Philippa Kaur

Duggins, D. O., Simenstad, C. A. & Estes, J. A. Magnification of secondary producition by kelp detritus in coastal marine ecosystems. Science 1979(245), 170–173 (1989).Article 
ADS 

Google Scholar 
Hill, R. et al. Can macroalgae contribute to blue carbon? An Australian perspective. Limnol. Oceanogr. 60, 1689–1706 (2015).Article 
ADS 

Google Scholar 
Mann, K. H. Seaweeds: Their productivity and strategy for growth. Science 1979(182), 975–981 (1973).Article 
ADS 

Google Scholar 
Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).Article 

Google Scholar 
Giordano, M., Beardall, J. & Raven, J. A. CO2 concentrating mechanisms in algae: Mechanisms, environmental modulation, and evolution. Annu. Rev. Plant Biol. 56, 99–131 (2005).Article 
CAS 

Google Scholar 
Raven, J. A. & Beardall, J. The ins and outs of CO2. J. Exp. Bot. 67, 1–13 (2016).Article 
CAS 

Google Scholar 
Raven, J. A. et al. Seaweeds in cold seas: Evolution and carbon acquisition. Ann. Bot. 90, 525–536. https://doi.org/10.1093/aob/mcf171 (2002).Article 
CAS 

Google Scholar 
Raven, J. et al. Ocean Acidification due to Increasing Atmospheric Carbon Dioxide 1–68 (The Royal Society, 2005).
Google Scholar 
Kübler, J. E. & Dudgeon, S. R. Predicting effects of ocean acidification and warming on algae lacking carbon concentrating mechanisms. PLoS ONE 10, 1–19 (2015).Article 

Google Scholar 
Fernández, P. A., Hurd, C. L. & Roleda, M. Y. Bicarbonate uptake via an anion excange protein is the main mechanism of inorganic carbon acquisition by the giant kelp Macrocystis pyrifera (Laminariales, Phaeophyceae) under variable pH1. J. Phycol. 50, 1–11 (2014).Article 

Google Scholar 
Raven, J. A. et al. Mechanistic interpretation of carbon isotope discrimination by marine macroalgae and seagrasses. Funct. Plant Biol. 29, 355 (2002).Article 
CAS 

Google Scholar 
Raven, J. A., Cockell, C. S. & De La Rocha, C. L. The evolution of inorganic carbon concentrating mechanisms in photosynthesis. Philos. Trans. R. Soc. B 363, 2641–2650 (2008).Article 
CAS 

Google Scholar 
Bidwell, R. G. S. S. & McLachlan, J. Carbon nutrition of seaweeds: Photosynthesis, photorespiration and respiration. J. Exp. Mar. Biol. Ecol. 86, 15–46 (1985).Article 
CAS 

Google Scholar 
Hurd, C. L. Water motion, marine macroalgal physiology and production. J. Phycol. 36, 453–472. https://doi.org/10.1046/j.1529-8817.2000.99139.x (2000).Article 
CAS 

Google Scholar 
Hurd, C. L., Stevens, C. L., Laval, B. E., Lawrence, G. A. & Harrison, P. J. Visualization of seawater flow around morphologically distinct forms of the giant kelp Macrocystis integrifolia from wave-sheltered and exposed sites. Limnol. Oceanogr. 42, 156–163. https://doi.org/10.4319/lo.1997.42.1.0156 (1997).Article 
ADS 

Google Scholar 
Smith, F. A. A. & Walker, N. A. A. Photosynthesis by aquatic plants: Effects of unstirred layers in relation to assimilation of CO2 and HCO3- to carbon isotope discrimination. N. Phytol. 86, 245–259 (1980).Article 
CAS 

Google Scholar 
Wheeler, W. N. Effect of boundary layer transport on the fixation of carbon by the giant kelp Macrocystis pyrifera. Mar. Biol. 56, 103–110 (1980).Article 
ADS 
CAS 

Google Scholar 
Hurd, C. L., Lenton, A., Tilbrook, B. & Boyd, P. W. Current understanding and challenges for oceans in a higher-CO2 world. Nat. Clim. Chang. 8, 686–694 (2018).Article 
ADS 
CAS 

Google Scholar 
Stocker, T. F. et al. Technical Summary. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 33–115 (2013).Hepburn, C. D. et al. Diversity of carbon use strategies in a kelp forest community: Implications for a high CO2 ocean. Glob. Chang. Biol. 17, 2488–2497 (2011).Article 
ADS 

Google Scholar 
Beer, S. & Koch, E. Photosynthesis of marine macroalgae and seagrasses in globally changing CO2 environments. Mar. Ecol. Prog. Ser. 141, 199–204 (1996).Article 
ADS 

Google Scholar 
Ihnken, S., Roberts, S. & Beardall, J. Differential responses of growth and photosynthesis in the marine diatom Chaetoceros muelleri to CO2 and light availability. Phycologia 50, 182–193 (2011).Article 
CAS 

Google Scholar 
Gerard, V. A. In situ water motion and nutrient uptake by the giant kelp Macrocystis pyrifera. Mar. Biol. 69, 51–54 (1982).Article 

Google Scholar 
Hepburn, C. D., Holborow, J. D., Wing, S. R., Frew, R. D. & Hurd, C. L. Exposure to waves enhances the growth rate and nitrogen status of the giant kelp Macrocystis pyrifera. Mar. Ecol. Prog. Ser. 339, 99–108 (2007).Article 
ADS 
CAS 

Google Scholar 
Hurd, C. L. Shaken and stirred: The fundamental role of water motion in resource acquisition and seaweed productivity. Persp. Phycol. 4, 73–81 (2017).ADS 

Google Scholar 
Sültemeyer, D. F., Miller, A. G., Espie, G. S., Fock, H. P. & Canvin, D. T. Active CO2 transport by the green alga Chlamydomonas reinhardtii. Plant Physiol. 89, 1213–1219 (1989).Article 

Google Scholar 
Koch, M., Bowes, G., Ross, C. & Zhang, X. H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Chang. Biol. 19, 103–132 (2013).Article 
ADS 

Google Scholar 
Britton, D., Cornwall, C. E., Revill, A. T., Hurd, C. L. C. L. & Johnson, C. R. Ocean acidification reverses the positive effects of seawater pH fluctuations on growth and photosynthesis of the habitat-forming kelp Ecklonia radiata. Sci. Rep. 6, 1–10 (2016).Article 

Google Scholar 
Cornwall, C. E. et al. Carbon-use strategies in macroalgae: Differential responses to lowered ph and implications for ocean acidification. J. Phycol. 48, 137–144 (2012).Article 
CAS 

Google Scholar 
Kram, S. L. et al. Variable responses of temperate calcified and fleshy macroalgae to elevated pCO2 and warming. ICES J. Mar. Sci. 73, 693–703 (2016).Article 

Google Scholar 
Kübler, J. E., Johnston, A. M. & Raven, J. A. The effects of reduced and elevated CO2 and O2 on the seaweed Lomentaria articulata. Plant Cell Environ. 22, 1303–1310 (1999).Article 

Google Scholar 
van der Loos, L. M. et al. Responses of macroalgae to CO2 enrichment cannot be inferred solely from their inorganic carbon uptake strategy. Ecol. Evol. 9, 125–140 (2019).Article 

Google Scholar 
Cornwall, C. E. & Hurd, C. L. Variability in the benefits of ocean acidification to photosynthetic rates of macroalgae without CO2-concentrating mechanisms. Mar. Freshw. Res. 71, 275–280 (2019).Article 

Google Scholar 
Cornwall, C. E., Revill, A. T. & Hurd, C. L. High prevalence of diffusive uptake of CO2 by macroalgae in a temperate subtidal ecosystem. Photosynth. Res. 124, 181–190 (2015).
Article 
CAS 

Google Scholar 
Lovelock, C. E., Reef, R., Raven, J. A. & Pandolfi, J. M. Regional variation in δ13C of coral reef macroalgae. Limnol. Oceanogr. 65, 2291–2302 (2020).Article 
ADS 
CAS 

Google Scholar 
Fischer, G. & Wiencke, C. Stable carbon isotope composition, depth distribution and fate of macroalgae from the Antarctic Peninsula region. Polar. Biol. 12, 341–348 (1992).Article 

Google Scholar 
Stephens, T. A. & Hepburn, C. D. Mass-transfer gradients across kelp beds influence Macrocystis pyrifera growth over small spatial scales. Mar. Ecol. Prog. Ser. 515, 97–109 (2014).Article 
ADS 

Google Scholar 
Kregting, L. T., Hepburn, C. D. & Savidge, G. Seasonal differences in the effects of oscillatory and uni-directional flow on the growth and nitrate-uptake rates of juvenile Laminaria digitata (Phaeophyceae). J. Phycol. 51, 1116–1126 (2015).Article 
CAS 

Google Scholar 
Parker, H. S. Influence of relative water motion on the growth, ammonium uptake and carbon and nitrogen composition of Ulva lactuca (Chlorophyta). Mar. Biol. 63, 309–318 (1981).Article 
CAS 

Google Scholar 
Bergstrom, E. et al. Inorganic carbon uptake strategies in coralline algae: Plasticity across evolutionary lineages under ocean acidification and warming. Mar. Environ. Res. 161, 105107 (2020).Article 
CAS 

Google Scholar 
Maberly, S. C., Raven, J. A. & Johnston, A. M. Discrimination between C-12 and C-13 by marine plants. Oecologia 91, 481–492 (1992).Article 
ADS 
CAS 

Google Scholar 
Gattuso, J. P. et al. Package ‘Seacarb ’. Preprint at http://cran.r-project.org/package=seacarb (2015).Raven, J. A., Beardall, J. & Giordano, M. Energy costs of carbon dioxide concentrating mechanisms in aquatic organisms. Photosynth. Res. 121, 111–124 (2014).Article 
CAS 

Google Scholar 
Raven, J. A., Walker, D. I., Johnston, A. M., Handley, L. L. & Kübler, J. E. Implications of 13C natural abundance measurements for photosynthetic performance by marine macrophytes in their natural environment. Mar. Ecol. Prog. Ser. 123, 193–205 (1995).Article 
ADS 

Google Scholar 
Raven, J. A. Inorganic carbon acquisition by marine autotrophs. Adv. Bot. Res. 27, 85–209 (1997).Article 
CAS 

Google Scholar 
Fernández, P. A., Roleda, M. Y. & Hurd, C. L. Effects of ocean acidification on the photosynthetic performance, carbonic anhydrase activity and growth of the giant kelp Macrocystis pyrifera. Photosynth. Res. 124, 293–304 (2015).Article 

Google Scholar 
Bailly, J. & Coleman, J. R. Effect of CO(2) concentration on protein biosynthesis and carbonic anhydrase expression in Chlamydomonas reinhardtii. Plant Physiol. 87, 833–840 (1988).Article 
CAS 

Google Scholar 
Dionisio-Sese, M. L., Fukuzawa, H. & Miyachi, S. Light-induced carbonic anhydrase expression in Chlamydomonas reinhardtii. Plant Physiol. 94, 1103–1110 (1990).Article 
CAS 

Google Scholar 
Pollock, S. V., Colombo, S. L., Prout, D. L., Godfrey, A. C. & Moroney, J. V. Rubisco activase is required for optimal photosynthesis in the green alga Chlamydomonas reinhardtii in a low-CO2 atmosphere. Plant Physiol. 133, 1854–1861 (2003).Article 
CAS 

Google Scholar 
Carlberg, S., Axelsson, L., Larsson, C., Ryberg, H. & Uusitalo, J. Inducible CO2 concentrating mechanisms in green seaweeds I. Taxonomical and physiological aspects. In Current Research in Photosynthesis (ed. Baltscheffsky, M.) (Springer, 1990). https://doi.org/10.1007/978-94-009-0511-5_749.Chapter 

Google Scholar 
Wheeler, W. N. Effect of boundary-layer transport on the fixation of carbon by the giant-kelp Macrocystis pyrifera. Mar. Biol. 56, 103–110 (1980).Article 
ADS 
CAS 

Google Scholar 
Johnston, A. M. & Raven, J. A. Effects of culture in high CO2 on the photosynthetic physiology of Fucus serratus. Br. J. Phycol. 25, 75–82 (1990).Article 

Google Scholar 
Connell, S. D., Kroeker, K. J., Fabricius, K. E., Kline, D. I. & Russell, B. D. The other ocean acidification problem: CO2 as a resource among competitors for ecosystem dominance. Philos. Trans. R. Soc. Lond. 368, 20120442 (2013).Article 

Google Scholar 
Porter, E. T., Sanford, L. P. & Suttles, S. E. Gypsum dissolution is not a universal integrator of water motion. Limnol. Oceanogr. 45, 145–158 (2000).Article 
ADS 

Google Scholar 
Gerard, V. A. & Mann, K. H. Growth and production of Laminaria longicruris (Phaeophyta) populations exposed to different intensities of water movement. J. Phycol. 15, 33–41 (1979).Article 

