Pretty good governance: balancing policy drivers and stakeholder interests in developing fisheries ecosystem plans

The central research question that this thesis addresses is whether there is a significant gap between fishery stakeholder values and the principles and policy goals implicit in an Ecosystem Approach to Fisheries Management (EAFM). The implications of such a gap for fisheries governance are explored. Furthermore an assessment is made of what may be practically achievable in the implementation of an EAFM in fisheries in general and in a case study fishery in particular. The research was mainly focused on a particular case study, the Celtic Sea Herring fishery and its management committee, the Celtic Sea Herring Management Advisory Committee (CSHMAC). The Celtic Sea Herring fishery exhibits many aspects of an EAFM and the fish stock has successfully recovered to healthy levels in the past 5 years. However there are increasing levels of governance related conflict within the fishery which threaten the future sustainability of the stock. Previous research on EAFM governance has tended to focus either on higher levels of EAFM governance or on individual behaviour but very little research has attempted to link the two spheres or explore the relationship between them. Two main themes within this study aimed to address this gap. The first was what role governance could play in facilitating EAFM implementation. The second theme concerned the degree of convergence between high-level EAFM goals and stakeholder values. The first method applied was governance benchmarking to analyse systemic risks to EAFM implementation. This found that there are no real EU or national level policies which provide stakeholders or managers with clear targets for EAFM implementation. The second method applied was the use of cognitive mapping to explore stakeholders understandings of the main ecological, economic and institutional driving forces in the Celtic Sea Herring fishery. The main finding from this was that a long-term outlook can and has been incentivised through a combination of policy drivers and participatory management. However the fundamental principle of EAFM, accounting for ecosystem linkages rather than target stocks was not reflected in stakeholders cognitive maps. This was confirmed in a prioritisation of stakeholders management priorities using Analytic Hierarchy Process which found that the overriding concern is for protection of target stock status but that wider ecosystem health was not a priority for most management participants. The conclusion reached is that moving to sustainable fisheries may be a more complex process than envisioned in much of the literature and may consist of two phases. The first phase is a transition to a long-term but still target stock focused approach. This achievable transition is mainly a strategic change, which can be incentivised by policies and supported by stakeholders. In the Celtic Sea Herring fishery, and an increasing number of global and European fisheries, such transitions have contributed to successful stock recoveries. The second phase however, implementation of an ecosystem approach, may present a greater challenge in terms of governability, as this research highlights some fundamental conflicts between stakeholder perceptions and values and those inherent in an EAFM. This phase may involve the setting aside of fish for non-valued ecosystem elements and will require either a pronounced mind-set and value change or some strong top-down policy incentives in order to succeed. Fisheries governance frameworks will need to carefully explore the most effective balance between such endogenous and exogenous solutions. This finding of low prioritisation of wider ecosystem elements has implications for rights based management within an ecosystem approach, regardless of whether those rights are individual or collective.

Application of signal detection methods to fisheries management

The abundance of many commercially important fish stocks are declining and this has led to widespread concern on the performance of traditional approach in fisheries management. Quantitative models are used for obtaining estimates of population abundance and the management advice is based on annual harvest levels (TAC), where only a certain amount of catch is allowed from specific fish stocks. However, these models are data intensive and less useful when stocks have limited historical information. This study examined whether empirical stock indicators can be used to manage fisheries. The relationship between indicators and the underlying stock abundance is not direct and hence can be affected by disturbances that may account for both transient and persistent effects. Methods from Statistical Process Control (SPC) theory such as the Cumulative Sum (CUSUM) control charts are useful in classifying these effects and hence they can be used to trigger management response only when a significant impact occurs to the stock biomass. This thesis explores how empirical indicators along with CUSUM can be used for monitoring, assessment and management of fish stocks. I begin my thesis by exploring various age based catch indicators, to identify those which are potentially useful in tracking the state of fish stocks. The sensitivity and response of these indicators towards changes in Spawning Stock Biomass (SSB) showed that indicators based on age groups that are fully selected to the fishing gear or Large Fish Indicators (LFIs) are most useful and robust across the range of scenarios considered. The Decision-Interval (DI-CUSUM) and Self-Starting (SS-CUSUM) forms are the two types of control charts used in this study. In contrast to the DI-CUSUM, the SS-CUSUM can be initiated without specifying a target reference point (‘control mean’) to detect out-of-control (significant impact) situations. The sensitivity and specificity of SS-CUSUM showed that the performances are robust when LFIs are used. Once an out-of-control situation is detected, the next step is to determine how much shift has occurred in the underlying stock biomass. If an estimate of this shift is available, they can be used to update TAC by incorporation into Harvest Control Rules (HCRs). Various methods from Engineering Process Control (EPC) theory were tested to determine which method can measure the shift size in stock biomass with the highest accuracy. Results showed that methods based on Grubb’s harmonic rule gave reliable shift size estimates. The accuracy of these estimates can be improved by monitoring a combined indicator metric of stock-recruitment and LFI because this may account for impacts independent of fishing. The procedure of integrating both SPC and EPC is known as Statistical Process Adjustment (SPA). A HCR based on SPA was designed for DI-CUSUM and the scheme was successful in bringing out-of-control fish stocks back to its in-control state. The HCR was also tested using SS-CUSUM in the context of data poor fish stocks. Results showed that the scheme will be useful for sustaining the initial in-control state of the fish stock until more observations become available for quantitative assessments.