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The Major Roles of Climate Warming and Ecological Competition in the Small-scale Coastal Fishery in French Guiana

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Abstract

Marine ecosystems, biodiversity, and fisheries are under strain worldwide due to global changes including climate warming and demographic pressure. To address this issue, many scientists and stakeholders advocate the use of an ecosystem approach for fisheries that integrates the numerous ecological and economic complexities at play rather than focusing on the management of individual target species. However, the operationalization of such an ecosystem approach remains challenging, especially from a bio-economic standpoint. Here, to address this issue, we propose a model of intermediate complexity (MICE) relying on multi-species, multi-fleet, and resource-based dynamics. Climate change effects are incorporated through an envelope model for the biological growth of fish species as a function of sea surface temperature. The model is calibrated for the small-scale fishery in French Guiana using a time series of fish landings and fishing effort from 2006 to 2018. From the calibrated model, a predictive fishing effort projection and RCP climate scenarios derived from IPCC, we explore the ecosystem dynamics and the fishery production at the horizon 2100. Our results demonstrate the long-term detrimental impact of both climate change and ecological competition on fish biodiversity. The prognosis is particularly catastrophic under the most pessimistic climate scenario, with a potential collapse of both biomass targeted species and fishing activity by 2100.

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Availability of data and material

Data of fishing landings and efforts are available in the website of the IFREMER Fisheries Information System (https://sih.ifremer.fr/). Observed sea surface temperature data (SST) are extracted from the NOAA Earth System Research Laboratory website (https://www.esrl.noaa.gov/).

Code availability

This study has been performed thanks to the scientific software SCILAB. In particular the calibration was done with “optim_ga” routine. The numerical codes for the calibration and the scenarios are available in the Google drive of codes.enmo.d.20.00261@gmail.com, password: ENMO.D.20.00261. Please contact Hélène Gomes in case of any problem.

Notes

  1. Using data on fish landings for a given effort as well as comprehensive data on fishing effort, IFREMER observers are able to extrapolate landings for all boats and landing points on a quarterly basis using the rule of three. During this period, the fishermen made no technical adaptations and installed no new equipment that would have increased fishing power. Therefore, we can assume that the efficiency of the French Guiana fishery remains constant over time.

  2. https://www.esrl.noaa.gov/

  3. In the model proposed for the French Guiana fishery by Cissé et al. [42], trophic interactions turn out to have a weak influence. Here, we have simplified the model by ignoring trophic interactions between fishes in order to focus on the influence of the environment on population dynamics.

  4. However we are aware that a longer timeframe introduces significant additional uncertainties to the model.

  5. We used the level of the resource stock at equilibrium in year 2085 to obtain \(B_{res,i}^*\)

    $$B_{res,i}^*=\frac{M_i+\displaystyle {\sum _{f=1}^{F}}q_{i,f}E_f(t_{2085})}{\gamma _i\big (\theta (t_{2085}-\tau _i)\big )g_i a_{res,i}},$$

    with \(t_{2085}\) corresponding to the value of the first quarter of the year 2085. The choice of the year 2085 is rather arbitrary. The idea is to consider a relatively distant year in which two species would already be extinct.

  6. https://www.fishbase.se

References

  1. Butchart, S. H. M., Walpole, M., Collen, B., Strien, A. v., Scharlemann, J. P. W., Almond, R. E. A., Baillie, J. E. M., Bomhard, B., Brown, C., Bruno, J., Carpenter, K. E., Carr, G. M., Chanson, J., Chenery, A. M., Csirke, J., Davidson, N. C., Dentener, F., Foster, M., Galli, A., Galloway, J. N., Genovesi, P., Gregory, R. D., Hockings, M., Kapos, V., Lamarque, J.-F., Leverington, F., Loh, J., McGeoch, M. A., McRae, L., Minasyan, A., Morcillo, M. H., Oldfield, T. E. E., Pauly, D., Quader, S., Revenga, C., Sauer, J. R., Skolnik, B., Spear, D., Stanwell-Smith, D., Stuart, S. N., Symes, A., Tierney, M., Tyrrell, T. D., Vié, J. C., & Watson, R. (2010). Global Biodiversity: Indicators of Recent Declines. Science 328, 5982 , 1164–1168. Publisher: American Association for the Advancement of Science Section: Report.

