Philippa Kaur

Editorial Committee of Vegetation Map China. Vegetation Map of China (1:1000 000) (Geology Press, 2007).Fu, B. et al. Current condition and protection strategies of Qinghai–Tibet Plateau ecological security barrier. Bull. Chin. Acad. Sci. 36, 1298–1306 (2021).
Google Scholar 
Zhang, Y. et al. Spatial and temporal variability in the net primary production (NPP) of alpine grassland on Tibetan Plateau from 1982 to 2009. Acta Geogr. Sin. 68, 1197–1211 (2013).
Google Scholar 
Bao, C. & Liu, R. Spatiotemporal evolution of the urban system in the Tibetan Plateau. J. Geoinf. Sci. 21, 1330–1340 (2019).
Google Scholar 
Miehe, G. et al. The Kobresia pygmaea ecosystem of the Tibetan highlands — origin, functioning and degradation of the world’s largest pastoral alpine ecosystem: Kobresia pastures of Tibet. Sci. Total Environ. 648, 754–771 (2019). This work describes features of K. pygmaea grassland and reveals that overstocking has caused pasture degradation and soil deterioration.Article 

Google Scholar 
Liu, Y. et al. Grassland dynamics in responses to climate variation and human activities in China from 2000 to 2013. Sci. Total Environ. 690, 27–39 (2019).Article 

Google Scholar 
Cao, J. et al. Grassland degradation on the Qinghai–Tibetan Plateau: reevaluation of causative factors. Rangel. Ecol. Manag. 72, 988–995 (2019).Article 

Google Scholar 
Zhao, X. Restoration and Sustainable Management of Degradaded Grassland in the Three Rivers Headwater (Science Press, 2011).Gao, Q. Exploration and Study on Eoclogical Revelization Fuatures in Qiangtang Plateau (China Agriculture Press, 2015).Gu, X. et al. Soil extractable organic C and N contents, methanotrophic activity under warming and degradation in a Tibetan alpine meadow. Agric. Ecosyst. Environ. 278, 6–14 (2019).Article 

Google Scholar 
Li, Y. et al. Changes of soil microbial community under different degraded gradients of alpine meadow. Agric. Ecosyst. Environ. 222, 213–222 (2016).Article 

Google Scholar 
Wang, W., Wang, Q. & Wang, H. The effect of land management on plant community composition, species diversity, and productivity of alpine Kobersia steppe meadow. Ecol. Res. 21, 181–187 (2005).Article 

Google Scholar 
Xu, H., Wang, X. & Zhang, X. Alpine grasslands response to climatic factors and anthropogenic activities on the Tibetan Plateau from 2000 to 2012. Ecol. Eng. 92, 251–259 (2016).Article 

Google Scholar 
Yu, L., Tang, L., Wei, D., Mei, M. & Zhou, H. Characteristics and causes of changes of alpine grassland productivity in the source region of Yellow River. Int. Conf. Geoinformatics https://doi.org/10.1109/GEOINFORMATICS.2010.5567879 (2010).Article 

Google Scholar 
Yang, Y. et al. Responses of the functional structure of soil microbial community to livestock grazing in the Tibetan alpine grassland. Glob. Chang. Biol. 19, 637–648 (2013). This work shows that soil microbial community functional structure is very sensitive to livestock grazing.Article 

Google Scholar 
Gao, Y. Z. et al. Belowground net primary productivity and biomass allocation of a grassland in Inner Mongolia is affected by grazing intensity. Plant Soil 307, 41–50 (2008).Article 

Google Scholar 
Dlamini, A. P. et al. Controlling factors of sheet erosion under degraded grasslands in the sloping lands of KwaZulu-Natal, South Africa. Agric. Water Manag. 98, 1711–1718 (2011).Article 

Google Scholar 
Niemandt, C. & Greve, M. Fragmentation metric proxies provide insights into historical biodiversity loss in critically endangered grassland. Agric. Ecosyst. Environ. 235, 172–181 (2016).Article 

Google Scholar 
Kang, S. C. et al. Review of climate and cryospheric change in the Tibetan Plateau. Environ. Res. Lett. 5, 15101–15101 (2010).Article 

Google Scholar 
Shen, H. et al. Effects of simulated N deposition on photosynthesis and productivity of key plants from different functional groups of alpine meadow on Qinghai–Tibetan Plateau. Environ. Pollut. 251, 731–737 (2019).Article 

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

Google Scholar 
Lu, C. & Tian, H. Spatial and temporal patterns of nitrogen deposition in China: synthesis of observational data. J. Geophys. Res. 112, D22S05 (2007).
Google Scholar 
National Bureau of Statistics of China. China Statistics Yearbook (China Statistics Press, 2020).Mo, X. Sustainable livestock carring capacity and overgrazing rate of grassland over Qinghai–Tibet plateau since 1980. Natl Tibetan Plateau Data Center https://doi.org/10.11888/Socioeco.tpdc.270347 (2020).Article 

Google Scholar 
Tian, Y. Y., Jiang, G. H., Zhou, D. Y. & Li, G. Y. Systematically addressing the heterogeneity in the response of ecosystem services to agricultural modernization, industrialization and urbanization in the Qinghai–Tibetan Plateau from 2000 to 2018. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2020.125323 (2021).Article 

Google Scholar 
Yao, Y. et al. Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years. Glob. Chang. Biol. 24, 184–196 (2018).Article 

Google Scholar 
Li, L. et al. Current challenges in distinguishing climatic and anthropogenic contributions to alpine grassland variation on the Tibetan Plateau. Ecol. Evol. 8, 5949–5963 (2018). This work finds that large inconsistencies exist in distinguishing the respective contribution of climatic and anthropogenic forces to grassland dynamics.Article 

Google Scholar 
Zhong, L., Ma, Y. M., Xue, Y. K. & Piao, S. L. Climate change trends and impacts on vegetation greening over the Tibetan Plateau. J. Geophys. Res. Atmos. 124, 7540–7552 (2019). This work demonstrates that the general increasing trends in vegetation density and greening of the QTP are mainly caused by climate factors, using satellite-derived climate and vegetation data from 1999 to 2014.Article 

Google Scholar 
Yang, K. & He, J. China meteorological forcing dataset (1979–2018). Natl Tibetan Plateau Data Center https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file (2019).Article 

Google Scholar 
Xiong, Q. et al. Monitoring the impact of climate change and human activities on grassland vegetation dynamics in the northeastern Qinghai–Tibet Plateau of China during 2000–2015. J. Arid. Land 11, 637–651 (2019).Article 

Google Scholar 
Pan, T., Zou, X. T., Liu, Y. J., Wu, S. H. & He, G. M. Contributions of climatic and non-climatic drivers to grassland variations on the Tibetan Plateau. Ecol. Eng. 108, 307–317 (2017).Article 

Google Scholar 
Hou, X. 1:1 Million vegetation map of China (National Tibetan Plateau Data Center, 2019).Peng, S. S. et al. Recent change of vegetation growth trend in China. Environ. Res. Lett. 6, 044027 (2011).Article 

Google Scholar 
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).Article 

Google Scholar 
Zhu, Z. C. et al. Greening of the earth and its drivers. Nat. Clim. Chang. 6, 791 (2016).Article 

Google Scholar 
Vermote, E. et al. NOAA climate data record (CDR) of normalized difference vegetation index (NDVI), version 4. NOAA Natl Cent. Environ. Inf. https://doi.org/10.7289/V5PZ56R6 (2014).Article 

Google Scholar 
Chen, H. et al. Attribution analyses of changes in alpine grasslands on the Qinghai–Tibetan Plateau. Chin. Sci. Bull. 65, 2406–2418 (2020). This work demonstrates that human activities play an increasingly important role in the restoration of degraded grasslands.Article 

Google Scholar 
Shen, M. et al. Evaporative cooling over the Tibetan Plateau induced by vegetation growth. Proc. Natl Acad. Sci. USA 112, 9299–9304 (2015).Article 

Google Scholar 
Cai, D. et al. Vegetation dynamics on the Tibetan Plateau (1982–2006): an attribution by ecohydrological diagnostics. J. Clim. 28, 4576–4584 (2015).Article 

Google Scholar 
Zhou, W. et al. Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010. Ecol. Indic. 83, 303–313 (2017).Article 

Google Scholar 
Liu, Z. et al. Patterns of plant species diversity along an altitudinal gradient and its effect on above-ground biomass in alpine meadows in Qinghai–Tibet Plateau. Biodivers. Sci. 23, 451–462 (2015).Article 

Google Scholar 
Harris, R. B. Rangeland degradation on the Qinghai–Tibetan Plateau: a review of the evidence of its magnitude and causes. J. Arid. Environ. 74, 1–12 (2010).Article 

Google Scholar 
Lu, S. et al. Basic characteristics of Stipa sareptana var. krylovii communities in China. Chin. J. Plant. Ecol. 44, 1087–1094 (2020).Article 

Google Scholar 
Qiao, X. et al. Distribution, community characteristics and classification of Stipa tianschanica var. klemenzii steppe in China. Chin. J. Plant. Ecol. 41, 231–237 (2017).Article 

Google Scholar 
Qiao, X., Guo, K., Zhao, L., Yang, Y. & Zhao, H. Distribution, community characteristics and classification of Stipa basiplumosa steppe on Tibetan Plateau. Geogr. Res. 36, 2432–2440 (2017).
Google Scholar 
Qiao, X., Guo, K., Zhao, L., Wang, Z. & Liu, C. Community characteristics of Stipa bungeana alliance in China. Chin. J. Plant Ecol. 44, 986–994 (2020).Article 

Google Scholar 
Zhu, Y., Qiao, X., Guo, K., Xu, R. & Zhao, L. Distribution, community characteristics and classification of Stipa tianschanica var. gobica steppe in China. Chin. J. Plant Ecol. 42, 785–792 (2018).Article 

Google Scholar 
Li, X. R., Jia, X. H. & Dong, G. R. Influence of desertification on vegetation pattern variations in the cold semi-arid grasslands of Qinghai–Tibet plateau, north-west China. J. Arid. Environ. 64, 505–522 (2006).Article 

Google Scholar 
Tang, L. et al. Changes in vegetation composition and plant diversity with rangeland degradation in the alpine region of Qinghai–Tibet Plateau. Rangel. J. 37, 107–115 (2015).Article 

Google Scholar 
Zhou, X. Chinese Kobresia Meadow (Science Press, 2001).Wang, B. Z. et al. Potential distribution patterns of Stipa bungeana in China and the major factors influencing distribution. Acta Prataculturae Sinica 28, 3–13 (2019).
Google Scholar 
Sun, H., Li, W., Zhang, M. & Han, Y. A comprehensive scientific expedition to the Qinghai–Tibet Plateau. Resour. Sci. 8, 22–30 (1986).
Google Scholar 
Zhu, F. X. et al. Spatiotemporal variations of annual shallow soil temperature on the Tibetan Plateau during 1983–2013. Clim. Dyn. 51, 2209–2227 (2018).Article 

Google Scholar 
Chen, L. T. et al. Changes of carbon stocks in alpine grassland soils from 2002 to 2011 on the Tibetan Plateau and their climatic causes. Geoderma 288, 166–174 (2017).Article 

Google Scholar 
Ding, J. et al. Decadal soil carbon accumulation across Tibetan permafrost regions. Nat. Geosci. 10, 420–424 (2017).Article 

Google Scholar 
Tian, L. M. et al. Variations in soil nutrient availability across Tibetan grassland from the 1980s to 2010s. Geoderma 338, 197–205 (2019).Article 

Google Scholar 
Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Chang. 5, 424–430 (2015).Article 

Google Scholar 
Chen, H. et al. The impacts of climate change and human activities on biogeochemical cycles on the Qinghai–Tibetan Plateau. Glob. Chang. Biol. 19, 2940–2955 (2013). This work suggests that warming enhanced NPP and soil respiration but many uncertainties remain.Article 

Google Scholar 
Shen, M. G. et al. Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges. Natl Sci. Rev. 2, 454–467 (2015).Article 

Google Scholar 
Liu, X. D., Yin, Z. Y., Shao, X. M. & Qin, N. S. Temporal trends and variability of daily maximum and minimum, extreme temperature events, and growing season length over the eastern and central Tibetan Plateau during 1961–2003. J. Geophys. Res. Atmos. https://doi.org/10.1029/2005jd006915 (2006).Article 

Google Scholar 
Yang, K. et al. Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: a review. Glob. Planet. Change 112, 79–91 (2014). This work reviews the main spatio-temporal characteristics of climate change on the QTP.Article 

Google Scholar 
Klein, J. A., Harte, J. & Zhao, X. Q. Experimental warming, not grazing, decreases rangeland quality on the Tibetan Plateau. Ecol. Appl. 17, 541–557 (2007).Article 

Google Scholar 
Li, C. et al. Productivity and quality of alpine grassland vary with soil water availability under experimental warming. Front. Plant. Sci. 9, 1790 (2018).Article 

Google Scholar 
Peng, A. H. et al. Plant community responses to warming modified by soil moisture in the Tibetan Plateau. Arct. Antarct. Alp. Res. 52, 60–69 (2020).Article 

Google Scholar 
Li, F. et al. Leaf area rather than photosynthetic rate determines the response of ecosystem productivity to experimental warming in an alpine steppe. J. Geophys. Res. Biogeosci. 124, 2277–2287 (2019).Article 

Google Scholar 
Zong, N. et al. Responses of ecosystem CO2 fluxes to short-term experimental warming and nitrogen enrichment in an alpine meadow, northern Tibet Plateau. Sci. World J. 2013, 415318 (2013).Article 

Google Scholar 
Chen, Q., Niu, B., Hu, Y., Luo, T. & Zhang, G. Warming and increased precipitation indirectly affect the composition and turnover of labile-fraction soil organic matter by directly affecting vegetation and microorganisms. Sci. Total Environ. 714, 136787 (2020).Article 

Google Scholar 
Wang, X. X. et al. Effects of short-term and long-term warming on soil nutrients, microbial biomass and enzyme activities in an alpine meadow on the Qinghai–Tibet Plateau of China. Soil. Biol. Biochem. 76, 140–142 (2014).Article 

Google Scholar 
Jiang, L. L. et al. Plant organic N uptake maintains species dominance under long-term warming. Plant Soil 433, 243–255 (2018).Article 

Google Scholar 
Li, N. et al. Short-term effects of temperature enhancement on community structure and biomass of alpine meadow in the Qinghai–Tibet Plateau. Acta Ecol. Sin. 31, 895–905 (2011).
Google Scholar 
Jiang, Y., Fan, M. & Zhang, Y. Effect of short-term warming on plant community features of alpine meadow in northern Tibet. Chin. J. Ecol. 36, 616–622 (2017).
Google Scholar 
Wang, S. et al. Effects of warming and grazing on soil N availability, species composition, and ANPP in an alpine meadow. Ecology 93, 2365–2376 (2012). This work shows the effects of asymmetric warming and moderate grazing on plant composition, diversity, productivity and their relationships.Article 

Google Scholar 
Liu, P. et al. Ambient climate determines the directional trend of community stability under warming and grazing. Glob. Change Biol. 27, 5198–5210 (2021). This work finds that the negative effect of warming on plant diversity disappears with experimental duration, and ambient climate modulates the effects of warming and grazing on productivity stability.Article 

Google Scholar 
Zhang, B. et al. Responses of soil microbial communities to experimental warming in alpine grasslands on the Qinghai–Tibet Plateau. PLoS ONE 9, e103859 (2014).Article 

Google Scholar 
Chen, X. et al. Effects of warming and nitrogen fertilization on GHG flux in the permafrost region of an alpine meadow. Atmos. Environ. 157, 111–124 (2017).Article 

Google Scholar 
Zhang, Y. et al. Effects of grazing and climate warming on plant diversity, productivity and living state in the alpine rangelands and cultivated grasslands of the Qinghai–Tibetan Plateau. Rangel. J. 37, 57–65 (2015).Article 

Google Scholar 
Quan, Q. et al. High-level rather than low-level warming destabilizes plant community biomass production. J. Ecol. 109, 1607–1617 (2021).Article 

