Ecology

1.Vogel, J. Recyling of carbon in a forest environment. Oecol. Plant. 13, 89–94 (1978).
Google Scholar 
2.Medina, E. & Minchin, P. Stratification of δ 13C values of leaves in Amazonian rain forests. Oecologia 45, 377–378 (1980).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Ehleringer, J. R., Field, C. B., Lin, Z. & Kuo, C. Leaf carbon isotope and mineral composition in subtropical plants along an irradiance cline. Oecologia 70, 520–526 (1986).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Medina, E., Sternberg, L. & Cuevas, E. Vertical stratification of δ13C values in closed natural and plantation forests in the Luquillo mountains, Puerto Rico. Oecologia 87, 369–372 (1991).ADS 
PubMed 
Article 

Google Scholar 
5.Graham, H. V. et al. Isotopic characteristics of canopies in simulated leaf assemblages. Geochim. Cosmochim. Acta 144, 82–95 (2014).ADS 
CAS 
Article 

Google Scholar 
6.Buchmann, N., Kao, W.-Y. & Ehleringer, J. Influence of stand structure on carbon-13 of vegetation, soils, and canopy air within deciduous and evergreen forests in Utah, United States. Oecologia 110, 109–119 (1997).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Sternberg, L. D. S. L., Mulkey, S. S. & Wright, S. J. Oxygen isotope ratio stratification in a tropical moist forest. Oecologia 81, 51–56 (1989).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Ometto, J. P. H. B. et al. The stable carbon and nitrogen isotopic composition of vegetation in tropical forests of the Amazon Basin, Brazil. Biogeochemistry 79, 251–274 (2006).CAS 
Article 

Google Scholar 
9.van der Merwe, N. J. & Medina, E. The canopy effect, carbon isotope ratios and foodwebs in Amazonia. J. Archaeol. Sci. 18, 249–259 (1991).Article 

Google Scholar 
10.Houle, A. & Wrangham, R. W. Contest competition for fruit and space among wild chimpanzees in relation to the vertical stratification of metabolizable energy. Anim. Behav. 175, 231–246 (2021).Article 

Google Scholar 
11.Roberts, P., Blumenthal, S. A., Dittus, W., Wedage, O. & Lee-Thorp, J. A. Stable carbon, oxygen, and nitrogen, isotope analysis of plants from a South Asian tropical forest: Implications for primatology. Am. J. Primatol. 79, e22656 (2017).Article 
CAS 

Google Scholar 
12.Barbour, M. M. Stable oxygen isotope composition of plant tissue: A review. Funct. Plant Biol. 34, 83–94 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Cernusak, L. A. et al. Stable isotopes in leaf water of terrestrial plants. Plant Cell Environ. 39, 1087–1102 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Ometto, J. P. H., Flanagan, L. B., Martinelli, L. A. & Ehleringer, J. R. Oxygen isotope ratios of waters and respired CO2 in Amazonian forest and pasture ecosystems. Ecol. Appl. 15, 58–70 (2005).Article 

Google Scholar 
15.Yakir, D. Variations in the natural abundance of oxygen-18 and deuterium in plant carbohydrates. Plant Cell Environ. 15, 1005–1020 (1992).CAS 
Article 

Google Scholar 
16.Wania, R., Hietz, P. & Wanek, W. Natural 15N abundance of epiphytes depends on the position within the forest canopy: Source signals and isotope fractionation. Plant Cell Environ. 25, 581–589 (2002).CAS 
Article 

Google Scholar 
17.Blumenthal, S. A., Rothman, J. M., Chritz, K. L. & Cerling, T. E. Stable isotopic variation in tropical forest plants for applications in primatology. Am. J. Primatol. 78, 1041–1054 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Schleser, G. H. & Jayasekera, R. 13C-variations of leaves in forests as an indication of reassimilated CO2 from the soil. Oecologia 65, 536–542 (1985).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.van der Merwe, N. J. & Medina, E. Photosynthesis and 13C12C ratios in Amazonian rain forests. Geochim. Cosmochim. Acta 53, 1091–1094 (1989).ADS 
Article 

Google Scholar 
20.Chazdon, R. L. & Pearcy, R. W. The importance of sunflecks for forest understory plants. Bioscience 41, 760–766 (1991).Article 

Google Scholar 
21.Lambers, H., Chapin, F. S. & Pons, T. L. Plant Physiological Ecology (Springer New York, 2008) https://doi.org/10.1007/978-0-387-78341-3.Book 

Google Scholar 
22.Hellkvist, J., Richards, G. P. & Jarvis, P. G. Vertical gradients of water potential and tissue water relations in sitka spruce trees measured with the pressure chamber. J. Appl. Ecol. 11, 637–667 (1974).Article 

Google Scholar 
23.Ambrose, A. R., Sillett, S. C. & Dawson, T. E. Effects of tree height on branch hydraulics, leaf structure and gas exchange in California redwoods. Plant Cell Environ. 32, 743–757 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Peltoniemi, M. S., Duursma, R. A. & Medlyn, B. E. Co-optimal distribution of leaf nitrogen and hydraulic conductance in plant canopies. Tree Physiol. 32, 510–519 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Araguás-Araguás, L., Froehlich, K. & Rozanski, K. Deuterium and oxygen-18 isotope composition of precipitation and atmospheric moisture. Hydrol. Process. 14, 1341–1355 (2000).ADS 
Article 

Google Scholar 
26.Gonfiantini, R., Roche, M.-A., Olivry, J.-C., Fontes, J.-C. & Zuppi, G. M. The altitude effect on the isotopic composition of tropical rains. Chem. Geol. 181, 147–167 (2001).ADS 
CAS 
Article 

Google Scholar 
27.Craine, J. M. et al. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytol. 183, 980–992 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Guenni, O., Romero, E., Guédez, Y., Bravo de Guenni, L. & Pittermann, J. Influence of low light intensity on growth and biomass allocation, leaf photosynthesis and canopy radiation interception and use in two forage species of Centrosema (DC.) Benth. Grass Forage Sci. 73, 967–978 (2018).CAS 
Article 

Google Scholar 
29.Ryan, M. G. & Yoder, B. J. Hydraulic limits to tree height and tree growth. Bioscience 47, 235–242 (1997).Article 

Google Scholar 
30.Dunham, N. T. & Lambert, A. L. The role of leaf toughness on foraging efficiency in Angola black and white colobus monkeys (Colobus angolensis palliatus). Am. J. Phys. Anthropol. 161, 343–354 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Poorter, L., van de Plassche, M., Willems, S. & Boot, R. G. A. Leaf traits and herbivory rates of tropical tree species differing in successional status. Plant Biol. 6, 746–754 (2004).CAS 
PubMed 
Article 

Google Scholar 
32.Sponheimer, M. et al. Using carbon isotopes to track dietary change in modern, historical, and ancient primates. Am. J. Phys. Anthropol. 140, 661–670 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Nelson, S. V. Chimpanzee fauna isotopes provide new interpretations of fossil ape and hominin ecologies. Proc. R. Soc. B 280, 20132324 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
34.Krigbaum, J., Berger, M. H., Daegling, D. J. & McGraw, W. S. Stable isotope canopy effects for sympatric monkeys at Taï Forest, Côte d’Ivoire. Biol. Lett. 9, 20130466 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Oelze, V. M., Head, J. S., Robbins, M. M., Richards, M. & Boesch, C. Niche differentiation and dietary seasonality among sympatric gorillas and chimpanzees in Loango National Park (Gabon) revealed by stable isotope analysis. J. Hum. Evol. 66, 95–106 (2014).PubMed 
Article 

Google Scholar 
36.McGraw, W. S. Positional behavior of Cercopithecus petaurista. Int. J. Primatol. 21, 157–182 (2000).Article 

Google Scholar 
37.McGraw, W. S. Comparative locomotion and habitat use of six monkeys in the Tai Forest, Ivory Coast. Am. J. Primatol. 105, 493–510 (1998).CAS 

Google Scholar 
38.Carter, M. L. & Bradbury, M. W. Oxygen isotope ratios in primate bone carbonate reflect amount of leaves and vertical stratification in the diet. Am. J. Primatol. 78, 1086–1097 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Bryant, J. D. & Froelich, P. N. A model of oxygen isotope fractionation in body water of large mammals. Geochim. Cosmochim. Acta 59, 4523–4537 (1995).ADS 
CAS 
Article 

Google Scholar 
40.Sharma, N. et al. Watering holes: The use of arboreal sources of drinking water by Old World monkeys and apes. Behav. Proc. 129, 18–26 (2016).Article 

Google Scholar 
41.Wittig, R. M. Taï chimpanzees. In Encyclopedia of Animal Cognition and Behavior (eds Vonk, J. & Shackelford, T.) 1–7 (Springer International Publishing, 2017) https://doi.org/10.1007/978-3-319-47829-6_1564-1.Chapter 