Google Scholar 
Bivand, R., Keitt, T. & Rowlingson, B. Package ‘rgdal’. R Package https://doi.org/10.1353/lib.0.0050 (2016).Article 

Google Scholar 
LINZ. LINZ Data Service. https://data.linz.govt.nz/layer/50258-nz-coastlines-topo-150k/history/ Accessed July 2021 (2021).Kirk, J. T. Characteristics of the light field in highly turbid waters: A Monte Carlo study. Limnol. Oceanogr. 39, 702–706 (1994).Article 
ADS 

Google Scholar 
Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis (Fisheries Research Board of Canada, 1968).
Google Scholar 
Kohler, K. E. & Gill, S. M. Coral Point Count with Excel extensions (CPCe): A visual basic program for the determination of coral and substrate coverage using random point count methodology. Comput. Geosci. 32, 1259–1269 (2006).Article 
ADS 

Google Scholar 
Axelsson, L., Mercado, J. & Figueroa, F. Utilization of HCO3− at high ph by the brown macroalga laminaria saccharina. Eur. J. Phycol. 35, 53–59 (2000).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. Preprint at (2017).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).Article 
MathSciNet 
MATH 

Google Scholar  More

Orr, M. R. & Smith, T. B. Ecology and speciation. Trends Ecol. Evol. 13, 502–506 (1998).Article 
CAS 

Google Scholar 
Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates, 2004).
Google Scholar 
Gillespie, R. G. Adaptive radiation: Convergence and non-equilibrium. Curr. Biol. 23, R71–R74 (2013).Article 
CAS 

Google Scholar 
Price, T. Speciation in Birds (Roberts and Company Publishers, 2008).
Google Scholar 
Schluter, D. Evidence for ecological speciation and its alternative. Science 323, 737–741 (2009).Article 
ADS 
CAS 

Google Scholar 
Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47, 507–532 (2016).Article 

Google Scholar 
Jønsson, K. A. et al. Ecological and evolutionary determinants for the adaptive radiation of the Madagascan vangas. Proc. Natl. Acad. Sci. 109, 6620–6625 (2012).Article 
ADS 

Google Scholar 
Wiens, J. J. Speciation and ecology revisited: Phylogenetic niche conservatism and the origin of species. Evolution 58, 193–197 (2004).
Google Scholar 
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).Article 

Google Scholar 
Wiens, J. J. & Graham, C. H. Niche Conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).Article 

Google Scholar 
Petitpierre, B. et al. Climatic niche shifts are rare among terrestrial plant invaders. Science 335, 1344–1348 (2012).Article 
ADS 
CAS 

Google Scholar 
Winger, B. M., Barker, F. K. & Ree, R. H. Temperate origins of long-distance seasonal migration in New World songbirds. Proc. Natl. Acad. Sci. 111, 12115–12120 (2014).Article 
ADS 
CAS 

Google Scholar 
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: Evolution and determinants. Oikos 103, 247–260 (2003).Article 

Google Scholar 
Gómez, C., Tenorio, E. A., Montoya, P. & Cadena, C. D. Niche-tracking migrants and niche-switching residents: Evolution of climatic niches in New World warblers (Parulidae). Proc. R. Soc. B Biol. Sci. 283, 20152458 (2016).Article 

Google Scholar 
Menchaca, A., Arteaga, M. C., Medellin, R. A. & Jones, G. Conservation units and historical matrilineal structure in the tequila bat (Leptonycteris yerbabuenae). Glob. Ecol. Conserv. 23, e01164 (2020).Article 

Google Scholar 
Medellín, R. A. et al. Follow me: Foraging distances of Leptonycteris yerbabuenae (Chiroptera: Phyllostomidae) in Sonora determined by fluorescent powder. J. Mammal. 99, 306–311 (2018).Article 

Google Scholar 
Broennimann, O. et al. Evidence of climatic niche shift during biological invasion. Ecol. Lett. 10, 701–709 (2007).Article 
CAS 

Google Scholar 
Martínez-Meyer, E., Peterson, A. T. & Hargrove, W. W. Ecological niches as stable distributional constraints on mammal species, with implications for Pleistocene extinctions and climate change projections for biodiversity. Glob. Ecol. Biogeogr. 13, 305–314 (2004).Article 

Google Scholar 
Soto-Centeno, J. A. & Steadman, D. W. Fossils reject climate change as the cause of extinction of Caribbean bats. Sci. Rep. 5, 7971 (2015).Article 
ADS 
CAS 

Google Scholar 
Avise, J. C. Phylogeography: The History and Formation of Species (Harvard University Press, 2000).Book 

Google Scholar 
Hickerson, M. J. et al. Phylogeography’s past, present, and future: 10 years after Avise, 2000. Mol. Phylogenet. Evol. 54, 291–301 (2010).Article 
CAS 

Google Scholar 
Pahad, G., Montgelard, C. & Jansen van Vuuren, B. Phylogeography and niche modelling: Reciprocal enlightenment. Mammalia 84, 10–25 (2019).Article 

Google Scholar 
Flanders, J. et al. Phylogeography of the greater horseshoe bat, Rhinolophus ferrumequinum: Contrasting results from mitochondrial and microsatellite data. Mol. Ecol. 18, 306–318 (2009).Article 
CAS 

Google Scholar 
Machado, A. F. et al. Integrating phylogeography and ecological niche modelling to test diversification hypotheses using a Neotropical rodent. Evol. Ecol. 33, 111–148 (2019).Article 

Google Scholar 
Kalkvik, H. M., Stout, I. J., Doonan, T. J. & Parkinson, C. L. Investigating niche and lineage diversification in widely distributed taxa: Phylogeography and ecological niche modeling of the Peromyscus maniculatus species group. Ecography 35, 54–64 (2012).Article 

Google Scholar 
Wang, Y. et al. Ring distribution patterns—diversification or speciation? Comparative phylogeography of two small mammals in the mountains surrounding the Sichuan Basin. Mol. Ecol. 30, 2641–2658 (2021).Article 

Google Scholar 
Soto-Centeno, J. A., Barrow, L. N., Allen, J. M. & Reed, D. L. Reevaluation of a classic phylogeographic barrier: New techniques reveal the influence of microgeographic climate variation on population divergence. Ecol. Evol. 3, 1603–1613 (2013).Article 

Google Scholar 
Amador, L. I., Moyers Arévalo, R. L., Almeida, F. C., Catalano, S. A. & Giannini, N. P. Bat systematics in the light of unconstrained analyses of a comprehensive molecular supermatrix. J. Mamm. Evol. 25, 37–70 (2018).Article 

Google Scholar 
Rojas, D., Warsi, O. M. & Dávalos, L. M. Bats (Chiroptera: Noctilionoidea) challenge a recent origin of extant neotropical diversity. Syst. Biol. 65, 432–448 (2016).Article 

Google Scholar 
Shi, J. J. & Rabosky, D. L. Speciation dynamics during the global radiation of extant bats. Evolution 69, 1528–1545 (2015).Article 

Google Scholar 
Dumont, E. R. et al. Morphological innovation, diversification and invasion of a new adaptive zone. Proc. Biol. Sci. 279, 1797–1805 (2012).
Google Scholar 
Leiser-Miller, L. B. & Santana, S. E. Morphological diversity in the sensory system of phyllostomid bats: Implications for acoustic and dietary ecology. Funct. Ecol. 34, 1416–1427 (2020).Article 

Google Scholar 
Hedrick, B. P. & Dumont, E. R. Putting the leaf-nosed bats in context: A geometric morphometric analysis of three of the largest families of bats. J. Mammal. 99, 1042–1054 (2018).Article 

Google Scholar 
Clare, E. L. Cryptic species? Patterns of maternal and paternal gene flow in eight neotropical bats. PLoS One 6, e21460 (2011).Article 
ADS 
CAS 

Google Scholar 
Chaverri, G. et al. Unveiling the hidden bat diversity of a neotropical montane forest. PLoS One 11, e0162712 (2016).Article 

Google Scholar 
Calahorra-Oliart, A., Ospina-Garcés, S. M. & León-Paniagua, L. Cryptic species in Glossophaga soricina (Chiroptera: Phyllostomidae): Do morphological data support molecular evidence?. J. Mammal. 102, 54–68 (2021).Article 

Google Scholar 
Lim, B. K., Loureiro, L. O. & Garbino, G. S. T. Cryptic diversity and range extension in the big-eyed bat genus Chiroderma (Chiroptera, Phyllostomidae). Zookeys 918, 41–63 (2020).Article 

Google Scholar 
Loureiro, L. O., Engstrom, M., Lim, B., González, C. L. & Juste, J. Not all Molossus are created equal: Genetic variation in the mastiff bat reveals diversity masked by conservative morphology. Acta Chiropterologica 21, 51 (2019).Article 

Google Scholar 
Morales, A., Villalobos, F., Velazco, P. M., Simmons, N. B. & Piñero, D. Environmental niche drives genetic and morphometric structure in a widespread bat. J. Biogeogr. 43, 1057–1068 (2016).Article 

Google Scholar 
Hedrick, B. P. et al. Morphological diversification under high integration in a hyper diverse mammal clade. J. Mamm. Evol. 27, 563–575 (2020).Article 

Google Scholar 
Morales, A. E. & Carstens, B. C. Evidence that myotis lucifugus “subspecies” are five nonsister species, despite gene flow. Syst. Biol. 67, 756–769 (2018).Article 

Google Scholar 
Simmons, N. B. & Cirranello, A. L. Bat species of the world: A taxonomic and geographic database. https://batnames.org.Russell, A. L., Pinzari, C. A., Vonhof, M. J., Olival, K. J. & Bonaccorso, F. J. Two tickets to paradise: Multiple dispersal events in the founding of hoary bat populations in Hawai’i. PLoS One 10, 1–13 (2015).
Google Scholar 
Shump, K. A. & Shump, A. U. Lasiurus cinereus. Mamm. Species 185, 1–5 (1982).
Google Scholar 
Ziegler, A. C., Howarth, F. G. & Simmons, N. B. A second endemic land mammal for the Hawaiian Islands: A new genus and species of fossil bat (Chiroptera: Vespertilionidae). Am. Museum Novit. 1–52 (2016).Bonaccorso, F. J. & McGuire, L. P. Modeling the colonization of Hawaii by hoary bats (Lasiurus cinereus). In Bat Evolution, Ecology, and Conservation (eds Adams, R. A. & Pedersen, S. C.) 187–205 (Springer, 2013).Chapter 

Google Scholar 
Baird, A. B. et al. Molecular systematic revision of tree bats (Lasiurini): Doubling the native mammals of the Hawaiian Islands. J. Mammal. 96, 1255–1274 (2015).Article 

Google Scholar 
Jacobs, D. S. Morphological divergence in an insular bat, Lasiurus cinereus semotus. Funct. Ecol. 10, 622–630 (1996).Article 

Google Scholar 
Baird, A. B. et al. Nuclear and mtDNA phylogenetic analyses clarify the evolutionary history of two species of native Hawaiian bats and the taxonomy of Lasiurini (Mammalia: Chiroptera). PLoS One 12, e0186085 (2017).Article 

Google Scholar 
Kumar, S. & Subramanian, S. Mutation rates in mammalian genomes. Proc. Natl. Acad. Sci. U.S.A. 99, 803–808 (2002).Article 
ADS 
CAS 

Google Scholar 
Gillespie, R. G. et al. Comparing adaptive radiations across space, time, and taxa. J. Hered. 111, 1–20 (2020).Article 

Google Scholar 
Fišer, C., Robinson, C. T. & Malard, F. Cryptic species as a window into the paradigm shift of the species concept. Mol. Ecol. 27, 613–635 (2018).Article 

Google Scholar 
Espíndola, A. et al. Identifying cryptic diversity with predictive phylogeography. Proc. R. Soc. B Biol. Sci. 283, 20161529 (2016).Article 

Google Scholar 
Padial, J. M., Miralles, A., De la Riva, I. & Vences, M. The integrative future of taxonomy. Front. Zool. 7, 1–14 (2010).Article 

Google Scholar 
Fujita, M. K., Leaché, A. D., Burbrink, F. T., McGuire, J. A. & Moritz, C. Coalescent-based species delimitation in an integrative taxonomy. Trends Ecol. Evol. 27, 480–488 (2012).Article 

Google Scholar 
Solari, S., Sotero-Caio, C. G. & Baker, R. J. Advances in systematics of bats: Towards a consensus on species delimitation and classifications through integrative taxonomy. J. Mammal. 100, 838–851 (2018).Article 

Google Scholar 
Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).
Google Scholar 
Morales, A. E., De-la-Mora, M. & Piñero, D. Spatial and environmental factors predict skull variation and genetic structure in the cosmopolitan bat Tadarida brasiliensis. J. Biogeogr. 45, 1529–1540 (2018).Article 

Google Scholar 
Pavan, A. C. & Marroig, G. Integrating multiple evidences in taxonomy: Species diversity and phylogeny of mustached bats (Mormoopidae: Pteronotus). Mol. Phylogenet. Evol. 103, 184–198 (2016).Article 