Google Scholar 
Wang, X. et al. Response of greenhouse gases emission fluxes to long-term warming in alpine meadow of northern Tibet. Chin. J. Agrometeorol. 39, 152–161 (2018).
Google Scholar 
Klein, J. A., Harte, J. & Zhao, X. Q. Experimental warming causes large and rapid species loss, dampened by simulated grazing, on the Tibetan Plateau. Ecol. Lett. 7, 1170–1179 (2004).Article 

Google Scholar 
Zhang, C. H. et al. Recovery of plant species diversity during long-term experimental warming of a species-rich alpine meadow community on the Qinghai–Tibet Plateau. Biol. Conserv. 213, 218–224 (2017).Article 

Google Scholar 
Li, X. et al. Responses of biotic interactions of dominant and subordinate species to decadal warming and simulated rotational grazing in Tibetan alpine meadow. Sci. China Life Sci. 61, 849–859 (2018).Article 

Google Scholar 
Klein, J. A., Harte, J. & Zhao, X. Q. Dynamic and complex microclimate responses to warming and grazing manipulations. Glob. Chang. Biol. 11, 1440–1451 (2005).Article 

Google Scholar 
Chen, J. et al. Warming effects on ecosystem carbon fluxes are modulated by plant functional types. Ecosystems 20, 515–526 (2017).Article 

Google Scholar 
Zhang, Y. Q. & Welker, J. M. Tibetan alpine tundra responses to simulated changes in climate: aboveground biomass and community responses. Arct. Alp. Res. 28, 203–209 (1996).Article 

Google Scholar 
Liu, H. et al. Shifting plant species composition in response to climate change stabilizes grassland primary production. Proc. Natl Acad. Sci. USA 115, 4051–4056 (2018). This work demonstrates that shifting plant species composition in response to climate change may have stabilized primary production in this high-elevation ecosystem, but also causes a shift from above-ground to below-ground productivity.Article 

Google Scholar 
Ganjurjav, H. et al. Differential response of alpine steppe and alpine meadow to climate warming in the central Qinghai–Tibetan Plateau. Agric. For. Meteorol. 223, 233–240 (2016).Article 

Google Scholar 
Jiang, F., Wei, X., Kang, B. & Shao, X. Effects of warming on alpine meadow diversity and primary productivity. Acta Agrestia Sin. 27, 298–305 (2019).
Google Scholar 
Zong, N. et al. Responses of plant community structure and species composition to warming and N addition in an alpine meadow, northern Tibetan Plateau, China. Chin. J. Appl. Ecol. 27, 3739–3748 (2016).
Google Scholar 
Peng, F. et al. Warming-induced shift towards forbs and grasses and its relation to the carbon sequestration in an alpine meadow. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aa6508 (2017).Article 

Google Scholar 
Dorji, T. et al. Grazing and spring snow counteract the effects of warming on an alpine plant community in Tibet through effects on the dominant species. Agric. For. Meteorol. 263, 188–197 (2018).Article 

Google Scholar 
Xue, X., Peng, F., You, Q., Xu, M. & Dong, S. Belowground carbon responses to experimental warming regulated by soil moisture change in an alpine ecosystem of the Qinghai–Tibet Plateau. Ecol. Evol. 5, 4063–4078 (2015).Article 

Google Scholar 
Jing, X. et al. No temperature acclimation of soil extracellular enzymes to experimental warming in an alpine grassland ecosystem on the Tibetan Plateau. Biogeochemistry 117, 39–54 (2014).Article 

Google Scholar 
Yu, C. Q., Shen, Z. X., Zhang, X. Z., Sun, W. & Fu, G. Response of soil C and N, dissolved organic C and N, and inorganic N to short-term experimental warming in an alpine meadow on the Tibetan Plateau. Sci. World J. 2014, 152576 (2014).
Google Scholar 
Zhang, Y. et al. Simulated warming enhances the responses of microbial N transformations to reactive N input in a Tibetan alpine meadow. Environ. Int. 141, 105795 (2020).Article 

Google Scholar 
Jia, J. et al. Climate warming alters subsoil but not topsoil carbon dynamics in alpine grassland. Glob. Chang. Biol. 25, 4383–4393 (2019).Article 

Google Scholar 
Ding, X. L. et al. Warming increases microbial residue contribution to soil organic carbon in an alpine meadow. Soil. Biol. Biochem. 135, 13–19 (2019).Article 

Google Scholar 
Guan, S. et al. Climate warming impacts on soil organic carbon fractions and aggregate stability in a Tibetan alpine meadow. Soil. Biol. Biochem. 116, 224–236 (2018).Article 

Google Scholar 
Rui, Y. C. et al. Warming and grazing affect soil labile carbon and nitrogen pools differently in an alpine meadow of the Qinghai–Tibet Plateau in China. J. Soils Sediment. 11, 903–914 (2011).Article 

Google Scholar 
Heng, T., Wu, J., Xie, S. & Wu, M. The responses of soil C and N, microbial biomass C or N under alpine meadow of Qinghai–Tibet Plateau to the change of temperature and precipitation. Chin. Agric. Sci. Bull. 27, 425–430 (2011).
Google Scholar 
Li, N., Wang, G., Yang, Y., Gao, Y. & Liu, G. Plant production, and carbon and nitrogen source pools, are strongly intensified by experimental warming in alpine ecosystems in the Qinghai–Tibet Plateau. Soil. Biol. Biochem. 43, 942–953 (2011).Article 

Google Scholar 
Zhao, J. X. et al. Increased precipitation offsets the negative effect of warming on plant biomass and ecosystem respiration in a Tibetan alpine steppe. Agric. For. Meteorol. https://doi.org/10.1016/j.agrformet.2019.107761 (2019). This work shows that increased precipitation offsets the negative effect of warming on plant biomass and ecosystem respiration in a Tibetan alpine steppe.Article 

Google Scholar 
Wu, H. et al. Effects of increased precipitation combined with nitrogen addition and increased temperature on methane fluxes in alpine meadows of the Tibetan Plateau. Sci. Total Environ. 705, 135818 (2020).Article 

Google Scholar 
Shi, F. S., Chen, H., Chen, H. F., Wu, Y. & Wu, N. The combined effects of warming and drying suppress CO2 and N2O emission rates in an alpine meadow of the eastern Tibetan Plateau. Ecol. Res. 27, 725–733 (2012).Article 

Google Scholar 
Fu, G., Zhang, H. R. & Sun, W. Response of plant production to growing/non-growing season asymmetric warming in an alpine meadow of the northern Tibetan Plateau. Sci. Total Environ. 650, 2666–2673 (2019).Article 

Google Scholar 
Xiong, Q. L. et al. Warming and nitrogen deposition are interactive in shaping surface soil microbial communities near the alpine timberline zone on the eastern Qinghai–Tibet Plateau, southwestern China. Appl. Soil. Ecol. 101, 72–83 (2016).Article 

Google Scholar 
Wang, C. et al. Responses of plant leaf traits to simulated rainfall changes in alpine region. Acta Ecol. Sin. 41, 1–13 (2021).Article 

Google Scholar 
Zhang, K. et al. Effects of short-term warming and altered precipitation on soil microbial communities in alpine grassland of the Tibetan Plateau. Front. Microbiol. 7, 1032 (2016).
Google Scholar 
Evans, R. D. & Ehleringer, J. R. Water and nitrogen dynamics in an arid woodland. Oecologia 99, 233–242 (1994).Article 

Google Scholar 
Swap, R. J., Aranibar, J. N., Dowty, P. R., Gilhooly, W. P. III & Macko, S. A. Natural abundance of 13C and 15N in C3 and C4 vegetation of southern Africa: patterns and implications. Glob. Chang. Biol. 10, 350–358 (2004).Article 

Google Scholar 
Jia, Y. et al. Spatial and decadal variations in inorganic nitrogen wet deposition in China induced by human activity. Sci. Rep. 4, 3763–3763 (2014).Article 

Google Scholar 
Liu, Y. W., Xu, R., Wang, Y. S., Pan, Y. P. & Piao, S. L. Wet deposition of atmospheric inorganic nitrogen at five remote sites in the Tibetan Plateau. Atmos. Chem. Phys. 15, 11683–11700 (2015).Article 

Google Scholar 
Wang, W. et al. Atmospheric nitrogen deposition to a southeast Tibetan forest ecosystem. Atmosphere https://doi.org/10.3390/atmos11121331 (2020).Article 

Google Scholar 
Zou, X. et al. Ice-core based assessment of nitrogen deposition in the central Tibetan Plateau over the last millennium. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2021.152692 (2022).Article 

Google Scholar 
Aerts, R., Wallen, B. & Malmer, N. Growth-limiting nutrients in sphagnum-dominated bogs subject to low and high amospheric nitrogen supply. J. Ecol. 80, 131–140 (1992).Article 

Google Scholar 
Bai, Y. F. et al. Tradeoffs and thresholds in the effects of nitrogen addition on biodiversity and ecosystem functioning: evidence from Inner Mongolia grasslands. Glob. Chang. Biol. 16, 358–372 (2010).Article 

Google Scholar 
Du, Y. Population statistics of Qinghai–Tibet Plateau (1952–2016) (National Tibetan Plateau Data Center, 2019).Zhang, Y. J., Zhang, X. Q., Wang, X. Y., Liu, N. & Kan, H. M. Establishing the carrying capacity of the grasslands of China: a review. Rangel. J. 36, 1–9 (2014).Article 

Google Scholar 
Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2, 720–735 (2021). This work shows that socio-ecological solutions are needed to combat degradation and promote restoration.Article 

Google Scholar 
Liu, M. et al. Effects of rotational and continuous overgrazing on newly assimilated C allocation. Biol. Fertil. Soils 57, 193–202 (2021).Article 

Google Scholar 
Yang, X. X. et al. Different responses of soil element contents and their stoichiometry (C:N:P) to yak grazing and Tibetan sheep grazing in an alpine grassland on the eastern Qinghai–Tibetan Plateau. Agric. Ecosyst. Environ. https://doi.org/10.1016/j.agee.2019.106628 (2019).Article 

Google Scholar 
Lin, B., Zhao, X. R., Zheng, Y., Qi, S. & Liu, X. Z. Effect of grazing intensity on protozoan community, microbial biomass, and enzyme activity in an alpine meadow on the Tibetan Plateau. J. Soils Sediment. 17, 2752–2762 (2017).Article 

Google Scholar 
Ma, W. M., Ding, K. Y. & Li, Z. W. Comparison of soil carbon and nitrogen stocks at grazing-excluded and yak grazed alpine meadow sites in Qinghai–Tibetan Plateau, China. Ecol. Eng. 87, 203–211 (2016).Article 

Google Scholar 
Li, W. et al. Effects of grazing regime on vegetation structure, productivity, soil quality, carbon and nitrogen storage of alpine meadow on the Qinghai–Tibetan Plateau. Ecol. Eng. 98, 123–133 (2017).Article 

Google Scholar 
Sun, J. et al. Effects of grazing regimes on plant traits and soil nutrients in an alpine steppe, northern Tibetan Plateau. PLoS ONE 9, e108821 (2014).Article 

Google Scholar 
Niu, K. C., He, J. S. & Lechowicz, M. J. Grazing-induced shifts in community functional composition and soil nutrient availability in Tibetan alpine meadows. J. Appl. Ecol. 53, 1554–1564 (2016).Article 

Google Scholar 
Luan, J. W. et al. Different grazing removal exclosures effects on soil C stocks among alpine ecosystems in east Qinghai–Tibet Plateau. Ecol. Eng. 64, 262–268 (2014).Article 

Google Scholar 
Wei, D. et al. Responses of CO2, CH4 and N2O fluxes to livestock exclosure in an alpine steppe on the Tibetan Plateau, China. Plant Soil 359, 45–55 (2012).Article 

Google Scholar 
Shen, H. et al. Grazing enhances plant photosynthetic capacity by altering soil nitrogen in alpine grasslands on the Qinghai–Tibetan Plateau. Agric. Ecosyst. Environ. 280, 161–168 (2019).Article 

Google Scholar 
Jiang, L. et al. Grazing modifies inorganic and organic nitrogen uptake by coexisting plant species in alpine grassland. Biol. Fertil. Soils 52, 211–221 (2016).Article 

Google Scholar 
Sun, Y., Schleuss, P. M., Pausch, J., Xu, X. L. & Kuzyakov, Y. Nitrogen pools and cycles in Tibetan Kobresia pastures depending on grazing. Biol. Fertil. Soils 54, 569–581 (2018).Article 

Google Scholar 
Chen, B. et al. The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai–Tibet Plateau. Agric. For. Meteorol. 189-190, 11–18 (2014).Article 

Google Scholar 
Wang, Z. Q. et al. Quantitative assess the driving forces on the grassland degradation in the Qinghai–Tibet Plateau, in China. Ecol. Inform. 33, 32–44 (2016).Article 

Google Scholar 
Huang, K. et al. The influences of climate change and human activities on vegetation dynamics in the Qinghai–Tibet Plateau. Remote Sens. 8, 876 (2016).Article 

Google Scholar 
Li, L. et al. Increasing sensitivity of alpine grasslands to climate variability along an elevational gradient on the Qinghai–Tibet Plateau. Sci. Total Environ. 678, 21–29 (2019).Article 

Google Scholar 
Wang, Z. et al. Vegetation expansion on the Tibetan Plateau and its relationship with climate change. Remote. Sens. https://doi.org/10.3390/rs12244150 (2020).Article 

Google Scholar 
Wu, J. et al. Disentangling climatic and anthropogenic contributions to nonlinear dynamics of alpine grassland productivity on the Qinghai–Tibetan Plateau. J. Environ. Manag. 281, 111875 (2021).Article 

Google Scholar 
Fu, G., Shen, Z. X. & Zhang, X. Z. Increased precipitation has stronger effects on plant production of an alpine meadow than does experimental warming in the northern Tibetan Plateau. Agric. For. Meteorol. 249, 11–21 (2018).Article 

Google Scholar 
Hu, Y. et al. Effect of increasing precipitation and warming on microbial community in Tibetan alpine steppe. Environ. Res. 189, 109917 (2020).Article 

Google Scholar 
Ma, Z. et al. Climate warming reduces the temporal stability of plant community biomass production. Nat. Commun. 8, 15378 (2017).Article 

Google Scholar 
Bai, W., Xi, J. & Wang, G. Effects of short-term warming and nitrogen addition on CO2 emission during growing season in an alpine swamp meadow ecosystem of Qinghai–Tibetan Plateau. Chin. J. Ecol. 38, 927–936 (2019).
Google Scholar 
Bai, W., Wang, G. X., Xi, J. Y., Liu, Y. W. & Yin, P. S. Short-term responses of ecosystem respiration to warming and nitrogen addition in an alpine swamp meadow. Eur. J. Soil Biol. 92, 16–23 (2019).Article 

Google Scholar 
Ge, Y. et al. Effects of warming and nitrogen addition on plant community structure and species diversity of alpine meadow in northern Tibet. Ecol. Environ. Sci. 28, 2185–2191 (2019).
Google Scholar 
Zong, N. et al. Effects of warming and nitrogen addition on community production and biomass allocation in an alpine meadow. Chin. J. Appl. Ecol. 29, 59–67 (2018).
Google Scholar 
Zhu, X. X. et al. Effects of warming, grazing/cutting and nitrogen fertilization on greenhouse gas fluxes during growing seasons in an alpine meadow on the Tibetan Plateau. Agric. For. Meteorol. 214, 506–514 (2015).Article 

Google Scholar 
Fu, G. et al. Clipping alters the response of biomass production to experimental warming: a case study in an alpine meadow on the Tibetan Plateau, China. J. Mt. Sci. 12, 935–942 (2015).Article 

Google Scholar 
Chen, S. P. et al. Plant diversity enhances productivity and soil carbon storage. Proc. Natl Acad. Sci. USA. 115, 4027–4032 (2018).Article 

Google Scholar 
Wu, J. S. et al. Effects of livestock exclusion and climate change on aboveground biomass accumulation in alpine pastures across the northern Tibetan Plateau. Chin. Sci. Bull. 59, 4332–4340 (2014).Article 

Google Scholar 
Sun, J., Cheng, G. W., Li, W. P., Sha, Y. K. & Yang, Y. C. On the variation of NDVI with the principal climatic elements in the Tibetan Plateau. Remote Sens. 5, 1894–1911 (2013).Article 

Google Scholar 
Sun, J. et al. Reconsidering the efficiency of grazing exclusion using fences on the Tibetan Plateau. Sci. Bull. 65, 1405–1414 (2020). This work finds that fencing enclosures lead to some negative impacts, such as hindering wildlife movement.Article 

Google Scholar 
Yu, C. et al. Grazing exclusion to recover degraded alpine pastures needs scientific assessments across the northern Tibetan Plateau. Sustainability https://doi.org/10.3390/su8111162 (2016).Article 

Google Scholar 
Wu, J. & Wang, X. Effect of enclosure ages on community characters and biomas of the degraded alpine steppe at the northern Tibet. Acta Agrestia Sin. 25, 261–266 (2017).
Google Scholar 
Zhao, J. X., Luo, T. X., Li, R. C., Li, X. & Tian, L. H. Grazing effect on growing season ecosystem respiration and its temperature sensitivity in alpine grasslands along a large altitudinal gradient on the central Tibetan Plateau. Agric. For. Meteorol. 218, 114–121 (2016).Article 

Google Scholar 
Deng, L., Sweeney, S. & Shangguan, Z. P. Grassland responses to grazing disturbance: plant diversity changes with grazing intensity in a desert steppe. Grass Forage Sci. 69, 524–533 (2014).Article 

Google Scholar 
Yuan, Z., Epstein, H. & Li, G. Grazing exclusion did not affect soil properties in alpine meadows in the Tibetan permafrost region. Ecol. Eng. https://doi.org/10.1016/j.ecoleng.2019.105657 (2020).Article 

Google Scholar 
Zhang, W. et al. Effects of banning grazing and delaying grazing on species diversity and biomass of alpine meadow in northern Tibet. J. Agric. Sci. Technol. 15, 143–149 (2013).
Google Scholar 
Miao, F., Guo, Y., Miao, P., Guo, Z. & Shen, Y. The northeast edge of Qinghai–Tibet Plateau area of alpine meadow community characteristics respond to nurture. Acta Prataculture Sin. 21, 11–16 (2012).
Google Scholar 
Lu, X. et al. Short-term grazing exclusion has no impact on soil properties and nutrients of degraded alpine grassland in Tibet, China. Solid Earth 6, 1195–1205 (2015).Article 

Google Scholar 
Gao, Y. H., Zeng, X. Y., Schumann, M. & Chen, H. Effectiveness of exclosures on restoration of degraded alpine meadow in the eastern Tibetan Plateau. Arid. Land. Res. Manag. 25, 164–175 (2011).Article 

Google Scholar 
Yao, X. X. et al. Effects of long term fencing on biomass, coverage, density, biodiversity and nutritional values of vegetation community in an alpine meadow of the Qinghai–Tibet Plateau. Ecol. Eng. 130, 80–93 (2019).Article 

Google Scholar 
Chen, W., Chang, H. & Liu, R. Fractal features of soil particle size distributions and their implications for indicating enclosure management in a semiarid grassland ecosystem. Pol. J. Ecol. 68, 132–144 (2020).
Google Scholar 
Smith, D., King, R. & Allen, B. L. Impacts of exclusion fencing on target and non-target fauna: a global review. Biol. Rev. 95, 1590–1606 (2020).Article 

Google Scholar 
Zhang, Y. et al. Assessment of effectiveness of nature reserves on the Tibetan Plateau based on net primary production and the large sample comparison method. J. Geogr. Sci. 26, 27–44 (2016).Article 

Google Scholar 
Hu, J. Research on the status quo and problems of natural reserve construction in Qinghai–Tibet Plateau. Environ. Dev. 32, 204–206 (2020).
Google Scholar 
Shao, Q., Fan, J., Liu, J., Cao, W. & Liu, L. Target-based assessment on effects of first-stage ecological conservation and restoration project in three-river source region, China and policy recommendations. Bull. Chin. Acad. Sci. 32, 35–44 (2017).
Google Scholar 
Liu, F. & Zeng, Y. N. Spatial–temporal change in vegetation net primary productivity and its response to climate and human activities in Qinghai Plateau in the past 16 years. Acta Ecol. Sin. 39, 1528–1540 (2019).
Google Scholar 
Zhang, Y., Wu, X., Qi, W., Li, S. & Bai, W. Characteristics and protection effectiveness of nature reserves on the Tibetan Plateau, China. Resources. Science 37, 1455–1464 (2015).
Google Scholar 
Buckley, M. C. & Crone, E. E. Negative off-site impacts of ecological restoration: understanding and addressing the conflict. Conserv. Biol. 22, 1118–1124 (2008).Article 

Google Scholar 
Cao, S. X. & Zhang, J. Political risks arising from the impacts of large-scale afforestation on water resources of the Tibetan Plateau. Gondwana Res. 28, 898–903 (2015).Article 

Google Scholar 
Li, Y. & Li, W. Why “Balance of Forage and Livestock” system failed to reach sustainable grassland utilization. China Agric. Univ. J. Soc. Sci. Ed. 29, 124–131 (2012).
Google Scholar 
Du, S. A Study on the Satisfaction Degree of Herdsmen’s Income and Grassland Ecological Compensation Policy. Master thesis, Lanzhou Univ. (2019).Yu, H., Wang, G., Yang, Y. & Lü, Y. Concept of grassland green carrying capacity and its application framework in national park. Acta Ecol. Sin. 40, 7248–7254 (2020).
Google Scholar 
Deng, Y. & Li, C. The investigation and research about the Farmland Retirement and Environment Project in the Yangtze River headwaters area. Ecol. Econ. 2, 77–80 (2006).
Google Scholar 
Zhou, Q. et al. Analysis on the relationship between grassland area and forage-livestock balance in Qinghai–Tibet Plateau. Chin. J. Grassl. 41, 110–117 (2019).
Google Scholar 
Li, Y. et al. Awareness and reaction of herdsmen to the policy of returning grazing land to grasslands in the Changtang Plateau,Tibet. Pratacultural Sci. 30, 788–794 (2013).
Google Scholar 
Fan, J. et al. Third pole national park group construction is scientific choice for implementing strategy of major function zoning and green development in Tibet, China. Bull. Chin. Acad. Sci. 32, 932–944 (2017).
Google Scholar 
Xu, Z., Cheng, S. & Gao, L. Impacts of herders sedentarization on regional spatial heterogeneity and grassland ecosystem change in pastoral area. J. Arid. Land. Resour. Environ. 31, 8–13 (2017).
Google Scholar 
Ptackova, J. Sedentarisation of Tibetan nomads in China: implementation of the Nomadic settlement project in the Tibetan Amdo area; Qinghai and Sichuan Provinces. Pastoralism https://doi.org/10.1186/2041-7136-1-4 (2011).Article 

Google Scholar 
Weber, K. T. & Horst, S. Desertification and livestock grazing: the roles of sedentarization, mobility and rest. Pastoralism https://doi.org/10.1186/2041-7136-1-19 (2011).Article 

Google Scholar 
Zhang, J. et al. Ecological consequence of nomad settlement policy in the pasture area of Qinghai–Tibetan Plateau: from plant and soil perspectives. J. Environ. Manage. https://doi.org/10.1016/j.jenvman.2020.110114 (2020).Article 

Google Scholar 
Li, C. X., de Jong, R., Schmid, B., Wulf, H. & Schaepman, M. E. Spatial variation of human influences on grassland biomass on the Qinghai–Tibetan Plateau. Sci. Total Environ. 665, 678–689 (2019).Article 

Google Scholar 
Kuang, W. Dataset of Urban Distribution, Urban Population and Built-up Area in Tibetan Plateau (2000–2015) (National Tibetan Plateau Data Center, 2021).Tian, L. & Chen, J. Urban expansion inferenced by ecosystem production on the Qinghai–Tibet plateau. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ac3178 (2022).Article 

Google Scholar 
Liu, Y. & Lu, C. Quantifying grass coverage trends to identify the hot plots of grassland degradation in the Tibetan Plateau during 2000–2019. Int. J. Environ. Res. Public Health https://doi.org/10.3390/ijerph18020416 (2021).Article 

Google Scholar 
Tang, L. et al. Warming counteracts grazing effects on the functional structure of the soil microbial community in a Tibetan grassland. Soil. Biol. Biochem. 134, 113–121 (2019).Article 

Google Scholar 
Zhong, L. Tourism Planning Case in Tibetan Plateau (China Tourism Press, 2018).La, M. Discussion of the coordinated development of tourism development and ecological Environment Protection in Tibetan. Soc. Sci. Res. 6, 118–120, (2013).
Google Scholar 
Zhuang, M. et al. Opportunities for household energy on the Qinghai–Tibet Plateau in line with United Nations’ Sustainable Development Goals. Renew. Sustain. Energy Rev. https://doi.org/10.1016/j.rser.2021.110982 (2021).Article 

Google Scholar 
Ruess, R. W. & Mcnaughton, S. J. Grazing and the dynamics of nutrient and energy regulated microbial processes in the serengeti grasslands. Oikos 49, 101–110 (1987).Article 

Google Scholar 
Li, M. et al. Changes in plant species richness distribution in Tibetan alpine grasslands under different precipitation scenarios. Glob. Ecol. Conserv. 21, 13 (2020).
Google Scholar 
Wang, Z. et al. Quantitative assess the driving forces on the grassland degradation in the Qinghai–Tibet Plateau, in China. Ecol. Inform. 33, 32–44 (2016).Article 

Google Scholar 
Muñoz Sabater, J. ERA5-Land monthly averaged data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.68d2bb30 (2019).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).Article 

Google Scholar  More

Moore, J., Pope, J., Woods, M. & Ellis, A. 2018 Aerial Survey Results: California (USDA, 2018).Stephens, S. L. et al. Drought, tree mortality, and wildfire in forests adapted to frequent fire. Bioscience 68, 77–88 (2018).Article 

Google Scholar 
Li, S. & Banerjee, T. Spatial and temporal pattern of wildfires in California from 2000 to 2019. Sci. Rep. 11, 8779 (2021).Article 

Google Scholar 
Wang, D. et al. Economic footprint of California wildfires in 2018. Nat. Sustain. 4, 252–260 (2020).Article 

Google Scholar 
Asner, G. P. et al. Progressive forest canopy water loss during the 2012–2015 California drought. Proc. Natl Acad. Sci. USA 113, E249–E255 (2016).
Google Scholar 
Brodrick, P. G., Anderegg, L. D. L. & Asner, G. P. Forest drought resistance at large geographic scales. Geophys. Res. Lett. 46, 2752–2760 (2019).Article 

Google Scholar 
Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).Article 

Google Scholar 
Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).Article 

Google Scholar 
Paz-Kagan, T. et al. What mediates tree mortality during drought in the southern Sierra Nevada? Ecol. Appl. 27, 2443–2457 (2017).Article 

Google Scholar 
Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is Tree Drought Mortality so Hard to Predict? Trends Ecol. Evol. 36, 520–532.(2021).Goodfellow, B. W. et al. The chemical, mechanical, and hydrological evolution of weathering granitoid. J. Geophys. Res. Earth Surf. 121, 1410–1435 (2016).Article 

Google Scholar 
Shen, X., Arson, C., Ferrier, K. L., West, N. & Dai, S. Mineral weathering and bedrock weakening: modeling microscale bedrock damage under biotite weathering. J. Geophys. Res. Earth Surf. 124, 2623–2646 (2019).Article 

Google Scholar 
McLaughlin, B. C. et al. Weather underground: subsurface hydrologic processes mediate tree vulnerability to extreme climatic drought. Glob. Change Biol. 26, 3091–3107 (2020).Article 

Google Scholar 
Hahm, W. J. et al. Low subsurface water storage capacity relative to annual rainfall decouples Mediterranean plant productivity and water use from rainfall variability. Geophys. Res. Lett. 46, 6544–6553 (2019).Article 

Google Scholar 
Zhang, Y., Keenan, T. F. & Zhou, S. Exacerbated drought impacts on global ecosystems due to structural overshoot. Nat. Ecol. Evol. 5, 1490–1498 (2021).Article 

Google Scholar 
Tague, C. & Peng, H. The sensitivity of forest water use to the timing of precipitation and snowmelt recharge in the California Sierra: implications for a warming climate. J. Geophys. Res. Biogeosci. 118, 875–887 (2013).Article 

Google Scholar 
Hahm, W. J., Riebe, C. S., Lukens, C. E. & Araki, S. Bedrock composition regulates mountain ecosystems and landscape evolution. Proc. Natl Acad. Sci. USA 111, 3338–3343 (2014).Article 

Google Scholar 
Uhlig, D., Schuessler, J. A., Bouchez, J., Dixon, J. L. & von Blanckenburg, F. Quantifying nutrient uptake as driver of rock weathering in forest ecosystems by magnesium stable isotopes. Biogeosciences 14, 3111–3128 (2017).Article 

Google Scholar 
Stone, E. C. Dew as an ecological factor: II. The effect of artificial dew on the survival of Pinus ponderosa and associated species. Ecology 38, 414–422 (1957).Article 

Google Scholar 
Wald, J. A., Graham, R. C. & Schoeneberger, P. J. Distribution and properties of soft weathered bedrock at ≤1 m depth in the contiguous United States. Earth Surf. Process. Landf. 38, 614–626 (2013).Article 

Google Scholar 
Klos, P. Z. et al. Subsurface plant-accessible water in mountain ecosystems with a Mediterranean climate. WIREs Water 5, e1277 (2018).Article 

Google Scholar 
Dawson, T. E., Hahm, W. J. & Crutchfield-Peters, K. Digging deeper: what the critical zone perspective adds to the study of plant ecophysiology. N. Phytol. 226, 666–671 (2020).Article 

Google Scholar 
Rempe, D. M. & Dietrich, W. E. Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proc. Natl Acad. Sci. USA 115, 2664–2669 (2018).Article 

Google Scholar 
Holbrook, W. S. et al. Links between physical and chemical weathering inferred from a 65-m-deep borehole through Earth’s critical zone. Sci. Rep. 9, 4495 (2019).Article 

Google Scholar 
Krone, L. V. et al. Deep weathering in the semi-arid Coastal Cordillera, Chile. Sci. Rep. 11, 13057 (2021).Article 

Google Scholar 
Callahan, R. P. et al. Subsurface weathering revealed in hillslope‐integrated porosity distributions. Geophys. Res. Lett. 47, e2020GL088322 (2020).Holbrook, W. S. et al. Geophysical constraints on deep weathering and water storage potential in the Southern Sierra Critical Zone Observatory. Earth Surf. Process. Landf. 39, 366–380 (2014).Article 

Google Scholar 
Hayes, J. L., Riebe, C. S., Holbrook, W. S., Flinchum, B. A. & Hartsough, P. C. Porosity production in weathered rock: where volumetric strain dominates over chemical mass loss. Sci. Adv. 5, eaao0834 (2019).Article 

Google Scholar 
Riebe, C. S. et al. Anisovolumetric weathering in granitic saprolite controlled by climate and erosion rate. Geology 49, 551–555 (2021).Article 

Google Scholar 
McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).Article 

Google Scholar 
Vitousek, P. M., Porder, S. & Houlton, B. Z. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).Article 

Google Scholar 
Bateman, P. C., Dodge, F. C. W. & Bruggman, P. E. Major Oxide Analyses, CPIW Norms, Modes, and Bulk Specific Gravities of Plutonic Rocks from the Mariposa 1° × 2° Sheet, Central Sierra Nevada, California Open-File Report 84–162 (USGS, 1984).Amundson, R., Richter, D. D., Humphreys, G. S., Jobbagy, E. G. & Gaillardet, J. Coupling between biota and earth materials in the critical zone. Elements 3, 327–332 (2007).Article 

Google Scholar 
Tune, A. K., Druhan, J. L., Wang, J., Bennett, P. C. & Rempe, D. M. Carbon dioxide production in bedrock beneath soils substantially contributes to forest carbon cycling. J. Geophys. Res. Biogeosci. 125, e2020JG005795 (2020).Gabet, E. J. & Mudd, S. M. Bedrock erosion by root fracture and tree throw: a coupled biogeomorphic model to explore the humped soil production function and the persistence of hillslope soils. J. Geophys. Res. 115, F04005 (2010).Bateman, P. C. Plutonism in the Central Part of the Sierra Nevada Batholith, California (USGS, 1992); http://pubs.er.usgs.gov/publication/pp1483Callahan, R. P. et al. Arrested development: erosional equilibrium in the southern Sierra Nevada, California, maintained by feedbacks between channel incision and hillslope sediment production. GSA Bull. 131, 1179–1202 (2019).Article 

