Marine biodiversity and ecosystem services

Télécharger le fichier original (Mémoire de fin d’études)

Risk management

It has also been recognized that wise use of fish resources over time should incorporate the in-herent risk and uncertainty of fishery systems (Garcia, 1996; Hilborn and Peterman, 1996). Hilborn and Peterman (1996) identified a set of sources of uncertainty associated with stock assessment and management, including uncertainty in resource abundance (for instance, uncertainty in re-cruitment), in model structure, in model parameters, in the behaviour of resource users, in future environmental conditions, and in future economic, political and social conditions. Implementation of risk management is therefore crucial to reduce the risk of collapse of fishing communities. As Hilborn et al. (2001) said: “if we are to succeed at management – if we are to maintain stable fishing communities – we have to begin to manage risk”. One way of reducing risk is by reducing fishing pressure in order to have larger average stock sizes, which would serve as a buffer for nat-ural fluctuations (Hilborn et al., 2001). To deal with the variety of uncertainties, the Precautionary Approach, which involves the application of prudent foresight by taking account of the uncertain-ties in fisheries systems and the need to take action with incomplete knowledge (Garcia, 1996), has been suggested. The approach is used by the International Council for the Exploration of the Sea (ICES) for fixing quotas.
The use of reference points is also recommended (Caddy and Mahon, 1995) as a guide for fish-eries management. A reference point indicates a particular state of a fishery indicator corresponding to a situation considered as desirable (‘target reference point’), or undesirable and requiring imme-diate action (‘limit reference point’ and ‘threshold reference point’) (Caddy and Mahon, 1995; Gar-cia, 1996). Reference points may be general (applicable to many stocks) or stock-specific. Defining reference points is not an easy task. Annex II of the UN (1995) suggests that the fishing mortality rate which generates MSY should be regarded as a standard for limit reference points. However, in implementing the Precautionary Approach, the ICES defined limit reference points in terms of the maximum fishing mortality and minimum biomass thresholds associated with stock collapse rather than with MSY. Furthermore the ICES has been using precautionary reference points in addition to the limit reference points to frame its advice to the European Commission, the Northeast Atlantic Fisheries Commission, and the North Atlantic Salmon Conservation, to cite some. Indicators for fisheries performance are an integral part of fisheries management plans providing dynamic signs of the relative position of such indicators with respect to predetermined reference points (Seijo and Caddy, 2000). The figure 1.5 illustrates how the precautionary approach can be implemented, through Harvest Control Rules (HCR), by specifying when a rebuilding plan is mandatory in terms of precautionary and limit reference points for spawning biomass and fishing mortality rate. The HCR describes how the harvest is intended to be controlled by management in relation to the state of some indicator of stock status.

Multiple management objectives

In any given context, fisheries management is clearly characterised by multiple objectives, some of which may be conflicting (Crutchfield, 1973; Charles, 1989). While management objectives are often not clearly stated, Charles (1989) summarized some of the most commonly declared objectives of fisheries management as: (i) resource conservation, (ii) maintaining the viability of fishing communities, (iii) food production, (iv) generation of economic wealth, (v) generation of reasonable income for fishers, and (vi) maintaining employment for fishers. These objectives can be grouped according to whether they reflect ecological, economic or social objectives.
An important issue is thus to determine management procedures that give acceptable results with respect to the sustainability objectives while being robust to uncertainties (De Lara and Mar-tinet, 2009). Management Strategy Evaluation (MSE) provides a framework for comparing differ-ent fisheries management strategies with respect to conflicting objectives, and taking into account uncertainties (Sainsbury et al., 2000; Kell et al., 2007). However, even if MSE allows us to de-scribe trade-offs in management objectives and to characterize potential management procedures with respect to a set of performance statistics, due to the absence of an agreed ‘common measure’ or conflicting performance measures, the decision-makers are left with clearer perspectives but without tools to rank the various management procedures (De Lara and Doyen, 2008).
Therefore, even if the current approaches for sustainable management of fisheries have been and are still useful; there is a need for alternative approaches that can help to deal with multiple objectives under uncertainty.

Viability models

Viability theory, introduced by Aubin (1990), aims at identifying decision rules or controls for dynamical systems (in particular non-linear control problems), such that the systems are main-tained at each instant inside a given set of admissible states of diverse nature, called the viability constraints set. Although dynamic optimisation problems are usually formulated under constraints, the role played by the constraints poses difficult technical problems and is generally not tackled as a specific issue (De Lara and Doyen, 2008). Furthermore, the optimization procedure reduces the diversity of feasible forms of evolution by, in general, selecting a single trajectory (De Lara and Doyen, 2008). In contrast, viability analysis, instead of maximizing an objective function, focuses on the role of constraints and on characterizing the safe paths and decisions.

