Ecology

Phung, T. X., Nascimento, J. C. S., Novarro, A. J. & Wiens, J. J. Correlated and decoupled evolution of adult and larval body size in frogs. Proc. R. Soc. Lond. B 287, 20201474–10 (2020).
Google Scholar 
Hall, B. K. & Wake, M. H. The Origin and Evolution of Larval Forms (Gulf Professional Publishing, 1999).Hime, P. M. et al. Phylogenomics reveals ancient gene tree discordance in the amphibian tree of life. Syst. Biol. 70, 49–66 (2021).CAS 
PubMed 

Google Scholar 
Duellman, W. E. & Trueb, L. Biology of Amphibians (Johns Hopkins University Press, 1994).Nunes-de-Almeida, C. H., Batista Haddad, C. F. & Toledo, L. F. A revised classification of the amphibian reproductive modes. Salamandra 57, 413–427 (2021).
Google Scholar 
Vences, M. & Köhler, J. Global diversity of amphibians (Amphibia) in freshwater. Hydrobiologia 595, 569–580 (2007).
Google Scholar 
Blackburn, D. G. Evolution of vertebrate viviparity and specializations for fetal nutrition: a quantitative and qualitative analysis. J. Morphol. 276, 961–990 (2015).PubMed 

Google Scholar 
AmphibiaWeb. Electronic Database (University of California, Berkeley, CA, USA, 2019). https://amphibiaweb.org.Frost, D. R. Amphibian Species of the World: an Online Reference. Version 6.0 (date of access: 01.08.2019). Electronic Database (American Museum of Natural History, New York, USA, 2019). https://amphibiansoftheworld.amnh.org/.Bonett, R. M., Ledbetter, N. M., Hess, A. J., Herrboldt, M. A. & Denoël, M. Repeated ecological and life cycle transitions make salamanders an ideal model for evolution and development. Developmental Dynamics 251, 957–972 (2022).Salthe, S. N. Reproductive modes and the number and sizes of ova in the urodeles. Am. Midl. Naturalist 81, 467490 (1969).
Google Scholar 
Salthe, S. N. & Duellman, W. E. in Evolutionary Biology of the Anurans (ed. Vial, J. L.) 229–249 (University of Missouri Press Columbia, 1973).Haddad, C. & Prado, C. P. A. Reproductive modes in frogs and their unexpected diversity in the Atlantic Forest of Brazil. BioScience 55, 207–217 (2005).
Google Scholar 
Lutz, B. Ontogenetic evolution in frogs. Evolution 2, 29–39 (1948).CAS 
PubMed 

Google Scholar 
Crump, M. L. Anuran reproductive modes: evolving perspectives. J. Herpetol. 49, 1–16 (2015).
Google Scholar 
Schoch, R. Evolution of life cycles in early amphibians. Annu. Rev. Earth Planet. Sci. 37, 135–162 (2009).ADS 
CAS 

Google Scholar 
Meegaskumbura, M. et al. Patterns of reproductive-mode evolution in Old World tree frogs (Anura, Rhacophoridae). Zool. Scr. 44, 509–522 (2015).
Google Scholar 
Portik, D. M. & Blackburn, D. C. The evolution of reproductive diversity in Afrobatrachia: A phylogenetic comparative analysis of an extensive radiation of African frogs. Evolution 70, 2017–2032 (2016).PubMed 
PubMed Central 

Google Scholar 
San Mauro, D. et al. Life-history evolution and mitogenomic phylogeny of caecilian amphibians. Mol. Phylogenet. Evol. 73, 177–189 (2014).PubMed 

Google Scholar 
Pereira, E. B., Collevatti, R. G., de Carvalho Kokubum, M. N., de Oliveira Miranda, N. E. & Maciel, N. M. Ancestral reconstruction of reproductive traits shows no tendency toward terrestriality in leptodactyline frogs. BMC Evol. Biol. 15, 91 (2015).Gomez-Mestre, I., Pyron, R. A. & Wiens, J. J. Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs. Evolution 66, 3687–3700 (2012).PubMed 

Google Scholar 
Wake, D. B. & Hanken, J. Direct development in the lungless salamanders: what are the consequences for developmental biology, evolution and phylogenesis? Int. J. Dev. Biol. 40, 859–869 (1996).CAS 
PubMed 

Google Scholar 
Dubois, A. Developmental pathway, speciation and supraspecific taxonomy in amphibians: 1. Why are there so many frog species in Sri Lanka? Alytes 22, 19–37 (2004).
Google Scholar 
Hedges, S. B., Duellman, W. E. & Heinicke, M. P. New World direct-developing frogs (Anura: Terrarana): molecular phylogeny, classification, biogeography, and conservation. Zootaxa 1737, 1–182 (2008).
Google Scholar 
Dugo-Cota, Á., Vilà, C., Rodríguez, A. & Gonzalez-Voyer, A. Ecomorphological convergence in Eleutherodactylus frogs: a case of replicate radiations in the Caribbean. Ecol. Lett. 22, 884–893 (2019).PubMed 

Google Scholar 
Simpson, G. G. The Major Features of Evolution (Columbia University Press, 1953).Vági, B., Végvári, Z., Liker, A., Freckleton, R. P. & Szekely, T. Parental care and the evolution of terrestriality in frogs. Proc. R. Soc. Lond. B 286, 20182737–10 (2019).
Google Scholar 
Furness, A. I. & Capellini, I. The evolution of parental care diversity in amphibians. Nat. Commun. 10, 1–12 (2019).CAS 

Google Scholar 
Furness, A. I., Venditti, C. & Capellini, I. Terrestrial reproduction and parental care drive rapid evolution in the trade-off between offspring size and number across amphibians. PLoS Biol. 20, e3001495 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wollenberg, K. C., Vieites, D. R., Glaw, F. & Vences, M. Speciation in little: the role of range and body size in the diversification of Malagasy mantellid frogs. BMC Evol. Biol. 11, 217 (2011).PubMed 
PubMed Central 

Google Scholar 
Cayuela, H. et al. Determinants and consequences of dispersal in vertebrates with complex life cycles: a review of pond-breeding amphibians. Q. Rev. Biol. 95, 1–36 (2020).
Google Scholar 
Phillimore, A. B., Freckleton, R. P., Orme, C. D. L. & Owens, I. P. F. Ecology predicts large‐scale patterns of phylogenetic diversification in birds. Am. Nat. 168, 220–229 (2006).PubMed 

Google Scholar 
Chen, J.-M. et al. An integrative phylogenomic approach illuminates the evolutionary history of Old World tree frogs (Anura: Rhacophoridae). Mol. Phylogenet. Evol. 145, 106724 (2020).PubMed 

Google Scholar 
Zimkus, B. M., Lawson, L., Loader, S. P. & Hanken, J. Terrestrialization, miniaturization and rates of diversification in African Puddle Frogs (Anura: Phrynobatrachidae). PLoS ONE 7, e35118 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moen, D. S. Improving inference and avoiding over-interpretation of hidden-state diversification models: specialized plant breeding has no effect on diversification in frogs. Evolution 76, 373–384 (2022).PubMed 

Google Scholar 
Eastman, J. M. & Storfer, A. Correlations of life-history and distributional-range variation with salamander diversification rates: evidence for species selection. Syst. Biol. 60, 503–518 (2011).PubMed 

Google Scholar 
Bonett, R. M., Steffen, M. A., Lambert, S. M., Wiens, J. J. & Chippindale, P. T. Evolution of paedomorphosis in plethodontid salamanders: ecological correlates and re-evolution of metamorphosis. Evolution 68, 466–482 (2014).PubMed 

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

Google Scholar 
Wagenmakers, E.-J. & Farrell, S. AIC model selection using Akaike weights. Psychon. Bull. Rev. 11, 192–196 (2004).PubMed 

Google Scholar 
Beaulieu, J. M., Oliver, J. C., O’Meara, B. & Boyko, J. R package ‘corHMM’: hidden markov models of character evolution. (2020).Pagel, M. & Meade, A. BayesTraits, version 4. University of Reading, Berkshire, UK. http://www.evolution.rdg.ac.uk (2022).Pagel, M. & Meade, A. Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo. Am. Nat. 167, 808–825 (2006).PubMed 

Google Scholar 
Maddison, W., Midford, P. & Otto, S. Estimating a binary character’s effect on speciation and extinction. Syst. Biol. 56, 701–710 (2007).PubMed 

Google Scholar 
FitzJohn, R. G., Maddison, W. P. & Otto, S. P. Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Syst. Biol. 58, 595–611 (2009).PubMed 

Google Scholar 
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).PubMed 

Google Scholar 
Herrera-Alsina, L., van Els, P. & Etienne, R. S. Detecting the dependence of diversification on multiple traits from phylogenetic trees and trait data. Syst. Biol. 68, 317–328 (2018).
Google Scholar 
Strathmann, R. R. Hypotheses on the origins of marine larvae. Annu. Rev. Ecol. Syst. 24, 89–117 (1993).
Google Scholar 
Wray, G. A. Evolution of larvae and developmental modes. In Ecology of Marine Invertebrate Larvae. (ed. McEdward, L.) 413–447 (CRC Press, 1995).Raff, R. A. Origins of the other metazoan body plans: the evolution of larval forms. Philos. T R. Soc. B 363, 1473–1479 (2008).
Google Scholar 
Collin, R. & Moran, A. in Evolutionary Ecology of Marine Invertebrate Larvae 50–66 (Oxford University Press, 2018).Collin, R. & Miglietta, M. P. Reversing opinions on Dollo’s Law. Trends Ecol. Evol. 23, 602–609 (2008).PubMed 

Google Scholar 
Wiens, J. J. Re-evolution of lost mandibular teeth in frogs after more than 200 million years, and re-evaluating Dollo’s law. Evolution 65, 1283–1296 (2011).PubMed 

