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    How cities are collaborating to help safeguard oceans

    NATURE INDEX
    24 September 2021

    How cities are collaborating to help safeguard oceans

    Despite missed deadlines in 2020 for key targets in marine conservation, momentum for these Sustainable Development Goals is growing.

    Michael Eisenstein

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    Michael Eisenstein

    Michael Eisenstein is a freelance writer in Philadelphia, Pennsylvania.

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    Bart Shepherd, co-leader of the Hope for Reefs initiative, guides fish into a decompression chamber while on expedition in Vanuatu.Credit: Luiz Rocha/California Academy of Sciences

    For about 30 minutes each year, vast colonies of corals in the waters of Palau, an island nation in the western Pacific, erupt in an almost perfectly synchronized mass-spawning event. Releasing buoyant packages of sperm and egg cells into the water to be fertilized by neighbouring colonies, these hermaphroditic species must make the most of rare opportunities to seed new life.In one of the world’s few indoor coral-culturing labs, Rebecca Albright and her team at the California Academy of Sciences in San Francisco are recreating the seasonal and lunar shifts that trigger such an event. The aim is to create multiple spawning systems that can be studied under controlled conditions. “Corals are notorious for being fickle animals to keep in captivity,” says Albright, a coral biologist and co-leader of Hope for Reefs, a global initiative to research and restore crucial coral-reef systems. “Most only sexually reproduce once a year, so you have to simulate all these environmental cues to elicit that.”
    Nature Index 2021 Science cities
    Strategies for cultivating and transplanting healthy corals into depleted areas are a crucial part of strengthening populations against what Albright describes as the “one-two punch effect” of climate change. Rising temperatures cause coral bleaching and death, while ocean acidification caused by increased levels of carbon dioxide makes corals less resilient and prevents regrowth. “If we are able to cap warming at 1.5  °C, we’re still going to lose 90% of reefs by 2050,” she says. “And if we edge towards 2 °C, we risk losing 97% to 99%.”Of the United Nations’ 17 Sustainable Development Goals (SDGs), Life below water (SDG14) and other SDGs related to environmental sustainability — Responsible consumption and production (SDG12), Climate action (SDG13) and Life on land (SDG15) — were the weakest in both donor funding and outcomes, attracting less than US$25 billion between them in 2000–13, according to the 2021 UNESCO Science Report (see go.nature.com/3zlojva). SDGs that are more directly related to economic growth — Industry, innovation and infrastructure (SDG9) and Sustainable cities and communities (SDG11) — by comparison, received $130 billion and $147 billion, respectively, over the same period.James Leape, co-director of Stanford University’s Center for Ocean Solutions in California, notes that four of the ten targets for SDG14, which aims to “conserve and sustainably use the oceans, seas and marine resources”, were due in 2020. All were missed. These include controlling the global damage wrought by illegal and unregulated fishing, which remains largely unchecked, and implementing scientifically grounded strategies for restoring affected fish stocks.But there are signs of momentum. The amount of ocean being conserved and managed within marine protected areas (MPAs), for example, has increased from 0.9% to 7.7% since 2000, says Leape. MPAs are regions in which fishing, mining and other activities are restricted. Efforts are under way to further expand the number of MPAs globally.Coastal collaborationsAs the world’s leading fishing nation, responsible for 15% of the reported global wild fish catch, China has ramped up efforts to designate new MPAs. Since 1980, China has designated more than 270 MPAs, comprising about 5% of its national waters. But it’s a long way off efforts by countries such as the United States, which has more than 1,000 MPAs that cover about 26% of its waters, and the United Kingdom, with 371 MPAs comprising 38% of its seas. In a 2019 Nature correspondence, fisheries researchers Yunzhou Li and Yiping Ren, from the Ocean University of China in Qingdao and Yong Chen from the University of Maine, Orono, say that effective monitoring and strict enforcement will also be essential to the success of China’s efforts (see Nature 573, 346; 2019).In a city-based analysis by the Nature Index, Beijing had the greatest output related to SDG14 in the 82 natural-sciences journals tracked by the index in 2015–20, with a Share of 17.88, followed by the coastal city of Townsville in northeastern Queensland, Australia (Share 15.59) and the Boston metropolitan area (Share 13.66). The San Francisco Bay Area, second only to Beijing in output related to all 17 SDGs, had the sixth-highest Share for SDG14 (13.24). (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

    Residents in the coastal town of Maroantsetra, in northeastern Madagascar, display their catch.Credit: Rebecca Gaal

    Many small island states face serious threats from the rapid decline of their coral reefs, which represent one of the world’s most diverse ecosystems. Gildas Todinanahary, a marine biologist at the Fisheries and Marine Science Institute at the University of Toliara in Madagascar, says the percentage of live coral cover surrounding the island nation has dropped from more than 80% in the 1980s to less than 10%, on average, today. “Decades ago, they used to say there will always be fish in the sea,” says Todinanahary. “Now they say there are no more fish.” This has jeopardized the livelihood of the fishing communities on the island’s western shore, he says.Christopher Golden, an ecologist and epidemiologist at the Harvard School of Public Health in Boston, is working with Todinanahary and his colleagues to deploy a series of small tiered platforms, designed to mimic the cracks and crevices of the reef, into healthy coral communities along the Madagascar coast. Once colonized, these structures are transported into degraded reefs in an effort to repopulate them. “If we can create a healthier reef, we can then rehabilitate some of the fish populations, and that will lead to improved fish-catch and greater access to seafood as a nutritional resource,” says Golden.Todinanahary is enthusiastic about the potential for seeding new reefs in barren coastal stretches, but says education and outreach to fishing communities will be key to ensuring that those restoration efforts endure. “It’s important to help communities change their habits and activities,” he says — for example, by providing training for alternative livelihoods such as aquaculture.Buy-in from community leaders is also crucial to the success of partnerships between researchers in leading science cities and colleagues in low- and middle-income maritime nations in SDG-related projects. In 2016, the government of Palau invited Leape and his team at Stanford to develop a strategy for turning 80% of its exclusive economic zone, a 370-km radius surrounding the island, into a protected area where fishing is prohibited. The initiative went into effect in January 2020. “We’re using satellite tracking to understand the patterns of use of the sanctuary by large pelagic species, and using DNA analysis to monitor biodiversity in the sanctuary,” says Leape. Palau’s programme has helped to motivate other island nations in the region to extend marine protection and conservation efforts as part of the Micronesia Challenge, an initiative to conserve 50% of marine resources and 30% of terrestrial resources by 2030.Golden’s research emphasizes both the sustainability and food-security sides of the fisheries-management coin, with routine health assessments of communities in places such as Madagascar and the Republic of Kiribati, an island nation in the central Pacific Ocean, coupled with close monitoring of the ecological health of their surrounding waters. To help this effort, Golden and his colleagues developed the Aquatic Food Composition Database, which compiles detailed nutritional information on more than 3,700 local plant and animal species to provide ecologically grounded guidance to local fishers. “We can look at what type of resilience there might be if we lose access to one species and have to focus on another,” says Golden. “We can understand the type of nourishment that people are actually getting from their catch.”Stanford’s Center for Ocean Solutions is also leveraging new technologies to guide sustainable fishing practices that benefit small-scale fishers, whose livelihood SDG14 aims to safeguard. “Their catches account for about two-thirds of the seafood we eat, and 90% of the fishery jobs,” says Leape. The centre is partnering with ABALOBI, an organization in South Africa founded by fisheries researcher Serge Raemaekers, from the University of Cape Town. ABALOBI has designed a mobile app toolbox to help fishers track specific fish populations, coordinate boats and crews, and bring catches to market. Leape is hopeful that early pilot testing in Africa and the Indian Ocean will pave the way for broader deployment in the near future.In parallel, Leape’s team is working on strategies to crack down on illegal fishing — currently estimated to account for roughly 20% of the global catch. This is being achieved partly through tools such as the satellite-based fishery monitoring efforts of Global Fishing Watch, a website run by Google in partnership with conservation non-profit organizations Oceana and SkyTruth. But technology is only part of the solution. Leape sees a crucial role for aggressive government enforcement and getting major corporations to engage in closer oversight of fishing practices. “We’ve been using Global Fishing Watch and other data sources to understand the patterns and areas for illegal fishing,” he says. “We’re working with these partners to try to translate that data into a more concerted effort to crack the problem.”

    doi: https://doi.org/10.1038/d41586-021-02407-8This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

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    Long-term trends in the body condition of parents and offspring of Tengmalm’s owls under fluctuating food conditions and climate change

    1.Brommer, J. E., Pietiäinen, H. & Kolunen, H. Reproduction and survival in a variable environment: Ural owls (Strix uralensis) and the three-year vole cycle. Auk 119, 544–550. https://doi.org/10.1642/0004-8038(2002)119[0544:rasiav]2.0.co;2 (2002).Article 

    Google Scholar 
    2.Begon, M., Townsend, C. R. & Harper, J. L. Ecology, Individuals, Populations and Communities 4th edn. (Blackwell, 2006).
    Google Scholar 
    3.Chang, A. M. & Wiebe, K. L. Body condition in snowy owls wintering on the prairies is greater in females and older individuals and may contribute to sex-biased mortality. Auk 133, 738–746. https://doi.org/10.1642/auk-16-60.1 (2016).Article 

    Google Scholar 
    4.McLean, N., van der Jeugd, H. P. & van de Pol, M. High intra-specific variation in avian body condition responses to climate limits generalisation across species. PLoS ONE 13, e0192401. https://doi.org/10.1371/journal.pone.0192401 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.McLean, N. M., van der Jeugd, H. P., van Turnhout, C. A. M., Lefcheck, J. S. & van de Pol, M. Reduced avian body condition due to global warming has little reproductive or population consequences. Oikos 129, 714–730. https://doi.org/10.1111/oik.06802 (2020).Article 

    Google Scholar 
    6.Aubry, L. M. et al. Climate change, phenology, and habitat degradation: Drivers of gosling body condition and juvenile survival in lesser snow geese. Glob. Change Biol. 19, 149–160. https://doi.org/10.1111/gcb.12013 (2013).ADS 
    Article 

    Google Scholar 
    7.Gardner, J. L., Amano, T., Sutherland, W. J., Clayton, M. & Peters, A. Individual and demographic consequences of reduced body condition following repeated exposure to high temperatures. Ecology 97, 786–795. https://doi.org/10.1890/15-0642.1 (2016).Article 
    PubMed 

    Google Scholar 
    8.Newton, I. Population Limitation in Birds (Academic Press, 1998).
    Google Scholar 
    9.Dunn, P. O. & Møller, A. P. Effects of Climate Change on Birds 2nd edn. (Oxford University Press, 2019).Book 

    Google Scholar 
    10.Crossin, G. T. et al. A carryover effect of migration underlies individual variation in reproductive readiness and extreme egg size dimorphism in Macaroni penguins. Am. Nat. 176, 357–366. https://doi.org/10.1086/655223 (2010).Article 
    PubMed 

    Google Scholar 
    11.Clausen, K. K., Madsen, J. & Tombre, I. M. Carry-over or compensation? The impact of winter harshness and post-winter body condition on spring-fattening in a migratory goose species. PLoS ONE 10(7), e0132312. https://doi.org/10.1371/journal.pone.0132312 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Selonen, V., Wistbacka, R. & Korpimäki, E. Food abundance and weather modify reproduction of two arboreal squirrel species. J. Mammal. 97, 1376–1384. https://doi.org/10.1093/jmammal/gyw096 (2016).Article 

    Google Scholar 
    13.Harrison, X. A., Blount, J. D., Inger, R., Norris, D. R. & Bearhop, S. Carry-over effects as drivers of fitness differences in animals. J. Anim. Ecol. 80, 4–18. https://doi.org/10.1111/j.1365-2656.2010.01740.x (2011).Article 
    PubMed 

    Google Scholar 
    14.O’Connor, C. M., Norris, D. R., Crossin, G. T. & Cooke, S. J. Biological carryover effects: Linking common concepts and mechanisms in ecology and evolution. Ecosphere 5, 1–11. https://doi.org/10.1890/es13-00388.1 (2014).Article 

    Google Scholar 
    15.Montreuil-Spencer, C., Schoenemann, K., Lendvai, A. Z. & Bonier, F. Winter corticosterone and body condition predict breeding investment in a nonmigratory bird. Behav. Ecol. 30, 1642–1652. https://doi.org/10.1093/beheco/arz129 (2019).Article 

    Google Scholar 
    16.Korpimäki, E. Body mass of breeding Tengmalm’s owls Aegolius funereus: Seasonal, between-year, site and age-related variation. Ornis Scand. 21, 169–178. https://doi.org/10.2307/3676776 (1990).Article 

    Google Scholar 
    17.Dijkstra, C., Daan, S., Meijer, T., Cave, A. J. & Foppen, R. P. B. Daily and seasonal-variations in body-mass of the kestrel in relation to food availability and reproduction. Ardea 76, 127–140 (1988).
    Google Scholar 
    18.Pietiäinen, H. & Kolunen, H. Female body condition and breeding of the Ural owl Strix uralensis. Funct. Ecol. 7, 726–735. https://doi.org/10.2307/2390195 (1993).Article 

    Google Scholar 
    19.Wijnandts, H. Ecological energetics of the long-eared owl (Asio otus). Ardea 72, 1–92 (1984).
    Google Scholar 
    20.Korpimäki, E. & Hakkarainen, H. Fluctuating food supply affects the cluch size of Tengmalm’s owl independent of laying date. Oecologia 85, 543–552 (1991).ADS 
    Article 

    Google Scholar 
    21.Korpimäki, E. & Wiehn, J. Clutch size of kestrels: Seasonal decline and experimental evidence for food limitation under fluctuating food conditions. Oikos 83, 259–272. https://doi.org/10.2307/3546837 (1998).Article 

