Technical University of Denmark
Browse

SEAwise Report on improved predictive models of natural mortality under different distributional scenarios and incorporating experimental results

Download (10.2 MB)
online resource
posted on 2024-06-21, 08:47 authored by Stefan NeuenfeldtStefan Neuenfeldt, Anna RindorfAnna Rindorf, Janneke Ransijn, Sophie Smout, Simon Northridge, Bernhard Kühn, Marc Taylor, Alexander Kempf, Robert Thorpe, Michael Spence, Morten VintherMorten Vinther, Ole HenriksenOle Henriksen, Mikaela Potier, Morgane Travers-Trolet, Paul Gatti, Nis Sand JacobsenNis Sand Jacobsen

The SEAwise project works to deliver a fully operational tool that will allow fishers, managers, and policy makers to easily apply Ecosystem Based Fisheries Management. This SEAwise report describes natural mortality models for the North Sea, the Celtic Sea, Bay of Biscay and Western waters. The models differ in type and structure, and together reveal both temporal differences and structural uncertainties linked to the assumptions made when building the model.

SMS, the model applied for the North Sea, is a stock assessment model including biological interaction estimated from a parameterised size-dependent food selection function. The model is formulated and fitted to observations of total catches, survey data and stomach contents for the North Sea. The predator and prey stocks available include as predators and prey (cod, whiting, haddock), prey only (herring, sprat, northern and southern sandeel, Norway pout), predator only (saithe, mackerel), no predator–prey interactions (sole and plaice) and ‘external predators’ (eight species of seabirds, starry ray, grey gurnard, North Sea horse-mackerel, western horse-mackerel, hake, grey seals and harbour porpoise). With respect to methodology, fish diets were recompiled with the R-package FishStomachs (with similar results as in the 2020 key run). These estimated uncertainties are now used as input to SMS, where such uncertainties were previously estimated within SMS.

The predictive ability of multispecies models was only sometimes enhanced compared to single species assessments. However, the choice of whether to use variable or constant natural mortality greatly impacted the estimated stock recruitment relationship and reference points for stock biomass based on this. Measures of the variability in the estimated natural mortality between years was estimated to provide guidance for Management strategy evaluations in single species environments. The estimation was compared between different areas for the same species, providing a general guidance on the likely level of variability in natural mortality for different species.
SEAwise updated the FLBEIA model to use output of the newest SMS key run from autumn 2023. A simplified one-fleet-only FLBEIA model, exploiting the gadoid stocks cod, saithe, whiting and haddock was used to model various intra- and interspecies relationships (cannibalism and predation), demonstrating the effects of assumed relationships between natural mortality and predator abundance.

An agent-based spatially explicit variant of the MIZER equations was used to understand some of the mechanisms and processes driving natural mortality. Features developed include spatial partitioning based on information from ICES DATRAS, explicit incorporation of seals as a dynamically interacting stock, incorporation of temperature-dependent recruitment for plaice, saithe, cod, herring, and whiting and consideration of climate scenarios out to 2100 for i) a continuation of past conditions, ii) RCP4.5, iii) RCP8.5.

The Ecopath with Ecosim (EwE) ecosystem model for the Celtic Sea area includes 53 functional groups exchanging matter and energy within the system: 2 seabirds’ groups, 2 cetaceans and seals’ groups, 31 fish groups (22 demersal and 9 pelagic), 2 cephalopod groups, 9 benthos groups, 4 zooplankton groups, 2 phytoplankton groups and a detritus group. Among all groups, 9 commercial groups qualified as “multi-stanza”, are separated in several life stages in order to consider ontogenetic changes. The parameterization of multi-stanza groups in Ecopath was made in consistency with the stock assessment information.

OSMOSE in the Bay of Biscay included 18 species or groups of species are explicitly modelled based on their abundance or commercial value. Environmental conditions considered in the model were temperature, oxygen concentration as well as benthic and pelagic primary and secondary producers, used as forcing prey field for fish. All the modelling choices (set of explicit species, spatial extent and resolution, forcing variables) are made, and all information required for parametrizing the model have been collected. In parallel, for data-rich species SMALK (sex – maturity – age – length – key) data were analyzed to derive required parameters for the bioenergetics module.

A DEB-IBM model coupled to the environment was developed to explore the future dynamics and productivity of the two main seabass stocks of the North East Atlantic by the end of the century. The approach combines a bioenergetics module, simulating individual life cycle, following the Dynamic Energy Budget theory, and a population module, based on an Individual Based Model (IBM) to up-scale individual bioenergetics at the population level. The model is not spatially explicit (0D) and applications for the European seabass stocks VIIIab and IVb–c & VIIa,d–h are developed independently assuming no connectivity. The main assumption of this modelling exercise is that seabass recruitment in both areas is shaped by temperature through growth and subsequently size selective survival. The DEB module predicts the whole life cycle of individuals, and thus growth, as a response to seawater temperature, with higher temperatures leading to faster growth and thus enhanced survival.

Funding

Shaping ecosystem based fisheries management

European Commission

Find out more...

History

ORCID for corresponding depositor

Usage metrics

    DTU Aqua

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC