The spawning potential ratio (SPR) is a well-established biological reference point, and estimates of SPR could be used to inform management decisions for data-poor fisheries. Simulations were used to investigate the utility of the length-based model (LB-SPR) developed in Hordyk et al. (2015). Some explorations of the life history ratios to describe length composition, spawning-per-recruit, and the spawning potential ratio. ICES Journal of Marine Science, 72: 204–216.) to estimate the SPR of a stock directly from the size composition of the catch. This was done by (i) testing some of the main assumptions of the LB-SPR model, including recruitment variability and dome-shaped selectivity, (ii) examining the sensitivity of the model to error in the input parameters, and (iii) completing an initial empirical test for the LB-SPR model by applying it to data from a well-studied species. The method uses maximum likelihood methods to find the values of relative fishing mortality (F/M) and selectivity-at-length that minimize the difference between the observed and the expected length composition of the catch, and calculates the resulting SPR. When parameterized with the correct input parameters, the LB-SPR model returned accurate estimates of F/M and SPR. With high variability in annual recruitment, the estimates of SPR became increasingly unreliable. The usefulness of the LB-SPR method was tested empirically by comparing the results predicted by the method with those for a well-described species with known length and age composition data. The results from this comparison suggest that the LB-SPR method has potential to provide a tool for the cost-effective assessment of data-poor fisheries. However, the model is sensitive to non-equilibrium dynamics, and requires accurate estimates of the three parameters (M/k, L∞, and CVL∞). Care must be taken to evaluate the validity of the assumptions and the biological parameters when the model is applied to data-poor fisheries.

Evaluating the status of data-poor fish stocks is often limited by incomplete knowledge of the basic life history parameters: the natural mortality rate (M), the von Bertalanffy growth parameters (L∞ and k), and the length at maturity (Lm). A common approach to estimate these individual parameters has been to use the Beverton–Holt life history invariants, the ratios M/k and Lm/L∞, especially for estimating M. In this study, we assumed no knowledge of the individual parameters, and explored how the information on life history strategy contained in these ratios can be applied to assessing data-poor stocks. We developed analytical models to develop a relationship between M/k and the von Bertalanffy growth curve, and demonstrate the link between the life history ratios and yield- and spawning-per-recruit. We further developed the previously recognized relationship between M/k and yield- and spawning-per-recruit by using information on Lm/L∞, knife-edge selectivity (Lc/L∞), and the ratio of fishing to natural mortality (F/M), to demonstrate the link between an exploited stock's expected length composition, and its spawning potential ratio (SPR), an internationally recognized measurement of stock status. Variation in length-at-age and logistic selectivity patterns were incorporated in the model to demonstrate how SPR can be calculated from the observed size composition of the catch; an advance which has potential as a cost-effective method for assessing data-poor stocks. A companion paper investigates the effects of deviations in the main assumptions of the model on the application of the analytical models developed in this study as a cost-effective method for stock assessment [Hordyk, A. R., Ono, K., Valencia, S., Loneragan, N. R., and Prince, J. D. 2015. A novel length based empirical estimation method of spawning potential ratio (SPR), and tests of its performance, for small-scale, data-poor fisheries. ICES Journal of Marine Science, 72: 217–231].

The complexity and cost of assessment techniques prohibits their application to 90% of fisheries. Simple generic approaches are needed for the world's small-scale and data-poor fisheries. This meta-analysis of the relationship between spawning potential and the normalized size and age of 123 marine species suggests that the so-called Beverton–Holt life-history invariants (BH-LHI; Lm/L∞, M/k, M × Agem) actually vary together in relation to life-history strategy, determining the relationship between size, age, and reproductive potential for each species. Although little realized, the common assumption of unique values for the BH-LHI also implies that all species share the same relationship between size, age, and reproductive potential. This implicit assumption is not supported by this meta-analysis, which suggests that there is considerable but predictable natural variation in the BH-LHI ratios and the relationships between size, age, and reproductive potential that they determine. We believe that this reconceptualization of the BH-LHI has potential to provide a theoretical framework for “borrowing” knowledge from well-studied species to apply to related, unstudied species and populations, and when applied together with the assessment technique described by Hordyk et al. (2015b), could make simple forms of size-based assessment possible for many currently unassessable fish stocks.

