Multiple indicator frameworks for assessment and precautionary management Mike Smith

Precautionary approach Implies that: a lack of full scientific certainty must not be used as a reason for postponing cost effective measures to prevent environmental degradation” (Principle 15, Rio de Janeiro Declaration; FAO, 1995) The PA requires the following tasks to be accomplished: Establishment of management objectives Specification of information required Assessment of the state of the stock, putting in evidence sources of uncertainty Definition of the rules for management decisions, which should be robust to uncertainty and incomplete knowledge on factors such as stock identity and dynamics and the effects of environment (FAO, 1995). Multiple indicator approaches (including TL systems) can provide a means of incorporating the above features for systems where data do not permit more sophisticated analytical stock assessments in support of management rules.

Multiple indicator frameworks Enable the synthesis of signals from a range of sometimes empirical indices and indicators by converting them into qualitative (or very simple scaled) terms (e.g. good, moderate, bad), such that they can then all be considered on the same scale. A framework to assemble, consider and combine data consisting of (time series of) indices or indicators that can inform on stock and fishery health (and potential) and provide a means for assessment and informing management They utilise relatively simple scoring systems, applied to each indicator to provide indications of stock and fishery health. Such scoring systems might include distributional thresholds (e.g. quantiles) and approaches such as fuzzy set theory/logic can be used (e.g. to soften knife edged thresholds). Scoring of individual indicator/indices or of compound signals may be conditional on the score or trend of another indicator/index. The choice of which indicators/indices to use and/or the weighting of their scores into compound signals is crucial to the overall outcome and could introduce subjectivity.

Indicators Indicators are defined as variables, pointers or indices of a phenomenon (Garcia et al. 2000). They can support the decision making process by: (i) describing the pressures affecting the ecosystem, the state of the ecosystem and the response of managers, (ii) tracking progress towards meeting management objectives (iii) communicating trends in complex impacts and management processes to a nonspecialist audience (Garcia et al. 2000; Rice 2000, 2003; Rochet and Trenkel 2003). As attributes (of a pressure, state, response system ) may not be directly measurable, indicators can act as proxies for them (Fulton et al. 2004a,b). However, for indicators to support management decision making, the relationship between the indicator current value and/or trend and the value and/or trend of the variable (or indicator) associated with meeting the operational objective needs to be known (Jennings, 2005). Most research has focussed on indicators for state, however management usually controls pressure and response describes the pressure and state changes (i.e. feedback relationships).

Desirable properties for indicators (based on ICES 2005, and Rice and Rochet, 2005) Concrete: directly observable and measurable rather than abstract or only estimated indirectly Theoretical based: reflect features of ecosystems and human impact relevant to objectives and be based on well-defined and validated theoretical links Consistently understood: public understanding and technical meaning should be consistent Cost: cost-effective given limited monitoring resources Measurable: measurable using existing instruments, monitoring programmes and analytical tools, available on spatial and temporal scales needed for management, have minimum or known bias and the signal should be distinguishable from noise Context: supported by existing or time-series of data to aid interpretation of trends and to allow a realistic setting of objectives Sensitive: sensitive to changes in the state, pressure or response it is intended to measure Responsive: provide rapid and reliable feedback on the consequences of management actions Specific: respond to the properties intended to be measured rather than to other factors and/or it should be possible to disentangle the other effects from the observed response

Structure of multiple indicator frameworks Typically, potential indices are grouped into 3 or 4 categories representing: Abundance Early season catch rate, survey counts, commercial fishing area, industry perspective Production Number of recruits, mean size, average maximum size, sex ratio in catches, proportion berried, density of larvae, condition factors, disease Fishing pressure Proportion immature in catch, total trap hauls (per area ground), incidental mortality (discards or other gears), landings alternative species, exploitation rate, total mortality, proportion females in catch, proportion of catch taken during ovigerous period Ecosystem and environment - harvest control rules for fish, have tended to focus on indicators monitoring (spawning) biomass, recruitment and fishing mortality, although environmental linkages are often more predictive for many invertebrates (Caddy, 2004) Predator abundance, prey abundance, temperature indices, wind/current flows

Reference points Reference points that might support management decision making include: (i) reference points for no impact, (ii) limit reference points for the values of indicators associated with serious or irreversible harm (iii) target reference points for preferred values of the indicators As indicator values include error, precautionary reference points may be needed to guarantee a high (preferably specified) probability of avoiding a limit. When indicators are used to guide management of target stocks, there is a tradition of setting reference points (FAO 1998). In other fields, reference points may not be specified and trajectories or directions may be used to guide decision making. (but I’m not sure how far!) Reference directions can guide management when the value of an indicator is unsatisfactory or close to a limit, but when a target has not or cannot be defined (Link, 2002; Trenkel and Rochet 2003; Jennings and Dulvy 2005). Cliff edge Increasing fishing impact unexploited targetprecautionary limit

An example (for shrimp, Koeller et al, 2002) Each indicator is considered under in methods, results (and brief interpretation) sections. Additional supporting information can also be presented. Traffic light colours were determined by pre-defined limits for individual indicators, with default transition boundaries of the 33 and 67 percentiles. In two cases (commercial CPUE) polarity was considered to have switched when considered with other indicators (increased aggregation & decreased survey abundance), but the TL system did not implement this.

