Ecology

Andersson, M. Sexual selection (Princeton University Press, 2019).
Google Scholar 
Davy, A. J. & Smith, H. Life-history variation and environment. In Plant Population Ecology (eds Davy, A. J., Hutchings, M. J. & Watkinson, A. R.) 1–22. 28th Symposium of the British Ecological Society, Sussex, 1987 (Blackwell, 1988).Wilson, K. L., De Gisi, J., Cahill, C. L., Barker, O. E. & Post, J. R. Life-history variation along environmental and harvest clines of a northern freshwater fish: plasticity and adaptation. J. Anim. Ecol. 88, 717–733 (2019).PubMed 

Google Scholar 
Laiolo, P. & Obeso, J. R. Life-history responses to the altitudinal gradient. In High Mountain Conservation in a Changing World (eds Catalan, J. et al.) 253–283 (Springer, 2017).
Google Scholar 
Schwarz, R. & Meiri, S. The fast-slow life-history continuum in insular lizards: a comparison between species with invariant and variable clutch sizes. J. Biogeogr. 44, 2808–2815 (2017).
Google Scholar 
Holm, S. et al. Size-related life-history traits in geometrid moths: a comparison of a temperate and a tropical community. Ecol. Entomol. 44, 711–716 (2019).
Google Scholar 
Ferguson, G. W. & Fox, S. F. Annual variation of survival advantage of large juvenile side-blotched lizards, Uta stansburiana: Its causes and evolutionary significance. Evolution 38, 342–349 (1984).PubMed 

Google Scholar 
Madsen, T., Ujvari, B., Shine, R. & Olsson, M. Rain, rats and pythons: Climate-driven population dynamics of predators and prey in tropical Australia. Austral Ecol. 31, 30–37 (2006).
Google Scholar 
Brown, G. P. & Shine, R. Rain, prey and predators: climatically driven shifts in frog abundance modify reproductive allometry in a tropical snake. Oecologia 154, 361–368 (2007).ADS 
PubMed 

Google Scholar 
James, C. & Shine, R. Life-history strategies of Australian lizards: a comparison between the tropics and the temperate zone. Oecologia 75, 307–316 (1988).ADS 
PubMed 

Google Scholar 
Mesquita, D. O. et al. Life-history patterns of lizards of the world. Am. Nat. 187, 689–705 (2016).PubMed 

Google Scholar 
Meiri, S. et al. The global diversity and distribution of lizard clutch sizes. Glob. Ecol. Biogeogr. 29, 1515–1530 (2020).
Google Scholar 
Andrews, R. M. Growth rate in island and mainland anoline lizards. Copeia 1976, 477–482 (1976).
Google Scholar 
Jessop, T. S. et al. Maximum body size among insular Komodo dragon populations covaries with large prey density. Oikos 112, 422–429 (2006).
Google Scholar 
Van Buskirk, J. & Crowder, L. B. Life-history variation in marine turtles. Copeia 1994, 66–81 (1994).
Google Scholar 
Broderick, A. C., Godley, B. J. & Hays, G. C. Trophic status drives interannual variability in nesting numbers of marine turtles. Proc. R. Soc. B 268, 1481–1487 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Broderick, A. C., Glen, F., Godley, B. J. & Hays, G. C. Variation in reproductive output of marine turtles. J. Exp. Mar. Biol. Ecol. 288, 95–109 (2003).
Google Scholar 
Pike, D. A. Climate influences the global distribution of sea turtle nesting. Glob. Ecol. Biogeogr. 22, 555–566 (2013).
Google Scholar 
Ujvari, B., Shine, R., Luiselli, L. & Madsen, T. Climate-induced reaction norms for life-history traits in pythons. Ecology 92, 1858–1864 (2011).PubMed 

Google Scholar 
Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).
Google Scholar 
Shine, R., Brown, G. P. & Goiran, C. Population dynamics of the sea snake Emydocephalus annulatus (Elapidae, Hydrophiinae). Sci. Rep. 11, 20701 (2021). ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 

Google Scholar 
Ineich, I. & Laboute, P. Les Serpents Marins de Nouvelle-Calédonie (IRD éditions, 2002).Udyawer, V., Goiran, C. & Shine, R. Peaceful coexistence between people and deadly wildlife: Why are recreational users of the ocean so rarely bitten by sea snakes? People Nat. 3, 335–346 (2021).
Google Scholar 
Goiran, C., Brown, G. P. & Shine, R. The behaviour of sea snakes (Emydocephalus annulatus) shifts with the tides. Sci. Rep. 10, 1–8 (2020).
Google Scholar 
Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).PubMed 
PubMed Central 

Google Scholar 
Shine, R., Goiran, C., Shine, T., Fauvel, T. & Brischoux, F. Phenotypic divergence between seasnake (Emydocephalus annulatus) populations from adjacent bays of the New Caledonian lagoon. Biol. J. Linn. Soc. 107, 824–832 (2012).
Google Scholar 
Avolio, C., Shine, R. & Pile, A. J. The adaptive significance of sexually dimorphic scale rugosity in sea snakes. Am. Nat. 167, 728–738 (2006).PubMed 

Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
Google Scholar 
Russell, B. C., Anderson, G. R. V. & Talbot, F. H. Seasonality and recruitment of coral reef fishes. Mar. Freshw. Res. 28, 521–528 (1977).
Google Scholar 
Shine, R., Bonnet, X., Elphick, M. J. & Barrott, E. G. A novel foraging mode in snakes: browsing by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae). Funct. Ecol. 18, 16–24 (2004).
Google Scholar 
Calow, P. Adaptive aspects of energy allocation. In Fish Energetics (eds Tytler, P. & Calow, P.) 13–31 (Springer, 1985).
Google Scholar 
Lenormand, T. Gene flow and the limits to natural selection. Trends Ecol. Evol. 17, 183–189 (2002).
Google Scholar 
Bronikowski, A. M. Experimental evidence for the adaptive evolution of growth rate in the garter snake Thamnophis elegans. Evolution 54, 1760–1767 (2000).CAS 
PubMed 

Google Scholar 
Cook, T. R., Bonnet, X., Fauvel, T., Shine, R. & Brischoux, F. Foraging behaviour and energy budgets of sea snakes from New Caledonia: Insights from implanted data-loggers. J. Zool. 298, 82–93 (2016).
Google Scholar 
Bonnet, X., Brischoux, F., Briand, M. & Shine, R. Plasticity matches phenotype to local conditions despite genetic homogeneity across 13 snake populations. Proc. R. Soc. B 288, 20202916 (2021).PubMed 
PubMed Central 

Google Scholar 
Lukoschek, V., Waycott, M. & Marsh, H. Phylogeography of the olive sea snake, Aipysurus laevis (Hydrophiinae) indicates Pleistocene range expansion around northern Australia but low contemporary gene flow. Mol. Ecol. 16, 3406–3422 (2007).CAS 
PubMed 

Google Scholar 
Nitschke, C. R., Hourston, M., Udyawer, V. & Sanders, K. L. Rates of population differentiation and speciation are decoupled in sea snakes. Biol. Lett. 14, 20180563 (2018).PubMed 
PubMed Central 

Google Scholar 
Heatwole, H., Grech, A., Monahan, J. F., King, S. & Marsh, H. Thermal biology of sea snakes and sea kraits. Integr. Comp. Biol. 52, 257–273 (2012).PubMed 

Google Scholar 
Brischoux, F., Rolland, V., Bonnet, X., Caillaud, M. & Shine, R. Effects of oceanic salinity on body condition in sea snakes. Integr. Comp. Biol. 52, 235–244 (2012).PubMed 

Google Scholar 
Bonnet, X. et al. Spatial variation in age structure among populations of a colonial marine snake: the influence of ectothermy. J. Anim. Ecol. 84, 925–933 (2015).PubMed 

Google Scholar 
Heatwole, H. Sea Snakes 2nd edn. (Krieger Publishing, 1999).
Google Scholar 
Blouin-Demers, G. & Weatherhead, P. J. Thermal ecology of black rat snakes (Elaphe obsoleta) in a thermally challenging environment. Ecology 82, 3025–3043 (2001).
Google Scholar 
Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).
Google Scholar 
Lowe, J. R. et al. Regional versus latitudinal variation in the life-history traits and demographic rates of a reef fish, Centropyge bispinosa, in the Coral Sea and Great Barrier Reef Marine Parks, Australia. J. Fish Biol. 99, 1602–1612. https://doi.org/10.1111/jfb.14865 (2021).PubMed 

Google Scholar 
Gust, N., Choat, J. & Ackerman, J. Demographic plasticity in tropical reef fishes. Mar. Biol. 140, 1039–1051 (2002).
Google Scholar 
Kingsford, M. J., Welch, D. & O’Callaghan, M. Latitudinal and cross-shelf patterns of size, age, growth, and mortality of a tropical damselfish Acanthochromis polyacanthus on the Great Barrier Reef. Diversity 11, 67 (2019).
Google Scholar  More

Mason, G. J. Stereotypies: A critical review. Anim. Behav. 41, 1015–1037 (1991).
Google Scholar 
Cussen, V. A. & Mench, J. A. The relationship between personality dimensions and resiliency to environmental stress in orange-winged Amazon parrots (Amazona amazonica), as indicated by the development of abnormal behaviors. PLoS ONE 10, 1–11 (2015).
Google Scholar 
Clubb, R. & Mason, G. Captivity effects on wide-ranging carnivores. Nature 425, 473–474 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Shepherdson, D., Lewis, K. D., Carlstead, K., Bauman, J. & Perrin, N. Individual and environmental factors associated with stereotypic behavior and fecal glucocorticoid metabolite levels in zoo housed polar bears. Appl. Anim. Behav. Sci. 147, 268–277 (2013).
Google Scholar 
Miller, L. J., Bettinger, T. & Mellen, J. The reduction of stereotypic pacing in tigers (Panthera tigris) by obstructing the view of neighbouring individuals. Anim. Welf. 17, 255–258 (2008).CAS 

Google Scholar 
Bachmann, I., Bernasconi, P., Herrmann, R., Weishaupt, M. A. & Stauffacher, M. Behavioural and physiological responses to an acute stressor in crib-biting and control horses. Appl. Anim. Behav. Sci. 82, 297–311 (2003).
Google Scholar 
Ahola, M. K., Vapalahti, K. & Lohi, H. Early weaning increases aggression and stereotypic behaviour in cats. Sci. Rep. 7, 10412 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Salonen, M. et al. Prevalence, comorbidity, and breed differences in canine anxiety in 13,700 Finnish pet dogs. Sci. Rep. 10, 2962 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Garner, J. P. Stereotypies and other abnormal repetitive behaviors: Potential impact on validity, reliability, and replicability of scientific outcomes. ILAR J. 46, 106–117 (2005).CAS 
PubMed 

Google Scholar 
Tynes, V. V. & Sinn, L. Abnormal repetitive behaviors in dogs and cats. A guide for practitioners. Vet. Clin. North Am. Small Anim. Pract. 44, 543–564 (2014).PubMed 

Google Scholar 
Luescher, A. U. Diagnosis and management of compulsive disorders in dogs and cats. Vet. Clin. North Am. Small Anim. Pract. 33, 253–267 (2003).PubMed 

Google Scholar 
Mason, G., Clubb, R., Latham, N. & Vickery, S. Why and how should we use environmental enrichment to tackle stereotypic behaviour?. Appl. Anim. Behav. Sci. 102, 163–188 (2007).
Google Scholar 
Overall, K. L. & Dunham, A. E. Clinical features and outcome in dogs and cats with obsessive-compulsive disorder: 126 Cases (1989–2000). J. Am. Vet. Med. Assoc. 221, 1445–1452 (2002).PubMed 

Google Scholar 
Tiira, K. et al. Environmental effects on compulsive tail chasing in dogs. PLoS One 7, e41684 (2012).Mason, G. & Rushen, J. Stereotypic Animal Behaviour: Fundamentals and Applications to Welfare 2nd edn. (CABI Publishing, 2006).
Google Scholar 
Moon-Fanelli, A. A., Dodman, N. H., Famula, T. R. & Cottam, N. Characteristics of compulsive tail chasing and associated risk factors in Bull Terriers. J. Am. Vet. Med. Assoc. 238, 883–889 (2011).PubMed 

Google Scholar 
Hewson, C. J., Luescher, U. A. & Ball, R. O. Measuring change in the behavioural severity of canine compulsive disorder: The construct validity of categories of change derived from two rating scales. Appl. Anim. Behav. Sci. 60, 55–68 (1998).
Google Scholar 
Vandeleest, J. J., McCowan, B. & Capitanio, J. P. Early rearing interacts with temperament and housing to influence the risk for motor stereotypy in rhesus monkeys (Macaca mulatta). Appl. Anim. Behav. Sci. 132, 81–89 (2011).PubMed 
PubMed Central 

Google Scholar 
Tang, R. et al. Candidate genes and functional noncoding variants identified in a canine model of obsessive-compulsive disorder. Genome Biol. 15, 25 (2014).
Google Scholar 
Dodman, N. H. et al. A canine chromosome 7 locus confers compulsive disorder susceptibility. Mol. Psychiatry 15, 8–10 (2010).CAS 
PubMed 

Google Scholar 
Jeppesen, L. L., Heller, K. E. & Bildsøe, M. Stereotypies in female farm mink (Mustela vison) may be genetically transmitted and associated with higher fertility due to effects on body weight. Appl. Anim. Behav. Sci. 86, 137–143 (2004).
Google Scholar 
Noh, H. J. et al. Integrating evolutionary and regulatory information with a multispecies approach implicates genes and pathways in obsessive-compulsive disorder. Nat. Commun. 8, 1–13 (2017).CAS 

Google Scholar 
Koran, L. M. Quality of life in obsessive-compulsive disorder. Psychiatr. Clin. North Am. 23, 509–517 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Murray, C. J. & Lopez, A. D. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020 (Harvard School of Public Health, 1996).
Google Scholar 
Calzà, J. et al. Altered cortico-striatal functional connectivity during resting state in obsessive-compulsive disorder. Front. Psychiatry 10, 319 (2019).PubMed 
PubMed Central 

