Philippa Kaur

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

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

1.Cramp, S. & Perrins, C. M. in The Birds of the Western Palearctic, Vol. 7 (eds. Cramp, S. & Perrins, C. M.) (Oxford University Press, 1993).2.Lack, D. Clutch and brood size in the Robin. Br. Birds 39(98–109), 130–135 (1946).
Google Scholar 
3.Lack, D. Further notes on clutch and brood size in the Robin. Br. Birds 41(98–104), 130–137 (1948).
Google Scholar 
4.Lack, D. The Life of Robin (Witherby, 1965).
Google Scholar 
5.Harper, D. G. C. Pairing strategies and mate choice in female Robins (Erithacus rubecula). Anim. Behav. 33, 862–875 (1985).Article 

Google Scholar 
6.Lebedeva, N. V. & Lomadze, N. H. in The Robin Erithacus Rubecula in the North-Western Caucasus (eds. Matishov, G. G. & Lebedeva, N. V.) 252–277 (SSC RAS Publishing, 2007).7.Knysh, N. P. Materials on the biology of Robin in forest-steppe deciduous forests of Sumy region. Berkut 17, 41–60 (2008).
Google Scholar 
8.Zimin V. B. in The Robin in the North of the Area, Vol. 1. Distribution. Number. Reproduction (ed. Zimin, V. B.) 401–422 (Karel’skiy nauchnyy centr RAN, 2009).9.Baranovskiy, A. V. & Ivanov, E. S. Features of reproductive biology of robins (Erithacus rubecula) in anthropogenic habitats (for example, the city of Ryazan). Principy èkologii 6, 17–25 (2017).
Google Scholar 
10.Wesołowski, T. Primeval conditions—What can we learn from them?. Ibis 149, 64–77 (2007).Article 

Google Scholar 
11.Tobias, J. & Seddon, N. Territoriality as a paternity guard in the European robin Erithacus rubecula. Anim. Behav. 60, 165–173 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Tobias, J. & Seddon, N. Female begging in European robins: Do neighbors eavesdrop for extrapair copulations?. Behav. Ecol. 13, 637–642 (2002).Article 

Google Scholar 
13.Lubjuhn, T., Strohbach, S., Brün, J., Gerken, T. & Epplen, J. T. Extra-pair paternity in great tits (Parus major)—A long term study. Behaviour 136, 1157–1172 (1999).Article 

Google Scholar 
14.Griffith, S. C., Owens, I. P. F. & Thuman, K. A. Extra pair paternity in birds: A review of interspecific variation and adaptive function. Mol. Ecol. 11, 2195–2212 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Cockburn, A. Prevalence of different modes of parental care in birds. Proc. Biol. Sci. 273, 1375–1383 (2006).PubMed 
PubMed Central 

Google Scholar 
16.Zagalska-Neubauer, M. & Dubiec, A. Techniki i markery molekularne w badaniach zmienności genetycznej ptaków. Not. Ornit. 48, 193–206 (2007).
Google Scholar 
17.Brouwer, L. & Griffith, S. C. Extra-pair paternity in birds. Mol. Ecol. 28, 4864–4882 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Petter, S. C., Miles, D. B. & White, M. M. Genetic evidence of mixed reproductive strategy in a monogamous bird. Condor 92, 702–708 (1990).Article 

Google Scholar 
19.Jennions, M. D. & Petrie, M. Why do females mate multiply? A review of the genetic benefits. Biol. Rev. 75, 21–64 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Akçay, E. & Roughgarden, J. Extra-pair paternity in birds: Review of the genetic benefits. Evol. Ecol. Res. 9, 855–868 (2007).
Google Scholar 
21.Dietzen, C., Witt, H.-H. & Wink, M. The phylogeographic differentiation of the European robin Erithacus rubecula on the Canary Islands revealed by mitochondrial DNA sequence data and morphometrics: Evidence for a new robin on Gran Canaria?. Avian Sci. 3, 115–131 (2003).
Google Scholar 
22.Rodrigues, P. et al. Phylogeography and genetic diversity of the Robin (Erithacus rubecula) in the Azores Islands: Evidence of a recent colonisation. J. Ornithol. 154, 889–900 (2013).Article 

