CPUE

Research Summary

Mark has been involved in extensive research into the use of catch-per-unit-effort (CPUE) data in fisheries stock assessment right from the start of his career (Maunder and Starr, 1995). In 2018 he co-organized and chaired the CAPAM workshop on spatio-temporal modelling of CPUE data and acted as a guest editor for the Fisheries Research special issue (Thorson et al., 2020). His review coauthored with Andre Punt has become the standard reference for CPUE standardization and is one of the most cited fisheries stock assessment technical papers (Maunder and Punt 2004). Another key paper reviews the problems with interpreting CPUE based indices of abundance for individual stocks and communities (Maunder et al., 2006). Mark argues that indices of abundance derived from CPUE data should be based on the whole stock, be constructed using area weighting of spatio-temporal models, have their composition data derived from the same spatio-temporal analysis and weighted by the CPUE, and that the composition for the related fishery removals should also be generated using spatio-temporal analysis but weighted by catch (Maunder et al., 2020). With his colleagues he has done a variety of studies into spatial analysis using a variety of techniques (e.g. Glaser et al., 2011; Martínez-Ortiz et al., 2015;  Lennert-Cody et al., 2018b; Xu et al., 2019), particularly modelling the CPUE and composition data simultaneously (Kai et al., 2017a,b; Satoh et al. 2021), as well as using regression tree methods to define areas with similar CPUE trends for fishery and stock structure definitions (Lennert-Cody et al., 2013.). Mark and his collaborators have also investigated depth as the third dimension on CPUE and its interaction with environment conditions (e.g. Bigelow and Maunder,2007). Mark converted the Habitat based CPUE standardization, which uses depth of longline hooks to associate fishing effort with environment conditions, into a statistical approach (Maunder et al., 2006) and also created and applied a Neural Network CPUE standardization approach to the same data (Maunder and Hinton, 2006). Mark was an early pioneer and advocate of the integrated assessment approach to stock assessment and showed how integrating CPUE standardization into stock assessment models improved uncertainty estimates (Maunder, 2001; Maunder, and Langley, 2004) presumably by automatically dealing with the correlations. Mark has also investigated various observation models to fit CPUE data including appropriate specification of the likelihood function's variance parameters, and how this relates to data weighting (Maunder and Starr, 2003; Hyun et al., 2015), and changes in catchability (Carvalho et al., 2014). Mark developed an in-season depletion estimator based on CPUE to provide in-season management advice (Maunder, 2010).  Other collaborations have included showing that the use of CPUE aggregated across species is not recommended (Kleiber and Maunder, 2008), highlighting biases and mis-use of CPUE data (Wang et al., 2009, 2015), identifying fishing strategies (Lennert-Cody et al., 2018a), reconstructing CPUE data to enable the calculation of abundance indices (Chang S-K et al., 2017), and creating indices for protected species (Lennert-Cody et al., 2016). Mark was also involved in efforts to counter the misinformation and misinterpretation of CPUE data which was used to promote preconceived personal beliefs over science (Sibert et al., 2006, 2007; Hampton et al., 2005.). 

Relevant Papers

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

Thorson, J.T., Maunder, M.N., Punt, A.E. 2020. The development of spatio-temporal models of fishery catch-per-unit-effort data to derive indices of relative abundance. Fish. Res. 105611. https://www.sciencedirect.com/science/article/abs/pii/S0165783620301284

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

Lennert-Cody, C.E. Moreno, G., Restrepo, V., Román, M.H., Maunder, M.N. 2018a. Recent purse-seine FAD fishing strategies in the eastern Pacific Ocean: what is the appropriate number of FADs at sea? ICES Journal of Marine Science 75 (5), 1748-1757. https://academic.oup.com/icesjms/article/75/5/1748/4976455

Lennert-Cody, C.E., Clarke, S.C., Aires-da-Silva, A., Maunder, M.N., Franks, P.J.S., Román, M., Miller, A.J., Minami, M. 2018b. The importance of environment and life stage on interpretation of silky shark relative abundance indices for the equatorial Pacific Ocean. Fisheries Oceanography 28: 43-53. https://onlinelibrary.wiley.com/doi/pdf/10.1111/fog.12385