Google Scholar 
Brem, S., Grünblatt, E., Drechsler, R., Riederer, P. & Walitza, S. The neurobiological link between OCD and ADHD. ADHD Atten. Deficit Hyperact. Disord. 6, 175–202 (2014).
Google Scholar 
Stein, D. J., Dodman, N. H., Borchelt, P. & Hollander, E. Behavioral disorders in veterinary practice: Relevance to psychiatry. Compr. Psychiatry 35, 275–285 (1994).CAS 
PubMed 

Google Scholar 
Overall, K. L. Natural animal models of human psychiatric conditions: Assessment of mechanism and validity. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 24, 727–776 (2000).CAS 

Google Scholar 
Flament, M. F. et al. Obsessive compulsive disorder in adolescence: An epidemiological study. J. Am. Acad. Child Adolesc. Psychiatry 27, 764–771 (1988).CAS 
PubMed 

Google Scholar 
Nestadt, G. et al. A family study of obsessive-compulsive disorder. Arch. Gen. Psychiatry 57, 358–363 (2000).CAS 
PubMed 

Google Scholar 
Protopopova, A., Hall, N. J. & Wynne, C. D. L. Association between increased behavioral persistence and stereotypy in the pet dog. Behav. Processes 106, 77–81 (2014).PubMed 

Google Scholar 
Valerius, G., Lumpp, A., Kuelz, A. K., Freyer, T. & Voderholzer, U. Reversal learning as a neuropsychological indicator for the neuropathology of obsessive compulsive disorder? A behavioral study. J. Neuropsychiatry Clin. Neurosci. 20, 210–218 (2008).PubMed 

Google Scholar 
Snyder, H. R., Kaiser, R. H., Warren, S. L. & Heller, W. Obsessive-compulsive disorder is associated with broad impairments in executive function: A meta-analysis. Clin. Psychol. Sci. 3, 301–330 (2015).PubMed 

Google Scholar 
Ogata, N. et al. Brain structural abnormalities in Doberman pinschers with canine compulsive disorder. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 45, 1–6 (2013).
Google Scholar 
Norman, L. J. et al. Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis. JAMA Psychiat. 73, 815–825 (2016).
Google Scholar 
Yalcin, E., Ilcol, Y. O. & Batmaz, H. Serum lipid concentrations in dogs with tail chasing. J. Small Anim. Pract. 50, 133–135 (2009).CAS 
PubMed 

Google Scholar 
Vermeire, S. et al. Serotonin 2A receptor, serotonin transporter and dopamine transporter alterations in dogs with compulsive behaviour as a promising model for human obsessive-compulsive disorder. Psychiatry Res. 201, 78–87 (2012).CAS 
PubMed 

Google Scholar 
Moon-Fanelli, A. A. & Dodman, N. H. Description and development of compulsive tail chasing in terriers and response to clomipramine treatment. J. Am. Vet. Med. Assoc. 212, 1252–1257 (1998).CAS 
PubMed 

Google Scholar 
Irimajiri, M. et al. Randomized, controlled clinical trial of the efficacy of fluoxetine for treatment of compulsive disorders in dogs. J. Am. Vet. Med. Assoc. 235, 705–709 (2009).CAS 
PubMed 

Google Scholar 
Walsh, B. R. A critical review of the evidence for the equivalence of canine and human compulsions. Appl. Anim. Behav. Sci. 234, 105166 (2021).
Google Scholar 
Wright, H. F., Mills, D. S. & Pollux, P. M. J. Development and validation of a psychometric tool for assessing impulsivity in the domestic dog (Canis familiaris). Int. J. Comp. Psychol. 24, 210–225 (2011).
Google Scholar 
Dinwoodie, I. R., Dwyer, B., Zottola, V., Gleason, D. & Dodman, N. H. Demographics and comorbidity of behavior problems in dogs. J. Vet. Behav. 32, 62–71 (2019).
Google Scholar 
Sulkama, S. et al. Canine hyperactivity, impulsivity, and inattention share similar demographic risk factors and behavioural comorbidities with human ADHD. Transl. Psychiatry 11, 501 (2021).PubMed 
PubMed Central 

Google Scholar 
Kooij, J. J. S. et al. Updated European Consensus Statement on diagnosis and treatment of adult ADHD. Eur. Psychiatry 56, 14–34 (2019).CAS 
PubMed 

Google Scholar 
Nakao, T., Okada, K. & Kanba, S. Neurobiological model of obsessive-compulsive disorder: Evidence from recent neuropsychological and neuroimaging findings. Psychiatry Clin. Neurosci. 68, 587–605 (2014).PubMed 

Google Scholar 
Milad, M. R. & Rauch, S. L. Obsessive-compulsive disorder: Beyond segregated cortico-striatal pathways. Trends Cogn. Sci. 16, 43–51 (2012).PubMed 

Google Scholar 
Hollander, E. Managing aggressive behavior in patients with obsessive-compulsive disorder and borderline personality disorder. J. Clin. Psychiatry 60, 38–44 (1999).PubMed 

Google Scholar 
Marsden, M. D. & Wood-Gush, D. G. M. The use of space by group-housed sheep. Appl. Anim. Behav. Sci. 15, 178 (1986).
Google Scholar 
Burn, C. C. A vicious cycle: A cross-sectional study of canine tail-chasing and human responses to it, using a free video-sharing website. PLoS ONE 6, e26553 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stubbs, B. et al. An examination of the anxiolytic effects of exercise for people with anxiety and stress-related disorders: A meta-analysis. Psychiatry Res. 249, 102–108 (2017).PubMed 

Google Scholar 
Schneider, B. M., Dodman, N. H. & Maranda, L. Use of memantine in treatment of canine compulsive disorders. J. Vet. Behav. Clin. Appl. Res. 4, 118–126 (2009).
Google Scholar 
Mihevc, S. P. & Majdic, G. Canine cognitive dysfunction and Alzheimer’s disease-two facets of the same disease?. Front. Neurosci. 13, 604 (2019).
Google Scholar 
Delorme, R. et al. Admixture analysis of age at onset in obsessive-compulsive disorder. Psychol. Med. 35, 237–243 (2005).PubMed 

Google Scholar 
Flaisher-Grinberg, S. et al. Ovarian hormones modulate ‘compulsive’ lever-pressing in female rats. Horm. Behav. 55, 356–365 (2009).CAS 
PubMed 

Google Scholar 
Fernández-Guasti, A., Agrati, D., Reyes, R. & Ferreira, A. Ovarian steroids counteract serotonergic drugs actions in an animal model of obsessive-compulsive disorder. Psychoneuroendocrinology 31, 924–934 (2006).PubMed 

Google Scholar 
Col, R., Day, C. & Phillips, C. J. C. An epidemiological analysis of dog behavior problems presented to an Australian behavior clinic, with associated risk factors. J. Vet. Behav. Clin. Appl. Res. 15, 1–11 (2016).
Google Scholar 
Rusbridge, C. Neurological diseases of the Cavalier King Charles spaniel. J. Small Anim. Pract. 46, 265–272 (2005).CAS 
PubMed 

Google Scholar 
Wrzosek, M., Płonek, M., Nicpoń, J., Cizinauskas, S. & Pakozdy, A. Retrospective multicenter evaluation of the ‘fly-catching syndrome’ in 24 dogs: EEG, BAER, MRI, CSF findings and response to antiepileptic and antidepressant treatment. Epilepsy Behav. 53, 184–189 (2015).PubMed 

Google Scholar 
Cao, X. et al. Balancing selection on CDH2 may be related to the behavioral features of the Belgian malinois. PLoS ONE 9, e110075 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Moon-Fanelli, A. A., Dodman, N. H. & Cottam, N. Blanket and flank sucking in Doberman Pinschers. J. Am. Vet. Med. Assoc. 231, 907–912 (2007).PubMed 

Google Scholar 
Tiira, K. & Lohi, H. Reliability and validity of a questionnaire survey in canine anxiety research. Appl. Anim. Behav. Sci. 155, 82–92 (2014).
Google Scholar 
Puurunen, J. et al. Inadequate socialisation, inactivity, and urban living environment are associated with social fearfulness in pet dogs. Sci. Rep. 10, 3527 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hakanen, E. et al. Active and social life is associated with lower non-social fearfulness in pet dogs. Sci. Rep. 10, 1–13 (2020).
Google Scholar 
Mikkola, S. et al. Aggressive behaviour is affected by demographic, environmental and behavioural factors in purebred dogs. Sci. Rep. 11, 9433 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hejjas, K. et al. Association of polymorphisms in the dopamine D4 receptor gene and the activity-impulsivity endophenotype in dogs. Anim. Genet. 38, 629–633 (2007).CAS 
PubMed 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2019).Hastie, T. gam: Generalized Additive Models. (2018).Robinson, D. & Hayes, A. broom: Convert Statistical Analysis Objects into Tidy Tibbles. https://cran.r-project.org/package=broom (2018).Wickham, H., François, R., Lionel, H. & Müller, K. dplyr: A Grammar of Data Manipulation. https://cran.r-project.org/package=dplyr (2019).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage Publications, 2011).
Google Scholar 
Robin, X. et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011).
Google Scholar 
Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. https://cran.r-project.org/package=emmeans (2019).Fox, J. Effect Displays in R for Generalised Linear Models. J. Stat. Softw. 8, 1–27 (2003).
Google Scholar 
Goto, A., Arata, S., Kiyokawa, Y., Takeuchi, Y. & Mori, Y. Risk factors for canine tail chasing behaviour in Japan. Vet. J. 192, 445–448 (2012).PubMed 

Google Scholar  More

OECD–FAO Agricultural Outlook 2017-2026 (OECD, 2017).Frenken, K. Irrigation in Southern and Eastern Asia in Figures—AQUASTAT Survey 2011 (FAO, 2012).FAOSTAT Production Data (FAO, accessed 2 May 2021); www.fao.org/faostat/en/#dataDawe, D., Jaffee, S. & Santos, N. Rice in the Shadow of Skyscrapers: Policy Choices in a Dynamic East and Southeast Asian Setting (FAO, 2014).Baldwin, K., Childs, N., Dyck, J. & Hansen, J. Southeast Asia’s Rice Surplus. Outlook No. RCS-121-01 (USDA, 2012).World Population Prospects (Department of Economic and Social Affairs, Population Division, UN, 2019).Rejesus, R. M., Mohanty, S. & Balagtas, J. V. Forecasting Global Rice Consumption (North Carolina State Univ., 2012).Clarete, R. L., Adriano, L. & Esteban A. Rice Trade and Price Volatility: Implications on ASEAN and Global Food Security (Asian Development Bank, 2013).Pandey, S. et al. Rice in the Global Economy: Strategic Research and Policy Issues for Food Security (International Rice Research Institute, 2010).Robinson, S. et al. The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description for Version 3, IFPRI Discussion Paper 1483 (International Food Policy Research Institute, 2015).d’Amour, C. B. et al. Future urban land expansion and implications for global croplands. Proc. Natl Acad. Sci. USA 114, 8939–8944 (2017).
Google Scholar 
de Fraiture, C. et al. Trends and Transitions in Asian Irrigation: What are the Prospects for the Future? IWMI-FAO Workshop on Asian Irrigation (FAO Regional Office for Asia and the Pacific, 2009)Global Rice Science Partnership. Rice Almanac 4th edn (International Rice Research Institute, 2013).Ladha, J. K. et al. Steady agronomic and genetic interventions are essential for sustaining productivity in intensive rice cropping. Proc. Natl Acad. Sci. USA 118, e2110807118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mutert, E. & Fairhurst, T. H. Developments in rice production in Southeast Asia. Better Crops Int. 15, 12–17 (2002).
Google Scholar 
Dawe, D. C., Piedad, M. & Cheryll B. C. Why Does the Philippines Import Rice?: Meeting the Challenge of Trade Liberalization (International Rice Research Institute, 2006).van Ittersum, M. K. et al. Can Sub-Saharan Africa feed itself? Proc. Natl Acad. Sci. USA 113, 14964–14969 (2016).PubMed 
PubMed Central 

Google Scholar 
Lobell, D. B., Cassman, K. G. & Field, C. B. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 179 (2009).
Google Scholar 
Agus, F. et al. Yield gaps in intensive rice–maize cropping sequences in the humid tropics of Indonesia. Field Crops Res. 237, 12–22 (2019).
Google Scholar 
Cosslett, T. L. & Cosslett, P. D. Rice Trade of the Mainland Southeast Asian Countries: Cambodia, Laos, Thailand, and Vietnam. Sustainable Development of Rice and Water Resources in Mainland Southeast Asia and Mekong River Basin (Springer, 2018).Tran, U. T. & Kajisa, K. The impact of Green Revolution on rice production in Vietnam. Dev. Econ. 44, 167–189 (2006).
Google Scholar 
Dobermann, A., Witt, C. & Dawe, D. Increasing Productivity of Intensive Rice Systems Through Site-Specific Nutrient Management (Science Publishers Inc. and International Rice Research Institute, 2004).Hoang, H. K. & Meyers, W. H. Price stabilization and impacts of trade liberalization in the Southeast Asian rice market. Food Policy 57, 26–39 (2015).
Google Scholar 
Clapp, J. Food self-sufficiency: making sense of it, and when it makes sense. Food Policy 66, 88–96 (2017).
Google Scholar 
Buresh, R. J., Correa, T. Q. Jr, Pabuayon, I. L. B., Laureles, E. V. & Choi, I. R. Yield of irrigated rice affected by asymptomatic disease in a long-term intensive monocropping experiment. Field Crops Res. 265, 108121 (2021).
Google Scholar 
Dawe, D. & Timmer, C. P. Why stable food prices are a good thing: lessons from stabilizing rice prices in Asia. Glob. Food Secur. 1, 127–133 (2012).
Google Scholar 
Deng, N. et al. Closing yield gaps for rice self-sufficiency in China. Nat. Commun. 10, 1725 (2019).PubMed 
PubMed Central 
ADS 

