Spatial Stock Assessment Models

Research Summary

Mark and his collaborators have been involved in a variety of projects developing spatial stock assessment models. Mark, in parallel with Dave Fournier and colleagues, pioneered the integration of tagging data into integrated spatial stock assessment models in his PhD dissertation (Maunder 1998; 2001). In 2018 he co-organized and chaired the CAPAM workshop on Spatial stock assessment models and acted as a guest editor for the Fisheries Research special issue (Cadrin et al. 2020). Mark has been involved in developing several spatially structured population dynamics and stock assessment models (e.g. Aires-da-Silva et al. 2009).  Mark and his colleagues reviewed the use of electronic tags in fisheries stock assessment (Sippel et al., 2015).

Mark and his colleagues investigated the areas as fleets approach to approximate spatial structure and movement finding that movement caused areas as fleets selectivity curves to be dome shaped and time varying (Waterhouse et al. 2014). They have also investigated alternative approaches to use selectivity curves to approximate movement in bluefin tuna (Lee et al. 2017).

All of the spatial-temporal models developed by Mark and his colleagues can be used to evaluate catch, CPUE, and composition data to input into spatially structured stock assessments (e.g. Satoh et al. 2021; Maunder et al., 2021, Xu et al., 2019).

Relevant Papers

Berger, A.M., Barcel, C., Goethel, D.R., … Maunder, M.N., … et al., 2024. Synthesizing the spatial functionality of contemporary stock assessment software to identify future needs for next generation assessment platforms. Fish. Res. 275, 107008 https://www.sciencedirect.com/science/article/pii/S0165783624000729 

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://www.sciencedirect.com/science/article/abs/pii/S0165783621001934

Cadrin, S.X., Maunder, M.N., Punt, A.E. 2020. Spatial Structure: Theory, estimation and application in stock assessment models. Fish. Res. 105608. https://www.sciencedirect.com/science/article/abs/pii/S0165783620301259

Maunder, M.N., Thorson, J.T., Xu, H., Oliveros-Ramos, R., … 2020. The need for spatio-temporal modeling to determine catch-per-unit effort based indices of abundance and associated composition data for inclusion in stock assessment models. Fish. Res. 105594. https://www.sciencedirect.com/science/article/abs/pii/S0165783620301119

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. Fisheries Research, 213, 121-131. https://www.sciencedirect.com/science/article/abs/pii/S016578361930013X

Lee, H.H., Thomas, L.R., Piner, K.R. and Maunder, M.N., 2017. Effects of age‐based movement on the estimation of growth assuming random‐at‐age or random‐at‐length data. Journal of Fish Biology, 90: 222-235. https://onlinelibrary.wiley.com/doi/full/10.1111/jfb.13177?casa_token=eTilR7sv_6EAAAAA%3Ae5KmmFDuw190ZmMEOCARKuc4vISS83ZwO6IdRUjY4Vzvm3By-94iKpe7lOvrifvt7jW5NdWkG9N1Yg

Lee, H-H., Piner, K.R., Maunder, M.N., Taylor, I.G., Methot Jr., R.D. 2017. Evaluation of alternative modelling approaches to account for spatial effects due to age-based movement. Canadian Journal of Fisheries and Aquatic Sciences, 2017, 74(11): 1832-1844. https://www.nrcresearchpress.com/doi/10.1139/cjfas-2016-0294#.Xpx448hKjD4

Sippel, T., Eveson, J.P., Galuardi, B., Lam, C., Hoyle, S., Maunder, M., Kleiber, P., Carvalho, F., Tsontos, V., Teo, S.L.H., Aires-da-Silva, A., Nicol, S. 2015. Using movement data from electronic tags in fisheries stock assessment: A review of models, technology and experimental design. Fisheries Research, 163: 152-160. https://www.sciencedirect.com/science/article/abs/pii/S0165783614001404

Waterhouse, L., Sampson, D. B., Maunder, M. Semmens, B. X. 2014. Using areas-as-fleets selectivity to model spatial fishing: Asymptotic curves are unlikely under equilibrium conditions. Fisheries Research, 158: 15-25. https://www.sciencedirect.com/science/article/abs/pii/S0165783614000174

Aires-da-Silva, A.M., Maunder, M.N. Gallucci, V.F., Kohler, N.E. and Hoey, J.J. 2009. A spatially-structured tagging model to estimate movement and fishing mortality rates for the blue shark (Prionace glauca) in the North Atlantic Ocean. Marine and Freshwater Research 60: 1029-1043. https://www.publish.csiro.au/mf/MF08235

Maunder, M.N. 2001. Integrated Tagging and Catch-at-Age ANalysis (ITCAAN). In Spatial Processes and Management of Fish Populations, edited by G.H. Kruse,N. Bez, A. Booth, M.W. Dorn, S. Hills, R.N. Lipcius, D. Pelletier, C. Roy, S.J. Smith, and D. Witherell, Alaska Sea Grant College Program Report No. AK-SG-01-02, University of Alaska Fairbanks, pp. 123-146. https://www.researchgate.net/publication/265335689_Integrated_Tagging_and_Catch-at-age_analysis_ITCAAN_model_development_and_simulation_testing

Maunder, M.N., 1998. Integration of Tagging and Population Dynamics Models in Fisheries Stock Assessment. PhD Thesis, University of Washington, 306 pp.

Reports

Maunder, M.N. 2013. Preliminary analysis of historical and recent skipjack tuna tagging data to explore information on exploitation rates. IATTC Stock Assessment Report 13: 77-101.  https://www.iattc.org/GetAttachment/d336c4e2-b84b-4d3e-8153-9d245e8bcd4b/No-13-2012_Status-of-the-tuna-and-billfish-stocks-in-2011.pdf