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Jim Thorson edited this page Nov 23, 2022 · 18 revisions

Overview

I would love to keep track of stock assessments, reports, and papers that either used or explored using SpatialDeltGLMM or VAST, to help make decisions about investment in maintaining and improving the software. Some documents listed below used code that was branched from SpatialDeltGLMM or VAST and is now independently maintained.

Used in stock assessment

I first list instances where VAST (or its precursors) was used and documented as base case for a stock assessment

  1. Canary rockfish, Pacific FMC, 2015 (link here)
  2. Darkblotched rockfish, Pacific FMC, 2015 (link here)
  3. Dusky rockfish, North Pacific FMC, 2015 (link here)
  4. Lingcod, Pacific FMC, 2017 (link here)
  5. Yellowtail rockfish, Pacific FMC, 2017 (link here)
  6. California Scorpionfish, Pacific FMC, 2017 (link here)
  7. Yelloweye rockfish, Pacific FMC, 2017 (link here)
  8. Pacific Ocean perch, Pacific FMC, 2017 (link here)
  9. Arrowtooth flounder, Pacific FMC, 2017 (link here)
  10. Albacore tuna, Secretariat of the Pacific Commission, 2018 (link here)
  11. The 2017 assessment of Snoek (Thyrsites atun) for the South African linefishery, 2017 (link here, see Fig 2.)
  12. Northern shrimp, New England FMC, 2018 (link here, see Fig. 3.4-3.6)
  13. Northern rockfish, North Pacific FMC, 2018 (link here, see e.g., pg. 20-21)
  14. Lophius budegassa, ICES, 2019 (link here, see e.g., Fig. 3.3.5 on pg. 74)
  15. SW Striped Marlin, Western and Central Pacific Fisheries Commission, 2019 (link here, see Fig. 6)
  16. Big skate, Pacific FMC, 2019 (link here, see Fig. 9-11 and 13-18)
  17. Longnose skate, Pacific FMC, 2019 (link here, see Fig. 21-25 and 37-40)
  18. Petrale, Pacific FMC, 2019 (link here, see Fig. 8-11, 18-26)
  19. Sablefish, Pacific FMC, 2019 (link here, Fig. 5-6, 12-13, 19-20, 23-24, 27-28, A7)
  20. Widow, Pacific FMC, 2019 (link here, Fig. 11-12, E1)
  21. Pacific cod in Bering Sea, North Pacific FMC, 2019 (link here, see Fig. 2.6-2.7, 2.11)
  22. Soupfin shark Galeorhinus galeus in South Africa, 2019. (link here,see Section 2.1.4, Fig. 4a)
  23. Smoothhound Mustelus mustelus in South Africa, 2019. (link here, see Section 2.1.4, Fig. 4a)
  24. Walleye pollock in Bering Sea, North Pacific FMC, 2019 (link here, see Fig. 17-18, 25-29, 32, 59-66)
  25. Yellowfin tuna in Western and Central Pacific Ocean, 2020. (link here)
  26. Bigeye tuna in Western and Central Pacific Ocean, 2020. (link here)
  27. Pacific cod in Bering Sea, North Pacific FMC, 2020 (link here, see e.g., 2.7-2.8)
  28. Walleye pollock in Bering Sea, North Pacific FMC, 2020 (link here, see e.g. Fig. 34-36)
  29. Dusky rockfish in the Gulf of Alaska, North Pacific FMC, 2020 (link here, see e.g., Fig. 12.4)
  30. Northern rockfish in the Gulf of Alaska, North Pacific FMC, 2020 (link here, see e.g. Fig. 10-4)
  31. Celtic Sea cod, haddock and whiting, ICES, 2020 (link here, see e.g. Fig. 9-10)
  32. Celtic Sea plaice, ICES, 2020 (link here)
  33. Lingcod north, Pacific FMC, 2021
  34. Lingcod south, Pacific FMC, 2021
  35. Dover sole, Pacific FMC, 2021
  36. Spiny dogfish, Pacific FMC, 2021
  37. Walleye pollock in Bering Sea, North Pacific FMC, 2021 (link here, see Fig. 1.16)
  38. Pacific cod in Bering Sea, North Pacific FMC, 2021 (link here, e.g., Fig. 2.4)
  39. Celtic Sea whiting, ICES, 2021 (link here, e.g., see Fig. 10)
  40. Celtic Sea gadids, ICES, 2022 (link here, see e.g. Fig. 8.11)
  41. Yellowfin sole, North Pacific FMC, 2022 (link here, see Fig. 14/16/28)
  42. Pacific cod, North Pacific FMC, 2022 (link here, see Fig. 18-22)
  43. Alaska pollock, North Pacific FMC, 2022 (link here, see Fig. 14/20)

Explored and documented in assessment report

  1. Walleye pollock in Eastern Bering Sea, North Pacific FMC, 2016 (link here, see Fig. 1.9)
  2. Hake complex in South Africa: Design-based vs. Geostastical GLMM (2 species), 2017 (link here)
  3. Hake complex in South Africa: Impacts on Stratification (2 species), 2017 (link here)
  4. St. Matthews Island Blue King Crab, North Pacific FMC, 2017 (link here, pg. 1172)
  5. 10 groundfish species in South and West Coast of South Africa, 2017 (link here)
  6. Bocaccio, Pacific FMC, 2017 (link here)
  7. Arrowtooth flounder in Gulf of Alaska, North Pacific FMC, 2017 (link here, see Fig. 7.4)
  8. Bigeye tuna in West and Central Pacific Ocean, Secretariat of the Pacific Commission, 2017 (link here)
  9. Yellowfin tuna in Western and Central Pacific Ocean, Secretariat of the Pacific Commission, 2017 (link here)
  10. Walleye pollock in Eastern Bering Sea, North Pacific FMC, 2018 (link here, see Fig. 60)
  11. Cowcod, Pacific FMC, 2019 (link here, see Fig. 18)
  12. Hake complex in South Africa: Estimating changes in survey catchability q for OMP robustness tests. (link here, p.
  13. Pacific cod in Gulf of Alaska, North Pacific FMC, 2019 (link here, see Fig. 1A.8)
  14. Yellowfin sole in the Bering Sea, North Pacific FMC, 2020 (link here, see e.g., 4.25)
  15. Pacific cod in the Gulf of Alaska, North Pacific FMC, 2020 (link here, see e.g. Appendix Fig. 2.1.5a)
  16. Walleye pollock in the Gulf of Alaska, North Pacific FMC, 2020 (link here, see e.g., Appendix Figure 1A.3a)
  17. Yellowfin sole in Bering Sea, North Pacific FMC, 2021 (link here, see Fig. 4.29)
  18. Northern rockfish in Gulf of Alaska, North Pacific FMC, 2021 (link here, see Fig. 10-2)
  19. Dusky rockfish in Gulf of Alaska, North Pacific FMC, 2021 (link here, see Fig. 12-2)
  20. Northern anchony in Central Subpopulation for US Management, California Current FMC, 2022 (link here, see Fig. C4-C5)