Google Scholar 
Wiens, J. J., Kuczynski, C. A., Duellman, W. E. & Reeder, T. W. Loss and re-evolution of complex life cycles in marsupial frogs: does ancestral trait reconstruction mislead? Evolution 61, 1886–1899 (2007).CAS 
PubMed 

Google Scholar 
Chippindale, P. T., Bonett, R. M., Baldwin, A. S. & Wiens, J. J. Phylogenetic evidence for a major reversal of life-history evolution in plethodontid salamanders. Evolution 58, 2809–2815 (2004).CAS 
PubMed 

Google Scholar 
Castroviejo-Fisher, S. et al. Phylogenetic systematics of egg-brooding frogs (Anura: Hemiphractidae) and the evolution of direct development. Zootaxa 4004, 1–75 (2015).PubMed 

Google Scholar 
Naumann, B., Schweiger, S., Hammel, J. U. & Müller, H. Parallel evolution of direct development in frogs—skin and thyroid gland development in African Squeaker Frogs (Anura: Arthroleptidae: Arthroleptis). Dev. Dyn. 250, 584–600 (2021).PubMed 

Google Scholar 
Goldberg, J., Taucce, P. P. G., Quinzio, S. I., Haddad, C. F. B. & Candioti, F. V. Increasing our knowledge on direct-developing frogs: the ontogeny of Ischnocnema henselii (Anura: Brachycephalidae). Zool. Anz. 284, 78–87 (2020).
Google Scholar 
Wassersug, R. J. & Duellman, W. E. Oral structures and their development in egg-brooding hylid frog embryos and larvae: evolutionary and ecological implications. J. Morphol. 182, 1–37 (1984).PubMed 

Google Scholar 
Kerney, R. R., Blackburn, D. C., Müller, H. & Hanken, J. Do larval traits re-evolve? Evidence from the embryogenesis of a direct-developing salamander, Plethodon cinereus. Evolution 66, 252–262 (2011).PubMed 

Google Scholar 
Theska, T. Musculoskeletal development of the Central African caecilian Idiocranium russeli (Amphibia: Gymnophiona: Indotyphlidae) and its bearing on the re-evolution of larvae in caecilian amphibians. Zoomorphology 138, 137–158 (2019).
Google Scholar 
Laslo, M., Denver, R. J. & Hanken, J. Evolutionary conservation of thyroid hormone receptor and deiodinase expression dynamics in ovo in a direct-developing frog, Eleutherodactylus coqui. Front. Endocrinol. 10, 307 (2019).
Google Scholar 
Gao, W. et al. Genomic and transcriptomic investigations of the evolutionary transition from oviparity to viviparity. Proc. Natl Acad. Sci. USA 116, 3646–3655 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Altig, R. & Crother, B. I. The evolution of three deviations from the biphasic anuran life cycle: alternatives to selection. Herpetol. Rev. 37, 321–356 (2006).
Google Scholar 
Venturelli, D. P., da Silva, W. R. & Giaretta, A. A. Tadpoles’ resistance to desiccation in species of Leptodactylus (Anura, Leptodactylidae). J. Herpetol. 55, 265–270 (2021).
Google Scholar 
Seymour, R. S. Respiration of aquatic and terrestrial amphibian embryos. American Zoologist 39, 261–270 (1999).Blackburn, D. G. Convergent evolution of viviparity, matrotrophy, and specializations for fetal nutrition in reptiles and other vertebrates. Am. Zool. 32, 313–321 (1992).
Google Scholar 
Buckley, D., Alcobendas, M., García-París, M. & Wake, M. H. Heterochrony, cannibalism, and the evolution of viviparity in Salamandra salamandra. Evol. Dev. 9, 105–115 (2007).PubMed 

Google Scholar 
Kusrini, M. D., Rowley, J. J. L., Khairunnisa, L. R., Shea, G. M. & Altig, R. The reproductive biology and larvae of the first tadpole-bearing frog, Limnonectes larvaepartus. PLoS ONE 10, e116154–e116159 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Lanza, B. & Leo, P. Sul primo caso sicuro di riproduzione vivipara nel genere Speleomantes. 1–54 (2000).Lunghi, E. et al. Comparative reproductive biology of European cave salamanders (Genus Hydromantes): nesting selection and multiple annual breeding. Salamandra 54, 101–108 (2018).
Google Scholar 
Liedtke, H. C. et al. Terrestrial reproduction as an adaptation to steep terrain in African toads. Proc. R. Soc. Lond. B 284, 20162598–20162599 (2017).
Google Scholar 
Wake, M. H. The reproductive biology of Eleutherodactylus jasperi (Amphibia, Anura, Leptodactylidae), with comments on the evolution of live-bearing systems. J. Herpetol. 12, 121–133 (1978).
Google Scholar 
Jennings, D. H. & Hanken, J. Mechanistic basis of life history evolution in anuran amphibians: thyroid gland development in the direct-developing frog, Eleutherodactylus coqui. Gen. Comp. Endocr. 111, 225–232 (1998).CAS 
PubMed 

Google Scholar 
Callery, E. M. & Elinson, R. P. Thyroid hormone-dependent metamorphosis in a direct developing frog. Proc. Natl Acad. Sci. USA 97, 2615–2620 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callery, E. M., Hung, F. & Elinson, R. P. Frogs without polliwogs: evolution of anuran direct development. BioEssays 23, 233–241 (2001).CAS 
PubMed 

Google Scholar 
Buckley, L. B. & Jetz, W. Environmental and historical constraints on global patterns of amphibian richness. Proc. R. Soc. Lond. B 274, 1167–1173 (2007).
Google Scholar 
Pyron, R. A. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. Lond. B 280, 20131622 (2013).
Google Scholar 
Gómez-Rodríguez, C., Baselga, A. & Wiens, J. J. Is diversification rate related to climatic niche width? Glob. Ecol. Biogeogr. 24, 383–395 (2015).
Google Scholar 
Moen, D. S. & Wiens, J. J. Microhabitat and climatic niche change explain patterns of diversification among frog families. Am. Nat. 190, 29–44 (2017).PubMed 

Google Scholar 
Kozak, K. H. & Wiens, J. J. Accelerated rates of climatic-niche evolution underlie rapid species diversification. Ecol. Lett. 13, 1378–1389 (2010).PubMed 

Google Scholar 
Jaramillo, A. F. et al. Vastly underestimated species richness of Amazonian salamanders (Plethodontidae: Bolitoglossa) and implications about plethodontid diversification. Mol. Phylogenet. Evol. 149, 106841 (2020).PubMed 

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

Google Scholar 
Bars-Closel, M., Kohlsdorf, T., Moen, D. S. & Wiens, J. J. Diversification rates are more strongly related to microhabitat than climate in squamate reptiles (lizards and snakes). Evolution 71, 2243–2261 (2017).PubMed 

Google Scholar 
Cyriac, V. P. & Kodandaramaiah, U. Digging their own macroevolutionary grave: fossoriality as an evolutionary dead end in snakes. J. Evolution. Biol. 31, 587–598 (2018).CAS 

Google Scholar 
Zamudio, K. R., Bell, R. C., Nali, R. C., Haddad, C. F. B. & Prado, C. P. A. Polyandry, predation, and the evolution of frog reproductive modes. Am. Nat. 188, S41–S61 (2016).PubMed 

Google Scholar 
Lion, M. B. et al. Global patterns of terrestriality in amphibian reproduction. Glob. Ecol. Biogeogr. 4, 679–13 (2019).
Google Scholar 
Müller, H. et al. Forests as promoters of terrestrial life-history strategies in East African amphibians. Biol. Lett. 9, 20121146–20121146 (2013).PubMed 
PubMed Central 

Google Scholar 
Velo-Antón, G., García-París, M., Galán, P. & Cordero Rivera, A. The evolution of viviparity in Holocene islands: ecological adaptation versus phylogenetic descent along the transition from aquatic to terrestrial environments. J. Zool. Syst. Evol. Res. 45, 345–352 (2007).
Google Scholar 
Liedtke, H. C. AmphiNom: an amphibian systematics tool. Syst. Biodivers. 17, 1–6 (2019).
Google Scholar 
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020). http://www.R-project.org/.IUCN. IUCN 2020. The IUCN Red List of Threatened Species. Version 2020-2. https://www.iucnredlist.org (2019).AmphibiaChina. The database of Chinese amphibians. Electronic Database (Kunming Institute of Zoology (CAS), Kunming, Yunnan, China, 2019) http://www.amphibiachina.org/.Ron, S. R., Yanez-Muñoz, M. H., Merino-Viteri, A. & Ortiz, D. A. Anfibios del Ecuador. Version 2019.0 (Museo de Zoología, Pontificia Universidad Católica del Ecuador, 2019). https://bioweb.bio/faunaweb/amphibiaweb.Greven, H. In Reproductive Biology and Phylogeny of Urodela 447–475 (Taylor & Francis, 2003).Marks, S. B. & Collazo, A. Direct development in Desmognathus aeneus (Caudata: Plethodontidae): a staging table. Copeia 1998, 637–648 (1998).
Google Scholar 
Müller, H., Loader, S. P., Ngalason, W., Howell, K. M. & Gower, D. J. Reproduction in brevicipitid frogs (Amphibia: Anura: Brevicipitidae)—evidence from Probreviceps m. macrodactylus. Copeia 2007, 726–733 (2007).
Google Scholar 
Velo-Antón, G., Santos, X., Sanmartín-Villar, I., Cordero-Rivera, A. & Buckley, D. Intraspecific variation in clutch size and maternal investment in pueriparous and larviparous Salamandra salamandra females. Evol. Ecol. 29, 185–204 (2014).
Google Scholar 
Beaulieu, J. M., O’Meara, B. C. & Donoghue, M. J. Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in campanulid angiosperms. Systematic biology 62, 725–737 (2013).Pupko, T., Pe’er, I., Shamir, R. & Graur, D. A fast algorithm for joint reconstruction of ancestral amino acid sequences. Mol. Biol. Evol. 17, 890–896 (2000).CAS 
PubMed 

