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Jim Thorson edited this page Jul 4, 2024 · 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)
  44. Pacific cod, North Pacific FMC, 2023 (link here)
  45. Alaska pollock, North Pacific FMC, 2023 (link here)
  46. Yellowfin sole, North Pacific FMC, 2023 (link here)
  47. Black Sea Bass, New England FMC, 2023 (link here, see Fig. 3.2)

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. Summer flounder in Northwest Atlantic, New England FMC, 2019 (link here, see Fig. A112)
  15. Yellowfin sole in the Bering Sea, North Pacific FMC, 2020 (link here, see e.g., Fig. 4.25)
  16. Pacific cod in the Gulf of Alaska, North Pacific FMC, 2020 (link here, see e.g. Appendix Fig. 2.1.5a)
  17. Walleye pollock in the Gulf of Alaska, North Pacific FMC, 2020 (link here, see e.g., Appendix Figure 1A.3a)
  18. Yellowfin sole in Bering Sea, North Pacific FMC, 2021 (link here, see Fig. 4.29)
  19. Northern rockfish in Gulf of Alaska, North Pacific FMC, 2021 (link here, see Fig. 10-2)
  20. Dusky rockfish in Gulf of Alaska, North Pacific FMC, 2021 (link here, see Fig. 12-2)
  21. Northern anchony in Central Subpopulation for US Management, California Current FMC, 2022 (link here, see Fig. C4-C5)
  22. George's Bank Haddock, New England FMC, 2022 (link here, Fig. 90-91)

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.
  28. Nishijima, S., Kanazawa, K., Ichinokawa, M., Yukami, R., Manabe, A., 2022. Standardizing monthly egg survey data as an abundance index for spawning stock biomass of chub mackerel in the Northwest Pacific (No. NPFC-2022-TWG CMSA06-WP10). North Pacific Fisheries Commission.
  29. Sawada, K., Okuda, T., 2023. Improved VAST (vector autoregressive spatio-temporal) modelling of grenadier relative abundance in Subarea 48.6 (No. WG-FSA-2023/33). CCAMLR.
  30. Kai, M., 2024. Spatio-temporal model for CPUE standardization: Application to shortfin mako caught by Japanese offshore and distant water shallow-set longliner in the western and central North Pacific1 (No. ISC/23/SHARKWG-1/2). ISC Shark Working Group Workshop.
  31. Chang, F.C., Matsumoto, T., Park, H., Lim, J., Kwon, Y., Lee, S.I., Lauretta, M., Kitakado, T., 2024. STANDARDIZED YELLOWFIN TUNA CPUE OF THE MULTIPLE LONGLINE FLEETS BY VECTOR AUTOREGRESSIVE SPATIOTEMPORAL GLMM IN THE ATLANTIC OCEAN. Collect Vol Sci Pap ICCAT 81, 1–23.
  32. Kai, M., 2024. SPATIO-TEMPORAL MODEL FOR CPUE STANDARDIZATION: APPLICATION TO ATLANTIC BLUE MARLIN CAUGHT BY JAPANESE TUNA LONGLINE FISHERY FROM 1994 TO 2022. Collect Vol Sci Pap ICCAT 81, 1–17.

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. Akia, S.A.V., Amandè, M.J., Gaertner, D., 2023. Towards “glocalised” management of tuna stocks based on causation between a stock and its component belonging temporally to local Exclusive Economic Zones. Aquat. Living Resour. 36, 23. https://doi.org/10.1051/alr/2023018

  4. 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

  5. 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

  6. Badger, J.J., Large, S.I., Thorson, J.T., 2023. Spatio-temporal species distribution models reveal dynamic indicators for ecosystem-based fisheries management. ICES J. Mar. Sci. 80, 1949–1962. https://doi.org/10.1093/icesjms/fsad123

  7. 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

  8. Bryan, M.D., Thorson, J.T., 2023. The performance of model-based indices given alternative sampling strategies in a climate-adaptive survey design. Front. Mar. Sci. 10. https://doi.org/10.3389/fmars.2023.1198260

  9. Cacciapaglia, C., Brooks, E.N., Adams, C.F., Legault, C.M., Perretti, C.T., Hart, D., 2024. Developing workflow and diagnostics for model selection of a vector autoregressive spatiotemporal (VAST) model in comparison to design-based indices. Fish. Res. 275, 107009. https://doi.org/10.1016/j.fishres.2024.107009