Google Scholar 
Ray, D. K. et al. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).PubMed 
ADS 

Google Scholar 
Stuart, A. M. et al. Yield gaps in rice-based farming systems: insights from local studies and prospects for future analysis. Field Crops Res. 194, 43–56 (2016).
Google Scholar 
Affholder, F., Poeydebat, C., Corbeels, M., Scopel, E. & Tittonell, P. The yield gap of major food crops in family agriculture in the tropics: assessment and analysis through field surveys and modelling. Field Crops Res. 143, 106–118 (2013).
Google Scholar 
Boling, A. A., Bouman, B. A., Tuong, T. P., Konboon, Y. & Harnpichitvitaya, D. Yield gap analysis and the effect of nitrogen and water on photoperiod-sensitive Jasmine rice in north-east Thailand. NJAS-Wagen. J. Life Sci. 58, 11–19 (2011).
Google Scholar 
van Oort, P. A. et al. Can yield gap analysis be used to inform R&D prioritisation? Glob. Food Sec. 12, 109–118 (2017).
Google Scholar 
Rattalino Edreira, J. I. et al. Spatial frameworks for robust estimation of yield gaps. Nat. Food 2, 773–779 (2021).
Google Scholar 
Grassini, P. et al. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Res. 177, 49–63 (2015).
Google Scholar 
Redfern, S. K., Azzu, N. & Binamira, J. S. Rice in Southeast Asia: Facing Risks and Vulnerabilities to Respond to Climate Change. Building Resilience for Adaptation to Climate Change in the Agriculture Sector (FAO, 2012).Angulo, C., Becker, M. & Wassmann, R. Yield gap analysis and assessment of climate-induced yield trends of irrigated rice in selected provinces of the Philippines. J. Agric. Rural Dev. Trop. Subtrop. 113, 61–68 (2012).
Google Scholar 
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).CAS 
PubMed 
ADS 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Gitz, V., Meybeck, A., Lipper, L., Young, C. D. & Braatz, S. Climate Change and Food Security: Risks and Responses (FAO, 2016).Collins, M. et al. in IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).ADS 

Google Scholar 
Pastor, A. V. et al. The global nexus of food–trade–water sustaining environmental flows by 2050. Nat. Sustain. 2, 499–507 (2019).
Google Scholar 
Kropff, M. J., Cassman, K. G., Peng, S., Matthews, R. B. & Setter, T. L. Quantitative Understanding of Yield Potential. Breaking the Yield Barrier (International Rice Research Institute, 1994).Matthews, R. B., Kropff, M. J., Bachelet, D. & van Laar, H. H. Modeling the Impact of Climate Change on Rice Production in Asia (CAB International and International Rice Research Institute, 1995).Mitchell P. L., Sheehy J. E. & Woodward F. I. Potential Yields and the Efficiency of Radiation Use in Rice. IRRI Discussion Paper Series 32 (International Rice Research Institute, 1998).Devkota, K. P. et al. Economic and environmental indicators of sustainable rice cultivation: a comparison across intensive irrigated rice cropping systems in six Asian countries. Ecol. Indic. 105, 199–214 (2019).CAS 

Google Scholar 
Peng, S. et al. The importance of maintenance breeding: a case study of the first miracle rice variety—IR8. Field Crops Res. 119, 342–347 (2010).ADS 

Google Scholar 
Peng, S., Cassman, K. G., Virmani, S. S., Sheehy, J. & Khush, G. S. Yield potential trends of tropical rice since the release of IR8 and the challenge of increasing rice yield potential. Crop Sci. 39, 1552–1559 (1999).
Google Scholar 
Kupkanchanakul, T. Bridging the Rice Yield Gap in Thailand. Bridging the Rice Yield Gap in the Asia-Pacific Region (FAO Regional Office for Asia and the Pacific, 2000).Monkham, T. et al. On-farm multi-location evaluation of occurrence of drought types and rice genotypes selected from controlled-water on-station experiments in northeast Thailand. Field Crops Res. 220, 27–36 (2018).
Google Scholar 
Naklang, K., Shu, F. & Nathabut, K. Growth of rice cultivars by direct seeding and transplanting under upland and lowland conditions. Field Crops Res. 48, 115–123 (1996).
Google Scholar 
Espe, M. B. et al. Rice yield improvements through plant breeding are offset by inherent yield declines over time. Field Crops Res. 222, 59–65 (2018).
Google Scholar 
Ermakova, M., Danila, F. R., Furbank, R. T. & von Caemmerer, S. On the road to C4 rice: advances and perspectives. Plant J. 101, 940–950 (2020).CAS 
PubMed 

Google Scholar 
Hari Prasad, A. S., Viraktamath, B. C. & Mohapatra, T. Hybrid Rice Development in Asia: Assessment of Limitations and Potential (FAO Regional Office for Asia and the Pacific, 2014).Report on the Regional Expert Consultation on Hybrid Rice Development in Asia Under FAO–China South–South Cooperation: Constraints and Opportunities (FAO Regional Office for Asia and the Pacific, 2016).Xie, F. & Peng, S. History and prospects of hybrid rice development outside of China. Sci. Bull. 35, 3858–3868 (2016).
Google Scholar 
Gummert, M. et al. Assessment of post-harvest losses and carbon footprint in intensive lowland rice production in Myanmar. Sci. Rep. 10, 1–13 (2020).
Google Scholar 
A Regional Rice Strategy for Sustainable Food Security in Asia and the Pacific (FAO Regional Office for Asia and the Pacific, 2014).Laborte, A. G. et al. Rice yields and yield gaps in Southeast Asia: past trends and future outlook. Eur. J. Agron. 36, 9–20 (2012).
Google Scholar 
Chivenge, P., Saito, K., Bunquin, M. A., Sharma, S. & Dobermann, A. Co-benefits of nutrient management tailored to smallholder agriculture. Glob. Food Sec. 30, 100570 (2021).PubMed 
PubMed Central 

Google Scholar 
Thomas, M. B. Ecological approaches and the development of “truly integrated” pest management. Proc. Natl Acad. Sci. USA 96, 5944–5951 (1999).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Adoption of Technologies for Sustainable Farming Systems. Wageningen Workshop Proceeding (OECD, 2001).OECD–FAO Agricultural Outlook 2021–2030 (OECD, 2021).Cassman, K. G. & Grassini, P. A global perspective on sustainable intensification research. Nat. Sustain. 3, 262–268 (2020).
Google Scholar 
Mortensen, D. A. & Smith, R. G. Confronting barriers to cropping system diversification. Front. Sustain. Food Syst. 4, 564197 (2020).
Google Scholar 
van Bussel, L. G. et al. From field to atlas: upscaling of location-specific yield gap estimates. Field Crops Res. 177, 98–108 (2015).
Google Scholar 
van Wart, J. et al. Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Res. 143, 44–55 (2013).
Google Scholar 
Bouman, B. A. M. et al. ORYZA2000: Modeling Lowland Rice (International Rice Research Institute, 2001).POWER Data Methodology (NASA, accessed 25 June 2020); https://power.larc.nasa.gov/docs/van Wart, J. et al. Creating long-term weather data from thin air for crop simulation modeling. Agric. For. Meteorol. 209, 49–58 (2015).ADS 

Google Scholar 
van Ittersum, M. K. et al. Yield gap analysis with local to global relevance—a review. Field Crops Res. 143, 4–17 (2013).
Google Scholar 
Khunthasuvon, S. et al. Lowland rice improvement in northern and northeast Thailand: 1. effects of fertiliser application and irrigation. Field Crops Res. 59, 99–108 (1998).
Google Scholar 
Naklang, K., Harnpichitvitaya, D., Amarante, S. T., Wade, L. J. & Haefele, S. M. Internal efficiency, nutrient uptake, and the relation to field water resources in rainfed lowland rice of northeast Thailand. Plant Soil 286, 193–208 (2006).CAS 

Google Scholar 
Roy, R. N., Finck, A., Blair, G. J. & Tandon, H. L. S. Plant Nutrition for Food Security—A Guide for Integrated Nutrient Management (FAO, 2006).White, P. F., Oberthür, T. & Sovuthy, P. The Soils Used for Rice Production in Cambodia: A Manual for Their Identification and Management (International Rice Research Institute, 1997).Agustiani, N. et al. Simulating rice and maize yield potential in the humid tropical environment of Indonesia. Eur. J. Agron. 101, 10–19 (2018).
Google Scholar 
Espe, M. B. et al. Yield gap analysis of US rice production systems shows opportunities for improvement. Field Crops Res. 196, 276–283 (2016).
Google Scholar 
Yuan, S., Peng, S. & Li, T. Evaluation and application of the ORYZA rice model under different crop managements with high-yielding rice cultivars in central China. Field Crops Res. 212, 115–125 (2017).
Google Scholar 
Li, T. et al. From ORYZA2000 to ORYZA (v3): an improved simulation model for rice in drought and nitrogen-deficient environments. Agric. For. Meteorol. 237, 246–256 (2017).PubMed 
ADS 

Google Scholar 
Bouman, B. A. M. Developing a System of Temperate and Tropical Aerobic Rice in Asia (STAR), CPWF Project Report (CGIAR Challenge Program on Water and Food, 2008).Regional: Development and Dissemination of Climate-Resilient Rice Varieties for Water-Short Areas of South Asia and Southeast Asia (Asian Development Bank, 2016).Nguyen, V. N. & Tran, D. V. Rice in Producing Countries, FAO Rice Information (FAO, Rome, Italy, 2002).Li, T. et al. Simulation of genotype performances across a larger number of environments for rice breeding using ORYZA2000. Field Crops Res. 149, 312–321 (2013).
Google Scholar 
Samson, B. K., Hasan, M. & Wade, L. J. Penetration of hardpans by rice lines in the rainfed lowlands. Field Crops Res. 76, 175–188 (2002).
Google Scholar 
Haefele, S. M. et al. Factors affecting rice yield and fertilizer response in rainfed lowlands of northeast Thailand. Field Crops Res. 98, 39–51 (2006).
Google Scholar 
Boling, A. A. et al. The effect of toposequence position on soil properties, hydrology, and yield of rainfed lowland rice in Southeast Asia. Field Crops Res. 106, 22–33 (2008).
Google Scholar 
Foreign Agricultural Service (USDA, accessed 2 May 2021); https://apps.fas.usda.gov/psdonline/app/index.html#/app/advQueryBalié, J. & Valera, H. G. Domestic and international impacts of the rice trade policy reform in the Philippines. Food Policy 92, 101876 (2020).
Google Scholar 
Koizumi, T., Gay, S. H. & Furuhashi, G. Reviewing Indica and Japonica Rice Market Developments (OECD, 2021).Standard Country or Area Codes for Statistical Use (M49) (United Nations Statistical Division, 1999).Rega, C., Helming, J. & Paracchini, M. L. Environmentalism and localism in agricultural and land-use policies can maintain food production while supporting biodiversity. Findings from simulations of contrasting scenarios in the EU. Land Use Policy 87, 103986 (2019).
Google Scholar 
Zhou, Y. & Staatz, J. Projected demand and supply for various foods in West Africa: implications for investments and food policy. Food Policy 61, 198–212 (2016).
Google Scholar 
Yuan, S. et al. Sustainable intensification for a larger global rice bowl. Nat. Commun. 12, 7163 (2021).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Andrade, J. F. et al. Impact of Urbanization trends on production of key staple crops. Ambio https://doi.org/10.1007/s13280-021-01674-z (2021). More

Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).PubMed 

Google Scholar 
Soto-Navarro, C. A. et al. Towards a multidimensional biodiversity index for national application. Nat. Sustain. 4, 933–942 (2021).Skidmore, A. K. et al. Priority list of biodiversity metrics to observe from space. Nat. Ecol. Evol. 5, 896–906 (2021).PubMed 

Google Scholar 
Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Girardello, M. et al. Global synergies and trade-offs between multiple dimensions of biodiversity and ecosystem services. Sci. Rep. 9, 5636 (2019).PubMed 
PubMed Central 

Google Scholar 
Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).CAS 
PubMed 

Google Scholar 
Pettorelli, N. et al. Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sens. Ecol. Conserv. 2, 122–131 (2016).
Google Scholar 
Paganini, M., Leidner, A. K., Geller, G., Turner, W. & Wegmann, M. The role of space agencies in remotely sensed essential biodiversity variables. Remote Sens. Ecol. Conserv. 2, 132–140 (2016).
Google Scholar 
O’Connor, B. et al. Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets. Remote Sens. Ecol. Conserv. 1, 19–28 (2015).
Google Scholar 
Skidmore, A. K. et al. Environmental science: agree on biodiversity metrics to track from space. Nature 523, 403–405 (2015).CAS 
PubMed 

Google Scholar 
Reddy, C. S. et al. Remote sensing enabled essential biodiversity variables for biodiversity assessment and monitoring: technological advancement and potentials. Biodivers. Conserv. 30, 1–14 (2021).
Google Scholar 
Vihervaara, P. et al. How essential biodiversity variables and remote sensing can help national biodiversity monitoring. Glob. Ecol. Conserv. 10, 43–59 (2017).
Google Scholar 
Luque, S., Pettorelli, N., Vihervaara, P. & Wegmann, M. Improving biodiversity monitoring using satellite remote sensing to provide solutions towards the 2020 conservation targets. Methods Ecol. Evol. 9, 1784–1786 (2018).
Google Scholar 
Moritz, C. Applications of mitochondrial DNA analysis in conservation: a critical review. Mol. Ecol. 3, 401–411 (1994).CAS 

Google Scholar 
Graham, C. H., Ferrier, S., Huettman, F., Moritz, C. & Peterson, A. T. New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol. Evol. 19, 497–503 (2004).PubMed 

Google Scholar 
Czyż, E. A. et al. Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series. Ecol. Evol. 10, 7419–7430 (2020).PubMed 
PubMed Central 