Google Scholar 
Bollback, J. P. SIMMAP: stochastic character mapping of discrete traits on phylogenies. BMC Bioinforma. 7, 88 (2006).
Google Scholar 
Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R. News 6, 7–11 (2006).
Google Scholar 
Tuffley, C. & Steel, M. Modeling the covarion hypothesis of nucleotide substitution. Math. Biosci. 147, 63–91 (1998).MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Xie, W., Lewis, P. O., Fan, Y., Kuo, L. & Chen, M. H. Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Syst. Biol. 60, 150–160 (2011).PubMed 

Google Scholar 
Nakov, T., Beaulieu, J. M. & Alverson, A. J. Diatoms diversify and turn over faster in freshwater than marine environments. Evolution 73, 2497–2511 (2019).PubMed 

Google Scholar 
Etienne, R. S. et al. Diversity-dependence brings molecular phylogenies closer to agreement with the fossil record. Proc. R. Soc. Lond. B 279, 1300–1309 (2011).
Google Scholar  More

The effects of extreme drought on soil biochemical propertiesAs shown in Fig. 1A, the range of SOC during the early, midterm and late extreme drought experiments, were 73.53–251.44 g kg−1, 54.75–256.16 g kg−1, and 66.37–282.16 g kg−1, respectively. Concomitantly, DOC was 171.85–323.74 mg kg−1, 158.15 – 504.62 mg kg−1, and 166.63–418.43 mg kg−1, MBC was 247.80 – 461.69 mg kg−1, 257.90–450.98 mg kg−1, and 264.10–458.15 mg kg−1, respectively (Fig. 1B, C). The variation ranges of soil TN were 3.50–16.60 g kg−1, 4.70–34.5 g kg−1, and 6.70–32.50 g kg−1, respectively (Fig. 1D). Similarly, the variation ranges of NH4+ were 5.96–12.03 g kg−1, 5.39–12.59 g kg−1, and 5.74–13.03 g kg−1, NO3− were 2.27–8.79 mg kg−1, 5.07–9.62 mg kg−1, and 5.09–9.52 mg kg−1, respectively (Fig. 1E, F). The changes of SOC and NH4+ with soil depth were significantly different in different extreme drought periods and decreased significantly with the increase of soil depth (Table 1, P  More

We investigated whether ricefield mosquito larval control and/or rice cultivation practices are associated with malaria vector densities through a systematic review and meta-analysis. Forty-seven experimental studies were eligible for inclusion in the qualitative analysis and thirty-three studies were eligible for the meta-analysis. It was demonstrated that the use of fish, chemical and biological larvicides in rice fields were effective in controlling larval malaria vector densities at all developmental stages. Intermittent irrigation, however, could only significantly reduce late-stage larvae. Based on a limited number of studies, meta-analyses on other forms of larval control such as monomolecular surface films (MSFs), neem, copepods and Azolla failed to demonstrate any consistent reduction in anopheline numbers. Similarly, rice cultivation practices such as plant variety and density, type of levelling and pesticide application were not generally associated with reduced malaria vectors. Nonetheless, in one study, minimal tillage was observed to reduce average numbers of larvae throughout a cropping season. In another study, herbicide application increased larval abundance over a 4-week period, as did one-time drainage in a third study.
Despite their different modes of action, the use of chemical and bacterial larvicides and MSFs were all relatively effective measures of larval control in rice fields, varying between a 57% to 76% reduction in vector abundance compared to no larviciding. Their effects were highest (often reaching 100% reduction) only shortly following application but did not persist for longer than two weeks. These larvicides mostly had short residual half-lives because they were applied to paddy water which was naturally not completely stagnant: there was a small but constant process of water loss (through drainage, evapotranspiration and percolation) and replacement through irrigation. Hence, even with a residual formulation, weekly re-application would be needed for sustained control47,40,41,50. This would be very labour- and cost-intensive to scale-up, to ensure that larvicides are evenly distributed across vast areas (even at plot/sub-plot level) throughout at least one 5-month long rice-growing season per year42,51. Aerial application (including unmanned aerial vehicles), although widely used in the US and Europe, is unlikely to be a feasible delivery system for smallholders in SSA, even in large irrigation schemes26,27,48,49. Furthermore, if synthetic organic chemicals were to be considered for riceland malaria vector control, their management in the current landscape of insecticide resistance across Africa must be considered.Biological control using fish was found to be, in general, slightly more effective than (chemical, bacterial and MSF) larviciding. The degree of effectiveness was dependent on the fish species and their feeding preferences: surface-feeding, larvivorous species provided better anopheline control than bottom-feeding selective feeders4,43. Selecting the most suitable fish for local rice fields is not straightforward; many criteria need to be considered4,52,53. Generally, fish were well-received by rice farmers, perceived to contribute to increased yield by reducing weeds and pests and providing fertiliser through excrement43,44. This was reportedly also observed in Guangxi, China, where a certain proportion of the field had to be deepened into a side-trench where the fish could take shelter when the fields were drained. Even with this reduction in rice production area, carp rearing still increased yields by 10% and farmer’s income per hectare by 70%53. Unfortunately, none of the eligible studies in this review had included yield or water use as an outcome. Future entomological studies need to measure these critical agronomic variables so that studies of vector control in rice can be understood by, and transferred to, agronomists. In SSA, irrigated rice-fish farming can be scaled up provided that an inventory of fish species suitable for specific locations is available and that water is consistently available in fields (an important limiting factor in African irrigation schemes)54. Lessons can be learnt from successful large-scale rice-fish systems in Asia, where they have served as win–win solutions for sustainable food production and malaria control16,55.Overall, there was only limited evidence that intermittent irrigation is effective at reducing late-instar anopheline larvae in rice fields. This finding contrasts with prior reviews, which found mixed results (regardless of larval stage) but emphasised that success was site-specific4,17,56. This contrast is presumably due to the inclusion criteria of our systematic review. These reviews excluded studies in various geographical settings and some older studies that reported successful anopheline control with intermittent irrigation but lacked either a contemporaneous control arm, adequate replication or adequate differentiation between culicines and anophelines16,57,50,51,52,61. It seems, from our review, that intermittent irrigation does not prevent the recruitment of early instars (and in one case, may have encouraged oviposition31) but tends to prevent their development into late-stage immatures. This important conclusion is, however, based only on four studies; more evidence is urgently needed where future trials should consider the basic principles of modern trials with adequate replication, controls and differentiation between larval instars and species.Generally, it is observed that drainage, passive or active, did not reliably reduce overall numbers of mosquito immatures. In India and Kenya, closer inspection revealed that soils were not drying sufficiently, so any stranded larvae were not killed31,46. Highlighted by van der Hoek et al.29 and Keiser et al.17, water management in rice fields is very dependent on the physical characteristics of the soil and the climate and is most suited to places that not only favour rapid drying, but also have a good control of water supply17,56. Moreover, repeated drainage, although directed against mosquitoes, can also kill their aquatic predators62. Since mosquitoes can re-establish themselves in a newly flooded rice field more quickly than their predators, intermittent irrigation with more than a week between successive drying periods can permit repeated cycles of mosquito breeding without any predation pressure. Its efficacy against malaria vectors is therefore highly reliant on the timing of the wetting and drying periods. Further site-specific research on timing, especially with regards to predator–prey interactions within the rice agroecosystem, is required to find the perfect balance.Another limitation in intermittent irrigation is that it cannot be applied during the first two to three weeks following transplanting, because rice plants must remain flooded to recover from transplanting shock. Unfortunately, this time coincides with peak vector breeding. Thus, other methods of larval control would be required to fill this gap. To agronomists, intermittent irrigation provides benefits to farmers, as it does not penalise yield but significantly reduces water consumption. Nonetheless, farmer compliance seems to be variable, especially in areas where water availability is inconsistent and intermittent irrigation would potentially require more labour31,32,39. Importantly, rice farmers doubted their ability to coordinate water distribution evenly amongst themselves, suggesting that there may be sharing issues, as in the “tragedy of the commons”63. Instead, they said that they preferred to have an agreed authority to regulate water46.No general conclusions could be made on the effect on malaria vectors of other rice cultivation practices (apart from water management) because only one study was eligible for each practice. Nevertheless, these experiments on pesticide application, tillage and weed control, as well as another study on plant spacing (not eligible since glass rods were used to simulate rice plants), do illustrate that small changes in agronomic inputs and conditions can have considerable effects on mosquito densities, not just rice yield36,38,64. Moreover, in partially- or shallowly-flooded plots, the larvae are often concentrated in depressions (usually footprints), suggesting that rice operations which leave or remove footprints (e.g. hand-weeding, drum seeders, levelling) will influence vector breeding4.Our study has some important limitations. First, in most trials, the units of intervention were replicate plots of rice, and success was measured as a reduction in larval densities within treated plots. This design focuses on the identification of effective and easy-to-implement ways of growing rice without growing mosquitoes, on the assumption that higher vector densities are harmful. However, from a public health perspective, the need for epidemiological outcomes is often, and reasonably, stressed22,65. Nonetheless, from a farmers’ perspective, it is also important to consider whether the vectors emerging from their rice fields significantly contribute to the local burden of malaria and to determine how this contribution can be minimised. There is evidence that riceland vectors do increase malaria transmission, since human biting rates are much higher in communities living next to rice schemes than their non-rice counterparts66 and that additional riceland vectors may intensify transmission and malaria prevalence in rice communities15. Hence, when investigating how rice-attributed malaria risk can be minimised, mosquito abundance as measured in the experimental rice trials is a useful indicator of potential impact on epidemiological outcomes.Second, larval density was not always separated into larval developmental stages. This can be misleading because some interventions work by reducing larval survival (but not by preventing oviposition) and development to late instars and pupae. Therefore, an intervention could completely eliminate late-stage larvae and pupae but have little effect on the total number of immatures. This was illustrated in our meta-analyses of intermittent irrigation in Table 3 and Supplementary Table 5, and could have been the case for some studies that failed to demonstrate consistent reductions in overall anopheline numbers but did not differentiate between larval instars34,45,67,60,69. We infer that when monitoring mosquito immatures in rice trials, it is important to distinguish between larval instars and pupae. Pupae should always be counted separately since its abundance is the most direct indicator of adult productivity70.Third, experimental trials rarely reported the timing of intervention application or accounted for different rice-growing phases, or “days after transplantation”, in the outcome. Both aspects are important to consider since an intervention may be suited to control larvae during certain growth phases but not others. This is illustrated by Djegbe et al.38, where, compared to deep tillage, minimal tillage could significantly reduce larvae during the early stages of rice cultivation but not during tillering and maturation38. In contrast, other interventions, such as Azolla and predatory copepods, took time to grow and accumulate, and were more effective during the later stages of a rice season45,67,71. This differentiation is important because it can identify components that could potentially form a complementary set of interventions against riceland malaria vectors, each component being effective at different parts of the season. Since rice fields, and hence the dynamics of riceland mosquito populations, vary from place to place, this set of interventions must also be robust. Special attention must be paid to the early stages of rice cultivation, particularly the first few weeks after transplanting (or sowing), since, with many vector species, a large proportion of adult mosquitoes are produced during this time.Fourth, the analysis of entomological counts is often inadequate. Many studies failed to provide the standard deviation (or any other measure of error) for larval counts and could not be included in the quantitative analysis. Often, due to the extreme (and not unexpected) variability of larval numbers, sample sizes were insufficient to calculate statistically significant differences between treatments. Fifth, a high risk of bias was found across both CTS and CITS studies, including high heterogeneity and some publication bias. Study quality was, in general, a shortcoming and limited the number of eligible studies for certain interventions, including intermittent irrigation. Moreover, there are conspicuous a priori reasons for bias in such experimental trials: trial locations are frequently chosen to maximise the probability of success.Finally, few studies were conducted in African countries, where the relationship between rice and malaria is most important because of the efficiency, and the “rice-philic” nature, of the vector An. gambiae s.l.15. In particular, there was a lack of studies on the effectiveness and scalability of biological control and rice cultivation practices. There is also very little information (particularly social science studies) on the views and perspectives of African rice farmers on mosquitoes in rice and interventions to control them72,73.In the future, as malaria declines (particularly across SSA), the contribution of rice production to increased malaria transmission is likely to become more conspicuous15. Unless this problem is addressed, rice growing will probably become an obstacle to malaria elimination. Current default methods of rice production provide near-perfect conditions for the larvae of African malaria vectors. Therefore, we need to develop modified rice-growing methods that are unfavourable to mosquitoes but still favourable for the rice. Although larviciding and biological control may be appropriate, their unsustainable costs remain the biggest barrier to uptake amongst smallholder farmers. Future investigations into riceland vector control should pay more attention to interventions that may be useful to farmers.Supported by medical entomologists, agronomists should lead the research task of identifying cultivation methods that achieve high rice productivity whilst suppressing vector productivity. Rice fields are a major global source of greenhouse gases, and agronomists have responded by successfully developing novel cultivation methods that minimise these emissions while maintaining yield. We need the same kind of response from agronomists, to achieve malaria control co-benefits within rice cultivation. At present, only a few aspects of rice cultivation have been investigated for their effects on mosquitoes, and the potential of many other practices for reducing anopheline numbers are awaiting study. Due to the spatial and temporal heterogeneity of rice agroecosystems, it is likely that no single control method can reduce mosquito numbers throughout an entire cropping season and in all soil types and irrigation methods. Thus, effective overall control is likely to come from a combination of local, site-specific set of complementary methods, each of which is active and effective during a different phase of the rice-growing season. More