Google Scholar 
42.Nelson, S. V. & Rook, L. Isotopic reconstructions of habitat change surrounding the extinction of Oreopithecus, the last European ape. Am. J. Phys. Anthropol. 160, 254–271 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Ryan, M. G., Phillips, N. & Bond, B. J. The hydraulic limitation hypothesis revisited. Plant Cell Environ. 29, 367–381 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Bachofen, C., D’Odorico, P. & Buchmann, N. Light and VPD gradients drive foliar nitrogen partitioning and photosynthesis in the canopy of European beech and silver fir. Oecologia 192, 323–339 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Chazdon, R. L., Williams, K. & Field, C. B. Interactions between crown structure and light environment in five rain forest piper species. Am. J. Bot. 75, 1459–1471 (1988).Article 

Google Scholar 
46.Ambrose, A. R. et al. Hydraulic constraints modify optimal photosynthetic profiles in giant sequoia trees. Oecologia 182, 713–730 (2016).ADS 
PubMed 
Article 

Google Scholar 
47.Voigt, C. C. Insights into strata use of forest animals using the ‘canopy effect’. Biotropica 42, 634–637 (2010).Article 

Google Scholar 
48.Ometto, J. P. H. B. et al. Carbon isotope discrimination in forest and pasture ecosystems of the Amazon Basin. Brazil. Glob. Biogeochem. Cycles 16, 56-1-56–10 (2002).
Google Scholar 
49.Loudon, J. E. et al. Stable isotope data from bonobo (Pan paniscus) faecal samples from the Lomako Forest Reserve, Democratic Republic of the Congo. Afr. J. Ecol. 57, 437–442 (2019).Article 

Google Scholar 
50.Medina, E., Klinge, H., Jordan, C. & Herrera, R. Soil respiration in Amazonian rain forests in the Rio Negro Basin. Flora 170, 240–250 (1980).Article 

Google Scholar 
51.Craine, J. M. et al. Ecological interpretations of nitrogen isotope ratios of terrestrial plants and soils. Plant Soil 396, 1–26 (2015).CAS 
Article 

Google Scholar 
52.Niinemets, Ü. & Tenhunen, J. D. A model separating leaf structural and physiological effects on carbon gain along light gradients for the shade-tolerant species Acer saccharum. Plant Cell Environ. 20, 845–866 (1997).Article 

Google Scholar 
53.Schoener, T. W. Theory of feeding strategies. Annu. Rev. Ecol. Syst. 2, 369–404 (1971).Article 

Google Scholar 
54.Onoda, Y., Schieving, F. & Anten, N. P. R. Effects of light and nutrient availability on leaf mechanical properties of plantago major: A conceptual approach. Ann. Bot. 101, 727–736 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Dasilva, G. L. Diet of Colobus polykomos on Tiwai Island: Selection of food in relation to its seasonal abundance and nutritional quality. Int. J. Primatol. 15, 655–680 (1994).Article 

Google Scholar 
56.Rothman, J. M., Chapman, C. A. & Pell, A. N. Fiber-bound nitrogen in gorilla diets: Implications for estimating dietary protein intake of primates. Am. J. Primatol. 70, 690–694 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Ganzhorn, J. U. et al. The importance of protein in leaf selection of folivorous primates. Am. J. Primatol. 79, e22550 (2017).Article 
CAS 

Google Scholar 
58.Tejada, J. V. et al. Comparative isotope ecology of western Amazonian rainforest mammals. Proc. Natl. Acad. Sci. USA 117, 26263–26272 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Cernusak, L. A. et al. Why are non-photosynthetic tissues generally 13C enriched compared with leaves in C3 plants? Review and synthesis of current hypotheses. Funct. Plant Biol. 36, 199–213 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Fannin, L. D. & McGraw, W. S. Does oxygen stable isotope composition in primates vary as a function of vertical stratification or folivorous behaviour?. Folia Primatol. 91, 219–227 (2020).Article 

Google Scholar 
61.Crowley, B. E., Melin, A. D., Yeakel, J. D. & Dominy, N. J. Do oxygen isotope values in collagen reflect the ecology and physiology of neotropical mammals?. Front. Ecol. Evol. 3, 127 (2015).Article 

Google Scholar 
62.DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45, 341–351 (1981).ADS 
CAS 
Article 

Google Scholar 
63.Lemoine, R. et al. Source-to-sink transport of sugar and regulation by environmental factors. Front. Plant Sci. 4, 272 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Anderson, D. L., Koomjian, W., French, B., Altenhoff, S. R. & Luce, J. Review of rope-based access methods for the forest canopy: Safe and unsafe practices in published information sources and a summary of current methods. Methods Ecol. Evol. 6, 865–872 (2015).Article 

Google Scholar  More

1.Duffy, L. M. et al. Global trophic ecology of yellowfin, bigeye, and albacore tunas: Understanding predation on micronekton communities at ocean-basin scales. Deep Sea Res. Part II Top. Stud. Oceanogr. 140, 55–73 (2017).ADS 
Article 

Google Scholar 
2.Mariani, P., Andersen, K. H., Lindegren, M. & MacKenzie, B. Trophic impact of Atlantic bluefin tuna migrations in the North Sea. ICES J. Mar. Sci. 74, 1552–1560 (2017).Article 

Google Scholar 
3.Block, B. A. et al. Tracking apex marine predator movements in a dynamic ocean. Nature 475, 86–90 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Arrizabalaga, H. et al. Chapter 3. Life history and migrations of Mediterranean bluefin tuna. In The Future Of Bluefin Tuna: Ecology, Fisheries Management, and Conservation (ed. Block, B. A.) 67–93 (Johns Hopkins University Press, 2019).
Google Scholar 
5.Rooker, J. R. et al. Population connectivity of pelagic megafauna in the Cuba–Mexico–United States triangle. Sci. Rep. 9, 1663 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Sun, J., Hinton, M. G. & Webster, D. G. Modeling the spatial dynamics of international tuna fleets. PLoS ONE 11, e0159626 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
7.Collette, B. B. et al. Conservation: High value and long life-double jeopardy for tunas and billfishes. Science 333, 291–292 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Kerr, L. A., Cadrin, S. X., Secor, D. H. & Taylor, N. G. Modeling the implications of stock mixing and life history uncertainty of Atlantic bluefin tuna. Can. J. Fish. Aquat. Sci. 74, 1990–2004 (2017).Article 

Google Scholar 
9.Fromentin, J. M. & Lopuszanski, D. Migration, residency, and homing of bluefin tuna in the western Mediterranean Sea. ICES J. Mar. Sci. 71, 510–518 (2014).Article 

Google Scholar 
10.Lam, C. H., Galuardi, B. & Lutcavage, M. E. Movements and oceanographic associations of bigeye tuna (Thunnus obesus) in the Northwest Atlantic. Can. J. Fish. Aquat. Sci. 71, 1529–1543 (2014).Article 

Google Scholar 
11.Rooker, J. R. et al. Wide-ranging temporal variation in transoceanic movement and population mixing of bluefin tuna in the North Atlantic Ocean. Front. Mar. Sci. 6, 398 (2019).Article 

Google Scholar 
12.Bayliff, W. H. A review of the biology and fisheries for northern bluefin tuna, Thunnus thynnus, in the Pacific Ocean. FAO Fish. Tech. Pap. 336, 244–295 (1994).
Google Scholar 
13.Collette, B. & Graves, J. Tunas and Billfishes of the World (Johns Hopkins University Press, 2019).
Google Scholar 
14.Madigan, D. J., Baumann, Z. & Fisher, N. S. Pacific bluefin tuna transport Fukushima-derived radionuclides from Japan to California. Proc. Natl. Acad. Sci. U. S. A. 109, 9483–9486 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Fujioka, K. et al. Spatial and temporal variability in the trans-Pacific migration of Pacific bluefin tuna (Thunnus orientalis) revealed by archival tags. Prog. Oceanogr. 162, 52–65 (2018).ADS 
Article 

Google Scholar 
16.Fujioka, K., Masujima, M., Boustany, A. M. & Kitagawa, T. Horizontal movements of Pacific bluefin tuna. In Biology and Ecology of Bluefin Tuna (eds Kitagawa, T. & Kimura, S.) 101–122 (CRC Press, 2015).
Google Scholar 
17.Fujioka, K. et al. Habitat use and movement patterns of small (age-0) juvenile Pacific bluefin tuna (Thunnus orientalis) relative to the Kuroshio. Fish. Oceanogr. 27, 185–198 (2018).Article 

Google Scholar 
18.Kitagawa, T., Kimura, S., Nakata, H. & Yamada, H. Diving behavior of immature, feeding Pacific bluefin tuna (Thunnus thynnus orientalis) in relation to season and area: The East China Sea and the Kuroshio–Oyashio transition region. Fish. Oceanogr. 13, 161–180 (2004).Article 

Google Scholar 
19.Rooker, J. R. et al. Natal homing and connectivity in Atlantic bluefin tuna populations. Science 322, 742–744 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Wells, R. J. D., Rooker, J. R. & Itano, D. G. Nursery origin of yellowfin tuna in the Hawaiian Islands. Mar. Ecol. Prog. Ser. 461, 187–196 (2012).ADS 
CAS 
Article 

Google Scholar 
21.Wells, R. J. D. et al. Natal origin of Pacific bluefin tuna from the California current large marine ecosystem. Biol. Lett. 16, 20190878 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Baumann, H. et al. Combining otolith microstructure and trace elemental analyses to infer the arrival of juvenile Pacific bluefin tuna in the California current ecosystem. ICES J. Mar. Sci. 72, 2128–2138 (2015).Article 

