Philippa Kaur

Rosenzweig, M. L. Habitat selection and population interactions: the search for mechanism. Am. Nat. 137, S5–S28 (1991).Article 

Google Scholar 
Binckley, C. A. & Resetarits, W. J. Habitat selection determines abundance, richness and species composition of beetles in aquatic communities. Biol. Lett. 1, 370–374 (2005).Article 
PubMed 
PubMed Central 

Google Scholar 
Foltz, S. J. & Dodson, S. I. Aquatic Hemiptera community structure in stormwater retention ponds: A watershed land cover approach. Hydrobiologia 621, 49–62 (2009).Article 

Google Scholar 
Resetarits, W. J. Habitat selection behaviour links local and regional scales in aquatic systems: Habitat selection at multiple spatial scales. Ecol. Lett. 8, 480–486 (2005).Article 
PubMed 

Google Scholar 
Leclerc, M. et al. Quantifying consistent individual differences in habitat selection. Oecologia 180, 697–705 (2016).Article 
PubMed 

Google Scholar 
Morris, D. W. Adaptation and habitat selection in the eco-evolutionary process. Proc. R. Soc. B Biol. Sci. 278, 2401–2411 (2011).Article 

Google Scholar 
Resetarits, W. J. Colonization under threat of predation: avoidance of fish by an aquatic beetle, Tropisternus lateralis (Coleoptera: Hydrophilidae). Oecologia 129, 155–160 (2001).Article 
PubMed 

Google Scholar 
Wellborn, G. A., Skelly, D. K. & Werner, E. E. Mechanisms creating community structure across a freshwater habitat gradient. Annu. Rev. Ecol. Evol. Syst. 27, 337–363 (1996).Article 

Google Scholar 
Klečka, J. & Boukal, D. S. Who eats whom in a pool? A comparative study of prey selectivity by predatory aquatic insects. PLoS ONE 7, e37741 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Nilsson, P. A. & Brönmark, C. Prey vulnerability to a gape-size limited predator: behavioural and morphological impacts on northern pike piscivory. Oikos 88, 539–546 (2000).Article 

Google Scholar 
Šigutová, H. et al. Specialization directs habitat selection responses to a top predator in semiaquatic but not aquatic taxa. Sci. Rep. 11, 18928 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Pintar, M. R. & Resetarits, W. J. Match and mismatch: integrating consumptive effects of predators, prey traits, and habitat selection in colonizing aquatic insects. Ecol. Evol. 11, 1902–1917 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Pintar, M. R. & Resetarits, W. J. Jr. Out with the old, in with the new: oviposition preference matches larval success in cope’s gray treefrog Hyla chrysoscelis. J. Herpetol. 51, 186–189 (2017).Article 

Google Scholar 
Wildermuth, H. Habitat selection and oviposition site recognition by the dragonfly Aeshna juncea (L.): an experimental approach in natural habitats (Anisoptera: Aeshnidae). Odonatologica 22, 27–44 (1993).Fortin, D., Morris, D. W. & McLoughlin, P. D. Habitat selection and the evolution of specialists in heterogeneous environments. Isr. J. Ecol. Evol. 54, 311–328 (2008).Article 

Google Scholar 
McLoughlin, P. D., Boyce, M. S., Coulson, T. & Clutton-Brock, T. Lifetime reproductive success and density-dependent, multi-variable resource selection. Proc. R. Soc. B Biol. Sci. 273, 1449–1454 (2006).Article 

Google Scholar 
Morris, D. W. Scales and costs of habitat selection in heterogeneous landscapes. Evol. Ecol. 6, 412–432 (1992).Article 

Google Scholar 
McLoughlin, P. D., Morris, D. W., Fortin, D., Wal, E. V. & Contasti, A. L. Considering ecological dynamics in resource selection functions. J. Anim. Ecol. 79, 4–12 (2010).Article 
PubMed 

Google Scholar 
Leclerc, M., Dussault, C. & St-Laurent, M.-H. Behavioural strategies towards human disturbances explain individual performance in woodland caribou. Oecologia 176, 297–306 (2014).Article 
PubMed 

Google Scholar 
Bolnick, D. I. et al. The ecology of individuals: incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).Article 
MathSciNet 
PubMed 

Google Scholar 
Sheppard, C. E. et al. Intragroup competition predicts individual foraging specialisation in a group-living mammal. Ecol. Lett. 21, 665–673 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Forstmeier, W. & Birkhead, T. R. Repeatability of mate choice in the zebra finch: consistency within and between females. Anim. Behav. 68, 1017–1028 (2004).Article 

Google Scholar 
Gómez-Laplaza, L. M. The influence of social status on shoaling preferences in the freshwater angelfish (Pterophyllum scalare). Behaviour 142, 827–844 (2005).Article 

Google Scholar 
Gillingham, M. P. & Parker, K. L. The importance of individual variation in defining habitat selection by moose in northern British Columbia. Alces 44, 7–20 (2008).
Google Scholar 
Lesmerises, R. & St-Laurent, M.-H. Not accounting for interindividual variability can mask habitat selection patterns: a case study on black bears. Oecologia 185, 415–425 (2017).Article 
PubMed 

Google Scholar 
van Beest, F. M. et al. Increasing density leads to generalization in both coarse-grained habitat selection and fine-grained resource selection in a large mammal. J. Anim. Ecol. 83, 147–156 (2014).Article 
PubMed 

Google Scholar 
Fretwell, S. D. & Lucas, H. L. On territorial behavior and other factors influencing habitat distribution in birds I. Theoretical development. Biotheoretica 19, 16–36 (1970).Article 

Google Scholar 
Binckley, C. A. & Resetarits, W. J. Functional equivalence of non-lethal effects: generalized fish avoidance determines distribution of gray treefrog, Hyla chrysoscelis, larvae. Oikos 102, 623–629 (2003).Article 

Google Scholar 
Kraus, J. M. & Vonesh, J. R. Feedbacks between community assembly and habitat selection shape variation in local colonization. J. Anim. Ecol. 79, 795–802 (2010).PubMed 

Google Scholar 
Pollard, C. J. et al. Removal of an exotic fish influences amphibian breeding site selection: Exotic fish removal. J. Wildl. Manag. 81, 720–727 (2017).Article 

Google Scholar 
Calenge, C., Dufour, A. B. & Maillard, D. K-select analysis: a new method to analyse habitat selection in radio-tracking studies. Ecol. Model. 186, 143–153 (2005).Article 

Google Scholar 
Freitas, C., Kovacs, K. M., Lydersen, C. & Ims, R. A. A novel method for quantifying habitat selection and predicting habitat use. J. Appl. Ecol. 45, 1213–1220 (2008).
Google Scholar 
Mitchell, L. J., Kohler, T., White, P. C. L. & Arnold, K. E. High interindividual variability in habitat selection and functional habitat relationships in European nightjars over a period of habitat change. Ecol. Evol. 10, 5932–5945 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Richter, L. et al. So close and yet so different: the importance of considering temporal dynamics to understand habitat selection. Basic Appl. Ecol. 43, 99–109 (2020).Article 

Google Scholar 
Tyler, J. A. & Rose, K. A. Individual variability and spatial heterogeneity in fish population models. Rev. Fish Biol. Fish. 4, 91–123 (1994).Article 

Google Scholar 
Córdoba-Aguilar, A. Dragonflies and Damselflies: Model Organisms for Ecological and Evolutionary Research. (Oxford University Press, 2008).Sandall, E. L. & Fischer, B. Be a professional: attend to the insects. Am. Entomol. 65, 176–179 (2019).Article 

Google Scholar 
Blaustein, L. Oviposition site selection in response to risk of predation: evidence from aquatic habitats and consequences for population dynamics and community. in Evolutionary theory and processes: modern perspectives (ed. Wasser, S. P.) 441–456 (Kluwer, 1999).Helebrandová, J. B., Pyszko, P. & Dolný, A. Behavioural phenotypic plasticity of submerged oviposition in damselflies (Insecta: Odonata). Insects 10, 124 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hollis, K. & Guillette, L. What associative learning in insects tells us about the evolution of learning and fixed behavior. Int. J. Comp. Psychol. 28, 25706 (2015).Article 

Google Scholar 
Papaj, D. R. & Lewis, A. C. Insect Learning: Ecological and Evolutinary Perspectives. (Chapman & Hall, 1993).Simons, M. & Tibbetts, E. Insects as models for studying the evolution of animal cognition. Curr. Opin. Insect Sci. 34, 117–122 (2019).Article 
PubMed 

Google Scholar 
Benard, M. F. Predator-induced phenotypic plasticity in organisms with complex life histories. Annu. Rev. Ecol. Evol. Syst. 35, 651–673 (2004).Article 