    Google Scholar 
    22.Pietiäinen, H. Seasonal and individual variation in the production of offspring in the Ural owl Strix uralensis. J. Anim. Ecol. 58, 905–920. https://doi.org/10.2307/5132 (1989).Article 

    Google Scholar 
    23.Wellicome, T. I. Effects of food on reproduction in burrowing owls (Athene cunicularia) during three stages of the breeding season (Ph.D. dissertation). (University of Alberta, 2000).24.Ilmonen, P. et al. Parental effort and blood parasitism in Tengmalm’s owl: Effects of natural and experimental variation in food abundance. Oikos 86, 79–86. https://doi.org/10.2307/3546571 (1999).Article 

    Google Scholar 
    25.Santangeli, A., Hakkarainen, H., Laaksonen, T. & Korpimäki, E. Home range size is determined by habitat composition but feeding rate by food availability in male Tengmalm’s owls. Anim. Behav. 83, 1115–1123. https://doi.org/10.1016/j.anbehav.2012.02.002 (2012).Article 

    Google Scholar 
    26.Griebel, R. L. & Savidge, J. A. Factors related to body condition of nestling burrowing owls in Buffalo Gap National Grassland, South Dakota. Wilson Bull. 115, 477–480. https://doi.org/10.1676/02-094 (2003).Article 

    Google Scholar 
    27.Valkama, J., Korpimäki, E., Holm, A. & Hakkarainen, H. Hatching asynchrony and brood reduction in Tengmalm’s owl Aegolius funereus: The role of temporal and spatial variation in food abundance. Oecologia 133, 334–341. https://doi.org/10.1007/s00442-002-1033-2 (2002).ADS 
    Article 
    PubMed 

    Google Scholar 
    28.König, C. & Weick, F. Owls of the World 2nd edn. (Yale University Press, 2008).
    Google Scholar 
    29.Mikkola, H. Owls of Europe (Poyser, 1983).
    Google Scholar 
    30.Korpimäki, E. On the Ecology and Biology of Tengmalm’s Owl (Aegolius funereus) in Southern Ostrobothnia and Soumenselkä, Western Finland Vol. 13, 1–84 (University of Oulu, 1981).
    Google Scholar 
    31.Korpimäki, E. Diet of breeding Tengmalm’s owls Aegolius funereus: Long-term changes and year-to-year variation under cyclic food conditions. Ornis Fenn. 65, 21–30 (1988).
    Google Scholar 
    32.Korpimäki, E. & Hakkarainen, H. The Boreal Owl: Ecology, Behaviour and Conservation of a Forest-Dwelling Predator (Cambridge University Press, 2012).Book 

    Google Scholar 
    33.Kouba, M., Bartoš, L., Šindelář, J. & Šťastný, K. Alloparental care and adoption in Tengmalm’s owl (Aegolius funereus). J. Ornithol. 158, 185–191. https://doi.org/10.1007/s10336-016-1381-z (2017).Article 

    Google Scholar 
    34.Eldegard, K. & Sonerud, G. A. Experimental increase in food supply influences the outcome of within-family conflicts in Tengmalm’s owl. Behav. Ecol. Sociobiol. 64, 815–826 (2010).Article 

    Google Scholar 
    35.Eldegard, K. & Sonerud, G. A. Sex roles during post-fledging care in birds: Female Tengmalm’s owls contribute little to food provisioning. J. Ornithol. 153, 385–398. https://doi.org/10.1007/s10336-011-0753-7 (2012).Article 

    Google Scholar 
    36.Kouba, M., Bartoš, L. & Šťastný, K. Differential movement patterns of juvenile Tengmalm’s owls (Aegolius funereus) during the post-fledging dependence period in two years with contrasting prey abundance. PLoS ONE 8(7), e67034. https://doi.org/10.1371/journal.pone.0067034 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Korpimäki, E. Fluctuating food abundance determines the lifetime reproductive success of male Tengmalm’s owls. J. Anim. Ecol. 61, 103–111 (1992).Article 

    Google Scholar 
    38.Kouba, M., Bartoš, L., Korpimäki, E. & Zárybnická, M. Factors affecting the duration of nestling period and fledging order in Tengmalm’s owl (Aegolius funereus): Effect of wing length and hatching sequence. PLoS ONE 10(3), e0121641. https://doi.org/10.1371/journal.pone.0121641 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Björklund, H., Saurola, P. & Valkama, J. Petolintuvuosi 2019 oli kohtalainen (Summary: Breeding and population trends of common raptors and owls in Finland in 2019). Yearb. Linnut Mag. 2019, 44–59 (2020).
    Google Scholar 
    40.Kouba, M., Bartoš, L., Bartošová, J., Hongisto, K. & Korpimäki, E. Interactive influences of fluctuations of main food resources and climate change on long-term population decline of Tengmalm’s owls in the boreal forest. Sci. Rep. 10, 20429. https://doi.org/10.1038/s41598-41020-77531-y (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Ferrero, J. J., Grande, J. M. & Negro, J. J. Copulation behavior of a potentially double-brooded bird of prey, the black-winged kite (Elanus caeruleus). J. Raptor Res. 37, 1–7 (2003).
    Google Scholar 
    42.Sergio, F. From individual behaviour to population pattern: Weather-dependent foraging and breeding performance in black kites. Anim. Behav. 66, 1109–1117. https://doi.org/10.1006/anbe.2003.2303 (2003).Article 

    Google Scholar 
    43.Korpimäki, E. Effects of age on breeding performance of Tengmalm’s owl Aegolius funereus in western Finland. Ornis Scand. 19, 21–26 (1988).Article 

    Google Scholar 
    44.Laaksonen, T., Korpimäki, E. & Hakkarainen, H. Interactive effects of parental age and environmental variation on the breeding performance of Tengmalm’s owls. J. Anim. Ecol. 71, 23–31. https://doi.org/10.1046/j.0021-8790.2001.00570.x (2002).Article 

    Google Scholar 
    45.Korpimäki, E. Highlights from a long-term study of Tengmalm’s owls: Cyclic fluctuations in vole abundance govern mating systems, population dynamics and demography. Brit. Birds 113, 316–333 (2020).
    Google Scholar 
    46.Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method. Oikos 118, 1883–1891. https://doi.org/10.1111/j.1600-0706.2009.17643.x (2009).Article 

    Google Scholar 
    47.Korpimäki, E., Norrdahl, K., Huitu, O. & Klemola, T. Predator-induced synchrony in population oscillations of coexisting small mammal species. Proc. R. Soc. B-Biol. Sci. 272, 193–202 (2005).Article 

    Google Scholar 
    48.Huitu, O., Norrdahl, K. & Korpimäki, E. Landscape effects on temporal and spatial properties of vole population fluctuations. Oecologia 135, 209–220. https://doi.org/10.1007/s00442-002-1171-6 (2003).ADS 
    Article 
    PubMed 

    Google Scholar 
    49.Schreiber-Gregory, D. N. & Jackson, H. M. Multicollinearity: What is it, why should we care, and how can it be controlled. In Proc. SAS R Global Forum 2017, Conference Paper 1404 (2017).50.Zuur, A., Ieno, E. N. & Smith, G. M. Analyzing Ecological Data (Springer, 2007).Book 

    Google Scholar 
    51.Tao, J., Littel, R., Patetta, M., Truxillo, C. & Wolfinger, R. Mixed Model Analyses Using the SAS System Course Notes (SAS Institute Inc., 2002).
    Google Scholar 
    52.Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Information-Theoretical Approach (Springer, 1998).Book 

    Google Scholar 
    53.Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
    MathSciNet 
    Article 

    Google Scholar 
    54.Vaida, F. & Blanchard, S. Conditional Akaike information for mixed-effects models. Biometrika 92, 351–370. https://doi.org/10.1093/biomet/92.2.351 (2005).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    55.Ward, E. J. A review and comparison of four commonly used Bayesian and maximum likelihood model selection tools. Ecol. Model. 211, 1–10. https://doi.org/10.1016/j.ecolmodel.2007.10.030 (2008).CAS 
    Article 

    Google Scholar 
    56.Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).MathSciNet 
    Article 

    Google Scholar 
    57.Christensen, W. Agreeing to disagree: Using SAS to make reasoned decisions when information criteria select different models. In SAS Conference Proceedings: Western Users of SAS Software 2018. September 5–7, 2018, Sacramento, California, Paper 099–2018 (2018).58.Posada, D. & Buckley, T. R. Model selection and model averaging in phylogenetics: Advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. Syst. Biol. 53, 793–808. https://doi.org/10.1080/10635150490522304 (2004).Article 
    PubMed 

    Google Scholar 
    59.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

    Google Scholar 
    60.Buckland, S. T., Burnham, K. P. & Augustin, N. H. Model selection: An integral part of inference. Biometrics 53, 603–618. https://doi.org/10.2307/2533961 (1997).Article 
    MATH 

    Google Scholar 
    61.Wagenmakers, E. J. & Farrell, S. AIC model selection using Akaike weights. Psychon. Bull. Rev. 11, 192–196. https://doi.org/10.3758/bf03206482 (2004).Article 
    PubMed 

    Google Scholar 
    62.Lack, D. The Natural Regulation of Animal Numbers (Oxford University Press, 1954).
    Google Scholar 
    63.Korpela, K. et al. Nonlinear effects of climate on boreal rodent dynamics: Mild winters do not negate high-amplitude cycles. Glob. Change Biol. 19, 697–710. https://doi.org/10.1111/gcb.12099 (2013).ADS 
    Article 

    Google Scholar 
    64.Wiehn, J. & Korpimäki, E. Food limitation on brood size: Experimental evidence in the Eurasian kestrel. Ecology 78, 2043–2050. https://doi.org/10.2307/2265943 (1997).Article 

    Google Scholar 
    65.Korpimäki, E. & Lagerström, M. Survival and natal dispersal of fledglings of Tengmalm’s owl in relation to fluctuating food conditions and hatching date. J. Anim. Ecol. 57, 433–441 (1988).Article 

    Google Scholar 
    66.Norris, K. J. Female choice and the quality of parental care in the great tit Parus major. Behav. Ecol. Sociobiol. 27, 275–281 (1990).Article 

    Google Scholar 
    67.Naef-Daenzer, B., Widmer, F. & Nuber, M. Differential post-fledging survival of great and coal tits in relation to their condition and fledging date. J. Anim. Ecol. 70, 730–738. https://doi.org/10.1046/j.0021-8790.2001.00533.x (2001).Article 

    Google Scholar 
    68.Grüebler, M. U. & Naef-Daenzer, B. Postfledging parental effort in barn swallows: Evidence for a trade-off in the allocation of time between broods. Anim. Behav. 75, 1877–1884. https://doi.org/10.1016/j.anbehav.2007.12.002 (2008).Article 

    Google Scholar 
    69.Jones, T. M., Ward, M. P., Benson, T. J. & Brawn, J. D. Variation in nestling body condition and wing development predict cause-specific mortality in fledgling dickcissels. J. Avian Biol. 48, 439–447. https://doi.org/10.1111/jav.01143 (2017).Article 

    Google Scholar 
    70.Magrath, R. D. Nestling weight and juvenile survival in the blackbird, Turdus merula. J. Anim. Ecol. 60, 335–351. https://doi.org/10.2307/5464 (1991).Article 

    Google Scholar 
    71.Naef-Daenzer, B. & Grüebler, M. U. Post-fledging survival of altricial birds: Ecological determinants and adaptation. J. Field Ornithol. 87, 227–250. https://doi.org/10.1111/jofo.12157 (2016).Article 

    Google Scholar 
    72.Winkler, D. W., Luo, M. K. & Rakhimberdiev, E. Temperature effects on food supply and chick mortality in tree swallows (Tachycineta bicolor). Oecologia 173, 129–138. https://doi.org/10.1007/s00442-013-2605-z (2013).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Hylton, R. A., Frederick, P. C., de la Fuente, T. E. & Spalding, M. G. Effects of nestling health on postfledging survival of wood storks. Condor 108, 97–106. https://doi.org/10.1650/0010-5422(2006)108[0097:Eonhop]2.0.Co;2 (2006).Article 

    Google Scholar 
    74.Imlay, T. L., Mann, H. A. R. & Leonard, M. L. No effect of insect abundance on nestling survival or mass for three aerial insectivores. Avian Conserv. Ecol. https://doi.org/10.5751/ace-01092-120219 (2017).Article 

    Google Scholar 
    75.Nooker, J. K., Dunn, P. O. & Whittingham, L. A. Effects of food abundance, weather, and female condition on reproduction in tree swallows (Tachycineta bicolor). Auk 122, 1225–1238. https://doi.org/10.1642/0004-8038(2005)122[1225:eofawa]2.0.co;2 (2005).Article 

    Google Scholar 
    76.Perrig, M., Gruebler, M. U., Keil, H. & Naef-Daenzer, B. Experimental food supplementation affects the physical development, behaviour and survival of little owl Athene noctua nestlings. Ibis 156, 755–767. https://doi.org/10.1111/ibi.12171 (2014).Article 

    Google Scholar 
    77.Perrig, M., Gruebler, M. U., Keil, H. & Naef-Daenzer, B. Post-fledging survival of little owls Athene noctua in relation to nestling food supply. Ibis 159, 532–540. https://doi.org/10.1111/ibi.12477 (2017).Article 

    Google Scholar 
    78.McDonald, P. G., Olsen, P. D. & Cockburn, A. Sex allocation and nestling survival in a dimorphic raptor: Does size matter? Behav. Ecol. 16, 922–930. https://doi.org/10.1093/beheco/ari071 (2005).Article 

