Farmery, A. K., Hendrie, G. A., O’Kane, G., McManus, A. & Green, B. S. Sociodemographic variation in consumption patterns of sustainable and nutritious seafood in Australia. Front. Nutr. 5, 118. https://doi.org/10.3389/fnut.2018.00118 (2018).
Google Scholar
Guillen, J. et al. Global seafood consumption footprint. Ambio 48, 111–122. https://doi.org/10.1007/s13280-018-1060-9 (2019).
Google Scholar
Norse, E. A. et al. Sustainability of deep-sea fisheries. Mar. Policy 36, 307–320. https://doi.org/10.1016/j.marpol.2011.06.008 (2012).
Google Scholar
Baco, A. R. et al. A synthesis of genetic connectivity in deep-sea fauna and implications for marine reserve design. Mol. Ecol. 25, 3276–3298. https://doi.org/10.1111/mec.13689 (2016).
Google Scholar
Victorero, L., Watling, L., Deng Palomares, M. L. & Nouvian, C. Out of sight, but within reach: A global history of bottom-trawled deep-sea fisheries from > 400 m depth. Front. Mar. Sci. 5, 98. https://doi.org/10.3389/fmars.2018.00098 (2018).
Google Scholar
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466. https://doi.org/10.1146/annurev.marine.010908.163757 (2009).
Google Scholar
Taylor, M. L. & Roterman, C. N. Invertebrate population genetics across Earth’s largest habitat: The deep-sea floor. Mol. Ecol. 26, 4872–4896. https://doi.org/10.1111/mec.14237 (2017).
Google Scholar
Allendorf, F. W., England, P. R., Luikart, G., Ritchie, P. A. & Ryman, N. Genetic effects of harvest on wild animal populations. Trends Ecol. Evol. 23, 327–337. https://doi.org/10.1016/j.tree.2008.02.008 (2008).
Google Scholar
Carreras, C. et al. Population genomics of an endemic Mediterranean fish: Differentiation by fine scale dispersal and adaptation. Sci. Rep. 7, 43417. https://doi.org/10.1038/srep43417 (2017).
Google Scholar
Coleman, F. C. & Williams, S. L. Overexploiting marine ecosystem engineers: Potential consequences for biodiversity. Trends Ecol. Evol. 17, 40–44. https://doi.org/10.1016/S0169-5347(01)02330-8 (2002).
Google Scholar
Neubauer, P., Jensen, O. P., Hutchings, J. A. & Baum, J. K. Resilience and recovery of overexploited marine populations. Science 340, 347–349. https://doi.org/10.1126/science.1230441 (2013).
Google Scholar
Ovenden, J. R., Berry, O., Welch, D. J., Buckworth, R. C. & Dichmont, C. M. Ocean’s eleven: A critical evaluation of the role of population, evolutionary and molecular genetics in the management of wild fisheries. Fish Fish. 16, 125–159. https://doi.org/10.1111/faf.12052 (2015).
Google Scholar
Pinsky, M. L. & Palumbi, S. R. Meta-analysis reveals lower genetic diversity in overfished populations. Mol. Ecol. 23, 29–39. https://doi.org/10.1111/mec.12509 (2014).
Google Scholar
Sundqvist, L., Keenan, K., Zackrisson, M., Prodöhl, P. & Kleinhans, D. Directional genetic differentiation and relative migration. Ecol. Evol. 6, 3461–3475. https://doi.org/10.1002/ece3.2096 (2016).
Google Scholar
Waples, R. S. et al. Guidelines for genetic data analysis. J. Cetac. Res. Manag. 18, 33–80 (2018).
Google Scholar
Hauser, L., Adcock, G. J., Smith, P. J., Bernal Ramírez, J. H. & Carvalho, G. R. Loss of microsatellite diversity and low effective population size in an overexploited population of New Zealand snapper (Pagrus auratus). Proc. Natl. Acad. Sci. 99, 11742–11747. https://doi.org/10.1073/pnas.172242899 (2002).
Google Scholar
Laikre, L., Palm, S. & Ryman, N. Genetic population structure of fishes: Implications for coastal zone management. AMBIO A J. Hum. Environ. 34, 111–119. https://doi.org/10.1579/0044-7447-34.2.111 (2005).