Chang S-K, Liu H-I, Fukuda, H, Maunder, M. N. 2017. Data reconstruction can improve abundance index estimation: An example using Taiwanese longline data for Pacific bluefin tuna. PLoS ONE 12(10): e0185784. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185784

Kai, M., Thorson, J.T., Piner, K.R, Maunder, M.N. 2017a. Predicting the spatio-temporal distributions of pelagic sharks in the western and central North Pacific. Fisheries Oceanography 2017;26:569–582. https://onlinelibrary.wiley.com/doi/abs/10.1111/fog.12217

Kai, M., Thorson, J.T., Piner, K.R., Maunder, M.N. 2017b. Spatiotemporal variation in size-structured populations using fishery data: an application to shortfin mako (Isurus oxyrinchus) in the Pacific Ocean. Canadian Journal of Fisheries and Aquatic Sciences, 2017, 74(11): 1765-1780. https://www.nrcresearchpress.com/doi/10.1139/cjfas-2016-0327#.Xpx4X8hKjD4

Lennert-Cody, C.E., Maunder, M.N., Fiedler, P.C., Minami, M., Gerrodette, T., Rusin, J., Minte-Vera, C.V., Scott, M. and Buckland, S.T., 2016. Purse-seine vessels as platforms for monitoring the population status of dolphin species in the eastern tropical Pacific Ocean. Fisheries Research, 178: 101-113. https://www.sciencedirect.com/science/article/abs/pii/S0165783615301028

Hyun, S.Y., Maunder, M.N., Rothschild, B.J. 2015. Importance of modeling heteroscedasticity of survey index data in fishery stock assessments. ICES Journal of Marine Science. 72 (1): 130-136. https://academic.oup.com/icesjms/article/72/1/130/823326

Martínez-Ortiz, J., Aires-da-Silva, A.M., Lennert-Cody, C.E. and Maunder, M.N., 2015. The Ecuadorian Artisanal Fishery for Large Pelagics: Species Composition and Spatio-Temporal Dynamics. PloS one, 10(8), p.e0135136. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4552643/

Wang, S. P., Maunder, M. N., Nishida, T., Chen,Y. R. 2015.  Influence of model misspecification, temporal changes, and data weighting in stock assessment models: Application to swordfish (Xiphias gladius) in the Indian Ocean. Fisheries Research, 166: 119-128. https://www.sciencedirect.com/science/article/abs/pii/S0165783614002483

Carvalho, F., Ahrens, R., Murie, D., Ponciano, J.M., Aires-da-Silva, A., Maunder, M.N., and Hazin, F. 2014. Incorporating specific change points in catchability in fisheries stock assessment models: An alternative approach applied to the blue shark (Prionace glauca) stock in the south Atlantic Ocean. Fisheries Research 154: 135-146. https://www.sciencedirect.com/science/article/abs/pii/S0165783614000381

Lennert-Cody, C.E., Maunder, M.N., Aires-da-Silva, A., Minami, M. 2013. Defining population spatial units: simultaneous analysis of frequency distributions and time series. Fisheries Research 139: 85-93. https://www.sciencedirect.com/science/article/abs/pii/S0165783612002974

Glaser, S. M., Ye, H., Maunder, M.N. MacCall A., Fogarty, M., and Sugihara, G. 2011. Detecting and forecasting complex nonlinear dynamics in spatially-structured catch-per-unit-effort time series for North Pacific albacore. Canadian Journal of Fisheries and Aquatic Science, 68: 400-412. https://www.nrcresearchpress.com/doi/abs/10.1139/f10-160#.Xp4USMhKjD4

Maunder, M.N. 2010. A depletion estimator for within-season management of yellowfin tuna. Managing Data-Poor Fisheries: Case Studies, Models & Solutions, 1: 251–258. https://caseagrant.ucsd.edu/publication/managing-data-poor-fisheries-case-studies-models-solutions