  2. Österblom, H., Jouffray, J. B., and Spijkers, J. (2016). Where and how to prioritize fishery reform? Proceedings of the National Academy of Sciences 113(25), E3473–E3474. Publisher: National Academy of Sciences Section: Letter.

  3. FAO, Ed. The State of World Fisheries and Aquaculture 2018- Meeting the sustainable development goals. No. (2018). in The state of world fisheries and aquaculture (p. 2018). Rome: FAO.

    Google Scholar 

  4. Barange, M. (2018). Impacts of climate change on fisheries and aquaculture: synthesis of current knowledge, adaptation and mitigation options. Food and Agriculture Organization of the United Nations, OCLC: 1101194814.

  5. Sumaila, U. R., Cheung, W. W. L., Lam, V. W. Y., Pauly, D., & Herrick, S. (2011). Climate change impacts on the biophysics and economics of world fisheries. Nature Climate Change, 1(9), 449–456.

    Article  Google Scholar 

  6. Fisheries, N. (2020). Ecosystem-Based Fisheries Management Policy | NOAA Fisheries. Archive Location: National Library Catalog: https://www.fisheries.noaa.gov/

  7. Cochrane, K. L. (2000). Reconciling sustainability, economic efficiency and equity in fisheries: the one that got away? Fish and Fisheries, 1(1), 3–21.

    Article  Google Scholar 

  8. Sinclair, M., Arnason, R., Csirke, J., Karnicki, Z., Sigurjonsson, J., Rune Skjoldal, H., & Valdimarsson, G. (2002). Responsible fisheries in the marine ecosystem. Fisheries Research, 58(3), 255–265.

    Article  Google Scholar 

  9. Doyen, L., Béné, C., Bertignac, M., Blanchard, F., Cissé, A. A., Dichmont, C., et al. (2017). Ecoviability for ecosystem-based fisheries management. Fish and Fisheries, 18(6), 1056–1072.

    Article  Google Scholar 

  10. Fulton, E. A., Punt, A. E., Dichmont, C. M., Harvey, C. J., & Gorton, R. (2019). Ecosystems say good management pays off. Fish and Fisheries 20(1), 66–96. https://onlinelibrary.wiley.com/doi/pdf/10.1111/faf.12324

  11. Link, J. S., Thébaud, O., Smith, D. C., Smith, A. D. M., Schmidt, J., Rice, J., Poos, J. J., Pita, C., Lipton, D., Kraan, M., Frusher, S., Doyen, L., Cudennec, A., Criddle, K., & Bailly, D. (2017). Keeping Humans in the Ecosystem. ICES Journal of Marine Science 74(7), 1947–1956. Publisher: Oxford Academic.

  12. Pitcher, T. J., Kalikoski, D., Short, K., Varkey, D., & Pramod, G. (2009). An evaluation of progress in implementing ecosystem-based management of fisheries in 33 countries. Marine Policy, 33(2), 223–232.

    Article  Google Scholar 

  13. Plagányi, É. E. (2007). Models for an ecosystem approach to fisheries. No. 477 in FAO fisheries technical paper. Food and Agriculture Organization of the United Nations, Rome. OCLC: 254027406.

  14. Plagányi, É. E., Punt, A. E., Hillary, R., Morello, E. B., Thébaud, O., Hutton, T., et al. (2014). Multispecies fisheries management and conservation: tactical applications using models of intermediate complexity. Fish and Fisheries, 15(1), 1–22.

    Article  Google Scholar 

  15. Sanchirico, J. N., Smith, M. D., & Lipton, D. W. (2008). An empirical approach to ecosystem-based fishery management. Ecological Economics, 64, 586–596.

    Article  Google Scholar 

  16. Christensen, V., & Walters, C. J. (2004). Ecopath with Ecosim: methods, capabilities and limitations. Ecological Modelling, 172(2), 109–139.

    Article  Google Scholar 

  17. Fulton, E. A., Link, J. S., Kaplan, I. C., Savina-Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R. J., Smith, A. D. M., & Smith, D. C. (2011). Lessons in modelling and management of marine ecosystems: the Atlantis experience. Fish and Fisheries 12(2), 171–188. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-2979.2011.00412.x

  18. Tromeur, E., & Doyen, L. (2019). Optimal Harvesting Policies Threaten Biodiversity in Mixed Fisheries. Environmental Modeling & Assessment, 24(4), 387–403.