Google Scholar 
23.Fulgione, D., Rippa, D., Manganiello, E., Caliendo, M. F. & Rastogi, R. K. Seasonal genetic structure analysis of a resident population of European Robin. Open Zool. J. 1, 11–17 (2008).CAS 
Article 

Google Scholar 
24.Morin, P. A., Messier, J. & Woodruff, D. S. DNA extraction, amplification, and direct sequencing from hornbill feathers. J. Sci. Soc. Thail. 20, 31–41 (1994).CAS 
Article 

Google Scholar 
25.Wright, T. F. et al. Microsatellite variation among divergent populations of stalk-eyed flies, genus Cyrtodiopsis. Genet. Res. 84, 27–40 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Yue, G.-H., Kovacs, B. & Orban, L. A new problem with cross-species amplification of microsatellites: Generation of non-homologous products. Dongwuxue Yanjiu 2, 131–140 (2010).
Google Scholar 
27.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Dąbrowski, M. J., Bornelöv, S., Kruczyk, M., Baltzer, N. & Komorowski, J. ‘True’ null allele detection in microsatellite loci: A comparison of methods, assessment of difficulties and survey of possible improvements. Mol. Ecol. Resour. 15, 477–488 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Primmer, C. R., Møller, A. P. & Ellegren, H. A wide-range survey of cross-species microsatellite amplification in birds. Mol. Ecol. 5, 365–378 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Jaroszewicz, B. et al. Białowieża forest—A relic of the high naturalness of European forests. Forests 10, 849 (2019).Article 

Google Scholar 
31.Campos, A. R. et al. How do Robins Erithacus rubecula resident in Iberia respond to seasonal flooding by conspecific migrants?. Bird Study 58, 435–442 (2011).Article 

Google Scholar 
32.Owen, J. C. Collecting, processing, and storing avian blood: A review. J. Field Ornithol. 82, 339–354 (2011).Article 

Google Scholar 
33.Horváth, M. B., Martínez-Cruz, B., Negro, J. J., Kalmár, L. & Godoy, J. A. An overlooked DNA source for non-invasive genetic analysis in birds. J. Avian Biol. 36, 84–88 (2005).Article 

Google Scholar 
34.Faircloth, B. C. MSATCOMMANDER: Detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour. 8, 92–94 (2008).CAS 
PubMed 
Article 

Google Scholar 
35.Schuelke, M. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 18, 233–234 (2000).CAS 
PubMed 
Article 

Google Scholar 
36.Austin, J. D. et al. Permanent genetic resources added to Molecular Ecology Resources Database 1 February 2011–31 March 2011. Mol. Ecol. Resour. 11, 757–758 (2011).CAS 
PubMed 
Article 

Google Scholar 
37.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Raymond, M. & Rousset, F. GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. Heredity 86, 248–249 (1995).Article 

Google Scholar 
39.Rousset, F. GENEPOP’007: A complete reimplementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
Article 

Google Scholar 
40.Goudet, J. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3. http://www.unil.ch/izea/softwares/fstat.htlm (2001).41.Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Grohme, M. A., Soler, R. F., Wink, M. & Frohme, M. Microsatellite marker discovery using single molecule real-time circular consensus sequencing on the Pacific Biosciences RS. Biotechniques 55, 253–256 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Liljegren, M. M., de Muinck, E. J. & Trosvik, P. Microsatellite length scoring by single molecule real time sequencing-effects of sequence structure and PCR regime. PLoS ONE 11, e0159232 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Dutta, N. et al. Microsatellite marker set for genetic diversity assessment of primitive Chitala chitala (Hamilton, 1822) derived through SMRT sequencing technology. Mol. Biol. Rep. 46, 41–49 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Corner, S., Yuzbasiyan-Gurkan, V., Agnew, D. & Venta, P. J. Development of a 12-plex of new microsatellite markers using a novel universal primer method to evaluate the genetic diversity of jaguars (Panthera onca) from North American zoological institutions. Conserv. Genet. Resour. 11, 487–497 (2019).Article 

