in

Tipping point realized in cod fishery

  • 1.

    Heinze, C. et al. The quiet crossing of ocean tipping points. Proc. Natl. Acad. Sci. 118, e2008478118 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 2.

    Dakos, V. et al. Ecosystem tipping points in an evolving world. Nat. Ecol. Evol. 3, 355–362 (2019).

    PubMed 
    Article 

    Google Scholar 

  • 3.

    Myers, R., Hutchings, J. & Barrowman, N. Hypotheses for the decline of cod in the North Atlantic. Mar. Ecol. Prog. Ser. 138, 293–308 (1996).

    ADS 
    Article 

    Google Scholar 

  • 4.

    Sguotti, C. et al. Catastrophic dynamics limit Atlantic cod recovery. Proc. R. Soc. B Biol. Sci. 286, 20182877 (2019).

    Article 

    Google Scholar 

  • 5.

    Levin, P. S. & Möllmann, C. Marine ecosystem regime shifts: Challenges and opportunities for ecosystem-based management. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130275 (2015).

    Article 

    Google Scholar 

  • 6.

    King, J. R., Mcfarlane, G. A. & Punt, A. E. Shifts in fisheries management: Adapting to regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130277 (2015).

    Article 

    Google Scholar 

  • 7.

    Döring, R., Berkenhagen, J., Hentsch, S. & Kraus, G. Small-Scale Fisheries in Germany: A Disappearing Profession? In Small-Scale Fisheries in Europe: Status, Resilience and Governance (eds. Pascual-Fernández, J. J., Pita, C. & Bavinck, M.) vol. 23 483–502 (Springer International Publishing, 2020).

  • 8.

    Papaioannou, E. A., Vafeidis, A. T., Quaas, M. F., Schmidt, J. O. & Strehlow, H. V. Using indicators based on primary fisheries’ data for assessing the development of the German Baltic small-scale fishery and reviewing its adaptation potential to changes in resource abundance and management during 2000–09. Ocean Coast. Manag. 98, 38–50 (2014).

    Article 

    Google Scholar 

  • 9.

    EU. Regulation (EU) 2016/1139 of the European Parliament and of the Council of 6 July 2016 establishing a multiannual plan for the stocks of cod, herring and sprat in the Baltic Sea and the fisheries exploiting those stocks, amending Council Regulation (EC) No 2187/2005 and repealing Council Regulation (EC) No 1098/2007. (2016).

  • 10.

    Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 11.

    Lenton, T. M. Environmental tipping points. Annu. Rev. Environ. Resour. 38, 1–29 (2013).

    ADS 
    Article 

    Google Scholar 

  • 12.

    Möllmann, C., Folke, C., Edwards, M. & Conversi, A. Marine regime shifts around the globe: Theory, drivers and impacts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130260 (2015).

    Article 

    Google Scholar 

  • 13.

    ICES. Advice cod in subdivisions 22–24, western Baltic stock (western Baltic Sea). (2019) https://doi.org/10.17895/ICES.ADVICE.5587.

  • 14.

    Conversi, A. et al. A holistic view of marine regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130279 (2015).

    Article 

    Google Scholar 

  • 15.

    Ratajczak, Z. et al. Abrupt change in ecological systems: Inference and diagnosis. Trends Ecol. Evol. 33, 513–526 (2018).

    PubMed 
    Article 

    Google Scholar 

  • 16.

    Turner, M. G. et al. Climate change, ecosystems and abrupt change: Science priorities. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190105 (2020).

    Article 

    Google Scholar 

  • 17.

    Scheffer, M. & Carpenter, S. R. Catastrophic regime shifts in ecosystems: Linking theory to observation. Trends Ecol. Evol. 18, 648–656 (2003).

    Article 

    Google Scholar 

  • 18.

    Beisner, B., Haydon, D. & Cuddington, K. Alternative stable states in ecology. Front. Ecol. Environ. 1, 376–382 (2003).

