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    Population dynamics of the sea snake Emydocephalus annulatus (Elapidae, Hydrophiinae)

    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

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    Genomic investigations provide insights into the mechanisms of resilience to heterogeneous habitats of the Indian Ocean in a pelagic fish

    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

  • in

    Effects of global warming on Mediterranean coral forests

    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

  • in

    Fitness consequences of targeted gene flow to counter impacts of drying climates on terrestrial-breeding frogs

    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

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    Iterative human and automated identification of wildlife images

    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

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    Constraining photosynthesis with ∆17O in CO2

    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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    Saving hawksbill sea turtles from rats, cats and Hurricane Ida

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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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    Carbon dioxide levels in initial nests of the leaf-cutting ant Atta sexdens (Hymenoptera: Formicidae)

    1.Hughes, W. O. H. & Goulson, D. The use of alarm pheromones to enhance bait harvest by grass-cutting ants. Bull. Entomol. Res. 92, 213–218 (2002).CAS 
    Article 

    Google Scholar 
    2.Staab, M. & Kleineidam, C. J. Initiation of swarming behavior and synchronization of mating flights in the leaf-cutting ant Atta vollenweideri Forel, 1893 (Hymenoptera: Formicidae). Myrmecol. News 19, 93–102 (2014).
    Google Scholar 
    3.Sales, T. A., Toledo, A. M. O. & Lopes, J. F. S. The best of heavy queens: Influence of post-flight weight on queens’ survival and productivity in Acromyrmex subterraneus (Forel, 1893) (Hymenoptera: Formicidae). Insectes Soc. 67, 383–390 (2020).Article 

    Google Scholar 
    4.Camargo, R. S., Forti, L. C., Fujihara, R. T. & Roces, F. Digging effort in leaf-cutting ant queens (Atta sexdens rubropilosa) and its effects on survival and colony growth during the claustral phase. Insectes Soc. 58, 17–22 (2011).Article 

    Google Scholar 
    5.Autuori, M. Contribuição para o conhecimento da saúva (Atta spp.) (Hymenoptera: Formicidae). I. Evolução do sauveiro (Atta sexdens rubropilosa Forel, 1908). Arq. Inst. Biol. 12, 197–228 (1941).
    Google Scholar 
    6.Aylward, F. O. et al. Leucoagaricus gongylophorus produces diverse enzymes for the degradation of recalcitrant plant polymers in leaf-cutter ant fungus gardens. Appl. Environ. Microbiol. 79, 3770–3778 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Costa, A. N., Vasconcelos, H. L., Vieira-Neto, E. H. M. & Bruna, E. M. Do herbivores exert top-down effects in Neotropical savannas? Estimates of biomass consumption by leaf-cutter ants. J. Veg. Sci. 19, 849–854 (2008).Article 

    Google Scholar 
    8.Bollazzi, M., Forti, L. C. & Roces, F. Ventilation of the giant nests of Atta leaf-cutting ants: Does underground circulating air enter the fungus chambers?. Insectes Soc. 59, 487–498 (2012).Article 

    Google Scholar 
    9.Sousa-Souto, L. et al. Increased CO2 emission and organic matter decomposition by leaf-cutting ant nests in a coastal environment. Soil Biol. Biochem. 44, 21–25 (2012).CAS 
    Article 

    Google Scholar 
    10.Hasin, S. et al. CO2 efflux from subterranean nests of ant communities in a seasonal tropical forest, Thailand. Ecol. Evol. 4, 3929–3939 (2014).Article 

    Google Scholar 
    11.Tschinkel, W. R. The nest architecture of the Florida harvester ant, Pogonomyrmex badius. J. Insect Sci. 4, 21 (2004).Article 

    Google Scholar 
    12.Kleineidam, C. & Roces, F. Carbon dioxide concentrations and nest ventilation in nests of the leaf-cutting ant Atta vollenweideri. Insectes Soc. 47, 241–248 (2000).Article 

    Google Scholar 
    13.Currie, J. A. Gas diffusion through soil crumbs: The effects of compaction and wetting. J. Soil Sci. 35, 1–10 (1984).CAS 
    Article 

    Google Scholar 
    14.Kleineidam, C., Ernst, R. & Roces, F. Wind-induced ventilation of the giant nests of the leaf-cutting ant Atta vollenweideri. Naturwissenschaften 88, 301–305 (2001).ADS 
    CAS 
    Article 