Used in ecosystem assessment or report

  1. Eastern Bering Sea ecosystem status report for forage fishes, groundfishes, jellyfish, and salmons (4 separate contributions), NPFMC, 2017 (Yasumiishi et al.) (link here)
  2. Gulf of Alaska ecosystem status report for forage fishes, groundfishes, and salmon (3 separate contributions), NPFMC, 2017 (Moss et al.) (link here)
  3. Eastern Bering Sea ecosystem status report for jellyfishes, groundfishes, and copepods (3 separate contributions), NPFMC, 2018 (link here, see Fig. 42/43/66/67; pg. 93/94/98)
  4. Eastern Bering Sea ecosystem status report for forage fish & groundfishes and copepods (2 separate contributions), NPFMC, 2019 (Yasumiishi et al., Eisner et al.) (link here, see Fig. 54/80/81; pg. 89/90/122/123)
  5. Eastern Bering Sea ecosystem status report for copepods (1 contribution), NPFMC, 2020 (Yasumiishi et al.) (link here, see Fig. 60/61; pg. 109/110)
  6. Ormseth, O.A., Yasumiishi, E., 2021. Status of forage species in the Bering Sea and Aleutian Islands region. Alsk. Fish. Sci. Cent. 1–47. (link here, see Fig. 7)

Used in climate assessment or report

  1. NOAA Arctic Report Card, 2019. Comparison of Near-bottom Fish Densities Show Rapid Community and Population Shifts in Bering and Barents Seas (link here, see Fig. 3)
  2. American Meteorological Society, State of the Climate in 2020. Chapter 5, Arctic (link here, see Fig. SB5.1)

Used in habitat assessment or report

  1. Mormede, S., Baird, S. J., & Roux, M.-J. (2021). Developing quantitative methods for the assessment of risk to benthic habitats from bottom fishing activities using the test case of holothurians on the Chatham Rise (New Zealand Aquatic Environment and Biodiversity Report No. 274; p. 28). Fisheries New Zealand. https://www.mpi.govt.nz/dmsdocument/48514-AEBR-274-Developing-quantitative-methods-for-the-assessment-of-risk-to-benthic-habitats-from-bottom-fishing-activities-using-the-test-case-of-holothurians-on-the-Chatham-Rise

