Philippa Kaur

Estes, J. et al. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Environ. Resour. 41, 83–116 (2016).
Google Scholar 
Ferretti, F., Worm, B., Britten, G., Heithaus, M. & Lotze, H. Patterns and ecosystem consequences of shark declines in the ocean. Ecol. Lett. 13, 1055–1071 (2010).PubMed 

Google Scholar 
Heithaus, M. R. et al. Seagrasses in the age of sea turtle conservation and shark overfishing. Front. Mar. Sci. 1, 1–6 (2014).
Google Scholar 
McCauley, D. et al. Marine defaunation: Animal loss in the global ocean. Science 347, 1255641 (2015).PubMed 

Google Scholar 
Pereira, H. et al. Scenarios for global biodiversity in the 21st century. Science 330, 1496–1501 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Oliver, S., Braccini, M., Newman, S. & Harvey, E. S. Global patterns in the bycatch of sharks and rays. Mar. Policy 54, 86–97 (2015).
Google Scholar 
Pacoureau, N. et al. Half a century of global decline in oceanic sharks and rays. Nature 589, 567–574 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Gilman, E. et al. Shark interactions in pelagic longline fisheries. Mar. Policy 32, 1–18 (2008).
Google Scholar 
Worm, B. et al. Global catches, exploitation rates, and rebuilding options for sharks. Mar. Policy 40, 194–204 (2013).
Google Scholar 
Bowlby, H. & Gibson, A. Implications of life history uncertainty when evaluating status in the Northwest Atlantic population of white shark (Carcharodon carcharias). Ecol. Evol. 10, 4990–5000 (2020).PubMed 
PubMed Central 

Google Scholar 
Dulvy, N. et al. Overfishing drives over one third of all sharks and rays toward a global extinction crisis. Curr. Biol. 31, 4773-4787.e8 (2021).CAS 
PubMed 

Google Scholar 
Heino, M., Pauli, B. & Dieckmann, U. Fisheries-induced evolution. Annu. Rev. Ecol. Evol. Syst. 46, 461–480 (2015).
Google Scholar 
Mitchell, J., McLean, D., Collins, S. & Langlois, T. Shark depredation in commercial and recreational fisheries. Rev. Fish Biol. Fish 28, 715–748 (2018).
Google Scholar 
Jaiteh, V. F., Loneragan, N. & Warren, C. The end of shark finning? Impacts of declining catches and fin demand on coastal community livelihoods. Mar. Policy 82, 224–233 (2017).
Google Scholar 
Seidu, I. et al. Fishing for survival: Importance of shark fisheries for the livelihoods of coastal communities in Western Ghana. Fish. Res. 246, 106157 (2022).
Google Scholar 
Gilman, E., Weijerman, M. & Suuronen, P. Ecological data from observer programs underpin ecosystem-based fisheries management. ICES J. Mar. Sci. 74, 1481–1495 (2017).
Google Scholar 
Melnychuk, M. et al. Identifying management actions that promote sustainable fisheries. Nat. Sustain. https://doi.org/10.1038/s41893-020-00668-1 (2021).Article 

Google Scholar 
Musyl, M. & Gilman, E. Meta-analysis of post-release fishing mortality in apex predatory pelagic sharks and white marlin. Fish Fish. 20, 466–500 (2019).
Google Scholar 
Clarke, S. A status snapshot of key shark species in the western and central pacific and potential management options. in WCPFC-SC7-2011/EB-WP-04. Western and Central Pacific Fisheries Commission, Kolonia, Federated States of Micronesia (2011).Dapp, D., Walker, T., Huveneers, C. & Reina, R. Respiratory mode and gear type are important determinants of elasmobranch immediate and post-release mortality. Fish Fish. 17, 507–524 (2016).
Google Scholar 
ICES. Report of the working group on elasmobranch fishes. in ICES CM 2018/ACOM:16. International Council for the Exploration of the Sea, Copenhagen (2018).Dicks, L. et al. A transparent process for “evidence-informed” policy making. Conserv. Lett. 7, 119–125 (2014).
Google Scholar 
Nichols, J., Kendall, W. & Boomer, G. Accumulating evidence in ecology: Once is not enough. Ecol. Evol. 9, 13991–14004 (2019).PubMed 
PubMed Central 

Google Scholar 
Nakagawa, S. et al. Meta-analysis of variation: Ecological and evolutionary applications and beyond. Methods Ecol. Evol. 6, 143–152 (2015).
Google Scholar 
Pfaller, J., Chaloupka, M., Bolten, A. & Bjorndal, K. Phylogeny, biogeography and methodology: A meta-analytic perspective on heterogeneity in adult marine turtle survival rates. Sci. Rep. 8, 5852. https://doi.org/10.1038/s41598-018-24262-w (2018).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Godin, A., Carlson, J. & Burgener, V. The effect of circle hooks on shark catchability and at-vessel mortality rates in longlines fisheries. Bull. Mar. Sci. 88, 469–483 (2012).
Google Scholar 
Reinhardt, J. et al. Catch rate and at-vessel mortality of circle hooks versus J-hooks in pelagic longline fisheries: A global meta-analysis. Fish Fish. 19, 413–430 (2018).
Google Scholar 
Rosa, D., Santos, C. & Coelho, R. Assessing the effects of hook, bait and leader type as potential mitigation measures to reduce bycatch and mortality rates of shortfin mako: A meta-analysis with comparisons for target, bycatch and vulnerable fauna interactions. in ICCAT Collective Volume of Scientifics Papers 76, 247–278 (2020).Santos, C., Rosa, D. & Coelho, R. Hook, bait and leader type effects on surface pelagic longline retention and mortality rates: A meta-analysis with comparisons for target, bycatch and vulnerable fauna interactions. in IOTC-2019-WPEB15-39. Indian Ocean Tuna Commission, Mahe, Seychelles (2019).Santos, C., Rosa, D. & Coelho, R. Progress on a meta-analysis for comparing hook, bait and leader effects on target, bycatch and vulnerable fauna interactions. in Collective Volume of Scientifics Papers ICCAT 77, 182–217 (2020).Condamine, F., Romieu, J. & Guinot, G. Climate cooling and clade competition likely drove the decline of lamniform sharks. PNAS 116, 20584–20590 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B. & Bürkner, P. Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC (with Discussion). Bayesian Anal. 16, 667–718 (2021).MathSciNet 

Google Scholar 
Cinar, O., Nakagawa, S. & Viechtbauer, W. Phylogenetic multilevel meta-analysis: A simulation study on the importance of modelling the phylogeny. Methods Ecol. Evol. 13, 383–395 (2022).
Google Scholar 
Lajeunesse, M. Meta-analysis and the comparative phylogenetic method. Am. Nat. 174, 369–381 (2009).PubMed 

Google Scholar 
Chamberlain, S. et al. Does phylogeny matter? Assessing the impact of phylogenetic information in ecological meta-analysis. Ecol. Lett. 15, 627–636 (2012).PubMed 

Google Scholar 
Burns, J. & Strauss, S. More closely related species are more ecologically similar in an experimental test. Proc. Natl. Acad. Sci. USA 108, 5302–5307 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cachera, M. & Le Loc’h, F. Assessing the relationships between phylogenetic and functional singularities in sharks (Chondrichthyes). Ecol. Evol. 7, 6292–6303 (2017).PubMed 
PubMed Central 

Google Scholar 
Münkemüller, T., Boucher, F. C., Thuiller, W. & Lavergne, S. Phylogenetic niche conservatism—Common pitfalls and ways forward. Funct. Ecol. 29, 627–639 (2015).PubMed 
PubMed Central 

Google Scholar 
Bazzi, M., Campione, N., Kear, B., Pimiento, C. & Ahlberg, P. Feeding ecology has shaped the evolution of modern sharks. Curr. Biol. 31, 5138–5148 (2021).CAS 
PubMed 

Google Scholar 
Sepulveda, C., Wegner, N., Bernal, D. & Graham, J. The red muscle morphology of the thresher sharks (family Alopiidae). J. Exp. Biol. 208, 4255–4261 (2005).CAS 
PubMed 

Google Scholar 
Wosnick, N. et al. Multispecies thermal dynamics of air-exposed ectothermic sharks and its implications for fisheries conservation. J. Exp. Mar. Biol. Ecol. 513, 1–9 (2019).
Google Scholar 
French, R. et al. High survivorship after catch-and-release fishing suggests physiological resilience in the endothermic shortfin mako shark (Isurus oxyrinchus). Conserv. Physiol. https://doi.org/10.1093/conphys/cov044 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Davis, M. Key principles for understanding fish bycatch discard mortality. Can. J. Fish. Aquat. Sci. 59, 1834–1843 (2002).
Google Scholar 
Massey, Y., Sabarros, P., Rabearisoa, N. & Bach, P. Drivers of at-haulback mortality of sharks caught during pelagic longline fishing experiments. in IOTC-2019-WPEB15-14_Rev1. Indian Ocean Tuna Commission, Mahe, Seychelles (2019).Musyl, M., Moyes, C., Brill, R. & Fragoso, N. Factors influencing mortality estimates in post-release survival studies: Comment on Campana et al. (2009). Mar. Ecol. Prog. Ser. 396, 157–159 (2009).Pimiento, C., Cantalapiedra, J., Shimada, K., Field, D. & Smaers, J. Evolutionary pathways towards gigantism in sharks and rays. Evolution 73, 588–599 (2019).PubMed 

Google Scholar 
Musyl, M. & Gilman, E. Post-release fishing mortality of blue (Prionace glauca) and silky shark (Carcharhinus falciformes) from a Palauan-based commercial longline fishery. Rev. Fish Biol. Fish. 28, 567–658 (2018).
Google Scholar 
Childs, D., Sheldon, B. & Rees, M. The evolution of labile traits in sex- and age-structured populations. J. Anim. Ecol. 85, 329–342 (2016).PubMed 
PubMed Central 

Google Scholar 
Comte, L., Murienne, J. & Grenouillet, G. Species traits and phylogenetic conservatism of climate-induced range shifts in stream fishes. Nat. Commun. 5, 5053 (2014).ADS 
PubMed Central 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2021-3. www.iucnredlist.org. ISSN 2307-8235 (International Union for the Conservation of Nature, Gland, Switzerland, 2022).García, V., Lucifora, L. & Ransom, M. The importance of habitat and life history to extinction risk in sharks, skates, rays and chimaeras. Proc. R. Soc. B 275, 83–89 (2008).PubMed 

Google Scholar 
Cortes, E. Perspectives on the intrinsic rate of population growth. Methods Ecol. Evol. 7, 1136–1145 (2016).
Google Scholar 
Ellis, J. et al. A review of capture and post-release mortality of elasmobranchs. J. Fish Biol. 90, 653–722 (2017).CAS 
PubMed 

Google Scholar 
Gallagher, A., Orbesen, E., Hammerschlag, N. & Serafy, J. Vulnerability of oceanic sharks as pelagic longline bycatch. Glob. Ecol. Conserv. 1, 50–59 (2014).
Google Scholar 
Afonso, A., Santiago, R., Hazin, H. & Hazin, F. Shark bycatch and mortality and hook bite-offs in pelagic longlines: Interactions between hook types and leader materials. Fish. Res. 131–133, 9–14 (2012).
Google Scholar 
Gilman, E., Chaloupka, M. & Musyl, M. Effects of pelagic longline hook size on species- and size-selectivity and survival. Rev. Fish Biol. Fish. 28, 417–433 (2018).
Google Scholar 
Epperly, S., Watson, J., Foster, D. & Shah, A. Anatomical hooking location and condition of animals captured with pelagic longlines: The grand banks experiments 2002–2003. Bull. Mar. Sci. 88, 513–527 (2012).
Google Scholar 
Amorim, S., Santos, M., Coelho, R. & Fernandez-Carvalho, J. Effects of 17/0 circle hooks and bait on fish catches in a southern Atlantic swordfish longline fishery. Aquat. Conserv. 25, 518–533 (2014).
Google Scholar 
Coelho, R., Fernandez-Carvalho, J., Lino, P. & Santos, M. An overview of the hooking mortality of elasmobranchs caught in a swordfish pelagic longline fishery in the Atlantic Ocean. Aquat. Living Resour. 25, 311–319 (2012).
Google Scholar 
Gilman, E. et al. A decision support tool for integrated fisheries bycatch management. Rev. Fish Biol. Fish. https://doi.org/10.1007/s11160-021-09693-5 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Pascoe, S. et al. Use of incentive-based management systems to limit bycatch and discarding. Int. Rev. Environ. Resour. Econ. 4, 123–161 (2010).
Google Scholar 
Somers, K., Pfeiffer, L., Miller, S. & Morrison, W. Using incentives to reduce bycatch and discarding: Results under the west coast catch share program. Coast. Manag. 46, 1–17 (2019).
Google Scholar 
Abbott, J. & Wilen, J. Regulation of fisheries bycatch with common-pool output quotas. J. Environ. Econ. Manag. 57, 195–204 (2009).MATH 

Google Scholar 
Gilman, E. et al. Increasing the functionalities and accuracy of fisheries electronic monitoring systems. Aquat. Conserv. 29, 901–926 (2019).
Google Scholar 
Watling, J. Fishing observers ‘intimidated and bribed by EU crews’. Quota checks allegedly being compromised aboard Northwest Atlantic fishery boats, as observers report surveillance and theft. The Guardian (2012, accessed 21 July 2022). https://www.theguardian.com/environment/2012/may/18/fishing-inspectors-intimidated-bribed-crews.Clarke, S., Harley, S., Hoyle, S. & Rice, J. Population trends in Pacific oceanic sharks and the utility of regulations on shark finning. Conserv. Biol. 27, 197–209 (2013).PubMed 

Google Scholar 
Tolotti, M. T. et al. Banning is not enough: The complexities of oceanic shark management by tuna regional fisheries management organizations. Glob. Ecol. Conserv. 4, 1–7 (2015).
Google Scholar 
Gilman, E., Chaloupka, M., Merrifield, M., Malsol, N. & Cook, C. Standardized catch and survival rates, and effect of a ban on shark retention, Palau pelagic longline fishery. Aquat. Conserv. 26, 1031–1062 (2016).
Google Scholar 
CITES. Appendices I, II and III. Valid from 22 June 2021. Convention on International Trade in Endangered Species of Wild Fauna and Flora, United Nations Environment Program, Geneva (2021).Ward-Paige, C. A global overview of shark sanctuary regulations and their impact on shark fisheries. Mar. Policy 82, 87–97 (2017).
Google Scholar 
E.U. Regulation (E.U.) No 1380/2013 of the European Parliament and of the Council of 11 December 2013 on the Common Fisheries Policy, amending Council Regulations (EC) No 1954/2003 and (EC) No 1224/2009 and repealing Council Regulations (EC) No 2371/2002 and (EC) No 639/2004 and Council Decision 2004/585/EC. Official Journal of the European Union L354, 22–61 (2013).FAO. International Guidelines on Bycatch Management and Reduction of Discards (Food and Agriculture Organization of the United Nations, Rome, 2011).CCSBT. Resolution to Align CCSBT’s Ecologically Related Species Measures with those of other Tuna RFMOs (Commission for the Conservation of Southern Bluefin Tuna, Deakin West, Australia, 2021).IATTC. Active Resolutions and Recommendations (Inter-American Tropical Tuna Commission, La Jolla, 2022).ICCAT. Compendium. Management Recommendations and Resolutions Adopted by ICCAT for the Conservation of Atlantic Tunas and Tuna-like Species (International Commission for the Conservation of Atlantic Tunas, Madrid, 2021).IOTC. Compendium of Active Conservation and Management Measures for the Indian Ocean Tuna Commission (Indian Ocean Tuna Commission, Mahe, 2021).WCPFC. Conservation and Management Measures and Resolutions of the Western and Central Pacific Fisheries Commission. Compiled 31 August 2021 (Western and Central Pacific Fisheries Commission, Kolonia, Federated States of Micronesia, 2021).Faith, D. Threatened species and the potential loss of phylogenetic diversity: Conservation scenarios based on estimated extinction probabilities and phylogenetic risk analysis. Conserv. Biol. 22, 1461–1470 (2008).PubMed 

Google Scholar 
Dolce, J. & Wilga, C. Evolutionary and ecological relationships of gill slit morphology in extant sharks. Bull. Mus. Comp. 161, 79–109 (2013).
Google Scholar 
MacLeod, N. & Forey, P. Morphology, Shape and Phylogeny (CRC Press, 2002).
Google Scholar 
Haddaway, N., Macura, B., Whaley, P. & Pullin, A. ROSES RepOrting standards for Systematic Evidence Syntheses: pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps. Environ. Evid. https://doi.org/10.1186/s13750-018-0121-7 (2018).Article 

Google Scholar 
Pullin, A., Frampton, G., Livoreil, B. & Petrokofsky, G., Eds. Section 5. Conducting a Search. Key CEE Standards for Conduct and Reporting. In Pullin, A., Frampton, G., Livoreil, B., Petrokofsky, G., Eds. Guidelines and Standards for Evidence Synthesis in Environmental Management. Version 5.0. Collaboration for Environmental Evidence (2020).Pullin, A., Frampton, G., Livoreil, B. & Petrokofsky, G. (eds) Section 3. Planning a CEE Evidence Synthesis. In Pullin, A., Frampton, G., Livoreil, B., Petrokofsky, G. (eds) Guidelines and Standards for Evidence Synthesis in Environmental Management. Version 5.0. Collaboration for Environmental Evidence (2021).Page, M. et al. The PRISMA statement: An updated guideline for reporting systematic reviews. BMJ https://doi.org/10.1136/bmj.n.71 (2020).Article 
PubMed 

Google Scholar 
Tuyl, F., Gerlach, R. & Mengersen, K. Comparison of Bayes-Laplace, Jeffreys, and other priors: The case of zero events. Am. Stat. 62, 40–44 (2008).MathSciNet 

