Ecology

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

Initial clinical courseAmong the 75 cesarean-delivered preterm piglets, nine were excluded before randomization (e.g. failed resuscitation, stillbirth), whereas the remaining 66 animals were group allocated. An additional seven animals were euthanized preschedule for reasons not related to the interventions (respiratory failure, iatrogenic complications). Two animals were euthanized preschedule with clinical NEC signs (1 CON, 1 FFTr), whereas the remaining 57 animals survived until day 5. During the course of the experiment, we observed rectal bleeding in 31% (5/16) of CON and 19% (3/16) of FMT animals relative to 0% (0/13) in both FFT groups (p  More

1.Bäckhed, F. et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl. Acad. Sci. U.S.A. 101, 15718–15723 (2004).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.LeBlanc, J. G. et al. Bacteria as vitamin suppliers to their host: A gut microbiota perspective. Curr. Opin. Biotechnol. 24, 160–168 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Huang, J. & Douglas, A. E. Consumption of dietary sugar by gut bacteria determines Drosophila lipid content. Biol. Lett. 11, 12–15 (2015).Article 
CAS 

Google Scholar 
4.Storelli, G. et al. Lactobacillus plantarum promotes Drosophila systemic growth by modulating hormonal signals through TOR-dependent nutrient sensing. Cell Metab. 14, 403–414 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Chandler, J. A., Lang, J., Bhatnagar, S., Eisen, J. A. & Kopp, A. Bacterial communities of diverse Drosophila species: Ecological context of a host-microbe model system. PLoS Genet. 7, e1002272 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Bing, X., Gerlach, J., Loeb, G. & Buchon, N. Nutrient-dependent impact of microbes on Drosophila suzukii development. MBio 9, e02199 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Wong, A. C. N., Chaston, J. M. & Douglas, A. E. The inconstant gut microbiota of Drosophila species revealed by 16S rRNA gene analysis. ISME J. 7, 1922–1932 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Chandler, J. A., James, P. M., Jospin, G. & Lang, J. M. The bacterial communities of Drosophila suzukii collected from undamaged cherries. PeerJ 2, e474 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Kapun, M. et al. Genomic analysis of European Drosophila malanogaster populations revels longitudinal structure, continent-wide selection, and previously unknown DNA viruses. Mol. Biol. Evol. 37, 2661 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Morais, P. B., Martins, M. B., Klaczko, L. B., Mendonca-Hagler, L. C. & Hagler, A. N. Yeast succession in the Amazon fruit Parahancornia amapa as resource partitioning among Drosophila spp. Appl. Environ. Microbiol. 61, 4251–4257 (1995).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Wolda, H. Season fluctuations in rainfall, food and abundance of tropical insects. J. Anim. Ecol. 47, 369–381 (1978).Article 

Google Scholar 
12.Simpson, S. J., Sibly, R. M., Lee, K. P., Behmer, S. T. & Raubenheimer, D. Optimal foraging when regulating intake of multiple nutrients. Anim. Behav. 68, 1299–1311 (2004).Article 

Google Scholar 
13.Lee, K. P. et al. Lifespan and reproduction in Drosophila: New insights from nutritional geometry. Proc. Natl. Acad. Sci. U.S.A. 105, 2498–2503 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Lee, K. P., Kim, J. S. & Min, K. J. Sexual dimorphism in nutrient intake and life span is mediated by mating in Drosophila melanogaster. Anim. Behav. 86, 987–992 (2013).Article 

Google Scholar 
15.Wong, A. C. N., Dobson, A. J. & Douglas, A. E. Gut microbiota dictates the metabolic response of Drosophila to diet. J. Exp. Biol. 217, 1894–1901 (2014).PubMed 
PubMed Central 

Google Scholar 
16.Rodrigues, M. A. et al. Drosophila melanogaster larvae make nutritional choices that minimize developmental time. J. Insect Physiol. 81, 69–80 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Davies, L. R., Schou, M. F., Kristensen, T. N. & Loeschcke, V. Linking developmental diet to adult foraging choice in Drosophila melanogaster. J. Exp. Biol. 221, 175554 (2018).Article 