Google Scholar 
Cook, W. L. & Streams, F. A. Fish predation on Notonecta (Hemiptera): relationship between prey risk and habitat utilization. Oecologia 64, 177–183 (1984).Article 
CAS 
PubMed 

Google Scholar 
Larson, D. J. The predaceous water beetles (Coleoptera: Dytiscidae) of Alberta: Systematics, natural history and distribution. Quaest. Entomol. 11, 245–498 (1985).
Google Scholar 
Svensson, B. G., Tallmark, B. & Petersson, E. Habitat heterogeneity, coexistence and habitat utilization in five backswimmer species (Notonecta spp.; Hemiptera, Notonectidae). Aquat. Insects 22, 81–98 (2000).Lock, K., Adriaens, T., Meutter, F. V. D. & Goethals, P. Effect of water quality on waterbugs (Hemiptera: Gerromorpha & Nepomorpha) in Flanders (Belgium): results from a large-scale field survey. Ann. Limnol. Int. J. Limnol. 49, 121–128 (2013).Article 

Google Scholar 
Macan, T. T. A twenty-one-year study of the water-bugs in a Moorland Fishpond. J. Anim. Ecol. 45, 913–922 (1976).Article 

Google Scholar 
Boukal, D. S. et al. Catalogue of water beetles of the Czech Republic. Klapalekiana 43 (Suppl.), 1–289 (2007).Åbjörnsson, K., Wagner, B. M. A., Axelsson, A., Bjerselius, R. & Olsén, K. H. Responses of Acilius sulcatus (Coleoptera: Dytiscidae) to chemical cues from perch (Perca fluviatilis). Oecologia 111, 166–171 (1997).Article 
PubMed 

Google Scholar 
Gioria, M., Schaffers, A., Bacaro, G. & Feehan, J. The conservation value of farmland ponds: Predicting water beetle assemblages using vascular plants as a surrogate group. Biol. Conserv. 143, 1125–1133 (2010).Article 

Google Scholar 
Bergsten, J. & Miller, K. B. Taxonomic revision of the Holarctic diving beetle genus Acilius Leach (Coleoptera: Dytiscidae): Acilius taxonomic revision. Syst. Entomol. 31, 145–197 (2005).Article 

Google Scholar 
Everard, M. Britain’s Freshwater Fishes. (Princeton University Press, 2013).Miller, K. B. & Bergsten, J. Predaceous diving beetle sexual systems. in Ecology, systematics, and the natural history of predaceous diving beetles (Coleoptera: Dytiscidae) (ed. Yee, D. A.) 199–234 (Springer Netherlands, 2014).Culler, L. E., Ohba, S. & Crumrine, P. Predator-prey interactions of dytiscids. in Ecology, Systematics, and the Natural History of Predaceous Diving Beetles (Coleoptera: Dytiscidae) (ed. Yee, D. A.) 363–379 (Springer, 2014).Baines, C. B., McCauley, S. J. & Rowe, L. Dispersal depends on body condition and predation risk in the semi-aquatic insect Notonecta undulata. Ecol. Evol. 5, 2307–2316 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Baines, C. B., Ferzoco, I. M. & McCauley, S. J. Sex-biased dispersal is independent of sex ratio in a semiaquatic insect. Behav. Ecol. Sociobiol. 71, 119 (2017).Article 

Google Scholar 
Hungerford, H. B. The biology and ecology of aquatic and semiaquatic Hemiptera. Univ. Kans. Sci. Bull. 11, 3–334 (1919).
Google Scholar 
Streams, F. A. Intrageneric predation by Notonecta (Hemiptera: Notonectidae) in the laboratory and in nature. Ann. Entomol. Soc. Am. 85, 265–273 (1992).Article 

Google Scholar 
Halekoh, U., Højsgaard, S. & Yan, J. The R Package geepack for generalized estimating equations. J. Stat. Softw. 15, 1–11 (2006).Article 

Google Scholar 
Lenth, R. V. Least-squares means: the R package lsmeans. J. Stat. Softw. 69, 1–33 (2016).Article 

Google Scholar 
Bates, A., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2021).Harvill, M. L. The antipredatory behavior of the aquatic diving beetle, Coptotomus venustus (Say)(Coleoptera: Dytiscidae) in response to fish predation. (Texas A&M University, 1994).McCauley, S. J. & Rowe, L. Notonecta exhibit threat-sensitive, predator-induced dispersal. Biol. Lett. 6, 449–452 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Schoeppner, N. M. & Relyea, R. A. Damage, digestion, and defence: the roles of alarm cues and kairomones for inducing prey defences. Ecol. Lett. 8, 505–512 (2005).Article 
PubMed 

Google Scholar 
Roberts, G. Why individual vigilance declines as group size increases. Anim. Behav. 51, 1077–1086 (1996).Article 

Google Scholar 
Giller, P. S. Locomotory efficiency in the predation strategies of the British Notonecta (Hempitera, Heteroptera). Oecologia 52, 273–277 (1982).Article 
CAS 
PubMed 

Google Scholar 
Gittelman, S. H. Locomotion and predatory strategy in backswimmers (Hemiptera: Notonectidae). Am. Midl. Nat. 92, 496–500 (1974).Article 

Google Scholar 
Morris, D. W. Density-dependent habitat selection: testing the theory with fitness data. Evol. Ecol. 3, 80–94 (1989).Article 

Google Scholar 
Holt, R. D. Population dynamics in two-patch environments: Some anomalous consequences of an optimal habitat distribution. Theor. Popul. Biol. 28, 181–208 (1985).Article 
MathSciNet 
MATH 

Google Scholar 
Briers, R. A. Metapopulation ecology of Notonecta in small ponds. Doctoral dissertation. (1999).Popham, E. J. The migration of aquatic bugs with special reference to the Corixidae (Hemiptera Heteroptera). Arch. Für Hydrobiol. 60, 450–496 (1964).
Google Scholar 
Doligez, B., Cadet, C., Danchin, E. & Boulinier, T. When to use public information for breeding habitat selection? The role of environmental predictability and density dependence. Anim. Behav. 66, 973–988 (2003).Article 

Google Scholar 
Pintar, M. R. & Resetarits, W. J. Aquatic beetles influence colonization of disparate taxa in small lentic systems. Ecol. Evol. 10, 12170–12182 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Sebastián-González, E., Sánchez-Zapata, J. A., Botella, F. & Ovaskainen, O. Testing the heterospecific attraction hypothesis with time-series data on species co-occurrence. Proc. R. Soc. B Biol. Sci. 277, 2983–2990 (2010).Article 

Google Scholar 
Giller, P. S. & McNeill, S. Predation strategies, resource partitioning and habitat selection in Notonecta (Hemiptera/Heteroptera). J. Anim. Ecol. 50, 789–808 (1981).Article 

Google Scholar 
Buxton, V. L., Enos, J. K., Sperry, J. H. & Ward, M. P. A review of conspecific attraction for habitat selection across taxa. Ecol. Evol. 10, 12690–12699 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Ferzoco, I. M. C., Baines, C. B. & McCauley, S. J. Co-occurring Notonecta (Hemiptera: Heteroptera: Notonectidae) species differ in their behavioral response to cues of Belostoma (Hemiptera: Heteroptera: Belostomatidae) predation risk. Ann. Entomol. Soc. Am. 112, 402–408 (2019).Article 

Google Scholar 
Roughgarden, J. Evolution of niche width. Am. Nat. 106, 683–718 (1972).Article 

Google Scholar 
Ruckstuhl, K. E. Sexual segregation in vertebrates: proximate and ultimate causes. Integr. Comp. Biol. 47, 245–257 (2007).Article 
CAS 
PubMed 

Google Scholar 
Hochkirch, A., Gröning, J. & Krause, S. Intersexual niche segregation in Cepero’s ground-hopper Tetrix ceperoi. Evol. Ecol. 21, 727–738 (2007).Article 

Google Scholar 
Romey, W. L. & Wallace, A. C. Sex and the selfish herd: sexual segregation within nonmating whirligig groups. Behav. Ecol. 18, 910–915 (2007).Article 

Google Scholar 
Main, M. B., Weckerly, F. W. & Bleich, V. C. Sexual segregation in ungulates: new directions for research. J. Mammal. 77, 449–461 (1996).Article 

Google Scholar 
Trivers, R. L. Parental investment and sexual selection. in Sexual Selection and the Descent of Man 1871–1971 (ed. Campbell, B.) (Aldine Publishing Company, 1972).Bonduriansky, R. The evolution of male mate choice in insects: a synthesis of ideas and evidence. Biol. Rev. Camb. Philos. Soc. 76, 305–339 (2001).Article 
CAS 
PubMed 

