Ecology

1.Krebs, C. J. Two paradigms of population regulation. Wildl. Res. 22, 1–10 (1995).MathSciNet 
Article 

Google Scholar 
2.McLaren, I. A. Natural Regulation of Animal Populations (Routledge, 2017).Book 

Google Scholar 
3.Deffner, D. & McElreath, R. The importance of life history and population regulation for the evolution of social learning. Philos. Trans. R. Soc. B 375, 20190492 (2020).Article 

Google Scholar 
4.Leão, S. M., Pianka, E. R. & Pelegrin, N. Is there evidence for population regulation in amphibians and reptiles? J. Herpetol. 52, 28–33 (2018).Article 

Google Scholar 
5.Hanski, I. A. Density dependence, regulation and variability in animal populations. Philos. Trans. R. Soc. B 330, 141–150 (1990).ADS 
Article 

Google Scholar 
6.Reznick, D., Bryant, M. J. & Bashey, F. r-and K-selection revisited: The role of population regulation in life-history evolution. Ecology 83, 1509–1520 (2002).Article 

Google Scholar 
7.Stenseth, N. C., Falck, W., Bjørnstad, O. N. & Krebs, C. J. Population regulation in snowshoe hare and Canadian lynx: Asymmetric food web configurations between hare and lynx. Proc. Natl Acad. Sci. USA 94, 5147–5152 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Lande, R. et al. Estimating density dependence from population time series using demographic theory and life-history data. Am. Nat. 159, 321–337 (2002).CAS 
PubMed 
Article 

Google Scholar 
9.Ferguson, G. W., Bohlen, C. H. & Woolley, H. P. Sceloporus undulatus: Comparative life history and regulation of a Kansas population. Ecology 61, 313–322 (1980).Article 

Google Scholar 
10.Andrews, R. M. Population stability of a tropical lizard. Ecology 72, 1204–1217 (1991).Article 

Google Scholar 
11.Tinkle, D. W., Dunham, A. E. & Congdon, J. D. Life history and demographic variation in the lizard Sceloporus graciosus: A long-term study. Ecology 74, 2413–2429 (1993).Article 

Google Scholar 
12.Altwegg, R., Dummermuth, S., Anholt, B. R. & Flatt, T. Winter weather affects asp viper Vipera aspis population dynamics through susceptible juveniles. Oikos 110, 55–66 (2005).Article 

Google Scholar 
13.Madsen, T. & Shine, R. Rain, fish and snakes: Climatically driven population dynamics of Arafura filesnakes in tropical Australia. Oecologia 124, 208–215 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Madsen, T., Ujvari, B., Shine, R. & Olsson, M. Rain, rats and pythons: Climate-driven population dynamics of predators and prey in tropical Australia. Austral Ecol. 31, 30–37 (2006).Article 

Google Scholar 
15.Brown, G. P. & Shine, R. Rain, prey and predators: Climatically driven shifts in frog abundance modify reproductive allometry in a tropical snake. Oecologia 154, 361–368 (2007).ADS 
PubMed 
Article 

Google Scholar 
16.Brown, G. P., Ujvari, B., Madsen, T. & Shine, R. Invader impact clarifies the roles of top-down and bottom-up effects on tropical snake populations. Funct. Ecol. 27, 351–361 (2013).Article 

Google Scholar 
17.Massot, M., Clobert, J., Pilorge, T., Lecomte, J. & Barbault, R. Density dependence in the common lizard: Demographic consequences of a density manipulation. Ecology 73, 1742–1756 (1992).Article 

Google Scholar 
18.Fordham, D. A., Georges, A. & Brook, B. W. Experimental evidence for density-dependent responses to mortality of snake-necked turtles. Oecologia 159, 271–281 (2009).ADS 
PubMed 
Article 

Google Scholar 
19.Burns, G. & Heatwole, H. Home range and habitat use of the olive sea snake, Aipysurus laevis, on the Great Barrier Reef, Australia. J. Herpetol. 32, 350–358 (1998).Article 

Google Scholar 
20.Ward, T. M. Age structures and reproductive patterns of two species of sea snake, Lapemis hardwickii Grey (1836) and Hydrophis elegans (Grey 1842), incidentally captured by prawn trawlers in northern Australia. Mar. Freshw. Res. 52, 193–203 (2001).Article 

Google Scholar 
21.Dennis, B. & Ponciano, J. M. Density-dependent state-space model for population-abundance data with unequal time intervals. Ecology 95, 2069–2076 (2014).PubMed 
Article 

Google Scholar 
22.Bonnet, X., Naulleau, G. & Shine, R. The dangers of leaving home: Dispersal and mortality in snakes. Biol. Conserv. 89, 39–50 (1999).Article 

Google Scholar 
23.Yacelga, M., Cayot, L. J. & Jaramillo, A. Dispersal of neonatal Galápagos marine iguanas Amblyrhynchus cristatus from their nesting zone: Natural history and conservation implications. Herpetol. Conserv. Biol. 7, 470–480 (2012).
Google Scholar 
24.Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Shine, R., Cogger, H. G., Reed, R. R., Shetty, S. & Bonnet, X. Aquatic and terrestrial locomotor speeds of amphibious sea-snakes (Serpentes, Laticaudidae). J. Zool. 259, 261–268 (2003).Article 

Google Scholar 
26.Forsman, A. Body size and net energy gain in gape-limited predators: A model. J. Herpetol. 30, 307–319 (1996).Article 

Google Scholar 
27.Shine, R., LeMaster, M. P., Moore, I. T., Olsson, M. M. & Mason, R. T. Bumpus in the snake den: Effects of sex, size and body condition on mortality in red-sided garter snakes. Evolution 55, 598–604 (2001).CAS 
PubMed 
Article 

Google Scholar 
28.Sherratt, E., Rasmussen, A. R. & Sanders, K. L. Trophic specialization drives morphological evolution in sea snakes. R. Soc. Open Sci. 5, 172141 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Lemen, C. A. & Voris, H. K. A comparison of reproductive strategies among marine snakes. J. Anim. Ecol. 50, 89–101 (1981).Article 

Google Scholar 
30.Shine, R., Shine, T. & Shine, B. Intraspecific habitat partitioning by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae): The effects of sex, body size, and colour pattern. Biol. J. Linn. Soc. 80, 1–10 (2003).Article 

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

Google Scholar 
32.Van Dyke, J. U., Beaupre, S. J. & Kreider, D. L. Snakes allocate amino acids acquired during vitellogenesis to offspring: Are capital and income breeding consequences of variable foraging success? Biol. J. Linn. Soc. 106, 390–404 (2012).Article 

Google Scholar 
33.Masunaga, G., Matsuura, R., Yoshino, T. & Ota, H. Reproductive biology of the viviparous sea snake Emydocephalus ijimae (Reptilia: Elapidae: Hydrophiinae) under a seasonal environment in the Northern Hemisphere. Herpetol. J. 13, 113–119 (2003).
Google Scholar 
34.Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 
Article 

Google Scholar 
35.Phillips, B. L. The evolution of growth rates on an expanding range edge. Biol. Lett. 5, 802–804 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).Article 

Google Scholar 
37.Goiran, C. & Shine, R. Decline in sea snake abundance on a protected coral reef system in the New Caledonian Lagoon. Coral Reefs 32, 281–284 (2013).ADS 
Article 

Google Scholar 
38.Somaweera, R. et al. Pinpointing drivers of extirpation in sea snakes: A synthesis of evidence from Ashmore Reef. Front. Mar. Sci. 8, 658756 (2021).Article 

Google Scholar 
39.Udyawer, V. et al. Future directions in the research and management of marine snakes. Front. Mar. Sci. 5, 399 (2018).Article 

Google Scholar 
40.Udyawer, V., Cappo, M., Simpfendorfer, C. A., Heupel, M. R. & Lukoschek, V. Distribution of sea snakes in the Great Barrier Reef Marine Park: Observations from 10 yrs of baited remote underwater video station (BRUVS) sampling. Coral Reefs 33, 777–791 (2014).ADS 
Article 

Google Scholar 
41.Udyawer, V., Goiran, C. & Shine, R. Peaceful coexistence between people and deadly wildlife: Why are recreational users of the ocean so rarely bitten by sea snakes? People Nat. 3, 335–346 (2021).Article 

Google Scholar 
42.Shine, R., Goiran, C., Shine, T., Fauvel, T. & Brischoux, F. Phenotypic divergence between seasnake (Emydocephalus annulatus) populations from adjacent bays of the New Caledonian Lagoon. Biol. J. Linn. Soc. 107, 824–832 (2012).Article 

Google Scholar 
43.Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).Article 

Google Scholar 
44.Li, M., Fry, B. G. & Kini, R. M. Eggs-only diet: Its implications for the toxin profile changes and ecology of the marbled sea snake (Aipysurus eydouxii). J. Mol. Evol. 60, 81–89 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
45.Heatwole, H. Predation on sea snakes. In The Biology of Sea Snakes (ed. Dunson, W. A.) 233–250 (University Park Press, 1975).
Google Scholar 
46.Rancurel, P. & Intes, A. L. requin tigre, Galeocerdo cuvieri Lacepede, des eaux neocaledoniennes examen des contenus stomacaux. Tethys 10, 195–199 (1982).
Google Scholar 
47.Ineich, I. & Laboute, P. Les Serpents Marins de Nouvelle-Calédonie (IRD éditions, 2002).
Google Scholar 
48.Masunaga, G., Kosuge, T., Asai, N. & Ota, H. Shark predation of sea snakes (Reptilia: Elapidae) in the shallow waters around the Yaeyama Islands of the southern Ryukyus, Japan. Mar. Biodivers. Rec. 1, e96 (2008).Article 

Google Scholar 
49.Wirsing, A. J. & Heithaus, M. R. Olive-headed sea snakes Disteria major shift seagrass microhabitats to avoid shark predation. Mar. Ecol. Progr. Ser. 387, 287–293 (2009).ADS 
Article 

Google Scholar 
50.White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).Article 

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

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

Google Scholar 
53.Dennis, B., Ponciano, J. M., Lele, S. R., Taper, M. L. & Staples, D. F. Estimating density dependence, process noise, and observation error. Ecol. Monogr. 76, 323–341 (2006).Article 

Google Scholar  More

1.Cowen, R. K., Gawarkiewicz, G., Pineda, J., Thorrold, S. R. & Werner, F. E. Population connectivity in marine systems an overview. Oceanography 20, 14–21 (2007).Article 