    Google Scholar 
    79.Morosinotto, C. et al. Fledging mass is color morph specific and affects local recruitment in a wild bird. Am. Nat. 196, 609–619. https://doi.org/10.1086/710708 (2020).Article 
    PubMed 

    Google Scholar 
    80.Overskaug, K., Bolstad, J. P., Sunde, P. & Øien, I. J. Fledgling behavior and survival in northern tawny owls. Condor 101, 169–174 (1999).Article 

    Google Scholar 
    81.Todd, L. D., Poulin, R. G., Wellicome, T. I. & Brigham, R. M. Post-fledging survival of burrowing owls in Saskatchewan. J. Wildl. Manage. 67, 512–519. https://doi.org/10.2307/3802709 (2003).Article 

    Google Scholar 
    82.Cox, W. A., Thompson, F. R., Cox, A. S. & Faaborg, J. Post-fledging survival in passerine birds and the value of post-fledging studies to conservation. J. Wildl. Manage. 78, 183–193. https://doi.org/10.1002/jwmg.670 (2014).Article 

    Google Scholar 
    83.Korpimäki, E. Timing of breeding of Tengmalm’s owl Aegolius funereus in relation to vole dynamics in western Finland. Ibis 129, 58–68 (1987).Article 

    Google Scholar 
    84.Pigeault, R., Cozzarolo, C. S., Glaizot, O. & Christe, P. Effect of age, haemosporidian infection and body condition on pair composition and reproductive success in great tits Parus major. Ibis 162, 613–626. https://doi.org/10.1111/ibi.12774 (2020).Article 

    Google Scholar 
    85.Hakkarainen, H. & Korpimäki, E. The effect of female body-size on clutch volume of Tengmalm’s owls Aegolius funereus in varying food conditions. Ornis Fenn. 70, 189–195 (1993).
    Google Scholar 
    86.Hanauska-Brown, L. A., Dufty, A. M. & Roloff, G. J. Blood chemistry, cytology, and body condition in adult northern goshawks (Accipiter gentilis). J. Raptor Res. 37, 299–306 (2003).
    Google Scholar 
    87.Chastel, O., Weimerskirch, H. & Jouventin, P. Body condition and seabird reproductive performance: A study of three petrel species. Ecology 76, 2240–2246. https://doi.org/10.2307/1941698 (1995).Article 

    Google Scholar 
    88.Grilli, M. G., Pari, M. & Ibanez, A. Poor body conditions during the breeding period in a seabird population with low breeding success. Mar. Biol. https://doi.org/10.1007/s00227-018-3401-4 (2018).Article 

    Google Scholar 
    89.Toland, B. Hunting success of some Missouri raptors. Wilson Bull. 98, 116–125 (1986).
    Google Scholar 
    90.Masoero, G., Morosinotto, C., Laaksonen, T. & Korpimäki, E. Food hoarding of an avian predator: Sex- and age-related differences under fluctuating food conditions. Behav. Ecol. Sociobiol. https://doi.org/10.1007/s00265-00018-02571-x (2018).Article 

    Google Scholar 
    91.Masoero, G., Laaksonen, T., Morosinotto, C. & Korpimäki, E. Age and sex differences in numerical responses, dietary shifts, and total responses of a generalist predator to population dynamics of main prey. Oecologia 192, 699–711. https://doi.org/10.1007/s00442-020-04607-x (2020).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    92.Norrdahl, K. & Korpimäki, E. Changes in population structure and reproduction during a 3-year population cycle of voles. Oikos 96, 331–345. https://doi.org/10.1034/j.1600-0706.2002.970319.x (2002).Article 

    Google Scholar 
    93.Merritt, J. F., Lima, M. & Bozinovic, F. Seasonal regulation in fluctuating small mammal populations: Feedback structure and climate. Oikos 94, 505–514. https://doi.org/10.1034/j.1600-0706.2001.940312.x (2001).Article 

    Google Scholar 
    94.Solonen, T. Overwinter population change of small mammals in southern Finland. Ann. Zool. Fenn. 43, 295–302 (2006).
    Google Scholar 
    95.Haapakoski, M. & Ylönen, H. Snow evens fragmentation effects and food determines overwintering success in ground-dwelling voles. Ecol. Res. 28, 307–315. https://doi.org/10.1007/s11284-012-1020-y (2013).Article 

    Google Scholar 
    96.Berlioz, J. & Bergman, G. (eds) Proc., XII International Ornithological Congress, Helsinki 5–12 Vol. 158, 586–591 (Tilgmannin Kirjapaino, 1960).
    Google Scholar 
    97.Fraixedas, S., Linden, A. & Lehikoinen, A. Population trends of common breeding forest birds in southern Finland are consistent with trends in forest management and climate change. Ornis Fenn. 92, 187–203 (2015).
    Google Scholar 
    98.Virkkala, R. Long-term decline of southern boreal forest birds: Consequence of habitat alteration or climate change? Biodivers. Conserv. 25, 151–167. https://doi.org/10.1007/s10531-015-1043-0 (2016).Article 

    Google Scholar 
    99.Björklund, H., Valkama, J., Tomppo, E. & Laaksonen, T. Habitat effects on the breeding performance of three forest-dwelling hawks. PLoS ONE 10(9), e0137877. https://doi.org/10.1371/journal.pone.0137877 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    100.Koskimäki, J. et al. Are habitat loss, predation risk and climate related to the drastic decline in a Siberian flying squirrel population? A 15-year study. Popul. Ecol. 56, 341–348. https://doi.org/10.1007/s10144-013-0411-4 (2014).Article 

    Google Scholar 
    101.Suzuki, N. & Parker, K. L. Proactive conservation of high-value habitat for woodland caribou and grizzly bears in the boreal zone of British Columbia, Canada. Biol. Conserv. 230, 91–103. https://doi.org/10.1016/j.biocon.2018.12.013 (2019).Article 

    Google Scholar 
    102.Venier, L. A. et al. Effects of natural resource development on the terrestrial biodiversity of Canadian boreal forests. Environ. Rev. 22, 457–490. https://doi.org/10.1139/er-2013-0075 (2014).Article 

    Google Scholar 
    103.Thomas, J. W. et al. A Conservation Strategy for the Northern Spotted Owl (US Government Printing Office 791-171/20026, 1990).
    Google Scholar 
    104.Laaksonen, T. & Lehikoinen, A. Population trends in boreal birds: Continuing declines in agricultural, northern, and long-distance migrant species. Biol. Conserv. 168, 99–107. https://doi.org/10.1016/j.biocon.2013.09.007 (2013).Article 

    Google Scholar  More

  • in

    Functional diversity outperforms taxonomic diversity in revealing short-term trampling effects

    1.Dengler, J. et al. Biodiversity of palaearctic grasslands: A synthesis. Agric. Ecosyst. Environ. 182(1), 1–14 (2014).Article 

    Google Scholar 
    2.Wang, H. et al. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecol. Lett. 23, 701–710 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Torok, P. et al. Step(pe) up! Raising the profile of the Palaearctic natural grasslands. Biodivers. Conserv. 25(12), 2187–2195 (2016).Article 

    Google Scholar 
    4.Kuss, F. R. & Graefe, A. R. Effects of recreation trampling on natural area vegetation. J. Leis. Res. 17, 165–183 (1985).Article 

    Google Scholar 
    5.Buckley, R. C. & Pannell, J. Environmental impacts of tourism and recreation in national parks and conservation reserves. J. Tourism Stud. 1, 24–32 (1990).
    Google Scholar 
    6.Yorks, T. et al. Toleration of traffic by vegetation: Life form conclusions and summary extracts from a comprehensive data base. Environ. Manage. 21, 12–131 (1997).Article 

    Google Scholar 
    7.Gouvenain, R. C. Indirect impacts of soil trampling on tree growth and plant succession in the north cascade mountains of Washington (1996).8.Xu, L., Yu, F. H. & Drunen, E. V. Trampling, defoliation and physiological integration affect growth, morphological and mechanical properties of a root-suckering clonal tree. Ann. Bot. 109, 1001–1008 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Bayfield, N. G. Use and deterioration of some Scottish hill paths. J. Appl. Ecol. 10, 639–648 (1973).Article 

    Google Scholar 
    10.Liddle, M. J. A selective review of the ecological effects of human trampling on natural ecosystems. Biol. Cons. 7, 17–36 (1975).Article 

    Google Scholar 
    11.Frissell, S. S. Judging recreational impacts on wilderness campsites. J. Forest. 76, 481–483 (1978).
    Google Scholar 
    12.Wagar, J. A. How to predict which vegetated areas will stand up best under ‘active’ recreation. Am. Recreat. J. 1, 20–21 (1961).
    Google Scholar 
    13.Cole, D. N. & Bayfield, N. G. Recreational trampling of vegetation: Standard experimental procedures. Biol. Cons. 63(3), 209–215 (1993).Article 

    Google Scholar 
    14.Prescott, O. & Stewart, G. Assessing the impacts of human trampling on vegetation: A systematic review and meta-analysis of experimental evidence. PeerJ 2, e360 (2014).Article 

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

    Google Scholar 
    16.Vandewalle, M. et al. Functional traits as indicators of biodiversity response to land use changes across ecosystems and organisms. Biodivers. Conserv. 19, 2921–2947 (2010).Article 

    Google Scholar 
    17.Petchey, O. L. & Gaston, K. J. Functional diversity, species richness and composition. Ecol. Lett. 5, 402–411 (2002).Article 

    Google Scholar 
    18.Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: Functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48(5), 1079–1087 (2011).Article 

    Google Scholar 
    19.Mouillot, D. et al. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28, 167–177 (2013).PubMed 
    Article 

    Google Scholar 
    20.Carmona, C. P. et al. Traits without borders: Integrating functional diversity across scales. Trends Ecol. Evol. 31(5), 382–394 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Conradi, T. et al. Impacts of visitor trampling on the taxonomic and functional community structure of calcareous grassland. Appl. Veg. Sci. 18, 359–367 (2015).Article 

    Google Scholar 
    22.Pickering, C. M. & Barros, A. Using functional traits to assess the resistance of subalpine grassland to trampling by mountain biking and hiking. J. Environ. Manage. 164, 129–136 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Baraloto, C., Herault, B. & Paine, C. E. T. Contrasting taxonomic and functional responses of a tropic tree community to selective logging. J. Appl. Ecol. 49, 861–870 (2012).Article 

    Google Scholar 
    24.Magnago, L. F. S. et al. Functional attributes change but functional richness is unchanged after fragmentation of Brazilian Atlantic forests. J. Ecol. 102, 475–485 (2014).Article 

    Google Scholar 
    25.Marion, J. L. & Cole, D. N. Spatial and temporal variation in soil and vegetation impacts on campsites. Ecol. Appl. 6, 520–530 (1996).Article 

    Google Scholar 
    26.Roovers, P. et al. Experimental trampling and vegetation recovery in some forest and heathland communities. Appl. Veg. Sci. 7, 111–118 (2004).Article 

    Google Scholar 
    27.Conradi, T. et al. Impacts of visitor trampling on the taxonomic and functional community structure of calcareous grassland. Appl. Veg. Sci. 18(3), 359–367 (2015).Article 

    Google Scholar 
    28.Zamora, R. Functional equivalence in plant-animal interactions: Ecological and evolutionary consequences. Oikos 88(2), 442–447 (2000).Article 

    Google Scholar 
    29.Hubbell, S. P. Neutral theory in community ecology and the hypothesis of functional equivalence. Funct. Ecol. 19, 166–172 (2005).Article 

    Google Scholar 
    30.Mouchet, M. A. et al. Functional diversity measures: An overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24, 867–876 (2010).Article 

    Google Scholar 
    31.Diaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl. Acad. Sci. U.S.A. 104, 20684–20689 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Suding, K. N. & Goldstein, L. J. Testing the Holy Grail framework: Using functional traits to predict ecosystem change. New Phytol. 180, 559–562 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.De Bello, F. et al. Towards an assessment of multiple ecosystem processes and services via functional traits. Biodivers. Conserv. 19, 2873–2893 (2010).Article 

    Google Scholar 
    34.Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    35.Tian, K., Guo, H. J. & Yang, Y. M. Ecological structures and functions of plateau wetlands in China (Chinese Science Press, 2009).
    Google Scholar 
    36.Garnier, E. et al. standardized protocol for the determination of specific leaf size and leaf dry matter content. Funct. Ecol. 15, 688–695 (2001).Article 

    Google Scholar 
    37.Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).Article 

    Google Scholar 
    38.Mason, N. W. H. et al. An index of functional diversity. J. Veg. Sci. 14, 571–578 (2003).Article 

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

    Google Scholar 
    40.Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).Article 

    Google Scholar 
    41.Cianciaruso, M. V. et al. Including intraspecific variability in functional diversity. Ecology 90, 81–89 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Lepš, J. et al. Community trait response to environment: Disentangling species turnover vs intraspecific trait variability effects. Ecography 34, 856–863 (2011).Article 

    Google Scholar 
    43.Garnier, E. et al. Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: A standardized methodology and lessons from an application to 11 European sites. Ann. Bot. 99, 967–985 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Pakeman, R. J. & Quested, H. M. Sampling plant functional traits: What proportion of the species need to be measured?. Appl. Veg. Sci. 10, 91–96 (2007).Article 

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

    Google Scholar 
    46.Oksanen, J., Blanchet, F. G., Friendly, M. et al. Vegan: Community Ecology Package. R package version 2.5–7 (2020).47.Laliberté, E., Legendre, P., Shipley, B. FD: measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0–12 (2014). More

  • in

    Refocusing multiple stressor research around the targets and scales of ecological impacts

