Assessment modelling approaches for stocks with spawning components, seasonal and spatial dynamics, and limited resources for data collection

English version

Autoři: Elisabeth Van Beveren ^aff001; Daniel E. Duplisea ^aff001; Pablo Brosset ^aff001; Martin Castonguay ^aff001
Působiště autorů: Fisheries and Oceans Canada, Institut Maurice-Lamontagne, Mont-Joli, QC, Canada ^aff001
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222472

Souhrn

The true spatiotemporal structure of a fish population is often more complex than represented in assessments because movement between spawning components is disregarded and data at the necessary scale are unavailable. This can generate poor advice. We explore the impacts of modelling choices and their associated risks given limited data and lack of biological knowledge on spawning component structure and connectivity. Pseudo-data for an age structured fish population were simulated with two spawning components that experience various levels of connectivity and that might overlap during a certain period but segregate during reproduction. A variety of implicit spatiotemporal and simpler models were fitted to the pseudo-datasets, mimicking different situations of data availability. To reproduce the true stock characteristics, the spatiotemporal models required total catch data disaggregated by spawning component; however, catch-at-age was not as important nor were disaggregated biomass indices to reproduce true dynamics. Even with just 5% connectivity between spawning components, both the spatiotemporal models and simpler alternatives generally overestimated stock biomass. Although bias was smallest when considering one unit population, spawning components might still need to be considered for management and conservation. In such case, the spatiotemporal model was less influenced by ignored connectivity patterns compared to a model focussing on one spawning component only.

Klíčová slova:

Population dynamics – Simulation and modeling – Death rates – Data acquisition – Spawning – Fish biology – Animal migration – Metapopulation dynamics

Zdroje

1. Shepherd JG, Pope JG. Dynamic Pool Models I: Interpreting the Past Using Virtual Population Analysis. In: Hart PJB, Reynolds JD, editors. Handbook of Fish Biology and Fisheries, Volume 2. Oxford, UK: Blackwell Science Ltd; 2008. pp. 127–163. doi: 10.1002/9780470693919.ch7

2. Cadrin SX, editor. Stock identification methods: applications in fishery science. 2nd ed. Amsterdam: Academic Press; 2013.

3. Kritzer JP, Sale PF. Metapopulation ecology in the sea: from Levins’ model to marine ecology and fisheries science. Fish Fish. 2004;5: 131–140. doi: 10.1111/j.1467-2979.2004.00131.x

4. McQuinn I. Metapopulations and the Atlantic herring. Rev Fish Biol Fish. 1997;7: 297–329.

5. Casey J, Jardim E, Martinsohn JT. The role of genetics in fisheries management under the E.U. common fisheries policy: genetics in fisheries management under the cfp. J Fish Biol. 2016;89: 2755–2767. doi: 10.1111/jfb.13151 27761916

6. Goethel DR, Quinn TJ, Cadrin SX. Incorporating Spatial Structure in Stock Assessment: Movement Modeling in Marine Fish Population Dynamics. Rev Fish Sci. 2011;19: 119–136. doi: 10.1080/10641262.2011.557451

7. Cope JM, Punt AE. Reconciling stock assessment and management scales under conditions of spatially varying catch histories. Fish Res. 2011;107: 22–38. doi: 10.1016/j.fishres.2010.10.002

8. Fu C, Fanning LP. Spatial Considerations in the Management of Atlantic Cod off Nova Scotia, Canada. North Am J Fish Manag. 2004;24: 775–784. doi: 10.1577/M03-134.1

9. Goethel DR, Berger AM. Accounting for spatial complexities in the calculation of biological reference points: effects of misdiagnosing population structure for stock status indicators. Can J Fish Aquat Sci. 2017;74: 1878–1894. doi: 10.1139/cjfas-2016-0290

10. Kell LT, Dickey-Collas M, Hintzen NT, Nash RDM, Pilling GM, Roel BA. Lumpers or splitters? Evaluating recovery and management plans for metapopulations of herring. ICES J Mar Sci. 2009;66: 1776–1783. doi: 10.1093/icesjms/fsp181

11. Kerr LA, Cadrin SX, Kovach AI. Consequences of a mismatch between biological and management units on our perception of Atlantic cod off New England. ICES J Mar Sci. 2014;71: 1366–1381. doi: 10.1093/icesjms/fsu113