Google Scholar
Gaggiotti, O. E. Population genetic models of source–sink metapopulations. Theor. Popul. Biol. 50, 178–208. https://doi.org/10.1006/tpbi.1996.0028 (1996).
Google Scholar
Hughes, A. R., Inouye, B. D., Johnson, M. T. J., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623. https://doi.org/10.1111/j.1461-0248.2008.01179.x (2008).
Google Scholar
Bracco, A., Liu, G., Galaska, M. P., Quattrini, A. M. & Herrera, S. Integrating physical circulation models and genetic approaches to investigate population connectivity in deep-sea corals. J. Mar. Syst. 198, 103189. https://doi.org/10.1016/j.jmarsys.2019.103189 (2019).
Google Scholar
Liu, S.-Y.V., Hsin, Y.-C. & Cheng, Y.-R. Using particle tracking and genetic approaches to infer population connectivity in the deep-sea scleractinian coral Deltocyathus magnificus in the South China sea. Deep Sea Res. Part I 161, 103297. https://doi.org/10.1016/j.dsr.2020.103297 (2020).
Google Scholar
Dambach, J., Raupach, M. J., Leese, F., Schwarzer, J. & Engler, J. O. Ocean currents determine functional connectivity in an Antarctic deep-sea shrimp. Mar. Ecol. 37, 1336–1344. https://doi.org/10.1111/maec.12343 (2016).
Google Scholar
Selkoe, K. A., Henzler, C. M. & Gaines, S. D. Seascape genetics and the spatial ecology of marine populations. Fish Fish. 9, 363–377. https://doi.org/10.1111/j.1467-2979.2008.00300.x (2008).
Google Scholar
Yan, R.-J., Schnabel, K. E., Rowden, A. A., Guo, X.-Z. & Gardner, J. P. A. Population structure and genetic connectivity of squat lobsters (Munida Leach, 1820) associated with vulnerable marine ecosystems in the southwest Pacific Ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00791 (2020).
Google Scholar
Breusing, C. et al. Biophysical and population genetic models predict the presence of “phantom” stepping stones connecting Mid-Atlantic Ridge vent ecosystems. Curr. Biol. 26, 2257–2267. https://doi.org/10.1016/j.cub.2016.06.062 (2016).
Google Scholar
Fisheries New Zealand. Fisheries Assessment: Scampi (SCI). https://fs.fish.govt.nz/Page.aspx?pk=113&dk=24443 (2017).
Botsford, L. W. et al. Connectivity, sustainability, and yield: Bridging the gap between conventional fisheries management and marine protected areas. Rev. Fish Biol. Fish. 19, 69–95. https://doi.org/10.1007/s11160-008-9092-z (2009).
Google Scholar
NIWA. Annual Distribution of Scampi. Ministry for Primary Industries, New Zealand. https://mpi.maps.arcgis.com/home/item.html?id=97da6c1a912b45a8855bf741211f5911 (2016).
Heasman, K. G. & Jeffs, A. G. Fecundity and potential juvenile production for aquaculture of the New Zealand scampi, Metanephrops challengeri (Balss, 1914) (Decapoda: Nephropidae). Aquaculture 511, 634184. https://doi.org/10.1016/j.aquaculture.2019.05.069 (2019).
Google Scholar
Smith, P. J. Allozyme variation in scampi (Metanephrops challengeri) fisheries around New Zealand. NZ J. Mar. Freshw. Res. 33, 491–497. https://doi.org/10.1080/00288330.1999.9516894 (1999).
Google Scholar
Berry, P. The biology of Nephrops andamanicus Wood-Mason (Decapoda, Reptantia). Report No. 22, 1–55 (South African Association for Marine Biological Research, Oceanographic Research Institute, Durban, South Africa, 1969).
Major, R. N. & Jeffs, A. G. Orientation and food search behaviour of a deep sea lobster in turbulent versus laminar odour plumes. Helgol. Mar. Res. 71, 9. https://doi.org/10.1186/s10152-017-0489-8 (2017).