Google Scholar 
23.Rooker, J. R. & Secor, D. H. Otolith microchemistry: Migration and ecology of Atlantic bluefin tuna. In The Future of Bluefin Tuna: Ecology, Fisheries Management, and Conservation (ed. Block, B. A.) 45–66 (Johns Hopkins University Press, 2019).
Google Scholar 
24.Kitchens, L. L. et al. Discriminating among yellowfin tuna Thunnus albacares nursery areas in the Atlantic Ocean using otolith chemistry. Mar. Ecol. Prog. Ser. 603, 201–213 (2018).ADS 
CAS 
Article 

Google Scholar 
25.Reeves, J., Chen, J., Wang, X. L., Lund, R. & Lu, Q. A review and comparison of changepoint detection techniques for climate data. J. Appl. Meteorol. Climatol. 46, 900–915 (2007).ADS 
Article 

Google Scholar 
26.Killick, R. & Eckley, I. A. Changepoint: An R package for changepoint analysis. J. Stat. Softw. 58, 1–19 (2014).Article 

Google Scholar 
27.Liu, H., Gilmartin, J., Li, C. & Li, K. Detection of time-varying pulsed event effects on estuarine pelagic communities with ecological indicators after catastrophic hurricanes. Ecol. Indic. 123, 107327 (2021).Article 

Google Scholar 
28.Millar, R. B. Comparison of methods for estimating mixed stock fishery composition. Can. J. Fish. Aquat. Sci. 47, 2235–2241 (1990).Article 

Google Scholar 
29.Rooker, J. R., Secor, D. H., Zdanowicz, V. S. & Itoh, T. Discrimination of northern bluefin tuna from nursery areas in the Pacific Ocean using otolith chemistry. Mar. Ecol. Prog. Ser. 218, 275–282 (2001).ADS 
CAS 
Article 

Google Scholar 
30.Wells, R. J. D. et al. Natural tracers reveal population structure of albacore (Thunnus alalunga) in the eastern North Pacific Ocean. ICES J. Mar. Sci. 72, 2118–2127 (2015).Article 

Google Scholar 
31.Elsdon, T. S. et al. Otolith chemistry to describe movements and life history parameters of fishes: Hypotheses, assumptions, limitations and inferences. Oceanogr. Mar. Biol. Annu. Rev. 46, 297–330 (2008).
Google Scholar 
32.Secor, D. H. Migration Ecology of Marine Fishes (Johns Hopkins University Press, 2015).
Google Scholar 
33.Chen, C. T. A., Ruo, R., Pai, S. C., Liu, C. T. & Wong, G. T. F. Exchange of water masses between East China Sea and the Kuroshio off northeastern Taiwan. Cont. Shelf Res. 15, 19–39 (1995).ADS 
Article 

Google Scholar 
34.Sasaki, Y. N., Minobe, S., Asai, T. & Inatsu, M. Influence of the Kuroshio in the East China Sea on the early summer (Baiu) rain. J. Climate 25, 6627–6645 (2012).ADS 
Article 

Google Scholar 
35.Sturrock, A. M., Trueman, C. N., Darnaude, A. M. & Hunter, E. Can otololith elemental chemistry retrospectively track migrations in marine fishes. J. Fish. Biol. 81, 766–795 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Lebrato, M. et al. Global variability in seawater Mg:Ca and Sr:Ca ratios in the modern ocean. Proc. Nat. Acad. Sci. 117, 22281–22292 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Rooker, J. R., Wells, R. J. D., Itano, D. G., Thorrold, S. R. & Lee, J. M. Natal origin and population connectivity of bigeye and yellowfin tuna in the Pacific Ocean. Fish. Oceanogr. 25, 277–291 (2016).Article 

Google Scholar 
38.Liao, W. H. & Ho, T. Y. Particulate trace metal composition and sources in the Kuroshio adjacent to the East China Sea: The importance of aerosol deposition. J. Geophys. Res. Oceans 123, 6207–6223 (2018).ADS 
CAS 
Article 

Google Scholar 
39.Campana, S. E. Chemistry and composition of fish otoliths: Pathways, mechanisms and applications. Mar. Ecol. Prog. Ser. 188, 263–297 (1999).ADS 
CAS 
Article 

Google Scholar 
40.Elsdon, T. S. & Gillanders, B. M. Relationship between water and otolith elemental concentrations in juvenile black bream Acanthopagrus butcheri. Mar. Ecol. Prog. Ser. 260, 263–272 (2003).ADS 
CAS 
Article 

Google Scholar 
41.Elsdon, T. S. & Gillanders, B. M. Interactive effects of temperature and salinity on otolith chemistry: Challenges for determining environmental histories of fish. Can. J. Fish. Aquat. Sci. 59, 1796–1808 (2002).CAS 
Article 

Google Scholar 
42.Stanley, R. R. E. et al. Environmentally mediated trends in otolith composition of juvenile Atlantic cod (Gadus morhua). ICES J. Mar. Sci. 72, 2350–2363 (2015).Article 

Google Scholar 
43.Macdonald, J. I. & Crook, D. A. Variability in Sr:Ca and Ba:Ca ratios in water and fish otoliths across an estuarine salinity gradient. Mar. Ecol. Prog. Ser. 413, 147–161 (2010).ADS 
CAS 
Article 

Google Scholar 
44.Reis-Santos, P., Tanner, S. E., Elsdon, T. S., Cabral, H. N. & Gillanders, B. M. Effects of temperature, salinity and water composition on otolith elemental incorporation of Dicentrarchus labrax. J. Exp. Mar. Biol. Ecol. 446, 245–252 (2013).CAS 
Article 

Google Scholar 
45.Rooker, J. R., Kraus, R. T. & Secor, D. H. Dispersive behaviors of black drum and red drum: Is otolith Sr:Ca a reliable indicator of salinity history?. Estuaries 27, 334–441 (2004).Article 

Google Scholar 
46.Hüssy, K. et al. Trace element patterns in otoliths: The role of biomineralization. Rev. Fish. Sci. Aquacult. https://doi.org/10.1080/23308249.2020.1760204 (2020).Article 

Google Scholar 
47.Thorrold, S. R., Jones, C. M. & Campana, S. E. Response of otolith microchemistry to environmental variations experienced by larval and juvenile Atlantic croaker (Micropogonias undulatus). Limnol. Oceanogr. 42, 102–111 (1997).ADS 
CAS 
Article 

Google Scholar 
48.Secor, D. H. & Rooker, J. R. Is otolith strontium a useful scalar of life-cycles in estuarine fishes?. Fish. Res. 1032, 1–14 (2000).
Google Scholar 
49.Izzo, C., Reis-Santos, P. & Gillanders, B. M. Otolith chemistry does not just reflect environmental conditions: A meta-analytic evaluation. Fish Fish. 19, 441–454 (2018).Article 

Google Scholar 
50.Sturrock, A. M. et al. Quantifying physiological influences on otolith chemistry. Methods Ecol. Evol. 6, 806–816 (2015).Article 

Google Scholar 
51.Bath, G. E. et al. Strontium and barium uptake in aragonitic otoliths of marine fish. Geochim. Cosmochim. Acta 64, 1705–1714 (2000).ADS 
CAS 
Article 

Google Scholar 
52.Arai, T., Kotake, A., Kayama, S., Ogura, M. & Watanabe, Y. Movements and life history patterns of the skipjack tuna Katsuwonus pelamis in the western Pacific, as revealed by otolith Sr:Ca ratios. J. Mar. Biol. Assoc. U. K. 85, 1211–1271 (2005).Article 

Google Scholar 
53.Shiozaki, T., Kondo, Y., Yuasa, D. & Takeda, S. Distribution of major diazotrophs in the surface water of the Kuroshio from northeastern Taiwan to south of mainland Japan. J. Plankton Res. 40, 407–419 (2018).CAS 
Article 

Google Scholar 
54.Nakata, K., Hada, A. & Masukawa, Y. Variation in food abundance for Japanese sardine larvae related to Kuroshio meander. Fish. Oceanogr. 3, 39–49 (1994).Article 

Google Scholar 
55.Kitagawa, T. et al. Horizontal and vertical movements of juvenile bluefin tuna (Thunnus orientalis) in relation to seasons and oceanographic conditions in the eastern Pacific Ocean. Fish. Oceanogr. 16, 409–421 (2007).Article 

Google Scholar 
56.Ichinokawa, M., Okamura, H., Oshima, K., Yokawa, K. & Takeuchi, Y. Spatiotemporal catch distribution of age-0 Pacific bluefin tuna Thunnus orientalis caught by the Japanese troll fishery in relation to surface sea temperature and seasonal migration. Fish. Sci. 80, 1181–1191 (2014).CAS 
Article 

Google Scholar 
57.Shimose, T., Tanabe, T., Chen, K. S. & Hsu, C. C. Age determination and growth of Pacific bluefin tuna, Thunnus orientalis, off Japan and Taiwan. Fish. Res. 100, 134–139 (2009).Article 

Google Scholar 
58.Chiba, S. et al. Large-scale climate control of zooplankton transport and biogeography in the Kuroshio–Oyashio extension region. Geophys. Res. Lett. 40, 5182–5187 (2013).ADS 
Article 

