates. Annu. Rev. Mar. Sci. 3, 509–535 (2011).Article 

Google Scholar 
Caplat, P. et al. Looking beyond the mountain: dispersal barriers in a changing world. Front. Ecol. Environ. 14, 261–268 (2016).Article 

Google Scholar 
Nickols, K. J., Wilson White, J., Largier, J. L. & Gaylord, B. Marine population connectivity: reconciling large-scale dispersal and high self-retention. Am. Nat. 185, 196–211 (2015).Article 

Google Scholar 
Pinsky, M. L., Comte, L. & Sax, D. F. Unifying climate change biology across realms and taxa. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2022.04.011 (2022).Fourcade, Y. et al. Habitat amount and distribution modify community dynamics under climate change. Ecol. Lett. 24, 950–957 (2021).Article 

Google Scholar 
Kappes, H., Tackenberg, O. & Haase, P. Differences in dispersal- and colonization-related traits between taxa from the freshwater and the terrestrial realm. Aquat. Ecol. 48, 73–83 (2014).Article 
CAS 

Google Scholar 
Kinlan, B. P. & Gaines, S. D. Propagule dispersal in marine and terrestrial environments: a community perspective. Ecology 84, 2007–2020 (2003).Article 

Google Scholar 
Kappes, H. & Haase, P. Slow, but steady: dispersal of freshwater molluscs. Aquat. Sci. 74, 1–14 (2012).Article 

Google Scholar 
Sasaki, M. & Dam, H. G. Global patterns in copepod thermal tolerance. J. Plankton Res. 43, 598–609 (2021).Article 

Google Scholar 
Cereja, R. Critical thermal maxima in aquatic ectotherms. Ecol. Indic. 119, 106856 (2020).Article 

Google Scholar 
Vinagre, C. et al. Upper thermal limits and warming safety margins of coastal marine species – Indicator baseline for future reference. Ecol. Indic. 102, 644–649 (2019).Article 

Google Scholar 
Muñoz, M. M. The Bogert effect, a factor in evolution. Evolution 76, 49–66 (2022).Article 

Google Scholar 
Muñoz, M. M. & Bodensteiner, B. L. Janzen’s hypothesis meets the Bogert effect: connecting climate variation, thermoregulatory behavior, and rates of physiological evolution. Integr. Org. Biol. 1, oby002 (2019).Spence, A. R. & Tingley, M. W. The challenge of novel abiotic conditions for species undergoing climate-induced range shifts. Ecography 43, 1571–1590 (2020).Article 

Google Scholar 
Burrows, M. T. et al. The pace of shifting climate in marine and terrestrial ecosystems. Science 334, 652–655 (2011).Article 
CAS 

Google Scholar 
Steele, J. H., Brink, K. H. & Scott, B. E. Comparison of marine and terrestrial ecosystems: suggestions of an evolutionary perspective influenced by environmental variation. ICES J. Mar. Sci. 76, 50–59 (2019).Article 

Google Scholar 
Sexton, J. P., McIntyre, P. J., Angert, A. L. & Rice, K. J. Evolution and ecology of species range limits. Annu. Rev. Ecol. Evol. Syst. 40, 415–436 (2009).Article 

Google Scholar 
Chuang, A. & Peterson, C. R. Expanding population edges: theories, traits, and trade-offs. Glob. Change Biol. 22, 494–512 (2016).Article 

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

Google Scholar 
Gaston, K. J. et al. Macrophysiology: a conceptual reunification. Am. Nat. 174, 595–612 (2009).Article 

Google Scholar 
Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).Article 
CAS 

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

Google Scholar 
Cooper, H., Hedges, L. V. & Valentine, J. C. The Handbook of Research Synthesis and Meta-Analysis (Russel Sage Foundation, 2009).Gleser, L. & Olkin, I. in The Handbook of Research Synthesis and Meta-Analysis (eds Cooper, H. et al.) Ch. 19 (Russel Sage Foundation, 2009).Huey, R. B., Hertz, P. E. & Sinervo, B. Behavioral drive versus behavioral inertia in evolution: a null model approach. Am. Nat. 161, 357–366 (2003).Article 

Google Scholar 
Bogert, C. M. Thermoregulation in reptiles, a factor in evolution. Evolution 3, 195–211 (1949).Article 
CAS 

Google Scholar 
Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer ‘cold-blooded’’ animals against climate warming. Proc. Natl Acad. Sci. USA 10, 3835–3840 (2009).Article 

Google Scholar 
Denney, D. A., Jameel, M. I., Bemmels, J. B., Rochford, M. E. & Anderson, J. T. Small spaces, big impacts: contributions of micro-environmental variation to population persistence under climate change. AoB Plants 12, plaa005 (2020).Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).Article 
CAS 

Google Scholar 
Clusella-Trullas, S., Garcia, R. A., Terblanche, J. S. & Hoffmann, A. A. How useful are thermal vulnerability indices? Trends Ecol. Evol. 36, 1000–1010 (2021).Article 

Google Scholar 
Wanders, N., van Vliet, M. T. H., Wada, Y., Bierkens, M. F. P. & van Beek, L. P. H. High-resolution global water temperature modeling. Water Resour. Res. 55, 2760–2778 (2019).Article 

Google Scholar 
Todgham, A. E. & Stillman, J. H. Physiological responses to shifts in multiple environmental stressors: relevance in a changing world. Integr. Comp. Biol. 53, 539–544 (2013).Article 

Google Scholar 
Hoffmann, A. A. & Sgró, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).Article 
CAS 

Google Scholar 
Pespeni, M. H. & Palumbi, S. R. Signals of selection in outlier loci in a widely dispersing species across an environmental mosaic. Mol. Ecol. 22, 3580–3597 (2013).Article 
CAS 

Google Scholar 
Hoey, J. A. & Pinsky, M. L. Genomic signatures of environmental selection despite near-panmixia in summer flounder. Evolut. Appl. 11, 1732–1747 (2018).Article 
CAS 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).Article 
CAS 

Google Scholar 
Young, H. S., McCauley, D. J., Galetti, M. & Dirzo, R. Patterns, causes, and consequences of Anthropocene defaunation. Annu. Rev. Ecol. Evol. Syst. 47, 333–358 (2016).Article 

Google Scholar 
Morelli, T. L. et al. Managing Climate Change refugia for climate adaptation. PLoS ONE 11, e0159909 (2016).Article 

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

Google Scholar 
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Internal Med. 151, 264–270 (2009).Article 

Google Scholar 
O’Dea, R. E. et al. Preferred reporting items for systematic reviews and meta-analyses in ecology and evolutionary biology: a PRISMA extension. Biol. Rev. https://doi.org/10.1111/brv.12721 (2021).Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, 89 (2021).Bennett, J. M. et al. GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 1198 (2018).Lancaster, L. T. & Humphreys, A. M. Global variation in the thermal tolerances of plants. Proc. Natl Acad. Sci. USA 117, 13580–13587 (2020).Article 
CAS 

Google Scholar 
Rohatgi, A. WebPlotDigitizer (2020); https://automeris.io/WebPlotDigitizerAssis, J. et al. Bio-ORACLE v2.0: extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).Article 

Google Scholar 
Dee, D. P. et al. The ERA–interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorolog. Soc. 137, 553–597 (2011).Article 

Google Scholar 
Helmuth, B. et al. Climate change and latitudinal patterns of intertidal thermal stress. Science 298, 1015–1017 (2002).Article 
CAS 

Google Scholar 
Helmuth, B. Thermal biology of rocky intertidal mussels: quantifying body temperature using climatological data. Ecology 80, 15–34 (1999).Article 

Google Scholar 
Bell, E. C. Environmental and morphological influences on thallus temperature and desiccation of the intertidal alga Mastocarpus papillatus Kützing. J. Exp. Mar. Biol. Ecol. 191, 29–55 (1995).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Software 36, 1–48 (2010).Article 

Google Scholar 
Sasaki, M. et al. Data for ‘greater local adaptation to temperature in the ocean than on land’. figshare https://doi.org/10.6084/m9.figshare.20173571 (2022). More

Department of Zoology, University of Cambridge, Cambridge, UKYucheng Wang, Bianca De Sanctis, Ruairidh Macleod, Daniel Money & Eske WillerslevLundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, DenmarkYucheng Wang, Ana Prohaska, Jialu Cao, Antonio Fernandez-Guerra, James Haile, Kurt H. Kjær, Thorfinn Sand Korneliussen, Nicolaj Krog Larsen, Ruairidh Macleod, Hugh McColl, Mikkel Winther Pedersen, Fernando Racimo, Alexandra Rouillard, Anthony H. Ruter, Lasse Vinner, David J. Meltzer & Eske WillerslevALPHA, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research (ITPCAS), Chinese Academy of Sciences (CAS), Beijing, ChinaYucheng WangKey Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Science, Lanzhou University, Lanzhou, ChinaHaoran DongGénomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry, FranceAdriana Alberti, France Denoeud & Patrick WinckerInstitute for Integrative Biology of the Cell (I2BC), Université Paris-Saclay, CEA, CNRS, Gif-sur-Yvette, FranceAdriana AlbertiThe Arctic University Museum of Norway, UiT—The Arctic University of Norway, Tromsø, NorwayInger Greve Alsos, Eric Coissac, Galina Gusarova, Youri Lammers & Marie Kristine Føreid MerkelDepartment of Geography and Environment, University of Hawaii, Honolulu, HI, USADavid W. BeilmanDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, DenmarkAnders A. BjørkInstitute of Earth Sciences, St Petersburg State University, St Petersburg, RussiaAnna A. Cherezova & Grigory B. FedorovArctic and Antarctic Research Institute, St Petersburg, RussiaAnna A. Cherezova & Grigory B. FedorovUniversité Grenoble-Alpes, Université Savoie Mont Blanc, CNRS, LECA, Grenoble, FranceEric CoissacDepartment of Genetics, University of Cambridge, Cambridge, UKBianca De Sanctis & Richard DurbinCarlsberg Research Laboratory, Copenhagen V, DenmarkChristoph Dockter & Birgitte SkadhaugeSchool of Geography and Environmental Science, University of Southampton, Southampton, UKMary E. EdwardsAlaska Quaternary Center, University of Alaska Fairbanks, Fairbanks, AK, USAMary E. EdwardsSchool of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, UKNeil R. Edwards & Philip B. HoldenCenter for the Environmental Management of Military Lands, Colorado State University, Fort Collins, CO, USAJulie EsdaleDepartment of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, CanadaDuane G. FroeseFaculty of Biology, St Petersburg State University, St Petersburg, RussiaGalina GusarovaDepartment of Glaciology and Climate, Geological Survey of Denmark and Greenland, Copenhagen K, DenmarkKristian K. KjeldsenDepartment of Earth Science, University of Bergen, Bergen, NorwayJan Mangerud & John Inge SvendsenBjerknes Centre for Climate Research, Bergen, NorwayJan Mangerud & John Inge SvendsenDepartment of Geology, Quaternary Sciences, Lund University, Lund, SwedenPer MöllerCenter for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Copenhagen Ø, DenmarkDavid Nogués-Bravo, Hannah Lois Owens & Carsten RahbekCentre d’Anthropobiologie et de Génomique de Toulouse, Faculté de Médecine Purpane, Université Paul Sabatier, Toulouse, FranceLudovic OrlandoCenter for Global Mountain Biodiversity, Globe Institute, University of Copenhagen, Copenhagen, DenmarkHannah Lois Owens & Carsten RahbekGates of the Arctic National Park and Preserve, US National Park Service, Fairbanks, AK, USAJeffrey T. RasicDepartment of Geosciences, UiT—The Arctic University of Norway, Tromsø, NorwayAlexandra RouillardZoological Institute, Russian academy of sciences, St Petersburg, RussiaAlexei TikhonovResource and Environmental Research Center, Chinese Academy of Fishery Sciences, Beijing, ChinaYingchun XingCollege of Plant Science, Jilin University, Changchun, Jilin, ChinaYubin ZhangDepartment of Anthropology, Southern Methodist University, Dallas, TX, USADavid J. MeltzerWellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UKEske WillerslevMARUM, University of Bremen, Bremen, GermanyEske WillerslevAll authors contributed to the conception of the presented ideas. Y.W. and H.D. analysed the data. Y.W., D.J.M., A.P. and E.W. wrote the paper with inputs from all authors. More

