Ecology

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

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

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

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

Sampling, bacterial isolation, and colony screeningSamples were collected from thermally active sediments as evidenced by temperature and/or emission of steam in November 2010 and 2012 from Harry’s Dream (HD2 and HD3), Warren Cave (WC1, WC2, WC3, WC4, WC7, and WC8), Haggis Hole, (HH), Mammoth Cave (MC), Hut Cave (Hut), and Heroine Cave (HC) (fumarolic ice caves on Mt. Erebus volcano [21]). The samples were divided into four types: Mainly weathered basaltic/phonolitic sand (HD2, HD3, WC3, WC4, WC8 and Hut); pebbles and rock fragments (WC7, HH, and MC); black porous glassy materials that appeared to be solidified lava (WC1, WC2, and HC); and ash sediment (WC10) with little to no organic material (Table S1). The sediments were collected aseptically using sterile 50 mm conical tubes and immediately sealed. Additional information and environmental parameters on the sampling locations are provided in Table S1 and in Tebo et al. [21].To isolate bacteria found in these oligotrophic environments, we employed a culture strategy of long-term incubation in a nutrient-poor medium and screening of slow-growing colonies by direct PCR identification. Reasoner’s 2 A gellan gum medium (10% R2AG) [23] and FS1VG medium [24] were used for bacterial isolation. The 10% R2AG is a 10-fold diluted R2A broth (Nihon Seiyaku, Tokyo, Japan), solidified with 15 g/L gellan gum (Kanto chemical, Tokyo, Japan) with 2 g/L CaCl2. The 10% R2AG and FS1VG media were adjusted to a pH of 4.5 or 6.0, with or without 30 mg/L sodium azide. Samples used for isolation were selected from Warren Cave and Harry’s Dream, where the presence of relatively large numbers of bacteria ( >107/g) was confirmed in a previous report [21], and a total of nine samples, WD1, 2, 3, 4, 7, 8, and 10 and HD2 and 3, were used without any other pretreatment such as drying or dilution. For the sandy and ash samples with fine particles (WC3, 4, 8, 10, and HD2, 3), approximately 50 mg of sample were spread directly on plates. Glassy materials (WC1 and WC2) were embedded directly in plates using 2-3 pieces (approx. 200 mg). Pebbles and rock fragments (WC7) were crushed and approximately 50 mg of debris were spread directly onto the plates. All plates were incubated at 15 °C, 30 °C or 37 °C under dark conditions. New colonies were marked as they appeared and selection of the isolates was performed by picking only colonies that appeared after four weeks of incubation. We selected colonies around the sediment or in the cracks created during spreading using a magnifying glass.For identification, colonies were picked with a sterile toothpick, re-streaked, stabbed on fresh medium, and subsequently suspended in 20 μL sterilized 0.05 M NaOH. Suspensions were heated at 100 °C for 15 min, and supernatants were used as template DNAs for PCR. Partial 16 S rRNA gene sequences were amplified by PCR using commonly used bacterial primer set 27 F (5′-AGATTTGATCCTGGCTCAG-3′) and 1492 R (5′-GGTTACCTTGTTACGACTT-3′) or 536 R (5′-GTA TTA CCG CGG CTG CTG-3′) with TaKaRa ExTaq DNA polymerase (Takara Bio, Shiga, Japan) as previously described [23]. Sequencing was performed at Eurofins Genomics (Louisville, KY, USA), using a 3730xl DNA analyzer (Applied Biosystems, CA, USA). Sequence similarities with closest species were calculated using EZbiocloud’s Identify Service (https://www.ezbiocloud.net/identify). Subsequently, cells in the stab identified as “Ca. Eremiobacterota” by the direct PCR identification were serially diluted and stabbed onto new plates until multiple pure cultures of Eremiobacterota were obtained. The isolate was designated as strain WC8-2.Whole genome sequencing and annotationGenomic DNA was extracted from WC8-2 cells grown in 10% R2A broth (pH6.0) with air/CO2 (90:10, v/v) at 30 °C for 30 days under a 12/12 h light/dark regime with incandescent light (250 μmol m–2s–1), using a Puregene