Sustainability in a context of uncertainties
In its meaning of continuance and duty regarding future generations, sustainabil-ity is deeply rooted in the future. Yet, its establishment as a guiding principle for human actions makes it a matter for present generations. In this regard, Baumgärt-ner and Quaas (2009) suggest to view sustainability as an attribute of present actions rather than one of future development. However, present decisions for sustainability are thwarted by what Faber et al. (1992) would call “surprises”.
Surprises can relate to the future evolution of the system, entity or process at stake. They can be the consequence of the present generation’s limited understanding of the dynamics involved and unpredictability of certain events. In the typology proposed by Faber et al. (1992), the former would fall under the “risk” (outcomes and associated probabilities are known) or “uncertainty” (outcomes are known without their associ-ated probabilities) categories, whereas the latter would be referred to as “ignorance”. Political declarations on sustainability often refer to the precautionary principle, the application of which is legitimized by the scientific uncertainty regarding future events (González-Laxe, 2005). Defined in the Rio Declaration, the principle states that « where there are threats of serious or irreversible damage, lack of full scientific certainty shall not be used as a reason for postponing cost-eﬀective measures to prevent environmental degradation » (U.N., 1992). Sustainable management is therefore one that must account for uncertainties regarding the consequences of present actions.
Relevant to the question of sustainability are also unknowns pertaining to the needs and preferences of future generations. In this regard, one can see in strong sustainability the recognition of our limited ability to speak for coming generations, justifying that we leave them with the ability to decide for themselves (Howarth, 1995; Baumgärtner and Quaas, 2009).
Viability theory to address the sustainability of renew-able resource extraction
Mathematically formalized by Jean-Pierre Aubin in the early 1990’s, viability the-ory has been recognized by many scholars as a relevant framework to address the question of sustainability in the exploitation of renewable resources (see for example a recent review carried out by Oubraham and Zaccour (2018)). Viability theory is a field of mathematics interested in the evolution of (controlled) dynamic systems whose state (and control) variables are subject to a set of constraints.
Solving a viability problem consists in the identification of trajectories X(.) and as-sociated controls (or decisions) U(.) that meet the following dynamics and constraints:
where A(t) is the set of acceptable states of the system that can be time-dependent, B(t, X(t)) the set of admissible controls which can be time- and state-dependent, T the time period over which the system is studied, and f(t, X(t), U(t)) the controlled dynamics function.
Viability theory introduces several mathematical objects of interest, among which the viability kernel (i.e. the ensemble of viable states, (Aubin, 1991)) or for a system out of its viability kernel, the notion of time of crisis (i.e. the time required for a system in crisis to reach its viability kernel (Doyen and Saint-Pierre, 1997)).
When the set of acceptable states A(.) reflects constraints pertaining to the con-tinuation of a system’s critical funds, functions or services, viability theory addresses the continuance aspect of sustainability. In addition to being strongly related to the idea of continuance, viability theory also deals with some of the ethical foundations embedded in the modern concept of sustainability. As highlighted by Baumgärtner and Quaas (2009), imposing constraints that aim at maintaining both natural and human forms of funds and services bears strong similarities with strong sustainability. Beyond justice between humans and nature, the framework can also incorporate objectives of intra-generational justice by ensuring that funds or services associated to the needs of diﬀerent groups of people are maintained over time. Finally, when acceptability constraints A(.) are the same at all times, viable paths are by nature meeting inter-generational equity requirements (Martinet and Doyen, 2007).
Originally developed for deterministic systems, the viability framework was ex-tended to formally address stochastic contexts by De Lara and Doyen (2008), thus enabling to address the uncertain nature of systems, entities or processes at stake.
