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Marine population modeling, which underpins the scientific advice to support fisheries interventions, is an active research field with recent advancements to address modern challenges (e.g., climate change) and enduring issues (e.g., data limitations). Based on discussions during the 'Land of Plenty' session at the 2021 World Fisheries Congress, we synthesize current challenges, recent advances, and interdisciplinary developments in biological fisheries models (i.e., data-limited, stock assessment, spatial, ecosystem, and climate), management strategy evaluation, and the scientific advice that bridges the science-policy interface. Our review demonstrates that proliferation of interdisciplinary research teams and enhanced data collection protocols have enabled increased integration of spatiotemporal, ecosystem, and socioeconomic dimensions in many fisheries models. However, not all management systems have the resources to implement model-based advice, while protocols for sharing confidential data are lacking and impeding research advances. We recommend that management and modeling frameworks continue to adopt participatory co-management approaches that emphasize wider inclusion of local knowledge and stakeholder input to fill knowledge gaps and promote information sharing. Moreover, fisheries management, by which we mean the end-to-end process of data collection, scientific analysis, and implementation of evidence-informed management actions, must integrate improved communication, engagement, and capacity building, while incorporating feedback loops at each stage. Increasing application of management strategy evaluation is viewed as a critical unifying component, which will bridge fisheries modeling disciplines, aid management decision-making, and better incorporate the array of stakeholders, thereby leading to a more proactive, pragmatic, transparent, and inclusive management framework-ensuring better informed decisions in an uncertain world.The online version contains supplementary material available at 10.1007/s11160-022-09726-7.

Over the past 50 years, the diversity of fisheries types being actively managed has changed from mainly data-rich, industrial sectors to more socially, economically, and environmentally complex multispecies and multisector fisheries. Accompanying this change has been a broadening of management objectives to include social and economic considerations with traditional resource sustainability objectives, the so-called triple bottom line, and the need to include these considerations into harvest strategies. The case of a line fishery in Australia’s Great Barrier Reef is used as a demonstration of the first steps in implementing triple bottom line harvest strategies. This fishery has several disparate sectors including commercial, tourism, and recreation; targets multiple but important reef species; and is undertaken in a World Heritage Site. This work highlights the need for a much-expanded set of objectives elicited from stakeholders that are either included in the trade-off analyses of the different harvest strategies or directly in an optimization. Both options demonstrated that a paradigm shift is required to emphasize representative participatory management systems that assemble teams from quite different backgrounds and viewpoints; use much broader set of objectives; and modify tools and (especially) the data collected within revised monitoring programmes to underpin these tools.

Recreational fishing (RF) is a popular pastime resulting in substantial fish mortality in many regions. Yet inclusion of RF in fishery harvest strategies is limited, because the sector's objectives are poorly understood, as are the data required to track their performance. To address this, we reviewed RF data sources available from a region of globally high participation (New South Wales [NSW], Australia) and evaluated their utility for RF-specific performance indicators within harvest strategies. We then linked these data sources to RF objectives they may be used to monitor. A total of 21 RF data sources were identified in NSW over the past two decades, spanning all major aquatic environments and 146 fished species. Numerous data sources were available to monitor ecological objectives, providing time-series and potential reference points for key indicators such as catch-per-unit-effort. Few data sources were available for social, economic, and institutional objectives, consistent with a global paucity of these data. We found that most social objectives of RF lie outside the scope of traditional harvest strategies, although some are linked to underlying ecological performance. Harvest strategy performance for RF will depend on the relative importance of social objectives and whether these can be achieved by controlling harvest.