Google Scholar 
59.Hiraoka, Y., Fujioka, K., Fukuda, H., Watai, M. & Ohshimo, S. Interannual variation of the diet shifts and their effects on the fatness and growth of age-0 Pacific bluefin tuna (Thunnus orientalis) off the southwestern Pacific coast of Japan. Fish. Oceanogr. 28, 419–433 (2019).Article 

Google Scholar 
60.Inagake, D. et al. Migration of young bluefin tuna, Thunnus orientalis Temminck et Schlegel, through archival tagging experiments and its relation with oceanographic conditions in the western north Pacific. Bull. Natl Res. Inst. Far Seas Fish. 38, 53–81 (2001).
Google Scholar 
61.Mohan, J. A. et al. Elements of time and place: Manganese and barium in shark vertebrae reflect age and upwelling histories. Proc. R. Soc. B Biol. Sci. 285, 20181760 (2018).Article 

Google Scholar 
62.Hsieh, Y. T. & Henderson, G. M. Barium stable isotopes in the global ocean: Tracer of Ba inputs and utilization. Earth Planet. Sci. Lett. 473, 269–278 (2017).ADS 
CAS 
Article 

Google Scholar 
63.Kimura, S. et al. Biological productivity of meso-scale eddies caused by front disturbances in the Kuroshio. ICES J. Mar. Sci. 54, 179–192 (1997).Article 

Google Scholar 
64.Tanaka, Y. et al. Occurrence of Pacific bluefin tuna (Thunnus orientalis) larvae off the Pacific coast of Tohoku area, northeastern Japan: Possibility of the discovery of the third spawning ground. Fish. Oceanogr. 29, 46–51 (2019).Article 

Google Scholar 
65.Shiao, J. C. et al. Contribution rates of different spawning and feeding grounds to adult Pacific bluefin tuna (Thunnus orientalis) in the northwestern Pacific Ocean. Deep Sea Res. Part I Oceanogr. Res. Pap. https://doi.org/10.1016/j.dsr.2020.103453 (2020).Article 

Google Scholar 
66.Uematsu, Y., Ishihara, T., Hiraoka, Y., Shimose, T. & Ohshimo, S. Natal origin identification of Pacific bluefin tuna (Thunnus orientalis) by vertebral first annulus. Fish. Res. 199, 26–31 (2018).Article 

Google Scholar 
67.Kitagawa, T., Fujioka, K. & Suzuki, N. Migrations of Pacific bluefin tuna in the western Pacific Ocean. In The Future of Bluefin Tuna: Ecology, Fisheries Management, and Conservation (ed. Block, B. A.) 147–164 (Johns Hopkins University Press, 2019).
Google Scholar  More

1.Rasher, D. B., Hoey, A. S. & Hay, M. E. Cascading predator effects in a Fijian coral reef ecosystem. Sci. Rep. 7, 1–10 (2017).CAS 
Article 

Google Scholar 
2.Roff, G. et al. The ecological role of sharks on coral reefs. Trends Ecol. Evol. 31, 395–407 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Ruppert, J. L. W., Travers, M. J., Smith, L. L., Fortin, M.-J. & Meekan, M. G. Caught in the middle: Combined impacts of shark removal and coral loss on the fish communities of coral reefs. PLoS ONE 8, e74648 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, e00590 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
5.Field, I. C., Meekan, M. G., Buckworth, R. C. & Bradshaw, C. J. A. Chapter 4 susceptibility of sharks, rays and chimaeras to global extinction. In Advances in Marine Biology vol. 56 275–363 (Elsevier, 2009).6.MacNeil, M. A. et al. Global status and conservation potential of reef sharks. Nature 583, 801–806 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Ward-Paige, C. A. et al. Large-scale absence of sharks on reefs in the Greater-Caribbean: A footprint of human pressures. PLoS ONE 5(8), e11968 (2010).8.Robbins, W. D., Hisano, M., Connolly, S. R. & Choat, J. H. Ongoing collapse of coral-reef shark populations. Curr. Biol. 16, 2314–2319 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Juhel, J.-B. et al. Reef accessibility impairs the protection of sharks. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13007 (2017).Article 

Google Scholar 
10.Nadon, M. O. et al. Re-creating missing population baselines for pacific reef sharks. Conserv. Biol. 26, 493–503 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Ferretti, F., Curnick, D., Liu, K., Romanov, E. V. & Block, B. A. Shark baselines and the conservation role of remote coral reef ecosystems. Sci. Adv. 4, eaaq0333 (2018).12.Ferretti, F., Worm, B., Britten, G. L., Heithaus, M. R. & Lotze, H. K. Patterns and ecosystem consequences of shark declines in the ocean: Ecosystem consequences of shark declines. Ecol. Lett. 13, 1055–1071 (2010).PubMed 
PubMed Central 

Google Scholar 
13.Cinner, J. E. et al. Gravity of human impacts mediates coral reef conservation gains. Proc. Natl. Acad. Sci. 115, E6116–E6125 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Davidson, L. N. K. & Dulvy, N. K. Global marine protected areas to prevent extinctions. Nat. Ecol. Evol. 1, 0040 (2017).Article 

Google Scholar 
15.O’Leary, B. C. et al. Effective coverage targets for ocean protection: Effective targets for ocean protection. Conserv. Lett. 9, 398–404 (2016).Article 

Google Scholar 
16.Sala, E. et al. Assessing real progress towards effective ocean protection. Mar. Policy 91, 11–13 (2018).Article 

Google Scholar 
17.D’agata, S. et al. Marine reserves lag behind wilderness in the conservation of key functional roles. Nat. Commun. 7, 12000 (2016).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.MacKeracher, T., Diedrich, A. & Simpfendorfer, C. A. Sharks, rays and marine protected areas: A critical evaluation of current perspectives. Fish Fish. 20, 255–267 (2019).Article 

Google Scholar 
19.Juhel, J.-B. et al. Isolation and no-entry marine reserves mitigate anthropogenic impacts on grey reef shark behavior. Sci. Rep. 9, 2897 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Robbins, W. D. Abundance, demography and population structure of the grey reef shark (Carcharhinus amblyrhynchos) and the white tip reef shark (Triaenodon obesus) (Fam. Charcharhinidae). (James Cook University, 2006).21.Kellner, J. B., Tetreault, I., Gaines, S. D. & Nisbet, R. M. Fishing the line near marine reserves in single and multispecies fisheries. Ecol. Appl. 17, 1039–1054 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Nillos Kleiven, P. J. et al. Fishing pressure impacts the abundance gradient of European lobsters across the borders of a newly established marine protected area. Proc. R. Soc. B Biol. Sci. 286, 20182455 (2019).Article 

Google Scholar 
23.Gerber, L. R. et al. Population models for marine reserve design: A retrospective and prospective synthesis. Ecol. Appl. 13, 47–64 (2003).Article 

Google Scholar 
24.Grüss, A., Kaplan, D. M., Guénette, S., Roberts, C. M. & Botsford, L. W. Consequences of adult and juvenile movement for marine protected areas. Biol. Conserv. 144, 692–702 (2011).Article 

Google Scholar 
25.Edgar, G. J. et al. Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216–220 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Abecasis, D., Afonso, P. & Erzini, K. Combining multispecies home range and distribution models aids assessment of MPA effectiveness. Mar. Ecol. Prog. Ser. 513, 155–169 (2014).ADS 
Article 

Google Scholar 
27.Di Franco, A. et al. Linking home ranges to protected area size: The case study of the Mediterranean Sea. Biol. Conserv. 221, 175–181 (2018).Article 

Google Scholar 
28.Krueck, N. C. et al. Reserve sizes needed to protect coral reef fishes: reserve sizes to protect coral reef fishes. Conserv. Lett. 11, e12415 (2018).29.Pittman, S. J. et al. Fish with chips: Tracking reef fish movements to evaluate size and connectivity of Caribbean marine protected areas. PLoS ONE 9, e96028 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Weeks, R., Green, A. L., Joseph, E., Peterson, N. & Terk, E. Using reef fish movement to inform marine reserve design. J. Appl. Ecol. 54, 145–152 (2017).Article 

Google Scholar 
31.Dwyer, R. G. et al. Individual and population benefits of marine reserves for reef sharks. Curr. Biol. 30, 117–118 (2020).32.Friedlander, A., Sandin, S., DeMartini, E. & Sala, E. Spatial patterns of the structure of reef fish assemblages at a pristine atoll in the central Pacific. Mar. Ecol. Prog. Ser. 410, 219–231 (2010).ADS 
Article 

Google Scholar 
33.Clarke, C., Lea, J. & Ormond, R. Comparative abundance of reef sharks in the Western Indian Ocean. In Proceedings of the 12th International Coral Reef Symposium, Cairns, Australia, 9-13 July 2012 (2012).34.Bonnin, L. et al. Repeated long-range migrations of adult males in a common Indo-Pacific reef shark. Coral Reefs https://doi.org/10.1007/s00338-019-01858-w (2019).Article 

Google Scholar 
35.Speed, C. W. et al. Reef shark movements relative to a coastal marine protected area. Reg. Stud. Mar. Sci. 3, 58–66 (2016).Article 

