Philippa Kaur

Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).Article 

Google Scholar 
Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).PubMed 
Article 

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

Google Scholar 
Wilkinson, D. P., Golding, N., Guillera-Arroita, G., Tingley, R. & McCarthy, M. A. A comparison of joint species distribution models for presence–absence data. Methods Ecol. Evol. 10, 198–211 (2018).Article 

Google Scholar 
Zimmermann, N. E., Edwards, T. C., Graham, C. H., Pearman, P. B. & Svenning, J.-C. New trends in species distribution modelling. Ecography (Cop.) 33, 985–989 (2010).Article 

Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop.) 29, 129–151 (2006).Article 

Google Scholar 
Kass, J. M. et al. Wallace: A flexible platform for reproducible modeling of species niches and distributions built for community expansion. Methods Ecol. Evol. 9, 1151–1156 (2018).Article 

Google Scholar 
Morisette, J. T. et al. VisTrails SAHM: Visualization and workflow management for species habitat modeling. Ecography (Cop.) 36, 129–135 (2013).Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography (Cop.) 32, 369–373 (2009).Article 

Google Scholar 
Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Syfert, M. M. et al. Using species distribution models to inform IUCN Red List assessments. Biol. Conserv. 177, 174–184 (2014).Article 

Google Scholar 
Robinson, A. P., Walshe, T., Burgman, M. A. & Nunn, M. Invasive Species: Risk Assessment and Management (Cambridge University Press, 2017).Book 

Google Scholar 
Fontaine, J. J. Improving our legacy: Incorporation of adaptive management into state wildlife action plans. J. Environ. Manag. 92, 1403–1408 (2011).Article 

Google Scholar 
Gantchoff, M., Conlee, L. & Belant, J. Conservation implications of sex-specific landscape suitability for a large generalist carnivore. Divers. Distrib. 25, 1488–1496 (2019).Article 

Google Scholar 
Camaclang, A. E., Maron, M., Martin, T. G. & Possingham, H. P. Current practices in the identification of critical habitat for threatened species. Conserv. Biol. 29, 482–492 (2014).PubMed 
Article 

Google Scholar 
Schwartz, M. W. The Performance of the Endangered Species Act. Annu. Rev. Ecol. Evol. Syst. 39, 279–299 (2008).Article 

Google Scholar 
Acevedo, P. et al. Generalizing and transferring spatial models: A case study to predict Eurasian badger abundance in Atlantic Spain. Ecol. Model. 275, 1–8 (2014).Article 

Google Scholar 
Werkowska, W., Márquez, A. L., Real, R. & Acevedo, P. A practical overview of transferability in species distribution modeling. Environ. Rev. 25, 127–133 (2017).Article 

Google Scholar 
Barbosa, A. M., Real, R. & MarioVargas, J. Transferability of environmental favourability models in geographic space: The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain. Ecol. Model. 220, 747–754 (2009).Article 

Google Scholar 
Randin, C. F. et al. Are niche-based species distribution models transferable in space?. J. Biogeogr. 33, 1689–1703 (2006).Article 

Google Scholar 
Jiménez-Valverde, A. et al. Use of niche models in invasive species risk assessments. Biol. Invasions 13, 2785–2797 (2011).Article 

Google Scholar 
Torres, R. T. et al. Favourableness and connectivity of a Western Iberian landscape for the reintroduction of the iconic Iberian ibex Capra pyrenaica. Oryx 51, 709–717 (2016).Article 

Google Scholar 
Luoto, M., Kuussaari, M. & Toivonen, T. Modelling butterfly distribution based on remote sensing data. J. Biogeogr. 29, 1027–1037 (2002).Article 

Google Scholar 
Cerasoli, F. et al. Determinants of habitat suitability models transferability across geographically disjunct populations: Insights from Vipera ursinii urs inii. Ecol. Evol. 11, 3991–4011 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Dobrowski, S. Z. et al. Modeling plant ranges over 75 years of climate change in California, USA: Temporal transferability and species traits. Ecol. Monogr. 81, 241–257 (2011).Article 

Google Scholar 
Townsend Peterson, A., Papeş, M. & Eaton, M. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography (Cop.) 30, 550–560 (2007).Article 

Google Scholar 
Qiao, H. et al. An evaluation of transferability of ecological niche models. Ecography (Cop.) 42, 521–534 (2018).Article 

Google Scholar 
Wenger, S. J. & Olden, J. D. Assessing transferability of ecological models: An underappreciated aspect of statistical validation. Methods Ecol. Evol. 3, 260–267 (2012).Article 

Google Scholar 
Gantchoff, M., Conlee, L. & Belant, J. L. Planning for carnivore recolonization by mapping sex-specific landscape connectivity. Glob. Ecol. Conserv. 21, e00869 (2020).Article 

Google Scholar 
Gantchoff, M. G. et al. Potential distribution and connectivity for recolonizing cougars in the Great Lakes region, USA. Biol. Conserv. 257, 109144 (2021).Article 

Google Scholar 
Boudreau, M. et al. Spatial prioritization of public outreach in the face of carnivore recolonization. J. Appl. Ecol. 59, 757–767 (2002).Article 

Google Scholar 
Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 80(346), 1517–1519 (2014).ADS 
Article 
CAS 

Google Scholar 
Laliberte, A. S. & Ripple, W. J. Range contractions of North American carnivores and ungulates. Bioscience 54, 123 (2004).Article 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 
Article 
CAS 

Google Scholar 
Gompper, M. E., Belant, J. L. & Kays, R. Carnivore coexistence: America’s recovery. Science 347, 382–383 (2015).CAS 
PubMed 
Article 

Google Scholar 
Mech, L. D. Where can wolves live and how can we live with them?. Biol. Conserv. 210, 310–317 (2017).Article 

Google Scholar 
United States Fish and Wildlife Service (USFWS). Endangered and threatened wildlife and plants; Removing the gray wolf (Canis lupus) from the list of endangered and threatened wildlife. Fed. Reg. 85(213), 69778–69895 (2020).
Google Scholar 
Gehring, T. M. & Potter, B. A. Wolf habitat analysis in Michigan: An example of the need for proactive land management for carnivore species. Wild. Soc. Bull. 33, 1237–1244 (2005).Article 

Google Scholar 
Falcucci, A., Maiorano, L., Tempio, G., Boitani, L. & Ciucci, P. Modeling the potential distribution for a range-expanding species: Wolf recolonization of the Alpine range. Biol. Conserv. 158, 63–72 (2013).Article 

Google Scholar 
Torres, L. G. et al. Poor transferability of species distribution models for a pelagic predator, the grey petrel, indicates contrasting habitat preferences across ocean basins. PLoS One 10, e0120014 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Olson, L. E. et al. Improved prediction of Canada lynx distribution through regional model transferability and data efficiency. Ecol. Evol. 11, 1667–1690 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Carroll, C., Rohlf, D. J., vonHoldt, B. M., Treves, A. & Hendricks, S. A. Wolf delisting challenges demonstrate need for an improved framework for conserving intraspecific variation under the endangered species act. Bioscience 71, 73–84 (2020).PubMed 
PubMed Central 

Google Scholar 
Yang, L. et al. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS J. Photogramm. Remote Sens. 146, 108–123 (2018).ADS 
Article 

Google Scholar 
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K. & Thuiller, W. Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib. 15, 59–69 (2009).Article 

Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
Paton, R. S. & Matthiopoulos, J. Defining the scale of habitat availability for models of habitat selection. Ecology https://doi.org/10.1890/14-2241.1 (2015).Article 

Google Scholar 
Derville, S., Torres, L. G., Iovan, C. & Garrigue, C. Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Divers. Distrib. 24, 1657–1673 (2018).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 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography https://doi.org/10.1111/j.1600-0587.2009.06142.x (2010).Article 

Google Scholar 
Arntzen, J. W. From descriptive to predictive distribution models: A working example with Iberian amphibians and reptiles. Front. Zool. 3, 1–11 (2006).Article 

Google Scholar 
Conway, K. Wolf recovery—GIS facilitates habitat mapping in the Great Lake States. GIS WORLD 9, 54–57 (1996).
Google Scholar 
Boitani, L. Wolf conservation and recovery. In Wolves: Behavior, Ecology, and Conservation (eds Mech, L. D. & Boitani, L.) (University of Chicago Press, 2007).
Google Scholar 
Mladenoff, D. J., Sickley, T. A., Haight, R. G. & Wydeven, A. P. A regional landscape analysis and prediction of favorable gray wolf habitat in the northern Great Lakes region. Conserv. Biol. 9, 279–294 (1995).Article 

Google Scholar 
Treves, A., Martin, K. A., Wiedenhoeft, J. E. & Wydeven, A. P. Dispersal of gray wolves in the Great Lakes region. In Recovery of Gray Wolves in the Great Lakes Region of the United States 191–204 (Springer, 2009).Chapter 

Google Scholar 
Nelson, M. E. Winter range arrival and departure of white-tailed deer in northeastern Minnesota. Can. J. Zool. 73, 1069–1076 (1995).Article 

Google Scholar 
Droghini, A. & Boutin, S. Snow conditions influence grey wolf (Canis lupus) travel paths: The effect of human-created linear features. Can. J. Zool. 96, 39–47 (2018).Article 

Google Scholar 
Beyer, D. E., Peterson, R. O., Vucetich, J. A. & Hammill, J. H. Wolf population changes in Michigan. In Recovery of Gray Wolves in the Great Lakes Region of the United States 65–85 (Springer, 2009).Chapter 

Google Scholar 
Claeys, G. B. Wolves in the Lower Peninsula of Michigan: Habitat modeling, evaluation of connectivity, and capacity estimation (Doctoral dissertation, Duke University) (2010)..Gehring, T. M. & Potter, B. A. Wolf habitat analysis in Michigan: An example of the need for proactive land management for carnivore species. Wildl. Soc. Bull. 33, 1237–1244 (2005).Article 

Google Scholar 
Mancinelli, S., Falco, M., Boitani, L. & Ciucci, P. Social, behavioural and temporal components of wolf (Canis lupus) responses to anthropogenic landscape features in the central Apennines, Italy. J. Zool. 309, 114–124 (2019).Article 

Google Scholar 
Potvin, M. J. et al. Monitoring and habitat analysis for wolves in upper Michigan. J. Wildl. Manag. 69, 1660–1669 (2005).Article 

Google Scholar 
Whittington, J. et al. Caribou encounters with wolves increase near roads and trails: A time-to-event approach. J. Appl. Ecol. 48, 1535–1542 (2011).Article 

Google Scholar 
Zimmermann, B., Nelson, L., Wabakken, P., Sand, H. & Liberg, O. Behavioral responses of wolves to roads: Scale-dependent ambivalence. Behav. Ecol. 25, 1353–1364 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Kojola, I. et al. Wolf visitations close to human residences in Finland: The role of age, residence density, and time of day. Biol. Conserv. 198, 9–14 (2016).Article 

Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kautz, T. M. et al. Large carnivore response to human road use suggests a landscape of coexistence. Glob. Ecol. Conserv. 30, e01772 (2021).Article 

Google Scholar 
Thuiller, W., Brotons, L., Araújo, M. B. & Lavorel, S. Effects of restricting environmental range of data to project current and future species distributions. Ecography (Cop.) 27, 165–172 (2004).Article 

Google Scholar 
Václavík, T. & Meentemeyer, R. K. Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion. Divers. Distrib. 18, 73–83 (2011).Article 

Google Scholar 
VanDerWal, J., Shoo, L. P., Graham, C. & Williams, S. E. Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?. Ecol. Model. 220, 589–594 (2009).Article 

Google Scholar 
Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl. Acad. Sci. U.S.A. 114, 7641–7646 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rosauer, D. F., Pollock, L. J., Linke, S. & Jetz, W. Phylogenetically informed spatial planning is required to conserve the mammalian tree of life. Proc. Biol. Sci. 284, 20170627 (2017).PubMed 
PubMed Central 

Google Scholar 
Guillera-Arroita, G. et al. Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292 (2015).Article 

Google Scholar  More

Agricultural and Horticultural Land Use (StatsNZ, 2021); https://www.stats.govt.nz/indicators/agricultural-and-horticultural-land-useThom, E. R. Hill Country Symposium Grassland Research and Practice Series No. 16 (New Zealand Grassland Association, 2016).Wardle, P. Vegetation of New Zealand (Cambridge Univ. Press, 1991).Maxwell, T. M. R., Moir, J. L. & Edwards, G. R. Grazing and soil fertility effect on naturalized annual clover species in New Zealand high country. Rangel. Ecol. Manage. 69, 444–448 (2016).Article 

Google Scholar 
Maxwell, T., Moir, J. & Edwards, G. Influence of environmental factors on the abundance of naturalised annual clovers in the South Island hill and high country. J. N. Z. Grassl. 72, 165–170 (2010).
Google Scholar 
Nölke, I., Tonn, B., Komainda, M., Heshmati, S. & Isselstein, J. The choice of the white clover population alters overyielding of mixtures with perennial ryegrass and chicory and underlying processes. Sci. Rep. 12, 1155 (2022).Article 

Google Scholar 
Fay, P. A. et al. Grassland productivity limited by multiple nutrients. Nat. Plants 1, 15080 (2015).CAS 
Article 

Google Scholar 
Zhang, W., Maxwell, T. M. R., Robinson, B. & Dickinson, N. Legume nutrition is improved by neighbouring grasses. Plant Soil (in the press).Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis (Academic Press, 2008)Phoenix, G. K., Johnson, D. A., Muddimer, S. P., Leake, J. R. & Cameron, D. D. Niche differentiation and plasticity in soil phosphorus acquisition among co-occurring plants. Nat. Plants 6, 349–354 (2020).CAS 
Article 

Google Scholar 
Lambers, H., Clements, J. C. & Nelson, M. N. How a phosphorus-acquisition strategy based on carboxylate exudation powers the success and agronomic potential of lupines (Lupinus, Fabaceae). Am. J. Bot. 100, 263–288 (2013).CAS 
Article 

Google Scholar 
Li, X. et al. Long-term increased grain yield and soil fertility from intercropping. Nat. Sustain. 4, 943–950 (2021).Article 

Google Scholar 
Homulle, Z., George, T. & Karley, A. Root traits with team benefits: understanding belowground interactions in intercropping systems. Plant Soil 471, 1–26 (2021).Article 

Google Scholar 
Burrows, C. J. Processes of Vegetation Change (Unwin Hyman, 1990).Fornara, D. & Tilman, D. Plant functional composition influences rates of soil carbon and nitrogen accumulation. J. Ecol. 96, 314–322 (2008).CAS 
Article 

Google Scholar 
Lynch, J. P., Strock, C. F., Schneider, H. M., Sidhu, J. S. & Ajmera, I. Root anatomy and soil resource capture. Plant Soil 466, 21–63 (2021).CAS 
Article 

Google Scholar 
Lambers, H., Wright, I. J., Caio, G. & Pereira, P. J. Leaf manganese concentrations as a tool to assess belowground plant functioning in phosphorus-impoverished environments. Plant Soil 461, 43–61 (2021).CAS 
Article 