Google Scholar 
Flinchum, B. A. et al. Estimating the water holding capacity of the critical zone using near-surface geophysics. Hydrol. Process. 32, 3308–3326 (2018).Article 

Google Scholar 
St. Clair, J. Geophysical Investigations of Underplating at the Middle American Trench, Weathering in the Critical Zone, and Snow Water Equivalent in Seasonal Snow. PhD thesis, Univ. Wyoming (2015).Dvorkin, J. & Nur, A. Elasticity of high‐porosity sandstones: theory for two North Sea data sets. Geophysics 61, 1363–1370 (1996).Article 

Google Scholar 
Gu, X. et al. Seismic refraction tracks porosity generation and possible CO2 production at depth under a headwater catchment. Proc. Natl Acad. Sci. USA 117, 18991–18997 (2020).Article 

Google Scholar 
Pasquet, S., Holbrook, W. S., Carr, B. J. & Sims, K. W. W. Geophysical imaging of shallow degassing in a Yellowstone hydrothermal system. Geophys. Res. Lett. 43, 12,027–12,035 (2016).Article 

Google Scholar 
Dahlgren, R. A., Boettinger, J. L., Huntington, G. L. & Amundson, R. G. Soil development along an elevational transect in the western Sierra Nevada, California. Geoderma 78, 207–236 (1997).Article 

Google Scholar 
Stone, E. L. & Kalisz, P. J. On the maximum extent of tree roots. For. Ecol. Manage. 46, 59–102 (1991).Article 

Google Scholar 
Carlson, T. N. & Ripley, D. A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 62, 241–252 (1997).Article 

Google Scholar 
Goulden, M. L. et al. Evapotranspiration along an elevation gradient in California’s Sierra Nevada. J. Geophys. Res. 117, G03028 (2012).Ma, Q. et al. Wildfire controls on evapotranspiration in California’s Sierra Nevada. J. Hydrol. 590, 125364 (2020).Article 

Google Scholar 
Roche, J. W., Goulden, M. L. & Bales, R. C. Estimating evapotranspiration change due to forest treatment and fire at the basin scale in the Sierra Nevada, California. Ecohydrology 11, e1978 (2018).Bales, R. C. et al. Mechanisms controlling the impact of multi-year drought on mountain hydrology. Sci. Rep. 8, 690 (2018).Article 

Google Scholar 
Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).Article 

Google Scholar 
Su, Y. et al. Emerging stress and relative resiliency of giant sequoia groves experiencing multiyear dry periods in a warming climate. J. Geophys. Res. Biogeosci. 122, 3063–3075 (2017).Article 

Google Scholar 
Moore, J., McAfee, L. & Iaccarino, J. 2016 Aerial Survey Results: California (USDA, 2017).Budyko, M. I., Miller, D. H. & Miller, D. H. Climate and Life (Academic Press, 1974).Hargreaves, G. H. & Samani, Z. A. Reference crop evapotranspiration from temperature. Appl. Eng. Agric. 1, 96–99 (1985).Article 

Google Scholar 
PRISM Climate Group PRISM Climate Data (Oregon State Univ., 2019).Bales, R. et al. Spatially distributed water-balance and meteorological data from the rain–snow transition, southern Sierra Nevada, California. Earth Syst. Sci. Data 10, 1795–1805 (2018).Article 

Google Scholar 
Callahan, R. P. Supplement for “Forest vulnerability to drought controlled by bedrock composition”. Hydroshare https://doi.org/10.4211/hs.edbb6ebfbc744186b5800932cd00b507 (2022).Earth Resources Observation and Science (EROS) Center USGS EROS Archive—Aerial Phorography—National Agriculture Imagery Program (NAIP) (USGS, 2017); https://doi.org/10.5066/F7QN651G More

Bedrock composition can play a critical role in determining the structure and water demand of forests, influencing their vulnerability to drought. The properties of bedrock can help explain within-region patterns of tree mortality in the 2011–2017 California drought.Montane forests are iconic natural resources that provide habitat, carbon sequestration, regulation of water, and, for many cultures, profound meaning. A warming climate and prolonged droughts threaten these forests, as shown by the 2011–2017 drought in California, USA, which killed over 140 million trees. However, the vulnerability of forests to climate-driven risks is not evenly distributed across these landscapes. In the 2011–2017 drought, some contiguous forested areas (or forest stands) suffered more than 70% mortality while forests in other locations experienced few or no losses1. Understanding these spatial patterns is critical for the projection of future risks and for targeted forest management. Writing in Nature Geoscience, Callahan and colleagues look beneath the surface at the composition of bedrock and find a link to these patterns of drought mortality in the California Sierra2. More

Zaneveld, J. R., McMinds, R. & Thurber, R. V. Stress and stability: Applying the Anna Karenina principle to animal microbiomes. Nat. Microbiol. 2, 1–8 (2017).Article 
CAS 

Google Scholar 
Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: A call for the consideration of host-associated microbiota in wildlife management practices. Proc. R. Soc. B 286, 2018–2448 (2019).Article 

Google Scholar 
Bennett, A. G. On the occurrence of diatoms on the skin of whales. Proc. R. Soc. Lond. B 91, 352–357 (1920).ADS 
Article 

Google Scholar 
Denys, L. Morphology and taxonomy of epizoic diatoms (Epiphalaina and Tursiocola) on a sperm whale (Physeter macrocephalus) stranded on the coast of Belgium. Diatom. Res. 12, 1–18 (1997).Article 

Google Scholar 
Majewska, R. Tursiocola neliana sp. nov (Bacillariophyceae) epizoic on South African leatherback sea turtles (Dermochelys coriacea) and new observations on the genus Tursiocola. Phytotaxa 453, 1–15 (2020).Article 

Google Scholar 
Majewska, R. et al. Chelonicola and Poulinea, two new gomphonemoid genera living on marine turtles from Costa Rica. Phytotaxa 233, 236–250 (2015).Article 

Google Scholar 
Majewska, R. et al. Shared epizoic taxa and differences in diatom community structure between green turtles (Chelonia mydas) from distant habitats. Microb Ecol. 74, 969–978 (2017).PubMed 
Article 

Google Scholar 
Majewska, R. et al. Two new epizoic Achnanthes species (Bacillariophyta) living on marine turtles from Costa Rica. Bot. Mar. 60, 303–318 (2017).Article 

Google Scholar 
Majewska, R., De Stefano, M. & Van de Vijver, B. Labellicula lecohuiana, a new epizoic diatom species living on green turtles in Costa Rica. Nova Hedwig Beih. 146, 23–31 (2018).Article 

Google Scholar 
Majewska, R. et al. Craspedostauros alatus sp. nov., a new diatom (Bacillariophyta) species found on museum sea turtle specimens. Diatom Res. 33, 229–240 (2018).Article 

Google Scholar 
Majewska, R. et al. Six new epibiotic Proschkinia (Bacillariophyta) species and new insights into the genus phylogeny. Eur. J. Phycol. 54, 609–631 (2019).Article 

Google Scholar 
Majewska, R., Robert, K., Van de Vijver, B. & Nel, R. A new species of Lucanicum (Cyclophorales, Bacillariophyta) associated with loggerhead sea turtles from South Africa. Bot. Lett. 167, 7–14 (2020).Article 

Google Scholar 
Frankovich, T. A., Sullivan, M. J. & Stacy, N. I. Tursiocola denysii sp. Nov. (Bacillariophyta) from the neck skin of Loggerhead sea turtles (Caretta caretta). Phytotaxa 234, 227–236 (2015).Article 

Google Scholar 
Frankovich, T. A., Ashworth, M. P., Sullivan, M. J., Vesela, J. & Stacy, N. I. Medlinella amphoroidea gen. et sp. Nov. (Bacillariophyta) from the neck skin of Loggerhead sea turtles (Caretta caretta). Phytotaxa 272, 101–114 (2016).Article 

Google Scholar 
Riaux-Gobin, C. et al. New epizoic diatom (Bacillariophyta) species from sea turtles in the Eastern Caribbean and South Pacific. Diatom Res. 32, 109–125 (2017).Article 

Google Scholar 
Riaux-Gobin, C., Witkowski, A., Chevallier, D. & Daniszewska-Kowalczyk, G. Two new Tursiocola species (Bacillariophyta) epizoic on green turtles (Chelonia mydas) in French Guiana and Eastern Caribbean. Fottea Olomouc 17, 150–163 (2017).Article 

Google Scholar 
Riaux-Gobin, C., Witkowski, A., Kociolek, J. P. & Chevallier, D. Navicula dermochelycola sp. Nov., presumably an exclusively epizoic diatom on sea turtles Dermochelys coriacea and Lepidochelys olivacea from French Guiana. Oceanol. Hydrobiol. Stud. 49, 132–139 (2020).CAS 
Article 

Google Scholar 
Robert, K., Bosak, S. & Van de Vijver, B. Catenula exigua sp. nov., a new marine diatom (Bacillariophyta) species from the Adriatic Sea. Phytotaxa 414, 113–118 (2019).Article 

Google Scholar 
Van de Vijver, B. & Bosak, S. Planothidium kaetherobertianum, a new marine diatom (Bacillariophyta) species from the Adriatic Sea. Phytotaxa 425, 105–112 (2019).Article 

Google Scholar 
Robinson, N. J. et al. Epibiotic diatoms are universally present on all sea turtle species. PLoS ONE 11, e0157011 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Van de Vijver, B. et al. Diversity of diatom communities (Bacillariophyta) associated with loggerhead sea turtles. PLoS ONE 15, e0236513 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Van de Vijver, B., Robert, K., Witkowski, A. & Bosak, S. Majewskaea gen. nov. (Bacillariophyta), a new marine benthic diatom genus from the Adriatic Sea. Fottea 20, 112–120 (2020).Article 

Google Scholar 
Majewska, R. Nagumoea hydrophicola sp. Nov. (Bacillariophyta), the first diatom species described from sea snakes. Diatom Res. 36, 49–59 (2021).Article 

Google Scholar 
Frankovich, T. A., Sullivan, M. J. & Stacey, N. I. Three new species of Tursiocola (Bacillariophyta) from the skin of the West Indian manatee (Trichechus manatus). Phytotaxa 204, 33–48 (2015).Article 

Google Scholar 
Frankovich, T. A., Ashworth, M. P., Sullivan, M. J., Theriot, E. C. & Stacy, N. I. Epizoic and apochlorotic Tursiocola species (Bacillariophyta) from the skin of Florida manatees (Trichechus manatus latirostris). Protist 169, 539–568 (2018).PubMed 
Article 

Google Scholar 
Azari, M. et al. Diatoms on sea turtles and floating debris in the Persian Gulf (Western Asia). Phycologia 59, 292–304 (2020).Article 

Google Scholar 
Majewska, R. & Goosen, W. E. For better, for worse: Manatee-associated Tursiocola (Bacillariophyta) remain faithful to their host. J. Phycol. 56, 1019–1027 (2020).CAS 
PubMed 
Article 

Google Scholar 
Smol, J. P. & Stoermer, E. F. The Diatoms: Applications for the Environmental and Earth Sciences (Cambridge University Press, 2010).Book 

Google Scholar 
Rivera, S. F. et al. DNA metabarcoding and microscopic analyses of sea turtles biofilms: Complementary to understand turtle behavior. PLoS ONE 13, e0195770 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Majewska, R. et al. On sea turtle-associated Craspedostauros with description of three novel species. J Phycol. 57, 199–208 (2021).CAS 
PubMed 
Article 

Google Scholar 
Holmes, R. W. The morphology of diatoms epizoic on cetaceans and their transfer from Cocconeis to two new genera, Bennettella and Epipellis. Br. Phycol. J. 20, 43–57 (1985).Article 

Google Scholar 
Woodworth, K. A., Frankovich, T. A. & Freshwater, D. W. Melanothamnus maniticola (Ceramiales, Rhodophyta): An epizoic species evolved for life on the West Indian Manatee. J. Phycol. 55, 1239–1245 (2019).CAS 
PubMed 
Article 

Google Scholar 
Vitt, L. J. & Caldwell, J. P. Herpetology: An Introductory Biology of Amphibians and Reptiles (Academic Press, 2013).
Google Scholar 
Pitman, L. R. et al. Skin in the game: Epidermal molt as a driver of long-distance migration in whales. Mar. Mamm. Sci. 36, 565–594 (2020).Article 

Google Scholar 
Pope, D. H. & Berger, L. R. Algal photosynthesis at increased hydrostatic pressure and constant pO2. Arch. Microbiol. 89, 321–325 (1973).CAS 

Google Scholar 
Calcagno, V., Jarne, P., Loreau, M., Mouquet, N. & David, P. Diversity spurs diversification in ecological communities. Nat. Commun. 8, 15810 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Robinson, N. J. & Pfaller, J. B. Sea turtle epibiosis: Global patterns and knowledge gaps. Trends Evol. Ecol. 10, 844021 (2021).
Google Scholar 
Conant, T. A., Dutton, P. H., Eguchi, T., Epperly, S. P., Fahy, C. C., Godfrey, M. H., MacPherson, S. L., Possardt, E. E., Schroeder, B. A., Seminoff, J. A., Snover, M. L. Loggerhead sea turtle (Caretta caretta) 2009 status review under the US Endangered Species Act. In Report of the loggerhead biological review Team to the National Marine Fisheries Service. 222, 1–230 (2009).Evans, K. M., Wortley, A. H. & Mann, D. G. An assessment of potential diatom ‘“barcode”’ genes (cox1, rbcL, 18S and ITS rDNA) and their effectiveness in determining relationships in Sellaphora (Bacillariophyta). Protist 158, 349–364 (2007).CAS 
PubMed 
Article 

Google Scholar 
Hamsher, S. E., Evans, K. M., Mann, D. G., Poulíčková, A. & Saunders, G. W. Barcoding diatoms: Exploring alternatives to COI-5P. Protist 162, 405–422 (2011).CAS 
PubMed 
Article 

Google Scholar 
Bowen, B. W. & Karl, S. A. Population genetics and phylogeography of sea turtles. Mol Ecol. 16, 4886–4907 (2007).CAS 
PubMed 
Article 

Google Scholar 
Shanker, K., Ramadevi, J., Choudhury, B. C., Singh, L. & Aggarwal, R. K. Phylogeography of olive ridley turtles (Lepidochelys olivacea) on the east coast of India: implications for conservation theory. Mol. Ecol. 13, 1899–1909 (2004).CAS 
PubMed 
Article 

Google Scholar 
Pinou, T. et al. Standardizing sea turtle epibiont sampling: Outcomes of the epibiont workshop at the 37th International Sea Turtle Symposium. Mar. Turt. Newsl. 157, 22–32 (2019).
Google Scholar 
Ehrhert L., Ogren L. H. Studies in foraging habitats: capturing and handling turtles. In Research and management techniques for the conservation of sea turtles (eds. Eckert, K. L., Bjorndal, K. A., Abreu-Grobois, F. A., Donnelly, M.). IUCN/SSC Marine Turtle Specialist Group. Publication No. 4. (1999).Guillard, R. R. Culture of phytoplankton for feeding marine invertebrates. In Culture of Marine Invertebrate Animals 29–60 (Springer, 1975).Theriot, E. C., Ashworth, M. P., Nakov, T., Ruck, E. & Jansen, R. K. Dissecting signal and noise in diatom chloroplast protein encoding genes with phylogenetic information profiling. Mol. Phylogenet. Evol. 89, 28–36 (2015).CAS 
PubMed 
Article 

Google Scholar 
Lobban, C. S., Ashworth, M. P., Calaor, J. J. & Theriot, E. C. Extreme diversity in fine-grained morphology reveals fourteen new species of conopeate Nitzschia (Bacillariophyta: Bacillariales). Phytotaxa. 401, 199–238 (2019).Article 

Google Scholar 
Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).CAS 
PubMed 

