Philippa Kaur

Pianka, E. R. Niche overlap and diffuse competition. Proc. Natl. Acad. Sci. 71, 2141–2145 (1974).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Tokeshi, M. Species Coexistence: Ecological and Evolutionary Perspectives. (Wiley-Blackwell, 2009).Grinnell, J. Geography and evolution. Ecology 5, 225–229 (1924).Article 

Google Scholar 
Roughgarden, J. Resource partitioning among competing species—A coevolutionary approach. Theor. Popul. Biol. 9, 388–424 (1976).Article 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Syme, J., Kiszka, J. J. & Parra, G. J. Dynamics of cetacean mixed-species groups: A review and conceptual framework for assessing their functional significance. Front. Mar. Sci. 8, 1–19 (2021).Article 

Google Scholar 
Stensland, E., Angerbjörn, A. & Berggren, P. Mixed species groups in mammals. Mamm. Rev. 33, 205–223 (2003).Article 

Google Scholar 
Cords, M. & Würsig, B. A Mix of Species: Associations of Heterospecifics Among Primates and Dolphins. in Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies (eds. Yamagiwa, J. & Karczmarski, L.) 409–431 (Springer, 2014). doi:https://doi.org/10.1007/978-4-431-54523-1_21.Goodale, E., Beauchamp, G. & Ruxton, G. D. Mixed-Species Groups of Animals: Behavior, Community Structure, and Conservation. (Academic Press, 2017).Krause, J. & Ruxton, G. D. Living in Groups. Oxford Series in Ecology and Evolution (Oxford University Press, 2002).Heymann, E. W. & Buchanan-Smith, H. M. The behavioural ecology of mixed-species troops of callitrichine primates. Biol. Rev. 75, 169–190 (2000).Article 
CAS 
PubMed 

Google Scholar 
Sridhar, H. & Guttal, V. Friendship across species borders: factors that facilitate and constrain heterospecific sociality. Philos. Trans. R. Soc. B Biol. Sci. 373, 1–9 (2018).Greenberg, R. Birds of many feathers: The formation and structure of mixed-species flocks of forest birds. in On the Move: How and Why Animals Travel in groups (eds. Boinski, S. & Gerber, P. A.) 521–558 (University of Chicago Press, 2000).Waser, P. M. ‘Chance’ and mixed-species associations. Behav. Ecol. Sociobiol. 15, 197–202 (1984).Article 

Google Scholar 
Whitesides, G. H. Interspecific associations of Diana monkeys, Cercopithecus diana, in Sierra Leone, West Africa: biological significance or chance?. Anim. Behav. 37, 760–776 (1989).Article 

Google Scholar 
Waser, P. M. Primate polyspecific associations: Do they occur by chance?. Anim. Behav. 30, 1–8 (1982).Article 

Google Scholar 
Alexander, R. D. The evolution of social behavior. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).Article 

Google Scholar 
Kasozi, H. & Montgomery, R. A. Variability in the estimation of ungulate group sizes complicates ecological inference. Ecol. Evol. 10, 6881–6889 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Syme, J., Kiszka, J. J. & Parra, G. J. How to define a dolphin ‘group’? Need for consistency and justification based on objective criteria. Ecol. Evol. 12, 1–18 (2022).Article 

Google Scholar 
Hutchinson, J. M. C. & Waser, P. M. Use, misuse and extensions of ‘ideal gas’ models of animal encounter. Biol. Rev. 82, 335–359 (2007).Article 
PubMed 

Google Scholar 
Gotelli, N. J. Null model analysis of species co-occurrence patterns. Ecology 81, 2606–2621 (2000).Article 

Google Scholar 
Astaras, C., Krause, S., Mattner, L., Rehse, C. & Waltert, M. Associations between the drill (Mandrillus leucophaeus) and sympatric monkeys in Korup National Park. Cameroon. Am. J. Primatol. 73, 127–134 (2011).Article 
PubMed 

Google Scholar 
Mammides, C., Chen, J., Goodale, U. M., Kotagama, S. W. & Goodale, E. Measurement of species associations in mixed-species bird flocks across environmental and human disturbance gradients. Ecosphere 9, 1–14 (2018).Article 

Google Scholar 
Ovaskainen, O., Abrego, N., Halme, P. & Dunson, D. Using latent variable models to identify large networks of species-to-species associations at different spatial scales. Methods Ecol. Evol. 7, 549–555 (2016).Article 

Google Scholar 
Pollock, L. J. et al. Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol. Evol. 5, 397–406 (2014).Article 

Google Scholar 
Warton, D. I. et al. So Many variables: Joint modeling in community ecology. Trends Ecol. Evol. 30, 766–779 (2015).Article 
PubMed 

Google Scholar 
Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).Article 
PubMed 

Google Scholar 
Ovaskainen, O. & Abrego, N. Joint Species Distribution Modelling. (Cambridge University Press, 2020). https://doi.org/10.1017/9781108591720.Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063 (2020).Article 
PubMed 

Google Scholar 
Haak, C. R., Hui, F. K., Cowles, G. W. & Danylchuk, A. J. Positive interspecific associations consistent with social information use shape juvenile fish assemblages. Ecology 101, 1–16 (2020).Article 

Google Scholar 
Bastianelli, G., Wintle, B. A., Martin, E. H., Seoane, J. & Laiolo, P. Species partitioning in a temperate mountain chain: Segregation by habitat vs. interspecific competition. Ecol. Evol. 7, 2685–2696 (2017).Aspin, T. & House, A. Alpha and beta diversity and species co-occurrence patterns in headwaters supporting rare intermittent-stream specialists. Freshw. Biol. n/a, (2022).Astarloa, A. et al. Identifying main interactions in marine predator-prey networks of the Bay of Biscay. ICES J. Mar. Sci. 76, 2247–2259 (2019).Article 

Google Scholar 
Parra, G. J. Resource partitioning in sympatric delphinids: space use and habitat preferences of Australian snubfin and Indo-Pacific humpback dolphins. J. Anim. Ecol. 75, 862–874 (2006).Article 
PubMed 

Google Scholar 
Parra, G. J., Wojtkowiak, Z., Peters, K. J. & Cagnazzi, D. Isotopic niche overlap between sympatric Australian snubfin and humpback dolphins. Ecol. Evol. 12, 1–11 (2022).Article 

Google Scholar 
Kiszka, J. J. et al. Ecological niche segregation within a community of sympatric dolphins around a tropical island. Mar. Ecol. Prog. Ser. 433, 273–288 (2011).Article 
ADS 

Google Scholar 
Bearzi, M. Dolphin sympatric ecology. Mar. Biol. Res. 1, 165–175 (2005).Article 

Google Scholar 
Zaeschmar, J. R. et al. Occurrence of false killer whales (Pseudorca crassidens) and their association with common bottlenose dolphins (Tursiops truncatus) off northeastern New Zealand. Mar. Mammal Sci. 30, 594–608 (2014).Article 

Google Scholar 
Elliser, C. R. & Herzing, D. L. Long-term interspecies association patterns of Atlantic bottlenose dolphins, Tursiops truncatus, and Atlantic spotted dolphins, Stenella frontalis, in the Bahamas. Mar. Mammal Sci. 32, 38–56 (2016).Article 

Google Scholar 
Kiszka, J. J., Perrin, W. F., Pusineri, C. & Ridoux, V. What drives island-associated tropical dolphins to form mixed-species associations in the southwest Indian Ocean?. J. Mammal. 92, 1105–1111 (2011).Article 

Google Scholar 
Brown, A. M., Bejder, L., Cagnazzi, D., Parra, G. J. & Allen, S. J. The north west cape, Western Australia: A potential hotspot for Indo-Pacific humpback dolphins Sousa chinensis?. Pacific Conserv. Biol. 18, 240–246 (2012).Article 

Google Scholar 
Allen, S. J., Cagnazzi, D., Hodgson, A. J., Loneragan, N. R. & Bejder, L. Tropical inshore dolphins of north-western Australia: Unknown populations in a rapidly changing region. Pacific Conserv. Biol. 18, 56–63 (2012).Article 

Google Scholar 
Palmer, C., Parra, G. J., Rogers, T. & Woinarski, J. Collation and review of sightings and distribution of three coastal dolphin species in waters of the Northern Territory. Australia. Pacific Conserv. Biol. 20, 116–125 (2014).Article 

Google Scholar 
Corkeron, P. J. Aspects of the Behavioral Ecology of Inshore Dolphins Tursiops truncatus and Sousa chinensis in Moreton Bay, Australia. in The Bottlenose Dolphin (eds. Leatherwood, S. & Reeves, R.) 285–293 (Elsevier, 1990). https://doi.org/10.1016/B978-0-12-440280-5.50018-4.Haughey, R. et al. Distribution and habitat preferences of Indo-Pacific Bottlenose Dolphins (Tursiops aduncus) inhabiting coastal waters with mixed levels of protection. Front. Mar. Sci. 8, 1–20 (2021).Article 

Google Scholar 
Hanf, D., Hodgson, A. J., Kobryn, H., Bejder, L. & Smith, J. N. Dolphin distribution and habitat suitability in North Western Australia: Applications and Implications of a Broad-Scale, Non-targeted Dataset. Front. Mar. Sci. 8, 1–18 (2022).Article 

Google Scholar 
Hunt, T. N., Allen, S. J., Bejder, L. & Parra, G. J. Identifying priority habitat for conservation and management of Australian humpback dolphins within a marine protected area. Sci. Rep. 10, 1–14 (2020).Article 

Google Scholar 
Hunt, T. N. Demography, habitat use and social structure of Australian humpback dolphins (Sousa sahulensis) around the North West Cape, Western Australia: Implications for conservation and management. PhD Thesis, Flinders University, Adelaide, Australia. (Flinders University, 2018).Cassata, L. & Collins, L. B. Coral reef communities, habitats, and substrates in and near sanctuary zones of Ningaloo marine park. J. Coast. Res. 241, 139–151 (2008).Article 

Google Scholar 
CALM MPRA. Management plan for the Ningaloo Marine Park and Muiron Islands Marine Management Area 2005–2015. (2005).Hunt, T. N. et al. Demographic characteristics of Australian humpback dolphins reveal important habitat toward the southwestern limit of their range. Endanger. Species Res. 32, 71–88 (2017).Article 

Google Scholar 
Mann, J. Behavioral sampling methods for cetaceans: A review and critique. Mar. Mammal Sci. 15, 102–122 (1999).Article 

Google Scholar 
Python Software Foundation. Python Language Reference, version 3.8.0. at https://www.python.org/ (2016).QGIS Development Team. QGIS Geographic Information System, version 3.8.3 Zanzibar. at http://qgis.osgeo.org (2019).Zanardo, N., Parra, G., Passadore, C. & Möller, L. Ensemble modelling of southern Australian bottlenose dolphin Tursiops sp. distribution reveals important habitats and their potential ecological function. Mar. Ecol. Prog. Ser. 569, 253–266 (2017).Hanberry, B. B. Finer grain size increases effects of error and changes influence of environmental predictors on species distribution models. Ecol. Inform. 15, 8–13 (2013).Article 

Google Scholar 
Gottschalk, T. K., Aue, B., Hotes, S. & Ekschmitt, K. Influence of grain size on species–habitat models. Ecol. Modell. 222, 3403–3412 (2011).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Passadore, C., Möller, L. M., Diaz-Aguirre, F. & Parra, G. J. Modelling dolphin distribution to inform future spatial conservation decisions in a marine protected area. Sci. Rep. 8, 1–14 (2018).Article 
CAS 

Google Scholar 
Parra, G. J., Schick, R. & Corkeron, P. J. Spatial distribution and environmental correlates of Australian snubfin and Indo-Pacific humpback dolphins. Ecography (Cop.) 29, 396–406 (2006).Article 

Google Scholar 
Conrad, O. et al. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 8, 1991–2007 (2015).R Core Team. R version 3.6.1. at https://www.r-project.org/ (2019).RStudio Team. RStudio: Integrated Develpment for R. at http://rstudio.com/ (2019).Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).Article 

Google Scholar 
Tikhonov, G. et al. Joint species distribution modelling with the r-package Hmsc. Methods Ecol. Evol. 11, 442–447 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).Article 
MATH 