12. Ying Y, Chen Y, Lin L, Gao T, Quinn T. Risks of ignoring fish population spatial structure in fisheries management. Can J Fish Aquat Sci. 2011;68: 2101–2120. doi: 10.1139/f2011-116

13. Little LR, Punt AE, Tuck GN, Mapstone BD. Exploring the effect of sampling, protogyny, and larval advection on stock estimates subject to no-take closures in a spatially complex coral reef line fishery on the Great Barrier Reef, Australia. Can J Fish Aquat Sci. 2017;74: 1950–1959. doi: 10.1139/cjfas-2016-0349

14. Berger AM, Goethel DR, Lynch PD, Quinn T, Mormede S, McKenzie J, et al. Space oddity: The mission for spatial integration. Can J Fish Aquat Sci. 2017;74: 1698–1716. doi: 10.1139/cjfas-2017-0150

15. McGilliard CR, Punt AE, Methot RD, Hilborn R. Accounting for marine reserves using spatial stock assessments. Jacobson L, editor. Can J Fish Aquat Sci. 2015;72: 262–280. doi: 10.1139/cjfas-2013-0364

16. 18 J, Truesdell SB, Chen Y. Impacts of seasonal stock mixing on the assessment of Atlantic cod in the Gulf of Maine. ICES J Mar Sci. 2014;71: 1443–1457. doi: 10.1093/icesjms/fsu066

17. Goethel DR, Legault CM, Cadrin SX. Testing the performance of a spatially explicit tag-integrated stock assessment model of yellowtail flounder (Limanda ferruginea) through simulation analysis. Wilberg M, editor. Can J Fish Aquat Sci. 2015;72: 582–601. doi: 10.1139/cjfas-2014-0244

18. Guan W, Cao J, Chen Y, Cieri M, Quinn T. Impacts of population and fishery spatial structures on fishery stock assessment. Can J Fish Aquat Sci. 2013;70: 1178–1189. doi: 10.1139/cjfas-2012-0364

19. Heath MR, Kunzlik PA, Gallego A, Holmes SJ, Wright PJ. A model of meta-population dynamics for North Sea and West of Scotland cod—The dynamic consequences of natal fidelity. Fish Res. 2008;93: 92–116. doi: 10.1016/j.fishres.2008.02.014

20. Kerr LA, Cadrin SX, Secor DH, Taylor NG. Modeling the implications of stock mixing and life history uncertainty of Atlantic bluefin tuna. Can J Fish Aquat Sci. 2016; 1–15. doi: 10.1139/cjfas-2016-0067

21. R core team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. [Internet]. Vienna, Austria; 2019. Available: http://www.R-project.org/

22. Gundmundsson G, Gunnlaugsson T. Selection and estimation of sequential catch-atage models. Can J Fish Aquat Sci. 2012;69: 1760–1772. doi: 10.1139/F2012-095

23. DFO. Assessment of the Atlantic Mackerel stock for the Northwest Atlantic (Subareas 3 and 4) in 2019. Canada; 2019 p. 16. Report No.: 2014/XXX.

24. Cadigan N. A state-space stock assessment model for northern cod, including under-reported catches and variable natural mortality rates. Can J Fish Aquat Sci. 2016;73: 296–308.

25. Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM. TMB: Automatic Differentiation and Laplace Approximation. J Stat Softw. 2016;70: 1–21.

26. Van Beveren E, Duplisea D, Castonguay M, Doniol-Valcroze T, Plourde S, Cadigan N. How catch underreporting can bias stock assessment of and advice for northwest Atlantic mackerel and a possible resolution using censored catch. Fish Res. 2017;194: 146–154. doi: 10.1016/j.fishres.2017.05.015

27. TRAC. Atlantic mackerel in the Northwest Atlantic [NAFO Subareas 2–6] [Internet]. 2010. Report No.: 2010/01. Available: http://www.mar.dfo-mpo.gc.ca/science/TRAC/trac.html

28. Punt AE, Haddon M, Tuck GN. Which assessment configurations perform best in the face of spatial heterogeneity in fishing mortality, growth and recruitment? A case study based on pink ling in Australia. Fish Res. 2015;168: 85–99. doi: 10.1016/j.fishres.2015.04.002

29. Punt AE, Haddon M, Little LR, Tuck GN. Can a spatially-structured stock assessment address uncertainty due to closed areas? A case study based on pink ling in Australia. Fish Res. 2016;175: 10–23. doi: 10.1016/j.fishres.2015.11.008

30. DFO. Assessment of the Atlantic Mackerel stock for the Northwest Atlantic (Subareas 3 and 4) in 2013. Canada; 2014 p. 16. Report No.: 2014/030.