Yeast/Bact. Kit B (Qiagen, Germantown, MD, USA) [25]. Sequencing was performed by Macrogen Japan Corp., on a NovaSeq 6000 (Illumina, Inc., San Diego, CA, USA) and PacBio RSII (Pacific Biosciences of California, Inc. Menlo Park, CA, USA). The gap-free complete genome was assembled de novo using the Unicycler version 0.4.8 hybrid assembly pipeline with default settings [26]. Completeness and contamination levels were estimated using CheckM [27]. The genome was annotated using the DDBJ Fast Annotation and Submission Tool (DFAST) [28] and the BlastKOALA web server version 2.2, and was visualized using CGView Server [29] (http://cgview.ca/).Phylogenetic analysis of “Ca. Eremiobacterota”Identification of strain WC8-2 was performed using the Genome Taxonomy Database Toolkit (GTDB-Tk) (ver. 2.1.0), which produces standardized taxonomic labels that are based on those used in the Genome Taxonomy Database [30]. Terrabacterial genomes including “Ca. Eremiobacterota” MAGs and related genomes were retrieved from the Genome Taxonomy Database (GTDB) (July 2022) and the NCBI RefSeq database (July 2022). Full-length 16 S rRNA gene sequences were retrieved from the WC8-2 genome (WPS_r00030) and the NCBI database (Table S2). Multiple sequences were aligned using SINA (version 1.2.11) [31]. IQ-TREE version 1.6.12 [32] was used to build the phylogeny. ModelFinder [33] was used to determine the optimal evolutionary model for phylogeny building, which selected the TNe+I + G4 model. Branch support was calculated using 1000 ultrafast bootstraps [34]. The pairwise 16 S rRNA gene sequence similarities were determined using SDT software. Phylogenomic analysis based on 400 marker proteins was carried out using PhyloPhlAn v3.0 [35]. Diamond v5.2.32 [36], MAFFT v7.453 [37], and TrimAI were utilized for orthologs searching, multiple sequence alignment within the superphylum Terrabacteria, and gap-trimming, respectively. Gappy sites and sequences with >50% gaps were deleted from the alignments. IQ-TREE version 1.6.12 [32] was used to build the phylogenomic tree. ModelFinder [33] was used to determine the optimal evolutionary model for phylogeny building, which selected the LG + F + R9 model. These analyses were conducted using the “AOBA-B” super- computer (NEC, Tokyo, Japan) with 2CPUs (EPYC7702, AMD, CA, US) and 256GB of RAM. The related similarity of genomes between strain WC8-2 and relatives was estimated using average nucleotide identity (ANI) values, which were calculated using OrthoANI calculator in the EzBio-Cloud web service [38]. The related similarity between strain WC8-2 and its sister phyla with one representative from each class was assessed by pairwise Average Amino acid Identity (AAI) values using the online tools at the Kostas Konstantinidis Lab website Environmental Microbial Genomics Laboratory (http://enve-omics.ce.gatech.edu/aai/). The MAGs used in the tree are listed in Table S3.Phylogeny of photosynthesis- and “atmospheric chemosynthesis”- associated genesWe retrieved phototrophy- and “atmospheric chemosynthesis”-related protein sequences from the WC8-2 genome, “Ca. Eremiobacterota” MAGs, and known phototrophs genomes (Table S3) using the local BLAST server (SequenceServer 1.0.14 [39]) with reference sequences (Table S3) or annotated sequences as queries. Sequences were aligned using MUSCLE and poorly aligned regions were removed using Gblocks version 0.91b [40] or by manual inspection. The alignment sequences were concatenated into a single sequence. The ML tree was constructed using IQ-TREE with the best-hit evolutionary rate model: LG + I + G4 for HhyL, CbbL, BchXYZ and CbbL, LG + F + I + G4 for BchLNB, and LG + F + G4 for PufML, BchI, and BchD. All trees were visualized using iTOL (version 5.0) [41]. All sequences used in the trees are listed in Table S3.Analysis of bacterial communities in the fumarolic ice cavesEnvironmental DNA was extracted from the samples (0.1 g) (WC7, WC8, HD3, MC, Hut, HH, HC) using a DNeasy PowerSoil Kit (Qiagen, Valencia, CA, USA) following the manufacturer’s instructions. The V4 region of the 16 S rRNA gene was PCR amplified