Multi-Indicator Harvest Strategies for Data-Limited Fisheries: A Practitioner Guide to Learning and Design

Abstract

As the world population grows, fisheries practitioners will be under increased pressure to address global challenges in data-limited fisheries management. With a focus on addressing localized and case-specific management needs, we provide a practical guide to the design and development of multi-indicator frameworks for fishery management. In a data-limited context, indicators are observations or estimates of the state of the fishery resource that are typically proxies for variables of interest, rather than quantities such as stock biomass estimated from data-rich stock assessments. Indicator frameworks structure the integration and interpretation of indicators to guide tactical fishery decision-making, often when the application of more formal analytical assessments is not feasible, yet where indicators in combination provide insight into stock status. With a focus on multi-indicator frameworks, we describe a pragmatic approach for their development via a set of organizational steps, considering a wide spectrum of types and severity of information limitations. We highlight where multi-indicator frameworks can be insightful and informative in relation to single indicator approaches but also point to potential pitfalls, with emphasis on critical evaluation and detection of performance flaws during the design phase using methods such as management strategy evaluation.

Data-rich but model-resistant: an evaluation of data-limited methods to manage fisheries with failed age-based stock assessments

“…Most DLMs do not have the ability to estimate a biomass reference point (e.g., B MSY ), which made consideration of socalled biomass-based harvest control rules that reduce F or catch in response to estimated changes in relative stock status impossible. Although reference points can be created for DLMs, they typically rely on local expert judgment (Harford et al 2021) and are geared towards either keeping the stock about where it is or else increasing it towards a relative amount that was thought to be good. Neither of these provide a proxy for maximum sustainable yield reference points, but might instead provide pretty good yield (Hilborn 2010).…”

Oceans of plenty? Challenges, advancements, and future directions for the provision of evidence-based fisheries management advice

“…For instance, when a measure of relative abundance, yield-per-recruit, egg-per-recruit, or an indicator based on a representative length-frequency sample can be developed, it provides the minimum requirement for an HCR to regulate exploitation levels (Hordyk et al 2015 ; Jardim et al 2015 ; Wakefield et al 2020 ). Multiple empirical indicators can be used in indicator-based decision frameworks, wherein greater insight into stock status is provided by considering indicators in combination (Harford et al 2021 ). Empirical and data-limited methods should be embedded within management strategies that are robust to the higher levels of uncertainty in assessment output by including precautionary management measures or buffers (Dowling et al 2019 ).…”

The FishPath approach for fisheries management in a data‐ and capacity‐limited world

“…Other complementary resources that can assist in harvest strategy development include Harford et al's (2021) guidance on developing multi‐indicator harvest strategy frameworks and the Environmental Defense Fund's online Framework for Integrated Stock and Habitat Evaluation (FISHE) tool (http://fishe.edf.org/). FISHE provides a step‐by‐step process and detailed written guidance for providing scientific guidance for the management of data‐limited fisheries and provides an explicit link to the FishPath Tool.…”

“…These can be used singly or combined in multi‐indicator frameworks (e.g. Harford et al, 2016, 2021), forming ‘empirical assessments’. Empirical indicators or assessments can incorporate local fisher knowledge and give indirect estimates of stock status.…”

“…These can be used singly or combined in multi-indicator frameworks (e.g. Harford et al, 2016Harford et al, , 2021 2019) provides a summary of the types of approaches we class as data-limited 'assessments', including empirical assessments.…”