Google Scholar 
2.Vendrami, D. L. et al. RAD sequencing sheds new light on the genetic structure and local adaptation of European scallops and resolves their demographic histories. Sci. Rep. UK 9, 1–13 (2019).CAS 

Google Scholar 
3.Holsinger, K. & Weir, B. Genetics in geographically structured populations: Defining, estimating and interpreting FST. Nat. Rev. Genet. 10, 639–650 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Smedbol, R. K., McPherson, A., Hansen, M. M. & Kenchington, E. Myths and moderation in marine metapopulations?. Fish Fish. 3, 20–35 (2002).Article 

Google Scholar 
5.Makinen, H. S., Cano, J. M. & Merila, J. Identifying footprints of directional and balancing selection in marine and freshwater three-spined stickleback (Gasterosteus aculeatus) populations. Mol. Ecol. 17, 3565–3582 (2008).CAS 
PubMed 
Article 

Google Scholar 
6.Tine, M. et al. European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation. Nat. Commun. 5, 5770 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Thompson, P. L. & Fronhofer, E. A. The conflict between adaptation and dispersal for maintaining biodiversity in changing environments. Proc. Natl. Acad. Sci. 116, 21061–21067 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Samuk, K. et al. Gene flow and selection interact to promote adaptive divergence in regions of low recombination. Mol. Ecol. 26, 4378–4390 (2017).PubMed 
Article 

Google Scholar 
9.van Tienderen, P. H., de Haan, A. A., van der Linden, C. G. & Vosman, B. Biodiversity assessment using markers for ecologically important traits. Trends Ecol. Evol. 17, 577–582 (2002).Article 

Google Scholar 
10.Cadrin, S. X., Kerr, L. A. & Mariani, S. Interdisciplinary evaluation of spatial population structure for definition of fishery management units. In Stock Identification Methods: Applications in Fishery Science (eds Cadrin, S. X. et al.) (Academic Press, 2014).Chapter 

Google Scholar 
11.Hoffmann, A. et al. A framework for incorporating evolutionary genomics into biodiversity conservation and management. Clim. Change Res. 2, 1–24 (2015).Article 

Google Scholar 
12.Narum, S. R., Buerkle, C. A., Davey, J. W., Miller, M. R. & Hohenlohe, P. A. Genotyping by sequencing in ecological and conservation genomics. Mol. Ecol. 22, 2841–2847 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Davey, J. W. & Blaxter, M. L. RADSeq: Next-generation population genetics. Brief Funct. Genom. 9, 416–423 (2010).CAS 
Article 

Google Scholar 
14.Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Valencia, L. M., Martins, A., Ortiz, E. M. & Di Fiore, A. A. RAD-sequencing approach to genome-wide marker discovery, genotyping, and phylogenetic inference in a diverse radiation of primates. PLoS ONE 13, e0201254 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Andrews, K. R., Good, J. M., Miller, M. R., Luikart, G. & Hohenlohe, P. A. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat. Rev. Genet. 17, 81 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Zalapa, J. E. et al. Using next-generation sequencing approaches to isolate simple sequence repeat (SSR) loci in the plant sciences. Am. J. Bot. 99, 193–208 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hohenlohe, P. et al. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. Plos Genet. 6, e1000862 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Emerson, K. J. et al. Resolving postglacial phylogeography using high-throughput sequencing. Proc. Natl. Acad. Sci. 107, 16196–16200 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.McCormack, J. E., Hird, S. M., Zellmer, A. J., Carstens, B. C. & Brumfield, R. T. Applications of next-generation sequencing to phylogeography and phylogenetics. Mol. Phylogenet. Evol. 62, 397–406 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Genner, M. J. & Turner, G. F. The mbuna cichlids of Lake Malawi: A model for rapid speciation and adaptive radiation. Fish Fish. 6, 1–34 (2005).Article 

Google Scholar 
22.Brawand, D. et al. The genomic substrate for adaptive radiation in African cichlid fish. Nature 513, 375–381 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.FAO. Fishery and Aquaculture Statistics Yearbook 2014 (Food and Agriculture Organization, 2016).
Google Scholar 
24.CMFRI. Marine Fish Landings in India 2019. Technical Report (ICAR-Central Marine Fisheries Research Institute, 2020).
Google Scholar 
25.Longhurst, A. R. & Wooster, W. S. Abundance of oil sardine (Sardinella longiceps) and upwelling in the southwest coast of India. Can. J. Fish Aquat. Sci. 47, 2407–2419 (1990).Article 

Google Scholar 
26.Krishnakumar, P. K. et al. How environmental parameters influenced fluctuations in oil sardine and mackerel fishery during 1926–2005 along the southwest coast of India. Mar. Fish. Inf. Service T & E Ser. No. 198, 1–5 (2008).
Google Scholar 
27.Xu, C. & Boyce, M. S. Oil sardine (Sardinella longiceps) off the Malabar coast: Density dependence and environmental effects. Fish. Oceanogr. 18, 359–370 (2009).Article 

Google Scholar 
28.Checkley, D. M. Jr., Asch, R. G. & Rykaczewski, R. R. Climate, anchovy and sardine. Annu. Rev. Mar. Sci. 9, 469–493 (2017).ADS 
Article 

Google Scholar 
29.Kripa, V. et al. Overfishing and climate drives changes in biology and recruitment of the Indian oil sardine Sardinella longiceps in southeastern Arabian Sea. Front. Mar. Sci. 5, 443 (2018).Article 

Google Scholar 
30.Kuthalingam, M. D. K. Observations on the life history and feeding habits of the Indian sardine, Sardinella longiceps (Cuv. & Val.). Treubia 25, 207–213 (1960).
Google Scholar 
31.Sebastian, W., Sukumaran, S., Zacharia, P. U. & Gopalakrishnan, A. Genetic population structure of Indian oil sardine, Sardinella longiceps assessed using microsatellite markers. Conserv. Genet. 18, 951–964 (2017).CAS 
Article 

Google Scholar 
32.Sebastian, W. et al. Signals of selection in the mitogenome provide insights into adaptation mechanisms in heterogeneous habitats in a widely distributed pelagic fish. Sci. Rep. UK 10, 1–14 (2020).Article 
CAS 

Google Scholar 
33.Sukumaran, S., Sebastian, W. & Gopalakrishnan, A. Population genetic structure of Indian oil sardine, Sardinella longiceps along Indian coast. Gene 576, 372–378 (2016).CAS 
PubMed 
Article 

Google Scholar 
34.Sukumaran, S. et al. Morphological divergence in Indian oil sardine, Sardinella longiceps Valenciennes, 1847 Does it imply adaptive variation?. J. Appl. Ichthyol. 32, 706–711 (2016).CAS 
Article 

Google Scholar 
35.Burgess, S. C., Treml, E. A. & Marshall, D. J. How do dispersal costs and habitat selection influence realized population connectivity?. Ecology 93, 1378–1387 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Pardoe, H. Spatial and temporal variation in life-history traits of Atlantic cod (Gadus morhua) in Icelandic waters, Reykjavik University of Iceland. PhD thesis https://doi.org/10.13140/RG.2.2.27158.70727 (2009).Article 

Google Scholar 
37.Devaraj, M. et al. Status, prospects and management of small pelagic fisheries in India. In Small Pelagic Resources and Their Fisheries in the Asia-Pacific Region: Proceedings of the APFIC Workshop (eds Devaraj, M. & Martosubroto, P.) 91–198 (Asia-Pacific Fishery Commission, Food and Agriculture Organization of the United Nations Regional Office for Asia and the Pacific, 1997).
Google Scholar 
38.Mohamed, K. S. et al. Minimum Legal Size (MLS) of capture to avoid growth overfishing of commercially exploited fish and shellfish species of Kerala. Mar. Fish. Inf. Service T & E Ser. No. 220, 3–7 (2014).
Google Scholar 
39.Hartl, D. L. & Clark, A. G. Principles of Population Genetics (Sinauer Associates, 2006).
Google Scholar 
40.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 

Google Scholar 
41.Chatterjee, A. et al. A new atlas of temperature and salinity for the North Indian Ocean. J. Earth. Syst. Sci. 121, 559–593 (2012).ADS 
Article 

Google Scholar 
42.Nair, A. K. K., Balan, K. & Prasannakumari, B. The fishery of the oil sardine (Sardinella longiceps) during the past 22 years. Indian J. Fish. 20, 223–227 (1973).
Google Scholar 
43.Krishnakumar, P. K. & Bhat, G. S. Seasonal and inter annual variations of oceanographic conditions off Mangalore coast (Karnataka, India) in the Malabar upwelling system during 1995–2004 and their influences on the pelagic fishery. Fish. Oceanogr. 17, 45–60 (2008).Article 

Google Scholar 
44.Hamza, F., Valsala, V., Mallissery, A. & George, G. Climate impacts on the landings of Indian oil sardine over the south-eastern Arabian Sea. Fish Fish. 22, 175–193 (2021).Article 

Google Scholar 
45.Shankar, D., Vinayachandran, P. N. & Unnikrishnan, A. S. The monsoon currents in the north Indian Ocean. Prog. Oceanogr. 52, 63–120 (2002).ADS 
Article 

Google Scholar 
46.Shetye, S. R. & Gouveia, A. D. Coastal Circulation in the North Indian Ocean: Coastal Segment (14, SW) (Wiley, 1998).
Google Scholar 
47.Kumar, S. P. et al. High biological productivity in the central Arabian Sea during the summer monsoon driven by Ekman pumping and lateral advection. Curr. Sci. India 1, 1633–1638 (2001).
Google Scholar 
48.Frichot, E. & Francois, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).Article 

Google Scholar 
49.Raja, A. B. T. The Indian Oil Sardine. Kochi. Central Mar. Fish. Res. Inst. Bull. No. 16, 151 (1969).
Google Scholar 
50.Nair, R. V. & Chidambaram, K. Review of the oil sardine fishery. Proc. Natl. Acad. Sci. India 17, 71–85 (1951).
Google Scholar 
51.Rijavec, L., Krishna Rao, K. & Edwin, D. G. P. Distribution and Abundance of Marine Fish Resources Off the Southwest Coast of India (Results of Acoustic Surveys, 1976–1978) (Food and Agriculture Organization of the United Nations, 1982).
Google Scholar 
52.Hauser, L. & Carvalho, G. R. Paradigm shifts in marine fisheries genetics: Ugly hypotheses slain by beautiful facts. Fish Fish. 9, 333–362 (2008).Article 