Google Scholar 
36.Udyawer, V. et al. A standardised framework for analysing animal detections from automated tracking arrays. Anim. Biotelem. 6, 17 (2018).Article 

Google Scholar 
37.Brodie, S. et al. Continental-scale animal tracking reveals functional movement classes across marine taxa. Sci. Rep. 8, 3717 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Espinoza, M., Heupel, M. R., Tobin, A. J. & Simpfendorfer, C. A. Residency patterns and movements of grey reef sharks (Carcharhinus amblyrhynchos) in semi-isolated coral reef habitats. Mar. Biol. 162, 343–358 (2015).CAS 
Article 

Google Scholar 
39.Vianna, G. M. S., Meekan, M. G., Meeuwig, J. J. & Speed, C. W. Environmental influences on patterns of vertical movement and site fidelity of grey reef sharks (Carcharhinus amblyrhynchos) at aggregation sites. PLoS ONE 8, e60331 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Barnett, A., Abrantes, K. G., Seymour, J. & Fitzpatrick, R. Residency and spatial use by reef sharks of an isolated seamount and its implications for conservation. PLoS ONE 7, e36574 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Field, I. C., Meekan, M. G., Speed, C. W., White, W. & Bradshaw, C. J. A. Quantifying movement patterns for shark conservation at remote coral atolls in the Indian Ocean. Coral Reefs 30, 61–71 (2010).ADS 
Article 

Google Scholar 
42.Heupel, M. R. & Simpfendorfer, C. A. Long-term movement patterns of a coral reef predator. Coral Reefs 34, 679–691 (2015).ADS 
Article 

Google Scholar 
43.Andréfouët, S., Torres-Pulliza, D., Dosdane, M., Kranenburg, C. & Murch, B. Atlas des récifs coralliens de Nouvelle-Calédonie. IFRECOR Nouv.-Caléd. IRD Nouméa 26 (2004).44.Lea, J. S. E., Humphries, N. E., von Brandis, R. G., Clarke, C. R. & Sims, D. W. Acoustic telemetry and network analysis reveal the space use of multiple reef predators and enhance marine protected area design. Proc. R. Soc. B Biol. Sci. 283, 20160717 (2016).Article 

Google Scholar 
45.Benhamou, S. & Cornélis, D. Incorporating movement behavior and barriers to improve kernel home range space use estimates. J. Wildl. Manag. 74, 1353–1360 (2010).Article 

Google Scholar 
46.Fieberg, J. & Börger, L. Could you please phrase “home range” as a question?. J. Mammal. 93, 890–902 (2012).Article 

Google Scholar 
47.Heupel, M. R. & Simpfendorfer, C. A. Importance of environmental and biological drivers in the presence and space use of a reef-associated shark. Mar. Ecol. Prog. Ser. 496, 47–57 (2014).ADS 
Article 

Google Scholar 
48.Dwyer, R. G. et al. Using individual-based movement information to identify spatial conservation priorities for mobile species. Conserv. Biol. 33, 1426–1437 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
49.IUCN, UNEP-WCMC. The World Database on Protected Areas (WDPA). [01/2019]. (UNEP World Conservation Monitoring Centre, Cambridge (UK), 2014). Available at: https://www.protectedplanet.net.50.UNEP-WCMC. Global Distribution of Warm-Water Coral Reefs, Compiled from Multiple Sources Including the Millennium Coral Reef Mapping Project. Version 4.0. (WorldFish Centre, WRI, TNC, 2018).51.Graham, N. A. J., Spalding, M. D. & Sheppard, C. R. C. Reef shark declines in remote atolls highlight the need for multi-faceted conservation action. Aquat. Conserv. Mar. Freshw. Ecosyst. 20, 543–548 (2010).Article 

Google Scholar 
52.Davis, K. L. F., Russ, G. R., Williamson, D. H. & Evans, R. D. Surveillance and poaching on inshore reefs of the Great Barrier Reef marine park. Coast. Manag. 32, 373–387 (2004).Article 

Google Scholar 
53.D’agata, S. et al. Human-mediated loss of phylogenetic and functional diversity in coral reef fishes. Curr. Biol. 24, 555–560 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
54.Gaines, S. D., White, C., Carr, M. H. & Palumbi, S. R. Designing marine reserve networks for both conservation and fisheries management. Proc. Natl. Acad. Sci. 107, 18286–18293 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Bessa-Gomes, C., Legendre, S. & Clobert, J. Allee effects, mating systems and the extinction risk in populations with two sexes. Ecol. Lett. 7, 802–812 (2004).Article 

Google Scholar 
56.Rankin, D. J. & Kokko, H. Do males matter? The role of males in population dynamics. Oikos 116, 335–348 (2007).Article 

Google Scholar 
57.Pratt, H. L. & Carrier, J. C. A review of elasmobranch reproductive behavior with a case study on the nurse shark, Ginglymostoma cirratum. Environ. Biol. Fish. 60, 157–188 (2001).Article 

Google Scholar 
58.Momigliano, P., Harcourt, R., Robbins, W. D. & Stow, A. Connectivity in grey reef sharks (Carcharhinus amblyrhynchos) determined using empirical and simulated genetic data. Sci. Rep. 5, 13229 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Momigliano, P. et al. Genetic structure and signatures of selection in grey reef sharks (Carcharhinus amblyrhynchos). Heredity 119(3), 142–153 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Bradley, D. et al. Resetting predator baselines in coral reef ecosystems. Sci. Rep. 5, 43131 (2017).61.Williams, J. J., Papastamatiou, Y. P., Caselle, J. E., Bradley, D. & Jacoby, D. M. P. Mobile marine predators: An understudied source of nutrients to coral reefs in an unfished atoll. Proc. R. Soc. B 285, 20172456 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
62.Mourier, J., Vercelloni, J. & Planes, S. Evidence of social communities in a spatially structured network of a free-ranging shark species. Anim. Behav. 83, 389–401 (2012).Article 

Google Scholar 
63.Mourier, J. et al. Extreme inverted trophic pyramid of reef sharks supported by spawning groupers. Curr. Biol. 26, 2011–2016 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Robbins, W. D. & Renaud, P. Foraging mode of the grey reef shark, Carcharhinus amblyrhynchos, under two different scenarios. Coral Reefs 35, 253–260 (2015).ADS 
Article 

Google Scholar 
65.Devillers, R. et al. Reinventing residual reserves in the sea: Are we favouring ease of establishment over need for protection?. Aquat. Conserv. Mar. Freshw. Ecosyst. 25, 480–504 (2015).Article 

Google Scholar 
66.Boerder, K., Miller, N. A. & Worm, B. Global hot spots of transshipment of fish catch at sea. Sci. Adv. 4, eaat7159 (2018).67.Kroodsma, D. A. et al. Tracking the global footprint of fisheries. Science 359, 904–908 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Watson, R. A. et al. Marine foods sourced from farther as their use of global ocean primary production increases. Nat. Commun. 6, 7365 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Januchowski-Hartley, F. A., Vigliola, L., Maire, E., Kulbicki, M. & Mouillot, D. Low fuel cost and rising fish price threaten coral reef wilderness. Conserv. Lett. 13, e12706 (2020).Article 

Google Scholar 
70.Dent, F. & Clarke, S. State of the global market for shark products. FAO Fish. Aquac. Tech. Pap. 590, 37 (2015).
Google Scholar 
71.Schofield, G. et al. Evidence-based marine protected area planning for a highly mobile endangered marine vertebrate. Biol. Conserv. 161, 101–109 (2013).72.Botsford, L. W., Micheli, F. & Hastings, A. Principles for the design of marine reserves. Ecol. Appl. 13, 25–31 (2003).Article 

Google Scholar 
73.Hastings, A. & Botsford, L. W. Comparing designs of marine reserves for fisheries and for biodiversity. Ecol. Appl. 13, 65–70 (2003).Article 

Google Scholar 
74.Green, A. L. et al. Larval dispersal and movement patterns of coral reef fishes, and implications for marine reserve network design. Biol. Rev. 90, 1215–1247 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
75.CBD. Decisions Adopted by the Conference of the Parties to the Convention on Biological Diversity at its Eighth Meeting (Decision VIII/15, Annex IV). (2006).76.Giakoumi, S. et al. Revisiting “success” and “failure” of marine protected areas: A conservation scientist perspective. Front. Mar. Sci. 5, 223 (2018).Article 

Google Scholar 
77.Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Rife, A. N., Erisman, B., Sanchez, A. & Aburto-Oropeza, O. When good intentions are not enough … Insights on networks of “paper park” marine protected areas. Conserv. Lett. 6, 200–212 (2013).Article 

Google Scholar 
79.Heupel, M. R., Simpfendorfer, C. A. & Fitzpatrick, R. Large-scale movement and reef fidelity of grey reef sharks. PLoS ONE 5, e9650 (2010). ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
80.Heupel, M. R., Reiss, K. L., Yeiser, B. G. & Simpfendorfer, C. A. Effects of biofouling on performance of moored data logging acoustic receivers. Limnol. Oceanogr. Methods 6, 327–335 (2008).Article 