Google Scholar 
Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: A fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).Article 
CAS 

Google Scholar 
Robinson, D. & Foulds, L. Comparison of phylogenetic trees. Math. Biosci. 53, 131–147 (1981).Article 
MathSciNet 
MATH 

Google Scholar 
Pattengale, N. D., Alipour, M., Bininda-Emonds, O. R., Moret, B. M. & Stamatakis, A. How many bootstrap replicates are necessary?. J. Comput. Biol. 17, 337–354 (2010).Article 
MathSciNet 
CAS 

Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).Article 
ADS 
CAS 

Google Scholar 
Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Article 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).Article 
CAS 

Google Scholar 
Kapli, P. et al. Multi-rate Poisson Tree Processes for single-locus species delimitation under Maximum Likelihood and Markov Chain Monte Carlo. Bioinformatics 33, 1630–1638 (2017).CAS 

Google Scholar 
Yang, Z. & Rannala, B. Unguided species delimitation using DNA sequence data from multiple loci. Mol. Biol. Evol. 31, 3125–3135 (2014).Article 
CAS 

Google Scholar 
Flouri, T., Jiao, X., Rannala, B. & Yang, Z. Species tree inference with BPP using genomic sequences and the multispecies coalescent. Mol. Biol. Evol. 35, 2585–2593 (2018).Article 
CAS 

Google Scholar 
Van Buuren, S. & Groothuis-Oudshoorn, K. Multivariate imputation by chained equations. J. Stat. Softw. 45, 1–67 (2011).Article 

Google Scholar 
Penone, C. et al. Imputation of missing data in life-history trait datasets: Which approach performs the best?. Methods Ecol. Evol. 5, 961–970 (2014).Article 

Google Scholar 
Berner, D. Size correction in biology: How reliable are approaches based on (common) principal component analysis?. Oecologia 166, 961–971 (2011).Article 
ADS 

Google Scholar 
Simmons, N. B. Order Chiroptera. In Mammal Species of the World: A Taxonomic and Geographic Reference (eds Wilson, D. E. & Reeder, D. M.) 312–529 (The John Hopkins University Press, 2005).
Google Scholar 
Wilson, D. E. & Mittermeier, R. A. Handbook of the Mammals of the World. Vol. 9. Bats (Lynx Editions, 2019).
Google Scholar 
R Core Team. R: A language and environment for statistical computing (2022).Kuhn, M. caret: Classification and Regression Training. R package version 6.0-86. https://CRAN.R-project.org/package=caret (2020).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).Book 
MATH 

Google Scholar 
Kuhn, M. & Johnson, K. Applied Predictive Modeling (Springer, 2013).Book 
MATH 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Hijmans, R. J. raster: Geographic Data Analysis and Modeling (2022).Barker, B. S., Rodríguez-Robles, J. A. & Cook, J. A. Climate as a driver of tropical insular diversity: Comparative phylogeography of two ecologically distinctive frogs in Puerto Rico. Ecography 38, 769–781 (2015).Article 

Google Scholar 
Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C. & Guisan, A. Selecting predictors to maximize the transferability of species distribution models: Lessons from cross-continental plant invasions. Glob. Ecol. Biogeogr. 26, 275–287 (2017).Article 

Google Scholar 
Akinwande, M. O., Dikko, H. G. & Samson, A. Variance inflation factor: As a condition for the inclusion of suppressor variable(s) in regression analysis. Open J. Stat. 05, 754–767 (2015).Article 

Google Scholar 
Izenman, A. J. Linear discriminant analysis. in Modern Multivariate Statistical Techniques 237–280 (2013).Lever, J., Krzywinski, M. & Altman, N. Points of significance: Principal component analysis. Nat. Methods 14, 641–642 (2017).Article 
CAS 

Google Scholar 
Guisan, A., Petitpierre, B., Broennimann, O., Daehler, C. & Kueffer, C. Unifying niche shift studies: Insights from biological invasions. Trends Ecol. Evol. 29, 260–269 (2014).Article 

Google Scholar 
Di Cola, V. et al. ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).Article 

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

Google Scholar 
Liu, C., Wolter, C., Xian, W. & Jeschke, J. M. Most invasive species largely conserve their climatic niche. Proc. Natl. Acad. Sci. 117, 23643–23651 (2020).Article 
ADS 
CAS 

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).Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).
Google Scholar  More

Overall frameworkCells in our model are arranged along independent radial files, with each cell in one of either the proliferation, enlargement-only, wall thickening, or mature zones, depending on the distance of the cell’s centre from the inside edge of the phloem and the time of year. Only cells that contribute to the formation of xylem tracheids are treated explicitly. A daily timestep is used, on which cells in the proliferation and enlargement-only zones can enlarge in the radial direction if these zones are non-dormant, and on which secondary-wall thickening can occur in the wall thickening zone. Cells in the proliferation zone divide periclinally if they reach a threshold radial length. Cell-size control at division is intermediate between a critical size and a critical increment22. Mother cells divide asymmetrically, with the subsequent relative growth rates of the daughters inversely proportional to their relative sizes. Size at division and asymmetry of division are computed with added statistical noise22, and therefore the model is run for an ensemble of independent radial files with perturbed initial conditions.Equations and parametersCell enlargement and divisionCells in the proliferation and enlargement-only zones, when not dormant, enlarge in the radial direction at a rate dependent on temperature and relative sibling birth size. A Boltzmann-Arrhenius approach is used for the temperature dependence30:$$mu={mu }_{0}{e}^{frac{{E}_{a}}{k}left(frac{1}{{T}_{0}}-frac{1}{T}right)}$$
(1)
where μ is the relative rate of radial cell growth at temperature T (μm μm−1 day−1), μ0 is μ at temperature T0 (=283.15 K), Ea is the effective activation energy for cell enlargement, k is the Boltzmann constant (i.e. 8.617 x 10−5 eV K−1), and T is temperature (K). μ0 was calibrated to an observed mean radial file length at the end of the elongation period dataset23 (Table 1; see “Observations”), and Ea was calibrated to an observed temperature dependence of annual ring width dataset31 (Table 1; Supplementary Fig. 4; see “Observations”).Table 1 Model parameters calibrated to observationsFull size tableRadial length of an individual cell then increases according to:$${{Delta }}{L}_{r}={L}_{r}({e}^{epsilon mu }-1)$$
(2)
where ΔLr is the radial increment of the cell (μm day−1), Lr is the radial length of the cell (μm), and ϵ is the cell’s growth dependence on relative birth size, given by22:$$epsilon=1-{g}_{asym}{alpha }_{b}$$
(3)
where gasym is the strength of the dependence of relative growth rate on asymmetric division (Table 2; unitless), and αb is the degree of asymmetry relative to the cell’s sister22 (scalar):$${alpha }_{b}=frac{{L}_{r}{,}_{b}-{L}_{r}{,}_{b}^{sis}}{{L}_{r}{,}_{b}+{L}_{r}{,}_{b}^{sis}}$$
(4)
where Lr,b is the radial length of the cell at birth (μm) and ({L}_{r}{,}_{b}^{sis}) is the radial length of its sister at birth (μm), which are calculated stochastically22:$${L}_{r}{,}_{b}={L}_{r}{,}_{d}(0.5-{Z}_{a})$$
(5)
$${L}_{r}{,}_{b}^{sis}={L}_{r}{,}_{d}(0.5+{Z}_{a})$$
(6)
where Lr,d is the length of the mother cell when it divides (μm) and Za is Gaussian noise with mean zero and standard deviation σa (Table 2; −0.49 ≤Za≤ 0.49 for numerical stability).Table 2 Parameters used in the model that are taken directly from literatureFull size tableLength at division is derived as22:$${L}_{r}{,}_{d}=f{L}_{r}{,}_{b}+{chi }_{b}(2-f+Z)$$
(7)
where f is the mode of cell-size regulation (Table 2; unitless), χb is the mean cell birth size (Table 3; μm), and Z is Gaussian noise with mean zero and standard deviation σ (Table 2).Table 3 Parameters used in the model that are calculated from observationsFull size tableThe first cell in each radial file is an initial, which produces phloem mother cells outwards and xylem mother cells inwards. It grows and divides as other cells in the proliferation zone, but on division one of the daughters is stochastically assigned to phloem or xylem, the other remaining as the initial. The probability of the daughter being on the phloem side is fphloem (Table 3).Cell-wall growthBoth primary and secondary cell-wall growth are influenced by temperature, carbohydrate concentration, and lumen volume. A Michaelis-Menten equation is used to relate the rate of wall growth to the concentration of carbohydrates in the cytoplasm:$${{Delta }}M=frac{{{Delta }}{M}_{max}theta }{theta+{K}_{m}}$$
(8)
where ΔM is the rate of cell-wall growth (mg cell−1 day−1), ΔMmax is the carbohydrate-saturated rate of wall growth (mg cell−1 day−1), θ is the concentration of carbohydrates in the cell’s cytoplasm (mg ml−1), and Km is the effective Michaelis constant (mg ml−1; Table 1).The maximum rate of cell-wall growth, ΔMmax, is assumed to depend linearly on lumen volume (a proxy for the amount of machinery for wall growth), and on temperature as in Eq. (1):$${{Delta }}{M}_{max}=omega {V}_{l}{e}^{frac{{E}_{aw}}{k}left(frac{1}{{T}_{0}}-frac{1}{T}right)}$$
(9)
where ω is the normalised rate of cell-wall mass growth (i.e. the rate at T0; Table 1; mg ml−1 day−1), Vl is the cell lumen volume (ml cell−1), and Eaw is the effective activation energy for wall building (eV; Table 1). ω and Km were calibrated to an observed distribution of carbohydrates23 (see next section). Eaw was calibrated to an observed temperature dependence of maximum density31 (Table 1; see “Observations”).Lumen volume is given by:$${V}_{l}={V}_{c}-{V}_{w}$$
(10)
where Vc is total cell volume (ml cell−1) and Vw is total wall volume (ml cell−1). Cells are assumed cuboid and therefore Vc is given by:$${V}_{c}={L}_{a}{L}_{t}{L}_{r}/1{0}^{12}$$
(11)
where La is axial length (μm; Table 2) and Lt is tangential length (μm; Table 3). Vw is given by:$${V}_{w}=M/rho$$
(12)
where M is wall mass (mg cell−1) and ρ is wall-mass density (Table 2; mg[DM] ml−1).Cells in the proliferation and enlargement-only zones only have primary cell walls. ΔMmax (Eq. (9)) is therefore given the following limit:$${{Delta }}{M}_{max}=min ({{Delta }}{M}_{max},rho {V}_{{w}_{p}}-M)$$
(13)
where ({V}_{{w}_{p}}) is the required primary wall volume:$${V}_{{w}_{p}}={V}_{c}-({L}_{a}-2{W}_{p})({L}_{t}-2{W}_{p})({L}_{r}-2{W}_{p})/1{0}^{12}$$
(14)
where Wp is primary cell-wall thickness (Table 3; μm).Carbohydrate distributionThe distribution of carbohydrates across each radial file is calculated independently from the balance of diffusion from the phloem and the uptake into primary and secondary cell walls. The carbohydrate concentration in the phloem is prescribed at the mean value observed across the three observational dates in23, as described below in “Observations”, and the resulting concentration in the cytoplasm of the furthest living cell from the phloem is solved numerically. The inside wall of this cell is assumed to be impermeable to carbohydrates and therefore provides the inner boundary to the solution. It is assumed that the rate of diffusion across each file is rapid relative to the rate of cell-wall building, and therefore concentrations are assumed to be in equilibrium on each day. Carbohydrate diffusion between living cells is assumed to be proportional to the concentration gradient:$${q}_{i}=({theta }_{i-1}-{theta }_{i})/eta$$
(15)
where qi is the rate of carbohydrate diffusion from cell i − 1 to cell i (mg day−1) and η is the resistance to flow between cells (calibrated to the observed distribution of carbohydrates23, see next section; Table 1; day ml−1).As it is assumed that carbohydrates cannot diffuse between radial files, at equilibrium the flux into a given cell must equal the rate of wall growth in that cell plus the wall growth in all cells further along the radial file away from the phloem. From this it can be shown that the equilibrium carbohydrate concentration in the furthest living cell from the phloem in each radial file is given by:$${theta }_{n}={theta }_{p}-eta mathop{sum }limits_{i=1}^{n}mathop{sum }limits_{j=i}^{n}{{Delta }}{M}_{j}$$
(16)
where θp is the concentration of carbohydrates in the phloem (Table 3; mg ml−1) and n is the number of living cells in the file (phloem mother cells are ignored for simplicity). The rate of wall growth in each cell depends on the concentration of carbohydrates (Eq. (8)), and therefore θn must be found that results in an equilibrium flow across the radial file. This is done using Brent’s method41 as implemented in the “ZBRENT” function42.Zone widthsThe widths of the proliferation, enlargement-only, and secondary wall thickening zones vary through the year, and are fitted to observations on three dates23 (see Supplementary Fig. 2 and “Observations”). Linear responses to daylength were found, which are therefore used to determine widths for the observational period and later days:$${z}_{k}={a}_{k}+{b}_{k}{{{{{{{rm{dl}}}}}}}};{{mathrm{DOY}}}ge 185$$
(17)
where zk is the distance of the inner edge of the zone from the inner edge of the phloem (μm), k is proliferation (p), secondary wall thickening (t), or enlargement-only (e), ak and bk are constants (Table 3), dl is daylength (s), and DOY is day-of-year. The proliferation zone width on earlier days when non-dormant was fixed at its DOY 185 width (assuming this to be its maximum, and that it would reach its maximum very soon after cambial dormancy is broken in the spring). During dormancy, the proliferation zone width is fixed at its value on DOY 231 (the first day of dormancy23). The enlargement-only zone width prior to DOY 185, the first observational day, is assumed to be a linear extension of the rate of change after DOY 185. The wall-thickening zone width plays little role prior to DOY 185 at the focal site, and so was set to its Eq. (17) value each earlier day. On all days the condition zt≥ze≥zp is imposed, and zone widths cannot exceed their values at 24 h daylength (necessary for sites north of the Arctic circle). Supplementary Figure 2 shows the resulting progression of zone widths through the year, together with the observed values.DormancyProliferation was observed to be finished by DOY 23123, and so the proliferation and enlargement-only zones are assumed to enter dormancy then. Release from dormancy in the spring is calculated using an empirical thermal time/chilling model33. It was necessary to adjust the asymptote and temperature threshold of the published model because the heat sum on the day of release calculated from observations in Sweden (see “Observations”) was much lower than reported for Sitka spruce buds in Britain in the original work:$${{{{{{{{rm{dd}}}}}}}}}_{req}=15+4401.8{e}^{-0.042{{{{{{{rm{cd}}}}}}}}}$$
(18)
where ddreq is the required sum of degree-days (°C) from DOY 32 for dormancy release and cd is the chill-day sum from DOY 306. The degree-day sum is the sum of daily mean temperatures above 0 °C, and the chill-day sum is the number of days with mean temperatures below 0 °C. Dormancy can only be released during the first half of the year.Simulation protocolsEach simulation consisted of an ensemble of 100 independent radial files. Each radial file was initialised by producing a file of 100 cells with radial lengths χb(1+Za), allowing these to divide once, ignoring the second daughter from each division, and then limiting the remaining daughters to only those falling inside the proliferation zone on DOY 1. Values for ϵ (the relative growth of daughter cells) and Lr,d (the radial length at division) were derived for each cell. The main simulations were conducted at the observation site in boreal Sweden (64.35°N, 19.77°E) over 1951–1995 to capture the growth period of the observed trees23. Results are mostly presented for 1995 when the observations were made. Simulations for calibration of the effective activation energies (i.e. Ea and Eaw) were performed at 68.26°N, 19.63°E in Arctic Sweden over 1901–200431. Daily mean temperatures for both sites were derived from the appropriate gridbox in a 6 h 1/2 degree global-gridded dataset43.ObservationsObservations of cellular characteristics and carbohydrate concentrations23 were used to derive a number of model parameters, and to test model output (model calibration and testing were performed using different outputs). According to the published study we used, samples were cut from three 44 yr old Scots pine trees growing in Sweden (64°21’ N; 19°46’ E) at different times during the growing season. 30 μm thick longitudinal tangential sections of the cambial region were made, and the radial distributions of soluble carbohydrates measured using microanalytical techniques23. Cell sizes, wall thicknesses, and positions in their Fig. 123, an image of transverse sections on three sampling dates, were digitised using “WebPlotDigitizer”44. These, together with the numbers of cells in each zone and their sizes given in the text of that paper, were used to estimate zone widths, which were then regressed against daylength to give the parameters for Eq. (17) (Table 3), mean cell size in the proliferation zone on the first sampling date (used to derive χb; Table 3), mean cell tangential length (Table 3), and final ring width (used to calibrate μ0; Table 1). The thickness of the primary cell wall (Table 3) was derived by plotting cell-wall thickness against time and taking the low asymptote.The distributions of carbohydrates along the radial files on the last sampling date for “Tree 1” and “Tree 3” (results for “Tree 2” were not shown for this date) shown in Fig. 2 of the observational paper23 were calculated. The masses for each of sucrose, glucose, and fructose in each 30 μm section were digitised using the same method as for cell properties and then summed and converted to concentrations, with the results shown in Supplementary Figure 5. Mean observed carbohydrate concentrations and cell masses at four points were used to calibrate values for the η, ω, and Km parameters in Table 1. Calibration was performed by minimising the summed relative error across the observations.The calibration target for the effective activation energy for wall deposition (i.e. Eaw) was the observed relationship between maximum density and mean June-July-August temperature over 1901-2004 in northern Sweden31 (Supplementary Fig. 3), and for the effective activation energy for cell enlargement the relationship between ring width and temperature (i.e. Ea) target was the same study (Supplementary Fig. 4). These observations were made on living and subfossil Scots pine sample material from the Lake Tornesträsk area (68.21–68.31°N; 19.45–19.80°E; 350–450 m a.s.l.) using X-ray densitometry for maximum density, and standardised to remove non-climatic information31.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