Tech memos or white-papers

  1. Strasburger, Moss, Siwicke, Yasumiishi, Pinchuk, Fenske. Eastern Gulf of Alaska Ecosystem Assessment, July through August 2017. 2018. Available at: https://apps-afsc.fisheries.noaa.gov/Publications/AFSC-TM/NOAA-TM-AFSC-367.pdf
  2. Xu, Lennert-Cody, Maunder, and Minte-Vera. Spatiotemporal dynamics of the dolphin-associated purse-seine fishery for yellowfin tuna in the eastern Pacific Ocean. 2018. Available at: https://www.iattc.org/Meetings/Meetings2018/SAC-09/PDFs/Docs/_English/SAC-09-09-EN_Spatial-tempora-modeling-of-yellowfin-CPUE-data.pdf
  3. Winker, H., Thorson, J.T., Fairweather, T., Leslie, R., Durholtz, D. (2017). Towards improving precision in South African demersal trawl survey indices using geostatistical GLMMs. International Stock Assessment Review Workshop, Cape Town. MARAM/IWS/2018/Hake/BG5. Available here
  4. Tremblay-Boyer, L. and McKechnie, S. (2018). Background analyses for the 2018 stock assessment of South Pacific albacore tuna. WCPFC-SC14-2018/SA-IP-07. Available at: https://www.wcpfc.int/node/31260
  5. Tremblay-Boyer, L. and Pilling, G. (2017). Exploratory geostatistical analyses of Pacific-wide operational longline CPUE data for WCPO tuna assessments. WCPFC-SC13-2017/SA-WP-03, Rarotonga, Cook Islands, 9–17 August 2017. Accessible here: https://www.wcpfc.int/node/29516
  6. Minte-Vera, C., Xu, H., Maunder, M.N., 2019. Stock status indicators for yellowfin tuna in the eastern Pacific Ocean (No. SAC-10-08). Inter-American Tropical Tuna Commission, San Diego, CA. Available at: https://www.iattc.org/Meetings/Meetings2019/SAC-10/Docs/_English/SAC-10-08_Yellowfin%20tuna%20Stock%20status%20indicators.pdf (i.e., Fig. 5)
  7. Durcharme-Barth, N., Pilling, G. Background analyses for the 2019 stock assessment of SW Pacific striped marlin. Western and Central Pacific Fisheries Commission, SA-IP-07, 2019. Available at: https://www.wcpfc.int/node/42944 (i.e., Fig. 21
  8. Kinoshita, J., Aoki, Y., Ducharme-Barth, N. and Kiyofuji, H. 2019. Standardized catch per unit effort (CPUE) of skipjack tuna of the Japanese pole-and-line fisheries in the WCPO from 1972 to 2018. WCPFC-SC15-2019/SA-WP-14. (link here, i.e., Fig. 11)
  9. Hashimoto, M., Tsukahara, Y., Fuji, T., Nakayama, I., Suyama, S., Naya, M., Oshima, K., Kai, M., 2019. Application of spatiotemporal model to fishery-independent survey data for Pacific saury (No. NPFC-2019-SSC PS05-WP17). North Pacific Fishery Commission.
  10. Chang, S-K., Yuan, T-L., Liu, H-I. and Xu, H.. Abundance index of Taiwanese PBF fisheries based on traditional and spatiotemporal delta-generalized linear mixed models ISC Pacific bluefin tuna Working Group, International Scientific Committee for Tuna and Tuna-Like Species in the North Pacific Ocean (ISC). ISC/20/PBFWG-1/03. (link here)
  11. Ducharme-Barth, N., Vincent, M., 2020. Analysis of Pacific-wide operational longline dataset for bigeye and yellowfin tuna catch-per-unit-effort (CPUE). Technical Report WCPFC-SC16-2020/SC16-SA-IP-07
  12. Vidal, T., Hamer, P., 2020. Developing yellowfin tuna recruitment indices from drifting FAD purse seine catch and effort data.
  13. Vidal, T., Hamer, P., Escalle, L., Pilling, G., 2020. Assessing trends in skipjack tuna abundance from purse seine catch and effort data in the WCPO.
  14. Sawada, K., Okuda, T., 2020. Spatial modeling of bycatch patterns for research fishing operations in Subarea 48.6 using VAST. CCAMLR.
  15. Hsu, J., Chang, Y.-J., 2021. CPUE standardization of blue marlin (Makaira nigricans) for the Taiwanese distant-water tuna longline fishery in the Pacific Ocean during 1971-2019 (No. ISC/20/BILLWG-03/03). Institute of Oceanography, National Taiwan University, Taipei, Taiwan.
  16. Manabe, A., Nishijima, S., Yukami, R., 2020. Review and update on fishery-independent and fishery-dependent indices of the chub mackerel of Japan (No. NPFC-2020-TWG CMSA03-WP03). North Pacific Fisheries Commission, Tokyo, Japan.
  17. ICES. 2020. ICES Workshop on evaluating survey information Celtic Sea gadoids (WKESIG). ICES Scientific Reports. 2:107. 26 pp. http://doi.org/10.17895/ices.pub.7574
  18. Yuan, T, Chang, S., Liu, H., and Huang, C. 2021. ISC Pacific bluefin tuna Working Group, International Scientific Committee for Tuna and Tuna-Like Species in the North Pacific Ocean (ISC). http://isc.fra.go.jp/pdf/PBF/ISC21_PBF_1/ISC_21_PBFWG_1_03_Chang_rev.pdf, e.g., Fig. 9-14.
  19. Sawada, K., Okuda, T., 2021. Progress on the spatial modeling of bycatch patterns for research fishing operations in Subarea 48.6 using VAST (No. WG-FSA-2021/48). CCAMLR, Hobart, Australia.
  20. Hansell, A., Hanke, A., Becker, S., Cadrin, S., Lauretta, M., Walter, J., Golet, W., Kerr, L., 2021. Development of a western large (> 177 cm) Atlantic bluefin tuna index of abundance based on Canadian and US rod and reel fisheries data. Collect Vol Sci Pap ICCAT 78, 438–453.
  21. Shinohara, N., Nishijima, S., Ichinokawa, M., Yukami, R., 2021. Standardizing monthly egg survey data as an abundance index for spawning stock biomass of chub mackerel in the Northwest Pacific (No. NPFC-2021-TWG CMSA04-WP04). North Pacific Fisheries Commission, Tokyo, Japan.
  22. Hoyle, S.D., Charsley, A.R., Rudd, M.B., Crow, S.K., Thorson, J.T., 2021. Modelling approaches and data requirements for a spatiotemporal index-based assessment of longfin eels (No. 2021/58), New Zealand Fisheries Assessment Report. Fisheries New Zealand.
  23. Kai, M., 2022. Spatio-temporal model for CPUE standardization: Application to blue shark caught by longline of Japanese research and training vessels in the western and central North Pacific1 (No. ISC/21/SHARKWG-2/03). International Scientific Committee for Tuna and Tuna-like Species in the North Pacific Ocean.
  24. Grüss, A., Moore, B.R., Pinkerton, M.H., Devine, J.A., 2022. Using VAST (vector autoregressive spatio–temporal) models to predict spatio–temporal changes in macrourid by-catch in the Ross Sea region Antarctic toothfish (Dissostichus mawsoni) fishery: Methods and preliminary results (No. WG-SAM-2022/15). CCAMLR.
  25. Akia, S., Guery, L., Grande, M., Kaplan, D., Pascual, P., Ramos, M.L., Uranga, J., Abascal, F., Santiago, J., Merino, G., 2022. European purse seiners cpue standardization of eastern atlantic skipjack caught under non-owned dfads using the vast methodology. Collect Vol Sci Pap ICCAT 79, 210–221.
  26. Sawada, K., Gruss, A., Okuda, T., 2022. Update on the VAST (vector autoregressive spatio-temporal) modelling of grenadier relative abundance in Subarea 48.6 (No. WG-FSA-2022/33). CCAMLR.
  27. Grüss, A., Moore, G., Pinkerton, M., Devine, J., 2022. VAST (vector autoregressive spatio-temporal) modelling of macrourid relative abundance in the Ross Sea region to support by-catch management (No. WG-FSA-2022/48). CCAMLR.

Journal articles

Finally, I list instances of peer-reviewed journal articles using VAST (or its precursors: SpatialDeltaGLMM and SpatialDFA)

  1. Adams, C.F., Brooks, E.N., Legault, C.M., Barrett, M.A., Chevrier, D.F., 2021. Quota allocation for stocks that span multiple management zones: analysis with a vector autoregressive spatiotemporal model. Fish. Manag. Ecol. n/a. https://doi.org/10.1111/fme.12488

  2. Akia, S., Amandé, M., Pascual, P., Gaertner, D., 2021. Seasonal and inter-annual variability in abundance of the main tropical tunas in the EEZ of Côte d’Ivoire (2000-2019). Fish. Res. 243, 106053. https://doi.org/10.1016/j.fishres.2021.106053

  3. Alglave, B., Rivot, E., Etienne, M.-P., Woillez, M., Thorson, J.T., Vermard, Y., 2022. Combining scientific survey and commercial catch data to map fish distribution. ICES J. Mar. Sci. fsac032. https://doi.org/10.1093/icesjms/fsac032

  4. Astarloa, A., Louzao, M., Andrade, J., Babey, L., Berrow, S., Boisseau, O., Brereton, T., Dorémus, G., Evans, P.G.H., Hodgins, N.K., Lewis, M., Martinez-Cedeira, J., Pinsky, M.L., Ridoux, V., Saavedra, C., Santos, M.B., Thorson, J.T., Waggitt, J.J., Wall, D., Chust, G., 2021. The Role of Climate, Oceanography, and Prey in Driving Decadal Spatio-Temporal Patterns of a Highly Mobile Top Predator. Front. Mar. Sci. 8, 1463. https://doi.org/10.3389/fmars.2021.665474

  5. Bell, R.J., McManus, M.C., McNamee, J., Gartland, J., Galuardi, B., McGuire, C., 2021. Perspectives from the water: Utilizing fisher’s observations to inform SNE/MA windowpane science and management. Fish. Res. 243, 106090. https://doi.org/10.1016/j.fishres.2021.106090

