Ecology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

1.Pollard, A. J., Reeves, R. & Baker, A. J. M. Facultative hyperaccumulation of heavy metals and metalloids. Plant Sci. 217–218, 8–17 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
2.Whiting, S. N. et al. Research priorities for conservation of metallophyte biodiversity and their potential for restoration and site remediation. Restor. Ecol. 12, 106–116 (2004).Article 

Google Scholar 
3.Baker, A. J. M. Accumulators and excluders—Strategies in the response of plants to heavy metals. J. Plant Nutr. 3, 643–654 (1981).CAS 
Article 

Google Scholar 
4.Ernest, W. H. O. Evolution of metal hyperaccumulation and phytoremediation hype. New Phytol. 146, 357–358 (2000).Article 

Google Scholar 
5.Pollard, A. J., Powell, K. D., Harper, F. A. & Smith, J. A. C. The genetic basis of metal hyperaccumulation in plants. Crit. Rev. Plant Sci. 21, 539–566 (2002).CAS 
Article 

Google Scholar 
6.Antosiewicz, D. M. Adaptation of plants to an environmental polluted with heavy metals. Acta Soc. Bot. Pol. 61, 281–299 (1992).CAS 
Article 

Google Scholar 
7.Brooks, R. R., Lee, J., Reeves, R. D. & JaVré, T. Detection of nickeliferous rocks by analysis of herbarium specimens of indicator plants. J. Geochem. Explor. 7, 49–77 (1977).CAS 
Article 

Google Scholar 
8.Jansen, S., Broadley, M., Robbrecht, E. & Smets, E. Aluminium hyperaccumulation in angiosperms: A review of its phylogenetic signifficance. Bot. Rev. 68, 235–269 (2002).Article 

Google Scholar 
9.van der Ent, A., Baker, A. J. M., Reeves, R. D., Pollard, J. & Schat, H. Hyperaccumulators of metal and metalloid trace elements: Facts and fiction. Plant Soil 362, 319–334 (2013).Article 
CAS 

Google Scholar 
10.Metali, F., Salim, K. A. & Burslem, D. F. R. P. Evidence of foliar aluminium accumulation in local, regional and global datasets of wild plants. New Phytol. 193, 637–649 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Noret, N., Meerts, P., Vanhaelen, M., Dos Santos, A. & Escarré, J. Do metal-rich plants deter herbivores? A field test of the defence hypothesis. Oecologia 152, 92–100 (2007).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Martens, S. N. & Boyd, R. S. The ecological significance of nickel hyperaccumulation: A plant chemical defense. Oecologia 98, 379–384 (1994).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Boyd, R. S. & Martens, S. N. The significance of metal hyperaccumulation for biotic interactions. Chemoecology 8, 1–7 (1998).CAS 
Article 

Google Scholar 
14.Hanson, B. et al. Selenium accumulation protects Brassica juncea from invertebrate herbivory and fungal infection. New Phytol. 159, 461–469 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Rascio, N. & Navari-Izzo, F. Heavy metal hyperaccumulating plants: How and why do they do it? And what makes them so interesting?. Plant Sci. 180, 169–181 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Freeman, J. L., Garcia, D., Kim, D., Hopf, A. & Salt, D. E. Constitutively elevated salicylic acid signals glutathione-mediated nickel tolerance in Thlaspi nickel hyperaccumulators. Plant Physiol. 137, 1082–1091 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Vesk, P. A. & Reichman, S. M. Hyperaccumulators and herbivores—A Bayesian meta-analysis of feeding choice trials. J. Chem. Ecol. 35, 289–296 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Pollard, A. J. & Baker, A. J. M. Deterrence of herbivory by zinc hyperaccumulation in Thlaspi caerulescens (Brassicaceae). New Phytol. 135, 655–658 (1997).CAS 
Article 

Google Scholar 
19.Ribeiro, S. P. & Brown, V. K. Insect herbivory in tree crowns of Tabebuia aurea and T. ochracea (Bignoniaceae): Contrasting the Brazilian Cerrado with the wetland Pantanal Matogrossense. Selbyana 20, 159–170 (1999).
Google Scholar 
20.Strauss, S. Y., Rudgers, J. A., Lau, J. A. & Irwin, R. E. Direct and ecological costs of resistance to herbivory. Trends Ecol. Evol. 17, 278–285 (2002).Article 

Google Scholar 
21.Hossain, M. A., Piyatida, P., Teixeria da Silva, J. A. & Fujita, M. Molecular mechanism of heavy metal toxicity and tolerance in plants: Central role of glutathione in detoxification of reactive oxygen species and methylglyoxal and in heavy metal chelation. J. Bot. 2012, 01–37 (2012).Article 
CAS 

Google Scholar 
22.McNaughton, S. J. Compensatory plant growth as a response to herbivory. Oikos 40, 329–336 (1983).Article 

Google Scholar 
23.Kozlov, M. V., Lanta, V., Zverev, V. E. & Zvereva, E. L. Delayed local responses of downy birch to damage by leafminers and leafrollers. Oikos 121, 428–434 (2012).Article 

Google Scholar 
24.Maestri, E., Marmiroli, M., Visioli, G. & Marmiroli, N. Metal tolerance and hyperaccumulation: Costs and trade-offs between traits and environment. Environ. Exp. Bot. 68, 1–13 (2010).CAS 
Article 

Google Scholar 
25.Khan, A. et al. Heavy metals effects on plant growth and dietary intake of trace metals in vegetables cultivated in contaminated soil. Int. J. Environ. Sci. Technol. 16, 2295–2304 (2019).CAS 
Article 

Google Scholar 
26.Barceló, J. & Poschenrieder, C. Respuestas de las plantas a la contaminación por metales pesados. Suelo y Planta 2, 345–361 (1992).
Google Scholar 
27.Ribeiro, S. P. et al. Plant defense against leaf herbivory based on metal accumulation: Examples from a tropical high altitude ecosystem. Plant Spec. Biol. 32, 147–155 (2017).Article 

Google Scholar 
28.Boyd, R. S. & Martens, S. N. Nickel hyperaccumulated by Thlaspi montanum var. montanum is acutely toxic to an insect herbivore. Oikos 70, 21–25 (1994).CAS 
Article 

Google Scholar 
29.Boyd, R. S. & Jhee, E. M. A test of elemental defence against slugs by Ni in hyperaccumulator and non-hyperaccumulator Streptanthus species. Chemoecology 15, 179–185 (2005).CAS 
Article 