Google Scholar 
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133, 225–245 (2000).Article 

Google Scholar 
Tjur, T. Coefficients of determination in logistic regression models—A new proposal: The coefficient of discrimination. Am. Stat. 63, 366–372 (2009).Article 
MathSciNet 
MATH 

Google Scholar 
Syme, J. The behavioural ecology of mixed-species groups of delphinids. PhD Thesis, Flinders University, Adelaide, Australia. (Flinders University, 2023).Wang, J. Y. Bottlenose Dolphin, Tursiops aduncus, Indo-Pacific Bottlenose Dolphin. in Encyclopedia of Marine Mammals (eds. Würsig, B., Thewissen, J. G. M. & Kovacs, K. M.) 125–130 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00073-X.Parra, G. J. & Jefferson, T. A. Humpback Dolphins. in Encyclopedia of Marine Mammals (eds. Würsig, B., Thewissen, J. G. M. & Kovacs, K. M.) 483–489 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00153-9.Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Spatial and temporal avoidance of risk within a large carnivore guild. Ecol. Evol. 7, 189–199 (2017).Article 
PubMed 

Google Scholar 
Browning, N. E., Cockcroft, V. G. & Worthy, G. A. J. Resource partitioning among South African delphinids. J. Exp. Mar. Bio. Ecol. 457, 15–21 (2014).Article 

Google Scholar 
Kiszka, J. J., Méndez-Fernandez, P., Heithaus, M. R. & Ridoux, V. The foraging ecology of coastal bottlenose dolphins based on stable isotope mixing models and behavioural sampling. Mar. Biol. 161, 953–961 (2014).Article 
CAS 

Google Scholar 
Saayman, G. S. & Tayler, C. K. The socioecology of humpback dolphins (Sousa sp.). in Behavior of Marine Animals Current Perspectives in Research Volume 3: Cetaceans (eds. Winn, H. E. & Olla, B. L.) 165–226 (Springer, 1979).Gowans, S. & Whitehead, H. Distribution and habitat partitioning by small odontocetes in the Gully, a submarine canyon on the Scotian Shelf. Can. J. Zool. 73, 1599–1608 (1995).Article 

Google Scholar 
Clua, E. Mixed-species feeding aggregation of dolphins, large tunas and seabirds in the Azores. Aquat. Living Resour. 14, 11–18 (2001).Article 

Google Scholar 
Quérouil, S. et al. Why do dolphins form mixed-species associations in the azores?. Ethology 114, 1183–1194 (2008).Article 

Google Scholar 
Heithaus, M. R. & Dill, L. M. Food availability and tiger shark predation risk influence bottlenose dolphin habitat use. Ecology 83, 480–491 (2002).Article 

Google Scholar  More

Rahaman, A. et al. The increasing hunger concern and current need in the development of sustainable food security in the developing countries. Trends Food Sci. Technol. 113, 423–429. https://doi.org/10.1016/j.tifs.2021.04.048 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Porter, J. R. et al. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 485–533 (Cambridge University Press, 2014).
Google Scholar 
Yan, H. et al. Crop traits enabling yield gains under more frequent extreme climatic events. Sci. Total Environ. 808, 152170. https://doi.org/10.1016/j.scitotenv.2021.152170 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Lobell, D. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change. 3, 497–501. https://doi.org/10.1038/nclimate1832 (2013).Article 
ADS 

Google Scholar 
Vermeulen, S. J. et al. Addressing uncertainty in adaptation planning for agriculture. Proc. Natl. Acad. Sci. 110, 8357–8362. https://doi.org/10.1073/pnas.1219441110 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
FAO. Climate Change and Food Security: Risks and Responses (FAO, 2015).
Google Scholar 
Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989. https://doi.org/10.1038/ncomms6989 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Ding, Z. et al. Modeling the combined impacts of deficit irrigation, rising temperature and compost application on wheat yield and water productivity. Agric. Water Manag. 244, 106626. https://doi.org/10.1016/j.agwat.2020.106626 (2021).Article 

Google Scholar 
Malhi, G. S., Kaur, M. & Kaushik, P. Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability 13, 1318 (2021).Article 
CAS 

Google Scholar 
Persson, T. & Kværnø, S. Impact of projected mid-21st century climate and soil extrapolation on simulated spring wheat grain yield in Southeastern Norway. J. Agric. Sci. 155, 361–377. https://doi.org/10.1017/S0021859616000241 (2017).Article 

Google Scholar 
Zhu, X. & Troy, T. J. Agriculturally relevant climate extremes and their trends in the world’s major growing regions. Earth’s Future 6, 656–672. https://doi.org/10.1002/2017EF000687 (2018).Article 
ADS 

Google Scholar 
Fischer, T. et al. Increase in irrigated wheat yield in north-west Mexico from 1960 to 2019: Unravelling the negative relationship to minimum temperature. Field Crops Res. 275, 108331. https://doi.org/10.1016/j.fcr.2021.108331 (2022).Article 
ADS 

Google Scholar 
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620. https://doi.org/10.1126/science.1204531 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Harkness, C. et al. Adverse weather conditions for UK wheat production under climate change. Agric. For. Meteorol. 282, 107862. https://doi.org/10.1016/j.agrformet.2019.107862 (2020).Article 
ADS 
PubMed 

Google Scholar 
Seehusen, T. & Uhlen, A. K. Analyses of yield gaps for the production of wheat and barley in Norway, potential to increase yields on existing farmland. Norwegian Institute for Bioeconomics, Report 5/166/2019 (2020).Hakala, K. et al. Sensitivity of barley varieties to weather in Finland. J. Agric. Sci. 150, 145–160. https://doi.org/10.1017/S0021859611000694 (2012).Article 
CAS 
PubMed 

Google Scholar 
Peltonen-Sainio, P., Jauhiainen, L., Hakala, K. & Ojanen, H. Climate change and prolongation of growing season, changes in regional potential for field crop production in Finland. Agric. Food Sci. 18, 171–190. https://doi.org/10.2137/145960609790059479 (2009).Article 

Google Scholar 
Fleisher, D. H. et al. A potato model intercomparison across varying climates and productivity levels. Glob. Change Biol. 23, 1258–1281. https://doi.org/10.1111/gcb.13411 (2017).Article 
ADS 

Google Scholar 
Moen, A. National Atlas of Norway: Vegetation (Hønefoss, 1999).
Google Scholar 
Bakkestuen, V., Erikstad, L. & Halvorsen, R. Step-less models for regional environmental variation in Norway. J. Biogeogr. 35, 1906–1922 (2008).Article 

Google Scholar 
Statistics-Norway. 2020. https://www.ssb.no/jord-skog-jakt-og-fiskeri/statistikker/stjord (Accessed 10 November 2020).Hanssen-Bauer, I. et al. Climate in Norway 2100 – a knowledge base for climate adaptation. Norwegian Centre for Climate Sciences, Report 1/2017 49 (2017).Blandford, D., Gaasland, I., Vårdal, E. & McIntosh, C. Greenhouse gas emissions, land use, and food supply under the paris climate agreement—Policy choice in Norway. Appl. Econ. Perspect. Policy 41, 249–264. https://doi.org/10.1093/aepp/ppy011 (2019).Article 

Google Scholar 
Rötter, R. P. et al. What would happen to barley production in Finland if global warming exceeded 4 °C? A model-based assessment. Eur. J. Agron. 35, 205–214. https://doi.org/10.1016/j.eja.2011.06.003 (2011).Article 

Google Scholar 
Ozturk, I., Sharif, B., Baby, S., Jabloun, M. & Olesen, J. E. The long-term effect of climate change on productivity of winter wheat in Denmark, scenario analysis using three crop models. J. Agric. Sci. 155, 733–750. https://doi.org/10.1017/S0021859616001040 (2017).Article 
CAS 

Google Scholar 
An, H. & Carew, R. Effect of climate change and use of improved varieties on barley and canola yield in Manitoba. Can. J. Plant Sci. 95, 127–139. https://doi.org/10.1139/CJPS-2014-221 (2014).Article 

Google Scholar 
Zhou, Z., Plauborg, F., Kristensen, K. & Andersen, M. Dry matter production, radiation interception and radiation use efficiency of potato in response to temperature and nitrogen application regimes. Agric. For. Meteorol. 232, 595–605. https://doi.org/10.1016/j.agrformet.2016.10.017 (2017).Article 
ADS 

Google Scholar 
Jensen, K. J. S. et al. Yield and development of winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.) in field experiments with variable weather and drainage conditions. Eur. J. Agron. 122, 126075. https://doi.org/10.1016/j.eja.2020.126075 (2021).Article 
CAS 

Google Scholar 
Lobell, D. B., Cahill, K. N. & Field, C. B. Historical effects of temperature and precipitation on California crop yields. Clim. Change 81, 187–203. https://doi.org/10.1007/s10584-006-9141-3 (2007).Article 
ADS 

Google Scholar 
Skjelvag, A. O. Climatic conditions for crop production in Nordic countries. Agric. Food Sci. Finland 7(2), 149–160 (1998).Article 

Google Scholar 
Norsk-Klimaservicesenter. https://seklima.met.no/ (2020).Erikstad, L. & Bakkestuen, V. Calculating cumulative effects in GIS using a stepless multivariate model. MethodsX 8, 101407. https://doi.org/10.1016/j.mex.2021.101407 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aune-Lundberg, L. & Strand, G.-H. The content and accuracy of the CORINE land cover dataset for Norway. Int. J. Appl. Earth Observ. Geoinform. 96, 102266. https://doi.org/10.1016/j.jag.2020.102266 (2021).Article 

Google Scholar 
QGIS Geographic Information System (QGIS Association, 2020).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
Lobell, D. B. & Field, C. B. Global scale climate–crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 014002. https://doi.org/10.1088/1748-9326/2/1/014002 (2007).Article 
ADS 

Google Scholar 
Shumway, R. H. & Stoffer, D. S. Time Series Analysis and its Applications Vol. 560 (Springer, 2016).MATH 

Google Scholar 
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).Article 

Google Scholar 
Lüdecke, D., Ben Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R Package for Assessment, Comparison and Testing of Statistical Models (2021).Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.3.3.0 (2020).Friedman, J. H., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(22), 2010. https://doi.org/10.18637/jss.v033.i01 (2010).Article 

Google Scholar 
Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B-Methodol. 58, 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x (1996).Article 
MathSciNet 
MATH 

Google Scholar 
Hastie, T., Tibshirani, R. & Friendman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009).Book 
MATH 

Google Scholar 
Meinshausen, N. & Bühlmann, P. Stability selection. J. Roy. Stat. Soc. B 72, 417–473. https://doi.org/10.2307/40802220 (2010).Article 
MathSciNet 
MATH 

Google Scholar 
Efron, B. & Stein, C. The jackknife estimate of variance. Ann. Stat. 9, 586–596. https://doi.org/10.1214/aos/1176345462 (1981).Article 
MathSciNet 
MATH 

Google Scholar 
Milborrow, S. plotmo: Plot a Model’s Residuals, Response, and Partial Dependence Plots. R package version 3.5.7 (2020).Liu, H. Xu, X. & Li, J.J. HDCI: High Dimensional Confidence Interval Based on Lasso and Bootstrap. R package version 1.0–2 (2017).. Seehusen, T. & Uhlen, A. K. Analyses of yield gaps for the production of wheat and barley in Norway, potential to increase yields on existing farmland. Norwegian Institute for Bioeconomics, Report 5/166/2019. http://hdl.handle.net/11250/2637490 (2019).Stabbetorp, H. Dyrkingsomfang og avling i kornproduksjonen. Norsk institutt for bioøkonomi, Report 4 (1) (2017).Ebrahimi, E. et al. Assessing the impact of climate change on crop management in winter wheat—A case study for Eastern Austria. J. Agric. Sci. 154, 1153–1170. https://doi.org/10.1017/S0021859616000083 (2016).Article 

Google Scholar 
Kristensen, K., Schelde, K. & Olesen, J. Winter wheat yield response to climate variability in Denmark. J. Agric. Sci. 148, 1–15. https://doi.org/10.1017/S0021859610000675 (2010).Article 