The majority of the world's fisheries, by number, are data-poor/limited, and there is a growing body of literature pertaining to approaches to estimate data-limited stock status. There are at least two drivers for assessing the status of data-limited fisheries. The first is to try to understand and report on the global or regional status of fisheries across many stocks. The second is to attempt to assess individual data-limited stocks, for status reporting and/or guiding management decisions. These drivers have led to attempts to find simple, generic, low-cost solutions, including the broad application of generically parameterised models, and the blanket application of a single, or limited number of possible, analytical approach(es). It is unclear that generic methods function as intended, especially when taken out of their original design context or used without care. If the intention is to resolve individual stock status for the purposes of management, there is concern with the indiscriminate application of a single method to a suite of stocks irrespective of the particular circumstances of each. We examine why caution needs to be exercised, and provide guidance on the appropriate application of data-limited assessment methods (DLMs). We recommend: (a) obtaining better data, (b) using care in acknowledging and interpreting uncertainties in the results of DLMs, (c) embedding DLMs in harvest strategies that are robust to the higher levels of uncertainty in the output of DLMs by including precautionary management measures or buffers and (d) selecting and applying DLMs appropriate to specific species’ and fisheries’ data and context. Fil: Dowling, Natalie A.. CSIRO Oceans and Atmosphere; Australia Fil: Smith, Anthony D. M.. CSIRO Oceans and Atmosphere; Australia Fil: Smith, David C.. CSIRO Oceans and Atmosphere; Australia Fil: Parma, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina Fil: Dichmont, Catherine M.. Cathy Dichmont Consulting; Australia Fil: Sainsbury, Keith. Sain Solutions; Australia Fil: Wilson, Jono R.. The Nature Conservancy; Estados Unidos Fil: Dougherty, Dawn T.. University of California Santa Barbara; Estados Unidos Fil: Cope, Jason M.. National Ocean And Atmospheric Administration; Estados Unidos

We proposed two hypotheses to explain why food chains are longer in pelagic than terrestrial ecosystems: greater trophic efficiency of pelagic animal taxa at lower trophic levels and a higher pelagic biomass production rate at lower trophic levels because of smaller pelagic body masses. Giacomini favored the former, invoking in support the energetic equivalence hypothesis. We reply that the energetic equivalence hypothesis does not describe populations at differing trophic levels and so does not refute a significant role for body-mass dependence in explaining faster trophic transfer in pelagic ecosystems. Metabolic scaling as body mass to the exponent 1/4, widely accepted, remains important for trophic dynamic models. We suggest a likelihood method to compare the two hypotheses on the basis of models of whole-ecosystem energetics.

In response to a vessel buyback scheme in a major Australian Commonwealth fishery, nominal catch-per-unit-effort (CPUE) showed dramatic increases. These led to a discontinuity in the statistically standardised CPUE time series, which may also suggest a discontinuity in the relationship between catch-per-unit-effort and abundance. We hypothesise the discontinuity in CPUE is due to reduced vessel interaction effects. Vessel interactions can arise due to, for example, competition or information sharing, and as such can influence CPUE independent of the underlying fish abundance. Thus there remains a need to quantitatively understand the nature of the effect of vessel interactions on CPUE. Awareness of such interactions is common, yet it has not been described in the context of rapid changes of effort brought about by management interventions such as a vessel buyback. Here we focus on vessel interference, ecologically defined as a depression in foraging (catch) rate with increased foraging (fisher, or vessel) density. We present a mechanistic model incorporating a vessel interference term, that could explain situations where CPUE is more responsive to effort buybacks than if no vessel interference is assumed. Our hypothesis is that vessel interference makes CPUE a nonlinear function of effort. We define an interference term that interacts with stock dynamics and catchability. Under a simulated buyback, the trajectories of abundance and CPUE change in a manner dependent on the magnitude of the interference. Our simple model of the non-linear effect of vessel interference holds up well under a large range of values for biological and operational parameters. The effect of interference also appears independent of stock carrying capacity, vessel numbers, and the magnitude of relative reduction in vessel numbers. We conclude that our model provides a promising approach to the technically challenging problem of estimating relative abundance under conditions of sudden changes in fishing effort, and thereby improving sustainable fisheries management via appropriate levels of control rules set in response to such estimates.

Stock assessments are integral to fisheries management, and the demand for stock assessments based on population dynamics models continues to increase. Historically, stock assessments have been based on bespoke methods and software. There is now a trend towards the use of flexible, documented, tested and maintained software packages, because use of such packages increases efficiency and consistency in assessments and should lead to more reliable and repeatable assessment outcomes. We have developed a web-site that collates available stock assessment packages (from data-limited to -rich) to provide assessment analysts with a single platform that summarizes the features of the available packages in a consistent manner. We also highlight current state-of-the-art packages, and those that are no longer supported or have been superseded. In addition to providing the packages, the web-site facilitates users of packages to provide feedback to developers regarding which features of other packages could be included in future versions of their packages. We see this effort as a way to support the design of the next generation stock assessment package.