In the autumn (November), L. dispar egg masses were collected at two sites: unpolluted and polluted forest. The first was a mixed oak forest at Kosmaj Mountain, 40 km south-east of Belgrade (coordinates 44°27′56″N 20°33′56″E). These woods are regarded as unpolluted because they are far from direct pollution and are part of the system of protected green areas around Belgrade, where the construction of industrial facilities and traffic infrastructure with potential negative effects on the environment is prohibited by legal regulations. The second site was Lipovica Forest (coordinates 44°38′11″N 20°24′12″E), with mixed Quercus frainetto and Quercus cerris trees, considered a polluted forest since it is located along the border of State Road 22, one of the most frequently used IB-class roads in Serbia.Collected egg masses were kept in a refrigerator at 4 °C until spring (March) when 200 eggs for each experimental group were set for hatching. After hatching in transparent Petri dishes (V = 200 mL), 10 first instar larvae were transferred and reared together at 23 °C with a 12:12 h light: dark photoperiod and relative humidity of 60%, until the third larval instar. Then, five 3rd instar larvae were reared together in the same Petri dish. After molting into the 4th instar, each larva was kept individually until the third day of the 5th instar, when they were sacrificed. Larvae were fed on an artificial diet designed for L. dispar42, and food was replaced every 48 h. Each experimental group contained between 50 and 60 larvae (Fig. 7).Figure 7A schematic figure of the experimental treatments.Full size imageThe optimal temperature for L. dispar larval development is 23 °C, and the control group was reared at this temperature. The highest summer temperature (2007–2010) measured in Serbian Quercus forests at a similar elevation was 28.4 °C, and the lowest 19.6 °C, while the average summer temperature was 26.3 °C43. Thus, we established variable temperature regimens that included brief (24 h) and daily (72 h) exposures to 28 °C. The control group of larvae were reared through the whole experiment on optimal 23 °C. Results of Huey et al.44 indicate that short term (daily) exposure to higher temperatures during development can increase both optimal temperature and maximal growth rate at the optimum, an example of beneficial thermal acclimation. In our previous research we found that induced thermotolerance modifies the activity of detoxifying enzymes in larvae originating from the polluted forest. We exposed L. dispar larvae in several experimental groups to that regime at 4th larval instar, with intention of analyze the effects of induce thermotolerance on observed parameters (ALP, ACP, hsp 70) in 5th instar larvae reared on optimal or elevated temperature28.At sacrifice on the third day of the 5th instar, the caterpillar midguts were dissected out on ice (n = 8–11 larval midguts per group for each enzyme assay). Midgut from single larvae was weighed and homogenized in insect physiological saline, as insect fluids have buffer values similar to vertebrates45. Homogenization was performed in ice-cold 0.15 M NaCl (final tissue concentration was 100 mg/mL in each sample), for 3 intervals of 10 s with a 15 s pause between them, at 5000 rpm, using Ultra Turrax homogenizer (IKA-Werke, Staufen, Germany). The homogenates were centrifuged for 10 min at 10,000 g at 4 ℃, and supernatants were used for enzyme assays and NATIVE gel electrophoresis. This protocol ensured that supernatants would contain cytosol and lysosomes.On the third day of the 5th instar, larval brain tissues were dissected out on ice and weighed. Pooled brain tissue (n = 30 brain tissues per experimental group) was diluted with 0.9% NaCl (1:9/w:V) and homogenized on ice at 5000 rpm during three 10 s intervals, separated by 15 s pauses (MHX/E Xenox homogenizer, Germany). Homogenates were centrifuged at 25,000 g for 10 min at 4 °C in an Eppendorf 5417R centrifuge (Germany). The supernatants were used for Western blotting and indirect non-competitive enzyme-linked immunosorbent assay (ELISA). Protein concentrations samples were determined using BSA as the standard46.A modified method by Nemec and Socha47 was used to determine the activity of ALP. The reaction mixture contained 0.1 M Tris HCl buffer pH 8.6, 5 mM MgCl2, midgut homogenate, and 5 mM p-nitrophenyl phosphate. During 30 min of incubation time at 30 ℃, the hydrolytic release of p-nitrophenol from p-nitrophenyl phosphate (pNPP) occurred under alkaline conditions.The reaction was stopped with 0.5 M NaOH, and the absorbance of p-nitrophenol was measured at 405 nm. Blank and non-catalytic probes were included. One unit of enzyme activity was defined as the amount of enzyme that released 1 mmol of p-nitrophenol per minute under the assay conditions.The same modified method of Nemec and Socha47 was employed to determine ACP activity, but under acidic conditions (0.1 M citrate buffer pH 5.6 was found optimal for L. dispar ACP), with a prolonged incubation time of 60 min. One unit of enzyme activity was defined as the amount of enzyme that released 1 μmol of p-nitrophenol per minute per mg of total protein. Total ACP activity determined in the midgut samples came from lysosomal ACP that ended up in the cytosol and non-lysosomal ACP, typically localized in the cytosol.Lysosomal ACP were detected indirectly48, under the same conditions, in a mixture containing the specific enzyme inhibitor NaF (50 mM). The absorbance determined at 405 nm is proportional to the activity of the non-lysosomal fraction of total ACP. The activity of the lysosomal fraction was obtained by subtracting not inhibited non-lysosomal acid phosphatases from the total phosphatase activity. Specific activities of ACP are given in mU per mg of total protein.A modified method by Allen et al.49 was used to detect ALP isoforms after native PAGE. Using 12% polyacrylamide gel, 10 μg protein aliquots per well were separated at 100 V and 4 ℃. The ALP isoform activity was visualized by soaking the gel in an incubation mixture consisting of 0.13% α-naphthyl phosphate, 100 mM Tris–HCl buffer (pH 8.6), and 0.1% Fast Blue B. The gels were incubated at room temperature until bands appeared.For ACP phosphatase detection, the same method of Allen et al.49 was also modified. After electrophoresis, the gel was washed with deionized water and equilibrated in 100 mM acetate buffer (pH 5.2) at 30 ℃. The nitrocellulose membrane was pre-soaked in 0.13% α-naphthyl phosphate dissolved in the same acetate buffer for 50 min at room temperature. The gel was covered with the membrane and incubated in a moist chamber for 60 min at 30 ℃. The membrane was soaked in 0.3% Fast Blue B stain dissolved in acetate buffer until bands became visible.Gels were scanned with a CanoScan LiDE 120 (Japan). The intensities of enzyme bands in the regions of ALP and ACP activities were analyzed using the ImageJ 1.42q software (U. S. National Institutes of Health, Bethesda, Maryland, USA).An indirect non-competitive ELISA was used to quantify the concentration of hsp70 in L. dispar brain tissue. Samples were diluted with carbonate-bicarbonate buffer (pH 9.6) and coated on a microplate (15 μg of tissue/well) (Multiwell immunoplate, NAXISORP, Thermo Scientific, Denmark) overnight at 4 °C, in the dark. The indirect non-competitive ELISA for L. dispar hsp70 was performed according to general practice: samples were first incubated with monoclonal anti-Hsp70 mouse IgG1 (dilution 1:5000) (clone BRM-22, Sigma Aldrich, USA) for 12 h at 4 °C, and then for 2 h at 25 °C with secondary anti-mouse IgG1 (gamma-chain)-HRP conjugate (dilution 1:5000) antibodies (Sigma Aldrich, USA). Chromogenic substrate 3, 3’, 5, 5’-Tetramethylbenzidine (TMB) was used as a visualizing reagent. Absorption was measured on a microplate reader (LKB 5060-006, Austria) at 450 nm. To enable statistically valid comparisons of experimental groups across multiple microplates, each microplate contained serial dilutions of standard hsp70 (recombinant hsp70, 50 ng/mL), used for the hsp70 standard curve, and homogenized brain tissues pulled by each treatment that were loaded on the microplates in a matched design, ensuring that each data point represented the mean of three replicates from each experimental group.Western blots were used to detect the presence of heat-shock protein 70 isoforms. Brain tissue homogenates were separated by SDS PAGE electrophoresis on 12% gels, according to Laemmli50. Protein transfer from the gel to the nitrocellulose membrane (Amersham Prothron, Premium 0.45 mm NC, GE Healthcare Life Sciences, UK) was left overnight at 40 V and 4 °C. Monoclonal anti-hsp70 mouse IgG1 (1:5000 dilution, clone BRM-22, Sigma Aldrich) and secondary mouse anti-mouse Hsp70 horseradish peroxidase conjugate antiserum (1:10,000 dilution, Sigma-Aldrich) were used for detection of hsp70 expression patterns in L. dispar larval brain tissue. Bands were visualized using chemiluminescence (ECL kit, Amersham).This study identified the hsp70 concentration in brain tissue and specific activities of total ACP and ALP in the larval midgut as the most promising biomarkers, which are sensitive and have consistent responses to thermal stress. These three biomarkers were combined into an IBR analysis according to Beliaeff and Burgeot51. The value of each biomarker (Xi) was standardized by the formula Yi = (Xi − mean)/SD, where Yi is the standardized biomarker response, and mean and SD were obtained from all values of the selected parameters. The next step was describing Zi as Zi = Yi or Zi = − Yi, depending on whether the temperature treatment caused induction or inhibition of the selected biomarkers. After finding the minimum value of Zi for each biomarker (min), the scores (Si) were computed as Si = Zi + |min|. Scores for biomarkers were used as the radius coordinates of the studied biomarker in the star plots. Star plot areas for the three-biomarker assembly, positioned in successive clockwise order—Hsp70, total ACP, and ALP, were obtained from the following formulas: ({A}_{i}=frac{{S}_{i}}{2*mathrm{sin}beta }left({S}_{i}*mathrm{cos}beta + {S}_{i+1}*mathrm{sin}beta right)), (beta = {mathrm{tan}}^{-1}left(frac{{S}_{i+1}*mathrm{sin}alpha }{{S}_{i}-{S}_{i+1}*mathrm{cos}alpha }right)),(alpha =2pi /n) radians (n is the number of biomarkers). The IBR values were calculated as follows:(IBR= sum_{i=1}^{n}{A}_{i}), where Ai is the area represented by two consecutive biomarkers on the star plot. Excel software (Microsoft, USA) was used to calculate IBR values and generate star plots.Statistical analyses were conducted in GraphPad Prism 6 (GraphPad Software, Inc., USA). Mean values ± standard errors of mean values (SEM) were calculated for the activity of enzymes, larval midgut mass, and the hsp70 concentration in brain tissue. D’Agostino-Pearson omnibus and Shapiro–Wilk tests were used to check the normality of data distribution. The effects of thermal treatments and their interaction on the variance of analyzed biomarkers in larvae from the polluted and the unpolluted forest were tested using two-way ANOVA with thermal treatments as fixed factors. For all comparisons, the level of significance was set at p  More