    Article 

    Google Scholar 

  • 19.

    Subbey, S., Devine, J. A., Schaarschmidt, U. & Nash, R. D. Modelling and forecasting stock–recruitment: Current and future perspectives. ICES J. Mar. Sci. 71, 2307–2322 (2014).

    Article 

    Google Scholar 

  • 20.

    Grasman, R. P. P. P., Maas, H. L. J. van der & Wagenmakers, E.-J. Fitting the Cusp Catastrophe in r : A cusp Package Primer. J. Stat. Softw. 32, 1-27 (2009).

  • 21.

    Thom, R. Structural Stability and Morphogenesis—An Outline of a General Theory of Models (Benjamin Inc, 1975).

    MATH 

    Google Scholar 

  • 22.

    Zeeman, E. Catastrophe theory. Sci. Am. 234, 65–83 (1976).

    Article 

    Google Scholar 

  • 23.

    Barunik, J. & Vosvrda, M. Can a stochastic cusp catastrophe model explain stock market crashes?. J. Econ. Dyn. Control 33, 1824–1836 (2009).

    MathSciNet 
    MATH 
    Article 

    Google Scholar 

  • 24.

    Xiaoping, Z., Jiahui, S. & Yuan, C. Analysis of crowd jam in public buildings based on cusp-catastrophe theory. Build. Environ. 45, 1755–1761 (2010).

    Article 

    Google Scholar 

  • 25.

    Guastello, S. J., Boeh, H., Shumaker, C. & Schimmels, M. Catastrophe models for cognitive workload and fatigue. Theor. Issues Ergon. Sci. 13, 586–602 (2012).

    Article 

    Google Scholar 

  • 26.

    Angelis, V., Angelis-Dimakis, A. & Dimaki, K. The Cusp Catastrophe model in describing a bank’s attractiveness as measured by its image. Proc. Econ. Finance 19, 261–277 (2015).

    Article 

    Google Scholar 

  • 27.

    Sideridis, G. D., Simos, P., Mouzaki, A. & Stamovlasis, D. Efficient word reading: Automaticity of print-related skills indexed by rapid automatized naming through cusp-catastrophe modeling. Sci. Stud. Read. 20, 6–19 (2016).

    Article 

    Google Scholar 

  • 28.

    Diks, C. & Wang, J. Can a stochastic cusp catastrophe model explain housing market crashes?. J. Econ. Dyn. Control 69, 68–88 (2016).

    Article 

    Google Scholar 

  • 29.

    Xu, Y. & Chen, X. Protection motivation theory and cigarette smoking among vocational high school students in China: A cusp catastrophe modeling analysis. Glob. Health Res. Policy 1, 3 (2016).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 30.

    Chen, D.-G., Lin, F., Chen, X., Tang, W. & Kitzman, H. Cusp Catastrophe Model: A nonlinear model for health outcomes in nursing research. Nurs. Res. 63, 211–220 (2014).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 31.

    Mostafa, M. M. Catastrophe theory predicts international concern for global warming. J. Quant. Econ. https://doi.org/10.1007/s40953-020-00199-8 (2020).

    Article 

    Google Scholar 

  • 32.

    Sguotti, C. et al. Non-linearity in stock–recruitment relationships of Atlantic cod: Insights from a multi-model approach. ICES J. Mar. Sci. 77, 1492–1502 (2020).

    Article 

    Google Scholar 

  • 33.

    Forster, P. M., Maycock, A. C., McKenna, C. M. & Smith, C. J. Latest climate models confirm need for urgent mitigation. Nat. Clim. Change 10, 7–10 (2020).

    ADS 
    Article 

    Google Scholar 

  • 34.

    Gröger, M., Arneborg, L., Dieterich, C., Höglund, A. & Meier, H. E. M. Summer hydrographic changes in the Baltic Sea, Kattegat and Skagerrak projected in an ensemble of climate scenarios downscaled with a coupled regional ocean–sea ice–atmosphere model. Clim. Dyn. 53, 5945–5966 (2019).

