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    Impact of test chamber design on spontaneous behavioral responses of model crustacean zooplankton Artemia franciscana

    1.Bai, Y., Henry, J. & Wlodkowic, D. Chemosensory avoidance behaviors of marine amphipods Allorchestes compressa revealed using a millifluidic perfusion technology. Biomicrofluidics 14, 014110 (2020).CAS 
    Article 

    Google Scholar 
    2.Bownik, A. Daphnia swimming behaviour as a biomarker in toxicity assessment: a review. Sci. Total Environ. 601–602, 194–205 (2017).Article 

    Google Scholar 
    3.Libralato, G., Prato, E., Migliore, L., Cicero, A. M. & Manfra, L. A review of toxicity testing protocols and endpoints with Artemia spp. Ecol. Indic. 69, 35–49 (2016).CAS 
    Article 

    Google Scholar 
    4.Henry, J. & Wlodkowic, D. Towards high-throughput chemobehavioural phenomics in neuropsychiatric drug discovery. Mar. Drugs 17, 340 (2019).CAS 
    Article 

    Google Scholar 
    5.Morgana, S., Estévez-Calvar, N., Gambardella, C., Faimali, M. & Garaventa, F. A short-term swimming speed alteration test with nauplii of Artemia franciscana. Ecotoxicol. Environ. Saf. 147, 558–564 (2018).CAS 
    Article 

    Google Scholar 
    6.Bartolomé, M. C. & Sánchez-Fortún, S. Acute toxicity and inhibition of phototaxis induced by benzalkonium chloride in Artemia franciscana larvae. Bull. Environ. Contam. Toxicol. 75, 1208–1213 (2005).Article 

    Google Scholar 
    7.Hellou, J. Behavioural ecotoxicology, an “early warning” signal to assess environmental quality. Environ. Sci. Pollut. Res. Int. 18, 1–11 (2011).CAS 
    Article 

    Google Scholar 
    8.Campana, O. & Wlodkowic, D. Ecotoxicology goes on a chip: embracing miniaturized bioanalysis in aquatic risk assessment. Environ. Sci. Technol. 52, 932–946 (2018).CAS 
    Article 

    Google Scholar 
    9.De Esch, C., Slieker, R., Wolterbeek, A., Woutersen, R. & de Groot, D. Zebrafish as potential model for developmental neurotoxicity testing. A mini review. Neurotoxicol. Teratol. 34, 545–553 (2012).Article 

    Google Scholar 
    10.Blackiston, D., Shomrat, T., Nicolas, C. L., Granata, C. & Levin, M. A second-generation device for automated training and quantitative behavior analyses of molecularly-tractable model organisms. PLoS ONE 5, e14370 (2010).Article 

    Google Scholar 
    11.Franco-Restrepo, J. E., Forero, D. A. & Vargas, R. A. A review of freely available, open-source software for the automated analysis of the behavior of adult. zebrafish. Zebrafish 16, 223–232 (2019).PubMed 

    Google Scholar 
    12.Henry, J., Rodriguez, A. & Wlodkowic, D. Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology. PeerJ 7, e7367 (2019).Article 

    Google Scholar 
    13.Henry, J. & Wlodkowic, D. High-throughput animal tracking in chemobehavioral phenotyping: current limitations and future perspectives. Behav. Processes 180, 104226 (2020).Article 

    Google Scholar 
    14.Garcia, G. R., Noyes, P. D. & Tanguay, R. L. Advancements in zebrafish applications for 21st century toxicology. Pharmacol. Ther. 161, 11–21 (2016).CAS 
    Article 

    Google Scholar 
    15.Rennekamp, A. J. & Peterson, R. T. 15 years of zebrafish chemical screening. Curr. Opin. Chem. Biol. 24, 58–70 (2015).CAS 
    Article 

    Google Scholar 
    16.Cartlidge, R. & Wlodkowic, D. Caging of planktonic rotifers in microfluidic environment for sub-lethal aquatic toxicity tests. Biomicrofluidics 12, 044111 (2018).Article 

    Google Scholar 
    17.Kohler, S. A., Parker, M. O. & Ford, A. T. Shape and size of the arenas affect amphipod behaviours: implications for ecotoxicology. PeerJ 6, e5271 (2018).Article 

    Google Scholar 
    18.Kohler, S. A., Parker, M. O. & Ford, A. T. Species-specific behaviours in amphipods highlight the need for understanding baseline behaviours in ecotoxicology. Aquat. Toxicol. 202, 173–180 (2018).CAS 
    Article 

    Google Scholar 
    19.Kohler, S. A., Parker, M. O. & Ford, A. T. High-throughput screening of psychotropic compounds: impacts on swimming behaviours in Artemia franciscana. Toxics 9, 64 (2021).Article 

    Google Scholar 
    20.Inoue, T., Hoshino, H., Yamashita, T., Shimoyama, S. & Agata, K. Planarian shows decision-making behavior in response to multiple stimuli by integrative brain function. Zoolog. Lett. 1, 7 (2015).Article 

    Google Scholar 
    21.Truong, L. et al. Multidimensional in vivo hazard assessment using zebrafish. Toxicol. Sci. 137, 212–233 (2014).CAS 
    Article 

    Google Scholar 
    22.Zhang, S., Hagstrom, D., Hayes, P., Graham, A. & Collins, E.-M. S. Multi-behavioral endpoint testing of an 87-chemical compound library in freshwater planarians. Toxicol. Sci. 167, 26–44 (2019).CAS 
    Article 

    Google Scholar 
    23.Akiyama, Y., Agata, K. & Inoue, T. Spontaneous behaviors and wall-curvature lead to apparent wall preference in planarian. PLoS ONE 10, e0142214 (2015).Article 

    Google Scholar 
    24.Blaser, R. E. & Rosemberg, D. B. Measures of anxiety in zebrafish (Danio rerio): dissociation of black/white preference and novel tank test. PLoS ONE 7, e36931 (2012).CAS 
    Article 

    Google Scholar 
    25.Harro, J. Animals, anxiety, and anxiety disorders: how to measure anxiety in rodents and why. Behav. Brain Res. 352, 81–93 (2018).Article 

    Google Scholar 
    26.Faimali, M. et al. Old model organisms and new behavioral end-points: swimming alteration as an ecotoxicological response. Mar. Environ. Res. 128, 36–45 (2017).CAS 
    Article 

    Google Scholar 
    27.Rashid, M. T. et al. Artemia swarm dynamics and path tracking. Nonlinear Dyn. 68, 555–563 (2012).Article 

    Google Scholar 
    28.Forward, R. B. & Rittschof, D. Brine shrimp larval photoresponses involved in diel vertical migration: activation by fish mucus and modified amino sugars. Limnol. Oceanogr. 44, 1904–1916 (1999).CAS 
    Article 

    Google Scholar 
    29.Gerhardt, A. Aquatic behavioral ecotoxicology—prospects and limitations. Hum. Ecol. Risk Assess. 13, 481–491 (2007).Article 

    Google Scholar 
    30.Ford, A. T. et al. The role of behavioral ecotoxicology in environmental protection. Environ. Sci. Technol. 55, 5620–5628 (2021).CAS 
    Article 

    Google Scholar 
    31.Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).Article 

    Google Scholar  More

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    Upper environmental pCO2 drives sensitivity to ocean acidification in marine invertebrates

    1.Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, aac4722 (2015).
    Google Scholar 
    2.Caldeira, K. & Wickett, M. E. Anthropogenic carbon and ocean pH. Nature 425, 365 (2003).CAS 

    Google Scholar 
    3.Hönisch, B. et al. The geological record of ocean acidification. Science 335, 1058–1063 (2012).
    Google Scholar 
    4.Turley, C. & Gattuso, J.-P. Future biological and ecosystem impacts of ocean acidification and their socioeconomic-policy implications. Curr. Opin. Environ. Sustain. 4, 278–286 (2012).
    Google Scholar 
    5.San Martin, V. A. et al. Linking social preferences and ocean acidification impacts in mussel aquaculture. Sci. Rep. 9, 4719 (2019).
    Google Scholar 
    6.Falkenberg, L. et al. Ocean acidification and human health. Int. J. Environ. Res. Public Health 17, 4563 (2020).CAS 

    Google Scholar 
    7.Loewe, M. & Rippin, N. The Sustainable Development Goals of the Post-2015 Agenda. Comments on the OWG and SDSN Proposals (German Development Institute 2015).8.Doney, S. C. et al. The impacts of ocean acidification on marine ecosystems and reliant human communities. Annu. Rev. Environ. Resour. 45, 83–112 (2020).
    Google Scholar 
    9.Ekstrom, J. et al. Vulnerability and adaptation of US shellfisheries to ocean acidification. Nat. Clim. Change 5, 207–214 (2015).
    Google Scholar 
    10.Ponce Oliva, R. D. et al. Ocean acidification, consumers’ preferences, and market adaptation strategies in the mussel aquaculture industry. Ecol. Econ. 158, 42–50 (2019).
    Google Scholar 
    11.Quatrinni, A. M. et al. Palaeoclimate ocean conditions shaped the evolution of corals and their skeletons through deep time. Nat. Ecol. Evol. 4, 1531–1538 (2020).
    Google Scholar 
    12.Thomsen, J. et al. Naturally acidified habitat selects for ocean acidification-tolerant mussels. Sci. Adv. 3, e1602411 (2017).
    Google Scholar 
    13.Rastrick, S. S. P. et al. Using natural analogues to investigate the effects of climate change and ocean acidification on Northern ecosystems. ICES J. Mar. Sci. 75, 2299–2311 (2018).
    Google Scholar 
    14.Hall-Spencer, J. M. et al. Volcanic carbon dioxide vents reveal ecosystem effects of ocean acidification. Nature 454, 96–99 (2008).CAS 

    Google Scholar 
    15.Agostini, S. et al. Ocean acidification drives community shifts towards simplified non-calcified habitats in a subtropical–temperate transition zone. Sci. Rep. 8, 11354 (2018).
    Google Scholar 
    16.Riquelme-Bugueño, R. et al. Diel vertical migration into anoxic and high-pCO2 waters: acoustic and net-based krill observations in the Humboldt Current. Sci. Rep. 10, 17181 (2020).
    Google Scholar 
    17.Pérez et al. Riverine discharges impact physiological traits and carbon sources for shell carbonate in the marine intertidal mussel Perumytilus purpuratus. Limnol. Oceanogr. 61, 969–983 (2016).
    Google Scholar 
    18.Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 0084 (2017).
    Google Scholar 
    19.Saavedra et al. Local habitat influences on feeding and respiration of the intertidal mussels Perumytilus purpuratus exposed to increased pCO2 levels. Estuaries Coast. 41, 1118–1129 (2018).CAS 

    Google Scholar 
    20.Riebesell, U. & Gattuso, J.-P. Lessons learned from ocean acidification research. Nat. Clim. Change 5, 12–14 (2015).CAS 

    Google Scholar 
    21.Tilbrook, B. et al. An enhanced ocean acidification observing network: from people to technology to data synthesis and information exchange. Front. Mar. Sci. 6, 337 (2019).
    Google Scholar 
    22.Barry, J. P., Hall-Spencer, J. M. and Tyrrell, T. in Guide to Best Practices for Ocean Acidification Research and Data Reporting (eds Riebesell, U. et al.) Ch. 3 (Publications Office of the European Union, 2010).23.Vargas, C. A. et al. Influence of glacier melting and river discharges on the nutrient distribution and DIC recycling in the southern Chilean Patagonia. J. Geophys. Res. Biogeosci. 123, 256–270 (2018).
    Google Scholar 
    24.Feely, R. A. et al. Evidence for upwelling of corrosive ‘acidified’ water onto the Continental Shelf. Science 320, 1490–1492 (2008).CAS 

    Google Scholar 
    25.Vargas, C. A. et al. Riverine and corrosive upwelling waters influences on the carbonate system in the coastal upwelling area off Central Chile: implications for coastal acidification events. J. Geophys. Res. Biogeosci. 121, 1468–1483 (2016).
    Google Scholar 
    26.Cao, Z. et al. Dynamics of the carbonate system in a large continental shelf system under the influence of both a river plume and coastal upwelling. J. Geophys. Res. Oceans 116, G02010 (2010).
    Google Scholar 
    27.Feely, R. A. et al. The combined effects of ocean acidification, mixing, and respiration on pH and carbonate saturation in an urbanized estuary. Est. Coast. Shelf Sci. 88, 442–449 (2010).CAS 

    Google Scholar 
    28.Cai, W.-J. et al. Acidification of subsurface coastal waters enhanced by eutrophication. Nat. Geosci. 4, 766–770 (2011).CAS 

    Google Scholar 
    29.Kwiatkowski, L. et al. Nighttime dissolution in a temperate coastal ocean ecosystem increases under acidification. Sci. Rep. 6, 22984 (2016).CAS 

    Google Scholar 
    30.Wolfe, K., Nguyen, H. D., Davey, M. & Byrne, M. Characterizing biogeochemical fluctuations in a world of extremes: a synthesis for temperate intertidal habitats in the face of global change. Glob. Change Biol. 26, 3858–3879 (2020).
    Google Scholar 
    31.Shaw, E. C., Phinn, S. R., Tilbrook, B. & Steven, A. Natural in situ relationships suggest coral reef calcium carbonate production will decline with ocean acidification. Limnol. Oceanogr. 60, 777–788 (2015).
    Google Scholar 
    32.Takeshita, Y. et al. Coral reef carbonate chemistry variability at different functional scales. Front. Mar. Sci. 5, 175 (2018).
    Google Scholar 
    33.Brodeur, J. R. et al. Chesapeake Bay inorganic carbon: spatial distribution and seasonal variability. Front. Mar. Sci. 6, 99 (2019).
    Google Scholar 
    34.Hoshijima, U. & Hofmann, G. E. Variability of seawater chemistry in a kelp forest environment is linked to in situ transgenerational effects in the purple sea urchin, Strongylocentrotus purpuratus. Front. Mar. Sci. 6, 62 (2019).
    Google Scholar 
    35.Koweek, D. A. et al. A year in the life of a central California kelp forest: physical and biological insights into biogeochemical variability. Biogeosciences 14, 31–44 (2017).CAS 

    Google Scholar 
    36.Cornwall, C. E. & Hurd, C. L. Experimental design in ocean acidification research: problems and solutions. ICES J. Mar. Sci. 73, 572–581 (2016).
    Google Scholar 
    37.Kapsenberg, L. & Hofmann, G. E. Ocean pH time-series and drivers of variability along the northern Channel Islands, California, USA. Limnol. Oceanogr. 61, 953–968 (2016).
    Google Scholar 
    38.Hofmann, G. E. et al. High-frequency dynamics of ocean pH: a multi-ecosystem comparison. PLoS ONE 6, e28983 (2011).CAS 

    Google Scholar 
    39.Baumann, H. Experimental assessments of marine species sensitivities to ocean acidification and co-stressors: how far have we come? Can. J. Zool. 97, 399–408 (2019).
    Google Scholar 
    40.Cornwall, C. E. et al. Diurnal fluctuations in seawater pH influence the response of a calcifying macroalga to ocean acidification. Proc. R. Soc. B 280, 20132201 (2013).
    Google Scholar 
    41.Rivest, E. B., Comeau, S. & Cornwall, C. E. The role of natural variability in shaping the response of coral reef organisms to climate change. Curr. Clim. 3, 271–281 (2017).
    Google Scholar 
    42.Sanford, E. & Kelly, M. W. Local adaptation in marine invertebrates. Annu. Rev. Mar. Sci. 3, 509–535 (2011).
    Google Scholar 
    43.Lewis, C. N. et al. Sensitivity to ocean acidification parallels natural pCO2 gradients experienced by Arctic copepods under winter sea ice. Proc. Natl Acad. Sci. USA 110, E4960–E4967 (2013).CAS 

    Google Scholar 
    44.Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience 57, 573–583 (2007).
    Google Scholar 
    45.Aguilera, V. M., Vargas, C. A. & Dewitte, B. Intraseasonal hydrographic variations and nearshore carbonates system off northern Chile during the 2015 El Niño event. J. Geophys. Res. Biogeosci. 125, e2020JG005704 (2020).CAS 

    Google Scholar 
    46.Fassbender, A. J. et al. Seasonal carbonate chemistry variability in marine surface waters of the US Pacific Northwest. Earth Syst. Sci. Data 10, 1367–1401 (2018).
    Google Scholar 
    47.Reum, J. C. P. et al. Seasonal carbonate chemistry covariation with temperature, oxygen, and salinity in a fjord estuary: implications for the design of ocean acidification experiments. PLoS ONE 9, e89619 (2014).
    Google Scholar 
    48.Wallace, R. B. et al. Coastal ocean acidification: the other eutrophication problem. Estuar. Coast. Shelf Sci. 148, 1–13 (2014).CAS 

    Google Scholar 
    49.Rutgersson, A. et al. The annual cycle of carbon dioxide and parameters influencing the air–sea carbon exchange in the Baltic Proper. J. Mar. Syst. 74, 381–394 (2008).
    Google Scholar 
    50.Clargo, N. M., Salt, L. A., Thomas, H. & de Baar, H. J. W. Rapid increase of observed DIC and pCO2 in the surface waters of the North Sea in the 2001–2011 decade ascribed to climate change superimposed by biological processes. Mar. Chem. 177, 566–581 (2015).CAS 

    Google Scholar 
    51.Ericson, Y. et al. Temporal variability in surface water pCO2 in Adventfjorden (West Spitsbergen) with emphasis on physical and biogeochemical drivers. J. Geophys. Res. Oceans 123, 4888–4905 (2018).CAS 

    Google Scholar 
    52.Geilfus, N.-X. et al. Spatial and temporal variability of seawater pCO2 within the Canadian Arctic Archipelago and Baffin Bay during the summer and autumn 2011. Cont. Shelf Res. 156, 1–10 (2018).
    Google Scholar 
    53.Islam, F. et al. Sea surface pCO2 and O2 dynamics in the partially ice-covered Arctic Ocean. J. Geophys. Res. Oceans 122, 1425–1438 (2016).
    Google Scholar 
    54.Copin-Montégut, C., Bégovic, M. & Merlivat, L. Variability of the partial pressure of CO2 on diel to annual time scales in the Northwestern Mediterranean Sea. Mar. Chem. 85, 169–189 (2004).
    Google Scholar 
    55.Pardo, P. C. et al. Surface ocean carbon dioxide variability in South Pacific boundary currents and Subantarctic waters. Sci. Rep. 9, 7592 (2019).
    Google Scholar 
    56.Gagliano, M., McCormick, M. I., Moore, J. A. & Depczynski, M. The basics of acidification: baseline variability of pH on Australian coral reefs. Mar. Biol. 157, 1849–1856 (2010).CAS 

    Google Scholar 
    57.Takeshita, Y. et al. Including high-frequency variability in coastal acidification projections. Biogeosciences 12, 5853–5870 (2015).
    Google Scholar 
    58.Carter, H. A., Ceballos-Osuna, L., Miller, N. A. & Stillman, J. H. Impact of ocean acidification on metabolism and energetics during early life stages of the intertidal porcelain crab Petrolisthes cinctipes. J. Exp. Biol. 216, 1412–1422 (2013).CAS 

    Google Scholar 
    59.Ceballos-Osuna, L., Carter, H. A., Miller, N. A. & Stillman, J. H. Effects of ocean acidification on early life-history stages of the intertidal porcelain crab Petrolisthes cinctipes. J. Exp. Biol. 216, 1405–1411 (2013).CAS 

    Google Scholar 
    60.Miller, S. H. et al. Effect of elevated pCO2 on metabolic responses of porcelain crab (Petrolisthes cinctipes) larvae exposed to subsequent salinity stress. PLoS ONE 9, e109167 (2014).
    Google Scholar 
    61.Bayne, B. L. Metabolic expenditure. Dev. Aquacult. Fish. Sci. 41, 331–415 (2017).
    Google Scholar 
    62.Waldbusser, G. G. et al. Slow shell building, a possible trait for resistance to the effects of acute ocean acidification. Limnol. Oceanogr. 61, 1969–1983 (2016).
    Google Scholar 
    63.Dorey, N., Lancon, P., Thorndyke, M. & Dupont, S. Assessing physiological tipping point for sea urchin larvae exposed to a broad range of pH. Glob. Change Biol. 19, 3355–3367 (2013).
    Google Scholar 
    64.Kelly, M. W., Padilla-Gamiño, J. L. & Hofmann, G. E. Natural variation and the capacity to adapt to ocean acidification in the keystone sea urchin Strongylocentrotus purpuratus. Glob. Change Biol. 19, 2536–2546 (2015).
    Google Scholar 
    65.Gaitán-Espitia, J. D. et al. Spatio–temporal environmental variation mediates geographical differences in phenotypic responses to ocean acidification. Biol. Lett. 13, 20160865 (2017).
    Google Scholar 
    66.Calosi, P. et al. Distribution of sea urchins living near shallow water CO2 vents is dependent upon species acid–base and ion-regulatory abilities. Mar. Pollut. Bull. 73, 470–484 (2013).CAS 

    Google Scholar 
    67.Foo, S. A., Dworjanyn, S. A., Poore, A. G. B. & Byrne, M. Adaptive capacity of the habitat modifying sea urchin Centrostephanus rodgersii to ocean warming and ocean acidification: performance of early embryos. PLoS ONE 7, e42497 (2012).CAS 

    Google Scholar 
    68.Chan, K. Y. K., Grünbaum, D., Arnberg, M. & Dupont, S. Impacts of ocean acidification on survival, growth, and swimming behaviours differ between larval urchins and brittlestars. ICES J. Mar. Sci. 73, 951–996 (2016).
    Google Scholar 
    69.Stumpp, M. et al. Acidified seawater impacts sea urchin larvae pH regulatory systems relevant for calcification. Proc. Natl Acad. Sci. USA 109, 18192–18197 (2012).CAS 

    Google Scholar 
    70.Stumpp, M. et al. Digestion in sea urchin larvae impaired under ocean acidification. Nat. Clim. Change 3, 1044–1049 (2013).CAS 

    Google Scholar 
    71.Thor, P. & Dupont, S. Transgenerational effects alleviate severe fecundity loss during ocean acidification in a ubiquitous planktonic copepod. Glob. Change Biol. 21, 2261–2271 (2015).
    Google Scholar 
    72.Gibbin, E. M. et al. The evolution of phenotypic plasticity under global change. Sci. Rep. 7, 17253 (2017).
    Google Scholar 
    73.Gibbin, E. M. et al. Can multi-generational exposure to ocean warming and acidification lead to the adaptation of life history and physiology in a marine metazoan? J. Exp. Biol. 220, 551–563 (2017).
    Google Scholar 
    74.Dam, H. G. et al. Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification. Nat. Clim. Change 11, 780–786 (2021).
    Google Scholar 
    75.Byrne, M. Impact of ocean warming and ocean acidification on marine invertebrate life history stages: vulnerabilities and potential for persistence in a changing ocean. Oceanogr. Mar. Biol. 49, 1–42 (2011).
    Google Scholar 
    76.Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).
    Google Scholar 
    77.Kroeker et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).
    Google Scholar 
    78.Takahashi, T., Sutherland, S. C. & Kozyr, A. LDEO Database (Version 2019): Global Ocean Surface Water Partial Pressure of CO2 Database: Measurements Performed During 1957–2019 (NCEI Accession 0160492) Version 9.9 (National Oceanic and Atmospheric Administration National Centers for Environmental Information); https://doi.org/10.3334/CDIAC/OTG.NDP088(V2015)79.Manly, B. F. J. Randomization, Bootstrap and Monte Carlo Methods in Biology (CRC Press, 1997).80.Martinez, W. L. & Martinez, A. R. Computational Statistics Handbook with MATLAB (CRC Press, 2002). More

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    Deciphering the multiple effects of climate warming on the temporal shift of leaf unfolding

    1.Arora, V. K. & Boer, G. J. A parameterization of leaf phenology for the terrestrial ecosystem component of climate models. Glob. Change Biol. 11, 39–59 (2005).
    Google Scholar 
    2.Richardson, A. D. et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob. Change Biol. 18, 566–584 (2012).
    Google Scholar 
    3.Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).
    Google Scholar 
    4.Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3227–3246 (2010).
    Google Scholar 
    5.Diez, J. M. et al. Forecasting phenology: from species variability to community patterns. Ecol. Lett. 15, 545–553 (2012).
    Google Scholar 
    6.Hegland, S. J., Nielsen, A., Lazaro, A., Bjerknes, A. L. & Totland, O. How does climate warming affect plant-pollinator interactions? Ecol. Lett. 12, 184–195 (2009).
    Google Scholar 
    7.Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 

    Google Scholar 
    8.Zhang, H., Yuan, W., Liu, S. & Dong, W. Divergent responses of leaf phenology to changing temperature among plant species and geographical regions. Ecosphere 6, art250 (2015).
    Google Scholar 
    9.Zhang, G., Zhang, Y., Dong, J. & Xiao, X. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proc. Natl Acad. Sci. USA 110, 4309–4314 (2013).CAS 