Google Scholar 
47.Graham, B. A., Carpenter, A. M., Friesen, V. L. & Burg, T. M. A comparison of neutral genetic differentiation and genetic diversity among migratory and resident populations of Golden-crowned-Kinglets (Regulus satrapa). J. Ornithol. 161, 509–519 (2020).Article 

Google Scholar 
48.Bensch, S., Grahn, M., Müller, N., Gay, L. & Akesson, S. A. Genetic, morphological, and feather isotope variation of migratory willow warblers show gradual divergence in a ring. Mol. Ecol. 18, 3087–3096 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Kralj, J., Procházka, P., Fainová, D., Patzenhauerová, H. & Tutiš, V. Intraspecific variation in the wing shape and genetic differentiation of reed warblers Acrocephalus scirpaceus in Croatia. Acta Ornithol. 45, 51–58 (2010).Article 

Google Scholar 
50.Mettler, R. et al. Contrasting patterns of genetic differentiation among blackcaps (Sylvia atricapilla) with divergent migratory orientations in Europe. PLoS ONE 8, e81365 (2013).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Gyllensten, U., Jakonsson, S. & Temrin, H. No evidence for illegitimate young in monogamous and polygynous warblers. Nature 343, 168–170 (1990).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Gil, D., Slater, P. J. B. & Graves, J. A. Extrapair paternity and song characteristics in the willow warbler Phylloscopus trochilus. J. Avian Biol. 38, 291–297 (2007).Article 

Google Scholar 
53.Moskalenko, V. N., Belokon, M. M., Belokon, Y. S. & Goretskaia, M. I. Extrapair young in nests of the Wood Warbler (Phylloscopus sibilatrix) in the Middle Russia (poster). In 26th International Ornithological Congress (2014).54.Grendelmeier, A., Arlettaz, R., Olano-Marin, J. & Pasinelli, G. Experimentally provided conspecific cues boost bird territory density but not breeding performance. Behav. Ecol. 28, 174–185 (2017).Article 

Google Scholar 
55.Petrie, M. & Kempenaers, B. Extrapair paternity in birds: Explaining variation between species and populations. Trends Ecol. Evol. 13, 52–58 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Wagner, R. H. Hidden leks: sexual selection and the clustering of avian territories. Ornithol. Monogr. 49, 123–145 (1998).Article 

Google Scholar 
57.Fletcher, R. J. & Miller, C. W. On the evolution of hidden leks and the implications for reproductive and habitat selection behaviours. Anim. Behav. 71, 1247–1251 (2006).Article 

Google Scholar 
58.Broughton, R. K., Bubnicki, J. W. & Maziarz, M. Multi-scale settlement patterns of a migratory songbird in a European primeval forest. Behav. Ecol. Sociobiol. 74, 1–12 (2020).Article 

Google Scholar  More

1.Clapham, P. J. Humpback whale: Megaptera novaeangliae. In Encyclopedia of Marine Mammals 489–492 (Elsevier, 2018).Chapter 

Google Scholar 
2.Corkeron, P. J. & Connor, R. C. Why do baleen whales migrate?. Mar. Mamm. Sci. 15, 1228–1245 (1999).Article 

Google Scholar 
3.Geijer, C. K. A., Notarbartolo di Sciara, G. & Panigada, S. Mysticete migration revisited: Are Mediterranean fin whales an anomaly?. Mamm. Rev. 46, 284–296 (2016).Article 

Google Scholar 
4.Baker, C. S. & Herman, L. M. Aggressive behavior between humpback whales (Megaptera novaeangliae) wintering in Hawaiian waters. Can. J. Zool. 62, 1922–1937 (1984).Article 

Google Scholar 
5.Herman, L. M. The multiple functions of male song within the humpback whale (Megaptera novaeangliae) mating system: Review, evaluation, and synthesis. Biol. Rev. 92, 1795–1818 (2017).PubMed 
Article 

Google Scholar 
6.Palsbøll, P. J., Clapham, P. J., Mattila, D. K. & Vasquez, O. Composition and dynamics of humpback whale competitive groups in the West Indies. Behaviour 122, 182–194 (1992).Article 