Google Scholar 
18Keebaugh, E. S., Yamada, R., Obadia, B., Ludington, W. B. & Ja, W. W. Microbial quantity impacts Drosophila nutrition, development, and lifespan. iScience 4, 247–259 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19Morimoto, J., Simpson, S. J. & Ponton, F. Direct and transgenerational effects of male and female gut microbiota in Drosophila melanogaster. Biol. Lett. 13, 20160966 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Simpson, S. J. & Raubenheimer, D. The Nature of Nutrition: A Unifying Framework from Animal Adaptation to Human Obesity (Princeton University Press, 2012).Book 

Google Scholar 
21.Wong, A. C. N. et al. Gut microbiota modifies olfactory-guided microbial preferences and foraging decisions in Drosophila. Curr. Biol. 27, 2397–2404 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Andersen, L. H., Kristensen, T. N., Loeschcke, V., Toft, S. & Mayntz, D. Protein and carbohydrate composition of larval food affects tolerance to thermal stress and desiccation in adult Drosophila melanogaster. J. Insect Physiol. 56, 336–340 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Kutz, T. C., Sgrò, C. M. & Mirth, C. K. Interacting with change: Diet mediates how larvae respond to their thermal environment. Funct. Ecol. 33, 1940–1951 (2019).Article 

Google Scholar 
24.Sørensen, T. A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biol. Writing 5, 1–34 (1948).
Google Scholar 
25.Broderick, N. & Lemaitre, B. Gut-associated microbes of Drosophila melanogaster. Gut Microbes 3, 307–321 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
26.De Ley, J. Comparative carbohydrate metabolism and a proposal for a phylogenetic relationship of the acetic acid bacteria. J. Gen. Microbiol. 24, 31–50 (1961).Article 

Google Scholar 
27Ameyama, M. Gluconobacter oxydans subsp. sphaericus, new subspecies isolated from grapes. Int. J. Syst. Bacteriol. 25, 365–370 (1948).Article 

Google Scholar 
28.Deppenmeier, U., Hoffmeister, M. & Prust, C. Biochemistry and biotechnological applications of Gluconobacter strains. Appl. Microbiol. Biotechnol. 60, 233–242 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Ryngajłło, M., Kubiak, K., Jędrzejczak-Krzepkowska, M., Jacek, P. & Bielecki, S. Comparative genomics of the Komagataeibacter strains—Efficient bionanocellulose producers. Microbiologyopen 8, 1–25 (2019).Article 
CAS 

Google Scholar 
30.Gilbert, D. G. Dispersal of yeasts and bacteria by Drosophila in a temperate forest. Oecologia 46, 135–137 (1980).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Blum, J. E., Fischer, C. N., Miles, J. & Handelsman, J. Frequent replenishment sustains the beneficial microbiome of Drosophila melanogaster. MBio 4, 1–8 (2013).CAS 
Article 

Google Scholar 
32.Staubach, F., Baines, J. F., Künzel, S., Bik, E. M. & Petrov, D. A. Host species and environmental effects on bacterial communities associated with Drosophila in the laboratory and in the natural environment. PLoS ONE 8, e70749 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Wong, A. C. N. et al. The host as the driver of the microbiota in the gut and external environment of Drosophila melanogaster. Appl. Environ. Microbiol. 81, 6232–6240 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Pais, I. S., Valente, R. S., Sporniak, M. & Teixeira, L. Drosophila melanogaster establishes a species-specific mutualistic interaction with stable gut-colonizing bacteria. PLoS Biol. 16(7), e2005710 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Buchon, N., Broderick, N. A. & Lemaitre, B. Gut homeostasis in a microbial world: Insights from Drosophila melanogaster. Nat. Rev. Microbiol. 11, 615–626 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Wong, A. C. N., Ng, P. & Douglas, A. E. Low-diversity bacterial community in the gut of the fruitfly Drosophila melanogaster. Environ. Microbiol. 13, 1889–1900 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Manteca, A. & Sanchez, J. Streptomyces development in colonies and soils. Appl. Environ. Microbiol. 75, 2920–2924 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Lee, K. P., Raubenheimer, D., Behmer, S. T. & Simpson, S. J. A correlation between macronutrient balancing and insect host-plant range: Evidence from the specialist caterpillar Spodoptera exempta (Walker). J. Insect Physiol. 49, 1161–1171 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Mevi-Schütz, J. & Erhardt, A. Larval nutrition affects female nectar amino acid preference in the map butterfly (Araschnia levana). Ecology 18, 2788–2794 (2003).Article 