Wang, S-.P., Maunder, M.N., and Aires-da-Silva, A. 2009. Implications of model and data assumptions: An illustration including data for the Taiwanese longline fishery into the eastern Pacific Ocean bigeye tuna (Thunnus obesus) stock assessment. Fisheries Research 97 (2009) 118–126. https://www.sciencedirect.com/science/article/abs/pii/S0165783609000344

Kleiber, P. and Maunder M.N. 2008. Inherent Bias in using Aggregate CPUE to Characterize Abundance of Fish Species Assemblages. Fisheries Research 93: 140-145. https://www.sciencedirect.com/science/article/abs/pii/S016578360800091X

Bigelow, K.A. and Maunder, M.N. 2007. Does habitat or depth influence catch rates of pelagic species? Can. J. Fish. Aquat. Sci. 64: 1581-1594. https://www.nrcresearchpress.com/doi/abs/10.1139/f07-115#.Xp4nJ8hKjD4

Sibert, J., Hampton, J., Kleiber, P., and Maunder, M.N. 2007. Fishing for Good News: response. Science 316: 200-201. https://science.sciencemag.org/content/316/5822/200.2

Maunder, M.N. and Hinton, M.G. 2006. Estimating relative abundance from catch and effort data, using neural networks. Inter-American Tropical Tunna Commission Special Report 15. pp. 19. https://www.iattc.org/GetAttachment/0b3efbb9-00f1-42c6-941c-5f7dc644cd45/No-15-2006-MAUNDER,-MARK-N-,-and-MICHAEL-G-HINTON_Estimating-relative-abundance-from-catch-and-effort-data,-using-neural-networks.pdf

Maunder, M.N., Hinton, M.G., Bigelow, K.A., Langley, A.D. 2006. Developing indices of abundance using habitat data in a statistical framework. Bulletin of Marine Science, 79(3): 545–559. https://www.ingentaconnect.com/content/umrsmas/bullmar/2006/00000079/00000003/art00010

Maunder, M.N., Sibert, J.R. Fonteneau, A., Hampton, J., Kleiber, P., and Harley, S. 2006. Interpreting catch-per-unit-of-effort data to asses the status of individual stocks and communities. ICES Journal of Marine Science, 63: 1373-1385. https://academic.oup.com/icesjms/article/63/8/1373/710477

Sibert, J., Hampton, J., Kleiber, P., and Maunder, M.N. 2006. Biomass, Size, and Trophic Status of Top Predators in the Pacific Ocean. Science 314: 1773-1776. https://science.sciencemag.org/content/314/5806/1773

Hampton, J., Sibert, J.R., Kleiber, P., Maunder, M.N., and Harley, S.J. 2005. Decline of Pacific tuna populations exaggerated? Nature E1/E2. https://www.nature.com/articles/nature03581

Maunder, M.N. and Langley, A.D. 2004. Integrating the standardization of catch-per-unit-of-effort into stock assessment models: testing a population dynamics model and using multiple data types. Fisheries Research 70(2-3): 389-395. https://www.sciencedirect.com/science/article/abs/pii/S0165783604001808

Maunder, M.N. and Punt, A.E. 2004. Standardizing Catch and Effort Data: A Review of Recent Approaches. Fisheries Research 70(2-3): 141-159. https://www.sciencedirect.com/science/article/abs/pii/S0165783604001638

Maunder, M.N. and Starr, P.J. 2003. Fitting fisheries models to standardised CPUE abundance indices. Fisheries Research 63: 43-50. https://www.sciencedirect.com/science/article/abs/pii/S016578360300002X

Maunder M.N. 2001. A general framework for integrating the standardization of catch-per-unit-of-effort into stock assessment models. Can. J. Fish. Aquat. Sci., 58: 795-803. https://www.nrcresearchpress.com/doi/abs/10.1139/f01-029#.Xp47wMhKjD4