Google Scholar 
Foster, S. E. & Soluk, D. A. Protecting more than the wetland: The importance of biased sex ratios and habitat segregation for conservation of the Hine’s emerald dragonfly Somatochlora hineana Williamson. Biol. Conserv. 127, 158–166 (2006).Article 

Google Scholar 
Miller, K. B. The phylogeny of diving beetles (Coleoptera: Dytiscidae) and the evolution of sexual conflict. Biol. J. Linn. Soc. 79, 359–388 (2003).Article 

Google Scholar 
Watson, P. J., Stallmann, R. R. & Arnqvist, G. Sexual conflict and the energetic costs of mating and mate choice in water striders. Am. Nat. 151, 46–58 (1998).Article 
CAS 
PubMed 

Google Scholar 
Rowe, L., Krupa, J. J. & Sih, A. An experimental test of condition-dependent mating behavior and habitat choice by water striders in the wild. Behav. Ecol. 7, 474–479 (1996).Article 

Google Scholar 
McLain, D. K. & Pratt, A. E. The cost of sexual coercion and heterospecific sexual harassment on the fecundity of a host-specific, seed-eating insect (Neacoryphus bicrucis). Behav. Ecol. Sociobiol. 46, 164–170 (1999).Article 

Google Scholar 
Stone, G. N. Female foraging responses to sexual harassment in the solitary bee Anthophora plumipes. Anim. Behav. 50, 405–412 (1995).Article 

Google Scholar 
Martens, A. & Rehfeldt, G. Female aggregation in Platycypha caligata (Odonata: Chlorocyphidae): A tactic to evade male interference during oviposition. Anim. Behav. 38, 369–374 (1989).Article 

Google Scholar 
Kolar, V. & Boukal, D. S. Habitat preferences of the endangered diving beetle Graphoderus bilineatus: implications for conservation management. Insect Conserv. Divers. 13, 480–494 (2020).Article 

Google Scholar 
Wilcox, C. Habitat size and isolation affect colonization of seasonal wetlands by predatory aquatic insects. Isr. J. Zool. 47, 459–475 (2001).Article 

Google Scholar 
Baines, C. B., Ferzoco, I. M. C. & McCauley, S. J. Phenotype-by-environment interactions influence dispersal. J. Anim. Ecol. 88, 1263–1274 (2019).Article 
PubMed 

Google Scholar 
Liao, W., Venn, S. & Niemelä, J. Diving beetle (Coleoptera: Dytiscidae) community dissimilarity reveals how low landscape connectivity restricts the ecological value of urban ponds. Landsc. Ecol. 37, 1049–1058 (2022).Article 

Google Scholar  More

Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Davis, K. F. et al. Assessing the sustainability of post-Green Revolution cereals in India. Proc. Natl Acad. Sci. USA 116, 25034 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoekstra, A. Y. & Wiedmann, T. O. Humanity’s unsustainable environmental footprint. Science 344, 1114 (2014).Article 
CAS 
PubMed 

Google Scholar 
O Neill, D. W., Fanning, A. L., Lamb, W. F. & Steinberger, J. K. A good life for all within planetary boundaries. Nat. Sustain. 1, 88 (2018).Article 

Google Scholar 
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).Article 
PubMed 

Google Scholar 
van Dijk, M., Morley, T., Rau, M. L. & Saghai, Y. A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nat. Food 2, 494 (2021).Article 

Google Scholar 
Grassini, P., Eskridge, K. M. & Cassman, K. G. Distinguishing between yield advances and yield plateaus in historical crop production trends. Nat. Commun. 4, 2918 (2013).Article 
PubMed 

Google Scholar 
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).Article 
PubMed 

Google Scholar 
Chen, X. et al. Integrated soil–crop system management for food security. Proc. Natl Acad. Sci. USA 108, 6399 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
FAOSTAT. FAO http://www.fao.org/faostat/en/#home (2021).Liu, Z. et al. Optimization of China’s maize and soy production can ensure feed sufficiency at lower nitrogen and carbon footprints. Nat. Food 2, 426 (2021).Article 

Google Scholar 
Zhang, Q. et al. Outlook of China’s agriculture transforming from smallholder operation to sustainable production. Glob. Food Secur. 26, 100444 (2020).Article 

Google Scholar 
Duan, J. et al. Consolidation of agricultural land can contribute to agricultural sustainability in China. Nat. Food 2, 1014 (2021).Article 
CAS 

Google Scholar 
Cui, Z. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363 (2018).Article 
CAS 
PubMed 

Google Scholar 
Zhou, F. et al. Deceleration of China’s human water use and its key drivers. Proc. Natl Acad. Sci. USA 117, 7702 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, H. et al. Estimating ammonia emissions from cropland in China based on the establishment of agro-region-specific models. Agr. For. Meteorol. 303, 108373 (2021).Article 

Google Scholar 
Yue, Q. et al. Deriving emission factors and estimating direct nitrous oxide emissions for crop cultivation in China. Environ. Sci. Technol. 53, 10246 (2019).Article 
CAS 
PubMed 

Google Scholar 
Ju, X., Gu, B., Wu, Y. & Galloway, J. N. Reducing China’s fertilizer use by increasing farm size. Global Environ. Chang. 41, 26 (2016).Article 

Google Scholar 
Costanza, R. et al. Changes in the global value of ecosystem services. Global Environ. Chang. 26, 152 (2014).Article 

Google Scholar 
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254 (2012).Article 
CAS 
PubMed 

Google Scholar 
Davis, K. F., Rulli, M. C., Seveso, A. & D. Odorico, P. Increased food production and reduced water use through optimized crop distribution. Nat. Geosci. 10, 919 (2017).Article 
CAS 

Google Scholar 
Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486 (2014).Article 
CAS 
PubMed 

Google Scholar 
UN Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019, Online Edition. Rev. 1 (2019). https://population.un.org/wpp/2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2019).Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2, 284 (2020).Article 

Google Scholar 
Cardinale, B. J. et al. Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl Acad. Sci. USA 104, 18123 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257 (2019).Article 
CAS 
PubMed 

Google Scholar 
Price Bureau of the National Development and Reform Commission of China. China Agricultural Products Cost–Benefit Compilation of Information 2017 (in Chinese) (China Statistics Press, 2017).Fan, S., Brzeska, J., Keyzer, M. & Halsema, A. From Subsistence to Profit: Transforming Smallholder Farms. (Inter. Food Policy Res. Inst., 2013).Wang, S. et al. Urbanization can benefit agricultural production with large-scale farming in China. Nat. Food 2, 183 (2021).Article 

Google Scholar 
Yin, Y. et al. A steady-state N balance approach for sustainable smallholder farming. Proc. Natl Acad. Sci. USA 118, e2106576118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guiding opinions of the ministry of agriculture on the adjustment of maize structure in the “sickle” area. Ministry of Agriculture and Rural Affairs of the People’s Republic of China http://www.moa.gov.cn/nybgb/2015/shiyiqi/201712/t20171219_6103893.htm (2017).Zhang, F., Chen, X. & Vitousek, P. An experiment for the world. Nature 497, 33 (2013).Article 
CAS 
PubMed 

Google Scholar 
Zhang, W. et al. Closing yield gaps in China by empowering smallholder farmers. Nature 537, 671 (2016).Article 
CAS 
PubMed 

Google Scholar 
Cyberspace Administration of China. State Council of the People’s Republic of China http://www.gov.cn/xinwen/2021-12/28/content_5664873.htm (2021).Kou, T. et al. Effects of long-term cropping regimes on soil carbon sequestration and aggregate composition in rainfed farmland of Northeast China. Soil Till. Res. 118, 132 (2012).Article 

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

Google Scholar 
Damerau, K. et al. India has natural resource capacity to achieve nutrition security, reduce health risks and improve environmental sustainability. Nat. Food 1, 631 (2020).Article 

Google Scholar 
Kuang, W. et al. Cropland redistribution to marginal lands undermines environmental sustainability. Natl Sci. Rev. 9, 1 (2021).
Google Scholar 
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ma, L. et al. Exploring future food provision scenarios for China. Environ. Sci. Technol. 53, 1385 (2018).Article 

Google Scholar 
National population development plan: 2016–2030. National Development and Reform Commission http://www.gov.cn/zhengce/content/2017-01/25/content_5163309.htm (2016).Ma, L. et al. Environmental assessment of management options for nutrient flows in the food chain in China. Environ. Sci. Technol. 47, 7260 (2013).Article 
CAS 
PubMed 

Google Scholar 
Lobell, D. B., Cassman, K. G. & Field, C. B. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 179 (2009).Article 