Google Scholar 
40.Lee, K. P. The interactive effects of protein quality and macronutrient imbalance on nutrient balancing in an insect herbivore. J. Exp. Biol. 210, 3236–3244 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Fanson, B. G., Weldon, C. W., Pérez-Staples, D., Simpson, S. J. & Taylor, P. W. Nutrients, not caloric restriction, extend lifespan in Queensland fruit flies (Bactrocera tryoni). Aging Cell 8, 514–523 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Spor, A., Koren, O. & Ley, R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. 9, 279–290 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Hooper, L. V., Littman, D. R. & Macpherson, A. J. Interactions between the microbiota and the immune system. Science 336, 1268–1273 (2007).ADS 
Article 
CAS 

Google Scholar 
44.Faith, J. J., McNulty, N. P., Rey, F. E. & Gordon, J. I. Predicting a human gut microbiota’s response to diet in gnotobiotic mice. Science 333, 101–104 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Ridley, E. V., Wong, A. C. N., Westmiller, S. & Douglas, A. E. Impact of the resident microbiota on the nutritional phenotype of Drosophila melanogaster. PLoS ONE 7, e36765 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Nguyen, B. et al. Interactions between ecological factors in the developmental environment modulate pupal and adult traits in a polyphagous fly. Ecol. Evol. 9, 6342–6352 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Drew, R. A. I., Courtice, A. C. & Teakle, D. S. Bacteria as a natural source of food for adult fruit flies (Diptera, Tephritidae). Oecologia 60, 279–284 (1983).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48Lesperance, D. N. A. & Broderick, N. Gut bacteria mediate nutrient availability in Drosophila diets. Appl. Environ. Microbiol. 59, 211 (2020).
Google Scholar 
49.Kristensen, T. N. et al. Fitness components of Drosophila melanogaster developed on a standard laboratory diet or a typical natural food source. Insect Sci. 23, 771–779 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Harrison, A. P. & Pelczar, M. J. Damage and survival of bacteria during freeze-drying and during storage over a ten-year period. J. Gen. Microbiol. 30, 395–400 (1963).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Rubin, B. E. R. et al. Investigating the impact of storage conditions on microbial community composition in soil samples. PLoS ONE 8, 1–9 (2013).
Google Scholar 
52.Sharon, G. et al. Commensal bacteria play a role in mating preference of Drosophila melanogaster. Proc. Natl. Acad. Sci. U.S.A. 107, 20051–20056 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Xu, X., Feng, G., Liu, H. & Li, X. Control of spoilage microorganisms in Soybean milk by nipagin complex esters, nisin, sodium dehydroaceate and heat treatment. IPCBEE 67, 35 (2014).ADS 
CAS 

Google Scholar 
54.Leftwich, P. T., Clarke, N. V. E., Hutchings, M. I. & Chapman, T. Gut microbiomes and reproductive isolation in Drosophila. Proc. Natl. Acad. Sci. U.S.A. 114, 12767–12772 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Leftwich, P. T., Clarke, N. V. E., Hutchings, M. I. & Chapman, T. Reply to Obadia et al.: Effect of methyl paraben on host–microbiota interactions in Drosophila melanogaster. Proc. Natl. Acad. Sci. U.S.A. 20, E4549–E4550 (2018).Article 

Google Scholar 
56.Ward, D. V. et al. Evaluation of 16s rDNA-based community profiling for human microbiome research. PLoS ONE 7, e39315 (2012).ADS 
CAS 
Article 