  6. Cao, J., Thorson, J.T., Richards, R.A., Chen, Y., 2017. Spatiotemporal index standardization improves the stock assessment of northern shrimp in the Gulf of Maine. Can. J. Fish. Aquat. Sci. 74, 1781–1793. https://doi.org/10.1139/cjfas-2016-0137

  7. Carroll, G., Holsman, K.K., Brodie, S., Thorson, J.T., Hazen, E.L., Bograd, S.J., Haltuch, M.A., Kotwicki, S., Samhouri, J., Spencer, P., Willis‐Norton, E., Selden, R.L., 2019. A review of methods for quantifying spatial predator–prey overlap. Glob. Ecol. Biogeogr. 28, 1561–1577. https://doi.org/10.1111/geb.12984

  8. Chasco, B.E., Hunsicker, M.E., Jacobson, K.C., Welch, O.T., Morgan, C.A., Muhling, B.A., Harding, J.A., 2022. Evidence of Temperature-Driven Shifts in Market Squid Doryteuthis opalescens Densities and Distribution in the California Current Ecosystem. Mar. Coast. Fish. 14, e10190. https://doi.org/10.1002/mcf2.10190

  9. Chen, Y., Shan, X., Han, Q., Gorfine, H., Dai, F., Jin, X., 2022. Long-term changes in the spatio-temporal distribution of snailfish Liparis tanakae in the Yellow Sea under fishing and environmental changes. Front Mar Sci. https://doi.org/10.3389/fmars.2022.1024086

  10. Currie, J.C., Thorson, J.T., Sink, K.J., Atkinson, L.J., Fairweather, T.P., Winker, H., 2019. A novel approach to assess distribution trends from fisheries survey data. Fish. Res. 214, 98–109. https://doi.org/10.1016/j.fishres.2019.02.004

  11. Dolder, P.J., Thorson, J.T., Minto, C., 2018. Spatial separation of catches in highly mixed fisheries. Sci. Rep. 8, 13886. https://doi.org/10.1038/s41598-018-31881-w

  12. Ducharme-Barth, N.D., Grüss, A., Vincent, M.T., Kiyofuji, H., Aoki, Y., Pilling, G., Hampton, J., Thorson, J.T., 2022. Impacts of fisheries-dependent spatial sampling patterns on catch-per-unit-effort standardization: A simulation study and fishery application. Fish. Res. 246, 106169. https://doi.org/10.1016/j.fishres.2021.106169

  13. Duffy‐Anderson, J.T., Stabeno, P., Andrews, A.G., Cieciel, K., Deary, A., Farley, E., Fugate, C., Harpold, C., Heintz, R., Kimmel, D., Kuletz, K., Lamb, J., Paquin, M., Porter, S., Rogers, L., Spear, A., Yasumiishi, E., 2019. Responses of the Northern Bering Sea and Southeastern Bering Sea Pelagic Ecosystems Following Record-Breaking Low Winter Sea Ice. Geophys. Res. Lett. 46, 9833–9842. https://doi.org/10.1029/2019GL083396

  14. Eisner, L.B., Yasumiishi, E.M., Andrews, A.G., O’Leary, C.A., 2020. Large copepods as leading indicators of walleye pollock recruitment in the southeastern Bering Sea: Sample-Based and spatio-temporal model (VAST) results. Fish. Res. 232, 105720. https://doi.org/10.1016/j.fishres.2020.105720

  15. Elo, M., Kajanus, M.H., Tolvanen, J., Devictor, V., Forsman, J.T., Lehikoinen, A., Mönkkönen, M., Thorson, J.T., Vollstädt, M.G.R., Kivelä, S.M., In press. Do large-scale associations in birds imply biotic interactions or environmental filtering? J. Biogeogr. n/a. https://doi.org/10.1111/jbi.14520

  16. Gao, J., Thorson, J.T., Szuwalski, C., Wang, H.-Y., 2020. Historical dynamics of the demersal fish community in the East and South China Seas. Mar. Freshw. Res. https://doi.org/10.1071/MF18472

  17. Godefroid, M., Boldt, J.L., Thorson, J.T., Forrest, R., Gauthier, S., Flostrand, L., Ian Perry, R., Ross, A.R.S., Galbraith, M., 2019. Spatio-temporal models provide new insights on the biotic and abiotic drivers shaping Pacific Herring (Clupea pallasi) distribution. Prog. Oceanogr. 178, 102198. https://doi.org/10.1016/j.pocean.2019.102198

  18. Goodman, M.C., Carroll, G., Brodie, S., Grüss, A., Thorson, J.T., Kotwicki, S., Holsman, K., Selden, R.L., Hazen, E.L., De Leo, G.A., 2022. Shifting fish distributions impact predation intensity in a sub-Arctic ecosystem. Ecography 2022, e06084. https://doi.org/10.1111/ecog.06084

  19. Grüss, A., Biggs, C., Heyman, W.D., Erisman, B., 2018a. Prioritizing monitoring and conservation efforts for fish spawning aggregations in the U.S. Gulf of Mexico. Sci. Rep. 8, 8473. https://doi.org/10.1038/s41598-018-26898-0

  20. Grüss, A., Biggs, C.R., Heyman, W.D., Erisman, B., 2019a. Protecting juveniles, spawners or both: A practical statistical modelling approach for the design of marine protected areas. J. Appl. Ecol. 56, 2328–2339. https://doi.org/10.1111/1365-2664.13468

  21. Grüss, A., Drexler, M.D., Ainsworth, C.H., Babcock, E.A., Tarnecki, J.H., Love, M.S., 2018b. Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation. Front. Mar. Sci. 5. https://doi.org/10.3389/fmars.2018.00016

  22. Grüss, A., Drexler, M.D., Chancellor, E., Ainsworth, C.H., Gleason, J.S., Tirpak, J.M., Love, M.S., Babcock, E.A., 2019b. Representing species distributions in spatially-explicit ecosystem models from presence-only data. Fish. Res. 210, 89–105. https://doi.org/10.1016/j.fishres.2018.10.011

  23. Grüss, A., Gao, J., Thorson, J.T., Rooper, C.N., Thompson, G., Boldt, J.L., Lauth, R., 2020a. Estimating synchronous changes in condition and density in eastern Bering Sea fishes. Mar. Ecol. Prog. Ser. 635, 169–185. https://doi.org/10.3354/meps13213