Meat, Milk and More: Policy Innovations to Shepherd Inclusive and Sustainable Livestock Systems in Africa (Malabo Montpellier Panel, 2020).Value of Agricultural Production (FAO, accessed August 25, 2022); https://www.fao.org/faostat/en/#data/QVJayne, T. & Sanchez, P. A. Agricultural productivity must improve in sub-Saharan Africa. Science 372, 1045–1047 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Dangal, S. R. S. et al. Methane emission from global livestock sector during 1890–2014: magnitude, trends and spatiotemporal patterns. Glob. Change Biol. 23, 4147–4161 (2017).Article 
ADS 

Google Scholar 
Mottet, A. et al. Climate change mitigation and productivity gains in livestock supply chains: insights from regional case studies. Reg. Env. Change 17, 129–141 (2016).Article 

Google Scholar 
Valin, H. et al. Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security? Environ. Res. Lett. 8, 035019 (2013).Article 
ADS 
CAS 

Google Scholar 
González-Quintero, R. et al. Yield gap analysis to identify attainable milk and meat productivities and the potential for greenhouse gas emissions mitigation in cattle systems of Colombia. Agric. Syst. 195, 103303 (2022).Article 

Google Scholar 
Crops and Livestock Products (FAO, accessed August 17,2022); https://www.fao.org/faostat/en/#data/QCLLedo, J. et al. Persistent challenges in safety and hygiene control practices in emerging dairy chains: the case of Tanzania. Food Control 105, 164–173 (2019).Article 

Google Scholar 
Häsler, B. et al. Integrated food safety and nutrition assessments in the dairy cattle value chain in Tanzania. Glob. Food Sec. 18, 102–113 (2018).Article 

Google Scholar 
Supply Utilization Accounts (FAO, accessed August 26, 2022); https://www.fao.org/faostat/en/#data/SCLMichael, S. et al. Tanzania Livestock Master Plan (International Livestock Research Institute, 2018).Tanzania Livestock Sector Analysis (2016/2017–2030/2031) (United Republic of Tanzania Ministry of Livestock and Fisheries, 2017); https://www.mifugouvuvi.go.tz/uploads/projects/1553602287-LIVESTOCK%20SECTOR%20ANALYSIS.pdfNicholson, C. et al. Assessment of Investment Priorities for Tanzania’s Dairy Sector: Report on Activities and Accomplishments (International Livestock Research Institute, 2021).Chagunda, M. G. C., Romer, D. A. M. & Roberts, D. J. Effect of genotype and feeding regime on enteric methane, non-milk nitrogen and performance of dairy cows during the winter feeding period. Livest. Sci. 122, 323–332 (2009).Article 

Google Scholar 
Notenbaert, A. et al. Towards environmentally sound intensification pathways for dairy development in the Tanga region of Tanzania. Reg. Environ. Change 20, 138 (2020).Yesuf, G. A. et al. Embedding stakeholders’ priorities into the low-emission development of the East African dairy sector. Env. Res. Lett. 16, 064032 (2021).Article 
CAS 

Google Scholar 
GLS (Greening Livestock Survey) (International Livestock Research Institute, 2019); https://data.ilri.org/portal/dataset/greeninglivestockIntended Nationally Determined Contributions (United Republic of Tanzania, 2021); https://unfccc.int/sites/default/files/NDC/2022-06/TANZANIA_NDC_SUBMISSION_30%20JULY%202021.pdfNdung’u, P. W. et al. Farm-level emission intensities of smallholder cattle (Bos indicus; B. indicus–B. taurus crosses) production systems in highlands and semi-arid regions. Animal 16, 100445 (2022).Article 
PubMed 

Google Scholar 
Goopy, J. P. et al. Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle. Br. J. Nutr. 123, 1239–1246 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goopy, J. P. et al. A new approach for improving emission factors for enteric methane emissions of cattle in smallholder systems of East Africa—results for Nyando, Western Kenya. Agric. Syst. 161, 72–80 (2018).Article 

Google Scholar 
Supporting Low Emissions Development in the Tanzanian Dairy Cattle Sector—Reducing Enteric Methane for Food Security and Livelihoods (FAO, 2019).Gerssen-Gondelach, S. J. et al. Intensification pathways for beef and dairy cattle production systems: impacts on GHG emissions, land occupation and land use change. Agric. Ecosyst. Environ. 240, 135–147 (2017).Article 

Google Scholar 
Havlik, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Herrero, M. et al. Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Change 6, 452–461 (2016).Article 
ADS 

Google Scholar 
Dizyee, K., Baker, D. & Omore, A. Upgrading the smallholder dairy value chain: a system dynamics ex-ante impact assessment in Tanzania’s Kilosa district. J. Dairy Res. 86, 440–449 (2019).Article 
CAS 
PubMed 

Google Scholar 
Simões, A. R. P., Nicholson, C. F., Novakovicc, A. M. & Protil, R. M. Dynamic impacts of farm-level technology adoption on the Brazilian dairy supply chain. Int. Food Agribus. Manag. Rev. 23, 71–84 (2020).Article 

Google Scholar 
Rahimi, J. et al. Heat stress will detrimentally impact future livestock production in East Africa. Nat. Food. 2, 88–96 (2021).Article 