Evans, K., Franco, J. & De Scurrah, M. M. Distribution of species of potato cyst-nematodes in South America. Nematologica 21, 365–369. https://doi.org/10.1163/187529275×00103 (1975).Article 

Google Scholar 
Plantard, O. et al. Origin and genetic diversity of Western European populations of the potato cyst nematode (Globodera pallida) inferred from mitochondrial sequences and microsatellite loci. Mol. Ecol. 17, 2208–2218. https://doi.org/10.1111/j.1365-294X.2008.03718.x (2008).Article 
CAS 

Google Scholar 
Price, J. A., Coyne, D., Blok, V. C. & Jones, J. T. Potato cyst nematodes Globodera rostochiensis and G. pallida. Mol. Plant Pathol. 22, 495–507. https://doi.org/10.1111/mpp.13047 (2021).Article 
CAS 

Google Scholar 
CABI. Globodera rostochiensis (yellow potato cyst nematode). https://www.cabi.org/isc/datasheet/27034 (2021).CABI. Globodera pallida (white potato cyst nematode). https://www.cabi.org/isc/datasheet/27033 (2021).Ruthes, A. C. & Dahlin, P. The impact of management strategies on the development and status of potato cyst nematode populations in Switzerland: An overview from 1958 to present. Plant Dis. 106, 1096–1104. https://doi.org/10.1094/pdis-04-21-0800-sr (2021).Article 

Google Scholar 
Minnis, S. T. et al. Potato cyst nematodes in England and Wales—Occurrence and distribution. Ann. Appl. Biol. 140, 187–195. https://doi.org/10.1111/j.1744-7348.2002.tb00172.x (2002).Article 

Google Scholar 
Djebroune, A. et al. Integrative morphometric and molecular approach to update the impact and distribution of potato cyst nematodes Globodera rostochiensis and Globodera pallida (Tylenchida: Heteroderidae) in Algeria. Pathogens 10, 216. https://doi.org/10.3390/pathogens10020216 (2021).Article 

Google Scholar 
Vallejo, D. et al. Occurrence and molecular characterization of cyst nematode species (Globodera spp.) associated with potato crops in Colombia. PLoS One 16, e0241256. https://doi.org/10.1371/journal.pone.0241256 (2021).Article 
CAS 

Google Scholar 
Hajjaji, A., Mhand, R. A., Rhallabi, N. & Mellouki, F. First report of morphological and molecular characterization of Moroccan populations of Globodera pallida. J. Nematol. 53, e2021-07. https://doi.org/10.21307/jofnem-2021-007 (2021).Article 
CAS 

Google Scholar 
Camacho, M. J. et al. Potato cyst nematodes: Geographical distribution, phylogenetic relationships and integrated pest management outcomes in Portugal. Front. Plant Sci. 11, 9. https://doi.org/10.3389/fpls.2020.606178 (2020).Article 

Google Scholar 
Handayani, N. D. et al. Distribution, DNA barcoding and genetic diversity of potato cyst nematodes in Indonesia. Eur. J. Plant Pathol. 158, 363–380. https://doi.org/10.1007/s10658-020-02078-7 (2020).Article 
CAS 

Google Scholar 
Bairwa, A. et al. Morphological and molecular characterization of potato cyst nematode populations from the Nilgiris. Indian J. Agric. Sci. 90, 273–278 (2020).Article 
CAS 

Google Scholar 
Mburu, H. et al. Potato cyst nematodes: A new threat to potato production in East Africa. Front. Plant Sci. 11, 13. https://doi.org/10.3389/fpls.2020.00670 (2020).Article 

Google Scholar 
Altas, A., Evlice, E., Ozer, G., Dababat, A. & Imren, M. Identification, distribution and genetic diversity of Globodera rostochiensis (Wollenweber, 1923) Skarbilovich, 1959 (Tylenchida: Heteroderidae) populations in Turkey. Turk. Entomol. Derg. Turk. J. Entomol. 44, 385–397. https://doi.org/10.16970/entoted.740223 (2020).Article 

Google Scholar 
Dandurand, L.-M., Zasada, I. A., Wang, X. & Mimee, B. Current status of potato cyst nematodes in North America. Annu. Rev. Phytopathol. 57, 117–133. https://doi.org/10.1146/annurev-phyto-082718-100254 (2019).Article 
CAS 

Google Scholar 
Blacket, M. J. et al. Molecular assessment of the introduction and spread of potato cyst nematode, Globodera rostochiensis, in Victoria, Australia. Phytopathology 109, 659–669. https://doi.org/10.1094/phyto-06-18-0206-r (2019).Article 
CAS 

Google Scholar 
Sullivan, M. J., Inserra, R. N., Franco, J., Moreno-Leheude, I. & Greco, N. Potato cyst nematodes: Plant host status and their regulatory impact. Nematropica 37, 193–201 (2007).
Google Scholar 
Hodda, M. & Cook, D. C. Economic impact from unrestricted spread of potato cyst nematodes in Australia. Phytopathology 99, 1387–1393. https://doi.org/10.1094/phyto-99-12-1387 (2009).Article 
CAS 

Google Scholar 
Koirala, S., Watson, P., McIntosh, C. S. & Dandurand, L. M. Economic impact of Globodera pallida on the Idaho economy. Am. J. Potato Res. 97, 214–220. https://doi.org/10.1007/s12230-020-09768-2 (2020).Article 

Google Scholar 
Trudgill, D. L., Elliott, M. J., Evans, K. & Phillips, M. S. The white potato cyst nematode (Globodera pallida)—A critical analysis of the threat in Britain. Ann. Appl. Biol. 143, 73–80. https://doi.org/10.1111/j.1744-7348.2003.tb00271.x (2003).Article 

Google Scholar 
Duan, Y. X. Plant Nematology (Science Press, 2011).
Google Scholar 
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen–Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644. https://doi.org/10.5194/hess-11-1633-2007 (2007).Article 
ADS 

Google Scholar 
Li, J. Suitable Risk Assessment for Six Potential Invasive Nematodes in China, Master thesis (Jilin Agriculture University, 2008).
Google Scholar 
Contina, J. B., Dandurand, L. M. & Knudsen, G. R. A spatiotemporal analysis and dispersal patterns of the potato cyst nematode Globodera pallida in Idaho. Phytopathology 110, 379–392. https://doi.org/10.1094/phyto-04-19-0113-r (2020).Article 
CAS 

Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models with Applications in R (Cambridge University Press, 2017). https://doi.org/10.1017/9781139028271.Book 

Google Scholar 
Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In Proceedings of the Twenty-First International Conference on Machine Learning 83. https://doi.org/10.1145/1015330.1015412 (Association for Computing Machinery, 2004).Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x (2008).Article 

Google Scholar 
Wan, J. et al. Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis (Diptera: Tephritidae) in China using MaxEnt ecological niche modeling. J. Integr. Agric. 19, 2072–2082. https://doi.org/10.1016/S2095-3119(19)62840-6 (2020).Article 
CAS 

Google Scholar 
Midmore, D. J. Potato production in the tropics. In The Potato Crop: The Scientific Basis for Improvement 728–793 (Springer Netherlands, 1992).Chapter 

Google Scholar 
Naika, S., de Jeude, J. V. L., de Goffau, M., Hilmi, M. & van Dam, B. Cultivation of Tomato: Production, Processing and Marketing (Digigrafi, 2005).
Google Scholar 
Chapman, M. A. Eggplant breeding and improvement for future climates. In Genomic Designing of Climate-Smart Vegetable Crops 257–276 (Springer, 2020).Chapter 

Google Scholar 
Management, D. o. C. List of National Agricultural Plant Quarantine Pests Distribution Administrative Areas. https://www.moa.gov.cn/nybgb/2019/201906/201907/t20190701_6320036.htm (Ministry of Agriculture and Rural Affairs of the People’s Republic of China, 2021).Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x (2011).Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 33, 607–611. https://doi.org/10.1111/j.1600-0587.2009.06142.x (2010).Article 

Google Scholar 
Foot, M. A. The Ecology of Globodera pallida (Stone) Mulvey & Stone (Nematoda, Heteroderidae) at Pukekohe, New Zealand, Doctoral thesis (The University of Auckland, 1978).
Google Scholar 
Phillips, S. J., Dudík, M., & Schapire, R. E. Maxent software for modeling species niches and distributions (Version 3.4.1) [Internet]. http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed 24-10-2020.Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x (2013).Article 

Google Scholar 
Wan, J., Wang, R., Ren, Y. & McKirdy, S. Potential distribution and the risks of Bactericera cockerelli and its associated plant pathogen Candidatus Liberibacter solanacearum for global potato production. Insects 11, 298. https://doi.org/10.3390/insects11050298 (2020).Article 

Google Scholar 
Cobos, M. E., Peterson, A. T., Barve, N. & Osorio-Olvera, L. kuenm: An R package for detailed development of ecological niche models using Maxent. PeerJ 7, e6281. https://doi.org/10.7717/peerj.6281 (2019).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/. Accessed 30-10-2020.Peterson, A. T., Papeş, M. & Soberón, J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Model. 213, 63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008 (2008).Article 

Google Scholar 
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342. https://doi.org/10.1890/10-1171.1 (2011).Article 

Google Scholar 
ESRI. ArcGIS Desktop: Release 10 (Version 10.4.1) (Environmental Systems Research Institute). Accessed 24-10-2020.Kong, W., Li, X. & Zou, H. Optimizing MaxEnt model in the prediction of species distribution. J. Appl. Ecol. 30, 2116–2128. https://doi.org/10.13287/j.1001-9332.201906.029 (2019).Article 

Google Scholar 
Fischer, G. et al. Global Agro-ecological Zones (GAEZ v3.0). http://pure.iiasa.ac.at/id/eprint/13290/ (2012).Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893. https://doi.org/10.1111/ecog.03049 (2017).Article 