    1.Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Threats Classification Scheme (Version 3.2) (International Union for Conservation of Nature and Natural Resources, 2020); https://www.iucnredlist.org/resources/threat-classification-scheme3.Living Planet Report 2018: Aiming Higher (World Wildlife Fund, 2018).4.Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Halpern, B. S. & Fujita, R. Assumptions, challenges, and future directions in cumulative impact analysis. Ecosphere 4, art131 (2013).Article 

    Google Scholar 
    6.Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. A. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Orr, J. A. et al. Towards a unified study of multiple stressors: divisions and common goals across research disciplines. Proc. R. Soc. B Biol. Sci. 287, 20200421 (2020).Article 

    Google Scholar 
    8.Piggott, J. J., Townsend, C. R. & Matthaei, C. D. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5, 1538–1547 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Burgess, B. J., Purves, D., Mace, G. & Murrell, D. J. Ecological theory predicts ecosystem stressor interactions in freshwater ecosystems, but highlights the strengths and weaknesses of the additive null model. Preprint at bioRxiv https://doi.org/10.1101/2020.08.10.243972 (2020).11.Didham, R. K., Tylianakis, J. M., Gemmell, N. J., Rand, T. A. & Ewers, R. M. Interactive effects of habitat modification and species invasion on native species decline. Trends Ecol. Evol. 22, 489–496 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Galic, N., Sullivan, L. L., Grimm, V. & Forbes, V. E. When things don’t add up: quantifying impacts of multiple stressors from individual metabolism to ecosystem processing. Ecol. Lett. 21, 568–577 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Kéfi, S. et al. Advancing our understanding of ecological stability. Ecol. Lett. 22, 1349–1356 (2019).PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    16.Ashauer, R. & Jager, T. Physiological modes of action across species and toxicants: the key to predictive ecotoxicology. Environ. Sci. Process Impacts 20, 48–57 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Caswell, H. in Ecotoxicology. A Hierarchical Treatment (eds Newman, M. C. & Jagoe, C. H) 255–292 (CRC Press, 1996).18.Judd, A., Backhaus, T. & Goodsir, F. An effective set of principles for practical implementation of marine cumulative effects assessment. Environ. Sci. Policy 54, 254–262 (2015).Article 

    Google Scholar 
    19.Schafer, R. B. & Piggott, J. J. Advancing understanding and prediction in multiple stressor research through a mechanistic basis for null models. Glob. Change Biol. 24, 1817–1826 (2018).Article 

    Google Scholar 
    20.Boyd, P. W. & Brown, C. J. Modes of interactions between environmental drivers and marine biota. Front. Mar. Sci. 2, 9 (2015).
    Google Scholar 
    21.Beyer, J. et al. Environmental risk assessment of combined effects in aquatic ecotoxicology: a discussion paper. Mar. Environ. Res. 96, 81–91 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Côté, I. M., Darling, E. S. & Brown, C. J. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. B Biol. Sci. 283, 20152592 (2016).Article 

    Google Scholar 
    23.Kroeker, K. J., Kordas, R. L. & Harley, C. D. Embracing interactions in ocean acidification research: confronting multiple stressor scenarios and context dependence. Biol. Lett. https://doi.org/10.1098/rsbl.2016.0802 (2017).24.De Laender, F. Community- and ecosystem-level effects of multiple environmental change drivers: beyond null model testing. Glob. Change Biol. 24, 5021–5030 (2018).Article 

    Google Scholar 
    25.Goussen, B., Price, O. R., Rendal, C. & Ashauer, R. Integrated presentation of ecological risk from multiple stressors. Sci. Rep. 6, 36004 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Liess, M., Foit, K., Knillmann, S., Schafer, R. B. & Liess, H. D. Predicting the synergy of multiple stress effects. Sci. Rep. 6, 32965 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Van den Brink, P. J. et al. Towards a general framework for the assessment of interactive effects of multiple stressors on aquatic ecosystems: results from the Making Aquatic Ecosystems Great Again (MAEGA) workshop. Sci. Total Environ. 684, 722–726 (2019).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    28.Kooijman, S. A. L. M. Dynamic Energy Budgets in Biological Systems: Applications to Ecotoxicology (Cambridge Univ. Press, 1993).29.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

    Google Scholar 
    30.Jeschke, J. M., Kopp, M. & Tollrian, R. Consumer-food systems: why type I functional responses are exclusive to filter feeders. Biol. Rev. 79, 337–349 (2004).PubMed 
    Article 

    Google Scholar 
    31.Bolker, B., Holyoak, M., Krivan, V., Rowe, L. & Schmitz, O. Connecting theoretical and empirical studies of trait-mediated interactions. Ecology 84, 1101–1114 (2003).Article 

    Google Scholar 
    32.Schmitz, O. J., Krivan, V. & Ovadia, O. Trophic cascades: the primacy of trait-mediated indirect interactions. Ecol. Lett. 7, 153–163 (2004).Article 

    Google Scholar 
    33.Abrams, P. A., Menge, B. A., Mittelbach, G. G., Spiller, D. A. & Yodzis, P. in Food Webs: Integration of Patterns and Dynamics (eds G. A. Polis & K. O. Winemiller) 371–395 (Chapman & Hall, 1996).34.Thompson, P. L., MacLennan, M. M. & Vinebrooke, R. D. Species interactions cause non‐additive effects of multiple environmental stressors on communities. Ecosphere 9, e02518 (2018).Article 

    Google Scholar 
    35.Loreau, M. Linking biodiversity and ecosystems: towards a unifying ecological theory. Philos. Trans. R. Soc. B Biol. Sci. 365, 49–60 (2010).Article 

    Google Scholar 
    36.Gonzalez, A. et al. Scaling-up biodiversity-ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Adler, P. B. et al. Productivity is a poor predictor of plant species richness. Science 333, 1750–1753 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Newman, E. A. Disturbance ecology in the Anthropocene. Front. Ecol. Evol. 7, 147 (2019).Article 

    Google Scholar 
    40.Ohlmann, M. et al. Diversity indices for ecological networks: a unifying framework using Hill numbers. Ecol. Lett. 22, 737–747 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Ohlmann, M. et al. Mapping the imprint of biotic interactions on β‐diversity. Ecol. Lett. 21, 1660–1669 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Brun, P. et al. The productivity–biodiversity relationship varies across diversity dimensions. Nat. Commun. 10, 5691 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Pellissier, L. et al. Comparing species interaction networks along environmental gradients. Biol. Rev. 93, 785–800 (2018).PubMed 
    Article 

    Google Scholar 
    44.Bracewell, S. et al. Qualifying the effects of single and multiple stressors on the food web structure of Dutch drainage ditches using a literature review and conceptual models. Sci. Total Environ. 684, 727–740 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Kohler, H. R. & Triebskorn, R. Wildlife ecotoxicology of pesticides: can we track effects to the population level and beyond? Science 341, 759–765 (2013).PubMed 
    Article 
    CAS 

    Google Scholar 
    46.Kooijman, S. A. L. M. Dynamic Energy and Mass Budgets in Biological Systems (Cambridge Univ. Press, 2000).47.Stearns, S. C. The Evolution of Life Histories (Oxford Univ. Press, 1992).48.Jackson, M. C., Pawar, S. & Woodward, G. The temporal dynamics of multiple stressor effects: from individuals to ecosystems. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.01.005 (2021).49.Billick, I. & Case, T. J. Higher order interactions in ecological communities: what are they and how can they be detected? Ecology 75, 1529–1543 (1994).Article 

    Google Scholar 
    50.Grilli, J., Barabás, G., Michalska-Smith, M. J. & Allesina, S. Higher-order interactions stabilize dynamics in competitive network models. Nature 548, 210–213 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.Gill, R. J., Ramos-Rodriguez, O. & Raine, N. E. Combined pesticide exposure severely affects individual- and colony-level traits in bees. Nature 491, 105–108 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Crespi, E. J., Williams, T. D., Jessop, T. S. & Delehanty, B. Life history and the ecology of stress: how do glucocorticoid hormones influence life‐history variation in animals? Funct. Ecol. 27, 93–106 (2013).Article 

    Google Scholar 
    53.Matthiopoulos, J., Moss, R. & Lambin, X. The kin-facilitation hypothesis for red grouse population cycles: territory sharing between relatives. Ecol. Modell. 127, 53–63 (2000).Article 

    Google Scholar 
    54.Moss, R., Watson, A. & Parr, R. Experimental prevention of a population cycle in red grouse. Ecology 77, 1512–1530 (1996).Article 

    Google Scholar 
    55.Kaiser-Bunbury, C. N. et al. Ecosystem restoration strengthens pollination network resilience and function. Nature 542, 223–227 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Lever, J. J., van Nes, E. H., Scheffer, M. & Bascompte, J. The sudden collapse of pollinator communities. Ecol. Lett. 17, 350–359 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Schmitz, O. J. Press perturbations and the predictability ofecological interactions in a food web. Ecology 78, 55–69 (1997).
    Google Scholar 
    58.Ernest, S. K. M. et al. Thermodynamic and metabolic effects on the scaling of production and population energy use. Ecol. Lett. 6, 990–995 (2003).Article 

    Google Scholar 
    59.Price, P. B. & Sowers, T. Temperature dependence of metabolic rates for microbial growth, maintenance, and survival. Proc. Natl Acad. Sci. USA 101, 4631–4636 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Apple, J. K., Del Giorgio, P. A. & Kemp, W. M. Temperature regulation of bacterial production, respiration, and growth efficiency in a temperate salt-marsh estuary. Aquat. Microb. Ecol. 43, 243–254 (2006).Article 

    Google Scholar 
    61.Pawar, S., Dell, A. I., Savage, V. M. & Knies, J. L. Real versus artificial variation in the thermal sensitivity of biological traits. Am. Nat. 187, E41–E52 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Yee, E. & Murray, S. Effects of temperature on activity, food consumption rates, and gut passage times of seaweed-eating Tegula species (Trochidae) from California. Mar. Biol. 145, 895–903 (2004).Article 

    Google Scholar 
    64.Savage, V. M., Gillooly, J. F., Brown, J. H., West, G. B. & Charnov, E. L. Effects of body size and temperature on population growth. Am. Nat. 163, E429–E441 (2004).Article 

    Google Scholar 
    65.Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2013.2612 (2014).66.Vasseur, D. A. & McCann, K. S. A mechanistic approach for modeling temperature-dependent consumer-resource dynamics. Am. Nat. 166, 184–198 (2005).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Gilbert, B. et al. A bioenergetic framework for the temperature dependence of trophic interactions. Ecol. Lett. 17, 902–914 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Binzer, A., Guill, C., Brose, U. & Rall, B. C. The dynamics of food chains under climate change and nutrient enrichment. Philos. Trans. R. Soc. B Biol. Sci. 367, 2935–2944 (2012).Article 

    Google Scholar 
    69.Binzer, A., Guill, C., Rall, B. C. & Brose, U. Interactive effects of warming, eutrophication and size structure: impacts on biodiversity and food-web structure. Glob. Change Biol. 22, 220–227 (2016).Article 

    Google Scholar 
    70.Sentis, A., Binzer, A. & Boukal, D. S. Temperature-size responses alter food chain persistence across environmental gradients. Ecol. Lett. 20, 852–862 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    71.Robinson, S. I., McLaughlin, Ó. B., Marteinsdóttir, B. & O’Gorman, E. J. Soil temperature effects on the structure and diversity of plant and invertebrate communities in a natural warming experiment. J. Anim. Ecol. 87, 634–646 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.McKee, D. et al. Response of freshwater microcosm communities to nutrients, fish, and elevated temperature during winter and summer. Limnol. Oceanogr. 48, 707–722 (2003).Article 

    Google Scholar 
    73.McKee, D. et al. Macro-zooplankter responses to simulated climate warming in experimental freshwater microcosms. Freshw. Biol. 47, 1557–1570 (2002).Article 

    Google Scholar 
    74.Allen, A., Gillooly, J. & Brown, J. Linking the global carbon cycle to individual metabolism. Funct. Ecol. 19, 202–213 (2005).Article 

    Google Scholar 
    75.Anderson, K. J., Allen, A. P., Gillooly, J. F. & Brown, J. H. Temperature‐dependence of biomass accumulation rates during secondary succession. Ecol. Lett. 9, 673–682 (2006).PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    77.Sokolova, I. M. & Lannig, G. Interactive effects of metal pollution and temperature on metabolism in aquatic ectotherms: implications of global climate change. Clim. Res. 37, 181–201 (2008).Article 

    Google Scholar 
    78.Petchey, O. L., Brose, U. & Rall, B. C. Predicting the effects of temperature on food web connectance. Philos. Trans. R. Soc. B Biol. Sci. 365, 2081–2091 (2010).Article 

    Google Scholar 
    79.Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    80.Relyea, R. A. The impact of insecticides and herbicides on the biodiversity and productivity of aquatic communities. Ecol. Appl. 15, 618–627 (2005).Article 

    Google Scholar 
    81.Beketov, M. A., Kefford, B. J., Schäfer, R. B. & Liess, M. Pesticides reduce regional biodiversity of stream invertebrates. Proc. Natl Acad. Sci. USA 110, 11039–11043 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    82.Clements, W. H. & Rohr, J. R. Community responses to contaminants: using basic ecological principles to predict ecotoxicological effects. Environ. Toxicol. Chem. 28, 1789–1800 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Case, T. J. An Illustrated Guide to Theoretical Ecology (Oxford Univ. Press, 2000).84.Jeschke, J. M., Kopp, M. & Tollrian, R. Predator functional responses: discriminating between handling and digesting prey. Ecol. Monogr. 72, 95–112 (2002).Article 