Google Scholar 
Yan, X., Akiyama, H., Yagi, K. & Akimoto, H., Global estimations of the inventory and mitigation potential of methane emissions from rice cultivation conducted using the 2006 Intergovernmental Panel on Climate Change Guidelines. Global Biogeochem. Cy. https://doi.org/10.1029/2008GB003299 (2009).Smith, P., Martino, Z. & Cai, D. ‘Agriculture’, in Climate Change 2007: Mitigation (Cambridge Univ. Press, 2007).Liang, D. et al. China’s greenhouse gas emissions for cropping systems from 1978–2016. Sci. Data 8, 171 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Dinosauria Owen, 1842Theropoda Marsh, 1881Dromaeosauridae Matthew and Brown, 1922Halszkaraptorinae Cau et al., 2017Revised diagnosisSmall dromaeosaurids that possess dorsoventrally flattened premaxillae, premaxillary bodies perforated by many neurovascular foramina, enlarged and closely packed premaxillary teeth that utilized delayed replacement patterns, reduced anterior maxillary teeth, dorsolateral placement of retracted external nares, greatly elongated cervical vertebrae, anterior cervical vertebrae with round lobes formed by the postzygapophyses, horizontal zygapophyses, and pronounced zygapophyseal laminae in the anterior caudal vertebrae, mediolaterally compressed ulnae with sharp posterior margins, second and third metacarpals with similar thicknesses, shelf-like supratrochanteric processes on the ilia, elongated fossae that border posterolateral ridges on the posterodistal surfaces of the femoral shafts, and third metatarsals in which the proximal halves are unconstricted and anteriorly convex.Natovenator polydontus gen. et sp. nov.HolotypeMPC-D 102/114 (Institute of Paleontology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia) is a mostly articulated skeleton with a nearly complete skull (See Supplementary Table 1 for measurements).Locality and horizonBaruungoyot Formation (Upper Cretaceous), Hermiin Tsav, Omnogovi Province, Mongolia13 (Supplementary Fig. 5).EtymologyNatovenator, from the Latin nato (swim) and venator (hunter), in reference to the hypothesized swimming behaviour and piscivorous diet of the new taxon; polydontus, from the Greek polys (many) and odous (tooth) in reference to the unusually many teeth.DiagnosisA small halszkaraptorine dromaeosaurid with the following autapomorphies: wide groove delimited by a pair of ridges on the anterodorsal surface of the premaxilla, premaxilla with an elongated internarial process that overlies nasal and extends posterior to the external naris, 13 premaxillary teeth with large and incisiviform crowns, first three anteriormost maxillary teeth are greatly reduced and are clustered together with the following tooth without any separations by interdental septa, anteroposteriorly long external naris (about 30% of the preorbital skull length), paroccipital process with a anteroposteriorly broad dorsal surface, elongate maxillary process of the palatine that extends anteriorly beyond the middle of the antorbital fenestra, pterygoid with a deep fossa on the medial surface of the quadrate ramus, distinct posterolaterally oriented projection on the lateral surface of atlas, absence of pleurocoels in cervical vertebrae (not confirmed in the missing fifth cervical centrum), posterolaterally oriented and nearly horizontal proximal shafts in the dorsal ribs, hourglass-shaped metacarpal II with distinctly concave medial and lateral surfaces.DescriptionThe skull of Natovenator is nearly complete, although the preorbital region has been affected by compression and is slightly offset from the rest of the skull (Figs. 1c, d, 2a–d and Supplementary Figs. 1, 2). Near the tip of the snout, the premaxilla is marked by a broad groove. The body of the premaxilla is also dorsoventrally low and is perforated by numerous foramina that lead into a complex network of neurovascular chambers (Supplementary Fig. 1b) as in Halszkaraptor4. Similarly, the external naris is positioned posteriorly and is level with the premaxilla-maxilla contact (Fig. 2a, b), although it is marginally behind this position in Halszkaraptor4. It is also dorsally placed compared to those of other non-avian theropods and faces dorsolaterally. The exceptionally long external naris and accordingly elongated internarial process of Natovenator (Fig. 2c) are unique among dromaeosaurids but comparable to those in aquatic toothed birds14 as well as in therizinosaurs15,16. The frontal is similar to those of other halszkaraptorines4,17 in that it is vaulted to accommodate a large orbit and has little contribution to the supratemporal fossa. A sharp nuchal crest is formed by the parietal and the squamosal (Supplementary Fig. 2a–e). The latter also produces a shelf that extends over the quadrate head as in other dromaeosaurids18. The paroccipital process curves gently on the occiput and has a broad dorsal surface that tapers laterally (Fig. 2f and Supplementary Fig. 2b, e). Its ventrolateral orientation is reminiscent of Mahakala17 but is different from the more horizontal paroccipital process of Halszkaraptor4. The occipital condyle is long and constricted at its base. A shallow dorsal tympanic recess on the lateral wall of the braincase is different from the deep one of Mahakala17. The palatine is tetraradiate with a greatly elongated maxillary process, which extends anteriorly beyond the level of the mid-antorbital fenestra. The pterygoid is missing its anterior portion (Fig. 2g and Supplementary Fig. 2a–e). A deep fossa on the medial surface of the thin quadrate ramus is not seen in any other dromaeosaurids. The mandibles of Natovenator preserve most of the elements, especially those on the left side (Fig. 1a, b, d and Supplementary Figs. 1a, 2). Each jaw is characterized by a slender dentary with nearly parallel dorsal and ventral margins, a surangular partially fused with the articular, a distinctive surangular shelf, and a fan-shaped retroarticular process that protrudes dorsomedially. The upper dentition of Natovenator is heterodont as the premaxillary teeth are morphologically distinct from the maxillary teeth (Fig. 2a, b, e and Supplementary Fig. 1a, c). There are unusually numerous premaxillary teeth tightly packed without any separation of the alveoli by bony septa. The roots of the teeth are long, and the crowns are tall and incisiviform as in Halszkaraptor4. Moreover, the large replacement teeth in the premaxilla suggest that the replacement of the premaxillary teeth was delayed as in Halszkaraptor4. However, the number of teeth in each premaxilla is 13 in Natovenator, whereas it is only 11 in Halszkaraptor4. In the maxilla, the three most anterior maxillary teeth are markedly shorter than the premaxillary teeth and the more posterior maxillary teeth. This pattern is also observed in Halszkaraptor, although the number of shorter maxillary teeth differs as it has two reduced ones7. Both the maxillary and dentary teeth have sharp fang-like crowns that lack serrations. Although posteriormost parts are poorly preserved, there are at least 23 alveoli in each of the maxilla and dentary, which suggests high numbers of teeth in both elements.The neck of Natovenator, as preserved, is twisted and includes ten elongated cervical vertebrae, although most of the 5th cervical is missing (Figs. 1, 3a–d). This elongation of the cervicals results in a noticeably longer neck than those of most dromaeosaurids and is estimated to be longer than the dorsal series. It is, however, proportionately shorter than that of Halszkaraptor, which has a neck as long as its dorsal and sacral vertebra combined4. Another peculiarity in the neck of the Natovenator is a pronounced posterolaterally extending projection on the neurapophysis of the atlas (Fig. 3a and Supplementary Fig. 2b, c, e). The postzygapophyses of each anterior cervical are fused into a single lobe-like process as in Halszkaraptor4. Pleurocoels are absent in the cervical vertebrae. In contrast, Halszkaraptor has pleurocoels on its 7th–9th cervicals4. A total of 12 dorsal vertebrae are preserved (Figs. 1a, b, 3e, 4a and Supplementary Figs. 3a–d). They all lack pleurocoels, and their parapophyses on the anterior and mid-dorsals are placed high on the anterodorsal end of each centrum. Interestingly, the positions of the parapophyses are similar to those of hesperornithiforms19,20,21 rather than other dromaeosaurids such as Deinonychus22 or Velociraptor23. The preserved dorsal ribs, articulated with the second to seventh dorsals, are flattened and posteriorly oriented (Figs. 1, 3e, 4a–d). The proximal shafts are also nearly horizontal, which is indicative of a dorsoventrally compressed ribcage. Each proximal caudal vertebra has a long centrum and horizontal zygapophyses with expanded laminae (Fig. 3f and Supplementary Fig. 3e–i), all of which are characters shared with other halszkaraptorines4,17. The forelimb elements are partially exposed (Figs. 1a, b, 2a–d, 3e, g). The nearly complete right humerus is proportionately short and distally flattened like that of Halszkaraptor4. The shaft of the ulna is mediolaterally compressed to produce a sharp posterior margin as in Halszkaraptor4 and Mahakala17. Metacarpal III is robust and is only slightly longer than metacarpal II. Similarly, metacarpal III is almost as thick and long as other second metacarpals of other halszkaraptorines4,17. The femur has a long ridge on its posterior surface, which is another characteristic shared among halszkaraptorines4. Typically for a dromaeosaurid, metatarsals II and III have ginglymoid distal articular surfaces (Fig. 3h and Supplementary Fig. 4f, h). The ventral surface of metatarsal III is invaded by a ridge near the distal end, unlike other halszkaraptorines (Fig. 3h)4,5,17,24.Phylogenetic analysisThe phylogenetic analysis found more than 99,999 most parsimonious trees (CI = 0.23, RI = 0.55) with 6574 steps. Deinonychosaurian monophyly is not supported by the strict consensus tree (Supplementary Fig. 6). Instead, Dromaeosauridae was recovered as a sister clade to a monophyletic clade formed by Troodontidae and Avialae, which is consistent with the results of Cau et al.4 and Cau7. Halszkaraptorinae is positioned at the base of Dromaeosauridae as in Cau et al.4, although there are claims that dromaeosaurid affinities of halszkaraptorines are not well supported25. Nine (seven ambiguous and two unambiguous) synapomorphies support the inclusion of Halszkaraptorinae in Dromaeosauridae. The two unambiguous synapomorphies are the anterior tympanic recess at the same level as the basipterygoid process and the presence of a ventral flange on the paroccipital process. A total of 20 synapomorphies (including one unambiguous synapomorphy) unite the four halszkaraptorines, including Natovenator (Supplementary Fig. 7). In Halszkaraptorinae, Halszkaraptor is the earliest branching taxon, and the remaining three taxa form an unresolved clade supported by three ambiguous synapomorphies (characters 121/1, 569/0, and 1153/1). Two of these synapomorphies are related to the paroccipital process (characters 121 and 569), which is not preserved in Hulsanpes5,24. The other is the presence of an expansion on the medial margin of the distal half of metatarsal III, which is not entirely preserved in the Natovenator. When scored as 0 for this character, Natovenator branches off from the unresolved clade. It suggests that the medial expansion of the dorsal surface of metatarsal III could be a derived character among halszkaraptorines. More