Google Scholar 
Lanfear, R., Calcott, B., Kainer, D., Mayer, C. & Stamatakis, A. Selecting optimal partitioning schemes for phylogenomic datasets. BMC Evol. Biol. 14, 1–14 (2014).Article 

Google Scholar 
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 
Article 

Google Scholar 
Chernomor, O., Von Haeseler, A. & Minh, B. Q. Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol. 65, 997–1008 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Aberer, A. J., Kobert, K. & Stamatakis, A. ExaBayes: Massively parallel bayesian tree inference for the whole-genome Era. Mol. Biol. Evol. 31, 2553–2556 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Metcalf, C. J. & Pavard, S. Why evolutionary biologists should be demographers. Trends Ecol. Evol. 22, 205–212 (2007).PubMed 
Article 

Google Scholar 
French, J. C., Riris, P., Fernandez-Lopez de Pablo, J., Lozano, S. & Silva, F. A manifesto for palaeodemography in the twenty-first century. Philos. Trans. R. Soc. Lond. B Biol. Sci. 376, 20190707 (2021).PubMed 
Article 

Google Scholar 
French, J. C. Demography and the Palaeolithic archaeological record. J. Archaeol. Method Th. 23, 150–199 (2016).Article 

Google Scholar 
Henrich, J. Demography and cultural evolution: how adaptive cultural processes can produce maladaptive losses – The Tasmanian case. Am. Antiquity 69, 197–214 (2004).Article 

Google Scholar 
Powell, A., Shennan, S. & Thomas, M. G. Late Pleistocene demography and the appearance of modern human behavior. Science 324, 1298–1301 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Shennan, S. Demography and cultural innovation: a model and its implications for the emergence of modern human culture. Camb. Archaeol. J. 11, 5–16 (2001).Article 

Google Scholar 
Jorgensen, E. K. The palaeodemographic and environmental dynamics of prehistoric Arctic Norway: an overview of human-climate covariation. Quat. Int. 549, 36–51 (2020).Article 

Google Scholar 
Jorgensen, E. K. & Riede, F. Convergent catastrophes and the termination of the Arctic Norwegian Stone Age: a multi-proxy assessment of the demographic and adaptive responses of mid-Holocene collectors to biophysical forcing. Holocene 29, 1782–1800 (2019).ADS 
Article 

Google Scholar 
Riede, F. Lateglacial and Postglacial Pioneers in Northern Europe (Archaeopress, 2014).Tallavaara, M. & Seppä, H. Did the mid-Holocene environmental changes cause the boom and bust of hunter-gatherer population size in eastern Fennoscandia? Holocene 22, 215–225 (2011).ADS 
Article 

Google Scholar 
Kavanagh, P. H. et al. Hindcasting global population densities reveals forces enabling the origin of agriculture. Nat. Hum. Behav. 2, 478–484 (2018).PubMed 
Article 

Google Scholar 
Tallavaara, M., Luoto, M., Korhonen, N., Jarvinen, H. & Seppa, H. Human population dynamics in Europe over the Last Glacial Maximum. Proc. Natl Acad. Sci. USA 112, 8232–8237 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bliege Bird, R. & Codding, B. F. Promise and peril of ecological and evolutionary modelling using cross-cultural datasets. Nat. Ecol. Evol. 6, 1–3 (2021).Hamilton, M. J. & Tallavaara, M. Statistical inference, scale and noise in comparative anthropology. Nat. Ecol. Evol. 6, 122 (2022).PubMed 
Article 

Google Scholar 
Gurven, M. D. & Davison, R. J. Periodic catastrophes over human evolutionary history are necessary to explain the forager population paradox. Proc. Natl Acad. Sci. USA 116, 12758–12766 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tallavaara, M. & Jorgensen, E. K. Why are population growth rate estimates of past and present hunter-gatherers so different? Philos. T R Soc. B 376, 20190708 (2021).Blackman, F. F. Optima and limiting factors. With two diagrams in the text. Ann. Bot. Lond. 19, 281–296 (1905).Article 

Google Scholar 
Maier, A. et al. Cultural evolution and environmental change in Central Europe between 40 and 15 ka. Quat. Int. 581-582, 225–240 (2021).Article 

Google Scholar 
Zhu, D., Galbraith, E. D., Reyes-Garcia, V. & Ciais, P. Global hunter-gatherer population densities constrained by influence of seasonality on diet composition. Nat. Ecol. Evol. 5, 1536 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Binford, L. R. Archaeology as anthropology. Am. Antiquity 28, 217–225 (1962).Article 

Google Scholar 
Lowe, J. J. et al. Synchronisation of palaeoenvironmental events in the North Atlantic region during the Last Termination: a revised protocol recommended by the INTIMATE group. Quat. Sci. Rev. 27, 6–17 (2008).ADS 
Article 

Google Scholar 
Bocquet-Appel, J. P., Demars, P. Y., Noiret, L. & Dobrowsky, D. Estimates of upper Palaeolithic meta-population size in Europe from archaeological data. J. Archaeol. Sci. 32, 1656–1668 (2005).Article 

Google Scholar 
Fort, J., Pujol, T. & Cavalli-Sforza, L. L. Palaeolithic populations and waves of advance (Human range expansions). Camb. Archaeol. J. 14, 53–61 (2004).Article 

Google Scholar 
Schmidt, I. et al. Approaching prehistoric demography: proxies, scales and scope of the Cologne Protocol in European contexts. Philos. Trans. R. Soc. Lond. B Biol. Sci. 376, 20190714 (2021).PubMed 
Article 

Google Scholar 
de Pablo, J. F. L. et al. Palaeodemographic modelling supports a population bottleneck during the Pleistocene-Holocene transition in Iberia. Nat. Commun. 10, 1872 (2019).Binford, L. R. Constructing Frames of Reference: An Analytical Method for Archaeological Theory Building Using Ethnographic and Environmental Data Sets. (Univ. California Press, 2019).Johnson, A. L. Exploring adaptive variation among hunter-gatherers with Binford’s frames of reference. J. Archaeol. Res. 22, 1–42 (2014).Article 

Google Scholar 
Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).Article 

Google Scholar 
Tallavaara, M., Eronen, J. T. & Luoto, M. Productivity, biodiversity, and pathogens influence the global hunter-gatherer population density. Proc. Natl Acad. Sci. USA 115, 1232–1237 (2018).CAS 
PubMed 
Article 

Google Scholar 
Cade, B. S. & Noon, B. R. A gentle introduction to quantile regression for ecologists. Front Ecol. Environ. 1, 412–420 (2003).Article 

Google Scholar 
Cade, B. S., Terrell, J. W. & Schroeder, R. L. Estimating effects of limiting factors with regression quantiles. Ecology 80, 311–323 (1999).Article 

Google Scholar 
Burman, P., Chow, E. & Nolan, D. A cross-validatory method for dependent data. Biometrika 81, 351–358 (1994).MathSciNet 
MATH 
Article 

Google Scholar 
Burke, K. D. et al. Differing climatic mechanisms control transient and accumulated vegetation novelty in Europe and eastern North America. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20190218 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Currie, D. J. Energy and large-scale patterns of animal-species and plant-species richness. Am. Nat. 137, 27–49 (1991).Article 

Google Scholar 
Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction (Cambridge Univ. Press, 2010).Harcourt, A. Human Biogeography (Univ. California Press, 2012).Marlowe, F. W. Hunter-gatherers and human evolution. Evol. Anthropol. 14, 54–67 (2005).Article 

Google Scholar 
Belovsky, G. E. An optimal foraging-based model of hunter-gatherer population-dynamics. J. Anthropol. Archaeol. 7, 329–372 (1988).Article 

Google Scholar 
Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).Article 

Google Scholar 
Ohlemuller, R. Climate. Running out of climate space. Science 334, 613–614 (2011).ADS 
PubMed 
Article 

Google Scholar 
Williams, J. W., Jackson, S. T. & Kutzbach, J. E. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl Acad. Sci. USA 104, 5738–5742 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).PubMed 
Article 

Google Scholar 
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).Article 

Google Scholar 
Warren, D. L., Cardillo, M., Rosauer, D. F. & Bolnick, D. I. Mistaking geography for biology: inferring processes from species distributions. Trends Ecol. Evol. 29, 572–580 (2014).PubMed 
Article 

Google Scholar 
Wobst, H. M. The archaeo-ethnology of hunter-gatherers or the tyranny of the ethnographic record in archaeology. Am Antiquity 43, 303–309 (1978).Maier, A. et al. Demographic estimates of hunter-gatherers during the Last Glacial Maximum in Europe against the background of palaeoenvironmental data. Quat. Int. 425, 49–61 (2016).Article 

Google Scholar 
Riede, F. Oxford Handbook of the Archaeology and Anthropology of Hunter-Gatherers (Oxford Univ. Press, 2014).Jochim, M., Herhahn, C. & Starr, H. The Magdalenian colonization of southern Germany. Am. Anthropol. 101, 129–142 (1999).Article 

Google Scholar 
Arts, N. & Deeben, J. On the Northwestern Border of Late Magdalenian Territory: Ecology and Archaeology of Early Late Glacial Band Societies in Northwestern Europe. In Late Glacial in Central Europe. Culture and Environment. (eds Burdukiewicz, J. M. & Kobusiewicz, M.) (Polska Akademia Nauk, Warszawa 1987).Maier, A. Population and settlement dynamics from the Gravettian to the Magdalenian. Mitteilungen der Ges. f.ür. Urgesch. 26, 83–101 (2017).
Google Scholar 
Maier, A., Liebermann, C. & Pfeifer, S. J. Beyond the Alps and Tatra Mountains-the 20-14 ka repopulation of the northern mid-latitudes as inferred from palimpsests deciphered with keys from Western and Central Europe. J. Paleolit. Archaeol. 3, 398–452 (2020).Article 

Google Scholar 
Gamble, C., Davies, W., Pettitt, P. & Richards, M., Climate change. and evolving human diversity in Europe during the last glacial. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359, 243–253 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
Housley, R. A., Gamble, C. S., Street, M. & Pettitt, P. Proceedings of the Prehistoric Society. (Cambridge Univ. Press).Bellwood, P. S. First Farmers: the Origins of Agricultural Societies. (Blackwell, Oxford 2005).d’Errico, F. et al. The origin and evolution of sewing technologies in Eurasia and North America. J. Hum. Evol. 125, 71–86 (2018).PubMed 
Article 

Google Scholar 
Moseler, F. Brandstrukturen im späten Magdalénien: Betrieb, Nutzung und Funktion (Verlag des Römisch-Germanischen Zentralmuseums, 2020).Simova, I. & Storch, D. The enigma of terrestrial primary productivity: measurements, models, scales and the diversity-productivity relationship. Ecography 40, 239–252 (2017).Rosenzweig, M. L. Net primary productivity of terrestrial communities – prediction from climatological data. Am. Nat. 102, 67 (1968).Article 

Google Scholar 
Jensen, H. J. & Møberg, T. Et røgeri fra ældre stenalder ved Bølling Sø? Midtjyske Fortaellinger 2007, 51–62 (2008).Holst, D. Hazelnut economy of early Holocene hunter-gatherers: a case study from Mesolithic Duvensee, northern Germany. J. Archaeol. Sci. 37, 2871–2880 (2010).Article 

Google Scholar 
Boethius, A. Something rotten in Scandinavia: the world’s earliest evidence of fermentation. J. Archaeol. Sci. 66, 169–180 (2016).Article 

Google Scholar 
Dyson‐Hudson, R. & Smith, E. A. Human territoriality: an ecological reassessment. Am. Anthropol. 80, 21–41 (1978).Article 

Google Scholar 
Finlayson, C. The water optimisation hypothesis and the human occupation of the mid-latitude belt in the Pleistocene. Quat. Int 300, 22–31 (2013).Article 

Google Scholar 
Laland, K. N. & Brown, G. R. Niche construction, human behavior, and the adaptive-lag hypothesis. Evol. Anthropol. 15, 95–104 (2006).Article 

Google Scholar 
Laland, K. N. & O’Brien, M. J. Niche construction theory and archaeology. J. Archaeol. Method Th. 17, 303–322 (2010).Article 

Google Scholar 
Riede, F. Handbook of Evolutionary Research in Archaeology (Springer, 2019).Jöris, O. & Terberger, T. Zur Rekonstruktion eines Zeltes mit Trapezförmigem Grundriss am Magdalénien-Fundplatz Gönnersdorf/Mittelrhein: Eine» Quadratur des Kreises «? Arch.äologisches Korrespondenzblatt 31, 163–172 (2001).
Google Scholar 
Salomon, H., Vignaud, C., Lahlil, S. & Menguy, N. Solutrean and Magdalenian ferruginous rocks heat-treatment: accidental and/or deliberate action? J. Archaeol. Sci. 55, 100–112 (2015).CAS 
Article 

Google Scholar 
Nakazawa, Y., Straus, L. G., Gonzalez-Morales, M. R., Solana, D. C. & Saiz, J. C. On stone-boiling technology in the Upper Paleolithic: behavioral implications from an Early Magdalenian hearth in El Miron Cave, Cantabria, Spain. J. Archaeol. Sci. 36, 684–693 (2009).Article 

Google Scholar 
Pedersen, J., Maier, A. & Riede, F. A punctuated model for the colonisation of the Late Glacial margins of northern Europe by Hamburgian hunter-gatherers. Quart.är. 65, 85–104 (2018).
Google Scholar 
Whallon, R. Social networks and information: non-“utilitarian” mobility among hunter-gatherers. J. Anthropol. Archaeol. 25, 259–270 (2006).Article 

Google Scholar 
Leal Filho, W. et al. Impacts of climate change to African indigenous communities and examples of adaptation responses. Nat. Commun. 12, 6224 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Heitz, C. F., Hinz, M., Laabs, J. & Hafner, A. Mobility as resilience capacity in northern Alpine Neolithic settlement communities. Archaeol. Rev. Camb. 36, 75–106 (2021).
Google Scholar 
Riede, F., Oetelaar, G. A. & VanderHoek, R. From crisis to collapse in hunter-gatherer societies. A comparative investigation of the cultural impacts of three large volcanic eruptions on past hunter-gatherers. Crisis to Collapse–The Archaeology of Social Breakdown. Louvain-la-Neuve: UCL Presses Universitaires De Louvian 23–39 (2017).Halstead, P., O’Shea, J. & O’Shea, J. M. Bad Year Economics: Cultural Responses to Risk and Uncertainty. (Cambridge Univ. Press, 2004).Brovkin, V. et al. Past abrupt changes, tipping points and cascading impacts in the Earth system. Nat. Geosci. 14, 550–558 (2021).ADS 
CAS 
Article 

Google Scholar 
Burke, A. et al. The archaeology of climate change: the case for cultural diversity. Proc Natl Acad Sci USA 118, e2108537118 (2021).Binford, L. R. Willow smoke and dogs tails – Hunter-gatherer settlement systems and archaeological site formation. Am. Antiquity 45, 4–20 (1980).Article 

Google Scholar 
Birdsell, J. B. Some environmental and cultural factors influencing the structuring of Australian aboriginal populations. Am. Nat. 87, 171–207 (1953).Article 

Google Scholar 
Kelly, R. L. The Lifeways of Hunter-Gatherers: The Foraging Spectrum (Cambridge Univ. Press, 2013).Penington, R. Hunter-gatherer demography. In Hunter-Gatherers: An Interdisciplinary Perspective. (eds. Panter-Brick, C., Layton, R. H. & Rowley-Conwy, P.) (Cambridge University Press, Cambridge, 2001).Wobst, H. M. Locational relationships in Paleolithic society. J. Hum. Evol. 5, 49–58 (1976).Article 

Google Scholar 
Richards, M. P., Pettitt, P. B., Stiner, M. C. & Trinkaus, E. Stable isotope evidence for increasing dietary breadth in the European mid-Upper Paleolithic. Proc. Natl Acad. Sci. USA 98, 6528–6532 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Drucker, D. & Bocherens, H. Carbon and nitrogen stable isotopes as tracers of change in diet breadth during Middle and Upper Palaeolithic in Europe. Int J. Osteoarchaeol. 14, 162–177 (2004).Article 