Non-linear dynamical system and viability constraints

Solving the viability problem relies on the consistency between a controlled dynamic and ac-ceptability constraints applying both to states and decisions of the system.

Stochastic co-viability

Risk and uncertainty of fishery systems constitute major issues in fisheries management, and acceptability constraints in a co-viability context have to be articulated with uncertainty in a prob-abilistic or stochastic sense. The probability of co-viability CVA of a fishery system, regarding control or decision u(t) and considering the multiple constraints defined in section 1.3.2, is ex-pressed by: CVA u(t0),…,u(T ) = P constraints (1.3.2) are satisfied for t = t0,…,T . (1.10)
The idea underlying stochastic viability is to require the respect of the constraints at a given confidence level. Therefore, of particular interest are the set of control or decision u(t) such that the probability of co-viability CVA is above a certain level as: CVA u(t0),…,u(T ) ≥ β. (1.11) With β some confidence level (typically 90%, 95% or 100%).
By compiling ecological and economic goals from stochastic simulation models, stochastic co-viability analysis (De Lara and Doyen, 2008; Baumgärtner and Quaas, 2009; Doyen and De Lara, 2010) can be used to address important issues of vulnerability, risk, safety and precaution, and to determine the ability of a particular resource system to achieve specified multiple sustainability ob-jectives with sufficiently high probability. In contrast to the MSE approach, stochastic co-viability analysis proposes a ‘sustainability metric’ to rank alternative management strategies through the co-viability probability. Indeed, as viable management requires all constraints (and hence objec-tives) to be satisfied, the approach is not based on arbitrary weights that may reflect priorities in the objectives. This approach therefore provides a useful tool to inform policy makers about the trade-offs involved in managing fisheries under multiple constraints in a stochastic environment. As depicted in box 3, the viability kernel plays a major mathematical role in the viability analysis.

Viability kernel.

A major mathematical tool to study the whole viability of the system is provided by the so-called viability kernel, denoted by Viab. It corresponds to the set of all initial conditions such that there exists at least one trajectory starting from the initial conditions that stays in the set of constraints A. Figure 1.6 gives a graphical representation of the viability kernel.
Figure 1.6: The state constraint set A defined by a set of ecological and economic viability constraints corresponds to the large blue set. It includes the smaller viability kernel Viab (in dark blue).
For decision makers, knowing the viability kernel has practical interest since it describes the states from which controls can be found that maintain the system in a desirable configuration until the horizon time T .
Only a few applied studies (Béné and Doyen, 2000; De Lara et al., 2007; Doyen et al., 2007; Martinet et al., 2007; De Lara and Martinet, 2009; Martinet et al., 2010; De Lara et al., 2011; Péreau et al., 2012) have made use of the co-viability approach to integrate economic and ecological ob-jectives for fisheries management. Among these studies, only Doyen et al. (2007) and De Lara and Martinet (2009) integrate uncertainty affecting biological dynamics. While these studies integrate several species in their analysis, the diversity among the fishery industry is not taken into account.

Thesis objective

There is a growing need for decision-support tools to assist in evaluating management poli-cies for the regulation of marine fisheries. These tools need to (i) integrate ecological and socio-economic drivers of changes in fisheries and ecosystems; (ii) include the complex dynamic dimen-sions of these drivers; (iii) deal with various sources of uncertainty and (iv) incorporate multiple, rather than single objectives. In this context, this thesis investigates the use of stochastic co-viability analysis as a decision-support tool to assist in evaluating management strategies for sustainable reg-ulation of mixed fisheries in different contexts. This analysis is developed for two case studies (the French Bay of Biscay mixed demersal fishery and the Australian Northern Prawn Fishery) each of which displays different features and thereby allowing the evaluation of the approach for different ecosystems. Consistent with the sustainability issue, the dynamic modelling integrates biologi-cal and economic components of the systems calibrated with ecological and economic data, and accounts for uncertainties and multiple objectives.
This thesis has four main objectives:
• Development of bio-economic models of the French Bay of Biscay (BoB) demersal mixed fishery and of the Australian Northern Prawn Fishery (NPF) which capture the key biolog-ical and economic processes governing these fisheries. The generality of their modelling is deliberately maintained, thereby allowing potential future extensions and their application to other fisheries and economic and management contexts.
• Comparison of fishing strategies in terms of viability and bio-economic risks for BoB and NPF fisheries.
• Identification of viable fishing strategies for both case studies.
• Identification of main drivers of ecological-economic viability regarding both fisheries.