Study siteThe Cariaco Basin, located on the continental shelf off Venezuela, is a large (about 160 km long and about 65 km wide) depression, composed of two approximately 1,400-m-deep sub-basins. It is partially isolated from the Caribbean Sea by a series of sills with depths of less than 150 m (ref. 47). This limits renewal of deep water in the basin and, paired with the high oxygen demand resulting from intense surface primary productivity, leads to anoxic waters below a depth of about 275 m at present47,48.The marked seasonality in the Cariaco Basin, combined with anoxic bottom waters that effectively prevent bioturbation, results in the accumulation of annually laminated (varved) sediments. As sediments are varved for the last deglaciation and the Holocene, and because of the sensitivity of the area to climate change, they are considered to be one of the most valuable high-resolution marine climate archives and have been successfully used to study climate variability in the tropics3,11,16,17,18. Varve thickness is about 1 mm or more during the YD–Holocene transition18.Core and age modelCore MD03-2621 was retrieved during IMAGES cruise XI (PICASSO) aboard R/V Marion Dufresne in 2003 (Laj and Shipboard Party 2004). Cariaco cores have been collected under the regulations of the Ocean Drilling Program and the IMAGES coring programme. In this study, data from depths between 480 and 540 cm below the seafloor are presented, encompassing the YD–Holocene transition. A detailed age model for core MD03-2621 was established by Deplazes et al.11 and is based on the cross-correlation of total reflectance to dated colour records from the Cariaco Basin49,50. For the studied interval, the original age model is based on a floating varve chronology anchored to tree ring data by matching 14C data49. The age model for core MD03-2621 was further fine-tuned by correlation of reflectance data to the NGRIP ice core δ18O record on the GICC05 age scale11. The transition from the YD to the Holocene is characterized by a decrease in the sedimentation rate from 1.4 to 0.5 mm year−1.To account for possible depth offsets during storage and subsampling, we matched sediment colour data expressed as greyscale (GS) to the reflectance data from Deplazes et al.11 with the software QAnalySeries51. To enable comparison with our record, ages in Lea et al.3 were corrected for the age difference between the sediment-colour-based midpoint of the YD–Holocene transition in their record (11.56 kyr b2k) and in data from Deplazes et al.11 (11.673 kyr b2k). The start and end of the change in reflectance were determined by the RAMPFIT software52.Sample preparationSamples for MSI of molecular proxies were prepared as described in Alfken et al.53: the original core was subsampled by LL channels, from which X-ray pictures (Hewlett-Packard Faxitron 43855A X-ray cabinet) and high-resolution digital images (smart-CIS 1600 Line Scanner) were obtained. The LL channels were then cut into 5-cm pieces, which were subsequently freeze-dried, embedded in a gelatin:carboxymethyl cellulose (4%:1%) mixture and thin-sectioned on a Microm HM 505 E cryomicrotome. From each piece, one 60-µm-thick and one 100-µm-thick, longitudinal slice (spanning the whole 5 cm piece) were prepared and affixed to indium-tin-oxide-coated glass slides (Bruker Daltonik, Bremen, Germany) for MSI and elemental mapping, respectively. Slices for MSI were further amended with a fullerite matrix54.For all slices, a high-resolution picture was taken on a M4 Tornado micro-X-ray fluorescence spectroscopy system (Bruker Nano Analytics). This picture was used as a reference to set up elemental mapping and MSI analysis, and also for the 2D comparison of elemental and proxy data to sediment colour. Sediment colour is expressed as GS value. To account for differences between single slices, ΔGS was calculated as the difference between a value and the median GS of each individual slice. Very low GS values corresponding to areas devoid of sediment, identified by a black background, were excluded from analysis.Elemental mappingElemental mapping of 100-µm-thick slices was performed on a M4 Tornado micro-X-ray fluorescence spectroscopy system (Bruker Nano Analytics) equipped with a micro-focused Rh source (50 kV, 600 µA) with a polycapillary optic. Measurements were conducted under vacuum, with a resolution of 50 µm, two scans per spot and a scan time of 5 ms per scan. Data were initially processed and visualized with M4 Tornado Software version 1.3. XY matrices of relevant elements and sediment colour were imported into MATLAB (R2016b) for further processing. To assess the correspondence between sediment colour and elemental composition, for each 5-cm piece, signal intensities of Ca, Fe, Ti and Si in single spots were binned according to ΔGS and average intensities were calculated for each bin (Extended Data Fig. 5). The bin size was 5 GS units.Molecular proxy analysis by MSIMSI was carried out on a 7T solariX XR Fourier transform ion cyclotron resonance mass spectrometer coupled to a matrix-assisted laser desorption/ionization source equipped with a Smartbeam II laser (Bruker Daltonik, Bremen, Germany). Analyses were performed in positive ionization mode selecting for a continuous accumulation of selective ions window of m/z 554 ± 12. Spectra were acquired with 25% data reduction to limit data size. Spatial resolution was obtained by rastering the ionizing laser across the sample in a defined rectangular area at a 100-µm spot distance. Considering laminae thickness in the millimetre range18, such raster resolution is suited for seasonally resolved SST reconstruction. Settings for laser power, frequency and number of shots were adjusted for optimal signal intensities before each measurement; typical values were 250 shots with 200 Hz frequency and 60% laser power. External mass calibration was performed in electrospray ionization mode with sodium trifluoroacetate (Sigma-Aldrich). Each spectrum was also calibrated after data acquisition by an internal lock mass calibration using the Na+ adduct of pyropheophorbide a (m/z 557.2523), a chlorophyll a derivative generally present in relatively young marine sediments. Around 20,000 individual spots were thereby obtained for every 5-cm slice, each spot containing information on the abundance of diunsaturated and triunsaturated C37 alkenones needed to calculate the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) SST proxy.We provide a two-pronged approach to decode SST proxy information: (1) a downcore ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) profile is obtained by pooling alkenone data from coeval horizons, and results in SST reconstructions with annual resolution, and (2) 2D images of alkenone distribution are examined in conjunction with maps of sediment colour and elemental distribution to filter single-spot alkenone data for season of deposition.SST reconstruction with yearly resolutionFor the downcore profile, MSI data were referenced to the X-ray image by the identification of three teaching points per 5-cm piece. Afterwards, the X-ray image was corrected for tilting of laminae in the LL channels. This was done by identification of single laminae in the X-ray image and selection of a minimum of four tie points per lamina. A detailed description can be found in Alfken et al.9. After applying the corresponding age model, downcore profiles were established with 1-year resolution: the intensity of the two alkenone species relevant to the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) proxy (C37:2 and C37:3) were recorded for each individual laser spot and filtered for a signal-to-noise threshold of 3. Only spots in which both compounds were detected were further considered. Intensity values were then summed over the depth corresponding to 1 year. By pooling proxy data into 1-year horizons, the effect of changing sedimentation rate and, thereby, changing downcore resolution is minimized. If at least ten spots presenting both compounds were available for a single horizon, data quality criteria were satisfied54 and a ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) value was calculated as defined by Prahl and Wakeham22:$${{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}=frac{{text{C}}_{37:2}}{{text{C}}_{37:2}+{text{C}}_{37:3}}$$
(1)
To apply the gas chromatography (GC)-based calibrations for the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) proxy, MSI-based data were converted to GC equivalents. Therefore, after MSI, sediment slices were extracted for conventional proxy analysis. Sediment was scraped off the slide and extracted following a modified Bligh and Dyer procedure55,56. Extracts were evaporated under a stream of nitrogen, re-dissolved in n-hexane and analysed on a Thermo Finnigan Trace GC-FID equipped with a Restek Rxi-5ms capillary column (30 m × 0.25 mm ID). For each 5-cm piece, a ratio between the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) values obtained by GC flame ionization detector analysis and MSI of the whole piece was calculated. The average ratio of all pieces for which GC-based values could be obtained was 1.194, with a standard deviation of 0.021.$${{rm{U}}}_{37,{rm{G}}{rm{C}}-{rm{F}}{rm{I}}{rm{D}}}^{{{rm{K}}}^{{prime} }}=1.194times {{rm{U}}}_{37,{rm{M}}{rm{S}}{rm{I}}}^{{{rm{K}}}^{{prime} }}$$
(2)
This ratio was used to calculate GC-equivalent ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) values, which were then translated into SST using the BAYSPLINE calibration57. The average standard error of the BAYSPLINE model is 0.049 ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) units (corresponding to 1.4 °C) for samples with SST below 23.4 °C, but increases at higher values (to up to 4.4 °C)57. This is explained by the fact that sensitivity of the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) to SST (that is, the slope of the regression) declines at higher values. In the current dataset, the 95% confidence interval is, on average, ±3.6 °C. The analytical precision of MSI-based SST reconstructions for the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}), using at least ten data points, according to Alfken et al.9, is about 0.3 °C. Sources of uncertainty are summarized in Extended Data Fig. 10a.For frequency analysis, a continuous, annually spaced record was constructed by linearly interpolating 49 missing values. The record was subsequently detrended. Spectral analysis was performed with the REDFIT module58 using a Hanning window (oversample 2, segments 2). Continuous wavelet transforms were applied to investigate changes in cyclicity over time, using the Morlet wavelet with code provided by Torrence and Compo59 for MATLAB. All steps, except for the wavelet analysis, were performed with the PAST software60.For the assessment of the interannual variability, the SST record was band-pass-filtered for periods between 2 and 8 years. The record is based on 1-year binned data; seasonality is thereby nullified and the highest frequency to be evaluated (Nyquist frequency) corresponds to a period of 2 years. Variability of this time series was quantified by calculating the standard deviation of the band-pass-filtered ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) signal in 25-year intervals. To account for the potential impact of analytical precision on the observed signal (Methods, section titled ‘The effect of changing sedimentation rate on reconstructed interannual SST variability during the YD–Holocene transition’), the variability experiment from Alfken et al.9 was revisited. A sediment extract had been sprayed on an ITO slide and analysed by MSI. We then randomly selected n spots and obtained a ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) value for the summed intensities of these spots. Precision was calculated as the standard deviation of five replicate experiments for n = 1, 5, 10, 20, 30, 40, 50 and 60. Decreasing analytical variability with increasing number of observations was fitted to a curve (R2 = 0.838) described by the equation$${rm{Analytical}},{rm{variability}}=0.0741times {rm{number}},{rm{of}},{{rm{spots}}}^{-0.558}$$
(3)
On the basis of this equation, analytical variability for each horizon could be calculated on the basis of the number of values included (Extended Data Fig. 10b). The mean variability for each 25-year window was then subtracted from the observed variability in the band-pass-filtered signal and the resulting proxy values were translated to SST following the equation by Müller et al.61. Statistical significance of the change in corrected SST variability after 11.66 kyr b2k was assessed with a t-test.Assessment of SST seasonalityFor the assessment of SST seasonality, alkenone intensities from individual spots were binned according to ΔGS, with a bin size of 1 unit. Spots were then separated into the categories upwelling season and non-upwelling season by identifying the threshold ΔGS value that maximized the difference between average SST in the bins above and below it. Furthermore, this value had to fulfill three conditions: (1) be higher (lighter) than the bins with the highest relative abundance of Ca, Ti and Fe, which is indicative of the dark sediments associated to non-upwelling season, (2) be lower (darker) than the bin with highest relative abundance for Si indicative of light sediment associated to the upwelling season and (3) the number of spots categorized as upwelling and non-upwelling had to account for at least 25% of total spots. If criteria 1 and 2 prevented criteria 3 from being fulfilled, a limit of 15% was set. After separating data into these two categories, data were processed separately as described above for the unfiltered dataset and a downcore temporal resolution of 5 years was applied. Seasonality was calculated as the difference between both records and thus represents the difference between 5-year average SST in the non-upwelling and upwelling seasons.Shift in seasonality was fitted to two different ramps with the RAMPFIT software52. An unconstrained approach and a constrained approach (in which the start and end points of the ramp were restricted to the intervals 11.725–11.8 kyr b2k and 11.6–11.675 kyr b2k) were applied. Negative values were excluded from this fitting. The resulting groups of data were compared by a Mann–Whitney rank test.SST seasonality in the modern Cariaco Basin was calculated for the years 1980 to 2020 based on the HadISST dataset62 by dividing monthly data from each year into two groups and searching for the largest difference between the average temperatures of both groups. Each group had to include at least three consecutive months. In 36 out of 41 years, the warm season was defined from May to November or from July to November.Decadal-scale to centennial-scale SST changes during the YD–Holocene transition and in the early HoloceneAnnually reconstructed SST (average SST = 24.3 °C) remains relatively stable during the YD–Holocene transition. At around 11.4 kyr b2k, a warming trend is observed. Averaging all data before 11.39 kyr and after 11.37 kyr results in a warming from 23.9 ± 1.6 °C to 25.5 ± 1.4 °C. Trends identified by MSI are consistent with conventional ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) analyses performed in the present study and those previously reported by Herbert and Schuffert23 on Ocean Drilling Program core 165-1002C (Extended Data Fig. 1). These authors observed a slight warming several hundred years after the transition into the Holocene, between about 11.53 and 11.32 kyr b2k.Three prominent SST maxima are observed between about 11.50 and 11.45 kyr b2k. The average SST in these 50 years is 1.3 °C higher than in the 50 years before and after. These maxima are synchronous with the 11.4-ka cold event or PBO characterized by a negative excursion in δ18O and reduced snow accumulation rates in Greenland ice cores63 (Extended Data Fig. 2). The PBO coincides with the oldest of the Bond events, that is, pulses of ice rafting in the Northern Atlantic indicative of climatic deterioration64.A warm tropical response to the PBO would be supported by the lower-resolution foraminiferal SST record of Lea et al.3, which shows two data points of increased SST shortly after the end of the YD–Holocene transition. To enable direct comparison, ages in Lea et al.3 were corrected for the age difference between the sediment-colour-based YD termination midpoint in their record and in data from Deplazes et al.11. After this correction, these maxima correspond to 11.43 and 11.50 kyr b2k (Extended Data Fig. 2). Further, the SST maxima coincide with a short-lived change to lighter-coloured sediments. Hughen et al.19 described a correlation between brief North Atlantic cold events, such as the PBO, and changes in tropical primary productivity mediated by stronger upwelling that result in lighter sediments in the Cariaco Basin. Far-reaching effects of the PBO have previously been described in West Asia, with increased dust plumes being related to a southward shift of the westerlies65.The identification of the mechanisms behind a potential TNA response to the PBO is beyond the scope of this study. However, we wish to point out that high-resolution records are crucial to identify such events and to differentiate between underlying changes coinciding in time and, as in the present case, sharp signals that act on the same multidecadal timescales and can potentially be triggered by the same processes66.The effect of changing sedimentation rate on reconstructed interannual SST variability during the YD–Holocene transitionPooling proxy data into 1-year horizons establishes a constant sampling rate and thereby prevents potential effects of changing sedimentation rates. The onset of the Holocene in the Cariaco Basin sediments is characterized by a sharp decrease in sedimentation rates from 1.4 to 0.5 mm year−1 (refs. 11,19). Consequently, in the yearly pooled data, we observe a reduction in the number of values summed for each horizon (Extended Data Fig. 10b), as fewer laser spots fit into the thinner Holocene annual layers. At the same time, the mean intensity in each of these spots slightly increases, consistent with a relative increase of the contribution of haptophytes to primary production20.We have previously shown that the precision of MSI-based molecular proxy analysis is dependent on both the number of spots pooled per data point and the signal intensity in these spots54. All horizons used in the downcore record are above the established threshold of ten spots and proxy variability was shown to stabilize above this threshold9,54. However, as a decrease in the number of values per horizon might still result in lower analytical precision and contribute to higher signal variability, we corrected variability in the 2–8-year window with the estimated analytical variability (see equation (3)). With this correction, the magnitude of the described variability decreases across the record, but the trend towards higher interannual variability in the Holocene persists (Fig. 2c).Varve formation and alkenone deposition in the sediments of the Cariaco Basin during the YD–Holocene transitionComparison of elemental maps and sediment colour (Extended Data Fig. 5) shows a consistent pattern of lamination across the YD–Holocene transition that results from the seasonal interplay of precipitation, upwelling and dominant phytoplankton community composition. Darker laminae represent the rainy, non-upwelling (summer/fall) season and are enriched in Fe and Ti from terrigenous material and Ca sourced from biogenic CaCO3 produced by foraminifera or coccolithophores. Lighter laminae are characterized by high abundance of Si and correspond to the increased production of biogenic opal by diatoms during the upwelling (winter/spring) season67. This is in agreement with observations by Hughen et al.18, who described the laminae couplets in the Cariaco Basin as representing annual cycles, whereby light laminae are an indicator of high productivity associated with the winter/spring upwelling season and dark laminae are an indicator of summer/fall runoff and accumulation of terrigenous material. Deplazes et al.68 described a divergent origin of lamination for a deeper section of the YD, with light laminae being rich in calcareous and terrigenous elements characteristic for the summer season, whereas dark layers were enriched in Si and Br, indicative of diatoms and organic-walled primary producers characteristic for the more productive winter season. Such an alteration of the characteristic pattern of lamination is not observed in the late YD investigated here.This blueprint of seasonality was used to assess the seasonal behaviour of alkenones. Alkenones were deposited throughout the year, as evidenced by the fact that the number of spots containing detectable amounts of both alkenone species are not restricted to the upwelling or non-upwelling seasons but distributed across a relatively wide range of GS values to both sides of the median (Extended Data Fig. 6). Average alkenone signal intensity is higher in the non-upwelling season, pointing to a preference of alkenone producers for this season and/or to a stronger dilution of the signal in the upwelling season. In regards to the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) SST proxy distribution in light versus dark layers, our observations are in agreement with the ability to capture the seasonal SST cycle with alkenones in sinking particles in the modern Cariaco Basin69.Effect of changing seasonality on YD and early Holocene SST records from the western TNAChanging seasonality can contribute to explaining contrasting lower-resolution SST records in the western TNA during the YD and the early Holocene. The strong warming during the YD–Holocene transition recorded in the foraminiferal Mg/Ca record of the Cariaco Basin (Lea et al.3; Extended Data Fig. 1) might be reflecting the more robust thermohaline stratification and increasingly warmer non-upwelling seasons, given the preference of Globigerinoides ruber for this season.Globigerinoides ruber (white), as used by Lea et al.3, is considered to be a dominant species in the tropics, with a relatively uniform annual distribution. However, in the modern Cariaco Basin, upwelling leads to a distinct foraminiferal community composition and seasonal turnover70, consistent with the notion of warm-water foraminifera narrowing their occurrence to the warmest season71. The relative abundance of G. ruber increases in the non-upwelling (warm) season but rarely exceeds 15%, whereas the upwelling season is clearly dominated by Globigerina bulloides72,73. Globigerinoides ruber fluxes are consistently lowest when upwelling is most vigorous, as expressed in annual minima in SST (Extended Data Fig. 9b). As upwelling during the YD and early Holocene was more intense than in the present70, the preference of G. ruber for the summer (non-upwelling) season might have been even more pronounced.The development of a stronger seasonality in the early Holocene would thus have led to a narrower temporal occurrence of G. ruber in the non-upwelling season, during which it would also be exposed to higher SST. The average SST difference between seasons obtained in our analysis can be converted into annual SST amplitude by assuming a sinusoidal curve. By doing so, we observe an increase in the seasonal amplitude of 1.5 to 1.9 °C (depending on the ramp fitted), which is similar to the warming described by Lea et al.3.This interpretation is in agreement with Bova et al.46, who observed that most Holocene climate reconstructions are biased towards the boreal summer/fall and reflect the evolution of seasonal rather than annual temperatures. As discussed above, this is probably not true for the ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) index in the Cariaco Basin, as alkenones are deposited throughout the year. The suggested weakening of summer stratification during the YD (as compared with the Holocene) might, however, explain why the lower-resolution ({{rm{U}}}_{37}^{{{rm{K}}}^{{prime} }}) records from the semi-enclosed Cariaco Basin show no or weaker warming23 than other, open-ocean, tropical YD records4, where the interplay of upwelling, freshwater input and stratification are less relevant to the SST signal. More