Google Scholar 
57.Caporaso, J. et al. Ultra-high-throughput microbial community analysis on Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 
CAS 

Google Scholar 
60.Overgaard, J., Kristensen, T. N. & Sørensen, J. G. Validity of thermal ramping assays used to assess thermal tolerance in arthropods. PLoS ONE 7, 1–7 (2012).Article 
CAS 

Google Scholar 
61.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021). Accessed February 2021. https://www.R-project.org/.62RStudio Team. RStudio: Integrated Development for R (RStudio, PBC, 2020).
Google Scholar 
63McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Oksanen, J. et al. vegan: Community Ecology Package. R package 2.5-7 (2019). Accessed October 2019. https://CRAN.R-project.org/package=vegan.65.Wickham, H. Ggplot2: Elegant Graphics for Data Analysis 2nd edn. (Springer, 2016).MATH 
Book 

Google Scholar  More

1.Bonan, G. B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).CAS 
PubMed 
Article 
ADS 

Google Scholar 
2.Pugh, T. A. M. et al. Role of forest regrowth in global carbon sink dynamics. Proc. Natl. Acad. Sci. U. S. A. 116, 4382–4387 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
3.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010).Article 

Google Scholar 
4.Williams, C. A., Gu, H., MacLean, R., Masek, J. G. & Collatz, G. J. Disturbance and the carbon balance of US forests: A quantitative review of impacts from harvests, fires, insects, and droughts. Glob. Planet. Change 143, 66–80 (2016).Article 
ADS 

Google Scholar 
5.Kurz, W. A. et al. Mountain pine beetle and forest carbon feedback to climate change. Nature 452, 987–990 (2008).CAS 
PubMed 
Article 
ADS 

Google Scholar 
6.Lovett, G. M. et al. Nonnative forest insects and pathogens in the United States: Impacts and policy options. Ecol. Appl. 26, 1437–1455 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Xu, L. et al. Changes in global terrestrial live biomass over the 21st century. Sci Adv 7, eabe9829 (2021).PubMed 
Article 
ADS 

Google Scholar 
8.Nave, L. E. et al. Reforestation can sequester two petagrams of carbon in US topsoils in a century. Proc. Natl. Acad. Sci. U. S. A. 115, 2776–2781 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Millar, C. I., Stephenson, N. L. & Stephens, S. L. Climate change and forests of the future: Managing in the face of uncertainty. Ecol. Appl. 17, 2145–2151 (2007).PubMed 
Article 

Google Scholar 
10.McCarthy, J. K., Dwyer, J. M. & Mokany, K. A regional-scale assessment of using metabolic scaling theory to predict ecosystem properties. Proc. Biol. Sci. 286, 20192221 (2019).PubMed 
PubMed Central 

Google Scholar 
11.Woodall, C. W., Miles, P. D. & Vissage, J. S. Determining maximum stand density index in mixed species stands for strategic-scale stocking assessments. For. Ecol. Manag. 216, 367–377 (2005).Article 

Google Scholar 
12.Reineke, L. H. Perfecting a stand-density index for even-aged forests. J. Agric. Res. 46, 627–638 (1933).
Google Scholar 
13.Long, J. N. A practical approach to density management. For. Chron. 61, 23–27 (1985).Article 

Google Scholar 
14.Domke, G. et al. Forests. In Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report (eds Cavallaro, N., Shrestha, G., Birdsey, R., Mayes, M. A., Najjar, R. G., Reed, S. C., Romero-Lankao, P. & Zhu, Z.) 365–398 (US Global Change Research Program, 2018).15.Yoda, K., Kira, T., Ogawa, H. & Hozumi, K. Self-thinning in overcrowded pure stands under cultivated and natural conditions. J. Biol. Osaka City Univ. 14, 106–129 (1963).
Google Scholar 
16.Drew, T. J. & Flewelling, J. W. Stand density management: An alternative approach and its application to Douglas-fir plantations. For. Sci. 25, 518–532 (1979).
Google Scholar 
17.Bechtold, W. A. & Patterson, P. L. The Enhanced Forest Inventory and Analysis Program: National Sampling Design and Estimation Procedures. SRS GTR-80. USDA Forest Service, Southern Research Station, Asheville, North Carolina, USA. (2005). https://doi.org/10.2737/SRS-GTR-80.18.McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).PubMed 
Article 