Honey bee colony loss and parasites across space and timeHoney bee colony loss strongly depends on spatio-temporal factors33,42, which in turn have to be jointly modeled with other stressors. Focusing on CONUS climatic regions, defined by the National Centers for Environmental Information40 (see Fig. 1), this is supported by the box plots in Fig. 2 which depict appropriately normalized honey bee colony loss (upper panel) and presence of V. destructor (lower panel) quarterly between 2015 and 2021. Specifically, Fig. 2a highlights that the first quarter generally accounts for a higher and more variable proportion of losses. Average losses are typically lower and less dispersed during the second quarter, and then tend to increase again during the third and fourth quarters. The Central region, which reports the highest median losses during the first quarter (larger than 20%) exemplifies this pattern, which is in line with existing studies that link overwintering with honey bee colony loss6,29,30,31,32,33,43. On the other hand, the West North Central region follows a different pattern, where losses are typically lower during the first quarter and peak during the third. This holds, albeit less markedly, also for Northwest and Southwest regions. These differing patterns are also depicted in Fig. 3, which shows the time series of normalized colony loss for each state belonging to Central and West North Central regions – with the smoothed conditional means highlighted in black and red, respectively. Figure 2b shows that also the presence of V. destructor tends to follow a specific pattern; in most regions it increases from the first to the third quarter, and then it decreases in the fourth – with the exception of the Southwest region, where it keeps increasing. This is most likely because most beekeepers try to get V. destructor levels low by fall, so that colonies are as healthy as possible going into winter, and also because of the population dynamics of V. destructor alongside honey bee colonies – i.e., their presence typically increases as the colony grows and has more brood cycles, since this parasite develops inside honey bee brood cells44,45. The West region (which encompasses only California since Nevada was missing in the honey bee dataset; see Data) reports high levels of V. destructor throughout the year, with very small variability. A comparison of Fig. 2a and b shows that honey bee colony loss and the presence of V. destructor tend to be higher than the corresponding medians during the third quarter, suggesting a positive association. This is further confirmed in Fig. 4, which shows a scatter plot of normalized colony loss against V. destructor presence, documenting a positive association in all quarters. Although with the data at hand we are not able to capture honey bee movement across states, as well as intra-quarter losses and honey production, these preliminary findings can be useful to support commercial beekeeper strategies and require further investigation.Figure 2Empirical distribution of honey bee (Apis mellifera) colony loss (a) and Varroa destructor presence (b) across quarters (the first one being January-March) and climatic regions; red dashed lines indicate the overall medians. (a) Box plots of normalized colony loss (number of lost colonies over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. At the contiguous United States level, this follows a stable pattern across the years, with higher and more variable losses during the first quarter (see Supplementary Figs. S2-S6), but some regions do depart from this pattern (e.g., West North Central). (b) Box plots of normalized V. destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each quarter of 2015–2021 and each climatic region. The maximum number of colonies is defined as the number of colonies at the beginning of a quarter, plus all colonies moved into that region during the same quarter.Full size imageFigure 3Comparison of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) between Central and West North Central climatic regions for each quarter of 2015–2021 (the first quarter being January-March). (a) Trajectory of each state belonging to Central (yellow) and West North Central (blue) climatic regions. (b) Smoothed conditional means for each of the two sets of curves based on a locally weighted running line smoother where the width of the sliding window is equal to 0.2 and corresponding standard error bands are based on a 0.95 confidence level46.Full size imageFigure 4Scatter plot of normalized honey bee (Apis mellifera) colony loss (number of lost colonies over the maximum number of colonies) against normalized Varroa destructor presence (number of colonies affected by V. destructor over the maximum number of colonies) for each state and each quarter of 2015–2021 (the first quarter being January-March). Points are color-coded by quarter, and ordinary least squares fits (with corresponding standard error bands based on a 0.95 confidence level) computed by quarter are superimposed to visualize the positive association.Full size imageUp-scaling weather dataThe data sets available to us for weather related variables had a much finer spatio-temporal resolution (daily and on a (4 times 4) kilometer grid) than the colony loss data (quarterly and at the state level). Therefore, we aggregated the former to match the latter. For similar data up-scaling tasks, sums or means are commonly employed to summarize the variables available at finer resolution47. The problem with aggregating data in such a manner is that one only preserves information on the “center” of the distributions – thus losing a potentially considerable amount of information. To retain richer weather related information in our study, we considered additional summaries capturing more complex characteristics, e.g., the tails of the distributions or their entropy, to ascertain whether they may help in predicting honey bee colony loss. Within each state and quarter we therefore computed, in addition to means, indexes such as standard deviation, skewness, kurtosis, (L_2)-norm (or energy), entropy and tail indexes48. This was done for minimum and maximum temperatures, as well as precipitation data (see Data processing for details).Next, as a first way to validate the proposed weather data up-scaling approach, we performed a likelihood ratio test between nested models. Specifically, we considered a linear regression for colony loss (see Statistical model) and compared an ordinary least squares fit comprising all the computed indexes as predictors (the full model) against one comprising only means and standard deviations (the reduced model). The test showed that the use of additional indexes provides a statistically significant improvement in the fit (p-(text {value}=0.03)). This test, which can be replicated for other choices of models and estimation methods (see Supplementary Table S5), supports the use of our up-scaling approach.Figure 5 provides a spatial representation of (normalized) honey bee colony losses and of three indexes relative to the minimum temperature distribution; namely, mean, kurtosis and skewness (these all turn out to be relevant predictors based on subsequent analyses; see Table 1). For each of the four quantities, the maps are color-coded by state based on the median of first quarter values over the period 2015-2021 (first quarters typically have the highest losses, but similar patterns can be observed for other quarters; see Supplementary Figs. S12-S14). Notably, the indexes capture characteristics of the within-state distributions of minimum temperatures that do vary geographically. For example, considering minimum temperature, skewness is an index that (broadly speaking) provides information on whether the data tends to accumulate at one end or the other of the observed range of minimum temperatures (i.e., a positive/negative skewness indicates that the data accumulates towards the lower/upper range, respectively). On the other hand, kurtosis is an index that captures the presence of “extreme” values in the tails of the data (i.e., a low/high value of kurtosis indicates that the tail minimum temperatures are relatively close/very far from the typical minimum temperatures). With this in mind, going back to Fig. 5, we can see that minimum temperatures in states in the north-west present large kurtosis (a prevalence of extreme values in the tails) and negative skewness (a tendency to accumulate towards the upper values of the minimum temperature range), while the opposite is true for states in the south-east. More generally, the mean minimum temperature separates northern vs southern states, kurtosis is higher for states located in the central band of the CONUS, and skewness separates western vs eastern states.We further note that the states with lower losses during the first quarter (e.g., Montana and Wyoming) do not report extreme values in any of the considered indexes. Although these states are generally characterized by low minimum temperatures, these are somewhat “stable” (they do not show marked kurtosis or skewness in their distributions) – perhaps allowing honey bees and beekeepers to adapt to more predictable conditions. On the other hand, states with higher losses during the first quarter such as New Mexico have higher minimum temperatures as well as marked kurtosis, and thus higher chances of extreme minimum temperatures – which may indeed affect honey bee behavior and colony loss. Overall, across all quarters of the years 2015-2021, we found that normalized colony losses and mean minimum temperatures are negatively associated (the Pearson correlation is -0.17 with a p-(text {value} More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Sasaki, M. et al. Greater evolutionary divergence of thermal limits within marine than terrestrial species. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01534-y (2022). More