using primers with adapter sequences (V3-V4f_MIX) [42]. The PCR cycling was carried out using the following parameters: 94 °C for 2 min followed by 25 cycles at 94 °C for 30 s, 56 °C for 30 s, and 72 °C for 1 min with a final extension at 72 °C for 2 min. Library construction and sequencing were performed at the Bioengineering Lab (Kanagawa, Japan) using MiSeq (Illumina). Briefly, adaptor and primer regions were trimmed using the FASTX-Toolkit v0.0.13 (http://hannonlab.cshl.edu/fastx_toolkit). Read sequences of ≤40 bp with ambiguous bases and low-quality sequences (quality score, ≤Q20), together with their paired-end reads, were filtered out using Sickle v1.33 (https://github.com/najoshi/sickle). High-quality paired-end reads were merged using PEAR v0.9.10 with default settings [43]. Merged sequences of ≤245 and ≥260 bp were discarded using SeqKit v0.8.0 [44]. Operational taxonomic units (OTUs) were classified using QIIME v1.9.1 and the SILVA database (release 132) with 97% identity. To study the phylogeny of the OTUs assigned to the “Ca. Eremiobacterota”, a neighbor-joining (NJ) tree of the OTUs was constructed [45] as described above.Microscopic observationElectron microscopic observations of the cells were performed at Tokai Electron Microscopy (Nagoya, Japan). Briefly, the cells grown in 10% R2A broth (pH6.0) with air/CO2 (90:10, v/v) at 30 °C for 15 days under a 12/12 h light/dark regime with incandescent light (250 μmol m–2s–1) were fixed with 4% paraformaldehyde (PFA) and 4% glutaraldehyde (GA) in 0.1 M phosphate buffer (PB) at pH7.4, and postfixed with 2% OsO4 in 0.1 M PB. Cells were then dehydrated using graded ethanol solutions. The dehydrated cells were polymerized with resin, ultrathin sectioned, stained with 2% uranyl acetate, then secondary-stained with Lead stain solution. A transmission electron microscope (TEM) (JEM-1400Pus; JEOL, Tokyo, Japan) was used to observe the ultrathin sectioned cells at 100 kV acceleration voltage. To observe the negative-stained cells, PFA- and GA-fixed cells were adsorbed on formvar film-coated copper grids and stained with 2% phosphotungstic acid solution (pH 7.0) and observed using a TEM at 100 kV. DAPI (4,6-diamidino2-phenylindole) and Nile-Red staining was performed by incubating 0.1 mL cell suspension with a 1 mL staining solution (1 mg/L DAPI and 1 mg/L Nile Red in PBS buffer) for 10 min. The stained cells were observed under a fluorescence microscope (Olympus AX80T; Olympus Optical; Tokyo, Japan).Growth assayUnless otherwise noted, all cultures were grown in 100 mL butyl stopper- and screw-cap-sealed glass vials containing 50 mL liquid medium (pH6.0) at 30 °C. Growth was monitored by optical density at 600 nm (OD600) using a spectrophotometer (BioSpectrometer Basic; Eppendorf; Tokyo, Japan). Initial cell density was adjusted to 0.005 (OD600). Specific culture conditions are described below. All anaerobic growth tests were conducted with 100% N2 gas in the headspaces and supplemented with a reducing agent (0.3 g/L cysteine-HCl) and a redox indicator (1 mg/L resazurin).Optimal culture conditionsCell growth in different media was examined using 1, 10, 20, 100% (a full strength) R2A broth and Basal_YE with air/CO2 (90:10, v/v) under a 12/12 h light/dark regime with incandescent light (250 μmol m–2s–1) for 25 days. Basal_YE contained (l−1) 0.44 g KH2PO4, 0.1 g (NH4)2SO4, 0.1 g MgSO4.7H2O, 0.3 g yeast extract, and 1 ml trace element SL-8 [refer to DSMZ745]. Cell growth at different temperatures (10, 13, 20, 25, 30, 33 and 37 °C; pH6.0), pH (3.7, 4.5, 6.0, 7.0, 8.0, and 9.0), and NaCl (0, 1, 10, 20, and 30 g [l−1]; pH6.0) was examined using Basal_YE with air/CO2 (90:10, v/v) for 25 days under a 12/12 h light/dark regime with incandescent light (250 μmol m−2s−1). The following pH buffer solutions were used: acetic acid/sodium acetate for pH 4–6, K2HPO4/KH2PO4 for pH 6–8, sodium bicarbonate/sodium carbonate for pH 9–10. To examine colony formation on solid media, WC8-2 was streaked or stabbed on 10% R2AG medium (pH 6.0) and incubated at 30 °C for 30 days under a 100% air atmosphere.Optimal oxygen