Google Scholar 
7.Payne, R. S. & Mcvay, S. Songs of Humpback Whales. Science 173, 585–597 (1971).CAS 
PubMed 
Article 
ADS 

Google Scholar 
8.Kroodsma, D. E. & Byers, B. E. The function (s) of bird song. Am. Zool. 31, 318–328 (1991).Article 

Google Scholar 
9.Garland, E. C. et al. Dynamic horizontal cultural transmission of humpback whale song at the Ocean Basin Scale. Curr. Biol. 21, 687–691 (2011).CAS 
PubMed 
Article 

Google Scholar 
10.Noad, M. J. & Cato, D. H. Swimming speeds of singing and non-singing humpback whales during migration. Mar. Mamm. Sci. 23, 481–495 (2007).Article 

Google Scholar 
11.Smith, J. N., Goldizen, A. W., Dunlop, R. A. & Noad, M. J. Songs of male humpback whales, Megaptera novaeangliae, are involved in intersexual interactions. Anim. Behav. 76, 467–477 (2008).Article 

Google Scholar 
12.Ross-Marsh, E., Elwen, S., Prinsloo, A., James, B. & Gridley, T. Singing in South Africa: Monitoring the occurrence of humpback whale (Megaptera novaeangliae) song near the Western Cape. Bioacoustics 30, 163–179 (2020).Article 

Google Scholar 
13.Stimpert, A. K., Peavey, L. E., Friedlaender, A. S. & Nowacek, D. P. Humpback whale song and foraging behavior on an Antarctic feeding ground. PLoS ONE 7, e51214 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
14.Vu, E. T. et al. Humpback whale song occurs extensively on feeding grounds in the western North Atlantic Ocean. Aquat. Biol. 14, 175–183 (2012).Article 

Google Scholar 
15.McSweeney, D., Chu, K., Dolphin, W. & Guinee, L. North Pacific humpback whale songs: A comparison of southeast Alaskan feeding ground songs with Hawaiian wintering ground songs. Mar. Mamm. Sci. 5, 139–148 (1989).Article 

Google Scholar 
16.Kowarski, K., Evers, C., Moors-Murphy, H., Martin, B. & Denes, S. L. Singing through winter nights: Seasonal and diel occurrence of humpback whale (Megaptera novaeangliae) calls in and around the Gully MPA, offshore eastern Canada. Mar. Mamm. Sci. 34, 169–189 (2018).Article 

Google Scholar 
17.Clark, C. W. & Clapham, P. J. Acoustic monitoring on a humpback whale (Megaptera novaeangliae) feeding ground shows continual singing into late spring. Proc. R. Soc. B 271, 1051–1057 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
18.International Whaling Commission. Report on the workshop on the comprehensive assessment of Southern Hemisphere humpback whales. J. Cetac. Res. Manag. 3, 1–50 (2011).
Google Scholar 
19.International Whaling Commission. Annex H: Report of the Sub-Committee on Other Southern Hemisphere Whale Stocks. (2016).20.Garland, E. C. et al. Humpback whale song on the Southern Ocean feeding grounds: Implications for cultural transmission. PLoS ONE 8, e79422 (2013).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
21.Gabriele, C. & Frankel, A. The occurrence and significance of humpback whale songs in Glacier Bay, Southeastern Alaska. Arctic Res. USA 16, 42–47 (2002).
Google Scholar 
22.Payne, R. & Guinee, L. Humpback whales (Megaptera novaeangliae) songs as an indicator of stocks. In Communication and Behavior of Whales (ed. Payne, R.) 333–358 (Westview Press, 1983).
Google Scholar 
23.Payne, K. & Payne, R. Large scale changes over 19 years in songs of humpback whales in Bermuda. Z. Tierpsychol. 68, 89–114 (1985).Article 

Google Scholar 
24.Winn, H. et al. Song of the humpback whale—population comparisons. Behav. Ecol. Sociobiol. 8, 41–46 (1981).Article 

Google Scholar 
25.Winn, H. & Winn, L. The song of the humpback whale Megaptera novaeangliae in the West Indies. Mar. Biol. 47, 97–114 (1978).Article 