Google Scholar 
53.Catchen, J. et al. The population structure and recent colonisation history of Oregon threespine stickleback determined using restriction-site associated DNA-sequencing. Mol. Ecol. 22, 2864–2883 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Schott, F. A. & McCreary, J. P. Jr. The monsoon circulation of the Indian Ocean. Prog. Oceanogr. 51, 1–123 (2001).ADS 
Article 

Google Scholar 
55.Aykanat, T. et al. Low but significant genetic differentiation underlies biologically meaningful phenotypic divergence in a large Atlantic salmon population. Mol. Ecol. 24, 5158–5174 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Xu, J. et al. Genomic basis of adaptive evolution: the survival of Amur ide (Leuciscus waleckii) in an extremely alkaline environment. Mol. Biol. Evol. 34, 145–149 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Pappas, F. & Palaiokostas, C. Genotyping strategies using ddRAD sequencing in farmed arctic charr (Salvelinus alpinus). Animals 11, 899 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Gleason, L. U. & Burton, R. S. Genomic evidence for ecological divergence against a background of population homogeneity in the marine snail Chlorostoma funebralis. Mol. Ecol. 25, 3557–3573 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
59.Bailey, D. A., Lynch, A. H. & Hedstrom, K. S. Impact of ocean circulation on regional polar climate simulations using the Arctic Region Climate System Model. Ann. Glaciol. 25, 203–207 (1997).ADS 
Article 

Google Scholar 
60.Oomen, R. A. & Hutchings, J. A. Variation in spawning time promotes genetic variability in population responses to environmental change in a marine fish. Conserv. Physiol. 3, p.cov027 (2015).Article 
CAS 

Google Scholar 
61.Cury, P. et al. Small pelagics in upwelling systems: Patterns of interaction and structural changes in “wasp-waist” ecosystems. ICES J. Mar. Sci. 57, 603–618 (2000).Article 

Google Scholar 
62.Marshall, D. J. & Morgan, S. G. Ecological and evolutionary consequences of linked life-history stages in the sea. Curr. Biol. 21, R718–R725 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Churchill, J. H., Runge, J. & Chen, C. Processes controlling retention of spring-spawned Atlantic cod (Gadus morhua) in the western Gulf of Maine and their relationship to an index of recruitment success. Fish Oceanogr. 20, 32–46 (2011).Article 

Google Scholar 
64.John, S., Muraleedharan, K. R., Azeez, S. A. & Cazenave, P. W. What controls the flushing efficiency and particle transport pathways in a tropical estuary? Cochin Estuary, Southwest Coast of India. Water 12, 908 (2020).Article 

Google Scholar 
65.Seena, G., Muraleedharan, K. R., Revichandran, C., Azeez, S. A. & John, S. Seasonal spreading and transport of buoyant plumes in the shelf off Kochi, South west coast of India A modeling approach. Sci. Rep. UK 9, 1–15 (2019).ADS 

Google Scholar 
66.Marshall, D. J., Monro, K., Bode, M., Keough, M. J. & Swearer, S. Phenotype environment mismatches reduce connectivity in the sea. Ecol. Lett. 13, 128–140 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Gruss, A. & Robinson, J. Fish populations forming transient spawning aggregations: Should spawners always be the targets of spatial protection efforts?. ICES J. Mar. Sci. 72, 480–497 (2015).Article 

Google Scholar 
68.Chollett, I., Priest, M., Fulton, S. & Heyman, W. D. Should we protect extirpated fish spawning aggregation sites?. Biol. Conserv. 241, 108395 (2020).Article 

Google Scholar 
69.Nielsen, E. E., Hemmer-Hansen, J. A. K. O. B., Larsen, P. F. & Bekkevold, D. Population genomics of marine fishes: Identifying adaptive variation in space and time. Mol. Ecol. 18, 3128–3150 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Johannesson, K., Smolarz, K., Grahn, M. & Andre, C. The future of Baltic Sea populations: Local extinction or evolutionary rescue?. Ambio 40, 179–190 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Wang, L. et al. Population genetic studies revealed local adaptation in a high gene-flow marine fish, the small yellow croaker (Larimichthys polyactis). PLoS ONE 8, e83493 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Brennan, R. S., Hwang, R., Tse, M., Fangue, N. A. & Whitehead, A. Local adaptation to osmotic environment in killifish, Fundulus heteroclitus, is supported by divergence in swimming performance but not by differences in excess post-exercise oxygen consumption or aerobic scope. Comp. Biochem. Phys. B 196, 11–19 (2016).CAS 
Article 

Google Scholar 
73.Fan, S., Elmer, K. R. & Meyer, A. Genomics of adaptation and speciation in cichlid fishes: Recent advances and analyses in African and Neotropical lineages. Philos. T. R. Soc. B. 367, 385–394 (2012).Article 

Google Scholar 
74.Turner, T. L. & Hahn, M. W. Genomic islands of speciation or genomic islands and speciation?. Mol. Ecol. 19, 848–850 (2010).PubMed 
Article 

Google Scholar 
75.Seehausen, O. et al. Genomics and the origin of species. Nat. Rev. Genet. 15, 176 (2014).CAS 
PubMed 
Article 

Google Scholar 
76.Wolf, J. B. & Ellegren, H. Making sense of genomic islands of differentiation in light of speciation. Nat. Rev. Genet. 18, 87 (2017).CAS 
PubMed 
Article 

Google Scholar 
77.Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
78.Christensen, C., Jacobsen, M. W., Nygaard, R. & Hansen, M. M. Spatiotemporal genetic structure of anadromous Arctic char (Salvelinus alpinus) populations in a region experiencing pronounced climate change. Conserv. Genet. 19, 687–700 (2018).Article 

Google Scholar 
79.Nielsen, E. E. et al. Genomic signatures of local directional selection in a high gene flow marine organism; the Atlantic cod (Gadus morhua). BMC Evol. Biol. 9, 1–11 (2009).Article 
CAS 

Google Scholar 
80.Vivekanandan, E., Rajagopalan, M. & Pillai, N. G. K. Recent trends in sea surface temperature and its impact on oil sardine. In Global Climate Change and Indian Agriculture (eds Aggarwal, P. K. et al.) 89–92 (Indian Council of Agricultural Research, 2009).
Google Scholar 
81.DeTolla, L. J. et al. Guidelines for the care and use of fish in research. Ilar J. 1(37), 159–173 (1995).Article 

Google Scholar 
82.Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: An analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
83.Andrews, S. FASTQC. A Quality Control Tool for High Throughput Sequence Data (Babraham Institute, 2010).
Google Scholar 
84.Paris, J. R., Stevens, J. R. & Catchen, J. M. Lost in parameter space: A road map for stacks. Methods Ecol. Evol. 8, 1360–1373 (2017).Article 

Google Scholar 
85.Rousset, F. genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
Article 

Google Scholar 
86.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 

Google Scholar 
88.Felsenstein, J. PHYLIP—Phylogeny inference package (Version 3.2). Cladistics 5, 164–166 (1989).
Google Scholar 
89.Andrew, R. Tree Figure Drawing Tool Version 1.4.2 2006–2014 (Institute of Evolutionary, Biology University of Edinburgh, 2014).
Google Scholar 
90.Bonnet, E. & Van de Peer, Y. zt: A sofware tool for simple and partial mantel tests. J. Stat. Softw. 7, 1 (2002).Article 

Google Scholar 
91.Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
93.Lischer, H. E. & Excoffier, L. PGDSpider: An automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: Calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
95.Chen, C., Liu, H. & Beardsley, R. C. An unstructured grid, finite-volume, three-dimensional, primitive equations ocean model: Application to coastal ocean and estuaries. J. Atmos. Ocean. Technol. 20, 159–186 (2003).ADS 
Article 

Google Scholar  More

1.Heron, S. F., Maynard, J. A., van Hooidonk, R. & Eakin, C. M. Warming trends and bleaching stress of the World’s coral reefs 1985–2012. Sci. Rep. 6, 38402 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).ADS 
Article 

Google Scholar 
3.Glynn, P. W. Widespread coral mortality and the 1982–83 El Nino warming events. Environ. Conserv. 11, 133–146 (1984).Article 

Google Scholar 
4.Spalding, M. D. & Brown, B. E. Warm-water coral reefs and climate change. Science 350, 769–771 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Eakin, C. M. et al. Global coral bleaching 2014–2017. Reef Curr. 31, 1 (2016).
Google Scholar 
6.Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Rossi, S., Bramanti, L., Gori, A. & Orejas, C. An overview of the animal forests of the world. In Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds Rossi, S. et al.) 1–26 (Springer, 2017).Chapter 

Google Scholar 
8.Chimienti, G. Vulnerable forests of the pink sea fan Eunicella verrucosa in the Mediterranean Sea. Diversity 12, 176 (2020).Article 

Google Scholar 
9.Chimienti, G., De Padova, D., Mossa, M. & Mastrototaro, F. A mesophotic black coral forest in the Adriatic Sea. Sci. Rep. 10, 8504 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.FAO, Food and Agricultural Organization. International Guidelines for the Management of Deep-Sea Fisheries in the High Seas (FAO, 2009).
Google Scholar 
11.Coll, M. et al. The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats. PLoS ONE 5(8), e11842 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Lejeusne, C., Chevaldonne, P., Pergent-Martini, C., Boudouresque, C.-F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25(4), 250–260 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Marbà, N., Jordà, G., Agustí, S., Girard, C. & Duarte, C. M. Footprints of climate change on Mediterranean Sea biota. Front. Mar. Sci. 2, 56 (2015).Article 

Google Scholar 
14.Cramer, W. et al. Climate change and interconnected risks to sustainable development in the Mediterranean. Nat. Clim. Change 8, 972–980 (2018).ADS 
Article 

Google Scholar 
15.Albano, P. G. et al. Native biodiversity collapse in the eastern Mediterranean. Proc. R. Soc. B 288, 20202469 (2021).PubMed 
Article 

Google Scholar 
16.Harmelin, J. G. Biologie du corail rouge. Paramètres de populations, croissance et mortalité naturelle. Etat des connaissances en France. FAO Fish. Rep. 306, 99–103 (1984).
Google Scholar 
17.Bavestrello, G. & Boero, F. Necrosi e rigenerazione in Eunicella cavolinii (Anthozoa, Cnidaria) in Mar Ligure. Boll. Mus. Ist. Biol. Univ. Genova 52, 295–300 (1986).
Google Scholar 
18.Cerrano, C. et al. Catastrophic mass-mortality episode of gorgonians and other organisms in the Ligurian Sea (North-western Mediterranean), Summer 1999. Ecol. Lett. 3, 284–293 (2000).Article 