Google Scholar 
19.Andrews, C., Weiskittel, A., D’Amato, A. W. & Simons-Legaard, E. Variation in the maximum stand density index and its linkage to climate in mixed species forests of the North American Acadian Region. For. Ecol. Manag. 417, 90–102 (2018).Article 

Google Scholar 
20.Nagel, L. M. et al. Adaptive silviculture for climate change: A national experiment in manager–scientist partnerships to apply an adaptation framework. J. For. 115, 167–178 (2017).
Google Scholar 
21.Pretzsch, H. & Biber, P. A re-evaluation of the Reineke’s rule and stand density index. For. Sci. 51, 304–320 (2005).
Google Scholar 
22.Condés, S. et al. Climate influences on the maximum size-density relationship in Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) stands. For. Ecol. Manag. 385, 295–307 (2017).Article 

Google Scholar 
23.Ducey, M. J., Woodall, C. W. & Bravo-Oviedo, A. Climate and species functional traits influence maximum live tree stocking in the Lake States, USA. For. Ecol. Manag. 386, 51–61 (2017).Article 

Google Scholar 
24.Zhao, D., Bullock, B. P., Montes, C. R. & Wang, M. Rethinking maximum stand basal area and maximum SDI from the aspect of stand dynamics. For. Ecol. Manag. 475, 118462 (2020).Article 

Google Scholar 
25.Weiskittel, A. R. & Kuehne, C. Evaluating and modeling variation in site-level maximum carrying capacity of mixed-species forest stands in the Acadian Region of northeastern North America. For. Chron. 95, 171–182 (2019).Article 

Google Scholar 
26.Pretzsch, H. & del Río, M. Density regulation of mixed and mono-specific forest stands as a continuum: A new concept based on species-specific coefficients for density equivalence and density modification. For. Int. J. For. Res. 93, 1–15 (2020).
Google Scholar 
27.Senf, C., Buras, A., Zang, C. S., Rammig, A. & Seidl, R. Excess forest mortality is consistently linked to drought across Europe. Nat. Commun. 11, 6200 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
28.Woodall, C. W., Perry, C. H. & Miles, P. D. The relative density of forests in the United States. For. Ecol. Manag. 226, 368–372 (2006).Article 

Google Scholar 
29.Venturas, M. D., Todd, H. N., Trugman, A. T. & Anderegg, W. R. L. Understanding and predicting forest mortality in the western United States using long-term forest inventory data and modeled hydraulic damage. New Phytol. 230, 1896–1910 (2020).PubMed 
Article 

Google Scholar 
30.Higuera, P. E. & Abatzoglou, J. T. Record-setting climate enabled the extraordinary 2020 fire season in the western United States. Glob. Change Biol. 27, 1–2 (2021).Article 
ADS 

Google Scholar 
31.Peters, M. P. & Iverson, L. R. Projected drought for the conterminous United States in the 21st century. In Effects of Drought on Forests and Rangelands in the United States (eds Vose, J. M., Peterson, D. L., Luce, C. H. & Patel-Weynand, T.) vol. Gen. Tech. Rep. WO-98 19–39 (USDA Forest Service, 2019).32.Coulston, J. W., Woodall, C. W., Domke, G. M. & Walters, B. F. Refined forest land use classification with implications for United States national carbon accounting. Land Use Policy 59, 536–542 (2016).Article 

Google Scholar 
33.Wear, D. N. & Coulston, J. W. From sink to source: Regional variation in U.S. forest carbon futures. Sci. Rep. 5, 16518 (2015).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
34.Senf, C., Sebald, J. & Seidl, R. Increasing canopy mortality affects the future demographic structure of Europe’s forests. One Earth 4, 749–755 (2021).Article 