Concomitant to the progressive aﬃrmation of sustainability as a major political engagement, the need to transition towards a more integrated approach to fisheries management, able to address the multiple facets of sustainability has made its way in the international political arena. The Ecosystem Approach to Fisheries (EAF) -also re-ferred to as Ecosystem-Based Fisheries Management (EBFM)- was introduced in 2001 during the Reykjavik Conference on Responsible Fisheries in the Marine Ecosystem as the framework to operationalize sustainability in the management of fisheries (Garcia and Cochrane, 2005). Recognized by many jurisdictions as the new standard to man-age fisheries (EU, 2013; NOAA, 2016; DFO, 2018; DAFF, 2018b) , its purpose was to « plan, develop and manage fisheries in a manner that addresses the multiplicity of
societal needs and desires, without jeopardizing the options for future generations to benefit from a full range of goods and services provided by marine ecosystems ». Pri-marily focusing on ensuring ecologically sustainable fisheries (Stephenson et al., 2017), fisheries management frameworks are increasingly transitioning towards an explicit in-tegration of the four pillars of sustainability, namely ecological, economic, social and institutional (Benson and Stephenson, 2018; Hobday et al., 2018; Stephenson et al., 2018, 2019; Alexander et al., 2019; Foley et al., 2020).
EAF also aimed to « balance diverse societal objectives, by taking account of the knowledge and uncertainties about biotic, abiotic and human components of ecosystems and their interactions and applying an integrated approach to fisheries within ecologi-cally meaningful boundaries » (FAO, 2003). The latter reference to knowledge and un-certainties highlights the crucial role that science has to play in the operationalization of EAF, further stressed by Garcia and Cochrane (2005). Smith et al. (2007) note that science can be involved in three steps of the fisheries management process, namely mon-itoring, assessment and decision-making. In subsequent developments, greater attention will be given to the two latter aspects as the areas covered in the present thesis. The assessment phase involves a large spectrum of methods, from quantitative models or empirical indicators assessing the status of fish stocks, to ecosystem indicators reflect-ing on ecosystem state or economic indicators evaluating the performance of fishing fleets. Scientific support to fisheries-related decision-making typically consists in the ex-ante evaluation of management options (e.g. Management Strategy Evaluation 2) and the development of methods that can help structure and solve decision problems (e.g. Multiple Criteria Decision Analysis 3).
Development of integrated models to operationalize the Ecosystem Approach to Fisheries
Biological single-species approaches have traditionally dominated the management of fisheries, with management objectives specified as species-specific reference points, stocks assessed individually, and, potentially, management strategies evaluated using single-species Management Strategy Evaluation frameworks (Punt, 2017). Meeting the ambitions of EAF yet requires the development of approaches that capture the ecolog-ical, economic and social complexities of the systems at stake, and assess the impacts of management decisions in these three dimensions. Integrated modelling has therefore become an active area of research to support decision-making in an EAF perspective.
2. Management Strategy Evaluation (MSE) refers to the simulation-based evaluation of manage-ment strategies to highlight the trade-oﬀs associated to a set of alternatives and assess the consequence of uncertainty on the achievement of management objectives (Punt et al., 2016a).
3. Multiple Criteria Decision Analysis (MCDA) is a field of operations research that aims at solving decision-making problems involving multiple (and usually conflicting) criteria.
Integrated models such as the ones identified by Nielsen et al. (2018) or Melbourne-Thomas et al. (2017) enable the representation of complex systems by accounting for interactions at various scales and across several dimensions.
To support tactical decision-making, the choice has often been to tailor the model to the question asked by only representing the relevant components and processes. It is for instance the case of Models of Intermediate Complexity for Ecosystem assessment (MICE) (Plagányi et al., 2014) which opt for a simplified representation of the ecological realm, hence standing halfway between single-species and whole-of-ecosystem models. Human dynamics in such models have generally remained fairly minimal. In the same vein, bio-economic simulation models tend to focus on the representation of socio-economic dynamics and that of harvested stocks, often to the expense of ecological complexity.