Google Scholar
Tuck, I. D., Parsons, D. M., Hartill, B. W. & Chiswell, S. M. Scampi (Metanephrops challengeri) emergence patterns and catchability. ICES J. Mar. Sci. 72, i199–i210. https://doi.org/10.1093/icesjms/fsu244 (2015).
Google Scholar
Chiswell, S. M. & Booth, J. D. Sources and sinks of larval settlement in Jasus edwardsii around New Zealand: Where do larvae come from and where do they go?. Mar. Ecol. Prog. Ser. 354, 201–217. https://doi.org/10.3354/meps07217 (2008).
Google Scholar
Silva, C. N. S., Macdonald, H. S., Hadfield, M. G., Cryer, M. & Gardner, J. P. A. Ocean currents predict fine-scale genetic structure and source-sink dynamics in a marine invertebrate coastal fishery. ICES J. Mar. Sci. 76, 1007–1018. https://doi.org/10.1093/icesjms/fsy201 (2019).
Google Scholar
Singh, S. P., Groeneveld, J. C., Hart-Davis, M. G., Backeberg, B. C. & Willows-Munro, S. Seascape genetics of the spiny lobster Panulirus homarus in the Western Indian Ocean: Understanding how oceanographic features shape the genetic structure of species with high larval dispersal potential. Ecol. Evol. 8, 12221–12237. https://doi.org/10.1002/ece3.4684 (2018).
Google Scholar
Singh, S. P., Groeneveld, J. C. & Willows-Munro, S. Between the current and the coast: Genetic connectivity in the spiny lobster Panulirus homarus rubellus, despite potential barriers to gene flow. Mar. Biol. 166, 36. https://doi.org/10.1007/s00227-019-3486-4 (2019).
Google Scholar
Thomas, L. & Bell, J. J. Testing the consistency of connectivity patterns for a widely dispersing marine species. Heredity 111, 345–354. https://doi.org/10.1038/hdy.2013.58 (2013).
Google Scholar
Baeza, J. A., Holstein, D., Umaña-Castro, R. & Mejía-Ortíz, L. M. Population genetics and biophysical modeling inform metapopulation connectivity of the Caribbean king crab Maguimithrax spinosissimus. Mar. Ecol. Prog. Ser. 610, 83–97 (2019).
Google Scholar
Hedgecock, D., Barber, P. H. & Edmands, S. Genetic approaches to measuring connectivity. Oceanography 20, 70–79 (2007).
Google Scholar
Jahnke, M. & Jonsson, P. R. Biophysical models of dispersal contribute to seascape genetic analyses. Philos. Trans. R. Soc. B Biol. Sci. 377, 20210024. https://doi.org/10.1098/rstb.2021.0024 (2022).
Google Scholar
Sebastian, W. et al. Genomic investigations provide insights into the mechanisms of resilience to heterogeneous habitats of the Indian Ocean in a pelagic fish. Sci. Rep. 11, 20690. https://doi.org/10.1038/s41598-021-00129-5 (2021).
Google Scholar
Lal, M. M., Southgate, P. C., Jerry, D. R., Bosserelle, C. & Zenger, K. R. A parallel population genomic and hydrodynamic approach to fishery management of highly-dispersive marine invertebrates: The case of the Fijian black-lip pearl oyster Pinctada margaritifera. PLoS ONE 11, e0161390. https://doi.org/10.1371/journal.pone.0161390 (2016).
Google Scholar
Xu, T. et al. Hidden historical habitat-linked population divergence and contemporary gene flow of a deep-sea patellogastropod limpet. Mol. Biol. Evol. 38, 5640–5654. https://doi.org/10.1093/molbev/msab278 (2021).
Google Scholar
de Souza, J. M. A. C. et al. Moana Ocean Hindcast—A 25+ years simulation for New Zealand Waters using the ROMS v3.9 model. EGUsphere https://doi.org/10.5194/egusphere-2022-41 (2022).
Norrie, C., Dunphy, B., Roughan, M., Weppe, S. & Lundquist, C. Spill-over from aquaculture may provide a larval subsidy for the restoration of mussel reefs. Aquac. Environ. Interact. 12, 231–249 (2020).