    Google Scholar 
    85.Jeschke, J. M. & Tollrian, R. Density-dependent effects of prey defences. Oecologia 123, 391–396 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    86.Jorgensen, C., Ernande, B. & Fiksen, O. Size-selective fishing gear and life history evolution in the Northeast Arctic cod. Evol. Appl. 2, 356–370 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Kuparinen, A., Kuikka, S. & Merila, J. Estimating fisheries-induced selection: traditional gear selectivity research meets fisheries-induced evolution. Evol. Appl. 2, 234–243 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    88.Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    89.Day, T., Abrams, P. A. & Chase, J. M. The role of size-specific predation in the evolution and diversification of prey life histories. Evolution 56, 877–887 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    90.Heino, M., Pauli, B. D. & Dieckmann, U. Fisheries-induced evolution. Annu. Rev. Ecol. Evol. Syst. 46, 461–480 (2015).Article 

    Google Scholar 
    91.Galloway, J. N. et al. The nitrogen cascade. Bioscience 53, 341–356 (2003).Article 

    Google Scholar 
    92.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).Article 
    CAS 

    Google Scholar 
    93.Birk, S. et al. Impacts of multiple stressors on freshwater biota across spatial scales and ecosystems. Nat. Ecol. Evol. 4, 1060–1068 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    94.Rosenzweig, M. L. Paradox of enrichment: destabilization of exploitation ecosystems in ecological time. Science 171, 385–387 (1971).CAS 
    PubMed 
    Article 

    Google Scholar 
    95.Oksanen, L., Fretwell, S. D., Arruda, J. & Niemela, P. Exploitation ecosystems in gradients of primary productivity. Am. Nat. 118, 240–261 (1981).Article 

    Google Scholar 
    96.Lotze, H. K. et al. Depletion, degradation, and recovery potential of estuaries and coastal seas. Science 312, 1806–1809 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    97.Doney, S. C. The growing human footprint on coastal and open-ocean biogeochemistry. Science 328, 1512–1516 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    98.Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    99.Duchet, C. et al. Pesticide‐mediated trophic cascade and an ecological trap for mosquitoes. Ecosphere 9, e02179 (2018).Article 

    Google Scholar 
    100.Halstead, N. T. et al. Community ecology theory predicts the effects of agrochemical mixtures on aquatic biodiversity and ecosystem properties. Ecol. Lett. 17, 932–941 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    101.Ferger, S. W. et al. Synergistic effects of climate and land use on avian beta‐diversity. Divers. Distrib. 23, 1246–1255 (2017).Article 

    Google Scholar 
    102.Maris, V. et al. Prediction in ecology: promises, obstacles and clarifications. Oikos 127, 171–183 (2018).Article 

    Google Scholar 
    103.Palmer, M. A. et al. Ecological science and sustainability for the 21st century. Front. Ecol. Environ. 3, 4–11 (2005).Article 

    Google Scholar 
    104.Folt, C. L., Chen, C. Y., Moore, M. V. & Burnaford, J. Synergism and antagonism among multiple stressors. Limnol. Oceanogr. 44, 864–877 (1999).Article 

    Google Scholar 
    105.Grimm, V. & Berger, U. Structural realism, emergence, and predictions in next-generation ecological modelling: synthesis from a special issue. Ecol. Modell. 326, 177–187 (2016).Article 

    Google Scholar 
    106.Geary, W. L. et al. A guide to ecosystem models and their environmental applications. Nat. Ecol. Evol. 4, 1459–1471 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    107.Rosenblatt, A. E., Smith-Ramesh, L. M. & Schmitz, O. J. Interactive effects of multiple climate change variables on food web dynamics: Modeling the effects of changing temperature, CO2, and water availability on a tri-trophic food web. Food Webs https://doi.org/10.1016/j.fooweb.2016.10.002 (2017).108.Bartley, T. J. et al. Food web rewiring in a changing world. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0772-3 (2019).109.CaraDonna, P. J. et al. Interaction rewiring and the rapid turnover of plant–pollinator networks. Ecol. Lett. 20, 385–394 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    110.Gilljam, D., Curtsdotter, A. & Ebenman, B. Adaptive rewiring aggravates the effects of species loss in ecosystems. Nat. Commun. 6, 8412 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    111.Staniczenko, P. P. A., Lewis, O. T., Jones, N. S. & Reed-Tsochas, F. Structural dynamics and robustness of food webs. Ecol. Lett. 13, 891–899 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    112.Thierry, A. et al. Adaptive foraging and the rewiring of size-structured food webs following extinctions. Basic Appl. Ecol. 12, 562–570 (2011).Article 

    Google Scholar 
    113.Petchey, O. L., Beckerman, A. P., Riede, J. O. & Warren, P. H. Size, foraging, and food web structure. Proc. Natl Acad. Sci. USA 105, 4191–4196 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    114.Beckerman, A. P., Petchey, O. L. & Warren, P. H. Foraging biology predicts food web complexity. Proc. Natl Acad. Sci. USA 103, 13745–13749 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    115.O’Gorman, E. J. et al. A simple model predicts how warming simplifies wild food webs. Nat. Clim. Change 9, 611–616 (2019).Article 

    Google Scholar 
    116.Williams, R. J., Brose, U. & Martinez, N. D. in From Energetics to Ecosystems: The Dynamics and Structure of Ecological Systems (eds Rooney, N. et al.) 37–51 (Springer, 2007).117.Blanchard, J. L. et al. How does abundance scale with body size in coupled size‐structured food webs? J. Anim. Ecol. 78, 270–280 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    118.Blanchard, J. L., Heneghan, R. F., Everett, J. D., Trebilco, R. & Richardson, A. J. From bacteria to whales: using functional size spectra to model marine ecosystems. Trends Ecol. Evol. 32, 174–186 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    119.Kerr, S. R. & Dickie, L. M. The Biomass Spectrum: A Predator–Prey Theory of Aquatic Production (Columbia Univ. Press, 2001).120.Adams, M. P. et al. Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data. Ecol. Lett. 23, 607–619 (2020).PubMed 
    Article 

    Google Scholar 
    121.Bode, M. et al. Revealing beliefs: using ensemble ecosystem modelling to extrapolate expert beliefs to novel ecological scenarios. Methods Ecol. Evol. 8, 1012–1021 (2017).Article 

    Google Scholar 
    122.McGowan, C. P., Runge, M. C. & Larson, M. A. Incorporating parametric uncertainty into population viability analysis models. Biol. Conserv. 144, 1400–1408 (2011).Article 

    Google Scholar 
    123.Delmas, E., Brose, U., Gravel, D., Stouffer, D. B. & Poisot, T. Simulations of biomass dynamics in community food webs. Methods Ecol. Evol. 8, 881–886 (2017).Article 

    Google Scholar 
    124.Scott, F., Blanchard, J. L. & Andersen, K. H. mizer: an R package for multispecies, trait-based and community size spectrum ecological modelling. Methods Ecol. Evol. 5, 1121–1125 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    125.Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    126.Tabi, A., Petchey, O. L. & Pennekamp, F. Warming reduces the effects of enrichment on stability and functioning across levels of organisation in an aquatic microbial ecosystem. Ecol. Lett. 22, 1061–1071 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    127.O’Brien, A. L., Dafforn, K. A., Chariton, A. A., Johnston, E. L. & Mayer-Pinto, M. After decades of stressor research in urban estuarine ecosystems the focus is still on single stressors: a systematic literature review and meta-analysis. Sci. Total Environ. 684, 753–764 (2019).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    128.Hampton, S. E. et al. Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models. Ecology 94, 2663–2669 (2013).PubMed 
    Article 

    Google Scholar 
    129.Ives, A. R., Dennis, B., Cottingham, K. L. & Carpenter, S. R. Estimating community stability and ecological interactions from time-series data. Ecol. Monogr. 73, 301–330 (2003).Article 

    Google Scholar 
    130.Geary, W. L., Nimmo, D. G., Doherty, T. S., Ritchie, E. G. & Tulloch, A. I. T. Threat webs: reframing the co‐occurrence and interactions of threats to biodiversity. J. Appl. Ecol. 56, 1992–1997 (2019).
    Google Scholar 
    131.Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    132.Rall, B. C. et al. Universal temperature and body-mass scaling of feeding rates. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 2923–2934 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    133.Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366, 886–890 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    134.Brennan, G. L., Colegrave, N. & Collins, S. Evolutionary consequences of multidriver environmental change in an aquatic primary producer. Proc. Natl Acad. Sci. USA 114, 9930–9935 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    135.De Valpine, P. & Hastings, A. Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72, 57–76 (2002).Article 

    Google Scholar 
    136.Ellner, S. P., Seifu, Y. & Smith, R. H. Fitting population dynamic models to time‐series data by gradient matching. Ecology 83, 2256–2270 (2002).Article 

    Google Scholar 
    137.Blanchard, J. L. A rewired food web. Nature 527, 173–174 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    138.Law, R., Plank, M. J., James, A. & Blanchard, J. L. Size‐spectra dynamics from stochastic predation and growth of individuals. Ecology 90, 802–811 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    139.Hampton, S. E., Scheuerell, M. D. & Schindler, D. E. Coalescence in the Lake Washington story: interaction strengths in a planktonic food web. Limnol. Oceanogr. 51, 2042–2051 (2006).Article 

    Google Scholar 
    140.Ives, A. R. Predicting the response of populations to environmental change. Ecology 76, 926–941 (1995).Article 

    Google Scholar  More

  • in

    Author Correction: Areas of global importance for conserving terrestrial biodiversity, carbon and water

    Biodiversity and Natural Resources Program (BNR), International Institute for Applied Systems Analysis (IIASA), Laxenburg, AustriaMartin Jung, Matthew Lewis, Dmitry Schepaschenko, Myroslava Lesiv, Steffen Fritz, Michael Obersteiner & Piero ViscontiUN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, UKAndy Arnell, Shaenandhoa García-Rangel, Jennifer Mark, Lera Miles, Corinna Ravilious, Oliver Tallowin, Arnout van Soesbergen, Valerie Kapos & Neil BurgessFood and Agriculture Organization of the United Nations (FAO), Rome, ItalyXavier de LamoDepartment of Zoology, University of Cambridge, Cambridge, UKMatthew LewisDepartment of Ecology and Evolutionary Biology, University of Connecticut, Stamford, CT, USACory MerowRoyal Botanic Gardens, Kew, Richmond, UKIan Ondo, Samuel Pironon & Rafaël GovaertsBotanic Gardens Conservation International, Richmondy, UKMalin RiversSiberian Federal University, Krasnoyarsk, RussiaDmitry SchepaschenkoDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USABradley L. Boyle, Brian J. Enquist, Brian Maitner & Erica A. NewmanDepartment of Geography, Florida State University, Tallahassee, FL, USAXiao FengDepartment of Biological Sciences, Macquarie University, North Ryde, New South Wales, AustraliaRachael GallagherSchool of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelShai Meiri & Gali OferDepartment of Geography, King’s College London, London, UKMark MulliganMitrani Department of Desert Ecology, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, IsraelUri RollCIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto, Vairão, PortugalJeffrey O. HansonDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USAWalter Jetz & D. Scott RinnanCenter for Biodiversity and Global Change, Yale University, New Haven, CT, USAWalter Jetz & D. Scott RinnanDepartment of Biology and Biotechnologies, Sapienza University of Rome, Rome, ItalyMoreno Di MarcoThe Nature Conservancy, Arlington, VA, USAJennifer McGowanColumbia University, New York, NY, USAJeffrey D. SachsSchool of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart, Tasmania, AustraliaVanessa M. AdamsCSIRO Land and Water, Canberra, Australian Capital Territory, AustraliaSamuel C. AndrewDepartment of Biology, University of Kentucky, Lexington, KY, USAJoseph R. BurgerBetty and Gordon Moore Center for Science, Conservation International, Arlington, VA, USALee Hannah & Patrick R. RoehrdanzDepartamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, ChilePablo A. MarquetInstituto de Ecología y Biodiversidad (IEB), Santiago, ChilePablo A. MarquetCentro de Cambio Global UC, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, ChilePablo A. MarquetThe Santa Fe Institute, Santa Fe, NM, USAPablo A. MarquetInstituto de Sistemas Complejos de Valparaíso (ISCV), Valparaíso, ChilePablo A. MarquetManaaki Whenua—Landcare Research, Lincoln, New ZealandJames K. McCarthyCenter for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, DenmarkNaia Morueta-HolmeDepartment of Biological Sciences, Purdue University, West Lafayette, IN, USADaniel S. ParkCenter for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Biology, Aarhus University, Aarhus, DenmarkJens-Christian SvenningSection for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, DenmarkJens-Christian SvenningCEFE, Univ. Montpellier, CNRS, EPHE, IRD, Univ. Paul Valéry Montpellier 3, Montpellier, FranceCyrille ViolleNaturalis Biodiversity Center, Leiden, The NetherlandsJan J. WieringaWorld Resources Institute, London, UKGraham WynneRio Conservation and Sustainability Science Centre, Department of Geography and the Environment, Pontifical Catholic University, Rio de Janeiro, BrazilBernardo B. N. StrassburgInternational Institute for Sustainability, Rio de Janeiro, BrazilBernardo B. N. StrassburgPrograma de Pós Graduacão em Ecologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, BrazilBernardo B. N. StrassburgBotanical Garden Research Institute of Rio de Janeiro, Rio de Janeiro, BrazilBernardo B. N. StrassburgEnvironmental Change Institute, Centre for the Environment, Oxford University, Oxford, UKMichael ObersteinerUN Sustainable Development Solutions Network, Paris, FranceGuido Schmidt-TraubCorrespondence to
    Martin Jung or Piero Visconti. More

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    Effects of fertilizer under different dripline spacings on summer maize in northern China

    1.China. China statistical yearbook. (China Statistics Press, 2020).2.Shiferaw, B., Prasanna, B. M., Hellin, J. & Bänziger, M. Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur. 3, 307–327 (2011).Article 