Google Scholar 
30.Freeman, J. L. et al. Selenium accumulation protects plants from herbivory by Orthoptera due to toxicity and deterrence. New Phytol. 175, 490–500 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Mathews, S., Ma, L. Q., Rathinasabapathi, C. & Stamps, R. H. Arsenic reduced scale-insect infestation on arsenic hyperaccumulator Pteris vittata L. Environ. Exp. Bot. 65, 282–286 (2009).CAS 
Article 

Google Scholar 
32.Coleman, C. M., Boyd, R. S. & Eubanks, M. D. Extending the elemental defense hypothesis: Dietary metal concentrations below hyperaccumulator levels could harm herbivores. J. Chem. Ecol. 31, 1669–1681 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Scheirs, J., Vandevyvere, I., Wollaert, K., Blust, R. & De Bruyn, L. Plant-mediated effects of heavy metal pollution on host choice of a grass miner. Environ. Pollut. 143, 138–145 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Boyd, R. S. The defence hypothesis of elemental hyperaccumulation: Status, challenges and new directions. Plant Soil 293, 53–176 (2007).Article 
CAS 

Google Scholar 
35.Porto, M. L. & Silva, M. F. F. Tipos de vegetação metalófila em áreas da Serra de Carajás e de Minas Gerais, Brasil. Acta Bot. Bras. 3, 13–21 (1989).Article 

Google Scholar 
36.Teixeira, W. A. & Lemos-Filho, J. P. Metais pesados em folhas de espécies lenhosas colonizadoras de uma área de mineração de ferro em Itabirito, Minas Gerais. Rev. Arvore 22, 381–388 (1998).
Google Scholar 
37.Lorenzi, H. Árvores Brasileiras: Manual De Identificação e Cultivo de Plantas Arbóreas Nativas Do Brasil Vol. 3 (Nova Odessa: Instituto Plantarum, 2009)38.Pérez, J. F. M. et al. Sistema de manejo para a candeia—Eremanthus erythropappus (DC.) Macleish—a opção do sistema de corte seletivo. Cerne 10, 257–273 (2004).
Google Scholar 
39.Keane, B., Collier, M., Shann, J. & Rogstad, S. Metal content of dandelion (Taraxacum officinale) leaves in relation to soil contamination and airborne particulate matter. Sci. Total Environ. 281, 63–78 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Assunção, A. G. L., Schat, H. & Aarts, M. G. M. Thlaspi caerulescens, an attractive model species to study heavy metal hyperaccumulation in plants. New Phytol. 159, 351–360 (2003).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
41.Basta, N. T., Ryan, J. A. & Chaney, R. L. Trace element chemistry in residual-treated soil: Key concepts and metal bioavailability. J. Environ. Qual. 34, 49–63 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Evans, L. J. Chemistry of metal retention by soils—Several processes are explained. Environ. Sci. Technol. 23, 1046–1056 (1989).ADS 
CAS 
Article 

Google Scholar 
43.Campos, N. B. Aptidão reprodutiva e estrutura da comunidade de um candeial com elevada mortalidade. Dissertation (Federal University of Ouro Preto, 2012).44.Pereira, J. A., Londe, V., Ribeiro, S. P. & De Sousa, H. C. Crown architecture and leaf anatomic traits influencing herbivory on Clethra scabra Pers.: Comparing adaptation to wetlands and drained habitats. Rev. Bras. Bot. 40, 481–490 (2017).Article 

Google Scholar 
45.Koslov, M. V., Zverev, V. & Zvereva, E. L. Combined effects of environmental disturbance and climate warming on insect herbivory in mountain birch in subarctic forests: Results of 26-year monitoring. Sci. Total Environ. 601–602, 802–811 (2017).ADS 
Article 
CAS 

Google Scholar 
46.Mendes, G. & Cornelissen, T. G. Effects of plant quality and ant defence on herbivory rates in a neotropical ant-plant. Ecol. Entomol. 2017, 1–8 (2017).
Google Scholar 
47.Jhee, E. M., Boyd, R. S. & Eubanks, M. D. Effectiveness of metal-metal and metal-organic compund combinations against Plutella xylostella: Implications for plant elemental defense. J. Chem. Ecol. 32, 239–259 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Boyd, R. S. Plant defense using toxic inorganic ions: Conceptual models of the defensive enhancement and joint effects hypotheses. Plant Sci. 195, 88–95 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Bronstein, J. L. Conditional outcomes in mutualistic interactions. TREE 9, 214–217 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Monteiro, I., Viana-Junior, A. B., Solar, R. R. C., Neves, F. S. & DeSouza, O. Disturbance-modulated symbioses in termitophily. Ecol. Evol. 7, 10829–10838 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Trumble, J. T., Kolodnyhirsch, D. M. & Ting, I. P. Plant compensation for arthropod herbivory. Annu. Rev. Entomol. 38, 93–119 (1993).Article 

Google Scholar 
52.Stowe, K. A., Marquis, R. J., Hochwender, C. G. & Simms, E. L. The evolutionary ecology of tolerance to consumer damage. Annu. Rev. Ecol. Syst. 31, 565–595 (2000).Article 

Google Scholar 
53.Poveda, K., Steffan-Dewenter, I., Scheu, S. & Tscharntke, T. Effects of below- and above-ground herbivores on plant growth, flower visitation and seed set. Oecologia 135, 601–605 (2003).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Tozer, K. N. et al. Growth responses of diploid and tetraploid perennial ryegrass (Lolium perenne) to soil-moisture deficit, defoliation and a root-feeding invertebrate. Crop Pasture Sci. 68, 632–642 (2017).Article 

Google Scholar 
55.Yuan, J., Wang, P. & Yang, Y. Effects of simulated herbivory on the vegetative reproduction and compensatory growth of Hordeum brevisubulatum at different ontogenic stages. Int. J. Environ. Res. Public Health 16, 1663 (2019).PubMed Central 
Article 

Google Scholar 
56.Seneviratne, M. et al. Heavy metal induced oxidative stress on seed germination and seedling development: A critical review. Environ. Geochem. Health. 41, 1813–1831 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Poschenrieder, C., Tolrà, R. & Barceló, J. Can metals defend plants against biotic stress?. Trends Plant Sci. 11, 288–295 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Coleman, J. E. Zinc proteins: Enzymes, storage proteins, transcription factors, and replication proteins. Annu. Rev. Biochem. 61, 897–946 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Jansen, S., Watanabe, T., Dessein, S., Smetes, E. & Robbrecht, E. A comparative study of metal levels in leaves of some Al-accumulating Rubiaceae. Ann. Bot. 91, 657–663 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Gall, J. E., Boyd, R. S. & Rajakaruna, N. Transfer of heavy metals through terrestrial food webs: A review. Environ. Monit. Asses. 187, 1–21 (2015).CAS 
Article 