Google Scholar 
Dorai-Raj, S. binom: Binomial confidence intervals for several parameterizations. R package version 1.1-1. https://CRAN.R-project.org/package=binom (2014).van Lissa, C. Small sample meta-analyses: Exploring heterogeneity using MetaForest. Chapter 13. In Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners (eds Van De Schoot, R. & Miočević, M.) 186–202 (Routledge, Oxford, 2020).
Google Scholar 
Wright, M. & Ziegler, A. ranger: A fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77, 1–17 (2017).
Google Scholar 
Janitza, S., Celik, E. & Boulesteix, A. A computationally fast variable importance test for random forests for high-dimensional data. Adv. Data Anal. Classif. 12, 885–915 (2018).MathSciNet 
MATH 

Google Scholar 
Mayer, M. missRanger: Fast imputation of missing values. R package version 2.1.3. https://CRAN.R-project.org/package=missRanger (2021).Konstantopoulos, S. Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis. Methods 2, 61–76 (2011).
Google Scholar 
Amaral, C., Pereira, F., Silva, D., Amorim, A. & de Carvalho, E. The mitogenomic phylogeny of the Elasmobranchii (Chondrichthyes). Mitochondrial DNA A 29, 867–878 (2017).
Google Scholar 
Hara, Y. et al. Shark genomes provide insights into elasmobranch evolution and the origin of vertebrates. Nat. Ecol. Evol. 2, 1761–1771 (2018).PubMed 

Google Scholar 
Naylor, G. et al. A DNA sequence-based approach to the identification of shark and ray species and its implications for global elasmobranch diversity and parasitology. Bull. Am. Mus. Nat. 367, 1–262 (2012).
Google Scholar 
Stein, R. et al. Global priorities for conserving the evolutionary history of sharks, rays and chimaeras. Nat. Ecol. Evol. 2, 288–298 (2018).PubMed 

Google Scholar 
Maddison, D., Swofford, D., Maddison, W. & Cannatella, D. Nexus: An extensible file format for systematic information. Syst. Biol. 46, 590–621 (1997).CAS 
PubMed 

Google Scholar 
Upham, N., Esselstyn, J. & Jetz, W. Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 

Google Scholar 
Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinform. 69, e96. https://doi.org/10.1002/cpbi.96 (2020).Article 

Google Scholar 
Hadfield, J. & Nakagawa, S. General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol 23, 494–508 (2010).CAS 
PubMed 

Google Scholar 
Lin, L. & Chu, H. Meta-analysis of proportions using generalized linear mixed models. Epidemiology 31, 713–717 (2020).PubMed 
PubMed Central 

Google Scholar 
Carpenter, B. et al. Stan: A probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).
Google Scholar 
Bürkner, P. brms: An R Package for Bayesian multilevel models using Stan. J. Stat. Softw. 81, 1–28 (2017).
Google Scholar 
Günhan, B., Röver, C. & Friede, T. Random-effects meta-analysis of few studies involving rare events. Res. Synth. Methods 11, 74–90 (2020).PubMed 

Google Scholar 
Pappalardo, P. et al. Comparing traditional and Bayesian approaches to ecological meta-analysis. Methods Ecol. Evol. 11, 1286–1295 (2020).
Google Scholar 
Ott, M., Plummer, M. & Roos, M. How vague is vague? How informative is informative? Reference analysis for Bayesian meta-analysis. Stat. Med. 40, 4505–4521 (2021).MathSciNet 
PubMed 
PubMed Central 

Google Scholar 
Wood, S. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall, 2017).MATH 

Google Scholar 
Kruschke, J. & Liddell, T. The Bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychon. Bull. Rev. 25, 178–206 (2018).PubMed 

Google Scholar 
Kay, M. tidybayes: Tidy data and geoms for Bayesian models. R package version 2.1.1. https://doi.org/10.5281/zenodo.1308151 (2020).Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).ADS 

Google Scholar 
Searle, S., Speed, F. & Milliken, G. Population marginal means in the linear model: An alternative to least squares means. Am. Stat. 34, 216–221 (1980).MathSciNet 
MATH 

Google Scholar 
Lenth, R. emmeans: Estimated marginal means, aka least-squares means. R package version 1.5.2-1. https://CRAN.R-project.org/package=emmeans (2020).Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).
Google Scholar 
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).MathSciNet 
MATH 

Google Scholar 
Yao, Y., Vehtari, A., Simpson, D. & Gelman, A. Using stacking to average Bayesian predictive distributions (with Discussion). Bayesian Anal. 13, 917–1003 (2018).MathSciNet 
MATH 

Google Scholar 
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Soc. Ser. A 182, 1–14 (2019).MathSciNet 

Google Scholar 
Lazic, S., Mellor, J., Ashby, M. & Munafo, M. A Bayesian predictive approach for dealing with pseudoreplication. Sci. Rep. 10, 2020. https://doi.org/10.1038/s41598-020-59384-7 (2020).Article 
CAS 

Google Scholar 
Page, M., Sterne, J., Higgins, J. & Egger, M. Investigating and dealing with publication bias and other reporting biases in meta-analyses of health research: A review. Res. Synth. Methods 12, 248–259 (2021).PubMed 

Google Scholar 
Peters, J., Sutton, A., Jones, D., Abrams, K. & Rushton, L. Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. J. Clin. Epidemiol. 61, 991–996 (2008).PubMed 

Google Scholar 
Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).
Google Scholar 
Gasparrini, A., Armstrong, B. & Kenward, M. Multivariate meta-analysis for non-linear and other multi-parameter associations. Stat. Med. 31, 3821–3839 (2012).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Darwin C. The structure and distribution of coral reefs, 3rd edn. D. Appleton & Company: New York, NY, USA, 1889.Lajeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. Systematic Revision of Symbiodiniaceae Highlights the Antiquity and Diversity of Coral Endosymbionts. Curr Biol. 2018;28:2570–80.e6.CAS 
PubMed 

Google Scholar 
Muscatine L, Porter JW. Reef corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience. 1977;27:454–60.
Google Scholar 
Rohwer F, Seguritan V, Azam F, Knowlton N. Diversity and distribution of coral-associated bacteria. Mar Ecol Prog Ser. 2002;243:1–10.
Google Scholar 
Rosenberg E, Koren O, Reshef L, Efrony R, Zilber-Rosenberg I. The role of microorganisms in coral health, disease and evolution. Nat Rev Microbiol. 2007;5:355–62.CAS 
PubMed 

Google Scholar 
Muscatine L. The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reefs. 1990;25:75–87.
Google Scholar 
Falkowski PG, Dubinsky Z, Muscatine L, McCloskey L. Population control in symbiotic corals. Bioscience. 1993;43:606–11.
Google Scholar 
Baker DM, Freeman CJ, Wong JCY, Fogel ML, Knowlton N. Climate change promotes parasitism in a coral symbiosis. ISME J. 2018;12:921–30.CAS 
PubMed 
PubMed Central 

Google Scholar 
Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Perna G, Geißler L, et al. Heat stress reduces the contribution of diazotrophs to coral holobiont nitrogen cycling. ISME J. 2022;16:1110–8.PubMed 

Google Scholar 
Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 2015;23:490–7.PubMed 

Google Scholar 
Bourne DG, Webster NS. Coral Reef Bacterial Communities. In: Rosenberg E, DeLong EF, editors. The Prokaryotes. Springer: Berlin Heidelberg; 2013. pp. 163–87.Ainsworth DT, Krause L, Bridge T, Torda G, Raina J-B, Zakrzewski M, et al. The coral core microbiome identifies rare bacterial taxa as ubiquitous endosymbionts. ISME J. 2015;9:2261–74.CAS 

Google Scholar 
Pernice M, Raina J-B, Rädecker N, Cárdenas A, Pogoreutz C, Voolstra CR. Down to the bone: the role of overlooked endolithic microbiomes in reef coral health. ISME J. 2020;14:325–34.PubMed 

Google Scholar 
Pogoreutz C, Oakley CA, Rädecker N, Cárdenas A, Perna G, Xiang N, et al. Coral holobiont cues prime Endozoicomonas for a symbiotic lifestyle. ISME J. 2022;16:1883–95.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pogoreutz C, Voolstra CR, Rädecker N, Weis V. The coral holobiont highlights the dependence of cnidarian animal hosts on their associated microbes. In: Bosch TCG, Hadfield MG, editors. Cellular Dialogues in the Holobiont. Boca Raton: CRC Press; 2020. pp. 91–118.Babbin AR, Tamasi T, Dumit D, Weber L, Rodríguez MVI, Schwartz SL, et al. Discovery and quantification of anaerobic nitrogen metabolisms among oxygenated tropical Cuban stony corals. ISME J. 2021;15:1222–35.CAS 
PubMed 

Google Scholar 
Glaze TD, Erler DV, Siljanen HMP. Microbially facilitated nitrogen cycling in tropical corals. ISME J. 2022;16:68–77.CAS 
PubMed 

Google Scholar 
Lesser MP, Morrow KM, Pankey SM, Noonan SHC. Diazotroph diversity and nitrogen fixation in the coral Stylophora pistillata from the Great Barrier Reef. ISME J. 2018;12:813–24.CAS 
PubMed 

Google Scholar 
Cardini U, Bednarz VN, Naumann MS, van Hoytema N, Rix L, Foster RA, et al. Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc R Soc B. 2015;282:20152257.PubMed 
PubMed Central 

Google Scholar 
Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Nitrogen fixation aligns with nifH abundance and expression in two coral trophic functional groups. Front Microbiol. 2017;8:1187.PubMed 
PubMed Central 

Google Scholar 
Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Voolstra CR, Wild C. Sugar enrichment provides evidence for a role of nitrogen fixation in coral bleaching. Glob Chang Biol. 2017;23:3838–48.PubMed 

Google Scholar 
Bednarz VN, van de Water JA, Rabouille S, Maguer JF, Grover R, Ferrier‐Pagès C. Diazotrophic community and associated dinitrogen fixation within the temperate coral Oculina patagonica. Environ Microbiol. 2019;21:480–95.CAS 
PubMed 

Google Scholar 
Lema KA, Willis BL, Bourne DG. Corals form characteristic associations with symbiotic nitrogen-fixing bacteria. Appl Environ Microbiol. 2012;78:3136–44.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lema KA, Clode PL, Kilburn MR, Thornton R, Willis BL, Bourne DG. Imaging the uptake of nitrogen-fixing bacteria into larvae of the coral Acropora millepora. ISME J. 2016;10:1804–8.CAS 
PubMed 

Google Scholar 
Santos HF, Carmo FL, Duarte G, Dini-Andreote F, Castro CB, Rosado AS, et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 2014;8:2272–9.PubMed 
PubMed Central 

Google Scholar 
Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F, Bougoure J, et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci USA. 2021;118:e2022653118.PubMed 
PubMed Central 

Google Scholar 
Braker G, Fesefeldt A, Witzel K-P. Development of PCR primer systems for amplification of nitrite reductase genes (nirK and nirS) to detect denitrifying bacteria in environmental samples. Appl Environ Microbiol. 1998;64:3769–75.CAS 
PubMed 
PubMed Central 

Google Scholar 
Tilstra A, El-Khaled YC, Roth F, Rädecker N, Pogoreutz C, Voolstra CR, et al. Denitrification aligns with N2 fixation in Red Sea corals. Sci Rep. 2019;9:1–9.Tilstra A, Roth F, El-Khaled YC, Pogoreutz C, Rädecker N, Voolstra CR, et al. Relative abundance of nitrogen cycling microbes in coral holobionts reflects environmental nitrate availability. R Soc Open Sci. 2021;8:201835.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiang N, Hassenrück C, Pogoreutz C, Rädecker N, Simancas-Giraldo SM, Voolstra CR, et al. Contrasting microbiome dynamics of putative denitrifying bacteria in two octocoral species exposed to dissolved organic carbon (DOC) and warming. Appl Environ Microbiol. 2022;88:e01886-21.El-Khaled YC, Roth F, Tilstra A, Rädecker N, Karcher DB, Kürten B, et al. In situ eutrophication stimulates dinitrogen fixation, denitrification, and productivity in Red Sea coral reefs. Mar Ecol Prog Ser. 2020;645:55–66.CAS 

Google Scholar 
Beauchamp EG, Trevors JT, Paul JW. Carbon sources for bacterial Denitrification. In: Stewart BA. Advances in Soil Science. Springer: New York, NY; 1989. pp. 113–42.Baker AC. Flexibility and Specificity in Coral-Algal Symbiosis: Diversity, Ecology, and Biogeography of Symbiodinium. Ann Rev Ecol Evol Syst. 2003;34:661–89.
Google Scholar 
Wang J-T, Chen Y-Y, Tew KS, Meng P-J, Chen CA. Physiological and Biochemical Performances of Menthol-Induced Aposymbiotic Corals. PLoS ONE. 2012;7:e46406.CAS 
PubMed 
PubMed Central 

Google Scholar 
Cui G, Liew YJ, Li Y, Kharbatia N, Zahran NI, Emwas A-H, et al. Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia. PLoS Genet. 2019;15:e1008189.CAS 
PubMed 
PubMed Central 

Google Scholar 
Rädecker N, Raina J-B, Pernice M, Perna G, Guagliardo P, Kilburn MR, et al. Using Aiptasia as a Model to Study Metabolic Interactions in Cnidarian-Symbiodinium Symbioses. Front Physiol. 2018;9:214.PubMed 
PubMed Central 

Google Scholar 
Voolstra CR. A journey into the wild of the cnidarian model systemAiptasiaand its symbionts. Mol Ecol. 2013;22:4366–8.PubMed 

Google Scholar 
Sunagawa S, Wilson EC, Thaler M, Smith ML, Caruso C, Pringle JR, et al. Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genom. 2009;10:258.
Google Scholar 
Xiang T, Hambleton EA, DeNofrio JC, Pringle JR, Grossman AR. Isolation of clonal axenic strains of the symbiotic dinoflagellate Symbiodinium and their growth and host specificity1. J Phycol. 2013;49:447–58.CAS 
PubMed 

Google Scholar 
Thornhill DJ, Lewis AM, Wham DC, Lajeunesse TC. Host‐specialist lineages dominate the adaptive radiation of reef coral endosymbionts. Evolution. 2014;68:352–67.CAS 
PubMed 

Google Scholar 
Bieri T, Onishi M, Xiang T, Grossman AR, Pringle JR. Relative Contributions of Various Cellular Mechanisms to Loss of Algae during Cnidarian Bleaching. PLoS ONE. 2016;11:e0152693.PubMed 
PubMed Central 

Google Scholar 
Baumgarten S, Simakov O, Esherick LY, Liew YJ, Lehnert EM, Michell CT, et al. The genome of Aiptasia, a sea anemone model for coral symbiosis. Proc Natl Acad Sci USA. 2015;112:11893–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Correa AMS, McDonald MD, Baker AC. Development of clade-specific Symbiodinium primers for quantitative PCR (qPCR) and their application to detecting clade D symbionts in Caribbean corals. Mar Biol. 2009;156:2403–11.CAS 

Google Scholar 
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods. 2001;25:402–8.CAS 
PubMed 

Google Scholar 
Lee JA, Francis CA. DeepnirSamplicon sequencing of San Francisco Bay sediments enables prediction of geography and environmental conditions from denitrifying community composition. Environ Microbiol. 2017;19:4897–912.CAS 
PubMed 

Google Scholar 
Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005;6:279–84.CAS 
PubMed 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBO J. 2011;17:10–2.
Google Scholar 
Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. UniProtKB/Swiss-Prot: the manually annotated section of the UniProt KnowledgeBase. Methods Mol Biol. 2007;406:89–112.Abascal F, Zardoya R, Telford MJ. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res. 2010;38:7–13.
Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28:1647–9.PubMed 
PubMed Central 

Google Scholar 
Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, et al. FunGene: the functional gene pipeline and repository. Front Microbiol. 2013;4:291.PubMed 
PubMed Central 

Google Scholar 
Wickham H. ggplot2. Wiley Interdiscip Rev Comput Stat. 2011;3:180–5.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Commun Ecol Package. 2007;10:719.
Google Scholar 
McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:1–11.CAS 

Google Scholar 
Meunier V, Geissler L, Bonnet S, Rädecker N, Perna G, Grosso O, et al. Microbes support enhanced nitrogen requirements of coral holobionts in a high CO 2 environment. Mol Ecol. 2021;30:5888–99.CAS 
PubMed 

Google Scholar 
Geissler L, Meunier V, Rädecker N, Perna G, Rodolfo-Metalpa R, Houlbrèque F, et al. Highly Variable and Non-complex Diazotroph Communities in Corals From Ambient and High CO2 Environments. Front Mar Sci. 2021;8:754682.Thornhill DJ, Xiang Y, Pettay DT, Zhong M, Santos SR. Population genetic data of a model symbiotic cnidarian system reveal remarkable symbiotic specificity and vectored introductions across ocean basins. Mol Ecol. 2013;22:4499–515.CAS 
PubMed 

Google Scholar 
Röthig T, Costa RM, Simona F, Baumgarten S, Torres AF, Radhakrishnan A, et al. Distinct bacterial communities associated with the coral model Aiptasia in aposymbiotic and symbiotic states with Symbiodinium. Front Mar Sci. 2016;3:234.
Google Scholar 
Hartman LM, Blackall LL, van Oppen MJH. Antibiotics reduce bacterial load in Exaiptasia diaphana, but biofilms hinder its development as a gnotobiotic coral model. Access Microbiol. 2022;4:000314.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lawson CA, Raina JB, Kahlke T, Seymour JR, Suggett DJ. Defining the core microbiome of the symbiotic dinoflagellate, Symbiodinium. Environ Microbiol Rep. 2018;10:7–11.CAS 
PubMed 