Google Scholar 
19.Linares, C. et al. Immediate and delayed effects of a mass mortality event on gorgonian population dynamics and benthic community structure in the NW Mediterranean Sea. Mar. Ecol. Prog. Ser. 305, 127–137 (2005).ADS 
Article 

Google Scholar 
20.Coma, R. et al. Consequences of a mass mortality in populations of Eunicella singularis (Cnidaria: Octocorallia) in Menorca (NW Mediterranean). Mar. Ecol. Prog. Ser. 327, 51–60 (2006).ADS 
Article 

Google Scholar 
21.Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).ADS 
Article 

Google Scholar 
22.Huete-Stauffer, C. et al. Paramuricea clavata (Anthozoa, Octocorallia) loss in the Marine Protected Area of Tavolara (Sardinia, Italy) due to a mass mortality event. Mar. Ecol. 32, 107–116 (2011).ADS 
Article 

Google Scholar 
23.Rubio-Portillo, E. et al. Effects of the 2015 heat wave on benthic invertebrates in the Tabarca Marine Protected Area (southeast Spain). Mar. Environ. Res. 122, 135–142 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Crisci, C., Bensoussan, N., Romano, J. C. & Garrabou, J. Temperature anomalies and mortality events in marine communities: Insights on factors behind differential mortality impacts in the NW Mediterranean. PLoS ONE 6, e23814 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Turicchia, E., Abbiati, M., Sweet, M. & Ponti, M. Mass mortality hits gorgonian forests at Montecristo Island. Dis. Aquat. Org. 131, 79–85 (2018).Article 

Google Scholar 
26.von Schuckmann, K. et al. Copernicus Marine Service Ocean State Report, issue 3. J. Oper. Oceanogr. 12(1), S1–S123 (2019).
Google Scholar 
27.Garrabou, J. et al. Collaborative database to track mass mortality events in the Mediterranean Sea. Front. Mar. Sci. 6, 707 (2019).Article 

Google Scholar 
28.Linares, C., Doak, D. F., Coma, R., Díaz, D. & Zabala, M. Life history and viability of a long-lived marine invertebrate: The octocoral Paramuricea clavata. Ecology 88, 918–928 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Linares, C., Coma, R., Garrabou, J., Díaz, D. & Zabala, M. Size distribution, density and disturbance in two Mediterranean gorgonians: Paramuricea clavata and Eunicella singularis. J. Appl. Ecol. 45(2), 688–699 (2008).Article 

Google Scholar 
30.Ponti, M., Turicchia, E., Ferro, F., Cerrano, C. & Abbiati, M. The understorey of gorgonian forests in mesophotic temperate reefs. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 1153–1166 (2018).Article 

Google Scholar 
31.Otero, M. M. et al. Overview of the conservation status of Mediterranean anthozoans. IUCN, x + 73 p (2017).32.Pastor, F., Valiente, J. A. & Khodayar, S. A. Warming Mediterranean: 38 years of increasing sea surface temperature. Remote Sens. 12(17), 2687 (2020).ADS 
Article 

Google Scholar 
33.DHI. Mike 3 Flow Model: Hydrodynamic Module-Scientific Documentation (DHI Software 2016, 2016).
Google Scholar 
34.Moore, S. K. et al. Impacts of climate variability and future climate change on harmful algal blooms and human health. Environ. Health 7(2), S4 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Piazza, G. et al. Prime osservazioni sul bloom mucillaginoso dell’estate 2018 sui fondali a coralligeno delle Isole Tremiti. Biol. Mar. Mediterr. 26(1), 320–321 (2019).
Google Scholar 
36.van de Water, J. A. J. M., Allemand, D. & Ferrier-Pagès, C. Host-microbe interactions in octocoral holobionts—Recent advances and perspectives. Microbiome 6, 64 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Bavestrello, G. et al. Mass mortality of Paramuricea clavata (Anthozoa: Cnidaria) on Portofino Promontory cliffs (Ligurian Sea). Mar. Life 4, 15–19 (1994).
Google Scholar 
38.Mistri, M. & Ceccherelli, V. U. Damage and partial mortality in the gorgonian Paramuricea clavata in the Strait of Messina (Tyrrhenian Sea). Mar. Life 5, 43–49 (1995).
Google Scholar 
39.Cerrano, C. & Bavestrello, G. Medium-term effects of dieoff of rocky benthos in the Ligurian Sea. What can we learn from gorgonians?. Chem. Ecol. 24, 73–82 (2008).Article 

Google Scholar 
40.Guiry, M. D. & Guiry, G. M. AlgaeBase (World-Wide Electronic Publication, National University of Ireland, 2021).
Google Scholar 
41.Cormaci, M., Furnari, G., Alongi, G., Catra, M. & Serio, D. The benthic algal flora on rocky substrata of the Tremiti Islands (Adriatic Sea). Plant Biosyst. 134(2), 133–152 (2000).Article 

Google Scholar 
42.Cebrian, E., Linares, C., Marschal, C. & Garrabou, J. Exploring the effects of invasive algae on the persistence of gorgonian populations. Biol. Invasions 14, 2647–2656 (2012).Article 

Google Scholar 
43.Verlaque, M., Ruitton, S., Mineur, F. & Boudouresque, C.-F. CIESM Atlas of Exotic Species of the Mediterranean: Macrophytes 1–362 (CIESM Publishers, 2015).
Google Scholar 
44.Ghabbourl, E. A. et al. Isolation of humic acid from the brown alga Pilayella littoralis. J. Appl. Phycol. 6, 459–468 (1994).Article 

Google Scholar 
45.Raberg, S., Jönsson, R. B., Björn, A., Granél, E. & Kautsky, L. Effects of Pilayella littoralis on Fucus vesiculosus recruitment: Implications for community composition. Mar. Ecol. Prog. Ser. 289, 131–139 (2005).ADS 
Article 

Google Scholar 
46.Adloff, F. et al. Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios. Clim. Dyn. 45(9–10), 2775–2802 (2015).Article 

Google Scholar 
47.Darmaraki, S. et al. Future evolution of marine heatwaves in the Mediterranean Sea. Clim. Dyn. 53, 1371–1392 (2019).Article 

Google Scholar 
48.Bavestrello, G., Cerrano, C., Zanzi, D. & Cattaneo-Vietti, R. Damage by fishing activities in the gorgonian coral Paramuricea clavata in the Ligurian Sea. Aquat. Conserv. 7, 253–262 (1997).Article 

Google Scholar 
49.Linares, C. & Doak, D. F. Forecasting the combined effects of disparate disturbances on the persistence of long-lived gorgonians: A case study of Paramuricea clavata. Mar. Ecol. Prog. Ser. 402, 59–68 (2010).ADS 
Article 

Google Scholar 
50.Chimienti, G. et al. An explorative assessment of the importance of Mediterranean Coralligenous habitat to local economy: The case of recreational diving. J. Environ. Account. Manag. 5(4), 310–320 (2017).
Google Scholar 
51.Di Camillo, C. G., Ponti, M., Bavestrello, G., Krzelj, M. & Cerrano, C. Building a baseline for habitat-forming corals by a multi-source approach, including web ecological knowledge. Biodivers. Conserv. 27, 1257–1276 (2018).Article 

Google Scholar 
52.Ingrosso, G. et al. Mediterranean bioconstructions along the Italian coast. Adv. Mar. Biol. 79, 61–136 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Chimienti, G., Angeletti, L., Rizzo, L., Tursi, A. & Mastrototaro, F. ROV vs trawling approaches in the study of benthic communities: The case of Pennatula rubra (Cnidaria: Pennatulacea). J. Mar. Biol. Assoc. U. K. 98(8), 1859–1869 (2018).Article 

Google Scholar 
54.Chimienti, G., Angeletti, L., Furfaro, G., Canese, S. & Taviani, M. Habitat, morphology and trophism of Tritonia callogorgiae sp. nov., a large nudibranch inhabiting Callogorgia verticillata forests in the Mediterranean Sea. Deep-Sea Res. Pt. I 165, 103364 (2020).Article 

Google Scholar 
55.Mastrototaro, F. et al. Mesophotic rocks dominated by Diazona violacea: A Mediterranean codified habitat. Eur. Zool. J. 87(1), 688–695 (2020).Article 

Google Scholar 
56.Walton, C. C., Pichel, W. G., Sapper, J. F. & May, D. A. The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites. J. Geophys. Res. 103(C12), 27999–28012 (1998).ADS 
Article 

Google Scholar 
57.Kilpatrick, K. A. et al. A decade of sea surface temperature from MODIS. Remote Sens. Environ. 165, 27–41 (2015).ADS 
Article 

Google Scholar 
58.Cleveland, R. B., Cleveland, W. S., McRae, J. E. & Terpenning, I. STL: A seasonal-trend decomposition procedure based on loess. J. Off. Stat. 6, 3–73 (1990).
Google Scholar 
59.Simoncelli, S. et al. Mediterranean Sea Physical Reanalysis (CMEMS MED-Physics) (Copernicus Monitoring Environment Marine Service (CMEMS), 2019). https://doi.org/10.25423/MEDSEA_REANALYSIS_PHYS_006_004.60.Copernicus Climate Change Service (C3S). ERA5: Fifth Generation of ECMWF Atmospheric Reanalyses of the Global Climate (Copernicus Climate Change Service Climate Data Store (CDS), 2017). https://cds.climate.copernicus.eu/cdsapp#!/home.61.Xie, P. & Arkin, P. A. Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteor. Soc. 78, 2539–2558 (1997).ADS 
Article 

Google Scholar 
62.Rodi, W. Examples of calculation methods for flow and mixing in stratified fluids. J. Geophys. Res. Ocean 92(C5), 5305–5328 (1987).ADS 
Article 