Ebenhard, T. Colonization in metapopulations: A review of theory and observations. Biol. J. Linn. Soc. 42, 105–121 (1991).Article 

Google Scholar 
Szucs, M., Melbourne, B. A., Tuff, T. & Hufbauer, R. A. The roles of demography and genetics in the early stages of colonization. Proc. R. Soc. B Biol. Sci. 281, 20141073 (2014).Article 

Google Scholar 
Williamson, M. Biological invasions Vol. 15 (Springer, 1996).
Google Scholar 
Dai, Z. C. et al. Synergy among hypotheses in the invasion process of alien plants: A road map within a timeline. Perspect. Plant Ecol. Evol. Syst. 47, 125575 (2020).Article 

Google Scholar 
Briski, E. et al. Beyond propagule pressure: Importance of selection during the transport stage of biological invasions. Front. Ecol. Environ. 16, 345–353 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. & Vitt, D. H. The dynamics of moss establishment: Temporal responses to nutrient gradients. Bryologist 97, 357–364 (1994).Article 

Google Scholar 
Li, Y. & Vitt, D. H. The dynamics of moss establishment: Temporal responses to a moisture gradient. J. Bryol. 18, 677–687 (1995).Article 

Google Scholar 
Wiklund, K. & Rydin, H. Ecophysiological constraints on spore establishment in bryophytes. Funct. Ecol. 18, 907–913 (2004).Article 

Google Scholar 
Zanatta, F. et al. Bryophytes are predicted to lag behind future climate change despite their high dispersal capacities. Nat. Commun. 11, 1–9 (2020).Article 

Google Scholar 
Seaborn, T. J., Goldberg, C. S. & Crespi, E. J. Integration of dispersal data into distribution modeling: What have we done and what have we learned?. Front. Biogeogr. 12, 1–14 (2020).Article 

Google Scholar 
Glime, J. M. Bryophyte Ecology (Vol. 1, Issue Physiological Ecology, Chapter 4–10 Adaptive strategies: vegetative propagules, pp. 1–44). (2021).Guerra, J., Brugués, M., Cano, M. J. & Cros, R. M. Bryum Hedw. in Flora Briofítica Ibérica, Vol. IV, Funariales, Splachnales, Schistostegales, Bryales, Timmiales (eds. Brugués, M. & Cros, R. M.) 105–178 (Universidad de Murcia. Sociedad Española de Briología, 2010).
Google Scholar 
Medina, N. G., Draper, I. & Lara, F. Biogeography of mosses and allies: Does size matter? in Biogeography of microscopic organisms: is everything small everywhere? 209–233 (2011). https://doi.org/10.1017/CBO9780511974878.012Miles, C. J. & Longton, R. E. The role of spores in reproduction in mosses. Bot. J. Linn. Soc. 104, 149–173 (1990).Article 

Google Scholar 
Estébanez, B., Draper, I. & Bujalance, R. M. Bryophytes: An approximation to the simplest land plants. in Biodiversidad. Aproximación a la diversidad botánica y zoológica de España 19 (2011).Frey, W. & Kürschner, H. Asexual reproduction, habitat colonization and habitat maintenance in bryophytes. Flora Morphol. Distrib. Funct. Ecol. Plants 206, 173–184 (2011).Article 

Google Scholar 
Giordano, S. et al. Regeneration from detached leaves of Pleurochaete squarrosa (Brid.) Lindb. in culture and in the wild. J. Bryol. 19, 219–227 (1996).Article 

Google Scholar 
La Farge, C., Williams, K. H. & England, J. H. Regeneration of Little Ice Age bryophytes emerging from a polar glacier with implications of totipotency in extreme environments. Proc. Natl. Acad. Sci. U. S. A. 110, 9839–9844 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Robinson, S. C. & Miller, N. G. Bryophyte diversity on Adirondack alpine summits is maintained by dissemination and establishment of vegetative fragments and spores. Bryologist 116, 382–391 (2013).Article 

Google Scholar 
Glime, J. M. Chapter 2–1 Meet the bryophytes. in Bryophyte Ecology 1 (2020).Korpelainen, H., Pohjamo, M. & Laaka-Lindberg, S. How efficiently does bryophyte dispersal lead to gene flow?. J. Hattori Bot. Lab. 205, 195–205 (2005).
Google Scholar 
Schuster, R. M. Phytogeography of the Bryophyta. in New manual of Bryology 1, 463–626 (Hattori Bot. Lab, 1983).Löbel, S., Schröder, B. & Snäll, T. Projected shifts in deadwood bryophyte communities under national climate and forestry scenarios benefit large competitors and impair small species. J. Biogeogr. https://doi.org/10.1111/jbi.14278 (2021).Article 

Google Scholar 
Laaka-Lindberg, S., Korpelainen, H. & Pohjamo, M. Dispersal of asexual propagules in bryophytes. J. Hattori Bot. Lab. 330, 319–330 (2003).
Google Scholar 
Miller, N. G. & Mogensen, G. S. Cyrtomnium hymenophylloides (Bryophyta, Mniaceae) in North America and Greenland: Male plants, sex-differential geographical distribution, and reproductive characteristics. Bryologist 100, 499–506 (1997).Article 

Google Scholar 
Muñoz, J., Felicísimo, Á. M., Cabezas, F., Burgaz, A. R. & Martínez, I. Wind as a long-distance dispersal vehicle in the Southern Hemisphere. Science 304, 1144–1147 (2004).Article 
PubMed 

Google Scholar 
Patiño, J. & Vanderpoorten, A. Bryophyte biogeography. CRC. Crit. Rev. Plant Sci. 37, 175–209 (2018).Article 

Google Scholar 
Pasiche-Lisboa, C. J., Booth, T., Belland, R. J. & Piercey-Normore, M. D. Moss and lichen asexual propagule dispersal may help to maintain the extant community in boreal forests. Ecosphere 10, e02823 (2019).Article 

Google Scholar 
Barbé, M., Fenton, N. J. & Bergeron, Y. So close and yet so far away: Long-distance dispersal events govern bryophyte metacommunity reassembly. J. Ecol. 104, 1707–1719 (2016).Article 

Google Scholar 
Hansson, L., Söderström, L. & Solbreck, C. The ecology of dispersal in relation to conservation. in Ecological principles of nature conservation. Conservation Ecology series: principles, practices and management. (ed. Hansson, L.) (Springer, 1992). https://doi.org/10.1007/978-1-4615-3524-9Chapter 

Google Scholar 
Miller, N. G. & Ambrose, L. J. H. Growth in culture of wind-blown bryophyte gametophyte fragments from Arctic Canada. Bryologist 79, 55 (1976).Article 

Google Scholar 
Barbé, M., Fenton, N. J., Caners, R. & Bergeron, Y. Inter-annual variation in bryophyte dispersal: Linking bryophyte phenophases and weather conditions. Botany 95, 1151–1169 (2017).Article 

Google Scholar 
Chmielewski, M. W. & Eppley, S. M. Forest passerines as a novel dispersal vector of viable bryophyte propagules. Proc. R. Soc. B Biol. Sci. 286, 20182253 (2019).Article 
CAS 

Google Scholar 
Davison, G. W. H. Role of birds in moss dispersal. Br. Birds 69, 65–66 (1976).
Google Scholar 
Heinken, T., Lees, R., Raudnitschka, D. & Runge, S. Epizoochorous dispersal of bryophyte stem fragments by roe deer (Capreolus capreolus) and wild boar (Sus scrofa). J. Bryol. 23, 293–300 (2001).Article 

Google Scholar 
Parsons, J. G. et al. Bryophyte dispersal by flying foxes: A novel discovery. Oecologia 152, 112–114 (2007).Article 
CAS 
PubMed 

Google Scholar 
Glime, J. M. Bryophyte Ecology (Vol. 2, Issue Bryological Interaction) (2021).Ware, C., Bergstrom, D. M., Müller, E. & Alsos, I. G. Humans introduce viable seeds to the Arctic on footwear. Biol. Invasions 14, 567–577 (2012).Article 

Google Scholar 
Shacklette, H. T. Unattached moss polsters on Amchitka Island, Alaska. Bryologist 69, 346–352 (1966).Article 

Google Scholar 
Moles, A. T. & Westoby, M. Seedling survival and seed size: A synthesis of the literature. J. Ecol. 92, 372–383 (2004).Article 