Google Scholar 
61.Poschenrieder, C., Gunsé, B., Corrales, I. & Barceló, J. A glance into aluminum toxicity and resistance in plants. Sci. Total. Environ. 400, 356–368 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Janssens, T. K. S., Roelofs, D. & Van Straalen, N. M. Molecular mechanisms of heavy metal tolerance and evolution in invertebrates. Insect Sci. 16, 3–18 (2009).CAS 
Article 

Google Scholar 
63.Hodson, M. E. Effects of heavy metals and metalloids on soil organisms. In Heavy metals in soils: trace metals and metalloids in soils and their bioavailability. Environmental Pollution (ed Alloway, B. J.) Vol. 22, 141–160 (Springer, 2012).64.Rahman, M. et al. Importance of mineral nutrition for mitigating aluminum toxicity in plants on acidic soils: Current status and opportunities. Int. J. Mol. Sci. 19, 1–28 (2018).ADS 

Google Scholar 
65.Kidd, P. S., Llugany, M., Poschenrieder, C., Gunsé, B. & Barceló, J. The role of root exudates in aluminium resistance and silicon-induced amelioration of aluminium toxicity in three varieties of maize (Zea mays L.). J. Exp. Bot. 52, 1339–1352 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Epstein, E. Silicon: Its manifold roles in plants. Ann. Appl. Biol. 155, 155–160 (2009).CAS 
Article 

Google Scholar 
67.Grevenstuk, T. & Romano, A. Aluminium speciation and internal detoxification mechanisms in plants: Where do we stand?. Metallomics 5, 1584–1594 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Panda, S. K., Baluška, F. & Matsumoto, H. Aluminum stress signaling in plants. Plant Signal Behav. 4, 592–597 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Borgström, P., Bommarco, R., Viketoft, M. & Strengbom, J. Below-ground herbivory mitigates biomass loss from above-ground herbivory of nitrogen fertilized plants. Sci. Rep. 10, 12752 (2020).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Bojórquez-Quintal, E., Escalante-Magaña, C., Echevarría-Machado, I. & Martínez-Estévez, M. Aluminum, a friend or foe of higher plants in acid soils. Front. Plant Sci. 8, 1–18 (2017).Article 

Google Scholar 
71.Massad, T. J. Ontogenetic differences of herbivory on woody and herbaceous plants: A meta-analysis demonstrating unique effects of herbivory on the young and the old, the slow and the fast. Oecologia 172, 1–10 (2013).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Messias, M. C. T. B. et al. Phanerogamic flora and vegetation of Itacolomi State Park, Minas Gerais, Brazil. Biota Neotrop. 17, 1–38 (2017).Article 

Google Scholar 
73.Peron, M. V. Listagem preliminar da flora fanerogâmica dos campos rupestres do Parque Estadual do Itacolomi–Ouro Preto/Mariana, MG. Rodriguésia 67, 63–69 (1989).Article 

Google Scholar 
74.Almeida, F. F. M. Províncias estruturais brasileiras. In SBG, Simpósio de Geologia do Nordeste, 8, Campina Grande, PB. Atas Campina Grande 363–391 (1977).75.Machado, N., Schrank, A., Noce, C. M. & Gauthier, G. Ages of detrital zircon from Archean-Paleoproterozoic sequences: Implications for Greenstone Belt setting and evolution of a Transamazonian foreland basin in Quadrilatero Ferrifero, southeast Brazil. Earth Planet Sci. Lett. 141, 259–276 (1996).ADS 
CAS 
Article 

Google Scholar 
76.Ribeiro, S. P. & Basset, Y. Gall-forming and free-feeding herbivory along vertical gradients in a lowland tropical rainforest: The importance of leaf sclerophylly. Ecography 30, 663–672 (2007).Article 

Google Scholar 
77.Ribeiro, S. P. & Basset, Y. Effects of sclerophylly and host choice on gall densities and herbivory distribution in an Australian subtropical forest. Austral. Ecol. 441, 219–226 (2016).Article 

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

Google Scholar 
79.R Core Team. R: A Language and Environment for Statistical Computing (R Found Stat Comp, 2020) https://www.R-project.org/. More

1.Landrigan, P. J. et al. The Lancet Commission on pollution and health. Lancet 391, 462–512 (2018).PubMed 
Article 

Google Scholar 
2.Grandjean, P. & Landrigan, P. J. Neurobehavioural effects of developmental toxicity. Lancet Neurol. 13, 330–338 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Boix, J., Cauli, O. & Felipo, V. Developmental exposure to polychlorinated biphenyls 52, 138 or 180 affects differentially learning or motor coordination in adult rats mechanisms involved. Neuroscience 167, 994–1003 (2010).CAS 
PubMed 
Article 

Google Scholar 
4.Boucher, O., Muckle, G. & Bastien, C. H. Prenatal exposure to polychlorinated biphenyls: a neuropsychologic analysis. Environ. Health Perspect. 117, 7–16 (2009).CAS 
PubMed 
Article 

Google Scholar 
5.Ennaceur, S., Gandoura, N. & Driss, M. R. Distribution of polychlorinated biphenyls and organochlorine pesticides in human breast milk from various locations in Tunisia: Levels of contamination, influencing factors, and infant risk assessment. Environ. Res. 108, 86–93 (2008).CAS 
PubMed 
Article 

Google Scholar 
6.Lancz, K. et al. Duration of breastfeeding and serum PCB 153 concentrations in children. Environ. Res. 136, 35–39 (2015).CAS 
PubMed 
Article 

Google Scholar 
7.Herbstman, J. B. et al. Determinants of prenatal exposure to polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) in an urban population. Environ. Health Perspect. 115, 1794–1800 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Lancz, K. et al. Ratio of cord to maternal serum PCB concentrations in relation to their congener-specific physicochemical properties. Int J. Hyg. Env. Health 218, 91–98 (2015).CAS 
Article 

Google Scholar 
9.Bergonzi, R. et al. Distribution of persistent organochlorine pollutants in maternal and foetal tissues: data from an Italian polluted urban area. Chemosphere 76, 747–754 (2009).CAS 
PubMed 
Article 