  24. Grüss, A., Perryman, H.A., Babcock, E.A., Sagarese, S.R., Thorson, J.T., Ainsworth, C.H., Anderson, E.J., Brennan, K., Campbell, M.D., Christman, M.C., Cross, S., Drexler, M.D., Marcus Drymon, J., Gardner, C.L., Hanisko, D.S., Hendon, J., Koenig, C.C., Love, M., Martinez-Andrade, F., Morris, J., Noble, B.T., Nuttall, M.A., Osborne, J., Pattengill-Semmens, C., Pollack, A.G., Sutton, T.T., Switzer, T.S., 2018c. Monitoring programs of the U.S. Gulf of Mexico: inventory, development and use of a large monitoring database to map fish and invertebrate spatial distributions. Rev. Fish Biol. Fish. 28, 667–691. https://doi.org/10.1007/s11160-018-9525-2

  25. Grüss, A., Rose, K.A., Justić, D., Wang, L., 2020b. Making the most of available monitoring data: A grid-summarization method to allow for the combined use of monitoring data collected at random and fixed sampling stations. Fish. Res. 229, 105623. https://doi.org/10.1016/j.fishres.2020.105623

  26. Grüss, A., Thorson, J.T., 2019. Developing spatio-temporal models using multiple data types for evaluating population trends and habitat usage. ICES J. Mar. Sci. 76, 1748–1761. https://doi.org/10.1093/icesjms/fsz075

  27. Grüss, A., Thorson, J.T., Babcock, E.A., Tarnecki, J.H., 2018d. Producing distribution maps for informing ecosystem-based fisheries management using a comprehensive survey database and spatio-temporal models. ICES J. Mar. Sci. 75, 158–177. https://doi.org/10.1093/icesjms/fsx120

  28. Grüss, A., Thorson, J.T., Carroll, G., Ng, E.L., Holsman, K.K., Aydin, K., Kotwicki, S., Morzaria-Luna, H.N., Ainsworth, C.H., Thompson, K.A., 2020c. Spatio-temporal analyses of marine predator diets from data-rich and data-limited systems. Fish Fish. 21, 718–739. https://doi.org/10.1111/faf.12457

  29. Grüss, A., Thorson, J.T., Sagarese, S.R., Babcock, E.A., Karnauskas, M., Walter, J.F., Drexler, M., 2017. Ontogenetic spatial distributions of red grouper (Epinephelus morio) and gag grouper (Mycteroperca microlepis) in the U.S. Gulf of Mexico. Fish. Res. 193, 129–142. https://doi.org/10.1016/j.fishres.2017.04.006

  30. Grüss, A., Thorson, J.T., Stawitz, C.C., Reum, J.C.P., Rohan, S.K., Barnes, C.L., 2021. Synthesis of interannual variability in spatial demographic processes supports the strong influence of cold-pool extent on eastern Bering Sea walleye pollock (Gadus chalcogrammus). Prog. Oceanogr. 194, 102569. https://doi.org/10.1016/j.pocean.2021.102569

  31. Grüss, A., Walter, J.F., Babcock, E.A., Forrestal, F.C., Thorson, J.T., Lauretta, M.V., Schirripa, M.J., 2019c. Evaluation of the impacts of different treatments of spatio-temporal variation in catch-per-unit-effort standardization models. Fish. Res. 213, 75–93. https://doi.org/10.1016/j.fishres.2019.01.008

  32. Guan, L., Chen, Y., Staples, K.W., Cao, J., Li, B., 2017. The influence of complex structure on the spatial dynamics of Atlantic cod (Gadus morhua) in the Gulf of Maine. ICES J. Mar. Sci. 74, 2379–2388. https://doi.org/10.1093/icesjms/fsx064

  33. Guan, L., Jin, X., Wu, Q., Shan, X., 2019. Statistical modelling for exploring diel vertical movements and spatial correlations of marine fish species: a supplementary tool to assess species interactions. ICES J. Mar. Sci. 76, 1776–1783. https://doi.org/10.1093/icesjms/fsz033

  34. Guan, L., Shan, X., Jin, X., Gorfine, H., Yang, T., Li, Z., 2020. Evaluating spatio-temporal dynamics of multiple fisheries-targeted populations simultaneously: A case study of the Bohai Sea ecosystem in China. Ecol. Model. 422, 108987. https://doi.org/10.1016/j.ecolmodel.2020.108987

  35. Han, Q., Grüss, A., Shan, X., Jin, X., Thorson, J.T., 2021. Understanding patterns of distribution shifts and range expansion/contraction for small yellow croaker (Larimichthys polyactis) in the Yellow Sea. Fish. Oceanogr. 30, 69–84. https://doi.org/10.1111/fog.12503

  36. Han, Q., Shan, X., Jin, X., Gorfine, H., Chen, Y., Su, C., 2022. Changes in distribution patterns for Larimichthys polyactis in response to multiple pressures in the Bohai Sea over the past four decades. Front. Mar. Sci.

  37. Hansell, A.C., Becker, S.L., Cadrin, S.X., Lauretta, M., Walter III, J.F., Kerr, L.A., 2022. Spatio-temporal dynamics of bluefin tuna (Thunnus thynnus) in US waters of the northwest Atlantic. Fish. Res. 255, 106460. https://doi.org/10.1016/j.fishres.2022.106460

  38. Hodgdon, C.T., Tanaka, K.R., Runnebaum, J., Cao, J., Chen, Y., 2020. A framework to incorporate environmental effects into stock assessments informed by fishery-independent surveys: a case study with American lobster (Homarus americanus). Can. J. Fish. Aquat. Sci. 1–11. https://doi.org/10.1139/cjfas-2020-0076

  39. Hovel, R.A., Thorson, J.T., Carter, J.L., Quinn, T.P., 2017. Within-lake habitat heterogeneity mediates community response to warming trends. Ecology 98, 2333–2342. https://doi.org/10.1002/ecy.1944

  40. Hsu, J., Chang, Y.-J., Ducharme-Barth, N.D., 2022. Evaluation of the influence of spatial treatments on catch-per-unit-effort standardization: A fishery application and simulation study of Pacific saury in the Northwestern Pacific Ocean. Fish. Res. 255, 106440.

