Kuriyama et al. 2015

[aaronmberger-nwfsc]

Andy,

Would you please add the below reference - I fixed citation in weight-at-age section.

Kuriyama, P.T. and 11 coauthors. 2015. An empirical weight-at-age approach reduces estimation bias compared to modeling parametric growth in integrated, statistical stock assessment models when growth is time varying. Fish Res: doi:10.1016/j.fishres.2015.09.007.

[allan-hicks]

Here is the bibtex entry:

@article{Kuriyama2015,

title = "An empirical weight-at-age approach reduces estimation bias compared to modeling parametric growth in integrated, statistical stock assessment models when growth is time varying ",

journal = "Fisheries Research ",

volume = "",

number = "",

pages = " - ",

year = "2015",

note = "",

issn = "0165-7836",

doi = "http://dx.doi.org/10.1016/j.fishres.2015.09.007",

url = "http://www.sciencedirect.com/science/article/pii/S0165783615300837",

author = "Peter T. Kuriyama and Kotaro Ono and Felipe Hurtado-Ferro and Allan C. Hicks and Ian G. Taylor and Roberto R. Licandeo and Kelli F. Johnson and Sean C. Anderson and Cole C. Monnahan and Merrill B. Rudd and Christine C. Stawitz and Juan L. Valero",

keywords = "Empirical weight-at-age information",

keywords = "Fisheries stock assessment",

keywords = "Time-varying growth",

keywords = "ss3sim",

keywords = "Stock Synthesis ",

abstract = "Abstract Somatic growth in fishes often varies through time. Despite this, most stock assessments either fix or estimate a time-invariant growth relationship because estimating time-varying growth parameters can be data intensive and subject to multiple sources of bias. Additionally, estimates of growth are often confounded with estimates of selectivity, particularly if selectivity is also time varying. Incorporating empirical weight-at-age (EWAA) information into assessments is a little-studied alternative that accounts for time-varying growth, but foregoes fixing or estimating growth and length–weight relationships. However, this method requires annual measures of fish weights at each age, and missing values must therefore be interpolated. We used Stock Synthesis in a simulation-testing framework to compare the effect of estimating a single time-invariant growth curve, time-varying growth curves, and incorporating {EWAA} information on management quantities and parameter estimates. We ran simulations across two fish life histories (hake-like and rockfish-like) and three data cases (data-rich, data-rich with a late-starting survey, and data-moderate). We found that when growth was time invariant, the {EWAA} approach was unbiased but had twice the median average relative error compared to a model that estimated growth from age and length data. However, for data-rich cases with time-varying growth, the {EWAA} method resulted in more accurate estimates of spawning stock biomass compared to the approach that estimated time-invariant and time-varying growth parameters, as evidenced by at least a five-fold reduction in range of median relative errors. The magnitude of this effect was greatest for the long-lived, slow-growing life history. For the relatively fast-growing species, estimates from the {EWAA} method were particularly sensitive to interpolating missing values. "

}

On Fri, Feb 5, 2016 at 2:28 PM, aaronmberger notifications@github.com wrote:

Andy,

Would you please add the below reference - I fixed citation in weight-at-age section.

Kuriyama, P.T. and 11 coauthors. 2015. An empirical weight-at-age approach reduces estimation bias compared to modeling parametric growth in integrated, statistical stock assessment models when growth is time varying. Fish Res: doi:10.1016/j.fishres.2015.09.007.

— Reply to this email directly or view it on GitHub https://github.com/cgrandin/hake-assessment/issues/53.

[andrew-edwards]

Thanks. Done.