Suborder Glossiphoniiformes Tessler & de Carle, 2018Family Glossiphoniidae Vaillant, 1890Comments. Our two-locus phylogeny reveals the presence of two large clades, corresponding to the subfamilies Glossiphoniinae and Haementeriinae (Fig. 1). The subfamily Theromyzinae Sawyer, 1986, delineated by some authors2,10,22, was not supported as a distant phylogenetic clade and their representatives are clustered within the monophyletic Glossiphoniinae. The same pattern was recovered by earlier phylogenetic reconstructions3,30,33,34. These data indicate that Theromyzinae may represent a synonym of the latter subfamily. However, a subfamily-level revision of the Glossiphoniidae is beyond the framework of the present study.Subfamily Glossiphoniinae Vaillant, 1890Genus Alboglossiphonia Lukin, 1976Type species: Alboglossiphonia heteroclita (Linnaeus, 1761) (= Hirudo heteroclita Linnaeus, 1761; by original designation).Arctic occurrences. Our results reveal that members of this genus are not common inhabitants of the Arctic but two species, A. heteroclita (Linnaeus, 1761) and A. sibirica sp. nov., cross the Arctic Circle on the Yamal Peninsula through the Ob and Taz rivers (Table 1). Previously, it was shown that A. heteroclita occurs in the lower Ob Basin, northern edge of Western Siberia23.Comments. This genus contains inconspicuous minute leeches and is characterized by a nearly global distribution1. It definitely requires an integrative taxonomic revision. Available genetic evidence (Fig. 1 and Supplementary Fig. S1) reveals that the North American populations of what was traditionally assigned to A. heteroclita should be considered a separate species, A. pallida (Verrill, 1872) (type locality: West River near New Haven, Connecticut, USA)35,36. Other species, which occurs in Siberia and the Far East, was tentatively assigned to Alboglossiphonia cf. papillosa (Braun, 1805) based on a darker pigmentation of its dorsum37,38 but it represents a separate North Asian species, which is described here.
Alboglossiphonia sibirica Bolotov, Eliseeva, Klass & Kondakov sp. nov = Alboglossiphonia heteroclita Lukin (1957): 27339 (identification error). = Alboglossiphonia heteroclita papillosa Kaygorodova et al. (2014): 337; Kaygorodova (2015): 4140 (identification error). = Alboglossiphonia cf. papillosa Klass et al. (2018): 2638 (identification error).Figures 4a, 5a, 7a, Supplementary Figs. S2a, S3a, S4, Supplementary Table S2.LSID: https://zoobank.org/urn:lsid:zoobank.org:act:19B581C3-E912-487C-B9EC-8E50DDEFD380.Holotype. RMBH Hir_0542_2-H (non-sequenced), RUSSIA: Lake Torfyanka, 43.0761° N, 131.9620° E, Vladivostok, Primorye, August 12, 2021, Y. E. Chapurina leg.Paratypes (N = 13). RUSSIA: 1 specimen RMBH Hir_0542_2 (sequenced: COI sequence acc. No. ON873332), the type locality, the same date, and collector; 1 specimen RMBH Hir_0396 (non-sequenced), an oxbow lake of Taz River, near Tazovsky settlement, 67.5063° N, 78.6751° E, Yamal-Nenets Region, August 22, 2019, E. S. Babushkin leg.; 1 specimen RMBH Hir_0394 (DNA voucher; sequenced: COI sequence acc. No. ON548508), Vitim River, 57.2010° N, 116.4300° E, Lena River basin, Vitimsky Nature Reserve, Irkutsk Region, July 12, 2019, E. S. Babushkin leg.; 4 specimens RMBH Hir_0013 (3 sequenced with DNA vouchers and one placed on 36 permanent slides as a series of slices; COI sequence acc. No. MH286267, MH286268, and MH286269; 18S rRNA sequence acc. No. MH286273), between zooids of a bryozoan colony, small floodplain lake of the Lena River near Yakutsk, 62.3076° N, 129.8999° E, Yakutia Republic, August 20, 2017, I. N. Bolotov leg.; 1 specimen RMBH Hir_0417_2 (DNA voucher; sequenced: COI sequence acc. No. ON548511), Oron Lake, Gnilaya Kurya Bay, 57.1750° N, 116.4031° E, Lena River basin, Vitimsky Nature Reserve, Irkutsk Region, July 1, 2019, E. S. Babushkin leg.; 1 specimen RMBH Hir_0409_1 (sequenced: COI sequence acc. No. ON548509), a roadside ditch in Knevichi settlement, 43.3886° N, 132.1880° E, Primorye, September 10, 2020, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0413 (sequenced: COI sequence acc. No. ON548510), a puddle near railway at Artem city, 43.3794° N, 132.2188° E, Primorye, September 10, 2020, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0003_3 (DNA voucher; sequenced: COI sequence acc. No. MN393256), Tumnin River, 49.9451° N, 139.9181° E, Khabarovsk Region, July 14, 2014, I. N. Bolotov & I. V. Vikhrev leg.; 1 specimen RMBH Hir_0509_1 (sequenced: COI sequence acc. No. ON548516), a reservoir on the Bolshoy Alim River, near Tolstovka settlement, 50.1981° N, 127.9431° E, Amur Region, July 3, 2021, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0510_1 (DNA voucher; sequenced: COI sequence acc. No. ON548517), an oxbow lake of Bureya River, near Novospassk, 49.6756° N, 129.7343° E, Amur Region, July 3, 2021, O. V. Aksenova et al. leg.Etymology. The name of this species reflects its broad distribution in Siberia.Differential diagnosis. Small leech, which could be distinguished from other congeners by a combination of the following characters: dorsum covered by numerous small, shallow, and indistinct papillae, light yellow, with multiple dark spots and short dashes arranged to 18–24 longitudinal rows; these spots and dashes merged into longitudinal lines in the anterior half of the body (the dark markings pattern often lost in ethanol-preserved animals). Externally, the new species is similar to A. heteroclita, A. hyalina (O. F. Müller, 1773), and A. striata (Apáthy, 1888). However, all these species do not have numerous dark spots and short dashes arranged to multiple longitudinal rows. Additionally, A. heteroclita differs from the new species by having a median row of segmentally arranged dark spots and a smooth dorsum without papillae. A. hyalina differs from A. sibirica sp. nov. by the general lack of dark pigmentation. A. striata differs from the new species by having a median row of segmentally arranged dark transverse stripes and a smooth dorsum without papillae.Molecular diagnosis. The new species represents a separate genetic lineage but is more closely related to A. heteroclita (mean pairwise COI p-distance = 5.1%; range 4.9–5.4%). The intraspecific pairwise COI p-distance ranges from 0.0 to 2.1% (mean ± s.e.m. = 1.31 ± 0.10%; N = 14 sequences and 91 pairwise distance values). The GenBank acc. numbers of reference DNA sequences (COI and 18S rRNA) are given in Supplementary Table S2 and Supplementary Datasets S1–S2.Description. Small leech (body length up to 11.9 mm). Measurements of the type series are given in Supplementary Table S2. Body broad, leaf-like, ovate. Dorsum with numerous small, shallow, and indistinct papillae. Posterior sucker small, circular (maximum diameter of 2.25 mm), ventrally directed. Proboscis pore in the center of anterior sucker. Coloration of living animals: body dirty yellow with multiple brown spots and dashes arranged to longitudinal rows; in the anterior half of the body, these spots and dashes merged into longitudinal lines. Coloration of ethanol-preserved animals: body light yellow; dorsum with multiple dark spots and short dashes arranged to 18–24 longitudinal rows; these spots and dashes merged into longitudinal lines in the anterior half of the body but the dark markings pattern often lost due to preservation. Three pairs of eyespots; the eyespots of the distal pair joined into a single spot; the eyespots of the next two pairs are spaced apart and fused together. Venter light yellow or whitish. Total number of annuli: 70. Somites I–IV joined to form a head region, somites V–XXIV triannulate, somites XXV–XXVII uniannulate. Gonopores joined and open in the furrow XIIa1/a2. Reproductive system: 6 pairs of large, bag-like testisacs inter-segmentally from XIII/XIV to XIX/XX; atrium small, spherical, the atrial cornua twisted anteriorly; paired ejaculatory ducts twisted, short; paired ovisacs narrow, very short. Digestive system: proboscis sheath massive, long, thick; salivary glands diffuse; crop with 6 pairs of crop caeca: 1st-5th uniform, bag-like, 6th pair (posterior caeca) with 3 blind processes; intestine with 4 pairs of rather short processes and an ovate extention after the last pair of processes.Distribution. North Asia: Western Siberia, Eastern Siberia, the Russian Far East, and Mongolia39.Habitats and ecology. This species is known to occur in a broad range of freshwater environments such as rivers, oxbow lakes, large to small natural lakes, reservoirs, road ditches, and even puddles (Supplementary Dataset S2). An unusual example of its association with a bryozoan species was described from Eastern Siberia38. Probably, the record of an Alboglossiphonia leech in the mantle cavity of an unidentified lymnaeid snail from the Altai Mountains, Russia41 could also be attributed to this species. The life cycle of the new species is unknown.Genus Glossiphonia Johnson, 1816Type species: Glossiphonia complanata (Linnaeus, 1758) (= Hirudo complanata Linnaeus, 1758; by subsequent designation).Arctic occurrences. Representatives of this genus are the most remarkable component of the Arctic Glossiphoniidae fauna. Altogether seven species were recorded north of the Arctic Circle, two of which are new to science and described here (Table 1).Comments. In general, sequenced representatives of the genus Glossiphonia could phylogenetically be delineated to three species groups (or subgenera): (1) the complanata-group (= subgenus Glossiphonia s. str.); (2) the verrucata-group (= subgenus Boreobdella Johansson, 1929; type species: Clepsine verrucata Müller, 1844); and (3) the concolor-group (= subgenus Paratorix Lukin & Epstein, 1960; type species: Torix baicalensis Stschegolew, 1922) (Table 1, Fig. 1 and Supplementary Fig. S1).
