Recruitment

Research Summary

Mark and his collaborators have been involved in extensive research into modelling recruitment ranging from the mechanisms driving the stock-recruitment relationship and the development of stock-recruitment relationships to the technical aspects of estimation and inclusion of covariates. In 2017 he co-organized the CAPAM workshop on recruitment and acted as a guest editor for the consequent Fisheries Research special issue (Sharma et al. 2019). As part of the workshop, he coauthored a review on how to model temporal variation in recruitment (Maunder and Thorson, 2019). He pioneered contemporary approaches to model recruitment as true random effects (or within state-space models) in a frequentist framework (i.e. integration across the random effects), before random effects were available in ADMB or before TMB was available (Maunder and Deriso, 2003). This work also highlighted that the estimate of the standard deviation of the recruitment variation is biased when using the penalized likelihood approach (pointed out by the reviewer Andre Punt) and identified the need for adjusting the log-normal bias correction factor for years with little information when developing computationally efficient methods for modelling recruitment variability in projections (Maunder et al. 2006). This work was followed by several publications on state-space models (e.g. Maunder and Deriso, 2011; Maunder et al., 2015). Mark led the creation of the low fecundity stock-recruitment curve now available in Stock Synthesis (Taylor et al., 2013). This work led to the development of the theory of the saturating life history (adults saturate the recruitment capacity at very low adult abundance) and the associated hocky stick stock-recruitment curve based on the general logistic curve for survival (Maunder, 2022). He also codeveloped a stock–recruitment model for highly fecund species, which is based on the temporal and spatial extent of spawning (Maunder and Deriso, 2013). This stock recruitment relationship was partly derived from his very first peer reviewed paper that used queuing theory to model the stock-recruitment relationship based on salmon redd superimposition (Maunder 1997). Mark and his collaborators have showed that estimates of the steepness of the Beverton-Holt stock-recruitment relationship conducted inside the stock assessment model are biased (Maunder, 2012; Lee et al., 2012), investigated how the stock-recruitment relationship relates to surplus production models (Maunder, 2003) and reference points (Maunder, 2003; Minte-Vera et al. 2018), and highlighted the tradeoffs of under or over estimating steepness (Zhu et al., 2012). Finally, he has conducted extensive research into including covariates in population dynamics models including the modelling of recruitment. Marks research has illustrated the need to estimate observation (Deriso et al. 2007.) and process error (annual recruitment deviates) when conducting covariate selection for recruitment or other model processes (Maunder and Watters, 2003) and the related use (Maunder and Deriso, 2011) and benefit (Maunder and Deriso, 2010) of state-space models when evaluating covariates (Maunder et al., 2015). He also investigated methods to deal with missing covariates (Maunder and Deriso, 2010) and early work on evaluating the use of covariates to model recruitment and their problems (Maunder, 1998; Maunder and Starr, 1998). 

Relevant Papers

Maunder, M.N. 2022. Stock-recruitment models from the viewpoint of density-dependent survival and the onset of strong density-dependence when a carrying capacity limit is reached. Fisheries Research 249, 106249. https://www.sciencedirect.com/science/article/abs/pii/S0165783622000261

Crone, P. R., Maunder, M. N., Lee, H. H., Piner, K. R. 2019. Good practices for including environmental data to inform spawner-recruit dynamics in integrated stock assessments: Small pelagic species case study. Fisheries Research. 217: 122-132. https://www.sciencedirect.com/science/article/abs/pii/S0165783618303825

Maunder M.N., Thorson, J.T. 2019. Modeling temporal variation in recruitment in fisheries stock assessment: A review of theory and practice. Fisheries Research. 217: 71-86. https://www.sciencedirect.com/science/article/abs/pii/S0165783618303564

Sharma, R., Porch, C. E., Babcock, E. A., Maunder, M. N., Punt, A. E. 2019. Recruitment: Theory, estimation, and application in fishery stock assessment models. Fisheries Research. 217: 1-4. https://www.sciencedirect.com/science/article/abs/pii/S0165783619300827

Minte-Vera, C.V., Maunder, M.N., Schaefer, K.M., Aires-da-Silva, A.M. 2018. The influence of metrics for spawning output on stock assessment results and evaluation of reference points: An illustration with yellowfin tuna in the eastern Pacific Ocean. Fisheries Research. 217: 35-45. https://www.sciencedirect.com/science/article/abs/pii/S0165783618302637

Maunder, M.N., Deriso, R.B., and Hanson, C.H. 2015. Use of state-space population dynamics models in hypothesis testing: advantages over simple log-linear regressions for modeling survival, illustrated with application to longfin smelt (Spirinchus thaleichthys). Fisheries Research, 164: 102–111. https://www.sciencedirect.com/science/article/pii/S0165783614003105

Maunder, M. N., Deriso, R. B. 2013. A stock–recruitment model for highly fecund species based on temporal and spatial extent of spawning. Fisheries Research, 146: 96-101.  https://www.sciencedirect.com/science/article/abs/pii/S0165783613001021