Google Scholar  More

SoilContents of risk metals in soilsLands of localities from which soil and plant samples were taken belong to agricultural lands.Soil reaction is one of the factors that most affects the behaviour of heavy metals in soil. Low pH values pose a risk of reduced nutrient intake and increase the availability of heavy metals for plants29,30.The presence of risk elements in the soil was evaluated based on their contents in bioavailable form (mobile forms), determined in soil extracts NH4NO3, and the total contents of risk elements were determined in soil extract by aqua regia (Table 1).Table 1 The contents of risk elements (Cu, Ni, Pb, Cd, Hg, Mn) in soil (mg/kg).Full size tableAccessible heavy metals for plants are those which are present in the soil solution as soluble components or those which are easily dissolved by root exudates31. The highest Cu contents determined in soil extract by NH4NO3, were in the cadastre of Gemerská Poloma (max. 0.390 mg/kg) (Table 1). However, even the highest determined concentration of Cu in its bioavailable form did not exceeded the determined critical value for this element18. Nickel is a beneficial element for plants. Elevated Ni concentrations in soils have a potential negative effect on plants32. Content of bioavailable forms of nickel is lower than the determined critical value in all analysed samples. Cadmium and lead present a risk to agricultural activity in this area. Cadmium in soil is highly bioavailable and has higher mobility in plants compared to other heavy metals. It is easily transported by roots to shoots. In contrast, lead is one of the least mobile heavy metals. It is naturally concentrated in the upper layers of the soil33. The contents of the available forms of cadmium and lead exceed the critical values for these elements. In case of lead, the determined contents are from 0.257 Henckovce to 0.676 Gemerská Poloma. Takáč et al.34 determined in 20 soil samples from the Central Spiš region 7.2–257.6 mg Cu/kg soil and 1.0–84.8 mg Pb/kg in their potentially mobilizable form and 0.4–1.4 mg Cu/kg soil and 4.3–7.1 mg Pb/kg in their mobile form. In comparison with our results, Vilček et al.35 determined a lower content of Cd (0.04), Pb (0.17), Ni (0.15) and higher Cu content (0.48) mg/kg in forms accessible to plants in 16 soil samples from locality Nižná Slaná in the years 2006–2008. However, high concentrations of metals in soil do not necessarily mean the availability of metals for plants36. As a result, extractable Mn is often a better indicator of Mn availability. Mn2+ is generally considered to be bioavailable22. The highest concentration of Mn was measured in soil samples from the cadastre of Nižná Slaná. On the contrary, the lowest concentrations were detected in samples from Gemerská Poloma cadastre, which is the furthest cadastre from the source. No critical limit is set up for manganese according to Slovak legislation, it is not possible to classify these soils as contaminated/uncontaminated. For comparison, the EDTA-extractable content of Mn ranged from 22.7 to 127 mg/kg dry soil (China)29; the mobile concentrations between 0.32 and 202.0 mg/kg and the available concentrations from 5.4 to 126.3 mg/kg (Egypt)37.Based on results of statistical analysis, significant higher content of Cu, Pb and Cd can be stated in samples from Gemerská Poloma cadastre. These soils are classified as gley fluvisols, soils from the other two localities are cambisols (from medium heavy to light) and acid cambisols (Henckovce), cambisols from medium heavy to light and typically acid cambisols (Nižná Slaná). The soil profile of fluvisols is repeatedly disrupted by floods, which often enriches them with a new layer of sludge sediments2.Another method for determination of metal content in soil is mineralisation using aqua regia, which dissolves most of the soil constituents except those strongly bound in silicate minerals. This content is sometimes referred to as pseudototal (determined in aqua regia). In this way, all elements that are likely to become bioavailable in the long term are determined38.Pseudototal contents of risk metals (Table 1) determined in soil extract using aqua regia were higher than their limit value in case of Cu (Gemerská Poloma cadastre), Cd (all cadastres) and Hg (cadastre of Henckovce and Gemerská Poloma).Due to the fact that the hygienic condition of agricultural soils is assessed according to the exceeding of the limit values of at least one risk substance, the monitored plots can be classified as contaminated (Cu  > 60.0, Cd  > 0.7, Hg  > 0.5 mg/kg soil).Manganese is not classified as risk element in Slovak legislation.Tóth et al.39 classified European soils into four categories: (1) no detectable content of HM, (2) the concentration of the investigated element is above the threshold value (Hg 0.5, Cd 1, Cu 100, Pb 60 and Ni 50 mg/kg), but below the lower guideline value (Hg 2, Cd 10, Cu 150, Pb 200 and Ni 100 mg/kg), (3) concentration of one or more element exceeds the lower guideline value but is below the higher guideline value (Hg 5, Cd 20, Cu 200, Pb 750 and Ni 150 mg/kg), (4) samples having concentrations above the higher guideline value.In comparison with the threshold and guideline values, soils in cadastres of Gemerská Poloma (Cu), Henckovce, Nižná Slaná, Gemerská Poloma (Cd, Hg) represent the ecological risk. Threshold and guideline values for Mn were not defined.The Spiš region and the northern part of the Gemer region belong to the most polluted areas in Slovakia in terms of soil contamination due to mining and metallurgical activities that have been carried out here in the past. Soils near the sludge in Nižná Slaná contain 3.17–53.3 (14.2–301, 0.71–20.6, 3.33–177, 12.9–223 and 675–11,510, respectively) mg Cd (Cu, Hg, Ni, Pb and Mn, respectively)/kg of soil14. In loaded area of Dongchuan, (China), contained Cd (Cu, Hg, Ni and Pb, resp.) 0.20–3.57 (45.38–2026, 0.02–0.23, 24.06–95.9 and 6.83–146.6, resp.) mg/kg40. In contrast, in the agricultural area of Punjab of the India, the soil contamination was caused by an excessive use of agrochemicals and polluted irrigation sources. Increased Cu (Pb and Cd) contents were determined in the soil samples: 9.0–48.5 (5.5–9.67 and 0.516–1.58, resp.) mg/kg41.However, in most cases, a large portion of the total element content is not available for immediate uptake by plants. Available forms represent a small proportion of this total content which is potentially available to plants. Availability is affected by many factors, including pH, redox state, macronutrient levels, available water content and temperature29,33,36,38.Indicators of soil contaminationContamination factors and degree of contaminationThe contamination character may be described in a uniform, adequate and standardised way by means of the contamination factor and the degree of contamination. Hakanson24 reported four Contamination degrees of individual metal (({mathrm{C}}_{mathrm{f}}^{mathrm{i}})) – low (({mathrm{C}}_{mathrm{f}}^{mathrm{i}}) < 1), moderate (1 ≤ ({mathrm{C}}_{mathrm{f}}^{mathrm{i}})   More

1.Gubler, D. J. Prevention and control of Aedes aegypti-borne diseases: lesson learned from past successes and failures. AsPac. J. Mol. Biol. Biotechnol. 19, 111–114 (2011).
Google Scholar 
2.Gratz, N. G. Critical review of the vector status of Aedes albopictus. Med. Vet. Entomol. 18, 215–227. https://doi.org/10.1111/j.0269-283X.2004.00513.x (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Unlu, I. Aedes albopictus in America: current perspectives and future challenges. CAB Rev. 14, 1–22 (2019).Article 

Google Scholar 
4.Schoof, H. Dispersal of Aedes taeniorhynchus Wiede-mann near Savannah. Georgia. Mosq. News 23, 1–10 (1963).
Google Scholar 
5.Fonseca, D. M. et al. Area-wide management of Aedes albopictus. Part 2: gauging the efficacy of traditional integrated pest control measures against urban container mosquitoes. Pest Manag. Sci. 69, 1351–1361 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.YiBin, Z., TongYan, Z. & PeiEn, L. Evaluation on the control efficacy of source reduction to Aedes albopictus in Shanghai, China. Chin. J. Vector Biol. Control 20, 3–6 (2009).
Google Scholar 
7.Rochlin, I., Ninivaggi, D. V., Hutchinson, M. L. & Farajollahi, A. Climate change and range expansion of the Asian tiger mosquito (Aedes albopictus) in Northeastern USA: implications for public health practitioners. PLoS ONE 8, e60874. https://doi.org/10.1371/journal.pone.0060874 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Hawley, W. A. The biology of Aedes albopictus. J. Am. Mosq. Control Assoc. Suppl. 1, 1–39 (1988).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Richards, S. L., Ghosh, S. K., Zeichner, B. C. & Apperson, C. S. Impact of source reduction on the spatial distribution of larvae and pupae of Aedes albopictus (Diptera: Culicidae) in suburban neighborhoods of a Piedmont community in North Carolina. J. Med. Entomol. 45, 617–628 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Unlu, I., Farajollahi, A., Strickman, D. & Fonseca, D. M. Crouching tiger, hidden trouble: Urban sources of Aedes albopictus (Diptera: Culicidae) refractory to source-reduction. PLoS ONE 8, e77999 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Lam, P. H. Y., Boon, C. S., Yng, N. Y. & Benjamin, S. Aedes albopictus control with spray application of Bacillus thuringiensis israelensis, strain AM 65-52. Southeast Asian J. Trop. Med. Public Health 41, 1071 (2010).PubMed 
PubMed Central 

Google Scholar 
12.Seleena, P., Lee, H. L., Nazni, W., Rohani, A. & Kadri, M. Microdroplet application of mosquitocidal Bacillus thuringiensis using ultra-low-volume generator for the control of mosquitos. Southeast Asian. J. Trop. Med. Public Health 27, 628–632 (1996).CAS 