Google Scholar 
Liu, G. & Martinoia, E. How to survive on low potassium. Nat. Plants 6, 332–333 (2020).Article 

Google Scholar 
Anke, M. & Seifert, M. The biological and toxicological importance of molybdenum in the environment and in the nutrition of plants, animals and man. Part 1: molybdenum in plants. Acta Biol. Hung. 58, 311–324 (2007).CAS 
Article 

Google Scholar 
Gylfadóttir, T., Helgadóttir, Á. & Høgh-Jensen, H. Consequences of including adapted white clover in northern European grassland: transfer and deposition of nitrogen. Plant Soil 297, 93–104 (2007).Article 

Google Scholar 
Maxwell, T. M. L. R. Ecology and Management of Adventive Annual Clover Species in the South Island Hill and High Country of New Zealand. PhD thesis, Lincoln Univ. (2013).Li, C. et al. Syndromes of production in intercropping impact yield gains. Nat. Plants 6, 653–660 (2020).Article 

Google Scholar 
McLaren, R. G. & Cameron, K. C. Soil Science: Sustainable Production and Environmental Science (Oxford Univ. Press, 1996).Chang, X., Duan, C. & Wang, H. Root excretion and plant resistance to metal toxicity. J. Appl. Ecol. 11, 315–320 (2000).CAS 
Article 

Google Scholar 
Puschenreiter, M., Gruber, B. & Wenzel, W. W. Phytosiderophore-induced mobilization and uptake of Cd, Cu, Fe, Ni, Pb and Zn by wheat plants grown on metal-enriched soils. Environ. Exp. Bot. 138, 67–76 (2017).CAS 
Article 

Google Scholar 
Lu, J. Y. et al. Rhizosphere priming effects of Lolium perenne and Trifolium repens depend on phosphorus fertilization and biological nitrogen fixation. Soil Biol. Biochem. 150, 108805 (2020).Article 

Google Scholar 
Wang, L., Chen, F. & Wan, K. Y. Research progress and prospects of plant growth efficiency and its evaluation. Soils 42, 164–170 (2010).CAS 

Google Scholar 
Tian, X. Y. et al. Physiological and molecular advances in magnesium nutrition of plants. Plant Soil 468, 1–17 (2021).CAS 
Article 

Google Scholar 
Wainwright, M. Sulfur oxidation in soils. Adv. Agron. 37, 349–376 (1984).CAS 
Article 

Google Scholar 
Kaiser, B. N., Gridley, K. L., Ngaire Brady, J., Phillips, T. & Tyerman, S. D. The role of molybdenum in agricultural plant production. Ann. Bot. 96, 745–754 (2005).CAS 
Article 

Google Scholar 
Khobra, R. & Singh, B. Phytosiderophore release in relation to multiple micronutrient metal deficiency in wheat. J. Plant Nutr. 41, 679–688 (2018).CAS 
Article 

Google Scholar 
Erenoglu, B., Eker, S., Cakmak, I., Derici, R. & Römheld, V. Effect of iron and zinc deficiency on release of phytosiderophores in barley cultivars differing in zinc efficiency. J. Plant Nutr. 23, 1645–1656 (2000).CAS 
Article 

Google Scholar 
Chen, C., Chaudhary, A. & Mathys, A. Nutritional and environmental losses embedded in global food waste. Resour. Conserv. Recycl. 160, 104912 (2020).Article 

Google Scholar 
Nyfeler, D., Huguenin-Elie, O., Suter, M., Frossard, E. & Lüscher, A. Grass–legume mixtures can yield more nitrogen than legume pure stands due to mutual stimulation of nitrogen uptake from symbiotic and non-symbiotic sources. Agric. Ecosyst. Environ. 140, 155–163 (2011).Article 

Google Scholar 
Zhang, C., Postma, J. A., York, L. M. & Lynch, J. P. Root foraging elicits niche complementarity-dependent yield advantage in the ancient ‘three sisters’ (maize/bean/squash) polyculture. Ann. Bot. 114, 1719–1733 (2014).CAS 
Article 

Google Scholar 
Ghestem, M., Sidle, R. C. & Stokes, A. The influence of plant root systems on subsurface flow: implications for slope stability. BioScience 61, 869–879 (2011).Article 

Google Scholar 
Huo, C., Luo, Y. & Cheng, W. Rhizosphere priming effect: a meta-analysis. Soil Biol. Biochem. 111, 78–84 (2017).CAS 
Article 

Google Scholar 
Coskun, D., Britto, D. T., Shi, W. & Kronzucker, H. J. How plant root exudates shape the nitrogen cycle. Trends Plant Sci. 22, 661–673 (2017).CAS 
Article 

Google Scholar 
Grelet, G. et.al. Regenerative Agriculture in Aotearoa New Zealand: Research Pathways to Build Science-Based Evidence and National Narratives (Landcare Research New Zealand, 2021).Sergei Schaub, R. F. et al. Plant diversity effects on forage quality, yield and revenues of semi-natural grasslands. Nat. Commun. 11, 768 (2020).Article 

Google Scholar 
Brooker, R. W. et al. Improving intercropping: a synthesis of research in agronomy, plant physiology and ecology. N. Phytol. 206, 107–117 (2015).Article 

Google Scholar 
Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2, 720–735 (2021).Article 

Google Scholar  More

Pörtner, H. O. et al. Climate Change 2022: Impacts, Adaptation and Vulnerability (IPCC, 2022).Patz, J. A., Campbell-Lendrum, D., Holloway, T. & Foley, J. A. Impact of regional climate change on human health. Nature 438, 310–317 (2005).CAS 
Article 

Google Scholar 
Smith, K. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 709–754 (Cambridge Univ. Press, 2014).Mora, C. et al. Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions. Nat. Clim. Change 8, 1062–1071 (2018).CAS 
Article 

Google Scholar 
Altizer, S., Ostfeld, R. S., Johnson, P. T., Kutz, S. & Harvell, C. D. Climate change and infectious diseases: from evidence to a predictive framework. Science 341, 514–519 (2013).CAS 
Article 

Google Scholar 
Epstein, P. The ecology of climate change and infectious diseases: comment. Ecology 91, 925–928 (2010).Article 

Google Scholar 
IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2014).Jaenisch, T. & Patz, J. Assessment of associations between climate and infectious diseases: a comparison of the reports of the Intergovernmental Panel on Climate Change (IPCC), the National Research Council (NRC), and United States Global Change Research Program (USGCRP). Glob. Change Hum. Health 3, 67–72 (2002).Article 

Google Scholar 
Hellberg, R. S. & Chu, E. Effects of climate change on the persistence and dispersal of foodborne bacterial pathogens in the outdoor environment: a review. Crit. Rev. Microbiol. 42, 548–572 (2016).Article 

Google Scholar 
Tabachnick, W. J. Climate change and the arboviruses: lessons from the evolution of the dengue and yellow fever viruses. Ann. Rev. Virol 29, 125–145 (2016).Article 
CAS 

Google Scholar 
Khasnis, A. A. & Nettleman, M. D. Global warming and infectious disease. Arch. Med. Res. 36, 689–696 (2005).Article 

Google Scholar 
McMichael, A. J. Extreme weather events and infectious disease outbreaks. Virulence 6, 543–547 (2015).Article 

Google Scholar 
Ahern, M., Kovats, R. S., Wilkinson, P., Few, R. & Matthies, F. Global health impacts of floods: epidemiologic evidence. Epidemiol. Rev. 27, 36–46 (2005).Article 

Google Scholar 
Hunter, P. R. Climate change and waterborne and vector‐borne disease. J. Appl. Microbiol. 94, 37–46 (2003).Article 

Google Scholar 
Gage, K. L., Burkot, T. R., Eisen, R. J. & Hayes, E. B. Climate and vector borne diseases. Am. J. Prev. Med. 35, 436–450 (2008).Article 

Google Scholar 
Semenza, J. C. et al. Climate change impact assessment of food- and waterborne diseases. Crit. Rev. Environ. Sci. Technol. 42, 857–890 (2012).Article 

Google Scholar 
Nichols, G., Lake, I. & Heaviside, C. Climate change and water-related infectious diseases. Atmosphere 9, 385 (2018).Article 

Google Scholar 
Cunliffe, J. A proliferation of pathogens through the 20th century. Scand. J. Immunol. 68, 120–128 (2008).CAS 
Article 

Google Scholar 
Cecchi, L. et al. Projections of the effects of climate change on allergic asthma: the contribution of aerobiology. Allergy 65, 1073–1081 (2010).CAS 

Google Scholar 
Demain, J. G. Climate change and the impact on respiratory and allergic disease: 2018. Curr. Allergy Asthma Rep. 18, 22 (2018).Article 

Google Scholar 
Andersen, L. K. & Davis, M. D. The effects of the El Niño Southern Oscillation on skin and skin-related diseases: a message from the International Society of Dermatology Climate Change Task Force. Int. J. Dermatol. 54, 1343–1351 (2015).Article 

Google Scholar 
Guenther, A. B., Zimmerman, P. R., Harley, P. C., Monson, R. K. & Fall, R. Isoprene and monoterpene emission rate variability: model evaluations and sensitivity analyses. J. Geophys. Res. Atmos 98, 12609–12617 (1993).Article 

Google Scholar 
Metcalf, C. J. E. & Lessler, J. Opportunities and challenges in modeling emerging infectious diseases. Science 357, 149–152 (2017).CAS 
Article 

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

Google Scholar 
Nava, A., Shimabukuro, J. S., Chmura, A. A. & Luz, S. L. B. The impact of global environmental changes on infectious disease emergence with a focus on risks for Brazil. ILAR J. 58, 393–400 (2017).CAS 
Article 

Google Scholar 
Gray, J. S., Dautel, H., Estrada-Peña, A., Kahl, O. & Lindgren, E. Effects of climate change on ticks and tick-borne diseases in Europe. Interdiscip. Perspect. Infect. Dis. 2009, 593232 (2009).CAS 
Article 

Google Scholar 
Ngongeh, L. A., Idika, I. K. & Ibrahim Shehu, A. R. warming and its impacts on parasitology/entomology. Open Parasitol. J 5, 1–11 (2014).Article 

Google Scholar 
LaDeau, S. L., Calder, C. A., Doran, P. J. & Marra, P. P. West Nile virus impacts in American crow populations are associated with human land use and climate. Ecol. Res. 26, 909–916 (2011).Article 

Google Scholar 
Gale, P., Drew, T., Phipps, L. P., David, G. & Wooldridge, M. The effect of climate change on the occurrence and prevalence of livestock diseases in Great Britain: a review. J. Appl. Microbiol. 106, 1409–1423 (2009).CAS 
Article 

Google Scholar 
Lancien, J., Muguwa, J., Lannes, C. & Bouvier, J. B. Tsetse and human trypanosomiasis challenge in south eastern Uganda. Int. J. Trop. Insect Sci. 11, 411–416 (1990).Article 

Google Scholar 
Karesh, W. B. et al. Ecology of zoonoses: natural and unnatural histories. Lancet 380, 1936–1945 (2012).Article 

Google Scholar 
Vezzulli, L., Colwell, R. R. & Pruzzo, C. Ocean warming and spread of pathogenic vibrios in the aquatic environment. Microb. Ecol. 65, 817–825 (2013).Article 

Google Scholar 
Arriaza, B. T., Reinhard, K. J., Araújo, A. G., Orellana, N. C. & Standen, V. G. Possible influence of the ENSO phenomenon on the pathoecology of diphyllobothriasis and anisakiasis in ancient Chinchorro populations. Mem. Inst. Oswaldo Cruz 105, 66–72 (2010).Article 

Google Scholar 
Kaffenberger, B. H., Shetlar, D., Norton, S. A. & Rosenbach, M. The effect of climate change on skin disease in North America. J. Am. Acad. Dermatol. 76, 140–147 (2017).Article 

Google Scholar 
Coates, S. J., Enbiale, W., Davis, M. D. & Andersen, L. K. The effects of climate change on human health in Africa, a dermatologic perspective: a report from the International Society of Dermatology Climate Change Committee. Int. J. Dermatol. 59, 265–278 (2020).Article 

Google Scholar 
Patz, J. A. et al. Unhealthy landscapes: policy recommendations on land use change and infectious disease emergence. Environ. Health Perspect. 112, 1092–1098 (2004).Article 

Google Scholar 
Nagy, G. J. et al. in Climate Change and Health (ed Leal, W) 475–514 (Springer, 2016).Kontra, J. M. Zombie infections and other infectious disease complications of global warming. J. Lancaster Gen. Hosp. 12, 12–16 (2017).
Google Scholar 
Charron, D., Fleury, M., Lindsay, L. R., Ogden, N. & Schuster, C. J. in Human Health in a Changing Climate (ed Séguin, J) 173–210 (Health Canada, 2008).Butler, C. D. & Harley, D. Primary, secondary and tertiary effects of eco-climatic change: the medical response. Postgrad. Med. J. 86, 230–234 (2010).Article 

Google Scholar 
Quarles, W. Global warming means more pathogens. IPM Pract. 35, 1–8 (2017).
Google Scholar 
Patz, J. A., Engelberg, D. & Last, J. The effects of changing weather on public health. Ann. Rev. Public Health 21, 271–307 (2000).CAS 
Article 

Google Scholar 
Yavarian, J., Shafiei-Jandaghi, N. Z. & Mokhtari-Azad, T. Possible viral infections in flood disasters: a review considering 2019 spring floods in Iran. Iran. J. Microbiol. 11, 85–89 (2019).
Google Scholar 
Boxall, A. B. A. et al. Impacts of climate change on indirect human exposure to pathogens and chemicals from agriculture. Environ. Health Perspect. 117, 508–514 (2009).CAS 
Article 

Google Scholar 
Wu, R., Trubl, G., Taş, N. & Jansson, J. K. Permafrost as a potential pathogen reservoir. One Earth 5, 351–360 (2022).Article 

Google Scholar 
Gross, M. Permafrost thaw releases problems. Curr. Biol. 29, R39–R41 (2019).CAS 
Article 

Google Scholar 
Baker-Austin, C. et al. Heat wave-associated vibriosis, Sweden and Finland, 2014. Emerg. Infect. Dis. 22, 1216 (2016).CAS 
Article 

Google Scholar 
Ghanchi, N. K. et al. Case series of Naegleria fowleri primary ameobic meningoencephalitis from Karachi, Pakistan. Am. J. Trop. Med. Hyg. 97, 1600–1602 (2017).Article 

Google Scholar 
Waits, A., Emelyanova, A., Oksanen, A., Abass, K. & Rautio, A. Human infectious diseases and the changing climate in the Arctic. Environ. Int. 121, 703–713 (2018).Article 

Google Scholar 
Oskorouchi, H. R., Nie, P. & Sousa-Poza, A. The effect of floods on anemia among reproductive age women in Afghanistan. PLoS ONE 13, e0191726 (2018).Article 
CAS 

