Proposed Abstract for the UseR Symposium Vienna May

Fisheries Research, 2013

Assessing the validity of a model is essential for its credibility especially when the model is used as decision making tool. Complex dynamic fishery models are recommended to investigate the functioning of fisheries and to assess the impact of management strategies, particularly spatial fishing regulations. However, their use is limited due to the difficulty and computational cost of parameterizing and gaining confidence, particularly for parameter rich models. These difficulties are compounded by uncertainty regarding parameter values, many of which are often taken from literature or estimated indirectly. Here we propose a methodology to improve confidence and understanding in the model, easily transferable to any complex model. The approach combines sensitivity analysis, scalability of parameters, optimization procedures, and model skill assessment in order to parameterize, validate and achieve the most plausible formulation of a model given the available knowledge while reducing the computational load. The methodology relies on five steps: (1) sensitivity analysis, (2) classification of parameters into a hierarchy according to their sensitivity and the nature of their uncertainty, (3) building of alternative formulations, (4) calibration and (5) skill evaluation. The approach is illustrated here by reviewing the parameterization of the ISIS-Fish model of the anchovy fishery in the Bay of Biscay. By using this approach, it is possible to make a thorough assessment of lacking information (e.g. accessibility to fishing and adult mortality) and to identify the strengths and weaknesses of the model in the context of different hypotheses. When applied to the ISIS-Fish model, the results suggest higher egg and adult mortality than formerly estimated, as well as new estimates for the migration towards spawning areas. They show the reliability of the model in terms of correlations with observations and the need for further efforts to model purse seiner catches. The methodology proved to be a cost-efficient tool for objectively assessing applied model validity in cases where parameter values are a mix of literature, expert opinion and calibration.

FLBEIA is an R library which provides a flexible and generic tool to conduct Bio-Economic Impact Assessment of fisheries management strategies. It has been built under a Management Strategy Evaluation framework which consist in simulating the fisheries system together with the management process. The fisheries system is simulated in the so called Operating Model which describes the true dynamics of the system and the management process is simulated in the Management Procedure which generates an observed system from the reality. The management advice is generated based on the observed system, instead of on the real one. The model is multistock, multifleet, seasonal and uncertainty is introduced by means of montecarlo simulation. In addition, it has a covariables component that allows introducing variables of interest not present in biological and fleets components. For example, it could be used to introduce relevant ecosystem components in a simple way. FLBEIA represents a middle way between complicated whole ecosystem models and simple bioeconomic fisheries models. The fishery system and management process are divided in low level interlinked processes, providing the library one or several models to describe each of the process. The user chooses the models to be used in each specific model implementation and if the functions provided within FLBEIA do not fulfill the requirements for some of the components, the user can code the functions that adequately describe the dynamics of those processes and use the existing ones for the rest. In this paper we present FLBEIA library describing how fishery system and management process are modeled, the low level process that build up the model and the available functions to model them, the necessary data and its form to condition the model and its principal advantages and limitations. Finally, we briefly present its application to 3 different case studies, Seabream artisanal fisheries in the Gulf of Cádiz, French deep-watter mixed-fisheries and Basque inshore sequential-fisheries.

RePEc: Research Papers in Economics, 1989

Uncertainty about the nature and significance of nonlinearities and the manner in which dynamics affect future realizations makes model specification the most difficult aspect of modeling dynamic systems. By interpreting several popular fishery models as subcases of a nesting dynamic Taylor series approximation, we isolate the specification differences between these models in a way that accounts for commonalities. On the argument that the differences due to alternative nonlinear forms are likely to be small compared to more mundane considerations such as delay difference and general dynamic lag specification, we propose an alternative model that uses the terms from the first order approximation common to all models combined with a data-based determination of the appropriate lags using the methods of state space time series analysis. Finally, the success of the alternative models is judged in an application to Pacific halibut data.

2021

In a dynamic, environment, the decision makers make use of many different resources where two or more can act as substitutes. At each decision moment in time, the market prices will be constant, and the relative prices of accessible resources will determine the economic rationale of the process. Ignoring or downplaying the effects of substitutability of resources in dynamic economic processes may lead to mismanagement of the fish stocks and result in serious economic consequences for the respective fishing industries. For nearly five decades’ fishery managers and policy makers have used bio-economic models and methods as foundation for their management schemes. These models and methods are for the most based on the deductive methodology of economics where central assumptions are the metaphors of “equilibrium“ and “bio-economic equilibrium“. Models based on equilibrium theories are usually deterministic where dynamics of the markets are a meager part of the problem. Less attenti...