Google Scholar 
Mbululo, Y. & Nyihirani, F. Climate characteristics over southern highlands Tanzania. Atmos. Clim. Sci. 2, 454–463 (2012).
Google Scholar 
Kihoro, E. M., Schoneveld, G. C. & Crane, T. A. Pathways toward inclusive low-emission dairy development in Tanzania: producer heterogeneity and implications for intervention design. Agric. Syst. 190, 103073 (2021).Mruttu, H. et al. Animal Genetics Strategy and Vision for Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).Agricultural Sample Survey 2018/19 Report on Livestock and Livestock Characteristics (Private Peasant Holdings) (Central Statistical Agency, 2019).2019/20 National Sample Census of Agriculture Main Report (Tanzania National Bureau of Statistics, 2022).Robinson, T. P. et al. Global Livestock Production Systems (FAO, 2011).Herrero, M. et al. Biomass use, production, feed efficiencies and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baseline Study of the Tanzania Dairy Value Chain (United Republic of Tanzania Ministry of Agriculture, Livestock and Fisheries, 2016).Mbwambo, N., Nandonde, S., Ndomba, C. & Desta, S. Assessment of Animal Feed Resources in Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., & Borriello, G. Open data kit: tools to build information services for developing regions. Proc. 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (Association for Computing Machinery, 2010).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).https://www.r-project.orgRufino, M. C. et al. Lifetime productivity of dairy cows in smallholder farming systems of the central highlands of Kenya. Animal 3, 1044–1056 (2009).Article 
CAS 
PubMed 

Google Scholar 
Hawkins, J. et al. Feeding efficiency gains can increase the greenhouse gas mitigation potential of the Tanzanian dairy sector. Sci. Rep. 11, 4190 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Python Software Foundation (Python Software Foundation, 2019); https://www.python.org/psf/Kashoma, I. P. B. et al. Predicting body weight of Tanzania shorthorn zebu cattle using heart girth measurements. Livest. Res. Rural. Dev. 23, Table 1 (2011).Galukande, E. B., Mahadevan, P. & Black, J. G. Milk production in East African zebu cattle. Anim. Sci. 4, 329–336 (1962).Article 

Google Scholar 
Gillah, K. A., Kifaro, G. C. & Madsen, J. Effects of pre partum supplementation on milk yield, reproduction and milk quality of crossbred dairy cows raised in a peri urban farm of Morogoro town Tanzania. Livest. Res. Rural. Dev. 26 (2014).Njau, F. B. C., Lwelamira, J. & Hyandye, C. Ruminant livestock production and quality of pastures in the communal grazing land of semi-arid central Tanzania. Livest. Res. Rural. Dev. 8, Table 4 (2013).Mwambene, P. L. et al. Selecting indigenous cattle populations for improving dairy production in the Southern Highlands and Eastern Tanzania. Livest. Res. Rural. Dev. 26 (2014).Rege, J. E. O. et al. Cattle of Kenya: Uses, Performance, Farmer Preferences, Measures of Genetic Diversity and Options for Improved Use (International Livestock Research Institute, 2001).Beffa, L. M. Genotype × Environment Interaction in Afrikaner Cattle. PhD thesis, Univ. of the Free State (2005).Meaker, H. J., Coetsee, T. P. N. & Lishman, A. W. The effects of age at 1st calving on the productive and reproductive-performance of beef-cows. S. Afr. J. Anim. Sci. 10, 105–113 (1980).
Google Scholar 
Chenyambuga, S. W. & Mseleko, K. F. Reproductive and lactation performances of Ayrshire and Boran crossbred cattle kept in smallholder farms in Mufindi district, Tanzania. Livest. Res. Rural. Dev. 21, 100 (2009).
Google Scholar 
Ojango, J. M. K. et al. Dairy production systems and the adoption of genetic and breeding technologies in Tanzania, Kenya, India and Nicaragua. Anim. Genet. Resour. 59, 81–95 (2016).Article 

Google Scholar 
Feedipedia—Animal Feed Resources Information System (FAO, accessed 2021); https://www.feedipedia.org/Lukuyu, B. et al. (eds) Feeding Dairy Cattle in East Africa (East Africa Dairy Development Project, 2012).Rubanza, C. D. K. et al. Biomass production and nutritive potential of conserved forages in silvopastoral traditional fodder banks (Ngitiri) of Meatu District of Tanzania. Asian-Aust. J. Anim. Sci. 19, 978–983 (2006).Article 

Google Scholar 
Food Balances (2010-) (FAO, accessed September 29, 2021); http://www.fao.org/faostat/en/#data/FBSCrop Data for the United Republic of Tanzania (FAO, accessed September 22, 2021); http://www.fao.org/faost at/en/#data/QCGilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data. 5, 180227 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
2014/15 Annual Agricultural Sample Survey Report (The United Republic of Tanzania, 2016).Basic Data for Livestock and Fisheries (The United Republic of Tanzania Ministry of Livestock and Fisheries, 2013).IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 Agriculture, Forestry and Other Land Use (IPCC, 2006).2019 Refinement to the IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2019).Fertilizers by Nutrient (FAO, accessed July 6, 2022); https://www.fao.org/faostat/en/#data/RFNHutton, M. O. et al. Toward a nitrogen footprint calculator for Tanzania. Env. Res. Lett. 12, 034016 (2017).Article 

Google Scholar 
Tanzania Fertilizer Assessment (International Fertilizer Development Center, 2012); http://tanzania.countrystat.org/fileadmin/user_upload/countrystat_fenix/congo/docs/Tanzania%20Fertilizer%20Assessment%202012.pdfA Common Carbon Footprint Approach for the Dairy Sector: The IDF Guide to Standard Life Cycle Methodology (International Dairy Federation, 2015); https://www.fil-idf.org/wp-content/uploads/2016/09/Bulletin479-2015_A-common-carbon-footprint-approach-for-the-dairy-sector.CAT.pdfBruzzone, L., Bovolo, F. & Arino, O. European Space Agency land cover climate change initiative. ESA LC CCI data: high resolution land cover data via Centre for Environmental Data Analysis; https://climate.esa.int/en/projects/high-resolution-land-cover/ (2021)Characteristics of Markets for Animal Feeds Raw Materials in the East African Community: Focus on Maize Bran and Sunflower Seed Cake (Kilimo Trust, 2017).Ngunga, D. & Mwendia, S. Forage Seed System in Tanzania: A Review Report (Alliance of Biodiversity and CIAT, 2020).Nkombe, B.M. Investigation of the Potential for Forage Species to Enhance the Sustainability of Degraded Rangeland and Cropland Soils. MSc thesis, Ohio State Univ. (2016).Producer Prices (FAO, accessed 2021); http://www.fao.org/faostat/en/#data/PP More

.readcube-buybox { display: none !important;}
Protecting an overfished lobster species helps the crustaceans to grow big, according to an analysis of European lobsters in marine sanctuaries1.

Access options

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0 0;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50%0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:””;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
/* style specs end */Subscribe to Nature+Get immediate online access to Nature and 55 other Nature journal$29.99monthlySubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueAll prices are NET prices.VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00All prices are NET prices.

Additional access options:

doi: https://doi.org/10.1038/d41586-022-03708-2

References

Subjects

Conservation biology More

Morrison, M. A., Francis, M. P., Hartill, B. W. & Parkinson, D. M. Diurnal and tidal variation in the abundance of the fish fauna of a temperate tidal mudflat. Estuar. Coast. Shelf Sci. 54, 793–807 (2002).Article 
ADS 

Google Scholar 
Ribeiro, J. et al. Seasonal, tidal and diurnal changes in fish assemblages in the Ria Formosa lagoon (Portugal). Estuar. Coast. Shelf Sci. 67, 461–474 (2006).Article 
ADS 

Google Scholar 
Takemura, A., Rahman, M. S. & Park, Y. J. External and internal controls of lunar-related reproductive rhythms in fishes. J. Fish Biol. 76, 7–26 (2010).Article 
CAS 
PubMed 

Google Scholar 
Mendes, S., Turrell, W., Lütkebohle, T. & Thompson, P. Influence of the tidal cycle and a tidal intrusion front on the spatio-temporal distribution of coastal bottlenose dolphins. Mar. Ecol. Prog. Ser. 239, 221–229 (2002).Article 
ADS 

Google Scholar 
Johnston, D. W., Thorne, L. H. & Read, A. J. Fin whales Balaenoptera physalus and minke whales Balaenoptera acutorostrata exploit a tidally driven island wake ecosystem in the Bay of Fundy. Mar. Ecol. Prog. Ser. 305, 287–295 (2005).Article 
ADS 

Google Scholar 
Ichikawa, K. et al. Dugong (Dugong dugon) vocalization patterns recorded by automatic underwater sound monitoring systems. J. Acoust. Soc. Am. 119, 3726–3733 (2006).Article 
ADS 
PubMed 

Google Scholar 
Akamatsu, T. et al. Seasonal and diurnal presence of finless porpoises at a corridor to the ocean from their habitat. Mar. Biol. 157, 1879–1887 (2010).Article 

Google Scholar 
Li, S. et al. Seasonal, lunar and tidal influences on habitat use of indo-pacific humpback dolphins in Beibu gulf, China. Zool. Stud. https://doi.org/10.6620/ZS.2018.57-01 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Zamon, J. E. Seal predation on salmon and forage fish schools as a function of tidal currents in the San Juan Islands, Washington, USA. Fish. Oceanogr. 10, 353–366 (2001).Article 

Google Scholar 
Van Parijs, S. M., Hastie, G. D. & Thompson, P. M. Geographical variation in temporal and spatial vocalization patterns of male harbour seals in the mating season. Anim. Behav. 58, 1231–1239 (1999).Article 
PubMed 

Google Scholar 
Bortolotto, G. A., Danilewicz, D., Hammond, P. S., Thomas, L. & Zerbini, A. N. Whale distribution in a breeding area: Spatial models of habitat use and abundance of western South Atlantic humpback whales. Mar. Ecol. Prog. Ser. 585, 213–227 (2017).Article 
ADS 

Google Scholar 
Johnson, J. H. & Wolman, A. A. The humpback whale, Megaptera novaeangliae. Mar. Fish. Rev. 46, 30–37 (1984).
Google Scholar 
Kobayashi, N. et al. Spatial distribution and habitat use patterns of humpback whales in Okinawa, Japan. Mammal Study 41, 207–214 (2016).Article 