Google Scholar 
Caminade, C., McIntyre, K. M. & Jones, A. E. Impact of recent and future climate change on vector‐borne diseases. Ann. N. Y. Acad. Sci. 1436, 157 (2019).Article 

Google Scholar 
Clegg, J. Influence of climate change on the incidence and impact of arenavirus diseases: a speculative assessment. Clin. Microbiol. Infect. 15, 504–509 (2009).CAS 
Article 

Google Scholar 
Nguyen, H. Q., Huynh, T. T. N., Pathirana, A. & Van der Steen, P. Microbial risk assessment of tidal-induced urban flooding in Can Tho City (Mekong Delta, Vietnam). Int. J. Environ. Res. Public. Health 14, 1485 (2017).Article 
CAS 

Google Scholar 
Ivers, L. C. & Ryan, E. T. Infectious diseases of severe weather-related and flood-related natural disasters. Curr. Opin. Infect. Dis. 19, 408–414 (2006).Article 

Google Scholar 
Cornell, K. Climate change and infectious disease patterns in the United States: public health preparation and ecological restoration as a matter of justice. MSc thesis, Goucher College (2016).Mishra, V. et al. Climate change and its impacts on global health: a review. Pharma Innov. 8, 316–326 (2019).
Google Scholar 
Lemonick, D. M. Epidemics after natural disasters. Am. J. Clin. Med. 8, 144–152 (2011).
Google Scholar 
Khan, A. E., Xun, W. W., Ahsan, H. & Vineis, P. Climate change, sea-level rise, and health impacts in Bangladesh. Environ. Sci. Policy Sustain. Dev. 53, 18–33 (2011).Article 

Google Scholar 
Jones, B. A. et al. Zoonosis emergence linked to agricultural intensification and environmental change. Proc. Natl Acad. Sci. USA 110, 8399–8404 (2013).CAS 
Article 

Google Scholar 
Zell, R., Krumbholz, A. & Wutzler, P. Impact of global warming on viral diseases: what is the evidence? Curr. Opin. Biotechnol. 19, 652–660 (2008).CAS 
Article 

Google Scholar 
McFarlane, R. A., Sleigh, A. C. & McMichael, A. J. Land-use change and emerging infectious disease on an island continent. Int. J. Environ. Res. Public. Health 10, 2699–2719 (2013).Article 

Google Scholar 
White, R. J. & Razgour, O. Emerging zoonotic diseases originating in mammals: a systematic review of effects of anthropogenic land‐use change. Mammal. Rev. 50, 336–352 (2020).Article 

Google Scholar 
Myers, S. S. et al. Human health impacts of ecosystem alteration. Proc. Natl Acad. Sci. USA 110, 18753–18760 (2013).CAS 
Article 

Google Scholar 
Munang’andu, H. M. et al. The effect of seasonal variation on anthrax epidemiology in the upper Zambezi floodplain of western Zambia. J. Vet. Sci. 13, 293–298 (2012).Article 

Google Scholar 
Liu, Q. et al. Changing rapid weather variability increases influenza epidemic risk in a warming climate. Environ. Res. Lett. 15, 044004 (2020).Article 

Google Scholar 
Kapoor, R. et al. God is in the rain: the impact of rainfall-induced early social distancing on COVID-19 outbreaks. J. Health Econ. 81, 102575 (2020).
Google Scholar 
Raza, A., Khan, M. T. I., Ali, Q., Hussain, T. & Narjis, S. Association between meteorological indicators and COVID-19 pandemic in Pakistan. Environ. Sci. Pollut. Res. 28, 40378–40393 (2021).CAS 
Article 

Google Scholar 
Nichols, G. L. et al. Coronavirus seasonality, respiratory infections and weather. BMC Infect. Dis. 21, 1101 (2021).El-Sayed, A. & Kamel, M. Climatic changes and their role in emergence and re-emergence of diseases. Environ. Sci. Pollut. Res. 27, 22336–22352 (2020).CAS 
Article 

Google Scholar 
Ruszkiewicz, J. A. et al. Brain diseases in changing climate. Environ. Res. 177, 108637 (2019).CAS 
Article 

Google Scholar 
Herrador, B. R. G. et al. Analytical studies assessing the association between extreme precipitation or temperature and drinking water-related waterborne infections: a review. Environ. Health 14, 29 (2015).Article 

Google Scholar 
Burge, C. A. et al. Climate Change influences on marine infectious diseases: implications for management and society. Ann. Rev. Mar. Sci. 6, 249–277 (2014).Article 

Google Scholar 
Mills, J. N., Gage, K. L. & Khan, A. S. Potential influence of climate change on vector-borne and zoonotic diseases: a review and proposed research plan. Environ. Health Perspect. 118, 1507–1514 (2010).Article 

Google Scholar 
Gubler, D. J. et al. Climate variability and change in the United States: potential impacts on vector-and rodent-borne diseases. Environ. Health Perspect. 109, 223–233 (2001).
Google Scholar 
Dayrit, J. F., Bintanjoyo, L., Andersen, L. K. & Davis, M. D. P. Impact of climate change on dermatological conditions related to flooding: update from the International Society of Dermatology Climate Change Committee. Int. J. Dermatol. 57, 901–910 (2018).Article 

Google Scholar 
Myaing, T. T. Climate change and emerging zoonotic diseases. KKU Vet. J. 21, 172–182 (2011).
Google Scholar 
Kimes, N. E. et al. Temperature regulation of virulence factors in the pathogen Vibrio coralliilyticus. ISME J. 6, 835–846 (2012).CAS 
Article 

Google Scholar 
Oh, M. H., Lee, S. M., Lee, D. H. & Choi, S. H. Regulation of the Vibrio vulnificus hupA gene by temperature alteration and cyclic AMP receptor protein and evaluation of its role in virulence. Infect. Immun. 77, 1208–1215 (2009).CAS 
Article 

Google Scholar 
Casadevall, A. Climate change brings the specter of new infectious diseases. J. Clin. Invest. 130, 553–555 (2020).CAS 
Article 

Google Scholar 
Beyer, R. M., Manica, A. & Mora, C. Shifts in global bat diversity suggest a possible role of climate change in the emergence of SARS-CoV-1 and SARS-CoV-2. Sci. Total Environ. 767, 145413 (2021).CAS 
Article 

Google Scholar 
Warburton, E. M., Pearl, C. A. & Vonhof, M. J. Relationships between host body condition and immunocompetence, not host sex, best predict parasite burden in a bat–helminth system. Parasitol. Res. 115, 2155–2164 (2016).Article 

Google Scholar 
Plowright, R. K. et al. Reproduction and nutritional stress are risk factors for Hendra virus infection in little red flying foxes (Pteropus scapulatus). Proc. R. Soc. B 275, 861–869 (2008).Article 

Google Scholar 
Beldomenico, P. M. & Begon, M. Disease spread, susceptibility and infection intensity: vicious circles? Trends Ecol. Evol. 25, 21–27 (2010).Article 

Google Scholar 
Mora, C. et al. Suitable days for plant growth disappear under projected climate change: potential human and biotic vulnerability. PLoS Biol. 13, e1002167 (2015).Article 
CAS 

Google Scholar 
Mora, C. et al. Biotic and human vulnerability to projected changes in ocean biogeochemistry over the 21st century. PLoS Biol. 11, e1001682 (2013).CAS 
Article 

Google Scholar 
Thiault, L. et al. Escaping the perfect storm of simultaneous climate change impacts on agriculture and marine fisheries. Sci. Adv. 5, eaaw9976 (2019).CAS 
Article 

Google Scholar 
Myers, S. S. et al. Increasing CO2 threatens human nutrition. Nature 510, 139–142 (2014).CAS 
Article 

Google Scholar 
Tirado, M. C., Clarke, R., Jaykus, L., McQuatters-Gollop, A. & Frank, J. Climate change and food safety: a review. Food Res. Int. 43, 1745–1765 (2010).Article 

Google Scholar 
Greene, M. Impact of the Sahelian drought in Mauritania, West Africa. Lancet 303, 1093–1097 (1974).Article 

Google Scholar 
Cabrol, J.-C. War, drought, malnutrition, measles—a report from Somalia. N. Engl. J. Med. 365, 1856–1858 (2011).CAS 
Article 

Google Scholar 
Cohen, S. et al. Chronic stress, glucocorticoid receptor resistance, inflammation, and disease risk. Proc. Natl Acad. Sci. USA 109, 5995–5999 (2012).CAS 
Article 

Google Scholar 
Calow, R. C., MacDonald, A. M., Nicol, A. L. & Robins, N. S. Ground water security and drought in Africa: linking availability, access, and demand. Groundwater 48, 246–256 (2010).CAS 
Article 

Google Scholar 
Salvador, C., Nieto, R., Linares, C., Díaz, J. & Gimeno, L. Effects of droughts on health: diagnosis, repercussion, and adaptation in vulnerable regions under climate change. Challenges for future research. Sci. Total Environ. 703, 134912 (2020).CAS 
Article 

Google Scholar 
Alhoot, M. A., Tong, W. T., Low, W. Y. & Sekaran, S. D. in Climate Change and Human Health Scenario in South and Southeast Asia (ed Akhtar, R) 243–268 (Springer, 2016).Yusa, A. et al. Climate change, drought and human health in Canada. Int. J. Environ. Res. Public Health 12, 8359–8412 (2015).CAS 
Article 

Google Scholar 
Ligon, B. L. Infectious Diseases that Pose Specific Challenges After Natural Disasters: A Review. Semin. Pediatr. Infect. Dis. 17, 36–45 (2006).Article 

Google Scholar 
Nsuami, M. J., Taylor, S. N., Smith, B. S. & Martin, D. H. Increases in gonorrhea among high school students following hurricane Katrina. Sex. Transm. Infect. 85, 194–198 (2009).CAS 
Article 

Google Scholar 
Jochelson, K. HIV and syphilis in the Republic of South Africa: the creation of an epidemic. Afr. Urban Q. 6, 20–34 (1991).
Google Scholar 
Sobral, M. F. F., Duarte, G. B., da Penha Sobral, A. I. G., Marinho, M. L. M. & de Souza Melo, A. Association between climate variables and global transmission of SARS-CoV-2. Sci. Total Environ. 729, 138997 (2020).CAS 
Article 

Google Scholar 
Liu, J. et al. Impact of meteorological factors on the COVID-19 transmission: a multi-city study in China. Sci. Total Environ. 726, 138513 (2020).CAS 
Article 

Google Scholar 
Chua, P. L. et al. Global projections of temperature-attributable mortality due to enteric infections: a modelling study. Lancet Planet. Health 5, e436–e445 (2021).Article 

Google Scholar 
McCreesh, N. & Booth, M. Challenges in predicting the effects of climate change on Schistosoma mansoni and Schistosoma haematobium transmission potential. Trends Parasitol. 29, 548–555 (2013).Article 

Google Scholar 
Wu, X., Tian, H., Zhou, S., Chen, L. & Xu, B. Impact of global change on transmission of human infectious diseases. Sci. China Earth Sci. 57, 189–203 (2014).Article 

Google Scholar 
Moreno, A. R. Climate change and human health in Latin America: drivers, effects, and policies. Reg. Environ. Change 6, 157–164 (2006).Article 

Google Scholar 
McCann, D. G., Moore, A. & Walker, M.-E. The water/health nexus in disaster medicine: I. drought versus flood. Curr. Opin. Environ. Sustain. 3, 480–485 (2011).Article 

Google Scholar 
Cutler, D. M. & Summers, L. H. The COVID-19 pandemic and the $16 trillion virus. JAMA 324, 1495–1496 (2020).CAS 
Article 

Google Scholar 
Vos, T. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1204–1222 (2020).Article 

Google Scholar 
Hsiao, M.-H. et al. Environmental factors associated with the prevalence of animal bites or stings in patients admitted to an emergency department. J. Acute Med. 2, 95–102 (2012).Article 

Google Scholar 
Jones, N. E. & Baker, M. D. Toxicologic exposures associated with natural disasters: gases, kerosene, ash, and bites. Clin. Pediatr. Emerg. Med. 13, 317–323 (2012).Article 