Google Scholar 
26.Cholewiak, D. M., Sousa-Lima, R. S. & Cerchio, S. Humpback whale song hierarchical structure: Historical context and discussion of current classification issues. Mar. Mamm. Sci. 29, E312–E332 (2013).Article 

Google Scholar 
27.Kowarski, K., Moors-Murphy, H., Maxner, E. & Cerchio, S. Western North Atlantic humpback whale fall and spring acoustic repertoire: Insight into onset and cessation of singing behavior. J. Acoust. Soc. Am. 145, 2305–2316 (2019).PubMed 
Article 
ADS 

Google Scholar 
28.Magnúsdóttir, E. E. & Lim, R. Subarctic singers: Humpback whale (Megaptera novaeangliae) song structure and progression from an Icelandic feeding ground during winter. PLoS ONE 14, e0210057 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Magnúsdóttir, E. E. et al. Humpback whale (Megaptera novaeangliae) song unit and phrase repertoire progression on a subarctic feeding ground. J. Acoust. Soc. Am. 138, 3362–3374 (2015).PubMed 
Article 
ADS 

Google Scholar 
30.Mattila, D. K., Guinee, L. N. & Mayo, C. A. Humpback whale songs on a North Atlantic feeding ground. J. Mammal. 68, 880–883 (1987).Article 

Google Scholar 
31.Teschke, K., Pehlke, H., Deininger, M., Jerosch, K. & Brey, T. Scientific Background Document in Support of the Development of a CCAMLR MPA in the Weddell Sea (Antarctica)–Version 2016. (2016).32.Gridley, T., Silva, M., Wilkinson, C., Seakamela, S. & Elwen, S. H. Song recorded near a super-group of humpback whales on a mid-latitude feeding ground off South Africa. J. Acoust. Soc. Am. 143, 298–304 (2018).Article 
ADS 

Google Scholar 
33.Spreen, G., Kaleschke, L. & Heygster, G. Sea ice remote sensing using AMSR-E 89-GHz channels. J. Geophys. Res.-Oceans 113, C02S03 (2008).Article 
ADS 

Google Scholar 
34.Tynan, C. T. & Thiele, D. Report on Antarctic ice edge definition by the ad hoc working group on ice data collection in the Antarctic. Paper: SC/55/19, submitted to the Scientific Committee of the International Whaling Commission (2003).35.Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945).Article 

Google Scholar 
36.Kohonen, T. Median strings. Pattern Recogn. Lett. 3, 309–313 (1985).Article 
ADS 

Google Scholar 
37.Schall, E. et al. Multi-year presence of humpback whales in the Atlantic sector of the Southern Ocean but not during El Niño. Commun. Biol. 4, 1–7 (2021).Article 

Google Scholar 
38.Ritschard, M. & Brumm, H. Zebra finch song reflects current food availability. Evol. Ecol. 26, 801–812 (2012).Article 

Google Scholar 
39.Darling, J. D., Acebes, J. M. V., Frey, O., Urbán, R. J. & Yamaguchi, M. Convergence and divergence of songs suggests ongoing, but annually variable, mixing of humpback whale populations throughout the North Pacific. Sci. Rep. 9, 1–14 (2019).ADS 

Google Scholar 
40.Schall, E. et al. Large-scale spatial variabilities in the humpback whale acoustic presence in the Atlantic sector of the Southern Ocean. R. Soc. Open Sci. 7, 201347 (2020).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
41.Van Opzeeland, I., Van Parijs, S., Kindermann, L., Burkhardt, E. & Boebel, O. Calling in the cold: Pervasive acoustic presence of humpback whales (Megaptera novaeangliae) in Antarctic coastal waters. PLoS ONE 8, 1–7 (2013).
Google Scholar 
42.Craig, A. S., Herman, L. M., Gabriele, C. M. & Pack, A. A. Migratory timing of humpback whales (Megaptera novaeangliae) in the central north Pacific varies with age, sex and reproductive status. Behaviour 140, 981–1001 (2003).Article 

