More stories

  • in

    Broad scale proteomic analysis of heat-destabilised symbiosis in the hard coral Acropora millepora

    Coral physiology in response to elevated temperatureSustained declines in photosynthetic health and symbiont density are well-defined characteristics of coral bleaching41. Consistent with previous studies10,11, the photosynthetic health of the coral symbionts, measured as dark-adapted quantum yield of PSII (FV/FM), decreased towards the end of the temperature ramping period (from day 4), declining further over the following three days (rmANOVA; F6,39 = 129.9, P  More

  • in

    Heterothermy as a mechanism to offset energetic costs of environmental and homeostatic perturbations

    1.Wingfield, J. C., Vleck, C. M. & Moore, M. C. Seasonal changes of the adrenocortical response to stress in birds of the Sonoran Desert. J. Exp. Zool. 264, 419–428 (1992).CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Boonstra, R. Coping with changing northern environments: The role of the stress axis in birds and mammals. Integr. Comp. Biol. 44, 95–108 (2004).PubMed 
    Article 

    Google Scholar 
    3.Lind, J. & Cresswell, W. Determining the fitness consequences of antipredation behavior. Behav. Ecol. 16, 945–956 (2005).Article 

    Google Scholar 
    4.Boyles, J. G., Smit, B. & McKechnie, A. E. A new comparative metric for estimating heterothermy in endotherms. Physiol. Biochem. Zool. 84, 115–123 (2011).PubMed 
    Article 

    Google Scholar 
    5.Boyles, J. G. et al. A global heterothermic continuum in mammals. Glob. Ecol. Biogeogr. 22, 1029–1039 (2013).Article 

    Google Scholar 
    6.Canale, C. I., Levesque, D. L. & Lovegrove, B. G. Tropical heterothermy: Does the exception prove the rule or force a re-definition? In Living in a Seasonal World: Thermoregulatory and Metabolic adaptations (eds Ruf, T. et al.) 29–40 (Springer, Berlin, 2012).Chapter 

    Google Scholar 
    7.Dammhahn, M., Landry-Cuerrier, M., Réale, D., Garant, D. & Humphries, M. M. Individual variation in energy-saving heterothermy affects survival and reproductive success. Funct. Ecol. 31, 866–875 (2017).Article 

    Google Scholar 
    8.McGuire, L. P., Jonasson, K. A. & Guglielmo, C. G. Bats on a budget: Torpor-assisted migration saves time and energy. PLoS ONE 9, e115724 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    9.Glazier, D. S. Metabolic level and size scaling of rates of respiration and growth in unicellular organisms. Funct. Ecol. 23, 963–968 (2009).Article 

    Google Scholar 
    10.Turbill, C. & Stojanovski, L. Torpor reduces predation risk by compensating for the energetic cost of antipredator foraging behaviours. Proc. R. Soc. B Biol. Sci. 285, 1–9 (2018).
    Google Scholar 
    11.Angilletta, M. J., Cooper, B. S., Schuler, M. S. & Boyles, J. G. The evolution of thermal physiology in endotherms. Front. Biosci. 2, 861–881 (2010).
    Google Scholar 
    12.Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, Oxford, 2009).Book 

    Google Scholar 
    13.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).ADS 
    CAS 
    Article 

    Google Scholar 
    14.Menzies, A. K. et al. Body temperature, heart rate, and activity patterns of two boreal homeotherms in winter: Homeostasis, allostasis, and ecological coexistence. Funct. Ecol. 34, 2292–2301 (2020).Article 

    Google Scholar 
    15.Humphries, M. M. & Careau, V. Heat for nothing or activity for free? Evidence and implications of activity-thermoregulatory heat substitution. Integr. Comp. Biol. 51, 419–431 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Daly, M., Behrends, P. R., Wilson, M. I. & Jacobs, L. F. Behavioural modulation of predation risk: Moonlight avoidance and crepuscular compensation in a nocturnal desert rodent, Dipodomys merriami. Anim. Behav. 44, 1–9 (1992).Article 

    Google Scholar 
    17.Price, M. V., Waser, N. M. & Bass, T. A. Effects of moonlight on microhabitat use by desert rodents. J. Mammal. 65, 353–356 (1984).Article 

    Google Scholar 
    18.Roschlau, C. & Scheibler, E. Foraging behaviour of a desert rodent community: Habitat or moon—Which is more influential?. Ethol. Ecol. Evol. 28, 394–413 (2016).Article 

    Google Scholar 
    19.Mandelik, Y., Jones, M. & Dayan, T. Structurally complex habitat and sensory adaptations mediate the behavioural responses of a desert rodent to an indirect cue for increased predation risk. Evol. Ecol. Res. 5, 501–515 (2003).
    Google Scholar 
    20.Gutman, R., Dayan, T., Levy, O., Schubert, I. & Kronfeld-Schor, N. The effect of the lunar cycle on fecal cortisol metabolite levels and foraging ecology of nocturnally and diurnally active spiny mice. PLoS ONE 6, 35–38 (2011).Article 
    CAS 

    Google Scholar 
    21.Upham, N. S. & Hafner, J. C. Do nocturnal rodents in the great basin desert avoid moonlight?. J. Mammal. 94, 59–72 (2013).Article 

    Google Scholar 
    22.Price, M. V. Structure of desert rodent communities: A critical review of questions and approaches. Integr. Comp. Biol. 26, 39–49 (1986).
    Google Scholar 
    23.Bennett, A. M. et al. Acute changes in whole body corticosterone in response to perceived predation risk: A mechanism for anti-predator behavior in anurans? Gen. Comp. Endocrinol. 229, 62–66 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Hernández, M. C., Navarro-Castilla, Á., Planillo, A., Sánchez-González, B. & Barja, I. The landscape of fear: Why some free-ranging rodents choose repeated live-trapping over predation risk and how it is associated with the physiological stress response. Behav. Process. 157, 125–132 (2018).Article 

    Google Scholar 
    25.Thaker, M., Lima, S. L. & Hews, D. K. Acute corticosterone elevation enhances antipredator behaviors in male tree lizard morphs. Horm. Behav. 56, 51–57 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Sapolsky, R. M., Romero, L. M. & Munck, A. U. How do glucocorticoids influence stress responses? Preparative actions. Endocr. Rev. 21, 55–89 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Mitra, R. & Sapolsky, R. M. Acute corticosterone treatment is sufficient to induce anxiety and amygdaloid dendritic hypertrophy. Proc. Natl. Acad. Sci. 105, 5573–5578 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Schroder, G. D. Foraging behavior and home range utilization of the bannertial kangaroo rat (Dipodomys spectabilis). Ecology 60, 657–665 (1979).ADS 
    Article 

    Google Scholar 
    29.Andersen, M. C. & Kay, F. R. Banner-tailed kangaroo rat burrow mounds and desert grassland habitats. J. Arid Environ. 41, 147–160 (1999).ADS 
    Article 

    Google Scholar 
    30.Harris, J. H. An experimental analysis of desert rodent foraging ecology. Ecology 65, 1579–1584 (1984).Article 

    Google Scholar 
    31.Lockard, R. B. Seasonal change in the activity pattern of Dipodomys spectabilis. J. Mammal. 59, 563–568 (1978).Article 

    Google Scholar 
    32.Lockard, R. B. & Owings, D. H. Seasonal variation in moonlight avoidance by bannertail kangaroo rats. J. Mammal. 55, 189–193 (1974).CAS 
    PubMed 
    Article 

    Google Scholar 
    33.Dawson, W. R. The relaxation of oxygen consumption to temperature in desert rodents. J. Mammal. 36, 543–553 (1955).Article 

    Google Scholar 
    34.Hart, J. S. Rodents. In Mammals. 1–149 (Academic Press, 1971).35.Quispe, R., Trappschuh, M., Gahr, M. & Goymann, W. Towards more physiological manipulations of hormones in field studies: Comparing the release dynamics of three kinds of testosterone implants, silastic tubing, time-release pellets and beeswax. Gen. Comp. Endocrinol. 212, 100–105 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    36.Sahores, A. et al. Novel, low cost, highly effective, handmade steroid pellets for experimental studies. PLoS ONE 8, e64049 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Sopinka, N. M. et al. Manipulating glucocorticoids in wild animals: Basic and applied perspectives. Conserv. Physiol. 3, cov031 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    38.Akana, S. F. et al. Feedback sensitivity of the rat hypothalamo-pituitary-adrenal axis and its capacity to adjust to exogenous corticosterone. Endocrinology 131, 585–594 (1992).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Bush, V. L., Middlemiss, D. N., Marsden, C. A. & Fone, K. C. F. Implantation of a slow release rorticosterone pellet induces long-term alterations in serotonergic neurochemistry in the rat brain. J. Neuroendocrinol. 15, 607–613 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    40.Meyer, J. S., Micco, D. J., Stephenson, B. S., Krey, L. C. & McEwen, B. S. Subcutaneous implantation method for chronic glucocorticoid replacement therapy. Physiol. Behav. 22, 867–870 (1979).CAS 
    PubMed 
    Article 

    Google Scholar 
    41.Chang, C. C. & Kincl, F. A. Sustained release hormonal preparations: 3. Biological effectiveness of 6-methyl-1717α-acetoxypregna-4,6-diene-3,20-dione. Steroids 12, 689–696 (1968).CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Kratochvíl, P., Benagiano, G. & Kincl, F. A. Sustained release hormonal preparations. 6. Permeability constant of various steroids. Steroids 15, 505–511 (1970).PubMed 
    Article 

    Google Scholar 
    43.Nash, H. A., Robertson, D. N., Moo Young, A. J. & Atkinson, L. E. Steroid release from silastic capsules and rods. Contraception 18, 367–394 (1978).CAS 
    PubMed 
    Article 

    Google Scholar 
    44.Borrow, A. P. et al. Chronic variable stress alters hypothalamic–pituitary–adrenal axis function in the female mouse. Physiol. Behav. 209, 112613 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Lajud, N., Roque, A., Cajero, M., Gutiérrez-Ospina, G. & Torner, L. Periodic maternal separation decreases hippocampal neurogenesis without affecting basal corticosterone during the stress hyporesponsive period, but alters HPA axis and coping behavior in adulthood. Psychoneuroendocrinology 37, 410–420 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    46.Mateo, J. M. & Cavigelli, S. A. A validation of extraction methods for noninvasive sampling of glucocorticoids in free-living ground squirrels. Physiol. Biochem. Zool. 78, 1069–1084 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Touma, C., Palme, R. & Sachser, N. Analyzing corticosterone metabolites in fecal samples of mice: A noninvasive technique to monitor stress hormones. Horm. Behav. 45, 10–22 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Torres-Medina, F. et al. Corticosterone implants produce stress-hyporesponsive birds. J. Exp. Biol. 221, jeb173864 (2018).PubMed 
    Article 

    Google Scholar 
    49.Adzic, M. et al. Acute or chronic stress induce cell compartment-specific phosphorylation of glucocorticoid receptor and alter its transcriptional activity in Wistar rat brain. J. Endocrinol. 202, 87–97 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Ellis, M. V. Development of a compact system for field euthanasia of small mammals. J. Mammal. 98, 1211–1214 (2017).Article 

    Google Scholar 
    51.Guglielmo, C. G., McGuire, L. P., Gerson, A. R. & Seewagen, C. L. Simple, rapid, and non-invasive measurement of fat, lean, and total water masses of live birds using quantitative magnetic resonance. J. Ornithol. 152, 75 (2011).Article 

    Google Scholar 
    52.McGuire, L. P. & Guglielmo, C. G. Quantitative magnetic resonance: A rapid, noninvasive body composition analysis technique for live and salvaged bats. J. Mammal. 91, 1375–1380 (2010).Article 

    Google Scholar 
    53.Warner, D. A., Johnson, M. S. & Nagy, T. R. Validation of body condition indices and quantitative magnetic resonance in estimating body composition in a small lizard. J. Exp. Zool. Part A Ecol. Genet. Physiol. 325, 588–597 (2016).CAS 
    Article 

    Google Scholar 
    54.Boyles, J. G. A brief introduction to methods for describing body temperature in endotherms. Physiol. Biochem. Zool. 92, 365–372 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Monson, G. & Kessler, W. Life history notes on the banner-tailed kangaroo rat, Merriam’s kangaroo rat, and the white-throated wood rat in Arizona and New Mexico. J. Wildl. Manag. 4, 37–43 (1940).Article 

    Google Scholar 
    56.Smit, B., Boyles, J. G., Brigham, R. M. & Mckechnie, A. E. Torpor in dark times: patterns of heterothermy are associated with the lunar cycle in a nocturnal bird. J. Biol. Rhythms 26, 241–248 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Kay, F. R. & Whitford, W. G. The burrow environment of the banner-tailed kangaroo rat, Dipodomys spectabilis, in southcentral New Mexico. Am. Midl. Nat. 99, 270–279 (1978).Article 

    Google Scholar 
    58.Randall, J. A. Territorial-defense interactions with neighbors and strangers in banner-tailed kangaroo rats. J. Mammal. 70, 308–315 (1989).Article 

    Google Scholar 
    59.Randall, J. A. Mating strategies of a nocturnal, desert rodent (Dipodomys spectabilis). Behav. Ecol. Sociobiol. 28, 215–220 (1991).Article 

    Google Scholar 
    60.Ward, D. W. & Randall, J. A. Territorial defense in the bannertail kangaroo rat (Dipodomys spectabilis): footdrumming and visual threats. Behav. Ecol. Sociobiol. 20, 323–328 (1987).Article 

    Google Scholar 
    61.Brown, J. S., Kotler, B. P., Smith, R. J. & Wirtz, W. O. The effects of owl predation on the foraging behavior of heteromyid rodents. Oecologia 76, 408–415 (1988).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Navarro-Castilla, Á., Barja, I. & Díaz, M. Foraging, feeding, and physiological stress responses of wild wood mice to increased illumination and common genet cues. Curr. Zool. 64, 409–417 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Sargunaraj, F., Kotler, B. P., Juliana, J. R. S. & Wielebnowski, N. Stress as an adaptation II: Does experimental cortisol supplementation affect predation risk assessment in foraging gerbils?. Evol. Ecol. Res. 18, 587–598 (2017).
    Google Scholar 
    64.Voellmy, I. K., Goncalves, I. B., Barrette, M. F., Monfort, S. L. & Manser, M. B. Mean fecal glucocorticoid metabolites are associated with vigilance, whereas immediate cortisol levels better reflect acute anti-predator responses in meerkats. Horm. Behav. 66, 759–765 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Kotler, B. P., Brown, J., Mukherjee, S., Berger-Tal, O. & Bouskila, A. Moonlight avoidance in gerbils reveals a sophisticated interplay among time allocation, vigilance and state-dependent foraging. Proc. R. Soc. B Biol. Sci. 277, 1469–1474 (2010).Article 

    Google Scholar 
    66.Pravosudov, V. V. Long-term moderate elevation of corticosterone facilitates avian food-caching behaviour and enhances spatial memory. Proc. R. Soc. B Biol. Sci. 270, 2599–2604 (2003).CAS 
    Article 

    Google Scholar 
    67.Speakman, J. R. & Król, E. Maximal heat dissipation capacity and hyperthermia risk: Neglected key factors in the ecology of endotherms. J. Anim. Ecol. 79, 726–746 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    68.Humphries, M. M., Kramer, D. L. & Thomas, D. W. The role of energy availability in mammalian hibernation: An experimental test in free-ranging eastern chipmunks. Physiol. Biochem. Zool. 76, 165–179 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Munro, D., Thomas, D. W. & Humphries, M. M. Torpor patterns of hibernating eastern chipmunks Tamias striatus vary in response to the size and fatty acid composition of food hoards. J. Anim. Ecol. 74, 692–700 (2005).Article 

    Google Scholar 
    70.Ernest, S. K. M. et al. Rodents, plants, and precipitation: Spatial and temporal dynamics of consumers and resources. Oikos 88, 470–482 (2017).Article 

    Google Scholar 
    71.Warne, R. W., Pershall, A. D. & Wolf, B. O. Linking precipitation and C3–C4 plant production to resource dynamics in higher-trophic-level consumers. Ecology 91, 1628–1638 (2010).PubMed 
    Article 

    Google Scholar 
    72.Warne, R. W., Baer, S. G. & Boyles, J. G. Community physiological ecology. Trends Ecol. Evol. 34, 510–518 (2019).PubMed 
    Article 

    Google Scholar  More

  • in

    Aposematism facilitates the diversification of parental care strategies in poison frogs

    1.Clutton-Brock, T. H. The Evolution of Parental Care Vol. 64 (Princeton University Press, 1991).Book 

    Google Scholar 
    2.Royle, N. J., Smiseth, P. T. & Kölliker, M. The Evolution of Parental Care (Oxford University Press, 2012).Book 

    Google Scholar 
    3.Hansell, M. Bird Nests and Construction Behaviour (Cambridge University Press, 2000).Book 

    Google Scholar 
    4.Doody, J. S., Freedberg, S. & Keogh, J. S. Communal egg-laying in reptiles and amphibians: evolutionary patterns and hypotheses. Q. Rev. Biol. 84, 229–252 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Boness, D. J. & Don Bowen, W. The evolution of maternal care in pinnipeds: new findings raise questions about the evolution of maternal feeding strategies. Bioscience 46, 645–654 (1996).Article 

    Google Scholar 
    6.Salomon, M., Mayntz, D., Toft, S. & Lubin, Y. Maternal nutrition affects offspring performance via maternal care in a subsocial spider. Behav. Ecol. Sociobiol. 65, 1191–1202 (2011).Article 

    Google Scholar 
    7.Summers, K. Mating and aggressive behaviour in dendrobatid frogs from Corcovado National Park, Costa Rica: a comparative study. Behaviour 137, 7–24 (2000).Article 

    Google Scholar 
    8.Li, D. & Jackson, R. R. A predator’s preference for egg-carrying prey: a novel cost of parental care. Behav. Ecol. Sociobiol. 55, 129–136 (2003).Article 

    Google Scholar 
    9.Stiver, K. A. & Alonzo, S. H. Parental and mating effort: is there necessarily a trade-off?. Ethology 115, 1101–1126 (2009).Article 

    Google Scholar 
    10.Ercit, K., Martinez-Novoa, A. & Gwynne, D. T. Egg load decreases mobility and increases predation risk in female black-horned tree crickets (Oecanthus nigricornis). PLoS ONE 9, e110298 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    11.Ghalambor, C. K. & Martin, T. E. Fecundity-survival trade-offs and parental risk-taking in birds. Science 292, 494–497 (2001).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    12.Thorogood, R., Ewen, J. G. & Kilner, R. M. Sense and sensitivity: responsiveness to offspring signals varies with the parents’ potential to breed again. Philos. Trans. R. Soc. B. 278, 2638–2645 (2011).
    Google Scholar 
    13.Stearns, S. C. The Evolution of Life Histories (Oxford University Press, 1992).
    Google Scholar 
    14.Weir, B. J. & Rowlands, I. Reproductive strategies of mammals. Annu. Rev. Ecol. Evol. Syst. 4, 139–163 (1973).Article 