Google Scholar
Larsson, J. et al. Regional genetic differentiation in the blue mussel from the Baltic Sea area. Estuar. Coast. Shelf Sci. 195, 98–109. https://doi.org/10.1016/j.ecss.2016.06.016 (2017).
Google Scholar
Nicolle, A. et al. Modelling larval dispersal of Pecten maximus in the English Channel: A tool for the spatial management of the stocks. ICES J. Mar. Sci. 74, 1812–1825. https://doi.org/10.1093/icesjms/fsw207 (2017).
Google Scholar
Hold, N. et al. Using biophysical modelling and population genetics for conservation and management of an exploited species, Pecten maximus L. Fish. Oceanogr. 30, 740–756. https://doi.org/10.1111/fog.12556 (2021).
Google Scholar
Truelove, N. K. et al. Biophysical connectivity explains population genetic structure in a highly dispersive marine species. Coral Reefs 36, 233–244. https://doi.org/10.1007/s00338-016-1516-y (2017).
Google Scholar
Busch, K. et al. Population connectivity of fan-shaped sponge holobionts in the deep Cantabrian Sea. Deep Sea Res. Part I 167, 103427. https://doi.org/10.1016/j.dsr.2020.103427 (2021).
Google Scholar
Ross, P. M., Hogg, I. D., Pilditch, C. A. & Lundquist, C. J. Phylogeography of New Zealand’s coastal benthos. NZ J. Mar. Freshw. Res. 43, 1009–1027. https://doi.org/10.1080/00288330.2009.9626525 (2009).
Google Scholar
Tuck, I. D. Characterisation and a length-based assessment model for scampi (Metanephrops challengeri) at the Auckland Islands (SCI 6A). Report No. 2015/21, 160 (Ministry for Primary Industries, Wellington, 2015).
Verry, A. J. F., Walton, K., Tuck, I. D. & Ritchie, P. A. Genetic structure and recent population expansion in the commercially harvested deepsea decapod, Metanephrops challengeri (Crustacea: Decapoda). NZ J. Mar. Freshw. Res. 54, 251–270. https://doi.org/10.1080/00288330.2019.1707696 (2020).
Google Scholar
Selkoe, K. A. et al. A decade of seascape genetics: Contributions to basic and applied marine connectivity. Mar. Ecol. Prog. Ser. 554, 1–19. https://doi.org/10.3354/meps11792 (2016).
Google Scholar
Hare, M. P. et al. Understanding and estimating effective population size for practical application in marine species management. Conserv. Biol. 25, 438–449. https://doi.org/10.1111/j.1523-1739.2010.01637.x (2011).
Google Scholar
Ashry, N. A. Plant biodiversity and biotechnology. In From Plant Genomics to Plant Biotechnology (eds Poltronieri, P. et al.) 205–222 (Woodhead Publishing, 2013).
Google Scholar
Sgrò, C. M., Lowe, A. J. & Hoffmann, A. A. Building evolutionary resilience for conserving biodiversity under climate change. Evol. Appl. 4, 326–337. https://doi.org/10.1111/j.1752-4571.2010.00157.x (2011).
Google Scholar
Kerr, L. A., Cadrin, S. X. & Secor, D. H. Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics. ICES J. Mar. Sci. 67, 1631–1639. https://doi.org/10.1093/icesjms/fsq053 (2010).
Google Scholar
Carroll, E. L. et al. Perturbation drives changing metapopulation dynamics in a top marine predator. Proc. R. Soc. B Biol. Sci. 287, 20200318. https://doi.org/10.1098/rspb.2020.0318 (2020).
Google Scholar
Chiswell, S. M., Bostock, H. C., Sutton, P. J. H. & Williams, M. J. M. Physical oceanography of the deep seas around New Zealand: A review. NZ J. Mar. Freshw. Res. 49, 286–317. https://doi.org/10.1080/00288330.2014.992918 (2015).
Google Scholar
Chiswell, S. M. & Roemmich, D. The East Cape Current and two eddies: A mechanism for larval retention?. NZ J. Mar. Freshw. Res. 32, 385–397. https://doi.org/10.1080/00288330.1998.9516833 (1998).