Google Scholar 
Matthews JL, Raina JB, Kahlke T, Seymour JR, van Oppen MJ, Suggett DJ. Symbiodiniaceae‐bacteria interactions: rethinking metabolite exchange in reef‐building corals as multi‐partner metabolic networks. Environ Microbiol. 2020;22:1675–87.PubMed 

Google Scholar 
Costa RM, Cárdenas A, Loussert-Fonta C, Toullec G, Meibom A, Voolstra CR. Surface Topography, Bacterial Carrying Capacity, and the Prospect of Microbiome Manipulation in the Sea Anemone Coral Model Aiptasia. Front Microbiol. 2021;12:637834.Pelve EA, Fontanez KM, DeLong EF. Bacterial succession on sinking particles in the ocean’s interior. Front Microbiol. 2017;8:2269.PubMed 
PubMed Central 

Google Scholar 
Welles L, Lopez-Vazquez CM, Hooijmans CM, Van Loosdrecht MCM, Brdjanovic D. Prevalence of ‘Candidatus Accumulibacter phosphatis’ type II under phosphate limiting conditions. AMB Express. 2016;6:1–12.Kaneko T. Complete Genomic Sequence of Nitrogen-fixing Symbiotic Bacterium Bradyrhizobium japonicum USDA110. DNA Res. 2002;9:189–97.PubMed 

Google Scholar 
Cziesielski MJ, Liew YJ, Cui G, Schmidt-Roach S, Campana S, Marondedze C, et al. Multi-omics analysis of thermal stress response in a zooxanthellate cnidarian reveals the importance of associating with thermotolerant symbionts. Proc R Soc B: Biol Sci. 2018;285:20172654.
Google Scholar 
Xiang T, Lehnert E, Jinkerson RE, Clowez S, Kim RG, Denofrio JC, et al. Symbiont population control by host-symbiont metabolic interaction in Symbiodiniaceae-cnidarian associations. Nat Commun. 2020;11:1–9.CAS 

Google Scholar  More

“This new study is inspired by some our earlier theoretical work applied to killer whales that suggests that age-related changes in relatedness are important for the evolution of menopause,” says Samuel Ellis, the first author of the study. “Reproduction can be thought of as a form of generalized harm as the birth of an offspring increases within-group competition for resources. Kinship dynamics — the ways in which local relatedness changes over an individual’s lifetime — are one way that menopause could be favored, because older females are more inclined to cease reproduction to not harm their group mates than younger females. Here we wanted to generalize this concept to both sexes, and to other species without menopause.” More

All animal care and experimental protocols complied with local and federal laws and guidelines and were approved by the appropriate governing body in Berlin, Germany, the Landesamt fur Gesundheit und Soziales (LaGeSo G-0224/20).Experimental breeding and designThe all-female Amazon molly (Poecilia formosa) is a naturally clonal, live-bearing fish species that gives birth to broods of genetically identical offspring. Like all unisexual vertebrates, Amazon mollies are the result of inter-specific hybridization44,45. As such, this ‘frozen hybrid’ has a heterozygous genome from its ancestral P. mexicana mother and P. latipinna father alleviating concerns about reduced genetic variation and the resulting inbreeding depression often associated with artificially selected isogenic animals. Additionally, despite their clonal nature, the Amazon’s genome shows no evidence of increased mutation accumulation, genomic decay or transposable element activity suggesting the genomes of these animals are evolving in similar ways as sexual species46. They reproduce through gynogenesis where the meiotic process is disrupted so that the eggs contain a full maternal genome. The egg must be fused with a sperm from one of their ancestral species to stimulate embryogenesis, but this paternal DNA is not incorporated into the egg. This provides the opportunity to control when reproduction occurs by controlling the females’ access to male sperm donors.We placed adult females, as potential mothers of experimental fish, in individual (5-gallon) breeding tanks with two Atlantic molly (P. mexicana) males for one week to act as sperm donors. Amazon mollies give birth to broods of generally ~8-30 individuals. A brood is born at once (i.e. all individuals are born within minutes of each other) and birth generally happens early in the day close to dawn. These parental fish were lab-bred and themselves sisters, so of the same age and lineage, and were kept at similar social densities and under standardized environmental conditions throughout their lives to further minimize potential variation in maternal experience. Each breeding tank contained an artificial plant as refuge and was checked frequently each day for the presence of offspring, especially during the morning hours when births are most likely. Newborn mollies were always found in the morning and then singly netted by trained animal caretakers, into individual experimental tanks where their behavior was automatically recorded for the next 70 days (see below). Moving the fish from the maternal tank to the experimental tanks was done in a standardized manner (i.e. individual fish were netted and placed into small dishes of water and then placed in the tracking tanks to limit exposure to the air) by the same caretakers to minimize variation in experience among individual fish. Altogether, eight mothers provided offspring that completed the entire 10-week experiment (Supplementary Table 1).Experimental tanks (27 x 27 cm), made of white Perspex, consisted of four equally sized compartments, and were evenly lit from below using 6500K-LEDs. Environmental conditions were highly standardized across tanks: all tanks were on the same 11:13 (L:D) light schedule, water depth was maintained at 10 cm depth, temperature was maintained at 25 ± 1 °C by a room air conditioning system, and fish received a standardized amount of powdered flake fish food (TetraMin™) twice daily. Opaque blinds surrounded the tanks to further limit outside disturbances. All experimental tanks were connected to the same filtration system where water could mix in the sump tank, allowing chemical cues to be shared across all experimental fish. Previous work has shown exposure to just chemical cues of conspecifics is sufficient in preventing the developmental of pathological behavior that could be associated with development in complete isolation14. We initially placed a total of 40 newborn individuals into the tracking tanks. At the end of the 10-week experiment, we were able to achieve complete tracking data on 26 individuals; camera malfunctions prevented data collection on four individuals, two individuals jumped into neighboring tanks causing the loss of data of all four individuals as we could not verify their identity; four newborn individuals escaped through holes in the water outlet of the tanks; and four individuals died as newborns. All results in the manuscript are on these 26 animals, though including data from all 40 (e.g. patterns of individual variation on the first day post birth) did not change the results or their interpretation (see Supplementary Table 2).Behavioral trackingWe developed a custom recording system using Raspberry Pi computers, which are an upcoming low-cost, highly adaptable solution for many applications in the biological sciences25. Specifically, we created a local network of Raspberry Pi 3B + ’s, each connected to a Raspberry Pi camera positioned exactly above an experimental tank, commanded by a lab computer, and connected to the server on the institute network (Supplementary Fig. 1). We programmed the Raspberry Pi’s using pirecorder26 to take timestamped photos every 3 s across the daily light period, each day, for 10 weeks, and store them automatically in dedicated, automatically named folders on the server. Image settings and resolution were thereby optimized to minimize file size while assuring image quality. After the experimental period, we created videos of all the recorded images of each fish of each day. These videos were subsequently tracked with the Biotracker software27, using background subtraction, providing the x, y coordinates of each fish in each frame. We then processed the data, including scaling and converting the coordinates to mm, and, for each frame, computed fish’s swimming speed (cm/s) and distance from the tank walls (cm). We then summarized these variables both on an hourly and daily basis to compute fish’s median swimming speed, inter-quartile range of swimming speeds, activity (proportion of time spent moving >0.5 cm/s), and median border distance. To quantify fish’s body size over time, we randomly selected five photos per week of each compartment, making sure the fish was away from the compartment walls and did not show strong body curvature, and then used ImageJ software to measure total body length (mm) from the tip of the snout to the end of the body. By averaging the measurements of the five images, we acquired one body size measurement per week.Error checkingWe collected up to 924,000 photos on each individual throughout the experimental period resulting in a total of over 24 million data points collected on our experimental animals (N = 26 individuals). To ensure that our tracking software accurately captured the behavior of our fish, we checked for potential tracking errors in two ways. First, we estimated overall error rates. To do this, we selected at random a starting frame from within a day; then we manually checked each of the subsequent 200 frames and identified whether an error was made (fish was not properly located by BioTracker) or not (fish was properly located) by visual inspection of the videos. We estimated the error rate as the number of errors divided by the total number of checked frames. The overall median error rate over the entire observation period was estimated to be 7%. Error rates increased earlier in the observation period when the fish were smaller (Supplementary Note I). As such, as a second step, we manually went through and corrected all frames for the very first day of tracking (i.e. day 1 post-birth) for all fish (~13,200 frames per individual) as this is a critical time period for one of our research questions. This ensured that the resulting behavioral data were completely accurate for this day. This manual correction allowed us the additional opportunity to compare how well our automatically tracked (i.e. not manually corrected) data performed compared to the manually corrected data. We found that the automatically tracked data re-created near identical estimates of among- and within-individual variance components and most importantly the among-individual correlation between the automatically tracked and manually corrected data was over 0.98 for our behavioral variables (Supplementary Note I). This strongly suggests that any errors introduced by our automated tracking software have minimal influence of our behavioral variables at best and do not affect our interpretation of the results.Statistical analysesWe used linear mixed, or hierarchical, models to partition the behavioral variation across different times periods into its among- and within-individual components. Throughout we focused our analysis on the 26 individuals for which we had complete data for the entire 10-week observation period to ensure comparable variation over time and across models.Our first question of interest was to test when individual differences in behavior first appeared over the course of the experiment. We started by investigating behavior on the first day post birth (Fig. 1A, Supplementary Table 2) and then planned to proceed in a day-by-day fashion until significant repeatability in behavior was apparent (Supplementary Table 3). We used hourly median swimming speed (11 observations for each of 26 individuals) as our response variable and included ‘hour’ and ‘total length (TL)’ as fixed effects and ‘individual’ was included as our random effect of interest. Including TL as a covariate allowed us to test whether behavior was related to an offspring’s body size on its first day of life. We set the first hour of the day as 0 and mean-centered TL as this would allow the among- (and within-) individual variance components to be estimated at these values (i.e. the earliest possible moment from when we could record behavior in the fish). We estimated the adjusted repeatability of median swimming speed as the variance attributable to individual identity over the total variance not explained by the fixed effects. We additionally estimated both marginal and conditional R-squared values which estimate the variance explained by the fixed effects only and the variance explained by the fixed and random effects combined, respectively. As our individual experimental fish came from different mothers, we first explored a number of different variance structures including random intercepts and slopes for both individual ID and maternal ID. This allowed us to test whether maternal identity explained variation in individual behavior. However, the most supported model included random intercepts and slopes for individual ID and not for mother ID, indicating that our methods to reduce variation among mothers were successful (Table 1). We used median swimming speed as our behavioral variable of interest throughout the main manuscript, as this behavior was tightly correlated with most of our other behavioral variables (Supplementary Fig. 2); though results using the other behavioral variables yielded the same interpretation (i.e. that significant individuality in (any) behavior was present on the very first day post-birth; Supplementary Table 2).Our second research question was to investigate how individual behavioral variance changed over the course of the entire observation period (70 days). Again, we first explored several different variance structures to test the importance of maternal identity and/or individual identity on behavioral variation. We found support for the inclusion of random slopes at the individual level, but not maternal level (Table 1). This indicates that levels of among- (and within-) individual variation may differ throughout the observation period. To investigate patterns of change in the variance components, we ran a series of models where we centered the observation covariate on different days. Individual intercepts are estimated when all covariates are set to zero, so this allowed us to ‘slice’ the data to estimate the among- and within-individual variance at different time points over the ten weeks. We ran 11 models as we chose to center the data every 7 days (first model was centered on observation 1; 11th model was centered on observation 70). The predicted individual intercepts (best linear unbiased predictors) and estimated variance components from each model are plotted in Fig. 3.We also closely investigated any potential influence of body size and/or growth rate differences on behavioral expression and individual behavioral variation in this entire 10-week data set. First, we estimated the repeatability of both weekly total length and weekly growth rates to determine if individuals consistently differed in these traits. Then, we ran a series of models with median weekly swimming speed as the response variable and included either weekly total length, weekly growth rate, and/or overall growth rate (estimated over the entire 10 weeks), as our fixed effects of interest. Each model also included the random effects of individual intercepts and slopes. Finally, because body size varies both among individuals (some individuals are on average larger than others) and within individuals (as they grow), we also performed within-individual centering of total length. In this fifth model, we included each individual’s average total length and their weekly deviation from their average length as the two fixed effects of interest. Individual identity and slopes were included as random effects. For all models, we estimated the variance explained by the fixed effects (marginal R2) and the fixed and random effects together (conditional R2). These results are reported in Table 2.For our third and final research question, we tested whether early-life behavior predicted later-life behavior. To test this, we estimated the among-individual correlation (including ‘individual ID’ as our random effect) in behavior using multivariate mixed models where the daily median swimming speeds in each week were the response variables (7 observations per week per individual; 10 weeks total; Fig. 4A). Then to investigate how the strength of these correlations may change over development, we used a linear model to test whether the correlation strength was predicted by the interaction between the first week included in the correlation and distance to the next week in the correlation (1, 2, 3, 4 or 5 weeks away in time; Fig. 4B).All models were performed using Markov Chain Monte Carlo estimation with the MCMCglmm package38 in R v3.6.139. We set our models to run 510,000 iterations with a 10,000 burn-in and thinning every 200 iterations. To ensure proper model mixing and convergence, we initially ran 5 independent chains and inspected posterior trace plots of parameter estimates (Supplementary Note II). In a preliminary analysis we tested three different prior settings (Supplementary Note II); results did not change with prior settings so we chose parameter-expanded priors for all models reported here as these are generally considered to be more robust. An R Markdown file with all the results presented here is included in Supplementary Note II.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

LaBarbera, M. The evolution and ecology of body size. In Patterns and Processes in the History of Life (eds Raup, D. M. & Jablonski, D.) 69–98 (Springer, 1986).
Google Scholar 
Peters, R. H. & Peters, R. H. The Ecological Implications of Body Size Vol. 2 (Cambridge University Press, 1986).
Google Scholar 
Klingenberg, C. P. & Spence, J. On the role of body size for life-history evolution. Ecol. Entomol. 22(1), 55–68 (1997).
Google Scholar 
Blanckenhorn, W. U. The evolution of body size: What keeps organisms small?. Q. Rev. Biol. 75(4), 385–407 (2000).CAS 
PubMed 

Google Scholar 
Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl. Acad. Sci. USA 104(45), 17707–17712 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schmidt-Nielsen, K. Scaling in biology: The consequences of size. J. Exp. Zool. 194(1), 287–307 (1975).CAS 
PubMed 

Google Scholar 
Calder, W. A. Size, Function, and Life History (Courier Corporation, 1996).
Google Scholar 
Gould, S. J. Allometry and size in ontogeny and phylogeny. Biol. Rev. 41(4), 587–638 (1966).CAS 
PubMed 

Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293(5538), 2248–2251 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Gearty, W. & Payne, J. L. Physiological constraints on body size distributions in Crocodyliformes. Evolution 74(2), 245–255 (2020).PubMed 

Google Scholar 
Maurer, B. A., Brown, J. H. & Rusler, R. D. The micro and macro in body size evolution. Evolution 46(4), 939–953 (1992).PubMed 

Google Scholar 
Hone, D. W. & Benton, M. J. The evolution of large size: How does Cope’s Rule work?. Trends Ecol. Evol. 20(1), 4–6 (2005).PubMed 

Google Scholar 
Reeve, J. P. & Fairbairn, D. J. Predicting the evolution of sexual size dimorphism. J. Evol. Biol. 14(2), 244–254 (2001).
Google Scholar 
Blanckenhorn, W. U. Behavioral causes and consequences of sexual size dimorphism. Ethology 111(11), 977–1016 (2005).
Google Scholar 
Wu, H., Jiang, T., Huang, X. & Feng, J. Patterns of sexual size dimorphism in horseshoe bats: Testing Rensch’s rule and potential causes. Sci. Rep. 8(1), 1–13 (2018).ADS 

Google Scholar 
Cox, R. M., Butler, M. A. & John-Alder, H. B. The evolution of sexual size dimorphism in reptiles. In Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (eds Fairbairn, D. J. et al.) 38–49 (Oxford University Press, 2007).
Google Scholar 
Stillwell, R. C., Blanckenhorn, W. U., Teder, T., Davidowitz, G. & Fox, C. W. Sex differences in phenotypic plasticity affect variation in sexual size dimorphism in insects: From physiology to evolution. Annu. Rev. Entomol. 55, 227–245 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rensch, B. Die Abhängigkeit der relativen Sexualdifferenz von der Körpergrösse. Bonn. Zool. Beitr. 1, 58–69 (1950).
Google Scholar 
Rensch, B. Evolution Above the Species Level (Columbia University Press, 1960).
Google Scholar 
Shine, R. Ecological causes for the evolution of sexual dimorphism: A review of the evidence. Q. Rev. Biol. 64(4), 419–461 (1989).CAS 
PubMed 

Google Scholar 
Portik, D. M., Blackburn, D. C. & McGuire, J. A. Macroevolutionary patterns of sexual size dimorphism among African tree frogs (Family: Hyperoliidae). J. Hered. 111(4), 379–391 (2020).PubMed 

Google Scholar 
Ceballos, C. P., Adams, D. C., Iverson, J. B. & Valenzuela, N. Phylogenetic patterns of sexual size dimorphism in turtles and their implications for Rensch’s rule. Evol. Biol. 40(2), 194–208 (2013).
Google Scholar 
Amado, T. F., Martinez, P. A., Pincheira-Donoso, D. & Olalla-Tárraga, M. Á. Body size distributions of anurans are explained by diversification rates and the environment. Glob. Ecol. Biogeogr. 30(1), 154–164 (2021).
Google Scholar 
Starostová, Z., Kubička, L. & Kratochvíl, L. Macroevolutionary pattern of sexual size dimorphism in geckos corresponds to intraspecific temperature-induced variation. J. Evol. Biol. 23(4), 670–677 (2010).PubMed 

Google Scholar 
Herczeg, G., Gonda, A. & Merilä, J. Rensch’s rule inverted–female-driven gigantism in nine-spined stickleback Pungitius pungitius. J. Anim. Ecol. 79(3), 581–588 (2010).PubMed 

Google Scholar 
Liao, W. B. & Chen, W. Inverse Rensch’s rule in a frog with female-biased sexual size dimorphism. Naturwissenschaften 99(5), 427–431 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Cooper, M. I. Sexual size dimorphism and the rejection of Rensch’s rule in Diplopoda (Arthropoda). J. Entomol. Zool. Stud. 6(1), 1582–1587 (2018).
Google Scholar 
Cheng, R. C. & Kuntner, M. Phylogeny suggests nondirectional and isometric evolution of sexual size dimorphism in argiopine spiders. Evolution 68(10), 2861–2872 (2014).PubMed 

Google Scholar 
Webb, T. J. & Freckleton, R. P. Only half right: Species with female-biased sexual size dimorphism consistently break Rensch’s rule. PLoS ONE 2(9), e897 (2007).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: Elements of a synthesis. J. Biogeogr. 35(3), 483–500 (2008).
Google Scholar 
Olalla-Tárraga, M. Á. & Rodríguez, M. Á. Energy and interspecific body size patterns of amphibian faunas in Europe and North America: Anurans follow Bergmann’s rule, urodeles its converse. Glob. Ecol. Biogeogr. 16(5), 606–617 (2007).
Google Scholar 
Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodríguez, M. Á. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
Google Scholar 
Gouveia, S. F. & Correia, I. Geographical clines of body size in terrestrial amphibians: Water conservation hypothesis revisited. J. Biogeogr. 43(10), 2075–2084 (2016).
Google Scholar 
Pincheira-Donoso, D., Meiri, S., Jara, M., Olalla-Tárraga, M. Á. & Hodgson, D. J. Global patterns of body size evolution are driven by precipitation in legless amphibians. Ecography 42(10), 1682–1690 (2019).
Google Scholar 
Nevo, E. Adaptive color polymorphism in cricket frogs. Evolution 27(3), 353–367 (1973).PubMed 

Google Scholar 
Ashton, K. G. Do amphibians follow Bergmann’s rule?. Can. J. Zool. 80(4), 708–716 (2002).MathSciNet 

Google Scholar 
Bergmann, C. Ueber die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse. Göttinger Studien. 1, 595–708 (1847).
Google Scholar 
Olalla-Tárraga, M. Á., Rodríguez, M. Á. & Hawkins, B. A. Broad-scale patterns of body size in squamate reptiles of Europe and North America. J. Biogeogr. 33(5), 781–793 (2006).
Google Scholar 
Trullas, S. C., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32(5), 235–245 (2007).
Google Scholar 
Rodríguez, M. Á., López-Sañudo, I. L. & Hawkins, B. A. The geographic distribution of mammal body size in Europe. Glob. Ecol. Biogeogr. 15(2), 173–181 (2006).
Google Scholar 
Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodriguez, M. A. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
Google Scholar 
Womack, M. C. & Bell, R. C. Two-hundred million years of anuran body-size evolution in relation to geography, ecology and life history. J. Evol. Biol. 33(10), 1417–1432 (2020).PubMed 

Google Scholar 
Frost, D. R. Amphibian Species of the World: An online reference, version 6. http://research.amnh.org/herpetology/amphibia/index.php. Accessed 12 July 2021 (2021).
Acevedo, A. A., Armesto, O. & Palma, R. E. Two new species of Pristimantis (Anura: Craugastoridae) with notes on the distribution of the genus in northeastern Colombia. Zootaxa 4750(4), 499–523 (2020).
Google Scholar 
Heinicke, M. P., Duellman, W. E. & Hedges, S. B. Major Caribbean and Central American frog faunas originated by ancient oceanic dispersal. Proc. Natl. Acad. Sci. USA 104(24), 10092–10097 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pinto-Sánchez, N. R. et al. The great American biotic interchange in frogs: Multiple and early colonization of Central America by the South American genus Pristimantis (Anura: Craugastoridae). Mol. Phylogenet. Evol. 62(3), 954–972 (2012).PubMed 

Google Scholar 
Zumel, D., Buckley, D. & Ron, S. R. The Pristimantis trachyblepharis species group, a clade of miniaturized frogs: Description of four new species and insights into the evolution of body size in the genus. Zool. J. Linn. Soc. zlab044 (2021).Pincheira-Donoso, D. et al. The multiple origins of sexual size dimorphism in global amphibians. Glob. Ecol. Biogeogr. 30(2), 443–458 (2021).
Google Scholar 
Woolbright, L. L. Sexual selection and size dimorphism in anuran amphibia. Am. Nat. 121(1), 110–119 (1983).
Google Scholar 
Nali, R. C., Zamudio, K. R., Haddad, C. F. & Prado, C. P. Size-dependent selective mechanisms on males and females and the evolution of sexual size dimorphism in frogs. Am. Nat. 184(6), 727–740 (2014).PubMed 

Google Scholar 
Hill, R. et al. Herpetological husbandry observations on the captive reproduction of gaige’s rain frog Pristimantis gaigeae (Dunn 1931). Herpetol. Rev. 41(4), 465 (2010).
Google Scholar 
Rojas-Rivera, A., Cortés-Bedoya, S., Gutiérrez-Cárdenas, P. D. A. & Castellanos, J. M. Pristimantis achatinus (Cachabi robber frog). Parental care and clutch size. Herpetol. Rev. 42, 588–589 (2011).
Google Scholar 
Granados-Pérez, Y. & Ramirez-Pinilla, M. P. Reproductive phenology of three species of Pristimantis in an Andean cloud forest. Revista Acad. Colomb. Ci. Exact. 44(173), 1083–1098 (2020).
Google Scholar 
Levy, D. L. & Heald, R. Biological scaling problems and solutions in amphibians. Cold Spring Harb. Perspect. Biol. 8(1), a019166 (2016).PubMed Central 

Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Survey Data Series. 691(10), 4–9 (2012).
Google Scholar 
Valenzuela-Sánchez, A., Cunningham, A. A. & Soto-Azat, C. Geographic body size variation in ectotherms: Effects of seasonality on an anuran from the southern temperate forest. Front. Zool. 12(1), 1–10 (2015).
Google Scholar 
Parsons, J. J. The northern Andean environment. Mt. Res. Dev. 2(3), 253–264 (1982).
Google Scholar 
Navas, C. A., Carvajalino-Fernández, J. M., Saboyá-Acosta, L. P., Rueda-Solano, L. A. & Carvajalino-Fernández, M. A. The body temperature of active amphibians along a tropical elevation gradient: Patterns of mean and variance and inference from environmental data. Funct. Ecol. 27(5), 1145–1154 (2013).
Google Scholar 
Swemmer, A. M., Knapp, A. K. & Snyman, H. A. Intra-seasonal precipitation patterns and above-ground productivity in three perennial grasslands. J. Ecol. 95(4), 780–788 (2007).
Google Scholar 
Losos, J. B. Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles (Univ. of California Press, 2011).
Google Scholar 
Pincheira-Donoso, D. & Hunt, J. Fecundity selection theory: Concepts and evidence. Biol. Rev. 92(1), 341–356 (2017).PubMed 

Google Scholar 
Morrison, C. & Hero, J. M. Geographic variation in life-history characteristics of amphibians: A review. J. Anim. Ecol. 72(2), 270–279 (2003).
Google Scholar 
Morrow, C. B., Ernest, S. M. & Kerkhoff, A. J. Macroevolution of dimensionless life-history metrics in tetrapods. Proc. Royal Soc. B. 288, 20210200 (2021).
Google Scholar 
Revell, L. J., Harmon, L. J. & Collar, D. C. Phylogenetic signal, evolutionary process, and rate. Syst. Biol. 57(4), 591–601 (2008).PubMed 

Google Scholar 
Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368(1618), 20120341 (2013).PubMed 
PubMed Central 

Google Scholar 
Meyer, A. L. & Wiens, J. J. Estimating diversification rates for higher taxa: BAMM can give problematic estimates of rates and rate shifts. Evolution 72(1), 39–53 (2018).PubMed 

Google Scholar 
Rabosky, D. L. et al. Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation. Nat. Commun. 4(1), 1–8 (2013).
Google Scholar 
Mendoza, A. M., Ospina, O. E., Cárdenas-Henao, H. & García-R, J. C. A likelihood inference of historical biogeography in the world’s most diverse terrestrial vertebrate genus: Diversification of direct-developing frogs (Craugastoridae: Pristimantis) across the Neotropics. Mol. Phylogenet. Evol. 85, 50–58 (2015).PubMed 

Google Scholar 
Baker, J., Meade, A., Pagel, M. & Venditti, C. Adaptive evolution toward larger size in mammals. Proc. Natl. Acad. Sci. USA 112(16), 5093–5098 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hariharan, I. K., Wake, D. B. & Wake, M. H. Indeterminate growth: Could it represent the ancestral condition?. Cold Spring Harb. Perspect. Biol. 8(2), a019174 (2016).PubMed Central 

Google Scholar 
Amado, T. F., Bidau, C. J. & Olalla-Tárraga, M. Á. Geographic variation of body size in New World anurans: Energy and water in a balance. Ecography 42(3), 456–466 (2019).
Google Scholar 
Watters, J. L., Cummings, S. T., Flanagan, R. L. & Siler, C. D. Review of morphometric measurements used in anuran species descriptions and recommendations for a standardized approach. Zootaxa 4072, 477–495 (2016).PubMed 

Google Scholar 
Lovich, J. E. & Gibbons, J. W. A review of techniques for quantifying sexual size dimorphism. Growth Dev. Aging. 56, 269–269 (1992).CAS 
PubMed 

Google Scholar 
Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29(6), 1695–1701 (2012).CAS 
PubMed 

Google Scholar 
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7(1), 1–8 (2007).
Google Scholar 
Drummond, A. J., Ho, S. Y., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4(5), e88 (2006).PubMed 
PubMed Central 

Google Scholar 
Rambaut, A. FigTree, A Graphical Viewer of Phylogenetic Trees. (2007)Olalla-Tárraga, M. A., Bini, L. M., Diniz-Filho, J. A. & Rodríguez, M. Á. Cross-species and assemblage-based approaches to Bergmann’s rule and the biogeography of body size in Plethodon salamanders of eastern North America. Ecography 33(2), 362–368 (2010).
Google Scholar 
QGIS.org. QGIS Geographic Information System. QGIS Association. http://www.qgis.org. Accessed 10 July 2021 (2022).Wei, T. et al. Package ‘corrplot’. Statistician. 56(316), e24 (2017).
Google Scholar 
James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51(3), 365–390 (1970).
Google Scholar 
Hawkins, B. A. & Felizola Diniz-Filho, J. A. Beyond Rapoport’s rule: Evaluating range size patterns of New World birds in a two-dimensional framework. Glob. Ecol. Biogeogr. 15(5), 461–469 (2006).
Google Scholar 
Eager, C. standardize: Tools for standardizing variables for regression in R. R package version 0.21 (2017).Meireles, J. E., O’Meara, B. & Cavender-Bares, J. Linking leaf spectra to the plant tree of life. In Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 155–172 (Springer, 2010).
Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401(6756), 877–884 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Pagel, M. The maximum likelihood approach to reconstructing ancestral character states of discrete characters on phylogenies. Syst. Biol. 48(3), 612–622 (1999).
Google Scholar 
Revell, L. J. Phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3(2), 217–223 (2012).
Google Scholar 
Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4(8), 754–759 (2013).
Google Scholar 
Rabosky, D. L. et al. BAMM tools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5(7), 701–707 (2014).
Google Scholar 
Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9(2), e89543 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66(4), 477–498 (2017).PubMed 
PubMed Central 

Google Scholar 
Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence diagnosis and output analysis for MCMC. R News. 6(1), 7–11 (2006).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2021). https://www.R-project.org. Accessed 1 June 2021 (2021).Fairbairn, D. J. Allometry for sexual size dimorphism: Pattern and process in the coevolution of body size in males and females. Annu. Rev. Ecol. Evol. Syst. 28(1), 659–687 (1997).
Google Scholar 
Fairbairn, D. J. Allometry for sexual size dimorphism: Testing two hypotheses for Rensch’s rule in the water strider Aquarius remigis. Am. Nat. 166(S4), S69–S84 (2005).PubMed 

Google Scholar 
Visser, A. G., Beevers, L. & Patidar, S. Complexity in hydroecological modelling: A comparison of stepwise selection and information theory. River Res. Appl. 34(8), 1045–1056 (2018).
Google Scholar 
Calcagno, V. & de Mazancourt, C. glmulti: An R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34(12), 1–29 (2010).
Google Scholar 
Callaghan, S., Guilyardi, E., Steenman-Clark, L. & Morgan, M. The METAFOR project. in Earth System Modelling-Volume 1 (Springer, 2013).Garamszegi, L. Z. & Mundry, R. Multimodel-inference in comparative analyses. In Modern Phylogenetic Comparative Methods and THEIR Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 305–331 (Springer Berlin, 2014).
Google Scholar  More

World Urbanization Prospects: The 2018 Revision (UN Department of Economic and Social Affairs, 2018).Global Vector Control Response 2017–2030 (World Health Organization & UNICEF, 2017).Gubler, D. J. Dengue, urbanization and globalization: the unholy trinity of the 21st century. Trop. Med. Health 39, S3–S11 (2011).Article 

Google Scholar 
Brady, O. J. et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl. Trop. Dis. 6, e1760 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Kraemer, M. U. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat. Microbiol. 4, 854–863 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown, J. E. et al. Worldwide patterns of genetic differentiation imply multiple ‘domestications’ of Aedes aegypti, a major vector of human diseases. Proc. R. Soc. B Biol. Sci. 278, 2446–2454 (2011).Article 

Google Scholar 
Padmanabha, H., Durham, D., Correa, F., Diuk-Wasser, M. & Galvani, A. The interactive roles of Aedes aegypti super-production and human density in dengue transmission. PLoS Negl. Trop. Dis. 6, e1799 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Stewart-Ibarra, A. M. et al. Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010. BMC Infect. Dis. 14, 610 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Cavany, S. M. et al. Optimizing the deployment of ultra-low volume and targeted indoor residual spraying for dengue outbreak response. PLoS Comput. Biol. 16, e1007743 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Stefopoulou, Α et al. Reducing Aedes albopictus breeding sites through education: a study in urban area. PLoS ONE 13, e0202451 (2018).Article 

Google Scholar 
Lindsay, S. W., Wilson, A., Golding, N., Scott, T. W. & Takken, W.Improving the built environment in urban areas to control Aedes aegypti-borne diseases. Bull. World Health Organ. 95, 607–608 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Echaubard, P. et al. Fostering social innovation and building adaptive capacity for dengue control in Cambodia: a case study. Infect. Dis. Poverty 9, 126 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Vazquez-Prokopec, G. M., Lenhart, A. & Manrique-Saide, P. Housing improvement: a novel paradigm for urban vector-borne disease control? Trans. R. Soc. Trop. Med. Hyg. 110, 567–569 (2016).Article 
PubMed 

Google Scholar 
Malone, R. W. et al. Zika virus: medical countermeasure development challenges. PLoS Negl. Trop. Dis. 10, e0004530 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Murdock, C. C., Evans, M. V., McClanahan, T. D., Miazgowicz, K. L. & Tesla, B. Fine-scale variation in microclimate across an urban landscape shapes variation in mosquito population dynamics and the potential of Aedes albopictus to transmit arboviral disease. PLoS Negl. Trop. Dis. 11, e0005640 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bradley, C. A. & Altizer, S. Urbanization and the ecology of wildlife diseases. Trends Ecol. Evol. 22, 95–102 (2007).Article 
PubMed 

Google Scholar 
McDonald, R. I., Kareiva, P. & Forman, R. T. The implications of current and future urbanization for global protected areas and biodiversity conservation. Biol. Conserv. 141, 1695–1703 (2008).Article 

Google Scholar 
Ferraguti, M. et al. Effects of landscape anthropization on mosquito community composition and abundance. Sci. Rep. 6, 29002 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Juliano, S. A., Westby, K. M. & Ower, G. D. Know your enemy: effects of a predator on native and invasive container mosquitoes. J. Med. Entomol. 56, 320–328 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mahendra, A. & Seto, K. C. Upward and Outward Growth: Managing Urban Expansion for More Equitable Cities in the Global South (World Resources Institute, 2019).Moretto, L. et al. Challenges of water and sanitation service co-production in the global South. Environ. Urban. 30, 425–443 (2018).Article 

Google Scholar 
Seto, K. C., Sánchez-Rodríguez, R. & Fragkias, M. The new geography of contemporary urbanization and the environment. Annu. Rev. Environ. Resour. 35, 167–194 (2010).Article 

Google Scholar 
Estallo, E. L. et al. A decade of arbovirus emergence in the temperate southern cone of South America: dengue, Aedes aegypti and climate dynamics in Córdoba, Argentina. Heliyon 6, e04858 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kaufman, M. G. & Fonseca, D. M.Invasion biology of Aedes japonicus japonicus (Diptera: Culicidae). Annu. Rev. Entomol. 59, 31–49 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kache, P. A. et al. Environmental determinants of Aedes albopictus abundance at a northern limit of its range in the United States. Am. J. Trop. Med. Hyg. 102, 436–447 (2020).Article 
PubMed 

Google Scholar 
Eskew, E. A. & Olival, K. J. De-urbanization and zoonotic disease risk. EcoHealth 15, 707–712 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Biehler, D. et al. in The Palgrave Handbook of Critical Physical Geography 295–318 (Springer, 2018).Stoddard, S. T. et al. The role of human movement in the transmission of vector-borne pathogens. PLoS Negl. Trop. Dis. 3, e481 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and Chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wong, P. P.-Y., Lai, P.-C., Low, C.-T., Chen, S. & Hart, M. The impact of environmental and human factors on urban heat and microclimate variability. Build. Environ. 95, 199–208 (2016).Article 