Glossiphonia arctica Bolotov, Eliseeva, Klass & Kondakov sp. novFigures 4B, 5b,c, 7c, Supplementary Figs. S2b, S3b, S5, Supplementary Table S2.LSID: https://zoobank.org/urn:lsid:zoobank.org:act:FADF0993-A946-413A-9680-25BA0F9BE90D.Holotype. RMBH Hir_0457_2_1-H (sequenced: COI sequence acc. No. ON810735; 18S rRNA sequence acc. No. ON819028), RUSSIA: a large lake near Sob’ railway station, 67.0480°N, 65.6316°E, Polar Urals, June 23, 2021, A. V. Kondakov et al. leg.Paratypes (N = 18). 18 specimens RMBH Hir_0457 (two specimens sequenced: COI sequence acc. No. ON810736 and ON810737; 18S rRNA sequence acc. No. ON819029; one specimen placed on 18 permanent slides as a series of slices), the type locality, the same date, and collectors.Etymology. The name of the new species indicates that its type locality is situated in the Arctic Region.Differential diagnosis. Medium-sized leech, which could be distinguished from other congeners by a combination of the following characters: dorsum with four rows of ovate, broad but very shallow and indistinct papillae on annulus a2 (outer paramedian and inner paramarginal series); each papilla bears ovate light yellow or white spot; dorsal black markings pattern absent. Externally, the new species is similar to G. mollissima. However, the latter species differs from G. arctica sp. nov. by having larger papillae and a well-developed black markings pattern dorsally.Molecular diagnosis. The new species represents a separate genetic lineage belonging to the verrucata-group (Fig. 1). The pairwise COI p-distance of the new species from other congeners varies from 7.0 to 12.4%. The intraspecific pairwise COI p-distance ranges from 0.0 to 0.2% (mean ± s.e.m. = 0.10 ± 0.05%; N = 3 sequences and 3 pairwise distance values). The GenBank acc. numbers of reference DNA sequences (COI and 18S rRNA) are given in Supplementary Table S2 and Supplementary Datasets S1–S2.Description. Medium-sized leech (body length up to 13.3 mm). Measurements of the type series are given in Supplementary Table S2. Body broad, leaf-like, ovate. Dorsum with four rows of ovate, broad but very shallow and indistinct papillae on annulus a2 (outer paramedian and inner paramarginal series). Posterior sucker small, circular (maximum diameter of 1.9 mm), ventrally directed. Proboscis pore in the center of anterior sucker. Coloration of living animals: body almost transparent, light brown, with multiple yellowish pigment cells. Coloration of ethanol-preserved animals: dorsum beige to light brown, with darker broad inner paramedian lines and light yellowish areas laterally and anteriorly; ovate light yellow or white spots at each papillae on annulus a2 arranged into four longitudinal rows (outer paramedian and inner paramarginal), sometimes with a few white spots between them. Three pairs of ovate eyespots arranged to two parallel rows; in some specimens eyes on each side are joined to a single large spot. Venter whitish to light brown, sometimes with irregular brownish shading. Total number of annuli: 70. Somites I–III uniannulate, IV biannulate, V–XXIV triannulate, XXV biannulate, XXVII uniannulate. The male and female genital pores are separated by two annuli and are located in the furrows XIa3/XIIa1 and XIIa2/a3, respectively. Reproductive system: 6 pairs of spherical testisacs inter-segmentally from XIII/XIV to XVIII/XIX; atrium spherical, the atrial cornua large, twisted anteriorly; paired ejaculatory ducts very long, extending to XVIII; paired ovisacs massive, long, with multiple lobes, arranged as loops, extending to XVIII (pregnant specimen with eggs). Digestive system: proboscis sheath massive, thick, elongated; esophagus narrow; salivary glands diffuse; crop with 7 pairs of crop caeca: 1st-6th uniform, bag-like, 7th pair (posterior caeca) with 4 blind processes and several smaller lobes; intestine enlarged, with 4 pairs of large, long, bag-like processes, expanding distally, each with several short lobes; a large circular extension after the last pair of processes.Distribution. Polar Urals (not known beyond the type locality).Habitats and ecology. The type series of this species was collected from a natural mountain lake with stony bottom. The leeches were recorded beneath flat stones (Fig. 3b); their feeding behavior and life cycle remain unknown.
Glossiphonia taymyrensis Bolotov, Eliseeva, Klass & Kondakov sp. novFigures 4E, 5d, 7b, Supplementary Figs. S2h, S3c, S6, Supplementary Table S2.LSID: https://zoobank.org/urn:lsid:zoobank.org:act:40269BF4-FE1C-4269-A7CC-41020789DC44.Holotype. RMBH Hir_0258_1-H (sequenced: COI sequence acc. No. ON810695), RUSSIA: small lake near Dudinka on Taymyr Peninsula, 69.4008°N, 86.3384°E, July 16, 2018, O. V. Aksenova et al. leg.Paratypes (N = 8). RUSSIA: 2 specimens RMBH Hir_0263_1 and RMBH Hir_0264_3 (sequenced: COI sequence acc. No. ON810701 and ON810705; 18S rRNA sequence acc. No. ON819017), the type locality, the same date, and collectors; 2 specimens RMBH Hir_0256_1 (one sequenced and one placed on 20 permanent slides as a series of slices; COI sequence acc. No. ON810693), small lake near Dudinka on Taymyr Peninsula, 69.3987° N, 86.3505° E, July 16, 2018, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0261_2 (sequenced: COI sequence acc. No. ON810699; 18S rRNA sequence acc. No. ON819016), small lake near Dudinka on Taymyr Peninsula, 69.4014° N, 86.3250° E, July 16, 2018, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0265_2 (sequenced: COI sequence acc. No. ON810706; 18S rRNA sequence acc. No. ON819021), Bolgokhtokh River near Dudinka, Taymyr Peninsula, 69.3780° N, 87.2215° E, July 21, 2018, O. V. Aksenova et al. leg.; 1 specimen RMBH Hir_0488 (sequenced: COI sequence acc. No. ON810755), a lake on Putorana Plateau, 68.7607° N, 91.9014° E, July, 2021, E. S. Chertoprud leg.; 1 specimen RMBH Hir_0449 (sequenced: COI sequence acc. No. ON810731), Pyzas River near Ust-Kabyrza settlement, 52.8277° N, 88.3973° E, Tashtagolsky District, Kemerovo Region, July 23, 2020, E. S. Babushkin & M. V. Vinarski leg.Etymology. The new species is named after the Taymyr Peninsula, where the majority of the type specimens were collected.Differential diagnosis. Small leech with broad, leaf-like, ovate body; three pairs of eyespots (distal pair joined; next two pairs separate); dorsal papillae absent; dorsal coloration with two inner paramedian rows of black spots, sometimes joining into unclear dashed lines; two annuli between the male (XIa3/XIIa1) and female (XIIa2/a3) genital pores. The new species largely resembles G. complanata but could be distinguished from it by having a smooth dorsum, without clear papillae. These taxa seem to have non-overlapping, allopatric ranges and, hence, could be separated on the basis of geographic criteria. However, the DNA approach seems to be the most appropriate way to distinguish these two species.Molecular diagnosis. The new species represents a separate genetic lineage belonging to the complanata-group (Fig. 1). The pairwise COI p-distance of the new species from other congeners varies from 6.0 to 12.2%. The intraspecific pairwise COI p-distance ranges from 0.0 to 1.1% (mean ± s.e.m. = 0.52 ± 0.07%; N = 8 sequences and 28 pairwise distance values). The GenBank acc. numbers of reference DNA sequences (COI and 18S rRNA) are given in Supplementary Table S2 and Supplementary Datasets S1–S2.Description. Small leech (body length up to 11.3 mm). Measurements of the type series are given in Supplementary Table S2. Body broad, leaf-like, ovate. Dorsum smooth, without clear papillae. Posterior sucker ovate (maximum diameter of 3.0 mm), ventrally directed. Proboscis pore in the center of anterior sucker. Coloration of living animals: not examined. Coloration of ethanol-preserved animals: (1) typical form having beige to light brown ground color without light spots but with darker brown coloration between inner paramedian lines; (2) melanic forms having dark brown ground color with four rows of large yellow spots (outer paramedian and marginal series) and yellow median stripe anteriorly (f. ‘maculosa’) or with strongly reduced yellow markings pattern. In all forms, there are two inner paramedian rows of black spots, sometimes joining into unclear dashed lines. Three pairs of ovate eyespots; the eyespots of the distal pair joined into a single spot; the eyespots of the next two pairs separate and are spaced apart. In the typical form, venter light yellow, with paired brown median and outer paramedian lines, which may be reduced to series of narrow brown longitudinal stripes. In melanic forms, ventral markings is more developed, with a series of brown longitudinal lines from median to inner paramarginal position and outer paramarginal brown spots. Posterior sucker with dense brown spots in melanic forms and with scarce brown spots in typical form. Total number of annuli: 68. Somites I–IV uniannulate, V–XXIV triannulate, XXV biannulate, XXVI–XXVII uniannulate. The male and female genital pores are separated by two annuli and are located in the furrows XIa3/XIIa1 and XIIa2/a3, respectively. Reproductive system: 6 pairs of spherical testisacs inter-segmentally from XII/XIII to XVIII/XIX; atrium ovate, the atrial cornua directed laterally; paired ejaculatory ducts twisted, short; paired ovisacs short, thick (undeveloped). Digestive system: salivary glands diffuse; proboscis sheath moderately thick; esophagus ovate; crop with 6 pairs of massive, bag-like, uniform crop caeca; intestine with 4 pairs of processes.Distribution. Western and Eastern Siberia.Habitats and ecology. The new species was recorded from natural lakes and rivers (Supplementary Dataset S2); its feeding behavior and life cycle are unknown.Genus Hyperboreomyzon Bolotov, Eliseeva, Klass & Kondakov gen. novLSID: https://zoobank.org/urn:lsid:zoobank.org:act:298FF41E-AF0D-4442-9F82-3022B8094A67.Type species: Hyperboreomyzon polaris gen. & sp. nov.Etymology. This name is compiled using two Greek words: ‘Hyperborea’ (meaning a mythical far northern land) and ‘myzon’ (meaning sucking).Diagnosis. Medium-sized, elongate, sub-fusiform glossiphoniid leeches; body and posterior sucker densely covered by shallow, ‘fish-scale’-like papillae; somite V biannulate; somites XII–XXIII secondarily sexannulate dorsally and ventrally due to the presence of very deep, prominent furrows separating each annulus to two semi-annuli; six rows of prominent dorsal tubercle-like papillae at a2 (inner paramedian, inner paramarginal, and marginal series) from V to XXVI; two pairs of circular eyespots on II and Va1 at inner paramedian position; gonopores at the furrows XIa3/XIIa1 (male) and XIIa2/a3 (female) and separated by two annuli; male atrium spherical; proboscis pore opens in a thick velar fold in the anterior half of oral sucker; one pair of compact, massive, elongated, incurved salivary glands, each gland with a bunch of a few short processes apically; 9 crop caeca pairs. Comparison of the new genus with other genera in the family based on morphological and anatomical features is presented in
Supplementary Table S3. Sexannulate condition was also recorded in the genus Actinobdella Moore, 1901 from North America36, but it differs from Hyperboreomyzon gen. nov. by having one pair of eyespots, diffuse salivary glands, and an apical position of proboscis pore (Supplementary Table S3).Comments. This genus is established for a single species, which is described below.
Hyperboreomyzon polaris Bolotov, Eliseeva, Klass & Kondakov gen. & sp. nov.Figures 4J, 5e, 6a-j, 7c, Supplementary Figs. S21, S8, S9, S10, S11, Supplementary Table S2.LSID: https://zoobank.org/urn:lsid:zoobank.org:act:503A9A26-CEDE-4747-952D-8416AE4EF4EB.Holotype. RMBH Hir_0486-H (sequenced: COI sequence acc. No. ON810753; 18S rRNA sequence acc. No. ON819030), RUSSIA: small alpine lake on Putorana Plateau, 68.9008°N, 94.1599°E, July, 2021, E. S. Chertoprud leg.Paratypes (N = 2). RUSSIA: 1 specimen RMBH Hir_0689 (dissected and placed on 60 permanent slides as a series of slices), small alpine lake on Putorana Plateau, 68.6659° N, 93.1365° E, August 11, 2021, E. S. Chertoprud leg.; 1 specimen RMBH Hir_0216 (sequenced and dissected; COI sequence acc. No. ON810677; 18S rRNA sequence acc. No. ON819005), water puddle on Kolguev Island, 68.9300° N, 49.0303° E, August 12, 2018, O. V. Travina & V. M. Spitsyn leg.Etymology. The name of the new species reflects its occurrences in polar (Arctic) areas of Eurasia.Differential diagnosis. As for the genus.Molecular diagnosis. None of congeneric species is known. Based on uncorrected pairwise COI p-distances between a haplotype of the new taxon and selected species-level haplotypes in each genus (Supplementary Table S1), Hyperboreomyzon seems to be more closely related to members of Hemiclepsis (mean distance ± s.e.m. = 11.62 ± 0.15%, range = 9.75–14.08%, N = 9) and Theromyzon (mean distance ± s.e.m. = 11.37 ± 0.07%, range = 10.47–12.64%, N = 9) without significant differences between distances from these two genera (Mann–Whitney test: p = 0.72). Other Glossiphoniidae genera are more distantly related, with a mean pairwise uncorrected COI p-distance of  > 13.0% (Mann–Whitney test: p  More