Google Scholar
Condie, S. & Condie, R. Retention of plankton within ocean eddies. Glob. Ecol. Biogeogr. 25, 1264–1277. https://doi.org/10.1111/geb.12485 (2016).
Google Scholar
Lesser, J. H. R. Phyllosoma larvae of Jasus edwardsii (Hutton) (Crustacea: Decapoda: Palinuridae) and their distribution off the east coast of the North Island, New Zealand. NZ J. Mar. Freshw. Res. 12, 357–370. https://doi.org/10.1080/00288330.1978.9515763 (1978).
Google Scholar
Kawecki, T. J. Ecological and evolutionary consequences of source-sink population dynamics. In Ecology, Genetics and Evolution of Metapopulations (eds Hanski, I. & Gaggiotti, O. E.) 387–414 (Academic Press, 2004).
Google Scholar
Figueira, W. F. & Crowder, L. B. Defining patch contribution in source-sink metapopulations: the importance of including dispersal and its relevance to marine systems. Popul. Ecol. 48, 215–224. https://doi.org/10.1007/s10144-006-0265-0 (2006).
Google Scholar
Heinrichs, J. A. et al. Recent advances and current challenges in applying source-sink theory to species conservation. Curr. Landsc. Ecol. Rep. 4, 51–60. https://doi.org/10.1007/s40823-019-00039-3 (2019).
Google Scholar
Hastings, A. & Botsford, L. W. Persistence of spatial populations depends on returning home. Proc. Natl. Acad. Sci. 103, 6067–6072. https://doi.org/10.1073/pnas.0506651103 (2006).
Google Scholar
Heinrichs, J. A., Lawler, J. J. & Schumaker, N. H. Intrinsic and extrinsic drivers of source-sink dynamics. Ecol. Evol. 6, 892–904. https://doi.org/10.1002/ece3.2029 (2016).
Google Scholar
Gilroy, J. J. & Edwards, D. P. Source-sink dynamics: A neglected problem for landscape-scale biodiversity conservation in the Tropics. Curr. Landsc. Ecol. Rep. 2, 51–60. https://doi.org/10.1007/s40823-017-0023-3 (2017).
Google Scholar
Lal, M. M., Bosserelle, C., Kishore, P. & Southgate, P. C. Understanding marine larval dispersal in a broadcast-spawning invertebrate: A dispersal modelling approach for optimising spat collection of the Fijian black-lip pearl oyster Pinctada margaritifera. PLoS ONE 15, e0234605. https://doi.org/10.1371/journal.pone.0234605 (2020).
Google Scholar
Chassé, J. & Miller, R. J. Lobster larval transport in the southern Gulf of St. Lawrence. Fish. Oceanogr. 19, 319–338. https://doi.org/10.1111/j.1365-2419.2010.00548.x (2010).
Google Scholar
Lindegren, M., Andersen, K. H., Casini, M. & Neuenfeldt, S. A metacommunity perspective on source–sink dynamics and management: the Baltic Sea as a case study. Ecol. Appl. 24, 1820–1832. https://doi.org/10.1890/13-0566.1 (2014).
Google Scholar
Tuck, I. D. et al. Estimating the abundance of scampi in SCI 6A (Auckland Islands) in 2013. Report No. 2015/10, 48 (Ministry for Primary Industries, 2015).
Brierley, A. S. & Kingsford, M. J. Impacts of climate change on marine organisms and ecosystems. Curr. Biol. 19, R602–R614. https://doi.org/10.1016/j.cub.2009.05.046 (2009).
Google Scholar
Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G. & Saba, V. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature 556, 191–196. https://doi.org/10.1038/s41586-018-0006-5 (2018).
Google Scholar
Thornalley, D. J. R. et al. Anomalously weak Labrador Sea convection and Atlantic overturning during the past 150 years. Nature 556, 227–230. https://doi.org/10.1038/s41586-018-0007-4 (2018).