Google Scholar  More

Forest Advanced Computing and Artificial Intelligence Laboratory (FACAI), Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USAJingjing Liang, Mo Zhou & Akane O. AbbasiForestry Division, Food and Agriculture Organization of the United Nations, Rome, ItalyJavier G. P. Gamarra & Antonello SalisGIP ECOFOR, Paris, FranceNicolas PicardDepartment of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USABryan Pijanowski, Douglass F. Jacobs & Minjee ParkInstitute for Global Change Biology, School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USAPeter B. ReichDepartment of Forest Resources, University of Minnesota, St. Paul, MN, USAPeter B. ReichHawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, AustraliaPeter B. ReichCrowther Lab, Department of Environmental Systems Science, Institute of Integrative Biology, ETH Zürich, Zürich, SwitzerlandThomas W. CrowtherWageningen Environmental Research, Wageningen University and Research, Wageningen, NetherlandsGert-Jan NabuursForest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, NetherlandsGert-Jan Nabuurs, Frans Bongers, Mathieu Decuyper, Marc Parren, Lourens Poorter & Douglas SheilDepartment of Crop and Forest Sciences, University of Lleida, Lleida, SpainSergio de-MiguelJoint Research Unit CTFC—Agrotecnio—CERCA, Solsona, SpainSergio de-Miguel & Albert MoreraInstitute of Ecology and Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Evironmental Sciences, Peking University, Beijing, ChinaJingyun FangNorthern Research Station, USDA Forest Service, Durham, NH, USAChristopher W. WoodallCenter for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Biology, Aarhus University, Aarhus C, DenmarkJens-Christian SvenningSection for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus C, DenmarkJens-Christian SvenningSchool of Biological Sciences, University of Bristol, Bristol, UKTommaso JuckerTERRA Teaching and Research Centre, Gembloux Agro Bio-Tech, University of Liege, Gembloux, BelgiumJean-Francois BastinManaaki Whenua Landcare Research, Lincoln, New ZealandSusan K. WiserEnvironmental and Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Gadong, Brunei DarussalamFerry SlikCentre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier, FranceBruno HéraultINP-HB (Institut National Polytechnique Félix Houphouet-Boigny), University of Montpellier, Yamoussoukro, Ivory CoastBruno HéraultDepartment of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Udine, ItalyGiorgio AlbertiFaculty of Science and Technology, Free University of Bolzano, Bolzano, ItalyGiorgio AlbertiInstitute of Bioeconomy, CNR, Sesto, ItalyGiorgio AlbertiNatural and Built Environments Research Centre, School of Natural and Built Environments, University of South Australia, Adelaide, South Australia, AustraliaGunnar KeppelBiometris, Wageningen University and Research, Wageningen, NetherlandsGeerten M. HengeveldWageningen University & Research, Forest and Nature Conservation Policy Group, Wageningen, NetherlandsGeerten M. HengeveldCentre for Econics and Ecosystem Management, Eberswalde University for Sustainable Development, Eberswalde, GermanyPierre L. IbischSchool of Forest, Fisheries, and Geomatics Sciences, Institute of Food & Agricultural Sciences, University of Florida, Gainesville, FL, USACarlos A. Silva, Eben N. Broadbent & Carine KlaubergNaturalis Biodiversity Center, Leiden, NetherlandsHans ter SteegeInstituto Nacional de Tecnología Agropecuaria (INTA), Santa Cruz, ArgentinaPablo L. PeriDepartment of Plant Sciences, University of Cambridge, Cambridge, UKDavid A. CoomesFaculty of Natural Resources Management, Lakehead University, Thunder Bay, Ontario, CanadaEric B. Searle & Han Y. H. ChenUniversity of Göttingen, Göttingen, GermanyKlaus von GadowBeijing Forestry University, Beijing, ChinaKlaus von GadowUniversity of Stellenbosch, Stellenbosch, South AfricaKlaus von GadowBiałowieża Geobotanical Station, Faculty of Biology, University of Warsaw, Białowieża, PolandBogdan JaroszewiczSwiss National Forest Inventory/Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, SwitzerlandMeinrad AbeggUFR Biosciences, University Félix Houphouët-Boigny, Abidjan, Ivory CoastYves C. Adou Yao & Anny E. N’GuessanEnvironmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UKJesús Aguirre-GutiérrezBiodiversity Dynamics, Naturalis Biodiversity Center, Leiden, NetherlandsJesús Aguirre-GutiérrezCenter for Latin American Studies, University of Florida, Gainesville, FL, USAAngelica M. Almeyda ZambranoInstitute of Botany, Academy of Sciences of the Czech Republic, Trebon, Czech RepublicJan Altman & Jiri DolezalFaculty of Forestry and Wood Sciences, Czech University of Life Sciences in Prague, Praha-Suchdol, Czech RepublicJan Altman & Miroslav SvobodaEscuela ECAPMA, National Open University and Distance (Colombia) | UNAD, Bogotá, ColombiaEsteban Alvarez-DávilaDepartamento de Ingeniería Agroforestal, Universidad de Santiago de Compostela, Lugo, SpainJuan Gabriel Álvarez-GonzálezCenter for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USALuciana F. AlvesUniversité Jean Lorougnon Guédé, Daloa, Ivory CoastBienvenu H. K. AmaniUniversité Officielle de Bukavu, Bukavu, Democratic Republic of CongoChristian A. AmaniSilviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Goettingen, GermanyChristian Ammer & Peter SchallInstitut National pour l’Etude et la Recherche Agronomiques, Kinshasa, Democratic Republic of CongoBhely Angoboy IlondeaNorwegian Institute of Bioeconomy Research (NIBIO), Division of Forestry and Forest Resources, Ås, NorwayClara Antón-FernándezEuropean Commission, Joint Research Centre, Ispra, ItalyValerio AvitabileCompensation International Progress S.A., Bogotá, ColombiaGerardo A. AymardLaboratory of Applied Ecology, University of Abomey-Calavi, Cotonou, BeninAkomian F. AzihouScientific Services, South African National Parks, Knysna, South AfricaJohan A. Baard & Graham P. DurrheimSchool of Geography, University of Leeds, Leeds, UKTimothy R. Baker, Simon L. Lewis & Oliver L. PhillipsDepartment of Geomatics, Forest Research Institute, Sekocin Stary, Raszyn, PolandRadomir Balazy & Krzysztof J. StereńczakProceedings of the National Academy of Sciences, Washington, DC, USAMeredith L. BastianDepartment of Evolutionary Anthropology, Duke University, Durham, NC, USAMeredith L. BastianDepartment of Environment, Universtité du Cinquantenaire de Lwiro, Bukavu, Democratic Republic of CongoRodrigue BatumikeDepartment of Environment, Ghent University, Ghent, BelgiumMarijn BautersDepartment of Green Chemistry and Technology, Ghent University, Ghent, BelgiumMarijn Bauters & Pascal BoeckxService of Wood Biology, Royal Museum for Central Africa, Tervuren, BelgiumHans Beeckman, Thales de Haulleville & Wannes HubauBalai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan, Manokwari, IndonesiaNithanel Mikael Hendrik Benu & Relawan KuswandiInstitute of Tropical Forest Conservation, Mbarara University of Science and Technology, Mbarara, UgandaRobert BitarihoUniversité de Liège, Gembloux Agro-Bio Tech, Gembloux, BelgiumJan Bogaert & Thales de HaullevilleIntegrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control (MANSiD), University Stefan cel Mare of Suceava, Suceava, RomaniaOlivier BouriaudDepartment of Forestry Sciences, ‘Luiz de Queiroz’ College of Agriculture, University of São Paulo, Piracicaba, BrazilPedro H. S. Brancalion, Ricardo G. César & Vanessa S. MorenoBavarian State Institute of Forestry, Freising, GermanySusanne BrandlDepartment of Natural Sciences, Manchester Metropolitan University, Manchester, UKFrancis Q. Brearley, Giacomo Sellan & Martin J. P. SullivanFacultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Durango, MexicoJaime Briseno-Reyes, José Javier Corral-Rivas & Daniel José Vega-NievaInstitute of Biology and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle (Saale), GermanyHelge BruelheideGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, GermanyHelge BruelheideDevelopment Economics Group, Wageningen University, Wageningen, NetherlandsErwin BulteRosen Center for Advanced Computing (RCAC), Purdue University, West Lafayette, IN, USAAnn Christine Catlin, Lev Gorenstein, Geoffrey Lentner & Xiao ZhuDepartment of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, ItalyRoberto Cazzolla GattiInstitute of Integrative Biology, ETH Zürich, Zürich, SwitzerlandChelsea ChisholmIFER – Institute of Forest Ecosystem Research, Jilove u Prahy, Czech RepublicEmil CiencialaGlobal Change Research Institute of the CAS, Brno, Czech RepublicEmil CiencialaPrograma de Pós-graduação em Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas, Campinas CEP, Biologia, BrazilGabriel D. CollettaDirección Nacional de Bosques (DNB), Ministerio de Ambiente y Desarrollo Sostenible (MAyDS), Ciudad Autónoma de Buenos Aires, Buenos Aires, ArgentinaAnibal CuchiettiDepartment of International Environment and Development Studies (Noragric), Faculty of Landscape and Society, Norwegian University of Life Sciences (NMBU), Ås, NorwayAida Cuni-SanchezDepartment of Environment and Geography, University of York, York, UKAida Cuni-SanchezDepartment of Environmental Science, School of Engineering and Sciences, SRM University-AP, Guntur, IndiaJavid A. DarDepartment of Botany, Dr. Harisingh Gour Vishwavidyalaya (A Central University), Madhya Pradesh, IndiaJavid A. Dar & Subashree KothandaramanDepartment of Ecology and Environmental Sciences, Pondicherry University, Puducherry, IndiaJavid A. Dar, Subashree Kothandaraman, Narayanaswamy Parthasarathy & Somaiah SundarapandianCentre for Structural and Functional Genomics & Quebec Centre for Biodiversity Science, Biology Department, Concordia University, Montreal, Quebec, CanadaSelvadurai DayanandanDepartment of Ecology, Faculty of Science, Charles University, Prague, Czech RepublicSylvain Delabye, Stepan Janecek, Yannick Klomberg, Vincent Maicher & Robert TropekBiology Centre of the Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech RepublicSylvain Delabye, Tom M. Fayle, Vincent Maicher & Robert TropekCirad, UMR EcoFoG (AgroParistech, CNRS, Inrae, Université des Antilles, Université de la Guyane), Campus Agronomique, Kourou, French GuianaGéraldine Derroire, Aurélie Dourdain & Eric MarconDepartment of Geography, Environment and Geomatics, University of Guelph, Guelph, Ontario, CanadaBen DeVriesNational Forest Authority, Kampala, UgandaJohn DiisiDepartment of Silviculture Foundation, Silviculture Research Institute, Vietnamese Academy of Forest Sciences, Hanoi, VietnamTran Van DoDepartment of Botany, Faculty of Science, University of South Bohemia, Bohemia, Czech RepublicJiri DolezalIPHAMETRA, IRET, CENAREST, Libreville, GabonNestor Laurier Engone ObiangFaculté de Gestion de Ressources Naturelles Renouvelables, Université de Kisangani, Kisangani, Democratic Republic of CongoCorneille E. N. Ewango, Faustin M. Mbayu & Eric Katembo WasingyaQueensland Herbarium, Department of Environment and Science, Toowong, Queensland, AustraliaTeresa J. Eyre, Victor J. Neldner & Michael R. NgugiSchool of Biological and Behavioural Sciences, Queen Mary University of London, London, UKTom M. FayleDepartment of Plant Biology, Faculty of Science, University of Yaoundé I, Yaoundé, CameroonLethicia Flavine N. Feunang, Banoho L. P. R. Kabelong, Moses B. Libalah, Louis N. Nforbelie, Emile Narcisse N. Njila & Melanie C. NyakoNatural Resources Institute Finland, Joensuu, FinlandLeena FinérInstitute of Plant Sciences, University of Bern, Bern, SwitzerlandMarkus FischerDepartment of Forest Resource Management, Swedish University of Agricultural Sciences, Umea, SwedenJonas Fridman & Bertil WesterlundResearch and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, ItalyLorenzo Frizzera, Damiano Gianelle & Mirco RodeghieroHerbário Dr. Roberto Miguel Klein, Universidade Regional de Blumenau, Blumenau, BrazilAndré L. de GasperGlick Designs, LLC, Hadley, MA, USAHenry B. GlickCIIDIR Durango, Instituto Politécnico Nacional, Durango, MexicoMaria Socorro Gonzalez-ElizondoDépartement des Sciences et Technologies de l’Environnement, Université du Burundi, Bujumbura, BurundiRichard HabonayoFaculté des Sciences, Evolutionary Biology and Ecology Unit, Université Libre de Bruxelles, Brussels, BelgiumOlivier J. HardyRoyal Botanic Garden Edinburgh, Edinburgh, UKDavid J. Harris & Axel Dalberg PoulsenDepartment of Plant Sciences, University of Oxford, Oxford, UKAndrew HectorDepartment of Plant Systematics, Bayreuth University, Bayreuth, GermanyAndreas HempHelmholtz GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Potsdam, GermanyMartin HeroldWild Chimpanzee Foundation, Liberia Representation, Monrovia, LiberiaAnnika HillersCentre for Conservation Science, The Royal Society for the Protection of Birds, Sandy, UKAnnika HillersDepartment of Environment, Laboratory for Wood Technology (UGent-Woodlab), Ghent University, Ghent, BelgiumWannes HubauAMAP, University of Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, FranceThomas IbanezDepartment of Forest Science, Tokyo University of Agriculture, Tokyo, JapanNobuo ImaiBiology Department, Université Officielle de Bukavu, Bukavu, Democratic Republic of CongoGerard ImaniInstitute of Dendrology, Polish Academy of Sciences, Kórnik, PolandAndrzej M. Jagodzinski & Jacek OleksynPoznan University of Life Sciences, Faculty of Forestry and Wood Technology, Department of Game Management and Forest Protection, Poznan, PolandAndrzej M. JagodzinskiDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, DenmarkVivian Kvist Johannsen & Sebastian Kepfer-RojasPlant Biology Department, Biology Institute, University of Campinas (UNICAMP), Campinas, BrazilCarlos A. JolyDepartment of Botany and Plant Pathology, Purdue University, West Lafayette, IN, USABlaise JumbamInstitute of Agricultural Research for Development (IRAD), Nkolbisson, Ministry of Scientific Research and Innovation, Yaounde, CameroonBlaise JumbamDepartment of Food and Resource Economics, University of Copenhagen, Copenhagen, DenmarkGoytom Abraha KahsayForestry Faculty, Bauman Moscow State Technical University, Mytischi, RussiaViktor Karminov & Olga MartynenkoIntegrative Research Center, The Field Museum, Chicago, IL, USAKuswata KartawinataLabo Botanique, Université Félix Houphouët-Boigny, Abidjan, Ivory CoastJustin N. KassiComputational and Applied Vegetation Ecology Lab, Ghent University, Ghent, BelgiumElizabeth Kearsley & Hans VerbeeckDepartment of Physical and Environmental Sciences, Colorado Mesa University, Grand Junction, CO, USADeborah K. KennardDepartment of Botany, Dr. Harisingh Gour Vishwavidalaya (A Central University), Sagar, IndiaMohammed Latif KhanKenya Forestry Research Institute, Department of Forest Resource Assessment, Nairobi, KenyaJohn N. KigomoDepartment of Forest Sciences, Seoul National University, Seoul, Republic of KoreaHyun Seok KimInterdisciplinary Program in Agricultural and Forest Meteorology, Seoul National University, Seoul, Republic of KoreaHyun Seok KimNational Center for Agro Meteorology, Seoul, Republic of KoreaHyun Seok KimResearch Institute for Agriculture and Life Sciences, Seoul National University, Seoul, Republic of KoreaHyun Seok KimInstitute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, EstoniaHenn Korjus & Mait LangInternational Institute for Applied Systems Analysis, Laxenburg, AustriaFlorian Kraxner, Dmitry Schepaschenko & Anatoly Z. ShvidenkoDepartment of Geoinformatics, Central University of Jharkhand, Ranchi, IndiaAmit KumarTartu Observatory, University of Tartu, Tõravere, EstoniaMait LangSchool of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South AfricaMichael J. LawesDepartment of Forest Engineering, Federal University of Viçosa (UFV), Viçosa, BrazilRodrigo V. LeiteDepartment of Geography, University College London, London, UKSimon L. LewisPlant Systematics and Ecology Laboratory (LaBosystE), Higher Teacher’s Training College, University of Yaoundé I, Yaoundé, CameroonMoses B. LibalahLaboratoire d’Écologie et Aménagement Forestier, Département d’Ecologie et de Gestion des Ressources Végétales, Université de Kisangani, Kisangani, Democratic Republic of CongoJanvier LisingoInstituto de Silvicultura e Industria de la Madera, Universidad Juarez del Estado de Durango, Durango, MexicoPablito Marcelo López-Serrano & Maria Guadalupe Nava-MirandaFaculty of Forestry, Qingdao Agricultural University, Qingdao, ChinaHuicui LuCenter for Forest Ecology and Productivity RAS (CEPF RAS), Moscow, RussiaNatalia V. LukinaDepartment of Ecoscience, Aarhus University, Silkeborg, DenmarkAnne Mette LykkeNicholas School of the Environment, Duke University, Durham, NC, USAVincent Maicher & John R. PoulsenDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USABrian S. MaitnerAgroParisTech, UMR AMAP, University of Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, FranceEric MarconUniversity of the Sunshine Coast, Sippy Downs, Queensland, AustraliaAndrew R. MarshallUniversity of York, York, UKAndrew R. MarshallFlamingo Land Ltd., North Yorkshire, UKAndrew R. MarshallDepartment of Wildlife Management, College of African Wildlife Management, Mweka, TanzaniaEmanuel H. MartinKenya Forestry Research Institute, Headquarters, Nairobi, KenyaMusingo T. E. MbuviDepartamento de Ecología y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, MexicoJorge A. MeaveEcology and Evolutionary Biology, University of Connecticut, Storrs, CT, USACory MerowDepartment of Forest Management and Forest Economics, Warsaw University of Life Sciences, Warsaw, PolandStanislaw MiscickiTropical Forests and People Research Centre, University of the Sunshine Coast, Maroochydore DC, Queensland, AustraliaSharif A. Mukul & Alain S. K. NguteFieldstation Fabrikschleichach, Julius-Maximilians University Würzburg, Würzburg, GermanyJörg C. MüllerBavarian Forest Nationalpark, Grafenau, GermanyJörg C. MüllerFakultas Kehutanan, Universitas Papua, Jalan Gunung Salju Amban, Manokwari Papua Barat, IndonesiaAgustinus MurdjokoLimbe Botanic Garden, Limbe, CameroonLitonga Elias NdiveInstitute of Forestry, Belgrade, SerbiaRadovan V. NevenicTropical Plant Exploration Group (TroPEG), Buea, CameroonMichael L. Ngoh & Moses Nsanyi SaingeDepartment of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USAMichael L. NgohApplied Biology and Ecology Research Unit, University of Dschang, Dschang, CameroonAlain S. K. NguteDepartment of Forestry and Natural Resources, University of Kentucky, Lexington, KY, USAThomas O. OchuodhoUQAM, Centre for Forest Research, Montreal, Quebec, CanadaAlain PaquetteV.N. Sukachev Forest Institute of FRC KSC SB RAS, Krasnoyarsk, RussiaElena I. Parfenova, Dmitry Schepaschenko & Nadja TchebakovaUrban Management and Planning, School of Social Sciences, Western Sydney University, Penrith, New South Wales, AustraliaSebastian PfautschInstituto Nacional de Pesquisas da Amazônia—INPA, Grupo Ecologia. Monitoramento e Uso Sustentável de Áreas Úmidas MAUA, Manaus, BrazilMaria T. F. Piedade, Jochen Schöngart & Natalia TarghettaCentro de Formação em Ciências Agroflorestais, Universidade Federal do Sul da Bahia, Ilhéus, BrazilDaniel Piotto & Samir G. RolimDepartment of Agriculture, Food, Environment and Forestry, University of Firenze, Firenze, ItalyMartina Pollastrini & Federico SelviTechnical University of Munich, School of Life Sciences Weihenstephan, Chair of Forest Growth and Yield Science, Munich, GermanyHans PretzschCentro Agricoltura, Alimenti, Ambiente, University of Trento, San Michele all’Adige, ItalyMirco RodeghieroDepartment of Biology, University of Florence, Sesto Fiorentino, ItalyFrancesco RoveroMUSE—Museo delle Scienze, Trento, ItalyFrancesco RoveroInfoflora c/o Botanical Garden of Geneva, Geneva, SwitzerlandErvan RutishauserAgricultural Research, Education and Extension Organization (AREEO), Research Institute of Forests and Rangelands (RIFR), Tehran, IranKhosro Sagheb-TalebiDepartment of Environmental Sciences, Central University of Jharkhand, Ranchi, IndiaPurabi SaikiaInstitute of International Education Scholar Rescue Fund (IIE-SRF), One World Trade Center, New York, NY, USAMoses Nsanyi SaingeCentro de Modelación y Monitoreo de Ecosistemas, Facultad de Ciencias, Universidad Mayor, Santiago, ChileChristian Salas-EljatibVicerrectoría de Investigación y Postgrado, Universidad de La Frontera, Temuco, ChileChristian Salas-EljatibDepartamento de Silvicultura y Conservación de la Naturaleza, Universidad de Chile, Santiago, ChileChristian Salas-EljatibРeoples Friendship University of Russia (RUDN University), Moscow, RussiaDmitry SchepaschenkoUniversity of Freiburg, Faculty of Biology, Freiburg, GermanyMichael Scherer-LorenzenInstitution with City, Department of Geography, University of Zurich, Zurich, SwitzerlandBernhard SchmidNational Forest Centre, Zvolen, Slovak RepublicVladimír ŠebeňCNRS-UMR LEEISA, Campus Agronomique, Kourou, French GuianaGiacomo SellanUniversite de Lorraine, AgroParisTech, INRA, Nancy, FranceJosep M. Serra-DiazCenter for International Forestry Research (CIFOR), Situ Gede, Bogor Barat, IndonesiaDouglas SheilCirad, University of Montpellier, Montpellier, FrancePlinio SistUniversidade Federal do Rio Grande do Norte, Departamento de Ecologia, Natal, BrazilAlexandre F. SouzaSchool of Biological Sciences, University of Aberdeen, Aberdeen, UKMike D. SwaineHerbarium Kew, Royal Botanic Gardens Kew, London, UKLiam A. TrethowanFaculté des Sciences Appliquées, Université de Mbujimayi, Mbujimayi, Democratic Republic of CongoJohn Tshibamba MukendiYale School of Forestry and Environmental Studies, New Haven, CT, USAPeter Mbanda UmunayUral State Forest Engineering University, Botanical Garden, Ural Branch of the Russian Academy of Sciences, Yekaterinburg, RussiaVladimir A. UsoltsevDIBAF Department, Tuscia University, Viterbo, ItalyGaia Vaglio Laurin & Riccardo ValentiniLINCGlobal, MNCN, CSIC, Madrid, SpainFernando ValladaresPlant Ecology and Nature Conservation Group, Wageningen University, AA Wageningen, NetherlandsFons van der PlasAgricultural High School, ESAV, Polytechnic Institute of Viseu, IPV, Viseu, PortugalHelder VianaCentre for the Research and Technology of Agro-Environmental and Biological Sciences, CITAB, UTAD, Quinta de Prados, Vila Real, PortugalHelder VianaDepartment of Forest Engineering, Universidade Regional de Blumenau, Blumenau, BrazilAlexander C. VibransNucleo de Estudos e Pesquisas Ambientais, Universidade Estadual de Campinas, Campinas (UNICAMP), SP, Campinas, BrazilSimone A. VieiraInternational Center for Tropical Botany, Department of Biological Sciences, Florida International University, Miami, FL, USAJason VleminckxForest Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, AustraliaCatherine E. WaiteSanya Nanfan Research Institute, Hainan Yazhou Bay Seed Laboratory, Hainan University, Sanya, ChinaHua-Feng Wang & Zhi-Xin ZhuKenya Forestry Research Institute, Taita Taveta Research Centre, Wundanyi, KenyaChemuku WekesaDepartment of Wetland Ecology, Institute for Geography and Geoecology, Karlsruhe Institute for Technology, Rastatt, GermanyFlorian WittmannDepartment of Forest Management, Centre for Agricultural Research in Suriname, Paramaribo, SurinameVerginia WortelPolish State Forests-Coordination Centre for Environmental Projects, Warsaw, PolandTomasz Zawiła-NiedźwieckiResearch Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, ChinaChunyu Zhang & Xiuhai ZhaoDepartment of Statistics, University of Wisconsin–Madison, Madison, WI, USAJun ZhuInstitut National Polytechnique Félix Houphouët-Boigny, DFR Eaux, Forêts et Environnement, BP, Yamoussoukro, Ivory CoastIrie C. Zo-BiCentre for Invasion Biology, Department of Mathematical Sciences, Stellenbosch University, Matieland, South AfricaCang HuiAfrican Institute for Mathematical Sciences, Muizenberg, South AfricaCang HuiConceptualization: J. Liang and C.H. Methodology: J. Liang, C.H., J.G.P.G. and N. Picard. Data coordination: J. Liang, M.Z., S.d.-M., T.W.C., G.-J.N., P.B.R., F. Slik, K.v.G., J.G.P.G. and N. Picard. Writing, revision and editing: all. More

IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2021).Giorgi, F. & Lionello, P. Climate change projections for the Mediterranean region. Glob. Planet. Change 63, 90–104 (2008).Article 

Google Scholar 
Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Körner, C. Alpine Plant Life (Springer International Publishing, 2021).Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).CAS 
PubMed 
Article 

Google Scholar 
Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).Article 

Google Scholar 
Gamm, C. M. et al. Declining growth of deciduous shrubs in the warming climate of continental western Greenland. J. Ecol. 106, 640–654 (2018).CAS 
Article 

Google Scholar 
AMAP. Arctic Climate Change Update 2021: Key Trends and Impacts. Arctic Monitoring and Assessment Programme (2021).Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nat. Clim. Change 2, 453–457 (2012).Article 

Google Scholar 
Bjorkman, A. D. et al. Plant functional trait change across a warming tundra biome. Nature 562, 57–62 (2018).CAS 
PubMed 
Article 

Google Scholar 
Zhang, W. et al. Self‐amplifying feedbacks accelerate greening and warming of the arctic. Geophys. Res. Lett. 45, 7102–7111 (2018).Article 

Google Scholar 
Körner, C. Treelines will be understood once the functional difference between a tree and a shrub is. Ambio 41, 197–206 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Pellizzari, E. et al. Diverging shrub and tree growth from the Polar to the Mediterranean biomes across the European continent. Glob. Change Biol. 23, 3169–3180 (2017).Article 

Google Scholar 
Dobbert, S., Pape, R. & Löffler, J. How does spatial heterogeneity affect inter‐ and intraspecific growth patterns in tundra shrubs. J. Ecol. 7, 1 (2021).
Google Scholar 
Ackerman, D., Griffin, D., Hobbie, S. E. & Finlay, J. C. Arctic shrub growth trajectories differ across soil moisture levels. Glob. Change Biol. 23, 4294–4302 (2017).Article 

Google Scholar 
Stendel, M., Christensen, J. H. & Petersen, D. in High-Arctic Ecosystem Dynamics in a Changing Climate (eds. Meltofte, H.) 13–43 (Elsevier, 2008).Prislan, P. et al. Annual cambial rhythm in Pinus halepensis and Pinus sylvestris as indicator for climate adaptation. Front. Plant Sci. 7, 1923 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Gazol, A. & Camarero, J. J. Mediterranean dwarf shrubs and coexisting trees present different radial-growth synchronies and responses to climate. Plant Ecol. 213, 1687–1698 (2012).Article 

Google Scholar 
Olano, J. M., Almería, I., Eugenio, M. & Arx, G. V. Under pressure: how a Mediterranean high-mountain forb coordinates growth and hydraulic xylem anatomy in response to temperature and water constraints. Funct. Ecol. 27, 1295–1303 (2013).Article 

Google Scholar 
Voltas, J. et al. A retrospective, dual-isotope approach reveals individual predispositions to winter-drought induced tree dieback in the southernmost distribution limit of Scots pine. Plant, Cell Environ. 36, 1435–1448 (2013).CAS 
Article 

Google Scholar 
Hanewinkel, M., Cullmann, D. A., Schelhaas, M.-J., Nabuurs, G.-J. & Zimmermann, N. E. Climate change may cause severe loss in the economic value of European forest land. Nat. Clim. Change 3, 203–207 (2013).Article 

Google Scholar 
Castagneri, D., Battipaglia, G., Arx, G. V., Pacheco, A. & Carrer, M. Tree-ring anatomy and carbon isotope ratio show both direct and legacy effects of climate on bimodal xylem formation in Pinus pinea. Tree Physiol. 38, 1098–1109 (2018).CAS 
PubMed 
Article 

Google Scholar 
Cabon, A., Peters, R. L., Fonti, P., Martínez-Vilalta, J. & Cáceres, M. Temperature and water potential co-limit stem cambial activity along a steep elevational gradient. N. Phytologist 226, 1325–1340 (2020).CAS 
Article 

Google Scholar 
Camarero, J. J., Valeriano, C., Gazol, A., Colangelo, M. & Sánchez-Salguero, R. Climate differently impacts the growth of coexisting trees and shrubs under semi-arid mediterranean conditions. Forests 12, 381 (2021).Article 

Google Scholar 
García-Cervigón Morales, A. I., Olano Mendoza, J. M., Eugenio Gozalbo, M. & Camarero Martínez, J. J. Arboreal and prostrate conifers coexisting in Mediterranean high mountains differ in their climatic responses. Dendrochronologia 30, 279–286 (2012).Article 

Google Scholar 
Oladi, R., Emaminasab, M. & Eckstein, D. The dendroecological potential of shrubs in north Iranian semi-deserts. Dendrochronologia 44, 94–102 (2017).Article 

Google Scholar 
McMahon, S. M. & Parker, G. G. A general model of intra-annual tree growth using dendrometer bands. Ecol. Evolution 5, 243–254 (2015).Article 

Google Scholar 
Drew, D. M., Downes, G. M. & Battaglia, M. CAMBIUM, a process-based model of daily xylem development in Eucalyptus. J. Theor. Biol. 264, 395–406 (2010).PubMed 
Article 

Google Scholar 
Delpierre, N., Berveiller, D., Granda, E. & Dufrêne, E. Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest. N. Phytologist 210, 459–470 (2016).CAS 
Article 

Google Scholar 
Rathgeber, C. B. K., Cuny, H. E. & Fonti, P. Biological basis of tree-ring formation: a crash course. Front. Plant Sci. 7, 734 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Körner, C. Carbon limitation in trees. J. Ecol. 91, 4–17 (2003).Article 

Google Scholar 
Thompson, J. D. Plant Evolution in the Mediterranean (Oxford University Press, 2005).Rossi, S. et al. Pattern of xylem phenology in conifers of cold ecosystems at the Northern Hemisphere. Glob. Change Biol. 22, 3804–3813 (2016).Article 

Google Scholar 
Löffler, J. & Pape, R. Thermal niche predictors of alpine plant species. Ecology 101, e02891 (2020).PubMed 
Article 

Google Scholar 
Zweifel, R. et al. Why trees grow at night. N. Phytologist 231, 2174–2185 (2021).Article 

Google Scholar 
González-Rodríguez, Á. M. et al. Seasonal cycles of sap flow and stem radius variation of Spartocytisus supranubius in the alpine zone of Tenerife, Canary Islands. Alp. Bot. 127, 97–108 (2017).Article 

Google Scholar 
Zweifel, R., Haeni, M., Buchmann, N. & Eugster, W. Are trees able to grow in periods of stem shrinkage. N. Phytologist 211, 839–849 (2016).Article 

Google Scholar 
Rossi, S., Deslauriers, A., Anfodillo, T. & Carraro, V. Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia 152, 1–12 (2007).PubMed 
Article 

Google Scholar 
Myers-Smith, I. H. et al. Climate sensitivity of shrub growth across the tundra biome. Nat. Clim. Change 5, 887–891 (2015).Article 

Google Scholar 
Mitrakos, K. A Theory for Mediterranean Plant Life (Acta oecologica, 1980).Camarero, J. J., Olano, J. M. & Parras, A. Plastic bimodal xylogenesis in conifers from continental Mediterranean climates. N. Phytologist 185, 471–480 (2010).Article 

Google Scholar 
Alday, J. G., Camarero, J. J., Revilla, J. & Resco de Dios, V. Similar diurnal, seasonal and annual rhythms in radial root expansion across two coexisting Mediterranean oak species. Tree Physiol. 40, 956–968 (2020).PubMed 
Article 

Google Scholar 
Lockhart, J. A. An analysis of irreversible plant cell elongation. J. Theor. Biol. 8, 264–275 (1965).CAS 
PubMed 
Article 

Google Scholar 
Descals, A. et al. Soil thawing regulates the spring growth onset in tundra and alpine biomes. Sci. total Environ. 742, 140637 (2020).CAS 
PubMed 
Article 

Google Scholar 
Morgner, E., Elberling, B., Strebel, D. & Cooper, E. J. The importance of winter in annual ecosystem respiration in the High Arctic: effects of snow depth in two vegetation types. Polar Res. 29, 58–74 (2010).CAS 
Article 

Google Scholar 
Weijers, S., Beckers, N. & Löffler, J. Recent spring warming limits near-treeline deciduous and evergreen alpine dwarf shrub growth. Ecosphere 9, e02328 (2018).Article 

Google Scholar 
Bret-Harte, M. S. et al. Developmental plasticity allows Betula nana to dominate tundra subjected to an altered environment. Ecology 82, 18–32 (2001).Article 

Google Scholar 
Wang, Y. et al. Warming‐induced shrubline advance stalled by moisture limitation on the Tibetan Plateau. Ecography 44, 1631–1641 (2021).Article 

Google Scholar 
Tape, K. D., Hallinger, M., Welker, J. M. & Ruess, R. W. Landscape heterogeneity of shrub expansion in Arctic Alaska. Ecosystems 15, 711–724 (2012).CAS 
Article 

Google Scholar 
Francon, L., Corona, C., Till-Bottraud, I., Carlson, B. Z. & Stoffel, M. Some (do not) like it hot: shrub growth is hampered by heat and drought at the alpine treeline in recent decades. Am. J. Bot. 107, 607–617 (2020).PubMed 
Article 

Google Scholar 
Lu, X., Liang, E., Babst, F., Camarero, J. J. & Büntgen, U. Warming-induced tipping points of Arctic and alpine shrub recruitment. Proc. Natl Acad. Sci. USA 119, e2118120119 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sabater, A. M. et al. Transpiration from subarctic deciduous woodlands: environmental controls and contribution to ecosystem evapotranspiration. Ecohydrology 13, e2190 (2019).
Google Scholar 
Larson, P. R. The indirect effect of photoperiod on tracheid diameter in Pinus resinosa. Am. J. Bot. 49, 132–137 (1962).Article 

Google Scholar 
Jackson, S. D. Plant responses to photoperiod. N. Phytologist 181, 517–531 (2009).CAS 
Article 

Google Scholar 
Waisel, Y. & Fahn, A. The effects of environment on wood formation and cambial activity in Robina Pseudacacia L. N. Phytologist 64, 436 (1965).Article 

Google Scholar 
Pasho, E., Camarero, J. J. & Vicente-Serrano, S. M. Climatic impacts and drought control of radial growth and seasonal wood formation in Pinus halepensis. Trees 26, 1875–1886 (2012).Article 

Google Scholar 
Gričar, J. et al. Plasticity in variation of xylem and phloem cell characteristics of Norway spruce under different local conditions. Front. Plant Sci. 6, 730 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).CAS 
PubMed 
Article 

Google Scholar 
Oberhuber, W., Sehrt, M. & Kitz, F. Hygroscopic properties of thin dead outer bark layers strongly influence stem diameter variations on short and long time scales in Scots pine (Pinus sylvestris L.). Agric. For. Meteorol. 290, 108026 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Sonntag, D. Important new values of the physical constants of 1986, vapour pressure formulations based on ITS-90, and psychrometer formulae. Z. f.ür. Meteorologie 70, 340–344 (1990).
Google Scholar 
Löffler, J., Dobbert, S., Pape, R. & Wundram, D. Dendrometer measurements of arctic-alpine dwarf shrubs and micro-environmental drivers of plant growth—Dataset from long-term alpine ecosystem research in central Norway. Erdkunde 75, DP311201 (2021).
Google Scholar 
Löffler, J., Albrecht, E. C., Dobbert, S., Pape, R. & Wundram, D. Dendrometer measurements of Mediterranean-alpine dwarf shrubs and micro-environmental drivers of plant growth—Dataset from long-term alpine ecosystem research in the Sierra Nevada, Spain (LTAER-ES). Erdkunde 76, DP311202 (2022).Article 

Google Scholar 
R Core Team. A Language and Environment for Statistical Computing. https://www.R-project.org/ (2020).Wood, S. N. Generalized Additive Models. An introduction with R (2nd edition) (Chapman & Hall/CRC, 2017).Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc.: Ser. B 73, 3–36 (2011).Article 

Google Scholar 
Byun, J. G. et al. Radial growth response of Pinus densiflora and Quercus spp. to topographic and climatic factors in South Korea. J. Plant Ecol. 6, 380–392 (2013).Article 

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

Google Scholar 
Gasparrini, A., Scheipl, F., Armstrong, B. & Kenward, M. G. A penalized framework for distributed lag non-linear models. Biometrics 73, 938–948 (2017).PubMed 
Article 

Google Scholar 
Scott, E. R., Uriarte, M. & Bruna, E. M. Delayed effects of climate on vital rates lead to demographic divergence in Amazonian forest fragments. https://doi.org/10.1101/2021.06.28.450186 (2021).Almon, S. The distributed lag between capital appropriations and expenditures. Econometrica 33, 178 (1965).Article 

Google Scholar 
Vanoni, M., Bugmann, H., Nötzli, M. & Bigler, C. Drought and frost contribute to abrupt growth decreases before tree mortality in nine temperate tree species. For. Ecol. Manag. 382, 51–63 (2016).Article 

Google Scholar 
Pukienė, R., Vitas, A., Kažys, J. & Rimkus, E. Four-decadal series of dendrometer measurements reveals trends in Pinus sylvestris L. inter- and intra-annual growth response to climatic conditions. Can. J. For. Res. 51, 445–454 (2020).Article 

Google Scholar 
Gasparrini, A., Armstrong, B. & Kenward, M. G. Distributed lag non-linear models. Stat. Med. 29, 2224–2234 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gasparrini, A. Distributed lag linear and non-linear models in R: the package dlnm. J. Stat. Softw. 43, https://doi.org/10.18637/jss.v043.i08 (2011).Kartverket. Terrain Map. https://www.norgeskart.no/ (Norwegian Mapping Authority, 2008).Autonomous body National Center for Geographic Information (CNIG). Digital Terrain Model – DTM25. http://centrodedescargas.cnig.es/ (2009). More

Luck, G. W., Daily, G. C. & Ehrlich, P. R. Population diversity and ecosystem services. Trends Ecol. Evol. 18, 331–336. https://doi.org/10.1016/S0169-5347(03)00100-9 (2003).Article 

Google Scholar 
Schindler, D. E. et al. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–612. https://doi.org/10.1038/nature09060 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Moore, J. W., Yeakel, J. D., Peard, D., Lough, J. & Beere, M. Life-history diversity and its importance to population stability and persistence of a migratory fish: Steelhead in two large North American watersheds. J. Anim. Ecol. 83, 1035–1046. https://doi.org/10.1111/1365-2656.12212 (2014).Article 
PubMed 

Google Scholar 
Barrett, R. D. H. et al. Linking a mutation to survival in wild mice. Science 363, 499. https://doi.org/10.1126/science.aav3824 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Jones, F. C. et al. The genomic basis of adaptive evolution in threespine sticklebacks. Nature 484, 55. https://doi.org/10.1038/nature10944 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Savage, A. E. & Zamudio, K. R. MHC genotypes associate with resistance to a frog-killing fungus. Proc. Natl. Acad. Sci. 108, 16705. https://doi.org/10.1073/pnas.1106893108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hofinger, B. J. et al. An exceptionally high nucleotide and haplotype diversity and a signature of positive selection for the eIF4E resistance gene in barley are revealed by allele mining and phylogenetic analyses of natural populations. Mol. Ecol. 20, 3653–3668. https://doi.org/10.1111/j.1365-294X.2011.05201.x (2011).CAS 
Article 
PubMed 

Google Scholar 
Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl. Acad. Sci. 114, E6089. https://doi.org/10.1073/pnas.1704949114 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hughes, J. B., Daily, G. C. & Ehrlich, P. R. Population diversity: its extent and extinction. Science 278, 689. https://doi.org/10.1126/science.278.5338.689 (1997).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Fernández-Llamazares, Á. et al. Scientists’ warning to humanity on threats to indigenous and local knowledge systems. J. Ethnobiol. 41(144–169), 126 (2021).
Google Scholar 
Womble, J. N., Willson, M. F., Sigler, M. F., Kelly, B. P. & VanBlaricom, G. R. Distribution of Steller sea lions (Eumetopias jubatus) in relation to spring-spawning fish in SE Alaska. Mar. Ecol. Prog. Ser. 294, 271–282 (2005).ADS 
Article 

Google Scholar 
Thomas, G. L. & Thorne, R. E. Night-time predation by Steller sea lions. Nature 411, 1013–1013. https://doi.org/10.1038/35082745 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Chamberlin, J. W., Beckman, B. R., Greene, C. M., Rice, C. A. & Hall, J. E. How relative size and abundance structures the relationship between size and individual growth in an ontogenetically piscivorous fish. Ecol. Evol. 7, 6981–6995. https://doi.org/10.1002/ece3.3218 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Hatch, S. A. Kittiwake diets and chick production signal a 2008 regime shift in the Northeast Pacific. Mar. Ecol. Prog. Ser. 477, 271–284 (2013).ADS 
Article 

Google Scholar 
Schrimpf, M. B., Parrish, J. K. & Pearson, S. F. Trade-offs in prey quality and quantity revealed through the behavioral compensation of breeding seabirds. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps09750 (2012).Article 

Google Scholar 
Willson, M. F. & Womble, J. N. Vertebrate exploitation of pulsed marine prey: A review and the example of spawning herring. Rev. Fish Biol. Fisheries 16, 183–200. https://doi.org/10.1007/s11160-006-9009-7 (2006).Article 

Google Scholar 
Sandell, T., Lindquist, A., Dionne, P. & Lowry, D. 2016 Washington State herring stock status report (Washington Department of Fish and Wildlife, 2019).Petrou, E. L. et al. Functional genetic diversity in an exploited marine species and its relevance to fisheries management. Proc. R. Soc. B Biol. Sci. 288, 20202398. https://doi.org/10.1098/rspb.2020.2398 (2021).CAS 
Article 

Google Scholar 
Chamberlin, J. et al. Phenological diversity of a prey species supports life-stage specific foraging opportunity for a mobile consumer. ICES J. Mar. Sci. 78, 3089–3100. https://doi.org/10.1093/icesjms/fsab176 (2021).Article 

Google Scholar 
Lok, E. K. et al. Spatiotemporal associations between Pacific herring spawn and surf scoter spring migration: Evaluating a silver wave hypothesis. Mar. Ecol. Prog. Ser. 457, 139–150 (2012).ADS 
Article 

Google Scholar 
Armstrong, J. B., Takimoto, G., Schindler, D. E., Hayes, M. M. & Kauffman, M. J. Resource waves: Phenological diversity enhances foraging opportunities for mobile consumers. Ecology 97, 1099–1112. https://doi.org/10.1890/15-0554.1 (2016).Article 
PubMed 

Google Scholar 
McKechnie, I. et al. Archaeological data provide alternative hypotheses on Pacific herring (Clupea pallasii) distribution, abundance, and variability. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1316072111 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Moss, M. L., Rodrigues, A. T., Speller, C. F. & Yang, D. Y. The historical ecology of Pacific herring: Tracing Alaska Native use of a forage fish. J. Archaeol. Sci. Rep. https://doi.org/10.1016/j.jasrep.2015.10.005 (2016).Article 

Google Scholar 
Kopperl, R. E. Herring use in southern Puget Sound: Analysis of fish remains at 45-KI-437. Northwest Anthropol. Res. Notes 35, 1–20 (2001).
Google Scholar 
McKechnie, I. & Moss, M. L. Meta-analysis in zooarchaeology expands perspectives on Indigenous fisheries of the Northwest Coast of North America. J. Archaeol. Sci. Rep. 8, 470–485. https://doi.org/10.1016/j.jasrep.2016.04.006 (2016).Article 

Google Scholar 
Caldwell, M. E. et al. A bird’s eye view of northern Coast Salish intertidal resource management features, southern British Columbia, Canada. J. Isl. Coast. Archaeol. 7, 219–233. https://doi.org/10.1080/15564894.2011.586089 (2012).Article 

Google Scholar 
Eells, M. & Castile, G. P. The Indians of Puget Sound: The Notebooks of Myron Eells (University of Washington Press, 1985).
Google Scholar 
Smith, M. W. The Puyallup-Nisqually (Columbia University Press, 1940).Book 

Google Scholar 
Thornton, T. F. & Moss, M. L. Herring and People in the North Pacific: Sustaining a Keystone Species (University of Washington Press, 2021).
Google Scholar 
Powell, M. Divided waters: Heiltsuk spatial management of herring fisheries and the politics of native sovereignty. West. Hist. Q. 43, 463–484. https://doi.org/10.2307/westhistquar.43.4.0463 (2012).Article 

Google Scholar 
Gauvreau, A. M., Lepofsky, D., Rutherford, M. & Reid, M. “Everything revolves around the herring”: The Heiltsuk–herring relationship through time. Ecol. Soc. https://doi.org/10.5751/ES-09201-220210 (2017).Article 

Google Scholar 
von der Porten, S., Lepofsky, D., McGregor, D. & Silver, J. Recommendations for marine herring policy change in Canada: Aligning with Indigenous legal and inherent rights. Mar. Policy 74, 68–76. https://doi.org/10.1016/j.marpol.2016.09.007 (2016).Article 

Google Scholar 
Hammond, J. Fish in puget sound. Am. Angler 25, 392–393 (1886).
Google Scholar 
Bargmann, G. Forage fish management plan (Washington Department of Fish and Wildlife, 1998).Stick, K. C. & Lindquist, A. 2008 Washington State herring stock status report (Washington Department of Fish and Wildlife, 2009).Erlandson, J. M. & Rick, T. C. Archaeology meets marine ecology: The antiquity of maritime cultures and human impacts on marine fisheries and ecosystems. Ann. Rev. Mar. Sci. 2, 231–251. https://doi.org/10.1146/annurev.marine.010908.163749 (2009).Article 