Google Scholar 
63.Galperin, B. & Orszag, S. A. Large Eddy Simulation of Complex Engineering and Geophysical Flows 3–36 (Cambridge University Press, 1993).
Google Scholar 
64.De Padova, D., De Serio, F., Mossa, M. & Armenio, E. Investigation of the current circulation offshore Taranto by using field measurements and numerical model. In Proceedings of the IEEE International Instrumentation and Measurement Technology Conference 1–5 (IEEE, 2017).65.Armenio, E., De Padova, D., De Serio, F. & Mossa, M. Monitoring system for the sea: Analysis of meteo, wave and current data. In Workshop on Metrology for the Sea, MetroSea 2017: Learning to Measure Sea Health Parameters 143–148 (IMEKO TC19, 2017).66.Armenio, E., Ben Meftah, M., De Padova, D., De Serio, F. & Mossa, M. Monitoring systems and numerical models to study coastal sites. Sensors 19(7), 1552 (2019).ADS 
PubMed Central 
Article 

Google Scholar 
67.Chu, P. C. & Fan, C. Global ocean synoptic thermocline gradient, isothermal-layer depth, and other upper ocean parameters. Sci. Data 6, 119 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Clementi, E. et al. Mediterranean Sea Analysis and Forecast (CMEMS MED-Currents, EAS5 System) (Copernicus Monitoring Environment Marine Service (CMEMS), 2019). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSIS_FORECAST_PHY_006_013_EAS5. More

1.Dulac, J. Global land transport infrastructure requirements. (2013).2.Baker, C. J., Chapman, L., Quinn, A. & Dobney, K. Climate change and the railway industry: A review. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 224, 519–528 (2010).Article 

Google Scholar 
3.IEA. The Future of Rail – Opportunities for energy and the environment. (2019). doi:https://doi.org/10.1787/9789264312821-en4.Popp, J. N. & Boyle, S. P. Railway ecology: Underrepresented in science?. Basic Appl. Ecol. 19, 84–93 (2017).Article 

Google Scholar 
5.IRF. IRF World Road Statistics 2019. (2019).6.UIC. Railisa UIC Statistics. (2019).7.Van Der Ree, R., Smith, D. J. & Grilo, C. Handbook of Road Ecology (Wiley, 2015). https://doi.org/10.1002/9781118568170.Book 

Google Scholar 
8.Railway Ecology. (Springer Open, 2017). https://doi.org/10.1007/978-3-319-57496-7_199.Barrientos, R. & Borda-de-Água, L. Railways as Barriers for Wildlife: Current Knowledge. in Railway Ecology (eds. Borda-de-Água, L., Barrientos, R., Beja, P. & Pereira, H. M.) 43–64 (Springer Open, 2017).10.Jackson, N. D. & Fahrig, L. Relative effects of road mortality and decreased connectivity on population genetic diversity. Biol. Conserv. 144, 3143–3148 (2011).Article 

Google Scholar 
11.van der Grift, E. Mammals and railroads: impacts and management implications. Lutra 42, 77–98 (1999).
Google Scholar 
12.Heske, E. J. Blood on the Tracks: Track Mortality and Scavenging Rate in Urban Nature Preserves. Urban Nat. 2, 1–13 (2015).
Google Scholar 
13.Huber, D., Kusak, J. & Frkovic, A. Traffic kills of brown bears in Gorski kotar, Croatia. Ursus 10, 167–171 (1998).
Google Scholar 
14.Waller, J. S. & Servheen, C. Effects of transportation infrastructure on grizzly bears in Northwestern Montana. J. Wildl. Manag. 69, 985–1000 (2005).Article 

Google Scholar 
15.Trombulak, S. C. & Frissell, C. A. Review of ecological effects of roads on terrestrial and aquatic communities. Conserv. Biol. 14, 18–30 (2000).Article 

Google Scholar 
16.Fahrig, L. & Rytwinski, T. Effects of roads on animal abundance: An empirical review and synthesis. Ecol. Soc. 14, 21 (2009).Article 

Google Scholar 
17.Kušta, T., Keken, Z., Ježek, M. & Kůta, Z. Effectiveness and costs of odor repellents in wildlife-vehicle collisions: A case study in Central Bohemia, Czech Republic. Transp. Res. Part D Transp. Environ. 38, 1–5 (2015).Article 

Google Scholar 
18.UIC. Railway noise in Europe – State of the art report. (2016).19.UIC. Railway induced vibration – State of the art report. (2017).20.Frost, M. & Ison, S. Comparison of noise impacts from urban transport. Proc. Inst. Civ. Eng. Transp. 160, 165–172 (2007).
Google Scholar 
21.Thompson, D. Railway Noise and Vibration-Mechanisms (Elsevier Ltd, 2009).
Google Scholar 
22.Vandevelde, J. C., Bouhours, A., Julien, J. F., Couvet, D. & Kerbiriou, C. Activity of European common bats along railway verges. Ecol. Eng. 64, 49–56 (2014).Article 

Google Scholar 
23.Barrientos, R., Ascensão, F., Beja, P., Pereira, H. M. & Borda-de-Água, L. Railway ecology vs. road ecology: similarities and differences. Eur. J. Wildl. Res. 65, (2019).24.Dorsey, B., Olsson, M. & Rew, L. J. Ecological effects of railways on wildlife. Handb. Road Ecol. https://doi.org/10.1002/9781118568170.ch26 (2015).Article 

Google Scholar 
25.Mickleburgh, S. P., Hutson, A. M. & Racey, P. A. A review of the global conservation status of bats. Oryx 36, 18–34 (2002).Article 

Google Scholar 
26.Ávila-Flores, R., Bolaina-Badal, A. L., Gallegos-Ruiz, A. & Sánchez-Gómez, W. S. Use of linear features by the common vampire bat (Desmodus rotundus) in a tropical cattle-ranching landscape. Therya 10, 229–234 (2019).Article 

Google Scholar 
27.Limpens, H. J. G. A. & Kapteyn, K. Bats, their behavior and linear landscape elements. Myotis 29, 39–48 (1991).
Google Scholar 
28.Verboom, B. & Huitema, H. The importance of linear landscape elements for the pipistrelle Pipistrellus pipistrellus and the serotine bat Eptesicus serotinus. Landsc. Ecol. 12, 117–125 (1997).Article 

Google Scholar 
29.Verboom, B. & Spoelstra, K. Effects of food abundance and wind on the use of tree lines by an insectivorous bat Pipistrellus pipistrellus. Can. J. Zool. 77, 1393–1401 (1999).Article 

Google Scholar 
30.Zurcher, A. A., Sparks, D. W. & Bennett, V. J. Why the bat did not cross the road?. Acta Chiropterol. 12, 337–340 (2010).Article 

Google Scholar 
31.Bennett, V. J. & Zurcher, A. A. When corridors collide: Road-related disturbance in commuting bats. J. Wildl. Manage. 77, 93–101 (2013).Article 

Google Scholar 
32.Anderson, D. & Wheatley, N. Mitigation of Wheel Squeal and Flanging Noise on the Australian Rail Network. in Noise and Vibration Mitigation for Rail Transportation Systems (eds. Schulte-Werning, B. et al.) 399–405 (Springer Berlin Heidelberg, 2007). doi:https://doi.org/10.1007/978-3-540-74893-9_5633.Rudd, M. J. Wheel/rail noise—Part II: Wheel squeal. J. Sound Vib. 46, 381–394 (1976).ADS 
Article 

Google Scholar 
34.Schaub, A., Ostwald, J. & Siemers, B. M. Foraging bats avoid noise. J. Exp. Biol. 211, 3174–3180 (2008).PubMed 
Article 

Google Scholar 
35.Luo, J., Siemers, B. M. & Koselj, K. How anthropogenic noise affects foraging. Glob. Chang. Biol. 21, 3278–3289 (2015).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Siemers, B. M. & Schaub, A. Hunting at the highway: Traffic noise reduces foraging efficiency in acoustic predators. Proc. R. Soc. B Biol. Sci. 278, 1646–1652 (2011).Article 

Google Scholar 
37.Schlaepfer, M. A., Runge, M. C. & Sherman, P. W. Ecological and evolutionary traps. Trends Ecol. Evol. 17, 474–480 (2002).Article 

Google Scholar 
38.Kaňuch, P., Fornůsková, A., Bartonička, T., Bryja, J. & Řehák, Z. Do two cryptic pipistrelle bat species differ in their autumn and winter roosting strategies within the range of sympatry?. Folia Zool. 59, 102–107 (2010).Article 

Google Scholar 
39.Dietz, C. & Kiefer, A. Bats of Britain and Europe (Bloomsbury Natural History, 2016).
Google Scholar 
40.Schnitzler, H. U. & Kalko, E. K. V. Echolocation by insect-eating bats. Bioscience 51, 557–569 (2001).Article 

Google Scholar 
41.Russ, J. M. & Montgomery, W. I. Habitat associations of bats in Northern Ireland: Implications for conservation. Biol. Conserv. 108, 49–58 (2002).Article 

Google Scholar 
42.Rachwald, A., Bradford, T., Borowski, Z. & Racey, P. A. Habitat preferences of soprano Pipistrelle Pipistrellus pygmaeus (Leach, 1825) and common Pipistrelle Pipistrellus pipistrellus (Schreber, 1774) in two different Woodlands in North East Scotland. Zool. Stud. 55, 1–8 (2016).
Google Scholar 
43.Nicholls, B. & Racey, A. Habitat selection as a mechanism of resource partitioning in two cryptic bat species Pipistrellus pipistrellus and Pipistrellus pygmaeus. Ecography (Cop.) 29, 697–708 (2006).Article 

Google Scholar 
44.Ciechanowski, M. Habitat preferences of bats in anthropogenically altered, mosaic landscapes of northern Poland. Eur. J. Wildl. Res. 61, 415–428 (2015).Article 

Google Scholar 
45.Mathews, F. et al. Barriers and benefits: Implications of artificial night-lighting for the distribution of common bats in britain and ireland. Philos. Trans. R. Soc. B Biol. Sci. 370, (2015).46.Spoelstra, K. et al. Experimental illumination of natural habitat—an experimental set-up to assess the direct and indirect ecological consequences of artificial light of different spectral composition. Philos. Trans. R. Soc. B Biol. Sci. 370, (2015).47.Brown, A. M. An investigation of the cochlear microphonic response of two species of echolocating bats: Rousettus aegyptiacus (geoffroy) and Pipistrellus pipistrellus (Schreber). J. Comp. Physiol. 83, 407–413 (1973).Article 

Google Scholar 
48.Wong, J. G. & Waters, D. A. The synchronisation of signal emission with wingbeat during the approach phase in soprano pipistrelles (Pipistrellus pygmaeus). J. Exp. Biol. 204, 575–583 (2001).CAS 
PubMed 
Article 