Google Scholar
van Gennip, S. J. et al. Going with the flow: The role of ocean circulation in global marine ecosystems under a changing climate. Glob. Change Biol. 23, 2602–2617. https://doi.org/10.1111/gcb.13586 (2017).
Google Scholar
Bashevkin, S. M. et al. Larval dispersal in a changing ocean with an emphasis on upwelling regions. Ecosphere 11, e03015. https://doi.org/10.1002/ecs2.3015 (2020).
Google Scholar
Gerber, L. R., Mancha-Cisneros, M. D. M., O’Connor, M. I. & Selig, E. R. Climate change impacts on connectivity in the ocean: Implications for conservation. Ecosphere 5, 1–18. https://doi.org/10.1890/es13-00336.1 (2014).
Google Scholar
Hoegh-Gulderg, O. & Pearse, J. Temperature, food availability, and the development of marine invertebrate larvae. Am. Zool. 35, 415–425. https://doi.org/10.1093/icb/35.4.415 (1995).
Google Scholar
O’Connor, M. I. et al. Temperature control of larval dispersal and the implications for marine ecology, evolution, and conservation. Proc. Natl. Acad. Sci. USA 104, 1266–1271. https://doi.org/10.1073/pnas.0603422104 (2007).
Google Scholar
Cetina-Heredia, P., Roughan, M., van Sebille, E., Feng, M. & Coleman, M. A. Strengthened currents override the effect of warming on lobster larval dispersal and survival. Glob. Change Biol. 21, 4377–4386. https://doi.org/10.1111/gcb.13063 (2015).
Google Scholar
Borja, A. et al. Past and future grand challenges in marine ecosystem ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00362 (2020).
Google Scholar
Ogilvie, S. et al. Mātauranga Māori driving innovation in the New Zealand scampi fishery. NZ J. Mar. Freshw. Res. 52, 590–602. https://doi.org/10.1080/00288330.2018.1532441 (2018).
Google Scholar
Andrews, S. FastQC: A quality control tool for high throughput sequence data v. 0.11.7 (Babraham Bioinformatics, 2010). http://www.bioinformatics.babraham.ac.uk/projects/fastqc.
Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754. https://doi.org/10.1111/mec.15253 (2019).
Google Scholar
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 (2011).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing v. 4.1.0 (R Studio v1.4.1106) (R Foundation for Statistical Computing, Vienna, Austria, 2021). https://www.R-project.org/.
Díaz-Arce, N. & Rodríguez-Ezpeleta, N. Selecting RAD-seq data analysis parameters for population genetics: The more the better?. Front. Genet. 10, 533. https://doi.org/10.3389/fgene.2019.00533 (2019).
Google Scholar
Potapov, V. & Ong, J. L. Examining sources of error in PCR by single-molecule sequencing. PLoS ONE 12, e0169774. https://doi.org/10.1371/journal.pone.0169774 (2017).
Google Scholar
Goudet, J. & Jombart, T. hierfstat: Estimation and Tests of Hierarchical F-Statistics v. 0.04-22 (Comprehensive R Archive Network (CRAN), 2015). https://CRAN.R-project.org/package=hierfstat.
Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).
Google Scholar
Nei, M. & Chesser, R. K. Estimation of fixation indices and gene diversities. Ann. Hum. Genet. 47, 253–259. https://doi.org/10.1111/j.1469-1809.1983.tb00993.x (1983).
Google Scholar
Archer, F. I., Adams, P. E. & Schneiders, B. B. stratag: An R package for manipulating, summarizing and analysing population genetic data. Mol. Ecol. Resour. 17, 5–11. https://doi.org/10.1111/1755-0998.12559 (2017).
Google Scholar
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281. https://doi.org/10.7717/peerj.281 (2014).
Google Scholar
Kamvar, Z. N., Brooks, J. C. & Grünwald, N. J. Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6, 208. https://doi.org/10.3389/fgene.2015.00208 (2015).
Google Scholar
Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405. https://doi.org/10.1093/bioinformatics/btn129 (2008).
Google Scholar
Jombart, T. & Ahmed, I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071. https://doi.org/10.1093/bioinformatics/btr521 (2011).