An example (for shrimp, Koeller et al, 2002) Halliday et al. (2001) proposed that decision rules should be based on an integrated score of indicators measuring at least three characteristics: abundance, production, and fishing mortality. If the proportion of indicators triggered within a management rule determines the severity of management response some redundancy and “smoothing” should be introduced because not all individual indicators are likely to trigger simultaneously (Caddy 1999a, 1999b). In this example, the aggregate signals for abundance were green in recent years, while the production and fishing mortality signals were declining to red in This example was used for assessment and information only. Management rules were based on separate simulation modelling.

An example using fuzzy logic for an HCR (Murta & Silvert, 2002) Using a knife edged threshold can lead to ‘flip-flop’ which should be avoided (Rice,2003). For example: If Biomass Threshold the TAC=0.4 * Biomass Fuzzy set theory can be used to allocate biomass as high or low (above or below a threshold). For example if a threshold were 500t, then below 250t could be considered 0% high biomass and above 750t 100% high biomass, with membership between interpolated. Stochastic output could also be used. Now if biomass is at the threshold it is 50% high and 50% low and the TAC would be obtained by averaging, i.e. Low biomass High biomassTAC 0.5 * * 0.4 = 0.2*B = 250t Fuzzy sets could also be used to define TAC and a range of indicators of fishery performance resulting in control rules of the form If Biomass is HIGH and Sampling is GOOD and Assessment model is GOOD and … Then TAC will be LARGE The membership of TAC is then ‘defuzzified’ to give a crisp value for the TAC

References Caddy, J.F., 1999a. Deciding on precautionary management measures for a stock based on a suite of limit reference points (LRPs) as a basis for a multi-LRP harvest law. NAFO Sci. Counc. Stud. 32: 55–68. Caddy, J.F., 1999b. A short review of precautionary reference points and some proposals for their use in data-poor situations. FAO Fish. Tech. Pap. No Caddy, J.F., Current usage of fisheries indicators and reference points, and their potential application to management of fisheries for marine invertebrates. J. Fish. Aquat. Sci. 61: 1307–1324. FAO, FAO, Precautionary approach to fisheries. Part I: Guidelines on the precautionary approach to capture fisheries and species introductions. FAO Fish. Tech. Pap. 350(1), FAO, Rome, 52 pp. Reproduced with minor editing as FAO Technical Guidelines for Responsible Fisheries. No. 2. FAO, A short review of precautionary reference points and some proposals for their use in data-poor situations. FAO Fisheries Technical Paper No. 379, 30 pp. Fulton, E.A., Smith, A.D.M., Webb, H. and Slater, J., 2004a. Ecological indicators for the impacts of fishing on non-target species, communities and ecosystems: review of potential indicators. Australian Fisheries Management Authority Final Research Report No. R99/1546, 116 pp.

References Fulton, E.A., Fuller, M., Smith, A.D.M. and Punt, A., 2004b. Ecological indicators of the ecosystem effects of fishing: final report. Australian Fisheries Management Authority Final Research Report No. R99/1546, 239 pp. Garcia, S.M., Staples, D.J. and Chesson, J., The FAO guidelines for the development and use of indicators of sustainable development of marine capture fisheries and an Australian example of their application. Ocean and Coastal Management 43, 537–556. Halliday, R.G., Fanning, L.P., and Mohn, R.K Use of the traffic light method in fishery management planning. Can. Sci. Advisory Secretariat Res. Doc. No. 2001/108. ICES (2005) Guidance on the application of the ecosystem approach to management of human activities in the European marine environment. ICES Co- operative Research Report No. 273, 22 pp. Jennings S., 2005, Indicators to support an ecosystem approach to fisheries. Fish Fish. 6, 212–232. Jennings, S. and Dulvy, N.K. (2005) Reference points and reference directions for size-based indicators of community structure. ICES Journal of Marine Science 62, 397– 404.

References Koeller, P.A., Cover, M. & King, M., A new traffic light assessment for the Eastern Scotian Shelf Shrimp Fishery in Canadian Science Advisory Secretariat, pp. Link, J.S. (2002) Ecological considerations in fisheries management: when does it matter? Fisheries 27, 10–17. Murta, A.G. & Silvert, W., A framework to put in practice a precautionary approach to fisheries assessment based on fuzzy set theory. ICES CM 2002/ACFM:10. WD. Rice, J.C. (2000) Evaluating fishery impacts using metrics of community structure. ICES Journal of Marine Science 57, 682–688. Rice, J.C. (2003) Environmental health indicators. Ocean and Coastal Management 46, 235–259. Rice, J.C. and Rochet, M.-J. (2005) A framework for selecting a suite of indicators for fisheries management. ICES Journal of Marine Science 62, 516–527.

References Rochet, M.-J. and Trenkel, V.M. (2003) Which community indicators can measure the impact of fishing? a review and proposals. Canadian Journal of Fisheries and Aquatic Science 60, 86–99. Trenkel, V.M. and Rochet, M.-J. (2003) Performance of indicators derived from abundance estimates for detecting the impact of fishing on a fish community. Canadian Journal of Fisheries and Aquatic Science 60, 67–85.