  41. Hsu, J., Chang, Y.-J., Kitakado, T., Kai, M., Li, B., Hashimoto, M., Hsieh, C., Kulik, V., Park, K.J., 2021. Evaluating the spatiotemporal dynamics of Pacific saury in the Northwestern Pacific Ocean by using a geostatistical modelling approach. Fish. Res. 235, 105821. https://doi.org/10.1016/j.fishres.2020.105821

  42. Johnson, K.F., Thorson, J.T., Punt, A.E., 2019. Investigating the value of including depth during spatiotemporal index standardization. Fish. Res. 216, 126–137. https://doi.org/10.1016/j.fishres.2019.04.004

  43. Kai, M., 2019. Spatio-temporal changes in catch rates of pelagic sharks caught by Japanese research and training vessels in the western and central North Pacific. Fish. Res. 216, 177–195. https://doi.org/10.1016/j.fishres.2019.02.015

  44. Kajanus, M.H., Forsman, J.T., Vollstädt, M.G.R., Devictor, V., Elo, M., Lehikoinen, A., Mönkkönen, M., Thorson, J.T., Kivelä, S.M., 2022. Titmice are a better indicator of bird density in Northern European than in Western European forests. Ecol. Evol. 12, e8479. https://doi.org/10.1002/ece3.8479

  45. Kakehi, S., Hashimoto, M., Naya, M., Ito, S., Miyamoto, H., Suyama, S., 2022. Reduced body weight of Pacific saury (Cololabis saira) causes delayed initiation of spawning migration. Fish. Oceanogr. 31, 319–332. https://doi.org/10.1111/fog.12579

  46. Kanamori, Y., Nishijima, S., Okamura, H., Yukami, R., Watai, M., Takasuka, A., 2021. Spatio-temporal model reduces species misidentification bias of spawning eggs in stock assessment of spotted mackerel in the western North Pacific. Fish. Res. 236, 105825. https://doi.org/10.1016/j.fishres.2020.105825

  47. Kanamori, Y., Takasuka, A., Nishijima, S., Okamura, H., 2019. Climate change shifts the spawning ground northward and extends the spawning period of chub mackerel in the western North Pacific. Mar. Ecol. Prog. Ser. 624, 155–166. https://doi.org/10.3354/meps13037

  48. Lowman, B.A., Jones, A.W., Pessutti, J.P., Mercer, A.M., Manderson, J.P., Galuardi, B., 2021. Northern Shortfin Squid (Illex illecebrosus) Fishery Footprint on the Northeast US Continental Shelf. Front. Mar. Sci. 8. https://doi.org/10.3389/fmars.2021.631657

  49. Marshall, K.N., Duffy-Anderson, J.T., Ward, E.J., Anderson, S.C., Hunsicker, M.E., Williams, B.C., 2019. Long-term trends in ichthyoplankton assemblage structure, biodiversity, and synchrony in the Gulf of Alaska and their relationships to climate. Prog. Oceanogr. 170, 134–145. https://doi.org/10.1016/j.pocean.2018.11.002

  50. Maureaud, A.A., Frelat, R., Pécuchet, L., Shackell, N., Mérigot, B., Pinsky, M.L., Amador, K., Anderson, S.C., Arkhipkin, A., Auber, A., Barri, I., Bell, R.J., Belmaker, J., Beukhof, E., Camara, M.L., Guevara‐Carrasco, R., Choi, J., Christensen, H.T., Conner, J., Cubillos, L.A., Diadhiou, H.D., Edelist, D., Emblemsvåg, M., Ernst, B., Fairweather, T.P., Fock, H.O., Friedland, K.D., Garcia, C.B., Gascuel, D., Gislason, H., Goren, M., Guitton, J., Jouffre, D., Hattab, T., Hidalgo, M., Kathena, J.N., Knuckey, I., Kidé, S.O., Koen‐Alonso, M., Koopman, M., Kulik, V., León, J.P., Levitt‐Barmats, Y., Lindegren, M., Llope, M., Massiot‐Granier, F., Masski, H., McLean, M., Meissa, B., Mérillet, L., Mihneva, V., Nunoo, F.K.E., O’Driscoll, R., O’Leary, C.A., Petrova, E., Ramos, J.E., Refes, W., Román‐Marcote, E., Siegstad, H., Sobrino, I., Sólmundsson, J., Sonin, O., Spies, I., Steingrund, P., Stephenson, F., Stern, N., Tserkova, F., Tserpes, G., Tzanatos, E., Rijn, I. van, Zwieten, P.A.M. van, Vasilakopoulos, P., Yepsen, D.V., Ziegler, P., Thorson, J.T., 2021. Are we ready to track climate-driven shifts in marine species across international boundaries? - A global survey of scientific bottom trawl data. Glob. Change Biol. 27, 220–236. https://doi.org/10.1111/gcb.15404

  51. Mazur, M.D., Tanaka, K.R., Shank, B., Chang, J.-H., Hodgdon, C.T., Reardon, K.M., Friedland, K.D., Chen, Y., 2022. Incorporating spatial heterogeneity and environmental impacts into stock-recruitment relationships for Gulf of Maine lobster. ICES J. Mar. Sci. fsab266. https://doi.org/10.1093/icesjms/fsab266

  52. McClatchie, S., Gao, J., Drenkard, E.J., Thompson, A.R., Watson, W., Ciannelli, L., Bograd, S.J., Thorson, J.T., 2018. Interannual and Secular Variability of Larvae of Mesopelagic and Forage Fishes in the Southern California Current System. J. Geophys. Res. Oceans 123, 6277–6295. https://doi.org/10.1029/2018JC014011

  53. McGowan, D., Horne, J., Rogers, L., 2019. Effects of temperature on the distribution and density of capelin in the Gulf of Alaska. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps12966

  54. McGowan, D.W., Horne, J.K., Thorson, J.T., Zimmermann, M., 2019. Influence of environmental factors on capelin distributions in the Gulf of Alaska. Deep Sea Res. Part II Top. Stud. Oceanogr., Understanding Ecosystem Processes in the Gulf of Alaska: Volume 2 165, 238–254. https://doi.org/10.1016/j.dsr2.2017.11.018

  55. Minte-Vera, C.V., Maunder, M.N., Aires-da-Silva, A.M., 2021. Auxiliary diagnostic analyses used to detect model misspecification and highlight potential solutions in stock assessments: application to yellowfin tuna in the eastern Pacific Ocean. ICES J. Mar. Sci. 78, 3521–3537. https://doi.org/10.1093/icesjms/fsab213

  56. Monnahan, C.C., Thorson, J.T., Kotwicki, S., Lauffenburger, N., Ianelli, J.N., Punt, A.E., 2021. Incorporating vertical distribution in index standardization accounts for spatiotemporal availability to acoustic and bottom trawl gear for semi-pelagic species. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsab085

  57. Mormede, S., Parker, S.J., Pinkerton, M.H., 2020. Comparing spatial distribution modelling of fisheries data with single-area or spatially-explicit integrated population models, a case study of toothfish in the Ross Sea region. Fish. Res. 221, 105381. https://doi.org/10.1016/j.fishres.2019.105381