31. Bousquet N, Cadigan N, Duchesne T, Rivest L-P. Detecting and correcting underreported catches in fish stock assessment: trial of a new method. Can J Fish Aquat Sci. 2010;67: 1247–1261.

32. Hammond TR, Trenkel VM. Censored catch data in fisheries stock assessment. ICES J Mar Sci. 2005;62: 1118–1130.

33. He X, Ralston S, MacCall AD. Interactions of age-dependent mortality and selectivity functions in age-based stock assessment models. Fish Bull. 2011;109: 198–216.

34. Deroba JJ, Butterworth DS, Methot RDJ, De Oliveira JAA, Fernandez C, Nielsen A, et al. Simulation testing the robustness of stock assessment models to error: some results from the ICES strategic initiative on stock assessment methods. ICES J Mar Sci. 2014; doi: 10.1093/icesjms/fst237

35. Liu X, Heino M. Overlooked biological and economic implications of within-season fishery dynamics. Hilborn R, editor. Can J Fish Aquat Sci. 2014;71: 181–188. doi: 10.1139/cjfas-2013-0029

36. Frank KT, Brickman D. Allee effects and compensatory population dynamics within a stock complex. Can J Fish Aquat Sci. 2000;57: 513–517. doi: 10.1139/f00-024

37. Kerr LA, Cadrin SX, Secor DH. Simulation modelling as a tool for examining the consequences of spatial structure and connectivity on local and regional population dynamics. ICES J Mar Sci. 2010;67: 1631–1639. doi: 10.1093/icesjms/fsq053

38. Kerr LA, Cadrin SX, Secor DH. The role of spatial dynamics in the stability, resilience, and productivity of an estuarine fish population. Ecol Appl. 2010;20: 497–507. doi: 10.1890/08-1382.1 20405802

39. Secor DH, Kerr LA, Cadrin SX. Connectivity effects on productivity, stability, and persistence in a herring metapopulation model. ICES J Mar Sci. 2009;66: 1726–1732. doi: 10.1093/icesjms/fsp154

40. Hart D., Cadrin SX. Yellowtail flounder (Limanda ferruginea) off the northeastern United States: implications of movement among stocks. In: Akcakaya HR, Burgman MA, Kindvall O, Wood CC, Sjogren-Gulve P, Hatfield JS, et al., editors. Species Conservation and Management: Case Studies. New York: Oxford University Press; 2004. pp. 230–244.

41. Redding SG, Cooper LW, Castonguay M, Wiernicki C, Secor DH. Northwest Atlantic mackerel (Scomber scombrus) population structure evaluated using otolith δ18O. in prep.;

42. Rudd M, Branch TA. Does unreported catch lead to overfishing? Fish Fish. 2016; doi: 10.1111/faf.12181

43. Hurtado-Ferro F, Punt AE, Hill KT. Use of multiple selectivity patterns as a proxy for spatial structure. Fish Res. 2014;158: 102–115. doi: 10.1016/j.fishres.2013.10.001

44. Maunder MN. Workshop on spatial analysis for stock assessment. La Jolla, California (USA): INTER-AMERICAN TROPICAL TUNA COMMISSION; 2008 p. 21.

45. Porch C, Kleiber P, Turner S, Sibert J, Bailey R, Cort JL. The efficacy of VPA models in the presence of complicated movement patterns. Col Vol Sci Pap ICCAT. 1998;50: 591–622.

46. Kerr LA, Goethel DR. Simulation Modeling as a Tool for Synthesis of Stock Identification Information. Stock Identification Methods. Elsevier; 2014. pp. 501–533. doi: 10.1016/B978-0-12-397003-9.00021–7

47. Punt AE, Butterworth DS, de Moor CL, De Oliveira JAA, Haddon M. Management strategy evaluation: best practices. Fish Fish. 2016;17: 303–334. doi: 10.1111/faf.12104

48. Cadigan N. Updates to a Northern Cod (Gadus morhua) State-Space Integrated Assessment Model. St. John’s, NL: Centre for Fisheries Ecosystem Research; 2016 p. 58. Report No.: 2016/022.

Assessment modelling approaches for stocks with spawning components, seasonal and spatial dynamics, and limited resources for data collection

Souhrn

Klíčová slova:

Zdroje

PLOS One

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