Google Scholar 
Guillén-Escribà, C. et al. Remotely sensed between-individual functional trait variation in a temperate forest. Ecol. Evol. 11, 10834–10867 (2021).PubMed 
PubMed Central 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
PubMed 

Google Scholar 
Shaw, R. G. & Etterson, J. R. Rapid climate change and the rate of adaptation: insight from experimental quantitative genetics. New Phytol. 195, 752–765 (2012).PubMed 

Google Scholar 
Wang, Z. et al. Foliar functional traits from imaging spectroscopy across biomes in the eastern North America. New Phytol. 228, 494–511 (2020).PubMed 

Google Scholar 
Poorter, L. et al. Are functional traits good predictors of demographic rates? Evidence from five neotropical forests. Ecology 89, 1908–1920 (2008).CAS 
PubMed 

Google Scholar 
Cornwell, W. K. & Ackerly, D. D. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol. Monogr. 79, 109–126 (2009).
Google Scholar 
Gao, Q. et al. Stimulation of soil respiration by elevated CO2 is enhanced under nitrogen limitation in a decade-long grassland study. Proc. Natl Acad. Sci. USA 117, 33317–33324 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, aad8466 (2016).PubMed 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Comparative studies of critical physiological limits and vulnerability to environmental extremes in small ectotherms: how much environmental control is needed? Integr. Zool. 13, 355–371 (2018).PubMed 
PubMed Central 

Google Scholar 
Marshall, C. R. A simple method for bracketing absolute divergence times on molecular phylogenies using multiple fossil calibration points. Am. Nat. 171, 726–742 (2008).PubMed 

Google Scholar 
Quental, T. B. & Marshall, C. R. Diversity dynamics: molecular phylogenies need the fossil record. Trends Ecol. Evol. 25, 434–441 (2010).PubMed 

Google Scholar 
Graham, C. H., Moritz, C. & Williams, S. E. Habitat history improves prediction of biodiversity in rainforest fauna. Proc. Natl Acad. Sci. USA 103, 632–636 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar 
Zipkin, E. F. et al. Addressing data integration challenges to link ecological processes across scales. Front. Ecol. Environ. 19, 30–38 (2021).
Google Scholar 
Cavender-Bares, J. et al. BII-Implementation: the causes and consequences of plant biodiversity across scales in a rapidly changing world. Res. Ideas Outcomes 7, e63850 (2021).
Google Scholar 
Hwang, D. et al. A data integration methodology for systems biology. Proc. Natl Acad. Sci. USA 102, 17296–17301 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Malley, M. A. & Soyer, O. S. The roles of integration in molecular systems biology. Stud. Hist. Philos. Sci. C 43, 58–68 (2012).
Google Scholar 
Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).von Humboldt, A. & Bonpland, A. Essai sur la Géographie des Plantes, Accompagné d’un Tableau Physique des Régions Equinoxiales (Levrault & Schoell, 1807).Darwin, C. On the Origin of Species by Means of Natural Selection 6th edn (with corrections and additions to 1872) (John Murray, 1888).Braun, E. L. Deciduous Forests of Eastern North America (Hafner Publishing Company, 1967).Slik, J. W. F. et al. Phylogenetic classification of the world’s tropical forests. Proc. Natl Acad. Sci. USA 115, 1837 (2018).PubMed 
PubMed Central 

Google Scholar 
Tingley, M. W., Monahan, W. B., Beissinger, S. R. & Moritz, C. Birds track their Grinnellian niche through a century of climate change. Proc. Natl Acad. Sci. USA 106, 19637–19643 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett. 13, 1310–1324 (2010).PubMed 

Google Scholar 
Cavender-Bares, J., Ackerly, D., Hobbie, S. & Townsend, P. Evolutionary legacy effects on ecosystems: biogeographic origins, plant traits, and implications for management in the era of global change. Annu. Rev. Ecol. Evol. Syst. 47, 433–462 (2016).
Google Scholar 
Crisp, M. D., Arroyo, M. T. K., Cook, L. G., Gandolfo, M. A. & Jordan, G. J. Phylogenetic biome conservatism on a global scale. Nature 458, 754–756 (2009).CAS 
PubMed 

Google Scholar 
Forrestel, E. J., Donoghue, M. J. & Smith, M. D. Convergent phylogenetic and functional responses to altered fire regimes in mesic savanna grasslands of North America and South Africa. New Phytol. 203, 1000–1011 (2014).PubMed 

Google Scholar 
Auler, A. S. & Smart, P. L. Late quaternary paleoclimate in semiarid northeastern Brazil from U-series dating of travertine and water-table speleothems. Quat. Res. 55, 159–167 (2001).CAS 

Google Scholar 
Cheng, H. et al. Climate change patterns in Amazonia and biodiversity. Nat. Commun. 4, 1411 (2013).PubMed 

Google Scholar 
Ledru, M.-P. et al. The last 50,000 years in the Neotropics (Southern Brazil): evolution of vegetation and climate. Palaeogeogr. Palaeoclimatol. Palaeoecol. 123, 239–257 (1996).
Google Scholar 
Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).PubMed 
PubMed Central 

Google Scholar 
Delsuc, F., Brinkmann, H. & Philippe, H. Phylogenomics and the reconstruction of the tree of life. Nat. Rev. Genet. 6, 361–375 (2005).CAS 
PubMed 

Google Scholar 
Ciccarelli, F. D. et al. Toward automatic reconstruction of a highly resolved tree of life. Science 311, 1283–1287 (2006).CAS 
PubMed 

Google Scholar 
Beck, P. S. A. & Goetz, S. J. Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences. Environ. Res. Lett. 6, 045501 (2011).
Google Scholar 
Kokaly, R. F., Asner, G. P., Ollinger, S. V., Martin, M. E. & Wessman, C. A. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens. Environ. 113, S78–S91 (2009).
Google Scholar 
Graham, C. H. et al. The origin and maintenance of montane diversity: integrating evolutionary and ecological processes. Ecography 37, 711–719 (2014).
Google Scholar 
Carnaval, A. C., Hickerson, M. J., Haddad, C. F., Rodrigues, M. T. & Moritz, C. Stability predicts genetic diversity in the Brazilian Atlantic forest hotspot. Science 323, 785–789 (2009).CAS 
PubMed 

Google Scholar 
Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Carnaval, A. C. et al. Prediction of phylogeographic endemism in an environmentally complex biome. Proc. R. Soc. B 281, 20141461 (2014).PubMed 
PubMed Central 

Google Scholar 
Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).PubMed 
PubMed Central 

Google Scholar 
Forest, F., Crandall, K. A., Chase, M. W. & Faith, D. P. Phylogeny, extinction and conservation: embracing uncertainties in a time of urgency. Philos. Trans. R. Soc. Lond. B 370, 20140002 (2015).
Google Scholar 
Faith, D. P. Phylogenetic diversity, functional trait diversity and extinction: avoiding tipping points and worst-case losses. Philos. Trans. R. Soc. Lond. B 370, 20140011 (2015).
Google Scholar 
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Google Scholar 
Lavorel, S. et al. Assessing functional diversity in the field—methodology matters! Funct. Ecol. 22, 134–147 (2008).
Google Scholar 
Petchey, O. L. & Gaston, K. J. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).PubMed 

Google Scholar 
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).
Google Scholar 
Suding, K. N. et al. Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants. Glob. Change Biol. 14, 1125–1140 (2008).
Google Scholar 
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).CAS 
PubMed 

Google Scholar 
Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dahlin, K. M., Asner, G. P. & Field, C. B. Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem. Proc. Natl Acad. Sci. USA 110, 6895–6900 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).PubMed 

Google Scholar 
Enquist, B., Condit, R., Peet, R., Schildhauer, M. & Thiers, B. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. PeerJ 4, e2615v2612 (2016).
Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 

Google Scholar 
Asner, G. P., Martin, R. E., Anderson, C. B. & Knapp, D. E. Quantifying forest canopy traits: imaging spectroscopy versus field survey. Remote Sens. Environ. 158, 15–27 (2015).
Google Scholar 
Fajardo, A. & Siefert, A. Phenological variation of leaf functional traits within species. Oecologia 180, 951–959 (2016).PubMed 

Google Scholar 
Townsend, P. A., Foster, J. R., Chastain, R. A. Jr. & Currie, W. S. Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. Geosci. Remote Sens. IEEE Trans. 41, 1347–1354 (2003).
Google Scholar 
Féret, J. B., Gitelson, A. A., Noble, S. D. & Jacquemoud, S. PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sens. Environ. 193, 204–215 (2017).
Google Scholar 
Berger, K. et al. Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. Int. J. Appl. Earth Obs. Geoinf. 92, 102174 (2020).
Google Scholar 
Jacquemoud, S. & Ustin, S. Leaf Optical Properties (Cambridge Univ. Press, 2019).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoffman, M., Koenig, K., Bunting, G., Costanza, J. & Williams, K. J. Biodiversity hotspots (version 2016.1). Zenodo https://doi.org/10.5281/zenodo.3261807 (2016).Folke, C. et al. Resilience thinking: integrating resilience, adaptability and transformability. Ecol. Soc. 15, 20 (2010).
Google Scholar 
Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).CAS 
PubMed 

Google Scholar 
Peterson, G., Allen, C. & Holling, C. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).
Google Scholar 
MacDougall, A. S., McCann, K. S., Gellner, G. & Turkington, R. Diversity loss with persistent human disturbance increases vulnerability to ecosystem collapse. Nature 494, 86–89 (2013).CAS 
PubMed 

Google Scholar 
Duncan, B. N. et al. Space‐based observations for understanding changes in the Arctic‐Boreal Zone. Rev. Geophys. 58, e2019RG000652 (2020).
Google Scholar 
Wittenberg, L., Malkinson, D., Beeri, O., Halutzy, A. & Tesler, N. Spatial and temporal patterns of vegetation recovery following sequences of forest fires in a Mediterranean landscape, Mt. Carmel Israel. CATENA 71, 76–83 (2007).
Google Scholar 
Meng, Y. et al. Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. Ecol. Inform. 57, 101064 (2020).
Google Scholar 
Wilson, A. M., Latimer, A. M. & Silander, J. A. Climatic controls on ecosystem resilience: postfire regeneration in the Cape Floristic Region of South Africa. Proc. Natl Acad. Sci. USA 112, 9058 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Xie, Z. et al. Landsat and GRACE observations of arid wetland dynamics in a dryland river system under multi-decadal hydroclimatic extremes. J. Hydrol. 543, 818–831 (2016).Allen, C. R. et al. Quantifying spatial resilience. J. Appl. Ecol. 53, 625–635 (2016).
Google Scholar 
Lausch, A. et al. Understanding and assessing vegetation health by in situ species and remote-sensing approaches. Methods Ecol. Evol. 9, 1799–1809 (2018).
Google Scholar 
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 
PubMed 

Google Scholar 
Faruk, A., Belabut, D., Ahmad, N., Knell, R. J. & Garner, T. W. J. Effects of oil-palm plantations on diversity of tropical anurans. Conserv. Biol. 27, 615–624 (2013).PubMed 

Google Scholar 
Yue, S., Brodie, J. F., Zipkin, E. F. & Bernard, H. Oil palm plantations fail to support mammal diversity. Ecol. Appl. 25, 2285–2292 (2015).PubMed 

Google Scholar 
Dislich, C. et al. A review of the ecosystem functions in oil palm plantations, using forests as a reference system. Biol. Rev. Camb. Philos. Soc. 92, 1539–1569 (2017).PubMed 

Google Scholar 
Slingsby, J. A., Moncrieff, G. R. & Wilson, A. M. Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics. ISPRS J. Photogramm. Remote Sens. 166, 15–25 (2020).
Google Scholar 
Spasojevic, M. J. et al. Scaling up the diversity–resilience relationship with trait databases and remote sensing data: the recovery of productivity after wildfire. Glob. Change Biol. 22, 1421–1432 (2016).
Google Scholar 
van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 

Google Scholar 
Williams, L. J. et al. Remote spectral detection of biodiversity effects on forest biomass. Nat. Ecol. Evol. 5, 46–54 (2021).PubMed 

Google Scholar 
Schweiger, A. K. et al. Coupling spectral and resource-use complementarity in experimental grassland and forest communities. Proc. R. Soc. B 288, 20211290 (2021).PubMed 
PubMed Central 

Google Scholar 
Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: patterns and processes. Ecol. Lett. 12, 443–451 (2009).PubMed 

Google Scholar 
Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).CAS 
PubMed 

Google Scholar 
Isbell, F., Tilman, D., Reich, P. B. & Clark, A. T. Deficits of biodiversity and productivity linger a century after agricultural abandonment. Nat. Ecol. Evol. 3, 1533–1538 (2019).PubMed 

Google Scholar 
Walters, M. & Scholes, R. The GEO Handbook on Biodiversity Observation Networks (Springer, 2017).Kühl, H. S. et al. Effective biodiversity monitoring needs a culture of integration. One Earth 3, 462–474 (2020).
Google Scholar 
Sasaki, T., Furukawa, T., Iwasaki, Y., Seto, M. & Mori, A. S. Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances. Ecol. Indic. 57, 395–408 (2015).
Google Scholar 
Thompson, B. K., Olden, J. D. & Converse, S. J. Mechanistic invasive species management models and their application in conservation. Conserv. Sci. Pract. 3, e533 (2021).
Google Scholar 
Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).CAS 
PubMed 

Google Scholar 
Ellis, E. C. et al. Used planet: a global history. Proc. Natl Acad. Sci. USA 110, 7978–7985 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
McKey, D. et al. Pre-Columbian agricultural landscapes, ecosystem engineers, and self-organized patchiness in Amazonia. Proc. Natl Acad. Sci. USA 107, 7823–7828 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bush, M. B. et al. A 6900-year history of landscape modification by humans in lowland Amazonia. Quat. Sci. Rev. 141, 52–64 (2016).
Google Scholar 
Wright, J. L. et al. Sixteen hundred years of increasing tree cover prior to modern deforestation in Southern Amazon and Central Brazilian savannas. Glob. Change Biol. 27, 136–150 (2021).
Google Scholar 
Boivin, N. & Crowther, A. Mobilizing the past to shape a better Anthropocene. Nat. Ecol. Evol. 5, 273–284 (2021).PubMed 