Harvest strategies (HSs) have been applied to many data-rich fisheries, and are now increasingly being applied in data-limited situations. These have been evaluated using simulation frameworks, including management strategy evaluation (MSE), but few studies have considered the full spectrum from data-rich to data-limited strategies, in the context of the risk-cost-catch trade-off. This involves evaluating whether the cost of implementing a HS, the risk to the resource and catch taken from the resource have been appropriately balanced, given the value of the resource. HSs implemented for Australian Commonwealth fisheries were placed in eight tiers, ranging from data-rich to data-limited, and their performance evaluated using an MSE based on a full end-to-end ecosystem model. Generally, the risk to the resource increased as fewer data were available, due to biases in the assessments and slow response times to unexpected declines in resource status. The most data-rich tiers maximize discounted catches and profits over a 45-year projection period. However, the opportunity costs response is variable, and shows that the benefit of short-term high catches have to be compensated by resource recovery in the long term. On average, more data leads to improved management in terms of risk of being overfished and not reaching a target, but this requires lower initial catches to recover the resources and lower short-term discounted profits.

Tier systems for fisheries assessment and management are widely used, but defined differently by jurisdiction. A principal component analysis was applied to the expanded Australian Commonwealth 8-tier system for fishery assessment and management to determine whether it adequately delineates across stocks according to data availability and quality. The original Australian tiers comprised four levels that were defined primarily according to the available stock assessment options, given the data availability and quality. We asked fishery experts to score information quality for each of the main Australian Commonwealth species and/or fisheries. Multivariate analysis indicated that the eight tiers delineated between the extreme tier levels on the first principal component, although there was overlap for intermediate tiers. More generally, it is important that the aim of tier systems and the basis for tier delineations are explicitly defined given the increasing association of tiers with trade-offs between overfishing risk, management cost and catch.

We propose a conceptual framework for evaluating fishery management performance using conservation, economic, and sociocultural metrics. We develop a value function that weights outcomes for each measure based on their relative importance to decision makers and show how it can be derived from fundamental economic principles (the latter initially in collaboration with Mark Plummer). This approach allows one to explore how Optimal Yield, as mandated by the Magnuson-Stevens Act, varies with biological, economic, and sociocultural weightings.

Fisheries are increasingly managed with involvement of fishers and other stakeholders; this approach is believed to create ownership and maximize stakeholder buy-in. Stakeholder involvement is critical where managers lack adequate knowledge, resources to gather additional information, or resources to implement management. Such ‘data-limited’ fisheries are of global import but face challenges to meeting management goals. Sustainable management of data-limited fisheries may be improved by decision support. This paper reports results of a field experiment testing the FishPath interactive decision-support software tool for data-limited fisheries (FishPath) and its influence on stakeholder buy-in to management. In Stage 1, participants were provided a hypothetical fishery that mimicked real-world data limitations, and a tailored shortlist of management options; participants did not interact with FishPath. In Stage 2, participants collectively input the fishery into FishPath; the tool output the same management options seen in Stage 1. In Stage 3, to assess the effect of expert support, participants were randomly assigned to control and treatment groups. The control group explored output without additional support, while the treatment group explored output with support from a FishPath expert. After each stage, participants were asked to rate: 1) their support for formal management of the fishery; 2) how easy or hard they expected management of the fishery to be; and 3) how effective they expected management of the fishery to be. FishPath use increased stakeholder participants’ perceptions of the ease and effectiveness of management at each stage of the experiment, although support for management was unchanged.