    Article 

    Google Scholar 

  • 35.

    Litzow, M. A., Mueter, F. J. & Hobday, A. J. Reassessing regime shifts in the North Pacific: Incremental climate change and commercial fishing are necessary for explaining decadal-scale biological variability. Glob. Change Biol. 20, 38–50 (2014).

    ADS 
    Article 

    Google Scholar 

  • 36.

    Auber, A., Travers-Trolet, M., Villanueva, M. C. & Ernande, B. Regime shift in an exploited fish community related to natural climate oscillations. PLoS One 10, e0129883 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 37.

    Karnauskas, M. et al. Evidence of climate-driven ecosystem reorganization in the Gulf of Mexico. Glob. Change Biol. 21, 2554–2568 (2015).

    ADS 
    Article 

    Google Scholar 

  • 38.

    Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 39.

    Kotta, J. et al. Novel crab predator causes marine ecosystem regime shift. Sci. Rep. 8, 4956 (2018).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 40.

    Vert-pre, K. A., Amoroso, R. O., Jensen, O. P. & Hilborn, R. Frequency and intensity of productivity regime shifts in marine fish stocks. Proc. Natl. Acad. Sci. 110, 1779–1784 (2013).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 41.

    Perretti, C. et al. Regime shifts in fish recruitment on the Northeast US Continental Shelf. Mar. Ecol. Prog. Ser. 574, 1–11 (2017).

    ADS 
    Article 

    Google Scholar 

  • 42.

    Litzow, M. A., Ciannelli, L., Cunningham, C. J., Johnson, B. & Puerta, P. Nonstationary effects of ocean temperature on Pacific salmon productivity. Can. J. Fish. Aquat. Sci. 76, 1923–1928 (2019).

    Article 

    Google Scholar 

  • 43.

    van der Maas, H. L. J., Kolstein, R. & van der Pligt, J. Sudden transitions in attitudes. Sociol. Methods Res. 32, 125–152 (2003).

    MathSciNet 
    Article 

    Google Scholar 

  • 44.

    Griffith, G. P. Closing the gap between causality, prediction, emergence, and applied marine management. ICES J. Mar. Sci. 77, 1456–1462 (2020).

    Article 

    Google Scholar 

  • 45.

    Hutchings, J. A. Collapse and recovery of marine fishes. Nature 406, 882–885 (2000).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 46.

    Hilborn, R., Hively, D. J., Jensen, O. P. & Branch, T. A. The dynamics of fish populations at low abundance and prospects for rebuilding and recovery. ICES J. Mar. Sci. 71, 2141–2151 (2014).

    Article 

    Google Scholar 

  • 47.

    Köster, F. Trophodynamic control by clupeid predators on recruitment success in Baltic cod?. ICES J. Mar. Sci. 57, 310–323 (2000).

    Article 

    Google Scholar 

  • 48.

    Rowe, S., Hutchings, J. A., Bekkevold, D. & Rakitin, A. Depensation, probability of fertilization, and the mating system of Atlantic cod (Gadus morhua L.). ICES J. Mar. Sci. 61, 1144–1150 (2004).

    Article 

    Google Scholar 

  • 49.

    Keith, D. M. & Hutchings, J. A. Population dynamics of marine fishes at low abundance. Can. J. Fish. Aquat. Sci. 69, 1150–1163 (2012).

    Article 

    Google Scholar 

  • 50.

    Kuparinen, A., Keith, D. M. & Hutchings, J. A. Allee effect and the uncertainty of population recovery: Allee effect and population recovery. Conserv. Biol. 28, 790–798 (2014).

    PubMed 
    Article 

    Google Scholar 

  • 51.

