Systmod II: Approaching a real dynamic computer model for fish stock assessment and development of fishery strategies
- Published
- Accepted
- Subject Areas
- Aquaculture, Fisheries and Fish Science, Computational Biology, Mathematical Biology
- Keywords
- Fisheries management, Computer model, Length structured model, Norwegian Spring Spawning herring, Fish stock development
- Copyright
- © 2016 Hamre et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Systmod II: Approaching a real dynamic computer model for fish stock assessment and development of fishery strategies. PeerJ Preprints 4:e2604v1 https://doi.org/10.7287/peerj.preprints.2604v1
Abstract
Simulating development of fish stocks may be as complex as calculation of the development of the atmosphere, which is treated in meteorology as an initial value problem in physics. This approach was first proposed by Abbe and Bjerknes in the beginning of the 20 th century and today huge systems of differential equations are used to predict the weather. A similar approach to fisheries biology and ecology requires a real dynamic population model, which calculates the development of fish stocks from an initial state with equations that are independent of time. Here we present Systmod II, which uses a length-based growth function with a parameter for environmental variation and length-based data structure. The model uses monthly time steps to integrate population growth by moving fish to higher length groups as they grow. Since fish growth and maturity correlate more with length than with age, this gives comprehensive and clear results. The model was validated for Norwegian Spring-Spawning herring, using observed data from ICES working groups, and correlations (R2) between simulated and observed stock (total stock, spawning stock and catchable stock, numbers and biomass) were above 0.93. At present, the model makes reliable predictions on the short term (3 year for herring). For long term forecasts, better predictions of recruitment are needed . Since length is the main variable of the growth function, the state of the fish stock, including variability in length per yearclass, can be measured in situ, using hydro-acoustic trawl surveys. Data for modelling of many of the relations are still lacking, but can be filled in from future field studies.
Author Comment
This is a preprint submission to PeerJ
Supplemental Information
Supplemantary file 1. More results of SystmodII runs
Yield in biomass per length group of NSS herring 1982-90; Effect of F on stock biomass; Sensitivity of the model to varation in k and Lmax
Supplementary file 2. Input data to SystmodII
Each year from 1982 until 2009 NSS herring stock (ICES working grop reports and Holst et al., 1996): No of individuals at age; Average weight at age; Average length at age; Standard deviation to length at age; VPA data for total Stock biomass and spawning Stock biomass;Yearly F-values. Natural mortality at age
Supplementary file 3. Output data from Systmod II
The model was run assuming observed F and historical recruitment: Simulated no of individuals per year and lengthgroup; Simulated length and biomass per year and lengthgroup, Simulated yield. A possiblity to calculate catch and resulting biomass when assuming different F-values.