Google Scholar 
Rey, J. R. & O’Connell, S. M. Oviposition by Aedes aegypti and Aedes albopictus: influence of congeners and of oviposition site characteristics. J. Vector Ecol. 39, 190–196 (2014).Article 
PubMed 

Google Scholar 
Leisnham, P. T. & Juliano, S. Spatial and temporal patterns of coexistence between competing Aedes mosquitoes in urban Florida. Oecologia 160, 343–352 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Paploski, I. A. D. et al. Storm drains as larval development and adult resting sites for Aedes aegypti and Aedes albopictus in Salvador, Brazil. Parasit. Vectors 9, 419 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Zainon, N., Rahim, F. A. M., Roslan, D. & Abd Samat, A. H. Prevention of Aedes breeding habitats for urban high-rise building in Malaysia. Plan. Malay. 14, 115–128 (2016).
Google Scholar 
Kenneson, A. et al. Social-ecological factors and preventive actions decrease the risk of dengue infection at the household-level: results from a prospective dengue surveillance study in Machala, Ecuador. PLoS Negl. Trop. Dis. 11, e0006150 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Harrington, L. C. et al. Dispersal of the dengue vector Aedes aegypti within and between rural communities. Am. J. Trop. Med. Hyg. 72, 209–220 (2005).Article 
PubMed 

Google Scholar 
Vavassori, L., Saddler, A. & Müller, P. Active dispersal of Aedes albopictus: a mark–release–recapture study using self-marking units. Parasit. Vectors 12, 583 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Ren, H., Wu, W., Li, T. & Yang, Z. Urban villages as transfer stations for dengue fever epidemic: a case study in the Guangzhou, China. PLoS Negl. Trop. Dis. 13, e0007350 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Charron, D. F. in Ecohealth Research in Practice 255–271 (Springer, 2012).Lippi, C. A. et al. Exploring the utility of social–ecological and entomological risk factors for dengue infection as surveillance indicators in the dengue hyper-endemic city of Machala, Ecuador. PLoS Negl. Trop. Dis. 15, e0009257 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Wijayanti, S. P. et al. The importance of socio-economic versus environmental risk factors for reported dengue cases in Java, Indonesia. PLoS Negl. Trop. Dis. 10, e0004964 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Zellweger, R. M. et al. Socioeconomic and environmental determinants of dengue transmission in an urban setting: an ecological study in Nouméa, New Caledonia. PLoS Negl. Trop. Dis. 11, e0005471 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Ryan, S. J. et al. Socio-ecological factors associated with dengue risk and Aedes aegypti presence in the Galápagos Islands, Ecuador. Int. J. Environ. Res. Public Health 16, 682 (2019).Article 
PubMed Central 

Google Scholar 
Roiz, D. et al. Integrated Aedes management for the control of Aedes-borne diseases. PLoS Negl. Trop. Dis. 12, e0006845 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Sanchez, L. et al. Aedes aegypti larval indices and risk for dengue epidemics. Emerg. Infect. Dis. 12, 800–806 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Cromwell, E. A. et al. The relationship between entomological indicators of Aedes aegypti abundance and dengue virus infection. PLoS Negl. Trop. Dis. 11, e0005429 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Honório, N. A. et al. Spatial evaluation and modeling of dengue seroprevalence and vector density in Rio de Janeiro, Brazil. PLoS Negl. Trop. Dis. 3, e545 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Chadee, D. Dengue cases and Aedes aegypti indices in Trinidad, West Indies. Acta Trop. 112, 174–180 (2009).Article 
CAS 
PubMed 

Google Scholar 
Fustec, B. et al. Complex relationships between Aedes vectors, socio-economics and dengue transmission—lessons learned from a case-control study in northeastern Thailand. PLoS Negl. Trop. Dis. 14, e0008703 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Scarpino, S. V. & Petri, G. On the predictability of infectious disease outbreaks. Nat. Commun. 10, 898 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Batty, M. in Encyclopedia of Complexity and Systems Science (ed. Meyers, R.) 1041–1071 (Springer, 2009).McPhearson, T., Haase, D., Kabisch, N. & Gren, Å. Advancing understanding of the complex nature of urban systems. Ecol. Indic. 70, 566–573 (2016).Rus, K., Kilar, V. & Koren, D. Resilience assessment of complex urban systems to natural disasters: a new literature review. Int. J. Disaster Risk Reduct. 31, 311–330 (2018).Article 

Google Scholar 
Bettencourt, L. M. Introduction to Urban Science: Evidence and Theory of Cities as Complex Systems (MIT Press, 2021).Handbook for Integrated Vector Management (World Health Organization, 2012).Kolimenakis, A. et al. The role of urbanisation in the spread of Aedes mosquitoes and the diseases they transmit—a systematic review. PLoS Negl. Trop. Dis. 15, e0009631 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Evans, M. V., Bhatnagar, S., Drake, J. M., Murdock, C. C. & Mukherjee, S.Socio‐ecological dynamics in urban systems: an integrative approach to mosquito‐borne disease in Bengaluru, India. People Nat. 4, 730–743 (2022).Article 

Google Scholar 
Cook, E. M., Hall, S. J. & Larson, K. L. Residential landscapes as social–ecological systems: a synthesis of multi-scalar interactions between people and their home environment. Urban Ecosyst. 15, 19–52 (2012).Article 

Google Scholar 
Bai, X., McAllister, R. R., Beaty, R. M. & Taylor, B. Urban policy and governance in a global environment: complex systems, scale mismatches and public participation. Curr. Opin. Environ. Sustain. 2, 129–135 (2010).Article 

Google Scholar 
Batty, M. Inventing Future Cities (MIT Press, 2018).McPhearson, T. et al. Advancing urban ecology toward a science of cities. BioScience 66, 198–212 (2016).Article 

Google Scholar 
Grimm, N. B., Cook, E. M., Hale, R. L. & Iwaniec, D. M. in The Routledge Handbook of Urbanization and Global Environmental Change 227–236 (Routledge, 2015).Haase, D. et al. A quantitative review of urban ecosystem service assessments: concepts, models, and implementation. Ambio 43, 413–433 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Filatova, T., Parker, D. & Van der Veen, A. Agent-based urban land markets: agent’s pricing behavior, land prices and urban land use change. J. Artif. Soc. Soc. Simul. 12, 3 (2009).
Google Scholar 
Acuto, M., Parnell, S. & Seto, K. C. Building a global urban science. Nat. Sustain. 1, 2–4 (2018).Article 

Google Scholar 
Collins, M. & Kapucu, N. Early warning systems and disaster preparedness and response in local government. Disaster Prev. Manag. 17, 587–600 (2008).Article 

Google Scholar 
Ahern, J. From fail-safe to safe-to-fail: sustainability and resilience in the new urban world. Landsc. Urban Plan. 100, 341–343 (2011).Article 

Google Scholar 
Gordon-Larsen, P., Nelson, M. C., Page, P. & Popkin, B. M. Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics 117, 417–424 (2006).Article 
PubMed 

Google Scholar 
Zhou, S. & Lin, R. Spatial–temporal heterogeneity of air pollution: the relationship between built environment and on-road PM2.5 at micro scale. Transp. Res. D Transp. Environ. 76, 305–322 (2019).Article 

Google Scholar 
Frank, L. D. & Engelke, P. Multiple impacts of the built environment on public health: walkable places and the exposure to air pollution. Int. Reg. Sci. Rev. 28, 193–216 (2005).Article 

Google Scholar 
Diuk-Wasser, M. A., VanAcker, M. C. & Fernandez, M. P. Impact of land use changes and habitat fragmentation on the eco-epidemiology of tick-borne diseases. J. Med. Entomol. 58, 1546–1564 (2021).Article 
PubMed 

Google Scholar 
Sengupta, U., Rauws, W. S. & De Roo, G.Planning and complexity: engaging with temporal dynamics, uncertainty and complex adaptive systems. Environ. Plann. B Plann. Des. 43, 970–974 (2016).Article 

Google Scholar 
Shi, Y. et al. Assessment methods of urban system resilience: from the perspective of complex adaptive system theory. Cities 112, 103141 (2021).Article 

Google Scholar 
Holland, J. H. Signals and Boundaries: Building Blocks for Complex Adaptive Systems (MIT Press, 2012).Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems. Ecol. Soc. 23, 46–61 (2018).Article 

Google Scholar 
Levin, S. et al. Social–ecological systems as complex adaptive systems: modeling and policy implications. Environ. Dev. Econ. 18, 111–132 (2013).Article 

Google Scholar 
Waldrop, M. M. Complexity: The Emerging Science at the Edge of Order and Chaos (Simon and Schuster, 1993).Nel, D., du Plessis, C. & Landman, K. Planning for dynamic cities: introducing a framework to understand urban change from a complex adaptive systems approach. Int. Plan. Stud. 23, 250–263 (2018).Article 

Google Scholar 
Sharifi, A. Resilient urban forms: a macro-scale analysis. Cities 85, 1–14 (2019).Article 

Google Scholar 
Borgström, S. T., Elmqvist, T., Angelstam, P. & Alfsen-Norodom, C. Scale mismatches in management of urban landscapes. Ecol. Soc. 11, 16 (2006).Article 

Google Scholar 
Walker, B. H., Carpenter, S. R., Rockstrom, J., Crépin, A.-S. & Peterson, G. D. Drivers, “slow” variables, “fast” variables, shocks, and resilience. Ecol. Soc. 17, 30 (2012).Article 

Google Scholar 
Carpenter, S. R. & Turner, M. G. Hares and tortoises: interactions of fast and slow variables in ecosystems. Ecosystems 3, 495–497 (2000).Article 

Google Scholar 
Peters, D. P., Bestelmeyer, B. T. & Turner, M. G. Cross-scale interactions and changing pattern–process relationships: consequences for system dynamics. Ecosystems 10, 790–796 (2007).Article 

Google Scholar 
Crépin, A.-S. Using fast and slow processes to manage resources with thresholds. Environ. Resour. Econ. 36, 191–213 (2007).Article 

Google Scholar 
Soranno, P. A. et al. Cross‐scale interactions: quantifying multi‐scaled cause–effect relationships in macrosystems. Front. Ecol. Environ. 12, 65–73 (2014).Article 

Google Scholar 
Pickett, S. T. et al. Theoretical perspectives of the Baltimore Ecosystem Study: conceptual evolution in a social–ecological research project. BioScience 70, 297–314 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Gunderson, L. H., Holling, C. S. & Light, S. S. Barriers and Bridges to the Renewal of Ecosystems and Institutions (Columbia Univ. Press, 1995).Turner, M. G., Dale, V. H. & Gardner, R. H. Predicting across scales: theory development and testing. Landsc. Ecol. 3, 245–252 (1989).Article 

Google Scholar 
Wu, J. & Loucks, O. L. From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. Q. Rev. Biol. 70, 439–466 (1995).Article 

Google Scholar 
Flores, A., Pickett, S. T., Zipperer, W. C., Pouyat, R. V. & Pirani, R. Adopting a modern ecological view of the metropolitan landscape: the case of a greenspace system for the New York City region. Landsc. Urban Plan. 39, 295–308 (1998).Article 

Google Scholar 
Fauchald, P. & Tveraa, T. Hierarchical patch dynamics and animal movement pattern. Oecologia 149, 383–395 (2006).Article 
PubMed 

Google Scholar 
Linton, J. & Budds, J. The hydrosocial cycle: defining and mobilizing a relational–dialectical approach to water. Geoforum 57, 170–180 (2014).Article 

Google Scholar 
Knox, P. & Pinch, S. Urban Social Geography: an Introduction (Routledge, 2014).Geels, F. W. From sectoral systems of innovation to socio-technical systems: insights about dynamics and change from sociology and institutional theory. Res. Policy 33, 897–920 (2004).Article 

Google Scholar 
West, S., Haider, L. J., Stålhammar, S. & Woroniecki, S. A relational turn for sustainability science? Relational thinking, leverage points and transformations. Ecosyst. People 16, 304–325 (2020).Article 

Google Scholar 
Jones, M. Phase space: geography, relational thinking, and beyond. Prog. Hum. Geogr. 33, 487–506 (2009).Article 

Google Scholar 
Wohl, S. Considering how morphological traits of urban fabric create affordances for complex adaptation and emergence. Prog. Hum. Geogr. 40, 30–47 (2016).Article 

Google Scholar 
Herold, M., Scepan, J. & Clarke, K. C. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ. Plan. A 34, 1443–1458 (2002).Article 

Google Scholar 
Morrison, A. C. et al. Temporal and geographic patterns of Aedes aegypti (Diptera: Culicidae) production in Iquitos, Peru. J. Med. Entomol. 41, 1123–1142 (2004).Article 
PubMed 

Google Scholar 
LaCon, G. et al. Shifting patterns of Aedes aegypti fine scale spatial clustering in Iquitos, Peru. PLoS Negl. Trop. Dis. 8, e3038 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Lai, S. et al. Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005–2015. PLoS Negl. Trop. Dis. 12, e0006743 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Gergel, S. E. & Turner, M. G. Learning Landscape Ecology: a Practical Guide to Concepts and Techniques (Springer, 2017).Hosseini, P. R. et al. Does the impact of biodiversity differ between emerging and endemic pathogens? The need to separate the concepts of hazard and risk. Phil. Trans. R. Soc. B Biol. Sci. 372, 20160129 (2017).Article 

Google Scholar 
LaDeau, S. L., Allan, B. F., Leisnham, P. T. & Levy, M. Z. The ecological foundations of transmission potential and vector‐borne disease in urban landscapes. Funct. Ecol. 29, 889–901 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Rowley, W. A. & Graham, C. L. The effect of temperature and relative humidity on the flight performance of female Aedes aegypti. J. Insect Physiol. 14, 1251–1257 (1968).Article 
CAS 
PubMed 

Google Scholar 
Evans, M. V. et al. Microclimate and larval habitat density predict adult Aedes albopictus abundance in urban areas. Am. J. Trop. Med. Hyg. 101, 362–370 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Alto, B. W. & Juliano, S. A. Temperature effects on the dynamics of Aedes albopictus (Diptera: Culicidae) populations in the laboratory. J. Med. Entomol. 38, 548–556 (2001).Article 
CAS 
PubMed 

Google Scholar 
Streutker, D. R. A remote sensing study of the urban heat island of Houston, Texas. Int. J. Remote Sens. 23, 2595–2608 (2002).Article 

Google Scholar 
Fikrig, K. et al. Sugar feeding patterns of New York Aedes albopictus mosquitoes are affected by saturation deficit, flowers, and host seeking. PLoS Negl. Trop. Dis. 14, e0008244 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Samson, D. M. et al. Resting and energy reserves of Aedes albopictus collected in common landscaping vegetation in St. Augustine, Florida. J. Am. Mosq. Control Assoc. 29, 231–236 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Grove, J. M., Locke, D. H. & O’Neil-Dunne, J. P. An ecology of prestige in New York City: examining the relationships among population density, socio-economic status, group identity, and residential canopy cover. Environ. Manag. 54, 402–419 (2014).Article 

Google Scholar 
Leong, M., Dunn, R. R. & Trautwein, M. D. Biodiversity and socioeconomics in the city: a review of the luxury effect. Biol. Lett. 14, 20180082 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Aronson, M. F. et al. Biodiversity in the city: key challenges for urban green space management. Front. Ecol. Environ. 15, 189–196 (2017).Article 

Google Scholar 
Hemme, R. R., Thomas, C. L., Chadee, D. D. & Severson, D. W. Influence of urban landscapes on population dynamics in a short-distance migrant mosquito: evidence for the dengue vector Aedes aegypti. PLoS Negl. Trop. Dis. 4, e634 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
García-Betancourt, T., Higuera-Mendieta, D. R., González-Uribe, C., Cortés, S. & Quintero, J. Understanding water storage practices of urban residents of an endemic dengue area in Colombia: perceptions, rationale and socio-demographic characteristics. PLoS ONE 10, e0129054 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Plummer, R., de Loë, R. & Armitage, D. A systematic review of water vulnerability assessment tools. Water Resour. Manag. 26, 4327–4346 (2012).Article 

Google Scholar 
Ledogar, R. J. et al. Mobilising communities for Aedes aegypti control: the SEPA approach. BMC Public Health 17, 103–114 (2017).Article 

Google Scholar 
Michalos, A. C. Encyclopedia of Quality of Life and Well-being Research (Springer Netherlands, 2014).Reiner, R. C. Jr, Stoddard, S. T. & Scott, T. W. Socially structured human movement shapes dengue transmission despite the diffusive effect of mosquito dispersal. Epidemics 6, 30–36 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Whiteford, L. M. The ethnoecology of dengue fever. Med. Anthropol. Q. 11, 202–223 (1997).Article 
CAS 
PubMed 

Google Scholar 
Ibarra, A. M. S. et al. A social–ecological analysis of community perceptions of dengue fever and Aedes aegypti in Machala, Ecuador. BMC Public Health 14, 1135 (2014).Article 

Google Scholar 
Mitchell-Foster, K. L. Interdisciplinary Knowledge Translation and Evaluation Strategies for Participatory Dengue Prevention in Machala, Ecuador. PhD thesis, Univ. British Columbia (2013).Kropf, K.Aspects of urban form. Urban Morphol. 13, 105–120 (2009).Article 

Google Scholar 
Rose, L. A. Topographical constraints and urban land supply indexes. J. Urban Econ. 26, 335–347 (1989).Article 