Google Scholar 
da Silva, J. C. P., de Medeiros, F. H. V. & Campos, V. P. Building soil suppressiveness against plant-parasitic nematodes. Biocontrol Sci. Technol. 28, 423–445. https://doi.org/10.1080/09583157.2018.1460316 (2018).Article 

Google Scholar 
Kim, E., Seo, Y., Kim, Y. S., Park, Y. & Kim, Y. H. Effects of soil textures on infectivity of root-knot nematodes on carrot. Plant Pathol. J. 33, 66–74. https://doi.org/10.5423/ppj.Oa.07.2016.0155 (2017).Article 
CAS 

Google Scholar 
Duyck, P.-F. et al. Niche partitioning based on soil type and climate at the landscape scale in a community of plant-feeding nematodes. Soil Biol. Biochem. 44, 49–55. https://doi.org/10.1016/j.soilbio.2011.09.014 (2012).Article 
CAS 

Google Scholar 
Stanton, J. C., Pearson, R. G., Horning, N., Ersts, P. & ReşitAkçakaya, H. Combining static and dynamic variables in species distribution models under climate change. Methods Ecol. Evol. 3, 349–357. https://doi.org/10.1111/j.2041-210X.2011.00157.x (2012).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Multimodel inference understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304. https://doi.org/10.1177/0049124104268644 (2004).Article 
MathSciNet 

Google Scholar 
Din, A. U. et al. The impact of COVID-19 on the food supply chain and the role of e-commerce for food purchasing. Sustainability 14, 3074. https://doi.org/10.3390/su14053074 (2022).Article 
CAS 

Google Scholar 
Lang, T. & McKee, M. The reinvasion of Ukraine threatens global food supplies. BMJ https://doi.org/10.1136/bmj.o676,o676,10.1136/bmj.o676 (2022).Article 

Google Scholar 
Hijmans, R. J. Global distribution of the potato crop. Am. J. Potato Res. 78, 403–412. https://doi.org/10.1007/bf02896371 (2001).Article 

Google Scholar 
Motti, R. The Solanaceae family: Botanical features and diversity. In The Wild Solanums Genomes 1–9 (Springer International Publishing, 2021).
Google Scholar 
Chytrý, M. et al. Projecting trends in plant invasions in Europe under different scenarios of future land-use change. Glob. Ecol. Biogeogr. 21, 75–87. https://doi.org/10.1111/j.1466-8238.2010.00573.x (2012).Article 

Google Scholar 
Lin, W., Cheng, X. & Xu, R. Impact of different economic factors on biological invasions on the global scale. PLoS One 6, e18797. https://doi.org/10.1371/journal.pone.0018797 (2011).Article 
ADS 
CAS 

Google Scholar 
Jones, L. M. et al. Climate change is predicted to alter the current pest status of Globodera pallida and G. rostochiensis in the United Kingdom. Glob. Change Biol. 23, 4497–4507. https://doi.org/10.1111/gcb.13676 (2017).Article 
ADS 
MathSciNet 

Google Scholar 
Kaczmarek, A., MacKenzie, K., Kettle, H. & Blok, V. C. Influence of soil temperature on Globodera rostochiensis and Globodera pallida. Phytopathol. Mediterr. 53, 396–405. https://doi.org/10.14601/Phytopathol_Mediterr-13512 (2014).Article 
CAS 

Google Scholar 
Hearne, R., Lettice, E. P. & Jones, P. W. Interspecific and intraspecific competition in the potato cyst nematodes Globodera pallida and G. rostochiensis. Nematology 19, 463–475. https://doi.org/10.1163/15685411-00003061 (2017).Article 
CAS 

Google Scholar 
Skelsey, P., Kettle, H., Mackenzie, K. & Blok, V. Potential impacts of climate change on the threat of potato cyst nematode species in Great Britain. Plant. Pathol. 67, 909–919. https://doi.org/10.1111/ppa.12807 (2018).Article 

Google Scholar 
Carlton, J. & Ruiz, G. M. Invasive Species: Vectors and Management Strategies (Island Press, 2003).
Google Scholar 
Singh, S. K., Paini, D. R., Ash, G. J. & Hodda, M. Prioritising plant-parasitic nematode species biosecurity risks using self organising maps. Biol. Invasions 16, 1515–1530. https://doi.org/10.1007/s10530-013-0588-7 (2014).Article 

Google Scholar 
Jiang, D., Chen, S., Hao, M. M., Fu, J. Y. & Ding, F. Y. Mapping the Potential global codling moth (Cydia pomonella L.) distribution based on a machine learning method. Sci. Rep. 8, 8. https://doi.org/10.1038/s41598-018-31478-3 (2018).Article 
CAS 

Google Scholar 
Simberloff, D. et al. Impacts of biological invasions: What’s what and the way forward. Trends Ecol. Evol. 28, 58–66. https://doi.org/10.1016/j.tree.2012.07.013 (2013).Article 

Google Scholar 
Ravichandra, N. Nematodes of quarantine importance. In Horticultural Nematology 369–385 (Springer, 2014).
Google Scholar  More

Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).Article 

Google Scholar 
Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Chang. 2, 121–124 (2012).Article 
ADS 

Google Scholar 
Princé, K. & Zuckerberg, B. Climate change in our backyards: The reshuffling of North America’s winter bird communities. Glob. Change Biol. 21, 572–585 (2015).Article 
ADS 

Google Scholar 
Brotons, L., Jiguet, F., Herando, S. & Lehikoinen, A. Bird communities and climate change. In Effects of Climate Change on Birds (eds Dunn, P. O. & Møller, A. P.) 221–235 (Oxford University Press, 2019).Chapter 

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).Article 
ADS 
CAS 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).Article 

Google Scholar 
Devictor, V., Julliard, R., Couvet, D. & Jiguet, F. Birds are tracking climate warming, but not fast enough. Proc. R. Soc. B Biol. Sci. 275, 2743–2748 (2008).Article 

Google Scholar 
Lehikoinen, A. et al. Wintering bird communities are tracking climate change faster than breeding communities. J. Anim. Ecol. 90, 1085–1095 (2021).Article 

Google Scholar 
McNaughton, S. J. Diversity and stability of ecological communities: A comment on the role of empiricism in ecology. Am. Nat. 111, 515–525 (1977).Article 

Google Scholar 
Loreau, M. et al. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science (80-. ) 294, 804–808 (2001).Article 
ADS 
CAS 

Google Scholar 
Loreau, M. & de Mazancourt, C. Biodiversity and ecosystem stability: A synthesis of underlying mechanisms. Ecol. Lett. 16, 106–115 (2013).Article 

Google Scholar 
Fonseca, C. R. & Ganade, G. Species functional redundancy, random extinctions and the stability of ecosystems. J. Ecol. 89, 118–125 (2001).Article 

Google Scholar 
Hodgson, D., McDonald, J. L. & Hosken, D. J. What do you mean, ‘resilient’?. Trends Ecol. Evol. 30, 503–506 (2015).Article 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).Article 
CAS 

Google Scholar 
Oksanen, J. et al. Community ecology package vegan, R package version 2.0-7 (2013).Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. R package (2014).García-Palacios, P., Gross, N., Gaitán, J. & Maestre, F. T. Climate mediates the biodiversity-ecosystem stability relationship globally. Proc. Natl. Acad. Sci. U. S. A. 115, 8400–8405 (2018).Article 
ADS 

Google Scholar 
De Boeck, H. J. et al. Patterns and drivers of biodiversity-stability relationships under climate extremes. J. Ecol. 106, 890–902 (2018).Article 

Google Scholar 
Fridley, J. D. et al. The invasion paradox: Reconciling pattern and process in species invasions. Ecology 88, 3–17 (2007).Article 
CAS 

Google Scholar 
Elton, C. S. The Ecology of Invasions by Plants and Animals (Methuen, 1958).Book 

Google Scholar 
Pigot, A. L., Trisos, C. H. & Tobias, J. A. Functional traits reveal the expansion and packing of ecological niche space underlying an elevational diversity gradient in passerine birds. Proc. R. Soc. B Biol. Sci. 283, 20152013 (2016).Article 

Google Scholar 
Pellissier, V., Barnagaud, J. Y., Kissling, W. D., Şekercioǧlu, Ç. H. & Svenning, J. C. Niche packing and expansion account for species richness–productivity relationships in global bird assemblages. Glob. Ecol. Biogeogr. 27, 604–615 (2018).Article 

Google Scholar 
Schipper, A. M. et al. Contrasting changes in the abundance and diversity of North American bird assemblages from 1971 to 2010. Glob. Change Biol. 22, 3948–3959 (2016).Article 
ADS 

Google Scholar 
Jarzyna, M. A. & Jetz, W. A near half-century of temporal change in different facets of avian diversity. Glob. Change Biol. 23, 2999–3011 (2017).Article 
ADS 

Google Scholar 
Catano, C. P., Fristoe, T. S., LaManna, J. A. & Myers, J. A. Local species diversity, β-diversity and climate influence the regional stability of bird biomass across North America. Proc. R. Soc. B Biol. Sci. 287, 20192520 (2020).Article 

Google Scholar 
Wang, S. et al. An invariability-area relationship sheds new light on the spatial scaling of ecological stability. Nat. Commun. 8, 1–8 (2017).ADS 

Google Scholar 
Pimm, S. L. & Redfearn, A. The variability of population densities. Nature 334, 613–614 (1988).Article 
ADS 

Google Scholar 
Santangeli, A. & Lehikoinen, A. Are winter and breeding bird communities able to track rapid climate change? Lessons from the high North. Divers. Distrib. 23, 308–316 (2017).Article 

Google Scholar 
Sauer, J. R. et al. The first 50 years of the North American Breeding Bird Survey. Condor 119, 576–593 (2017).Article 

Google Scholar 
Meehan, T. D., Michel, N. L. & Rue, H. Spatial modeling of Audubon Christmas Bird Counts reveals fine-scale patterns and drivers of relative abundance trends. Ecosphere 10, e020707 (2019).Article 
ADS 

Google Scholar 
Meller, K., Piha, M., Vähätalo, A. V. & Lehikoinen, A. A positive relationship between spring temperature and productivity in 20 songbird species in the boreal zone. Oecologia 186, 883–893 (2018).Article 
ADS 

Google Scholar 
Lefcheck, J. S. & Duffy, J. E. Multitrophic functional diversity predicts ecosystem functioning in experimental assemblages of estuarine consumers. Ecology 96, 2973–2983 (2015).Article 

Google Scholar 
Alerstam, T. & Högstedt, G. Bird migration and reproduction in relation to habitats for survival and breeding. Scand. J. Ornithol. 13, 25–37 (1982).
Google Scholar 
Dingle, H. Migration: The Biology of Life on the Move (Oxford University Press, 1996).
Google Scholar 
Jones, P. D. et al. Hemispheric and large-scale land-surface air temperature variations: An extensive revision and an update to 2010. J. Geophys. Res. Atmos. https://doi.org/10.1029/2011JD017139 (2012).Article 
ADS 

Google Scholar 
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).Article 

Google Scholar 
Bartley, T. J. et al. Food web rewiring in a changing world. Nat. Ecol. Evol. 3, 345–354 (2019).Article 

Google Scholar 
Thébault, E. & Loreau, M. Trophic interactions and the relationship between species diversity and ecosystem stability. Am. Nat. 166, E95–E114 (2005).Article 

Google Scholar 
Kokkoris, G. D., Jansen, V. A. A., Loreau, M. & Troumbis, A. Y. Variability in interaction strength and implications for biodiversity. J. Anim. Ecol. 71, 362–371 (2002).Article 

Google Scholar 
Vázquez, D. P. & Simberloff, D. Ecological specialization and susceptibility to disturbance: Conjectures and refutations. Am. Nat. 159, 606–623 (2002).Article 

Google Scholar 
Carvalheiro, L. G. et al. The potential for indirect effects between co-flowering plants via shared pollinators depends on resource abundance, accessibility and relatedness. Ecol. Lett. 17, 1389–1399 (2014).Article 

Google Scholar 
Morris, R. J., Lewis, O. T. & Godfray, H. C. J. Experimental evidence for apparent competition in a tropical forest food web. Nature 428, 310–313 (2004).Article 
ADS 
CAS 

Google Scholar 
Pace, M. L., Cole, J. J., Carpenter, S. R. & Kitchell, J. F. Trophic cascades revealed in diverse ecosystems. Trends Ecol. Evol. 14, 483–488 (1999).Article 
CAS 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science (80-. ) 345, 401–406 (2014).Article 
ADS 
CAS 

Google Scholar 
Lindström, Å., Green, M., Paulson, G., Smith, H. G. & Devictor, V. Rapid changes in bird community composition at multiple temporal and spatial scales in response to recent climate change. Ecography (Cop.) 36, 313–322 (2013).Article 

Google Scholar 
Pearce-Higgins, J. W., Eglington, S. M., Martay, B. & Chamberlain, D. E. Drivers of climate change impacts on bird communities. J. Anim. Ecol. 84, 943–954 (2015).Article 