    Article  Google Scholar 

  19. Tilman, D., & Sterner, R. W. (1984). Invasions of equilibria: tests of resource competition using two species of algae. Oecologia, 61(2), 197–200.

    Article  Google Scholar 

  20. Doyen, L. (2018). Mathematics for Scenarios of Biodiversity and Ecosystem Services. Environmental Modeling & Assessment, 23(6), 729–742.

    Article  Google Scholar 

  21. Daan, N. (1980). A review of replacement of depleted stocks by other species and the mechanisms underlying such replacement [sardine, anchovy, pilchard, herring, blue whiting, mackerel, cod, California, South Africa, Japan, Norway, North Sea, Baltic Sea, USA, East Coast, Gulf of Thailand]. Rapp. P.-v. Réun. Cons. int. Explor. Mer 177, 405–421.

  22. Botsford, L. W., Castilla, J. C., & Peterson, C. H. (1997). The management of fisheries and marine ecosystems. Science, 277(5325), 509–515.

    Article  CAS  Google Scholar 

  23. Stock, C. A., Alexander, M. A., Bond, N. A., Brander, K. M., Cheung, W. W. L., Curchitser, E. N., et al. (2011). On the use of IPCC-class models to assess the impact of climate on Living Marine Resources. Progress in Oceanography, 88(1), 1–27.

    Article  Google Scholar 

  24. de Lange, C. (2013). Fishery forced to close as shrimp stocks collapse. New Scientist, 220(2947), 7.

    Article  Google Scholar 

  25. Lopes, P. F. M., Pennino, M. G., & Freire, F. (2018). Climate change can reduce shrimp catches in equatorial Brazil. Regional Environmental Change, 18(1), 223–234.

    Article  Google Scholar 

  26. Parmesan, C. (2006). Ecological and Evolutionary Responses to Recent Climate Change. Annual Review of Ecology, Evolution, and Systematics, 37(1), 637–669.

    Article  Google Scholar 

  27. Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J., Collingham, Y. C., Erasmus, B. F. N., Siqueira, M. F. D., Grainger, A., Hannah, L., Hughes, L., Huntley, B., Jaarsveld, A. S. V., Midgley, G. F., Miles, L., Ortega-Huerta, M. A., Peterson, A. T., Phillips, O. L., & Williams, S. E. (2004). Extinction risk from climate change. Nature 427(6970), 145–148.

  28. Cheung, W., Lam, V., & Pauly, D. (2008). Dynamic bioclimate envelope model to predict climate-induced changes in distribution of marine fishes and invertebrates. Modelling Present and Climate-shifted Distributions of Marine Fishes and Invertebrates, 16, 5–50.

    Google Scholar 

  29. Zhao, Q., Boomer, G. S., & Royle, J. A. (2019). Integrated modeling predicts shifts in waterbird population dynamics under climate change. Ecography, 42(9), 1470–1481.

    Article  Google Scholar 

  30. Garza-Gil, M. D., Torralba-Cano, J., & Varela-Lafuente, M. M. (2011). Evaluating the economic effects of climate change on the European sardine fishery. Regional Environmental Change, 11(1), 87–95.

    Article  Google Scholar 

  31. Alfonso, Á., López, U., Santana, A., Polanco-Martínez, J., Santana-del Pino, A., Ibarra-Berastegi, G., & Castro, J. (2010). The role of climatic variability on the short-term fluctuations of octopus captures at the Canary Islands. Fisheries Research, 102, 258–265.

    Article  Google Scholar 

  32. Lehodey, P., Alheit, J., Barange, M., Baumgartner, T., Beaugrand, G., Drinkwater, K., Fromentin, J. M., Hare, S. R., Ottersen, G., Perry, R. I., Roy, C., van der Lingen, C. D., & Werner, F. (2006). Climate Variability, Fish, and Fisheries. Journal of Climate 19,(20) 5009–5030. Publisher: American Meteorological Society.

  33. Brander, K. M. (2007). Global fish production and climate change. Proceedings of the National Academy of Sciences, 104(50), 19709–19714.

    Article  CAS  Google Scholar 

  34. Cheung, W. W. L., Lam, V. W. Y., Sarmiento, J. L., Kearney, K., Watson, R., & Pauly, D. (2009). Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries, 10(3), 235–251.