Google Scholar 
Mori, K., Sata, F., Yamaguchi, M., Suganuma, H. & Ueyanagi, S. Distribution, migration and local movements of humpback whale (Megaptera novaeangliae) in the adjacent waters of the Ogasawara (Bonin) Islands Japan. J. Fac. Mar. Sci. Technol. Tokai Univ. 45, 197–213 (1998).
Google Scholar 
Rasmussen, K., Calambokidis, J. & Steiger, G. H. Distribution and migratory destinations of humpback whales off the Pacific coast of Central America during the boreal winters of 1996–2003. Mar. Mammal Sci. 28, 1–13 (2012).Article 

Google Scholar 
Calambokidis, J. et al. SPLASH: structure of populations, levels of abuncance and status of humpback whales in the North Pacific. Final report for Contract AB133F-03-RP-00078, to U.S. Dept. of Comm. Western Administrative Center, Seattle, WA. https://cascadiaresearch.org/files/SPLASH-contract-Report-May08.pdf (2008).Hill, M. et al. Found: A missing breeding ground for endangered western North Pacific humpback whales in the Mariana Archipelago. Endanger. Species Res. 41, 91–103 (2020).Article 

Google Scholar 
Payne, R. S. & McVay, S. Songs of humpback whales. Science 173, 585–597 (1971).Article 
ADS 
CAS 
PubMed 

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

Google Scholar 
Tyack, P. Interactions between singing Hawaiian humpback whales and conspecifics nearby. Behav. Ecol. Sociobiol. 8, 105–116 (1981).Article 

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

Google Scholar 
Au, W. W. L., Mobley, J., Burgess, W. C., Lammers, M. O. & Nachtigall, P. E. Seasonal and diurnal trends of chorusing humpback whales wintering in waters off western Maui. Mar. Mammal Sci. 16, 530–544 (2000).Article 

Google Scholar 
Cerchio, S., Collins, T., Strindberg, S., Bennett, C. & Rosenbaum, H. Humpback whale singing activity off northern Angola: An indication of the migratory cycle, breeding habitat and impact of seismic surveys on singer number in Breeding. Int. Whal. Comm. P. SC/62/SH12 (2010).Kobayashi, N., Okabe, H., Higashi, N., Miyahara, H. & Uchida, S. Diel patterns in singing activity of humpback whales in a winter breeding area in Okinawan (Ryukyuan) waters. Mar. Mammal Sci. 37, 982–992 (2021).Article 

Google Scholar 
Munger, L. M., Lammers, M. O., Fisher-Pool, P. & Wong, K. Humpback whale (Megaptera novaeangliae) song occurrence at American Samoa in long-term passive acoustic recordings, 2008–2009. J. Acoust. Soc. Am. 132, 2265–2272 (2012).Article 
ADS 
PubMed 

Google Scholar 
Barlow, D. R., Fournet, M. & Sharpe, F. Incorporating tides into the acoustic ecology of humpback whales. Mar. Mammal Sci. 35, 234–251 (2019).Article 

Google Scholar 
Chenoweth, E., Gabriele, C. & Hill, D. Tidal influences on humpback whale habitat selection near headlands. Mar. Ecol. Prog. Ser. 423, 279–289 (2011).Article 
ADS 

Google Scholar 
Sousa-Lima, R. S., Clark, C. W. & Road, S. W. Modeling the effect of boat traffic on singing activity of humpback whales (Megaptera novaeangliae) in the abrolhos national marine park, Brazil. Can. Acoust 36, 174–181 (2008).
Google Scholar 
Cerchio, S., Strindberg, S., Collins, T., Bennett, C. & Rosenbaum, H. Seismic surveys negatively affect humpback whale singing activity off Northern Angola. PLoS ONE 9, e86464. https://doi.org/10.1371/journal.pone.0086464 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Darling, J. D. & Mori, K. Recent observations of humpback whales (Megaptera novaeangliae) in Japanese waters off Ogasawara and Okinawa. Can. J. Zool. 71, 325–333 (1993).Article 

Google Scholar 
Calambokidis, J. et al. Movements and population structure of humpback whales in the North Pacific. Mar. Mammal Sci. 17, 769–794 (2001).Article 

Google Scholar 
Wessel, P., Smith, W. H. F., Scharroo, R., Luis, J. & Wobbe, F. Generic mapping tools: Improved version released. Eos Trans. Am. Geophys. Union 94, 409–410 (2013).Article 
ADS 

Google Scholar 
Helweg, D. A. & Herman, L. M. Diurnal patterns of behaviour and group membership of humpback whales (Megaptera novaeangliae) wintering in Hawaiian waters. Ethology 98, 298–311 (1994).Article 

Google Scholar 
Darling, J. D. & Berube, M. Interactions of singing humpback whales with other males. Mar. Mammal Sci. 17, 570–584 (2001).Article 

Google Scholar 
Whitlow, W. L. et al. Acoustic properties of humpback whale songs. J. Acoust. Soc. Am. 120, 1103–1110 (2006).Article 

Google Scholar 
Japan Coast Guard. Sailing Directions for South and East Coasts of Honshu. (1981).Tsujii, K. et al. Change in singing behavior of humpback whales caused by shipping noise. PLoS ONE 13, e0204112. https://doi.org/10.1371/journal.pone.0204112 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ryan, J. P. et al. Humpback whale song occurrence reflects ecosystem variability in feeding and migratory habitat of the northeast Pacific. PLoS ONE 14, e0222456. https://doi.org/10.1371/journal.pone.0222456 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. 4.0.0 version. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2020).Wood, S.N. Generalized Additive Models: An Introduction with R 2nd edn, (Chapman and Hall/CRC, 2017). More

Kazarov, E. The Role of Zoos in Creating a Conservation Ethic in Visitors. SIT Digital Collections (2022). at https://digitalcollections.sit.edu/isp_collection/584.Hosey, G. How does the zoo environment affect the behaviour of captive primates?. Appl. Anim. Behav. Sci. 90, 107–129 (2005).
Google Scholar 
Morgan, K. & Tromborg, C. Sources of stress in captivity. Appl. Anim. Behav. Sci. 102, 262–302 (2007).
Google Scholar 
Sherwen, S. & Hemsworth, P. The visitor effect on zoo animals: Implications and opportunities for zoo animal welfare. Animals 9, 366 (2019).PubMed Central 

Google Scholar 
Chamove, A., Hosey, G. & Schaetzel, P. Visitors excite primates in zoos. Zoo Biol. 7, 359–369 (1988).
Google Scholar 
Tetley, C. L. & O’Hara, S. J. Ratings of animal personality as a tool for improving the breeding, management and welfare of zoo mammals. Anim. Welf. UFAW J. 21(4), 463 (2012).CAS 

Google Scholar 
Stoinski, T. S., Jaicks, H. F. & Drayton, L. A. Visitor effects on the behavior of captive western lowland gorillas: The importance of individual differences in examining welfare. Zoo Biol. 31(5), 586–599 (2012).PubMed 

Google Scholar 
Queiroz, M. B. & Young, R. J. The different physical and behavioural characteristics of zoo mammals that influence their response to visitors. Animals 8(8), 139 (2018).PubMed Central 

Google Scholar 
Fanson, K. V. & Wielebnowski, N. C. Effect of housing and husbandry practices on adrenocortical activity in captive Canada lynx (Lynx canadensis). Anim. Welf. 22, 159–165 (2013).CAS 

Google Scholar 
Pirovino, M. et al. Fecal glucocorticoid measurements and their relation to rearing, behavior, and environmental factors in the population of pileated gibbons (Hylobates pileatus) held in European zoos. Int. J. Primatol. 32(5), 1161–1178 (2011).
Google Scholar 
Williams, I., Hoppitt, W. & Grant, R. The effect of auditory enrichment, rearing method and social environment on the behavior of zoo-housed psittacines (Aves: Psittaciformes); implications for welfare. Appl. Anim. Behav. Sci. 186, 85–92 (2017).
Google Scholar 
Fernandez, E., Tamborski, M., Pickens, S. & Timberlake, W. Animal–visitor interactions in the modern zoo: Conflicts and interventions. Appl. Anim. Behav. Sci. 120, 1–8 (2009).
Google Scholar 
Hosey, G. & Skyner, L. Self-injurious behavior in zoo primates. Int. J. Primatol. 28, 1431–1437 (2007).
Google Scholar 
Mallapur, A., Sinha, A. & Waran, N. Influence of visitor presence on the behaviour of captive lion-tailed macaques (Macaca silenus) housed in Indian zoos. Appl. Anim. Behav. Sci. 94, 341–352 (2005).
Google Scholar 
Davey, G. Visitors’ Effects on the Welfare of Animals in the Zoo: A Review. J. Appl. Anim. Welf. Sci. 10, 169–183 (2007).CAS 
PubMed 

Google Scholar 
Jones, H., McGregor, P., Farmer, H. & Baker, K. The influence of visitor interaction on the behavior of captive crowned lemurs (Eulemur coronatus) and implications for welfare. Zoo Biol. 35, 222–227 (2016).CAS 
PubMed 

Google Scholar 
Cook, S. & Hosey, G. R. Interaction sequences between chimpanzees and human visitors at the zoo. Zoo Biol. 14(5), 431–440 (1995).
Google Scholar 
Baker, K. C. Benefits of positive human interaction for socially-housed chimpanzees. Anim. Welf. (South Mimms, Engl.nd) 13(2), 239 (2004).CAS 