  58. Ng, E.L., Deroba, J.J., Essington, T.E., Grüss, A., Smith, B.E., Thorson, J.T., 2021. Predator stomach contents can provide accurate indices of prey biomass. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsab026

  59. O’Leary, C.A., DeFilippo, L.B., Thorson, J.T., Kotwicki, S., Hoff, G.R., Kulik, V.V., Ianelli, J.N., Punt, A.E., 2022. Understanding transboundary stocks’ availability by combining multiple fisheries-independent surveys and oceanographic conditions in spatiotemporal models. ICES J. Mar. Sci. 79, 1063–1074. https://doi.org/10.1093/icesjms/fsac046

  60. O’Leary, C.A., Thorson, J.T., Ianelli, J.N., Kotwicki, S., 2020. Adapting to climate-driven distribution shifts using model-based indices and age composition from multiple surveys in the walleye pollock (Gadus chalcogrammus) stock assessment. Fish. Oceanogr. 29, 541–557. https://doi.org/10.1111/fog.12494

  61. Omori, K.L., Thorson, J.T., 2022. Identifying species complexes based on spatial and temporal clustering from joint dynamic species distribution models. ICES J. Mar. Sci. fsac015. https://doi.org/10.1093/icesjms/fsac015

  62. Oyafuso, Z.S., Barnett, L.A.K., Kotwicki, S., 2021. Incorporating spatiotemporal variability in multispecies survey design optimization addresses trade-offs in uncertainty. ICES J. Mar. Sci. 78, 1288–1300. https://doi.org/10.1093/icesjms/fsab038

  63. Perretti, C.T., Thorson, J.T., 2019. Spatio-temporal dynamics of summer flounder (Paralichthys dentatus) on the Northeast US shelf. Fish. Res. 215, 62–68. https://doi.org/10.1016/j.fishres.2019.03.006

  64. Robertson, M.D., Gao, J., Regular, P.M., Morgan, M.J., Zhang, F., 2021. Lagged recovery of fish spatial distributions following a cold-water perturbation. Sci. Rep. 11, 9513. https://doi.org/10.1038/s41598-021-89066-x

  65. Runnebaum, J., Guan, L., Cao, J., O’Brien, L., Chen, Y., 2017. Habitat suitability modeling based on a spatiotemporal model: an example for cusk in the Gulf of Maine. Can. J. Fish. Aquat. Sci. 75, 1784–1797. https://doi.org/10.1139/cjfas-2017-0316

  66. Runnebaum, J., Tanaka, K.R., Guan, L., Cao, J., O�Brien, L., Chen, Y., 2020. Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data. Mar. Ecol. Prog. Ser. 641, 159–175. https://doi.org/10.3354/meps13302

  67. Satoh, K., Xu, H., Minte-Vera, C.V., Maunder, M.N., Kitakado, T., 2021. Size-specific spatiotemporal dynamics of bigeye tuna (Thunnus obesus) caught by the longline fishery in the eastern Pacific Ocean. Fish. Res. 243, 106065. https://doi.org/10.1016/j.fishres.2021.106065

  68. Sculley, M.L., Brodziak, J., 2020. Quantifying the distribution of swordfish (Xiphias gladius) density in the Hawaii-based longline fishery. Fish. Res. 230, 105638. https://doi.org/10.1016/j.fishres.2020.105638

  69. Selden, R.L., Thorson, J.T., Samhouri, J.F., Bograd, S.J., Brodie, S., Carroll, G., Haltuch, M.A., Hazen, E.L., Holsman, K.K., Pinsky, M.L., Tolimieri, N., Willis-Norton, E., 2020. Coupled changes in biomass and distribution drive trends in availability of fish stocks to US West Coast ports. ICES J. Mar. Sci. 77, 188–199. https://doi.org/10.1093/icesjms/fsz211

  70. Smart, T.I., Bubley, W.J., Glasgow, D.M., Reichert, M.J.M., 2020. Spatial Distribution Changes and Habitat Use in Red Porgy in Waters off the Southeast U.S. Atlantic Coast. Mar. Coast. Fish. 12, 381–394. https://doi.org/10.1002/mcf2.10135

  71. Stevenson, D.E., Kotwicki, S., Thorson, J.T., Correa, G.M., Buckley, T., 2022. The influence of age and cohort on the distribution of walleye pollock (Gadus chalcogrammus) in the eastern Bering Sea. Can. J. Fish. Aquat. Sci. https://doi.org/10.1139/cjfas-2021-0300

  72. Thorson, J.T., 2022. Development and simulation testing for a new approach to density dependence in species distribution models. ICES J. Mar. Sci. 79, 117–128. https://doi.org/10.1093/icesjms/fsab247

  73. Thorson, J.T., 2019a. Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fish. Res. 210, 143–161. https://doi.org/10.1016/j.fishres.2018.10.013

  74. Thorson, J.T., 2019b. Measuring the impact of oceanographic indices on species distribution shifts: The spatially varying effect of cold-pool extent in the eastern Bering Sea. Limnol. Oceanogr. 64, 2632–2645. https://doi.org/10.1002/lno.11238

  75. Thorson, J.T., 2018. Three problems with the conventional delta-model for biomass sampling data, and a computationally efficient alternative. Can. J. Fish. Aquat. Sci. 75, 1369–1382. https://doi.org/10.1139/cjfas-2017-0266

  76. Thorson, J.T., 2015. Spatio-temporal variation in fish condition is not consistently explained by density, temperature, or season for California Current groundfishes. Mar. Ecol. Prog. Ser. 526, 101–112. https://doi.org/10.3354/meps11204

  77. Thorson, James T, Adams, C.F., Brooks, E.N., Eisner, L.B., Kimmel, D.G., Legault, C.M., Rogers, L.A., Yasumiishi, E.M., 2020. Seasonal and interannual variation in spatio-temporal models for index standardization and phenology studies. ICES J. Mar. Sci. 77, 1879–1892. https://doi.org/10.1093/icesjms/fsaa074

  78. Thorson, J.T., Adams, G., Holsman, K., 2019. Spatio-temporal models of intermediate complexity for ecosystem assessments: A new tool for spatial fisheries management. Fish Fish. 20, 1083–1099. https://doi.org/10.1111/faf.12398