Google Scholar 
35.Morin, X., Fahse, L., Scherer-Lorenzen, M. & Bugmann, H. Tree species richness promotes productivity in temperate forests through strong complementarity between species. Ecol. Lett. 14, 1211–1219 (2011).PubMed 
Article 

Google Scholar 
36.Griscom, B. W. et al. Natural climate solutions. Proc. Natl. Acad. Sci. U. S. A. 114, 11645–11650 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
37.Gunn, J. S., Ducey, M. J. & Belair, E. Evaluating degradation in a North American temperate forest. For. Ecol. Manag. 432, 415–426 (2019).Article 

Google Scholar 
38.Domke, G. M., Oswalt, S. N., Walters, B. F. & Morin, R. S. Tree planting has the potential to increase carbon sequestration capacity of forests in the United States. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.2010840117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
39.King, D. I. & Schlossberg, S. Synthesis of the conservation value of the early-successional stage in forests of eastern North America. For. Ecol. Manag. 324, 186–195 (2014).Article 

Google Scholar 
40.Stephens, S. L. et al. Forest restoration and fuels reduction: Convergent or divergent?. Bioscience 71, 85–101 (2020).
Google Scholar 
41.Berner, L. T., Law, B. E., Meddens, A. J. H. & Hicke, J. A. Tree mortality from fires, bark beetles, and timber harvest during a hot and dry decade in the western United States (2003–2012). Environ. Res. Lett. 12, 065005 (2017).Article 
ADS 

Google Scholar 
42.Stanke, H., Finley, A. O., Domke, G. M., Weed, A. S. & MacFarlane, D. W. Over half of western United States’ most abundant tree species in decline. Nat. Commun. 12, 451 (2021).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
43.Weiskittel, A. R., Gould, P. J. & Temesgen, H. Sources of variation in the self-thinning boundary line for three species with varying levels of shade tolerance. For. Sci. 55, 84–93 (2009).
Google Scholar 
44.Ducey, M. J. & Knapp, R. A. A stand density index for complex mixed species forests in the northeastern United States. For. Ecol. Manag. 260, 1613–1622 (2010).Article 

Google Scholar 
45.Kurz, W. A., Stinson, G., Rampley, G. J., Dymond, C. C. & Neilson, E. T. Risk of natural disturbances makes future contribution of Canada’s forests to the global carbon cycle highly uncertain. Proc. Natl. Acad. Sci. U. S. A. 105, 1551–1555 (2008).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
46.Seidl, R., Schelhaas, M.-J. & Lexer, M. J. Unraveling the drivers of intensifying forest disturbance regimes in Europe. Glob. Change Biol. 17, 2842–2852 (2011).Article 
ADS 

Google Scholar 
47.Nelson, M. D. et al. Defining the United States land base: A technical document supporting the USDA Forest Service 2020 RPA assessment. In Gen. Tech. Rep. NRS-191, Vol. 191, 1–70 (2020).48.Patterson, P. L. & Reams, G. A. Combining panels for forest inventory and analysis estimation. Gen. Tech. Rep. SRS-80. Asheville, NC: US Department of Agriculture, Forest Service, 79–84 (2005).49.Bailey, R. G. Delineation of ecosystem regions. Environ. Manag. 7, 365–373 (1983).Article 
ADS 

Google Scholar 
50.Salas-Eljatib, C. & Weiskittel, A. R. Evaluation of modeling strategies for assessing self-thinning behavior and carrying capacity. Ecol. Evol. 8, 10768–10779 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Geraci, M. Linear quantile mixed models: The lqmm package for Laplace quantile regression. J. Stat. Softw. 57(13), 1–29. http://www.jstatsoft.org/v57/i13/ (2013).
Google Scholar 
52.R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
53.Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, e0156720 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
54.Omernik, J. M. & Griffith, G. E. Ecoregions of the conterminous United States: Evolution of a hierarchical spatial framework. Environ. Manag. 54, 1249–1266 (2014).Article 
ADS 

Google Scholar 
55.De’ath, G. Boosted trees for ecological modeling and prediction. Ecology 88, 243–251 (2007).PubMed 
Article 