    Neuenhoff, R. D. et al. Continued decline of a collapsed population of Atlantic cod (Gadus morhua) due to predation-driven Allee effects. Can. J. Fish. Aquat. Sci. 76, 168–184 (2019).

    Article 

    Google Scholar 

  • 52.

    Vergnon, R., Shin, Y.-J. & Cury, P. Cultivation, Allee effect and resilience of large demersal fish populations. Aquat. Living Resour. 21, 287–295 (2008).

    Article 

    Google Scholar 

  • 53.

    Saha, B., Bhowmick, A. R., Chattopadhyay, J. & Bhattacharya, S. On the evidence of an Allee effect in herring populations and consequences for population survival: A model-based study. Ecol. Model. 250, 72–80 (2013).

    Article 

    Google Scholar 

  • 54.

    Perälä, T. & Kuparinen, A. Detection of Allee effects in marine fishes: Analytical biases generated by data availability and model selection. Proc. R. Soc. B Biol. Sci. 284, 20171284 (2017).

    Article 

    Google Scholar 

  • 55.

    Lundquist, C. J. & Botsford, L. W. Estimating larval production of a broadcast spawner: The influence of density, aggregation, and the fertilization Allee effect. Can. J. Fish. Aquat. Sci. 68, 30–42 (2011).

    Article 

    Google Scholar 

  • 56.

    Sæther, B.-E., Engen, S., Lande, R. & Saether, B.-E. Density-dependence and optimal harvesting of fluctuating populations. Oikos 76, 40 (1996).

    MATH 
    Article 

    Google Scholar 

  • 57.

    Rowe, S. & Hutchings, J. A. Mating systems and the conservation of commercially exploited marine fish. Trends Ecol. Evol. 18, 567–572 (2003).

    Article 

    Google Scholar 

  • 58.

    Swain, D. P. & Chouinard, G. A. Predicted extirpation of the dominant demersal fish in a large marine ecosystem: Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Can. J. Fish. Aquat. Sci. 65, 2315–2319 (2008).

    Article 

    Google Scholar 

  • 59.

    Kuparinen, A. & Hutchings, J. A. Increased natural mortality at low abundance can generate an Allee effect in a marine fish. R. Soc. Open Sci. 1, 140075 (2014).

    ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 60.

    Swain, D. & Benoît, H. Extreme increases in natural mortality prevent recovery of collapsed fish populations in a Northwest Atlantic ecosystem. Mar. Ecol. Prog. Ser. 519, 165–182 (2015).

    ADS 
    Article 

    Google Scholar 

  • 61.

    Walters, C. & Kitchell, J. F. Cultivation/depensation effects on juvenile survival and recruitment: Implications for the theory of fishing. Can. J. Fish. Aquat. Sci. 58, 39–50 (2001).

    Article 

    Google Scholar 

  • 62.

    Andreasen, H. et al. Diet composition and food consumption rate of harbor porpoises (Phocoena phocoena) in the western Baltic Sea. Mar. Mamm. Sci. 33, 1053–1079 (2017).

    Article 

    Google Scholar 

  • 63.

    Hüssy, K. Review of western Baltic cod (Gadus morhua) recruitment dynamics. ICES J. Mar. Sci. 68, 1459–1471 (2011).

    Article 

    Google Scholar 

  • 64.

    Winter, A., Richter, A. & Eikeset, A. M. Implications of Allee effects for fisheries management in a changing climate: Evidence from Atlantic cod. Ecol. Appl. 30, 1–14 (2020).

  • 65.

    Munch, S. B., Giron-Nava, A. & Sugihara, G. Nonlinear dynamics and noise in fisheries recruitment: A global meta-analysis. Fish Fish. 19, 964–973 (2018).

    Article 

    Google Scholar 

  • 66.

    Szuwalski, C. S., Vert-Pre, K. A., Punt, A. E., Branch, T. A. & Hilborn, R. Examining common assumptions about recruitment: A meta-analysis of recruitment dynamics for worldwide marine fisheries. Fish Fish. 16, 633–648 (2015).