Google Scholar 
Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. Tropical forests in the Anthropocene. Ann. Rev. Environ. Res. 39, 125–159 (2014).Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).CAS 

Google Scholar 
Verburg, P. H., Erb, K.-H., Mertz, O. & Espindola, G. Land system science: between global challenges and local realities. Curr. Opin. Environ. Sustain. 5, 433–437 (2013).PubMed 
PubMed Central 

Google Scholar 
Pendrill, F., Persson, U. M., Godar, J. & Kastner, T. Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition. Environ. Res. Lett. 14, 055003 (2019).
Google Scholar 
Burke, M., Driscoll, A., Lobell, D. B. & Ermon, S. Using satellite imagery to understand and promote sustainable development. Science 371, eabe8628 (2021).CAS 
PubMed 

Google Scholar 
Schell, C. J. et al. The ecological and evolutionary consequences of systemic racism in urban environments. Science 369, eaay4497 (2020).Trounstine, J. The geography of inequality: how land use regulation produces segregation. Am. Political Sci. Rev. 114, 443–455 (2020).
Google Scholar 
Su, S., Pi, J., Xie, H., Cai, Z. & Weng, M. Community deprivation, walkability, and public health: highlighting the social inequalities in land use planning for health promotion. Land Use Policy 67, 315–326 (2017).
Google Scholar 
Coomes, O. T., Takasaki, Y. & Rhemtulla, J. M. Forests as landscapes of social inequality tropical forest cover and land distribution among shifting cultivators. Ecol. Soc. 21, 20 (2016).Watmough, G. R. et al. Socioecologically informed use of remote sensing data to predict rural household poverty. Proc. Natl Acad. Sci. USA 116, 1213 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Verburg, P. H. et al. Land system science and sustainable development of the earth system: a global land project perspective. Anthropocene 12, 29–41 (2015).
Google Scholar 
Bickenbach, F., Bode, E., Nunnenkamp, P. & Söder, M. Night lights and regional GDP. Rev. World Econ. 152, 425–447 (2016).
Google Scholar 
Mayer, A. et al. Applying the human appropriation of net primary production framework to map provisioning ecosystem services and their relation to ecosystem functioning across the European Union. Ecosyst. Serv. 51, 101344 (2021).PubMed 
PubMed Central 

Google Scholar 
Li, Y. Urban Green Space Analysis on UBC Vancouver Campus: Integrating Virtual Gaming Technology to Map Cultural Use and Biodiversity Value of Urban Green Space (Univ. British Columbia, 2021).Ghaffarian, S., Roy, D., Filatova, T. & Kerle, N. Agent-based modelling of post-disaster recovery with remote sensing data. Int. J. Disaster Risk Reduct. 60, 102285 (2021).
Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).PubMed 

Google Scholar 
Zeng, Y. et al. Environmental destruction not avoided with the Sustainable Development Goals. Nat. Sustain. 3, 795–798 (2020).
Google Scholar 
Mirza, M. U., Xu, C., Bavel, B. V., van Nes, E. H. & Scheffer, M. Global inequality remotely sensed. Proc. Natl Acad. Sci. USA 118, e1919913118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kavvada, A. et al. Towards delivering on the Sustainable Development Goals using Earth observations. Remote Sens. Environ. 247, 111930 (2020).
Google Scholar 
Hooper, D. U. & Vitousek, P. M. Effects of plant composition and diversity on nutrient cycling. Ecol. Monogr. 68, 121–149 (1998).
Google Scholar 
Craine, J. M. et al. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytol. 183, 992 (2009).
Google Scholar 
Madritch, M. D. et al. Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales. Philos. Trans. R. Soc. B 369, 20130194 (2014).
Google Scholar 
Hobbie, S. E. Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol. Evol. 30, 357–363 (2015).PubMed 

Google Scholar 
Cline, L. C. et al. Resource availability underlies the plant–fungal diversity relationship in a grassland ecosystem. Ecology 99, 204–216 (2018).PubMed 

Google Scholar 
Wardle, D. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).CAS 
PubMed 

Google Scholar 
Meier, C. L. & Bowman, W. D. Links between plant litter chemistry, species diversity, and below-ground ecosystem function. Proc. Natl Acad. Sci. USA 105, 19780–19785 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gold, K. M. et al. Hyperspectral measurements enable pre-symptomatic detection and differentiation of contrasting physiological effects of late blight and early blight in potato. Remote Sens. 12, 286 (2020).Serbin, S. P., Singh, A., McNeil, B. E., Kingdon, C. C. & Townsend, P. A. Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecol. Appl. 24, 1651–1669 (2014).
Google Scholar 
Fisher, J. B., Perakalapudi, N. V., Turner, B. L., Schimel, D. S. & Cusack, D. F. Sci. Rep. 10, 6725 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
van der Heijden, M. G. A., Martin, F. M., Selosse, M.-A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).PubMed 

Google Scholar 
Meireles, J. E., O’Meara, B. & Cavender-Bares, J. in Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J. et al.) 155–172 (Springer, 2020).Kothari, S. et al. Community-wide consequences of variation in photoprotective physiology among prairie plants. Photosynthetica 56, 455–467 (2018).CAS 

Google Scholar 
Anderegg, L. D. L. et al. Representing plant diversity in land models: an evolutionary approach to make “functional types” more functional. Glob. Change Biol., https://doi.org/10.1111/gcb.16040 (2022).Cavender-Bares, J. M. et al. Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments. Ecol. Monogr., https://doi.org/10.1002/ecm.1488 (2021).Niemann, K. O., Quinn, G., Stephen, R., Visintini, F. & Parton, D. Hyperspectral remote sensing of mountain pine beetle with an emphasis on previsual assessment. Can. J. Remote Sens. 41, 191–202 (2015).
Google Scholar 
Chu, H. et al. Soil microbial biogeography in a changing world: recent advances and future perspectives. mSystems 5, e00803–e00819 (2020).King, G. M. Enhancing soil carbon storage for carbon remediation: potential contributions and constraints by microbes. Trends Microbiol. 19, 75–84 (2011).CAS 
PubMed 

Google Scholar 
Singh, A. K., Sisodia, A., Sisodia, V. & Padhi, M. in New and Future Developments in Microbial Biotechnology and Bioengineering (eds. Singh, J. S. & Singh, D. P.) 57–68 (Elsevier, 2019).Eviner, V. T. Plant traits that influence ecosystem processes vary independently among species. Ecology 85, 2215–2229 (2004).
Google Scholar 
Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).PubMed 

Google Scholar 
Paneque-Gálvez, J. et al. High overlap between traditional ecological knowledge and forest conservation found in the Bolivian Amazon. Ambio 47, 908–923 (2018).PubMed 
PubMed Central 

Google Scholar 
Hilbert, M. The bad news is that the digital access divide is here to stay: domestically installed bandwidths among 172 countries for 1986–2014. Telecommun. Policy 40, 567–581 (2016).
Google Scholar 
Prados, A. I. et al. Impact of the ARSET program on use of remote-sensing data. ISPRS Int. J. Geo-Inf. 8, 261 (2019).Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).
Google Scholar 
Chase, A. S. Z., Chase, D. & Chase, A. Ethics, new colonialism, and lidar data: a decade of lidar in Maya archaeology. J. Comput. Appl. Archaeol. 3, 51–62 (2020).
Google Scholar 
Carrino, T. A., Crósta, A. P., Toledo, C. L. B. & Silva, A. M. Hyperspectral remote sensing applied to mineral exploration in southern Peru: a multiple data integration approach in the Chapi Chiara gold prospect. Int. J. Appl. Earth Obs. Geoinf. 64, 287–300 (2018).
Google Scholar 
Scafutto, R. D. P. M., de Souza Filho, C. R. & de Oliveira, W. J. Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: implications for onshore exploration and monitoring. ISPRS J. Photogramm. Remote Sens. 128, 146–157 (2017).
Google Scholar 
Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).CAS 
PubMed 

Google Scholar 
Ustin, S. L. & Middleton, E. M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1 (2021).Randin, C. F. et al. Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens. Environ. 239, 111626 (2020).
Google Scholar 
Geller, G. N. et al. in Remote Sensing of Plant Biodiversity (eds. Cavender Bares, J. et al.) 519–526 (Springer, 2020).Asner, G. P. & Martin, R. E. Spectranomics: emerging science and conservation opportunities at the interface of biodiversity and remote sensing. Glob. Ecol. Conserv. 8, 212–219 (2016).
Google Scholar 
Schneider, F. D. et al. Towards mapping the diversity of canopy structure from space with GEDI. Environ. Res. Lett. 15, 115006 (2020).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Green, R. O. et al. Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ. 65, 227–248 (1998).
Google Scholar 
Hook, S. & Fisher, J. ECO3ETPTJPL v001 ECOSTRESS Evapotranspiration PT-JPL Daily L3 Global 70 m https://doi.org/10.5067/ECOSTRESS/ECO3ETPTJPL.001 (LP DAAC, accessed 8 December 2021).Turner, A. J. et al. A double peak in the seasonality of California’s photosynthesis as observed from space. Biogeosciences 17, 405–422 (2020).CAS 

Google Scholar 
Radeloff, V. C. et al. The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity. Remote Sens. Environ. 222, 204–214 (2019).
Google Scholar 
Crameri, F. Scientific colour-maps. Zenodo https://doi.org/10.5281/zenodo.1287763 (2018).Li, X. & Xiao, J. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: a global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sensing 11, 2563 (2019).Keil, P. & Chase, J. M. Global patterns and drivers of tree diversity integrated across a continuum of spatial grains. Nat. Ecol. Evol. 3, 390–399 (2019).PubMed 

Google Scholar 
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).Boonman, C. C. F. et al. Assessing the reliability of predicted plant trait distributions at the global scale. Glob. Ecol. Biogeogr. 29, 1034–1051 (2020).PubMed 
PubMed Central 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).
Google Scholar 
Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).PubMed 
PubMed Central 

Google Scholar 
Mokany, K. et al. Reconciling global priorities for conserving biodiversity habitat. Proc. Natl Acad. Sci. USA 117, 9906 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lausch, A. et al. Linking Earth Observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives. Ecol. Indic. 70, 317–339 (2016).
Google Scholar 
Schneider, F. D. et al. Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat. Commun. 8, 1441 (2017).PubMed 
PubMed Central 

Google Scholar 
Rocchini, D. et al. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol. Inform. 5, 318–329 (2010).
Google Scholar 
Schneider, F. D., Ferraz, A. & Schimel, D. Watching Earth’s interconnected systems at work. Eos, https://doi.org/10.1029/2019EO136205 (2019).Laliberté, E., Schweiger, A. K. & Legendre, P. Partitioning plant spectral diversity into alpha and beta components. Ecol. Lett. 23, 370–380 (2020).PubMed 

Google Scholar 
Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).
Google Scholar 
Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).PubMed 

Google Scholar 
Dubayah, R. et al. The Global Ecosystem Dynamics Investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).
Google Scholar 
Omasa, K., Hosoi, F. & Konishi, A. 3D lidar imaging for detecting and understanding plant responses and canopy structure. J. Exp. Bot. 58, 881–898 (2007).CAS 
PubMed 

Google Scholar 
Bae, S. et al. Radar vision in the mapping of forest biodiversity from space. Nat. Commun. 10, 4757 (2019).PubMed 
PubMed Central 

Google Scholar 
Stavros, E. N. et al. ISS observations offer insights into plant function. Nat. Ecol. Evol. 1, 0194 (2017).Turner, W. et al. Free and open-access satellite data are key to biodiversity conservation. Biol. Conserv. 182, 173–176 (2015).
Google Scholar 
Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).CAS 
PubMed 

Google Scholar 
Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).PubMed 

Google Scholar 
Kissling, W. D. et al. Towards global data products of essential biodiversity variables on species traits. Nat. Ecol. Evol. 2, 1531–1540 (2018).PubMed 

Google Scholar 
Kissling, W. D. et al. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).PubMed 

Google Scholar 
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).PubMed 

Google Scholar 
Fretwell, P. T. & Trathan, P. N. Penguins from space: faecal stains reveal the location of emperor penguin colonies. Glob. Ecol. Biogeogr. 18, 543–552 (2009).
Google Scholar 
Davies, A. B. & Asner, G. P. Advances in animal ecology from 3D-LiDAR ecosystem mapping. Trends Ecol. Evol. 29, 681–691 (2014).PubMed 

Google Scholar 
Paz, A. et al. in Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J. et al.) 255–266 (Springer International Publishing, 2020).Pinto-Ledezma, J. N. & Cavender-Bares, J. Predicting species distributions and community composition using satellite remote sensing predictors. Sci. Rep. 11, 16448 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Papeş, M., Tupayachi, R., Martínez, P., Peterson, A. T. & Powell, G. V. N. Using hyperspectral satellite imagery for regional inventories: a test with tropical emergent trees in the Amazon Basin. J. Veg. Sci. 21, 342–354 (2010).
Google Scholar 
Wang, Z. et al. Mapping foliar functional traits and their uncertainties across three years in a grassland experiment. Remote Sens. Environ. 221, 405–416 (2019).
Google Scholar  More

Hou, X., et al. The Cambrian fossils of Chengjiang, China: the flowering of early animal life. 316p., (Wiley Blackwell, Second Edition, 2017).Zhao, F., Zhu, M. & Hu, S. Community structure and composition of the Cambrian Chengjiang biota. Sci. China Earth Sci. 53, 1784–1799 (2010).ADS 

Google Scholar 
Yang, X. et al. A juvenile-rich palaeocommunity of the lower Cambrian Chengjiang biota sheds light on palaeo-boom or palaeo-bust environments. Nat. Ecol. Evol. 5, 1082–1090 (2021).PubMed 

