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).
Google Scholar
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).
Google Scholar
Havird, J. C. et al. Distinguishing between active plasticity due to thermal acclimation and passive plasticity due to Q10 effects: why methodology matters. Funct. Ecol. 34, 1015–1028 (2020).
Google Scholar
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).
Google Scholar
White, C. R., Alton, L. A., Bywater, C. L., Lombardi, E. J. & Marshall, D. J. Metabolic scaling is the product of life history optimization. Science 377, 834–839 (2022).
Google Scholar
Savage, V. M., Gilloly, J. F., Brown, J. H. & Charnov, E. L. Effects of body size and temperature on population growth. Am. Nat. 163, 429–441 (2004).
Google Scholar
Bernhardt, J. R., Sunday, J. M. & O’Connor, M. I. Metabolic theory and the temperature–size rule explain the temperature dependence of population carrying capacity. Am. Nat. 192, 687–697 (2018).
Google Scholar
Damuth, J. Population density and body size in mammals. Nature 290, 699–700 (1981).
Google Scholar
Schuster, L., Cameron, H., White, C. R. & Marshall, D. J. Metabolism drives demography in an experimental field test. Proc. Natl Acad. Sci. USA 118, e2104942118 (2021).
Google Scholar
Amarasekare, P. & Coutinho, R. M. The intrinsic growth rate as a predictor of population viability under climate warming. J. Anim. Ecol. 82, 1240–1253 (2013).
Google Scholar
Amarasekare, P. & Savage, V. A framework for elucidating the temperature dependence of fitness. Am. Nat. 179, 178–191 (2012).
Google Scholar
Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).
Google Scholar
Comeault, A. A. & Matute, D. R. Temperature-dependent competitive outcomes between the fruit flies Drosophila santomea and Drosophila yakuba. Am. Nat. 197, 312–323 (2021).
Google Scholar
Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).
Google Scholar
Davis, A. J., Lawton, J. H., Shorrocks, B. & Jenkinson, L. S. Individualistic species responses invalidate simple physiological models of community dynamics under global environmental change. J. Anim. Ecol. 67, 600–612 (1998).
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).
Google Scholar
Janča, M. & Gvoždík, L. Costly neighbours: heterospecific competitive interactions increase metabolic rates in dominant species. Sci. Rep. 7, 5177 (2017).
Google Scholar
Pettersen, A. K., Hall, M. D., White, C. R. & Marshall, D. J. Metabolic rate, context-dependent selection, and the competition–colonization trade-off. Evol. Lett. 4, 333–344 (2020).
Google Scholar
DeLong, J. P., Hanley, T. C. & Vasseur, D. A. Competition and the density dependence of metabolic rates. J. Anim. Ecol. 83, 51–58 (2014).
Google Scholar
Reid, D., Armstrong, J. D. & Metcalfe, N. B. Estimated standard metabolic rate interacts with territory quality and density to determine the growth rates of juvenile Atlantic salmon. Funct. Ecol. 25, 1360–1367 (2011).
Google Scholar
Ayala, F. J. in Essays in Evolution and Genetics in Honor of Theodosius Dobzhansky (eds Hecht, M. K. & Steere, W. C.) 121–158 (Springer, 1970).
Atkinson, W. D. & Shorrocks, B. Aggregation of larval Diptera over discrete and ephemeral breeding sites: the implications for coexistence. Am. Nat. 124, 336–351 (1984).
Google Scholar
McKenzie, J. A. & McKechnie, S. W. A comparative study of resource utilization in natural populations of Drosophila melanogaster and D. simulans. Oecologia 40, 299–309 (1979).
Google Scholar
Alton, L. A. et al. Developmental nutrition modulates metabolic responses to projected climate change. Funct. Ecol. 34, 2488–2502 (2020).
Google Scholar
Mitchell, K. A. & Hoffmann, A. A. Thermal ramping rate influences evolutionary potential and species differences for upper thermal limits in Drosophila. Funct. Ecol. 24, 694–700 (2010).
Google Scholar
Overgaard, J., Kristensen, T. N., Mitchell, K. A. & Hoffmann, A. A. Thermal tolerance in widespread and tropical Drosophila species: does phenotypic plasticity increase with latitude? Am. Nat. 178, S80–S96 (2011).
Google Scholar
Kellermann, V. et al. Comparing thermal performance curves across traits: how consistent are they? J. Exp. Biol. 222, jeb193433 (2019).
Google Scholar
Terblanche, J. S., Clusella-Trullas, S. & Chown, S. L. Phenotypic plasticity of gas exchange pattern and water loss in Scarabaeus spretus (Coleoptera: Scarabaeidae): deconstructing the basis for metabolic rate variation. J. Exp. Biol. 213, 2940–2949 (2010).
Google Scholar
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).
Google Scholar
Bos, M., Burnet, B., Farrow, R. & Woods, R. A. Mutual facilitation between larvae of the sibling species Drosophila melanogaster and D. simulans. Evolution 31, 824–828 (1977).
Google Scholar
Arthur, W. On the complexity of a simple environment: competition, resource partitioning and facilitation in a two-species Drosophila system. Phil. Trans. R. Soc. B 313, 471–508 (1986).
Hodge, S., Mitchell, P. & Arthur, W. Factors affecting the occurrence of facilitative effects in interspecific interactions: an experiment using two species of Drosophila and Aspergillus niger. Oikos 87, 166–174 (1999).
Google Scholar
Bath, E., Morimoto, J. & Wigby, S. The developmental environment modulates mating-induced aggression and fighting success in adult female Drosophila. Funct. Ecol. 32, 2542–2552 (2018).
Google Scholar
Thibert, J., Farine, J. P., Cortot, J. & Ferveur, J. F. Drosophila food-associated pheromones: effect of experience, genotype and antibiotics on larval behavior. PLoS ONE 11, e0151451 (2016).
Google Scholar
Chown, S. L. et al. Scaling of insect metabolic rate is inconsistent with the nutrient supply network model. Funct. Ecol. 21, 282–290 (2007).
Google Scholar
Becker, R. A., Wilks, A. R. & Brownrigg, R. mapdata: extra map databases. R version 2.3.0 https://CRAN.R-project.org/package=mapdata (2018).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Google Scholar
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar
Bolker, B. & R Development Core Team bbmle: tools for general maximum likelihood estimation. R version 1.0.25 https://CRAN.R-project.org/package=bbmle (2022).
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Google Scholar
Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn (Sage, 2019).
Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R version 0.4.6 https://CRAN.R-project.org/package=DHARMa (2022).
Messamah, B., Kellermann, V., Malte, H., Loeschcke, V. & Overgaard, J. Metabolic cold adaptation contributes little to the interspecific variation in metabolic rates of 65 species of Drosophilidae. J. Insect Physiol. 98, 309–316 (2017).
Google Scholar
Chamberlain, S. et al. rgbif: interface to the global biodiversity information facility API. R version 3.7.3 https://CRAN.R-project.org/package=rgbif (2022).
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).
Google Scholar
Hijmans, R. J. raster: geographic data analysis and modeling. R version 3.6-3 https://CRAN.R-project.org/package=raster (2022).
Alton, L. A. & Kellermann, V. Data for “Interspecific interactions alter the metabolic costs of climate warming”. Zenodo https://doi.org/10.5281/zenodo.7475922 (2023).
White, C. R. et al. Geographical bias in physiological data limits predictions of global change impacts. Funct. Ecol. 35, 1572–1578 (2021).
Google Scholar
Source: Ecology - nature.com