    Article 

    Google Scholar 

  • 67.

    Funk, S., Krumme, U., Temming, A. & Möllmann, C. Gillnet fishers’ knowledge reveals seasonality in depth and habitat use of cod (Gadus morhua) in the Western Baltic Sea. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsaa071 (2020).

    Article 

    Google Scholar 

  • 68.

    Hüssy, K., Hinrichsen, H.-H. & Huwer, B. Hydrographic influence on the spawning habitat suitability of western Baltic cod (Gadus morhua). ICES J. Mar. Sci. 69, 1736–1743 (2012).

    Article 

    Google Scholar 

  • 69.

    Hinrichsen, H.-H., Hüssy, K. & Huwer, B. Spatio-temporal variability in western Baltic cod early life stage survival mediated by egg buoyancy, hydrography and hydrodynamics. ICES J. Mar. Sci. 69, 1744–1752 (2012).

    Article 

    Google Scholar 

  • 70.

    Petereit, C., Hinrichsen, H.-H., Franke, A. & Köster, F. Floating along buoyancy levels: Dispersal and survival of western Baltic fish eggs. Prog. Oceanogr. 122, 131–152 (2014).

    ADS 
    Article 

    Google Scholar 

  • 71.

    Stiasny, M. H. et al. Ocean acidification effects on Atlantic Cod larval survival and recruitment to the fished population. PLoS One 11, e0155448 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 72.

    Voss, R. et al. Ecological-economic sustainability of the Baltic cod fisheries under ocean warming and acidification. J. Environ. Manag. 238, 110–118 (2019).

    Article 

    Google Scholar 

  • 73.

    Lindegren, M., Möllmann, C., Nielsen, A. & Stenseth, N. C. Preventing the collapse of the Baltic cod stock through an ecosystem-based management approach. Proc. Natl. Acad. Sci. 106, 14722–14727 (2009).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 74.

    Lindegren, M. et al. Ecological forecasting under climate change: The case of Baltic cod. Proc. R. Soc. B Biol. Sci. 277, 2121–2130 (2010).

    Article 

    Google Scholar 

  • 75.

    Holsman, K. K. et al. Ecosystem-based fisheries management forestalls climate-driven collapse. Nat. Commun. 11, 4579 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 76.

    Levin, P. S. et al. Building effective fishery ecosystem plans. Mar. Policy 92, 48–57 (2018).

    Article 

    Google Scholar 

  • 77.

    Dawson, C. & Levin, P. S. Moving the ecosystem-based fisheries management mountain begins by shifting small stones: A critical analysis of EBFM on the U.S. West Coast. Mar. Policy 100, 58–65 (2019).

    Article 

    Google Scholar 

  • 78.

    Link, J. S. & Marshak, A. R. Characterizing and comparing marine fisheries ecosystems in the United States: Determinants of success in moving toward ecosystem-based fisheries management. Rev. Fish Biol. Fish. 29, 23–70 (2019).

    Article 

    Google Scholar 

  • 79.

    Townsend, H. et al. Progress on implementing ecosystem-based fisheries management in the United States through the use of ecosystem models and analysis. Front. Mar. Sci. 6, 641 (2019).

    Article 

    Google Scholar 

  • 80.

    Koehn, L. E. et al. Case studies demonstrate capacity for a structured planning process for ecosystem-based fisheries management. Can. J. Fish. Aquat. Sci. 77, 1256–1274 (2020).

    Article 

    Google Scholar 

  • 81.

    Skern-Mauritzen, M. et al. Ecosystem processes are rarely included in tactical fisheries management. Fish Fish. 17, 165–175 (2016).

    Article 

    Google Scholar 

  • 82.

    Marshall, K. N., Koehn, L. E., Levin, P. S., Essington, T. E. & Jensen, O. P. Inclusion of ecosystem information in US fish stock assessments suggests progress toward ecosystem-based fisheries management. ICES J. Mar. Sci. 76, 1–9 (2019).