Google Scholar 
Hadly, E. A. & Barnosky, A. D. in Conservation Paleobiology: Using the Past to Manage for the Future Vol. 15 (eds Dietl, G. P. & Flessa, K. W.) 39–59 (Paleontological Society Papers, 2009).Wolverton, S. & Lyman, R. L. in Conservation Biology and Applied Zooarchaeology (eds Wolverton, S. & Lyman, R. L.) 1–22 (University of Arizona Press, 2012).Rogers, L. A. et al. Centennial-scale fluctuations and regional complexity characterize Pacific salmon population dynamics over the past five centuries. Proc. Natl. Acad. Sci. 110, 1750. https://doi.org/10.1073/pnas.1212858110 (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wright, C. A., Dallimore, A., Thomson, R. E., Patterson, R. T. & Ware, D. M. Late Holocene paleofish populations in Effingham Inlet, British Columbia, Canada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 224, 367–384. https://doi.org/10.1016/j.palaeo.2005.03.041 (2005).Article 

Google Scholar 
Thompson, T. Q. et al. Anthropogenic habitat alteration leads to rapid loss of adaptive variation and restoration potential in wild salmon populations. Proc. Natl. Acad. Sci. 116, 177. https://doi.org/10.1073/pnas.1811559115 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Halffman, C. M. et al. Early human use of anadromous salmon in North America at 11,500 y ago. Proc. Natl. Acad. Sci. 112, 12344–12348. https://doi.org/10.1073/pnas.1509747112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Quinn, T. An environmental and historical overview of the Puget Sound ecosystem (U.S. Geological Survey, 2010).Kopperl, R. E., Taylor, A. K., Miss, C. J., Ames, K. M. & Hodges, C. M. The Bear Creek Site (45KI839), a Late Pleistocene-Holocene transition occupation in the Puget Sound lowland, King County, Washington. PaleoAmerica 1, 116–120. https://doi.org/10.1179/2055556314Z.0000000004 (2015).Article 

Google Scholar 
Gunther, E. Klallam Ethnography (University of Washington Press, 1927).
Google Scholar 
Elmendorf, W. W. & Kroeber, A. L. The Structure of Twana Culture (Washington State University Press, 1992).
Google Scholar 
The Suquamish Tribe. Fish consumption survey of the Suquamish Indian tribe of the Port Madison Indian Reservation, Puget Sound Region (Suquamish, WA, 2000).Suttles, W. P. The Economic Life of the Coast Salish of Haro and Rosario Straits (Garland Publishing, 1974).
Google Scholar 
Lane, B. The Indian herring fishery from the earliest times to the mid-nineteenth century (United States Depatment of the Interior, 1974).Stein, J. K. in Vashon Island Archaeology: A View from Burton Acres Shell Midden (eds Stein, J. K. & Phillips, L. S.) 5–16 (Burke Musseum, 2002).Lewarch, D. E. et al. Data recovery excavations at the Bay Street Shell Midden (45KP115), Kitsap County, Washington (Larson Anthropological Archaeological Services Limited, 2002).De Danaan, L. in Vashon Island Archaeology: A View from Burton Acres Shell Midden (eds Stein, J. K. & Phillips, L. S.) 17–31 (Burke Museum, 2002).Stein, J. K. in Vashon Island Archaeology: A View from Burton Acres Shell Midden (eds Stein, J. K. & Phillips, L. S.) 47–64 (Burke Musseum, 2002).Yang, D. Y. & Watt, K. Contamination controls when preparing archaeological remains for ancient DNA analysis. J. Archaeol. Sci. 32, 331–336. https://doi.org/10.1016/j.jas.2004.09.008 (2005).Article 

Google Scholar 
Yang, D. Y., Liu, L., Chen, X. & Speller, C. F. Wild or domesticated: DNA analysis of ancient water buffalo remains from north China. J. Archaeol. Sci. 35, 2778–2785. https://doi.org/10.1016/j.jas.2008.05.010 (2008).Article 

Google Scholar 
Maddox, D. M. et al. A mutation in Syne2 causes early retinal defects in photoreceptors, secondary neurons, and Müller Glia. Invest. Ophthalmol. Vis. Sci. 56, 3776–3787. https://doi.org/10.1167/iovs.14-16047 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Han, F. et al. Ecological adaptation in Atlantic herring is associated with large shifts in allele frequencies at hundreds of loci. Elife 9, e61076. https://doi.org/10.7554/eLife.61076 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Smith, M. J. et al. Multiplex preamplification PCR and microsatellite validation enables accurate single nucleotide polymorphism genotyping of historical fish scales. Mol. Ecol. Resour. 11, 268–277. https://doi.org/10.1111/j.1755-0998.2010.02965.x (2011).Article 
PubMed 

Google Scholar 
Speller, C. F. et al. High potential for using DNA from ancient herring bones to inform modern fisheries management and conservation. PLoS ONE 7, e51122. https://doi.org/10.1371/journal.pone.0051122 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Weir, B. & Cockerham, C. Estimating F-Statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 
PubMed 

Google Scholar 
Archer, F. I., Adams, P. E. & Schneiders, B. B. stratag: An r package for manipulating, summarizing and analysing population genetic data. Mol. Ecol. Resour. 17, 5–11. https://doi.org/10.1111/1755-0998.12559 (2017).CAS 
Article 
PubMed 

Google Scholar 
Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
Article 

Google Scholar 
Rousset, F. Genepop’007: A complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x (2008).Article 
PubMed 

Google Scholar 
Moran, B. M. & Anderson, E. C. Bayesian inference from the conditional genetic stock identification model. Can. J. Fish. Aquat. Sci. 76, 551–560. https://doi.org/10.1139/cjfas-2018-0016 (2018).Article 

Google Scholar 
Moss, M. L. Understanding variability in Northwest Coast faunal assemblages: Beyond economic intensification and cultural complexity. J. Isl. Coast. Archaeol. 7, 1–22. https://doi.org/10.1080/15564894.2011.586090 (2012).Article 

Google Scholar 
Greene, C., Kuehne, L., Rice, C., Fresh, K. & Penttila, D. Forty years of change in forage fish and jellyfish abundance across greater Puget Sound, Washington (USA): Anthropogenic and climate associations. Mar. Ecol. Prog. Ser. 525, 153–170 (2015).ADS 
Article 

Google Scholar 
Rice, C. A., Duda, J. J., Greene, C. M. & Karr, J. R. Geographic patterns of fishes and jellyfish in Puget Sound surface waters. Mar. Coast. Fish. 4, 117–128. https://doi.org/10.1080/19425120.2012.680403 (2012).Article 

Google Scholar 
Haegele, C. W. & Schweigert, J. F. Distribution and characteristics of herring spawning grounds and description of spawning behavior. Can. J. Fish. Aquat. Sci. 42, s39–s55. https://doi.org/10.1139/f85-261 (1985).Article 

Google Scholar 
Gao, Y. W., Joner, S. H. & Bargmann, G. G. Stable isotopic composition of otoliths in identification of spawning stocks of Pacific herring (Clupea pallasi) in Puget Sound. Can. J. Fish. Aquat. Sci. 58, 2113–2120. https://doi.org/10.1139/f01-146 (2001).Article 

Google Scholar 
West, J. E., O’Neill, S. M. & Ylitalo, G. M. Spatial extent, magnitude, and patterns of persistent organochlorine pollutants in Pacific herring (Clupea pallasi) populations in the Puget Sound (USA) and Strait of Georgia (Canada). Sci. Total Environ. 394, 369–378. https://doi.org/10.1016/j.scitotenv.2007.12.027 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Moss, M. L. The nutritional value of Pacific herring: An ancient cultural keystone species on the Northwest Coast of North America. J. Archaeol. Sci. Rep. 5, 649–655. https://doi.org/10.1016/j.jasrep.2015.08.041 (2016).Article 

Google Scholar 
Brown, F. & Brown, Y. K. Staying the course, staying alive- Coastal First Nations fundamental truths: Biodiversity, stewardship and sustainability 82 (2009).Dugmore, A. J. et al. Cultural adaptation, compounding vulnerabilities and conjunctures in Norse Greenland. Proc. Natl. Acad. Sci. 109, 3658. https://doi.org/10.1073/pnas.1115292109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nunn, P. D. et al. Times of plenty, times of less: Last-millennium societal disruption in the Pacific Basin. Hum. Ecol. 35, 385–401. https://doi.org/10.1007/s10745-006-9090-5 (2007).Article 

Google Scholar 
Rose, K. A., Megrey, B. A., Hay, D., Werner, F. & Schweigert, J. Climate regime effects on Pacific herring growth using coupled nutrient-phytoplankton-zooplankton and bioenergetics models. Trans. Am. Fish. Soc. 137, 278–297. https://doi.org/10.1577/T05-152.1 (2008).Article 

Google Scholar 
Rosenthal, Y., Linsley, B. K. & Oppo, D. W. Pacific ocean heat content during the past 10,000 years. Science 342, 617. https://doi.org/10.1126/science.1240837 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hopt, J. & Grier, C. Continuity amidst change: Village organization and fishing subsistence at the Dionisio Point locality in coastal southern British Columbia. J. Isl. Coast. Archaeol. 13, 21–42. https://doi.org/10.1080/15564894.2016.1257526 (2018).Article 

Google Scholar 
Butler, V. L., Campbell, S. K., Bovy, K. M. & Etnier, M. A. Exploring ecodynamics of coastal foragers using integrated faunal records from Čḯxwicən village (Strait of Juan de Fuca, Washington, U.S.A.). J. Archaeol. Sci. Rep. 23, 1143–1167. https://doi.org/10.1016/j.jasrep.2018.09.031 (2019).Article 

Google Scholar 
Lamichhaney, S. et al. Parallel adaptive evolution of geographically distant herring populations on both sides of the North Atlantic Ocean. Proc. Natl. Acad. Sci. 114, E3452–E3461 (2017).CAS 
Article 

Google Scholar 
Carpenter, M. L. et al. Pulling out the 1%: Whole-genome capture for the targeted enrichment of ancient DNA sequencing libraries. Am. J. Hum. Genet. 93, 852–864. https://doi.org/10.1016/j.ajhg.2013.10.002 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Oosting, T. et al. Unlocking the potential of ancient fish DNA in the genomic era. Evol. Appl. 12, 1513–1522. https://doi.org/10.1111/eva.12811 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Abrahms, B. et al. Emerging perspectives on resource tracking and animal movement ecology. Trends Ecol. Evol. 36, 308–320. https://doi.org/10.1016/j.tree.2020.10.018 (2021).Article 
PubMed 

Google Scholar 
Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51, 337–360. https://doi.org/10.1017/S0033822200033865 (2009).Article 

Google Scholar 
Reimer, P. J. et al. The IntCal20 Northern Hemisphere radiocarbon age calibration curve (0–55 cal kBP). Radiocarbon 62, 725–757. https://doi.org/10.1017/RDC.2020.41 (2020).CAS 
Article 

Google Scholar 
Deo, J. N., Stone, J. O. & Stein, J. K. Building confidence in shell: Variations in the marine radiocarbon reservoir correction for the Northwest Coast over the past 3,000 years. Am. Antiq. 69, 771–786. https://doi.org/10.2307/4128449 (2004).Article 

Google Scholar  More

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

Google Scholar 
Knapp, A. K. & Smith, M. D. Variation among biomes in temporal dynamics of aboveground primary production. Science. 291, 481–484 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gillman, L. N. et al. Latitude, productivity and species richness. Glob. Ecol. Biogeogr. 24, 107–117 (2015).Article 

Google Scholar 
Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: A review. Rev. Geophys. 53, 1–34 (2015).Article 

Google Scholar 
Goldman, C. R., Jassby, A. & Powell, T. Interannual fluctuations in primary production: Meteorological forcing at two subalpine lakes. Limnol. Oceanogr. 34, 310–323 (1989).ADS 
CAS 
Article 

Google Scholar 
Sayers, M. J., Fahnenstiel, G. L., Shuchman, R. A. & Bosse, K. R. A new method to estimate global freshwater phytoplankton carbon fixation using satellite remote sensing: initial results. Int. J. Remote Sens. 42, 3708–3730 (2021).Article 

Google Scholar 
Behrenfeld, M. J. et al. Climate-driven trends in contemporary ocean productivity. Nature 444, 752–755 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Uitz, J., Claustre, H., Gentili, B. & Stramski, D. Phytoplankton class-specific primary production in the world’s oceans: Seasonal and interannual variability from satellite observations. Global Biogeochem. Cycles 24, GB3016 (2010).ADS 
Article 
CAS 

Google Scholar 
Holt, J. et al. Modelling the global coastal ocean. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 367, 939–951 (2009).ADS 
MATH 
Article 

Google Scholar 
Duarte, C. M., Losada, I. J., Hendriks, I. E., Mazarrasa, I. & Marbà, N. The role of coastal plant communities for climate change mitigation and adaptation. Nat. Clim. Chang. 3, 961–968 (2013).ADS 
CAS 
Article 

Google Scholar 
Saba, V. S. et al. An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe. Biogeosciences 8, 489–503 (2011).ADS 
CAS 
Article 

Google Scholar 
Duarte, C. M. et al. Seagrass community metabolism: Assessing the carbon sink capacity of seagrass meadows. Global Biogeochem. Cycles 24, 1–8 (2010).Article 
CAS 

Google Scholar 
Charpy-Roubaud, C. & Sournia, A. The comparative estimation of phytoplanktonic, microphytobenthic and macrophytobenthic primary production in the oceans. Mar. Microb. Food Webs 4, 31–57 (1990).
Google Scholar 
Duarte, C. M. et al. Global estimates of the extent and production of macroalgal forests. Global Ecology and Biogeography. 31(7), 1422–1439, https://doi.org/10.1111/geb.13515 (2022).Duggins, D. O. & Estes, J. A. Magnification of secondary production by kelp detritus in coastal marine ecosystems. Science. 245, 170–173 (1989).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Dunton, K. H. & Schell, D. M. Dependence of consumers on macroalgal (Laminaria solidungula) carbon in an arctic kelp community: 13C evidence. Mar. Biol. 625, 615–625 (1987).Article 

Google Scholar 
Krumhansl, K. A. & Scheibling, R. E. Production and fate of kelp detritus. Mar. Ecol. Prog. Ser. 467, 281–302 (2012).ADS 
Article 

Google Scholar 
Ortega, A. et al. Important contribution of macroalgae to oceanic carbon sequestration. Nat. Geosci. 12, 748–754 (2019).ADS 
CAS 
Article 

Google Scholar 
Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. 9, 737–742 (2016).ADS 
CAS 
Article 