Google Scholar 
56.Long, J. N. & Daniel, T. W. Assessment of growing stock in uneven-age stands. West. J. Appl. For. 11, 59–61 (1990).Article 

Google Scholar 
57.Yang, L. et al. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS J. Photogramm. Remote Sens. 146, 108–123 (2018).Article 
ADS 

Google Scholar  More

1.Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Duarte, C. M. et al. Rebuilding marine life. Nature 580, 39–51 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Darling, E. S. et al. Relationships between structural complexity, coral traits, and reef fish assemblages. Coral Reefs 36, 561–575 (2017).ADS 
Article 

Google Scholar 
4.McWilliam, M., Chase, T. J. & Hoogenboom, M. O. Neighbor diversity regulates the productivity of coral assemblages. Curr. Biol. 28, 3634–3639 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Cornwall, C. E. et al. Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proc. Natl. Acad. Sci. 118, e2015265118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Gardner, T. A. Long-term region-wide declines in caribbean corals. Science 301, 958–960 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.De’ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27—year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1208909109 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Madin, J. S. et al. Cumulative effects of cyclones and bleaching on coral cover and species richness at Lizard Island. Mar. Ecol. Prog. Ser. 604, 263–268 (2018).ADS 
Article 

Google Scholar 
11.Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. Long-term shifts in the colony size structure of coral populations along the Great Barrier Reef: Demographic change in Australia’s corals. Proc. R. Soc. B Biol. Sci. 287, 20201432 (2020).Article 

Google Scholar 
12.Claar, D. C. et al. Dynamic symbioses reveal pathways to coral survival through prolonged heatwaves. Nat. Commun. 11, 1–10 (2020).ADS 
Article 
CAS 

Google Scholar 
13.Claar, D. C. & Baum, J. K. Timing matters: Survey timing during extended heat stress can influence perceptions of coral susceptibility to bleaching. Coral Reefs 38, 559–565 (2019).ADS 
Article 

Google Scholar 
14.Edmunds, P. J. Vital rates of small reef corals are associated with variation in climate. Limnol. Oceanogr. 66, 901–913 (2021).ADS 
Article 

Google Scholar 
15.Hall, T. E. et al. Stony coral populations are more sensitive to changes in vital rates in disturbed environments. Ecol. Appl. 31, 1–11 (2021).Article 

Google Scholar 
16.Madin, J. S., Baird, A. H., Dornelas, M. & Connolly, S. R. Mechanical vulnerability explains size-dependent mortality of reef corals. Ecol. Lett. 17, 1008–1015 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Edmunds, P. J. & Riegl, B. Urgent need for coral demography in a world where corals are disappearing. Mar. Ecol. Prog. Ser. 635, 233–242 (2020).ADS 
Article 

Google Scholar 
18.Hughes, T. P. et al. Ecological memory modifies the cumulative impact of recurrent climate extremes. Nat. Clim. Chang. 9, 40–43 (2019).ADS 
Article 

Google Scholar 
19.Pratchett, M. et al. Spatial, temporal and taxonomic variation in coral growth—Implications for the structure and function of coral reef ecosystems. Oceanogr. Mar. Biol. Ann. Rev. 53, 215–295 (2015).
Google Scholar 
20.Cantin, N. E. & Lough, J. M. Surviving coral bleaching events: Porites growth anomalies on the great barrier reef. PLoS ONE 9, e88720 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Linares, C., Pratchett, M. S. & Coker, D. J. Recolonisation of Acropora hyacinthus following climate-induced coral bleaching on the Great Barrier Reef. Mar. Ecol. Prog. Ser. 438, 97–104 (2011).ADS 
Article 

Google Scholar 
22.Victor, S., Golbuu, Y., Yukihira, H. & Van Woesik, R. Acropora size-frequency distributions reflect spatially variable conditions on coral reefs of Palau. Bull. Mar. Sci. 85, 149–157 (2009).
Google Scholar 
23.Wilson, S. K., Robinson, J. P. W., Chong-Seng, K., Robinson, J. & Graham, N. A. J. Boom and bust of keystone structure on coral reefs. Coral Reefs 38, 625–635 (2019).ADS 
Article 