Google Scholar 
43.Dawbin, W. Temporal segregation of humpback whales during migration in southern hemisphere waters. Mem. Qld. Mus. 42, 105–138 (1997).
Google Scholar 
44.Magnúsdóttir, E., Rasmussen, M., Lammers, M. & Svavarsson, J. Humpback whale songs during winter in subarctic waters. Polar Biol. 37, 427–433 (2014).Article 

Google Scholar 
45.Bombosch, A. et al. Predictive habitat modelling of humpback (Megaptera novaeangliae) and Antarctic minke (Balaenoptera bonaerensis) whales in the Southern Ocean as a planning tool for seismic surveys. Deep Sea Res. Part 1 91, 101–114 (2014).Article 

Google Scholar 
46.Thiele, D. et al. Seasonal variability in whale encounters in the Western Antarctic Peninsula. Deep Sea Research (Part II, Topical Studies in Oceanography) 51, 2311–2325 (2004).Article 
ADS 

Google Scholar 
47.Brown, M. R., Corkeron, P. J., Hale, P. T., Schultz, K. W. & Bryden, M. M. Evidence for a sex-segregated migration in the humpback whale (Megaptera novaeangliae). Proc. R. Soc. Lond. B 259, 229–234 (1995).CAS 
Article 
ADS 

Google Scholar 
48.McDonald, M. A., Mesnick, S. L. & Hildebrand, J. A. Biogeographic characterisation of blue whale song worldwide: Using song to identify populations. J. Cetac. Res. Manage. 8, 55–65 (2006).
Google Scholar 
49.Thomisch, K. et al. Spatio-temporal patterns in acoustic presence and distribution of Antarctic blue whales Balaenoptera musculus intermedia in the Weddell Sea. Endanger. Species Res. 30, 239–253 (2016).Article 

Google Scholar 
50.Oleson, E. M., Širović, A., Bayless, A. R. & Hildebr, J. A. Synchronous seasonal change in fin whale song in the North Pacific. PLoS ONE 9, e115678 (2014).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
51.Simon, M., Stafford, K. M., Beedholm, K., Lee, C. M. & Madsen, P. T. Singing behavior of fin whales in the Davis Strait with implications for mating, migration and foraging. J. Acoust. Soc. Am. 128, 3200–3210 (2010).PubMed 
Article 
ADS 

Google Scholar 
52.Stafford, K. M. et al. Spitsbergen’s endangered bowhead whales sing through the polar night. Endanger. Species Res. 18, 95–103 (2012).Article 

Google Scholar 
53.Risch, D. et al. Minke whale acoustic behavior and multi-year seasonal and diel vocalization patterns in Massachusetts Bay, USA. Mar. Ecol. Prog. Ser. 489, 279–295 (2013).Article 
ADS 

Google Scholar 
54.Brenowitz, E. A., Margoliash, D. & Nordeen, K. W. An introduction to birdsong and the avian song system. J. Neurobiol. 33, 495–500 (1997).CAS 
PubMed 
Article 

Google Scholar 
55.Tobias, J., Gamarra-Toledo, V., García-Olaechea, D., Pulgarin, P. & Seddon, N. Year-round resource defence and the evolution of male and female song in suboscine birds: Social armaments are mutual ornaments. J. Evol. Biol. 24, 2118–2138 (2011).CAS 
PubMed 
Article 

Google Scholar 
56.Vu, E. T., Clark, C., Catelani, K., Kellar, N. M. & Calambokidis, J. Seasonal blubber testosterone concentrations of male humpback whales (Megaptera novaeangliae). Mar. Mam. Sci. 31, 1258–1264 (2015).Article 

Google Scholar 
57.Yamada, K. & Soma, M. Diet and birdsong: Short-term nutritional enrichment improves songs of adult Bengalese finch males. J. Avian Biol. 47, 865–870 (2016).Article 

Google Scholar 
58.Casagrande, S., Pinxten, R., Zaid, E. & Eens, M. Positive effect of dietary lutein and cholesterol on the undirected song activity of an opportunistic breeder. PeerJ 4, e2512 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Weinrich, M. Humpback whale competitive groups observed on a high-latitude feeding ground. Mar. Mamm. Sci. 11, 251–254 (1995).Article 

Google Scholar 
60.Chittleborough, R. The breeding cycle of the female humpback whale, Megaptera nodosa (Bonnaterre). Mar. Freshw. Res. 9, 1–18 (1958).Article 

Google Scholar 
61.Chittleborough, R. Studies on the ovaries of the humback whale, Megaptera nodosa (bonnaterre), on the western Australian coast. Mar. Freshw. Res. 5, 35–63 (1954).Article 

Google Scholar 
62.Cerchio, S., Jacobsen, J. K. & Norris, T. F. Temporal and geographical variation in songs of humpback whales, Megaptera novaeangliae: Synchronous change in Hawaiian and Mexican breeding assemblages. Anim. Behav. 62, 313–329 (2001).Article 

Google Scholar 
63.Garland, E. C. et al. Quantifying humpback whale song sequences to understand the dynamics of song exchange at the ocean basin scale. J. Acoust. Soc. Am. 133, 560–569 (2013).PubMed 
Article 
ADS 

Google Scholar 
64.Allen, J. A., Garland, E. C., Dunlop, R. A. & Noad, M. J. Cultural revolutions reduce complexity in the songs of humpback whales. Proc. R. Soc. B 285, 20182088 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Stevick, P. T. et al. Migrations of individually identified humpback whales between the Antarctic Peninsula and South America. J. Cetac. Res. Manag. 6, 109–113 (2004).
Google Scholar 
66.Engel, M. et al. Mitochondrial DNA diversity of the Southwestern Atlantic humpback whale (Megaptera novaeangliae) breeding area off Brazil, and the potential connections to Antarctic feeding areas. Conserv. Genet. 9, 1253–1262 (2008).CAS 
Article 

Google Scholar 
67.Amaral, A. R. et al. Population genetic structure among feeding aggregations of humpback whales in the Southern Ocean. Mar. Biol. 163, 1–13 (2016).Article 

Google Scholar 
68.Rekdahl, M. L. et al. Culturally transmitted song exchange between humpback whales (Megaptera novaeangliae) in the southeast Atlantic and southwest Indian Ocean basins. R. Soc. Open Sci. 5, 172305 (2018).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
69.Darling, J. D. & Sousa-Lima, R. S. Songs indicate interaction between humpback whale (Megaptera novaeangliae) populations in the western and eastern South Atlantic Ocean. Mar. Mamm. Sci. 21, 557–566 (2005).Article 

Google Scholar 
70.Razafindrakoto, Y., Cerchio, S., Collins, T., Rosenbaum, H. & Ngouessono, S. Similarity of humpback whale song from Madagascar and Gabon indicates significant contact between South Atlantic and southwest Indian Ocean populations. PLoS ONE 8, e79422 (2009).
Google Scholar 
71.Zerbini, A. et al. Migration and summer destinations of humpback whales (Megaptera novaeangliae) in the western South Atlantic Ocean. J. Cetac. Res. Manag. 3, 113–118 (2011).
Google Scholar 
72.Rosenbaum, H. C., Maxwell, S. M., Kershaw, F. & Mate, B. Long-range movement of humpback whales and their overlap with anthropogenic activity in the South Atlantic Ocean. Conserv. Biol. 28, 604–615 (2014).PubMed 
Article 

Google Scholar 
73.Filun, D. et al. Frozen verses: Antarctic minke whales (Balaenoptera bonaerensis) call predominantly during austral winter. R. Soc. Open Sci. 7, 192112 (2020).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
74.Rettig, S. et al. In International Confence and Exhibition on Underwater Acoustics. (eds Papadakis, J. & Bjorno, L.) 1669–1674.75.Baumgartner, M. F. & Mussoline, S. E. A generalized baleen whale call detection and classification system. J. Acoust. Soc. Am. 129, 2889–2902 (2011).PubMed 
Article 
ADS 

Google Scholar 
76.Klinck, H. et al. Long-range underwater vocalizations of the crabeater seal (Lobodon carcinophaga). J. Acoust. Soc. Am. 128, 474–479 (2010).PubMed 
Article 
ADS 