Natural resource management has long recognised that the multi-objective nature of management is important, but has struggled to operationalise this into quantitative, measurable objectives for functional use in management. Operationalising broader ecological and social objectives has been particularly problematic. In fisheries management, the focus has mainly been on target species sustainability and, in the past few decades, on profitability. However, multi-objective management is now essential as fisheries have become recognised as complex social-ecological-systems. Policy and legislation demand a move towards quantitative approaches for reconciling multiple objectives and operationalising these within harvest strategies. We present a quantitative, non-commensurable-unit approach, via a multi-indicator value function with explicit objective preference weights. We use a simulation to set Total Allowable Catches (TACs) for three main species groups in a reef line fishery in Australia's Great Barrier Reef. Our method enables stakeholders to consider a richer range of tradeoffs than is possible with bio-economic models. Moreover, it allows the formal evaluation of performance across alternative stakeholder group preferences, providing an impartial way to obtain an overall optimum TAC. The simulation requires extensive fishery data and requires the performance indicators associated with each objective to be quantitatively and defensibly defined. Thus, our approach provides a pathway forward that forces managers and stakeholders to confront the associated data requirements.

Highly valuable and easily accessible marine natural resources fished under traditional “open access” arrangements pose immense challenges for sustainable management. This is particularly the case for communities struggling to balance food security, conservation and economic trade-offs. Sea cucumber (beche-de-mer) fisheries across the Indo-Pacific and Caribbean are mostly fished for economic benefit (exported predominantly to Asian markets), and many species are considered heavily overfished. Most of these fisheries are data-poor and management is challenged by the dispersed and small-scale nature of fishing operations, and sometimes inadequate buy-in to sustainable harvest practices from stakeholders. In this study we developed a tiered harvest strategy for a commercial Indigenous-owned multi-species sea cucumber fishery in Torres Strait, northern Australia. The harvest strategy specifies pre-agreed, transparent rules for monitoring and use of fishery-dependent and fishery-independent data as inputs to decision rules to regulate the fishery. The strategy outlines a tiered, step-wise approach for how data can be used to adjust the fishery take while managing risks to a resource and supporting community aspirations. This creates incentives for data collection to support the fishery. The strategy encourages voluntary application of customary and traditional laws at the local scale. It advances attempts for modern fisheries management approaches to be cognisant of the history of Indigenous Community’s stewardship knowledge in order to reinforce compliance and improve sustainable outcomes. The tiered harvest strategy framework has been implemented in Torres Strait and is also likely to be applicable to other sea cucumber and data-poor fisheries.

There can be substantial differences in data quality and quantity among fished species. Consequently, the quality and type of assessments can also vary substantially. However, all species, especially those that are targeted, need to be managed. Several jurisdictions have developed hierarchical tier systems that categorize stocks based on, for example, the data available for assessment purposes and/or the extent to which quantities on which management advice is based can be estimated. Four case-studies (Australia's Southern and Eastern Scalefish and Shark Fishery, the USA west coast groundfishery, the USA Alaskan crab fishery and EU fisheries) are used to contrast the types of hierarchical tier systems available, and to assess the extent to which each system constrains risk to be equivalent among the tiers (termed risk equivalency). Only the Australian system explicitly aims to achieve risk equivalency. However, this intent has not been fully operationalized. Our review reveals that best practice is not to define tiers simply on data availability, but also on what the assessments based on those data are capable of estimating. In addition, clearly differentiating the quantification of uncertainty from how decision-makers wish to address that uncertainty would simplify justification of buffers (the gap between the assessment-produced target catch or effort and the final management decision that accounts for uncertainty and risk). Risk equivalency can be achieved using management strategy evaluation to select the values for control variables, which determine the buffer given the uncertainty associated with the assessment.