Google Scholar 
Bach, L. T. et al. Testing the climate intervention potential of ocean afforestation using the Great Atlantic Sargassum belt. Nat. Commun. 12, 2556 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Duarte, C. M., Wu, J., Xiao, X., Bruhn, A. & Krause-Jensen, D. Can Seaweed Farming Play a Role in Climate Change Mitigation and Adaptation? Front. Mar. Sci. 4 (2017).Kanwisher, J. W. Photosynthesis and respiration in some seaweeds. in Some contemporary studies in marine science:: a collection of original scientific papers presented to Dr. S.M. Marshall, O.B.E., F.R.S. in recognition of her contribution with the late Dr. A.P. Orr to marine biological progress (eds. Barnes, H. & Marshall, S. M.) 407 (Allen & Unwin, 1966).Blinks, L. R. Photosynthesis and productivity of littoral marine algae. J. Mar. Res. 14, 363–373 (1955).
Google Scholar 
Printz, H. Seasonal growth and production of dry matter in Ascophyllum nodosum. Avh. Utg. Av Det Nor. Videnskaps-akademi i Oslo. I. Mat. Klasse 4, 1–15 (1950).
Google Scholar 
Rassweiler, A., Reed, D. C., Harrer, S. L. & Nelson, J. C. Improved estimates of net primary production, growth and standing crop of Macrosystis pryifera in Southern California. Ecology 99, 2132 (2018).PubMed 
Article 

Google Scholar 
Littler, M. M. & Arnold, K. E. Primary Productivity of Marine Macroalgal Functional-Form Groups From Southwestern North America. Journal of Phycology 18, 307–311 (1982).Article 

Google Scholar 
Krause-Jensen, D. et al. Seasonal sea ice cover as principal driver of spatial and temporal variation in depth extension and annual production of kelp in Greenland. Glob. Chang. Biol. 18, 2981–2994 (2012).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Smale, D. A. et al. Environmental factors influencing primary productivity of the forest – forming kelp Laminaria hyperborea in the northeast Atlantic. Sci. Rep. 10, 12161 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pessarrodona, A. et al. Global seaweed productivity. Science Advances https://doi.org/10.1126/sciadv.abn2465 (2022) (in press).Assis, J. et al. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).Article 

Google Scholar 
Fulton, C. J. et al. Form and function of tropical macroalgal reefs in the Anthropocene. Funct. Ecol. 33, 989–999 (2019).Article 

Google Scholar 
Tebbett, S. B. & Bellwood, D. R. Algal turf productivity on coral reefs: A meta-analysis. Mar. Environ. Res. 168, 105311 (2021).CAS 
PubMed 
Article 

Google Scholar 
Wernberg, T., Krumhansl, K., Filbee-Dexter, K. & Pedersen, M. F. Status and trends for the world’s kelp forests. in World Seas: An Environmental Evaluation: Ecological Issues and Environmental Impacts (ed. Sheppard, C.) 57–78, https://doi.org/10.1016/B978-0-12-805052-1.00003-6 (Academic Press, 2019).Gómez, I. et al. Light and temperature demands of marine benthic microalgae and seaweeds in polar regions. Bot. Mar. 52, 593–608 (2009).Article 

Google Scholar 
Kindig, A. C. & Littler, M. M. Growth and primary productivity of marine macrophytes exposed to domestic sewage effluents. Mar. Environ. Res. 3, 81–100 (1980).Article 

Google Scholar 
Wanders, J. B. W. The role of benthic algae in the shallow reef of Curaçao (Netherlands Antilles) III: The significance of grazing. Aquat. Bot. 3, 357–390 (1977).Article 

Google Scholar 
Hatcher, B. G. Reef primary productivity: a beggar’s banquet. Trends Ecol. Evol. 3, 106–111 (1988).CAS 
PubMed 
Article 

Google Scholar 
Odum, H. T. & Odum, E. P. Trophic Structure and Productivity of a Windward Coral Reef Community on Eniwetok Atoll. Ecol. Monogr. 25, 291–320 (1955).Article 

Google Scholar 
Owen, D. P., Long, M. H., Fitt, W. K. & Hopkinson, B. M. Taxon-specific primary production rates on coral reefs in the Florida Keys. Limnol. Oceanogr. 1–14, https://doi.org/10.1002/lno.11627 (2020).Attard, K. M. et al. Benthic oxygen exchange in a live coralline algal bed and an adjacent sandy habitat: An eddy covariance study. Mar. Ecol. Prog. Ser. 535, 99–115 (2015).ADS 
CAS 
Article 

Google Scholar 
Attard, K. M. Seasonal metabolism and carbon export potential of a key coastal habitat: The perennial canopy-forming macroalga Fucus vesiculosus. Limnol. Oceanogr. 64, 149–164 (2019).ADS 
Article 

Google Scholar 
Rohatgi, A. WebPlotDigitizer. (2019).Brey, T., Müller-Wiegmann, C., Zittier, Z. M. C. & Hagen, W. Body composition in aquatic organisms – A global data bank of relationships between mass, elemental composition and energy content. J. Sea Res. 64, 334–340 (2010).ADS 
Article 

Google Scholar 
Thom, R. M. Spatial and Temporal Patterns of Fucus distichus ssp. edentatus (de la Pyl.) Pow. (Phaeophyceae: Fucales) in Central Puget Sound. Bot. Mar. 26, 471–486 (1983).Article 

Google Scholar 
Johnston, C. S., Jones, R. G. & Hunter, D. R. A seasonal carbon budget for a laminarian population in a Scottish sea-loch. Helgoländer wissenschaftliche Meeresuntersuchungen 30, 527–545 (1977).ADS 
CAS 
Article 

Google Scholar 
Blain, C. O., Hansen, S. C. & Shears, N. T. Coastal darkening substantially limits the contribution of kelp to coastal carbon cycles. Glob. Chang. Biol. 1–17, https://doi.org/10.1111/gcb.15837 (2021).Randall, J., Wotherspoon, S., Ross, J., Hermand, J. & Johnson, C. An in situ study of production from diel oxygen modelling, oxygen exchange, and electron transport rate in the kelp Ecklonia radiata. Mar. Ecol. Prog. Ser. 615, 51–65 (2019).ADS 
CAS 
Article 

Google Scholar 
Rodgers, K. L., Rees, T. A. V. & Shears, N. T. A novel system for measuring in situ rates of photosynthesis and respiration of kelp. Mar. Ecol. Prog. Ser. 528, 101–115 (2015).ADS 
CAS 
Article 

Google Scholar 
Sanderson, J. C. Subtidal Macroalgal Studies in East and South Eastern Tasmanian Coastal Waters. (University of Tasmania, 1990).Miller, R. J., Reed, D. C. & Brzezinski, M. A. Community structure and productivity of subtidal turf and foliose algal assemblages. Mar. Ecol. Prog. Ser. 388, 1–11 (2009).ADS 
CAS 
Article 

Google Scholar 
Pessarrodona, A. et al. A global dataset of seaweed net primary productivity, Figshare, https://doi.org/10.6084/m9.figshare.14882322 (2021).Berg, P., Huettel, M., Glud, R. N., Reimers, C. E. & Attard, K. M. Aquatic Eddy Covariance: The Method and Its Contributions to Defining Oxygen and Carbon Fluxes in Marine Environments. Ann. Rev. Mar. Sci. 14, 431–455 (2022).PubMed 
Article 

Google Scholar 
Lees, D. C., Houghton, J. P., Erickson, D. E., Driskell, W. B. & Boettcher, D. E. Ecological studies of intertidal and shallow subtidal habitats in lower Cook Inlet, Alaska. (1980).Kelly, E. L. A. et al. A budget of algal production and consumption by herbivorous fish in an herbivore fisheries management area, Maui, Hawaii. Ecosphere 8, e01899 (2017).Article 

Google Scholar 
Pedersen, M. F., Nejrup, L. B., Fredriksen, S., Christie, H. C. & Norderhaug, K. M. Effects of wave exposure on population structure, demography, biomass and productivity of the kelp Laminaria hyperborea. Mar. Ecol. Prog. Ser. 451, 45–60 (2012).ADS 
Article 

Google Scholar 
Kain, J. M. The biology of Laminaria hyperborea X. The effect of depth on some populations. J. Mar. Biol. Assoc. United Kingdom 57, 587–607 (1977).Article 

Google Scholar 
Yatsuya, K., Nishigaki, T., Douke, A., Itani, M. & Wada, Y. Annual net productions of sargassacean species in coastal areas with different environmental characteristics in Kyoto Prefecture, the Sea of Japan. Nippon Suisan Gakkaishi 73, 880–890 (2007).Article 

Google Scholar 
Carter, A. R. & Simons, R. H. Regrowth and Production Capacity of Gelidium pristoides (Gelidiales, Rhodophyta) under Various Harvesting Regimes at Port Alfred, South Africa. Bot. Mar. 30, 227–232 (1987).Article 

Google Scholar 
Santelices, B., Vásquez, J., Ohme, U. & Fonck, E. Managing wild crops of Gracilaria in central Chile. in Eleventh International Seaweed Symposium (eds. Bird, C. J. & Ragan, M. A.) 77–89 (Springer Netherlands, 1984).Pessarrodona, A., Foggo, A. & Smale, D. A. Can ecosystem functioning be maintained despite climate-driven shifts in species composition? Insights from novel marine forests. J. Ecol. 10, 91–104 (2018).
Google Scholar 
Dunton, K. H. An annual carbon budget for an arctic kelp community. in The Alaskan Beaufort Sea: ecosystems and environments. (eds. Barnes, P. W., Schell, D. & Reimnitz, E.) 311–326 (Academic press, 1984).Klumpp, D. W. & McKinnon, A. D. Commmunity structure, biomass and productivity of epilithic algal communities on the Great Barrier Reef; dynamics at different spatial scales. Mar. Ecol. Prog. Ser. 86, 77–89 (1992).ADS 
Article 

Google Scholar 
Westphalen, G. & Cheshire, A. C. Quantum efficiency and photosynthetic production of a temperate turf algal community. Aust. J. Bot. 45, 343–349 (1997).Article 

Google Scholar 
Morrissey, J. Primary productivity of coral reef benthic macroalgae. Proceedings of the 5th International Coral Reef Congress 77–82 (1985).Howard, K. L. & Menzies, R. J. Distribution and Production of Sargassum in the Waters off the Carolina Coast. Bot. Mar. 12, 244–254 (1969).Article 

Google Scholar 
Weigel, B. L. & Pfister, C. A. The dynamics and stoichiometry of dissolved organic carbon release by kelp. Ecology 102, 1–17 (2020).
Google Scholar 
Tait, L. W., South, P. M., Lilley, S. A., Thomsen, M. S. & Schiel, D. R. Assemblage and understory carbon production of native and invasive canopy-forming macroalgae. J. Exp. Mar. Bio. Ecol. 469, 10–17 (2015).CAS 
Article 

Google Scholar 
Rodgers, K. & Shears, N. Modelling kelp forest primary production using in situ photosynthesis, biomass and light measurements. Mar. Ecol. Prog. Ser. 553, 67–79 (2016).ADS 
CAS 
Article 

Google Scholar  More

Study areaThere is a long history of diversified cropping patterns due to the climatic and topographic complexity in China4. Cropping intensity increases from north to south, and multiple cropping dominates in regions south of 400N4. For example, multiple cropping systems of double rice and winter wheat plus maize are popular in the Middle-lower Yangtze river plain and the Huang-Huai-Hai plain, respectively (Fig. 1)22. Three staple crops, maize, paddy rice, and wheat, are widely distributed across the country (Figure S1). These three major crops contributed to more than half (57.08%) of the total sown area in China in 2020 (http://www.stats.gov.cn/english/).Fig. 1The distribution map of cropping patterns in 2021, 9 agricultural regions and validation sites in China. Notes: A to I represented nine agricultural regions in China. (A) Middle-lower Yangtze River Plain; (B) Huang-Huai-Hai plain; (C) Northeast China; (D) Inner Mongolia and along the Great Wall; (E) Loess plateau; (F) Southwest China; (G) Southern China; (H) Gansu-Xinjiang region; (I) Qinghai-Tibet region.Full size imageMODIS images and pre-processingWe used the 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products (MOD09A1) from 2015 to 2021. Three spectral indices were calculated: the 2-band Enhanced Vegetation Index (EVI2)23, LSWI16, and Normalized Multi-band Drought Index (NMDI)24 (Fig. 2). The functions of EVI2, LSWI, and NMDI are provided in Eqs. 1–3 as follows.$${rm{EVI2}}=2.5times left({rho }_{NIR}-{rho }_{{rm{Red}}}right)/left({rho }_{NIR}+2.4times {rho }_{{rm{Red}}}+1right)$$
(1)
$${rm{LSWI}}=left({rho }_{NIR}-{rho }_{SWIR6}right)/left({rho }_{NIR}+{rho }_{SWIR6}right)$$
(2)
$$NMDI=frac{{rho }_{NIR}-left({rho }_{SWIR6}-{rho }_{SWIR7}right)}{{rho }_{NIR}+left({rho }_{SWIR6}-{rho }_{SWIR7}right)}$$
(3)
where, ρNIR, ρRed, ρSWIR6 and ρSWIR7 represented the surface reflectance values from the red (620–670 nm), Near-infrared (841–875 nm), short wave infrared band centered at 1640 nm (1628–1652 nm) and 2130 nm (2105–2155 nm), respectively.Fig. 2The workflow of the methodology: Data preprocessing, deriving cropping intensity, mapping three staple crops and obtaining annual maps of cropping patterns in China.Full size imageFor each spectral index (EVI2, LSWI, and NMDI), a daily continuous time series was developed based on the cloud-free observations using the Whittaker Smoother (WS)25. The WS smoother performed well in multiple cropping regions and therefore was applied here26.Validation data and other datasetsThe validation data in this study included the ground truth reference data and agricultural census data. The ground truth reference data were collected in major agricultural regions with GPS receivers and digital cameras during the study period (2015–2021) (Fig. 1, Table S1). For each sampling site, the geographic location and crop types were recorded. The reliability of ground survey data was improved through visual confirmation based on high-resolution images in Google Earth. Some reference sites with small field sizes were removed to considering the mixed-pixel problems of MODIS images. Finally, we obtained a total of 18,379 ground samples collected during 2015–2021 (Table S1). All the ground truth reference data were used to validate the cropping pattern data in its corresponding year. Agricultural census data were obtained from the National Statistical Bureau of China (NSBC) (http://www.stats.gov.cn/english/), which was collected through sampling statistics. The crop calendar data from agro-meteorological stations recorded the sowing, seedling, tillering, heading, and harvesting dates of winter wheat (210 sites) or spring wheat (90 sites). The calendar data were applied to establish the trend surfaces of key phenological stages of winter wheat and spring wheat, respectively. The crop calendar data were provided by the National Meteorological Information Center, China Meteorological Administration.The cropland distribution data were derived from the 30 m GlobeLand30 global land cover data in 202027. The total accuracy of GlobeLand30 in 2020 is 85.72%, and the Kappa coefficient is 0.82 (www.globallandcover.com). To correspond to MODIS images, the 30 m cropland pixels from GlobeLand30 data were spatially aggregated to a 500 m cropland fraction map. For simplification, we classified pixel purity of MODIS pixels into three groups: cropland percentages of >90%, 50–90%, and More