Google Scholar 
10.Patel, J. F., Hartman, T. J., Sjodin, A., Northstone, K. & Taylor, E. V. Prenatal exposure to polychlorinated biphenyls and fetal growth in British girls. Environ. Int. 116, 116–121 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Rice, D. & Barone, S. Critical periods of vulnerability for the developing nervous system: Evidence from humans and animal models. Environ. Health Perspect. 108, 511–533 (2000).PubMed 
PubMed Central 

Google Scholar 
12.Trnovec, T. et al. Serum PCB concentrations and cochlear function in 12-year-old children. Environ. Sci. Technol. 44, 2884–2889 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Jusko, T. A. et al. Prenatal and postnatal serum PCB concentrations and cochlear function in children at 45 months of age. Environ. Health Perspect. 122, 1246–1252 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
14.Ribas-Fitó, N., Sala, M., Kogevinas, M. & Sunyer, J. Polychlorinated biphenyls (PCBs) and neurological development in children: A systematic review. J. Epidemiol. Community Health 55, 537–546 (2001).PubMed 
PubMed Central 
Article 

Google Scholar 
15.Walkowiak, J. et al. Environmental exposure to polychlorinated biphenyls and quality of the home environment: effects on psychodevelopment in early childhood. Lancet 358, 1602–1607 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Chen, Y.-C. J., Guo, Y.-L., Hsu, C.-C. & Rogan, W. J. Cognitive development of Yu-Cheng (‘Oil Disease’) children prenatally exposed to heat-degraded PCBs. JAMA 268, 3213 (1992).CAS 
PubMed 
Article 

Google Scholar 
17.Berger, D. F. et al. Hyperactivity and impulsiveness in rats fed diets supplemented with either Aroclor 1248 or PCB-contaminated St. Lawrence river fish. Behav. Brain Res. 126, 1–11 (2001).CAS 
PubMed 
Article 

Google Scholar 
18.Johansen, E. B. et al. Behavioral changes following PCB 153 exposure in the spontaneously hypertensive rat—an animal model of attention-deficit/hyperactivity disorder. Behav. Brain Funct. 10, 1–19 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Crofton, K. M., Ding, D.-L., Padich, R., Taylor, M. & Henderson, D. Hearing loss following exposure during development to polychlorinated biphenyls: a cochlear site of action. Hear. Res. 144, 196–204 (2000).CAS 
PubMed 
Article 

Google Scholar 
20.Goldey, E. S., Kehn, L. S., Lau, C., Rehnberg, G. L. & Crofton, K. M. Developmental exposure to polychlorinated biphenyls (Aroclor 1254) reduces circulating thyroid hormone concentrations and causes hearing deficits in rats. Toxicol. Appl. Pharmacol. 135, 77–88 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Lilienthal, H., Korkalainen, M., Andersson, P. L. & Viluksela, M. Developmental exposure to purity-controlled polychlorinated biphenyl congeners (PCB74 and PCB95) in rats: Effects on brainstem auditory evoked potentials and catalepsy. Toxicology 327, 22–31 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Lilienthal, H., Heikkinen, P., Andersson, P. L., van der Ven, L. T. M. & Viluksela, M. Auditory effects of developmental exposure to purity-controlled polychlorinated biphenyls (PCB52 and PCB180) in rats. Toxicol. Sci. 122, 100–111 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Poon, E., Bandara, S. B., Allen, J. B., Sadowski, R. N. & Schantz, S. L. Developmental PCB exposure increases susceptibility to audiogenic seizures in adulthood. Neurotoxicology 46, 117–124 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Stewart, A. M., Braubach, O., Spitsbergen, J., Gerlai, R. & Kalueff, A. V. Zebrafish models for translational neuroscience research: From tank to bedside. Trends Neurosci. 37, 264–278 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Fonnum, F. & Mariussen, E. Mechanisms involved in the neurotoxic effects of environmental toxicants such as polychlorinated biphenyls and brominated flame retardants. J. Neurochem. 111, 1327–1347 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Dervola, K. S. N., Johansen, E. B., Walaas, S. I. & Fonnum, F. Gender-dependent and genotype-sensitive monoaminergic changes induced by polychlorinated biphenyl 153 in the rat brain. Neurotoxicology 50, 38–45 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Campagna, R. et al. Cerebellum Proteomics addressing the cognitive deficit of rats perinatally exposed to the food-relevant polychlorinated biphenyl 138. Toxicol. Sci. 123, 170–179 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Enayah, S. H., Vanle, B. C., Fuortes, L. J., Doorn, J. A. & Ludewig, G. PCB95 and PCB153 change dopamine levels and turn-over in PC12 cells. Toxicology 394, 93–101 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Langeveld, W. T., Meijer, M. & Westerink, R. H. S. Differential effects of 20 non-dioxin-like PCBs on basal and depolarization-evoked intracellular calcium levels in PC12 cells. Toxicol. Sci. 126, 487–496 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Keil, K. P., Sethi, S. & Lein, P. J. Sex-dependent effects of 2,2′,3,5′,6-pentachlorobiphenyl on dendritic arborization of primary mouse neurons. Toxicol. Sci. 168, 95–109 (2019).CAS 
PubMed 
Article 

Google Scholar 
31.Pruitt, D. L., Meserve, L. A. & Bingman, V. P. Reduced growth of intra- and infra-pyramidal mossy fibers is produced by continuous exposure to polychlorinated biphenyl. Toxicology 138, 11–17 (1999).CAS 
PubMed 
Article 

Google Scholar 
32.Yang, D. et al. Developmental exposure to polychlorinated biphenyls interferes with experience-dependent dendritic plasticity and ryanodine receptor expression in weanling rats. Environ. Health Perspect. 117, 426–435 (2009).CAS 
PubMed 
Article 

Google Scholar 
33.Lein, P. J. et al. Ontogenetic alterations in molecular and structural correlates of dendritic growth after developmental exposure to polychlorinated biphenyls. Environ. Health Perspect. 115, 556–563 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Tropepe, V. & Sive, H. L. Can zebrafish be used as a model to study the neurodevelopmental causes of autism? Genes. Brain Behav. 2, 268–281 (2003).CAS 

Google Scholar 
35.Maximino, C. & Herculano, A. M. A review of monoaminergic neuropsychopharmacology in zebrafish. Zebrafish 7, 359–378 (2010).CAS 
PubMed 
Article 