Google Scholar 
49.Adams, A. M., Jantzen, M. K., Hamilton, R. M. & Fenton, M. B. Do you hear what I hear? Implications of detector selection for acoustic monitoring of bats. Methods Ecol. Evol. 3, 992–998 (2012).Article 

Google Scholar 
50.Lintott, P. R. et al. Ecobat: An online resource to facilitate transparent, evidence-based interpretation of bat activity data. Ecol. Evol. 8, 935–941 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Shaw-Taylor, L. & You, X. The development of the railway network in Britain 1825–1911. in The Online Historical Atlas of Transport, Urbanization and Economic Development in England and Wales c.1680–1911 (eds. Shaw-Taylor, L., Bogart, D. & Satchell, M.) (2018).52.Hatano, L., Smith, R. A. & Hillmansen, S. International railway comparisons. Proc. Inst. Mech Eng. Part F J. Rail Rapid Transit 221, 117–123 (2007).Article 

Google Scholar 
53.Robinson, R. A. & Sutherland, W. J. Post-war changes in arable farming and biodiversity in Great Britain. J. Appl. Ecol. 39, 157–176 (2002).Article 

Google Scholar 
54.Myczko, Ł et al. Effects of local roads and car traffic on the occurrence pattern and foraging behaviour of bats. Transp. Res. Part D Transp. Environ. 56, 222–228 (2017).Article 

Google Scholar 
55.Ueda, K., Sekoguchi, T. & Yanagisawa, H. How predictability affects habituation to novelty ?. Biorxiv https://doi.org/10.1101/2020.07.24.219253 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
56.JNCC & Bat Conservation Trust. National Bat Monitoring Programme annual report. (2019).57.Voigt, C. C. & Kingston, T. Bats in the Anthropocene. in Bats in the Anthropocene: Conservation of Bats in a Changing World 245–262 (2015). doi:https://doi.org/10.1007/978-3-319-25220-9_958.Burgin, C. J., Colella, J. P., Kahn, P. L. & Upham, N. S. How many species of mammals are there?. J. Mammal. 99, 1–14 (2018).Article 

Google Scholar 
59.Frick, W. F., Kingston, T. & Flanders, J. A review of the major threats and challenges to global bat conservation. Ann. N. Y. Acad. Sci. https://doi.org/10.1111/nyas.14045 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
60.Fenton, M. B. A technique for monitoring bat activity with results obtained from different environments in southern Ontario. Can. J. Zool. 48, 847–851 (1970).Article 

Google Scholar 
61.Švec, J. G. & Granqvist, S. Tutorial and guidelines on measurement of sound pressure level in voice and speech. J. Speech Lang. Hear. Res. 61, 441–461 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Boersma, P. & Weenink, D. Praat: doing phonetics by computer. (2019).63.Sueur, J., Aubin, T. & Simonis, C. Seewave, a free and modular tool for sound analysis and synthesis. Bioacoustics-the Int. J. Anim. Sound Its Rec. 18, 213–226 (2008).
Google Scholar 
64.Harrell, F. E. Hmisc: Harrell Miscellaneous. (2014).65.Met Office. MIDAS: UK Hourly Weather Observation Data. NCAS Br. Atmos. Data Cent. (2019).66.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2019).67.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378 (2017).Article 

Google Scholar 
68.Swift, S. M. Activity patterns of Pipistrelle bats (Pipistrellus pipistrellus) in north-east Scotland. J. Zool. 190, 285–295 (2009).Article 

Google Scholar 
69.Petrželková, K. J., Downs, N. C., Zukal, J. & Racey, P. A. A comparison between emergence and return activity in pipistrelle bats Pipistrellus pipistrellus and P. pygmaeus. Acta Chiropterol. 8, 381–390 (2006).Article 

Google Scholar 
70.Ciechanowski, M., Zając, T., Biłas, A. & Dunajski, R. Spatiotemporal variation in activity of bat species differing in hunting tactics: Effects of weather, moonlight, food abundance, and structural clutter. Can. J. Zool. 85, 1249–1263 (2007).Article 

Google Scholar 
71.Bejder, L., Samuels, A., Whitehead, H., Finn, H. & Allen, S. Impact assessment research: Use and misuse of habituation, sensitisation and tolerance in describing wildlife responses to anthropogenic stimuli. Mar. Ecol. Prog. Ser. 395, 177–185 (2009).ADS 
Article 

Google Scholar 
72.Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 
Article 

Google Scholar 
73.Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 2018, 1–32 (2018).
Google Scholar 
74.Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).Article 

Google Scholar 
75.Barton, K. MuMIn: Multi-Model Inference (R Package v3). (2017).76.Pasch, B., Bolker, B. M. & Phelps, S. M. Interspecific dominance via vocal interactions mediates altitudinal zonation in neotropical singing mice. Am. Nat. 182, 2 (2013).Article 

Google Scholar 
77.Bates, D., Kliegl, R., Vasishth, S. & Baayen, H. Parsimonious mixed models. (2015).78.Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level / mixed) regression models. (2020).79.Lüdecke, D. sjPlot: Data visualization for statistics in social science. (2020). More

1.Lande, R. & Shannon, S. The role of genetic variation in adaptation and population persistence in a changing environment. Evolution 50, 434–437 (1996).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Barrett, R. D. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Young, A., Boyle, T. & Brown, T. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11, 413–418 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Cushman, S. A. Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol. Conserv. 128, 231–240 (2006).Article 

Google Scholar 
5.Opdam, P. & Wascher, D. Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biol. Conserv. 117, 285–297 (2004).Article 

Google Scholar 
6.Broadhurst, L. M. et al. Seed supply for broadscale restoration: maximizing evolutionary potential. Evol. Appl. 1, 587–597 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Vitt, P., Havens, K., Kramer, A. T., Sollenberger, D. & Yates, E. Assisted migration of plants: changes in latitudes, changes in attitudes. Biol. Conserv. 143, 18–27 (2010).Article 

Google Scholar 
8.Aitken, S. N. & Bemmels, J. B. Time to get moving: assisted gene flow of forest trees. Evol. Appl. 9, 271–290 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Evans, B. J. et al. Speciation over the edge: gene flow among non-human primate species across a formidable biogeographic barrier. R. Soc. Open Sci. 4, 170351 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Weeks, A. R. et al. Assessing the benefits and risks of translocations in changing environments: a genetic perspective. Evol. Appl. 4, 709–725 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Pavlova, A. et al. Severe consequences of habitat fragmentation on genetic diversity of an endangered Australian freshwater fish: a call for assisted gene flow. Evol. Appl. 10, 531–550 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
12.Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).Article 

Google Scholar 
13.Rajpurohit, S. & Nedved, O. Clinal variation in fitness related traits in tropical drosophilids of the Indian subcontinent. J. Therm. Biol. 38, 345–354 (2013).Article 

Google Scholar 
14.Kawecki, T. J. & Ebert, D. Conceptual issues in local adaptation. Ecol. Lett. 7, 1225–1241 (2004).Article 

Google Scholar 
15.Kottler, E. J., Dickman, E. E., Sexton, J. P., Emery, N. C. & Franks, S. J. Draining the swamp hypothesis: little evidence that gene flow reduces fitness at range edges. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.02.004 (2021).16.Kelly, E. & Phillips, B. L. Targeted gene flow for conservation. Conserv. Biol. 30, 259–267 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Macdonald, S. L., Llewelyn, J., Moritz, C. & Phillips, B. L. Peripheral isolates as sources of adaptive diversity under climate change. Front. Ecol. Evol. 5, 88 (2017).Article 

Google Scholar 
18.Edmands, S. Between a rock and a hard place: evaluating the relative risks of inbreeding and outbreeding for conservation and management. Mol. Ecol. 16, 463–475 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Edmands, S. Heterosis and outbreeding depression in interpopulation crosses spanning a wide range of divergence. Evolution 53, 1757–1768 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Frankham, R. et al. Predicting the probability of outbreeding depression. Conserv. Biol. 25, 465–475 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Whiteley, A. R., Fitzpatrick, S. W., Funk, W. C. & Tallmon, D. A. Genetic rescue to the rescue. Trends Ecol. Evol. 30, 42–49 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Schierup, M. H. & Christiansen, F. B. Inbreeding depression and outbreeding depression in plants. Heredity 77, 461–468 (1996).Article 

Google Scholar 
23.Bjorkman, A. D., Vellend, M., Frei, E. R. & Henry, G. H. Climate adaptation is not enough: warming does not facilitate success of southern tundra plant populations in the high Arctic. Glob. Change Biol. 23, 1540–1551 (2017).Article 

Google Scholar 
24.Frankham, R. Where are we in conservation genetics and where do we need to go? Conserv. Genet. 11, 661–663 (2010).Article 

Google Scholar 
25.Tallmon, D. A., Luikart, G. & Waples, R. S. The alluring simplicity and complex reality of genetic rescue. Trends Ecol. Evol. 19, 489–496 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Weeks, A. R. et al. Genetic rescue increases fitness and aids rapid recovery of an endangered marsupial population. Nat. Commun. 8, 1071 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Le Cam, S., Perrier, C., Besnard, A.-L., Bernatchez, L. & Evanno, G. Genetic and phenotypic changes in an Atlantic salmon population supplemented with non-local individuals: a longitudinal study over 21 years. Proc. Roy. Soc. B-Biol. Sci. 282, 20142765 (2015).Article 
CAS 

Google Scholar 
28.Fitzpatrick, S. W. et al. Gene flow from an adaptively divergent source causes rescue through genetic and demographic factors in two wild populations of Trinidadian guppies. Evol. Appl. 9, 879–891 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Robinson, Z. L. et al. Experimental test of genetic rescue in isolated populations of brook trout. Mol. Ecol. 26, 4418–4433 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Byrne, P. G. & Silla, A. J. An experimental test of the genetic consequences of population augmentation in an amphibian. Conserv. Sci. Pract. 2, e194 (2020).31.Stuart, S. N. et al. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783–1786 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Urban, M. C., Richardson, J. L. & Freidenfelds, N. A. Plasticity and genetic adaptation mediate amphibian and reptile responses to climate change. Evol. Appl. 7, 88–103 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Carey, C. & Alexander, M. A. Climate change and amphibian declines: is there a link? Divers. Distrib. 9, 111–121 (2003).Article 