    Article  Google Scholar 

  35. Diop, B., Sanz, N., Duplan, Y. J. J., Guene, E. H. M., Blanchard, F., Pereau, J. C., & Doyen, L. (2018). Maximum Economic Yield Fishery Management in the Face of Global Warming. Ecological Economics, 154, 52–61.

    Article  Google Scholar 

  36. Lagarde, A., Doyen, L., Ahad-Cissé, A., Caill-Milly, N., Gourguet, S., Pape, O. L., et al. (2018). How Does MMEY Mitigate the Bioeconomic Effects of Climate Change for Mixed Fisheries. Ecological Economics, 154, 317–332.

    Article  Google Scholar 

  37. Cheung, W. W. L., Reygondeau, G., & Frölicher, T. L. (2016). Large benefits to marine fisheries of meeting the 1.5C global warming target. Science 354, 6319 , 1591–1594.

  38. Ferrier, S., Ninan, K., Leadley, P., Alkemade, R., Acosta, L., Akçakaya, H., Brotons, L., Cheung, W. W. L., Christensen, V., Harhash, K., Kabubo-Mariara, J., Lundquist, C., Obersteiner, M., Pereira, H., Peterson, G., Pichs-Madruga, R., Ravindranath, N., Rondinini, C., & Wintle, B. (2016). IPBES: The methodological assessment report on scenarios and models of biodiversity and ecosystem services. Technical Report, Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany.

  39. Pörtner, H. O., Roberts, D., & Masson-Delmotte, V. (2019). IPCC (2019): Special Report on the Ocean and Cryosphere in a Changing Climate. IPCC: Rationale Reference.

    Google Scholar 

  40. de la Guyane, P. (2010). Arrete préfectoral n1157.

  41. Cissé, A. A., Doyen, L., Blanchard, F., Béné, C., & Péreau, J. C. (2015). Ecoviability for small-scale fisheries in the context of food security constraints. Ecological Economics, 119, 39–52.

    Article  Google Scholar 

  42. Cissé, A. A., Gourguet, S., Doyen, L., Blanchard, F., & Péreau, J. C. (2013). A bio-economic model for the ecosystem-based management of the coastal fishery in French Guiana. Environment and Development Economics 18(3), 245–269. Publisher: Cambridge University Press.

  43. Demougeot, L., & Baert, X. (2019). La population guyanaise à l’horizon 2050 : vers un doublement de la population? INSEE Analyses 36.

  44. Walters, C., Christensen, V., & Pauly, D. (1997). Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments. Reviews in Fish Biology and Fisheries, 7(2), 139–172.

    Article  Google Scholar 

  45. Ainsworth, C. H., Samhouri, J. F., Busch, D. S., Cheung, W. W. L., Dunne, J., & Okey, T. A. (2011). Potential impacts of climate change on Northeast Pacific marine foodwebs and fisheries. ICES Journal of Marine Science, 68(6), 1217–1229.

    Article  Google Scholar 

  46. Thompson, P. M., & Ollason, J. C. (2001). Lagged effects of ocean climate change on fulmar population dynamics. Nature, 413(6854), 417–420.

    Article  CAS  Google Scholar 

  47. Brock, W., & Xepapadeas, A. (2002). Optimal Ecosystem Management when Species Compete for Limiting Resources. Journal of Environmental Economics and Management, 44(2), 189–220.

    Article  Google Scholar 

  48. Lara, M. D., and Doyen, L. Sustainable Management of Natural Resources: Mathematical Models and Methods. Springer Science & Business Media, Aug. 2008. Google-Books-ID: KRKsZZZxNh0C.

  49. Candela, L., Castelli, D., Coro, G., Pagano, P., & Sinibaldi, F. (2016). Species distribution modeling in the cloud. Concurrency and Computation: Practice and Experience, 28(4), 1056–1079.

    Article  Google Scholar 

  50. Mardle, S., & Pascoe, S. (2000). Use of evolutionary methods for bioeconomic optimization models: an application to fisheries. Agricultural Systems, 66(1), 33–49.