    Article 

    Google Scholar 

  • 83.

    Otto, S. A., Kadin, M., Casini, M., Torres, M. A. & Blenckner, T. A quantitative framework for selecting and validating food web indicators. Ecol. Ind. 84, 619–631 (2018).

    Article 

    Google Scholar 

  • 84.

    Kadin, M. et al. Trophic interactions, management trade-offs and climate change: The need for adaptive thresholds to operationalize ecosystem indicators. Front. Mar. Sci. 6, 249 (2019).

    ADS 
    Article 

    Google Scholar 

  • 85.

    Samhouri, J. F. et al. Defining ecosystem thresholds for human activities and environmental pressures in the California Current. Ecosphere 8, 1–21 (2017).

  • 86.

    Payne, M. R. et al. Lessons from the first generation of marine ecological forecast products. Front. Mar. Sci. 4, 289 (2017).

    Article 

    Google Scholar 

  • 87.

    Tommasi, D. et al. Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts. Prog. Oceanogr. 152, 15–49 (2017).

    ADS 
    Article 

    Google Scholar 

  • 88.

    Haltuch, M. et al. Unraveling the recruitment problem: A review of environmentally-informed forecasting and management strategy evaluation. Fish. Res. 217, 198–216 (2019).

    Article 

    Google Scholar 

  • 89.

    Hobday, A. J. et al. A framework for combining seasonal forecasts and climate projections to aid risk management for fisheries and aquaculture. Front. Mar. Sci. 5, 137 (2018).

    Article 

    Google Scholar 

  • 90.

    Hobday, A. J. et al. Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsy210 (2019).

    Article 

    Google Scholar 

  • 91.

    Punt, A. E., Butterworth, D. S., de Moor, C. L., De Oliveira, J. A. A. & Haddon, M. Management strategy evaluation: Best practices. Fish Fish. 17, 303–334 (2016).

    Article 

    Google Scholar 

  • 92.

    Grüss, A. et al. Recommendations on the use of ecosystem modeling for informing ecosystem-based fisheries management and restoration outcomes in the Gulf of Mexico. Mar. Coast. Fish. 9, 281–295 (2017).

    Article 

    Google Scholar 

  • 93.

    Hollowed, A. B. et al. Integrated modeling to evaluate climate change impacts on coupled social-ecological systems in Alaska. Front. Mar. Sci. 6, 775 (2020).

    Article 

    Google Scholar 

  • 94.

    Okamoto, D. K. et al. Attending to spatial social–ecological sensitivities to improve trade-off analysis in natural resource management. Fish Fish. 21, 1–12 (2020).

    Article 

    Google Scholar 

  • 95.

    Möllmann, C. et al. Implementing ecosystem-based fisheries management: From single-species to integrated ecosystem assessment and advice for Baltic Sea fish stocks. ICES J. Mar. Sci. 71, 1187–1197 (2014).

    Article 

    Google Scholar 

  • 96.

    Voss, R. et al. Assessing social—ecological trade-offs to advance ecosystem-based fisheries management. PLoS One 9, e107811 (2014).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 97.

    Schmidt, J. O. et al. Future ocean observations to connect climate, fisheries and marine ecosystems. Front. Mar. Sci. 6, 550 (2019).

    Article 

    Google Scholar 

  • 98.

    Hicks, C. C. et al. Engage key social concepts for sustainability. Science 352, 38–40 (2016).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 99.

    Hornborg, S. et al. Ecosystem-based fisheries management requires broader performance indicators for the human dimension. Mar. Policy 108, 103639 (2019).

    Article 

    Google Scholar 

  • 100.

    Levin, P. S. et al. Conceptualization of social-ecological systems of the california current: An examination of interdisciplinary science supporting ecosystem-based management. Coast. Manag. 44, 397–408 (2016).