Google Scholar 
Liu, F. “Interrupted Development”: The Effects of Blighted Neighborhoods and Topographic Barriers on Cities. PhD thesis, George Washington Univ. (2006).Durand-Lasserve, A. & Selod, H. in Urban Land Markets 101–132 (Springer, 2009).Talen, E. City Rules: How Regulations Affect Urban Form (Island Press, 2012).Scheer, B. C. The Evolution of Urban Form: Typology for Planners and Architects (Routledge, 2017).Dimoudi, A., Kantzioura, A., Zoras, S., Pallas, C. & Kosmopoulos, P. Investigation of urban microclimate parameters in an urban center. Energy Build. 64, 1–9 (2013).Article 

Google Scholar 
Middel, A., Häb, K., Brazel, A. J., Martin, C. A. & Guhathakurta, S. Impact of urban form and design on mid-afternoon microclimate in Phoenix local climate zones. Landsc. Urban Plan. 122, 16–28 (2014).Article 

Google Scholar 
Honório, N. A. et al. Dispersal of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in an urban endemic dengue area in the State of Rio de Janeiro, Brazil. Mem. Inst. Oswaldo Cruz 98, 191–198 (2003).Article 
PubMed 

Google Scholar 
Seto, K. C. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) 923–1000 (Cambridge Univ. Press, 2014).Romeo-Aznar, V., Freitas, L. P., Cruz, O. G., King, A. & Pascual, M. Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics. Nat. Commun. 13, 996 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lafferty, K. D. et al. Local extinction of the Asian tiger mosquito (Aedes albopictus) following rat eradication on Palmyra Atoll. Biol. Lett. 14, 20170743 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Rodríguez, M. C., Dupont-Courtade, L. & Oueslati, W. Air pollution and urban structure linkages: evidence from European cities. Renew. Sustain. Energy Rev. 53, 1–9 (2016).Article 

Google Scholar 
Venter, Z. S., Krog, N. H. & Barton, D. N. Linking green infrastructure to urban heat and human health risk mitigation in Oslo, Norway. Sci. Total Environ. 709, 136193 (2020).Article 
CAS 
PubMed 

Google Scholar 
Little, E., Barrera, R., Seto, K. C. & Diuk-Wasser, M. Co-occurrence patterns of the dengue vector Aedes aegypti and Aedes mediovitattus, a dengue competent mosquito in Puerto Rico. EcoHealth 8, 365–375 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Pereira dos Santos, T. et al. Potential of Aedes albopictus as a bridge vector for enzootic pathogens at the urban–forest interface in Brazil. Emerg. Microbes Infect. 7, 191 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Cardoso, J. et al. Yellow fever virus in Haemagogus leucocelaenus and Aedes serratus mosquitoes, southern Brazil, 2008. Emerg. Infect. Dis. 16, 1918–1924 (2010).Article 
PubMed Central 

Google Scholar 
Grobbelaar, A. A. et al. Resurgence of yellow fever in Angola, 2015–2016. Emerg. Infect. Dis. 22, 1854–1855 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Tonkiss, F. Cities by Design: the Social Life of Urban Form (John Wiley & Sons, 2014).Hillier, B., Greene, M. & Desyllas, J. Self-generated neighbourhoods: the role of urban form in the consolidation of informal settlements. Urban Des. Int. 5, 61–96 (2000).Article 

Google Scholar 
Li, X., Mou, Y., Wang, H., Yin, C. & He, Q. How does polycentric urban form affect urban commuting? Quantitative measurement using geographical big data of 100 cities in China. Sustainability 10, 4566 (2018).Article 

Google Scholar 
Wen, T.-H., Lin, M.-H., Teng, H.-J. & Chang, N.-T. Incorporating the human–Aedes mosquito interactions into measuring the spatial risk of urban dengue fever. Appl. Geogr. 62, 256–266 (2015).Article 

Google Scholar 
Achee, N. L. et al. A critical assessment of vector control for dengue prevention. PLoS Negl. Trop. Dis. 9, e0003655 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Scott, T. W. & Morrison, A. C. Vector dynamics and transmission of dengue virus: implications for dengue surveillance and prevention strategies: vector dynamics and dengue prevention. Curr. Top. Microbiol. Immunol. 338, 115–128 (2010).PubMed 

Google Scholar 
Delmelle, E., Kim, C., Xiao, N. & Chen, W. Methods for space–time analysis and modeling: an overview. Int. J. Appl. Geospat. Res. 4, 1–18 (2013).Article 

Google Scholar 
Kua, K. P. & Lee, S. W. H. Randomized trials of housing interventions to prevent malaria and Aedes-transmitted diseases: a systematic review and meta-analysis. PLoS ONE 16, e0244284 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chareonviriyaphap, T. et al. The use of an experimental hut for evaluating the entering and exiting behavior of Aedes aegypti (Diptera: Culicidae), a primary vector of dengue in Thailand. J. Vector Ecol. 30, 344–346 (2005).PubMed 

Google Scholar 
Maneerat, S. & Daudé, E. A spatial agent-based simulation model of the dengue vector Aedes aegypti to explore its population dynamics in urban areas. Ecol. Model. 333, 66–78 (2016).Article 

Google Scholar 
Barbu, C. M. et al. The effects of city streets on an urban disease vector. PLoS Comput. Biol. 9, e1002801 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stewart Ibarra, A. M. et al. Dengue vector dynamics (Aedes aegypti) influenced by climate and social factors in Ecuador: implications for targeted control. PLoS ONE 8, e78263 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Mesch, G. S. & Manor, O. Social ties, environmental perception, and local attachment. Environ. Behav. 30, 504–519 (1998).Article 

Google Scholar 
Matthews, L. & Haydon, D. Introduction. Cross-scale influences on epidemiological dynamics: from genes to ecosystems. J. R. Soc. Interface 4, 763–765 (2007).Article 
PubMed Central 

Google Scholar 
Strauss, A. T., Shoemaker, L. G., Seabloom, E. W. & Borer, E. T. Cross‐scale dynamics in community and disease ecology: relative timescales shape the community ecology of pathogens. Ecology 100, e02836 (2019).Article 
PubMed 

Google Scholar 
Schreiber, S. J. et al. Cross-scale dynamics and the evolutionary emergence of infectious diseases. Virus Evol. 7, veaa105 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ramalho, C. E. & Hobbs, R. J. Time for a change: dynamic urban ecology. Trends Ecol. Evol. 27, 179–188 (2012).Article 
PubMed 

Google Scholar 
Waggoner, J. J. et al. Homotypic dengue virus reinfections in Nicaraguan children. J. Infect. Dis. 214, 986–993 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ezeakacha, N. F. & Yee, D. A. The role of temperature in affecting carry-over effects and larval competition in the globally invasive mosquito Aedes albopictus. Parasit. Vectors 12, 123 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Evans, M. V. et al. Carry-over effects of urban larval environments on the transmission potential of dengue-2 virus. Parasit. Vectors 11, 426 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Lowe, R. et al. Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study. Lancet Planet. Health 5, e209–e219 (2021).Article 
PubMed 

Google Scholar 
Chen, S.-C. et al. Lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: insights from a statistical analysis. Sci. Total Environ. 408, 4069–4075 (2010).Article 
CAS 
PubMed 

Google Scholar 
Elsinga, J. et al. Knowledge, attitudes, and preventive practices regarding dengue in Maracay, Venezuela. Am. J. Trop. Med. Hyg. 99, 195–203 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Wong, L. P., Shakir, S. M. M., Atefi, N. & AbuBakar, S. Factors affecting dengue prevention practices: nationwide survey of the Malaysian public. PLoS ONE 10, e0122890 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Des Roches, S. et al. Socio‐eco‐evolutionary dynamics in cities. Evol. Appl. 14, 248–267 (2021).Article 
PubMed 

Google Scholar 
Pickett, S. T. et al. Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Annu. Rev. Ecol. Syst. 32, 127–157 (2001).Article 

Google Scholar 
Combs, M. A. et al. Socio‐ecological drivers of multiple zoonotic hazards in highly urbanized cities. Glob. Change Biol. 28, 1705–1724 (2022).Article 
CAS 

Google Scholar 
Zhou, Q.A review of sustainable urban drainage systems considering the climate change and urbanization impacts. Water 6, 976–992 (2014).Article 

Google Scholar 
Stewart-Ibarra, A. M. et al. Co-developing climate services for public health: stakeholder needs and perceptions for the prevention and control of Aedes-transmitted diseases in the Caribbean. PLoS Negl. Trop. Dis. 13, e0007772 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hastings, A. Timescales, dynamics, and ecological understanding. Ecology 91, 3471–3480 (2010).Article 
PubMed 

Google Scholar 
Lippi, C. A. et al. A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador. Int. J. Health Geogr. 19, 3 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Projection of the Ecuadorian Population, per Calendar Years, by Cantons 2010–2020 (National Institute of Statistics and Census, 2012).Pertumbuhan Ekonomi Indonesia Triwulan II (Badann Pusat Statistik, 2021).Rašić, G. et al. Aedes aegypti has spatially structured and seasonally stable populations in Yogyakarta, Indonesia. Parasit. Vectors 8, 610 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Schmidt, T. L. et al. Genome-wide SNPs reveal the drivers of gene flow in an urban population of the Asian tiger mosquito, Aedes albopictus. PLoS Negl. Trop. Dis. 11, e0006009 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Schmidt, T. L., Filipović, I., Hoffmann, A. A. & Rašić, G. Fine-scale landscape genomics helps explain the slow spatial spread of Wolbachia through the Aedes aegypti population in Cairns, Australia. Heredity 120, 386–395 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tantowijoyo, W. et al. Stable establishment of wMel Wolbachia in Aedes aegypti populations in Yogyakarta, Indonesia. PLoS Negl. Trop. Dis. 14, e0008157 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Telle, O. et al. The spread of dengue in an endemic urban milieu—the case of Delhi, India. PLoS ONE 11, e0146539 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Telle, O. et al. Social and environmental risk factors for dengue in Delhi city: a retrospective study. PLoS Negl. Trop. Dis. 15, e0009024 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Stokes, E. C. & Seto, K. C. Characterizing and measuring urban landscapes for sustainability. Environ. Res. Lett. 14, 045002 (2019).Article 

Google Scholar 
Jackson-Smith, D. B. et al. Differentiating urban forms: a neighborhood typology for understanding urban water systems. Cities Environ. 9, 5 (2016).
Google Scholar 
Population Census by Age (Department of Provincial Administration, accessed March 2022); https://stat.bora.dopa.go.th/new_stat/webPage/statByAge.phpSalje, H. et al. Revealing the microscale spatial signature of dengue transmission and immunity in an urban population. Proc. Natl Acad. Sci. USA 109, 9535–9538 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Salje, H. et al. Reconstructing unseen transmission events to infer dengue dynamics from viral sequences. Nat. Commun. 12, 1810 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chai, B. & Seto, K. C. Conceptualizing and characterizing micro-urbanization: a new perspective applied to Africa. Landsc. Urban Plan. 190, 103595 (2019).Article 

Google Scholar 
Zhu, G., Liu, J., Tan, Q. & Shi, B. Inferring the spatio-temporal patterns of dengue transmission from surveillance data in Guangzhou, China. PLoS Negl. Trop. Dis. 10, e0004633 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ab Hamid, N. et al. Vertical infestation profile of Aedes in selected urban high-rise residences in Malaysia. Trop. Med. Infect. Dis. 5, 114 (2020).Article 
PubMed Central 

Google Scholar 
Sun, H. et al. Spatio-temporal analysis of the main dengue vector populations in Singapore. Parasit. Vectors 14, 41 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ho, C.-M. et al. Surveillance for dengue fever vectors using ovitraps at Kaohsiung and Tainan in Taiwan. Formos. Entomol. 25, 159–174 (2005).
Google Scholar 
McKenzie, D. & Ray, I. Urban water supply in India: status, reform options and possible lessons. Water Policy 11, 442–460 (2009).Article 

Google Scholar 
Qian, S. S., Cuffney, T. F., Alameddine, I., McMahon, G. & Reckhow, K. H. On the application of multilevel modeling in environmental and ecological studies. Ecology 91, 355–361 (2010).Article 
PubMed 

Google Scholar 
Parham, P. E. et al. Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission. Phil. Trans. R. Soc. B Biol. Sci. 370, 20130551 (2015).Article 

Google Scholar 
Slocum, M. G., Beckage, B., Platt, W. J., Orzell, S. L. & Taylor, W. Effect of climate on wildfire size: a cross-scale analysis. Ecosystems 13, 828–840 (2010).Article 

Google Scholar 
Chiu, C.-H., Wen, T.-H., Chien, L.-C. & Yu, H.-L. A probabilistic spatial dengue fever risk assessment by a threshold-based-quantile regression method. PLoS ONE 9, e106334 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Higuera-Mendieta, D. R., Cortés-Corrales, S., Quintero, J. & González-Uribe, C. KAP surveys and dengue control in Colombia: disentangling the effect of sociodemographic factors using multiple correspondence analysis. PLoS Negl. Trop. Dis. 10, e0005016 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, J. H., Yuan, J. & Wang, T. Direct cost of dengue hospitalization in Zhongshan, China: associations with demographics, virus types and hospital accreditation. PLoS Negl. Trop. Dis. 11, e0005784 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Tam, C. C. et al. Estimates of dengue force of infection in children in Colombo, Sri Lanka. PLoS Negl. Trop. Dis. 7, e2259 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Lee, S. & Castillo-Chavez, C. The role of residence times in two-patch dengue transmission dynamics and optimal strategies. J. Theor. Biol. 374, 152–164 (2015).Article 
PubMed 

Google Scholar 
Adams, B. & Kapan, D. D. Man bites mosquito: understanding the contribution of human movement to vector-borne disease dynamics. PLoS ONE 4, e6763 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Colizza, V. & Vespignani, A. Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. J. Theor. Biol. 251, 450–467 (2008).Article 
PubMed 

Google Scholar 
Otero, M., Schweigmann, N. & Solari, H. G. A stochastic spatial dynamical model for Aedes aegypti. Bull. Math. Biol. 70, 1297–1325 (2008).Article 
PubMed 

Google Scholar 
Otero, M. & Solari, H. G. Stochastic eco-epidemiological model of dengue disease transmission by Aedes aegypti mosquito. Math. Biosci. 223, 32–46 (2010).Article 
CAS 
PubMed 

Google Scholar 
Li, X. & Liu, X. Embedding sustainable development strategies in agent‐based models for use as a planning tool. Int. J. Geogr. Inf. Sci. 22, 21–45 (2008).Article 

Google Scholar 
Mozaffaree Pour, N. & Oja, T. Urban expansion simulated by integrated cellular automata and agent-based models; an example of Tallinn, Estonia. Urban Sci. 5, 85 (2021).Article 

Google Scholar 
Gilbert, N. Agent-Based Models Vol. 153 (Sage Publications, 2019).Roster, K. & Rodrigues, F. A. Neural networks for dengue prediction: a systematic review. Preprint at https://arxiv.org/abs/2106.12905 (2021).Zhao, N. et al. Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl. Trop. Dis. 14, e0008056 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhai, Y. et al. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata. Int. J. Geogr. Inf. Sci. 34, 1475–1499 (2020).Article 

Google Scholar 
Verma, D. & Jana, A. LULC classification methodology based on simple Convolutional Neural Network to map complex urban forms at finer scale: evidence from Mumbai. Preprint at https://arxiv.org/abs/1909.09774 (2019).Djenontin, I. N. S. & Meadow, A. M. The art of co-production of knowledge in environmental sciences and management: lessons from international practice. Environ. Manag. 61, 885–903 (2018).Article 

Google Scholar 
Meschede, C. & Mainka, A. Including citizen participation formats for drafting and implementing local sustainable development strategies. Urban Sci. 4, 13 (2020).Article 

Google Scholar 
Mansfield, R. G., Batagol, B. & Raven, R. “Critical agents of change?”: opportunities and limits to children’s participation in urban planning. J. Plan. Lit. 36, 170–186 (2021).Article 

Google Scholar 
Curtis, A., Quinn, M., Obenauer, J. & Renk, B. M. Supporting local health decision making with spatial video: dengue, Chikungunya and Zika risks in a data poor, informal community in Nicaragua. Appl. Geogr. 87, 197–206 (2017).Article 

Google Scholar 
Norström, A. V. et al. Principles for knowledge co-production in sustainability research. Nat. Sustain. 3, 182–190 (2020).Article 

Google Scholar 
Dickens, L. & Butcher, M. Going public? Re‐thinking visibility, ethics and recognition through participatory research praxis. Trans. Inst. Br. Geogr. 41, 528–540 (2016).Article 

Google Scholar 
Wallerstein, N. et al. Power dynamics in community-based participatory research: a multiple-case study analysis of partnering contexts, histories, and practices. Health Educ. Behav. 46, 19S–32S (2019).Article 
PubMed 

Google Scholar 
Parra, C. et al. Synergies between technology, participation, and citizen science in a community-based dengue prevention program. Am. Behav. Sci. 64, 1850–1870 (2020).Article 

Google Scholar 
Lozano–Fuentes, S. et al. Cell phone-based system (Chaak) for surveillance of immatures of dengue virus mosquito vectors. J. Med. Entomol. 50, 879–889 (2013).Article 
PubMed 

Google Scholar 
Kelvin, A. A. et al. ZIKATracker: a mobile app for reporting cases of ZIKV worldwide. J. Infect. Dev. Ctries. 10, 113–115 (2016).Article 
PubMed 

Google Scholar 
Fernandez, M. P. et al. Usability and feasibility of a smartphone app to assess human behavioral factors associated with tick exposure (The Tick App): quantitative and qualitative study. JMIR mHealth uHealth 7, e14769 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hamer, S. A., Curtis-Robles, R. & Hamer, G. L. Contributions of citizen scientists to arthropod vector data in the age of digital epidemiology. Curr. Opin. Insect Sci. 28, 98–104 (2018).Article 
PubMed 