and carbon dioxide conditionsTo determine the preferred O2 concentration, WC8-2 was grown in Basal_YE (pH 6.0, 30 °C, no NaCl) in butyl stopper-sealed glass bottles with the atmosphere in the headspace adjusted to different N2/O2/CO2 ratios (70:20:10%, 80:10:10%, 89:1:10%, 90:0:10% v/v) after removing oxygen with 100% N2 gas. Cultures were incubated for 25 days under a 12/12 h light/dark regime with incandescent light (250 μmol m–2s–1). To determine the CO2 preference, the gas phase was adjusted to different N2/O2/CO2 ratios (70:20:10%,75:20:5%, 80:20:0% v/v) in sealed bottles or 100% air (plugged with a BIO-SILICO N-38 sponge plug; Shin-Etsu Polymer Co., Ltd, Tokyo, Japan; breathable culture-plug) under the same conditions as the O2 preference test.Photoorganoheterotrophic (or photoorganoautotrophic) and chemoorganoheterotrophic (or chemoorganoautotrophic) growthThe utilization of organic compounds as carbon sources/organic electron donors was tested in Basal medium (Basal_YE without yeast extract, pH 6.0) supplemented with one of the following sources (l-1): 0.3 ml of glycerol, or 0.3 g of sucrose, d-glucose, d-ribose, maltose, l-leucine, l-isoleucine, l-valine, l-serine, l-lysine, taurine, yeast extract or gellan gum, 1 ml of vitamin B12 solution (2 mg/L). Utilization was assessed by measuring growth of the cultures during a 25-day incubation at 30 °C in continuous light (250 μmol m–2s–1) for photoorganoheterotrophy, or continuous dark for chemoorganoheterotrophy under aerobic (air/CO2 [90:10, v/v]) and anaerobic (N2/CO2 [90:10, v/v]) conditions.Photolithoautotrophic and chemolithoautotrophic growthCells were inoculated into amended PSB2 [46] as described below or Basal medium with 5 mM Na2S or Na2S2O3 or 1% H2 (v/v; in the gas phase) as electron donors, and cultivated in continuous light (250 μmol m–2s–1) for photolithoautotrophic growth, or continuous dark for chemolithoautotrophic growth under aerobic (air/CO2 [90:10, v/v]) and anaerobic (N2/CO2 [90:10, v/v]) conditions for 60 days at 30 °C. The amended PBS2 contained (L-1): 0.5 g NH4Cl, 1.0 g KH2PO4, 0.2 g NaCl, 0.4 g MgSO4.7H2O, 0.05 g CaCl2.2H2O, 4.2 g NaHCO3, 1 ml trace element SL-8 [refer to DSMZ745], and 1 ml vitamin B12 solution (2 mg/L), pH6.0.Fermentative or anaerobic growthAnaerobic growth was examined in continuous dark in 20% R2A broth (pH 6.0) with N2/CO2 (90:10, v/v) supplemented with 5 mM Na2SO4, NaNO3, or dimethyl sulfoxide (DMSO) as electron acceptors for 60 days at 30 °C.Pigment assaysCells grown in Basal_YE (pH6.0) with air/CO2 (90:10, v/v) at 30 °C for 14 days (exponential growth phase) under continuous light (250 μmol m–2 s–1) and continuous dark were used for the pigment assays. The absorption spectrum was determined in a cell suspension in 60% (w/v) sucrose and in a 100% methanol extract using a spectrophotometer (V-630; JASCO, Tokyo, Japan) at 350–1100 nm. The BChl a concentration was determined spectroscopically in 100% methanol [47]. Dry cell weight was measured after harvested cells were washed twice with Milli-Q water and dried at 80 °C for 3 days. The extract was also analyzed by HPLC (NEXERA X2; Shimadzu; Kyoto, Japan) equipped with a 4.6 × 250 mm COSMOSIL 5C18-AR (Nakarai Taque; Tokyo, Japan) with isocratic elution of 92.5% (v/v) methanol in water at a flow rate of 1.0 mL/min. BChl a was monitored at 766 nm using a diode-array spectrophotometer detector (SPD-M20A; Shimadzu; Kyoto, Japan).Observation of taxisTo study phototaxis in WC8-2, cells were grown in 20% R2A broth with air/CO2 (90:10, v/v) for 14 days under a 12/12 h light/dark regimen. Cultures were transferred to tissue culture flasks (175 cm2, canted neck, Iwaki, Shizuoka, Japan). Light sources were ultraviolet (UV) at 395 nm (Linkman, Fukui, Japan), blue at 470 nm (CREE, Durham, NC, USA), green at 502 nm (Linkman), red at 653 nm (LENOO, Shinpei, Taiwan), and near-infrared (NIR) at 880 nm (LENOO). The light-emitting device was constructed by assembling LEDs on a breadboard with a power supply. Cultivations were illuminated with each wavelength using a light-emitting device from the underside and