Google Scholar 
36.Howe, K. et al. The zebrafish reference genome sequence and its relationship to the human genome. Nature 496, 498–503 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Wolman, M. A. A. et al. A genome-wide screen identifies PAPP-AA-mediated IGFR signaling as a novel regulator of habituation learning. Neuron 85, 1200–1211 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Meserve Id, J. H. et al. A forward genetic screen identifies Dolk as a regulator of startle magnitude through the potassium channel subunit Kv1.1. PLoS Genet. 17, e1008943 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Voesenek, C. J., Muijres, F. T. & Van Leeuwen, J. L. Biomechanics of swimming in developing larval fish. J. Exp. Biol. 221, jeb149583 (2018).40.Roberts, A. Early functional organization of spinal neurons in developing lower vertebrates. Brain Res. Bull. 53, 585–593 (2000).CAS 
PubMed 
Article 

Google Scholar 
41.Liu, Y. C. & Hale, M. E. Local spinal cord circuits and bilateral mauthner cell activity function together to drive alternative startle behaviors. Curr. Biol. 27, 697–704 (2017).CAS 
PubMed 
Article 

Google Scholar 
42.Burgess, H. A. & Granato, M. Sensorimotor gating in larval zebrafish. J. Neurosci. 27, 4984–4994 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Troconis, E. L. et al. Intensity-dependent timing and precision of startle response latency in larval zebrafish. J. Physiol. 595, 265–282 (2017).CAS 
PubMed 
Article 

Google Scholar 
44.Smith, N. L. & Kimelman, D. Establishing the body plan: The first 24 h of zebrafish development. The Zebrafish in Biomedical Research: Biology, Husbandry, Diseases, and Research Applications, https://doi.org/10.1016/B978-0-12-812431-4.00007-5 (Elsevier, 2019).45.Meyers, J. R. et al. Lighting up the senses: FM1-43 loading of sensory cells through nonselective ion channels. J. Neurosci. 23, 4054–4065 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Tabor, K. M. et al. Direct activation of the Mauthner cell by electric field pulses drives ultrarapid escape responses. J. Neurophysiol. 112, 834–844 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Jain, R. A. et al. A forward genetic screen in zebrafish identifies the G-protein-coupled receptor CaSR as a modulator of sensorimotor decision making. Curr. Biol. 28, 1357–1369.e5 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Mariussen, E. & Fonnum, F. The effect of polychlorinated biphenyls on the high affinity uptake of the neurotransmitters, dopamine, serotonin, glutamate and GABA, into rat brain synaptosomes. Toxicology 159, 11–21 (2001).CAS 
PubMed 
Article 

Google Scholar 
49.Wigestrand, M. B., Stenberg, M., Walaas, S. I., Fonnum, F. & Andersson, P. L. Non-dioxin-like PCBs inhibit [3H]WIN-35,428 binding to the dopamine transporter: a structure–activity relationship study. Neurotoxicology 39, 18–24 (2013).CAS 
PubMed 
Article 

Google Scholar 
50.Oikonomou, G. et al. The serotonergic raphe promote sleep in zebrafish and mice. Neuron 103, 686–701.e8 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Fosque, B. F. et al. Labeling of active neural circuits in vivo with designed calcium integrators. Science 347, 755–760 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Klocke, C. & Lein, P. J. Evidence implicating non-dioxin-like congeners as the key mediators of polychlorinated biphenyl (PCB) developmental neurotoxicity. Int. J. Mol. Sci. 21, 1013 (2020).53.Puel JL, Pujol R, Ladrech S, Eybalin M. Alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid electrophysiological and neurotoxic effects in the guinea-pig cochlea. Neuroscience. Nat. Rev. Neurosci. 45, 63–72. https://doi.org/10.1016/0306-4522(91)90103-u (1991).CAS 
Article 

Google Scholar 
54.Sebe JY, et al. Ca2+-Permeable AMPARs Mediate Glutamatergic Transmission and Excitotoxic Damage at the Hair Cell Ribbon Synapse. J Neurosci. 37, 6162–6175. https://doi.org/10.1523/JNEUROSCI.3644-16.2017 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Coleman, M. Axon degeneration mechanisms: commonality amid diversity. Nat. Rev. Neurosci. 6, 889–898 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Lacoste, A. M. B. et al. A convergent and essential interneuron pathway for mauthner-cell-mediated escapes. Curr. Biol. 25, 1526–1534 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Kang, K. S., Wilson, M. R., Hayashi, T., Chang, C. C. & Trosko, J. E. Inhibition of gap junctional intercellular communication in normal human breast epithelial cells after treatment with pesticides, PCBs, and PBBs, alone or in mixtures. Environ. Health Perspect. 104, 192–200 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Bager, Y., Lindebro, M. C., Martel, P., Chaumontet, C. & Wärngård, L. Altered function, localization and phosphorylation of gap junctions in rat liver epithelial, IAR 20, cells after treatment with PCBs or TCDD. Environ. Toxicol. Pharmacol. 3, 257–266 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Machala, M. et al. Inhibition of gap junctional intercellular communication by noncoplanar polychlorinated biphenyls: Inhibitory potencies and screening for potential mode(s) of action. Toxicol. Sci. 76, 187–195 (2003).Article 
CAS 

Google Scholar 
60.Nyffeler, J. et al. A structure–activity relationship linking non-planar PCBs to functional deficits of neural crest cells: new roles for connexins. Arch. Toxicol. 92, 1225–1247 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Kang, K.-S., Park, J.-E., Ryu, D.-Y. & Lee, Y.-S. Effects and neuro-toxic mechanisms of 2,2’,4,4’,5,5’-hexachlorobiphenyl and endosulfan in neuronal stem cells. J. Vet. Med. Sci. 63, 1183–1190 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Aluru, N., Krick, K. S., Mcdonald, A. M. & Karchner, S. I. Developmental exposure to PCB153 (2,2′,4,4′,5,5′-hexachlorobiphenyl) alters circadian rhythms and the expression of clock and metabolic genes. Toxicol. Sci. 173, 41–52 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
63.GEO Accession viewer. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2450663 (2017).64.Serrano-Velez, J. L. et al. Abundance of gap junctions at glutamatergic mixed synapses in adult Mosquitofish spinal cord neurons. Front. Neural Circuits 8, 66 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Tanaka, Y. et al. Aroclor 1254 and BDE-47 inhibit dopaminergic function manifesting as changes in locomotion behaviors in zebrafish embryos. Chemosphere 193, 1207–1215 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Rungta, R. L. et al. The cellular mechanisms of neuronal swelling underlying cytotoxic edema. Cell 161, 610–621 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Cesetti, T., Ciccolini, F. & Li, Y. GABA not only a neurotransmitter: osmotic regulation by GABA AR signaling. Front Cell Neurosci. 6, 3 (2012).68.Fernandes, E. C. A. et al. Potentiation of the human GABAA receptor as a novel mode of action of lower-chlorinated non-dioxin-like PCBs. Environ. Sci. Technol. 44, 2864–2869 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Magnusson, O., Mohringe, B., Thorell, G. & Lake-Bakaar, D. M. Effects of the dopamine D2 selective receptor antagonist remoxipride on dopamine turnover in the rat brain after acute and repeated administration. Pharmacol. Toxicol. 60, 368–373 (1987).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Seegal, R. F., Bush, B. & Shain, W. Lightly chlorinated ortho-substituted PCB congeners decrease dopamine in nonhuman primate brain and in tissue culture. Toxicol. Appl. Pharmacol. 106, 136–144 (1990).CAS 
PubMed 
Article 

