Journal article
A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: application to spawning potential ratios for data-poor stocks
Canadian Journal of Fisheries and Aquatic Sciences, Vol.73(12), pp.1787-1799
2016
Abstract
Selectivity in fish is often size-dependent, which results in differential fishing mortality rates across fish of the same age, an effect known as "Lee’s Phenomenon". We extend previous work on using length composition to estimate the spawning potential ratio (SPR) for data-limited stocks by developing a computationally efficient length-structured per-recruit model that splits the population into a number of subcohorts, or growth-type-groups, to account for size-dependent fishing mortality rates. Two simple recursive equations, using the life history ratio of the natural mortality rate to the von Bertalanffy growth parameter (M/K), were developed to generate length composition data, reducing the complexity of the previous approach. Using simulated and empirical data, we demonstrate that ignoring Lee’s Phenomenon results in overestimates of fishing mortality and negatively biased estimates of SPR. We also explored the behaviour of the model under various scenarios, including alternative life history strategies and the presence of size-dependent natural mortality. The model developed in this paper may be a useful tool to estimate the SPR for data-limited stock where it is not possible to apply more conventional methods.
Details
- Title
- A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: application to spawning potential ratios for data-poor stocks
- Authors/Creators
- A.R. Hordyk (Author/Creator)K. Ono (Author/Creator)J.D. Prince (Author/Creator)C.J. Walters (Author/Creator)
- Publication Details
- Canadian Journal of Fisheries and Aquatic Sciences, Vol.73(12), pp.1787-1799
- Publisher
- National Research Council of Canada
- Identifiers
- 991005543835807891
- Copyright
- © 2016, Canadian Science Publishing.
- Murdoch Affiliation
- School of Veterinary and Life Sciences
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
821 File views/ downloads
173 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.2 Marine Biology
- 3.2.92 Fisheries Ecology
- Web Of Science research areas
- Fisheries
- Marine & Freshwater Biology
- ESI research areas
- Plant & Animal Science