Case studies

Two mixed fisheries are analysed in this thesis: the French Bay of Biscay (BoB) demersal mixed fishery and the Australian Northern Prawn Fishery (NPF). These two fisheries are relevant to in-vestigate the question of how to manage multi-species fisheries in a stochastic context taking into account multiple objectives regarding specifically the biological and economic sustainability of the fisheries. These two fisheries are multi-species, and both make use of multiple fishing strategies, with direct and indirect impacts on the ecosystems. Both fisheries use indeed non-selective trawl technology inducing important by-catches and discards. Furthermore they are of great commer-cial and industrial interest. Therefore their sustainable management is a major societal concern. While drawing on a common methodology, this thesis examines the two cases in a way that recog-nises their different histories, social and political contexts and captures differences in management strategies and objectives. A summary of the main features of both case studies is given in box 4.

French Bay of Biscay demersal fishery (BoB)

The Bay of Biscay demersal mixed fishery operates in divisions VIIIa and b of the ICES grid and includes French, Spanish and Belgian fishery fleets. This thesis focuses on the French fleets. The main gears used in these fisheries are trawl, gill-net and longline, and all induce variable levels of impacts on a wide range of species. Among the 200 species caught in the Bay of Biscay, three of the most important species in percentage of the total French national landing value include Nor-way lobster (Nephrops norvegicus, 6%), Hake (Merlucius merlucius, 7%) and Sole (Solea solea, 11%). The management of the Bay of Biscay demersal fisheries mainly relies on conservation measures: a Total Allowable Catch (TAC) revised each year, a minimum landing size (MLS), and a minimum trawl mesh size. In accordance with the framework of the Common Fishery Policy reform (EC, 2009), a multi-species management plan for the Bay of Biscay is to replace the former mono-specific management plan on Sole and Hake. The analysis implemented in this thesis will provide important evidence of management effectiveness that can inform the development of the new management plan.

Australian Northern Prawn Fishery (NPF)

The NPF, located off Australia’s northern coast and established in the late 1960s, is a multi-species trawl fishery based on several tropical prawn species, each with different biology. The main revenue of the fishery (95% of the total annual landed catch value) comes from an unpredictable nat-urally fluctuating resource, the white banana prawn (Penaeus merguiensis), and a more predictable resource, comprising two tiger prawn species (Peaneus semisulcatus and Penaeus esculentus). The fishery operates over two ‘seasons’ spanning the period April to November with a mid-season closure of variable length from June to August. The fishery effectively consists of two sub-fisheries that are (to a large degree) spatially and temporally separate. The ‘banana prawn sub-fishery’ is a single species fishery based on the white banana prawn, while the ‘tiger prawn sub-fishery’ is a mixed species fishery targeting grooved and brown tiger prawns, as well as blue endeavour prawns (Metapenaeus endeavouri) which are caught as by product. The NPF was the first fishery in Australia to adopt biomass at maximum economic yield (MEY) as its management target (Woodhams et al., 2011). The fishery is primarily managed through input controls, mainly in the form of restricted quantities of tradeable units of effort (based on the length of trawl net headrope) and seasonal closures. Seasonal closures are in place to protect small prawns (closure from December to March), as well as spawning individuals (June to August closure). In the future, the fishery will be moving to an individual transferable quota (ITQ) management regime. This fishery is one of Australia’s most valuable federally managed commercial fisheries, and since its establishment in the late 1960s has regularly returned positive profit (Rose and Kompas, 2004). However, in recent years the fishery has experienced a decline in value as a result of the increased supply of aquaculture-farmed prawns to both domestic and international markets, strong Australian currency and increasing fuel prices (Punt et al., 2011). The bio-economic model and co-viability analysis developed for this fishery allows for the exploration of alternative management strategies for different economic scenarios.

The bio-economic model

We consider the two species, Nephrops and Hake, exploited by several fleets using different ‘metiers’ (trawlers targeting Nephrops or targeting demersal fish, gill-netters targeting Hake, long-liners, etc.) based on information provided in ICES (2009). To capture this complexity but keep it manageable, we group these fleets into three generic sets of fleets operating in the Bay of Biscay: one constituted of the Nephrops trawlers, one constituted of gill-netters targeting Hake grouped together under a general Hake fleet category and a third ‘fleet’ termed ‘others’ constituted by all other vessels impacting Hake or Nephrops. The economic analysis focuses on the two first fleets.

A multi-species, multi-fleet and age-structured dynamics

We develop an age-structured population model derived from the standard fish stock assessment approach (Quinn and Deriso, 1999). Time t ∈ N is measured in years. Let A = 9 ∈ N∗ denote a maximum age limit, and a ∈ {1,…, A} an age class index, all expressed in years. The state variables N s,a(t) ∈ R2+A are the abundances of species s = 1,2 at age3 a, where index s = 1 refers to Hake and s = 2 refers to Nephrops. Similarly the index f = 1 is used to denote the fleet targeting Hake while index f = 2 denotes the fleets targeting Nephrops. The third fleet f = 3 encompasses all other vessels involved. For age a = 1,…, A − 1 and each species s = 1,2, the dynamics of the two species are assumed to follow the discrete equation system: Ns,a+1(t + 1) = Ns,a(t) exp −Ms,a − 3 u f (t)Fs,a,f , (2.1) f =1 where
• Ms,a is the natural mortality rate of individuals of species s at age a;
• Fs,a,f is the current (here 2008) fishing mortality rate of species s at age a due to fleet f , and
• the controls u f (t) are multipliers of the current fishing mortality Fs,a,f for fleets f . As we assume that there is no control on the third fleet ‘others’, we fix its fishing mortality by writing u3(t) = 1.
More globally, the vector u = (1,1,1) represents the fishing baseline for year t0 = 2008.
The parameter values used in the analysis are detailed in appendix (table 2.1 for Hake and table 2.2 for Nephrops respectively). They are derived from ICES databases4, working group WGHMM (ICES, 2009) and the Ifremer databases5. Note that the mortality of Hake (s = 1) due to the Nephrops fleet’s ( f = 2) by-catch is included in the dynamics through the positive parameters F1,a,2.
Recruitment involves complex biological and environmental processes that vary over time. The recruits Ns,1(t +1) for each species are therefore supposed to be uncertain functions of the spawning stock biomass.

Table des matières

1 General introduction 
1.1 General context
1.1.1 Marine biodiversity and ecosystem services
1.1.2 Marine biodiversity under pressure
1.1.3 Open access resource exploitation issues
1.1.4 Marine resource and managements
1.2 Bio-economic models
1.2.1 Schaefer model
1.2.2 Maximum Sustainable Yield
1.2.3 Maximum Economic Yield
1.2.4 Risk management
1.2.5 Multiple management objectives
1.3 Viability models
1.3.1 Non-linear dynamical system and viability constraints
1.3.2 Co-viability analysis
1.3.3 Stochastic co-viability
1.4 Thesis objective
1.5 Case studies
1.5.1 French Bay of Biscay demersal fishery (BoB)
1.5.2 Australian Northern Prawn Fishery (NPF)
1.6 Structure of the thesis
1.7 Context of the thesis
2 A stochastic viability approach to ecosystem-based fisheries management 
2.1 Introduction
2.2 The bio-economic model
2.2.1 A multi-species, multi-fleet and age-structured dynamics
2.2.2 Catches and gross incomes
2.2.3 Profits
2.3 A viability diagnosis
2.4 Results
2.4.1 Population viability analysis
2.4.2 Socio-economic viability analysis
2.4.3 Co-viability analysis
2.4.4 Status quo strategy: neither viable nor sustainable
2.4.5 Optimizing scenario: a high bio-socio-economic risk
2.4.6 Biological Conservation scenario: viable but not sustainable
2.4.7 Economic scenario: sustainable and almost viable
2.4.8 Co-viability scenario: A win-win situation
2.5 Discussion and perspectives
2.6 Appendix
3 Managing mixed fisheries for bio-economic viability 
3.1 Introduction
3.2 Material and methods
3.2.1 The Bay of Biscay case study
3.2.2 The bio-economic model
3.2.3 Fuel scenarios
3.2.4 The co-viability diagnostic
3.2.5 Management strategies
3.3 Results
3.3.1 Status quo strategy: not socio-economically viable
3.3.2 npv strategy : high total NPV but not socio-economically viable
3.3.3 cva strategy: biologically and socio-economically viable
3.3.4 Sole strategy: not socio-economically viable
3.3.5 Nephrops strategy: not socio-economically viable
3.4 Discussion
3.4.1 Decision support for the Bay of Biscay mixed fishery
3.4.2 CVA as step towards integrated management for mixed fisheries
3.4.3 Perspectives
3.5 Appendix
4 Risk versus economic performance in a mixed fishery: the case of the Northern Prawn Fishery in Australia 
4.1 Introduction
4.2 Material and Methods
4.2.1 A multi-species, stochastic and dynamic model
4.2.2 Fishing mortality and catch
4.2.3 Economic component
4.2.4 Parameter estimation
4.2.5 Effort allocation strategy Tadapt
4.2.6 Other effort allocation strategies
4.2.7 Fishing capacity management strategies
4.2.8 Economic scenarios
4.3 Results
4.3.1 Sensitivity of biological and economic performance indicators to economic scenarios
4.3.2 Mean-variance analyses
4.4 Discussion
4.4.1 The interest of an integrated bio-economic model
4.4.2 Trade-off between mean annual levels of profit and their variability
4.4.3 Expected effects of economic scenarios on the biological and economic performance of the fishery, and possible adaptation options
4.4.4 Perspectives
4.5 Appendix A. Dynamics details of the bio-economic model
4.5.1 Tiger prawn abundance dynamics
4.5.2 Recruitment estimation
4.5.3 CPUE
4.6 Appendix B. Bio-economic parameter values
4.7 Appendix C. Statistical analyses
4.8 Appendix D. Algorithm of weekly tiger effort allocation
4.9 Appendix E. To go further: Mean-variance analyses of S
4.9.1 Performance of various effort allocation strategies
4.9.2 Performance of various fishing capacity strategies
4.9.3 Discussion around mean-variance analyses of S
5 Co-viability analysis in the Northern Prawn Fishery 
5.1 Introduction
5.2 Material and Methods
5.2.1 A multi-species, multi fishing strategies, stochastic and dynamic bio-economic model
5.2.2 Biological component
5.2.3 Effort allocation strategies
5.2.4 Fishing capacity strategies
5.2.5 Stochastic co-viability analysis
5.3 Results
5.3.1 Management strategy performances
5.3.2 Exploratory approach for the best strategies
5.3.3 Which number of vessels for a viable management?
5.4 Discussion
5.4.1 Assessment of trade-offs associated with managing the fishery
5.4.2 CVA as a step towards integrated management of mixed fisheries
5.4.3 Perspectives
5.5 Appendix A. Bio-economic parameter values
5.6 Appendix B. Statistics
6 General discussion 
6.1 Important common features of the bio-economic models
6.1.1 Complexity of the integrated models
6.1.2 Uncertainties
6.1.3 The role of data in integrated models
6.2 Viability analyses and bio-economic risk
6.2.1 Multiple objectives and potential conflicts
6.2.2 Sustainability and bio-economic risk
6.3 Fishing strategies and co-viability drivers
6.3.1 Status quo: socio-economic viability at stake
6.3.2 Mono-specific and specialized strategies: bio-economic risks
6.3.3 CVA strategies: win-win strategies exist
6.3.4 Cost of sustainability
6.4 Perspectives
6.4.1 Implications of other species and repartition in stocks
6.4.2 Toward Ecosystem-Based Fishery Management
6.4.3 Climate change and its implications for NPF and BoB modelling
6.4.4 Towards output control variables
6.4.5 Setting maximum admissible variabilities
6.4.6 Capturing fleet dynamics
List of Acronyms
List of Variables
Bibliography 
Appendix A Genetic algorithm for optimisation
Appendix B Calibration
Appendix C Trophic interactions and co-viability analysis
Appendix D Résumé long
D.1 Introduction générale
D.1.1 Contexte général
D.1.2 Modèles bio-économiques
D.1.3 Approche de viabilité
D.1.4 Objectifs de la thèse
D.1.5 Cas d’études
D.2 Chapitre 2
D.3 Chapitre 3
D.4 Chapitre 4
D.5 Chapitre 5
D.6 Discussion
D.6.1 Points communs clés des modèles bio-économiques
D.6.2 Analyses de viabilité et risques bio-économiques
D.6.3 Stratégies de pêche et facteurs de co-viabilité
D.6.4 Perspectives
List of Figures
List of Tables

Télécharger le rapport complet