    Google Scholar 
    15.Vogel, S., Ellington, C. P. & Kilgore, D. L. Wind-induced ventilation of the burrow of the prairie-dog, Cynomys ludovicianus. J. Comp. Physiol. 85, 1–14 (1973).Article 

    Google Scholar 
    16.Jonkman, J. C. M. The external and internal structure and growth of nests of the leaf-cutting ant Atta vollenweideri Forel, 1893 (Hym: Formicidae) Part II. Zeitschrift für Angew. Entomol. 89, 158–173 (1980).Article 

    Google Scholar 
    17.Gutiérrez, J. L. & Jones, C. G. Physical ecosystem engineers as agents of biogeochemical heterogeneity. Bioscience 56, 227–236 (2006).Article 

    Google Scholar 
    18.Fernandez-Bou, A. S. et al. The role of the ecosystem engineer, the leaf-cutter ant Atta cephalotes, on soil CO2 dynamics in a wet tropical rainforest. J. Geophys. Res. Biogeosciences 124, 260–273 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Moitinho, M. R. et al. Does fresh farmyard manure introduce surviving microbes into soil or activate soil-borne microbiota?. J. Environ. Manag. 11, 1–15 (2021).
    Google Scholar 
    20.Roces, F. Variable thermal sensitivity as output of a circadian clock controlling the bimodal rhythm of temperature choice in the ant Camponotus mus. J. Comp. Physiol. A 177, 637–643 (1995).Article 

    Google Scholar 
    21.Römer, D., Bollazzi, M. & Roces, F. Carbon dioxide sensing in an obligate insect-fungus symbiosis: CO2 preferences of leaf-cutting ants to rear their mutualistic fungus. PLoS ONE 12, e0174597 (2017).Article 

    Google Scholar 
    22.Halboth, F. & Roces, F. The construction of ventilation turrets in Atta vollenweideri leaf-cutting ants: Carbon dioxide levels in the nest tunnels, but not airflow or air humidity, influence turret structure. PLoS ONE 12, e0188162 (2017).Article 

    Google Scholar 
    23.Kleineidam, C. & Tautz, J. Perception of carbon dioxide and other “air-condition” parameters in the leaf cutting ant Atta cephalotes. Naturwissenschaften 83, 566–568 (1996).ADS 
    CAS 

    Google Scholar 
    24.Kleineidam, C., Romani, R., Tautz, J. & Isidoro, N. Ultrastructure and physiology of the CO2 sensitive sensillum ampullaceum in the leaf-cutting ant Atta sexdens. Arthropod Struct. Dev. 29, 43–55 (2000).CAS 
    Article 

    Google Scholar 
    25.Camargo, R. S. & Forti, L. C. Queen lipid content and nest growth in the leaf cutting ant (Atta sexdens rubropilosa) (Hymenoptera: Formicidae). J. Nat. Hist. 47, 65–73 (2013).Article 

    Google Scholar 
    26.Seal, J. N. Scaling of body weight and fat content in fungus-gardening ant queens: Does this explain why leaf-cutting ants found claustrally?. Insectes Soc. 56, 135–141 (2009).Article 

    Google Scholar 
    27.Camargo, R. D. S., Fonseca, J. A., Lopes, J. F. S. & Forti, L. C. Influência do ambiente no desenvolvimento de colônias iniciais de formigas cortadeiras (Atta sexdens rubropilosa). Ciência Rural 43, 1375–1380 (2013).Article 

    Google Scholar 
    28.Silva, E. J., da Silva Camargo, R. & Forti, L. C. Flight and digging effort in leaf-cutting ant males and gynes. Sociobiology 62, 334–339 (2015).Article 

    Google Scholar 
    29.Kuzyakov, Y. Sources of CO2 efflux from soil and review of partitioning methods. Soil Biol. Biochem. 38, 425–448 (2006).CAS 
    Article 

    Google Scholar 
    30.Camargo, R. S., Silva, E. J., Forti, L. C. & Matos, C. A. O. Initial development and production of CO2 in colonies of the leaf-cutting ant Atta sexdens during the claustral foundation. Sociobiology 63, 720–723 (2016).Article 

    Google Scholar 
    31.Cribari-Neto, F. & Zeileis, A. Beta regression in R. J. Stat. Softw. 34, 1–24 (2010).Article 

    Google Scholar 
    32.Ferrari, S. & Cribari-Neto, F. Beta regression for modelling rates and proportions. J. Appl. Stat. 31, 799–815 (2004).MathSciNet 
    Article 

    Google Scholar 
    33.Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54 (2006).Article 

    Google Scholar  More