    Google Scholar 
    15.Kvarnemo, C. In Evolutionary Behavioral Ecology (ed. FoxWestneat, C. W.) (Oxford University Press, 2010).
    Google Scholar 
    16.Alonso-Alvarez, C. & Velando, A. Benefits and costs of parental care. The evolution of parental care, 40–61 (2012).17.Farmer, C. Parental care: the key to understanding endothermy and other convergent features in birds and mammals. Am. Nat. 155, 326–334 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Ar, A. & Yom-Tov, Y. The evolution of parental care in birds. Evolution 32, 655–669 (1978).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Gubernick, D. J. Parent and infant attachment in mammals. In Parental care in mammals 243–305 (Springer, 1981).20.Case, T. J. Endothermy and parental care in the terrestrial vertebrates. Am. Nat. 112, 861–874 (1978).Article 

    Google Scholar 
    21.Gross, M. R. & Shine, R. Parental care and mode of fertilization in ectothermic vertebrates. Evolution 35, 775–793 (1981).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Balshine, S. Patterns of parental care in vertebrates. Evol. Parental Care 62, 80 (2012).
    Google Scholar 
    23.Furness, A. I. & Capellini, I. The evolution of parental care diversity in amphibians. Nat. Commun. 10, 1–12 (2019).CAS 
    Article 

    Google Scholar 
    24.Schulte, L. M., Ringler, E., Rojas, B. & Stynoski, J. L. Developments in amphibian parental care research: history, present advances, and future perspectives. Herpetol. Monogr. 34, 71–97 (2020).Article 

    Google Scholar 
    25.Wells, K. D. The Ecology and Behavior of Amphibians (University of Chicago Press, 2010).
    Google Scholar 
    26.Weygoldt, P. Evolution of parental care in dart poison frogs (Amphibia: Anura: Dendrobatidae). J. Zoolog. Syst. Evol. 25, 51–67 (1987).Article 

    Google Scholar 
    27.Summers, K. & Tumulty, J. in Sexual Selection 289–320 (Elsevier, 2014).28.Lehtinen, R., Lannoo, M. J. & Wassersug, R. J. Phytotelm-breeding anurans: past, present and future research. Misc. Publ. Museum Zool. Univ. Michigan 193, 1–9 (2004).
    Google Scholar 
    29.Brust, D. G. Maternal brood care by Dendrobates pumilio: a frog that feeds its young. J. Herpetol. 27, 96–98 (1993).Article 

    Google Scholar 
    30.Bourne, G. R., Collins, A. C., Holder, A. M. & McCarthy, C. L. Vocal communication and reproductive behavior of the frog Colostethus beebei in Guyana. J. Herpetol. 35, 272–281 (2001).Article 

    Google Scholar 
    31.Schulte, L. M. Feeding or avoiding? Facultative egg feeding in a Peruvian poison frog (Ranitomeya variabilis). Ethol. Ecol. Evol. 26, 58–68. https://doi.org/10.1080/03949370.2013.850453 (2014).Article 

    Google Scholar 
    32.Beck, K. B., Loretto, M.-C., Ringler, M., Hödl, W. & Pašukonis, A. Relying on known or exploring for new? Movement patterns and reproductive resource use in a tadpole-transporting frog. PeerJ 5, e3745 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Pašukonis, A., Loretto, M.-C. & Rojas, B. How far do tadpoles travel in the rainforest? Parent-assisted dispersal in poison frogs. Evol. Ecol. 33, 613–623 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Summers, K. Metabolism and parental care in ectotherms: a comment on Beekman et al. Behav. Ecol. 30, 593–594 (2019).Article 

    Google Scholar 
    35.Santos, J. C. & Cannatella, D. C. Phenotypic integration emerges from aposematism and scale in poison frogs. Proc. Natl. Acad. Sci. 108, 6175–6180 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Stynoski, J. L., Schulte, L. M. & Rojas, B. Poison frogs. Curr. Biol. 25, R1026–R1028 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Rojas, B., Valkonen, J. & Nokelainen, O. Aposematism. Curr. Biol. 25, R350–R351 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Poulton, E. B. The Colours of Animals: Their Meaning and Use, Especially Considered in the Case of Insects (D. Appleton, 1990).
    Google Scholar 
    39.Santos, J. C., Coloma, L. A. & Cannatella, D. C. Multiple, recurring origins of aposematism and diet specialization in poison frogs. Proc. Natl. Acad. Sci. 100, 12792–12797 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Vences, M. et al. Convergent evolution of aposematic coloration in Neotropical poison frogs: a molecular phylogenetic perspective. Org. Divers. Evol. 3, 215–226 (2003).Article 

    Google Scholar 
    41.Daly, J. W. et al. An uptake system for dietary alkaloids in poison frogs (Dendrobatidae). Toxicon 32, 657–663 (1994).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Saporito, R. A., Spande, T. F., Garraffo, H. M. & Donnelly, M. A. Arthropod alkaloids in poison frogs: a review of the dietary hypothesis. Heterocycles 79, 277–297 (2009).CAS 
    Article 

    Google Scholar 
    43.Santos, J. C. et al. Aposematism increases acoustic diversification and speciation in poison frogs. Philos. Trans. R. Soc. B. 281, 20141761 (2014).
    Google Scholar 
    44.Caldwell, J. P. The evolution of myrmecophagy and its correlates in poison frogs (Family Dendrobatidae). J. Zool. 240, 75–101 (1996).Article 