Google Scholar 
Kimmerer, R. W. Patterns of dispersal and establishment of bryophytes colonizing natural and experimental treefall mounds in northern hardwood forests. Bryologist 108, 391–401 (2005).Article 

Google Scholar 
Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Article 

Google Scholar 
Stieha, C. R., Middleton, A. R., Stieha, J. K., Trott, S. H. & Mcletchie, D. N. The dispersal process of asexual propagules and the contribution to population persistence in Marchantia (Marchantiaceae). Am. J. Bot. 101, 348–356 (2014).Article 
PubMed 

Google Scholar 
Hugonnot, V. Comparative investigations of niche, growth rates and reproduction between the native moss Campylopus pilifer and the invasive C. introflexus. J. Bryol. 39, 79–84 (2017).Article 

Google Scholar 
Benscoter, B. W. Post-fire bryophyte establishment in a continental bog. J. Veg. Sci. 17, 647–652 (2006).Article 

Google Scholar 
Esposito, A., Mazzoleni, S. & Strumia, S. Post-fire bryophyte dynamics in Mediterranean vegetation. J. Veg. Sci. 10, 261–268 (1999).Article 

Google Scholar 
Naeth, M. A. & Wilkinson, S. R. Establishment of restoration trajectories for upland tundra communities on diamond mine wastes in the Canadian arctic. Restor. Ecol. 22, 534–543 (2014).Article 

Google Scholar 
Lamarre, J. J. M. Tundra bryophyte revegetation: novel methods for revegetating northern ecosystems (University of Alberta, 2016).Dierßen, K. Distribution, ecological amplitude and phytosociological characterization of European bryophytes. (Bryophytorum Bibliotheca 56. J. Cramer, Berlin, 289 pp., 2001).Smith, A. J. E. The moss flora of Britain and Ireland (Cambridge University Press, 2004).Book 

Google Scholar 
Casas, C., Brugués, M., Cros, R. M. & Sérgio, C. Handbook of Mosses of the Iberian Peninsula and the Balearic Islands. (Instituts d’Estudis Catalans, 2006).Medina, N., Mazimpaka Nibarere, V., Hortal, J. & Lara García, F. Catálogo de los briófitos epífitos que crecen en bosques de quercíneas del cuadrante noroccidental ibérico. Boletín la Soc. Esp. Briol. 30, 1–30 (2015).
Google Scholar 
Ron Alvarez, M. E. & Vicente, J. Contribución al conocimiento de la flora briológica de Canencia, Sierra de Guadarrama (Madrid). Bot. Complut. https://doi.org/10.5209/BOCM.7415 (1989).Article 

Google Scholar 
Pressel, S., Matcham, H. W. & Duckett, J. G. Studies of protonemal morphogenesis in mosses. XI. Bryum and allied genera: A plethora of propagules. J. Bryol. 29, 241–258 (2007).Article 

Google Scholar 
Söderström, L. & Herben, T. Dynamics of bryophyte metapopulations. in Advances in Briology 6. Population studies (ed. Longton, R. E.) 6, 205–240 (International Association of Briologists. Schweizerbart Science Publishers, 1997).
Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cox, E. P. A method of assigning numerical and percentage values to the degree of roundness of sand grains. J. Paleontol. 1, 179–183 (1927).
Google Scholar 
R Core Team. R: A language and environment for Statistical Computing (2021).Kassambara, A. rstatix: Pipe-friendly framework for basic statistical tests (2020).Zeileis, A., Meyer, D. & Hornik, K. Residual-based shadings for visualizing (conditional) independence. J. Comput. Graph. Stat. 16, 507–525 (2007).Article 
MathSciNet 

Google Scholar 
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A grammar of Data Manipulation (2022).Fox, J. & Weisberg, S. An R Companion to Applied Regression (2019).Maechler, M. et al. robustbase: Basic Robust Statistics (2022).Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots (2020).Revelle, W. psych: Procedures for psychological, psychometric, and personality research (2021).Kuhn, M., Jackson, S. & Cimentada, J. corrr: correlations in R. R package version 0.4.3 (2020).Wei, T. & Simko, V. R package ‘corrplot’: visualization of a correlation matrix (Version 0.84) (2017).Wilke, C. O. ggtext: improved text rendering support for ‘ggplot2’ (2020).Auguie, B. gridExtra: miscellaneous functions for ‘Grid’ graphics (2017).Wilke, C. O. cowplot: streamlined plot theme and plot annotations for ‘ggplot2’. R package version 1.1.1 (2020).Stark, L. R., Nichols, L. II., McLetchie, D. N., Smith, S. D. & Zundel, C. Age and sex-specific rates of leaf regeneration in the Mojave Desert moss Syntrichia caninervis. Am. J. Bot. 91, 1–9 (2004).Article 
PubMed 

Google Scholar 
Fernandez-Mendoza, F., Estebanez, B., Gomez-Sanz, D. & Ron, E. Sporophyte-bearing specimens of Pleurochaete squarrosa in Zamora, Spain. Cryptogam. Bryol. 23, 211–215 (2002).
Google Scholar 
Chen, K. H., Liao, H. L., Arnold, A. E., Bonito, G. & Lutzoni, F. RNA-based analyses reveal fungal communities structured by a senescence gradient in the moss Dicranum scoparium and the presence of putative multi-trophic fungi. New Phytol. 218, 1597–1611 (2018).Article 
CAS 
PubMed 

Google Scholar 
Kruijer, H. J. D., Raes, N. & Stech, M. Modelling the distribution of the moss species Hypopterygium tamarisci (Hypopterygiaceae, Bryophyta) in Central and South America. Nov. Hedwigia 91, 399–420 (2010).Article 

Google Scholar 
Van Zanten, B. O. Preliminary report on germination experiments designed to estimate the survival chances of moss spores during aerial trans-oceanic long-range dispersal in the Southern Hemisphere, with particular reference to New Zealand. J. Hattori Bot. Lab. 41, 133–140 (1976).
Google Scholar 
Van Zanten, B. O. Experimental studies on trans-oceanic long-range dispersal of moss spores in the Southern Hemisphere. J. Hattori Bot. Lab. 44, 455–482 (1978).
Google Scholar 
De Meester, L., Gómez, A., Okamura, B. & Schwenk, K. The monopolization hypothesis and the dispersal-gene flow paradox in aquatic organisms. Acta Oecologica 23, 121–135 (2002).Article 

Google Scholar 
Izquieta-Rojano, S. et al. Pleurochaete squarrosa (Brid.) Lindb. as an alternative moss species for biomonitoring surveys of heavy metal, nitrogen deposition and δ15N signatures in a Mediterranean area. Ecol. Indic. 60, 1221–1228 (2016).Article 
CAS 

Google Scholar 
Kimmerer, R. W. & Young, C. C. Effect of gap size and regeneration niche on species coexistence in bryophyte communities. J. Torrey Bot. Soc. 123, 16–24 (1996).Article 

Google Scholar 
Refoyo, P., Peláez, M., García-Rodríguez, M., López-Sánchez, A. & Perea, R. Moss cover and browsing scores as sustainability indicators of mountain ungulate populations in Mediterranean environments. Biodivers. Conserv. https://doi.org/10.1007/s10531-022-02454-1 (2022).Article 

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 
CAS 

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

Google Scholar 
De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).Article 

Google Scholar 
Anderegg, W. R., Kane, J. M. & Anderegg, L. D. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Change 3, 30–36 (2013).Article 

Google Scholar 
Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 129 (2015).Article 

Google Scholar 
Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).Article 
CAS 

Google Scholar 
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).Article 
CAS 

Google Scholar 
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).Article 
CAS 

Google Scholar 
Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Change 7, 395–402 (2017).Article 

Google Scholar 
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).Article 
CAS 

Google Scholar 
Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).Article 
CAS 

Google Scholar 
Anderegg, W. R., Trugman, A. T., Badgley, G., Konings, A. G. & Shaw, J. Divergent forest sensitivity to repeated extreme droughts. Nat. Clim. Change 10, 1091–1095 (2020).Article 

Google Scholar 
Zhang, T., Niinemets, Ü., Sheffield, J. & Lichstein, J. W. Shifts in tree functional composition amplify the response of forest biomass to climate. Nature 556, 99–102 (2018).Article 
CAS 

Google Scholar 
Engelbrecht, B. M. et al. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007).Article 
CAS 

Google Scholar 
Lenoir, J., Gégout, J.-C., Marquet, P., De Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).Article 
CAS 

Google Scholar 
Au, T. F. et al. Demographic shifts in eastern US forests increase the impact of late‐season drought on forest growth. Ecography 43, 1475–1486 (2020).Article 

Google Scholar 
Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).Article 
CAS 