arising from Y. Wang et al. Nature https://doi.org/10.1038/s41586-021-04016-x (2021)A unique challenge for environmental DNA (eDNA)-based palaeoecological reconstructions and extinction estimates is that organisms can contribute DNA to sediments long after their death. Recently, Wang et al.1 discovered mammoth eDNA in sediments that are between approximately 4.6 and 7 thousand years (kyr) younger than the most recent mammoth fossils in North America and Eurasia, which they interpreted as mammoths surviving on both continents into the Middle Holocene epoch. Here we present an alternative explanation for these offsets: the slow decomposition of mammoth tissues on cold Arctic landscapes is responsible for the release of DNA into sediments for thousands of years after mammoths went extinct. eDNA records are important palaeobiological archives, but the mixing of undatable DNA from long-dead organisms into younger sediments complicates the interpretation of eDNA, particularly from cold and high-latitude systems.All animal tissues, including faeces, contribute DNA to eDNA records2, but the durations across which tissues can contribute genetic information must vary depending on tissue type and local rates of destruction and decomposition. On high-latitude landscapes, soft tissues and skeletal remains of large mammals may persist, unburied, for millennia3,4,5. For example, unburied antlers of caribou (Rangifer tarandus) from Svalbard (Norway) and Ellesmere Island (Canada) have been dated3,4 to between 1 and 2 cal kyr bp (calibrated kyr before present). Elephant seal (Mirounga leonina) remains near the Antarctic coastline5,6 can persist for more than 5,000 years. This is in contrast to bones in warmer settings, which persist for only centuries or decades7,8. Because bones are particularly resistant to decay, quantifying how their persistence changes across environments enables us to constrain the durations that dead individuals generally contribute to eDNA archives. To do this, we consolidated data on the oldest radiocarbon-dated surface-collected bones from different ecosystems. We included bones that we are reasonably confident persisted without being completely buried (‘never buried’), and bones for which exhumation cannot be confidently excluded (‘potentially never buried’). Pairing bone persistence with mean annual temperatures (MAT) from their sample localities, we find a strong link between the local temperature and the logged duration of bone persistence (Fig. 1, never buried bones: R2 = 0.94, P  More