Fisheries management operates in an environment characterized by multiple risks. These risks are often complementary and can be traded off against each other. An important goal for managers is to develop strategies to minimize the overall risk exposure at minimal cost. We show a simple model that quantifies a range of risks faced by fisheries management agencies in terms of long-term expected budgetary expenditure. The model includes not only the cost a management agency would be expected to incur from overfishing a stock, or from being seen to overfish it, but also the social cost incurred from not achieving its objectives, such as the opportunity cost of foregoing catches and economic returns. These costs can be controlled by adjusting the biomass level targeted by management, or increasing expenditures for data collection to improve the precision of biomass estimates. The overall risk, expressed as the long-term total expected cost to a management agency, depends strongly on the fisheries management objectives, and the emphasis on conservation or economic motives. In general, management under a conservation-oriented objective would reduce risk either by increasing target biomass levels or by expenditure on monitoring and assessment, while a catch-focused objective would seek to lower management costs by reducing expenditure on data collection and assessment. Increased natural stock variability affects the risk of overfishing, and long-term expected costs as the ability to make a meaningful estimate of biomass declines. Management of catch-focused fisheries would reduce the biomass target as stock variability increases, because the benefit of catches are seen to outweigh the cost, or risk of being overfished. The model provides the basis for more extensive risk analyses, and serves as a simple lesson that the consequences of reducing the short-term costs associated with managing a fishery can come with a concomitant increase in overall risk.

Harvest strategy approaches based around empirical indicators and/or control rules are beginning to be accepted in a growing range of data- and capacity-poor fisheries. While there is an increasing body of work around developing empirical indicators and control rules in data-poor contexts, this has typically been done on a case-specific basis. There remains a need for general guidance on formulating control rules that link empirical indicators with suitable management responses. Additionally, in the data-poor context, most literature has focused on empirical indicators and assessments, with less focus on decision rules and the incorporation of indicators and assessments in a harvest strategy framework. This review considers a range of harvest strategy options, focusing on empirical indicators and decision rules available for data-poor species and fisheries. These clearly illustrate that a paucity of information is not a reason to avoid developing harvest strategies, and that a range of pragmatic approaches are available regardless of the available data, life-history of the target species, nature of fishing operations, or the available research capacity. There is considerable scope for further work in this field, but arguably there is a comprehensive repository of approaches and decision rules that, when combined with the guidelines, form a solid foundation and toolkit for all but the most data-poor species and fisheries.

There has been extensive literature discussion regarding data-poor assessments, but considerably less on harvest strategies for data-poor fisheries. There is also a large body of work around harvest strategy development for data-rich fisheries. However, there has been little discussion or specific guidance regarding the process of developing and implementing formal harvest strategies for data-poor fisheries. We outline such a process, illustrated using case studies of data-poor Australian Commonwealth fisheries. The process comprises: (1) compile and review available information; (2) identify possibly indicators; (3) identify reference points for key indicators; (4) select an appropriate harvest strategy and decision rules; (5) if possible, formally evaluate whether the harvest strategy options are likely to achieve the management objectives; and (6) implementation. While this approach is similar to that for data-rich cases, there is less statistical or estimation detail within each step. Even with minimal capacity, the guidelines outlined here, backed by even the simplest form of management strategy evaluation, provide an approach that should enable harvest strategies to be proposed and associated monitoring to be implemented. Monitoring requirements may be explicitly built into harvest strategies via trigger reference points and control rules related to data-collection. While prior formal evaluation provides the best basis for testing the efficacy of a harvest strategy, there remains disparity between the corresponding required capacity and what many agencies and institutions worldwide are capable of providing. Thus, the extent to which harvest strategies may be effectively evaluated and implemented remains an unresolved challenge for data-poor fisheries, but one whose resolution is predicated, at least in the first instance, on adequate monitoring.

Fishery managers are faced with the challenge of maintaining sustainable fisheries at the lowest possible cost while conforming to international and national obligations. Given that fisheries range from low to high value, there is a real need to understand how to trade ecological and economic risks, and the various costs associated with their management, against the benefits from catch. Key to this is an understanding of (a) the costs corresponding to a given level of acceptable risk, or conversely, (b) the change in risk given a change in cost investment. This paper first defines biological, economic and ecosystem risk at a whole-of-fishery level, and then develops a simple model to quantify the trade-offs between risk, cost and catch. Using as case studies Australia’s federally managed fisheries that range from data-rich to data-poor, risk was quantified for target species in terms of both their limit and target reference points (defined as ‘‘biological risk’’ and ‘‘economic risk’’, respectively), and for ecosystems in terms of overall ecological impact (defined as ‘‘ecosystem risk’’). A statistical linear model was used to quantify the risk–cost–catch frontier for each of the three forms of risk. The most parsimonious models were statistically significant for each. However, the management and research costs were mostly positively correlated with risk, indicating that these tended to be reactive to risk, as opposed to risk decreasing in response to increased costs. The only model where this was not the case was for the ecosystem risk, which is probably because these risks have only recently been assessed and the management response to these risks across all the fisheries has so far been limited. For target species risks, it was not possible to develop a model for proactive use. However, the method itself has merit and, if the costs were defined to a greater level of resolution, and/or a time-dynamic modelling approach considered, these issues could potentially be addressed.