Google Scholar 
Socolar, J. B., Epanchin, P. N., Beissinger, S. R. & Tingley, M. W. Phenological shifts conserve thermal niches in North American birds and reshape expectations for climate-driven range shifts. Proc. Natl. Acad. Sci. 114, 12976–12981 (2017).Article 
ADS 
CAS 

Google Scholar 
Pollock, H. S., Brawn, J. D. & Cheviron, Z. A. Heat tolerances of temperate and tropical birds and their implications for susceptibility to climate warming. Funct. Ecol. https://doi.org/10.1111/1365-2435.13693 (2020).Article 

Google Scholar 
Wu, J. X., Wilsey, C. B., Taylor, L. & Schuurman, G. W. Projected avifaunal responses to climate change across the U.S. National Park System. PLoS ONE 13, 1–18 (2018).
Google Scholar 
Root, T. Environmental factors associated with avian distributional boundaries. J. Biogeogr. 15, 489 (1988).Article 

Google Scholar 
Zuckerberg, B. et al. Climatic constraints on wintering bird distributions are modified by urbanization and weather. J. Anim. Ecol. 80, 403–413 (2011).Article 

Google Scholar 
Newton, I. The Migration Ecology of Birds (Academic Press Inc., 2008).
Google Scholar 
La Sorte, F. A., Johnston, A. & Ault, T. R. Global trends in the frequency and duration of temperature extremes. Clim. Change 166, 1–14 (2021).Article 
ADS 

Google Scholar 
Faurby, S. & Araújo, M. B. Anthropogenic range contractions bias species climate change forecasts. Nat. Clim. Change 8, 252–256 (2018).Article 
ADS 

Google Scholar 
Jiguet, F., Brotons, L. & Devictor, V. Community responses to extreme climatic conditions. Curr. Zool. 57, 406–413 (2011).Article 

Google Scholar 
Tayleur, C. et al. Swedish birds are tracking temperature but not rainfall: Evidence from a decade of abundance changes. Glob. Ecol. Biogeogr. 24, 859–872 (2015).Article 

Google Scholar 
Clements, C. F. & Ozgul, A. Indicators of transitions in biological systems. Ecol. Lett. 21, 905–919 (2018).Article 

Google Scholar 
Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).Article 
ADS 
CAS 

Google Scholar 
Fischer, J. et al. Functional richness and relative resilience of bird communities in regions with different land use intensities. Ecosystems 10, 964–974 (2007).Article 

Google Scholar 
Olivier, T., Thébault, E., Elias, M., Fontaine, B. & Fontaine, C. Urbanization and agricultural intensification destabilize animal communities differently than diversity loss. Nat. Commun. 11, 1–9 (2020).Article 
ADS 

Google Scholar 
Michel, N. L. et al. Metrics for conservation success: Using the “Bird‐Friendliness Index” to evaluate grassland and aridland bird community resilience across the Northern Great Plains ecosystem. Divers. Distrib. 26, 1687–1702 (2020).National Audubon Society. The Christmas bird count historical results [online]. http://www.christmasbirdcount.org (2019).BirdLife International & NatureServe. Bird species distribution maps of the world. Version 4.0 (2015).Billerman, S. M., Keeney, B. K., Rodewald, P. G. & Schulenberg, T. S. Birds of the world (2020).BirdLife International. IUCN red list for birds. http://www.birdlife.org (2020).De Magalhães, J. P. & Costa, J. A database of vertebrate longevity records and their relation to other life-history traits. J. Evol. Biol. 22, 1770–1774 (2009).Article 

Google Scholar 
Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).Article 

Google Scholar 
IUCN. The IUCN red list of threatened species. Version 2019-2. http://www.iucnredlist.org (2019).Morelli, F., Benedetti, Y., Møller, A. P. & Fuller, R. A. Measuring avian specialization. Ecol. Evol. 9, 8378–8386 (2019).Article 

Google Scholar 
MacLean, S. A. & Beissinger, S. R. Species’ traits as predictors of range shifts under contemporary climate change: A review and meta-analysis. Glob. Change Biol. 23, 4094–4105 (2017).Article 
ADS 

Google Scholar 
Jiguet, F., Gadot, A. S., Julliard, R., Newson, S. E. & Couvet, D. Climate envelope, life history traits and the resilience of birds facing global change. Glob. Change Biol. 13, 1672–1684 (2007).Article 
ADS 

Google Scholar 
Julliard, R., Jiguet, F. & Couvet, D. Common birds facing global changes: What makes a species at risk?. Glob. Change Biol. 10, 148–154 (2004).Article 
ADS 

Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).Article 
ADS 
CAS 

Google Scholar 
Grimm, A. et al. Earlier breeding, lower success: Does the spatial scale of climatic conditions matter in a migratory passerine bird?. Ecol. Evol. 5, 5722–5734 (2015).Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

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

Google Scholar 
Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).Article 

Google Scholar 
Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).Article 

Google Scholar 
Barnagaud, J. Y. et al. Biogeographical, environmental and anthropogenic determinants of global patterns in bird taxonomic and trait turnover. Glob. Ecol. Biogeogr. 26, 1190–1200 (2017).Article 

Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and nonlinear mixed effects model (2019).Barton, K. MuMIn: Multi-model inference. R package (2020).R Core Team. R: A language and environment for statistical computing. Version 3.5.3. http://www.r-project.org/ (2019).Devictor, V. et al. Functional biotic homogenization of bird communities in disturbed landscapes. Glob. Ecol. Biogeogr. 17, 252–261 (2008).Article 

Google Scholar 
Neutel, A. M., Heesterbeek, J. A. P. & De Ruiter, P. C. Stability in real food webs: weak links in long loops. Science (80-. ) 296, 1120–1123 (2002).Article 
ADS 
CAS 

Google Scholar 
Wallach, A. D. et al. Trophic cascades in 3D: Network analysis reveals how apex predators structure ecosystems. Methods Ecol. Evol. 8, 135–142 (2017).Article 

Google Scholar  More

Empirical dataWe applied our theory to two datasets (Table 1): the plant survey dataset and the biodiversity-manipulated experiment dataset. The plant survey dataset contains nine sites of long-term grassland experiments across the United States (see also Hallett et al.10, and Zhao et al.23). Five of nine sites are from the Long Term Ecological Research (LTER) network (see Table 1). Plant abundances were measured either as biomass or as percent cover. In percent-cover cases, summed values can exceed 100% due to vertically overlapping canopies. All sites were sampled annually and were spatially replicated. We only used data of the plant survey dataset from unmanipulated control plots. Methods for data collection were constant over time.The biodiversity-manipulated experimental dataset comprises two long-term grassland experiments, BigBio and BioCON, at the Cedar Creek Ecosystem Science Reserve. Both experiments directly manipulated plant species number (1, 2, 4, 8, 16 for BigBio; and 1, 4, 9, 16 for BioCON). BioCON also contains different treatment levels for nitrogen and atmospheric CO2, but here only data from the ambient CO2 and ambient N treatments were used. We excluded plots with only one species. BigBio comprises 125 plots over 17 years, and BioCON comprises 59 plots over 22 years (Table 1).TheoryLet xi(t) denote the biomass of species i = 1, …, S at time t = 1, …, t and let μi = mean (xi (t)), σi = ({{mbox{sd}}})(xi (t)), and ({v}_{i}={sigma }_{i}^{2}) be the mean, standard deviation and variance of species i, computed through time. Let vij = cov (({x}_{i}left(tright),, {x}_{j}left(tright))) be the covariance, through time, of the dynamics of species i and j. Let xtot (left(tright)={sum }_{i}{x}_{i}(t)), ({mu }_{{{mbox{tot}}}}={sum }_{i}{mu }_{i}), ({v}_{{{mbox{tot}}}}={sum }_{i,j}{v}_{{ij}}), and ({{{{{{rm{sigma }}}}}}}_{{{{{{rm{tot}}}}}}}=sqrt{{v}_{{{{{{rm{tot}}}}}}}}). When population time series are uncorrelated, ({v}_{{{{{{rm{tot}}}}}}}={sum }_{i}{v}_{i}).As defined previously10,15, community stability is the inverse coefficient of variation of ({x}_{{{mbox{tot}}}}left(tright)), ({S}_{{{{{{rm{com}}}}}}}={mu }_{{{{{{rm{tot}}}}}}}/{sigma }_{{{{{{rm{tot}}}}}}}). Population stability is the inverse of weighted-average population variability9, ({sum }_{i}frac{{mu }_{i}}{{mu }_{{{{{{rm{tot}}}}}}}}{{CV}}_{i}={sum }_{i}frac{{mu }_{i}}{{mu }_{{{{{{rm{tot}}}}}}}}frac{{sigma }_{i}}{{mu }_{i}}={sum }_{i}frac{{sigma }_{i}}{{mu }_{{{{{{rm{tot}}}}}}}}), i.e, ({S}_{{pop}}={mu }_{{{{{{rm{tot}}}}}}}/{sum }_{i}{sigma }_{i}). The ratio of community stability over population stability is the Loreau-de Mazancourt asynchrony index14, Φ = ({sum }_{i}{sigma }_{i}/{sigma }_{{{{{{rm{tot}}}}}}}), so that$${S}_{{{{{{rm{com}}}}}}}=varPhi {S}_{{{{{{rm{pop}}}}}}}.$$
(1)
Now we suppose a hypothetical community with the same species-level variances and means as the original community but with species covariances equal to zero. Then, (1) becomes Scom_ip = (SAE)Spop, where ({S}_{{{{{{rm{com}}}}}}_{{{{{rm{ip}}}}}}}=frac{{mu }_{{{{{{rm{tot}}}}}}}}{sqrt{{sum }_{i}{v}_{i}}}=frac{{mu }_{{{{{{rm{tot}}}}}}}}{sqrt{{sum }_{i}{sigma }_{i}^{2}}}) is the value of community stability in the case of uncorrelated or independent populations and SAE is the component of Φ due to statistical averaging (here, “ip” stands for “independent populations”). The equation Scom_ip = (SAE)Spop can be interpreted as a definition of SAE. We then have$$SAE=frac{{S}_{{{{{{rm{com}}}}}}_{{{{{rm{ip}}}}}}}}{{S}_{{{{{{rm{pop}}}}}}}}=frac{{sum }_{i}{sigma }_{i}}{sqrt{{sum }_{i}{sigma }_{i}^{2}}}.$$
(2)
The compensatory effect is then the rest of Φ, i.e.,$$CPE=frac{{S}_{{{{{{rm{com}}}}}}}}{{S}_{{{{{{rm{pop}}}}}}}times SAE}=frac{{sum }_{i}{sigma }_{i}}{{sigma }_{{{{{{rm{tot}}}}}}}left({sum }_{i}{sigma }_{i}/sqrt{{sum }_{i}{sigma }_{i}^{2}}right)}=frac{sqrt{{sum }_{i}{sigma }_{i}^{2}}}{{sigma }_{{{{{{rm{tot}}}}}}}}.$$
(3)
Considering the classic variance ratio ({{{{{rm{varphi }}}}}}=frac{{V}_{{{{{{rm{tot}}}}}}}}{{sum }_{i}{V}_{i}}=frac{{sigma }_{{{{{{rm{tot}}}}}}}^{2}}{{sum }_{i}{sigma }_{i}^{2}}), our CPE is (1/sqrt{varphi }). Values CPE  > 1 (respectively, More

Reygondeau, G. & Beaugrand, G. Future climate-driven shifts in distribution of Calanus finmarchicus. Glob. Change Biol. 17, 756–766 (2011).Article 
ADS 

Google Scholar 
Grieve, B. D., Hare, J. A. & Saba, V. S. Projecting the effects of climate change on Calanus finmarchicus distribution within the U.S. Northeast Continental Shelf. Sci. Rep. 7, 6264 (2017).Article 
ADS 

Google Scholar 
Bosso, L. et al. The rise and fall of an alien: Why the successful colonizer Littorina saxatilis failed to invade the Mediterranean Sea. Biol. Invasions 24, 3169–3187 (2022).Article 

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

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

Google Scholar 
Kaschner, K., Watson, R., Trites, A. W. & Pauly, D. Mapping world-wide distributions of marine mammal species using a relative environmental suitability (RES) model. Mar. Ecol. Prog. Ser. 316, 285–310 (2006).Article 
ADS 

Google Scholar 
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?. Glob. Ecol. Biogeogr. 12, 361–371 (2003).Article 

Google Scholar 
Buckley, L. B. Linking traits to energetics and population dynamics to predict lizard ranges in changing environments. Am. Nat. 171, E1–E19 (2008).Article 

Google Scholar 
Kolbe, J. J., Kearney, M. & Shine, R. Modeling the consequences of thermal trait variation for the cane toad invasion of Australia. Ecol. Appl. 20, 2273–2285 (2010).Article 