incubated at 20 °C for 18 h. Cells aggregating toward a light source was taken to indicate phototaxis. As a control, culture vessels were wrapped in aluminum foil to block light. Images and time-lapse video were captured using an iPhone 6 S camera.Stable carbon isotope ratio mass spectrometry (IRMS)The WC8-2 cells were grown in Basal_YE (pH6.0) with air/13CO2 [90:10, v/v] and air/unlabeled CO2 [90:10, v/v] under continuous light (250 μmol m–2s–1) for photoheterotroph and continuous dark for chemoheterotroph at 30 °C for 14 days (exponential growth phase). Approximately 10 mg culture biomass was collected, washed in HCl overnight, rinsed three times with deionized water, and placed into tin capsules. Stable carbon isotope ratios (δ13C) were analyzed at Shoko Science (Saitama, Japan) using a Delta V Advantage (EA-IRMS; Thermo Fisher Scientific, Bremen, Germany). The standard for C isotope ratio analysis was Vienna PeeDee Belemnite (VPDB). The δ13C values of 13CO2-cultivated cells exceeded the optimum calibration range of the instrument, but were used in this study to provide conclusive evidence that inorganic carbon was incorporated into the biomass.Quantitative reverse transcription PCR (qRT-PCR)Total RNA extraction and cDNA synthesisTotal RNA was extracted from cells grown in Basal_YE (pH6.0) with air/CO2 [90:10, v/v] in continuous light (250 μmol m–2s–1) for photoheterotrophic conditions and in continuous dark for chemoheterotrophic conditions at 30 °C for 14 days (exponential growth phase) using the Total RNA Purification Kit (Norgen, Biotek Corp, Ontario, Canada). DNA was removed from the extracted nucleic acids using an RNase-Free DNase I Kit (Norgen, Biotek Corp) according to the manufacturer’s protocol. The absence of DNA in the RNA samples was confirmed by PCR without reverse transcriptase. cDNA was generated from 500 ng total RNA using a TaKaRa PrimeScript™ 1st strand cDNA Synthesis Kit (TaKaRa Bio) with random hexamers according to the manufacturer’s protocol.Primer design, specificity and efficiencyThe following three photosynthesis- and CO2-fixation-related genes in the WC8-2 genome were selected for qRT-PCR: bchM encoding an enzyme involved in BChl synthesis, pufL encoding the anoxygenic Type II photochemical reaction centers L-subunit, and cbbL encoding the large subunit of type IE RuBisCO. The RNA polymerase subunit beta (rpoB) was used as a housekeeping reference gene. Primers for qRT-PCR were designed with Primer3 (v. 0.4.0) [48] with the following criteria: product size ranging from 80 to 150 bp, optimum Tm of 60 °C and GC content about 50 to 55%. Standard RT-PCR confirmed that each primer set amplified only a single product with expected size (data not shown), and the product was also sequenced using Sanger sequencing at Macrogen Japan Corp to confirm the candidate products. Primer efficiency was calculated for qRT-PCR using the slope of the calibration curve based on a 20-, 40-, 80-, 160-fold dilution series of cDNA samples [49]. In addition, the specificity of the primers was determined by the confirmation of a single peak in the melting curve. All information about the primers is shown in Table S4.qRT-PCRqRT-PCR was performed using a MiniOpticon Real-Time PCR System (Bio-Rad, Marnes la Coquette, France). The reaction mixture contained 10 μL TB Green Premix Ex Taq II (Tli RNaseH Plus, Takara Bio), 0.8 μL 10 mM primer, 2 μL of a 20-, 40-, 80-, 160-fold dilution series of cDNA, and 6.4 μL water. qRT-PCR was performed using the following protocol: denaturation at 95 °C for 30 s; denaturation and amplification at 95 °C for 5 s and 60 °C for 30 s, respectively (40 cycles). Fluorescence was measured at the end of the amplification step, and amplified products were examined by melting curve analysis from 60 to 95 °C. Each reaction was performed in three independent cultivations. Relative gene expression fold change was calculated using the comparative Ct method (2−ΔΔCt) [49]. Normalized expressions were used for reaction in dark. The 2−ΔΔCt values ≤0.5 were defined as downregulated and values ≥2.0 as upregulated, with P  More

