Covariates

Research Summary

Mark pioneered methods to include covariates into stock assessment models to explain process variation. This work was initiated by showing alternative ways to model recruitment (Maunder and Starr, 1998;  Maunder and Starr, 2001) and the problems with the early methods to include environmental data to explain recruitment (Maunder, 1998) and was followed by developing a statistically rigorous method that included the modelling of the unexplained process variation to ensure that the hypothesis tests were appropriate (Maunder and Watters, 2003) including integrating over the unexplained variation and estimating its standard deviation (Maunder and Deriso, 2003). The latter research introduced a general approach to state-space models based in random effects, which Mark extended for other applications that looked at determining factors that influence recruitment or survival (e.g., Delta smelt: Maunder and Deriso, 2011; Longfin smelt: Maunder et al. 2015; pacific herring: Deriso et al., 2008). He also evaluated methods to deal with missing covariate values (Maunder and Deriso, 2010).

Mark has also evaluated covariates in relation to standardizing CPUE data to provide indices of abundance (e.g. Lennert-Cody et al., 2018). He developed the statistical habitat-based model statHBS, extending Hinton’s habitat-based model, which matches vertical habit preference of a species with environmental conditions, into a statistical frame work and extended it to estimate factors such as retrieval and shoaling (Maunder et al., 2006). Related work included using neural networks to implement the statHBS (Maunder and Hinton, 2006) and evaluating whether habitat or depth influences catch rates (Bigelow and Maunder, 2007). Mark also developed methods to integrate covariates to standardize CPUE directly into stock assessment models (Maunder, 2001; Maunder and Langley, 2004)

Mark’s work has also involved evaluating the important of variance estimation when evaluating covariates (Deriso et al., 2007) and evaluating using covariates as data (Crone et al. 2019).        

Relevant Papers

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

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. 2018. 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

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. 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 Pearson, W.H. 2008. Incorporating covariates into fisheries stock assessment models with application to Pacific herring of Prince William Sound, Alaska. Ecological Applications 18(5): 1270-1286. https://esajournals.onlinelibrary.wiley.com/doi/epdf/10.1890/07-0708.1

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

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., 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. 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. 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. 2001. Bayesian Assessment of the SNA1 snapper (Pagrus auratus) stock on the northeast coast of New Zealand. New Zealand Journal of Marine and Freshwater Research, 35: 87-110. https://www.tandfonline.com/doi/abs/10.1080/00288330.2001.9516980

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. 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 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. http://aquaticcommons.org/6778/

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

Reports

Brandon, J. R., Punt, A. E., Wade, P. R., Perryman, W. L., Methot, R. D., and Maunder, M. N. 2007. Incorporating environmental time series into a population dynamics model for eastern North Pacific gray whales. IWC Scientific Paper SC/59/BRG26.