Google Scholar 
Carder, G. & Semple, S. Visitor effects on anxiety in two captive groups of western lowland gorillas. Appl. Anim. Behav. Sci. 115, 211–220 (2008).
Google Scholar 
Wood, W. Interactions among environmental enrichment, viewing crowds, and zoo chimpanzees (Pantroglodytes). Zoo Biol. 17, 211–230 (1998).
Google Scholar 
Todd, P., Macdonald, C. & Coleman, D. Visitor-associated variation in captive Diana monkey (Cercopithecus diana diana) behaviour. Appl. Anim. Behav. Sci. 107, 162–165 (2007).
Google Scholar 
Davis, N., Schaffner, C. & Smith, T. Evidence that zoo visitors influence HPA activity in spider monkeys (Ateles geoffroyii rufiventris). Appl. Anim. Behav. Sci. 90, 131–141 (2005).
Google Scholar 
Sherwen, S. L. et al. Effects of visual contact with zoo visitors on black-capped capuchin welfare. Appl. Anim. Behav. Sci. 167, 65–73 (2015).
Google Scholar 
Choo, Y., Todd, P. & Li, D. Visitor effects on zoo orangutans in two novel, naturalistic enclosures. Appl. Anim. Behav. Sci. 133, 78–86 (2011).
Google Scholar 
Sherwen, S., Magrath, M., Butler, K., Phillips, C. & Hemsworth, P. A multi-enclosure study investigating the behavioural response of meerkats to zoo visitors. Appl. Anim. Behav. Sci. 156, 70–77 (2014).
Google Scholar 
Hosey, G. & Druck, P. The influence of zoo visitors on the behaviour of captive primates. Appl. Anim. Behav. Sci. 18, 19–29 (1987).
Google Scholar 
Mitchell, G. et al. More on the ‘influence’of zoo visitors on the behaviour of captive primates. Appl. Anim. Behav. Sci. 35(2), 189–198 (1992).
Google Scholar 
Sellinger, R. & Ha, J. The effects of visitor density and intensity on the behavior of two captive jaguars (Panthera onca). J. Appl. Anim. Welfare Sci. 8, 233–244 (2005).CAS 

Google Scholar 
Azevedo, C., Lima, M., Silva, V., Young, R. & Rodrigues, M. Visitor Influence on the Behavior of Captive Greater Rheas (Rhea americana, Rheidae Aves). J. Appl. Anim. Welfare Sci. 15, 113–125 (2012).
Google Scholar 
Das Gupta, M., Das, A., Sumy, M. C. & Islam, M. M. An explorative study on visitor’s behaviour and their effect on the behaviour of primates at Chittagong zoo. Bangladesh J. Vet. Anim. Sci. 5(2), 24–32 (2017).
Google Scholar 
Hemsworth, P. Human–animal interactions in livestock production. Appl. Anim. Behav. Sci. 81, 185–198 (2003).
Google Scholar 
Stoinski, T., Czekala, N., Lukas, K. & Maple, T. Urinary androgen and corticoid levels in captive, male Western lowland gorillas (Gorilla g. gorilla): Age- and social group-related differences. Am. J. Primatol. 56, 73–87 (2002).CAS 
PubMed 

Google Scholar 
Stoinski, T., Lukas, K., Kuhar, C. & Maple, T. Factors influencing the formation and maintenance of all-male gorilla groups in captivity. Zoo Biol. 23, 189–203 (2004).
Google Scholar 
Olsson, I. & Westlund, K. More than numbers matter: The effect of social factors on behaviour and welfare of laboratory rodents and non-human primates. Appl. Anim. Behav. Sci. 103, 229–254 (2007).
Google Scholar 
Martin, J. E. Early life experiences: Activity levels and abnormal behaviours in resocialised chimpanzees. Anim Welf. 11(4), 419–436 (2002).CAS 

Google Scholar 
Birkett, L. P. & Newton-Fisher, N. E. How abnormal is the behaviour of captive, zoo-living chimpanzees?. PLoS ONE 6(6), e20101 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ballen, C., Shine, R. & Olsson, M. Effects of early social isolation on the behaviour and performance of juvenile lizards Chamaeleo calyptratus. Anim. Behav. 88, 1–6 (2014).
Google Scholar 
Coe, C., Mendoza, S., Smotherman, W. & Levine, S. Mother-infant attachment in the squirrel monkey: Adrenal response to separation. Behav. Biol. 22, 256–263 (1978).CAS 
PubMed 

Google Scholar 
Mendoza, S., Smotherman, W., Miner, M., Kaplan, J. & Levine, S. Pituitary-adrenal response to separation in mother and infant squirrel monkeys. Dev. Psychobiol. 11, 169–175 (1978).CAS 
PubMed 

Google Scholar 
Gilbert, M. & Baker, K. Social buffering in adult male rhesus macaques (Macaca mulatta): Effects of stressful events in single vs. pair housing. J. Med. Primatol. 40, 71–78 (2010).PubMed 
PubMed Central 

Google Scholar 
Schapiro, S. Effects of social manipulations and environmental enrichment on behavior and cell-mediated immune responses in rhesus macaques. Pharmacol. Biochem. Behav. 73, 271–278 (2002).CAS 
PubMed 

Google Scholar 
Chen, W. et al. Effects of social isolation and re-socialization on cognition and ADAR1 (p110) expression in mice. PeerJ 4, e2306 (2016).PubMed 
PubMed Central 

Google Scholar 
Glatston, A., Geilvoet-Soeteman, E., Hora-Pecek, E. & Van Hooff, J. The influence of the zoo environment on social behavior of groups of cotton-topped tamarins Saguinus oedipus oedipus. Zoo Biol. 3, 241–253 (1984).
Google Scholar 
Mitchell, G. et al. Effects of visitors and cage changes on the behaviors of mangabeys. Zoo Biol. 10, 417–423 (1991).
Google Scholar 
Geissmann, T. & Orgeldinger, M. The relationship between duet songs and pair bonds in siamangs Hylobates syndactylus. Anim. Behav. 60, 805–809 (2000).CAS 
PubMed 

Google Scholar 
Palombit, R. Pair bonds in monogamous apes: A comparison of the siamang hylobates syndactylus and the white-handed gibbon hylobates lar. Behaviour 133, 321–356 (1996).
Google Scholar 
Rutberg, A. The evolution of monogamy in primates. J. Theor. Biol. 104, 93–112 (1983).ADS 
CAS 
PubMed 

Google Scholar 
Giorgi, A., Montebovi, G., Vitale, A. & Alleva, E. A behavioural case study of early social isolation of a subadult white-handed gibbon (Hylobates lar). Folia Primatol. 89, 287–294 (2018).
Google Scholar 
Skynner, L. A., Amory, J. R. & Hosey, G. The effect of visitors on the self-injurious behaviour of a male pileated gibbon (Hylobates pileatus). Zool. Garten 74(1), 38–41 (2004).
Google Scholar 
Smith, K. & Kuhar, C. Siamangs (Hylobates syndactylus) and white-cheeked gibbons (Hylobates leucogenys) show few behavioral differences related to zoo attendance. J. Appl. Anim. Welfare Sci. 13, 154–163 (2010).CAS 

Google Scholar 
Lukas, K. E. et al. Longitudinal study of delayed reproductive success in a pair of white-cheeked gibbons (Hylobates leucogenys). Zoo Biol. 21, 413–434 (2002).
Google Scholar 
Cooke, C. & Schillaci, M. Behavioral responses to the zoo environment by white handed gibbons. Appl. Anim. Behav. Sci. 106, 125–133 (2007).
Google Scholar 
Mootnick, A. & Baker, E. Masturbation in captiveHylobates (gibbons). Zoo Biol. 13, 345–353 (1994).
Google Scholar 
Geissmann, T. Reassessment of age of sexual maturity in gibbons (hylobates spp.). American Journal of Primatology 23, 11–22 (1991).Altmann, J. Observational study of behavior: Sampling methods. Behaviour 49(3–4), 227–266 (1974).CAS 
PubMed 

Google Scholar 
Pomerantz, O. & Terkel, J. Effects of positive reinforcement training techniques on the psychological welfare of zoo-housed chimpanzees (Pan troglodytes). Am. J. Primatol. 71, 687–695 (2009).PubMed 

Google Scholar 
Orgeldinger, M. Protective and territorial behavior in captive siamangs (Hylobates syndactylus). Zoo Biol. 16, 309–325 (1997).
Google Scholar 
Fox, J. et al. Package ‘car’. Vienna: R Foundation for Statistical Computing, 16 https://cran.uni-muenster.de/web/packages/car/car.pdf (2012).Magnusson, A., Skaug, H., Nielsen, A., Berg, C., Kristensen, K., Maechler, M., van Bentham, K., Bolker, B., Brooks, M. & Brooks, M. M. Package ‘glmmtmb’. R Package Version 0.2. 0 (2017).Hartig, F., & Hartig, M. F. Package ‘DHARMa’. Vienna, Austria: R Development Core Team (2017).Troisi, A. Displacement activities as a behavioral measure of stress in nonhuman primates and human subjects. Stress 5, 47–54 (2002).PubMed 

Google Scholar 
Baker, K. & Aureli, F. Behavioural indicators of anxiety: An empirical test in chimpanzees. Behaviour 134, 1031–1050 (1997).
Google Scholar 
Vick, S. J. & Paukner, A. Variation and context of yawns in captive chimpanzees (Pan troglodytes). Am. J. Primatol. Off. J. Am. Soc. Primatol. 72(3), 262–269 (2010).
Google Scholar 
Norscia, I. & Palagi, E. When play is a family business: Adult play, hierarchy, and possible stress reduction in common marmosets. Primates 52, 101–104 (2010).PubMed 

Google Scholar 
Held, S. & Špinka, M. Animal play and animal welfare. Anim. Behav. 81, 891–899 (2011).
Google Scholar 
Davey, G. Visitor behavior in zoos: A review. Anthrozoös 19, 143–157 (2006).
Google Scholar 
Nimon, A. & Dalziel, F. Cross-species interaction and communication: a study method applied to captive siamang (Hylobates syndactylus) and long-billed corella (Cacatua tenuirostris) contacts with humans. Appl. Anim. Behav. Sci. 33, 261–272 (1992).
Google Scholar 
Suomi, S. Early determinants of behaviour: Evidence from primate studies. Br. Med. Bull. 53, 170–184 (1997).CAS 
PubMed 