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

Google Scholar 
Somero, G. N., Lockwood, B. L. & Tomanek, L. Biochemical Adaptation: Response to Environmental Challenges, From Life’s Origins to the Anthropocene (Sinauer Associates, 2017).
Google Scholar 
Kuo, E. S. & Sanford, E. Geographic variation in the upper thermal limits of an intertidal snail: Implications for climate envelope models. Mar. Ecol. Prog. Ser. 388, 137–146 (2009).Article 
ADS 

Google Scholar 
Smeraldo, S. et al. Ignoring seasonal changes in the ecological niche of non-migratory species may lead to biases in potential distribution models: lessons from bats. Biodivers. Conserv. 27, 2425–2441 (2018).Article 

Google Scholar 
Gamliel, I. et al. Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species. Ecography 43, 1090–1106 (2020).Article 

Google Scholar 
Kearney, M. R., Wintle, B. A. & Porter, W. P. Correlative and mechanistic models of species distribution provide congruent forecasts under climate change. Conserv. Lett. 3, 203–213 (2010).Article 

Google Scholar 
Buckley, L. B., Waaser, S. A., MacLean, H. J. & Fox, R. Does including physiology improve species distribution model predictions of responses to recent climate change?. Ecology 92, 2214–2221 (2011).Article 

Google Scholar 
Fry, F. E. J. Effects of the environment on animal activity. Pub. Ontario Fish. Lab. No. 68. Toronto Studies Biol. Ser. 55, 1–52 (1947).
Google Scholar 
Brett, J. R. Energetic responses of salmon to temperature. A study of some thermal relations in the physiology and freshwater ecology of sockeye salmon (Oncorhynchus nerkd). Am Zoologist 11, 99–113 (1971).Article 

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

Google Scholar 
Pörtner, H. O. & Farrell, A. P. Physiology and climate change. Science 322, 690–692 (2008).Article 

Google Scholar 
Eliason, E. J. et al. Differences in thermal tolerance among sockeye salmon populations. Science 332, 109–112 (2011).Article 
ADS 
CAS 

Google Scholar 
Donelson, J. M., Munday, P. L., McCormick, M. I. & Pitcher, C. R. Rapid transgenerational acclimation of a tropical reef fish to climate change. Nat. Clim. Change 2, 30–32 (2012).Article 
ADS 

Google Scholar 
Pörtner, H. Climate change and temperature-dependent biogeography: Oxygen limitation of thermal tolerance in animals. Naturwissenschaften 88, 137–146 (2001).Article 
ADS 

Google Scholar 
Pörtner, H.-O. Oxygen-and capacity-limitation of thermal tolerance: A matrix for integrating climate-related stressor effects in marine ecosystems. J. Exp. Biol. 213, 881–893 (2010).Article 

Google Scholar 
Clark, T. D., Sandblom, E. & Jutfelt, F. Response to Farrell and to Pörtner and Giomi. J. Exp. Biol. 216, 4495–4497 (2013).Article 

Google Scholar 
Farrell, A. P. Aerobic scope and its optimum temperature: Clarifying their usefulness and limitations: Correspondence on J. Exp. Biol. 216, 2771–2782. J. Exp. Biol. 216, 4493–4494 (2013).Article 

Google Scholar 
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).Article 
ADS 
CAS 

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

Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).Article 
ADS 
CAS 

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

Google Scholar 
Clarke, A. Is there a universal temperature dependence of metabolism?. Funct. Ecol. 18, 252–256 (2004).Article 

Google Scholar 
Clarke, A. & Fraser, K. P. P. Why does metabolism scale with temperature?. Funct. Ecol. 18, 243–251 (2004).Article 

Google Scholar 
Fangue, N. A., Hofmeister, M. & Schulte, P. M. Intraspecific variation in thermal tolerance and heat shock protein gene expression in common killifish, Fundulus heteroclitus. J. Exp. Biol. 209, 2859–2872 (2006).Article 
CAS 

Google Scholar 
Dhillon, R. S. & Schulte, P. M. Intraspecific variation in the thermal plasticity of mitochondria in killifish. J. Exp. Biol. 214, 3639–3648 (2011).Article 
CAS 

Google Scholar 
Fangue, N. A., Podrabsky, J. E., Crawshaw, L. I. & Schulte, P. M. Countergradient variation in temperature preference in populations of killifish Fundulus heteroclitus. Physiol. Biochem. Zool. 82, 776–786 (2009).Article 

Google Scholar 
Healy, T. M. & Schulte, P. M. Thermal acclimation is not necessary to maintain a wide thermal breadth of aerobic scope in the common killifish (Fundulus heteroclitus). Physiol. Biochem. Zool. 85, 107–119 (2012).Article 
CAS 

Google Scholar 
Chust, G. et al. Are Calanus spp. shifting poleward in the North Atlantic? A habitat modelling approach. ICES J. Mar. Sci. 71, 241–253 (2014).Article 

Google Scholar 
Norin, T., Malte, H. & Clark, T. D. Aerobic scope does not predict the performance of a tropical eurythermal fish at elevated temperatures. J. Exp. Biol. 217, 244–251 (2014).
Google Scholar 
Payne, N. L. et al. Temperature dependence of fish performance in the wild: links with species biogeography and physiological thermal tolerance. Funct. Ecol. 30, 903–912 (2016).Article 

Google Scholar 
Raffel, T. R. et al. Disease and thermal acclimation in a more variable and unpredictable climate. Nat. Clim. Change 3, 146–151 (2013).Article 
ADS 

Google Scholar 
Sinclair, B. J. et al. Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures?. Ecol. Lett. 19, 1372–1385 (2016).Article 

Google Scholar 
Dahlke, F. T. et al. Northern cod species face spawning habitat losses if global warming exceeds 1.5°C. Sci. Adv. 4, 8821 (2018).Article 
ADS 

Google Scholar 
Pörtner, H.-O. & Giomi, F. Nothing in experimental biology makes sense except in the light of ecology and evolution: Correspondence on J. Exp. Biol. 2771-2782. J. Exp. Biol. 216, 4494–4495 (2013).Article 

Google Scholar 
Pörtner, H.-O. How and how not to investigate the oxygen and capacity limitation of thermal tolerance (OCLTT) and aerobic scope: Remarks on the article by Gräns et al. J. Exp. Biol. 217, 4432–4433 (2014).Article 

Google Scholar 
Kleiber, M. Body size and metabolism. Hilgardia 6, 315–353 (1932).Article 
CAS 

Google Scholar 
Killen, S. S., Atkinson, D. & Glazier, D. S. The intraspecific scaling of metabolic rate with body mass in fishes depends on lifestyle and temperature. Ecol. Lett. 13, 184–193 (2010).Article 

Google Scholar 
Norin, T. & Gamperl, A. K. Metabolic scaling of individuals vs. populations: Evidence for variation in scaling exponents at different hierarchical levels. Funct. Ecol. 32, 379–388 (2018).Article 

Google Scholar 
Jayasundara, N., Kozal, J. S., Arnold, M. C., Chan, S. S. L. & Giulio, R. T. D. High-throughput tissue bioenergetics analysis reveals identical metabolic allometric scaling for teleost hearts and whole organisms. PLoS ONE 10, e0137710 (2015).Article 

Google Scholar 
Kinnison, M. T., Unwin, M. J. & Quinn, T. P. Migratory costs and contemporary evolution of reproductive allocation in male chinook salmon. J. Evol. Biol. 16, 1257–1269 (2003).Article 
CAS 

Google Scholar 
Clarke, A. & Johnston, N. M. Scaling of metabolic rate with body mass and temperature in teleost fish. J. Anim. Ecol. 68, 893–905 (1999).Article 

Google Scholar 
Duvernell, D. D., Lindmeier, J. B., Faust, K. E. & Whitehead, A. Relative influences of historical and contemporary forces shaping the distribution of genetic variation in the Atlantic killifish, Fundulus heteroclitus. Mol. Ecol. 17, 1344–1360 (2008).Article 

Google Scholar 
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A. & Ramirez-Villegas, J. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci. Data 7, 1–14 (2020).Article 

Google Scholar 
Franke, R. Scattered data interpolation: Tests of some methods. Math. Comput. 38, 181–200 (1982).MathSciNet 
MATH 

Google Scholar 
Levitus, S. et al. World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys. Res. Lett. 39, 15. https://doi.org/10.1029/2012GL051106 (2012).Article 

Google Scholar 
Kaschner, K. et al. AquaMaps: Predicted Range Maps for Aquatic Species (Worldwide Web Electronic Publication, 2019).
Google Scholar 
Jayasundara, N. Ecological significance of mitochondrial toxicants. Toxicology 391, 64–74 (2017).Article 
CAS 

Google Scholar 
Beers, J. M. & Jayasundara, N. Antarctic notothenioid fish: what are the future consequences of ‘losses’ and ‘gains’ acquired during long-term evolution at cold and stable temperatures?. J. Exp. Biol. 218, 1834–1845 (2015).Article 

Google Scholar 
Lear, K. O. et al. Thermal performance responses in free-ranging elasmobranchs depend on habitat use and body size. Oecologia 191, 829–842 (2019).Article 
ADS 

Google Scholar 
Good, S. et al. The current configuration of the OSTIA system for operational production of foundation sea surface temperature and ice concentration analyses. Remote Sens. 12, 720 (2020).Article 
ADS 

Google Scholar 
Stocker, T. Climate Change 2013: The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2014).
Google Scholar 
Ready, J. et al. Predicting the distributions of marine organisms at the global scale. Ecol. Model. 221, 467–478 (2010).Article 

Google Scholar 
Pawlowicz, R. M_Map: A Mapping Package for MATLAB, Version 1.4 m. Computer Software, UBC EOAS. https://www.eoas.ubc.ca/rich/map.html (2020).Schulzweida, U., Kornblueh, L. & Quast, R. CDO User’s Guide. Climate Data Operators, Version 1, (2006).Nychka, D., Furrer, R., Paige, J. & Sain, S. Fields: Tools for Spatial Data. R Package Version 11.6. (2017).Chen, Z., Farrell, A. P., Matala, A. & Narum, S. R. Mechanisms of thermal adaptation and evolutionary potential of conspecific populations to changing environments. Mol. Ecol. 27, 659–674 (2018).Article 

Google Scholar 
da Silva, C. R. B., Riginos, C. & Wilson, R. S. An intertidal fish shows thermal acclimation despite living in a rapidly fluctuating environment. J. Comp. Physiol. B. 189, 385–398 (2019).Article 

Google Scholar 
Slesinger, E. et al. The effect of ocean warming on black sea bass (Centropristis striata) aerobic scope and hypoxia tolerance. PLoS ONE 14, e0218390 (2019).Article 
CAS 

Google Scholar 
Moffett, E. R., Fryxell, D. C., Palkovacs, E. P., Kinnison, M. T. & Simon, K. S. Local adaptation reduces the metabolic cost of environmental warming. Ecology 99, 2318–2326 (2018).Article 

Google Scholar 
Turker, H. The effect of water temperature on standard and routine metabolic rate in two different sizes of Nile tilapia. Kafkas Universitesi Veteriner Fakultesi Dergisi 17, 575–580 (2011).
Google Scholar 
Hvas, M., Folkedal, O., Imsland, A. & Oppedal, F. The effect of thermal acclimation on aerobic scope and critical swimming speed in Atlantic salmon, Salmo salar. J. Exp. Biol. 220, 2757–2764 (2017).
Google Scholar 
Ohlberger, J., Mehner, T., Staaks, G. & Hölker, F. Intraspecific temperature dependence of the scaling of metabolic rate with body mass in fishes and its ecological implications. Oikos 121, 245–251 (2012).Article 

Google Scholar 
Kunz, K. L. et al. New encounters in Arctic waters: A comparison of metabolism and performance of polar cod (Boreogadus saida) and Atlantic cod (Gadus morhua) under ocean acidification and warming. Polar Biol. 39, 1137–1153 (2016).Article 

Google Scholar 
Norin, T., Bailey, J. A. & Gamperl, A. K. Thermal biology and swimming performance of Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus). PeerJ 7, e7784 (2019).Article 

Google Scholar 
Nowell, L. B. et al. Swimming energetics and thermal ecology of adult bonefish (Albula vulpes): A combined laboratory and field study in Eleuthera, The Bahamas. Environ. Biol. Fishes 98, 2133–2146 (2015).Article 

Google Scholar 
Pang, X., Yuan, X.-Z., Cao, Z.-D., Zhang, Y.-G. & Fu, S.-J. The effect of temperature on repeat swimming performance in juvenile qingbo (Spinibarbus sinensis). Fish Physiol. Biochem. 41, 19–29 (2015).Article 
CAS 

Google Scholar 
Schwieterman, G. D. et al. Metabolic Rates and Hypoxia Tolerences of clearnose skate (Rostaraja eglanteria), summer flounder (Paralichthys dentatus), and thorny skate (Amblyraja radiata). Biology 8, 56 (2019).Article 
CAS 

Google Scholar 
Xie, H. et al. Effects of acute temperature change and temperature acclimation on the respiratory metabolism of the snakehead. Turk. J. Fish. Aquat. Sci. 17, 535–542 (2017).Article 

Google Scholar  More