Maunder, M. N. and Starr, P. J. (1995) Rock lobster standardised CPUE analysis. New Zealand Fisheries Assessment Research Document 95/11 28p. http://docs.niwa.co.nz/library/public/95_11_FARD.pdf

Workshop reports

Hoyle, S.D., Bigelow, K.A., Langley, A.D., and Maunder, M.N. 2007. Proceedings of the pelagic longline catch rate standardisation meeting. WCPFC-SC3-ME SWG/IP-1 http://www.soest.hawaii.edu/pfrp/pdf/bigelow_ATT00089.pdf

Maunder, M.N. (compiled by) (2005). Report from the workshop on developing indices of abundance from purse seine catch and effort data, IATTC, La Jolla, California, USA, 3-5 November, 2004.

Reports

Maunder, M.N. and Hinton, M.G. (2006) Estimating relative abundance from catch and effort data, using neural networks. Inter-American Tropical Tunna Commission Special Report 15. pp. 19. https://iattc.org/GetAttachment/0b3efbb9-00f1-42c6-941c-5f7dc644cd45/No-15-2006-MAUNDER,-MARK-N-,-and-MICHAEL-G-HINTON_Estimating-relative-abundance-from-catch-and-effort-data,-using-neural-networks.pdf

Lennert-Cody, C.E., Okamoto, H., and Maunder, M.N. 2014. Analysis of Japanese longline operational-level catch and effort data for bigeye tuna in the eastern Pacific Ocean. IATTC Stock Assessment Report 14: 186-216. https://www.iattc.org/GetAttachment/7463ef95-8e47-4ec0-ad48-5a168e5b8d31/No-14-2014_Status-of-the-tuna-and-billfish-stocks-in-2012.pdf

Maunder, M.N. and Hoyle, S.D. (2007) A novel method to estimate relative abundance from purse-seine catch-per-set data using known abundance of another species. Inter-Amer. Trop. Tuna Comm., Stock Assessment Report, 7: 283-297. https://www.iattc.org/GetAttachment/3a07c1d5-7988-407a-80d3-c288778f8ef9/No-7-2007_Status-of-the-tuna-and-billfish-stocks-in-2005.pdf

Hoyle, S.D. and Maunder M.N. (2006) Standardisation of yellowfin and bigeye CPUE data from Japanese longliners, 1975-2004. IATTC Working Group on Stock Assessments, 6th Meeting, SAR-7-07.

Maunder, M.N. and Hoyle, S.D. (2006) Analysis of skipjack catch per unit of effort (CPUE). IATTC Working Group on Stock Assessments, 6th Meeting, SAR-7-07b.

Maunder, M.N. and Hoyle, S.D. (2006) Estimating relative indices of abundance for yellowfin tuna from catch-per-unit-of-effort on schools associated with dolphins. IATTC Working Group on Stock Assessments, 6th Meeting, SAR-7-07a.ii.

Langley, A. Bigelow, K. Maunder, M.N. and Miyabe, N. (2005) Longline CPUE indices for bigeye and yellowfin in the Pacific Ocean using GLM and statistical habitat standardisation methods. WCPFC–SC1 SA WP–8, 40p.

Hinton, M.G. and Maunder, M.N. (2003) Methods for standardizing CPUE and how to select among them. ICCAT SCRS/2003/034.

Hinton, M.G. and Maunder, M.N. (2003) Methods for standardizing CPUE and how to select among them. ICCAT SCRS/2003/034.

Bigelow, K., Maunder, M.N., Hinton, M. (2003) Comparison of deterministic and statistical habitat-based models to estimate effective longline effort and standardized cpue for bigeye and yellowfin tuna. SCTB16 RG-3.

Maunder, M.N., Hinton, M.G., Bigelow, K.A., and Harley, S.J. (2002) Statistical comparisons of habitat standardized effort and nominal effort, SCTB 15, MWG-7, 18p.

Maunder, M. N. and Starr, P. J. (1995) Rock lobster standardised CPUE analysis. New Zealand Fisheries Assessment Research Document 95/11 28p.