    Google Scholar 
    10.Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).
    Google Scholar 
    11.Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. & Schwartz, M. D. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).
    Google Scholar 
    12.Menzel, A., Sparks, T. H., Estrella, N. & Roy, D. B. Altered geographic and temporal variability in phenology in response to climate change. Glob. Ecol. Biogeogr. 15, 498–504 (2006).
    Google Scholar 
    13.Zhang, X., Tarpley, D. & Sullivan, J. T. Diverse responses of vegetation phenology to a warming climate. Geophys. Res. Lett. https://doi.org/10.1029/2007gl031447 (2007).14.Fitter, A. H. & Fitter, R. S. Rapid changes in flowering time in British plants. Science 296, 1689–1691 (2002).CAS 

    Google Scholar 
    15.Primack, R. B. et al. Spatial and interspecific variability in phenological responses to warming temperatures. Biol. Conserv. 142, 2569–2577 (2009).
    Google Scholar 
    16.Cleland, E. E., Chiariello, N. R., Loarie, S. R., Mooney, H. A. & Field, C. B. Diverse responses of phenology to global changes in a grassland ecosystem. Proc. Natl Acad. Sci. USA 103, 13740–13744 (2006).CAS 

    Google Scholar 
    17.Wang, H., Dai, J., Zheng, J. & Ge, Q. Temperature sensitivity of plant phenology in temperate and subtropical regions of China from 1850 to 2009. Int. J. Climatol. 35, 913–922 (2015).
    Google Scholar 
    18.Chuine, I. M., Morin, X. & Bugmann, H. Warming, photoperiods, and tree phenology. Science 329, 277–278 (2010).
    Google Scholar 
    19.Chuine, I. A unified model for budburst of trees. J. Theor. Biol. 207, 337–347 (2000).CAS 

    Google Scholar 
    20.Murray, M., Cannell, M. G. R. & Smith, R. I. Date of budburst of fifteen tree species in Britain following climatic warming. J. Appl. Ecol. 26, 693–700 (1989).
    Google Scholar 
    21.Man, R., Lu, P. & Dang, Q. L. Insufficient chilling effects vary among boreal tree species and chilling duration. Front. Plant Sci. 8, 1354 (2017).
    Google Scholar 
    22.Cannell, M. G. R. & Smith, R. I. L. Thermal time, chill days and prediction of budburst in Picea sitchensis. J. Appl. Ecol. 20, 951–963 (1983).
    Google Scholar 
    23.Fu, Y. H. et al. Increased heat requirement for leaf flushing in temperate woody species over 1980-2012: effects of chilling, precipitation and insolation. Glob. Change Biol. 21, 2687–2697 (2015).
    Google Scholar 
    24.Zhang, H., Liu, S., Regnier, P. & Yuan, W. New insights on plant phenological response to temperature revealed from long-term widespread observations in China. Glob. Change Biol. 24, 2066–2078 (2018).
    Google Scholar 
    25.Yu, H., Luedeling, E. & Xu, J. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 107, 22151–22156 (2010).CAS 

    Google Scholar 
    26.Asse, D. et al. Warmer winters reduce the advance of tree spring phenology induced by warmer springs in the Alps. Agric. For. Meteorol. 252, 220–230 (2018).
    Google Scholar 
    27.Ettinger, A. K. et al. Winter temperatures predominate in spring phenological responses to warming. Nat. Clim. Change 10, 1137–1142 (2020).
    Google Scholar 
    28.Chuine, I. & Régnière, J. Process-based models of phenology for plants and animals. Annu. Rev. Ecol. Evol. Syst. 48, 159–182 (2017).
    Google Scholar 
    29.Caffarra, A., Donnelly, A., Chuine, I. & Jones, M. B. Modelling the timing of Betula pubescens budburst. I. Temperature and photoperiod: a conceptual model. Clim. Res. 46, 147–157 (2011).
    Google Scholar 
    30.Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M. & Wanner, H. European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303, 1499–1503 (2004).CAS 

    Google Scholar 
    31.Ciais, P. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).32.Fu, Y. H. et al. Daylength helps temperate deciduous trees to leaf-out at the optimal time. Glob. Change Biol. 25, 2410–2418 (2019).
    Google Scholar 
    33.Wolkovich, E. M. et al. A simple explanation for declining temperature sensitivity with warming. Glob. Change Biol. 27, 4947–4949 (2021).CAS 

    Google Scholar 
    34.Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).
    Google Scholar 
    35.Kramer, K. Selecting a model to predict the onset of growth of Fagus sylvatica. J. Appl. Ecol. 31, 172–181 (1994).
    Google Scholar 
    36.Chuine, I., Cour, P. & Rousseau, D.-D. Selecting models to predict the timing of flowering of temperate trees: implications for tree phenology modelling. Plant Cell Environ. 22, 1–13 (1999).37.Savas, R. Investigations on the annual cycle of development of forest trees. II. Autumn dormancy and winter dormancy https://eurekamag.com/research/000/414/000414639.php (1974).38.Hänninen, H. Modelling bud dormancy release in trees from cool and temperate regions. Acta. Fenn. 14, 499–454 (1990).
    Google Scholar 
    39.Harrington, C. A., Gould, P. J. & St. Clair, J. B. Modeling the effects of winter environment on dormancy release of Douglas-fir. Ecol. Manag. 259, 798–808 (2010).
    Google Scholar 
    40.Zhang, H., Yuan, W., Liu, S., Dong, W. & Fu, Y. Sensitivity of flowering phenology to changing temperature in China. J. Geophys. Res. Biogeosci. 120, 1658–1665 (2015).
    Google Scholar 
    41.Richardson, A. D. et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol. 29, 321–331 (2009).CAS 

    Google Scholar 
    42.Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
    Google Scholar 
    43.Körner, C. & Basler, D. Phenology under global warming. Science 327, 1461–1462 (2010).
    Google Scholar 
    44.Zohner, C. M. & Renner, S. S. Common garden comparison of the leaf-out phenology of woody species from different native climates, combined with herbarium records, forecasts long-term change. Ecol. Lett. 17, 1016–1025 (2014).
    Google Scholar 
    45.Vitasse, Y. & Basler, D. What role for photoperiod in the bud burst phenology of European beech. Eur. J. For. Res. 132, 1–8 (2012).
    Google Scholar 
    46.Lenz, A., Hoch, G., Körner, C. & Vitasse, Y. Convergence of leaf-out towards minimum risk of freezing damage in temperate trees. Funct. Ecol. 30, 1480–1490 (2016).
    Google Scholar 
    47.Wang, Y. et al. Forest controls spring phenology of juvenile Smith fir along elevational gradients on the southeastern Tibetan Plateau. Int. J. Biometeorol. 63, 963–972 (2019).
    Google Scholar 
    48.Marquis, B., Bergeron, Y., Simard, M. & Tremblay, F. Probability of sping frosts, not growing degree-days, drives onset of spruce bud burst in plantations at the boreal-temperate forest ecotone. Front. Plant Sci. 11, 1031 (2020).
    Google Scholar 
    49.Shen, M., Piao, S., Cong, N., Zhang, G. & Jassens, I. A. Precipitation impacts on vegetation spring phenology on the Tiberan Plateau. Glob. Change Biol. 21, 3647–3656 (2015).
    Google Scholar 
    50.Liu et al. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Glob. Change Biol. 22, 644–655 (2016).CAS 

    Google Scholar 
    51.Minder, J. R., Mote, P. W. & Lundquist, J. D. Surface temperature lapse rates over complex terrain: lessons from the Cascade Mountains. J. Geophys. Res. 115, D14122 (2010).
    Google Scholar 
    52.Navarro-Serrano et al. Elevation effects on air temperature in a topographically complex mountain valley in the Spanish Pyrenees. Atmosphere 11, 656 (2020).
    Google Scholar 
    53.Chen, L. et al. Leaf senescence exhibits stronger climatic responses during warm than during cold autumns. Nat. Clim. Change 10, 777–780 (2020).CAS 

    Google Scholar 
    54.Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
    Google Scholar 
    55.Beer, C. et al. Harmonized European long-term climate data for assessing the effect of changing temporal variability on land–atmosphere CO2 fluxes. J. Clim. 27, 4815–4834 (2014).
    Google Scholar 
    56.Olsson, C. & Jönsson, A. M. Process-based models not always better than empirical models for simulating budburst of Norway spruce and birch in Europe. Glob. Change Biol. 20, 3492–3507 (2014).
    Google Scholar 
    57.Duan, Q., Sorooshian, S. & Gupta, V. K. Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 158, 265–284 (1994).
    Google Scholar 
    58.Bluemel, K. & Chmielewski, F. Shortcomings of classical phenological forcing models and a way to overcome them. Agric. For. Meteorol. 164, 10–19 (2012).
    Google Scholar  More