Olszewska-Guizzo, A., Fogel, A., Benjumea, D. & Tahsin, N. Sustainable Policies and Practices in Energy, Environment and Health Research 223–243 (Springer, 2022).Book 

Google Scholar 
Gascon, M. et al. Mental health benefits of long-term exposure to residential green and blue spaces: A systematic review. Int. J. Environ. Res. Public Health 12, 4354–4379. https://doi.org/10.3390/ijerph120404354 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Houlden, V., Weich, S., Porto-de-Albuquerque, J., Jarvis, S. & Rees, K. The relationship between greenspace and the mental wellbeing of adults: A systematic review. PLoS ONE 13, 3000 (2018).Article 

Google Scholar 
Hung, S.-H. & Chang, C.-Y. Health benefits of evidence-based biophilic-designed environments: A review. J. People Plants Env. 24, 1–16 (2021).Article 

Google Scholar 
Berman, M. G., Jonides, J. & Kaplan, S. The cognitive benefits of interacting with nature. Psychol. Sci. 19, 1207–1212. https://doi.org/10.1111/j.1467-9280.2008.02225.x (2008).Article 
PubMed 

Google Scholar 
Kaplan, S. Meditation, restoration, and the management of mental fatigue. Environ. Behav. 33, 480–506. https://doi.org/10.1177/00139160121973106 (2001).Article 

Google Scholar 
Ulrich, R. S. et al. Stress recovery during exposure to natural and urban environments. J. Environ. Psychol. 11, 201–230 (1991).Article 

Google Scholar 
Kellert, S. R. & Wilson, E. O. The Biophilia Hypothesis (Island Press, 1993).
Google Scholar 
Stack, K. & Shultis, J. Implications of attention restoration theory for leisure planners and managers. Leisure/Loisir 37, 1–16 (2013).Article 

Google Scholar 
Steel, Z. et al. The global prevalence of common mental disorders: A systematic review and meta-analysis 1980–2013. Int. J. Epidemiol. 43, 476–493. https://doi.org/10.1093/ije/dyu038 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Mueller, D. P. The current status of urban-rural differences in psychiatric disorder. An emerging trend for depression. J. Nerv. Ment. Dis. 169, 18–27 (1981).Article 
CAS 
PubMed 

Google Scholar 
Peen, J., Schoevers, R. A., Beekman, A. T. & Dekker, J. The current status of urban-rural differences in psychiatric disorders. Acta Psychiatr. Scand. 121, 84–93. https://doi.org/10.1111/j.1600-0447.2009.01438.x (2010).Article 
CAS 
PubMed 

Google Scholar 
Taylor, L. & Hochuli, D. F. Defining greenspace: Multiple uses across multiple disciplines. Landsc. Urban Plan. 158, 25–38 (2017).Article 

Google Scholar 
en K Staats, H. Restorative Environments The Oxford Handbook of Environmental and Conservation Psychology 445th edn. (Oxford University Press, 2012).
Google Scholar 
Wood, L., Hooper, P., Foster, S. & Bull, F. Public green spaces and positive mental health–investigating the relationship between access, quantity and types of parks and mental wellbeing. Health Place 48, 63–71 (2017).Article 
PubMed 

Google Scholar 
Tsunetsugu, Y. et al. Physiological and psychological effects of viewing urban forest landscapes assessed by multiple measurements. Landsc. Urban Plan. 113, 90–93 (2013).Article 

Google Scholar 
Gidlow, C. J. et al. Where to put your best foot forward: Psycho-physiological responses to walking in natural and urban environments. J. Environ. Psychol. 45, 22–29 (2016).Article 

Google Scholar 
Lee, J. Experimental study on the health benefits of garden landscape. Int. J. Environ. Res. Public Health 14, 829 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Fuller, R. A., Irvine, K. N., Devine-Wright, P., Warren, P. H. & Gaston, K. J. Psychological benefits of greenspace increase with biodiversity. Biol. Lett. 3, 390–394 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Thompson, C. W., Aspinall, P. & Bell, S. Innovative Approaches to Researching Landscape and Health: Open Space: People Space 2 (Routledge, 2010).Book 

Google Scholar 
Tsutsumi, M., Nogaki, H., Shimizu, Y., Stone, T. E. & Kobayashi, T. Individual reactions to viewing preferred video representations of the natural environment: A comparison of mental and physical reactions. Jpn. J. Nurs. Sci. 14, 3–12 (2017).Article 
PubMed 

Google Scholar 
Grazuleviciene, R. et al. Tracking restoration of park and urban street settings in coronary artery disease patients. Int. J. Environ. Res. Public Health 13, 550 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Bostancı, S. H. In New Approaches to Spatial Planning and Design (ed Murat Özyavuz) Ch. 32, 435–451 (Peter Lang, 2019).Daniel, T. C. Measuring Landscape Esthetics: The Scenic Beauty Estimation Method, vol. 167 (Department of Agriculture, Forest Service, Rocky Mountain Forest and Range…, 1976).Bacon, W. R. In (eds Elsner G. H. et al) Technical Coordinators. Proceedings of our national landscape: A conference on applied techniques for analysis and management of the visual resource [Incline Village, Nev., April 23–25, 1979]. Gen. Tech. Rep. PSW-GTR-35. Berkeley, CA. Pacific Southwest Forest and Range Exp. Stn., Forest Service, US Department of Agriculture 660–665 (1979).Gavrilidis, A. A., Ciocănea, C. M., Niţă, M. R., Onose, D. A. & Năstase, I. I. Urban landscape quality index—planning tool for evaluating urban landscapes and improving the quality of life. Procedia Environ. Sci. 32, 155–167. https://doi.org/10.1016/j.proenv.2016.03.020 (2016).Article 

Google Scholar 
Knobel, P. et al. Development of the urban green space quality assessment tool (RECITAL). Urban For. Urban Green. 57, 126895 (2021).Article 

Google Scholar 
Bacon, W. R. & Dell, J. National Forest Landscape Management (Forest Service, US Department of Agriculture, 1973).Kaplan, R., Kaplan, S. & Ryan, R. With People in Mind: Design and Management of Everyday Nature (Island Press, 1998).
Google Scholar 
Smardon, R., Palmer, J. & Felleman, J. P. Foundations for Visual Project Analysis (Wiley, 1986).
Google Scholar 
Jung, C. G. Man and His Symbols Garden City (Doubleday and Co, 1964).
Google Scholar 
Olszewska, A., Marques, P. F., Ryan, R. L. & Barbosa, F. What makes a landscape contemplative?. Env. Plan. B Urban Anal. City Sci. 45, 7–25. https://doi.org/10.1177/0265813516660716 (2016).Article 

Google Scholar 
Tarkka, I. M. & Hallett, M. Cortical topography of premotor and motor potentials preceding self-paced, voluntary movement of dominant and non-dominant hands. Electroencephalogr. Clin. Neurophysiol. 75, 36–43 (1990).Article 
CAS 
PubMed 

Google Scholar 
Olszewska-Guizzo, A., Paiva, T. O. & Barbosa, F. Effects of 3D contemplative landscape videos on brain activity in a passive exposure EEG experiment. Front. Psychiatry 9, 317. https://doi.org/10.3389/fpsyt.2018.00317 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Bradley, M. M. & Lang, P. J. Measuring emotion: The self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25, 49–59. https://doi.org/10.1016/0005-7916(94)90063-9 (1994).Article 
CAS 
PubMed 

Google Scholar 
Beck, A. T., Steer, R. A. & Brown, G. K. Beck depression inventory-II. San Antonio 78, 490–498 (1996).
Google Scholar 
Ferree, T. C., Luu, P., Russell, G. S. & Tucker, D. M. Scalp electrode impedance, infection risk, and EEG data quality. Clin. Neurophysiol. 112, 536–544. https://doi.org/10.1016/S1388-2457(00)00533-2 (2001).Article 
CAS 
PubMed 

Google Scholar 
Stroganova, T. A. & Orekhova, E. V. EEG and infant states. Infant EEG Event-Relat. Potentials 251, 280 (2007).
Google Scholar 
Cacioppo, J. T., Tassinary, L. G. & Berntson, G. Handbook of Psychophysiology (Cambridge University Press, 2007).
Google Scholar 
Ulrich, R. S. Natural versus urban scenes: Some psychophysiological effects. Environ. Behav. 13, 523–556 (1981).Article 

Google Scholar 
Choi, Y., Kim, M. & Chun, C. Measurement of occupants’ stress based on electroencephalograms (EEG) in twelve combined environments. Build. Environ. 88, 65–72 (2015).Article 

Google Scholar 
Gorji, M. A. H., Davanloo, A. A. & Heidarigorji, A. M. The efficacy of relaxation training on stress, anxiety, and pain perception in hemodialysis patients. Indian J. Nephrol. 24, 356 (2014).Article 

Google Scholar 
Cahn, B. R. & Polich, J. Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychol. Bull. 132, 180 (2006).Article 
PubMed 

Google Scholar 
Gruzelier, J. A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cogn. Process. 10, 101–109 (2009).Article 

Google Scholar 
Vecchiato, G. et al. Neurophysiological correlates of embodiment and motivational factors during the perception of virtual architectural environments. Cogn. Process. 16, 425–429 (2015).Article 
PubMed 

Google Scholar 
Lagopoulos, J. et al. Increased theta and alpha EEG activity during nondirective meditation. J. Altern. Complement. Med. 15, 1187–1192 (2009).Article 
PubMed 

Google Scholar 
Wascher, E. et al. Frontal theta activity reflects distinct aspects of mental fatigue. Biol. Psychol. 96, 57–65 (2014).Article 
PubMed 

Google Scholar 
Kabat-Zinn, J. Mindfulness. Mindfulness 6, 1481–1483 (2015).Article 

Google Scholar 
McGarrigle, T. & Walsh, C. A. Mindfulness, self-care, and wellness in social work: Effects of contemplative training. J. Relig. Spiritual. Soc. Work Soc. Thought 30, 212–233 (2011).
Google Scholar 
Grossman, P., Niemann, L., Schmidt, S. & Walach, H. Mindfulness-based stress reduction and health benefits: A meta-analysis. J. Psychosom. Res. 57, 35–43 (2004).Article 
PubMed 

Google Scholar 
Bailey, A. W., Allen, G., Herndon, J. & Demastus, C. Cognitive benefits of walking in natural versus built environments. World Leisure J. 60, 293–305 (2018).Article 

Google Scholar 
Qin, J., Zhou, X., Sun, C., Leng, H. & Lian, Z. Influence of green spaces on environmental satisfaction and physiological status of urban residents. Urban For. Urban Green. 12, 490–497 (2013).Article 

Google Scholar 
Kolb, B. & Whishaw, I. Q. Fundamentals of Human Neuropsychology (Freeman, 1990).
Google Scholar 
Milner, B. Visual recognition and recall after right temporal-lobe excision in man. Neuropsychologia 6, 191–209 (1968).Article 

Google Scholar 
Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215. https://doi.org/10.1038/nrn755 (2002).Article 
CAS 
PubMed 

Google Scholar 
Chang, C.-Y. & Chen, P.-K. Human response to window views and indoor plants in the workplace. HortScience 40, 1354–1359 (2005).Article 

Google Scholar 
Herzog, T. R., Black, A. M., Fountaine, K. A. & Knotts, D. J. Reflection and attentional recovery as distinctive benefits of restorative environments. J. Environ. Psychol. 17, 165–170 (1997).Article 

Google Scholar 
Baehr, E., Rosenfeld, J. P. & Baehr, R. Clinical use of an alpha asymmetry neurofeedback protocol in the treatment of mood disorders: Follow-up study one to five years post therapy. J. Neurother. 4, 11–18 (2001).Article 

Google Scholar 
Sia, A. et al. Nature-based activities improve the well-being of older adults. Sci. Rep. 10, 1–8 (2020).Article 

Google Scholar 
Olszewska-Guizzo, A., Sia, A., Fogel, A. & Ho, R. Can exposure to certain urban green spaces trigger frontal alpha asymmetry in the brain?—Preliminary findings from a passive task EEG study. Int. J. Environ. Res. Public Health 17, 394 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Olszewska-Guizzo, A. et al. Therapeutic garden with contemplative features induces desirable changes in mood and B rain activity in depressed adults. Front. Psychiatry https://doi.org/10.3389/fpsyt.2022.757056 (2021).Article 

Google Scholar 
Tan, S. B., Vignesh, L. N. & Donald, L. Public Housing in Singapore: Examining Fundamental Shifts (Lee Kuan Yew School of Public Policy, National University of Singapore, 2014).Tan, P. Y. Nature, Place & People: Forging Connections Through Neighbourhood Landscape Design (World Scientific Publishing Co., 2018).Book 