Google Scholar 
34.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Pounds, J. A. et al. Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439, 161–167 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Rudin-Bitterli, T. S., Evans, J. P. & Mitchell, N. J. Geographic variation in adult and embryonic desiccation tolerance in a terrestrial-breeding frog. Evolution 74, 1186–1199 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Eads, A., Mitchell, N. J. & Evans, J. Patterns of genetic variation in desiccation tolerance in embryos of the terrestrial-breeding frog, Pseudophryne guentheri. Evolution 66, 2865–2877 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Cummins, D., Kennington, W. J., Rudin‐Bitterli, T. & Mitchell, N. J. A genome‐wide search for local adaptation in a terrestrial‐breeding frog reveals vulnerability to climate change. Glob. Change Biol. 25, 3151–3162 (2019).Article 

Google Scholar 
40.Bureau of Meteorology. Climate Data Online, http://www.bom.gov.au/climate/data/ (2020).41.Turelli, M. & Moyle, L. C. Asymmetric postmating isolation: Darwin’s corollary to Haldane’s rule. Genetics 176, 1059–1088 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Dobzhansky, T. Studies on hybrid sterility. II. Localization of sterility factors in Drosophila pseudoobscura hybrids. Genetics 21, 113 (1936).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Muller, H. J. Isolating mechanisms, evolution and temperature. Biol. Symp. 6, 71–125 (1942).
Google Scholar 
44.Orr, H. A. The population genetics of speciation: the evolution of hybrid incompatibilities. Genetics 139, 1805–1813 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Arntzen, J. W., Jehle, R., Bardakci, F., Burke, T. & Wallis, G. P. Asymmetric viability of reciprocal-cross hybrids between crested and marbled newts (Trituris cristatus and Trituris marmoratus). Evolution 63, 1191–1202 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Lee-Yaw, J. A., Jacobs, C. G. C. & Irwin, D. E. Individual performance in relation to cytonuclear discordance in a northern contact zone between long-toed salamander (Ambystoma macrodactylum) lineages. Mol. Ecol. 23, 4590–4602 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Sanchez, S. et al. Within-colony spatial segregation leads to foraging behaviour variation in a seabird. Mar. Ecol. Prog. Ser. 606, 215–230 (2018).Article 

Google Scholar 
48.Sasa, M. M., Chippindale, P. T. & Johnson, N. A. Patterns of postzygotic isolation in frogs. Evolution 52, 1811–1820 (1998).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Sánchez‐Guillén, R., Córdoba‐Aguilar, A., Cordero‐Rivera, A. & Wellenreuther, M. Genetic divergence predicts reproductive isolation in damselflies. J. Evol. Biol. 27, 76–87 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
50.Coyne, J. A. & Orr, H. A. Patterns of speciation in Drosophila. Evolution 43, 362–381 (1989).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Kelemen, L. & Moritz, C. Comparative phylogeography of a sibling pair of rainforest Drosophila species (Drosophila serrata and D. birchii). Evolution 53, 1306–1311 (1999).PubMed 
PubMed Central 

Google Scholar 
52.Hercus, M. J. & Hoffmann, A. A. Desiccation resistance in interspecific Drosophila crosses: genetic interactions and trait correlations. Genetics 151, 1493–1502 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Rudin-Bitterli, T. S., Mitchell, N. J. & Evans, J. P. Extensive geographical variation in testes size and ejaculate traits in a terrestrial-breeding frog. Biol. Lett. 16, 20200411 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Shaver, J., Barch, S. & Shivers, C. Tissue-specificity of frog egg-jelly antigens. J. Exp. Zool. 151, 95–103 (1962).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Bradford, D. F. & Seymour, R. S. Influence of environmental PO2 on embryonic oxygen consumption, rate of development, and hatching in the frog, Pseudophryne bibroni. Physiol. Zool. 61, 475–482 (1988).Article 

Google Scholar 
56.Seymour, R. S., Geiser, F. & Bradford, D. F. Metabolic cost of development in terrestrial frog eggs (Pseudophryne bibronii). Physiol. Zool. 64, 688–696 (1991).Article 

Google Scholar 
57.Warkentin, K. M. Adaptive plasticity in hatching age: a response to predation risk trade-offs. Proc. Natl Acad. Sci. USA 92, 3507–3510 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Webb, P. Effect of body form and response threshold on the vulnerability of four species of teleost prey attacked by largemouth bass (Micropterus salmoides). Can. J. Fish. Aquat. Sci. 43, 763–771 (1986).Article 

Google Scholar 
59.Watkins, T. B. Predator-mediated selection on burst swimming performance in tadpoles of the Pacific tree frog, Pseudacris regilla. Physiol. Zool. 69, 154–167 (1996).Article 

Google Scholar 
60.Wilson, R. & Franklin, C. Thermal acclimation of locomotor performance in tadpoles of the frog Limnodynastes peronii. J. Comp. Physiol. B 169, 445–451 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Teplitsky, C. et al. Escape behaviour and ultimate causes of specific induced defences in an anuran tadpole. J. Evol. Biol. 18, 180–190 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Walker, J., Ghalambor, C., Griset, O., McKenney, D. & Reznick, D. Do faster starts increase the probability of evading predators? Funct. Ecol. 19, 808–815 (2005).Article 

Google Scholar 
63.Langerhans, R. B. Morphology, performance, fitness: functional insight into a post-Pleistocene radiation of mosquitofish. Biol. Lett. 5, 488–491 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
64.Plowman, M. C., Grbac-lvankovic, S., Martin, J., Hopfer, S. M. & Sunderman, F. W. Jr Malformations persist after metamorphosis of Xenopus laevis tadpoles exposed to Ni2+, Co2+, or Cd2+ in FETAX assays. Teratog. Carcinog. Mutagen. 14, 135–144 (1994).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits. Vol. 1 (Sinauer Sunderland, MA, 1998).66.Remington, D. L. & O’Malley, D. M. Whole-genome characterization of embryonic stage inbreeding depression in a selfed loblolly pine family. Genetics 155, 337–348 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Lynch, M. The genetic interpretation of inbreeding depression and outbreeding depression. Evolution 45, 622–629 (1991).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Armbruster, P., Bradshaw, W. E., Steiner, A. L. & Holzapfel, C. M. Evolutionary responses to environmental stress by the pitcher-plant mosquito, Wyeomyia smithii. Heredity 83, 509–519 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Marr, A. B., Keller, L. F. & Arcese, P. Heterosis and outbreeding depression in descendants of natural immigrants to an inbred population of song sparrows (Melospiza melodia). Evolution 56, 131–142 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Marshall, T. & Spalton, J. Simultaneous inbreeding and outbreeding depression in reintroduced Arabian oryx. Anim. Conserv. 3, 241–248 (2000).Article 

Google Scholar 
71.Rudin-Bitterli, T. S., Mitchell, N. J. & Evans, J. P. Environmental stress increases the magnitude of nonadditive genetic variation in offspring fitness in the frog Crinia georgiana. Am. Nat. 192, 461–478 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
72.Drummond, E., Short, E. & Clancy, D. Mitonuclear gene X environment effects on lifespan and health: How common, how big? Mitochondrion 49, 12–18 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Morales, H. E. et al. Concordant divergence of mitogenomes and a mitonuclear gene cluster in bird lineages inhabiting different climates. Nat. Ecol. Evol. 2, 1258–1267 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
74.Schmid, M., Evans, B. J. & Bogart, J. P. Polyploidy in amphibia. Cytogenet. Genome Res. 145, 315–330 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
75.Silla, A. J. Artificial fertilisation in a terrestrial toadlet (Pseudophryne guentheri): effect of medium osmolality, sperm concentration and gamete storage. Reprod. Fertil. Dev. 25, 1134–1141 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
76.Phillip, G. B. & Keogh, J. S. Extreme sequential polyandry insures against nest failure in a frog. Proc. Roy. Soc. B-Biol. Sci. 276, 115–120 (2009).Article 

Google Scholar 
77.Brandies, P., Peel, E., Hogg, C. J. & Belov, K. The value of reference genomes in the conservation of threatened species. Genes 10, 846 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
78.Scheele, B. C. et al. Interventions for reducing extinction risk in chytridiomycosis‐threatened amphibians. Conserv. Biol. 28, 1195–1205 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
79.Osborne, W. S. & Norman, J. A. Conservation genetics of Corroboree frogs, Psuedophryne corroboree (Anura: Myobatrachidae): population subdivision and genetic divergence. Aust. J. Zool. 39, 285–297 (1991).Article 

Google Scholar 
80.Browne, R. K. et al. Sperm collection and storage for the sustainable management of amphibian biodiversity. Theriogenology 133, 187–200 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
81.Silla, A. J. & Byrne, P. G. Hormone-induced ovulation and artificial fertilisation in four terrestrial-breeding anurans. Reprod. Fertil. Dev. https://doi.org/10.1071/RD20243 (2021).82.O’Brien, D. M., Keogh, J. S., Silla, A. J. & Byrne, P. G. Female choice for related males in wild red-backed toadlets (Pseudophryne coriacea). Behav. Ecol. 30, 928–937 (2019).Article 

Google Scholar 
83.Gosner, K. L. A simplified table for staging anuran embryos and larvae with notes on identification. Herpetologica 16, 183–190 (1960).
Google Scholar 
84.Anstis, M. Tadpoles and Frogs of Australia. (New Holland Publishers, 2013).85.CSIRO, and Bureau of Meteorology. State of the Climate 2018 (CSIRO Publishing, 2018).86.Andrich, M. A. & Imberger, J. The effect of land clearing on rainfall and fresh water resources in Western Australia: a multi-functional sustainability analysis. Int. J. Sustain. Dev. World Ecol. 20, 549–563 (2013).Article 

Google Scholar 
87.Raut, B. A., Jakob, C. & Reeder, M. J. Rainfall changes over southwestern Australia and their relationship to the Southern Annular Mode and ENSO. J. Clim. 27, 5801–5814 (2014).Article 

Google Scholar 
88.Arnold, G. in Greenhouse: Planning for Climate Change (ed. Pearman, G. I.) 375–386 (CSIRO Publishing, 1988).89.Hobbs, R. J. Effects of landscape fragmentation on ecosystem processes in the Western Australian wheatbelt. Biol. Conserv. 64, 193–201 (1993).Article 