    Google Scholar 
    45.Summers, K., Symula, R., Clough, M. & Cronin, T. Visual mate choice in poison frogs. Philos. Trans. R. Soc. B. 266, 2141–2145 (1999).CAS 

    Google Scholar 
    46.Duellman, W. E. & Trueb, L. Biology of Amphibians (JHU Press, 1994).
    Google Scholar 
    47.Summers, K. & McKeon, C. S. The evolutionary ecology of phytotelmata use in Neotropical poison frogs. Misc. Publ. Mus. Zool. Univ. Mich. 193, 55–73 (2004).
    Google Scholar 
    48.Summers, K., Sea McKeon, C. & Heying, H. The evolution of parental care and egg size: a comparative analysis in frogs. Philos. Trans. R. Soc. B. 273, 687–692 (2006).
    Google Scholar 
    49.Wells, K. D. Courtship and parental behavior in a Panamanian poison-arrow frog (Dendrobates auratus). Herpetologica 34, 148–155 (1978).
    Google Scholar 
    50.Summers, K. Sexual selection and intra-female competition in the green poison-dart frog, Dendrobates auratus. Anim. Behav. 37, 797–805 (1989).Article 

    Google Scholar 
    51.Summers, K. Paternal care and the cost of polygyny in the green dart-poison frog. Behav. Ecol. Sociobiol. 27, 307–313 (1990).Article 

    Google Scholar 
    52.Summers, K. & Amos, W. Behavioral, ecological, and molecular genetic analyses of reproductive strategies in the Amazonian dart-poison frog, Dendrobates ventrimaculatus. Behav. Ecol. 8, 260–267 (1997).Article 

    Google Scholar 
    53.Limerick, S. Courtship behavior and oviposition of the poison-arrow frog Dendrobates pumilio. Herpetologica 36, 69–71 (1980).
    Google Scholar 
    54.Pröhl, H. & Hödl, W. Parental investment, potential reproductive rates, and mating system in the strawberry dart-poison frog, Dendrobates pumilio. Behav. Ecol. Sociobiol. 46, 215–220 (1999).Article 

    Google Scholar 
    55.Brown, J. L., Morales, V. & Summers, K. A key ecological trait drove the evolution of biparental care and monogamy in an amphibian. Am. Nat. 175, 436–446 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Yang, Y., Blomenkamp, S., Dugas, M. B., Richards-Zawacki, C. L. & Pröhl, H. Mate choice versus mate preference: inferences about color-assortative mating differ between field and lab assays of poison frog behavior. Am. Nat. 193, 598–607 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Wells, K. D. Behavoral ecology and social organization of a dendrobatid frog (Colostethus inguinalis). Behav. Ecol. Sociobiol. 6, 199–209 (1980).Article 

    Google Scholar 
    58.Luddecke, H. Behavioral aspects of the reproductive biology of the Andean frog Colostethus palmatus (Amphibia: Dendrobatidae). Rev. Acad. Colomb. Cienc. Exact. Fis. Nat. 23, S303–S303 (1999).
    Google Scholar 
    59.Montanarin, A., Kaefer, I. L. & Lima, A. P. Courtship and mating behaviour of the brilliant-thighed frog Allobates femoralis from Central Amazonia: Implications for the study of a species complex. Ethol. Ecol. Evol. 23, 141–150 (2011).Article 

    Google Scholar 
    60.Ursprung, E., Ringler, M., Jehle, R. & Hoedl, W. Strong male/male competition allows for nonchoosy females: High levels of polygynandry in a territorial frog with paternal care. Mol. Ecol. 20, 1759–1771 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Stückler, S. et al. Spatio-temporal characteristics of the prolonged courtship in brilliant-thighed poison frogs, Allobates femoralis. Herpetologica 75, 268–279 (2019).Article 

    Google Scholar 
    62.Symula, R., Schulte, R. & Summers, K. Molecular phylogenetic evidence for a mimetic radiation in Peruvian poison frogs supports a Müllerian mimicry hypothesis. Philos. Trans. R. Soc. B 268, 2415–2421 (2001).CAS 

    Google Scholar 
    63.Summers, K. Mating strategies in two species of dart-poison frogs: a comparative study. Anim. Behav. 43, 907–919 (1992).Article 

    Google Scholar 
    64.Rojas, B. & Pašukonis, A. From habitat use to social behavior: natural history of a voiceless poison frog, Dendrobates tinctorius. PeerJ 7, e7648 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Maan, M. E. & Cummings, M. E. Poison frog colors are honest signals of toxicity, particularly for bird predators. Am. Nat. 179, E1–E14 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Grant, T. et al. Phylogenetic systematics of dart-poison frogs and their relatives (Amphibia: Athesphatanura: Dendrobatidae). Bull. Am. Mus. Nat. 2006, 1–262 (2006).
    Google Scholar 
    67.Grant, T. et al. Phylogenetic systematics of dart-poison frogs and their relatives revisited (Anura: Dendrobatoidea). S. Am. J. Herpetol. 12, S1–S90 (2017).Article 