    Google Scholar 
    3.Chen, M. P., Sun, F. & Shindo, J. China’s agricultural nitrogen flows in 2011: Environmental assessment and management scenarios. Resour. Conserv. Recycl. 111, 10–27 (2016).Article 

    Google Scholar 
    4.He, Y. X. et al. Tracking ammonia morning peak, sources and transport with 1 Hz measurements at a rural site in North China Plain. Atmos. Environ. 235, 117630 (2020).CAS 
    Article 

    Google Scholar 
    5.Zhang, Y. et al. Agricultural ammonia emissions inventory and spatial distribution in the North China Plain. Environ. Pollut. 158, 490–501 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Ayars, J. E., Fulton, A. & Taylor, B. Subsurface drip irrigation in California—Here to stay?. Agric. Water Manag. 157, 39–47 (2015).Article 

    Google Scholar 
    7.Chauhdary, J. N., Bakhsh, A., Engel, B. A. & Ragab, R. Improving corn production by adopting efficient fertigation practices: Experimental and modeling approach. Agric. Water Manag. 221, 449–461 (2019).Article 

    Google Scholar 
    8.Mali, S. S., Naik, S. K., Jha, B. K., Singh, A. K. & Bhatt, B. P. Planting geometry and growth stage linked fertigation patterns: Impact on yield, nutrient uptake and water productivity of Chilli pepper in hot and sub-humid climate. Sci. Hortic. (Amsterdam) 249, 289–298 (2019).Article 

    Google Scholar 
    9.Silber, A. et al. High fertigation frequency: the effects on uptake of nutrients, water and plant growth. Plant Soil 253, 467–477 (2003).CAS 
    Article 

    Google Scholar 
    10.Wu, D. L. et al. Effect of different drip fertigation methods on maize yield, nutrient and water productivity in two-soils in Northeast China. Agric. Water Manag. 213, 200–211 (2019).Article 

    Google Scholar 
    11.Ning, D. et al. Deficit irrigation combined with reduced N-fertilizer rate can mitigate the high nitrous oxide emissions from Chinese drip-fertigated maize field. Glob. Ecol. Conserv. 20, e00803 (2019).Article 

    Google Scholar 
    12.Sandhu, O. S. et al. Drip irrigation and nitrogen management for improving crop yields, nitrogen use efficiency and water productivity of maize-wheat system on permanent beds in north-west India. Agric. Water Manag. 219, 19–26 (2019).Article 

    Google Scholar 
    13.Li, H. et al. Effects of different nitrogen fertilizers on the yield, water- and nitrogen-use efficiencies of drip-fertigated wheat and maize in the North China Plain. Agric. Water Manag. 243, 106474 (2021).Article 

    Google Scholar 
    14.Lamm, F. R., Stone, L. R., Manges, H. L. & O’Brien, D. M. Optimum lateral spacing for subsurface drip-irrigated corn. Trans. ASAE 40, 1021–1027 (1997).Article 

    Google Scholar 
    15.Bozkurt, Y., Yazar, A., Gençel, B. & Sezen, M. S. Optimum lateral spacing for drip-irrigated corn in the Mediterranean Region of Turkey. Agric. Water Manag. 85, 113–120 (2006).Article 

    Google Scholar 
    16.Chen, R. et al. Lateral spacing in drip-irrigated wheat: The effects on soil moisture, yield, and water use efficiency. Field Crop. Res. 179, 52–62 (2015).Article 

    Google Scholar 
    17.Zhou, L. et al. Drip irrigation lateral spacing and mulching affects the wetting pattern, shoot-root regulation, and yield of maize in a sand-layered soil. Agric. Water Manag. 184, 114–123 (2017).Article 

    Google Scholar 
    18.Eissa, M. A. Efficiency of P fertigation for drip-irrigated potato grown on calcareous sandy soils. Potato Res. 62, 97–108 (2019).CAS 
    Article 

    Google Scholar 
    19.Irmak, S., Djaman, K. & Rudnick, D. R. Effect of full and limited irrigation amount and frequency on subsurface drip-irrigated maize evapotranspiration, yield, water use efficiency and yield response factors. Irrig. Sci. 34, 271–286 (2016).Article 

    Google Scholar 
    20.Yao, Y. L. et al. Urea deep placement for minimizing NH3 loss in an intensive rice cropping system. Field Crop. Res. 218, 254–266 (2018).Article 

    Google Scholar 
    21.Ziadi, N., Cambouris, A. N., Nyiraneza, J. & Nolin, M. C. Across a landscape, soil texture controls the optimum rate of N fertilizer for maize production. Field Crop. Res. 148, 78–85 (2013).Article 

    Google Scholar 
    22.Fang, H. et al. An optimized model for simulating grain-filling of maize and regulating nitrogen application rates under different film mulching and nitrogen fertilizer regimes on the Loess Plateau. China. Soil Tillage Res. 199, 104546 (2020).Article 

    Google Scholar 
    23.Zheng, J. et al. Interactive effects of mulching practice and nitrogen rate on grain yield, water productivity, fertilizer use efficiency and greenhouse gas emissions of rainfed summer maize in northwest China. Agric. Water Manag. 248, 106778 (2021).Article 

    Google Scholar 
    24.Qi, X. L. et al. Grain yield and apparent N recovery efficiency of dry direct-seeded rice under different N treatments aimed to reduce soil ammonia volatilization. Field Crop. Res. 134, 138–143 (2012).Article 

    Google Scholar 
    25.Han, K., Zhou, C. J. & Wang, L. Q. Reducing ammonia volatilization from maize fields with separation of nitrogen fertilizer and water in an alternating furrow irrigation system. J. Integr. Agric. 13, 1099–1112 (2014).CAS 
    Article 

    Google Scholar 
    26.Amin, A.E.-E.A.Z. Carbon sequestration, kinetics of ammonia volatilization and nutrient availability in alkaline sandy soil as a function on applying calotropis biochar produced at different pyrolysis temperatures. Sci. Total Environ. 726, 138489 (2020).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Li, H. T. et al. Film mulching, residue retention and N fertilization affect ammonia volatilization through soil labile N and C pools. Agric. Ecosyst. Environ. 308, 107272 (2021).CAS 
    Article 

    Google Scholar 
    28.Sun, B. et al. Bacillus subtilis biofertilizer mitigating agricultural ammonia emission and shifting soil nitrogen cycling microbiomes. Environ. Int. 144, 105989 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Tabli, N. et al. Plant growth promoting and inducible antifungal activities of irrigation well water-bacteria. Biol. Control 117, 78–86 (2018).Article 

    Google Scholar 
    30.Zhong, X. M. et al. Reducing ammonia volatilization and increasing nitrogen use efficiency in machine-transplanted rice with side-deep fertilization in a double-cropping rice system in Southern China. Agric. Ecosyst. Environ. 306, 107183 (2021).CAS 
    Article 

    Google Scholar 
    31.Li, C., Sun, M. X., Xu, X. B. & Zhang, L. X. Characteristics and influencing factors of mulch film use for pollution control in China: Microcosmic evidence from smallholder farmers. Resour. Conserv. Recycl. 164, 105222 (2021).Article 

    Google Scholar 
    32.Li, M. N., Wang, Y. L., Adeli, A. & Yan, H. J. Effects of application methods and urea rates on ammonia volatilization, yields and fine root biomass of alfalfa. Field Crop. Res. 218, 115–125 (2018).Article 

    Google Scholar 
    33.Pinheiro, P. L. et al. Straw removal reduces the mulch physical barrier and ammonia volatilization after urea application in sugarcane. Atmos. Environ. 194, 179–187 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    34.Zhu, H. et al. Interactive effects of soil amendments (biochar and gypsum) and salinity on ammonia volatilization in coastal saline soil. CATENA 190, 104527 (2020).CAS 
    Article 

    Google Scholar 
    35.Oppong Danso, E. et al. Effect of different fertilization and irrigation methods on nitrogen uptake, intercepted radiation and yield of okra (Abelmoschus esculentum L.) grown in the Keta Sand Spit of Southeast Ghana. Agric. Water Manag. 147, 34–42 (2015).Article 

    Google Scholar 
    36.Liu, R. H. et al. Chemical fertilizer pollution control using drip fertigation for conservation of water quality in Danjiangkou Reservoir. Nutr. Cycl. Agroecosystems 98, 295–307 (2014).CAS 
    Article 

    Google Scholar 
    37.Sanz-Cobena, A. et al. Strategies for greenhouse gas emissions mitigation in mediterranean agriculture: A review. Agric. Ecosyst. Environ. 238, 5–24 (2017).CAS 
    Article 

    Google Scholar 
    38.Zhou, J. B., Xi, J. G., Chen, Z. J. & Li, S. X. Leaching and transformation of nitrogen fertilizers in soil after application of n with irrigation: A soil column method. Pedosphere 16, 245–252 (2006).CAS 
    Article 

    Google Scholar 
    39.Rosemary, F., Vitharana, U. W. A., Indraratne, S. P., Weerasooriya, R. & Mishra, U. Exploring the spatial variability of soil properties in an Alfisol soil catena. CATENA 150, 53–61 (2017).CAS 
    Article 

    Google Scholar 
    40.Liu, Y., Lv, J. S., Zhang, B. & Bi, J. Spatial multi-scale variability of soil nutrients in relation to environmental factors in a typical agricultural region, Eastern China. Sci. Total Environ. 450–451, 108–119 (2013).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    41.Vasu, D. et al. Assessment of spatial variability of soil properties using geospatial techniques for farm level nutrient management. Soil Tillage Res. 169, 25–34 (2017).Article 

    Google Scholar 
    42.Jin, J. Y., Bai, Y. L. & Yang, L. P. High Efficiency Soil Nutrient Testing Technology and Equipment (China Agriculture Press, 2006) (in Chinese).
    Google Scholar 
    43.Tan, Y. et al. Improving wheat grain yield via promotion of water and nitrogen utilization in arid areas. Sci. Rep. 11, 13821 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Ren, Y. et al. Effect of sowing proportion on above- and below-ground competition in maize–soybean intercrops. Sci. Rep. 11, 15760 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Wang, Z. H., Liu, X. J., Ju, X. T., Zhang, F. S. & Malhi, S. S. Ammonia volatilization loss from surface-broadcast urea: comparison of vented- and closed-chamber methods and loss in winter wheat–summer maize rotation in North China plain. Commun. Soil Sci. Plant Anal. 35, 2917–2939 (2004).CAS 
    Article 

    Google Scholar 
    46.Zhou, L. P. et al. Comparison of several slow-released nitrogen fertilizers in ammonia volatilization and nitrogen utilization in summer maize field. J. Plant Nutr. Fertil. 22, 1449–1457 (2016) (in Chinese).
    Google Scholar 
    47.Huang, T. M. et al. Grain zinc concentration and its relation to soil nutrient availability in different wheat cropping regions of China. Soil Tillage Res. 191, 57–65 (2019).Article 

    Google Scholar 
    48.Wang, Z., Li, J. & Li, Y. Effects of drip system uniformity and nitrogen application rate on yield and nitrogen balance of spring maize in the North China Plain. Field. Crop. Res. 159, 10–20 (2014).Article 

    Google Scholar 
    49.Brar, H. S., Vashist, K. K. & Bedi, S. Phenology and yield of spring maize (Zea mays L.) under different drip irrigation regimes and planting methods. J. Agric. Sci. Technol. 18, 831–843 (2016).
    Google Scholar 
    50.Poch-Massegú, R., Jiménez-Martínez, J., Wallis, K. J., Ramírez de Cartagena, F. & Candela, L. Irrigation return flow and nitrate leaching under different crops and irrigation methods in Western Mediterranean weather conditions. Agric. Water Manag. 134, 1–13 (2014).Article 

    Google Scholar 
    51.Yuan, Z. Q. et al. Film mulch with irrigation and rainfed cultivations improves maize production and water use efficiency in Ethiopia. Ann. Appl. Biol. 175, 215–227 (2019).Article 

    Google Scholar 
    52.Wang, J. L. Research on the use of water and fertilizer for drip irrigation multiple cropping silage maize (Shihezi University, 2016) (in Chinese).
    Google Scholar 
    53.Lamm, F. R. & Trooien, T. P. Subsurface drip irrigation for corn production: a review of 10 years of research in Kansas. Irrig. Sci. 22, 195–200 (2003).Article 

    Google Scholar 
    54.Yan, X. L., Jia, L. M. & Dai, T. F. Effects of water and nitrogen coupling under drip irrigation on tree growth and soil nitrogen content of Populus × euramericana cv. ‘Guariento’. Chin. J. Appl. Ecol. 29, 2195 (2018) (in Chinese).
    Google Scholar 
    55.Sun, W. T., Sun, Z. X., Wang, C. X., Gong, L. & Zhang, Y. L. Coupling effect of water and fertilizer on corn yield under drip fertigation. Sci. Agric. Sin. 39, 563–568 (2006) (in Chinese).
    Google Scholar 
    56.Banerjee, B., Pathak, H. & Aggarwal, P. Effects of dicyandiamide, farmyard manure and irrigation on crop yields and ammonia volatilization from an alluvial soil under a rice (Oryza sativa L.)-wheat (Triticum aestivum L.) cropping system. Biol. Fertil. Soils 36, 207–214 (2002).CAS 
    Article 

    Google Scholar 
    57.Yang, Q. L., Liu, P., Dong, S. T., Zhang, J. W. & Zhao, B. Effects of fertilizer type and rate on summer maize grain yield and ammonia volatilization loss in northern China. J. Soils Sediments 19, 2200–2211 (2019).CAS 
    Article 