Google Scholar 
Kretschmer, I. Demographische Untersuchungen zu Bevölkerungsdichten, Mobilität und Landnutzungsmustern im späten Jungpaläolithikum (Verlag Marie Leidorf GmbH, 2015).Langley, M. C. & Street, M. Long range inland-coastal networks during the Late Magdalenian: evidence for individual acquisition of marine resources at Andernach-Martinsberg, German Central Rhineland. J. Hum. Evol. 64, 457–465 (2013).PubMed 
Article 

Google Scholar 
Lanczont, M. et al. Late Glacial environment and human settlement of the Central Western Carpathians: a case study of the Nowa Biala 1 open-air site (Podhale Region, southern Poland). Quat. Int 512, 113–132 (2019).Article 

Google Scholar 
Cziesla, E. Robbenjagd in Brandenburg? Gedanken zur Verwendung großer Widerhakenspitzen. Ethnographisch-archaologische Z. 48, 1–48 (2007).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Le Cook, B. & Manning, W. G. Thinking beyond the mean: a practical guide for using quantile regression methods for health services research. Shanghai Arch. Psychiatry 25, 55 (2013).PubMed 
PubMed Central 

Google Scholar 
Yee, T. W. & Mitchell, N. D. Generalized additive-models in plant ecology. J. Veg. Sci. 2, 587–602 (1991).Article 

Google Scholar 
Guisan, A., Edwards, T. C. & Hastie, T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol. Model 157, 89–100 (2002).Article 

Google Scholar 
Fewster, R. M., Buckland, S. T., Siriwardena, G. M., Baillie, S. R. & Wilson, J. D. Analysis of population trends for farmland birds using generalized additive models. Ecology 81, 1970–1984 (2000).Article 

Google Scholar 
Drexler, M. & Ainsworth, C. H. Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool. PLos ONE 8, e64458 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moisen, G. G. & Frescino, T. S. Comparing five modelling techniques for predicting forest characteristics. Ecol. Model 157, 209–225 (2002).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R (CRC Press, 2006).Zuur, A. F. A Beginner’s Guide to Generalized Additive Models with R (Highland Statistics Limited, 2012).Team, R. C. R: a language and environment for statistical computing. (2013).Wood, S. N. Generalized Additive Models: An Introduction with R (CRC Press, 2017).Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R. & Goude, Y. Fast calibrated additive quantile regression. J. Am. Stat. Assoc. 116, 1402–1412 (2021).MathSciNet 
CAS 
Article 

Google Scholar 
AlejoOrdonez/PaleoPopDen: (Version NatCommV0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.6962693 (2022).Liu, Z. et al. Transient simulation of last deglaciation with a new mechanism for Bolling-Allerod warming. Science 325, 310–314 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lorenz, D. J., Nieto-Lugilde, D., Blois, J. L., Fitzpatrick, M. C. & Williams, J. W. Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD. Sci. Data 3, 160048 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Peltier, W. R., Argus, D. & Drummond, R. Space geodesy constrains ice age terminal deglaciation: the global ICE‐6G_C (VM5a) model. J. Geophys. Res. Solid Earth 120, 450–487 (2015).ADS 
Article 

Google Scholar 
Vermeersch, P. M. European population changes during the Marine Isotope Stages 2 and 3. Quat. Int 137, 77–85 (2005).Article 

Google Scholar 
Gamble, C., Davies, W., Pettitt, P., Hazelwood, L. & Richards, M. The archaeological and genetic foundations of the European population during the late glacial: Implications for ‘agricultural thinking’. Camb. Archaeol. J. 15, 193–223 (2005).Article 

Google Scholar 
Steele, J. Radiocarbon dates as data: quantitative strategies for estimating colonization front speeds and event densities. J. Archaeol. Sci. 37, 2017–2030 (2010).Article 

Google Scholar 
Shennan, S. et al. Regional population collapse followed initial agriculture booms in mid-Holocene Europe. Nat. Commun. 4, 2486 (2013).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Surovell, T. A., Finley, J. B., Smith, G. M., Brantingham, P. J. & Kelly, R. Correcting temporal frequency distributions for taphonomic bias. J. Archaeol. Sci. 36, 1715–1724 (2009).Article 

Google Scholar 
Williams, A. N. The use of summed radiocarbon probability distributions in archaeology: a review of methods. J. Archaeol. Sci. 39, 578–589 (2012).Article 

Google Scholar 
Kelly, R. L., Surovell, T. A., Shuman, B. N. & Smith, G. M. A continuous climatic impact on Holocene human population in the Rocky Mountains. Proc. Natl Acad. Sci. USA 110, 443–447 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hijmans, R. J. et al. Package ‘raster’. R package 734, (2015).Lewin-Koh, N. J. et al. Package ‘maptools’. Internet: http://cran.r-project.org/web/packages/maptools/maptools.pdf (2012).Thornthwaite, C. W. An approach toward a rational classification of climate. Geogr. Rev. 38, 55–94 (1948).Article 

Google Scholar 
Lieth, H.Primary Productivity of the Biosphere (Springer, 1975). More