Google Scholar 
Thaler, S., Eitzinger, J., Trnka, M. & Dubrovsky, M. Impacts of climate change and alternative adaptation options on winter wheat yield and water productivity in a dry climate in Central Europe. J. Agric. Sci. 150, 537–555. https://doi.org/10.1017/S0021859612000093 (2012).Article 
CAS 

Google Scholar 
Ortiz, R. et al. Climate change, can wheat beat the heat?. Agr. Ecosyst. Environ. 126, 46–58. https://doi.org/10.1016/j.agee.2008.01.019 (2008).Article 

Google Scholar 
Semenov, M., Stratonovitch, P., Alghabari, F. & Gooding, M. Adapting wheat in Europe for climate change. J. Cereal Sci. 59, 245–256. https://doi.org/10.1016/j.jcs.2014.01.006 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Roberts, M. J., Braun, N. O., Sinclair, T. R., Lobell, D. B. & Schlenker, W. Comparing and combining process-based crop models and statistical models with some implications for climate change. Environ. Res. Lett. 12, 095010. https://doi.org/10.1088/1748-9326/aa7f33 (2017).Article 
ADS 

Google Scholar 
Zhu, X., Troy, T. & Devineni, N. Stochastically modeling the projected impacts of climate change on rainfed and irrigated US crop yields. Environ. Res. Lett. 14, 074021. https://doi.org/10.1088/1748-9326/ab25a1 (2019).Article 
ADS 

Google Scholar 
Lobell, D. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001. https://doi.org/10.1088/1748-9326/aa518a (2017).Article 
ADS 
CAS 

Google Scholar 
Flø, S. et al. Rom for bruk av Norsk korn. Felleskjøpet, Report 49 (2017).Lillemo, M., Reitan, L. & Bjornstad, A. Increasing impact of plant breeding on barley yields in central Norway from 1946 to 2008. Plant Breeding 129, 484–490. https://doi.org/10.1111/j.1439-0523.2009.01710.x (2010).Article 

Google Scholar 
Wonneberger, R., Ficke, A. & Lillemo, M. Mapping of quantitative trait loci associated with resistance to net form net blotch (Pyrenophora teres f. teres) in a doubled haploid Norwegian barley population. PLoS One 12, e0175773. https://doi.org/10.1371/journal.pone.0175773 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moore, F. C. & Lobell, D. B. The fingerprint of climate trends on European crop yields. Proc. Natl. Acad. Sci. 112, 2670–2675. https://doi.org/10.1073/pnas.1409606112 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin, P. et al. Recent warming across the North Atlantic region may be contributing to an expansion in barley cultivation. Clim. Change 145, 351–365. https://doi.org/10.1007/s10584-017-2093-y (2017).Article 
ADS 

Google Scholar 
Martin, P., Wishart, J., Dalmannsdottir, S., Halland, H. & Thomsen, a. M. Recent warming and the thermal requirement of barley in coastal Norway. Norwegian Institute for Bioeconomics, Report T2.4.3 ii (2018).Cattivelli, L., Ceccarelli, S., Romagosa, I. & Stanca, M. Abiotic stresses in Barley: Problems and solutions. In Barley: Production, Improvement, and Uses Vol. 4 (ed. Ullrich, S.) 282–306 (Blackwell UP, 2011).
Google Scholar 
Hura, T. Wheat and barley acclimatization to abiotic and biotic stress. Int. J. Mol. Sci. 21, 7423. https://doi.org/10.3390/ijms21197423 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kolberg, D., Persson, T., Mangerud, K. & Riley, H. Impact of projected climate change on workability, attainable yield, profitability and farm mechanization in Norwegian spring cereals. Soil Till. Res. 185, 122–138. https://doi.org/10.1016/j.still.2018.09.002 (2019).Article 

Google Scholar 
Olesen, J. E. et al. Impacts and adaptation of European crop production systems to climate change. Eur. J. Agron. 34, 96–112. https://doi.org/10.1016/j.eja.2010.11.003 (2011).Article 

Google Scholar 
Gammans, M., Mérel, P. & Ortiz-Bobea, A. Negative impacts of climate change on cereal yields: Statistical evidence from France. Environ. Res. Lett. 12, 054007. https://doi.org/10.1088/1748-9326/aa6b0c (2017).Article 
ADS 
CAS 

Google Scholar 
Ahmed, I., Harrison, M., Meinke, H. & Zhou, M. Barley phenology: physiological and molecular mechanisms for heading date and modelling of genotype-environment- management interactions. Plant Growth InTech 8, 175–202. https://doi.org/10.5772/64827 (2016).Article 
CAS 

Google Scholar 
Hossain, A., da Silva, J. A. T., Lozovskaya, M. V. & Zvolinsky, V. P. High temperature combined with drought affect rainfed spring wheat and barley in South-Eastern Russia. Saudi J. Biol. Sci. 19, 473–487. https://doi.org/10.1016/j.sjbs.2012.07.005 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Møllerhagen, P. Norsk potetproduksjon 2011. Bioforsk, Report 7(1) (2012).Hermansen, A., Lu, D. & Forbes, G. Potato production in China and Norway, similarities, differences and future challenges. Potato Res. 55, 197–203. https://doi.org/10.1007/s11540-012-9224-7 (2012).Article 

Google Scholar 
Hermansen, A., Nærstad, R., Le, V. & Nordskog, B. In Proceedings of the Eleventh EuroBlight Workshop (The Norwegian Institute for Agricultural and Environmental Research, Hamar, 2018).Raymundo, R. et al. Climate change impact on global potato production. Eur. J. Agron. 100, 87–98. https://doi.org/10.1016/j.eja.2017.11.008 (2018).Article 

Google Scholar 
Rabia, A., Yacout, D., Shahin, S., Mohamed, A. & Abdelaty, E. Towards sustainable production of potato under climate change conditions. Curr. J. Appl. Sci. Technol. 18, 200–207. https://doi.org/10.14456/cast.2018.15 (2018).Article 

Google Scholar 
Haverkort, A. J., Franke, A. C., Engelbrecht, F. A. & Steyn, J. M. Climate change and potato production in contrasting South African agro-ecosystems. Potato Res. 56, 67–84. https://doi.org/10.1007/s11540-013-9230-4 (2013).Article 

Google Scholar 
Martinelli, F. et al. Advanced methods of plant disease detection A review. Agron. Sustain. Dev. 35, 1–25. https://doi.org/10.1007/s13593-014-0246-1 (2015).Article 

Google Scholar 
Borus, D. Impacts of Climate Change on the Potato (Solanum Tuberosum L.) Productivity in Tasmania, Australia and Kenya (University of Tasmania, 2017).
Google Scholar 
Fageria, N., Baligar, V. & Jones, C. Growth and Mineral Nutrition of Field Crops Vol. 5, 586 (CRC Press, 2010).Book 

Google Scholar 
Fleisher, D. H. et al. Effects of elevated CO2 and cyclic drought on potato under varying radiation regimes. Agric. For. Meteorol. 171, 270–280. https://doi.org/10.1016/j.agrformet.2012.12.011 (2013).Article 
ADS 

Google Scholar 
Haverkort, A. J. & Struik, P. C. Yield levels of potato crops: Recent achievements and future prospects. Field Crop Res. 182, 76–85. https://doi.org/10.1016/j.fcr.2015.06.002 (2015).Article 

Google Scholar 
Van Oort, P. A. J., Timmermans, B. G. H., Meinke, H. & Van Ittersum, M. K. Key weather extremes affecting potato production in the Netherlands. Eur. J. Agron. 37, 11–22. https://doi.org/10.1016/j.eja.2011.09.002 (2012).Article 

Google Scholar 
Najafi, E., Devineni, N., Khanbilvardi, R. & Kogan, F. Understanding the changes in global crop yields through changes in climate and technology. Earth’s Future 6, 410–427. https://doi.org/10.1002/2017EF000690 (2018).Article 
ADS 
CAS 

Google Scholar 
Pulatov, B., Anna Maria, J. N., Karin, H. & Maj-Lena, L. Modeling climate change impact on potato crop phenology, and risk of frost damage and heat stress in northern Europe. Agric. For. Meteorol. 214, 281–292. https://doi.org/10.1016/j.agrformet.2015.08.266 (2015).Article 
ADS 

Google Scholar  More

Mounier, A. et al. Deciphering African late middle Pleistocene hominin diversity and the origin of our species. Nat. Commun. https://doi.org/10.1038/s41467-019-11213-w (2019).Schlebusch, C. M. et al. Southern African ancient genomes estimate modern human divergence to 350,000 to 260,000 years ago. Science 358, 652–655 (2017).CAS 
PubMed 

Google Scholar 
Lombard, M. et al. Ancient human DNA: how sequencing the genome of a boy from Ballito Bay changed human history. S Afr. J. Sci. 114, 1–3 (2018).
Google Scholar 
Grün, R. et al. Direct dating of Florisbad hominid. Nature 382, 500–501 (1996).PubMed 

Google Scholar 
Grine, F. et al. The Middle Stone Age human fossil record from Klasies River Main Site. J. Hum. Evol. 103, 53–78 (2017).PubMed 

Google Scholar 
Henshilwood, C. S. et al. A 100,000-year-old ochre-processing workshop at Blombos Cave, South Africa. Science 33, 219–222 (2011).
Google Scholar 
Lombard, M. et al. Four-field co-evolutionary model for human cognition: variation in the Middle Stone Age/Middle Palaeolithic. J. Archeol. Method Theory 28, 142–177 (2021).
Google Scholar 
Wadley, L. What stimulated rapid, cumulative innovation after 100,000 years ago? J. Archeol. Method Theory 28, 120–141 (2021).
Google Scholar 
Tylen, K. et al. The evolution of early symbolic behavior in Homo sapiens. Proc. Natl Acad. Sci. USA 117, 4578–4584 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rifkin, R. F. et al. Ancient oncogenesis, infection, and human evolution. Evol. Appl. https://doi.org/10.1111/eva.12497 (2017).Pittman, K. J. et al. The legacy of past pandemics: common human mutations that protect against infectious disease. PLoS Pathog. 12, e1005680 (2016).PubMed 
PubMed Central 

Google Scholar 
Andam, C. P. et al. Microbial genomics of ancient plagues and outbreaks. Trends Microbiol. 24, 978–990 (2016).CAS 
PubMed 

Google Scholar 
Houldcroft, C. J. et al. Migrating microbes: what pathogens can tell us about population movements and human evolution. Ann. Hum. Biol. 44, 397–407 (2017).PubMed 

Google Scholar 
Reyes-Centeno, H. et al. Testing modern human out-of-Africa dispersal models using dental nonmetric data. Curr. Anthropol. 58, 406–417 (2017).
Google Scholar 
Pimenoff, V. N. et al. The role of aDNA in understanding the co-evolutionary patterns of human sexually transmitted infections. Genes https://doi.org/10.3390/genes9070317 (2018).Ferwerda, B. et al. Functional consequences of Toll-like Receptor 4 polymorphisms. Mol. Med. 14, 346–352 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tanabe, K. et al. Plasmodium falciparum accompanied the human expansion out of Africa. Curr. Biol. 20, 1283–1289 (2010).CAS 
PubMed 

Google Scholar 
Linz, B. et al. An African origin for the intimate association between humans and Helicobacter pylori. Nature 445, 915–918 (2007).PubMed 
PubMed Central 

Google Scholar 
Nédélec, Y. et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167, 657–669 (2016).PubMed 

Google Scholar 
Owers, K. A. et al. Adaptation to infectious disease exposure in indigenous Southern African populations. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2017.0226 (2017).Schlebusch, C. M. et al. Khoe-San genomes reveal unique variation and confirm the deepest population divergence in Homo sapiens. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msaa140 (2020).Kessler, S. E. et al. Selection to outsmart the germs: the evolution of disease recognition and social cognition. J. Hum. Evol. 108, 92–109 (2017).PubMed 

Google Scholar 
Thornhill, R. et al. The parasite-stress theory of sociality, the behavioral immune system, and human social and cognitive uniqueness. Evol. Behav. Sci. 8, 257–264 (2014).
Google Scholar 
Gurven, M. et al. Longevity among hunter‐gatherers: a cross‐cultural examination. Popul Dev. Rev. 33, 321–365 (2007).
Google Scholar 
Pfeiffer, S. et al. The people behind the samples: biographical features of past hunter-gatherers from KwaZulu-Natal who yielded aDNA. Int. J. Paleopathol. 24, 158–164 (2019).PubMed 