  • in

    Global mapping reveals increase in lacustrine algal blooms over the past decade

    1.Brooks, B. W. et al. Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems? Environ. Toxicol. Chem. 35, 6–13 (2016).
    Google Scholar 
    2.Lopez, C., Jewett, E., Dortch, Q., Walton, B. & Hudnell, H. Scientific Assessment of Freshwater Harmful Algal Blooms (United States National Ocean Service, 2008)3.Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).
    Google Scholar 
    4.Paerl, H. W. & Paul, V. J. Climate change: links to global expansion of harmful cyanobacteria. Water Res. 46, 1349–1363 (2012).
    Google Scholar 
    5.Carmichael, W. W. The toxins of cyanobacteria. Sci. Am. 270, 78–86 (1994).
    Google Scholar 
    6.Carmichael, W. W. et al. Human fatalities from cyanobacteria: chemical and biological evidence for cyanotoxins. Environ. Health Persp. 109, 663–668 (2001).
    Google Scholar 
    7.Botswana: mystery elephant deaths caused by cyanobacteria. BBC News https://www.bbc.com/news/world-africa-54234396 (2020).8.Paerl, H. W. & Huisman, J. Blooms like it hot. Science 320, 57–58 (2008).
    Google Scholar 
    9.O’Neil, J. M., Davis, T. W., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: the potential roles of eutrophication and climate change. Harmful Algae 14, 313–334 (2012).
    Google Scholar 
    10.Kutser, T. Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing. Limnol. Oceanogr. 49, 2179–2189 (2004).
    Google Scholar 
    11.Kutser, T., Metsamaa, L., Strömbeck, N. & Vahtmäe, E. Monitoring cyanobacterial blooms by satellite remote sensing. Estuar. Coast. Shelf Sci. 67, 303–312 (2006).
    Google Scholar 
    12.Binding, C. E., Pizzolato, L. & Zeng, C. EOLakeWatch; delivering a comprehensive suite of remote sensing algal bloom indices for enhanced monitoring of Canadian eutrophic lakes. Ecol. Indic. 121, 106999 (2021).
    Google Scholar 
    13.Stumpf, R. P. et al. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria. Harmful Algae 54, 160–173 (2016).
    Google Scholar 
    14.Matthews, M. W. Eutrophication and cyanobacterial blooms in South African inland waters: 10 years of MERIS observations. Remote Sens. Environ. 155, 161–177 (2014).
    Google Scholar 
    15.Mishra, S. et al. Measurement of cyanobacterial bloom magnitude using satellite remote sensing. Sci. Rep. 9, 18310 (2019).
    Google Scholar 
    16.Hu, C. et al. Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. J. Geophys. Res. 115, C04002 (2010).
    Google Scholar 
    17.Song, K. et al. Climatic versus anthropogenic controls of decadal trends (1983–2017) in algal blooms in lakes and reservoirs across China. Environ. Sci. Technol. 55, 2929–2938 (2021).
    Google Scholar 
    18.Coffer, M. M., Schaeffer, B. A., Darling, J. A., Urquhart, E. A. & Salls, W. B. Quantifying national and regional cyanobacterial occurrence in US lakes using satellite remote sensing. Ecol. Indic. 111, 105976 (2020).
    Google Scholar 
    19.Ho, J., Michalak, A. & Pahlevan, N. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574, 667–670 (2019).
    Google Scholar 
    20.Dierssen, H. M., Kudela, R. M., Ryan, J. P. & Zimmerman, R. C. Red and black tides: quantitative analysis of water-leaving radiance and perceived color for phytoplankton, colored dissolved organic matter, and suspended sediments. Limnol. Oceanogr. 51, 2646–2659 (2006).
    Google Scholar 
    21.Michalak, A. M. et al. Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions. Proc. Natl Acad. Sci. USA 110, 6448–6452 (2013).
    Google Scholar 
    22.Binding, C., Greenberg, T., McCullough, G., Watson, S. & Page, E. An analysis of satellite-derived chlorophyll and algal bloom indices on Lake Winnipeg. J. Great Lakes Res. 44, 436–446 (2018).
    Google Scholar 
    23.Guo, L. Doing battle with the green monster of Taihu Lake. Science 317, 1166–1166 (2007).
    Google Scholar 
    24.Moradi, M. Comparison of the efficacy of MODIS and MERIS data for detecting cyanobacterial blooms in the southern Caspian Sea. Mar. Pollut. Bull. 87, 311–322 (2014).
    Google Scholar 
    25.Schindler, D. W. Eutrophication and recovery in experimental lakes: implications for lake management. Science 184, 897–899 (1974).
    Google Scholar 
    26.Qin, B. et al. Water depth underpins the relative roles and fates of nitrogen and phosphorus in lakes. Environ. Sci. Technol. 54, 3191–3198 (2020).
    Google Scholar 
    27.Beman, J. M., Arrigo, K. R. & Matson, P. A. Agricultural runoff fuels large phytoplankton blooms in vulnerable areas of the ocean. Nature 434, 211–214 (2005).
    Google Scholar 
    28.Yu, C. et al. Managing nitrogen to restore water quality in China. Nature 567, 516–520 (2019).
    Google Scholar 
    29.Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).
    Google Scholar 
    30.Hobbie, S. E. et al. Contrasting nitrogen and phosphorus budgets in urban watersheds and implications for managing urban water pollution. Proc. Natl Acad. Sci. USA 114, 4177–4182 (2017).
    Google Scholar 
    31.Wang, Z. China’s wastewater treatment goals. Science 338, 604–604 (2012).
    Google Scholar 
    32.Sutton, M. A. et al. The European Nitrogen Assessment: Sources, Effects and Policy Perspectives (Cambridge Univ. Press, 2011).33.Litke, D. W. Review of Phosphorus Control Measures in the United States and Their Effects on Water Quality (US Geological Survey, 1999).34.Kosten, S. et al. Warmer climates boost cyanobacterial dominance in shallow lakes. Glob. Change Biol. 18, 118–126 (2012).
    Google Scholar 
    35.Carey, C. C., Ibelings, B. W., Hoffmann, E. P., Hamilton, D. P. & Brookes, J. D. Eco-physiological adaptations that favour freshwater cyanobacteria in a changing climate. Water Res. 46, 1394–1407 (2012).
    Google Scholar 
    36.Wells, M. L. et al. Harmful algal blooms and climate change: learning from the past and present to forecast the future. Harmful Algae 49, 68–93 (2015).
    Google Scholar 
    37.Elliott, J. A. The seasonal sensitivity of cyanobacteria and other phytoplankton to changes in flushing rate and water temperature. Glob. Change Biol 16, 864–876 (2010).
    Google Scholar 
    38.Jeppesen, E. et al. in Shallow Lakes ’95 (eds Kufel, L. et al.) 151–164 (Springer, 1997).39.O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42, 773–710,781 (2015).
    Google Scholar 
    40.Janssen, A. B. G. et al. How to model algal blooms in any lake on earth. Curr. Opin. Environ. Sustain 36, 1–10 (2019).
    Google Scholar 
    41.Woodcock, C. E. et al. Free access to Landsat imagery. Science 320, 1011 (2008).
    Google Scholar 
    42.Zhu, Z. & Woodcock, C. E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83–94 (2012).
    Google Scholar 
    43.Masek, J. G. et al. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci. Remote Sens. Lett. 3, 68–72 (2006).
    Google Scholar 
    44.Vermote, E., Justice, C., Claverie, M. & Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 185, 46–56 (2016).
    Google Scholar 
    45.Irish, R. R. Landsat 7 Science Data Users Handbook 415–430 (US Geological Survey, 2000).46.Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).
    Google Scholar 
    47.Wang, J. et al. Recent global decline in endorheic basin water storages. Nat. Geosci. 11, 926–932 (2018).
    Google Scholar 
    48.Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
    Google Scholar 
    49.McNally, A. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci. Data 4, 170012 (2017).
    Google Scholar 
    50.CIESIN Gridded Population of the World v.4 (NASA SEDAC, 2018).51.Bouwman, L. et al. Exploring global changes in nitrogen and phosphorus cycles in agriculture induced by livestock production over the 1900–2050 period. Proc. Natl Acad. Sci. USA 110, 20882–20887 (2013).
    Google Scholar 
    52.Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).
    Google Scholar 
    53.Feng, L. & Hu, C. Land adjacency effects on MODIS Aqua top-of-atmosphere radiance in the shortwave infrared: statistical assessment and correction. J. Geophys. Res. Oceans 122, 4802–4818 (2017).
    Google Scholar 
    54.Walsh, S. E. et al. Global patterns of lake ice phenology and climate: model simulations and observations. J. Geophys. Res. Atmos. 103, 28825–28837 (1998).
    Google Scholar 
    55.Yang, X., Pavelsky, T. M. & Allen, G. H. The past and future of global river ice. Nature 577, 69–73 (2020).
    Google Scholar 
    56.Hu, C. et al. Dynamic range and sensitivity requirements of satellite ocean color sensors: learning from the past. Appl. Opt. 51, 6045–6062 (2012).
    Google Scholar 
    57.Kuhn, C. & Butman, D. Declining greenness in Arctic-boreal lakes. Proc. Natl Acad. Sci. USA 118, e2021219118 (2021).
    Google Scholar 
    58.Kirillin, G. et al. Physics of seasonally ice-covered lakes: a review. Aquat. Sci. 74, 659–682 (2012).
    Google Scholar 
    59.Kotovirta, V., Toivanen, T., Järvinen, M., Lindholm, M. & Kallio, K. Participatory surface algal bloom monitoring in Finland in 2011–2013. Environ. Syst. Res. 3, 24 (2014).
    Google Scholar 
    60.Cronberg, G., Annadotter, H. & Lawton, L. A. The occurrence of toxic blue-green algae in Lake Ringsjön, southern Sweden, despite nutrient reduction and fish biomanipulation. Hydrobiologia 404, 123–129 (1999).
    Google Scholar 
    61.Romarheim, A. T. & Riise, G. Development of Cyanobacteria in Årungen (Norsk vannforening, 2009)62.Robertson, A. R. The CIE 1976 color‐difference formulae. Color Res. Appl. 2, 7–11 (1977).
    Google Scholar 
    63.Mouw, C. B. et al. Aquatic color radiometry remote sensing of coastal and inland waters: challenges and recommendations for future satellite missions. Remote Sens. Environ. 160, 15–30 (2015).
    Google Scholar 
    64.Wasmund, N., Nausch, G. & Matthäus, W. Phytoplankton spring blooms in the southern Baltic Sea—spatio-temporal development and long-term trends. J. Plankton Res. 20, 1099–1117 (1998).
    Google Scholar 
    65.Hu, C. A novel ocean color index to detect floating algae in the global oceans. Remote Sens. Environ. 113, 2118–2129 (2009).
    Google Scholar 
    66.Fairman, H. S., Brill, M. H. & Hemmendinger, H. How the CIE 1931 color-matching functions were derived from Wright-Guild data. Color Res. Appl. 22, 11–23 (1997).
    Google Scholar 
    67.Chander, G., Markham, B. L. & Helder, D. L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903 (2009).
    Google Scholar 
    68.Feng, L. et al. Radiometric cross-calibration of Gaofen-1 WFV cameras using Landsat-8 OLI images: a solution for large view angle associated problems. Remote Sens. Environ. 174, 56–68 (2016).
    Google Scholar 
    69.Yu, X. et al. An empirical algorithm to seamlessly retrieve the concentration of suspended particulate matter from water color across ocean to turbid river mouths. Remote Sens. Environ. 235, 111491 (2019).
    Google Scholar 
    70.Hou, X., Feng, L., Chen, X. & Zhang, Y. Dynamics of the wetland vegetation in large lakes of the Yangtze Plain in response to both fertilizer consumption and climatic changes. ISPRS J. Photogramm. Remote Sens. 141, 148–160 (2018).
    Google Scholar 
    71.Lee, Z., Pahlevan, N., Ahn, Y.-H., Greb, S. & O’Donnell, D. Robust approach to directly measuring water-leaving radiance in the field. Appl. Opt. 52, 1693–1701 (2013).
    Google Scholar 
    72.Liu, L., Peng, W., Wu, L. & Liu, L. Water quality assessment of Danjiangkou Reservoir and its tributaries in China. IOP Conf. Ser. Earth Environ. Sci. 112, 012008 (2018).
    Google Scholar 
    73.Li, X. et al. The color formation mechanism of the blue karst lakes in Jiuzhaigou Nature Reserve, Sichuan, China. Water 12, 771 (2020).
    Google Scholar 
    74.Wurtsbaugh, W. & Marcarelli, A. Eutrophication in Farmington Bay, Great Salt Lake, Utah 2005 Annual Report (Utah State Univ., 2006).75.Hammer, U. T. Saline Lake Ecosystems of the World Vol. 59 (Springer, 1986). More

  • in

    Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060

    1.MacLachlan, N. J. & Guthrie, A. J. Re-emergence of bluetongue, African horse sickness, and other Orbivirus diseases. Vet. Res. 41, 35 (2010).Article 

    Google Scholar 
    2.Zientara, S., Weyer, C. T. & Lecollinet, S. African horse sickness. OIE Revue Sci. Tech. 34, 315–327 (2015).CAS 
    Article 

    Google Scholar 
    3.Ayelet, G. et al. Outbreak investigation and molecular characterization of African horse sickness virus circulating in selected areas of Ethiopia. Acta Trop. 127, 91–96 (2013).Article 

    Google Scholar 
    4.Diarra, M. et al. Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal. Parasit. Vectors 11, 1–15 (2018).Article 

    Google Scholar 
    5.Karamalla, S. T. et al. Sero-epidemioloical survey on African horse sickness virus among horses in Khartoum State, Central Sudan. BMC Vet. Res. 14, 1–6 (2018).Article 

    Google Scholar 
    6.Escobar, L. E. Ecological Niche modeling: An introduction for veterinarians and epidemiologists. Front. Vet. Sci. 7, 519059. https://doi.org/10.3389/fvets.2020.519059 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Okely, M., Anan, R., Gad-Allah, S. & Samy, A. M. Mapping the environmental suitability of etiological agent and tick vectors of Crimean-Congo hemorrhagic fever. Acta Trop. 203, 105319 (2020).CAS 
    Article 

    Google Scholar 
    8.Chavy, A. et al. Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome. PLoS Negl. Trop. Diseases 13, e0007629 (2019).Article 

    Google Scholar 
    9.Sloyer, K. E. et al. Ecological niche modeling the potential geographic distribution of four Culicoides species of veterinary significance in Florida, USA. PLoS ONE 14, e0206648 (2019).CAS 
    Article 

    Google Scholar 
    10.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 
    11.Cao, Z., Jin, Y., Shen, T., Xu, F. & Li, Y. Risk factors and distribution for peste des petits ruminants (PPR) in Mainland China. Small Rumin. Res. 162, 12–16 (2018).Article 

    Google Scholar 
    12.Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).Article 

    Google Scholar 
    13.Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling. undefined 37, 191–203 (2014).
    Google Scholar 
    14.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, (2020).15.Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

    Google Scholar 
    16.Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).Article 

    Google Scholar 
    17.Uusitalo, R. et al. Predicting spatial patterns of sindbis virus (Sinv) infection risk in finland using vector, host and environmental data. Int. J. Environ. Res. Public Health 18, 7064 (2021).Article 

    Google Scholar 
    18.Raffini, F. et al. From nucleotides to satellite imagery: Approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability (Switzerland) 12, 4508 (2020).CAS 
    Article 

    Google Scholar 
    19.Phillips, S. B., Aneja, V. P., Kang, D. & Arya, S. P. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

    Google Scholar 
    20.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

    Google Scholar 
    21.Hernández-Urcera, J., Murillo, F. J., Regueira, M., Cabanellas-Reboredo, M. & Planas, M. Preferential habitats prediction in syngnathids using species distribution models. Marine Environ. Res. 172, 105488 (2021).Article 

    Google Scholar 
    22.Smeraldo, S. et al. Generalists yet different: distributional responses to climate change may vary in opportunistic bat species sharing similar ecological traits. Mammal Rev. 51, 571–584 (2021).Article 

    Google Scholar 
    23.Thomson, A. M. et al. RCP4.5: A pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    24.QGIS Development Team. QGIS Geographic Information System. Open-Source Geospatial Foundation Project. (2020).25.Ramirez-Reyes, C. et al. Embracing ensemble species distribution models to inform at-risk species status assessments. J. Fish Wildl. Manag. 12, 98–111 (2021).Article 