l-cry mutants show higher spawning synchrony than wild-type animals under non-natural light conditionsIn order to test for a functional involvement of L-Cry in monthly oscillator function, we generated two l-cry mutant alleles (Δ34 and Δ11bp) (Fig. 1a) using TALENs28. In parallel, we generated a monoclonal antibody against Platynereis L-Cry. By testing mutant versus wildtype worms with the anti-L-Cry antibody in Western blots (Fig. 1b) and immunohistochemistry (Fig. 1e–j), we verified the absence of L-Cry protein in mutants. Furthermore, we confirmed that the staining of the antibody in wildtype worms (Fig. 1e–h) matches the regions where l-cry mRNA is expressed (Fig. 1d). These tests confirmed that the engineered l-cry mutations result in loss-of-function alleles. In turn, they validate the specificity of the raised anti-L-Cry antibody.Fig. 1: l-cry–/– mutants are loss-of-function alleles.a Overview of the l-cry genomic locus for wt and mutants. Both mutant alleles result in an early frameshift and premature stop codons. The Δ34 allele has an additional 9 bp deletion in exon 3. b Western Blots of P. dumerilii heads probed with anti-L-Cry antibody. In the context of further investigations such Western blots of mutant versus wild types have been performed more than 10 times with highly consistent results. Also see further analyses in this manuscript and ref. 36. c overview of P. dumerilii. d whole mount in situ hybridization against l-cry mRNA on worm head. ae, anterior eye; pe, posterior eye. e–j Immunohistochemistry of premature wild-type (e–h) and mutant (i, j) worm heads sampled at zt19/20 using anti-L-Cry antibody (green) and Hoechst staining (magenta), dorsal views, anterior up. e, f: z-stack images (maximal projections of 50 layers, 1.28 µm each) in the area highlighted by the rectangle in (d), whereas (g–j) are single layer images of the area highlighted by the white rectangles in (e, f). In the context of further investigations such stainings of mutant versus wild types have been performed more than 10 times with highly consistent results. Also see further analyses in this manuscript and ref. 36.Full size imageWe next assessed the circalunar maturation timing of wild types and l-cry mutant populations in conventional culture conditions, i.e. worms grown under typical indoor room lighting (named here artificial sun- and moonlight, Supplementary Fig. 1b).We expected either no phenotype (if L-Cry was not involved in circalunar clock entrainment) or a decreased spawning precision (if L-Cry was functioning as moonlight receptor in circalunar clock entrainment). Instead we observed an increased precision of the entrained worm population:We analyzed the maturation data using two statistical approaches, linear and circular statistics. We used the classical linear plots5 and statistics to compare the monthly spawning data distribution (Fig. 2a–c, i). This revealed a clear difference between mutant animals, which exhibited a stronger spawning peak at the beginning of the NM phase, compared to their wildtype and heterozygous counterparts (Fig. 2a–c, Kolmogorov–Smirnov test on overall data distribution, Fig. 2i).Fig. 2: L-Cry shields the circalunar clock from light that is not naturalistic moonlight.a–d, j Spawning of l-cry +/+ (a), l-cry +/– (Δ34) (b) and l-cry −/−(Δ34/ Δ34) (c) animals over the lunar month in the lab with 8 nights of artificial moonlight (a–c), under natural conditions in the sea (d, replotted from ref. 34,50,) and in the lab using naturalistic sun- and moonlight (j, 8 nights moonlight). e–h, k Data as in (a–d, j) as circular plot. 360° correspond to 30 days of the lunar month. The arrow represents the mean vector, characterized by the direction angle µ and r (length of µ). r indicates phase coherence (measure of population synchrony). p-values inside the plots: result of Rayleigh Tests. Significance indicates non-random distribution of data points. The inner circle represents the Rayleigh critical value (p = 0.05). i–l Results of two-sided multisample statistics on spawning data shown in (a–h, j, k). The phase differences in days can be calculated from the angle between the two mean vectors (i.e. 12°= 1 day).Full size imageWe then analyzed the same data using circular statistics (as the monthly cycle is repeating, see details in Methods section), which allowed us to describe the data with the mean vector (defined by the direction angle µ and its length r, shown as arrows in Fig. 2e–g). The phase coherence r (ranging from 0 to 1) serves as a measure for synchrony of the population data. As expected for entrained populations, all genotypes distributed their spawning across a lunar month significantly different from random (Fig. 2e–g, p values in circles, Rayleigh’s Uniformity test29). In line with the observed higher spawning peak of the l-cry−/− mutants in the linear plots, the circular analysis revealed a significant difference in spawning distribution (Mardia–Watson-Wheeler test, for details see Methods section) and higher spawning synchrony of mutants (r = 0.614) than in wild types and heterozygotes (r = 0.295 and r = 0.222) (Fig. 2i). The specificity of this phenotype of higher spawning precision for l-cry homozygous mutants was confirmed by analyses on trans-heterozygous l-cry (Δ34/Δ11) mutants (Supplementary Fig. 2), and by the fact that such a phenotype is not detectable in any other light receptor mutant available in Platynereis (r-opsin130: Supplementary Fig. 3a, b, e, f, i; c-opsin131: Supplementary Fig. 3c, d, g, h, i, Go-opsin: refs. 32, 33).The higher spawning synchrony of l-cry mutants under artificial light mimics the spawning precision of wild-type at its natural habitatThis increased spawning precision of l-cry mutants under artificial (but conventional indoor) laboratory light conditions let us wonder about the actual population synchrony of the worms under truly natural conditions. The lunar spawning synchrony of P. dumerilii at the Bay of Naples (the origin of our lab culture) has been worked on for more than 100 y. This allowed us to re-investigate very detailed spawning data records from the worms’ natural habitat published prior to environmental/light pollution. For better accessibility and comparability we combined all months and replotted the data published in 192934 (Fig. 2d, h, I; see details in Methods section; r = 0.631). This analysis revealed that the higher spawning synchrony in l-cry–/– worms mimics the actual spawning synchrony of P. dumerillii populations in their natural habitat34 (compare Fig. 2c, g with 2d, h.)Given that recent, non-inbred isolates from the same habitat as our lab inbred strains (which is the same habitat as the data collected in ref. 34) exhibit a broad spawning distribution under standard worm culture light conditions (which includes the bright artificial moonlight)35, we hypothesized that the difference in spawning synchrony between wildtype laboratory cultures and populations in their natural habitat is caused by the rather bright nocturnal light stimulus typically used for the standard laboratory culture (Supplementary Fig. 1a vs. b).Lunar spawning precision of wild-type animals depends on naturalistic moonlight conditionsWe next tested the resulting prediction that naturalistic moonlight should increase the spawning precision of the wildtype population, using naturalistic sun- and moonlight devices we specifically designed based on light measurements at the natural habitat of P. dumerilii31 (Supplementary Fig. 1a, c). We assessed the impact of the naturalistic sun- and moonlight (Supplementary Fig. 1a, c) on wildtype animals, maintaining the temporal aspects of the lab light regime (i.e. 8 nights of “full moon”). Indeed, merely adjusting the light intensity to naturalistic conditions increased the precision and phase coherence of population-wide reproduction: After several months under naturalistic sun- and moonlight, wildtype worms spawned with a major peak highly comparable to the wildtype precision reported at its natural habitat (Fig. 2d, h vs. j, k), and also exhibited an increased population synchrony (r = 0.398 compared to r = 0.295 under standard worm room light conditions). This increased similarity to the spawning distribution at the natural habitat (“Sea”) is confirmed by statistical analyses (Fig. 2l): The phase difference (angle between the two mean vectors) is only one day (corresponding to 12°). In contrast, the spawning distribution of wild types under standard worm room light versus naturalistic light conditions is highly significantly different in linear and circular statistical tests and has a phase difference of 7.7 days (Fig. 2l).These findings show that it is the naturalistic light that is critical for a highly precise entrainment of the monthly clock of wild-type worms. Given that l-cry–/– animals reach this high precision with the artificial light (i.e. standard lab light) implies that in wildtype L-Cry blocks artificial, but not naturalistic full-moonlight from efficiently synchronizing the circalunar clock. This block is removed in l-cry–/– animals, leading to a better synchronization of the l-cry–/– population. This finding suggests that L-Cry’s major role could be that of a gatekeeper controlling which ambient light is interpreted as full-moonlight stimulus for circalunar clock entrainment.
l-cry functions as a light signal gatekeeper for circalunar clock entrainmentA prediction of this hypothesis is that mutants should entrain better to an artificial full-moonlight stimulus provided out-of-phase than their wild type counterparts (in which L-Cry should block the “wrong” moonlight at least partially from re-entraining the circalunar oscillator).We thus compared the spawning rhythms of l-cry+/+ and l-cry–/– worms under a re-entrainment paradigm, where we provided our bright artificial culture full-moonlight at the time of the subjective new moon phase (Fig. 3a). In order to compare the spawning data distribution relative to the initial full moon (FM) stimulus, as well as to the new full moon stimulus (i.e. new FM), we used two nomenclatures for the months: months with numbers are analyzed relative to the initial nocturnal light stimulus (i.e. FM), whereas months with letters are analyzed relative to the new (phase-shifted) nocturnal light stimulus (i.e. new FM, Fig. 3a). When the nocturnal light stimulus is omitted (to test for the oscillator function) we then refer to ‘free-running FM’ (FR-FM) or ‘new free-running FM’ (new FR-FM), respectively (Fig. 3a). Using these definitions, the efficiency of circalunar clock re-entrainment will be reflected in the similarity of spawning data distributions between month 1 and month D, i.e. the more similar the distribution, the more the population has shifted to the new phase.Fig. 3: l-cry−/− mutants entrain the circalunar clock faster than wt to a high-intensity artificial moonlight stimulus.a Nocturnal moonlight exposure protocol of lunar phase shift (entrained by 8 nights, phased shifted by 6 nights of artificial culture moon, light green). b, c Number of mature animals (percent per month, rolling mean with a window of 3 days) of l-cry wild-type (b) and homozygous mutant (c) animals. p-values indicate results of Kolomogorov–Smirnov tests. Dark blue arrowheads- old FM phase: wt show a spawning minimum, indicative that the worms are not properly phase shifted. Mutants spawn in high numbers, but don’t spawn at the old NM indicated by light blue arrowhead. Also compare to initial FM and NM in months 1,2. d, e Circular plots of the data shown in (b) and (c). Each circle represents one lunar month. Each dot represents one mature worm. The arrow represents the mean vector characterized by the direction angle µ and r. r (length of µ) indicates phase coherence (measure of population synchrony). The inner circle represents the Rayleigh critical value (p = 0.05). f, g Results of two-sided multisample statistics of data in (d, e). Phase differences in days can be calculated from the angle between the two mean vectors (i.e. 12°= 1 day).Full size imageWhen using the artificial nocturnal light conditions, the re-entrainment of l-cry–/– animals was both faster and more complete than for their wildtype relatives, as predicted from our gate keeper hypothesis. This is evident from the linear data analysis and Kolmogorov–Smirnov tests when comparing the month before the entrainment (month 1) with two months that should be shifted after the entrainment (months C,D, Fig. 3b, c, f, g).Most notably, while l-cry−/− worms were fully shifted in month D (Fig. 3c: compare boxes and see complete lack of spawning at the light blue arrowhead indicating the old NM/new FR-FM phase versus massive spawning at new NM phase around dark blue arrowhead), wildtype animals were still mostly spawning according to the initial lunar phase (Fig. 3b: compare boxes and see spawning at the light blue arrowhead versus almost lack of spawning at dark blue arrowhead). The faster re-entrainment of l-cry–/–, compared to l-cry+/+ animals is also confirmed by the Mardia–Watson-Wheeler test (see Methods section for details). For l-cry+/+ animals, the comparisons of the spawning distributions before and after re-entrainment show a 1000-fold (months 1 versus C) and tenfold (months 1 versus D) higher statistical significance difference than the corresponding comparisons for l-cry−/− worms (Fig. 3f, g). Consistently, the phase differences in days calculated from the angle between the two mean vectors from the circular analysis is smaller in the mutants than in the wild types when comparing the phase of the month before the entrainment (month 1) with two months after the entrainment (months C, D) (Fig. 3d–g). The fact that there are still differences in the mutant population before and after entrainment is likely due to the fact that even the mutants are not fully re-entrained. However, they have shifted more robustly in response to an artificial nocturnal light stimulus than the wild types. This provides further evidence that in wildtype worms L-Cry indeed blocks the “wrong” light from entering into the circalunar clock and thus functions as a light gatekeeper.L-Cry functions mainly as light interpreter, while its contribution as direct moonlight entraining photoreceptor is (at best) minorWe next tested to which extent L-Cry is itself a sensor for the re-entrainment signal under naturalistic light conditions. Based on the finding that l-cry−/− worms can still re-entrain the circalunar oscillator (see above), it is clear that even if L-Cry also directly contributed to the entrainment, it cannot be the only moonlight receptor mediating entrainment. With the experiments below, we aimed to test if L-Cry has any role as an entraining photoreceptor to the monthly oscillator.Thus, we tested how the circalunar clock is shifted in response to a re-entrainment with naturalistic moonlight in Platynereis wt versus l-cry−/− worms. For this, animals initially raised and entrained under standard worm room light conditions of artificial sun- and moonlight (Supplementary Fig. 1b, e) were challenged by a deviating FM stimulus of 8 nights of naturalistic moonlight (Fig. 4a, Supplementary Fig. 1c, e). This re-entraining stimulus was repeated for three consecutive months (Fig. 4a).Fig. 4: l-cry has a minor contribution as entraining photoreceptor to circalunar clock entrainment.a Nocturnal moonlight exposure protocol of lunar phase shift with 8 nights of naturalistic moonlight (dark green). Number of mature animals (percent per month, rolling mean with a window of 3 days) of l-cry wild-type (b) and mutant (c) animals. p-values: Kolomogorov–Smirnov tests. Black arrowheads indicate spawning-free intervals of the wildtype, which shifted to the position of the new FM (under free-running conditions: FR-FM). d, e Data as in (b, c) plotted as circular data. 360° correspond to 30 days of the lunar month. The arrow represents the mean vector characterized by the direction angle µ and r. r (length of µ) indicates phase coherence (measure of population synchrony). p values are results of Rayleigh Tests: Significance indicates non-random distribution of data points. The inner circle represents the Rayleigh critical value (p = 0.05). f, g Results of two-sided multisample statistics on spawning data shown in (a–e). Phase differences in days can be calculated from the angle between the two mean vectors (i.e. 12°= 1 day).Full size imageThe resulting spawning distribution was analyzed for the efficacy of the naturalistic moonlight to phase-shift the circalunar oscillator. In order to test if the animals had shifted their spawning to the new phase, we again compared the spawning pattern before the exposure to the new full moon stimulus (months with numbers: data distribution analyzed relative to the initial/old FM, see Fig. 4a for an overview) to the spawning pattern after the exposure to the new full moon stimulus (months with letters: data distribution analyzed relative to the new FM, Fig. 4a). The more similar the data distributions of month 1 is to the months C, D, the more the population was shifted to the new phase.The first re-entraining full moon stimulus (Fig. 4b, first dark green box) is given in the middle of the main spawning period. The nocturnal light itself does not cause immediate effects on the number of spawning worms (Fig. 4b, see also Fig. 2b, c), but the repeated exposure resulted in a noticeable shift of the spawning distribution indicating a phase shift of the monthly oscillator in wildtype. Already at the third re-entraining full moon stimulus, wildtype animals exhibited a completely shifted spawning pattern (Fig. 4b, d-d″, month 1, 2 vs. month C). This is supported by statistical analyses: When comparing the months 1 and 2 (relative to the old FM before the shift) to the month C (relative to the new FM after the shift), both the Kolmogorov–Smirnov test (Fig. 4b: gray rectangles, 4f) and the Mardia–Watson–Wheeler test of the same data were non-significant (Fig. 4f), indicative of the population shifting to the new phase. Consistently, the direction angle (µ) of the mean vectors before and after the shift was highly similar, resulting in a phase difference of only 0.2 days between months 1 and C and 0.5 days between month 2 and month C (Fig. 4f, for details see methods). The month under circalunar free-running conditions (month D) supports this observation, albeit with lower statistical support (Fig. 4b, d″, f).Of note, wild-type worms would eventually reach the high spawning precision found under naturalistic moonlight only after several more months based on independent experiments (Fig. 2j, k).When we analyzed the spawning distribution of l-cry mutants in the same way as the wild types, we found that the data distribution exhibited significant differences in the linear Kolmogorov–Smirnov test when comparing months 1 and 2 before the shift to the months C and D after the shift (Fig. 4c: gray rectangles, Fig. 4g); as well as in the phase distribution in the circular analyses when comparing the months before the shift (months 1 and 2) with the last months of the shift (months C,D) (Fig. 4e, e′ versus e″, e‴, g). The populations also exhibited a noticeable phase difference of ≥3.5 days (Fig. 4g).Based on the statistical significant difference in the re-entrainment of l-cry–/–, but not wild-type populations under a naturalistic sun- and moonlight regime, we conclude that L-Cry also likely contributes to circalunar entrainment as a photoreceptor. However, as these differences are rather minor, compared to the much stronger differences seen under artificial light regime, we conclude that its major role is the light gatekeeping function.In an independent study that focused on the impact of moonlight on daily timing, we identified r-Opsin1 as a lunar light receptor that mediates moonlight effects on the worms’ ~24 h clock36. We tested if r-opsin1 is similarly important for mediating the moonlight effects on the monthly oscillator of the worm, analyzed here. This is not the case. r-opsin1–/– animals re-entrain as well as wildtype worms under naturalistic light conditions (Supplementary Fig. 4). This adds to and is also consistent with our above observation that the spawning distribution is un-altered between r-opsin1–/– and wildtype animals under artificial light conditions (Supplementary Fig. 3a, b, e, f). This finding also further enforces the notion that monthly and daily oscillators use distinct mechanisms, but both require L-Cry as light interpreter.L-Cry discriminates between naturalistic sun- and moonlight by forming differently photoreduced statesGiven that the phenotype of l-cry–/– animals suggests a role of L-Cry as light gatekeeper, i.e. only allowing the ‘right’ light to most efficiently impact on the circalunar oscillator, we next investigated how this could function on the biochemical and cell biological level.While we have previously shown that Pdu-L-Cry is degraded upon light exposure in S2 cell culture15, it has remained unclear if L-Cry has the spectral properties and sensitivity to sense moonlight and whether this would differ from sunlight sensation. To test this, we purified full length L-Cry from insect cells (Supplementary Fig. 5a–c). Multi-angle light scattering (SEC-MALS) analyses of purified dark-state L-Cry revealed a molar mass of about 130 kDa, consistent with the formation of an L-Cry homodimer (theoretical molar mass of L-Cry monomer is 65.6 kDa) (Fig. 5a). Furthermore, purified L-Cry binds Flavin Adenine Dinucleotide (FAD) as its chromophore (Supplementary Fig. 5d, e). We then used UV/Vis absorption spectroscopy to analyze the FAD photoreaction of purified L-Cry in presence of 1 mM TCEP to prevent protein oxidation. The absorption spectrum of dark-state L-Cry showed maxima at 450 nm and 475 nm, consistent with the presence of oxidized FAD (Supplementary Fig. 5f, black line). As basic starting point to analyze its photocycle, L-Cry was photoreduced using a LED (PerkinElmer ACULED Dyo) with a blue-light dominated spectrum and spectral peak at 450 nm (Supplementary Fig. 1d, d′, henceforth referred to as “blue-light”) for 110 s37. The light-activated spectrum showed that blue-light irradiation of L-Cry leads to the complete conversion of FADox into an anionic FAD radical (FADo-) with characteristic FADo- absorption maxima at 370 nm and 404 nm and reduced absorbance at 450 nm (Supplementary Fig. 5f, blue spectrum, black arrows). In darkness, L-Cry reverted back to the dark-state with time constants of 2 min (18 °C), 4 min (6 °C) and 4.7 min (ice) (Supplementary Fig. 5g–k).Fig. 5: L-Cry forms differently photoreduced sunlight- and moonlight states.a Multi-Angle Light Scattering (MALS) analyses of dark-state L-Cry fractionated by size exclusion chromatography (SEC). Black dashed line: normalized UV absorbance, solid line: normalized scattering signal. The molar mass of about 130 kDa derived from MALS (mass signal shown in red) corresponds to an L-Cry homodimer. b Absorption spectrum of L-Cry in darkness (black) and after sunlight exposure (orange). Additional timepoints: Supplementary Fig. 6a. c Dark recovery of L-Cry after 20 min sunlight on ice. Absorbance at 450 nm in Supplementary Fig. 6b. d, e Absorption spectra of L-Cry after exposure to naturalistic moonlight for different durations. f Full spectra of dark recovery after 6 h moonlight. Absorbance at 450 nm: Supplementary Fig. 6d. g Absorption spectrum of L-Cry after 6 h of moonlight followed by 20 min of sunlight. h Absorption spectrum of L-Cry after 20 min sunlight followed by moonlight first results in dark-state recovery. Absorbance at 450 nm: Supplementary Fig. 6e. i Absorption spectrum of L-Cry after 20 min sunlight followed by 4 h and 6 h moonlight builds up the moonlight state. j Model of L-Cry responses to sunlight (orange), moonlight (green) and darkness (black). Only transitions between stably accumulating states are shown. Absorbances in (b–i) were normalized when a shift in the baseline occurred between different measurements of the same measurement set, which is then indicated on the Y-axis as “normalized absorbance”.Full size imageWe then investigated the response of L-Cry to ecologically relevant light, i.e. sun- and moonlight using naturalistic sun- and moonlight devices that we designed based on light measurements at the natural habitat of P. dumerilii31 (Supplementary Fig. 1a, c, e). Upon naturalistic sunlight illumination, FAD was photoreduced to FADo-, but with slower kinetics than under the stronger blue-light source, likely due to the intensity differences between the two lights (Supplementary Fig. 1c–e).While blue-light illumination led to a complete photoreduction within 110 s (Supplementary Fig. 5f), sunlight induced photoreduction to FADo- was completed after 5–20 min (Fig. 5b) and did not further increase upon continued illumation for up to 2 h (Supplementary Fig. 6a). Dark recovery kinetics had time constants of 3.2 min (18 °C) and 5 min (ice) (Fig. 1c, Supplementary Fig. 6b, c).As the absorbance spectrum of L-Cry overlaps with that of moonlight at the Platynereis natural habitat (Supplementary Fig. 1a), L-Cry has the principle spectral prerequisite to sense moonlight. However, the most striking characteristic of moonlight is its very low intensity (5.8 × 1010 photons/cm2/s at −5m, Supplementary Fig. 1a–e). To test if Pdu-L-Cry is sensitive enough for moonlight, we illuminated purified L-Cry with our custom-built naturalistic moonlight, closely resembling full-moonlight intensity and spectrum at the Platynereis natural habitat (Supplementary Fig. 1a, c, e). Naturalistic moonlight exposure up to 2.75 h did not markedly photoreduce FAD, notably there was no difference between 1 h and 2.75 h (Fig. 5d). However, further continuous naturalistic moonlight illumination of 4 h and longer resulted in significant changes (Fig. 5d), whereby the spectrum transitioned towards the light activated state of FADo- (note peak changes at 404 nm and at 450 nm). This photoreduction progressed further until 6 h naturalistic moonlight exposure (Fig. 5d). No additional photoreduction could be observed after 9 h and 12 h of naturalistic moonlight exposure (Fig. 5e), indicating a distinct state induced by naturalistic moonlight that reaches its maximum after ~6 h, when about half of the L-Cry molecules are photoreduced. This time of ~6 h is remarkably consistent with classical work showing that a minimum of ~6 h of continuous nocturnal light is important for circalunar clock entrainment, irrespective of the preceding photoperiod5. The dark recovery of L-Cry after 6 h moonlight exposure occurred with a time constant of 6.7 min at 18 °C (Fig. 5f, Supplementary Fig. 6d). Given that both sunlight and moonlight cause FAD photoreduction, but with different kinetics and different final FADo- product/FADox educt ratios, we wondered how purified L-Cry would react to transitions between naturalistic sun- and moonlight (i.e. during “sunrise” and “sunset”).Mimicking the sunrise scenario, L-Cry was first illuminated with naturalistic moonlight for 6 h followed by 20 min of sunlight exposure. This resulted in an immediate enrichment of the FADo- state (Fig. 5g). Hence, naturalistic sunlight immediately photoreduces remaining oxidized flavin molecules, that are characteristic of moonlight activated L-Cry, to FADo-, to reach a distinct fully reduced sunlight state.In contrast, when we next mimicked the day-night transition (“sunset”) by first photoreducing with naturalistic sunlight (or strong blue-light) and subsequently exposed L-Cry to moonlight, L-Cry first returned to its full dark-state within about 30 min (naturalistic sunlight: τ = 7 min (ice), Fig. 5h, Supplementary Fig. 6e; blue-light: τ = 9 min (ice), Supplementary Fig. 6f–h), despite the continuous naturalistic moonlight illumination. Prolonged moonlight illumination then led to the conversion of dark-state L-Cry to the moonlight state (Fig. 5i, Supplementary Fig. 6f). Hence, fully photoreduced sunlight-state L-Cry first has to return to the dark-state before accumulating the moonlight state characterized by the stable presence of the partial FADo- product/FADox educt. In contrast to sunlight-state L-Cry, moonlight-state L-Cry does not return to the oxidized (dark) state under naturalistic moonlight (Fig. 5e), i.e. moonlight maintains the moonlight state, but not the sunlight state. We note, that a partially photoreduced L-Cry state may be formed transiently during dark-state recovery of the sunlight state under moonlight. However, this transiently occurring partially photoreduced L-Cry state would differ from the “true” moonlight state (e.g. by an allosteric change) preventing its accumulation (see discussion and Supplementary Fig. 6i).Given that L-Cry forms a homodimer and moonlight photoreduces about half of the FAD molecules, we propose that the moonlight state corresponds to a half-reduced FADo- FADox dimer, where FAD is only photoreduced in one L-Cry monomer, whereas in the sunlight state both monomers are photoreduced (FADo- FADo-) (Fig. 5j). This implies that the quantum yield for FADox to FADo- photoreduction differs between the two L-Cry monomers. One monomer (referred to as “A” in Fig. 5j) acts as “very low intensity light sensor” with a high quantum yield ΦA. Hence, the very low photon number provided after 6 h of moonlight illumination is sufficient to photoreduce its flavin co-factor, resulting in the partially photoreduced FADo- FADox moonlight state (Fig. 5j).For direct comparison, our naturalistic moonlight’s emission (in the main absorbance range of L-Cry: 330 nm–510 nm) is 5.4 × 1010 photons/cm2/s (Supplementary Fig. 1e), which accumulates to ~1.2 × 1015 photons/cm2 in the 6 h required to reach the half-reduced moonlight state (Fig. 5d, e). For naturalistic sunlight, emitting ~7.5 × 1014 photons/cm2/s (330–510 nm), at least 5 min of sunlight illumination (i.e. > ~1.8 × 1017 photons/cm2) are required to photoreduce the flavin in both L-Cry monomers in order to reach the fully photoreduced FADo- FADo- sunlight state (Fig. 5b, j). Thus, the second L-Cry monomer (monomer “B” in Fig. 5j) has a significantly lower quantum yield ΦB for FAD photoreduction (ΦB  More

Izdebski, A. et al. Palaeoecological data indicates land-use changes across Europe linked to spatial heterogeneity in mortality during the Black Death pandemic. Nat. Ecol. Evol. 6, 297–306 (2022).CAS 
Article 