Google Scholar 
Van Leeuwen, J. P., Hermans, K., Jylhä, A., Quanjer, A. J. & Nijman, H. Effectiveness of virtual reality in participatory urban planning: a case study. In Proc. Media Architecture Biennale 128–136 (Association for Computing Machinery, 2018).Kahila-Tani, M. Reshaping the Planning Process Using Local Experiences: Utilising PPGIS in Participatory Urban Planning. PhD thesis, Aalto Univ. (2015).Iwaniec, D. M. et al. The co-production of sustainable future scenarios. Landsc. Urban Plan. 197, 103744 (2020).Article 

Google Scholar 
Dickin, S. K., Schuster-Wallace, C. J. & Elliott, S. J. Mosquitoes & vulnerable spaces: mapping local knowledge of sites for dengue control in Seremban and Putrajaya Malaysia. Appl. Geogr. 46, 71–79 (2014).Article 

Google Scholar 
Chircop, A., Bassett, R. & Taylor, E. Evidence on how to practice intersectoral collaboration for health equity: a scoping review. Crit. Public Health 25, 178–191 (2015).Article 

Google Scholar 
Gamache, S., Diallo, T. A., Shankardass, K. & Lebel, A. The elaboration of an intersectoral partnership to perform health impact assessment in urban planning: the experience of Quebec City (Canada). Int. J. Environ. Res. Public Health 17, 7556 (2020).Article 
PubMed Central 

Google Scholar 
Herdiana, H., Sari, J. F. K. & Whittaker, M. Intersectoral collaboration for the prevention and control of vector borne diseases to support the implementation of a global strategy: a systematic review. PLoS ONE 13, e0204659 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Lee, S. A., Economou, T., de Castro Catão, R., Barcellos, C. & Lowe, R. The impact of climate suitability, urbanisation, and connectivity on the expansion of dengue in 21st century Brazil. PLoS Negl. Trop. Dis. 15, e0009773 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Johansson, M. A., Cummings, D. A. & Glass, G. E. Multiyear climate variability and dengue—El Nino southern oscillation, weather, and dengue incidence in Puerto Rico, Mexico, and Thailand: a longitudinal data analysis. PLoS Med. 6, e1000168 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Barrera, R., Amador, M. & MacKay, A. J. Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl. Trop. Dis. 5, e1378 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Hess, G. Disease in metapopulation models: implications for conservation. Ecology 77, 1617–1632 (1996).Article 

Google Scholar 
Hanski, I. Metapopulation dynamics: does it help to have more of the same? Trends Ecol. Evol. 4, 113–114 (1989).Article 
CAS 
PubMed 

Google Scholar 
Masui, H. et al. Assessing potential countermeasures against the dengue epidemic in non-tropical urban cities. Theor. Biol. Med. Model. 13, 12 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Stone, C. M., Schwab, S. R., Fonseca, D. M. & Fefferman, N. H. Contrasting the value of targeted versus area-wide mosquito control scenarios to limit arbovirus transmission with human mobility patterns based on different tropical urban population centers. PLoS Negl. Trop. Dis. 13, e0007479 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
O’Reilly, K. M. et al. Projecting the end of the Zika virus epidemic in Latin America: a modelling analysis. BMC Med. 16, 180 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Santé, I., García, A. M., Miranda, D. & Crecente, R. Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc. Urban Plan. 96, 108–122 (2010).Article 

Google Scholar 
Yang, J., Gong, J., Tang, W. & Liu, C. Patch-based cellular automata model of urban growth simulation: integrating feedback between quantitative composition and spatial configuration. Comput. Environ. Urban Syst. 79, 101402 (2020).Article 

Google Scholar 
Rozos, E., Butler, D. & Makropoulos, C. An integrated system dynamics–cellular automata model for distributed water-infrastructure planning. Water Sci. Technol. Water Supply 16, 1519–1527 (2016).Article 

Google Scholar 
Enduri, M. K. & Jolad, S. Dynamics of dengue disease with human and vector mobility. Spat. Spatiotemporal Epidemiol. 25, 57–66 (2018).Article 
PubMed 

Google Scholar 
Medeiros, L. C. et al. Modeling the dynamic transmission of dengue fever: investigating disease persistence. PLoS Negl. Trop. Dis. 5, e942 (2011).Article 
PubMed Central 

Google Scholar 
Ali, A. M., Shafiee, M. E. & Berglund, E. Z. Agent-based modeling to simulate the dynamics of urban water supply: climate, population growth, and water shortages. Sustain. Cities Soc. 28, 420–434 (2017).Article 

Google Scholar 
Philippon, D. et al. in Multi-Agent Based Simulation XVII. MABS 2016. Lecture Notes in Computer Science Vol 10399 (eds Nardin, L. & Antunes, L.) 111–127 (Springer, 2016).Agyemang, F. S., Silva, E. & Fox, S.Modelling and simulating ‘informal urbanization’: an integrated agent-based and cellular automata model of urban residential growth in Ghana. Urban Anal. City Sci. 0, 1–15 (2022).
Google Scholar 
Chouhan, S. S., Kaul, A. & Singh, U. P. Image segmentation using computational intelligence techniques. Arch. Comput. Methods Eng. 26, 533–596 (2019).Article 

Google Scholar 
Andersson, V. O., Birck, M. A. F. & Araujo, R. M. Towards predicting dengue fever rates using convolutional neural networks and street-level images. Proc. 2018 Int. Jt Conf. Neural Netw. 1–8 (IEEE, 2018).Chrysler, A., Gunarso, R., Puteri, T. & Warnars, H. A Literature Review of Crowd-Counting System on Convolutional Neural Network 012029 (IOP Conference Series: Earth and Environmental Science Volume 729, IOP Publishing, 2021).Bharambe, A., Chandorkar, A. A. & Kalbande, D. A deep learning approach for dengue tweet classification. Proc. 3rd Int. Conf. Invent. Res. Comput. Appl. 1043–1047 (IEEE, 2021).Kumar, A. & Garg, G. Sentiment analysis of multimodal twitter data. Multimed. Tools Appl. 78, 24103–24119 (2019).Article 

Google Scholar 
Marin, A. & Wellman, B. in The SAGE Handbook of Social Network Analysis Ch. 2 (2011).Snijders, T. A. & Steglich, C. E. Representing micro–macro linkages by actor-based dynamic network models. Sociol. Methods Res. 44, 222–271 (2015).Article 
PubMed 

Google Scholar 
Warren, C. R., Burton, R., Buchanan, O. & Birnie, R. V. Limited adoption of short rotation coppice: the role of farmers’ socio-cultural identity in influencing practice. J. Rural Stud. 45, 175–183 (2016).Article 

Google Scholar 
Beal Cohen, A. A., Muneepeerakul, R. & Kiker, G. Intra-group decision-making in agent-based models. Sci. Rep. 11, 17709 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Frederiks, E. R., Stenner, K. & Hobman, E. V. Household energy use: applying behavioural economics to understand consumer decision-making and behaviour. Renew. Sustain. Energy Rev. 41, 1385–1394 (2015).Article 

Google Scholar 
Spiegel, J. et al. Barriers and bridges to prevention and control of dengue: the need for a social–ecological approach. EcoHealth 2, 273–290 (2005).Article 

Google Scholar 
Arellano, C. et al. Knowledge and beliefs about dengue transmission and their relationship with prevention practices in Hermosillo, Sonora. Front. Public Health 3, 142 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Gertler, M. S. & Wolfe, D. A. Local social knowledge management: community actors, institutions and multilevel governance in regional foresight exercises. Futures 36, 45–65 (2004).Article 

Google Scholar 
Brown, R. R., Farrelly, M. A. & Loorbach, D. A. Actors working the institutions in sustainability transitions: the case of Melbourne’s stormwater management. Glob. Environ. Change 23, 701–718 (2013).Article 

Google Scholar 
Castilla-Rho, J. C., Mariethoz, G., Rojas, R., Andersen, M. S. & Kelly, B. F. An agent-based platform for simulating complex human–aquifer interactions in managed groundwater systems. Environ. Model. Softw. 73, 305–323 (2015).Article 

Google Scholar 
Sabatier, P. A. Toward better theories of the policy process. PS Polit. Sci. Polit. 24, 147–156 (1991).Article 

Google Scholar 
Abrantes, P. et al. Modelling urban form: a multidimensional typology of urban occupation for spatial analysis. Environ. Plan. B Urban Anal. City Sci. 46, 47–65 (2019).Article 

Google Scholar 
McGarigal, K. FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure Vol. 351 (US Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1995).Vazquez-Prokopec, G. M. et al. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS ONE 8, e58802 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ligtenberg, A., van Lammeren, R. J., Bregt, A. K. & Beulens, A. J. Validation of an agent-based model for spatial planning: a role-playing approach. Comput. Environ. Urban Syst. 34, 424–434 (2010).Article 

Google Scholar  More

De Grave, S., & Fransen, C. H. J. M. Carideorum Catalogus: The Recent Species of the Dendrobranchiate, Stenopodidean, Procarididean and Caridean Shrimps (Crustacea: Decapoda). Zool. Meded. 85, (2011).Garassino, A. The macruran decapod crustaceans of the Lower Cretaceous (Lower Barremian) of Las Hoyas (Cuenca, Spain). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 137, 101–126 (1997).Bravi, S., Coppa, M. G., Garassino, A., & Patricelli, R. Palaemon vesolensis n. sp. (Crustacea, Decapoda) from the Plattenkalk of Vesole Mount (Salerno, Southern Italy). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 140, 141–169 (1999).Colleary, C. et al. Chemical, experimental, and morphological evidence for diagenetically altered melanin in exceptionally preserved fossils. Proc. Natl. Acad. Sci. U.S.A. 11241, 12592–12597 (2015).Article 
ADS 

Google Scholar 
Vinther, J., Briggs, D. E., Clarke, J., Mayr, G. & Prum, R. O. Structural coloration in a fossil feather. Biol. Lett. 6, 128–131 (2010).Article 
PubMed 

Google Scholar 
McNamara, M. E. et al. Fossilised biophotonic nanostructures reveal the original colors of 47 million-year-old moths. PLoS Biol. 9, e1001200 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rietschel, S. Taphonomic biasing in the Messel Fauna and Flora. Cour. Forsch. Inst. Senckenberg 107, 169–182 (1988).
Google Scholar 
Wolf, H. W. Schätze im Schiefer (Westermann, 1991).Rabenstein, R. Messel 2000 – Das Weltnaturerbe Deutschlands (eds Forschungsinstitut Senckenberg) (2000).Gruber, G., & Micklich, N. Messel – Treasures of the Eocene (Hessisches Landesmuseum Darmstadt, 2007).Wedmann, S. Annotated taxon-list of the invertebrate animals from the Eocene fossil site Grube Messel near Darmstadt Germany. Cour. Forsch. Inst. Senckenberg 255, 103–110 (2005).
Google Scholar 
Schaal, S. F. K. & Rabenstein, R. D. Tagebau Messel in Linien und Zahlen. Senckenberg Nat. Forsch. Mus. 142, 376–377 (2012).
Google Scholar 
Moshayedi, M., Lenz, O. K., Wilde, V. & Hinderer, M. The recolonisation of volcanically disturbed Eocene habitats of Central Europe: the maar lakes of Messel and Offenthal (SW Germany) compared. Paleobiodivers. Paleoenviron. 100, 951–973 (2020).Article 

Google Scholar 
Schulz, R., Harms, F.-J. & Felder, M. Die Forschungsbohrung Messel 2001: Ein Beitrag zur Entschlüsselung der Genese einer Ölschieferlagerstätte. Z. angew. Geol. 2002, 9–17 (2002).
Google Scholar 
Felder, M. & Harms, F. J. Lithologie und genetische Interpretation der vulkano-sedimentären Ablagerungen aus der Grube Messel anhand der Forschungsbohrung Messel 2001 und weiterer Bohrungen (Eozän, Messel-Formation, Sprendlinger Horst, Südhessen). Cour. Forsch. Inst. Senckenberg 252, 151–203 (2004).
Google Scholar 
Büchel, G. N., & Schaal, S. F. K. The formation of the Messel maar in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Der, G. K. Messeler Ölschiefer – ein Algenlaminit. Cour. Forsch. Inst. Senckenberg 131, 1–143 (1990).
Google Scholar 
Lenz, O. K., Wilde, V. & Riegel, W. Recolonization of a Middle Eocene volcanic site: quantitative palynology of the initial phase of the maar lake of Messel (Germany). Rev. Palaeobot. Palynol. 145, 217–242 (2007).Article 

Google Scholar 
Bauersachs, T., Schouten, S. & Schwark, L. Characterization of the sedimentary organic matter preserved in Messel oil shale by bulk geochemistry and stable isotopes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 410, 390–400 (2014).Article 

Google Scholar 
Mertz, D. F. & Renne, P. R. A numerical age for the Messel fossil deposit (UNESCO world natural heritage site) from 40Ar/39Ar dating. Cour. Forsch. Inst. Senckenberg 255, 67–75 (2005).
Google Scholar 
Lenz, O. K., Wilde, V., Mertz, D. F. & Riegel, W. New palynology-based astronomical and revised 40Ar/39Ar ages for the Eocene maar lake of Messel (Germany). Int. J. Earth Sci. 104, 873–889 (2015).Article 
CAS 

Google Scholar 
Lenz, O. K. & Wilde, V. Changes in Eocene plant diversity and composition of vegetation: The lacustrine archive of Messel (Germany). Paleobiology 44, 709–735 (2018).Article 

Google Scholar 
Lenz, O. K., Wilde, V, Riegel, W., & Harms, F-J. A 600 k.y. record of El Niño–Southern Oscillation (ENSO): evidence for persisting teleconnections during the Middle Eocene greenhouse climate of Central Europe. Geology 38, 627–630 (2010).Lenz, O. K., Wilde, V, & Riegel, W. Paleoclimate – Learning from the past for the future in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 16–23 (Schweizerbart, 2018).Grein, M., Utescher, T., Wilde, V. & Roth-Nebelsick, A. Reconstruction of the middle Eocene climate of Messel using palaeobotanical data. Neues Jb. Geol. Paläontol. Abh. 260, 305–318 (2011).Article 

Google Scholar 
Tütken, T. Isotope compositions (C, O, Sr, Nd) of vertebrate fossils from the Middle Eocene oil shale of Messel, Germany: Implications for their taphonomy and palaeoenvironment. Palaeogeogr. Palaeoclimatol. Palaeoecol. 416, 92–109 (2014).Article 

Google Scholar 
Wilde, V. The fossil flora of Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 42–61 (Schweizerbart, 2018).Smith, K. T., Schaal, S. F. K. & Habersetzer, J. (eds.) Messel: An Ancient Greenhouse Ecosystem. (Schweizerbart, 2018).Wedmann, S., Hörnschemeyer, T., Engel, M. S., Zetter, R. & Grímsson, F. The last meal of an Eocene pollen-feeding fly. Curr. Biol. 31, 2020–2026 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wedmann, S. Jewels in the oil shale – insects and other invertebrates in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Franzen J. L. Odd-toed ungulates – Early horses and tapiromorphs in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 292–301 (Schweizerbart, 2018).Franzen, J. L., Aurich, C. & Habersetzer, J. Description of a well preserved fetus of the European Eocene Equoid Eurohippus messelensis. PLoS ONE 10, e0137985 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Franzen J. L., & Gingerich, P. D. Primates – Rareties in Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 240–247 (Schweizerbart, 2018).Franzen, J. L. et al. Complete primate skeleton from the middle Eocene of Messel in Germany: Morphology and paleobiology. PLoS ONE 4(5), e5723 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Houša, V. Bechleja inopinata n. g., n. sp., nový ráček z českých třetihor (Decapoda, Palaemonidae). Ústřed. Ústavu Geol. Sborník 23, 365–377 (1957).Glaessner, M. F. Decapoda. In Part R Arthropoda 4(2) Treatise on Invertebrate Paleontology (ed Moore, R. C.) (The University of Kansas Press and The Geological Society of America, 1969).De Grave, S., Cai, Y. & Anker, A. Global diversity of shrimps (Crustacea: Decapoda: Caridea) in freshwater. Hydrobiologia 595, 287–293 (2008).Article 

Google Scholar 
Garassino, A. & Bravi, S. Palaemon antonellae new species (Crustacea, Decapoda, Caridea) from the Lower Cretaceous “Platydolomite” of profeti (Caserta, Italy). J. Paleontol. 77, 589–592 (2003).Article 

Google Scholar 
Schweitzer, C., Karasawa, H., Schweigert, G., Feldmann, R. & Garassino, A. Systematic list of fossil decapod crustacean species. Crustac. Monogr. 10, 1–222 (2010).
Google Scholar 
Plotnick, R. E. Taphonomy of a modern shrimp: implications for the arthropod fossil record. Palaios 1, 286–293 (1986).Article 
ADS 

Google Scholar 
Klompmaker, A. A., Portell, R. W. & Frick, M. G. Comparative experimental taphonomy of eight marine arthropods indicates distinct differences in preservation potential. Palaeontology 60, 773–794 (2017).Article 

Google Scholar 
Vannier, J., Schoenemann, B., Gillot, T., Charbonnier, S. & Clarkson, E. Exceptional preservation of eye structure in arthropod visual predators from the Middle Jurassic. Nat. Commun. 7, 1–9 (2016).Article 

Google Scholar 
Jauvion, C., Audo, D., Charbonnier, S. & Vannier, J. Virtual dissection and lifestyle of a 165-million-year-old female polychelidan lobster. Arthropod Struct. Dev. 45, 122–132 (2016).Article 
PubMed 

Google Scholar 
Pazinato, P. G., Jauvion, C., Schweigert, G., Haug, J. T. & Haug, C. After 100 years: a detailed view of an eumalacostracan crustacean from the Upper Jurassic Solnhofen Lagerstätte with raptorial appendages unique to Euarthropoda. Lethaia 54, 55–72 (2021).Article 

Google Scholar 
Briggs, D. E. G. & Kear, A. J. Decay and mineralization of shrimps. Palaios 9, 431–456 (1994).Article 
ADS 