The theoretical basis of a new approach to data poor fisheries assessment, length-based assessment of spawning potential ratio, has been recently published. This paper describes its first application over two years to assess 12 of the 15 most numerous species of Indo-15Pacific coral reef fish in Palau. This study demonstrates the techniques applicability to small-scale data-poor fisheries and illustrates the type of data required, and the assessment's outputs. A methodology is developed for extending the principles of Beverton-Holt Life History Invariants to use the literature on related species within the Indo-Pacific reef fish assemblage to 'borrow' the information needed to parameterize 20 assessments for Palau's poorly studied stocks. While the assessments will continue to be improved through the collection of more size and maturity data, and through further synthesis of the literature, a consistent and coherent picture emerges of a heavily fished assemblage with most assessed species having SPR <20% and many <10%. Beyond the technical aspects of this study, the relative simplicity of the data being collected and the 25 underlying concept of spawning potential facilitated the involvement of fishers in collecting their own data and community ownership of the results.

The cost, complexity and the lack of technical capacity in many countries have made the scientific assessment and sustainable management of data‐poor fisheries a persistent problem. New and innovative approaches are needed to stop the ongoing decline of data‐poor fisheries and loss of coastal biodiversity they are driving. In recent decades, marine protected areas have become the most preferred form of management for study and have been widely implemented as broadly applicable powerful management tools for data‐poor fisheries, but although clearly capable of building biomass within sanctuaries, their effectiveness for sustaining fisheries is proving more difficult to substantiate. This study suggests the new approach needed is actually a return to the established basics of managing size selectivity. Previous studies have established the wisdom of managing size selectivity and fishing pressure to catch fish above the size or age of maturity, but their prescriptions are difficult to implement without age studies, or the capacity for controlling catches and fishing pressure. This study develops an easily implementable rule of thumb based simply on multiples of size of maturity and quantifies its benefit where controlling fishing pressure is not yet possible. Our study provides a timely reminder that even if used alone, size selectivity, the oldest form of management, still produces pretty good sustainable yields. We suggest our rule of thumb can be used to prevent data‐poor fisheries declining while capacity for more complex forms of assessment and management are developed.

Selectivity in fish is often size-dependent, which results in differential fishing mortality rates across fish of the same age, an effect known as “Lee’s Phenomenon”. We extend previous work on using length composition to estimate the spawning potential ratio (SPR) for data-limited stocks by developing a computationally efficient length-structured per-recruit model that splits the population into a number of subcohorts, or growth-type-groups, to account for size-dependent fishing mortality rates. Two simple recursive equations, using the life history ratio of the natural mortality rate to the von Bertalanffy growth parameter (M/K), were developed to generate length composition data, reducing the complexity of the previous approach. Using simulated and empirical data, we demonstrate that ignoring Lee’s Phenomenon results in overestimates of fishing mortality and negatively biased estimates of SPR. We also explored the behaviour of the model under various scenarios, including alternative life history strategies and the presence of size-dependent natural mortality. The model developed in this paper may be a useful tool to estimate the SPR for data-limited stock where it is not possible to apply more conventional methods.

For over two decades, Indonesia has reported higher average shark landings than any other nation, but very little local information exists on the fishery and life histories of targeted species. This poses severe challenges to shark sustainability and conservation in this vast archipelago. We draw on diverse sources of data to evaluate the sustainability of the shark fishery in eastern Indonesia, a particularly data-poor region where sharks are primarily targeted for their fins. Shark fishers from three coastal communities were interviewed on their perceptions of catch trends over the past twenty years and asked to collect fishing data during fishing trips in the Seram, Arafura and Timor Seas. For the most frequently harvested species, we estimated maximum intrinsic rates of increase (rmax) to predict their resilience to fishing pressure. Our results indicate that shark fishing practices in the region are likely to be unsustainable. The catches of several species largely comprised of immature individuals and most fishers attributed observed declines in shark numbers, size and species diversity to overfishing. Hammerhead sharks have relatively high intrinsic resilience but are nevertheless at risk of local extinction due to their availability to the fishery and the value of their fins. Sandbar, dusky and grey reef sharks have lower resilience and are frequently caught but not managed. We recommend a composite management approach, including consistent implementation of existing trade restrictions, fisheries research and opportunities for fishers’ livelihood diversification, to stem shark harvests in eastern Indonesia.

A simulation‐based approach known as management strategy evaluation (MSE) is increasingly used by resource managers to identify management procedures that are robust to uncertainties in system dynamics. The majority of global fish populations are data limited and there is large uncertainty over their population and exploitation dynamics. The Data‐Limited Methods Toolkit (DLMtool) is an R package that allows for rapid and flexible MSE specification. The package consolidates a large number of existing data‐limited management procedures and allows for rapid MSE testing of new approaches. The DLMtool package has supported transparent and rigorous decision‐making for a number of data‐limited populations, identifying robust management procedures and revealing performance trade‐offs.