Google Scholar 
13.Chandel, K. et al. Targeting a hidden enemy: Pyriproxyfen autodissemination strategy for the control of the container mosquito Aedes albopictus in cryptic habitats. PLoS Negl. Trop. Dis. 10, e0005235 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Pruszynski, C. A., Hribar, L. J., Mickle, R. & Leal, A. L. A large scale biorational approach using Bacillus thuringiensis israeliensis (strain AM65-52) for managing Aedes aegypti populations to prevent dengue, chikungunya and Zika transmission. PLoS ONE 12, e0170079 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
15.Unlu, I., Faraji, A., Indelicato, N. & Fonseca, D. M. The hidden world of Asian tiger mosquitoes: immature Aedes albopictus (Skuse) dominate in rainwater corrugated extension spouts. Trans. R. Soc. Trop. Med. Hyg. 108, 699–705. https://doi.org/10.1093/trstmh/tru1139 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Itoh, T. Utilization of blood fed females of Aedes aegypti as a vehicle for the transfer of the insect growth regulator, pyriproxyfen, to larval habitats. Trop. Med. 36, 243–248 (1995).
Google Scholar 
17.Gaugler, R., Suman, D. & Wang, Y. An autodissemination station for the transfer of an insect growth regulator to mosquito oviposition sites. Med. Vet. Entomol. 26, 37–45 (2012).CAS 
PubMed 
Article 

Google Scholar 
18.Mbare, O., Lindsay, S. W. & Fillinger, U. Testing a pyriproxyfen auto-dissemination station attractive to gravid Anopheles gambiae sensu stricto for the development of a novel attract-release-and-kill strategy for malaria vector control. BMC Infect. Dis. 19, 1–12 (2019).CAS 
Article 

Google Scholar 
19.Devine, G. J. et al. Using adult mosquitoes to transfer insecticides to Aedes aegypti larval habitats. Proc. Natl. Acad. Sci. 106, 11530–11534 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Caputo, B. et al. The “auto-dissemination” approach: a novel concept to fight Aedes albopictus in urban areas. PLoS Negl. Trop. Dis. 6, e1793 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
21.Lwetoijera, D., Kiware, S., Okumu, F., Devine, G. J. & Majambere, S. Autodissemination of pyriproxyfen suppresses stable populations of Anopheles arabiensis under semi-controlled settings. Malar. J. 18, 1–10 (2019).Article 

Google Scholar 
22.Unlu, I. et al. Large-scale operational pyriproxyfen autodissemination deployment to suppress the immature Asian Tiger Mosquito (Diptera: Culicidae) populations. J. Med. Entomol. 57, 1120–1130 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Mains, J. W., Brelsfoard, C. L. & Dobson, S. L. Male mosquitoes as vehicles for insecticide. PLoS Negl. Trop. Dis. 9, e0003406–e0003406 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Bibbs, C. S., Anderson, C. S., Smith, M. L. & Xue, R.-D. Direct and indirect efficacy of truck-mounted applications of s-methoprene against Aedes albopictus (Diptera: Culicidae). Int. J. Pest Manag. 64, 19–26 (2018).CAS 
Article 

Google Scholar 
25.Wang, Y. et al. Heterodissemination: precision targeting container Aedes mosquitoes with a cohabiting midge species carrying insect growth regulator. Pest Manag. Sci. 76, 2105–2112 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Lopez, L. C. S., Filizola, B., Deiss, I. & Rios, R. I. Phoretic behaviour of bromeliad annelids (Dero) and ostracods (Elpidium) using frogs and lizards as dispersal vectors. Hydrobiologia 549, 15–22 (2005).Article 

Google Scholar 
27.Torresdal, J. D., Farrell, A. D. & Goldberg, C. S. Environmental DNA detection of the golden tree frog (Phytotriades auratus) in bromeliads. PLoS ONE 12, e0168787 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Wilke, A. B., Vasquez, C., Mauriello, P. J. & Beier, J. C. Ornamental bromeliads of Miami-Dade County, Florida are important breeding sites for Aedes aegypti (Diptera: Culicidae). Parasit. Vectors 11, 1–7 (2018).Article 

Google Scholar 
29.Council, N. R. Guide for the Care and Use of Laboratory Animals (National Academies Press, Washington, 2010).
Google Scholar 
30.Unlu, I. et al. Effectiveness of autodissemination stations containing pyriproxyfen in reducing immature Aedes albopictus populations. Parasit. Vectors 10, 1–10 (2017).Article 
CAS 

Google Scholar 
31.Unlu, I. et al. Effects of a red marker dye on Aedes and Culex larvae: are there implications for operational mosquito control?. J. Am. Mosq. Control Assoc. 31, 375–379 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Development, R. & Team, C. A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria ( https://www.R-project.org/ ) (2019).33.Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: Linear mixed-effects models using Eigen and S4. R package version 1 (2014).34.Crawley, M. J. The R Book (Wiley, Chichester, 2012).MATH 
Book 

Google Scholar 
35.Lenth, R. V. Using lsmeans. J. Stat. Softw. 69, 1–33 (2017).
Google Scholar 
36.Plummer, M. in Proceedings of the 3rd international workshop on distributed statistical computing. 1–10 (Vienna, Austria.).37.Kellner, K. jagsUI: a wrapper around rjags to streamline JAGS analyses. R Package Vers. 1, 2015 (2015).
Google Scholar 
38.Khan, G. Z., Khan, I., Khan, I. A., Salman, M. & Ullah, K. Evaluation of different formulations of IGRs against Aedes albopictus and Culex quinquefasciatus (Diptera: Culicidae). Asian. Pac. J. Trop. Biomed. 6, 485–491 (2016).CAS 
Article 

Google Scholar 
39.Bury, R. B. & Whelan, J. A. Ecology and Management of the Bullfrog Vol. 155 (Fish and Wildlife Service, Washington, 1985).
Google Scholar 
40.WHO. Review of the insect growth regulator pyriproxyfen GR, pp. 50–67. InReport of the 4th WHOPES Working Group Meeting, 2000 December 4–5, Geneva Switzerland Geneva. WHO/CDS, WHOPES/2001. (2001).41.Devillers, J. Fate and ecotoxicological effects of pyriproxyfen in aquatic ecosystems. Environ. Sci. Pollut. Res. 1–17 (2020).42.Schaefer, C. & Miura, T. Chemical persistence and effects of S-31183, 2-[1-methyl-2-(4-phenoxyphenoxy) ethoxy] pyridine, on aquatic organisms in field tests. J. Econ. Entomol. 83, 1768–1776 (1990).CAS 
Article 

Google Scholar 
43.Ose, K., Miyamoto, M., Fujisawa, T. & Katagi, T. Bioconcentration and metabolism of pyriproxyfen in tadpoles of African clawed frogs, Xenopus laevis. J. Agric. Food Chem. 65, 9980–9986 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Lajmanovich, R. C. et al. Insecticide pyriproxyfen (Dragón®) damage biotransformation, thyroid hormones, heart rate, and swimming performance of Odontophrynus americanus tadpoles. Chemosphere 220, 714–722 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.https://edis.ifas.ufl.edu/uw259. The Cuban Treefrog (Osteopilus septentrionalis) in Florida. This document is WEC218, one of a series of the Department of Wildlife Ecology and Conservation, UF/IFAS Extension. (2017).46.Glorioso, B. M. et al. Osteopilus septentrionalis (Cuban treefrog). Herpetol. Rev. 49, 70–71 (2018).
Google Scholar 
47.Wermelinger, E. D. & Carvalho, RWd. Methods and procedures used in Aedes aegypti control in the successful campaign for yellow fever prophylaxis in Rio de Janeiro, Brazil, in 1928 and 1929. Epidemiol. Serv. Saude. 25, 837–844 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Santos França, L. et al. Challanges for the control and prevention of the Aedes aegypti mosquito. Rev. Enferm. UFPE. 11, 4913 (2017).Article 

Google Scholar 
49.Minakawa, N., Mutero, C. M., Githure, J. I., Beier, J. C. & Yan, G. Spatial distribution and habitat characterization of anopheline mosquito larvae in western Kenya. Am. J. Trop. Med. Hyg. 61, 1010–1016 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Mutuku, F. M. et al. Pupal habitat productivity of Anopheles gambiae complex mosquitoes in a rural village in western Kenya. Am. J. Trop. Med. Hyg. 74, 54–61 (2006).PubMed 
Article 
PubMed Central 