Eco-viability modelling for holistic sustainability assess- ments of fisheries
Building on fisheries bio-economic models, applications of eco-viability modelling to fishery systems have outlined the potential of the approach to operationalize EAF (Doyen et al., 2017). As a framework assessing the sustainability of complex systems subject to uncertainties, eco-viability indeed addresses the main principles of EAF. Whereas early-age viability models were mostly stylized applications to allow for an-alytical solutions, more recent applications involving simulation models –generally of intermediate complexity as described in Section 1.2.2- have enabled greater realism in the representation of fisheries systems and accounting of uncertainties in the modelled processes.
As noted by Oubraham and Zaccour (2018), nearly half of the applications of via-bility theory to the management of renewable resources have been on fisheries study-cases. These applications have considered a variety of sustainability or acceptability constraints. Those pertaining to the ecological viability of the studied systems have mostly been lower bounds on stock abundance, but some authors have also considered minimum thresholds of biodiversity indices (Cissé et al., 2013, 2015) or maximum catch levels for TEP (Threatened, Endangered or Protected) species (Gourguet et al., 2016). Economic viability has generally been interpreted as the generation of positive profits (De Lara and Martinet, 2009; Gourguet et al., 2016), with sometimes consideration of minimum crew remuneration (Maynou, 2019). Finally, the social dimension of EAF has been addressed by constraints pertaining to matters of food security (Eisenack et al., 2006; Hardy et al., 2013; Cissé et al., 2013, 2015), employment (Péreau et al., 2012; Gourguet et al., 2013), recreational catch (Thébaud et al., 2014) or equality in access to the resource (Curtin and Martinet, 2013).
When focusing on the identification of viable management levers (i.e. controls in the viability formalism), viability modelling becomes a useful tool to support decision-making. The main management lever considered in viability studies of fisheries has been fishing eﬀort (Oubraham and Zaccour, 2018), hence providing operational advice for fisheries under eﬀort regulation. Although a large fraction of the world’s fisheries, particularly in Europe, Oceania and North-America, are managed using output con-trols, fewer studies have considered the outputs as control variables (a few exceptions lie in the works of Eisenack et al. (2006); Péreau et al. (2012); Curtin and Martinet (2013) using catch levels or the allocation of catch quota as control variables), thereby underlining a current lack of eco-viability models to advise decision-making in fisheries under output controls.
Managing mixed fisheries
Mixed fisheries refer to those where the species caught are connected in various ways. Interactions among species in the harvesting process are referred to as technical and involve catching various species either simultaneously through the course of unse-lective fishing operations or sequentially throughout the season. Species can also be at the heart of economic interactions, with the revenue and costs of their harvest being interdependent 4.
Far from being anecdotal cases, mixed fisheries actually represent the dominant type of fishing worldwide (Cashion et al., 2018) and are found in all regions of the globe. In addition to their large commonality, they are also of great relevance to the study of sustainable fisheries management because of the multi-dimensional (i.e. eco-logical, economic and social) trade-oﬀs that emerge from the numerous (and uncertain) interactions involved. In this regard, they make good candidates to explore the useful-ness of eco-viability approaches to assist in identifying harvesting strategies that meet multi-dimensional sustainability requirements.
4. When attention is also given to the trophic interactions among caught species (i.e. the fact that they are connected in the food web), one usually refers to the term multispecies fishery (Santurtún et al., 2014). The present thesis focuses on mixed fisheries interactions and therefore does not address this latter aspect.