Estes, J. A. Predators and ecosystem management. Wildl. Soc. Bull. 24, 390–396 (1996).
Google Scholar 
Licht, D. S., Millspaugh, J. J., Kunkel, K. E., Kochanny, C. O. & Peterson, R. O. Using small populations of wolves for ecosystem restoration and stewardship. Bioscience 60, 147–153 (2010).Article 

Google Scholar 
Schwartz, C. C., Swenson, J. E. & Miller, S. D. Large carnivores, moose, and humans: A changing paradigm of predator management in the 21st century. Alces J. Devot. Biol. Manag. Moose 39, 41–63 (2003).
Google Scholar 
Valente, A. M., Acevedo, P., Figueiredo, A. M., Fonseca, C. & Torres, R. T. Overabundant wild ungulate populations in Europe: Management with consideration of socio-ecological consequences. Mammal Rev. 50, 353–366 (2020).Article 

Google Scholar 
Lee, S. D. & Miller-Rushing, A. J. Degradation, urbanization, and restoration: A review of the challenges and future of conservation on the Korean Peninsula. Biol. Cons. 176, 262–276 (2014).Article 

Google Scholar 
Kodera, Y. Habitat selection of Japanese wild boar in Iwami district, Shimane Prefecture, western Japan. Wildl. Conserv. Jpn. 6, 119–129 (2001).
Google Scholar 
Ohashi, H. et al. Differences in the activity pattern of the wild boar Sus scrofa related to human disturbance. Eur. J. Wildl. Res. 59, 167–177 (2013).Article 

Google Scholar 
Ministry of Environment Republic of Korea. Management Plan of Pest Wild Boars. (Sejong, 2010).National Institute of Biological Resources. Analysis of Hunting Effect on Pest Animals and its Monitoring Techniques. (Incheon, 2017).Lee, S. M. & Lee, E. J. Diet of the wild boar (Sus scrofa): Implications for management in forest-agricultural and urban environments in South Korea. PeerJ 7, e7835 (2019).Article 

Google Scholar 
Bieber, C. & Ruf, T. Population dynamics in wild boar Sus scrofa: ecology, elasticity of growth rate and implications for the management of pulsed resource consumers. J. Appl. Ecol. 42, 1203–1213 (2005).Article 

Google Scholar 
Brogi, R. et al. Capital-income breeding in wild boar: A comparison between two sexes. Sci. Rep. 11, 1–10 (2021).Article 

Google Scholar 
Frauendorf, M., Gethöffer, F., Siebert, U. & Keuling, O. The influence of environmental and physiological factors on the litter size of wild boar (Sus scrofa) in an agriculture dominated area in Germany. Sci. Total Environ. 541, 877–882 (2016).Article 
ADS 
CAS 

Google Scholar 
Massei, G., Genov, P. V. & Staines, B. W. Diet, food availability and reproduction of wild boar in a Mediterranean coastal area. Acta Theriol. 41, 307–320 (1996).Article 

Google Scholar 
Sabrina, S., Jean-Michel, G., Carole, T., Serge, B. & Eric, B. Pulsed resources and climate-induced variation in the reproductive traits of wild boar under high hunting pressure. J. Anim. Ecol. 78, 1278–1290 (2009).Article 

Google Scholar 
Fonseca, C. et al. Reproduction in the wild boar (Sus scrofa Linnaeus, 1758) populations of Portugal. Galemys 16, 53–65 (2004).
Google Scholar 
Morreti, M. Birth distribution, structure and dynamics of a hunted mountain populatin of wild boars (Sus scrofa L.), Ticino, Switzerland. J. Mt. Ecol. 3, 192–196 (1995).
Google Scholar 
Rosell, C., Navas, F. & Romero, S. Reproduction of wild boar in a cropland and coastal wetland area: Implications for management. Anim. Biodivers. Conserv. 35, 209–217 (2012).Article 