    Article 

    Google Scholar 

  • 101.

    ICES. Herring (Clupea harengus) in subdivisions 20-24, spring spawners (Skagerrak, Kattegat, and western Baltic). https://doi.org/10.17895/ICES.ADVICE.4715 (2019).

  • 102.

    Quentin Grafton, R. Adaptation to climate change in marine capture fisheries. Mar. Policy 34, 606–615 (2010).

    Article 

    Google Scholar 

  • 103.

    Lindegren, M. & Brander, K. Adapting fisheries and their management to climate change: A review of concepts, tools, frameworks, and current progress toward implementation. Rev. Fish. Sci. Aquac. 26, 400–415 (2018).

    Article 

    Google Scholar 

  • 104.

    Holsman, K. K. et al. Towards climate resiliency in fisheries management. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsz031 (2019).

    Article 

    Google Scholar 

  • 105.

    Bell, R. J., Odell, J., Kirchner, G. & Lomonico, S. Actions to promote and achieve climate-ready fisheries: Summary of current practice. Mar. Coast. Fish. 12, 166–190 (2020).

    Article 

    Google Scholar 

  • 106.

    Gaichas, S. K., Link, J. S. & Hare, J. A. A risk-based approach to evaluating northeast US fish community vulnerability to climate change. ICES J. Mar. Sci. 71, 2323–2342 (2014).

    Article 

    Google Scholar 

  • 107.

    Pecl, G. T. et al. Rapid assessment of fisheries species sensitivity to climate change. Clim. Change 127, 505–520 (2014).

    ADS 
    Article 

    Google Scholar 

  • 108.

    Hare, J. A. et al. A vulnerability assessment of fish and invertebrates to climate change on the Northeast U.S. Continental Shelf. PLoS One 11, e0146756 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 109.

    Johnson, J. E. et al. Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support. Mar. Policy 74, 220–229 (2016).

    Article 

    Google Scholar 

  • 110.

    Whitney, C. K. et al. Adaptive capacity: From assessment to action in coastal social-ecological systems. Ecol. Soc. 22, art22 (2017).

    Article 

    Google Scholar 

  • 111.

    Johnson, F. A., Eaton, M. J., Mikels-Carrasco, J. & Case, D. Building adaptive capacity in a coastal region experiencing global change. Ecol. Soc. 25, art9 (2020).

    Article 

    Google Scholar 

  • 112.

    ICES. Baltic Fisheries Assessemant Working Group. (2019). https://doi.org/10.17895/ICES.PUB.5949.

  • 113.

    ICES. Baltic Fisheries Assessemant Working Group. ICES CM 2014/ACOM:10 (2014).

  • 114.

    Hüssy, K. et al. Spatio-temporal trends in stock mixing of eastern and western Baltic cod in the Arkona Basin and the implications for recruitment. ICES J. Mar. Sci. J. Conseil 73, 293–303 (2016).

    Article 

    Google Scholar 

  • 115.

    Weist, P. et al. Assessing SNP-markers to study population mixing and ecological adaptation in Baltic cod. PLoS One 14, e0218127 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 116.

    R Core Team. R: A Language and Environment for Statistical Computing. (Accessed 2 July 2021); https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).

  • 117.

    Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).

    ADS 
    Article 

    Google Scholar 

  • 118.

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

  • 119.

    Zeileis, A., Kleiber, C., Krämer, W. & Hornik, K. Testing and dating of structural changes in practice. Comput. Stat. Data Anal. 44, 109–123 (2003).

    MathSciNet 
    MATH 
    Article 

    Google Scholar 

  • 120.

    Otto, S. A. Comparison of change point detection methods. (Accessed 2 July 2021); https://www.marinedatascience.co/blog/2019/09/28/comparison-of-change-point-detection-methods/. (2019).


  • Source: Ecology - nature.com

    Asegun Henry has a big idea for tackling climate change: Store up the sun

    New directions in real estate practice