    Google Scholar 
    68.Duellman, W. E. Frogs of the genus Colostethus (Anura; Dendrobatidae) in the Andes of northern Peru (2004).69.Fairbairn, D. J. Odd Couples: Extraordinary Differences Between the Sexes in the Animal Kingdom (Princeton University Press, 2013).Book 

    Google Scholar 
    70.Fairbairn, D. J., Blanckenhorn, W. U. & Székely, T. Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (Oxford University Press, 2007).Book 

    Google Scholar 
    71.Vági, B., Végvári, Z., Liker, A., Freckleton, R. P. & Székely, T. Parental care and the evolution of terrestriality in frogs. Philos. Trans. R. Soc. B. 286, 20182737 (2019).
    Google Scholar 
    72.Gosner, K. L. A simplified table for staging anuran embryos and larvae with notes on identification. Herpetologica 16, 183–190 (1960).
    Google Scholar 
    73.Kelber, A., Vorobyev, M. & Osorio, D. Animal colour vision–behavioural tests and physiological concepts. Biol. Rev. 78, 81–118 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Renoult, J. P., Kelber, A. & Schaefer, H. M. Colour spaces in ecology and evolutionary biology. Biol. Rev. 92, 292–315 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    75.Endler, J. A. On the measurement and classification of colour in studies of animal colour patterns. Biol. J. Linn. Soc. 41, 315–352 (1990).Article 

    Google Scholar 
    76.Kemp, D. J. et al. An integrative framework for the appraisal of coloration in nature. Am. Nat. 185, 705–724 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—a free software suite for objectively measuring reflectance, colour and pattern. Methods. Ecol. Evol. 6, 1320–1331 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Maia, R. & White, T. E. Comparing colors using visual models. Behav. Ecol. 29, 649–659 (2018).Article 

    Google Scholar 
    79.Bergeron, Z. T. & Fuller, R. C. Using human vision to detect variation in avian coloration: how bad is it?. Am. Nat. 191, 269–276 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Yang, Z., Kumar, S. & Nei, M. A new method of inference of ancestral nucleotide and amino acid sequences. Genetics 141, 1641–1650 (1995).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    82.Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series. B. Stat. Methodo. 57, 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    84.Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).Article 

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

    Google Scholar 
    86.Barker, D., Meade, A. & Pagel, M. Constrained models of evolution lead to improved prediction of functional linkage from correlated gain and loss of genes. Bioinformatics 23, 14–20 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    87.Lindstedt, C., Boncoraglio, G., Cotter, S. C., Gilbert, J. D. J. & Kilner, R. M. Parental care shapes evolution of aposematism and provides lifelong protection against predators. bioRxiv 25, 644864 (2019).
    Google Scholar 
    88.Donnelly, M. A. Demographic effects of reproductive resource supplementation in a territorial frog, Dendrobates pumilio. Ecol. Monogr. 59, 207–221 (1989).Article 

    Google Scholar 
    89.Rojas, B. & Endler, J. A. Sexual dimorphism and intra-populational colour pattern variation in the aposematic frog Dendrobates tinctorius. Evol. Ecol. 27, 739–753 (2013).Article 

    Google Scholar 
    90.Pröhl, H. Territorial behavior in dendrobatid frogs. J. Herpetol. 39, 354–365 (2005).Article 

    Google Scholar 
    91.Speed, M. P., Brockhurst, M. A. & Ruxton, G. D. The dual benefits of aposematism: predator avoidance and enhanced resource collection. Evolution 64, 1622–1633 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    92.Fincke, O. M. Organization of predator assemblages in Neotropical tree holes: effects of abiotic factors and priority. Ecol. Entomol. 24, 13–23 (1999).Article 

    Google Scholar 
    93.Summers, K. The effects of cannibalism on Amazonian poison frog egg and tadpole deposition and survivorship in Heliconia axil pools. Oecologia 119, 557–564 (1999).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    94.McKeon, C. S. & Summers, K. Predator driven reproductive behavior in a tropical frog. Evol. Ecol. 27, 725–737 (2013).Article 

    Google Scholar 
    95.Amézquita, A., Castro, L., Arias, M., González, M. & Esquivel, C. Field but not lab paradigms support generalisation by predators of aposematic polymorphic prey: the Oophaga histrionica complex. Evol. Ecol. 27, 769–782 (2013).Article 

    Google Scholar 
    96.Lawrence, J. P. et al. Weak warning signals can persist in the absence of gene flow. PNAS 116, 19037–19045 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    97.Lack, D. The natural regulation of animal numbers. The Natural Regulation of Animal Numbers. (1954).98.Williams, G. C. Natural selection, the costs of reproduction, and a refinement of Lack’s principle. Am. Nat. 100, 687–690 (1966).Article 