Google Scholar 
Peirce, J. et al. PsychoPy2: Experiments in behavior made easy. Behav. Res. Methods 51, 195–203. https://doi.org/10.3758/s13428-018-01193-y (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Edwards, A. L. Balanced Latin-square designs in psychological research. Am. J. Psychol. 64, 598–603 (1951).Article 
CAS 
PubMed 

Google Scholar 
Korpela, K. M., Ylén, M., Tyrväinen, L. & Silvennoinen, H. Determinants of restorative experiences in everyday favorite places. Health Place 14, 636–652 (2008).Article 
PubMed 

Google Scholar 
Ojala, A., Korpela, K., Tyrväinen, L., Tiittanen, P. & Lanki, T. Restorative effects of urban green environments and the role of urban-nature orientedness and noise sensitivity: A field experiment. Health Place 55, 59–70 (2019).Article 
PubMed 

Google Scholar 
Tyrväinen, L. et al. The influence of urban green environments on stress relief measures: A field experiment. J. Environ. Psychol. 38, 1–9 (2014).Article 

Google Scholar 
Herzog, T. R. & Barnes, G. J. Tranquility and preference revisited. J. Environ. Psychol. 19, 171–181 (1999).Article 

Google Scholar 
Neale, C. et al. The impact of walking in different urban environments on brain activity in older people. Cities Health 4, 94–106. https://doi.org/10.1080/23748834.2019.1619893 (2020).Article 

Google Scholar 
Kaplan, R. & Kaplan, S. The Experience of Nature: A Psychological Perspective (CUP Archive, 1989).
Google Scholar 
Treib, M. In Contemporary Landscapes of Contemplation (ed Rebecca Krinke) 27–49 (Routledge, 2005).Appleton, J. The Experience of Landscape (Wiley Chichester, 1996).
Google Scholar 
Grahn, P., Ottosson, J. & Uvnäs-Moberg, K. The oxytocinergic system as a mediator of anti-stress and instorative effects induced by nature: The calm and connection theory. Front. Psychol. 2021, 12 (2021).
Google Scholar 
Hartig, T., Mang, M. & Evans, G. W. Restorative effects of natural environment experiences. Environ. Behav. 23, 3–26. https://doi.org/10.1177/0013916591231001 (1991).Article 

Google Scholar 
Stamps Iii, A. E. Use of photographs to simulate environments: A meta-analysis. Percept. Mot. Skills 71, 907–913 (1990).Article 

Google Scholar 
Menardo, E., Brondino, M., Hall, R. & Pasini, M. Restorativeness in natural and urban environments: A meta-analysis. Psychol. Rep. 124, 417–437 (2021).Article 
PubMed 

Google Scholar  More

Kirilova, E. P., Cremer, H., Heiri, O. & Lotter, A. F. Eutrophication of moderately deep Dutch lakes during the past century: Flaws in the expectations of water management? Hydrobiologia 637, 157–171 (2010).Article 
CAS 

Google Scholar 
Scharf, B. & Viehberg, F. A. Living Ostracoda (Crustacea) from the town moat of Bremen, Germany. Crustaceana 87(8–9), 1124–1135 (2014).Article 

Google Scholar 
Rees, S. E. The historical and cultural importance of ponds and small lakes in Wales, UK. Aquat. Conserv. 7(2), 133–139 (1997).Article 

Google Scholar 
Brown, A. et al. The ecological impact of conquest and colonisation on a medieval frontier landscape: Combined palynological and geochemical analysis of lake sediments from Radzyń Chełmiński, northern Poland. Geoarchaeology 30, 511–527 (2015).Article 

Google Scholar 
Kittel, P. et al. The palaeoecological development of the Late Medieval moat—Multiproxy research at Rozprza Central Poland. Quat. Int. 482, 131–156 (2018).Article 

Google Scholar 
Hildebrandt-Radke, I. Geoarchaeological aspects in the studies of prehistoric and early historic settlement complexes. In Studia interdyscyplinarne nad środowiskiem i kulturą w Polsce. Tom 1. Środowisko-Człowiek-Cywilizacja (eds Makohonienko, M. et al.) 57–70 (Bogucki Wyd Naukowe, 2007).
Google Scholar 
Łyskowski, M. & Wardas-Lasoń, M. Georadar investigations and geochemical analysis in contemporary archaeological studies. Geol. Geophys. Environ. 38(3), 307–315 (2012).Article 

Google Scholar 
Korhola, A. & Rautio, M. Cladocera and other branchiopod crustaceans. In Tracking Environmental Change Using Lake Sediments, Vol. 4: Zoological Indicators (eds Smol, J. P. et al.) 5–41 (Kluwer Academic Publishers, 2001).Chapter 

Google Scholar 
Birks, H. H. Plant macrofossils. In Tracking Environmental Change Using Lake Sediments, 3: Terrestrial, Algal, and Siliceous Indicators (eds Smol, J. P. et al.) 49–74 (Kluwer Academic Publishers, 2001).
Google Scholar 
Battarbee, R. W. Diatom analysis. In Handbook of Holocene Palaeoecology and Palaeohydrology (ed. Berglund, B. E.) 527–570 (Wiley, 1986).
Google Scholar 
Luoto, T. P., Nevalainen, L., Kultti, S. & Sarmaja-Korjonen, K. An evaluation of the influence of water depth and river inflow on quantitative Cladocera-based temperature and lake level inferences in a shallow boreal lake. Hydrobiologia 676, 143–154 (2011).Article 
CAS 

Google Scholar 
Luoto, T. P. Intra-lake patterns of aquatic insect and mite remains. J. Paleolimnol. 47, 141–157 (2012).Article 

Google Scholar 
Hann, B. J. Methods in Quaternary ecology. Cladocera. Geosci. Canada 16, 17–26 (1989).
Google Scholar 
Dimbleby, G. W. The Palynology of Archaeological Sites (Academic Press. Inc., 1985).
Google Scholar 
Edwards, K. J. Using space in cultural palynology: The value of the off-site pollen record. In Modelling Ecological Change: Perspectives from Neoecology, Palaeoecology and Environmental Archaeology (eds Harris, D. R. & Thomas, K. D.) 61–74 (Routledge Taylor & Francis Group, 2016).
Google Scholar 
Kittel, P., Sikora, J. & Wroniecki, P. A Late Medieval motte-and-bailey settlement in a lowland river valley landscape of central Poland. Geoarchaeology 33(5), 558–578 (2018).Article 

Google Scholar 
Antczak-Orlewska, O. et al. The environmental history of the oxbow in the Luciąża River valley—Study on the specific microclimate during Allerød and Younger Dryas in central Poland. Quat. Int. https://doi.org/10.1016/j.quaint.2021.08.011 (2021).Article 

Google Scholar 
Dearing, J. A. Core correlation and total sediment influx. In Handbook of Holocene Palaeoecology and Palaeohydrology (ed. Berglund, B. E.) 247–270 (Wiley, 1986).
Google Scholar 
O’Brien, C. et al. A sediment-based multiproxy palaeoecological approach to the environmental archaeology of lake dwellings (crannogs), central Ireland. Holocene 15, 707–719 (2005).Article 

Google Scholar 
Ruiz, Z., Brown, A. G. & Langdon, P. G. The potential of chironomid (Insecta: Diptera) larvae in archaeological investigations of floodplain and lake settlements. J. Archaeol. Sci. 33, 14–33 (2006).Article 

Google Scholar 
Kittel, P. et al. A multi-proxy reconstruction from Lutomiersk-Koziówki, Central Poland, in the context of early modern hemp and flax processing. J. Archaeol. Sci. 50, 318–337 (2014).Article 

Google Scholar 
Kittel, P. et al. On the border between land and water: the environmental conditions of the Neolithic occupation from 4.3 until 1.6 ka BC at Serteya, Western Russia. Geoarchaeology 36, 173–202 (2021).Article 

Google Scholar 
Makohonienko, M. et al. Environmental changes during Mesolithic-Neolithic transition in Kuyavia Lakeland, Central Poland. Quat. Int. https://doi.org/10.1016/j.quaint.2021.11.020 (2021).Article 

Google Scholar 
Porinchu, D. F. & MacDonald, G. M. The use and application of freshwater midges (Chironomidae: Insecta: Diptera) in geographical research. Prog. Phys. Geogr. 27, 378–422 (2003).Article 

Google Scholar 
Brooks, S. J., Langdon, P. G. & Heiri, O. The Identification and Use of Palaearctic Chironomidae Larvae in Palaeoecology. QRA Technical guide no. 10 (Quaternary Research Association, 2007).Heiri, O., Birks, H. J. B., Brooks, S. J., Velle, G. & Willassen, E. Effects of within-lake variability of fossil assemblages on quantitative chironomid-inferred temperature reconstruction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 199, 95–106 (2003).Article 

Google Scholar 
Kittel, P., Sikora, J. & Wroniecki, P. The morphology of the Luciąża River valley floor in the vicinity of the Rozprza medieval ring-fort in light of geophysical survey. Bull. Geogr. Phys. Geogr. Ser. 8, 95–106 (2015).Article 

Google Scholar 
Hingham, R. & Barker, P. Timber Castles (University of Exeter Press, 2002).
Google Scholar 
Marciniak-Kajzer, A. Archaeology on Medieval Knights’ Manor Houses in Poland (Wyd. Uniwersytetu Łódzkiego, Wyd. Uniwersytetu Jagiellońskiego, 2016).Book 

Google Scholar 
Moller Pillot, H. K. M. Chironomidae Larvae of the Netherlands and Adjacent Lowlands. Biology and Ecology of the Aquatic Orthocladiinae, Prodiamesinae, Diamesinae, Buchonomyiinae, Podonominae, Telmatogetoninae (KNNV Publishing, 2013).Book 

Google Scholar 
Luoto, T. P. An assessment of lentic ceratopogonids, ephemeropterans, trichopterans and oribatid mites as indicators of past environmental change in Finland. Ann. Zool. Fenn. 46, 259–270 (2009).Article 

Google Scholar 
Cierniewski, J. Spatial complexity of the Cybina river valley organic soils against the background of physiographic conditions. Soil Sci. Annu. 32(4), 3–51 (1981).CAS 

Google Scholar 
Rydelek, P. Origin and composition of mineral constituents of fen peats from Eastern Poland. J. Plant Nutr. 36(6), 911–928 (2013).Article 
CAS 

Google Scholar 
Wachecka-Kotkowska, L. Rozwój rzeźby obszaru między Piotrkowem Trybunalskim, Radomskiem a Przedborzem w czwartorzędzie (Wyd. Uniwersytetu Łódzkiego, 2015).Book 

Google Scholar 
Kittel, P. et al. Lacustrine, fluvial and slope deposits in the wetland shore area in Serteya, Western Russia. Acta Geogr. Lodz 110, 103–124 (2020).
Google Scholar 
Ciszewski, D. Pollution of Mała Panew River sediments by heavy metals: Part I. Effect of changes in river bed morphology. Pol. J. Environ. Stud. 13(6), 589–595 (2004).CAS 

Google Scholar 
Borówka, R. Late Vistulian and Holocene denudation magnitude in morainic plateaux: Case studies in the zone of maximum extent of the last ice sheet. Quat. Stud. Pol. 9, 5–31 (1990).
Google Scholar 
Prusinkiewicz, Z., Bednarek, R., Kośko, A. & Szmyt, M. Palaeopedological studies of the age and properties of illuvial bands at an archaeological site. Quat. Int. 51(52), 195–201 (1998).Article 