  79. Thorson, J.T., Arimitsu, M.L., Barnett, L.A.K., Cheng, W., Eisner, L.B., Haynie, A.C., Hermann, A.J., Holsman, K., Kimmel, D.G., Lomas, M.W., Richar, J., Siddon, E.C., 2021a. Forecasting community reassembly using climate-linked spatio-temporal ecosystem models. Ecography 44, 612–625. https://doi.org/10.1111/ecog.05471

  80. Thorson, J.T., Barnett, L.A.K., 2017. Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat. ICES J. Mar. Sci. 74, 1311–1321. https://doi.org/10.1093/icesjms/fsw193

  81. Thorson, James T., Bryan, M.D., Hulson, P.-J.F., Xu, H., Punt, A.E., 2020a. Simulation testing a new multi-stage process to measure the effect of increased sampling effort on effective sample size for age and length data. ICES J. Mar. Sci. 77, 1728–1737. https://doi.org/10.1093/icesjms/fsaa036

  82. Thorson, James T., Ciannelli, L., Litzow, M.A., 2020b. Defining indices of ecosystem variability using biological samples of fish communities: A generalization of empirical orthogonal functions. Prog. Oceanogr. 181, 102244. https://doi.org/10.1016/j.pocean.2019.102244

  83. Thorson, J.T., Cunningham, C.J., Jorgensen, E., Havron, A., Hulson, P.-J.F., Monnahan, C.C., von Szalay, P., 2021b. The surprising sensitivity of index scale to delta-model assumptions: Recommendations for model-based index standardization. Fish. Res. 233, 105745. https://doi.org/10.1016/j.fishres.2020.105745

  84. Thorson, J.T., Fonner, R., Haltuch, M.A., Ono, K., Winker, H., 2017a. Accounting for spatiotemporal variation and fisher targeting when estimating abundance from multispecies fishery data. Can. J. Fish. Aquat. Sci. 74, 1794–1807. https://doi.org/10.1139/cjfas-2015-0598

  85. Thorson, J.T., Haltuch, M.A., 2018. Spatiotemporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment. Can. J. Fish. Aquat. Sci. 76, 401–414. https://doi.org/10.1139/cjfas-2018-0015

  86. Thorson, J.T., Ianelli, J.N., Kotwicki, S., 2017b. The relative influence of temperature and size-structure on fish distribution shifts: A case-study on Walleye pollock in the Bering Sea. Fish Fish. 18, 1073–1084. https://doi.org/10.1111/faf.12225

  87. Thorson, J.T., Ianelli, J.N., Larsen, E.A., Ries, L., Scheuerell, M.D., Szuwalski, C., Zipkin, E.F., 2016a. Joint dynamic species distribution models: a tool for community ordination and spatio-temporal monitoring. Glob. Ecol. Biogeogr. 25, 1144–1158. https://doi.org/10.1111/geb.12464

  88. Thorson, J.T., Kristensen, K., 2016. Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples. Fish. Res. 175, 66–74. https://doi.org/10.1016/j.fishres.2015.11.016

  89. Thorson, J.T., Pinsky, M.L., Ward, E.J., 2016b. Model-based inference for estimating shifts in species distribution, area occupied and centre of gravity. Methods Ecol. Evol. 7, 990–1002. https://doi.org/10.1111/2041-210X.12567

  90. Thorson, J.T., Rindorf, A., Gao, J., Hanselman, D.H., Winker, H., 2016c. Density-dependent changes in effective area occupied for sea-bottom-associated marine fishes. Proc R Soc B 283, 20161853. https://doi.org/10.1098/rspb.2016.1853

  91. Thorson, J.T., Scheuerell, M.D., Olden, J.D., Schindler, D.E., 2018. Spatial heterogeneity contributes more to portfolio effects than species variability in bottom-associated marine fishes. Proc R Soc B 285, 20180915. https://doi.org/10.1098/rspb.2018.0915

  92. Thorson, J.T., Shelton, A.O., Ward, E.J., Skaug, H.J., 2015. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES J. Mar. Sci. J. Cons. 72, 1297–1310. https://doi.org/10.1093/icesjms/fsu243

  93. Timbs, J.R., Powell, E.N., Mann, R., 2019. Changes in the spatial distribution and anatomy of a range shift for the Atlantic surfclam Spisula solidissima in the Mid-Atlantic Bight and on Georges Bank. Mar. Ecol. Prog. Ser. 620, 77–97. https://doi.org/10.3354/meps12964

  94. Timbs, J.R., Powell, E.N., Mann, R., 2018. Assessment of the Relationship of Stock and Recruitment in the Atlantic Surfclam Spisula solidissima in the Northwestern Atlantic Ocean. J. Shellfish Res. 37, 965–978. https://doi.org/10.2983/035.037.0507

  95. Tolimieri, N., Wallace, J., Haltuch, M., 2020. Spatio-temporal patterns in juvenile habitat for 13 groundfishes in the California Current Ecosystem. PLOS ONE 15, e0237996. https://doi.org/10.1371/journal.pone.0237996

  96. Vert-pre, K.A., Trancart, T., Feunteun, E., 2020. Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel. Cybium 44, 095–112. https://doi.org/10.26028/CYBIUM/2020-442-002

  97. Xu, H., Lennert-Cody, C.E., Maunder, M.N., Minte-Vera, C.V., 2019. Spatiotemporal dynamics of the dolphin-associated purse-seine fishery for yellowfin tuna (Thunnus albacares) in the eastern Pacific Ocean. Fish. Res. 213, 121–131. https://doi.org/10.1016/j.fishres.2019.01.013

  98. Yasumiishi, E.M., Cieciel, K., Andrews, A.G., Murphy, J., Dimond, J.A., 2020. Climate-related changes in the biomass and distribution of small pelagic fishes in the eastern Bering Sea during late summer, 2002-2018. Deep Sea Res. Part II Top. Stud. Oceanogr. 104907. https://doi.org/10.1016/j.dsr2.2020.104907

  99. 幹彦甲斐, 洋平塚原, 緑橋本, 2021. 時空間統計モデルおよびrのパッケージvastの概要と国際水産資源への適用事例. 日本水産学会誌 advpub. https://doi.org/10.2331/suisan.20-00034

Example applications:

  1. Index standardization
  2. Empirical Orthogonal Functions
  3. Ordination using joint species distribution model
  4. End-of-century projections
  5. Expand length and age-composition samples
  6. Combine condition and biomass data
  7. Expand stomach content samples
  8. Combine presence/absence, counts, and biomass data
  9. Seasonal and annual variation
  10. Combine acoustic and bottom trawl data
  11. Surplus production models
  12. Multispecies model of biological interactions
  13. Stream network models

Usage demos:

Project structure and utilities:

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