    Google Scholar 
    99.Brown, J., Morales, V. & Summers, K. Divergence in parental care, habitat selection and larval life history between two species of Peruvian poison frogs: an experimental analysis. J. Evol. Biol. 21, 1534–1543 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    100.Brown, J. L., Morales, V. & Summers, K. Tactical reproductive parasitism via larval cannibalism in Peruvian poison frogs. Biol. Lett. 5, 148–151 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    101.Brown, J. L., Morales, V. & Summers, K. Home range size and location in relation to reproductive resources in poison frogs (Dendrobatidae): a Monte Carlo approach using GIS data. Anim. Behav. 77, 547–554 (2009).Article 

    Google Scholar 
    102.Kok, P. J., Willaert, B. & Means, D. B. A new diagnosis and description of Anomaloglossus roraima (La Marca, 1998) (Anura: Aromobatidae: Anomaloglossinae), with description of its tadpole and call. S. Am. J. Herpetol. 8, 29–45 (2013).Article 

    Google Scholar 
    103.Pašukonis, A. et al. The significance of spatial memory for water finding in a tadpole-transporting frog. Anim. Behav. 116, 89–98 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    104.Pašukonis, A., Warrington, I., Ringler, M. & Hödl, W. Poison frogs rely on experience to find the way home in the rainforest. Biol. Lett. 10, 20140642 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    105.Poelman, E. H. & Dicke, M. Offering offspring as food to cannibals: oviposition strategies of Amazonian poison frogs (Dendrobates ventrimaculatus). Evol. Ecol. 21, 215–227 (2007).Article 

    Google Scholar 
    106.Caldwell, J. P. & de Araujo, M. C. Cannibalistic interactions resulting from indiscriminate predatory behavior in tadpoles of poison frogs (Anura: Dendrobatidae). Biotropica 30, 92–103 (1998).Article 

    Google Scholar 
    107.Gray, H. M., Summers, K. & Ibáñez, R. Kin discrimination in cannibalistic tadpoles of the Green Poison Frog, Dendrobates auratus (Anura, Dendrobatidae). Phyllomedusa (2009).108.Rojas, B. Strange parental decisions: fathers of the dyeing poison frog deposit their tadpoles in pools occupied by large cannibals. Behav. Ecol. Sociobiol. 68, 551–559 (2014).Article 

    Google Scholar 
    109.Schulte, L. M. & Mayer, M. Poison frog tadpoles seek parental transportation to escape their cannibalistic siblings. J. Zool. 303, 83–89, 12472 (2017).110.Ringler, E., Pašukonis, A., Hödl, W. & Ringler, M. Tadpole transport logistics in a Neotropical poison frog: indications for strategic planning and adaptive plasticity in anuran parental care. Front. Zool. 10, 67 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    111.Pröhl, H. Variation in male calling behaviour and relation to male mating success in the strawberry poison frog (Dendrobates pumilio). Ethology 109, 273–290 (2003).Article 

    Google Scholar 
    112.Summers, K. & Earn, D. J. The cost of polygyny and the evolution of female care in poison frogs. Biol. J. Linn. Soc. 66, 515–538 (1999).Article 

    Google Scholar 
    113.Ringler, E. et al. Flexible compensation of uniparental care: female poison frogs take over when males disappear. Behav. Ecol. 26, 1219–1225 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    114.Pyron, R. A. & Wiens, J. J. A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Mol. Phylogenet. Evol. 61, 543–583 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    115.Streicher, J. W. et al. Evaluating methods for phylogenomic analyses, and a new phylogeny for a major frog clade (Hyloidea) based on 2214 loci. Mol. Phylogenet. Evol. 119, 128–143 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    116.Gilbert, J. D. Thrips domiciles protect larvae from desiccation in an arid environment. Behav. Ecol. 25, 1338–1346 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    117.Hime, P. M. et al. Phylogenomics reveals ancient gene tree discordance in the amphibian tree of life. Syst. Biol. 70, 49–66 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    118.Moen, D. S., Morlon, H. & Wiens, J. J. Testing convergence versus history: convergence dominates phenotypic evolution for over 150 million years in frogs. Syst. Biol. 65, 146–160 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    119.Gomez-Mestre, I., Pyron, R. A. & Wiens, J. J. Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs. Evolution 66, 3687–3700 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    120.Liu, Y., Day, L. B., Summers, K. & Burmeister, S. S. Learning to learn: advanced behavioural flexibility in a poison frog. Anim. Behav. 111, 167–172 (2016).Article 

    Google Scholar 
    121.Liu, Y., Day, L. B., Summers, K. & Burmeister, S. S. A cognitive map in a poison frog. J. Exp. Biol. 222, jeb97467 (2019).Article 

    Google Scholar 
    122.Liu, Y., Jones, C. D., Day, L. B., Summers, K. & Burmeister, S. S. Cognitive phenotype and differential gene expression in a hippocampal homologue in two species of frog. Integr. Comp Biol. 60, 1007–1023 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

  • in

    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

  • in

    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