    Google Scholar 
    26.Stephenson, F. et al. Presence-only habitat suitability models for vulnerable marine ecosystem indicator taxa in the South Pacific have reached their predictive limit. ICES J. Mar. Sci. 78, 2830–2843 (2021).Article 

    Google Scholar 
    27.Zhu, G., Fan, J. & Peterson, A. T. Cautions in weighting individual ecological niche models in ensemble forecasting. Ecol. Modelling 448, 109502 (2021).Article 

    Google Scholar 
    28.Leta, S. et al. Modeling the global distribution of Culicoides imicola: an Ensemble approach. Sci. Rep. 9, 1–9 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    29.Onyango, M. G. et al. Delineation of the population genetic structure of Culicoides imicola in East and South Africa. Parasit. Vectors 8, 660 (2015).Article 

    Google Scholar 
    30.Carpenter, S., Mellor, P. S., Fall, A. G., Garros, C. & Venter, G. J. African horse sickness virus: history. Transm. Curr. Status. 62, 343–358. https://doi.org/10.1146/annurev-ento-031616-035010 (2017).CAS 
    Article 

    Google Scholar 
    31.Carpenter, S., Mellor, P. S., Fall, A. G., Garros, C. & Venter, G. J. African Horse Sickness Virus: History, Transmission, and Current Status. Annu. Rev. Entomol. 62, 343–358 (2017).CAS 
    Article 

    Google Scholar 
    32.Fall, M. et al. Culicoides (Diptera: Ceratopogonidae) midges, the vectors of African horse sickness virus—a host/vector contact study in the Niayes area of Senegal. Parasit. Vectors 8, 1–13 (2015).Article 

    Google Scholar 
    33.Mellor, P. S. Epizootiology and vectors of African horse sickness virus. Comp. Immunol. Microbiol. Infect. Dis. 17, 287–296 (1994).CAS 
    Article 

    Google Scholar 
    34.Wu, X., Lu, Y., Zhou, S., Chen, L. & Xu, B. Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environ. Int. 86, 14–23 (2016).Article 

    Google Scholar 
    35.Nosrat, C. et al. Impact of recent climate extremes on mosquito-borne disease transmission in Kenya. PLOS Negl. Trop. Diseases 15, e0009182 (2021).CAS 
    Article 

    Google Scholar 
    36.Abiodun, G. J., Maharaj, R., Witbooi, P. & Okosun, K. O. Modelling the influence of temperature and rainfall on the population dynamics of Anopheles arabiensis. Malar. J. 15, 1–15 (2016).Article 

    Google Scholar  More

  • in

    Behavioural traits of rainbow trout and brown trout may help explain their differing invasion success and impacts

    1.Holway, D. A. & Suarez, A. V. Animal behavior: An essential component of invasion biology. TREE 14, 328–330 (1999).CAS 
    PubMed 

    Google Scholar 
    2.Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Can behavioral and personality traits influence the success of unintentional species introductions? Trends Ecol. Evol. 27, 57–64 (2012).PubMed 

    Google Scholar 
    3.Weis, J. & Sol, D. Behaviour and the Invasion Process. in Biological Invasions and Animal Behaviour 5–116 (Cambridge University Press, 2016).4.Cote, J., Fogarty, S., Weinersmith, K., Brodin, T. & Sih, A. Personality traits and dispersal tendency in the invasive mosquitofish (Gambusia affinis). Proc. R. Soc. B Biol. Sci. 277, 1571–1579 (2010).
    Google Scholar 
    5.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).
    Google Scholar 
    6.Mutascio, H. E., Pittman, S. E. & Zollner, P. A. Investigating movement behavior of invasive Burmese pythons on a shy–bold continuum using individual-based modeling. Perspect. Ecol. Conserv. 15, 25–31 (2017).
    Google Scholar 
    7.Chuang, A. Living Life on the Edge: The Role of Invasion Processes in Shaping Personalities in a Non-Native Spider Species (The University of Tennessee, Knoxville, 2019). https://doi.org/10.1017/CBO9781107415324.004.Book 

    Google Scholar 
    8.Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).PubMed 

    Google Scholar 
    9.Pintor, L. M., Sih, A. & Kerby, J. L. Behavioral correlations provide a mechanism for explaining high invader densities and increased impacts on native prey. Ecology 90, 581–587 (2009).PubMed 

    Google Scholar 
    10.Petren, K. & Case, T. J. An experimental demonstration of exploitation competition in an ongoing invasion. Ecology 77, 118–132 (1996).
    Google Scholar 
    11.Wright, T. F., Eberhard, J. R., Hobson, E. A., Avery, M. L. & Russello, M. A. Behavioral flexibility and species invasions: The adaptive flexibility hypothesis. Ethol. Ecol. Evol. 22, 393–404 (2010).
    Google Scholar 
    12.Dick, J. T. A. Role of behaviour in biological invasions and species distributions; lessons from interactions between the invasive Gammarus pulex and the native G. duebeni (Crustacea: Amphipoda). Contrib. Zool. 77, 91–98 (2008).
    Google Scholar 
    13.Dick, J. T. A. et al. Invader Relative Impact Potential: A new metric to understand and predict the ecological impacts of existing, emerging and future invasive alien species. J. Appl. Ecol. 54, 1259–1267 (2017).
    Google Scholar 
    14.Dick, J. T. A., Elwood, R. W. & Montgomery, W. I. The behavioural basis of a species replacement: differential aggresssion and predation between the introduced Gammarus pulex and the native G. duebeni celticus (Amphipoda). Behav. Ecol. Sociobiol. 37, 393–398 (1995).
    Google Scholar 
    15.Dick, J. T. A. et al. Ecological impacts of an invasive predator explained and predicted by comparative functional responses. Biol. Invasions 15, 837–846 (2013).
    Google Scholar 
    16.Dick, J. T. A. et al. Advancing impact prediction and hypothesis testing in invasion ecology using a comparative functional response approach. Biol. Invasions 16, 735–753 (2014).
    Google Scholar 
    17.Iacarella, J. C., Dick, J. T. A. & Ricciardi, A. A spatio-temporal contrast of the predatory impact of an invasive freshwater crustacean. Divers. Distrib. 21, 803–812 (2015).
    Google Scholar 
    18.Toscano, B. J. & Griffen, B. D. Trait-mediated functional responses: Predator behavioural type mediates prey consumption. J. Anim. Ecol. 83, 1469–1477 (2014).PubMed 

    Google Scholar 
    19.MacCrimmon, H. R. World distribution of rainbow trout (Salmo gairdneri): further observations. J. Fish. Res. Board Canada 28, 663–704 (1971).
    Google Scholar 
    20.MacCrimmon, H. R., Marshall, T. L. & Gots, B. L. World distribution of brown trout, Salmo trutta: further observations. J. Fish. Res. Board Canada 27, 811–818 (1970).
    Google Scholar 
    21.Crawford, S. S. & Muir, A. M. Global introductions of salmon and trout in the genus Oncorhynchus: 1870–2007. Rev. Fish Biol. Fish. 18, 313–344 (2008).
    Google Scholar 
    22.Crowl, T. A., Townsend, C. R. & Mcintosh, A. R. The impact of introduced brown and rainbow trout on native fish: The case of Australasia. Rev. Fish Biol. Fish. 241, 217–241 (1992).
    Google Scholar 
    23.Hasegawa, K. Invasions of rainbow trout and brown trout in Japan: A comparison of invasiveness and impact on native species. Ecol. Freshw. Fish 29, 419–428 (2020).
    Google Scholar 
    24.Cambray, J. A. The global impact of alien trout species—A review; with reference to their impact in South Africa. African J. Aquat. Sci. 28, 61–67 (2003).
    Google Scholar 
    25.Dunham, J. B., Wheeler, A. & Rosenberger, A. Assessing the consequences of nonnative trout in headwater ecosystems in western North America. Fisheries 29, 37–41 (2004).
    Google Scholar 
    26.Fausch, K. D., Taniguchi, Y., Nakano, S., Grossman, G. D. & Townsend, C. R. Flood disturbance regimes influence rainbow trout invasion success among five holarctic regions. Ecol. Appl. 11, 1438–1455 (2001).
    Google Scholar 
    27.Anderson, R. M. & Nehring, R. B. Effects of a catch-and-release regulation on a wild trout population in Colorado and its acceptance by Anglers. North Am. J. Fish. Manag. 4, 257–265 (1984).
    Google Scholar 
    28.Young, K. A. et al. A trial of two trouts: Comparing the impacts of rainbow and brown trout on a native galaxiid. Anim. Conserv. 13, 399–410 (2010).
    Google Scholar 
    29.Conrad, J. L., Weinersmith, K. L., Brodin, T., Saltz, J. B. & Sih, A. Behavioural syndromes in fishes: A review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).CAS 
    PubMed 

    Google Scholar 
    30.Mowles, S. L., Cotton, P. A. & Briffa, M. Consistent crustaceans: The identification of stable behavioural syndromes in hermit crabs. Behav. Ecol. Sociobiol. 66, 1087–1094 (2012).
    Google Scholar 
    31.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 

    Google Scholar 
    32.Bell, A. M. Behavioural differences between individuals and two populations of stickleback (Gasterosteus aculeatus). J. Evol. Biol. 18, 464–473 (2005).CAS 
    PubMed 

    Google Scholar 
    33.Bourne, G. R. & Sammons, A. J. Boldness, aggression and exploration: evidence for a behavioural syndrome in male pentamorphic livebearing fish, Poecilia parae. AACL Bioflux 1, 39–50 (2008).
    Google Scholar 
    34.Lukas, J. et al. Consistent behavioral syndrome across seasons in an invasive freshwater fish. Front. Ecol. Evol. 8, 466 (2021).ADS 

    Google Scholar 
    35.Gjedrem, T., Gjøen, H. M. & Gjerde, B. Genetic origin of Norwegian farmed Atlantic salmon. Aquaculture 98, 41–50 (1991).
    Google Scholar 
    36.Huntingford, F. & Adams, C. Behavioural syndromes in farmed fish: Implications for production and welfare. Behaviour 142, 1207–1221 (2005).
    Google Scholar 
    37.Alvarez, D. & Nicieza, A. G. Predator avoidance behaviour in wild and hatchery-reared brown trout : The role of experience and domestication. J. Fish Biol. 63, 1565–1577. https://doi.org/10.1046/j.1095-8649.2003.00267.x (2003).Article 

    Google Scholar 
    38.Geffroy, B. et al. Evolutionary dynamics in the anthropocene: Life history and intensity of human contact shape antipredator responses. PLoS Biol. 18, 1–17 (2020).
    Google Scholar 
    39.Lincoln, R. F. & Scott, A. P. Production of all-female triploid rainbow trout. Aquaculture 30, 375–380 (1983).
    Google Scholar 
    40.Maxime, V. The physiology of triploid fish: Current knowledge and comparisons with diploid fish. Fish Fish. 9, 67–78 (2008).
    Google Scholar 
    41.Chatterji, R., Longley, D., Sandford, D., Roberts, D. & Stubbing, D. Performance of stocked triploid and diploid brown trout and their effects on wild brown trout in UK rivers. (2008).42.Benfey, T. J. The physiology and behavior of triploid fishes. Rev. Fish. Sci. 7, 39–67 (1999).
    Google Scholar 
    43.Carter, C. G. et al. Food consumption, feeding behaviour, and growth of triploid and diploid Atlantic salmon, Salmo salar L., parr.. Can. J. Zool. 72, 609–617 (1994).
    Google Scholar 
    44.Weber, G. M., Hostuttler, M. A., Cleveland, B. M. & Leeds, T. D. Growth performance comparison of intercross-triploid, induced triploid, and diploid rainbow trout. Aquaculture 433, 85–93 (2014).
    Google Scholar 
    45.Øverli, Ø., Pottinger, T. G., Carrick, T. R., Øverli, E. & Winberg, S. Differences in behaviour between rainbow trout selected for high- and low-stress responsiveness. J. Exp. Biol. 205, 391–395 (2002).PubMed 

    Google Scholar 
    46.Sadoul, B., Leguen, I., Colson, V., Friggens, N. C. & Prunet, P. A multivariate analysis using physiology and behavior to characterize robustness in two isogenic lines of rainbow trout exposed to a confinement stress. Physiol. Behav. 140, 139–147 (2015).CAS 
    PubMed 

    Google Scholar 
    47.Adriaenssens, B. & Johnsson, J. I. Learning and context-specific exploration behaviour in hatchery and wild brown trout. Appl. Anim. Behav. Sci. 132, 90–99 (2011).
    Google Scholar 
    48.Näslund, J. & Johnsson, J. I. State-dependent behavior and alternative behavioral strategies in brown trout (Salmo trutta L.) fry. Behav. Ecol. Sociobiol. 70, 2111–2125 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    49.Mortensen, E. Density-dependent mortality of trout fry (Salmo trutta L.) and its relationship to the management of small streams. J. Fish Biol. 11, 613–617 (1977).
    Google Scholar 
    50.Armstrong, J. D. & Nislow, K. H. Critical habitat during the transition from maternal provisioning in freshwater fish, with emphasis on Atlantic salmon (Salmo salar) and brown trout (Salmo trutta). J. Zool. 269, 403–413 (2006).
    Google Scholar 
    51.Walsh, R. N. & Cummins, R. A. The open-field test: A critical review. Psychol. Bull. 83, 482–504 (1976).CAS 
    PubMed 

    Google Scholar 
    52.Adriaenssens, B. & Johnsson, J. I. Shy trout grow faster: Exploring links between personality and fitness-related traits in the wild. Behav. Ecol. 22, 135–143 (2010).
    Google Scholar 
    53.Sneddon, L. U. The bold and the shy: Individual differences in rainbow trout. J. Fish Biol. 62, 971–975 (2003).
    Google Scholar 
    54.Adriaenssens, B. Individual variation in behaviour: personality and performance of brown trout in the wild (University of Gothenburg, 2010).55.Elias, A., Thrower, F. & Nichols, K. M. Rainbow trout personality: Individual behavioural variation in juvenile Oncorhynchus mykiss. Behaviour 155, 205–230 (2018).
    Google Scholar 
    56.Dick, J. T. A. et al. Functional responses can unify invasion ecology. Biol. Invasions 19, 1667–1672 (2017).
    Google Scholar 
    57.Sloman, K. A., Metcalfe, N. B., Taylor, A. C. & Gilmour, K. M. Plasma cortisol concentrations before and after social stress in rainbow trout and brown trout. Physiol. Biochem. Zool. 74, 383–389 (2001).CAS 
    PubMed 

    Google Scholar 
    58.Sadoul, B., Blumstein, D. T., Alfonso, S. & Geffroy, B. Human protection drives the emergence of a new coping style in animals. PLoS Biol. 19, 1–11 (2021).
    Google Scholar 
    59.Campbell, J. M., Carter, P. A., Wheeler, P. A. & Thorgaard, G. H. Aggressive behavior, brain size and domestication in clonal rainbow trout lines. Behav. Genet. 45, 245–254 (2015).PubMed 

    Google Scholar 
    60.Berejikian, B. A., Mathews, S. B. & Quinn, T. P. Effects of hatchery and wild ancestry and rearing environments on the development of agonistic behavior in steelhead trout (Oncorhynchus mykiss) fry. Can. J. Fish. Aquat. Sci. 53, 2004–2014 (1996).
    Google Scholar 
    61.Laverty, C. et al. Assessing the ecological impacts of invasive species based on their functional responses and abundances. Biol. Invasions 19, 1653–1665 (2017).
    Google Scholar 
    62.Alexander, M. E., Dick, J. T. A., Weyl, O. L. F., Robinson, T. B. & Richardson, D. M. Existing and emerging high impact invasive species are characterized by higher functional responses than natives. Biol. Lett. 10, 20130946 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    63.Dickey, J. W. E., Cuthbert, R. N., Steffen, G. T., Dick, J. T. A. & Briski, E. Sea freshening may drive the ecological impacts of emerging and existing invasive non-native species. Divers. Distrib. 27, 144–156 (2021).
    Google Scholar 
    64.Sadler, J., Pankhurst, P. M. & King, H. R. High prevalence of skeletal deformity and reduced gill surface area in triploid Atlantic salmon (Salmo salar L.). Aquaculture 198, 369–386 (2001).
    Google Scholar 
    65.Benfey, T. J. & Biron, M. Acute stress response in triploid rainbow trout (Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis). Aquaculture 184, 167–176 (2000).CAS 

    Google Scholar 
    66.Sadler, J., Pankhurst, N. W., Pankhurst, P. M. & King, H. Physiological stress responses to confinement in diploid and triploid Atlantic salmon. J. Fish Biol. 56, 506–518 (2000).
    Google Scholar 
    67.Berrebi, P., Splendiani, A., Palm, S. & Berna, R. Genetic diversity of domestic brown trout stocks in Europe. Aquaculture 544, 737043 (2021).CAS 

    Google Scholar 
    68.Gross, R., Lulla, P. & Paaver, T. Genetic variability and differentiation of rainbow trout (Oncorhynchus mykiss) strains in northern and Eastern Europe. Aquaculture 272, 139–146 (2007).
    Google Scholar 
    69.Whelan, K. Assessing and mitigating the impact of a major rainbow trout escape on the wild salmon and trout populations of the Mourne river system, Northern Ireland. (2017).70.Shelton, J. et al. Temperature mediates the impact of non-native rainbow trout on native freshwater fishes in South Africa’s Cape Fold Ecoregion. Biol. Invasions 20, 2927–2944 (2018).
    Google Scholar 
    71.Michelangeli, M. et al. Sex-dependent personality in two invasive species of mosquitofish. Biol. Invasions 22, 1353–1364 (2020).
    Google Scholar 
    72.Friard, O. & Gamba, M. BORIS: A free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).
    Google Scholar 
    73.R Core Team. R: A language and environment for statistical computing. (2018).74.RStudio Team. RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/. 2019 (2020).75.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. Springer https://doi.org/10.1086/648138 (2008).Article 
    MATH 

    Google Scholar 
    76.Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 18637 (2015).
    Google Scholar 
    77.Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version. Media https://doi.org/10.1007/978-0-387-98141-3 (2019).Article 

    Google Scholar 
    78.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

    Google Scholar 
    79.Barton, K. MuMIn: Multi-Model Inference. 2020 (2020).80.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. emmeans: estimated marginal means, aka least-squares means. R package version 1.5.2-1 (2020).81.Pritchard, D. frair: tools for functional response analysis. R package version 0.0.100 (2017).82.Juliano, S. A. Predation and functional response curves. in Design and Analysis of Ecological Experiments (eds. Scheiner, S. & Gurevitch, J.) Chapter 10 (2001).83.Rogers, D. Random search and insect population models. J. Anim. Ecol. 41, 369–383 (1972).
    Google Scholar 
    84.Bolker, B. M. Rogers random predator equation: extensions and estimation by numerical integration. 1–20 (2012). More

  • in

    Parallel evolution of urban–rural clines in melanism in a widespread mammal

    1.Angel, S. et al. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Prog. Plan. 75, 53–107 (2011).
    Google Scholar 
    2.Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    3.McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
    Google Scholar 
    4.Groffman, P. M. et al. Ecological homogenization of urban USA. Front. Ecol. Environ. 12, 74–81 (2014).
    Google Scholar 
    5.Bolnick, D. I. et al. (Non)Parallel evolution. Annu. Rev. Ecol. Evol. Syst. 49, 303–330 (2018).
    Google Scholar 
    6.Donihue, C. M. & Lambert, M. R. Adaptive evolution in urban ecosystems. Ambio 44, 194–203 (2015).PubMed 

    Google Scholar 
    7.Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).
    Google Scholar 
    8.Rivkin, L. R. et al. A roadmap for urban evolutionary ecology. Evol. Appl. 12, 384–398 (2019).PubMed 

    Google Scholar 
    9.Santangelo, J. S. et al. Urban environments as a framework to study parallel evolution. In Urban Evolutionary Biology (eds Szulkin, M. et al.) (Oxford University Press, 2020).
    Google Scholar 
    10.Cosentino, B. J., Moore, J.-D., Karraker, N. E., Ouellet, M. & Gibbs, J. P. Evolutionary response to global change: Climate and land use interact to shape color polymorphism in a woodland salamander. Ecol. Evol. 7, 5426–5434 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    11.Koprowski, J. L., Munroe, K. E. & Edelman, A. J. Gray not grey: Ecology of Sciurus carolinensis in their native range in North America. In Grey Squirrels: Ecology and Management of an Invasive Species in Europe (eds Shuttleworth, C. M. et al.) (European Squirrel Initiative, 2016).
    Google Scholar 
    12.McRobie, H., Thomas, A. & Kelly, J. The genetic basis of melanism in the gray squirrel (Sciurus carolinensis). J. Hered. 100, 709–714 (2009).CAS 
    PubMed 

    Google Scholar 
    13.Gibbs, J. P., Buff, M. F. & Cosentino, B. J. The biological system: Urban wildlife, adaptation and evolution: Urbanization as a driver of contemporary evolution in gray squirrels (Sciurus carolinensis). In Understanding Urban Ecology (eds Hall, M. A. & Balogh, S.) (Springer, 2019).
    Google Scholar 
    14.Lehtinen, R. M. et al. Dispatches form the neighborhood watch: Using citizen science and field survey data to document color morph frequency in space and time. Ecol. Evol. 10, 1526–1538 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    15.Perlut, N. G. Long-distance dispersal by eastern gray squirrels in suburban habitats. Northeast. Nat. 27, 195–200 (2020).
    Google Scholar 
    16.Goheen, J. R., Swihart, R. K., Gehring, T. M. & Miller, M. S. Forces structuring tree squirrel communities in landscapes fragmented by agriculture: Species differences in perceptions of forest connectivity and carrying capacity. Oikos 102, 95–103 (2003).
    Google Scholar 
    17.Ducharme, M. B., Larochelle, J. & Richard, D. Thermogenic capacity in gray and black morphs of the gray squirrel, Sciurus carolinensis. Physiol. Zool. 62, 1273–1292 (1989).
    Google Scholar 
    18.Linnen, C. R. & Hoekstra, H. E. Measuring natural selection on genotypes and phenotypes in the wild. Cold Spring Harb. Symp. Quant. Biol. 74, 155–168 (2010).PubMed Central 

    Google Scholar 
    19.Campbell-Staton, S. C. et al. Parallel selection on thermal physiology facilitates repeated adaptation of city lizards to urban heat islands. Nat. Ecol. Evol. 4, 652–658 (2020).PubMed 

    Google Scholar 
    20.Reid, N. M. et al. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish. Science 354, 1305–1308 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Bowers, M. A. & Breland, B. Foraging of gray squirrels on an urban-rural gradient: Use of the GUD to assess anthropogenic impact. Ecol. Appl. 6, 1135–1142 (1996).
    Google Scholar 
    22.McCleery, R. A., Lopez, R. R., Silvy, N. J. & Gallant, D. L. Fox squirrel survival in urban and rural environments. J. Wildl. Manage. 72, 133–137 (2008).
    Google Scholar 
    23.Benson, E. The urbanization of the eastern gray squirrel in the United States. J. Am. Hist. 100, 691–710 (2013).
    Google Scholar 
    24.Leveau, L. United colours of the city: A review about urbanization impact on animal colours. Austral Ecol. 46, 670–679 (2021).
    Google Scholar 
    25.Ducrest, A.-L., Keller, L. & Roulin, A. Pleiotropy in the melanocortin system, coloration, and behavioural syndromes. Trends Ecol. Evol. 23, 502–510 (2008).PubMed 

    Google Scholar 
    26.Stothart, M. R. & Newman, A. E. M. Shades of grey: Host phenotype dependent effect of urbanization on the bacterial microbiome of a wild mammal. Anim. Microbiome. 3, 46 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    27.Vasemägi, A. The adaptive hypothesis of clinal variation revisited: Single-locus clines as a result of spatially restricted gene flow. Genetics 173, 2411–2414 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    28.Merrick, M. J., Evans, K. L. & Bertolino, S. Urban grey squirrel ecology, associated impacts, and management challenges. In Grey Squirrels: Ecology and Management of an Invasive Species in Europe (eds Shuttleworth, C. M. et al.) (European Squirrel Initiative, 2016).
    Google Scholar 
    29.Chipman, R., Slate, D., Rupprecht, C. & Mendoza, M. Downside risk of wildlife translocation. In Towards the Elimination of Rabies in Eurasia (eds Dodet, B. et al.) (Dev. Biol Basel, Karger, 2008).
    Google Scholar 
    30.Allen, D. L. Michigan Fox Squirrel Management (Michigan Department of Conservation, 1943).
    Google Scholar 
    31.Schorger, A. W. Squirrels in early Wisconsin. Trans. Wis. Acad. Sci. Arts Lett. 39, 195–247 (1949).
    Google Scholar 
    32.Robertson, G. I. Distribution of Color Morphs of Sciurus carolinensis in Eastern North America (University of Western Ontario, 1973).
    Google Scholar 
    33.MacCleery, D. W. American Forests: A History of Resiliency and Recovery (Forest History Society, 2011).
    Google Scholar 
    34.Foster, D. R. et al. Wildlands and Woodlands: A Vision for the New England Landscape (Harvard University Press, 2010).
    Google Scholar 
    35.Thompson, R. T., Carpenter, D. N., Cogbill, C. V. & Foster, D. R. Four centuries of change in northeastern United States forests. PLoS ONE 8(9), e72540 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Lambert, M. R. et al. Adaptive evolution in cities: Progress and misconceptions. Trends Ecol. Evol. 36, 239–257 (2021).PubMed 