Google Scholar 
77.Risch, D. et al. Mysterious bio-duck sound attributed to the Antarctic minke whale (Balaenoptera bonaerensis). Biol. Lett. 10, 20140175 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
78.Schall, E. & Van Opzeeland, I. Calls produced by Ecotype C killer whales (Orcinus orca) off the Eckstrom iceshelf, Antarctica. Aquat. Mamm. 43, 117–126 (2017).Article 

Google Scholar 
79.Van Opzeeland, I. et al. Acoustic ecology of Antarctic pinnipeds. Mar. Ecol. Prog. Ser. 414, 267–291 (2010).Article 
ADS 

Google Scholar 
80.Dunlop, R. A., Cato, D. H. & Noad, M. J. Non-song acoustic communication in migrating humpback whales (Megaptera novaeangliae). Mar. Mamm. Sci. 24, 613–629 (2008).Article 

Google Scholar 
81.Stimpert, A. K., Au, W. W., Parks, S. E., Hurst, T. & Wiley, D. N. Common humpback whale (Megaptera novaeangliae) sound types for passive acoustic monitoring. J. Acoust. Soc. Am. 129, 476–482 (2011).PubMed 
Article 
ADS 

Google Scholar 
82.Cavalieri, D., Parkinson, C., Gloersen, P. & Zwally, H. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1 (NASA National Snow and Ice Data Center Distributed Active Archive Center, 1996).
Google Scholar 
83.Greene, C. A., Gwyther, D. E. & Blankenship, D. D. Antarctic mapping tools for MATLAB. Comput. Geosci. 104, 151–157 (2017).Article 
ADS 

Google Scholar 
84.Greene, C. A. Daily Antarctic Sea Ice Concentration (2020).85.Schall, E., Roca, I. & Van Opzeeland, I. Acoustic metrics to assess humpback whale song unit structure from the Atlantic sector of the Southern ocean. J. Acoust. Soc. Am. 149, 4649–4658 (2021).PubMed 
Article 
ADS 

Google Scholar 
86.Dalla Rosa, L., Secchi, E., Maia, Y. G., Zerbini, A. & Heide-Jørgensen, M. Movements of satellite-monitored humpback whales on their feeding ground along the Antarctic Peninsula. Polar Biol. 31, 771–781 (2008).Article 

Google Scholar 
87.Zann, R. & Cash, E. Developmental stress impairs song complexity but not learning accuracy in non-domesticated zebra finches (Taeniopygia guttata). Behav. Ecol. Sociobiol. 62, 391–400 (2008).Article 

Google Scholar 
88.Woodgate, J. L., Mariette, M. M., Bennett, A. T., Griffith, S. C. & Buchanan, K. L. Male song structure predicts reproductive success in a wild zebra finch population. Anim. Behav. 83, 773–781 (2012).Article 

Google Scholar 
89.Boogert, N. J., Giraldeau, L.-A. & Lefebvre, L. Song complexity correlates with learning ability in zebra finch males. Anim. Behav. 76, 1735–1741 (2008).Article 

Google Scholar 
90.Templeton, C. N., Laland, K. N. & Boogert, N. J. Does song complexity correlate with problem-solving performance in flocks of zebra finches?. Anim. Behav. 92, 63–71 (2014).Article 

Google Scholar 
91.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018). https://www.R-project.org/.92.Suzuki, R., Terada, Y. & Shimodaira, H. pvclust: Hierarchical Clustering with P-values via Multiscale Bootstrap Resampling. R Package Version 2.2–0 (2019).93.Garland, E. C. et al. Improved versions of the Levenshtein distance method for comparing sequence information in animals’ vocalisations: Tests using humpback whale song. Behaviour 149, 1413–1441 (2012).Article 

Google Scholar 
94.Van der Loo, M. P. The stringdist package for approximate string matching. R J. 6, 111–122 (2014).Article 

Google Scholar 
95.Pawlowicz, R. M_Map: A Mapping Package for MATLAB v. Version 1.4m. www.eoas.ubc.ca/~rich/map.html (2020). More