Google Scholar 
Ma, X., Hou, X., Edgecombe, G. D. & Strausfeld, N. J. Complex brain and optic lobes in an early Cambrian arthropod. Nature 490, 258–261 (2012).CAS 
PubMed 
ADS 

Google Scholar 
Saleh, F. et al. Taphonomic bias in exceptionally preserved biotas. Earth Planet. Sci. Lett. 529, 115873 (2020).CAS 

Google Scholar 
Saleh, F. et al. A novel tool to untangle the ecology and fossil preservation knot in exceptionally preserved biotas. Earth Planet. Sci. Lett. 569, 117061 (2021).CAS 

Google Scholar 
Harper, D. A. et al. The Sirius Passet Lagerstätte of North Greenland: a remote window on the Cambrian explosion. J. Geol. Soc. 176, 1023–1037 (2019).ADS 

Google Scholar 
Nanglu, K., Caron, J. B. & Gaines, R. R. The Burgess Shale paleocommunity with new insights from Marble Canyon, British Columbia. Paleobiology 46, 58–81 (2020).
Google Scholar 
Tanaka, G., Hou, X., Ma, X., Edgecombe, G. D. & Strausfeld, N. J. Chelicerate neural ground pattern in a Cambrian great appendage arthropod. Nature 502, 364–367 (2013).CAS 
PubMed 
ADS 

Google Scholar 
Cong, P., Ma, X., Hou, X., Edgecombe, G. D. & Strausfeld, N. J. Brain structure resolves the segmental affinity of anomalocaridid appendages. Nature 513, 538–542 (2014).CAS 
PubMed 
ADS 

Google Scholar 
Liu, Y., Ortega-Hernández, J., Zhai, D. & Hou, X. A reduced labrum in a Cambrian great-appendage euarthropod. Curr. Biol. 30, 3057–3061 (2020).CAS 
PubMed 

Google Scholar 
Liu, Y. et al. Computed tomography sheds new light on the affinities of the enigmatic euarthropod Jianshania furcatus from the early Cambrian Chengjiang biota. BMC Evol. Biol. 20, 1–17 (2020).
Google Scholar 
Gabbott, S. E., Hou, X.-G., Norry, M. J. & Siveter, D. J. Preservation of Early Cambrian animals of the Chengjiang biota. Geology 32, 901–904 (2004).CAS 
ADS 

Google Scholar 
Gaines, R. R. et al. Mechanism for Burgess Shale-type preservation. Proc. Natl. Acad. Sci. 109, 5180–5184 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Forchielli, A., Steiner, M., Kasbohm, J., Hu, S. & Keupp, H. Taphonomic traits of clay-hosted early Cambrian Burgess Shale-type fossil Lagerstätten in South China. Palaeogeogr, Palaeoclimatol. Palaeoecol. 398, 59–85 (2014).
Google Scholar 
Ma, X., Edgecombe, G. D., Hou, X., Goral, T. & Strausfeld, N. J. Preservational pathways of corresponding brains of a Cambrian euarthropod. Curr. Biol. 25, 2969–2975 (2015).CAS 
PubMed 

Google Scholar 
Hammarlund, E. U. et al. Early Cambrian oxygen minimum zone-like conditions at Chengjiang. Earth Planet. Sci. Lett. 475, 160–168 (2017).CAS 
ADS 

Google Scholar 
Qi, C. et al. Influence of redox conditions on animal distribution and soft-bodied fossil preservation of the Lower Cambrian Chengjiang Biota. Palaeogeogr. Palaeoclimatol. Palaeoecol. 507, 180–187 (2018).
Google Scholar 
Saleh, F., Daley, A. C., Lefebvre, B., Pittet, B. & Perrillat, J. P. Biogenic iron preserves structures during fossilization: a hypothesis: iron from decaying tissues may stabilize their morphology in the fossil record. BioEssays 42, 1900243 (2020).CAS 

Google Scholar 
Daley, A. C. et al. Insights into soft-part preservation from the Early Ordovician Fezouata Biota. Earth Sci. Rev. 213, 103464 (2021).
Google Scholar 
Pu, X. C., et al. Cambrian lithofacies, paleogeography and mineralization in south China, Geological Publishing House, Beijing, 191 p. (1992).Zhu, M. Y., Zhang, J. M. & Li, G. X. Sedimentary environments of the early Cambrian Chengjiang biota: sedimentology of the Yu’anshan Formation in Chengjiang County, eastern Yunnan. Acta Palaeontol. Sin. 40, 80–105 (2001).
Google Scholar 
Babcock, L. E. & Zhang, W. Stratigraphy, palaeontology, and depositional setting of the Chengjiang Lagerstätte (Lower Cambrian), Yunnan, China. Palaeoworld 13, 66–86 (2001).
Google Scholar 
Babcock, L. E., Zhang, W. & Leslie, S. A. The Chengjiang biota: record of the Early Cambrian diversification of life and clues to exceptional preservation of fossils. GSA Today 11, 4–9 (2001).
Google Scholar 
MacKenzie, L. A., Hofmann, M. H., Junyuan, C. & Hinman, N. W. Stratigraphic controls of soft-bodied fossil occurrences in the Cambrian Chengjiang Biota Lagerstätte, Maotianshan Shale, Yunnan Province, China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 420, 96–115 (2015).
Google Scholar 
Chen, J. Y. & Lindström, M. A lower Cambrian soft-bodied fauna from Chengjiang, Yunnan, China. Geologiska Föreningen i Stockholm Förhandlingar 113, 79–81 (1991).
Google Scholar 
Jin, Y. G., Wang, H. Y. & Wang, W. Palaeoecological aspect of branchiopods from Chiungchussu Formation of Early Cambrian Age, Eastern Yunnan, China. Palaeoecol. China 1, 25–47 (1991).CAS 

Google Scholar 
Hu, S. Taphonomy and palaeoecology of the Early Cambrian Chengjiang Biota from eastern Yunnan, China. Berl. Paläobiologische Abhandlungen 7, 189 (2005).ADS 

Google Scholar 
Schieber, J., Southard, J. & Thaisen, K. Accretion of mudstone beds from migrating floccule ripples. Science 318, 1760–1763 (2007).CAS 
PubMed 
ADS 

Google Scholar 
Lamb, M. P., Myrow, P. M., Lukens, C., Houck, K. & Strauss, J. Deposits from wave-influenced turbidity currents: Pennsylvanian Minturn Formation, Colorado, USA. J. Sediment. Res. 78, 480–498 (2008).ADS 

Google Scholar 
Baas, J. H., Best, J. L., Peakall, J. & Wang, M. A phase diagram for turbulent, transitional, and laminar clay suspension flows. J. Sediment. Res. 79, 162–183 (2009).ADS 

Google Scholar 
Plint, A. G. & Macquaker, J. H. Bedload Transport of Mud Across a Wide, Storm-Influenced Ramp: Cenomanian–Turonian Kaskapau Formation, Western Canada Foreland Basin—Reply. J. Sediment. Res. 83, 1200–1201 (2013).
Google Scholar 
Bohacs, K. M., Lazar, O. R. & Demko, T. M. Parasequence types in shelfal mudstone strata—Quantitative observations of lithofacies and stacking patterns, and conceptual link to modern depositional regimes. Geology 42, 131–134 (2014).ADS 

Google Scholar 
Lazar, O. R., Bohacs, K. M., Macquaker, J. H., Schieber, J. & Demko, T. M. Capturing key attributes of fine-grained sedimentary rocks in outcrops, cores, and thin sections: nomenclature and description guidelines. J. Sediment. Res. 85, 230–246 (2015).CAS 
ADS 

Google Scholar 
Wheatcroft, R. A. Oceanic flood sedimentation: a new perspective. Continent. Shelf Res. 20, 2059–2066 (2000).ADS 

Google Scholar 
Wright, L. D. & Friedrichs, C. T. Gravity-driven sediment transport on continental shelves: a status report. Continent. Shelf Res. 26, 2092–2107 (2006).ADS 

Google Scholar 
Bhattacharya, J. P. & MacEachern, J. A. Hyperpycnal rivers and prodeltaic shelves in the Cretaceous seaway of North America. J. Sediment. Res. 79, 184–209 (2009).ADS 

Google Scholar 
Ichaso, A. A. & Dalrymple, R. W. Tide-and wave-generated fluid mud deposits in the Tilje Formation (Jurassic), offshore Norway. Geology 37, 539–542 (2009).ADS 

Google Scholar 
Schieber, J. Experimental testing of the transport-durability of shale lithics and its implications for interpreting the rock record. Sediment. Geol. 331, 162–169 (2016).ADS 

Google Scholar 
Zavala, C. & Arcuri, M. Intrabasinal and extrabasinal turbidites: Origin and distinctive characteristics. Sediment. Geol. 337, 36–54 (2016).ADS 

Google Scholar 
Boulesteix, K., Poyatos-Moré, M., Hodgson, D. M., Flint, S. S. & Taylor, K. G. Fringe or background: characterizing deep-water mudstones beyond the basin-floor fan sandstone pinchout. J. Sediment. Res. 90, 1678–1705 (2020).ADS 

Google Scholar 
Dumas, S. & Arnott, R. W. C. Origin of hummocky and swaley cross-stratification—the controlling influence of unidirectional current strength and aggradation rate. Geology 34, 1073–1076 (2006).ADS 

Google Scholar 
Perillo, M. M. et al. A unified model for bedform development and equilibrium under unidirectional, oscillatory and combined‐flows. Sedimentology 61, 2063–2085 (2014).
Google Scholar 
Jelby, M. E., Grundvåg, S. A., Helland‐Hansen, W., Olaussen, S. & Stemmerik, L. Tempestite facies variability and storm‐depositional processes across a wide ramp: Towards a polygenetic model for hummocky cross‐stratification. Sedimentology 67, 742–781 (2020).
Google Scholar 
Collins, D. S., Johnson, H. D., Allison, P. A., Guilpain, P. & Damit, A. R. Coupled ‘storm‐flood’depositional model: application to the Miocene–Modern Baram Delta Province, north‐west Borneo. Sedimentology 64, 1203–1235 (2017).
Google Scholar 
Dillinger, A., Vaucher, R. & Haig, D. W. Refining the depositional model of the lower Permian Carynginia Formation in the northern Perth Basin: anatomy of an ancient mouth bar. Aust. J. Earth Sci. 69, 135–151 (2022).CAS 
ADS 

Google Scholar 
Zavala, C. Hyperpycnal (over density) flows and deposits. J. Palaeogeogr. 9, 1–21 (2020).
Google Scholar 
Lin, W. & Bhattacharya, J. P. Storm‐flood‐dominated delta: a new type of delta in stormy oceans. Sedimentology 68, 1109–1136 (2021).
Google Scholar 
MacEachern, J. A., Raychaudhuri, I. & Pemberton, S. G. Stratigraphic applications of the Glossifungites ichnofacies: delineating discontinuities in the rock record. In Applications of Ichnology to Petroleum Exploration: a Core Workshop, ed. S.G. Pemberton. Soc. Sediment. Geol. Core Workshop 17, 169–198 (1992).
Google Scholar 
Hubbard, S. M. & Shultz, M. R. Deep burrows in submarine fan-channel deposits of the Cerro Toro Formation (Cretaceous), Chilean Patagonia: implications for firmground development and colonization in the deep sea. Palaios 23, 223–232 (2008).ADS 

Google Scholar 
Buatois, L. A. & Mángano, M. G. Ichnology: organism-substrate interactions in space and time. Cambridge University Press (2011).Droser, M. L., Jensen, S. & Gehling, J. G. Trace fossils and substrates of the terminal Proterozoic–Cambrian transition: implications for the record of early bilaterians and sediment mixing. Proc. Natl Acad. Sci. 99, 12572–12576 (2002).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Droser, M. L., Jensen, S. & Gehlîng, J. G. Development of early Palaeozoic ichnofabrics: evidence from shallow marine siliciclastics. Geological Society, London, Special Publications 228, 383–396 (2004).Macquaker, J. H., Bentley, S. J. & Bohacs, K. M. Wave-enhanced sediment-gravity flows and mud dispersal across continental shelves: Reappraising sediment transport processes operating in ancient mudstone successions. Geology 38, 947–950 (2010).ADS 

Google Scholar 
Myrow, P. M., Fischer, W. & Goodge, J. W. Wave-modified turbidites: combined-flow shoreline and shelf deposits, Cambrian, Antarctica. J. Sediment. Res. 72, 641–656 (2002).CAS 
ADS 

Google Scholar 
Mackay, D. A. & Dalrymple, R. W. Dynamic mud deposition in a tidal environment: the record of fluid-mud deposition in the Cretaceous Bluesky Formation, Alberta, Canada. J. Sediment. Res. 81, 901–920 (2011).ADS 

Google Scholar 
Birgenheier, L. P., Horton, B., McCauley, A. D., Johnson, C. L. & Kennedy, A. A depositional model for offshore deposits of the lower Blue Gate Member, Mancos Shale, Uinta Basin, Utah, USA. Sedimentology 64, 1402–1438 (2017).
Google Scholar 
Lobza, V. & Schieber, J. Biogenic sedimentary structures produced by worms in soupy, soft muds; observations from the Chattanooga Shale (Upper Devonian) and experiments. J. Sediment. Res. 69, 1041–1049 (1999).ADS 

Google Scholar 
Savrda, C. E. & Bottjer, D. J. Trace-fossil model for reconstruction of paleo-oxygenation in bottom waters. Geology 14, 3–6 (1986).CAS 
ADS 

Google Scholar 
Dashtgard, S. E., Snedden, J. W. & MacEachern, J. A. Unbioturbated sediments on a muddy shelf: hypoxia or simply reduced oxygen saturation? Palaeogeogr. Palaeoclimatol. Palaeoecol. 425, 128–138 (2015).
Google Scholar 
Dashtgard, S. E. & MacEachern, J. A. Unburrowed mudstones may record only slightly lowered oxygen conditions in warm, shallow basins. Geology 44, 371–374 (2016).ADS 