Google Scholar
Miller, J. M., Cullingham, C. I. & Peery, R. M. The influence of a priori grouping on inference of genetic clusters: Simulation study and literature review of the DAPC method. Heredity 125, 269–280. https://doi.org/10.1038/s41437-020-0348-2 (2020).
Google Scholar
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788. https://doi.org/10.1111/2041-210x.12067 (2013).
Google Scholar
Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. 70, 3321–3323. https://doi.org/10.1073/pnas.70.12.3321 (1973).
Google Scholar
Dagestad, K. F., Röhrs, J., Breivik, Ø. & Ådlandsvik, B. OpenDrift v1.0: A generic framework for trajectory modelling. Geosci. Model Dev. 11, 1405–1420. https://doi.org/10.5194/gmd-11-1405-2018 (2018).
Google Scholar
Jeffs, A., Daniels, C. & Heasman, K. In Fisheries and Aquaculture: Natural History of Crustacea, Vol. 9 (eds Lovrich, G. & Thiel, M.) 285–311 (Oxford University Press, 2020).
Lundquist, C. J., Oldman, J. W. & Lewis, M. J. Predicting suitability of cockle Austrovenus stutchburyi restoration sites using hydrodynamic models of larval dispersal. NZ J. Mar. Freshw. Res. 43, 735–748. https://doi.org/10.1080/00288330909510038 (2009).
Google Scholar
Lundquist, C. J., Thrush, S. F., Oldman, J. W. & Senior, A. K. Limited transport and recolonization potential in shallow tidal estuaries. Limnol. Oceanogr. 49, 386–395. https://doi.org/10.4319/lo.2004.49.2.0386 (2004).
Google Scholar
Okubo, A. & Ebbesmeyer, C. C. Determination of vorticity, divergence, and deformation rates from analysis of drogue observations. Deep-Sea Res. Oceanogr. Abstr. 23, 349–352. https://doi.org/10.1016/0011-7471(76)90875-5 (1976).
Google Scholar
Pierce, D. ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files v. 1.17 (Comprehensive R Archive Network (CRAN), 2019). https://CRAN.R-project.org/package=ncdf4.
Coelho, S. C. C., Gherardi, D. F. M., Gouveia, M. B. & Kitahara, M. V. Western boundary currents drive sun-coral (Tubastraea spp.) coastal invasion from oil platforms. Sci. Rep. 12, 5286. https://doi.org/10.1038/s41598-022-09269-8 (2022).
Google Scholar
Demmer, J. et al. The role of wind in controlling the connectivity of blue mussels (Mytilus edulis L.) populations. Mov. Ecol. 10, 3. https://doi.org/10.1186/s40462-022-00301-0 (2022).
Google Scholar
Atalah, J., South, P. M., Briscoe, D. K. & Vennell, R. Inferring parental areas of juvenile mussels using hydrodynamic modelling. Aquaculture 555, 738227. https://doi.org/10.1016/j.aquaculture.2022.738227 (2022).
Google Scholar
McGeady, R., Lordan, C. & Power, A. M. Long-term interannual variability in larval dispersal and connectivity of the Norway lobster (Nephrops norvegicus) around Ireland: When supply-side matters. Fish. Oceanogr. 31, 255–270. https://doi.org/10.1111/fog.12576 (2022).
Google Scholar
Pante, E. & Simon-Bouhet, B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8, e73051. https://doi.org/10.1371/journal.pone.0073051 (2013).
Google Scholar
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Google Scholar
Becker, R. A., Wilks, A. R. & Brownrigg, R. mapdata: Extra Map Databases v. 2.3.0 (Comprehensive R Archive Network (CRAN), 2018). https://CRAN.R-project.org/package=mapdata.
McIlroy, D., Brownrigg, R., Minka, T. P. & Bivan, R. mapproj: Map Projections v. 1.2.7 (Comprehensive R Archive Network (CRAN), 2020). https://CRAN.R-project.org/package=mapproj.
South, A. rnaturalearth: World Map Data from Natural Earth v. 0.1.0 (Comprehensive R Archive Network (CRAN), 2017). https://CRAN.R-project.org/package=rnaturalearth.
Source: Ecology - nature.com