  10. 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

  11. 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

  12. Charsley, A.R., Grüss, A., Thorson, J.T., Rudd, M.B., Crow, S.K., David, B., Williams, E.K., Hoyle, S.D., 2023. Catchment-scale stream network spatio-temporal models, applied to the freshwater stages of a diadromous fish species, longfin eel (Anguilla dieffenbachii). Fish. Res. 259, 106583. https://doi.org/10.1016/j.fishres.2022.106583

  13. Charsley, A.R., Sibanda, N., Hoyle, S.D., Crow, S.K., 2022. Comparing the performance of three common species distribution modelling frameworks for freshwater environments through application to eel species in New Zealand. Can. J. Fish. Aquat. Sci. https://doi.org/10.1139/cjfas-2022-0212

  14. 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

  15. Chen, J., Gao, J., Zhang, F., 2024. Spatiotemporal model improves survey indices for witch flounder stock assessment in the Grand Banks. Can. J. Fish. Aquat. Sci. 81, 459–487. https://doi.org/10.1139/cjfas-2023-0101

  16. 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

  17. 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

  18. DeFilippo, L., Kotwicki, S., Barnett, L., Richar, J., Litzow, M.A., Stockhausen, W.T., Palof, K., 2023. Evaluating the impacts of reduced sampling density in a systematic fisheries-independent survey design. Front. Mar. Sci. 10. https://doi.org/10.3389/fmars.2023.1219283

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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., 2023. Do large-scale associations in birds imply biotic interactions or environmental filtering? J. Biogeogr. 50, 169–182. https://doi.org/10.1111/jbi.14520

  24. Farley, E.V., Yasumiishi, E.M., Murphy, J.M., Strasburger, W., Sewall, F., Howard, K., Garcia, S., Moss, J.H., 2024. Critical periods in the marine life history of juvenile western Alaska chum salmon in a changing climate. Mar. Ecol. Prog. Ser. 726, 149–160. https://doi.org/10.3354/meps14491

  25. Gaichas, S.K., Gartland, J., Smith, B.E., Wood, A.D., Ng, E.L., Celestino, M., Drew, K., Tyrell, A.S., Thorson, J.T., 2023. Assessing small pelagic fish trends in space and time using piscivore stomach contents. Can. J. Fish. Aquat. Sci. https://doi.org/10.1139/cjfas-2023-0093

  26. 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

  27. 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

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  104. Thorson, J.T., Barnes, C.L., Friedman, S.T., Morano, J.L., Siple, M.C., 2023. Spatially varying coefficients can improve parsimony and descriptive power for species distribution models. Ecography 2023, e06510. https://doi.org/10.1111/ecog.06510

  105. 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

  106. 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

  107. 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

  108. 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

  109. 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

  110. 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

  111. 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

  112. 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

  113. 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

  114. 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

  115. 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

  116. 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

  117. 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

  118. 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

  119. 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

  120. 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

  121. 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

  122. Von Szalay, P.G., Kotwicki, S., Barnett, L.A.K., Rugolo, L.J., Ono, K., 2023. Reducing uncertainty in survey abundance estimates by considering alternative designs and estimators: a case study with 3 species in the Gulf of Alaska. Fish. Bull. 121, 50–66. https://doi.org/10.7755/FB.121.1-2.5

  123. Wells, B., Santora, J., Bizzarro, J., Billings, A., Brodeur, R., Daly, E., Field, J., Richerson, K., Thorson, J., 2023. Trophoscapes of predatory fish reveal biogeographic structuring of spatial dietary overlap and inform fisheries bycatch patterns. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps14319

  124. 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

  125. 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

  126. Yasumiishi, E.M., Cunningham, C.J., Farley Jr., E.V., Eisner, L.B., Strasburger, W.W., Dimond, J.A., Irvin, P., 2024. Biological and environmental covariates of juvenile sockeye salmon distribution and abundance in the southeastern Bering Sea, 2002–2018. Ecol. Evol. 14, e11195. https://doi.org/10.1002/ece3.11195

  127. Yazdanian, M., Kankaanpää, T., Itämies, J., Leinonen, R., Merckx, T., Pöyry, J., Sihvonen, P., Suuronen, A., Välimäki, P., Kivelä, S.M., 2023. Ecological and life-history traits predict temporal trends in biomass of boreal moths. Insect Conserv. Divers. 16, 600–615. https://doi.org/10.1111/icad.12657

  128. 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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