Google Scholar 
Schriefer, M. E. et al. Identification of a novel rickettsial infection in a patient diagnosed with murine typhus. J. Clin. Microbiol. 32, 949–954 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pages, F. et al. The past and present threat of vector-borne diseases in deployed troops. Clin. Microbiol. Infect. 16, 209–224 (2010).CAS 
PubMed 

Google Scholar 
Wood, D. E. et al. Improved metagenomic analysis with Kraken 2. Genome Biol. https://doi.org/10.1186/s13059-019-1891-0 (2019).Jónsson, H. et al. mapDamage 2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).PubMed 
PubMed Central 

Google Scholar 
Gillespie, J. J. et al. Genomic diversification in strains of Rickettsia felis isolated from different arthropods. Genome Biol. Evol. 7, 35–56 (2015).CAS 

Google Scholar 
Cardwell, M. M. et al. The Sca2 autotransporter protein from Rickettsia conorii is sufficient to mediate adherence to and invasion of cultured mammalian cells. Infect. Immun. 77, 5272–5280 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kay, G. L. et al. Recovery of a Medieval Brucella melitensis genome using shotgun metagenomics. mBio. https://doi.org/10.1128/mBio.01337-14 (2014).Schuenemann, V. J. et al. Genome-wide comparison of medieval and modern Mycobacterium leprae. Science 341, 179–183 (2013).CAS 
PubMed 

Google Scholar 
Müller, R. et al. Genotyping of ancient Mycobacterium tuberculosis strains reveals historic genetic diversity. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2013.3236 (2014).Rasmussen, S. et al. Early divergent strains of Yersinia pestis in Eurasia 5,000 years ago. Cell 163, 571–582 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vågene, A. J. et al. Salmonella enterica genomes from victims of a major sixteenth-century epidemic in Mexico. Nat. Ecol. Evol. 2, 520–528 (2018).PubMed 

Google Scholar 
Guellil, M. et al. Genomic blueprint of a relapsing fever pathogen in 15th century Scandinavia. Proc. Natl Acad. Sci. USA 115, 10422–10427 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Patterson Ross, Z. et al. The paradox of HBV evolution as revealed from a 16th century mummy. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1006750 (2015).Marciniak, S. et al. Plasmodium falciparum malaria in 1st-2nd century CE southern Italy. Curr. Biol. 26, 1220–1222 (2016).
Google Scholar 
Margaryan, A. et al. Ancient pathogen DNA in human teeth and petrous bones. Ecol. Evol. https://doi.org/10.1002/ece3.3924 (2018).Zhou, Z. et al. Pan-genome analysis of ancient and modern Salmonella enterica demonstrates genomic stability of the invasive Para C lineage for millennia. Curr. Biol. 28, 2420–2428 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, K. M. Update on bone health in paediatric chronic disease. Endocrinol. Metab. Clin. North Am. https://doi.org/10.1016/j.ecl.2016.01.009 (2016).Latham, K.E. et al. DNA recovery and analysis from skeletal material in modern forensic contexts. Forensic Sci. Res. https://doi.org/10.1080/20961790.2018.1515594 (2019).Briggs, H. M. et al. Diagnosis and management of tickborne Rickettsial diseases: rocky mountain spotted fever and other spotted fever group Rickettsioses, Ehrlichioses, and Anaplasmosis – United States. MMWR Recomm. Rep. 65, 1–44 (2016).
Google Scholar 
Jonker, F. A. M. et al. Anaemia, iron deficiency and susceptibility to infection in children in sub‐Saharan Africa, guideline dilemmas. Br. J. Haematol. https://doi.org/10.1111/bjh.14593. (2017).Key, F. M. et al. Emergence of human-adapted Salmonella enterica is linked to the Neolithization process. Nat. Ecol. Evol. 4, 324–333 (2020).
Google Scholar 
Angelakis, E. et al. Rickettsia felis: the complex journey of an emergent human pathogen. Trends Parasitol. https://doi.org/10.1016/j.pt.2016.04.009 (2016).Legendre, K. P. et al. Rickettsia felis: A review of transmission mechanisms of an emerging pathogen. Trop. Med. Infect. Dis. https://doi.org/10.3390/tropicalmed2040064 (2017).Mediannikov, O. et al. Common epidemiology of Rickettsia felis infection and malaria, Africa. Emerg. Infect. Dis. https://doi.org/10.3201/eid1911.130361 (2014).Gonçalves, B. P. et al. Examining the human infectious reservoir for Plasmodium falciparum malaria in areas of differing transmission intensity. Nat. Commun. https://doi.org/10.1038/s41467-017-01270-4 (2017).Snowden, J. et al. Rickettsia rickettsiae (Rocky Mountain Spotted Fever). StatPearls Publishing, available from https://www.ncbi.nlm.nih.gov/books/NBK430881/ (2017).Azad, A. A. Pathogenic Rickettsiae as bioterrorism agents. Ann. N. Y Acad. Sci. 990, 734–738 (2007).
Google Scholar 
Oliveira, R. P. et al. Rickettsia felis in Ctenocephalides spp. fleas, Brazil. Emerg. Infect. Dis. https://doi.org/10.3201/eid0803.010301 (2002).Parola, P. et al. Rickettsia felis: The next mosquito-borne outbreak? Lancet Infect. Dis. https://doi.org/10.1016/S1473-3099(16)30331-0 (2016).Wadley, L. Legacies from the Later Stone Age. S Afr Archaeol Bull. Goodwin Ser. 6, 42–53 (1989).
Google Scholar 
Henn, B. M. et al. The great human expansion. Proc. Natl Acad. Sci. USA 109, 17758–17764 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang, D. Y. et al. Technical note: improved DNA extraction from ancient bones using silica-based spin columns. Am. J. Phys. Anthropol. 105, 539–543 (1998).CAS 
PubMed 

Google Scholar 
Malmström, E. M. et al. More on contamination: the use of asymmetric molecular behavior to identify authentic ancient human DNA. Mol. Biol. Evol. 24, 998–1004 (2007).PubMed 

Google Scholar 
Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Meyer, M. et al. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harbor Protoc. https://doi.org/10.1101/pdb.prot5448 (2010).Li, H. et al. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).PubMed 
PubMed Central 

Google Scholar 
Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl Acad. Sci. USA 104, 14616–14621 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Borry, M. et al. PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly. PeerJ. https://doi.org/10.7717/peerj.11845 (2021).Schubert, M. et al. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res. https://doi.org/10.1186/s13104-016-1900-2 (2016).Langmead, B. et al. Fast gapped-read alignment with Bowtie 2. Nat. Methods. https://doi.org/10.1038/nmeth.1923 (2012).Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. https://doi.org/10.1089/cmb.2012.0021 (2012).Jain, C. et al. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. https://doi.org/10.1038/s41467-018-07641-9 (2018).Gardner, S. H. et al. kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome. Bioinformatics. https://doi.org/10.1093/bioinformatics/btv271 (2015).Contreras-Moreira, B. et al. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.02411-13 (2013).Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. https://doi.org/10.1101/gr.092759.109 (2009).Parks, D. H. et al. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes Genome Res. https://doi.org/10.1101/gr.186072.114 (2015).Suyama, M. et al. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dereeper, A. et al. Phylogeny. fr: Robust phylogenetic analysis for the non-specialist. Nucleic Acids Res. https://doi.org/10.1093/nar/gkn180 (2008).Nguyen, L. T. et al. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msu300 (2015).Hoang, D. T. et al. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msx281 (2018).Kalyaanamoorthy, S. et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods. https://doi.org/10.1038/nmeth.4285 (2017).Price, M. N. et al. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One. https://doi.org/10.1371/journal.pone.0009490 (2010).Stamatakis, A. RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. https://doi.org/10.1093/bioinformatics/btl446 (2006).Kumar, S. et al. MEGA-CC: Computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis. Bioinformatics. https://doi.org/10.1093/bioinformatics/bts507 (2012).Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. https://doi.org/10.1080/10635150290069913 (2002).Olm, M. R. et al. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. https://doi.org/10.1038/ismej.2017.126 (2017).Posada, D. jModelTest: phylogenetic model averaging. Mol. Biol. Evol. 7, 1253–1256 (2008).
Google Scholar 
Letunic, I. et al. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2007).CAS 
PubMed 

Google Scholar  More

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 
CAS 

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

Google Scholar 
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).Article 

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

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).Article 
CAS 

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).Article 

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).Article 

Google Scholar 
Damuth, J. Population density and body size in mammals. Nature 290, 699–700 (1981).Article 

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).Article 
CAS 

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).Article 

Google Scholar 
Amarasekare, P. & Savage, V. A framework for elucidating the temperature dependence of fitness. Am. Nat. 179, 178–191 (2012).Article 

Google Scholar 
Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).Article 

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).Article 

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).Article 
CAS 

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).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 
Janča, M. & Gvoždík, L. Costly neighbours: heterospecific competitive interactions increase metabolic rates in dominant species. Sci. Rep. 7, 5177 (2017).Article 

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).Article 

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).Article 

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).Article 

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).Article 

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).Article 
CAS 

Google Scholar 
Alton, L. A. et al. Developmental nutrition modulates metabolic responses to projected climate change. Funct. Ecol. 34, 2488–2502 (2020).Article 

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).Article 

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).Article 

Google Scholar 
Kellermann, V. et al. Comparing thermal performance curves across traits: how consistent are they? J. Exp. Biol. 222, jeb193433 (2019).Article 

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).Article 

Google Scholar 
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).Article 
CAS 

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).Article 
CAS 

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).
Google Scholar 
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).Article 

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).Article 

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).Article 

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).Article 

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).Article 

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

Google Scholar 
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).Article 

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).Article 
CAS 

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).Article 

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).Article 

Google Scholar  More

Ritz, K. & Young, I. M. Interactions between soil structure and fungi. Mycologist 18, 52–59 (2004).Article 

Google Scholar 
Schimel, J. P. & Schaeffer, S. M. Microbial control over carbon cycling in soil. Front. Microbiol. 3, 348 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).Article 

Google Scholar 
van der Heijden, M. G. A. & Wagg, C. Soil microbial diversity and agro-ecosystem functioning. Plant Soil 363, 1–5 (2013).Article 
CAS 

Google Scholar 
Winter, S. et al. Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis. J. Appl. Ecol. 55, 2484–2495 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Belmonte, S. A. et al. Effect of long-term soil management on the mutual interaction among soil organic matter, microbial activity and aggregate stability in a vineyard. Pedosphere 28, 288–298 (2018).Article 
CAS 

Google Scholar 
Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005).Article 
ADS 
CAS 

Google Scholar 
Kratschmer, S. et al. Enhancing flowering plant functional richness improves wild bee diversity in vineyard inter-rows in different floral kingdoms. Ecol. Evol. 11, 7927–7945 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Constancias, F. et al. Microscale evidence for a high decrease of soil bacterial density and diversity by cropping. Agron. Sustain. Dev. 34, 831–840 (2014).Article 
CAS 

Google Scholar 
Schmidt, R., Gravuer, K., Bossange, A. V., Mitchell, J. & Scow, K. Long-term use of cover crops and no-till shift soil microbial community life strategies in agricultural soil. PLoS One 13, e0192953 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Vink, S. N., Chrysargyris, A., Tzortzakis, N. & Salles, J. F. Bacterial community dynamics varies with soil management and irrigation practices in grapevines (Vitis vinifera L.). Appl. Soil Ecol. 158, 103807 (2021).Article 

Google Scholar 
Pingel, M., Reineke, A. & Leyer, I. A 30-years vineyard trial: plant communities, soil microbial communities and litter decomposition respond more to soil treatment than to N fertilization. Agr. Ecosyst. Environ. 272, 114–125 (2019).Article 
CAS 

Google Scholar 
Sharma-Poudyal, D., Schlatter, D., Yin, C., Hulbert, S. & Paulitz, T. Long-term no-till: a major driver of fungal communities in dryland wheat cropping systems. PLoS One 12, e0184611 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Hungria, M., Franchini, J. C., Brandão-Junior, O., Kaschuk, G. & Souza, R. A. Soil microbial activity and crop sustainability in a long-term experiment with three soil-tillage and two crop-rotation systems. Appl. Soil. Ecol. 42, 288–296 (2009).Article 