Table of contents :
1 General introduction
1.1 Addressing the modern concept of sustainability
1.1.1 Definition and normative foundations
1.1.2 Sustainability in a context of uncertainties
1.1.3 Viability theory to address the sustainability of renewable resource extraction
1.2 Sustainability in fisheries management: the Ecosystem Approach to Fisheries and science in support to its implementation
1.2.1 Ecosystem Approach to Fisheries: the incarnation of sustainability in fisheries management
1.2.2 Development of integrated models to operationalize the Ecosystem Approach to Fisheries
1.2.3 Eco-viability modelling for holistic sustainability assessments of fisheries
1.3 Managing mixed fisheries
1.3.1 Limitations of single-species approaches for the management of mixed fisheries
1.3.2 Specific management arrangements in mixed fisheries
1.3.3 Scientific advice for mixed fisheries: some current approaches and limitations
1.4 Thesis objectives
1.5 Context of the PhD
1.6 Case studies
1.6.1 Bay of Biscay French demersal fishery (BoB)
1.6.2 Australian Southern and Eastern Scalefish and Shark Fishery (SESSF)
1.7 Structure of the thesis
2 Providing integrated total catch advice for the management of mixed
fisheries with an eco-viability approach
2.2 The Bay of Biscay mixed demersal fishery
2.3 Bio-economic simulation model
2.3.1 Operating model
2.3.2 Management procedures
2.4 Eco-viability evaluation
2.4.1 Eco-viability framework
2.4.2 Eco-viability under uncertainty
2.5 Model’s dimensions and calibration
2.6 Management strategies
2.7.1 The joint production problem
2.7.2 Biological viability
2.7.3 Fleets’ viability
2.7.4 From target reference points on fishing mortality to TAC advice
2.8.1 Towards operational eco-viable TAC advice for mixed fisheries
2.8.2 Its application to the Bay of Biscay demersal mixed fishery
2.8.3 Limitations and perspectives
2.8.4 Articulation with current ICES mixed fisheries advice
3 Modelling quota uptake in multi-species fisheries managed with ITQs
3.2 Equilibrium of multispecies ITQ markets
3.2.1 The model
3.2.2 Market equilibrium
3.3 The convergence towards ITQ market equilibrium
3.3.1 The tâtonnement algorithm
3.3.2 Implementation considerations
3.3.3 Numerical application to the Australian Southern and Eastern Scalefish and Shark Fishery
3.4.1 Fishing incentives under perfectly competitive ITQ markets
3.4.2 Observations from multi-species ITQ markets
3.4.3 Perspectives for process-based simulation models of multi-species ITQ markets
4 Flexibility of joint production in mixed fisheries and implications for management
4.2 The Australian Southern and Eastern Scalefish and Shark Fishery (SESSF) 76
4.3.1 IAM bio-economic model
4.3.2 Model calibration
4.3.3 Exploring flexibility in joint productions
4.4.1 Flexibility in joint productions
4.4.2 Socio-economic performance
5 From fish stocks to fishers and consumers: eco-viability in the Australian Southern and Eastern Scalefish and Shark Fishery
5.2 The Australian Southern and Eastern Scalefish and Shark Fishery (SESSF)
5.3.1 IAM bio-economic model
5.3.2 Eco-viability framework
5.3.3 Simulation plan
5.4.1 Operating domain accounting for market dynamics
5.4.2 Eco-viability analysis
5.4.3 Trade-offs within the eco-viable space
5.5.1 Definition of sustainability thresholds
5.5.2 From sustainability to trade-offs
5.5.3 Market dynamics in multispecies fisheries: what is the addedvalue for management advice?
6 General discussion
6.1 Key results of the thesis
6.2 The representation of mixed fisheries dynamics in the models used to support tactical decision-making in mixed fisheries
6.2.1 Modelling the biological dynamics of mixed fisheries
6.2.2 Modelling fishing activity
6.2.3 Modelling ITQ markets in multi-species fisheries
6.2.4 Modelling fish price dynamics
6.3 Integrating biological, economic and social considerations in the advisory process
6.3.1 Assessing eco-viability
6.3.2 Highlighting trade-offs
6.3.3 Contributions of the approach to advisory procedures in both European and Australian federal contexts
6.4.1 Modelling human behaviour under uncertainty
6.4.2 Tackling the curse of dimensionality
6.4.3 Better accounting of ecological dimensions of EBFM
6.4.4 Beyond eco-viability