Google Scholar 
71.Toro, C. et al. Dopamine modulates the activity of sensory hair cells. J. Neurosci. 35, 16494–16503 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Gafni, J., Wong, P. W. & Pessah, I. N. Non-coplanar 2,2’,3,5’,6-pentachlorobiphenyl (PCB 95) amplifies ionotropic glutamate receptor signaling in embryonic cerebellar granule neurons by a mechanism involving ryanodine receptors. Toxicol. Sci. 77, 72–82 (2003).PubMed 
Article 
CAS 

Google Scholar 
73.Ta, T. A., Feng, W., Molinski, T. F. & Pessah, I. N. Hydroxylated xestospongins block inositol-1,4,5-trisphosphate-induced Ca2+ release and sensitize Ca2+-induced Ca2+ release mediated by ryanodine receptors. Mol. Pharmacol. 69, 532–538 (2006).CAS 
PubMed 
Article 

Google Scholar 
74.Brunelli, L. et al. Insight into the neuroproteomics effects of the food-contaminant non-dioxin like polychlorinated biphenyls. J. Proteomics. 75, 2417–2430 (2012).CAS 
PubMed 
Article 

Google Scholar 
75.McCormick, M. I., Fakan, E. & Allan, B. J. M. Behavioural measures determine survivorship within the hierarchy of whole-organism phenotypic traits. Funct. Ecol. 32, 958–969 (2018).Article 

Google Scholar 
76.Lai, Z. et al. Residual distribution and risk assessment of polychlorinated biphenyls in surface sediments of the Pearl River Delta, South China. Bull. Environ. Contam. Toxicol. 95, 37–44 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Gräns, J. et al. Regulation of pregnane-X-receptor, CYP3A and P-glycoprotein genes in the PCB-resistant killifish (Fundulus heteroclitus) population from New Bedford Harbor. Aquat. Toxicol. 159, 198–207 (2015).PubMed 
Article 
CAS 

Google Scholar 
78.Hudspeth, A. The cellular basis of hearing: the biophysics of hair cells. Science 230, 745–752 (1985).CAS 
PubMed 
Article 

Google Scholar 
79.Nakayama, H. Common sensory inputs and differential excitability of segmentally homologous reticulospinal neurons in the hindbrain. J. Neurosci. 24, 3199–3209 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
80.Pujol-Martí, J. & López-Schier, H. Developmental and architectural principles of the lateral-line neural map. Front. Neural Circuits 7, 47 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Panlilio, J. M., Jones, I. T., Salanga, M. C., Aluru, N. & Hahn, M. E. Developmental exposure to domoic acid disrupts startle response behavior and circuitry in zebrafish. Toxicol. Sci. 182, 310–326 (2021).PubMed 
Article 

Google Scholar 
82.Park, H.-C., Shin, J., Roberts, R. K. & Appel, B. An olig2 reporter gene marks oligodendrocyte precursors in the postembryonic spinal cord of zebrafish. Dev. Dyn. 236, 3402–3407 (2007).CAS 
PubMed 
Article 

Google Scholar 
83.Shin, J., Park, H.-C., Topczewska, J. M., Mawdsley, D. J. & Appel, B. Neural cell fate analysis in zebrafish using olig2 BAC transgenics. Methods Cell Sci. 25, 7–14 (2003).CAS 
PubMed 
Article 

Google Scholar 
84.Takada, N., Kucenas, S. & Appel, B. Sox10 is necessary for oligodendrocyte survival following axon wrapping. Glia 58, 996–1006 (2010).PubMed 
PubMed Central 

Google Scholar 
85.Almeida, R. G., Czopka, T., Ffrench-Constant, C. & Lyons, D. A. Individual axons regulate the myelinating potential of single oligodendrocytes in vivo. Development 138, 4443–4450 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Kimmel, C. B. Patterning the brain of the zebrafish embryo. Annu. Rev. Neurosci. 16, 707–732 (1993).CAS 
PubMed 
Article 

Google Scholar 
87.Ranasinghe, P. et al. Embryonic exposure to 2,2′,3,5′,6-pentachlorobiphenyl (PCB-95) causes developmental malformations in zebrafish. Environ. Toxicol. Chem. 39, 162–170 (2019).Article 
CAS 

Google Scholar 
88.Jönsson, M. E., Kubota, A., Timme-Laragy, A. R., Woodin, B. & Stegeman, J. J. Ahr2-dependence of PCB126 effects on the swim bladder in relation to expression of CYP1 and cox-2 genes in developing zebrafish. Toxicol. Appl. Pharmacol. 265, 166–174 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
89.Panlilio, J. M., Aluru, N. & Hahn, M. E. Developmental neurotoxicity of the harmful algal bloom toxin domoic acid: Cellular and molecular mechanisms underlying altered behavior in the zebrafish model. Environ. Health Perspect. 128, 117002 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
90.Inoue, D. & Wittbrodt, J. One for all-a highly efficient and versatile method for fluorescent immunostaining in fish embryos. PLoS One 6, 1–7 (2011).Article 
CAS 

Google Scholar 
91.Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
Article 

Google Scholar 
92.Edwards KA, Hoppa MB, Bosco G. The Photoconvertible Fluorescent Probe, CaMPARI, Labels Active Neurons in Freely-Moving Intact Adult Fruit Flies. Front Neural Circuits. 14, 22 https://doi.org/10.3389/fncir.2020.00022. (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
93.Zhao, Y. et al. Rare earth elements lanthanum and praseodymium adversely affect neural and cardiovascular development in zebrafish (Danio rerio). Environ. Sci. Technol. https://doi.org/10.1021/acs.est.0c06632 (2020).94.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar  More