Google Scholar 
Pascault, N. et al. In situ dynamics of microbial communities during decomposition of wheat, rape, and alfalfa residues. Microb. Ecol. 60, 816–828 (2010).Article 
PubMed 

Google Scholar 
Tresch, S. et al. Litter decomposition driven by soil fauna, plant diversity and soil management in urban gardens. Sci. Total Environ. 658, 1614–1629 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Faust, S., Koch, H.-J., Dyckmans, J. & Joergensen, R. G. Response of maize leaf decomposition in litterbags and soil bags to different tillage intensities in a long-term field trial. Appl. Soil. Ecol. 141, 38–44 (2019).Article 

Google Scholar 
Liu, Y.-R. et al. New insights into the role of microbial community composition in driving soil respiration rates. Soil Biol. Biochem. 118, 35–41 (2018).Article 
CAS 

Google Scholar 
Yang, C., Liu, N. & Zhang, Y. Soil aggregates regulate the impact of soil bacterial and fungal communities on soil respiration. Geoderma 337, 444–452 (2019).Article 
ADS 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bruggisser, O. T., Schmidt-Entling, M. H. & Bacher, S. Effects of vineyard management on biodiversity at three trophic levels. Biol. Cons. 143, 1521–1528 (2010).Article 

Google Scholar 
Lienhard, P. et al. Pyrosequencing evidences the impact of cropping on soil bacterial and fungal diversity in Laos tropical grassland. Agron. Sustain. Dev. 34, 525–533 (2014).Article 

Google Scholar 
Schnoor, T. K., Lekberg, Y., Rosendahl, S. & Olsson, P. A. Mechanical soil disturbance as a determinant of arbuscular mycorrhizal fungal communities in semi-natural grassland. Mycorrhiza 21, 211–220 (2011).Article 
PubMed 

Google Scholar 
Kazakou, E. et al. A plant trait-based response-and-effect framework to assess vineyard inter-row soil management. Bot. Lett. 163, 373–388 (2016).Article 

Google Scholar 
Svensson, J. R., Lindegarth, M., Jonsson, P. R. & Pavia, H. Disturbance-diversity models: What do they really predict and how are they tested?. Proc. Biol. Sci. 279, 2163–2170 (2012).PubMed 
PubMed Central 

Google Scholar 
Bao, T. et al. Moderate disturbance increases the PLFA diversity and biomass of the microbial community in biocrusts in the Loess Plateau region of China. Plant Soil 451, 499–513 (2020).Article 
CAS 

Google Scholar 
Liu, J. et al. Soil carbon content drives the biogeographical distribution of fungal communities in the black soil zone of northeast China. Soil Biol. Biochem. 83, 29–39 (2015).Article 
CAS 

Google Scholar 
Cotton, J. & Acosta-Martínez, V. Intensive tillage converting grassland to cropland immediately reduces soil microbial community size and organic carbon. Agric. Environ. Lett. 3, 180047 (2018).Article 

Google Scholar 
Poeplau, C. et al. Temporal dynamics of soil organic carbon after land-use change in the temperate zone – carbon response functions as a model approach. Glob. Change Biol. 17, 2415–2427 (2011).Article 
ADS 

Google Scholar 
Burns, K. N. et al. Vineyard soil bacterial diversity and composition revealed by 16S rRNA genes: differentiation by vineyard management. Soil Biol. Biochem. 103, 337–348 (2016).Article 
CAS 

Google Scholar 
Steiner, M. et al. Local conditions matter: minimal and variable effects of soil disturbance on microbial communities and functions in European vineyards. PLoS One 18, e0280516 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zeng, J. et al. Nitrogen fertilization directly affects soil bacterial diversity and indirectly affects bacterial community composition. Soil Biol. Biochem. 92, 41–49 (2016).Article 
CAS 

Google Scholar 
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U.S.A. 103, 626–631 (2006).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Eisenhauer, N. Plant diversity effects on soil microorganisms: spatial and temporal heterogeneity of plant inputs increase soil biodiversity. Pedobiologia 59, 175–177 (2016).Article 

Google Scholar 
Porazinska, D. L. et al. Plant diversity and density predict belowground diversity and function in an early successional alpine ecosystem. Ecology 99, 1942–1952 (2018).Article 
PubMed 

Google Scholar 
Prober, S. M. et al. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecol. Lett. 18, 85–95 (2015).Article 
PubMed 

Google Scholar 
Sun, Y.-Q., Wang, J., Shen, C., He, J.-Z. & Ge, Y. Plant evenness modulates the effect of plant richness on soil bacterial diversity. Sci. Total Environ. 662, 8–14 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kuzyakov, Y. Priming effects: interactions between living and dead organic matter. Soil Biol. Biochem. 42, 1363–1371 (2010).Article 
CAS 

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

Google Scholar 
Dimassi, B. et al. Effect of nutrients availability and long-term tillage on priming effect and soil C mineralization. Soil Biol. Biochem. 78, 332–339 (2014).Article 
CAS 

Google Scholar 
Prescott, C. E. Litter decomposition: What controls it and how can we alter it to sequester more carbon in forest soils?. Biogeochemistry 101, 133–149 (2010).Article 
CAS 

Google Scholar 
Petraglia, A. et al. Litter decomposition: effects of temperature driven by soil moisture and vegetation type. Plant Soil 435, 187–200 (2019).Article 
CAS 

Google Scholar 
Vukicevich, E., Lowery, T., Bowen, P., Úrbez-Torres, J. R. & Hart, M. Cover crops to increase soil microbial diversity and mitigate decline in perennial agriculture. A review. Agron. Sustain. Dev. (2016).Bani, A. et al. The role of microbial community in the decomposition of leaf litter and deadwood. Appl. Soil. Ecol. 126, 75–84 (2018).Article 

Google Scholar 
Bonanomi, G., Capodilupo, M., Incerti, G., Mazzoleni, S. & Scala, F. Litter quality and temperature modulate microbial diversity effects on decomposition in model experiments. Community Ecol. 16, 167–177 (2015).Article 

Google Scholar 
Daebeler, A. et al. Pairing litter decomposition with microbial community structures using the Tea Bag Index (TBI). SOIL Discuss. [preprint]; 10.5194/soil-2021-110 (2021).Keuskamp, J. A., Dingemans, B. J. J., Lehtinen, T., Sarneel, J. M. & Hefting, M. M. Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. Methods Ecol. Evol. 4, 1070–1075 (2013).Article 

Google Scholar 
Schaller, K. Praktikum zur Bodenkunde und Pflanzenernährung. Hochschule Geisenheim, (2000).Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).Article 
CAS 
PubMed 

Google Scholar 
Ihrmark, K. et al. New primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).Article 
CAS 
PubMed 

Google Scholar 
Schoch, C. L. et al. SI: Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc. Natl. Acad. Sci. U.S.A. 109, 6241–6246 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. Available at https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).Joshi, N. A. & Fass, J. N. sickle – A Windowed Adaptive Trimming Tool for FASTQ Files Using Quality. Available at https://github.com/najoshi/sickle (2011).Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
CAS 
PubMed 

Google Scholar 
Westcott, S. L. & Schloss, P. D. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units. mSphere 2, e00073 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).Article 
CAS 
PubMed 

Google Scholar 
Gweon, H. S. et al. PIPITS: an automated pipeline for analyses of fungal internal transcribed spacer sequences from the Illumina sequencing platform. Methods Ecol. Evol. 6, 973–980 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).Article 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. Available at https://www.R-project.org/ (2019).McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Haegeman, B. et al. Robust estimation of microbial diversity in theory and in practice. ISME J. 7, 1092–1101 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Scheu, S. Automated measurement of the respiratory response of soil microcompartments: Active microbial biomass in earthworm faeces. Soil Biol. Biochem. 24, 1113–1118 (1992).Article 

Google Scholar 
Mori, T. Validation of the Tea Bag Index as a standard approach for assessing organic matter decomposition: a laboratory incubation experiment. Ecol. Ind. 141, 109077 (2022).Article 
CAS 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–142. Available at https://CRAN.R-project.org/package=nlme (2019).Lenth, R. Emmeans: Estimated Marginal Means, Aka Least-Squares Means. R package version 1.4.4. Available at https://CRAN.R-project.org/package=emmeans (2020).Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Grace, J. B., Anderson, T. M., Olff, H. & Scheiner, S. M. On the specification of structural equation models for ecological systems. Ecol. Monogr. 80, 67–87 (2010).Article 

Google Scholar 
Shipley, B. A new inferential test for path models based on directed acyclic graphs. Struct. Equ. Model. 7, 206–218 (2000).Article 
MathSciNet 

Google Scholar  More

Test organisms and exposuresIn this study, we used test organisms and reagents according to the Acute Toxicity Test Method of Daphnia magna Straus(Cladocera, Crustacea); ES 04704.1b29. Daphnia magna were fostered at the National Institute of Environmental Research and were adopted. During the test, adult female Daphnia magna over two weeks of age, cultured over several generations, were transferred to a freshly prepared container the day before the test. Daphnia magna are neonates for less than 24 h after birth29. To maintain the sensitivity of the organism, young individuals less than 24 h old that reproduced the following day were used. Individuals of a similar size were selected for the test. Daphnia magna was fed YCT, which is a mixture of green algae in Chlorella sp., yeast, Cerophy II(R), and trout chow. Sufficient amounts of prey were supplied 2 h before the test to minimize the effects of prey during the test. The test medium was prepared by dissolving KCl (8 mg/L), (text {MgSO}_4) (120 mg/L), (text {CaSO}_4 cdot 2 text {H}_2 text {O} ) (120 mg/L), and (text {NaHCO}_3) (192 mg/L) in deionized water.Automatic high-throughput Daphnia magna tracking systemTo build an automatic high-throughput Daphnia magna tracking system, we equipped the system with a video analysis algorithm as well as flow cells (Fig. 1). In the tracking system, six flow cells filled with culture medium were installed in the device. Each flow cell contained 10 Daphnia magna. Subsequently, to automatically measure the state of Daphnia magna, the six flow cells were photographed at 15 frames per second using a camera (Industrial Development Systems imaging) equipped with a CMOSIS sensor capable of infrared imaging. A red light close to the infrared spectrum was placed at the back of the flow cells for uniform illumination and to minimize stress on Daphnia magna. To capture the size and movement of the Daphnia magna as accurately as possible, the camera was set to a frame rate of 15 fps and a resolution of 2048 (times ) 1088 (2.23 MB), using a 12 mm lens. The distance between the flow cell and the camera was set to 16 cm. To measure the number of mobile Daphnia magna, their lethality, and swimming inhibition automatically and simultaneously, one camera for every two cells was used to collect the status data of Daphnia magna. For assessing ecotoxicity, the video analysis system used images obtained from the six flow cells to track each Daphnia magna and estimate key statistics such as the number of mobile individuals, average distance, and radius of activity.Figure 1New automatic high-throughput video tracking system for behavioral analysis using Daphnia magna as a model organismFull size imageThe automatic high-throughput video tracking system in the ecotoxicity measuring device was designed to continuously measure the ecotoxicity of Daphnia magna (Fig. 2). Daphnia magna moves faster at high temperatures and is less active at low temperatures. Thus, a constant temperature module that can be set to an appropriate Daphnia magna habitat temperature (20 ± 2 (^{circ })C) was added to create a suitable culture environment for Daphnia magna29. Natural pseudo-light ((lambda >590) nm, 3000 k) was installed on the upper part of the detector for proper habitat light intensity (500 Lux–1000 Lux). The size of the flow cell was set as small as possible while observing the movement of the Daphnia magna. An automatic feeding system was installed so that food could be injected during the replacement cycle. The six independent multi-flow cells were designed with an automatic dilution injection module; therefore, these flow cells were diluted to six different concentrations (100%, 50%, 25%, 12.5%, 6.25%, and 0%).Figure 2Schematic representation of the automatic high-throughput video tracking systemFull size imageAutomatic tracking algorithmThe CPU used for Daphnia magna tracking was Intel i5-9300H @ 2.40 GHz, with 8 GB of memory and Windows 10 Pro 64-bit operating system. In this experiment, the algorithms were trained using 12 Daphnia magna videos and tested using an additional four Daphnia magna videos. Subsequently, the detection and tracking methods were compared. The videos, each of which had a duration of 30 s, were captured at a rate of 15 frames per second. Generally, for long-time or real-time videos, the following factors must be considered in tracking Daphnia magna: automatic binarization between the object and background, effective classification of Daphnia magna or noise, and the speed of the algorithm. Therefore, to develop an efficient tracking algorithm, we propose the following tracking process (Fig. 3A). In this process, each frame is initially converted into an image and the background is identified from the obtained video (Fig. 3B). The background is the average of the frames over the previous 20 s, and the tracking system takes 20 s to capture the first background image. The background is subtracted from the image for object detection (Fig. 3C). The objects include Daphnia magna and noise such as droplets and sediment. The difference between the background and frame images is binarized, and each area of the binarized values is regarded as an object. Conventionally, the binarized values are manually generated using specific thresholds. In this study, the images are automatically binarized using k-means clustering to select the threshold value. After binarization, several machine learning methods are used to classify the objects as Daphnia magna or noise (Fig. 3D). For a faster tracking algorithm, we use simple machine learning methods such as random forest (RF) and support vector machine (SVM). The predicted Daphnia magna are tracked using SORT24, which is a fast and highly accurate tracking algorithm (Fig. 3E). Finally, based on the tracked results, statistics for assessing ecotoxicity, such as the number of mobile individuals, average distance, and radius of activity, are estimated to evaluate the toxicity of the aquatic environment.Figure 3Automatic Daphnia magna tracking algorithm process. (A) Overview of automatic tracking algorithm process. (B) Image extraction step. (C) Background subtraction step. (D) Daphnia magna detection step. (E) Daphnia magna tracking step.Full size imagek-means clustering for automatic background subtractionMany tracking algorithms assume that the background is fixed. With fixed backgrounds, the difference between the frame and background can be used to identify objects. However, automatically selecting the precise threshold value for image pixel binarization becomes one of the key problems in identifying objects. The proposed method applies k-means clustering to the pixel values of the subtracted image30, and the center value of each calculated cluster mean is selected as the threshold value (Fig. 4). In the k-means clustering method, grouping is repeatedly performed using the distance between data points31. For binarization, two groups are formed. Let (mu _1 (t)) be the mean of pixels less than the threshold and (mu _2(t)) be the mean of pixels greater than the threshold. At first, (mu _1(t), mu _2(t)) are randomly initialized. Subsequently, each pixel is grouped into a closer mean of each group. The above steps are repeated several times until the group experiences a few changes. Finally, the threshold is calculated as an average of the two means.Figure 4Example of automatic threshold value setting for binarization between objects and background using k-means clusteringFull size imageClassification methodsObject detection based solely on the subtraction between the background and frame images may have low accuracy. As the background in the proposed process is the average value of the frame images, noise may occur. Although this noise is removed by threshold selection in binarization, using only the threshold selection is not efficient for long or real-time videos. Therefore, additional noise must be classified and removed using machine learning models, requiring the construction of a database. In the database, the obtained objects are manually labeled as noise or Daphnia magna and are called ground truth. For classification, the resized 8 (times ) 8 image of each object is stored in the database. The resized image is transformed into a feature using the Sobel edge detection algorithm32 and entered as inputs to the classification models. In this study, classification models such as RF33 SVM34 were used.RF is a model that integrates several decision tree models35. All training data are sampled with a replacement for training each decision tree model. The decision tree model is trained to split intervals of each independent variable by minimizing the gini index (Eq. 1) or entropy index (Eq. 2). The gini index and entropy index denote the impurity within the intervals.$$begin{aligned} G= & {} 1- sum _{i=1}^{c} p_i ^2 end{aligned}$$
(1)
$$begin{aligned} E= & {} – sum _{i=1}^{c} p_i log_2 p_i end{aligned}$$
(2)
where (p_i) is a probability within i-th interval, and c is the number of intervals. For better performance, the RF selects independent variables of training data randomly. This step serves to reduce the correlation of each model. If predictions of each decision tree are uncorrelated, then the variance of an integrated prediction of models is smaller than the variance of each model. RF integrates several model predictions using the voting method. An advantage of the RF method is that it avoids overfitting because the model uses the average of many predictions.SVM is a model designed to search for a hyperplane to maximize the distance, or margin, between support vectors. The hyperplane refers to the plane that divides two different groups, and the support vector represents the closest vector to the hyperplane. Let (D=({textbf{x}}_i, y_i), i=1, ldots , n, {textbf{x}}_i in {mathbb {R}}^p, y_n in { -1,1 }) be training data. Suppose that the training data are completely separated linearly by a hyperplane; then, the hyperplane is expressed as Eq. 3.$$begin{aligned} {textbf{w}}^T {textbf{x}} + b = 0, end{aligned}$$
(3)
where ({textbf{w}}) is a weight vector of the hyperplane, and b is a bias. The weight vector is updated by minimizing Eq. 4.$$begin{aligned} L = {1 over 2} {textbf{w}}^T {textbf{w}} text { subject to } y_i ({textbf{w}}^T {textbf{x}} + b) ge 1 end{aligned}$$
(4)
We can transform Eqs. 4 to  5 by using the Lagrange multiplier method.$$begin{aligned} L^* = {1 over 2} {textbf{w}}^T {textbf{w}} – sum _{i=1}^n a_i { y_i ({textbf{w}}^T x_i + {-}) – 1 }, end{aligned}$$
(5)
where (a_i) is the Lagrange multiplier. We can efficiently solve Eq. 5 using a dual form. Furthermore, Eq. 5 can be solved in a case where it is not completely separated using a slack variable and a kernel trick can be used to estimate the nonlinear hyperplane.SORT trackerSORT, one of the frameworks for solving the multiple object tracking (MOT) problem, aims to achieve efficient real-time tracking24. The SORT method framework is created by combining the estimation step and the association step. The estimation step forecasts the next position of each predicted Daphnia magna. The association step matches the forecasting position and next true position of each predicted Daphnia magna. In the estimation step, the SORT framework uses the Kalman filter to forecast the position of the predicted Daphnia magna in the next frame. The position of each predicted Daphnia magna is expressed as Eq. 6.$$begin{aligned} {textbf{x}} = [u,v,s,r,{dot{u}}, {dot{v}}, {dot{s}}]^T end{aligned}$$
(6)
where u and v are the center positions of each predicted Daphnia magna, s is the scale size of the bounding box, and r is the aspect ratio of the bounding box. ({dot{u}}), ({dot{v}}), and ({dot{s}}) are the amounts of change in each variable. In the association step, to associate the forecasting position and true position, the framework adopts the intersection-over-union (IOU)36 as the association metric. The Hungarian algorithm is loaded into the SORT framework to perform fast and efficient Daphnia magna association prediction. In this study, a mixed metric of IOU36 and Euclidean distance37 was used instead of only the IOU that is used in SORT (Eq. 7) for more efficient association.$$begin{aligned} C_{ij} = (1-lambda ) {max_d – d_{ij} over max_d} + lambda cdot IOU_{ij} end{aligned}$$
(7)
where (d_{ij}) is the Euclidean distance between the i-th predicted Daphnia magna in the before frame and the j-th predicted Daphnia magna in the next frame, and (lambda ) is the weight of (IOU_{ij}). (IOU_{ij}) is the IOU between the i-th predicted Daphnia magna in the before-frame and the j-th predicted Daphnia magna in the next frame.MetricsThe binary confusion matrix consists of true positive (TP), true negative (TN), false positive (FP), and false negative (FN)38. TP is the number of cases where the predicted Daphnia magna matches the actual Daphnia magna, TN is the number of cases where the objects predicted as noise are actual noise, FP is the number of cases where the predicted Daphnia magna differs from the actual Daphnia magna, and FN is the number of cases where the objects predicted as noise are not actual noise. In this study, accuracy, recall, precision, and F1 scores (Eq. 8) were used as the metrics for comparing the machine learning methods.$$begin{aligned} begin{aligned} Accuracy&= {TP + FP over TP + TN + FP + FN} \ Recall&= {TP over TP + TN} \ Precision&= {TP over TP + FP} \ F1 score&= 2 times {Precision times Recall over Precision + Recall} end{aligned} end{aligned}$$
(8)
Standard MOT metrics to evaluate tracking performance include multi-object tracking accuracy (MOTA) and multi-object tracking precision (MOTP). An important task of MOT is to identify and track the same object across two frames. Identification (ID) precision (IDP), ID recall (IDR), ID F1 measure (IDF1), and ID switches (IDs) may be used as measures for evaluating the identification and tracking of the same objects39,40.Data analysisThe toxicity test using Daphnia magna was performed following the Korean official Acute Toxicity Test Method29. The test medium was prepared by dissolving KCl (8 mg/L), (text {MgSO}_4) (120 mg/L), (text {CaSO}_4 cdot 2 text {H}_2 text {O} ) (120 mg/L), and (text {NaHCO}_3) (192 mg/L) in deionized water. Considering that Daphnia magna are neonates for less than 24 h after birth29, five neonates were exposed to 50 mL of different concentrations of heavy metals such as Potassium dichromate, Copper(II) sulfate pentahydrate, and Lead(II) sulfate (6.25, 12.5, 25, 50, and 100%) and 50 mL of culture media. Potassium dichromate is a common inorganic reagent used as an oxidizing agent in chemical industries. Copper(II) sulfate pentahydrate is a trace material widely used in industrial processes and agriculture. A significant amount of copper is emitted in semiconductor manufacturing processes, which adversely impacts the aquatic ecosystem. When present as an ion in water, copper can be acutely toxic to aquatic organisms such as Daphnia magna. Lead(II) sulfate is another nonessential and nonbiodegradable heavy metal. It is highly toxic to numerous organisms even at low concentrations and can accumulate in aquatic ecosystems41. Twenty Daphnia magna (four replicates of five each) were exposed to each test solution for 24 h. The term “immobility” means that the Daphnia magna remains stationary after exposure to chemicals such as Potassium dichromate, Copper(II) sulfate pentahydrate, and Lead(II) sulfate. In this study, immobility was used as an endpoint identifier, and the number of mobile Daphnia magna were counted to evaluate the EC50 values for the samples using the ToxCalc 5.0 program (Tidepoll Software, USA).The locomotory responses of Daphnia magna were tested after 0, 12, 18, and 24 h of exposure at different concentrations. Potassium dichromate ((text {K}_2text {Cr}_2text {O}_7)) at 2 mg/L was connected to the Daphnia magna tracking system, and standard toxic substances were automatically diluted to 100%, 50%, 25%, 12.5%, and 6.25%. The automatic high-throughput Daphnia magna tracking system automatically measured the tracking results of a 1-minute-long video at hourly intervals. The average moving distance for 20 s of each Daphnia magna in each chamber was analyzed using a repeated measures ANOVA (RMANOVA). RMANOVA was used for the analysis of data obtained by repeatedly measuring the same Daphnia magna42. It analyzes the concentration effect excluding the time effect at each hour. The time effect means the change in average distance per 20 s. RMANOVA was implemented using the agricolae package of the R 4.0.4 program43. To remove the noise affecting RMANOVA, the Daphnia magna that remained stationary for 20 s or more were removed from the observations. In this study, we used the significance level at 5%. More

Massey, J. H. & Wittkopp, P. J. The genetic basis of pigmentation differences within and between Drosophila species. Curr. Top. Dev. Biol. 119, 27–61 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yassin, A. et al. The pdm3 locus is a hotspot for recurrent evolution of female-limited color dimorphism in Drosophila. Curr. Biol. 26, 2412–2422 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, T. M. et al. The regulation and evolution of a genetic switch controlling sexually dimorphic traits in Drosophila. Cell 134, 610–623 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bastide, H. et al. A genome-wide, fine-scale map of natural pigmentation variation in Drosophila melanogaster. PLoS Genet. 9, e1003534 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pool, J. E. & Aquadro, C. F. The genetic basis of adaptive pigmentation variation in Drosophila melanogaster. Mol. Ecol. 16, 2844–2851 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Wittkopp, P. J. et al. Intraspecific polymorphism to interspecific divergence: genetics of pigmentation in Drosophila. Science 326, 540–544 (2009).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Jeong, S. et al. The evolution of gene regulation underlies a morphological difference between two Drosophila sister species. Cell 132, 783–793 (2008).Article 
CAS 
PubMed 