Google Scholar 
24.Pratchett, M. S., McWilliam, M. J. & Riegl, B. Contrasting shifts in coral assemblages with increasing disturbances. Coral Reefs 39, 783–793 (2020).Article 

Google Scholar 
25.Loya, Y. et al. Coral bleaching: The winners and the losers. Ecol. Lett. 4, 122–131 (2001).Article 

Google Scholar 
26.Van Woesik, R., Sakai, K., Ganase, A. & Loya, Y. Revisiting the winners and the losers a decade after coral bleaching. Mar. Ecol. Prog. Ser. 434, 67–76 (2011).ADS 
Article 

Google Scholar 
27.McWilliam, M., Pratchett, M. S., Hoogenboom, M. O. & Hughes, T. P. Deficits in functional trait diversity following recovery on coral reefs. Proc. R. Soc. B Biol. Sci. 287, 20192628 (2020).Article 

Google Scholar 
28.Marshall, P. A. & Baird, A. H. Bleaching of corals on the Great Barrier Reef: Differential susceptibilities among taxa. Coral Reefs 19, 155–163 (2000).Article 

Google Scholar 
29.Graham, N. A. J., Cinner, J. E., Norström, A. V. & Nyström, M. Coral reefs as novel ecosystems: Embracing new futures. Curr. Opin. Environ. Sustain. 7, 9–14 (2014).Article 

Google Scholar 
30.Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. Nat. Commun. 10, 1–5 (2019).CAS 
Article 

Google Scholar 
31.Gilmour, J. P., Smith, L. D., Heyward, A. J., Baird, A. H. & Pratchett, M. S. Recovery of an isolated coral reef system following severe disturbance. Science 340, 69–71 (2013).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Hughes, T. P. et al. Global warming impairs stock–recruitment dynamics of corals. Nature 568, 387–390 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Vercelloni, J. et al. Forecasting intensifying disturbance effects on coral reefs. Glob. Chang. Biol. 26, 2785–2797 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Team, R. C. R: A Language and Environment for Statistical Computing. (2020).35.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378 (2017).Article 

Google Scholar 
36.Evans, R. D. et al. Early recovery dynamics of turbid coral reefs after recurring bleaching events. J. Environ. Manag. 268, 110666 (2020).Article 

Google Scholar 
37.Carlot, J. et al. Juvenile corals underpin coral reef carbonate production after disturbance. Glob. Chang. Biol. 27, 2623–2632 (2021).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Bellwood, D. R. et al. Coral reef conservation in the Anthropocene: Confronting spatial mismatches and prioritizing functions. Biol. Conserv. 236, 604–615 (2019).Article 

Google Scholar 
39.Baird, A., Emslie, M. & Lewis, A. Extended periods of coral recruitment on the Great Barrier Reef. In Proc. 12th Int. Coral Reef Symp. (2012).40.Foster, N. L., Baums, I. B. & Mumby, P. J. Sexual vs. asexual reproduction in an ecosystem engineer: The massive coral Montastraea annularis. J. Anim. Ecol. 76, 384–391 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Edmunds, P. J. Patterns in the distribution of juvenile corals and coral reef community structure in St. John, US Virgin Islands. Mar. Ecol. Prog. Ser. 202, 113–124 (2000).ADS 
Article 

Google Scholar 
42.Hughes, T. P., Linares, C., Dakos, V., van de Leemput, I. A. & van Nes, E. H. Living dangerously on borrowed time during slow, unrecognized regime shifts. Trends Ecol. Evol. 28, 149–155 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Wismer, S., Tebbett, S. B., Streit, R. P. & Bellwood, D. R. Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Sci. Total Environ. 650, 1487–1498 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Wismer, S., Tebbett, S. B., Streit, R. P. & Bellwood, D. R. Young fishes persist despite coral loss on the Great Barrier Reef. Commun. Biol. 2, 1–7 (2019).Article 

Google Scholar 
46.Abràmoff, M. D., Hospitals, I., Magalhães, P. J. & Abràmoff, M. Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).
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