1.Vaganov, E. A. et al. Influence of snowfall and melt timing on tree growth in subarctic Eurasia. Nature 400(6740), 149–151. https://doi.org/10.1038/22087 (1999).ADS 
CAS 
Article 

Google Scholar 
2.Impacts of a Warming Arctic—Arctic Climate Impact Assessment (ACIA). 144 (Cambridge University Press, 2004).3.Apps, M. J., Shvidenko, A. Z. & Vaganov, E. A. Boreal forests and the environment: A mitigation and adaptation strategies for global change. BFE. 11(1), 1–4 (2006).
Google Scholar 
4.Fedotov, A. P. et al. A reconstruction of the thawing of the permafrost during the last 170 years on the Taimyr Peninsula (East Siberia, Russia). Glob. Planet. Change 98–99, 139–152 (2002).
Google Scholar 
5.Kharuk, V. I., Dvinskaya, M. L. & Ranson, J. Fire return intervals within the northern boundary of the larch forest in Central Siberia. Int. J. Wildland Fire 22(2), 207–211. https://doi.org/10.1071/WF11181 (2011).Article 

Google Scholar 
6.Knorre, A. A., Kirdyanov, A. V., Prokushkin, A. S., Krusic, P. J. & Büntgen, U. Tree ring-based reconstruction of the long-term influence of wildfires on permafrost active layer dynamics in Central Siberia. Sci. Total Environ. 652, 314–319 (2019).ADS 
Article 

Google Scholar 
7.Kim, J.-S., Kug, J.-S., Jeong, S.-J., Park, H. & Schaepman-Strub, G. Extensive fires in southeastern Siberian permafrost linked to preceding Arctic Oscillation. Sci. Adv. 6(2), eaax330. https://doi.org/10.1126/sciadv.aax3308 (2020).Article 

Google Scholar 
8.Kirdyanov, A. V. et al. Long-term ecological consequences of forest fires in the permafrost zone of Siberia. Environ. Res. Lett. 15, 034061. https://doi.org/10.1088/1748-9326/ab7469 (2020).ADS 
Article 

Google Scholar 
9.Fritts, H. C. Tree-Rings and Climate 567 (Academic Press, 1976).
Google Scholar 
10.Schweingruber, F. H. Tree Rings and Environment Dendroecology (Paul Haupt Publ, 1996).
Google Scholar 
11.Hughes, M. K., Vaganov, E. A., Shiyatov, S. G., Touchan, R. & Funkhouser, G. Twentieth-century summer warmth in northern Yakutia in a 600-year context. Holocene 9(5), 603–608 (1999).Article 

Google Scholar 
12.Briffa, K. R. Annual climate variability in the Holocene: Interpreting the message of ancient trees. Quat. Sci. Rev. 19, 87–105 (2000).ADS 
Article 

Google Scholar 
13.Naurzbaev, M., Vaganov, E. A., Sidorova, O. V. & Schweingruber, F. H. Summer temperatures in eastern Taimyr inferred from a 2427-year late-Holocene tree-ring chronology and earlier floating series. Holocene 12(6), 727–736 (2002).ADS 
Article 

Google Scholar 
14.Grudd, H. Torneträsk tree-ring width and density AD 500–2004: A test of climatic sensitivity and a new 1500-year reconstruction of north Fennoscandian summers. Clim. Dyn. 31, 843–857 (2008).Article 

Google Scholar 
15.Sidorova, O. V., Siegwolf, R., Saurer, M., Naurzbaev, M. M. & Vaganov, E. A. Isotopic composition (δ13C, δ18O) in Siberian tree-ring chronology. Geophys. Res. Biogeosci. 113, G02019. https://doi.org/10.1029/2007JG000473 (2008).CAS 
Article 

Google Scholar 
16.Sidorova, O. V. et al. Spatial patterns of climatic changes in the Eurasian north reflected in Siberian larch tree-ring parameters and stable isotopes. Glob. Change Biol. 16, 1003–1018. https://doi.org/10.1111/j.1365-2486.2009.02008.x (2010).ADS 
Article 

Google Scholar 
17.Sidorova, O. V. et al. Is the 20th century warming unprecedented in the Siberian north?. Quat. Sci. Rev. 73, 93–102. https://doi.org/10.1016/j.quascirev.2013.05.015 (2013).ADS 
Article 

Google Scholar 
18.Kirdyanov, A. V., Treydte, K. S., Nikolaev, A., Helle, G. & Schleser, G. H. Climate signals in tree-ring width, density an δ13C from larches in Eastern Siberia (Russia). Chem. Geol. 252, 31–41. https://doi.org/10.1016/j.chemgeo.2008.01.023 (2008).ADS 
CAS 
Article 

Google Scholar 
19.Hilasvuori, E., Berninger, F., Sonninen, E., Tuomenvirta, H. & Jungner, H. Stability of climate signal in carbon and oxygen isotope records and ring width from Scots pine (Pinus sylvestris L.) in Finland. J. Quat. Sci. 24(5), 469–480 (2009).Article 

Google Scholar 
20.Loader, N. J., Young, G. H. F., Grudd, H. & McCarroll, D. Stable carbon isotopes from Torneträsk, norther Sweden provide a millennial length reconstruction of summer sunshine and its relationship to Arctic circulation. Quat. Sci. Rev. 62, 97–113 (2013).ADS 
Article 

Google Scholar 
21.Churakova (Sidorova), O. V. et al. Recent atmospheric drying in Siberia is not unprecedented over the last 1500 years. Sci. Rep. 10, 15024 (2020).CAS 
Article 

Google Scholar 
22.Young, G. H. F. et al. Changes in atmospheric circulation and the Arctic Oscillation preserved within a millennial length reconstruction of summer cloud cover from northern Fennoscandia. Clim. Dyn. 39, 495–507. https://doi.org/10.1007/s00382-011-1246-3 (2012).Article 

Google Scholar 
23.Saurer, M., Schweingruber, F., Vaganov, E. A., Shiyatov, S. G. & Siegwolf, R. Spatial and temporal oxygen isotope trends at the northern tree-line in Eurasia. Geophys. Res. Lett. https://doi.org/10.1029/2001GL013739 (2002).Article 

Google Scholar 
24.Saurer, M. et al. Influence of atmospheric circulation patterns on the oxygen isotope ratio of tree rings in the Alpine region. J. Geophys. Res. 117, D05118. https://doi.org/10.1029/2011JD016861 (2012).ADS 
CAS 
Article 