Google Scholar 
Pattison, S. A., Bruce Ainsworth, R. & Hoffman, T. A. Evidence of across‐shelf transport of fine‐grained sediments: turbidite‐filled shelf channels in the Campanian Aberdeen Member, Book Cliffs, Utah, USA. Sedimentology 54, 1033–1064 (2007).ADS 

Google Scholar 
Buatois, L. A. et al. Sedimentological and ichnological signatures of changes in wave, river and tidal influence along a Neogene tropical deltaic shoreline. Sedimentology 59, 1568–1612 (2012).CAS 
ADS 

Google Scholar 
Vaucher, R. et al. Tectonic controls on late Cambrian-Early Ordovician deposition in Cordillera Oriental (Northwest Argentina). Int. J. Earth Sci. 109, 1897–1920 (2020).CAS 

Google Scholar 
Paz, M. et al. Bottomset and foreset sedimentary processes in the mixed carbonate-siliciclastic Upper Jurassic-Lower Cretaceous Vaca Muerta Formation, Picún Leufú Area, Argentina. Sediment. Geol. 389, 161–185 (2019).CAS 
ADS 

Google Scholar 
Zavala, C. et al. Deltas: a new classification expanding Bates’s concepts. J. Palaeogeogr. 10, 1–15 (2021).
Google Scholar 
Davies, N. S. & Gibling, M. R. Cambrian to Devonian evolution of alluvial systems: the sedimentological impact of the earliest land plants. Earth-Sci. Rev. 98, 171–200 (2010).ADS 

Google Scholar 
McMahon, W. J. & Davies, N. S. Evolution of alluvial mudrock forced by early land plants. Science 359, 1022–1024 (2018).CAS 
PubMed 
ADS 

Google Scholar 
MacEachern, J. A., Bann, K. L., Bhattacharya, J. P. & Howell Jr, C. D. Ichnology of deltas: organism responses to the dynamic interplay of rivers, waves, storms, and tides. In River Deltas — Concepts, Models, and Examples: SEPM (eds Bhattacharya, J. P. & Giosan, L.), 49–85 (Special Publication, 2005).Buatois, L. A. & Mángano, M. G. Recurrent patterns and processes: the significance of ichnology in evolutionary paleoecology. In The trace-fossil record of major evolutionary events (eds Mángano, M. G. & Buatois, L. A.), Vol. 2, 449–473, Mesozoic and Cenozoic (Topics in Geobiology 40, 2016).Buatois, L. A. & Mángano, M. G. The other biodiversity record: Innovations in animal-substrate interactions through geologic time. GSA Today 28, 4–10 (2018).
Google Scholar 
Thayer, C. W. Biological bulldozers and the evolution of marine benthic communities. Science 203, 458–461 (1979).CAS 
PubMed 
ADS 

Google Scholar 
Thayer C. W. Sediment-mediated biological disturbance and the evolution of the marine benthos. In: Tevesz M. J. S., McCall P. L. (eds) Biotic interactions in recent and fossil benthic communities. Plenum, Zeitschr (1983).Buatois, L. A., et al. The Mesozoic marine revolution. In The trace-fossil record of major evolutionary events, (eds Mángano, M. G. & Buatois, L. A.), Vol. 40, 19–134. Mesozoic and Cenozoic (Topics in Geobiology, 2016).Gougeon, R. C., Mángano, M. G., Buatois, L. A., Narbonne, G. M. & Laing, B. A. Early Cambrian origin of the shelf sediment mixed layer. Nat. Commun. 9, 1909 (2018).PubMed 
PubMed Central 
ADS 

Google Scholar 
Herbers, D. S., MacNaughton, R. B., Timmer, E. R., Gingras, M. K. & Hubbard, S. Sedimentology and ichnology of an Early-Middle Cambrian storm-influenced barred shoreface succession, Colville Hills, Northwest territories. Bull. Can. Petrol. Geol. 64, 538–554 (2016).
Google Scholar 
Jensen, S. Trace fossils from the Lower Cambrian Mickwitzia sandstone, south-central Sweden. Foss. Strat. 42, 1–111 (1997).
Google Scholar 
Mángano, M. G. & Buatois, L. A. Decoupling of body-plan diversification and ecological structuring during the Ediacaran-Cambrian transition: Evolutionary and geobiological feedbacks. Proc. R. Soc. B. 281, 1–9 (2014).
Google Scholar 
Gaines, R. R. Burgess Shale-type preservation and its distribution in space and time. In Reading and Writing of the Fossil Record: Preservational Pathways to Exceptional Fossilization, (eds Laflamme, M., Schiffbauer, J. D. & Darroch, S. A. F.) Vol. 20, 123–146 (Paleontol. Soc. Pap. 2014).Enright, O. G. B., Minter, N. J., Sumner, E. J., Mángano, M. G. & Buatois, L. A. Flume experiments reveal flows in the Burgess Shale can sample and transport organisms across substantial distances. Commun. Earth Environ. 2, 1–7 (2021).
Google Scholar 
Daily, B., Moore, P. S. & Rust, B. R. Terrestrial‐marine transition in the Cambrian rocks of Kangaroo Island, South Australia. Sedimentology 27, 379–399 (1980).ADS 

Google Scholar 
Buatois, L. A., Mángano, M. G. & Pattison, S. A. Ichnology of prodeltaic hyperpycnite–turbidite channel complexes and lobes from the Upper Cretaceous Prairie Canyon Member of the Mancos Shale, Book Cliffs, Utah, USA. Sedimentology 66, 1825–1860 (2019).
Google Scholar 
Serra, F., Balseiro, D., Vaucher, R. & Waisfeld, B. G. Structure of Trilobite communities along a delta-marine gradient (lower Ordovician; Northwestern Argentina). Palaios 36, 39–52 (2021).ADS 

Google Scholar 
Saleh, F. et al. Storm-induced community dynamics in the Fezouata Biota (Lower Ordovician, Morocco). Palaios 33, 535–541 (2018).ADS 

Google Scholar 
Saleh, F. et al. Large trilobites in a stress-free Early Ordovician environment. Geol. Mag. 158, 261–270 (2021).ADS 

Google Scholar 
Tabb, D. C. & Jones, A. C. Effect of Hurricane Donna on the aquatic fauna of North Florida Bay. Trans. Am. Fish. Soc. 91, 375–378 (1962).
Google Scholar 
Barry, J. P. & Dayton, P. K. Physical heterogeneity and the organization of marine communities. In Ecological heterogeneity pp. 270–320. (Springer, New York, NY 1991).Shu, D. G., Zhang, X. L. & Chen, L. Reinterpretation of Yunnanozoon as the earliest known hemichordate. Nature 380, 428–430 (1996).CAS 
ADS 

Google Scholar 
Russell, M. P. Echinoderm responses to variation in salinity. Adv. Mar. Biol. 66, 171–212 (2013).PubMed 

Google Scholar 
Zhao, Y. et al. Kaili Biota: a taphonomic window on diversification of metazoans from the basal Middle Cambrian: Guizhou, China. Acta Geol. Sin. 79, 751–765 (2005).
Google Scholar  More

Alerstam, T., Hedenström, A. & Åkesson, S. Long‐distance migration: evolution and determinants. Oikos 103, 247–260 (2003).Article 

Google Scholar 
Newton, I. The migration ecology of birds (Elsevier, London, 2008).Putman, N. F. et al. An inherited magnetic map guides ocean navigation in juvenile Pacific salmon. Curr. Biol. 24, 446–450 (2014).CAS 
Article 

Google Scholar 
Jesmer, B. R. et al. Is ungulate migration culturally transmitted? Evidence of social learning from translocated animals. Science 361, 1023–1025 (2018).ADS 
CAS 
Article 

Google Scholar 
Milner-Gulland, E. J., Fryxell, J. M. & Sinclair, A. R. (Eds.) Animal migration: a synthesis (Oxford University Press, New York, 2011).Conradt, L. & Roper, T. J. Consensus decision making in animals. Trends Ecol. Evol. 20, 449–456 (2005).Article 

Google Scholar 
Couzin, I. D., Krause, J., Franks, N. R. & Levin, S. A. Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005).ADS 
CAS 
Article 

Google Scholar 
Vansteelant, W. M. G., Kekkonen, J. & Byholm, P. Wind conditions and geography shape the first outbound migration of juvenile honey buzzards and their distribution across sub-Saharan Africa. Proc. R. Soc. B 284, 20170387 (2017).Article 

Google Scholar 
Flack, A., Nagy, M., Fiedler, W., Couzin, I. D. & Wikelski, M. From local collective behavior to global migratory patterns in white storks. Science 360, 911–914 (2018).ADS 
CAS 
Article 

Google Scholar 
Chernetsov, N., Berthold, P. & Querner, U. Migratory orientation of first-year white storks (Ciconia ciconia): inherited information and social interactions. J. Exp. Biol. 207, 937–943 (2004).Article 

Google Scholar 
Mueller, T., O’Hara, R. B., Converse, S. J., Urbanek, R. P. & Fagan, W. F. Social learning of migratory performance. Science 341, 999–1002 (2013).ADS 
CAS 
Article 

Google Scholar 
Whiten, A. Cultural evolution in animals. Annu. Rev. Ecol. Evol. Syst. 50, 27–48 (2019).Article 

Google Scholar 
Whitehead, H. & Rendell, L. The Cultural Lives of Whales and Dolphins (Chicago University Press, Chicago, 2015).Franks, N. R. & Richardson, T. Teaching in tandem-running ants. Nature 439, 153–153 (2006).ADS 
CAS 
Article 

Google Scholar 
Thornton, A. & McAuliffe, K. Teaching in wild meerkats. Science 313, 227–229 (2006).ADS 
CAS 
Article 

Google Scholar 
Cramp, S. (Ed.) The birds of the Western Palearctic. Vol. IV. Terns to woodpeckers (Oxford University Press, New York, 1985).Méndez, V. et al. Paternal effects in the initiation of migratory behaviour in birds. Sci. Rep. 11, 2782 (2021).ADS 
Article 

Google Scholar 
Olson, V. A., Liker, A., Freckleton, R. P. & Székely, T. Parental conflict in birds: comparative analyses of offspring development, ecology and mating opportunities. Proc. R. Soc. B 275, 301–307 (2008).CAS 
Article 

Google Scholar 
Ledwoń, M. & Neubauer, G. Offspring desertion and parental care in the Whiskered Tern Chlidonias hybrida. Ibis 159, 860–872 (2017).Article 

Google Scholar 
Arnqvist, G. & Rowe, L. Sexual conflict (Princeton University Press, New York, 2005).Goodenough, K. S. & Patton, R. T. Satellite telemetry reveals strong fidelity to migration routes and wintering grounds for the gull-billed tern (Gelochelidon nilotica). Waterbirds 42, 400–410 (2019).Article 

Google Scholar 
Gu, Z. et al. Climate-driven flyway changes and memory-based long-distance migration. Nature 591, 259–264 (2021).ADS 
CAS 
Article 

Google Scholar 
Baert, J. M. et al. Resource predictability drives interannual variation in migratory behavior in a long-lived bird. Behav. Ecol. arab132, https://doi.org/10.1093/beheco/arab132 (2021).Papageorgiou, D. & Farine, D. R. Group size and composition influence collective movement in a highly social terrestrial bird. eLife 9, e59902 (2020).CAS 
Article 

Google Scholar 
Caro, T. M. & Hauser, M. D. Is there teaching in nonhuman animals? Q. Rev. Biol. 67, 151–174 (1992).CAS 
Article 

Google Scholar 
Thornton, A. & Raihani, N. J. The evolution of teaching. Anim. Behav. 75, 1823–1836 (2008).Article 

Google Scholar 
Riedman, M. L. The evolution of alloparental care and adoption in mammals and birds. Q. Rev. Biol. 57, 405–435 (1982).Article 

Google Scholar 
Sheppard, C. E. et al. Decoupling of genetic and cultural inheritance in a wild mammal. Curr. Biol. 28, 1846–1850 (2018).CAS 
Article 

Google Scholar 
Åkesson, S. & Helm, B. Endogenous programs and flexibility in bird migration. Front. Ecol. Evol. 8, 1–20 (2020).Article 

Google Scholar 
Sasaki, T. & Biro, D. Cumulative culture can emerge from collective intelligence in animal groups. Nat. Commun. 8, 1–6 (2017).ADS 
Article 

Google Scholar 
Whiten, A., Ayala, F. J., Feldman, M. W. & Laland, K. N. The extension of biology through culture. Proc. Natl Acad. Sci. USA 114, 7775–7781 (2017).CAS 
Article 

Google Scholar 
Aplin, L. M. Culture and cultural evolution in birds: a review of the evidence. Anim. Behav. 147, 179–187 (2019).Article 

Google Scholar 
Laland, K. N., Toyokawa, W. & Oudman, T. Animal learning as a source of developmental bias. Evol. Dev. 22, 126–142 (2020).Article 

Google Scholar 
Sergio, F. et al. Individual improvements and selective mortality shape lifelong migratory performance. Nature 515, 410–413 (2014).ADS 
CAS 
Article 

Google Scholar 
Guttal, V. & Couzin, I. D. Social interactions, information use, and the evolution of collective migration. Proc. Natl Acad. Sci. USA 107, 16172–16177 (2010).ADS 
CAS 
Article 

Google Scholar 
Oudman, T. et al. Young birds switch but old birds lead: how barnacle geese adjust migratory habits to environmental change. Front. Ecol. Evol. 7, 106–120 (2020).Article 

Google Scholar 
Pearson, R. G. et al. Life history and spatial traits predict extinction risk due to climate change. Nat. Clim. Chang 4, 217–221 (2014).ADS 
Article 

Google Scholar 
Vickery, J. A. The decline of Afro-Palaearctic migrants and an assessment of potential causes. Ibis 156, 1–22 (2014).Article 