Google Scholar 
Wuttke, M. Conservation-dissolution-transformation. On the behaviour of biogenic materials during fossilization In Messel: an insight into the history of life and of the earth (eds. Schaal, S. & Ziegler, W.) 263–275 (Claredon, 1992).Thompson, J. R. Comments on phylogeny of section Caridea (Decapoda Natantia) and the phylogenetic importance of the Oplophoridea. Proc. Symp. Crustacea Part 1, 314–326 (1967).
Google Scholar 
Ashelby, C. W., De Grave, S. & Johnson, M. L. Preliminary observations on the mandibles of palaemonoid shrimp (Crustacea: Decapoda: Caridea: Palaemonoidea). PeerJ 3, e846 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Felgenhauer, B. E., & Abele, L. G. Phylogenetic relationships among shrimp-like decapods. In Crustacean Phylogeny (ed Schram, F. R.) 291–311 (A. A. Balkema, 1983).Wowor, D., Cai, Y., & Ng, P. K. L. Crustacea: Decapoda, Caridea. In Freshwater Invertebrates of the Malaysian Region (eds Yule, C. M. & Y. H. Sen, Y. H.) 337–357 (Academy of Sciences Malaysia, 2004).Rodd, F. H., & Reznick, D. N. Life History Evolution in Guppies: III. The Impact of Prawn Predation on Guppy Life Histories. Oikos 62, 13–19 (1991).Felgenhauer, B. E. & Abele, L. G. Feeding structures of two atyid shrimps, with comments on Caridean phylogeny. J. Crustac. Biol. 5, 397–419 (1985).Article 

Google Scholar 
de Mazancourt, V., Marquet, G., & Keith, P. The “Pinocchio-shrimp effect”: First evidence of variation in rostrum length with the environment in Caridina H. Milne-Edwards, 1837 (Decapoda: Caridea: Atyidae). J. Crustac. Biol. 37, 249–257 (2017).Zimmermann, G. et al. Geometric morphometrics of carapace of Macrobrachium australe (Crustacea: Palaemonidae) from Reunion Island. Acta Zool. 93, 492–500 (2012).Article 

Google Scholar 
Bauer, R. T. Amphidromy in shrimps: a life cycle between rivers and the sea. Lat. Am. J. Aquat. Res. 41, 633–650 (2013).Article 

Google Scholar 
Jalihal, D. R., Sankolli, K. N. & Shenoy, S. Evolution of larval developmental patterns and the process of freshwaterization in the prawn genus Macrobrachium Bate, 1868 (Decapoda, Palaemonidae). Crustaceana 65, 365–376 (1993).Article 

Google Scholar 
Grande, L. Paleontology of the Green River Formation, with a review of the fish fauna. Bull. Geol. Surv. Wyoming 63, 1–333 (1984).
Google Scholar 
Grande, L. The Lost World of Fossil Lake: snapshots from deep time (University of Chicago Press, 2013).Micklich, N. Peculiarities of the Messel fish fauna and their palaeoecological implications: A case study. Palaeobiodivers. Palaeoenviron. 92, 585–629 (2012).Article 

Google Scholar 
Micklich, N. Actinopterygians—the fishes of the Messel lake. in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 104–111 (Schweizerbart, 2018).Christodoulou, M., Anastasiadou, C., Jugovic, J., & Tzomos, T. Freshwater Shrimps (Atyidae, Palaemonidae, Typhlocarididae) in the Broader Mediterranean Region: Distribution, Life Strategies, Threats, Conservation Challenges and Taxonomic Issues. In A Global Overview of the Conservation of Freshwater Decapod Crustaceans (eds Kawai, T. & Cumberlidge, N.) 199–236 (Springer, 2016).Anger, K. Neotropical Macrobrachium (Caridea: Palaemonidae): On the biology, origin, and radiation of freshwater-invading shrimp. J. Crustac. Biol. 33, 151–183 (2013).Article 

Google Scholar  More

OverviewWe compiled systematically sampled empirical taxa occurrence across the landscape, and inferentially assembled respective blue and green local food webs by combining these data with a metaweb approach. We quantified key properties of the inferred food webs, then analysed with GIS-derived environmental information how focal food-web metrics change along elevation and among different land-use types in blue versus green systems. Details are given below.Assemble food webs using a metaweb approachWe applied a metaweb method to obtain the composition and structure of multiple local food webs across a landscape spatial scale10. A metaweb is an accumulation of all interactions (here, trophic relationships) among the focal taxa. In this study, we built our metaweb based on known trophic interactions derived from literature and published datasets, which themselves were all based on primary empirical natural history observations. We further complemented or refined the trophic interactions in the metaweb based on expert knowledge of primary observations that are not yet published or only accessible in grey literature. The expert knowledge covers authors and collaborators who have specific natural history knowledge on Central European plants, herbivorous insects, birds, fish, and aquatic invertebrates. Importantly, these observations were all based on empirical observations and/or unpublished data accumulated over considerable field research experience. The respective literature we referred, as well as the metaweb itself with information source of each trophic link (online repository), are provided in Supplementary Methods. By assuming that any interaction in the metaweb will realise if the interacting taxa co-occur, the metaweb approach allows an inference of local food webs if taxa occurrence is known. Such an assumption of fixed diets may lead to an over-estimation of the locally realised trophic links32, as it essentially ignores the possible intraspecific diet variation caused by resource availability61,62, predation risk63, temperature64, ontogenetic shift65, or other genetic and environmental sources66. Therefore, the food webs we inferred systematically using this method capture trophic relationships driven by community composition (species presence versus absence) but not the above-mentioned processes. Nonetheless, since the trophic interactions were based on empirical observations, the fixed diets can be seen as collapsing all intraspecific variations of diet-determining traits (or trait-matching) at species level, within which we know realisable interactions surely exist. This, together with co-occurrence as a pre-requisite, gives realistic boundaries for the potential interaction realisation, which is plausible and non-biased when applying to localised sites. With this approach, we were addressing a systematic comparison among potential local food webs between the blue and green systems and across the selected gradients. For sensitivity analyses considering the potential inaccuracy of the metaweb approach mentioned here, please see further below Food-web metrics and analyses and Supplementary Discussion.We compiled taxa occurrence of four terrestrial and two aquatic broad taxonomic groups (“focal groups”) to assemble local green and blue communities, respectively and independently, based on the well-resolved data available. Each focal group referred to a distinct taxonomic group, and the within- and among-group trophic relationships captured most of the realised interactions. These focal groups were vascular plants, butterflies, grasshoppers, and birds in the green biome, and stream invertebrates and fishes in the blue biome. Notably, with “butterflies” we refer to their larval stage and accordingly their mostly-herbivorous trophic interactions throughout this study. Larval interactions were also the predominant interaction assessed for stream invertebrates (i.e., all interactions of stream invertebrates focussed on their aquatic stage, which is predominant larval). The occurrence data of these focal groups were compiled from highly standardised multiple-year empirical surveys of various authorities, all conducted by trained biologists with fixed protocols (Supplementary Methods). The information across sites should thus be representative and can be up-scaled to the landscape. The occurrences of plants, butterflies, birds, and stream invertebrates were from the Biodiversity Monitoring Switzerland programme (BDM Coordination Office67) managed by the Swiss Federal Office for the Environment (BAFU/FOEN). The occurrences of grasshoppers and fishes were from the Swiss database on faunistic records, info fauna (CSCF), where we further complemented fish occurrence from the data of Progetto Fiumi Project (Eawag). In terms of biological resolution, taxa were resolved to species level in most cases, while the plant and butterfly groups included some multi-species complexes. Insects of the order Ephemeroptera, Plecoptera, and Trichoptera were resolved to species, while all other stream invertebrates were resolved to family level. These were each treated as a node later in our food-web assembly, and referred to as “species”, as the species within such complexes and families mostly share the same trophic role. Spatially, the occurrence datasets adopted coordinates resolved to 1 × 1 km2. The species that were recorded in the same 1 × 1 km2 grid were considered to co-occurred. We took the co-occurring four/two focal groups to form local green/blue local communities, respectively. To obtain better co-occurrence across group-specific data from different sources (e.g., BDM and info fauna), we intentionally coarsened the grasshopper and fish occurrence to 5 × 5 km2 coordinates. This is arguably a biologically acceptable approximation considering the high mobility of these two groups. Also, we only included known stream-borne fishes and dropped pure lake-borne ones to match our stream-only invertebrate occurrence data. Across all 462 green and 465 blue communities we assembled, we covered 2016 plant, 191 butterfly, 109 grasshopper, 155 bird, 248 stream invertebrate, and 78 stream fish species. Unlike the knowledge of plant occurrence in green communities, we did not have detailed occurrence information of the basal components (e.g., primary producers) in blue ones. Therefore, we assumed three mega nodes—namely plant (including all alive or dead plant materials), plankton (including zooplankton, phytoplankton, and other algae), and detritus—as the basal nodes occurring in all blue communities, without further discrimination of identities or biology within. These adding to our focal groups thus cover major taxonomic groups as well as trophic roles from producers to top consumers in both blue and green systems.Taking the above-assembled local communities then drawing trophic links among species (nodes) according to the metaweb yielded the local food webs (illustrated in Fig. 1), representatively covering the whole Swiss area. Notably, although our understanding of trophic interactions indeed encompassed some links across the blue and green taxa (e.g., between piscivorous birds and fishes), our occurrence datasets did not present sufficient spatial grids where these taxa co-occur. We, therefore, did not include such links, nor assembled blue-green interconnected food webs, but the blue and green food webs separately instead (but see Supplementary Discussion). Also, we dropped isolated nodes, i.e., basal nodes without any co-occurring consumer and consumer nodes without any co-occurring resource, from the inferred food webs. These could possibly be passing-by species that were recorded but had no trophic interaction locally, or those that interact with non-focal taxa whose occurrence information was unknown to us. We thus had to exclude them to focus on evidence-supported occurrences and trophic interactions. Nonetheless, across all cases, isolated nodes were rather rare (averaged less than 3% of species occurred in either blue or green communities).Environmental dataWe acquired environmental data across all of Switzerland (42,000 km2) on a 1 × 1 km2 grid basis (i.e., values are averaged over the grid) from GIS databases, with which we mapped environmental conditions to the grids where we assembled food webs. These included: topographical information from DHM25 (Swisstopo, FOT), land-cover information from CLC (EEA), and climate information (averaged over the decade of 2005–2015) from CHELSA. Among environmental variables, elevation and temperature are essentially highly correlated. In this study, we took elevation as the focal environmental gradient throughout, as after accounting for the main effects of elevation on temperature, the residual temperature was not a good predictor of the food-web metrics we looked at (see next section, and Supplementary Table 4). In other words, by analysing along the elevation gradient, we already captured most of the temperature influences on food webs. Based on the labels provided by the GIS databases, we categorised the originally detailed land cover into the five major land-use types that we used in this study, namely forest, scrubland, open space, farmland, and urban area. Forest includes broad-leaved, coniferous, and mixed forests. Scrub includes bushy and herbaceous vegetation, heathlands, and natural grasslands. Open space encompasses sparsely vegetated areas, such as dunes, bare rocks, glaciers and perpetual snow. Farmland include any form of arable, pastures, and agro-forestry areas. Finally, urban area is where artificial constructions and infrastructure prevail. As each grid could contain multiple land-use types, we then defined the dominant land-use type of the grid as any of the five above that occupied more than 50% of the grid’s area. Analyses separated by land-use types with subsetted food webs (land-use-specific analyses) were based on the grids’ dominant land-use type. There were a few grids where the dominant land-use type did not belong to the focal major five, e.g., wetlands or water bodies, and a few where no single land-use type covered more than 50% of the area. Food webs of these grids were still included in the overall analyses but excluded from any land-use-specific analyses (as revealed in the difference in sample sizes between all versus land-use type subsetted food webs in Fig. 2; analyses details below).Food-web metrics and analysesWe quantified five metrics as the measures of the food webs’ structural and ecological properties. For the fundamental structure of the food webs, the number of nodes (“No. Nodes”) reflects the size of the web, meanwhile represents local species richness (though the few isolated nodes were excluded as above-mentioned). Connectance is the proportion of realised links among all potential ones (thus bounded 0–1), reflecting how connected the web is. We also derived holistic topological measures, namely nestedness and modularity. Nestedness of a food web, on the one hand, describes the tendency that some nodes’ narrower diets being subsets of other’s broader diets. We adopted a recently developed UNODF index68 (bounded 0–1) that is especially suitable for quantifying such a feature in our unipartite food webs. On the other hand, modularity (bounded 0–1 with our index) reflects the tendency of a food web to form modules, where nodes are highly connected within but only loosely connected between. Nestedness and modularity are two commonly investigated structures in ecological networks and have been considered relevant to species feeding ecology24 and the stability of the system69. Finally, we measured the level of consumers’ diet niche overlap of the food webs (Horn’s index70, bounded 0–1), which essentially depends on the arrangement of trophic relationships (thus the structure of the webs), and could have strong ecological implications as niche partitioning has been recognised to be a key mechanism that drives species coexistence71,72. We selected these fundamental and holistic properties as they are potentially more relevant to the processes that may have shaped food webs across a landscape scale (e.g., community assembly), in comparison to some node- or link-centric properties. Also, addressing similar metrics as in the literature13,69 would facilitate potential cross-study comparison or validation.To first gain a glimpse of the structure of the blue and green food webs, we performed a principal component analysis (PCA; Fig. 3a) on the inferred food webs (n = 462 and 465 in green and blue, respectively) taking the four structural metrics (number of nodes, connectance, nestedness, and modularity) as the explaining variables of blue versus green system types. We then confirmed that system type, elevation, and land-use type were all important predictors of food-web metrics (whereas the residual temperature after accounting elevation effects was not) by conducting general linear model analyses, taking the former as interactive predictors while the latter response variables (Supplementary Tables 3, 4). To check how elevation influences food-web properties in blue and green systems separately, and how food-web properties depend on each other, we ran a series of piecewise structural equation modelling (SEM)73 analyses on inferred food webs (Fig. 3b, c) whose dominant land use can be defined (n = 421 and 430 in green and blue, respectively). This was also conducted on subsetted webs of each of the five major land-use types (Supplementary Figs. 1 and 2). The SEM relationships were derived from linear mixed model analyses with dominant land-use type as a random effect (assumption tests see Supplementary Figs. 12–17). The SEM structure of direct effects was set according to the literature13,69 and is illustrated in Fig. 3b. In short, this structure tests the dependencies from elevation (an environmental predictor) to food-web metrics (ecological responses). The further dependencies among food-web metrics themselves were assigned with the principle of pointing from relative lower-level properties to higher-level ones. That is, from number of nodes (purely determined by nodes) to connectance (determined by numbers of nodes and links), further to nestedness and modularity (holistic topologies, determined further by the arrangement of links), then to diet niche overlap (ecological functional outcome).Finally, to check and visualise the exact changing patterns of food webs, we applied generalised additive models (GAMs) to reveal the relationships between food-web metrics and the whole-ranged elevation (Figs. 4 and 5), as well as a particular comparison between food webs in forests and farmlands below 1500 m a.s.l. (Supplementary Fig. 5), as this elevation segment covered most of the sites belonged to these two land-use types. We also performed a series of linear models (LMs) and least-squared slope comparisons based on land-use-specific subsets of food webs (Figs. 4 and 5; Supplementary Figs. 3 and 4), to investigate whether food-web elevational patterns are different among land-use types (assumption tests see Supplementary Tables 5 and 6). In the GAMs analyses, specifically, we simulated two sets of randomised webs, i.e., “keep-group” and “fully”, as the null models to compare with the inferred ones74. Both randomisations generated ten independently simulated webs from each input inferred local food web, keeping the same number of nodes and connectance as of the latter. On the one hand, the keep-group randomisation shuffled trophic links from an input local web but only allowed them to realised fulfilling some pre-set within- and among-group relationships. That is, in green communities, birds can feed on all groups, grasshoppers on any groups but birds, while butterflies only on plants; in blue communities, fishes can feed on all groups, while invertebrates on themselves and the basal resources. These pre-set group-wide relationships captured the majority of realistic trophic interactions compiled in our metaweb. On the other hand, the fully randomised webs shuffled trophic links disregarding the biological identity of nodes. The GAMs of nestedness, modularity, and niche overlap illustrated the patterns of these randomised webs (Fig. 5). Comparing among the three types of webs, the patterns exhibited already by fully randomised webs should be those contributed by variations in web size and connectance, while the difference between keep-group and fully randomised webs by the focal-group composition of local communities, and the difference between inferred and keep-group randomised webs further by the realistic species-specific diets. In addition, we also applied the same GAMs and LMs approach to analyse node richness, as well as both realised and potential diet generality (vulnerability for plants) of each focal group (Supplementary Figs. 6–11). These analyses provided hints about the changes in community composition and species diet breadths along elevation and among land-use types, which helped explain the detected food-web responses in mechanistic ways.In addition, to check if our findings were shaped or strongly influenced by the potential inaccuracy of using the metaweb, we repeated the above PCA, SEM, and GAM analyses as a series of sensitivity analyses. We generated food webs based on our locally inferred ones (i.e., the observations) but with random 10% link removal. This procedure mimics the effect of potential intraspecific diet variation (mentioned earlier) so that some trophic interactions in the metaweb do not realise locally. Overall, these analyses with link removal showed that our conclusions are qualitatively and quantitatively highly robust, and only very minorly affected by the such potential inaccuracy of metawebs, which is also in accordance to other food-web studies (see e.g., Pearse & Altermatt 201575). All details and outcomes of these additional analyses are given in Supplementary discussion.All metric quantification and analyses were performed under R version 4.0.3 (R Core Team76). All applied packages and functions were described in Supplementary Methods, while the R scripts performing these tasks can be accessed at the online repository provided.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More