Taylor, I.G., Gertseva, V. Methot, R.D. Jr., and Maunder, M.N. 2013. A stock-recruitment relationship based on pre-recruit survival, illustrated with application to spiny dogfish shark. Fisheries Research 142: 15– 21. https://www.sciencedirect.com/science/article/abs/pii/S0165783612001567

Lee, H-H., Maunder, M.N., Piner, K.R., and Methot, R.D. 2012. Can steepness of the stock-recruitment relationship be estimated in fishery stock assessment models? Fisheries Research 125-126: 254-261. https://www.sciencedirect.com/science/article/abs/pii/S0165783612001099

Maunder M.N. 2012. Evaluating the stock-recruitment relationship and management reference points: Application to summer flounder (Paralichthys dentatus) in the U.S. mid-Atlantic. Fisheries Research, 125–126: 20–26. https://www.sciencedirect.com/science/article/abs/pii/S0165783612000811

Zhu, J-F, Chen, Y., Dai, X.J., Harley, S.J., Hoyle, S.D., Maunder, M.N., Aires-da-Silva, A. 2012. Implications of uncertainty in the spawner-recruitment relationship for fisheries management: an illustration using bigeye tuna (Thunnus obesus) in the eastern Pacific Ocean. Fisheries Research 119– 120: 89– 93. https://www.sciencedirect.com/science/article/abs/pii/S0165783611003869

Maunder, M.N. and Deriso, R.B. 2011. A state–space multistage life cycle model to evaluate population impacts in the presence of density dependence: illustrated with application to delta smelt (Hyposmesus transpacificus) Can. J. Fish. Aquat. Sci. 68: 1285–1306. https://www.nrcresearchpress.com/doi/full/10.1139/f2011-071#.Xp4TTchKjD4

Maunder, M.N. and Deriso, R.B. 2010. Dealing with missing covariate data in fishery stock assessment models. Fisheries Research 101: 80-86. https://www.sciencedirect.com/science/article/abs/pii/S0165783609002537

Deriso, R.B., Maunder, M.N., and Skalski, J.R. 2007. Variance estimation in integrated assessment models and its importance for hypothesis testing. Can. J. Fish. Aquat. Sci. 64: 187-197. https://www.nrcresearchpress.com/doi/abs/10.1139/f06-178#.Xp4nR8hKjD4

Maunder M.N., Harley, S.J., and Hampton, J. 2006. Including parameter uncertainty in forward projections of computationally intensive statistical population dynamic models. ICES Journal of Marine Science 63: 969-979. https://academic.oup.com/icesjms/article/63/6/969/618044

Maunder, M.N. 2003. Is it time to discard the Schaefer model from the stock assessment scientist’s toolbox? Fisheries Research, 61: 145-149. https://www.sciencedirect.com/science/article/abs/pii/S0165783602002734

Maunder, M.N. and Deriso, R.B. 2003. Estimation of recruitment in catch-at-age models. Can. J. Fish. Aquat. Sci. 60: 1204-1216. https://www.nrcresearchpress.com/doi/abs/10.1139/f03-104#.Xp40ZshKjD4

Maunder, M.N. and Watters, G.M. 2003. A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example. Fishery Bulletin, 101: 89-99. http://aquaticcommons.org/15108/

Maunder, M.N. 1998. Problems with using an environmental based recruitment index: examples from a New Zealand snapper (Pagrus auratus) assessment. In fishery stock assessment models, edited by F. Funk, T.J. Quinn II, J. Heifetz, J.N. Ianelli, J.E. Powers, J.J. Schweigert, P.J. Sullivan, and C.I. Zhang, Alaska Sea Grant College Program Report No. AK-SG-98-01, University of Alaska Fairbanks, pp. 679-692. https://eos.ucs.uri.edu/seagrant_Linked_Documents/aku/akuw97002/akuw97002_full.pdf

Maunder, M.N. and Starr, P.J. 1998. Validating the Hauraki Gulf snapper pre-recruit trawl surveys and temperature recruitment relationship using catch at age analysis with auxiliary information. New Zealand Fisheries Assessment Research Document 98/15 23p. https://fs.fish.govt.nz/Doc/17645/1998%20FARDs/98_15_FARD.pdf.ashx

Maunder, M.N. 1997. Investigation of density dependence in salmon spawner-egg relationships using queueing theory. Ecological modelling 104: 189-197. https://www.sciencedirect.com/science/article/abs/pii/S0304380097001269 

CAPAM workshop reports

Sharma, R., C. E. Porch, E. A. Babcock, M. Maunder, and A. E. Punt, editors. 2019. Recruitment: Theory, Estimation, and Application in Fishery Stock Assessment Models. U.S. Department of Commerce, NOAA Technical Memorandum NMFS-NWFSC-148. https://www.webapps.nwfsc.noaa.gov/assets/25/9438_03212019_102753_TechMemo148.pdf

Reports

Maunder, M.N. and Starr, P.J. (1998) Validating the Hauraki Gulf snapper pre-recruit trawl surveys and temperature recruitment relationship using catch at age analysis with auxiliary information. New Zealand Fisheries Assessment Research Document 98/15 23p.