Google Scholar 
Kühtreiber, T. The medieval castle Lanzenkirchen in Lower Austria: reconstruction of economical and ecological development of an average-sized manor (12th–15th century). Archaeol. Pol. 37, 135–144 (1999).
Google Scholar 
Kočár, P., Čech, P., Kozáková, R. & Kočárová, R. Environment and economy of the early medieval settlement in Žatec. Interdiscip. Archaeol. 1, 45–60 (2010).
Google Scholar 
Brown, A. D. & Pluskowski, A. G. Detecting the environmental impact of the Baltic Crusades on a late medieval (13th-15th century) frontier landscape: Palynological analysis from Malbork Castle and hinterland, Northern Poland. J. Archaeol. Sci. 38, 1957–1966 (2011).Article 

Google Scholar 
Beneš, J. et al. Archaeobotany of the Old Prague Town defence system, Czech Republic: Archaeology, macro-remains, pollen, and diatoms. Veg. Hist. Archaeobot. 11(1/2), 107–119 (2002).Article 

Google Scholar 
Badura, M. & Latałowa, M. Szczątki makroskopowe roślin z obiektów archeologicznych Zespołu Przedbramia w Gdańsku. In Zespół Przedbramia ul. Długiej w Gdańsku. Studium archeologiczne (ed. Pudło, A.) 231–247 (Muzeum Historii Miasta Gdańska, 2016).
Google Scholar 
Dobrowolski, R. et al. Environmental conditions of settlement in the vicinity of the mediaeval capital of the Cherven Towns (Czermno site, Hrubieszów Basin, Eastern Poland). Quat. Int. 493, 258–273 (2018).Article 

Google Scholar 
Makohonienko, M. Środowisko przyrodnicze i gospodarka w otoczeniu średniowiecznego grodu w Łęczycy w świetle analizy palinologicznej. In Początki Łęczycy. Tom I—Archeologia środowiskowa średniowiecznej Łęczycy. Przyroda–Gospodarka–Społeczeństwo (eds Grygiel, R. & Jurek, T.) 95–190 (MAiE w Łodzi, 2014).
Google Scholar 
Koszałka, J. Źródła archeobotaniczne do rekonstrukcji uwarunkowań przyrodniczych oraz gospodarczych grodu w Łęczycy. In Początki Łęczycy. Tom I – Archeologia środowiskowa średniowiecznej Łęczycy. Przyroda–Gospodarka–Społeczeństwo (eds Grygiel, R. & Jurek, T.) 191–241 (MAiE w Łodzi, 2014).
Google Scholar 
Digerfeldt, G. Studies on past lake-level fluctuations. In Handbook of Holocene Palaeoecology and Palaeohydrology (ed. Berglund, B. E.) 127–143 (Wiley, 1986).
Google Scholar 
Magny, M. Palaeoclimatology and archaeology in the wetlands. In The Oxford Handbook of Wetland Archaeology (eds Menotti, F. & O’Sullivan, A.) 585–597 (Oxford University Press, 2013).
Google Scholar 
Płóciennik, M. et al. Summer temperature drives the lake ecosystem during the Late Weichselian and Holocene in Eastern Europe: A case study from East European Plain. CATENA 214, 106206 (2022).Article 

Google Scholar 
Święta-Musznicka, J., Badura, M., Pędziszewska, A. & Latałowa, M. Environmental changes and plant use during the 5th–14th centuries in medieval Gdańsk, northern Poland. Veget. Hist. Archaeobot. 30, 363–381 (2021).Article 

Google Scholar 
Rackham, J. & Sidell, J. London’s landscapes: The changing environment. In The Archaeology of Greater London. An Assessment of Archaeological Evidence for Human Presence in the Area Now Covered by Greater London (ed. Kendall, M.) 12–27 (Museum of London, 2000).
Google Scholar 
Ledger, P., Edwards, K. & Schofield, J. A multiple profile approach to the palynological reconstruction of Norse landscapes in Greenland’s Eastern Settlement. Quat. Res. 82(1), 22–37 (2014).Article 

Google Scholar 
Albert, B. & Innes, J. Multi-profile fine-resolution palynological and micro-charcoal analyses at Esklets, North York Moors, UK, with special reference to the Mesolithic-Neolithic transition. Veget. Hist. Archaeobot. 24, 357–375 (2015).Article 

Google Scholar 
Sikora, J., Kittel, P. & Wroniecki, P. From a point on the map to a shape in the landscape. Non-invasive verification of medieval ring-forts in Central Poland: Rozprza case study. Archaeol. Pol. 53, 510–514 (2015).
Google Scholar 
Sikora, J. et al. A palaeoenvironmental reconstruction of the rampart construction of the medieval ring-fort in Rozprza, Central Poland. Archaeol. Anthropol. Sci. 11(8), 4187–4219 (2019).Article 

Google Scholar 
Tolksdorf, J. F., Turner, F., Nelle, O., Peters, S. & Bruckner, H. Environmental development and local human impact in the Jeetzel valley (N Germany) since 10 ka BP as detected by geoarchaeological analyses in a coupled aeolian and lacustrine sediment archive at Soven. E&G Quat. Sci. J. 64, 95–110 (2015).Article 

Google Scholar 
Oonk S., Slomp C. P. & Huisman D. J. Geochemistry as an aid in archaeological prospection and site interpretation: Current issues and research directions. Archaeol. Prospect. 16, 35–51 (2009).Article 

Google Scholar 
Zieliński, T. & Pisarska-Jamroży, M. Which features of deposits should be included in a code and which not? Przegl. Geol. 60, 387–397 (2012).
Google Scholar 
Clift, P. D. et al. Grain-size variability within a mega-scale point-bar system, False River, Louisiana. Sedimentology 66, 408–434 (2019).Article 

Google Scholar 
Blott, S. J. & Pye, K. GRADISTAT: A grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf. Process. Landf. 26, 1237–1248 (2001).Article 

Google Scholar 
Rolland, N. & Larocque, I. The efficiency of kerosene flotation for extraction of chironomid head capsules from lake sediments samples. J. Paleolimnol. 37, 565–572 (2007).Article 

Google Scholar 
Schmid, P. E. A Key to the Chironomidae and Their Instars from Austrian Danube Region Streams and Rivers. Part I. Diamesinae Prodiamesinae and Orthocladiinae (Federal Institute for Water Quality of the Ministry of Agriculture and Forestry, 1993).
Google Scholar 
Andersen, T., Cranston, P. S. & Epler, J. H. Chironomidae of the Holarctic Region: Keys and Diagnoses. Part 1. Larvae. Insect Systematics and Evolution. Supplement 66 (Scandinavian Entomology, 2013).
Google Scholar 
Walker, I. R. Midges: Chironomidae and related Diptera. In Tracking Environmental Change Using Lake Sediments, Volume 4: Zoological Indicators (eds Smol, J. P. et al.) 43–66 (Kluwer Academic Press, 2001).Chapter 

Google Scholar 
Vallenduuk, H. J. & Moller Pillot, H. K. M. Chironomidae Larvae of the Netherlands and Adjacent Lowlands. General Ecology and Tanypodinae (KNNV Publishing, 2007).
Google Scholar 
Moller Pillot, H. K. M. Chironomidae Larvae Biology and Ecology of the Chironomini (KNNV Publishing, 2009).Book 

Google Scholar 
Juggins, S. C2 Version 1.5 User Guide. Software for Ecological and Palaeoecological Data Analysis and Visualisation (Newcastle University, 2007).
Google Scholar 
Schweingruber, F. H. Tree Rings. Basics and Applications of Dendrochronology (Kluwer Academic Publishers, 1988).
Google Scholar 
Skripkin, V. V. & Kovaliukh, N. N. Recent developments in the procedures used at the SSCER Laboratory for the routine preparation of lithium carbide. Radiocarbon 40(1), 211–214 (1998).Article 
CAS 

Google Scholar 
Krąpiec, M., Rakowski, A. Z., Huels, M., Wiktorowski, D. & Hamann, C. A new graphitization system for radiocarbon dating with AMS on the dendrochronological laboratory at AGH-UST Kraków. Radiocarbon 60(4), 1091–1100 (2018).Article 

Google Scholar 
Zoppi, U., Crye, J., Song, Q. & Arjomand, A. Performance evaluation of the new AMS system at Accium BioSciences. Radiocarbon 49, 173–182 (2007).Article 
CAS 

Google Scholar 
Reimer, P. et al. The IntCal20 Northern Hemisphere radiocarbon age calibration curve (0–55 cal kBP). Radiocarbon 62(4), 725–757 (2020).Article 
CAS 

Google Scholar 
Bronk Ramsey, C. OxCal Version 4.4.2. Available at: https://c14.arch.ox.ac.uk (2020).Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51(1), 337–360 (2009).Article 

Google Scholar 
Bronk Ramsey, C. Deposition models for chronological records. Quat. Sci. Rev. 27(1–2), 42–60 (2008).Article 

Google Scholar 
Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982).Article 
MathSciNet 
MATH 

Google Scholar 
Kohonen, T. Self-Organizing Maps (Springer, 2001).Book 
MATH 

Google Scholar 
Park, Y.-S. et al. Application of a self-organizing map to select representative species in multivariate analysis: A case study determining diatom distribution patterns across France. Ecol. Inform. 1, 247–257 (2006).Article 

Google Scholar 
Zhang, Q. et al. Self-organizing feature map classification and ordination of Larix principis-rupprechtii forest in Pangquangou Nature Reserve. Acta Ecol. Sin. 31, 2990–2998 (2011).
Google Scholar 
Ney, J. J. Practical use of biological statistics. In Inland Fisheries Management in North America (eds Kohler, C. C. et al.) 137–158 (American Fisheries Society, 1993).
Google Scholar 
Płóciennik, M. et al. Fen ecosystem responses to water-level fluctuations during the early and middle Holocene in central Europe: A case study from Wilczków, Poland. Boreas 44(4), 721–740 (2015).Article 

Google Scholar 
Brosse, S., Giraudel, J. L. & Lek, S. Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages. Ecol. Model. 146(1), 159–166 (2001).Article 

Google Scholar 
Lek, S., Scardi, M., Verdonschot, P. F. M., Descy, J. P. & Park, Y. S. Modelling Community Structure in Freshwater Ecosystems (Springer, 2005).Book 

Google Scholar 
Quinn, G. P. & Keough, M. Experimental Design and Data Analysis for Biologists (University of Cambridge, 2002).Book 

Google Scholar 
Płóciennik, M., Kruk, A., Michczyńska, D. J. & Birks, H. J. B. Kohonen artificial neural networks and the IndVal index as supplementary tools for the quantitative analysis of palaeoecological data. Geochronometria 42, 189–201 (2015).Article 

Google Scholar 
Vesanto, J. & Alhoniemi, E. Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11, 586–600 (2000).Article 
CAS 
PubMed 

Google Scholar 
Ward, J. H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963).Article 
MathSciNet 

Google Scholar 
Alhoniemi, E., Hollmén, J., Simula, O. & Vesanto, J. Process monitoring and modeling using the self-organizing map. Integr. Comput. Aided Eng. 6(1), 3–14 (1999).Article 

Google Scholar 
Dufrêne, M. & Legendre, P. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 
McCune, B. & Mefford, M. S. PcOrd Multivariate Analysis of Ecological Data. Version 6.06 (MjM Software, 2011).
Google Scholar 
Hadfield, J. D., Krasnov, B. R., Poulin, R. & Nakagawa, S. A tale of two phylogenies: Comparative analyses of ecological interactions. Am. Nat. 183(2), 174–187 (2014).Article 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R Journal 9(2), 378–400 (2017).Article 

Google Scholar 
Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-level/Mixed) Regression Models. R Package Version 0.4.5. https://CRAN.R-project.org/package=DHARMa (2022).Bartoń, K. MuMIn: Multi-model Inference. R Package Version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020).de Rosario-Martinez, H. phia: Post-Hoc Interaction Analysis. R Package Version 0.2-1. https://CRAN.R-project.org/package=phia (2015). More