Google Scholar 
Cellina, S. Effects of supplemental feeding on the body condition and reproductive state of wild boar Sus scrofa in Luxembourg PhD Dissertation thesis, University of Sussex, (2008).Gamelon, M. et al. High hunting pressure selects for earlier birth date: Wild boar as a case study. Evol. Int. J. Org. Evol. 65, 3100–3112 (2011).Article 

Google Scholar 
Gethöffer, F., Sodeikat, G. & Pohlmeyer, K. Reproductive parameters of wild boar (Sus scrofa) in three different parts of Germany. Eur. J. Wildl. Res. 53, 287–297 (2007).Article 

Google Scholar 
Fonseca, C., Da Silva, A., Alves, J., Vingada, J. & Soares, A. Reproductive performance of wild boar females in Portugal. Eur. J. Wildl. Res. 57, 363–371 (2011).Article 

Google Scholar 
Gaillard, J.-M., Brandt, S. & Jullien, J.-M. Body weight effect on reproduction of young wild boar (Sus scrofa) females: A comparative analysis. Folia Zool. (Brno) 42, 204–212 (1993).
Google Scholar 
Poteaux, C. et al. Socio-genetic structure and mating system of a wild boar population. J. Zool. 278, 116–125 (2009).Article 

Google Scholar 
Spitz, F., Valet, G. & Lehr Brisbin, I. Jr. Variation in body mass of wild boars from southern France. J. Mammal. 79, 251–259 (1998).Article 

Google Scholar 
Trivers, R. L. & Willard, D. E. Natural selection of parental ability to vary the sex ratio of offspring. Science 179, 90–92 (1973).Article 
ADS 
CAS 

Google Scholar 
Clutton-Brock, T. H., Albon, S. & Guinness, F. Parental investment in male and female offspring in polygynous mammals. Nature 289, 487–489 (1981).Article 
ADS 

Google Scholar 
Hewison, A. M. & Gaillard, J.-M. Successful sons or advantaged daughters? The Trivers-Willard model and sex-biased maternal investment in ungulates. Trends Ecol. Evol. 14, 229–234 (1999).Article 
CAS 

Google Scholar 
Clutton-Brock, T. H. & Iason, G. R. Sex ratio variation in mammals. Q. Rev. Biol. 61, 339–374 (1986).Article 
CAS 

Google Scholar 
Fernández-Llario, P. & Mateos-Quesada, P. Body size and reproductive parameters in the wild boar Sus scrofa. Acta Theriol. 43, 439–444 (1998).Article 

Google Scholar 
Meikle, D. B., Drickamer, L. C., Vessey, S. H., Rosenthal, T. L. & Fitzgerald, K. S. Maternal dominance rank and secondary sex ratio in domestic swine. Anim. Behav. 46, 79–85 (1993).Article 

Google Scholar 
Servanty, S., Gaillard, J.-M., Allainé, D., Brandt, S. & Baubet, E. Litter size and fetal sex ratio adjustment in a highly polytocous species: The wild boar. Behav. Ecol. 18, 427–432 (2007).Article 

Google Scholar 
Mendl, M., Zanella, A. J., Broom, D. M. & Whittemore, C. T. Maternal social status and birth sex ratio in domestic pigs: An analysis of mechanisms. Anim. Behav. 50, 1361–1370 (1995).Article 

Google Scholar 
Cameron, E. Z. Facultative adjustment of mammalian sex ratios in support of the Trivers-Willard hypothesis: Evidence for a mechanism. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271, 1723–1728 (2004).Article 

Google Scholar 
Clutton-Brock, T., Albon, S. & Guinness, F. Maternal dominance, breeding success and birth sex ratios in red deer. Nature 308, 358–360 (1984).Article 
ADS 