    Google Scholar 
    37.Farquhar, D. N. Some Aspects of Thermoregulation as Related to the Geographic Distribution of the Northern Melanic Phase of the Grey Squirrel (York University, 1974).
    Google Scholar 
    38.Innes, S. & Lavigne, D. M. Comparative energetics of coat colour polymorphs in the eastern gray squirrel Sciurus carolinensis. Can. J. Zool. 57, 585–592 (1979).
    Google Scholar 
    39.Santangelo, J. S. et al. Predicting the strength of urban-rural clines in a Mendelian polymorphism along a latitudinal gradient. Evol. Lett. 4, 212–225 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    40.Fidino, M. et al. Landscape-scale differences among cities alter common species’ responses to urbanization. Ecol. Appl. 31, e02253 (2021).PubMed 

    Google Scholar 
    41.Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
    Google Scholar 
    42.Alberti, M. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl. Acad. Sci. U.S.A. 114, 8951–8956 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.United States Census Bureau. 2019 TIGER/Line Shapefiles (machine-readable data files) https://www2.census.gov/geo/tiger/TIGER2019/UAC/ (2019).44.XX. Statistics Canada. Population Centre Boundary File, Census year 2016 https://www150.statcan.gc.ca/n1/en/catalogue/92-166-X (2017).45.Aiello-Lammens, M. E. et al. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
    Google Scholar 
    46.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2020).47.Brown de Colstoun, E. C. et al. Documentation for the Global Man-made Impervious Surface (GMIS) Dataset from Landsat (NASA Socioeconomic Data and Applications Center, 2017).
    Google Scholar 
    48.Steele, M. A. & Koprowski, J. L. North American Tree Squirrels (Smithsonian Books, 2001).
    Google Scholar 
    49.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    50.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Google Scholar 
    51.Hijmans, R. L. raster: Geographic data analysis and modeling. R package version 3.3–13. https://CRAN.R-project.org/package=raster (2020).52.Baston, D. exactextractr: Fast extraction from raster datasets using polygons. R package version 0.5.1. https://CRAN.R-project.org/package=exactextractr (2020).53.Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    54.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Google Scholar 
    55.Gelman, A. & Su, Y. arm: Data analysis using regression and multilevel/hierarchical models. R package version 1.11–2. https://CRAN.R-project.org/package=arm (2020).56.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, 2007).
    Google Scholar 
    57.Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012).
    Google Scholar 
    58.Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).MathSciNet 
    MATH 

    Google Scholar 
    59.Bardos, D. C., Guillera-Arroita, G. & Wintle, B. A. Valid auto-models for spatially autocorrelated occupancy and abundance data. Methods Ecol. Evol. 6, 1137–1149 (2015).
    Google Scholar  More

  • in

    Species delimitation and mitonuclear discordance within a species complex of biting midges

    1.De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56, 879–886. https://doi.org/10.1080/10635150701701083 (2007).Article 
    PubMed 

    Google Scholar 
    2.Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates Inc, 2004).
    Google Scholar 
    3.Endler, J. A. Gene flow and population differentiation: studies of clines suggest that differentiation along environmental gradients may be independent of gene flow. Science 179, 243–250 (1973).CAS 
    PubMed 
    ADS 

    Google Scholar 
    4.Mayr, E. Systematics and the Origin of Species, from the Viewpoint of a Zoologist (Harvard University Press, 1999).
    Google Scholar 
    5.Richardson, J. L., Urban, M. C., Bolnick, D. I. & Skelly, D. K. Microgeographic adaptation and the spatial scale of evolution. Trends Ecol. Evol. 29, 165–176 (2014).PubMed 

    Google Scholar 
    6.Nosil, P. Ernst Mayr and the integration of geographic and ecological factors in speciation. Biol. J. Lin. Soc. 95, 26–46 (2008).
    Google Scholar 
    7.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 

    Google Scholar 
    8.Leliaert, F. et al. DNA-based species delimitation in algae. Eur. J. Phycol. 49, 179–196 (2014).
    Google Scholar 
    9.Carstens, B. C., Pelletier, T. A., Reid, N. M. & Satler, J. D. How to fail at species delimitation. Mol. Ecol. 22, 4369–4383 (2013).PubMed 

    Google Scholar 
    10.Schlick-Steiner, B. C. et al. Integrative taxonomy: a multisource approach to exploring biodiversity. Annu. Rev. Entomol. 55, 421–438 (2010).CAS 
    PubMed 

    Google Scholar 
    11.Capblancq, T., Mavárez, J., Rioux, D. & Després, L. Speciation with gene flow: evidence from a complex of alpine butterflies (Coenonympha, Satyridae). Ecol. Evol. 9, 6444–6457 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    12.Pedraza-Marrón, C. d. R. et al. Genomics overrules mitochondrial DNA, siding with morphology on a controversial case of species delimitation. Proc. R. Soc. B 286, 20182924 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    13.Hinojosa, J. C. et al. A mirage of cryptic species: genomics uncover striking mitonuclear discordance in the butterfly Thymelicus sylvestris. Mol. Ecol. 28, 3857–3868 (2019).PubMed 

    Google Scholar 
    14.Nygren, A. et al. A mega-cryptic species complex hidden among one of the most common annelids in the North East Atlantic. PLoS ONE 13, e0198356 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    15.Thielsch, A., Knell, A., Mohammadyari, A., Petrusek, A. & Schwenk, K. Divergent clades or cryptic species? Mito-nuclear discordance in a Daphnia species complex. BMC Evol. Biol. 17, 1–9 (2017).
    Google Scholar 
    16.Eyer, P. A. & Hefetz, A. Cytonuclear incongruences hamper species delimitation in the socially polymorphic desert ants of the Cataglyphis albicans group in Israel. J. Evol. Biol. 31, 1828–1842 (2018).CAS 
    PubMed 

    Google Scholar 
    17.Borkent, A. Biology of Disease Vectors. 2nd edn, i–xxiii + 1–785 (Elsevier Academic Press, 2004).18.Mellor, P., Boorman, J. & Baylis, M. Culicoides biting midges: their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
    PubMed 

    Google Scholar 
    19.Rushton, J. & Lyons, N. Economic impact of Bluetongue: a review of the effects on production. Veterinaria italiana 51, 401–406 (2015).PubMed 

    Google Scholar 
    20.Tabachnick, W. J. Culicoides vriipennis and Bluetongue-Virus eidemiology in the United States. Annu. Rev. Entomol. 41, 23–43. https://doi.org/10.1146/annurev.en.41.010196.000323 (1996).CAS 
    Article 
    PubMed 

    Google Scholar 
    21.Wirth, W. W. & Jones, R. H. The North American Subspecies of Culicoides variipennis (Diptera, Heleidae). U. S. Dep. Agric. Tech. Bull 1170, 1–35 (1957).
    Google Scholar 
    22.Holbrook, F. R. et al. Sympatry in the Culicoides variipennis Complex (Diptera: Ceratopogonidae): a Taxonomic Reassessment. J. Med. Entomol. 37, 65–76. https://doi.org/10.1603/0022-2585-37.1.65 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    23.Hopken, M. W. Pathogen Vectors at the Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Biology (University of Colorado, 2016).
    Google Scholar 
    24.Shults, P. A Study of the Taxonomy, Ecology, and Systematics of Culicoides Species (Diptera: Ceratopogonidae) Including those Associated with Deer Breeding Facilities in Southeast Texas (Texas A&M University, 2015).
    Google Scholar 
    25.Velten, R. K. & Mullens, B. A. Field morphological variation and laboratory hybridization of Culicoides variipennis sonorensis and C. v. occidentalis (Diptera:Ceratopogonidae) in southern California. J. Med. Entomol. 34, 277–284 (1997).CAS 
    PubMed 

    Google Scholar 
    26.Fontaine, M. C. et al. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 347, 1258522 (2015).PubMed 

    Google Scholar 
    27.Bolnick, D. I. & Otto, S. P. The magnitude of local adaptation under genotype-dependent dispersal. Ecol. Evol. 3, 4722–4735 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    28.Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).PubMed 

    Google Scholar 
    29.Pante, E. et al. Species are hypotheses: avoid connectivity assessments based on pillars of sand. Mol. Ecol. 24, 525–544 (2015).PubMed 

    Google Scholar 
    30.Jacquet, S. et al. Colonization of the Mediterranean basin by the vector biting midge species Culicoides imicola: an old story. Mol. Ecol. 24, 5707–5725. https://doi.org/10.1111/mec.13422 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    31.Onyango, M. G. et al. Genotyping of whole genome amplified reduced representation libraries reveals a cryptic population of Culicoides brevitarsis in the Northern Territory, Australia. BMC Genomics 17, 769. https://doi.org/10.1186/s12864-016-3124-1 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Onyango, M. G. et al. Delineation of the population genetic structure of Culicoides imicola in East and South Africa. Parasit. Vectors 8, 660. https://doi.org/10.1186/s13071-015-1277-4 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Mignotte, A. et al. High dispersal capacity of Culicoides obsoletus (Diptera: Ceratopogonidae), vector of bluetongue and Schmallenberg viruses, revealed by landscape genetic analyses. Parasit. Vectors 14, 1–14 (2021).
    Google Scholar 
    34.Sanders, C. J. & Carpenter, S. Assessment of an immunomarking technique for the study of dispersal of Culicoides biting midges. Infect. Genet. Evol. 28, 583–587 (2014).PubMed 

    Google Scholar 
    35.Kluiters, G., Swales, H. & Baylis, M. Local dispersal of palaearctic Culicoides biting midges estimated by mark-release-recapture. Parasit. Vectors 8, 86 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    36.Ducheyne, E. et al. Quantifying the wind dispersal of Culicoides species in Greece and Bulgaria. Geospat. Health 10, 177–189 (2007).
    Google Scholar 
    37.Purse, B. V. et al. Climate change and the recent emergence of bluetongue in Europe. Nat. Rev. Microbiol. 3, 171–181 (2005).CAS 
    PubMed 

    Google Scholar 
    38.Jacquet, S. et al. Range expansion of the Bluetongue vector, Culicoides imicola, in continental France likely due to rare wind-transport events. Sci. Rep. https://doi.org/10.1038/srep27247 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Rundle, H. D. & Nosil, P. Ecological speciation. Ecol. Lett. 8, 336–352 (2005).
    Google Scholar 
    40.Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).PubMed 

    Google Scholar 
    41.Shults, P. A Study of Culicoides Biting Midges in the Subgenus Monoculicoides: Population Genetics, Taxonomy, Systematics, and Control. Ph.D. thesis, Texas A&M University (2021).42.Jewiss-Gaines, A., Barelli, L. & Hunter, F. F. First records of Culicoides sonorensis (Diptera: Ceratopogonidae), a known vector of bluetongue virus, Southern Ontario. J. Med. Entomol. 54, 757–762. https://doi.org/10.1093/jme/tjw215 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Chan, K. M. & Levin, S. A. Leaky prezygotic isolation and porous genomes: rapid introgression of maternally inherited DNA. Evolution 59, 720–729 (2005).CAS 
    PubMed 

    Google Scholar 
    44.Harrison, R. G. Hybrid zones: windows on evolutionary process. Oxf. Surv. Evol. Biol. 7, 69–128 (1990).
    Google Scholar 
    45.Harrison, R. G. Animal mitochondrial DNA as a genetic marker in population and evolutionary biology. Trends Ecol. Evol. 4, 6–11 (1989).CAS 
    PubMed 

    Google Scholar 
    46.Després, L. One, Two or More Species? Mitonuclear Discordance and Species Delimitation. Molecular ecology 28(17), 3845–3847 (2019).PubMed 

    Google Scholar 
    47.Janes, J. K. et al. The K= 2 conundrum. Mol. Ecol. 26, 3594–3602 (2017).PubMed 

    Google Scholar 
    48.De Meester, L., Vanoverbeke, J., Kilsdonk, L. J. & Urban, M. C. Evolving perspectives on monopolization and priority effects. Trends Ecol. Evol. 31, 136–146 (2016).PubMed 