Google Scholar 
Anderson, J. & Chamove, A. Self-aggression and social aggression in laboratory-reared macaques. J. Abnorm. Psychol. 89, 539–550 (1980).CAS 
PubMed 

Google Scholar 
Mallapur, A. & Choudhury, B. Behavioral abnormalities in captive nonhuman primates. J. Appl. Anim. Welfare Sci. 6, 275–284 (2003).CAS 

Google Scholar 
Barlow, C., Caldwell, C. & Lee, P. Individual differences and response to visitors in zoo-housed diana monkeys (Cercopithecus diana diana). Cabdirect.org (2022). at https://www.cabdirect.org/cabdirect/abstract/20123180753.Gartner, M. & Weiss, A. Studying primate personality in zoos: Implications for the management, welfare and conservation of great apes. International Zoo Yearbook 52, 79–91 (2018).
Google Scholar 
Mitchell, G., Raymond, E., Ruppenthal, G. & Harlow, H. Long-term effects of total social isolation upon behavior of rhesus monkeys. Psychol. Rep. 18, 567–580 (1966).
Google Scholar 
Martín, O., Vinyoles, D., García-Galea, E. & Maté, C. Improving the welfare of a zoo-housed male drill (Mandrillus leucophaeus poensis) aggressive toward visitors. J. Appl. Anim. Welfare Sci. 19, 323–334 (2016).
Google Scholar 
Ross, S., Melber, L., Gillespie, K. & Lukas, K. The impact of a modern, naturalistic exhibit design on visitor behavior: A cross-facility comparison. Visitor Stud. 15, 3–15 (2012).
Google Scholar 
Quadros, S., Goulart, V., Passos, L., Vecci, M. & Young, R. Zoo visitor effect on mammal behaviour: Does noise matter?. Appl. Anim. Behav. Sci. 156, 78–84 (2014).
Google Scholar 
Bonnie, K., Ang, M. & Ross, S. Effects of crowd size on exhibit use by and behavior of chimpanzees (Pan troglodytes) and Western lowland gorillas (Gorilla gorilla) at a zoo. Appl. Anim. Behav. Sci. 178, 102–110 (2016).
Google Scholar  More

Using a robust recent dataset, our analyses show that cocoa production is significantly affected by the maximum magnitude of ENSO phase during the current and previous purchase years (Fig. 2). The instantaneous effect is negative, followed by delayed positive effects in the two following years and negative in the third following year, combining to give a picture of multi-year fluctuations in cocoa production as a result of El Niño/La Niña events. Using a 70-year dataset, we show significant changes in these instantaneous and delayed ENSO-production relationships between recent and past time periods (Fig. 3). Using ERA5 data for the cocoa production area of Ghana, summarised at the same temporal resolution as the production data, we demonstrate significant relationships between ENSO phase and climate, with significant changes in mean climate and in ENSO-climate relationships (Fig. 4) between recent and past time periods. This agrees with prior work suggesting that ENSO may impact West Africa5,15, despite no current evidence of teleconnections between ENSO phase and West African climate17.Our 70-year production dataset represents a temporal extent unmatched by other research, however was aggregated to fewer replicates than the 21-year analysis (6 regions vs 68 districts). While this may represent reduced power, results from the overlapping time period of the two datasets strongly agree. The computation of yield, a more comparable metric between different-sized areas than total production, was not possible because data on area under production (AUP) were not available. However, the detrending process employed successfully eliminated variation between districts or regions (of which AUP is likely a substantial component) and long-term technological trends that would otherwise confound our ability to isolate the ENSO signal (Supplementary results).Perennial crops have multi-year growing patterns, with allocation of resources to growth, development and reproduction driven by climate in ways that are not fully understood29. ENSO generally peaks between October and December, also the busiest cocoa purchase period: thus we observe a relatively instantaneous apparent effect of ENSO phase on cocoa production. This reduction in cocoa production under El Niño inis consistent with results from farm monitoring8 and large-scale farm surveys30 evidencing production declines in from other regions (where teleconnections are understood), and with analyses of production data from West Africa31. During the main cocoa purchase period, coinciding with the minor wet and major dry seasons, we observe increases in water deficit during El Niño, leading to drought stress conditions. In small-scale cocoa studies, drought stress is correlated with reduction in pod production and increased tree mortality8,32, and in similar studies of other tree crops drought is directly linked to reduction in fruit or nut production33, although in all cases the mechanisms are unclear. Drought may generally create unfavourable conditions for growth and reproduction through reduced availability of water for vital processes, or more specifically by promoting disease incidence and pod rot8, increasing the chance of fire, increasing competition for soil moisture32, and/or reducing pollinator populations34. Alternatively, cocoa may respond to reduced water availability by reallocation of resources away from energetically expensive reproduction: rainfall exclusion experiments suggest that in the medium term, while bean production drops, vegetative growth is not significantly reduced during drought32.The significant increases in mean temperature and average drought stress we observed in some seasons over time is such that the climate experienced during El Niño events in recent decades represent novel extreme conditions for Ghana’s cocoa agriculture. This causes significant changes in the responses of cocoa production to ENSO phase over the same time period. One explanation for this may be that the warm, dry El Niño conditions in Ghana in the past were within the environmental tolerance of cocoa, leading to allocation of resources to reproduction in response to drought, increasing cocoa bean production and resulting in less severe instantaneous and delayed responses to ENSO phase (Fig. 3a–d) However, in recent decades this level or greater drought stress has become the norm (Fig. 4i–l), with El Niño conditions apparently triggering a different response mode, allocating resources away from reproduction in the short term and creating oscillating resource allocation over the following years.However, understanding the delayed responses of cocoa is challenging, especially as these represent a novel finding. There is little research that explores multi-annual physiological or ecological responses of cocoa to drought, and the explanation is likely to be a combination of both residual/delayed climatic responses to ENSO phase, and of life history strategies. The observed increase in production during the two years following El Niño may be explained by post-drought reallocation of resources to reproduction as remediation for lost reproductive output in the instantaneous response, or a shift to a ‘faster’ strategy by allocating resources to reproduction over the longer term, becoming evident in the data in subsequent years. Alternatively, this may be explained by favourable climatic conditions occurring during an El Niño event that impact the following years’ crop. March and April is a crucial time for cocoa pod development in Ghana and in recent years El Niño appears to bring greater rainfall during these months. Given the 6–9 months development of cocoa beans, the effects of this increased rainfall and reduced water deficit on cocoa production will be seen in the delayed response. We see evidence of this in the climate-change driven reversal of March–April rainfall patterns: while in the past El Niño has consistently resulted in drought stress, this reversal provides a respite from drought, buffering trees from reduced rainfall during the major wet season and giving sufficient resources for improved production in the following year.The robustness of our results provide evidence that may aid development of resilience strategies for ENSO-driven cocoa production variation in Ghana, but we may also consider whether these results can be generalised to the production of cocoa and/or perennial tree crops globally. The climatic impact of ENSO observed in Ghana is broadly consistent with many regions of the tropics2, the instantaneous cocoa production responses to El Niño are consistent with findings in these regions, and so we may expect these regions to see a similar pattern of multi-annual cocoa production variation in response to ENSO phase. However, there is considerable variation in ENSO responses among and within other perennial tree crops in regions where climatic responses to ENSO are similar to Ghana. Oil palm yields have been negatively associated with ENSO phase in Malaysia9, as have olive yields in Morocco (delayed by a year)33. Conversely, apple yields have been positively associated with ENSO phase in China10, as have coffee yields in Brazil35; however, no effect at all is seen in coffee in India over a 35-year time series7. Most of these analyses considered only a single ENSO phase (usually El Niño), and most considered only instantaneous impacts. However, it is clear that most of these crops do respond to ENSO, and given the shared biology it is reasonable to assume that delayed effects of ENSO phase are likely and should be considered to understand the full picture of ENSO impacts on perennial tree crops.The larger body of research into ENSO impacts on annual crops includes many studies using long time series, reporting high heterogeneity in space and among crops11,36,37. However, there appears to be little examination of changes in the direction and magnitude of ENSO responses over time; thus our findings are timely and signal that further research is needed to examine how changing climates may force novel extreme climatic conditions and shift response patterns to ENSO phase. Given that perennial tree crops are generally cash crops, and the utility of these crops to farmers are to a greater or lesser extent mediated by market forces, there is a need for improved forecasting of yield in response to changing climate and ENSO patterns to withstand production fluctuations. The low perishability of many perennial tree crops means that with accurate forecasting, supply may be managed or even exploited to ensure consistency of income both for farmers and those whose livelihoods depend on related food manufacturing industries.Our approach to understanding the responses of a perennial tree crop to ENSO phase and anthropogenic climate change exploited existing global, national and subnational datasets for climate and production with appropriate spatial and temporal resolution. We use freely available geographic and climate data, and employ highly replicable methods: a simple pipeline of climate data aggregation and summary computation, coupled with standard detrending and straightforward analytical methods with a relatively small computational requirement. This “big data” approach to agriculture-climate research demonstrates a relatively straightforward framework for understanding responses of agricultural productivity to climate and identifying temporal changes in these relationships. While small-scale studies examine the mechanisms of climate impacts through the interacting effects of agricultural practices, abiotic conditions, disease incidence and multi-trophic interactions, large-scale studies across regions and over time scales encompassing many ENSO oscillations are required to understand the global picture of perennial tree crop production security. Combined with local context-specific studies on governance arrangements16, such approaches could be crucial for reducing future vulnerability of these industries to increasing volatility under anthropogenic climate change. The main barrier to this research is the availability of production data from state or commercial entities. More