Google Scholar 
Lindenmayer, D. B., Laurance, W. F. & Franklin, J. F. Global decline in large old trees. Science 338, 1305–1306 (2012).Article 
CAS 

Google Scholar 
McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).Article 
CAS 

Google Scholar 
Ellsworth, D. & Reich, P. Canopy structure and vertical patterns of photosynthesis and related leaf traits in a deciduous forest. Oecologia 96, 169–178 (1993).Article 
CAS 

Google Scholar 
Stephenson, N. L. et al. Rate of tree carbon accumulation increases continuously with tree size. Nature 507, 90–93 (2014).Article 
CAS 

Google Scholar 
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).Article 
CAS 

Google Scholar 
Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 1, 15139 (2015).Article 

Google Scholar 
Piovesan, G. & Biondi, F. On tree longevity. N. Phytol. 231, 1318–1337 (2021).Article 

Google Scholar 
Jucker, T. et al. Tallo: a global tree allometry and crown architecture database. Glob. Change Biol. 28, 5254–5268 (2022).Article 
CAS 

Google Scholar 
Körner, C. A matter of tree longevity. Science 355, 130–131 (2017).Article 

Google Scholar 
D’orangeville, L. et al. Drought timing and local climate determine the sensitivity of eastern temperate forests to drought. Glob. Change Biol. 24, 2339–2351 (2018).Article 

Google Scholar 
Luo, Y. & Chen, H. Y. Observations from old forests underestimate climate change effects on tree mortality. Nat. Commun. 4, 1655 (2013).Article 

Google Scholar 
Dannenberg, M. P., Wise, E. K. & Smith, W. K. Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes. Sci. Adv. 5, eaaw0667 (2019).Article 

Google Scholar 
Anderegg, W. R. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).Article 
CAS 

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

Google Scholar 
Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).Article 
CAS 

Google Scholar 
Phillips, R. P. et al. A belowground perspective on the drought sensitivity of forests: towards improved understanding and simulation. For. Ecol. Manage. 380, 309–320 (2016).Article 

Google Scholar 
Meinzer, F. C., Lachenbruch, B. & Dawson, T. E. Size- and Age-Related Changes in Tree Structure and Function Vol. 4 (Springer, 2011).Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).Article 
CAS 

Google Scholar 
Klein, T. The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Funct. Ecol. 28, 1313–1320 (2014).Article 

Google Scholar 
Cavender-Bares, J. & Bazzaz, F. Changes in drought response strategies with ontogeny in Quercus rubra: implications for scaling from seedlings to mature trees. Oecologia 124, 8–18 (2000).Article 
CAS 

Google Scholar 
Gallé, A., Haldimann, P. & Feller, U. Photosynthetic performance and water relations in young pubescent oak (Quercus pubescens) trees during drought stress and recovery. N. Phytol. 174, 799–810 (2007).Article 

Google Scholar 
Keith, H., Mackey, B. G. & Lindenmayer, D. B. Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests. Proc. Natl Acad. Sci. USA 106, 11635–11640 (2009).Article 
CAS 

Google Scholar 
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).Article 
CAS 

Google Scholar 
Zhao, S. et al. The International Tree‐Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).Article 

Google Scholar 
Fisher, R. A. et al. Vegetation demographics in Earth system models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).Article 

Google Scholar 
Rayback, S. A. et al. The DendroEcological Network: a cyberinfrastructure for the storage, discovery and sharing of tree-ring and associated ecological data. Dendrochronologia 60, 125678 (2020).Article 

Google Scholar 
Maxwell, J. T. et al. Sampling density and date along with species selection influence spatial representation of tree-ring reconstructions. Climate of the Past 16, 1901–1916 (2020).Article 

Google Scholar 
Maxwell, J. T. et al. Higher CO2 concentrations and lower acidic deposition have not changed drought response in tree growth but do influence iWUE in hardwood trees in the Midwestern USA. J. Geophys. Res. Biogeosci. 124, 3798–3813 (2019).Article 
CAS 

Google Scholar 
Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).Article 

Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021); https://www.R-project.org/Cook, E. R. & Kairiukstis, L. A. Methods of Dendrochronology: Applications in the Environmental Sciences (Springer, 2013).Cook, E. R. & Peters, K. The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies. Tree-Ring Bull. 41, 45–53 (1981).
Google Scholar 
Fritts, H. Tree Rings and Climate (Academic Press, 1976).
Google Scholar 
Wilson, R. et al. Last millennium Northern Hemisphere summer temperatures from tree rings: part I: the long term context. Quat. Sci. Rev. 134, 1–18 (2016).Article 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

Google Scholar 
Holmes, R. Program COFECHA User’s Manual (Univ. Arizona Laboratory of Tree-Ring Research, 1983).Palmer, J. G. et al. Drought variability in the eastern Australia and New Zealand summer drought atlas (ANZDA, CE 1500–2012) modulated by the Interdecadal Pacific Oscillation. Environ. Res. Lett. 10, 124002 (2015).Article 

Google Scholar 
Cook, E. R. et al. Asian monsoon failure and megadrought during the last millennium. Science 328, 486–489 (2010).Article 
CAS 

Google Scholar 
Cook, E. R., Woodhouse, C. A., Eakin, C. M., Meko, D. M. & Stahle, D. W. Long-term aridity changes in the western United States. Science 306, 1015–1018 (2004).Article 
CAS 

Google Scholar 
Cook, E. R. et al. Megadroughts in North America: placing IPCC projections of hydroclimatic change in a long‐term palaeoclimate context. J. Quat. Sci. 25, 48–61 (2010).Article 

Google Scholar 
Cook, E. R. et al. Old World megadroughts and pluvials during the Common Era. Sci. Adv. 1, e1500561 (2015).Article 

Google Scholar 
Morales, M. S. et al. Six hundred years of South American tree rings reveal an increase in severe hydroclimatic events since mid-20th century. Proc. Natl Acad. Sci. USA 117, 16816–16823 (2020).Article 
CAS 

Google Scholar 
Stokes, M. & Smiley, T. An Introduction to Tree-Ring Dating. (Univ. Chicago Press, 1968).
Google Scholar 
Lockwood, B. R., Maxwell, J. T., Robeson, S. M, & Au, T. F. Assessing bias in diameter at breast height estimated from tree rings and its effects on basal area increment and biomass. Dendrochronologia 67, 125844 (2021).Locosselli, G. M. et al. Global tree-ring analysis reveals rapid decrease in tropical tree longevity with temperature. Proc. Natl Acad. Sci. USA 117, 33358–33364 (2020).Article 
CAS 

Google Scholar 
Rozas, V., DeSoto, L. & Olano, J. M. Sex‐specific, age‐dependent sensitivity of tree‐ring growth to climate in the dioecious tree Juniperus thurifera. N. Phytol. 182, 687–697 (2009).Article 

Google Scholar 
Carrer, M. & Urbinati, C. Age‐dependent tree‐ring growth responses to climate in Larix decidua and Pinus cembra. Ecology 85, 730–740 (2004).Article 

Google Scholar 
Gazol, A., Camarero, J., Anderegg, W. & Vicente‐Serrano, S. Impacts of droughts on the growth resilience of Northern Hemisphere forests. Glob. Ecol. Biogeogr. 26, 166–176 (2017).Article 

Google Scholar 
Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 4, 1075–1083 (2020).Article 

Google Scholar 
Pardos, M. et al. The greater resilience of mixed forests to drought mainly depends on their composition: analysis along a climate gradient across Europe. For. Ecol. Manage. 481, 118687 (2021).Article 

Google Scholar 
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: thestandardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R (CRC Press, 2017).Rollinson, C. R. et al. Climate sensitivity of understory trees differs from overstory trees in temperate mesic forests. Ecology 102, e03264 (2021).Article 

Google Scholar 
Lloret, F., Keeling, E. G. & Sala, A. Components of tree resilience: effects of successive low‐growth episodes in old ponderosa pine forests. Oikos 120, 1909–1920 (2011).Article 

Google Scholar 
Li, X. et al. Reply to: Disentangling biology from mathematical necessity in twentieth-century gymnosperm resilience trends. Nat. Ecol. Evol. 5, 736–737 (2021).Article 

Google Scholar 
Zheng, T. et al. Disentangling biology from mathematical necessity in twentieth-century gymnosperm resilience trends. Nat. Ecol. Evol. 5, 733–735 (2021).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Long, J. A. jtools: Analysis and Presentation of Social Scientific Data R Package v.2.2.0 https://cran.r-project.org/package=jtools (2022).Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on AIC R Package v.2.3-1 https://cran.r-project.org/package=AICcmodavg (2020).Au, T. F. Au_et_al_NCC.R. Figshare https://doi.org/10.6084/m9.figshare.21263676.v1 (2022). More