Google Scholar 
Thaxter, C. B. et al. A trial of three harness attachment methods and their suitability for long-term use on Lesser Black-backed Gulls and Great Skuas. Ringing Migr. 29, 65–76 (2014).Article 

Google Scholar 
Byholm, P., Beal, M., Isaksson, N., Lötberg, U. & Åkesson, S. Data from: paternal transmission of migration knowledge in a long-distance bird migrant. Movebank Data Repos. https://doi.org/10.5441/001/1.352qf1cv (2022).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, 2020). https://www.R-project.org/. More

This section provides an overview of methods. The Supplementary Information provides additional detailed descriptions of the leech collections, laboratory processing, bioinformatics pipeline, and site-occupancy modelling. Code for our bioinformatics pipeline is available at Ji72 and Yu73. Code for our site-occupancy modelling and analysis is available at Baker et al.74.Leech collectionsSamples were collected during the rainy season, from July to September 2016, by park rangers from the Ailaoshan Forestry Bureau. The nature reserve is divided into 172 non-overlapping patrol areas defined by the Yunnan Forestry Survey and Planning Institute. These areas range in size from 0.5 to 12.5 km2 (mean 3.9 ± sd 2.5 km2), in part reflecting accessibility (smaller areas tend to be more rugged). These patrol areas pre-existed our study, and are used in the administration of the reserve. The reserve is divided into six parts, which are managed by six cities or autonomous counties (NanHua, ChuXiong, JingDong, ZhenYuan, ShuangBai, XinPing) which assign patrol areas to the villages within their jurisdiction based on proximity. The villages establish working groups to carry out work within the patrol areas. Thus, individual park rangers might change every year, but the patrol areas and the villages responsible for them are fixed.Each ranger was supplied with several small bags containing tubes filled with RNAlater preservative. Rangers were asked to place any leeches they could collect opportunistically during their patrols (e.g. from the ground or clothing) into the tubes, in exchange for a one-off payment of RMB 300 ( ~USD 45) for participation, plus RMB 100 if they caught one or more leeches. Multiple leeches could be placed into each tube, but the small tube sizes generally required the rangers to use multiple tubes for their collections.A total of 30,468 leeches were collected in 3 months by 163 rangers across all 172 patrol areas. When a bag of tubes contained  More

Whittaker, R. H. Vegetation of the Siskiyou mountains, Oregon and California. Ecol. Monogr. 30, 279–338 (1960).
Google Scholar 
Wilson, R. J., Thomas, C. D., Fox, R., Roy, D. B. & Kunin, W. E. Spatial patterns in species distributions reveal biodiversity change. Nature 432, 393–396 (2004).CAS 
PubMed 

Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
PubMed 

Google Scholar 
Ings, T. C. et al. Ecological networks—beyond food webs. J. Anim. Ecol. 78, 253–269 (2009).PubMed 

Google Scholar 
Dunne, J. A. & Williams, R. J. Cascading extinctions and community collapse in model food webs. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 364, 1711–1723 (2009).
Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).PubMed 

Google Scholar 
Vellend, M. The Theory of Ecological Communities Vol. 57 229 (Princeton University Press, 2016).Altermatt, F. Diversity in riverine metacommunities: a network perspective. Aquat. Ecol. 47, 365–377 (2013).
Google Scholar 
Peterson, E. E. et al. Modelling dendritic ecological networks in space: an integrated network perspective. Ecol. Lett. 16, 707–719 (2013).PubMed 

Google Scholar 
Tonkin, J. D. et al. The role of dispersal in river network metacommunities: patterns, processes, and pathways. Freshw. Biol. 63, 141–163 (2018).
Google Scholar 
Muneepeerakul, R. et al. Neutral metacommunity models predict fish diversity patterns in Mississippi-Missouri basin. Nature 453, 220–222 (2008).CAS 
PubMed 

Google Scholar 
Besemer, K. et al. Headwaters are critical reservoirs of microbial diversity for fluvial networks. Proc. Biol. Sci. 280, 20131760 (2013).PubMed 
PubMed Central 

Google Scholar 
Finn, D. S., Bonada, N., Múrria, C. & Hughes, J. M. Small but mighty: headwaters are vital to stream network biodiversity at two levels of organization. J. North Am. Benthol. Soc. 30, 963–980 (2011).
Google Scholar 
Altermatt, F., Seymour, M. & Martinez, N. River network properties shape α-diversity and community similarity patterns of aquatic insect communities across major drainage basins. J. Biogeogr. 40, 2249–2260 (2013).
Google Scholar 
Harvey, E., Gounand, I., Fronhofer, E. A. & Altermatt, F. Disturbance reverses classic biodiversity predictions in river-like landscapes. Proc. R. Soc. B: Biol. Sci. 285, 20182441 (2018).
Google Scholar 
Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).
Google Scholar 
Thompson, R. M. et al. Food webs: reconciling the structure and function of biodiversity. Trends Ecol. Evol. 27, 689–697 (2012).PubMed 

Google Scholar 
Woodward, G. & Hildrew, A. G. Food web structure in riverine landscapes. Freshw. Biol. 47, 777–798 (2002).
Google Scholar 
Williams, R. J. & Martinez, N. D. Limits to trophic levels and omnivory in complex food webs: theory and data. Am. Nat. 163, 458–468 (2004).PubMed 

Google Scholar 
Thompson, R. M. & Townsend, C. R. The effect of seasonal variation on the community structure and food-web attributes of two streams: implications for food-web science. Oikos 87, 75–88 (1999).
Google Scholar 
Wood, S. A., Russell, R., Hanson, D., Williams, R. J. & Dunne, J. A. Effects of spatial scale of sampling on food web structure. Ecol. Evol. 5, 3769–3782 (2015).PubMed 
PubMed Central 

Google Scholar 
Tylianakis, J. M. & Morris, R. J. Ecological networks across environmental gradients. Annu. Rev. Ecol., Evolution, Syst. 48, 25–48 (2017).
Google Scholar 
Romanuk, T. N. et al. The structure of food webs along river networks. Ecography 29, 3–10 (2006).
Google Scholar 
Olivier, P. et al. Exploring the temporal variability of a food web using long‐term biomonitoring data. Ecography 42, 2107–2121 (2019).
Google Scholar 
Poisot, T., Canard, E., Mouillot, D., Mouquet, N. & Gravel, D. The dissimilarity of species interaction networks. Ecol. Lett. 15, 1353–1361 (2012).PubMed 

Google Scholar 
Delmas, E. et al. Analysing ecological networks of species interactions. Biol. Rev. Camb. Philos. Soc. https://doi.org/10.1111/brv.12433 (2018).Article 
PubMed 

Google Scholar 
Tavares-Cromar, A. F. & Williams, D. D. The importance of temporal resolution in food web analysis: Evidence from a detritus-based stream. Ecol. Monogr. 66, 91–113 (1996).
Google Scholar 
Poisot, T., Stouffer, D. B. & Gravel, D. Beyond species: why ecological interaction networks vary through space and time. Oikos 124, 243–251 (2015).
Google Scholar 
Thomsen, P. F. & Willerslev, E. Environmental DNA—an emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).
Google Scholar 
Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 12544 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dunne, J. A. In Ecological Networks: Linking Structure and Dynamics (eds. Pascual, J. A. & Dunne, J. A.) 27–86 (University Press, 2006).Neff, F. et al. Changes in plant-herbivore network structure and robustness along land-use intensity gradients in grasslands and forests. Sci Adv 7, eabf3985 (2021).O’Connor, M. J. et al. Unveiling the food webs of tetrapods across Europe through the prism of the Eltonian niche. J. Biogeogr. 47, 181–192 (2020).
Google Scholar 
Pellissier, L. et al. Comparing species interaction networks along environmental gradients. Biol. Rev. Camb. Philos. Soc. 93, 785–800 (2018).PubMed 

Google Scholar 
Saravia, L. A. et al. Ecological network assembly: how the regional metaweb influences local food webs. BioRxiv, https://doi.org/10.1101/340430 (2021).Blackman, R. C. et al. Mapping biodiversity hotspots of fish communities in subtropical streams through environmental DNA. Sci. Rep. 4, e65352 (2021).
Google Scholar 
Baselga, A. & Orme, C. D. L. betapart: an R package for the study of beta diversity: Betapart package. Methods Ecol. Evol. 3, 808–812 (2012).
Google Scholar 
Seymour, M. et al. Executing multi-taxa eDNA ecological assessment via traditional metrics and interactive networks. Sci. Total Environ. 729, 138801 (2020).CAS 
PubMed 

Google Scholar 
D’Alessandro, S. & Mariani, S. Sifting environmental DNA metabarcoding data sets for rapid reconstruction of marine food webs. Fish Fish 22, 822–833 (2021).
Google Scholar 
Zhang, Y. et al. Holistic pelagic biodiversity monitoring of the Black Sea via eDNA metabarcoding approach: From bacteria to marine mammals. Environ. Int. 135, 105307 (2020).PubMed 

Google Scholar 
Altermatt, F. et al. Uncovering the complete biodiversity structure in spatial networks: the example of riverine systems. Oikos 129, 607–618 (2020).
Google Scholar 
Widder, S. et al. Fluvial network organization imprints on microbial co-occurrence networks. Proc. Natl Acad. Sci. USA 111, 12799–12804 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Seymour, M. et al. Environmental DNA provides higher resolution assessment of riverine biodiversity and ecosystem function via spatio-temporal nestedness and turnover partitioning. Commun. Biol. 4, 512 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mächler, E. et al. Assessing different components of diversity across a river network using eDNA. Environ. DNA 1, 290–301 (2019).
Google Scholar 
Peralta-Maraver, I., López-Rodríguez, M. J. & de Figueroa, J. M. T. Structure, dynamics and stability of a Mediterranean river food web. Mar. Freshw. Res. 68, 484–495 (2017).
Google Scholar 
Woodward, G. et al. Ecological networks in a changing climate. Ecol. Netw. 42, 71–138 (2010).
Google Scholar 
Kondoh, M., Kato, S. & Sakato, Y. Food webs are built up with nested subwebs. Ecology 91, 3123–3130 (2010).PubMed 

Google Scholar 
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The River Continuum Concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).
Google Scholar 
Power, M. E. & Dietrich, W. E. Food webs in river networks. Ecol. Res. https://doi.org/10.1046/j.0912-3814.2002.00503.x (2002).Montoya, D., Yallop, M. L. & Memmott, J. Functional group diversity increases with modularity in complex food webs. Nat. Commun. 6, 7379 (2015).CAS 
PubMed 

Google Scholar 
Gravel, D., Albouy, C. & Thuiller, W. The meaning of functional trait composition of food webs for ecosystem functioning. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 371, 20150268 (2016).Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: a systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).
Google Scholar 
Carraro, L., Mächler, E., Wüthrich, R. & Altermatt, F. Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nat. Commun. 11, 3585 (2020).PubMed 
PubMed Central 

Google Scholar 
Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 

Google Scholar 
Bista, I. et al. Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nat. Commun. 8, 14087 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Erickson, R. A., Merkes, C. M., Jackson, C. A., Goforth, R. R. & Amberg, J. J. Seasonal trends in eDNA detection and occupancy of bigheaded carps. J. Gt. Lakes Res. 43, 762–770 (2017).
Google Scholar 
Troth, C. R., Sweet, M. J., Nightingale, J. & Burian, A. Seasonality, DNA degradation and spatial heterogeneity as drivers of eDNA detection dynamics. Sci. Total Environ. 768, 144466 (2021).CAS 
PubMed 

Google Scholar 
Thalinger, B. et al. The effect of activity, energy use, and species identity on environmental DNA shedding of freshwater fish. Front. Ecol. Evolution 9, 73 (2021).
Google Scholar 
Kelly, R. P., Port, J. A., Yamahara, K. M. & Crowder, L. B. Using environmental DNA to census marine fishes in a large mesocosm. PLoS ONE 9, e86175 (2014).PubMed 
PubMed Central 

Google Scholar 
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
PubMed Central 

Google Scholar 
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
PubMed 

Google Scholar 
Liu, C. M. et al. BactQuant: An enhanced broad-coverage bacterial quantitative real-time PCR assay. BMC Microbiol. 12, 56 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mansfeldt, C. et al. Microbial community shifts in streams receiving treated wastewater effluent. Sci. Total Environ. 709, 135727 (2020).CAS 
PubMed 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 

Google Scholar 
Andrews, S. FASTQC A Quality Control tool for High Throughput Sequence Data (Babraham Institute, 2015).Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
PubMed Central 

Google Scholar 
Hänfling, B. et al. Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Mol. Ecol. 25, 3101–3119 (2016).PubMed 

Google Scholar 
Csárdi, G. & Nepusz, T. The igraph software package for complex network research. Int. J. Complex Syst. 1695, 1–9 (2006).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package 2.5-6. https://CRAN.Rproject.org/package=vegan (2019).Tachet, H., Bournaud, M., Richoux, P. & Usseglio-Polatera, P. Invertébrés d’eau douce—systématique, biologie, écologie (CNRS Editions, 2010).Schmidt-Kloiber, A. & Hering, D. www.freshwaterecology.info—an online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecol. Indic. 53, 271–282 (2015).Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).PubMed 

Google Scholar 
Johnson, S., Domínguez-García, V., Donetti, L. & Muñoz, M. A. Trophic coherence determines food-web stability. Proc. Natl Acad. Sci. USA 111, 17923–17928 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wootton, K. L. Omnivory and stability in freshwater habitats: Does theory match reality? Freshw. Biol. 62, 821–832 (2017).
Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw., Artic. 82, 1–26 (2017).
Google Scholar 
Lenth, R. V. Estimated Marginal Means, aka Least-Squares Means [R package emmeans version 1.6.1] (2021).RStudio Team RStudio: Integrated development for R. RStudio, PBC, Boston, MA. R version 4.0.4 Retrieved from http://www.rstudio.com/ (2021) More