    Article  Google Scholar 

  51. Fulton, E. A., Smith, A. D. M., Smith, D. C., & Putten, I. E. V. (2011). Human behaviour: the key source of uncertainty in fisheries management. Fish and Fisheries 12(1), 2–17. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-2979.2010.00371.x

  52. Tilman, D. (2004). Niche tradeoffs, neutrality, and community structure: A stochastic theory of resource competition, invasion, and community assembly. Proceedings of the National Academy of Sciences, 101(30), 10854–10861.

    Article  CAS  Google Scholar 

  53. Pecuchet, L., Törnroos, A., & Lindegren, M. (2016). Patterns and drivers of fish community assembly in a large marine ecosystem. Marine Ecology Progress Series 546.

  54. Vandermeer, J. H. (1972). Niche Theory. Annual Review of Ecology and Systematics 3(1), 107–132. https://doi.org/10.1146/annurev.es.03.110172.000543

  55. Quinn, T. J., & Deriso, R. B. (1999). Quantitative Fish Dynamics. Oxford University Press. Google-Books-ID: 5FVBj8jnh6sC.

  56. Garcia, S. M., Allison, E. H., Andrew, N., Béné, C., Bianchi, G., de Graaf, G., Kalikoski, D., Mahon, R., & Orensanz, L. (2008). Towards integrated assessment and advice in small-scale fisheries: principles and processes. No. 515 in FAO fisheries and aquaculture technical paper. Food and Agriculture Organization of the United Nations, Rome.

  57. Communities, C. (2000). o. t. E. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Office for Official Publications of the European Communities.

  58. Briton, F., Macher, C., Merzeréaud, M., Le Grand, C., Fifas, S., & Thébaud, O. (2020). Providing Integrated Total Catch Advice for the Management of Mixed Fisheries with an Eco-viability Approach. Environmental Modeling & Assessment, 25(3), 307–325.

    Article  Google Scholar 

  59. Grafton, R. Q., Doyen, L., Béné, C., Borgomeo, E., Brooks, K., Chu, L., Cumming, G. S., Dixon, J., Dovers, S., Garrick, D., Helfgott, A., Jiang, Q., Katic, P., Kompas, T., Little, L. R., Matthews, N., Ringler, C., Squires, D., Steinshamn, S. I., Villasante, S., Wheeler, S., Williams, J., & Wyrwoll, P. R. (2019). Realizing resilience for decision-making. Nature Sustainability 2(10), 907–913. Number: 10 Publisher: Nature Publishing Group.

  60. Poudel, D., Sandal, L., Steinshamn, S., & Kvamsdal, S. (2012). Do Species Interaction and Stochasticity Matter to Optimal Management of Multispecies Fisheries? SSRN Electronic Journal.

  61. Lorance, P., & Dupouy, H. (2001). CPUE abundance indices of the main target species of the French deep-water fishery in ICES Sub-areas V-VII. Fisheries Research, 51(2), 137–149.

    Article  Google Scholar 

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Funding

This study, including data collection and analyzes, was funded by the IFREMER (Institut Français de Recherche pour l’Exploitation de la Mer; english: French Research Institute for Exploitation of the Sea). It also benefited from the CNRS research project entitled ENTROPIC (Ecological-ecoNomic resilience of TROPIcal coastal eCosystem).

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Authors and Affiliations

  1. Ifremer, USR 3456, LEEISA, CNRS, Universite de Guyane, Ifremer, 275 route de Montabo, 97300, Cayenne, Guyane, France

    Helene Gomes & Fabian Blanchard

  2. University of Bordeaux, GREThA, avenue Leon Duguit, Pessac, 33608 , France

    Coralie Kersulec & Luc Doyen

  3. Universite de Guyane, USR 3456, LEEISA, CNRS, Universite de Guyane, Ifremer, 2091 route de Baduel, Cayenne, 97300, Guyane, France

    Abdoul Ahad Cisse & Nicolas Sanz

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  1. Helene Gomes
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  2. Coralie Kersulec
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  3. Luc Doyen
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  4. Fabian Blanchard
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  5. Abdoul Ahad Cisse
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Contributions

All authors contributed to the study conception and design. The model was developed by Abdoul Ahad Cissé, Luc Doyen, Hélène Gomes and Coralie Kersulec. Scenarios for the projections were performed by Luc Doyen and Nicolas Sanz. Analysis were conducted by Fabian Blanchard and Hélène Gomes. The first draft was written by Hélène Gomes and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Helene Gomes.