Google Scholar 
Benedictow, O. J. The Complete History of the Black Death (The Boydell Press, 2021).Palermo, L. Mercati del Grano a Roma tra Medioevo e Rinascimento. Il Mercato Distrettuale del Grano in Età Comunale (Istituto Nazionale di Studi Romani, 1990).Cortonesi, A. I cereali nell’Italia del tardo medioevo. Note sugli aspetti qualitativi del consumo. Riv. Stor. Agricol. 37, 3–30 (1997).
Google Scholar 
Nanni, P. in The Crisis of the 14th Century. Teleconnections Between Environmental and Societal Change? (eds Bauch M. & Schenk G. J.) 169–189 (De Gruyter, 2020).Lagerås, P. Environment, Society and the Black Death: An Interdisciplinary Approach to the Late-Medieval Crisis in Sweden (Oxbow Books, 2016).Roosen, J. & Curtis, D. The ‘light touch’ of the Black Death in the southern Netherlands: an urban trick? Econ. Hist. Rev. 72, 32–56 (2019).Article 

Google Scholar 
Preiser-Kapeller, J. Der Lange Sommer und die Kleine Eiszeit: Klima, Pandemien und der Wandel der Alten Welt 500–1500 n. Chr. (Mandelbaum, 2021).Sadori, L. The Lateglacial and Holocene vegetation and climate history of Lago di Mezzano (central Italy). Quat. Sci. Rev. 202, 30–44 (2018).Article 

Google Scholar 
Cortonesi, A. Ruralia. Economie e Paesaggi del Medioevo Italiano (Il Calamo, 1995).Cortonesi, A. L’olivo nell’Italia medievale. Reti Medievali Riv. 6, 1–29 (2005).
Google Scholar 
Mensing, S. A. et al. Historical ecology reveals landscape transformation coincident with cultural development in central Italy since the Roman Period. Sci. Rep. 8, 2138 (2018).Article 

Google Scholar 
Cortonesi, A. in Il Paesaggio Agrario Italiano Medievale: Storia e Didattica, 113–120 (Istituto Alcide Cervi, 2011). More

Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jackson, R. B. et al. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
PubMed 
Article 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
PubMed 
Article 

Google Scholar 
IPCC. Climate Change 2021: The Physical Science Basis. (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, in press).Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).Wood, T. E. et al. in Ecosystem Consequences of Soil Warming: Microbes, Vegetation, Fauna and Soil Biogeochemistry (ed. Mohan, J.) Ch. 14 (Academic Press, 2019).Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).CAS 
PubMed 
Article 

Google Scholar 
van Gestel, N. et al. Predicting soil carbon loss with warming. Nature 554, E4–E5 (2018).PubMed 
Article 
CAS 

Google Scholar 
Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–104 (2017).CAS 
PubMed 
Article 

Google Scholar 
Romero-Olivares, A. L., Allison, S. D. & Treseder, K. K. Soil microbes and their response to experimental warming over time: a meta-analysis of field studies. Soil Biol. Biochem. 107, 32–40 (2017).CAS 
Article 

Google Scholar 
Anderson-Teixeira, K. J., Wang, M. M. H., McGarvey, J. C. & LeBauer, D. S. Carbon dynamics of mature and regrowth tropical forests derived from a pantropical database (TropForC-db). Glob. Change Biol. 22, 1690–1709 (2016).Article 

Google Scholar 
Nottingham, A. T., Meir, P., Velasquez, E. & Turner, B. L. Soil carbon loss by experimental warming in a tropical forest. Nature 584, 234–237 (2020).CAS 
PubMed 
Article 

Google Scholar 
Kimball, B. A. et al. Infrared heater system for warming tropical forest understory plants and soils. Ecol. Evol. 8, 1932–1944 (2018).DeAngelis, K. M. et al. Long-term forest soil warming alters microbial communities in temperate forest soils. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00104 (2015)Bååth, E. Temperature sensitivity of soil microbial activity modeled by the square root equation as a unifying model to differentiate between direct temperature effects and microbial community adaptation. Glob. Change Biol. 24, 2850–2861 (2018).Article 

Google Scholar 
Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3, 909–912 (2013).CAS 
Article 

Google Scholar 
Ratkowsky, D. A., Olley, J., Mcmeekin, T. A. & Ball, A. Relationship between temperature and growth-rate of bacterial cultures. J. Bacteriol. 149, 1–5 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rinnan, R., Rousk, J., Yergeau, E., Kowalchuk, G. A. & Bååth, E. Temperature adaptation of soil bacterial communities along an Antarctic climate gradient: predicting responses to climate warming. Glob. Change Biol. 15, 2615–2625 (2009).Article 

Google Scholar 
Nottingham, A. T., Bååth, E., Reischke, S., Salinas, N. & Meir, P. Adaptation of soil microbial growth to temperature: using a tropical elevation gradient to predict future changes. Glob. Change Biol. https://doi.org/10.1111/gcb.14502 (2019).Li, J. Q., Bååth, E., Pei, J. M., Fang, C. M. & Nie, M. Temperature adaptation of soil microbial respiration in alpine, boreal and tropical soils: an application of the square root (Ratkowsky) model. Glob. Change Biol. 27, 1281–1292 (2021).CAS 
Article 

Google Scholar 
Rousk, J., Frey, S. D. & Bååth, E. Temperature adaptation of bacterial communities in experimentally warmed forest soils. Glob. Change Biol. 18, 3252–3258 (2012).Article 

Google Scholar 
Nottingham, A. T. et al. Annual to decadal temperature adaptation of the soil bacterial community after translocation across an elevation gradient in the Andes. Soil Biol. Biochem. 158, 108217 (2021).CAS 
Article 

Google Scholar 
Nottingham, A. T. et al. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 22, 1889–1899 (2019).PubMed 
Article 

Google Scholar 
Donhauser, J., Niklaus, P. A., Rousk, J., Larose, C. & Frey, B. Temperatures beyond the community optimum promote the dominance of heat-adapted, fast growing and stress resistant bacteria in alpine soils. Soil Biol. Biochem. 148, 107873 (2020).CAS 
Article 

Google Scholar 
Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).Pold, G., Melillo, J. M. & DeAngelis, K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00480 (2015).Zhou, J. Z. et al. Temperature mediates continental-scale diversity of microbes in forest soils. Nat. Commun. 7, 12083 (2016).Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).Wu, L. et al. Reduction of microbial diversity in grassland soil is driven by long-term climate warming. Nat. Microbiol. 7, 1054–1062 (2022).Oliverio, A. M., Bradford, M. A. & Fierer, N. Identifying the microbial taxa that consistently respond to soil warming across time and space. Glob. Change Biol. 23, 2117–2129 (2017).Article 

Google Scholar 
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).CAS 
PubMed 
Article 

Google Scholar 
Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L. & Marsham, J. H. The effects of tropical vegetation on rainfall. Annu. Rev. Env. Resour. 43, 193–218 (2018).Article 

Google Scholar 
Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. https://doi.org/10.3389/Fmicb.2013.00333 (2013).Pietikäinen, J., Pettersson, M. & Bååth, E. Comparison of temperature effects on soil respiration and bacterial and fungal growth rates. FEMS Microbiol. Ecol. 52, 49–58 (2005).PubMed 
Article 
CAS 

Google Scholar 
Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 
Article 

Google Scholar 
Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nottingham, A. T. et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018).PubMed 
Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).PubMed 
Article 

Google Scholar 
LaManna, J. A. et al. Plant diversity increases with the strength of negative density dependence at the global scale. Science 356, 1389–1392 (2017).CAS 
PubMed 
Article 

Google Scholar 
Bagchi, R. et al. Pathogens and insect herbivores drive rainforest plant diversity and composition. Nature 506, 85–88 (2014).CAS 
PubMed 
Article 

Google Scholar 
Lapebie, P., Lombard, V., Drula, E., Terrapon, N. & Henrissat, B. Bacteroidetes use thousands of enzyme combinations to break down glycans. Nat. Commun. https://doi.org/10.1038/s41467-019-10068-5 (2019).Makhalanyane, T. P. et al. Microbial ecology of hot desert edaphic systems. FEMS Microbiol. Rev. 39, 203–221 (2015).CAS 
PubMed 
Article 

Google Scholar 
Aydogan, E. L., Moser, G., Muller, C., Kampfer, P. & Glaeser, S. P. Long-term warming shifts the composition of bacterial communities in the phyllosphere of Galium album in a permanent grassland field-experiment. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.00144 (2018).Hu, D. Y., Zang, Y., Mao, Y. J. & Gao, B. L. Identification of molecular markers that are specific to the class thermoleophilia. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01185 (2019).Mohan, J. E. et al. Mycorrhizal fungi mediation of terrestrial ecosystem responses to global change: mini-review. Fungal Ecol. 10, 3–19 (2014).Article 

Google Scholar 
Manzoni, S., Taylor, P., Richter, A., Porporato, A. & Agren, G. I. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol. 196, 79–91 (2012).CAS 
PubMed 
Article 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).CAS 
Article 

Google Scholar 
Reed, S. C. et al. Soil biogeochemical responses of a tropical forest to warming and hurricane disturbance. Adv. Ecol. Res. 62, 225–252 (2020).Article 

Google Scholar 
Nottingham, A. T., Turner, B. L., Stott, A. W. & Tanner, E. V. J. Nitrogen and phosphorus constrain labile and stable carbon turnover in lowland tropical forest soils. Soil Biol. Biochem. 80, 26–33 (2015).CAS 
Article 

Google Scholar 
Walker, T. W. N. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Change 8, 885–889 (2018).Kemmitt, S. J. et al. Mineralization of native soil organic matter is not regulated by the size, activity or composition of the soil microbial biomass—a new perspective. Soil Biol. Biochem. 40, 61–73 (2008).CAS 
Article 

Google Scholar 
Nannipieri, P., Trasar-Cepeda, C. & Dick, R. P. Soil enzyme activity: a brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol. Fert. Soils 54, 11–19 (2018).CAS 
Article 

Google Scholar 
Wallenstein, M., Allison, S., Ernakovich, J., Steinweg, J. M. & Sinsabaugh, R. in Soil Enzymology. Soil Biology Vol. 22 (eds Shukla, G. & Varma, A.) Ch. 13 (Springer, 2011).Zhou, X. Y., Chen, L., Xu, J. M. & Brookes, P. C. Soil biochemical properties and bacteria community in a repeatedly fumigated-incubated soil. Biol. Fert. Soils 56, 619–631 (2020).CAS 
Article 

Google Scholar 
Sanchez-Julia, M. & Turner, B. L. Abiotic contribution to phenol oxidase activity across a manganese gradient in tropical forest soils. Biogeochemistry https://doi.org/10.1007/s10533-021-00764-0 (2021).Razavi, B. S., Liu, S. B. & Kuzyakov, Y. Hot experience for cold-adapted microorganisms: temperature sensitivity of soil enzymes. Soil Biol. Biochem. 105, 236–243 (2017).CAS 
Article 

Google Scholar 
Pinney, M. M. et al. Parallel molecular mechanisms for enzyme temperature adaptation. Science 371, eaay2784 (2021).CAS 
PubMed 
Article 

Google Scholar 
Fanin, N. et al. Soil enzymes in response to climate warming: mechanisms and feedbacks. Funct. Ecol. https://doi.org/10.1111/1365-2435.14027 (2022).Hall, S. J. & Silver, W. L. Iron oxidation stimulates organic matter decomposition in humid tropical forest soils. Glob. Change Biol. 19, 2804–2813 (2013).Article 

Google Scholar 
Freeman, C., Ostle, N. & Kang, H. An enzymic ‘latch’ on a global carbon store. Nature 409, 149 (2001).CAS 
PubMed 
Article 

Google Scholar 
Sarmiento, C. et al. Soilborne fungi have host affinity and host-specific effects on seed germination and survival in a lowland tropical forest. Proc. Natl Acad. Sci. USA 114, 11458–11463 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Condit, R., Perez, R., Lao, S., Aguilar, S. & Hubbell, S. P. Demographic trends and climate over 35 years in the Barro Colorado 50 ha plot. For. Ecosyst. https://doi.org/10.1186/s40663-017-0103-1 (2017).Woodring, W. P. Geology of Barro Colorado Island. Smithson. Misc. Collect. 135, 1–39 (1958).
Google Scholar 
Sanchez, P. A. & Logan, T. J. Myths and science about the chemistry and fertility of soils in the tropics. SSSA Spec. Publ. 29, 35–46 (1992).CAS 

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

Google Scholar 
Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).CAS 
Article 

Google Scholar 
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).CAS 
Article 

Google Scholar 
Jenkinson, D. S., Brookes, P. C. & Powlson, D. S. Measuring soil microbial biomass. Soil Biol. Biochem. 36, 5–7 (2004).CAS 
Article 

Google Scholar 
Kouno, K., Tuchiya, Y. & Ando, T. Measurement of soil microbial biomass phosphorus by an anion-exchange membrane method. Soil Biol. Biochem. 27, 1353–1357 (1995).CAS 
Article 

Google Scholar 
Tabatabai, M. A. in Methods of Soil Analysis. Part 2. Microbiological and Biochemical Properties (ed. Page, A.L.) 778–833 (SSSA, 1994).Marx, M. C., Wood, M. & Jarvis, S. C. A microplate fluorimetric assay for the study of enzyme diversity in soils. Soil Biol. Biochem. 33, 1633–1640 (2001).CAS 
Article 

Google Scholar 
Price, N. & Stevens, L. Fundamentals of Enzymology: Cell and Molecular Biology of Catalytic Proteins (Oxford Univ. Press, 1999).Hagerty, S. B., Allison, S. D. & Schimel, J. P. Evaluating soil microbial carbon use efficiency explicitly as a function of cellular processes: implications for measurements and models. Biogeochemistry 140, 269–283 (2018).CAS 
Article 

Google Scholar 
Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Change 3, 395–398 (2013).CAS 
Article 

Google Scholar 
Spohn, M. et al. Soil microbial carbon use efficiency and biomass turnover in a long-term fertilization experiment in a temperate grassland. Soil Biol. Biochem. 97, 168–175 (2016).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L. et al. Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86, 172–189 (2016).Article 

Google Scholar 
Geyer, K. M., Dijkstra, P., Sinsabaugh, R. & Frey, S. D. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biol. Biochem. 128, 79–88 (2019).CAS 
Article 

Google Scholar 
Bååth, E., Pettersson, M. & Söderberg, K. H. Adaptation of a rapid and economical microcentrifugation method to measure thymidine and leucine incorporation by soil bacteria. Soil Biol. Biochem. 33, 1571–1574 (2001).Article 

Google Scholar 
Bárcenas-Moreno, G., Gomez-Brandon, M., Rousk, J. & Bååth, E. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob. Change Biol. 15, 2950–2957 (2009).Article 

Google Scholar 
Smirnova, E., Huzurbazar, S. & Jafari, F. PERFect: PERmutation Filtering test for microbiome data. Biostatistics 20, 615–631 (2019).PubMed 
Article 

Google Scholar 
Alberdi, A. & Gilbert, M. T. P. hilldiv: an R package for the integral analysis of diversity based on Hill numbers. Preprint at bioRxiv https://doi.org/10.1101/545665 (2019).Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).PubMed 
Article 

Google Scholar 
Oksanen, J. et al. vegan: Community ecology package, R Package version 2 https://cran.r-project.org/web/packages/vegan/ (2018).Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. https://doi.org/10.1186/gb-2011-12-6-r60 (2011).Roesch, L. F. W. et al. PIME: a package for discovery of novel differences among microbial communities. Mol. Ecol. Resour. 20, 415–428 (2020).CAS 
PubMed 
Article 

Google Scholar 
Roberts, D.W. labdsv: Ordination and multivariate analysis for ecology. R package version 2.0-1 https://cran.r-project.org/web/packages/labdsv/ (2019).Cao, Y. et al. microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. Bioinformatics 38, 4027–4029 (2022).Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. Peerj 3, e1319 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Peterson, R. A. & Cavanaugh, J. E. Ordered quantile normalization: a semiparametric transformation built for the cross-validation era. J. Appl. Stat. 47, 2312–2327 (2020).PubMed 
Article 

Google Scholar  More