2004

A simulation framework is used to investigate the performance of a fishery with respect to biological and economic sustainability under three alternative management scenarios. The fishery operates in a two-species system that mimics the exploitation of sole and plaice in the North Sea. At present a large proportion of plaice is caught as bycatch of the main beam trawl fleet targeting sole, and yet current management of the two stocks assumes no interaction in their exploitation. Within the simulation, the annual stock assessment (using XSA) and projections are still carried out in a single species manner, but the management choices and decisions are executed interactively. The contrasting management scenarios are: ICES CM 2004/FF:11

2010

Understanding the complexities of ecosystems is difficult enough, but when the human dimension is added to the inherent uncertainty and risk in fisheries management, the actual versus expected results move from the counter-intuitive to the paradoxical. Without an adequate understanding of the interrelationships between ecosystem components, including the human dimension, scientific management becomes intuition-based management, which is often counterproductive to achieving desired goals and objectives. A computer simulation model is developed for multiple species, resource areas, stocks, and cohorts. To explicitly incorporate ecosystem effects predator-prey and competitor relationships are constructed to interpret interactions between different species of fish. Fishing fleet dynamics are captured by modeling multiple vessel classes based on specifications for catchability and concentration profiles. Additional uncertainty is included through the effects of market supply and demand on price for different discount rates. Economic impacts are also estimated for each point in time as biological and market conditions change. This simulation model results are then compared to determine if the management objective of maximization of net benefits subject to the fish stock conservation goal are achievable in an ecosystem context while also considering impacts on jobs, income, and sales. Insights from this simulation of an ecosystem should provide information on the needed research required for the ecosystem approach to fisheries management to be successful in achieving its multiple objectives.

Aquatic Living Resources, 2010

Simulation of fisheries systems is a widely used approach that integrates monitoring and assessment tools. We applied the ALADYM (age-length based dynamic model) simulation model to three different studies aimed at investigating correlations between pressure and population metrics, exploring the viability of different mortality levels in long-term scenarios and predicting the effects of combined management measures. Uncertainty was incorporated into the simulations following the Monte Carlo paradigm. Three stocks were used for these exercises: red mullet in the central-southern Tyrrhenian Sea and European hake in both the Bay of Biscay and the Aegean Sea. The analysis of the relationships between total mortality and indicators highlighted significant pairwise negative correlations for red mullet. These signals of decline were supported by the spawning potential ratio indicator (mean exploited to mean unexploited spawning-stock biomass ESSB/USSB), which was low compared to target levels. It only remained within safe bounds (> 0.2; probability: 0.90-0.95) at total mortality levels lower than 1.6. The simulation results for European hake in the Bay of Biscay showed that a sustainable exploitation rate might range from 0.87 to 1.04. The benefits of combined management measures were demonstrated for European hake in the Aegean Sea, and with a further dataset on the Eastern cod stock in the Baltic Sea.

We have constructed an experiment to test to what extent di erent types of model improves the management o f a t wo-species shery. Thus we are not trying to determine what models or tools are the best from a theoretical point o f v i e w. Rather we i n vestigate the usefulnes of the models when applied in a practical management task. In particular we compare a simplistic stoachastic optimization model with a more complex one species model of the shery. We nd that bothmodels lead to better management, and when applied together they strengthen each other. That is the models are complementary rather than the competing substitues that theoretical discussions might i m p l y. At the initial stage of this project, the project group consisted of Asbj rn Aaheim, Magnus Hatlebakk and the authors. The authors are grateful for the discussion with the other project participants at this stage. We also had very useful discussion with the marine researcher Sigurd Tjelmeland about the design of the virtual reality. Thanks also to Solfrid Malo for assistance during some of the experiments. dne Cappelen, ystein Olsen and Karine Nyborg has given helpful comments on an earlier draft of the paper. The usual disquali er applies.

Reviews in Fish Biology and Fisheries, 2012

As tools within ecosystem-based fisheries management (EBFM), a wide range of Ecosystem Models (EMs) have been designed to represent ecosystem complexity, but it is not always clear how the outputs of these models can be applied. We address this debate in a literature review to illustrate how a better understanding of ecosystem modeling within the EBFM framework could facilitate the use of EMs in the decision-making process. We classify EMs according to their complexity, and qualitatively evaluate their level of success with regard to five general goals of EBFM. In principle, no single EM is found to successfully accomplish all the EBFM goals. Therefore, we suggest that the way in which ecosystem modeling can effectively contribute to EBFM is through a structured modeling process, which should be pursued according to the context of each specific area. Within this planning strategy a range of Ems should be considered, from rather simple ones with few parameters, whose outputs are scientifically robust but possibly of limited use within the EBFM, to those which include a large number of ecosystem elements yet at the expense of increased uncertainty. If multiple EMs, despite their different assumptions, leads to consistent and converging results then robust management decisions will be supported. The present paper appears particularly useful to anyone confronted with the selection of modeling tools for the implementation of fisheries management strategies considering the particular situation of the fishery.

2003

Many fisheries in developed countries are seriously over-harvested in spite of the efforts of dedicated scientists and management agencies and a concerned public. Many of these fisheries are well studied – lack of data is not the primary problem. Complexity with the fisheries and management systems conspires to defeat seemingly obvious solutions. System dynamics modeling may help provide solutions via its transparent framework for describing and analyzing the complex decision making systems. In fisheries, such system descriptions often become enmeshed in the many aspects of fish population dynamics and fail to adequately describe decision making activities of fishers, management agencies, and politicians. This paper is an attempt at providing a simple, but acceptably complex, population model meshed with both fishery activities and management decision making. The model is based on the well-known Schaefer biomass dynamic model but allows for delayed entry of young into the fish stock...