Google Scholar 
Rajpurohit, S. et al. Pigmentation and fitness trade-offs through the lens of artificial selection. Biol. Lett. 12, (2016).Massey, J. H. et al. Pleiotropic effects of ebony and tan on pigmentation and cuticular hydrocarbon composition in Drosophila melanogaster. Front. Physiol. 10, 518 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Parkash, R., Rajpurohit, S. & Ramniwas, S. Impact of darker, intermediate and lighter phenotypes of body melanization on desiccation resistance in Drosophila melanogaster. J. Insect Sci. 9, 1–10 (2009).Article 
PubMed 

Google Scholar 
Dombeck, I. & Jaenike, J. Ecological genetics of abdominal pigmentation in Drosophila falleni: A pleiotropic link to nematode parasitism. Evolution 58, 587–596 (2004).PubMed 

Google Scholar 
Kutch, I. C., Sevgili, H., Wittman, T. & Fedorka, K. M. Thermoregulatory strategy may shape immune investment in Drosophila melanogaster. J. Exp. Biol. 217, 3664–3669 (2014).PubMed 

Google Scholar 
Wittkopp, P. J. & Beldade, P. Development and evolution of insect pigmentation: Genetic mechanisms and the potential consequences of pleiotropy. Semin. Cell Dev. Biol. 20, 65–71 (2009).Article 
CAS 
PubMed 

Google Scholar 
Bastide, H., Yassin, A., Johanning, E. J. & Pool, J. E. Pigmentation in Drosophila melanogaster reaches its maximum in Ethiopia and correlates most strongly with ultra-violet radiation in sub-Saharan Africa. BMC Evol. Biol. 14, 179 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Arnold, S. J. Morphology, performance and fitness. Am. Zool. 23, 347–361 (1983).Article 

Google Scholar 
Gibert, P., Moreteau, B. & David, J. R. Developmental constraints on an adaptive plasticity: Reaction norms of pigmentation in adult segments of Drosophila melanogaster. Evol. Dev. 2, 249–260 (2000).Article 
CAS 
PubMed 

Google Scholar 
Parkash, R., Rajpurohit, S. & Ramniwas, S. Changes in body melanisation and desiccation resistance in highland vs. lowland populations of D. melanogaster. J. Insect Physiol. 54, 1050–1056 (2008).Article 
CAS 
PubMed 

Google Scholar 
Telonis-Scott, M., Hoffmann, A. A. & Sgro, C. M. The molecular genetics of clinal variation: A case study of ebony and thoracic trident pigmentation in Drosophila melanogaster from eastern Australia. Mol. Ecol. 20, 2100–2110 (2011).Article 
PubMed 

Google Scholar 
Munjal, A. K. et al. Thoracic trident pigmentation in Drosophila melanogaster: latitudinal and altitudinal clines in Indian populations. Genet. Sel. Evol. 29, 601–610 (1997).Article 
PubMed Central 

Google Scholar 
David, J. R., Capy, P., Payant, V. & Tsakas, S. Thoracic trident pigmentation in Drosophila melanogaster: Differentiation of geographical populations. Genet. Sel. Evol. 17, 211–224 (1985).Article 
CAS 

Google Scholar 
Clusella Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32, 235–245 (2007).Cordero, R. J. B. et al. Impact of yeast pigmentation on heat capture and latitudinal distribution. Curr. Biol. 28, 2657-2664.e3 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sibilia, C. D. et al. Thermal Physiology and Developmental Plasticity of Pigmentation in the Harlequin Bug (Hemiptera: Pentatomidae). J. Insect Sci. 18, (2018).Jong, P., Gussekloo, S. & Brakefield, P. Differences in thermal balance, body temperature and activity between non-melanic and melanic two-spot ladybird beetles (Adalia bipunctata) under controlled conditions. J. Exp. Biol. 199, 2655–2666 (1996).Article 
CAS 
PubMed 

Google Scholar 
Zverev, V., Kozlov, M. V., Forsman, A. & Zvereva, E. L. Ambient temperatures differently influence colour morphs of the leaf beetle Chrysomela lapponica: Roles of thermal melanism and developmental plasticity. J. Therm. Biol 74, 100–109 (2018).Article 
PubMed 

Google Scholar 
Watt, W. B. Adaptive significance of pigment polymorphisms in Colias butterflies, II. Thermoregulation and photoperiodically controlled melanin variation in Colias eurytheme. Proc. Natl. Acad. Sci. USA 63, 767–74 (1969).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kuyucu, A. C., Sahin, M. K. & Caglar, S. S. The relation between melanism and thermal biology in a colour polymorphic bush cricket, Isophya rizeensis. J. Therm. Biol. 71, 212–220 (2018).Article 
PubMed 

Google Scholar 
Köhler, G. & Schielzeth, H. Green-brown polymorphism in alpine grasshoppers affects body temperature. Ecol. Evol. 10, 441–450 (2020).Article 
PubMed 

Google Scholar 
Willmer, P. G. & Unwin, D. M. Field analyses of insect heat budgets: Reflectance, size and heating rates. Oecologia 50, 250–255 (1981).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Pecsenye, K., Bokor, K., Lefkovitch, L. P., Giles, B. E. & Saura, A. Enzymatic responses of Drosophila melanogaster to long- and short-term exposures to ethanol. Mol. Gen. Genet. 255, 258–268 (1997).Article 
CAS 
PubMed 

Google Scholar 
De Castro, S., Peronnet, F., Gilles, J.-F., Mouchel-Vielh, E. & Gibert, J.-M. bric à brac (bab), a central player in the gene regulatory network that mediates thermal plasticity of pigmentation in Drosophila melanogaster. PLoS Genet. 14, e1007573 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Cooley, A. M., Shefner, L., McLaughlin, W. N., Stewart, E. E. & Wittkopp, P. J. The ontogeny of color: Developmental origins of divergent pigmentation in Drosophila americana and D. novamexicana. Evol. Dev. 14, 317–25 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
John, A. V., Sramkoski, L. L., Walker, E. A., Cooley, A. M. & Wittkopp, P. J. Sensitivity of allelic divergence to genomic position: Lessons from the Drosophila tan Gene. G3 (Bethesda) (2016) doi:https://doi.org/10.1534/g3.116.032029.Liu, Y. et al. Changes throughout a genetic network mask the contribution of hox gene evolution. Curr. Biol. 29, 2157-2166.e6 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
David, J. R. et al. Evolution of assortative mating following selective introgression of pigmentation genes between two Drosophila species. Ecol. Evol. 12, e8821 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Wittkopp, P. J., True, J. R. & Carroll, S. B. Reciprocal functions of the Drosophila yellow and ebony proteins in the development and evolution of pigment patterns. Development 129, 1849–1858 (2002).Article 
CAS 
PubMed 

Google Scholar 
Davis, J. S. & Moyle, L. C. Desiccation resistance and pigmentation variation reflects bioclimatic differences in the Drosophila americana species complex. BMC Evol. Biol. 19, 204 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Nagy, O. et al. Correlated evolution of two copulatory organs via a single cis-regulatory nucleotide change. Curr. Biol. 28, 3450-3457.e13 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lachaise, D. et al. Evolutionary novelties in islands: Drosophila santomea, a new melanogaster sister species from São Tomé. Proc. Biol. Sci. 267, 1487–1495 (2000).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Haldane, J. B. S. Sex ratio and unisexual sterility in hybrid animals. J. Gen. 12, 101–109 (1922).Article 

Google Scholar 
Turissini, D. A. & Matute, D. R. Fine scale mapping of genomic introgressions within the Drosophila yakuba clade. PLoS Genet. 13, e1006971 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann, A. A. Physiological climatic limits in Drosophila: Patterns and implications. J. Exp. Biol. 213, 870–880 (2010).Article 
CAS 
PubMed 

Google Scholar 
Sunaga, S., Akiyama, N., Miyagi, R. & Takahashi, A. Factors underlying natural variation in body pigmentation of Drosophila melanogaster. Genes Genet. Syst. 91, 127–137 (2016).Article 
CAS 
PubMed 

Google Scholar 
Rajpurohit, S. & Schmidt, P. S. Latitudinal pigmentation variation contradicts ultraviolet radiation exposure: A case study in Tropical Indian Drosophila melanogaster. Front. Physiol. 10, 84 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Bergland, A. O., Behrman, E. L., O’Brien, K. R., Schmidt, P. S. & Petrov, D. A. Genomic evidence of rapid and stable adaptive oscillations over seasonal time scales in Drosophila. PLoS Genet. 10, e1004775 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Rudman, S. M. et al. Direct observation of adaptive tracking on ecological time scales in Drosophila. Science 375, eabj7484 (2022).Fabian, D. K. et al. Genome-wide patterns of latitudinal differentiation among populations of Drosophila melanogaster from North America. Mol. Ecol. 21, 4748–4769 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Zeuss, D., Brandl, R., Brändle, M., Rahbek, C. & Brunzel, S. Global warming favours light-coloured insects in Europe. Nat. Commun. 5, 3874 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Brakefield, P. M. & de Jong, P. W. A steep cline in ladybird melanism has decayed over 25 years: A genetic response to climate change?. Heredity (Edinb) 107, 574–578 (2011).Article 
CAS 
PubMed 

Google Scholar 
Zvereva, E. L., Hunter, M. D., Zverev, V., Kruglova, O. Y. & Kozlov, M. V. Climate warming leads to decline in frequencies of melanic individuals in subarctic leaf beetle populations. Sci. Total Environ. 673, 237–244 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Balanyá, J., Oller, J. M., Huey, R. B., Gilchrist, G. W. & Serra, L. Global genetic change tracks global climate warming in Drosophila subobscura. Science 313, 1773–1775 (2006).Article 
ADS 
PubMed 

Google Scholar  More

Hui, C. & Richardson, D. M. Invasion Dynamics (Oxford University Press, 2017).Book 
MATH 

Google Scholar 
Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford University Press, 2012).Book 

Google Scholar 
Shigesada, N., Kawasaki, K. & Takeda, Y. Modeling stratified diffusion in biological invasions. Am. Nat. 146, 229–251 (1995).Article 

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

Google Scholar 
Cayuela, H. et al. Determinants and consequences of dispersal in vertebrates with complex life cycles: A review of pond-breeding amphibians. Q. Rev. Biol. 95, 36 (2020).Article 

Google Scholar 
Measey, G. J. et al. A global assessment of alien amphibian impacts in a formal framework. Divers. Distrib. 22, 970–981 (2016).Article 

Google Scholar 
Antonelli, A., Smith, R. J., Perrigo, A. L. & Crottini, A. Madagascar’s extraordinary biodiversity: Evolution, distribution, and use. Science 378, eabf0869 (2022).
Article 
CAS 
PubMed 

Google Scholar 
Marshall, B. M. et al. Widespread vulnerability of Malagasy predators to the toxins of an introduced toad. Curr. Biol. 28, R654–R655 (2018).Article 
CAS 
PubMed 

Google Scholar 
Licata, F. et al. Toad invasion of Malagasy forests triggers severe mortality of a predatory snake. Biol. Inv. 24, 1189–1198 (2022).Article 

Google Scholar 
Licata, F. et al. Abundance, distribution and spread of the invasive Asian toad Duttaphrynus melanostictus in eastern Madagascar. Biol. Inv. 21, 1615–1626 (2019).Article 

Google Scholar 
McClelland, P., Reardon, J. T., Kraus, F., Raxworthy, C. J. & Randrianantoandro, C. Asian toad eradication feasibility report for Madagascar (Te Anau, 2015).Smith, M. A. & Green, D. M. Dispersal and the metapopulation paradigm in amphibian ecology and conservation: Are all amphibian populations metapopulations?. Ecography 28, 110–128 (2005).Article 