Google Scholar 
25.Ortega, P. et al. A model-tested North Atlantic Oscillation reconstruction for the past millennium. Nature 523, 71–74 (2015).ADS 
CAS 
Article 

Google Scholar 
26.Butzin, M. et al. Variations of oxygen-18 in West Siberian precipitation during the last 50 years. Atmos. Chem. Phys. 14, 5853–5869 (2014).ADS 
Article 

Google Scholar 
27.Gagen, M. et al. North Atlantic summer storm tracks over Europe dominated by internal variability over the past millennium. Nat. Geosci. 9(8), 630–635 (2016).ADS 
CAS 
Article 

Google Scholar 
28.Thompson, D. W. & Wallace, J. M. Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Clim. 13, 1000–1016 (2000).ADS 
Article 

Google Scholar 
29.Wang, J. et al. Impacts of the Siberian High and Arctic Oscillation on the East Asia winter monsoon: Driving down welling in the western Bering Sea Aquatic Ecosystem. Health Manag. 15(1), 20–30. https://doi.org/10.1080/14634988.2012.648860 (2012).Article 

Google Scholar 
30.Buermann, W. et al. Interannual covariability in Northern Hemisphere air temperatures and greenness associated with El-Nino-Southern Oscillation and the Arctic Oscillation. J. Geophys. Res. 108(D13), 4396. https://doi.org/10.1029/2002JD002630 (2003).Article 

Google Scholar 
31.Baltzer, H. et al. Impact of the Arctic Oscillation pattern on interannual forest fire variability in Central Siberia. Geophys. Res. Lett. https://doi.org/10.1029/2005GL022526 (2005).Article 

Google Scholar 
32.Zhang, J. et al. Analysis of the positive Arctic Oscillation index event and its influence in the winter and spring of 2019/2020. Front. Earth Sci. https://doi.org/10.3389/feart.2020.580601 (2021).Article 

Google Scholar 
33.Zielinski, G. A. Use of paleo-records in determining variability within the volcanism- climate system. Quat. Sci. Rev. 19, 417–438 (2000).ADS 
Article 

Google Scholar 
34.Panuyshkina, I. P. & Arbatskaya, M. K. Dendrochronological approach to study flammability of forests in Evenkia (Siberia). Sib. Ecol. J. 2, 167–173 (1999).
Google Scholar 
35.Valendik, E. N., Kisilyakhov, E. K., Rizova, V. A., Ponamarev, E. I. & Danilova, I. V. Large fires in taiga landscape of Central Siberia. Geogr. Nat. Resour. 14(1), 52–59 (2014).
Google Scholar 
36.Naulier, M. et al. A millennial summer temperature reconstruction for northeastern Canada using oxygen isotopes in subfossil trees. Clim. Past. 11, 1153–1164. https://doi.org/10.5194/cp-11-1153-2015 (2015).Article 

Google Scholar 
37.Churakova (Sidorova), O. V. et al. Siberian tree-ring and stable isotope proxies as indicators of temperature and moisture changes after major stratospheric volcanic eruptions. Clim. Past. https://doi.org/10.5194/cp-2018-70.y (2019).Article 

Google Scholar 
38.Furyaev, V. V., Vaganov, E. A., Tchebakova, N. M. & Valendik, E. N. Effects of fire and climate on successions and structural changes in the Siberian boreal forest. Eurasian J. For. Res. 2, 1–15 (2001).
Google Scholar 
39.Keller, K. M. et al. 20th-century changes in carbon isotopes and water-use efficiency: Tree-ring based evaluation of the CLM4.5 and LPX-Bern models. Biogeosciences 14, 2641–2673 (2017).ADS 
CAS 
Article 

Google Scholar 
40.D’Arrigo, R. D., Cook, E. R., Mann, M. E. & Jacoby, G. C. Tree-ring reconstructions of temperature and sea-level pressure variability associated with the warm-season Arctic Oscillation since AD 1650. Geophys. Res. Lett. 30(11), 1549. https://doi.org/10.1029/2003GL017250 (2003).ADS 
Article 

Google Scholar 
41.Kress, A. et al. Swiss tree rings reveal warm and wet summers during medieval times. Geophys. Res. Lett. 41, 1732–1737. https://doi.org/10.1002/2013GL059081 (2014).ADS 
Article 

Google Scholar 
42.Büntgen, U. et al. Recent European drought extremes beyond Common Era background variability. Nat. Geosci. 14, 190–196. https://doi.org/10.1038/s41561-021-00698-0 (2021).ADS 
CAS 
Article 

Google Scholar 
43.Kodera, K. & Kuroda, Y. Regional and hemispheric circulation patterns in the northern hemisphere winter, or the NAO and AO. Geophys. Res. Lett. 30(18), 2003. https://doi.org/10.1029/2003GL017290 (1934).Article 

Google Scholar 
44.Abaimov, A. P., Bondarev, A. I., Ziryanova, O. A. & Shitova, S. A. Forest Krasnoyarsk Polar (Nauka, 1997).
Google Scholar 
45.Ary-Mas Natural Conditions, Flora and Vegetation. (eds. Norin, B.N.) (Nauka, Leningrad, 1978).46.Ogi, M., Yamazaki, K. & Tachibana, Y. The summertime annular mode in the Northern Hemisphere and its linkage to the winter mode. J. Geophys. Res. 109, D20114 (2004).ADS 
Article 

Google Scholar 
47.Gagen, M. H., McCarroll, D., Loader, N. J., Robertson, I. & Jalkanen, R. Exorcising the ‘segment length curse’ summer temperature reconstruction since AD 1640 using non de-trend stable carbon isotope ratios from line trees in northern Finland. Holocene 17, 433–444 (2007).ADS 
Article 

Google Scholar 
48.Boettger, T. et al. Wood cellulose preparation methods and mass spectrometric analyses of δ13C, δ18O, and nonexchangeable δ2H values in cellulose, sugar, and starch: An inter-laboratory comparison. Anal. Chem. 15, 4603–4612 (2007).Article 

Google Scholar 
49.Weigt, R. B. et al. Comparison of δ18O and δ13C values between tree-ring whole wood and cellulose in five species growing under two different site conditions. Rapid Commun. Mass Spectrom. 29(29), 2233–2244. https://doi.org/10.1002/rcm.7388 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
50.Francey, R. J. et al. A 1000-year high precision record of δ13C in atmospheric CO2. Tellus B51, 170–193 (1999).ADS 
Article 

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

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