    Google Scholar 
    58.Zhou, G. W. et al. Effects of saline water irrigation and N application rate on NH3 volatilization and N use efficiency in a drip-irrigated cotton field. Water Air Soil Pollut. 227, 103 (2016).ADS 
    Article 
    CAS 

    Google Scholar 
    59.Zheng, J., Kilasara, M. M., Mmari, W. N. & Funakawa, S. Ammonia volatilization following urea application at maize fields in the East African highlands with different soil properties. Biol. Fertil. Soils 54, 411–422 (2018).CAS 
    Article 

    Google Scholar 
    60.Li, Z. et al. Nitrogen use efficiency and ammonia oxidation of corn field with drip irrigation in Hetao irrigation district. J. Irrig. Drain. 37, 37–42,49 (2018) (in Chinese).61.Zheng, L. et al. Impact of fertilization on ammonia volatilization and N2O emissions in an open vegetable field. Chin. J. Appl. Ecol. 29, 4063–4070 (2018) (in Chinese).
    Google Scholar 
    62.Li, Y. Q., Liu, G., Hong, M., Wu, Y. & Chang, F. Effect of optimized nitrogen application on nitrous oxide emission and ammonia volatilization in Hetao irrigation area. Acta Sci. Circumst. 39, 578–584 (2019) (in Chinese).CAS 

    Google Scholar 
    63.Das, P. et al. Emissions of ammonia and nitric oxide from an agricultural site following application of different synthetic fertilizers and manures. Geosci. J. 12, 177–190 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    64.Cai, G. X. et al. Nitrogen losses from fertilizers applied to maize, wheat and rice in the North China Plain. Nutr. Cycl. Agroecosyst. 63, 187–195 (2002).CAS 
    Article 

    Google Scholar 
    65.Wang, X. L. et al. Corn compensatory growth upon post-drought rewatering based on the effects of rhizosphere soil nitrification on cytokinin. Agric. Water Manag. 241, 106436 (2020).Article 

    Google Scholar 
    66.Li, G. et al. Effect of drip fertigation on summer maize in north China. Sci. Agric. Sin. 52, 1930–1941 (2019) (in Chinese).
    Google Scholar  More

  • in

    Specialization directs habitat selection responses to a top predator in semiaquatic but not aquatic taxa

    1.Binckley, C. A. & Resetarits, W. J. Habitat selection determines abundance, richness and species composition of beetles in aquatic communities. Biol. Lett. 1, 370–374 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Foltz, S. J. & Dodson, S. I. Aquatic Hemiptera community structure in stormwater retention ponds: A watershed land cover approach. Hydrobiologia 621, 49–62 (2009).Article 

    Google Scholar 
    3.Goldberg, F. J., Quinzio, S. & Vaira, M. Oviposition-site selection by the toad Melanophryniscus rubriventris in an unpredictable environment in Argentina. Can. J. Zool. 84, 699–705 (2006).Article 

    Google Scholar 
    4.Blaustein, L. Oviposition site selection in response to risk of predation: Evidence from aquatic habitats and consequences for population dynamics and community. In Evolutionary Theory and Processes: Modern Perspectives (ed. Wasser, S. P.) 441–456 (Kluwer, 1999).5.Resetarits, W. J. & Binckley, C. A. Spatial contagion of predation risk affects colonization dynamics in experimental aquatic landscapes. Ecology 90, 869–876 (2009).PubMed 
    Article 

    Google Scholar 
    6.Kraus, J. M. & Vonesh, J. R. Feedbacks between community assembly and habitat selection shape variation in local colonization. J. Anim. Ecol. 79, 795–802 (2010).PubMed 

    Google Scholar 
    7.Resetarits, W. J. Oviposition site choice and life history evolution. Am. Zool. 36, 205–215 (1996).Article 

    Google Scholar 
    8.Morris, D. W. Toward an ecological synthesis: A case for habitat selection. Oecologia 136, 1–13 (2003).ADS 
    PubMed 
    Article 

    Google Scholar 
    9.Resetarits, W. J. & Wilbur, H. M. Choice of oviposition site by Hyla chrysoscelis: Role of predators and competitors. Ecology 70, 220–228 (1989).Article 

    Google Scholar 
    10.Resetarits, W. J., Binckley, C. A. & Chalcraft, D. R. Habitat selection, species interactions, and processes of community assembly in complex landscapes: A metacommunity perspective. In Metacommunities: Spatial Dynamics and Ecological Communities (eds. Holyoak, M., Leybold, A. & Holt, R. D.) 374–398 (University of Chicago Press, Chicago, 2005).11.Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

    Google Scholar 
    12.Langellotto, G. A. & Denno, R. F. Responses of invertebrate natural enemies to complex-structured habitats: A meta-analytical synthesis. Oecologia 139, 1–10 (2004).ADS 
    PubMed 
    Article 

    Google Scholar 
    13.Åbjörnsson, K., Brönmark, C. & Hansson, L.-A. The relative importance of lethal and non-lethal effects of fish on insect colonisation of ponds: Influence of fish on insect colonisation. Freshw. Biol. 47, 1489–1495 (2002).Article 

    Google Scholar 
    14.Pintar, M. R. & Resetarits, W. J. Jr. Out with the old, in with the new: Oviposition preference matches larval success in cope’s gray treefrog, Hyla chrysoscelis. J. Herpetol. 51, 186–189 (2017).Article 

    Google Scholar 
    15.Wellborn, G. A., Skelly, D. K. & Werner, E. E. Mechanisms creating community structure across a freshwater habitat gradient. Annu. Rev. Ecol. Evol. Syst. 27, 337–363 (1996).Article 

    Google Scholar 
    16.Caudill, C. C. & Peckarsky, B. L. Lack of appropriate behavioral or developmental responses by mayfly larvae to trout predators. Ecology 84, 2133–2144 (2003).Article 

    Google Scholar 
    17.Binckley, C. A. & Resetarits, W. J. Functional equivalence of non-lethal effects: Generalized fish avoidance determines distribution of gray treefrog, Hyla chrysoscelis, larvae. Oikos 102, 623–629 (2003).Article 

    Google Scholar 
    18.Pollard, C. J. et al. Removal of an exotic fish influences amphibian breeding site selection: Exotic fish removal. J. Wildl. Manag. 81, 720–727 (2017).Article 

    Google Scholar 
    19.Petranka, J. W. & Fakhoury, K. Evidence of a chemically-mediated avoidance response of ovipositing insects to bluegills and green frog tadpoles. Copeia 1991, 234–239 (1991).Article 

    Google Scholar 
    20.McPeek, M. A. Differential dispersal tendencies among Enallagma damselflies (Odonata) inhabiting different habitats. Oikos 56, 187–195 (1989).Article 

    Google Scholar 
    21.Šigutová, H., Šigut, M. & Dolný, A. Intensive fish ponds as ecological traps for dragonflies: An imminent threat to the endangered species Sympetrum depressiusculum (Odonata: Libellulidae). J. Insect Conserv. 19, 961–974 (2015).Article 

    Google Scholar 
    22.Potts, K. M. Survival and development of larval odonates (Anisoptera) and female oviposition site choice in response to predatory fish. https://egrove.olemiss.edu/etd/1854 (2020).23.Blaustein, L., Kiflawi, M., Eitam, A., Mangel, M. & Cohen, J. E. Oviposition habitat selection in response to risk of predation in temporary pools: Mode of detection and consistency across experimental venue. Oecologia 138, 300–305 (2004).ADS 
    PubMed 
    Article 

    Google Scholar 
    24.Wildermuth, H. Habitat selection and oviposition site recognition by the dragonfly Aeshna juncea (L.): An experimental approach in natural habitats (Anisoptera: Aeshnidae). Odonatologica 22, 27–44 (1993).25.Wildermuth, H. Habitatselektion bei Libellen. Adv. Odonatol. 6, 223–257 (1994).
    Google Scholar 
    26.Laurila, A. Breeding habitat selection and larval performance of two anurans in freshwater rock-pools. Ecography 21, 484–494 (1998).Article 

    Google Scholar 
    27.Schwind, R. Spectral regions in which aquatic insects see reflected polarized light. J. Comp. Physiol. A 177, 439–448 (1995).Article 

    Google Scholar 
    28.Horváth, G. & Kriska, G. Polarization vision in aquatic insects and ecological traps for polarotactic insects in Aquatic Insects: Challenges to Populations (eds. Lancaster, J. & Briers, R. A.) 204–229 (CAB International Publishing, 2008).29.Schulte, L. M. et al. The smell of success: Choice of larval rearing sites by means of chemical cues in a Peruvian poison frog. Anim. Behav. 81, 1147–1154 (2011).Article 

    Google Scholar 
    30.Corbet, P. S. Dragonflies: Behavior and ecology of Odonata. (Harley Books, 1999).31.Nicolet, P. et al. The wetland plant and macroinvertebrate assemblages of temporary ponds in England and Wales. Biol. Conserv. 120, 261–278 (2004).Article 

    Google Scholar 
    32.Henrikson, B.-I. Sphagnum mosses as a microhabitat for invertebrates in acidified lakes and the colour adaptation and substrate preference in Leucorrhinia dubia (Odonata, Anisoptera). Ecography 16, 143–153 (1993).Article 

    Google Scholar 
    33.Kokko, H. & Sutherland, W. J. Ecological traps in changing environments: Ecological and evolutionary consequences of a behaviourally mediated Allee effect. Evol. Ecol. Res. 3, 537–551 (2001).
    Google Scholar 
    34.Gilroy, J. J. & Sutherland, W. J. Beyond ecological traps: Perceptual errors and undervalued resources. Trends Ecol. Evol. 22, 351–356 (2007).PubMed 
    Article 

    Google Scholar 
    35.Abrams, P. A., Cressman, R. & Křivan, V. The role of behavioral dynamics in determining the patch distributions of interacting species. Am. Nat. 169, 505–518 (2007).PubMed 
    Article 

    Google Scholar 
    36.Denton, J. & Beebee, T. J. C. Palatability of anuran eggs and embryos. Amphib. Reptil. 12, 111–112 (1991).Article 

    Google Scholar 
    37.Larson, D. J. The predaceous water beetles (Coleoptera: Dytiscidae) of Alberta: Systematics, natural history and distribution. Quaest. Entomol. 11, 245–498 (1985).
    Google Scholar 
    38.Mikolajewski, D. J. & Rolff, J. Benefits of morphological defence demonstrated by direct manipulation in larval dragonflies. Evol. Ecol. Res. 6, 619–626 (2004).
    Google Scholar 
    39.Relyea, R. A. Morphological and behavioral plasticity of larval anurans in response to different predators. Ecology 82, 523–540 (2001).Article 

    Google Scholar 
    40.Benard, M. F. Predator-induced phenotypic plasticity in organisms with complex life histories. Annu. Rev. Ecol. Evol. Syst. 35, 651–673 (2004).Article 

    Google Scholar 
    41.McCauley, S. J., Davis, C. J. & Werner, E. E. Predator induction of spine length in larval Leucorrhinia intacta (Odonata). Evol. Ecol. Res. 10, 435–447 (2008).
    Google Scholar 
    42.Nöllert, A. & Nöllert, C. Die Amphibien Europas. (Franckh-Kosmos Verlags-GmbH and Company, 1992).43.Maštera, J., Zavadil, V. & Dvořák, J. Vajíčka a larvy obojživelníků České republiky. (Academia, 2015).44.Speybroeck, J., Beukema, W., Bok, B. & Van der Voort, J. Field Guide to the Amphibians and Reptiles of Britain and Europe. (Bloomsbury Natural History, 2016).45.Sternberg, K. & Buchwald, R. Die Libellen Baden-Württembergs. Band 2: Großlibellen (Anisoptera). (Verlag Eugen Ulmer Gmbh & Co., 2000).46.Mikolajewski, D. J. & Johansson, F. Morphological and behavioral defenses in dragonfly larvae: Trait compensation and cospecialization. Behav. Ecol. 15, 614–620 (2004).Article 

    Google Scholar 
    47.Kjærstad, G., Dolmen, D., Olsvik, H. A. & Tilseth, E. The backswimmer Notonecta glauca L. (Hemiptera, Notonectidae) in Central Norway. Nor. J. Entomol. 56, 44–49 (2009).
    Google Scholar 
    48.Svensson, B. G., Tallmark, B. & Petersson, E. Habitat heterogeneity, coexistence and habitat utilization in five backswimmer species (Notonecta spp.; Hemiptera, Notonectidae). Aquat. Insects 22, 81–98 (2000).Article 

    Google Scholar 
    49.Macan, T. T. A twenty-one-year study of the water-bugs in a Moorland Fishpond. J. Anim. Ecol. 45, 913–922 (1976).Article 

    Google Scholar 
    50.Lock, K., Adriaens, T., Meutter, F. V. D. & Goethals, P. Effect of water quality on waterbugs (Hemiptera: Gerromorpha & Nepomorpha) in Flanders (Belgium): Results from a large-scale field survey. Ann. Limnol. Int. J. Limnol. 49, 121–128 (2013).Article 

    Google Scholar 
    51.Cook, W. L. & Streams, F. A. Fish predation on Notonecta (Hemiptera): Relationship between prey risk and habitat utilization. Oecologia 64, 177–183 (1984).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    52.Swevers, L., Lambert, J. G. D. & De Loof, A. Synthesis and metabolism of vertebrate-type steroids by tissues of insects: A critical evaluation. Experientia 47, 687–698 (1991).CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Bergsten, J. & Miller, K. B. Taxonomic revision of the Holarctic diving beetle genus Acilius Leach (Coleoptera: Dytiscidae): Acilius taxonomic revision. Syst. Entomol. 31, 145–197 (2005).Article 