Google Scholar 
Maillard, D. & Fournier, P. Timing and synchrony of births in the wild boar (Sus scrofa Linnaeus, 1758) in a Mediterranean habitat: The effect of food availability. Galemys 16, 67–74 (2004).
Google Scholar 
Bywater, K. A., Apollonio, M., Cappai, N. & Stephens, P. A. Litter size and latitude in a large mammal: the wild boar Sus scrofa. Mammal Rev. 40, 212–220 (2010).
Google Scholar 
Orłowska, L., Rembacz, W. & Florek, C. Carcass weight, condition and reproduction of wild boars harvested in north-western Poland. Pest Manag. Sci. 69, 367–370 (2013).Article 

Google Scholar 
Carranza, J. Sexual selection for male body mass and the evolution of litter size in mammals. Am. Nat. 148, 81–100 (1996).Article 

Google Scholar 
FernáNdez-Llario, P., Carranza, J. & Mateos-Quesada, P. Sex allocation in a polygynous mammal with large litters: The wild boar. Anim. Behav. 58, 1079–1084 (1999).Article 

Google Scholar 
McBride, G. The” teat order” and communication in young pigs. Animal Behaviour (1963).McBride, G., James, J. & Hodgens, N. Social behaviour of domestic animals. IV. Growing pigs. Anim. Sci. 6, 129–139 (1964).Article 

Google Scholar 
McBride, G., James, J. & Wyeth, G. Social behaviour of domestic animals VII. Variation in weaning weight in pigs. Anim. Sci. 7, 67–74 (1965).Article 

Google Scholar 
Geochang County. Geochang Statistical yearbook. (Geochang, 2015).Seoul Metropolitan Government. Seoul Statistical Yearbook. (Seoul, 2017).Animal and Plant Quarantine Agency and Ministry of Food and Drug Safety. Institutional Animal Care and Use Committee Standard Operation Guideline. (Gimcheon, 2020).Magnell, O. & Carter, R. The chronology of tooth development in wild boar – A guide to age determination of linear enamel hypoplasia in prehistoric and medieval pigs. Verterrinarija Ir Zootechnika. T. 40, 43–48 (2007).
Google Scholar 
Oroian, T. E., Oroian, R. G., Pasca, I., Oroian, E. & Covrig, I. Methods of age estimation by dentition in Sus scrofa ferus sp. Bull. UASVM Anim. Sci. Biotechnol. 67, 1–2 (2010).
Google Scholar 
Vericad, R. Fetal age, conception and birth period estimation on wild boar (Sus scrofa) in West Pyrenees. in Actas del XV Congresso Int. Fauna Cinegética y Silvestre. (Trujillo, 1983).Rosell, C., Fernández-Llario, P. & Herrero, J. The wild boar (Sus scrofa Linnaeus, 1758). Galemys 13, 1–25 (2001).
Google Scholar 
R core team. R: A language and environment for statistical computing v. 3.6.0 (Austria, 2019). More

I’m a conservation land manager at the Port of San Diego in California. My team and I aim to manage the tidelands around San Diego Bay, an area of more than 4,850 hectares, three-quarters of which is covered by water at high tide. At least 60% of the bay’s shoreline is ‘hardened’ — that is, it is edged with either a solid seawall or rip rap, piles of artificial boulders.To prevent erosion of the adjacent natural shoreline and restore wetlands, we’re participating in the San Diego Bay Native Oyster Living Shoreline project. As part of that, in December 2021, we placed 360 reef balls — depicted in this photograph from September this year — along 260 metres of shoreline to form the foundation of a native-oyster reef in the south bay. Here, I’m looking for oysters that have settled and are growing on the spheres.The reef balls are made out of ‘baycrete’, a concrete mixture made with local sand and the shells of farmed oysters. These attract wild oysters, which come to live there. We’re targeting the native Olympia oysters (Ostrea lurida), which can filter up to 190 litres of water per day. And sediment should accumulate behind the reef balls, encouraging the growth of eelgrass (Zostera marina). The grass is the foundation of the bay’s food chain.In a couple of years, native oysters will cover the reef balls, forming an artificial reef offshore. This reef will cause storm waves to break farther from the shoreline, protecting the adjacent salt marsh. Just inland from this area is a wetlands habitat refuge for the endangered California least tern (Sternula antillarum browni), and many birds are already hopping onto the reef balls and eating what’s living there.Living shorelines are an important part of sequestering carbon to combat climate change — both eelgrass and oysters store a lot of carbon. The reef balls are win–win–win. I often joke that we’re trying to save the planet one acre (0.4 hectares) at a time. 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