Google Scholar 
Shine, R. et al. Increased rates of dispersal of free-ranging cane toads (Rhinella marina) during their global invasion. Sci. Rep. 11, 23574 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Myles-Gonzalez, E., Burness, G., Yavno, S., Rooke, A. & Fox, M. G. To boldly go where no goby has gone before: Boldness, dispersal tendency, and metabolism at the invasion front. Behav. Ecol. 26, 1083–1090 (2015).Article 

Google Scholar 
Van Petegem, K. H. P. et al. Empirically simulated spatial sorting points at fast epigenetic changes in dispersal behaviour. Evol. Ecol. 29, 299–310 (2015).Article 

Google Scholar 
Stuart, Y. E. et al. Rapid evolution of a native species following invasion by a congener. Science 346, 463–466 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Licata, F., Andreone, F., Crottini, A., Harison, R. F. & Ficetola, G. F. Does spatial sorting occur in the invasive Asian toad in Madagascar? Insights into the invasion unveiled by morphological analyses. JZSER 2021, 1–9 (2021).
Google Scholar 
Schwarzkopf, L. & Alford, R. A. Nomadic movement in tropical toads. Oikos 96, 492–506 (2002).Article 

Google Scholar 
Brown, G. P., Kelehear, C. & Shine, R. Effects of seasonal aridity on the ecology and behaviour of invasive cane toads in the Australian wet–dry tropics. Funct. Ecol. 25, 1339–1347 (2011).Article 

Google Scholar 
Duellman, W. E. & Trueb, L. Biology of Amphibians (JHU Press, 1994).Book 

Google Scholar 
Wells, K. D. The Ecology and Behavior of Amphibians (University of Chicago Press, 2010). https://doi.org/10.7208/9780226893334.Book 

Google Scholar 
Shaw, A. K., Kokko, H. & Neubert, M. G. Sex difference and Allee effects shape the dynamics of sex-structured invasions. J. Anim. Ecol. 87, 36–46 (2018).Article 
PubMed 

Google Scholar 
Schwarzkopf, L. & Alford, R. A. Desiccation and shelter-site use in a tropical amphibian: Comparing toads with physical models. Funct. Ecol. 10, 193–200 (1996).Article 

Google Scholar 
Wogan, G. O. U., Stuart, B. L., Iskandar, D. T. & McGuire, J. A. Deep genetic structure and ecological divergence in a widespread human commensal toad. Biol. Lett. 12, 20150807 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Licata, F. Exploring the invasion dynamics and impacts of the invasive Asian common toad in Madagascar (University of Porto, 2022).
Google Scholar 
Reilly, S. B. et al. Toxic toad invasion of Wallacea: A biodiversity hotspot characterized by extraordinary endemism. Glob. Change Biol. 23, 5029–5031 (2017).Article 
ADS 

Google Scholar 
Jørgensen, C. B., Shakuntala, K. & Vijayakumar, S. Body size, reproduction and growth in a tropical toad, Bufo melanostictus, with a comparison of ovarian cycles in tropical and temperate zone anurans. Oikos 46, 379 (1986).Article 

Google Scholar 
Vences, M. et al. Tracing a toad invasion: Lack of mitochondrial DNA variation, haplotype origins, and potential distribution of introduced Duttaphrynus melanostictus in Madagascar. Amphib. Reptilia 38, 197–207 (2017).Article 

Google Scholar 
Ngo, B. V. & Ngo, C. D. Reproductive activity and advertisement calls of the Asian common toad Duttaphrynus melanostictus (Amphibia, Anura, Bufonidae) from Bach Ma National Park, Vietnam. Zool. Stud. 52, 12 (2013).Article 

Google Scholar 
Licata, F. et al. The Asian toad (Duttaphrynus melanostictus) in Madagascar: A report of an ongoing invasion. In Problematic Wildlife II: New Conservation and Management Challenges in the Human-Wildlife Interactions (eds Angelici, F. M. & Rossi, L.) 617–638 (Springer, 2020). https://doi.org/10.1007/978-3-030-42335-3_21.Chapter 

Google Scholar 
Moore, M., Solofo Niaina Fidy, J. F. & Edmonds, D. The new toad in town: Distribution of the Asian toad, Duttaphrynus melanostictus, in the Toamasina area of eastern Madagascar. Trop. Conserv. Sci. 8, 440–455 (2015).Article 

Google Scholar 
Licata, F. et al. Using public surveys to rapidly profile biological invasions in hard-to-monitor areas. Anim. Conserv. https://doi.org/10.1111/acv.12835 (2023).Article 

Google Scholar 
Zhang, M. et al. Automatic high-resolution land cover production in madagascar using sentinel-2 time series, tile-based image classification and google earth engine. Remote Sensing 12, 3663 (2020).Article 
ADS 

Google Scholar 
Peel, M. C., Finlayson, B. L. & Mcmahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 4, 439–473 (2007).
Google Scholar 
Merkel, A. Toamasina Climate (Madagascar). Accessed 20 July 2022. https://en.climate-data.org/africa/madagascar/toamasina/toamasina-4029/
(2021).Gordon, A. Secondary sexual characters of Bufo melanostictus schneider. Copeia 1933, 204–207 (1933).Article 

Google Scholar 
Alford, R. & Rowley, J. Techniques for tracking amphibians: The effects of tag attachment, and harmonic direction finding versus radio telemetry. Amphib. Reptilia 28, 367–376 (2007).Article 

Google Scholar 
Lassueur, T., Joost, S. & Randin, C. F. Very high resolution digital elevation models: Do they improve models of plant species distribution?. Ecol. Modell. 198, 139–153 (2006).Article 

Google Scholar 
Abrams, M., Crippen, R. & Fujisada, H. ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD). Remote Sensing 12, 1156 (2020).Article 
ADS 

Google Scholar 
Brown, G. P., Phillips, B. L., Webb, J. K. & Shine, R. Toad on the road: Use of roads as dispersal corridors by cane toads (Bufo marinus) at an invasion front in tropical Australia. Biol. Conserv. 133, 88–94 (2006).Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 
MATH 

Google Scholar 
Hijmans, R. J. et al. raster: Geographic data analysis and modeling. https://CRAN.R-project.org/package=raster (2021).Yagi, K. T. & Green, D. M. Performance and movement in relation to postmetamorphic body size in a pond-breeding amphibian. J. Herpetol. 51, 482–489 (2017).Article 

Google Scholar 
Labocha, M. K., Schutz, H. & Hayes, J. P. Which body condition index is best?. Oikos 123, 111–119 (2014).Article 

Google Scholar 
Tingley, R. & Shine, R. Desiccation risk drives the spatial ecology of an invasive anuran (Rhinella marina) in the australian semi-desert. PLoS ONE 6, e25979 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Richards, S. J., Sinsch, U. & Alford, R. A. Radio Tracking. In Measuring and Monitoring Biological Diversity: Standard Methods for Amphibians (eds Heyer, R. et al.) 155–158 (Smithsonian Institution, 1994).
Google Scholar 
Altobelli, J. T., Dickinson, K. J. M., Godfrey, S. S. & Bishop, P. J. Methods in amphibian biotelemetry: Two decades in review. Austral. Ecol. 47, 1382–1395 (2022).Article 

Google Scholar 
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002). https://doi.org/10.1007/978-1-4757-2917-7_3.Book 
MATH 

Google Scholar 
Richards, S. A., Whittingham, M. J. & Stephens, P. A. Model selection and model averaging in behavioural ecology: The utility of the IT-AIC framework. Behav. Ecol. Sociobiol. 65, 77–89 (2011).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2021).Bates, D. et al. lme4: Linear Mixed-Effects Models using ‘Eigen’ and S4. (2020).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. (2022).Hodges, C. W., Marshall, B. M., Hill, J. G. & Strine, C. T. Malayan kraits (Bungarus candidus) show affinity to anthropogenic structures in a human dominated landscape. bioRxiv https://doi.org/10.1101/2021.09.08.459477 (2021).Article 

Google Scholar 
Muller, B. J., Cade, B. S. & Schwarzkopf, L. Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts. Ecosphere 9, e02067 (2018).Article 

Google Scholar 
Linsenmair, K. E. & Spieler, M. Migration patterns and diurnal use of shelter in a ranid frog of a West African savannah: A telemetric study. Amphib. Reptilia 19, 43–64 (1998).Article 

Google Scholar 
Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).Article 
PubMed 

Google Scholar 
Ward-Fear, G., Greenlees, M. J. & Shine, R. Toads on lava: spatial ecology and habitat use of invasive cane yoads (Rhinella marina) in Hawai’i. PLoS ONE 11, e0151700 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Huang, W.-S., Lin, J.-Y. & Yu, J.Y.-L. Male reproductive cycle of the toad Bufo melanostictus in Taiwan. Zool. Sci. 14, 497–503 (1997).Article 

Google Scholar 
Brown, G. P., Phillips, B. L. & Shine, R. The straight and narrow path: the evolution of straight-line dispersal at a cane toad invasion front. Proc. R. Soc. B 281, 20141385 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Perkins, T. A., Phillips, B. L., Baskett, M. L. & Hastings, A. Evolution of dispersal and life history interact to drive accelerating spread of an invasive species. Ecol. Lett. 16, 1079–1087 (2013).Article 
PubMed 

Google Scholar 
Ochocki, B. M. & Miller, T. E. X. Rapid evolution of dispersal ability makes biological invasions faster and more variable. Nat. Commun. 8, 14315 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Phillips, B. L., Brown, G. P., Travis, J. M. J. & Shine, R. Reid’s paradox revisited: The evolution of dispersal kernels during range expansion. Am. Nat. 172, S34–S48 (2008).Article 
PubMed 

Google Scholar 
Kot, M., Lewis, M. A. & van den Driessche, P. Dispersal data and the spread of invading organisms. Ecology 77, 2027–2042 (1996).Article 

Google Scholar 
Deguise, I. & Richardson, J. S. Movement behaviour of adult western toads in a fragmented, forest landscape. Can. J. Zool. 87, 1184–1194 (2009).Article 

Google Scholar 
Mitrovich, M. J., Gallegos, E. A., Lyren, L. M., Lovich, R. E. & Fisher, R. N. Habitat use and movement of the endangered Arroyo toad (Anaxyrus californicus) in coastal southern California. J. Herpetol. 45, 319–328 (2011).Article 

Google Scholar 
Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. A toad more traveled: The heterogeneous invasion dynamics of cane toads in Australia. Am. Nat. 171, E134–E148 (2008).Article 
PubMed 

Google Scholar 
Enriquez-Urzelai, U., Montori, A., Llorente, G. A. & Kaliontzopoulou, A. Locomotor mode and the evolution of the hindlimb in western mediterranean anurans. Evol. Biol. 42, 199–209 (2015).Article 

Google Scholar 
Junior, B. T. & Gomes, F. R. Relation between water balance and climatic variables associated with the geographical distribution of anurans. PLoS ONE 10, e0140761 (2015).Article 

Google Scholar 
Klockmann, M., Günter, F. & Fischer, K. Heat resistance throughout ontogeny: Body size constrains thermal tolerance. Glob. Change Biol. 23, 686–696 (2017).Article 
ADS 

Google Scholar 
Petrovskii, S., Mashanova, A. & Jansen, V. A. A. Variation in individual walking behavior creates the impression of a Lévy flight. PNAS 108, 8704–8707 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lindström, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. PNAS 110, 13452–13456 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Tingley, R. et al. New weapons in the toad toolkit: A review of methods to control and mitigate the biodiversity impacts of invasive Cane toads (Rhinella marina). Q. Rev. Biol. 92, 123–149 (2017).Article 
PubMed 

Google Scholar 
Novoa, A. et al. Invasion syndromes: A systematic approach for predicting biological invasions and facilitating effective management. Biol. Invasions 22, 1801–1820 (2020).Article 

Google Scholar 
DeVore, J. L., Crossland, M. R., Shine, R. & Ducatez, S. The evolution of targeted cannibalism and cannibal-induced defenses in invasive populations of cane toads. Proc. Natl. Acad. Sci. 118, e2100765118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Muller, B. J. & Schwarzkopf, L. Relative effectiveness of trapping and hand-capture for controlling invasive cane toads (Rhinella marina). Int. J. Pest Manag. 64, 185–192 (2018).Article 
CAS 

Google Scholar  More