    Google Scholar 
    54.Åbjörnsson, K., Wagner, B. M. A., Axelsson, A., Bjerselius, R. & Olsén, K. H. Responses of Acilius sulcatus (Coleoptera: Dytiscidae) to chemical cues from perch (Perca fluviatilis). Oecologia 111, 166–171 (1997).ADS 
    PubMed 
    Article 

    Google Scholar 
    55.Boukal, D. S. et al. Catalogue of water beetles of the Czech Republic. Klapalekiana 43(Suppl.), 1–289 (2007).
    Google Scholar 
    56.Gioria, M., Schaffers, A., Bacaro, G. & Feehan, J. The conservation value of farmland ponds: Predicting water beetle assemblages using vascular plants as a surrogate group. Biol. Conserv. 143, 1125–1133 (2010).Article 

    Google Scholar 
    57.Everard, M. Britain’s Freshwater Fishes. (Princeton University Press, 2013).58.Briers, R. A. & Warren, P. H. Competition between the nymphs of two regionally co-occurring species of Notonecta (Hemiptera: Notonectidae). Freshw. Biol. 42, 11–20 (1999).Article 

    Google Scholar 
    59.Wiggins, G. B., Mackay, R. J. & Smith, I. M. Evolutionary and ecological strategies of animals on annual temporary pools. Arch. Für Hydrobiol. Suppl. 58, 197–206 (1980).
    Google Scholar 
    60.Culler, L. E., Ohba, S. & Crumrine, P. Predator-Prey Interactions of Dytiscids. In Ecology, Systematics, and the Natural History of Predaceous Diving Beetles (Coleoptera: Dytiscidae) (ed. Yee, D. A.) 363–379 (Springer, 2014).61.Schuh, R. T. & Slater, J. A. True Bugs of the World (Hemiptera:Heteroptera): Classification and Natural History (Cornell University Press, Cornell, 1995).
    Google Scholar 
    62.Streams, F. A. Intrageneric predation by Notonecta (Hemiptera: Notonectidae) in the laboratory and in nature. Ann. Entomol. Soc. Am. 85, 265–273 (1992).Article 

    Google Scholar 
    63.Giacoma, C., Zugolaro, C. & Beani, L. The advertisement calls of the green toad (Bufo viridis): Variability and role in mate choice. Herpetologica 53, 454–464 (1997).
    Google Scholar 
    64.Pekár, S. & Brabec, M. Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences. Ethology 124, 86–93 (2018).Article 

    Google Scholar 
    65.Halekoh, U., Højsgaard, S. & Yan, J. The R Package geepack for generalized estimating equations. J. Stat. Softw. 15, 1–11 (2006).Article 

    Google Scholar 
    66.R Core Team. R: A Language and Environment for Statistical Computing (The R Foundation for Statistical Computing, Vienna, Austria). https://www.r-project.org/ (2020).67.Wells, K. D. The Ecology and Behavior of Amphibians. (University of Chicago Press, 2007).68.Purrenhage, J. L. & Boone, M. D. Amphibian community response to variation in habitat structure and competitor density. Herpetologica 65, 14–30 (2009).Article 

    Google Scholar 
    69.Formanowicz, D. R. & Bobka, M. S. Predation risk and microhabitat preference: An experimental study of the behavioral responses of prey and predator. Am. Midl. Nat. 121, 379–386 (1989).Article 

    Google Scholar 
    70.Egan, R. S. & Paton, P. W. C. Within-pond parameters affecting oviposition by wood frogs and spotted salamanders. Wetlands 24, 1–13 (2004).Article 

    Google Scholar 
    71.Ward, S. A. Optimal habitat selection in time-limited dispersers. Am. Nat. 129, 568–579 (1987).Article 

    Google Scholar 
    72.Fretwell, S. D. & Lucas, H. L. On territorial behavior and other factors influencing habitat distribution in birds. I. Theoretical development. Biotheoretica 19, 16–36 (1970).Article 

    Google Scholar 
    73.Austad, S. N. A classification of alternative reproductive behaviors and methods for field-testing ESS models. Am. Zool. 24, 309–319 (1984).Article 

    Google Scholar 
    74.Crespo, J. G. A review of chemosensation and related behavior in aquatic insects. J. Insect Sci. 11, 1–39 (2011).Article 

    Google Scholar 
    75.Wildermuth, H. Dragonflies recognize the water of rendezvous and oviposition sites by horizontally polarized light: A behavioural field test. Naturwissenschaften 85, 297–302 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    76.Chislock, M. F., Doster, E., Zitomer, R. A. & Wilson, A. E. Eutrophication: Causes, consequences, and controls in aquatic ecosystems. Nat. Educ. Knowl. 4, 10 (2013).
    Google Scholar 
    77.Dolný, A., Mižičová, H. & Harabiš, F. Natal philopatry in four European species of dragonflies (Odonata: Sympetrinae) and possible implications for conservation management. J. Insect Conserv. 17, 821–829 (2013).Article 

    Google Scholar 
    78.Refsnider, J. M. & Janzen, F. J. Putting eggs in one basket: Ecological and evolutionary hypotheses for variation in oviposition-site choice. Annu. Rev. Ecol. Evol. Syst. 41, 39–57 (2010).Article 

    Google Scholar 
    79.Brodin, T., Mikolajewski, D. J. & Johansson, F. Behavioural and life history effects of predator diet cues during ontogeny in damselfly larvae. Oecologia 148, 162–169 (2006).ADS 
    PubMed 
    Article 

    Google Scholar 
    80.Kershenbaum, A., Spencer, M., Blaustein, L. & Cohen, J. E. Modelling evolutionarily stable strategies in oviposition site selection, with varying risks of predation and intraspecific competition. Evol. Ecol. 26, 955–974 (2012).Article 

    Google Scholar 
    81.Hopper, K. R. Risk-spreading and bet-hedging in insect population biology. Annu. Rev. Entomol. 44, 535–560 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    82.Gioria, M. Habitats. In Ecology, Systematics, and the Natural History of predaceous diving beetles (Coleoptera: Dytiscidae) (ed. Yee, D. A.) 307–362 (Springer, Netherlands, 2014).
    Google Scholar 
    83.Diehl, S. Fish predation and benthic community structure: The role of omnivory and habitat complexity. Ecology 73, 1646–1661 (1992).Article 

    Google Scholar 
    84.Giller, P. S. & McNeill, S. Predation strategies, resource partitioning and habitat selection in Notonecta (Hemiptera/Heteroptera). J. Anim. Ecol. 50, 789–808 (1981).Article 

    Google Scholar 
    85.Ribera, I. & Nilsson, A. N. Morphometric patterns among diving beetles (Coleoptera: Noteridae, Hygrobiidae, and Dytiscidae). Can. J. Zool. 73, 2343–2360 (2011).Article 

    Google Scholar 
    86.Roberts, G. Why individual vigilance declines as group size increases. Anim. Behav. 51, 1077–1086 (1996).Article 

    Google Scholar 
    87.Schoeppner, N. M. & Relyea, R. A. Damage, digestion, and defence: The roles of alarm cues and kairomones for inducing prey defences. Ecol. Lett. 8, 505–512 (2005).PubMed 
    Article 

    Google Scholar 
    88.Schoeppner, N. M. & Relyea, R. A. Interpreting the smells of predation: How alarm cues and kairomones induce different prey defences. Funct. Ecol. 23, 1114–1121 (2009).Article 

    Google Scholar 
    89.McCauley, S. J. & Rowe, L. Notonecta exhibit threat-sensitive, predator-induced dispersal. Biol. Lett. 6, 449–452 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

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    Persistence and accumulation of environmental DNA from an endangered dragonfly

    We developed environmental DNA (eDNA) detection protocols to assist in habitat identification for conservation for the US federally endangered Hine’s emerald dragonfly (Somatochlora hineana). Larval S. hineana have been observed in groundwater-fed calcareous fen habitats in Illinois, Wisconsin, Michigan, and Missouri in the USA, and Ontario, Canada. Habitat destruction and fragmentation have been the primary cause of S. hineana population decline1. Therefore, a key part of conservation efforts to benefit S. hineana is the identification and protection of any remaining habitat areas. Conventional sampling for the presence of S. hineana often includes both adult and larval sampling.Larval S. hineana surveys include benthic-sampling and the pumping of crayfish burrows. Larval S. hineana are most often found in the burrows of Cambarus (= Lacunicambarus) diogenes throughout the year and are almost exclusively found in C. diogenes burrows during their overwintering period2. Comprehensive larval surveys can take months to complete, require intensive training of field personnel, are reliant on favorable weather conditions, and are only effective if late instar larvae can be collected for identification. Adult S. hineana surveys are difficult due to short flight season, habitat segregation by sex, large potential flight range (adults can range for many kilometers from larval habitat), risk of harm when netting adult dragonflies, and difficulty observing genitalia characteristics necessary for accurate species identification when in flight1.Given the restrictions of conventional sampling techniques, there has been a great need to develop a method to expedite field site identification. Environmental DNA can be used to guide and prioritize locations for conventional surveying methods, increasing the speed at which habitats can be identified for protection and restoration.Environmental DNA (eDNA) is a relatively new surveillance method used to detect the presence of a species within a habitat by collecting environmental samples (e.g., soil and water) that contain cell fragments and exogenous DNA3. Mitochondrial genes, which are more plentiful and have a higher resistance to degradation than nuclear genes, are targeted and amplified to determine species presence or absence4,5,6,7.Currently, there is a taxonomic skew toward fish, amphibian, and mollusk eDNA studies7,8 suggesting the need to determine if eDNA methods can be useful for detecting aquatic insects. Environmental DNA analysis from 27 taxa of freshwater arthropods had been published as of 2019; some of these taxa include Procambarus clarkii, Pacifastacus leniusculus, and Gammarus pulex8. Additionally, the critically endangered plecopteran Isogenus nubecula was detected using eDNA methods9.The potential advantages of using eDNA rather than traditional surveying methods include the reduction of field labor hours10, reduced impact to sensitive habitats7, and a lower threshold of detection11,12. Additionally, eDNA has proven to be an effective tool when traditional methods require timely/costly surveying efforts6 and for detecting cryptic invasive species10.Although there is always some risk of damaging the habitat when studying a system, environmental DNA sampling (i.e., water, soil, ice) is much less invasive and has far less potential for harming native and endangered species than many traditional surveying methods7. For example, electrofishing can cause damage in the form of removing/killing fish from the sample site13. Traditional sampling methods for larval populations of S. hineana include benthic sampling (monitoring populations in stream beds) and burrow-pumping (a novel technique used to locate larvae within crayfish burrows)2. These techniques can disrupt flow patterns within shallow streams, collapse burrows, and harm/kill sampled individuals.While there has been some speculation that eDNA sampling may have high false-positive rates due to ancient DNA contamination from extirpated populations, studies show that eDNA typically becomes undetectable in water within 1–44 days after source removal10,14,15,16,17,18,19,20,21 and approximately 144 days in soil22. This suggests that eDNA surveys are contemporaneous and can be used to inform conservation efforts.Environmental DNA degradation is likely more complex in a field setting, and the persistence (defined here as the length of time eDNA remains detectable within a habitat or mesocosm) and net-accumulation (defined here as the difference between the amount of eDNA produced and the amount of eDNA degraded over time) are likely to vary depending on numerous factors that alter source/sink dynamics3. Spatiotemporal dynamics are especially important in affecting the persistence and accumulation of eDNA in the field and need to be accounted for when developing eDNA methodologies23. Concentrations of eDNA may fluctuate spatially and/or temporally as a result of fluctuations in biomass18,24,25, transport through a flowing system17,26,27,28, age structuring of target populations7,16, feeding activity29, life-history events5, seasonal habitat preference13,30, water temperature24,31,32,33, hydrology13,27, inhibition13,27, and microbial activity34. Some studies show that water pH affects eDNA degradation rates19, while others do not35. Similarly, some studies show that UV light exposure affects eDNA degradation rates17, while others show no such effect36.In this study, we focused on the effects that seasonal shifts in temperature have on the persistence and net-accumulation of larval S. hineana eDNA. Since temperature drives the production of eDNA through metabolic processes31 and directly alters the rate of microbial degradation of eDNA32, it may be the most important variable driving seasonal shifts in eDNA detection.Somatochlora hineana larval molting activity varies with seasonal changes, the net-accumulation of S. hineana eDNA within a habitat. Adult S. hineana females lay eggs within streams and streamlets during their flight period (July–early August). Eggs typically mature over winter. In the following year, hatching of pro-larva from eggs occurs between April and June. All S. hineana larvae go through approximately 12 larval instars (F-11 to F-0). The first 6 larval instars (F-11 through F-6) occur rapidly within the first year, and the final 6 (F-5 through F-0) occur more slowly over a period of 2–4 years1. Since S. hineana larvae take several years to fully mature, they survive overwintering in shallow, partially frozen streams within Cambarus (= Lacunicambarus) diogenes crayfish burrows. While S. hineana larvae overwinter within burrows, they rarely consume food or molt, thus reducing the amount of eDNA shed2.The net-accumulation of larval S. hineana eDNA was likely to increase with increasing temperatures2,31,37, while the persistence of larval S. hineana eDNA was likely to decrease with increasing temperatures32. Therefore, we assessed the seasonal shift in persistence and net-accumulation of larval S. hineana eDNA in temperature-controlled mesocosms that reflect the larval overwintering period (5.0 °C) and the larval active period (16.0 °C). This study provided preliminary information regarding the seasonal shift in eDNA production for larval S. hineana. Understanding the seasonal dynamics of larval S. hineana eDNA is vital for efficient detection of this rare aquatic species using eDNA protocols. Our mesocosm results have informed subsequent field sampling of S. hineana eDNA. More