Google Scholar 
90.Silla, A. J. Effect of priming injections of luteinizing hormone-releasing hormone on spermiation and ovulation in Gϋnther’s toadlet, Pseudophryne guentheri. Reprod. Biol. Endocrinol. 9, 68 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
91.Lymbery, R. A., Kennington, W. J. & Evans, J. P. Multivariate sexual selection on ejaculate traits under sperm competition. Am. Nat. 192, 94–104 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
92.Browne, R. K., Clulow, J. & Mahony, M. Short-term storage of cane toad (Bufo marinus) gametes. Reproduction 121, 167–173 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Kouba, A. J., Vance, C. K., Frommeyer, M. A. & Roth, T. L. Structural and functional aspects of Bufo americanus spermatozoa: effects of inactivation and reactivation. J. Exp. Zool. A. Comp. Exp. Biol. 295, 172–182 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
94.Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with Image. J. Biophotonics Int. 11, 36–42 (2004).
Google Scholar 
95.Noldus, L. P., Spink, A. J. & Tegelenbosch, R. A. EthoVision: a versatile video tracking system for automation of behavioral experiments. Behav. Res. Methods Instrum. Comput. 33, 398–414 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2014).97.Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
98.Harrison, X. A. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2, e616 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
99.Rudin-Bitterli, T. S., Evans, J. P. & Mitchell, N. J. Fitness consequences of targeted gene flow to counter impacts of drying climates on terrestrial-breeding frogs. Data sets. https://doi.org/10.5061/dryad.6m905qg09 (2021). More

1.Steenweg, R. et al. Scaling-up camera traps: monitoring the planet’s biodiversity with networks of remote sensors. Front. Ecol. Environ. 15, 26–34 (2017).Article 

Google Scholar 
2.Rich, L. N. et al. Assessing global patterns in mammalian carnivore occupancy and richness by integrating local camera trap surveys. Global Ecol. Biogeogr. 26, 918–929 (2017).Article 

Google Scholar 
3.Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).Article 

Google Scholar 
4.Ahumada, J. A. et al. Wildlife insights: a platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. Environ. Conserv. 47, 1–6 (2020).MathSciNet 
Article 

Google Scholar 
5.Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl Acad. Sci. 115, E5716–E5725 (2018).Article 

Google Scholar 
6.Miao, Z. et al. Insights and approaches using deep learning to classify wildlife. Sci. Rep. 9, 8137 (2019).Article 

Google Scholar 
7.Liu, Z. et al. Large-scale long-tailed recognition in an open world. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2537–2546 (IEEE, 2019).8.Liu, Z. et al. Open compound domain adaptation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 12406–12415 (IEEE, 2020).9.Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).Article 

Google Scholar 
10.Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).Article 

Google Scholar 
11.Ripple, W. J. et al. Conserving the world’s megafauna and biodiversity: the fierce urgency of now. Bioscience 67, 197–200 (2017).Article 

Google Scholar 
12.Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).Article 

Google Scholar 
13.O’Connell, A. F., Nichols, J. D. & Karanth, K. U. Camera Traps in Animal Ecology: Methods and Analyses (Springer Science & Business Media, 2010).14.Burton, A. C. et al. Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article 

Google Scholar 
15.Kays, R., McShea, W. J. & Wikelski, M. Born-digital biodiversity data: millions and billions. Divers. Distrib. 26, 644–648 (2020).Article 

Google Scholar 
16.Swanson, A. et al. Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Sci. Data 2, 1–14 (2015).Article 

Google Scholar 
17.Ahumada, J. A. et al. Community structure and diversity of tropical forest mammals: data from a global camera trap network. Philos. Trans. R. Soc. B Biol. Sci. 366, 2703–2711 (2011).Article 

Google Scholar 
18.Pardo, L. E. et al. Snapshot Safari: a large-scale collaborative to monitor Africa’s remarkable biodiversity. South Africa J. Sci. https://doi.org/10.17159/sajs.2021/8134 (2021).19.Anderson, T. M. et al. The spatial distribution of African savannah herbivores: species associations and habitat occupancy in a landscape context. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150314 (2016).Article 

Google Scholar 
20.Palmer, M., Fieberg, J., Swanson, A., Kosmala, M. & Packer, C. A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol. Lett. 20, 1364–1373 (2017).Article 

Google Scholar 
21.Tabak, M. A. et al. Machine learning to classify animal species in camera trap images: applications in ecology. Methods Ecol. Evol. 10, 585–590 (2019).Article 

Google Scholar 
22.Whytock, R. C. et al. Robust ecological analysis of camera trap data labelled by a machine learning model. Methods Ecol. Evol 12, 1080–1092 (2021).Article 

Google Scholar 
23.Beery, S., Van Horn, G. & Perona, P. Recognition in terra incognita. In Proc. European Conference on Computer Vision (ECCV) 456–473 (IEEE, 2018).24.Tabak, M. A. et al. Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2. Ecol. Evol. 10, 10374–10383 (2020).Article 

Google Scholar 
25.Shahinfar, S., Meek, P. & Falzon, G. How many images do I need? Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecol. Inform. 57, 101085 (2020).Article 

Google Scholar 
26.Norouzzadeh, M. S. et al. A deep active learning system for species identification and counting in camera trap images. Methods Ecol. Evol. 12, 150–161 (2020).Article 

Google Scholar 
27.Willi, M. et al. Identifying animal species in camera trap images using deep learning and citizen science. Methods Ecol. Evol. 10, 80–91 (2019).Article 

Google Scholar 
28.Schneider, S., Greenberg, S., Taylor, G. W. & Kremer, S. C. Three critical factors affecting automated image species recognition performance for camera traps. Ecol. Evol. 10, 3503–3517 (2020).Article 

Google Scholar 
29.Kays, R. et al. An empirical evaluation of camera trap study design: how many, how long and when? Methods Ecol. Evol. 11, 700–713 (2020).Article 

Google Scholar 
30.Prach, K. & Walker, L. R. Four opportunities for studies of ecological succession. Trends Ecol. Evol. 26, 119–123 (2011).Article 

Google Scholar 
31.Mech, L. D., Isbell, F., Krueger, J. & Hart, J. Gray wolf (Canis lupus) recolonization failure: a Minnesota case study. Can. Field-Nat. 133, 60–65 (2019).Article 

Google Scholar 
32.Taylor, G. et al. Is reintroduction biology an effective applied science? Trends Ecol. Evol. 32, 873–880 (2017).Article 

Google Scholar 
33.Clavero, M. & Garcia-Berthou, E. Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20, 110 (2005).Article 

Google Scholar 
34.Caravaggi, A. et al. An invasive-native mammalian species replacement process captured by camera trap survey random encounter models. Remote Sens. Ecol. Conserv. 2, 45–58 (2016).Article 

Google Scholar 
35.Arjovsky, M., Bottou, L., Gulrajani, I. & Lopez-Paz, D. Invariant risk minimization. Preprint at https://arxiv.org/abs/1907.02893 (2019).36.Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 3320–3328 (IEEE, 2014).37.Deng, J. et al. ImageNet: a large-scale hierarchical image database. In Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).38.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution and protection. Science https://doi.org/10.1126/science.1246752 (2014).39.Liu, W., Wang, X., Owens, J. & Li, Y. Energy-based out-of-distribution detection. In Advances in Neural Information Processing Systems (eds Larochelle, H. et al.) 21464–21475 (Curran Associates, 2020).40.Lee, D.-H. Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In Workshop on Challenges in Representation Learning, ICML, Vol. 3 (2013).41.He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016).42.Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. Preprint at https://arxiv.org/abs/1503.02531 (2015).43.Gaynor, K. M., Daskin, J. H., Rich, L. N. & Brashares, J. S. Postwar wildlife recovery in an African savanna: evaluating patterns and drivers of species occupancy and richness. Anim. Conserv. 24, 510–522 (2020).Article 

Google Scholar 
44.Paszke, A. et al. in Advances in Neural Information Processing Systems Vol. 32 (eds Wallach, H. et al.) 8024–8035 http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (Curran Associates, 2019)45.Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. Preprint at https://arxiv.org/abs/2002.05709 (2020).46.He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 9729–9738 (IEEE, 2020).47.Xiao, T., Wang, X., Efros, A. A. & Darrell, T. What should not be contrastive in contrastive learning. Preprint at https://arxiv.org/abs/2008.05659 (2020). More

The net uptake of CO2 by the biosphere offsets roughly a quarter of current fossil fuel emissions. However, climate change is expected to impact photosynthesis and ecosystem respiration differently. Quantification of these individual processes is required to better understand and predict the consequences for carbon cycling. Variations in oxygen isotope signatures (δ18O and Δ17O) in atmospheric CO2 can be used as tracers for photosynthesis. Δ17O is much less dependent on variations in the hydrological cycle, which often obscure photosynthesis signals in the more widely measured δ18O. Although, measurement techniques for Δ17O in tropospheric CO2 only became sufficiently accurate to interpret variations since the ~2010s, providing new insights into the carbon cycle. More

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It was turtle-nesting season when this photograph was taken one night in June. I am on Needham’s Point beach measuring a critically endangered female hawksbill turtle (Eretmochelys imbricata). As field director of the Barbados sea turtle project, I run the day-to-day conservation activities and train and manage volunteers.We also run research projects that inform our conservation activities. We collect data such as shell length, which can tell us the age at which females become sexually mature and can indicate growth rates. These data help us to keep track of turtle health and survival. For example, if we start seeing smaller turtles, this could indicate that they are maturing faster, or that food is scarce and the turtles are growing more slowly.In August, the baby turtles hatch. I was on call 7 days a week for around 8 hours a day, responding to emergencies. These included hatchlings wandering off in the wrong direction, putting them at risk of being hit by a car or eaten by predators such as rats and cats. We took the hatchlings to a safe spot on the beach and released them. I also had to prepare for the expected swells as Hurricane Ida passed us by: when beaches flood, nests can wash away. We took rescued eggs and premature hatchlings to a makeshift intensive-care unit until they were ready for release. We aim to leave no turtle behind.I have worked at the project for 15 years. I recently finished a master’s degree on the coloration of the Barbados bullfinch (Loxigilla barbadensis) at the University of the West Indies, which hosts the turtle project. Next year I hope to start a PhD, part of which will look at the conflict between tourism and sea-turtle survival in Barbados. Here, interactions between sea turtles and humans occur at every stage of the turtles’ lives and can affect their survival. After my doctorate, I will continue to focus on helping sea turtles in the Caribbean. There is something addictive about making a real-time, tangible difference to their lives.

Nature 598, 532 (2021)
doi: https://doi.org/10.1038/d41586-021-02851-6

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