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Appendix

Appendix

1.1 Geographic Coordinates of the Points used for the SST

See Table 7.

Table 7 Geographic coordinates of each point
Full size table

1.2 Temperatures \(\theta _{i,10}\) , \(\theta _{i,opt}\), \(\theta _{i,90}\) used for Climate Change Modelling

See Table 8

Table 8 Temperatures \(\theta _{i,10}\) , \(\theta _{i,opt}\), \(\theta _{i,90}\) for each stock i
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1.3 Rate \(\delta ^{hist}_f\) used for Projected Efforts

See Table 9.

Table 9 Rate \(\delta ^{hist}_f\) for each fleet
Full size table

1.4 Sensitivity Analysis

A sensitivity analysis was carried out to evaluate reliability and contribution to the outputs for each calibrated parameter. To this end, additional simulations were run based on the PS, RCP 2.6, and RCP 8.5 scenarios. Due to the large number of parameters, we simplified the sensitivity analysis for each climate scenario by simultaneously perturbing all of the calibrated parameters of the same category. There were seven categories in total: trophic interactions, catchability, mortality rate, growth efficiency, initial biomass, time lag, and values of I. For each category of parameters, a level of noise ranging from -10% to +10% of the calibrated values was added. Relative differences in average catch per quarter \(\bar{H}=\frac{1}{t_f-t_1} \sum _{t=t_1}^{t_f}\sum _{f=1}^{F}\sum _{i=1}^{N}H_{i,f}(t)\) were computed for each climate scenario.

Fig. 11
figure 11

Relative change in average catch per quarter \(\bar{H}\) for scenarios RCP 8.5 (red) and RCP 2.6 (blue) as a function of variations in input parameters by 1% increments from -10% to +10%. The baseline is the predictive scenario

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Figure 11 displays the sensitivity results for both climate scenarios. These results show that the parameters with the greatest impact, in both cases, are catchability, mortality, growth efficiency, and values of I. With the exception of the time-lag parameter, the relative change in average catch appears to vary more or less linearly with the magnitude of the perturbations, with the absolute value of the slopes ranging from 0.008 to 0.97, indicating the bounds of the marginal effects of the parameters. Since the relative changes are smaller than the magnitude of the perturbations for these parameters, it appears that the effects of these parameters are small. We can see that the impact of the perturbations on trophic interactions, growth efficiency, initial biomass, and parameter I is similar for both climate scenarios: the red and blue lines practically coincide. For the perturbations of the time lag, the graph of the relative change in catch is stepped; time lag is an integer value, so the magnitude of the perturbation has an impact when its value is higher than 1. Finally, we can see that the time lag has a greater effect on relative change in average catch in the RCP 8.5 scenario than in the RCP 2.6 scenario. This is because the temperature increase \(\Delta _{\omega ,t_f}\) is higher under RCP 8.5 than under RCP 2.6.

1.5 Comparison of Historical and Estimated CPUE

As the efficiency of the fishing time of the French Guiana coastal fishery is assumed to be constant between 2006 and 2018, the catch per unit effort (CPUE) can be considered a proxy for abundance [61]. Since ours is a multi-species, multi-fleet model, we cannot calculate the CPUE for each species. However, we can compute the CPUE for each fleet and for the aggregated catch. Figure 12 compares historical CPUE with the estimated CPUE. The position of the crosses on the diagonal suggests a satisfactory goodness-of-fit for every biomass.

Fig. 12
figure 12

Comparison between historical and estimated CPUE in the 2006-2018 time series for CC, CCA, T and aggregated catches

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1.6 Sea Surface Temperatures at Extinctions Dates of Each Species for Both Climate Scenario

See Table 10.

Table 10 Sea surface temperatures at extinctions dates of each species for both climate scenario
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1.7 Biological Efficiency for the 13 Most-fished Species in the Coastal Fishery in French Guiana.

See Fig. 13

Fig. 13
figure 13

Biological efficiency for the 13 most-fished species in the coastal fishery in French Guiana. The species included in the model are represented by red curves, while the others are represented by black curves

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Gomes, H., Kersulec, C., Doyen, L. et al. The Major Roles of Climate Warming and Ecological Competition in the Small-scale Coastal Fishery in French Guiana. Environ Model Assess 26, 655–675 (2021). https://doi.org/10.1007/s10666-021-09772-8

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