    Google Scholar 
    49.Ballard, J. W. O., Chernoff, B. & James, A. C. Divergence of mitochondrial DNA is not corroborated by nuclear DNA, morphology, or behavior in Drosophila simulans. Evolution 56, 527–545 (2002).PubMed 

    Google Scholar 
    50.Behura, S., Sahu, S., Mohan, M. & Nair, S. Wolbachia in the Asian rice gall midge, Orseolia oryzae (Wood-Mason): Correlation between host mitotypes and infection status. Insect Mol. Biol. 10, 163–171 (2001).CAS 
    PubMed 

    Google Scholar 
    51.Covey, H. et al. Cryptic Wolbachia (Rickettsiales: Rickettsiaceae) detection and prevalence in Culicoides (Diptera: Ceratopogonidae) midge populations in the United States. J. Med. Entomol. 57, 1262–1269. https://doi.org/10.1093/jme/tjaa003 (2020).Article 
    PubMed 

    Google Scholar 
    52.Pagès, N., Muñoz-Muñoz, F., Verdún, M., Pujol, N. & Talavera, S. First detection of Wolbachia-infected Culicoides (Diptera: Ceratopogonidae) in Europe: Wolbachia and Cardinium infection across Culicoides communities revealed in Spain. Parasit. Vectors 10, 582. https://doi.org/10.1186/s13071-017-2486-9 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Pilgrim, J. et al. Cardinium symbiosis as a potential confounder of mtDNA based phylogeographic inference in Culicoides imicola (Diptera: Ceratopogonidae), a vector of veterinary viruses. Parasit. Vectors 14, 100. https://doi.org/10.1186/s13071-020-04568-3 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Hare, M. P. Prospects for nuclear gene phylogeography. Trends Ecol. Evol. 16, 700–706 (2001).
    Google Scholar 
    55.Onyango, M. G. et al. Assessment of population genetic structure in the arbovirus vector midge, Culicoides brevitarsis (Diptera: Ceratopogonidae), using multi-locus DNA microsatellites. Vet. Res. 46, 108. https://doi.org/10.1186/s13567-015-0250-8 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Fonseca, D. M., Smith, J. L., Kim, H.-C. & Mogi, M. Population genetics of the mosquito Culex pipiens pallens reveals sex-linked asymmetric introgression by Culex quinquefasciatus. Infect. Genet. Evol. 9, 1197–1203 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Goubert, C., Minard, G., Vieira, C. & Boulesteix, M. Population genetics of the Asian tiger mosquito Aedes albopictus, an invasive vector of human diseases. Heredity 117, 125–134 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Lehmann, T. et al. Microgeographic structure of Anopheles gambiae in western Kenya based on mtDNA and microsatellite loci. Mol. Ecol. 6, 243–253 (1997).CAS 
    PubMed 

    Google Scholar 
    59.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631. https://doi.org/10.1093/molbev/msl191 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Manni, M. et al. Molecular markers for analyses of intraspecific genetic diversity in the Asian Tiger mosquito, Aedes albopictus. Parasit. Vectors 8, 1–11 (2015).
    Google Scholar 
    61.Arntzen, J. W., Jehle, R., Bardakci, F., Burke, T. & Wallis, G. P. Asymmetric viability of reciprocal-cross hybrids between Crested and Marbled Newts (Triturus cristatus and T. marmoratus). Evolution 63, 1191–1202. https://doi.org/10.1111/j.1558-5646.2009.00611.x (2009).Article 
    PubMed 

    Google Scholar 
    62.Gibeaux, R. et al. Paternal chromosome loss and metabolic crisis contribute to hybrid inviability in Xenopus. Nature 553, 337. https://doi.org/10.1038/nature25188 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    63.Werren, J. H., Baldo, L. & Clark, M. E. Wolbachia: master manipulators of invertebrate biology. Nat. Rev. Microbiol. 6, 741 (2008).CAS 
    PubMed 

    Google Scholar 
    64.Servedio, M. R. & Kirkpatrick, M. The effects of gene flow on reinforcement. Evolution 51, 1764–1772. https://doi.org/10.1111/j.1558-5646.1997.tb05100.x (1997).Article 
    PubMed 

    Google Scholar 
    65.Howard, D. J. Reinforcement: origin, dynamics, and fate of an evolutionary hypothesis. Hybrid zones and the evolutionary process, 46–69 (1993).66.Yukilevich, R. Asymmetrical patterns of speciation uniquely support reinforcement in Drosophila. Evolution 66, 1430–1446. https://doi.org/10.1111/j.1558-5646.2011.01534.x (2012).Article 
    PubMed 

    Google Scholar 
    67.Downes, J. A. The Culicoides variipennis complex: a necessary re-alignment of nomenclature (Diptera: Ceratopogonidae). Can. Entomol. 110, 63–69 (1978).
    Google Scholar 
    68.Toews, D. P. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
    PubMed 

    Google Scholar 
    69.Smith, H. & Mullens, B. A. Seasonal activity, size, and parity of Culicoides occidentalis (Diptera: Ceratopogonidae) in a coastal southern California salt marsh. J. Med. Entomol. 40, 352–355. https://doi.org/10.1603/0022-2585-40.3.352 (2003).Article 
    PubMed 

    Google Scholar 
    70.Linley, J. The effect of salinity on oviposition and egg hatching in Culicoides variipennis sonorensis (Diptera: Ceratopogonidae). J. Am. Mosq. Control Assoc. 2, 79–82 (1986).CAS 
    PubMed 

    Google Scholar 
    71.Gerry, A. C. & Mullens, B. A. Response of Male Culicoides variipennis sonorensis (Diptera: Ceratopogonidae) to carbon dioxide and observations of mating behavior on and near cattle. J. Med. Entomol. 35, 239–244. https://doi.org/10.1093/jmedent/35.3.239 (1998).CAS 
    Article 
    PubMed 

    Google Scholar 
    72.Nolan, D. V. et al. Rapid diagnostic PCR assays for members of the Culicoides obsoletus and Culicoides pulicaris species complexes, implicated vectors of bluetongue virus in Europe. Vet. Microbiol. 124, 82–94 (2007).CAS 
    PubMed 

    Google Scholar 
    73.Sebastiani, F. et al. Molecular differentiation of the Old World Culicoides imicola species complex (Diptera, Ceratopogonidae), inferred using random amplified polymorphic DNA markers. Mol. Ecol. 10, 1773–1786 (2001).CAS 
    PubMed 

    Google Scholar 
    74.Carlson, D. Identification of mosquitoes of Anopheles gambiae species complex A and B by analysis of cuticular components. Science 207, 1089–1091 (1980).CAS 
    PubMed 
    ADS 

    Google Scholar 
    75.Palacios, G. et al. Characterization of the Sandfly fever Naples species complex and description of a new Karimabad species complex (genus Phlebovirus, family Bunyaviridae). J. Gen. Virol. 95, 292 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Rivas, G., Souza, N. & Peixoto, A. A. Analysis of the activity patterns of two sympatric sandfly siblings of the Lutzomyia longipalpis species complex from Brazil. Med. Vet. Entomol. 22, 288–290 (2008).CAS 
    PubMed 

    Google Scholar 
    77.Wilson, W. C. et al. Current status of bluetongue virus in the Americas. Bluetongue 10, 197–220 (2009).
    Google Scholar 
    78.Allen, S. E. et al. Epizootic Hemorrhagic Disease in White-Tailed Deer, Canada. Emerg. Infect. Dis. 25, 832–834. https://doi.org/10.3201/eid2504.180743 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.McGregor, B. L. et al. Field data implicating Culicoides stellifer and Culicoides venustus (Diptera: Ceratopogonidae) as vectors of epizootic hemorrhagic disease virus. Parasit. Vectors 12, 258. https://doi.org/10.1186/s13071-019-3514-8 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Shults, P., Ho, A., Martin, E. M., McGregor, B. L. & Vargo, E. L. Genetic diversity of Culicoides stellifer (Diptera: Ceratopogonidae) in the Southeastern United States compared with sequences from Ontario, Canada. J. Med. Entomol. 57, 1324–1327. https://doi.org/10.1093/jme/tjaa025 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    81.Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237 (2005).PubMed 

    Google Scholar 
    82.Ciota, A. T., Chin, P. A. & Kramer, L. D. The effect of hybridization of Culex pipiens complex mosquitoes on transmission of West Nile virus. Parasit. Vectors 6, 1–4 (2013).
    Google Scholar 
    83.Meiswinkel, R., Gomulski, L., Delécolle, J., Goffredo, M. & Gasperi, G. The taxonomy of Culicoides vector complexes-unfinished business. Vet. Ital. 40, 151–159 (2004).CAS 
    PubMed 

    Google Scholar 
    84.Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics (Oxford, England) 32, 3047–3048. https://doi.org/10.1093/bioinformatics/btw354 (2016).CAS 
    Article 

    Google Scholar 
    85.Andrews, S. Babraham bioinformatics-FastQC a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).86.Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).CAS 
    PubMed 

    Google Scholar 
    87.Morales-Hojas, R. et al. The genome of the biting midge Culicoides sonorensis and gene expression analyses of vector competence for bluetongue virus. BMC Genomics 19, 624. https://doi.org/10.1186/s12864-018-5014-1 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    88.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics (Oxford, England) 25, 1754–1760 (2009).CAS 

    Google Scholar 
    89.Pante, E. et al. Use of RAD sequencing for delimiting species. Heredity 114, 450–459 (2015).CAS 
    PubMed 

    Google Scholar 
    90.Benestan, L. M. et al. Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol. Ecol. 25, 2967–2977 (2016).PubMed 

    Google Scholar 
    91.Lischer, H. E. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics (Oxford, England) 28, 298–299 (2012).CAS 

    Google Scholar 
    92.Pina-Martins, F., Silva, D. N., Fino, J. & Paulo, O. S. Structure_threader: An improved method for automation and parallelization of programs structure, fastStructure and MavericK on multicore CPU systems. Mol. Ecol. Resour. 17, e268–e274 (2017).CAS 
    PubMed 

    Google Scholar 
    93.Raj, A., Stephens, M. & Pritchard, J. K. Variational Inference of Population Structure in Large SNP Datasets. bioRxiv 10, 001073 (2013).
    Google Scholar 
    94.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.http://www.R-project.org/ (2013).95.Jombart, Thibaut, and Caitlin Collins. A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.0. 0. London: Imperial College London, MRC Centre for Outbreak Analysis and Modelling (2015).96.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics (Oxford, England) 30, 1312–1313 (2014).CAS 

    Google Scholar 
    97.Leaché, A. D., Banbury, B. L., Felsenstein, J., De Oca, A.N.-M. & Stamatakis, A. Short tree, long tree, right tree, wrong tree: New acquisition bias corrections for inferring SNP phylogenies. Syst. Biol. 64, 1032–1047 (2015).PubMed 
    PubMed Central 

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

    Google Scholar 
    99.Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    100.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K., Von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    101.Nguyen, L.-T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    PubMed 

    Google Scholar 
    102.Hoang, D. T., Chernomor, O., Von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).CAS 
    PubMed 

    Google Scholar 
    103.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 30. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    104.Rousset, F. genepop’007: a complete re‐implementation of the genepop software for Windows and Linux. Molecular ecology resources 8(1), 103–106 (2008).
    Google Scholar 
    105.Rousset, F. Genetic differentiation between individuals. J Evol Biol 13, 58–62 (2000).
    Google Scholar 
    106.Loiselle, B. A., Sork, V. L., Nason, J. & Graham, C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am. J. Bot. 82, 1420–1425 (1995).
    Google Scholar 
    107.Hardy, O. & Vekemans, X. SPAGeDi 1.5. A program for Spatial Pattern Analysis of Genetic Diversity. User’s manual http://ebe.ulb.ac.be/ebe/SPAGeDi_files/SPAGeDi_1.5_Manual.pdf. Université Libre de Bruxelles, Brussells, Belgium.[Google Scholar] (2015).108.Jay, F., Sjödin, P., Jakobsson, M. & Blum, M. G. Anisotropic isolation by distance: the main orientations of human genetic differentiation. Mol. Biol. Evol. 30, 513–525 (2013).CAS 
    PubMed 

    Google Scholar 
    109.Piry, S. et al. Mapping Averaged Pairwise Information (MAPI): a new exploratory tool to uncover spatial structure. Methods Ecol. Evol. 7, 1463–1475 (2016).
    Google Scholar 
    110.Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics (Oxford, England) 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).Article 

    Google Scholar 
    111.Hopken, M. W. Pathogen Vectors at The Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Ph.D. thesis, Colorado State University (2016).112.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    113.Bandelt, H. J., Forster, P. & Rohl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 (1999).CAS 
    Article 
    PubMed 

    Google Scholar  More