Google Scholar  More

ParticipantsData were drawn from the NCDS, which is a nationally representative study that has followed a cohort of participants all born in a single week in the United Kingdom since 1958. Since birth, they have been followed up a total of 11 times at ages 7, 11, 16, 23, 33, 42, 44, 46, 50 and 55. As data on time spent caring for grandchildren is only available from the most recent interview, all analyses here are cross-sectional, with all women included in the sample being aged either 55 or 56 (depending on whether the interview was conducted in 2013 or 2014) and representing the third generation of women in Fig. 1. The sample was limited to women who had at least one parent alive and at least one grandchild (n = 934). Data from the NCDS are available from the UK Data Service, and the participant characteristics shown in Supplementary Table S1.VariablesHours spent helping parents per weekInformation regarding parental caregiving was included as a count variable. In the most recent interviews, participants were asked whether they ever do various activities for their parents (e.g. shopping for them, helping with basic personal needs, giving them lifts, etc.), and if they do, how many hours on average per week do they spend doing so. Any women who reported not helping their parents do any of the activities were coded as helping their parents for zero hours per week.Hours spent caring for grandchildren per monthThe number of hours spent caring for grandchildren per month was also included as a count variable. Women were asked whether they ever look after their grandchildren without the grandchild’s parents being present, and if they do, at what frequency and for how many hours. Women who stated that they did not care for their grandchildren or did so less often than monthly were coded as caring for their grandchildren for zero hours per month. This measure also includes overnight stays.Fecundity status at age 55Fecundity status was derived from information on age, year and reason for last menstrual period, which was collected at ages 44, 50, and 55. Based on this, a binary categorical variable was derived where women were coded as either ‘Still menstruating’ or ‘No longer menstruating’. The latter category comprised of women who were post-menopausal or who had stopped menstruating for another reason, such as a surgical menopause. Women who had stopped menstruating due to menopause or other reasons were grouped together as the direct fitness implications of no longer menstruating are the same, regardless of the reason for it.Control variablesCovariates included were selected based on their expected effect on the woman’s ability to help other family members. As a proxy of socioeconomic status, the age at which the woman left education was included. Employment status was utilised to give an indication of the woman’s time constraints (i.e. if she was employed, it can be expected she had less time to care for kin)24, with women being coded as either employed, unemployed, or other, with the latter category including those who are doing something other than formal employment but do not classify themselves as unemployed (e.g. retired, volunteering, studying, etc.). Self-perceived health was used as a measure of how physically able the woman is to help family members25, and number of grandchildren was also included to adjust for how many grandparenting responsibilities a woman had. We also included information on the mortality status of the woman’s parents (i.e. whether she had both parents alive or not), which was derived from interviews at ages 7, 11, 16, 23, 42, 46, 50 and 55. The focal woman’s mother’s and father’s age at birth (collected in the perinatal interview) were also included to control for the amount of help her parents may need, as older parents would expected to be more in need of assistance. Finally, in models predicting hours spent caring for parents, time spent caring for grandchildren was adjusted for, and vice versa for models where hours spent caring for grandchildren was the outcome.AnalysesTime spent helping parents and caring for grandchildren were both modelled using zero-inflated negative binomial regression (ZINB). This modelling procedure was selected both due to the over-dispersed nature of the data with excess zeros, and because zero-inflated models allow for zeros to be generated through two distinct processes. Here, the model distinguishes between excess zeroes, which occur when the event could not have happened, and true zeros, which occur when there could have been an event. Therefore, the model estimates a binary outcome (does not care versus does care) and a count outcome (the number of hours spent caring). This method is theoretically appropriate, as there are many different reasons people would offer no care to kin: while some people may choose to invest less, for some people the choice is out of their control, with external factors influencing caring behaviours, such as living far away from kin26. In addition to this, ZINB was found to better fit the data than negative binomial regression (Supplementary Table S2).Time spent helping parents was first modelled. A ‘base’ model was first made containing the age the woman left education, employment status, marital status, self-perceived health, number of grandchildren, parent mortality status, age of parents, and time spent caring for grandchildren. Fecundity status was subsequently added, and model fitting then carried out on these two models, utilising their Akaike Information Criterion (AIC) value to understand whether a model including fecundity better fit the data than one without. The model with the lowest AIC value is taken to best fit the data. As AIC values penalise models for complexity, it means the model with the most terms will not automatically be selected as the best. The ΔAIC was also calculated, which is the difference between the candidate models AIC and the AIC value of the best fitting candidate model. If the ΔAIC value is ≤ 2, then it indicates that there is still good evidence to support the candidate model, meaning that a candidate model with a ΔAIC of ≤ 2 is almost as good as the best fitting model. A ΔAIC value of between 4 and 7 is taken to indicate the candidate model has considerably less support, and a ΔAIC of greater than 10 indicates there is no support for the candidate model27. The Akaike weights (wi) were also calculated to evaluate model fit, which give the probability that the candidate model is the best among the set of presented candidate models27. The same procedure was then used to model time spent caring for grandchild per month: a model including just the covariates was first made, but this time adjusting for time spent helping parents rather than time caring for grandchildren, with fecundity status then being added, and model fitting was once again carried out using the methods outlined above. All analyses were carried in R using the zeroinfl function with a negative binomial distribution specified28, and model fitting carried out with the package AICcmodavg29. All visualisations were created using ggplot230. More

1.System of Environmental-Economic Accounting—Ecosystem Accounting: Final Draft (United Nations, 2021).2.Bateman, I. J. & Mace, G. M. Nat. Sustain. 3, 777–783 (2020).Article 

Google Scholar 
3.Scarlett, L. & Boyd, J. Ecol. Econ. 115, 3–10 (2015).Article 

Google Scholar 
4.Cohen, F., Hepburn, C. J. & Teytelboym, A. Annu. Rev. Environ. Resour. 44, 425–448 (2019).Article 

Google Scholar 
5.Dakos, V. et al. Nat. Ecol. Evol. 3, 355–362 (2019).Article 

Google Scholar 
6.Rockström, J. et al. Nature 461, 472–475 (2009).Article 

Google Scholar 
7.Bergstrom, D. et al. Glob. Change Biol. 27, 1692–1703 (2021).Article 

Google Scholar 
8.Nordhaus, W. D & Tobin, J. Is Growth Obsolete? (National Bureau of Economic Research, 1972).9.Keith, H. et al. Nat. Ecol. Evol. 1, 1683–1692 (2017).Article 

Google Scholar 
10.Boyd, J. W. et al. BioScience 68, 940–943 (2018).Article 

Google Scholar 
11.Hein, L. et al. Science 367, 514–515 (2020).CAS 
Article 

Google Scholar 
12.Hamilton, K. & Hepburn, C. National Wealth: What is Missing and Why it Matters (Oxford Univ. Press, 2017).13.Pearce, D. W. & Atkinson, G. Ecol. Econ. 8, 103–108 (1993).Article 

Google Scholar 
14.Lange, G.-M. et al. The Changing Wealth of Nations (World Bank, 2018).15.Dasgupta, P. Final Report – The Economics of Biodiversity: The Dasgupta Review (HM Treasury, 2021).16.Hoekstra, R. Replacing GDP by 2030: Towards a Common Language for the Well-being and Sustainability Community (Cambridge Univ. Press, 2019).17.Dikau, S. & Volz, U. Ecol. Econ. 184, 107022 (2021).Article 

Google Scholar 
18.Vardon, M., Burnett, P. & Dovers, S. Ecol. Econ. 124, 145–152 (2016).Article 

Google Scholar 
19.Biodiversity, Natural Capital and the Economy: A Policy Guide for Finance, Economic and Environment Ministers (OECD, 2021).20.A Call for Action, Climate Change as a Source of Financial Risk (NGFS, 2019). More

1.Leroi, A. M., Lambert, B., Rosindell, J., Zhang, X. & Kokkoris, G. D. Nat. Hum. Behav. 4, 780–790 (2020).Article 

Google Scholar 
2.Hahn, M. W. & Bentley, R. A. Proc. R. Soc. B: Biol. Sci. 270, S120–S123 (2003).Article 

Google Scholar 
3.Bentley, R. A., Hahn, M. W. & Shennan, S. J. Proc. R. Soc. B Biol. Sci. 271, 1443–1450 (2004).Article 

Google Scholar 
4.Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Nat. Genet. 48, 238–244 (2016).CAS 
Article 

Google Scholar 
5.Tarabichi, M. et al. Nat. Genet. 50, 1630–1633 (2018).CAS 
Article 

Google Scholar 
6.Heide, T. et al. Nat. Genet. 50, 1633–1637 (2018).CAS 
Article 

Google Scholar 
7.McDonald, T. O., Chakrabarti, S. & Michor, F. Nat. Genet. 50, 1620–1623 (2018).CAS 
Article 

Google Scholar 
8.Werner, B., Williams, M. J., Barnes, C. P., Graham, T. A. & Sottoriva, A. Nat. Genet. 50, 1624–1626 (2018).CAS 
Article 

Google Scholar 
9.Balaparya, A. & De, S. Nat. Genet. 50, 1626–1628 (2018).CAS 
Article 

Google Scholar 
10.Williams, M. J. et al. Nat. Genet. 50, 895–903 (2018).CAS 
Article 

Google Scholar 
11.Bentley, R. A., Carrignon, S., Ruck, D. J., Valverde, S. & O’Brien, M. J. Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01149-x (2020).12.O’Dwyer, J. P. & Kandler, A. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160426 (2017).Article 

Google Scholar 
13.Bentley, R. A., Lipo, C. P., Herzog, H. A. & Hahn, M. W. Evol. Hum. Behav. 28, 151–158 (2007).Article 

Google Scholar 
14.Bentley, R. A. PLoS ONE 3, e3057 (2008).Article 

Google Scholar 
15.Acerbi, A. & Bentley, R. A. Evol. and Hum. Behav. 35, 228–236 (2014).Article 

Google Scholar  More