Factors constraining the structure of food webs can be investigated by comparing classes of ecosystems. We find that pelagic ecosystems, those based on one-celled primary producers, have longer food chains than terrestrial ecosystems. Yet pelagic ecosystems have lower primary productivity, contrary to the hypothesis that greater energy flows permit higher trophic levels. We hypothesize that longer food chain length in pelagic ecosystems, compared with terrestrial ecosystems, is associated with smaller pelagic animal body size permitting more rapid trophic energy transfer. Assuming negative allometric dependence of biomass production rate on body mass at each trophic level, the lowest three pelagic animal trophic levels are estimated to add biomass more rapidly than their terrestrial counterparts by factors of 12, 4.8, and 2.6. Pelagic animals consequently transport primary production to a fifth trophic level 50-190 times more rapidly than animals in terrestrial webs. This difference overcomes the approximately fivefold slower pelagic basal productivity, energetically explaining longer pelagic food chains. In addition, ectotherms, dominant at lower pelagic animal trophic levels, have high metabolic efficiency, also favoring higher rates of trophic energy transfer in pelagic ecosystems. These two animal trophic flow mechanisms imply longer pelagic food chains, reestablishing an important role for energetics in food web structure.

Sustainable fisheries management into the future will require both understanding of and adaptation to climate change. A risk management approach is appropriate due to uncertainty in climate projections and the responses of target species. Management strategy evaluation (MSE) can underpin and support effective risk management. Climate change impacts are likely to differ by species and spatially. We use a spatial MSE applied to a multi-species data-poor sea cucumber/beche-de-mer fishery to demonstrate the utility of MSE to test the performance of alternative harvest strategies in meeting fishery objectives; this includes the ability to manage through climate variability and change, and meeting management objectives pertaining to resource status and fishery economic performance. The impacts of fishing relative to the impacts of climate change are distinguished by comparing future projection distributions relative to equivalent no-fishing no-climate-change trials. The 8 modelled species exhibit different responses to environmental variability and have different economic value. Status quo management would result in half the species falling below target levels, moderate risks of overall and local depletion, and significant changes in species composition. The three simple strategies with no monitoring (spatial rotation, closed areas, multi-species composition) were all successful in reducing these risks, but with fairly substantial decreases in the average profit. Higher profits (for the same risk levels) could only be achieved with strategies that included monitoring and hence adaptive management. Spatial management approaches based on adaptive feedback performed best overall.

Prince, J. D., Dowling, N. A., Davies, C. R., Campbell, R. A., and Kolody, D. S. 2011. A simple cost-effective and scale-less empirical approach to harvest strategies. – ICES Journal of Marine Science, 68: 947–960. An empirical harvest strategy for an Australian longline fishery was developed and tested using harvest strategy evaluation. The approach is based on catch rate and size composition indices and iteratively drives the population towards a target level of spawning potential ratio. The simulation model assumed traditional age-structured population dynamics. The results demonstrate that the empirical approach represents a promising feedback strategy that becomes more precautionary as data errors and recruitment variance increase. Given the urgent need for formal harvest strategies to replace ad hoc decision-making in data-poor fisheries, the approach offers considerable promise. Independent implementation at local scales across the range of a species is speculated to result in management that is robust to uncertainty about stock structure.

The greenlip abalone (Haliotis laevigata) population in Waterloo Bay, South Australia, has undergone collapse and fishery closure twice since 1978. A rich data set, including survey measures of degree of spatial aggregation, has been gathered over that time and provides a unique opportunity to identify factors accounting for persistence or collapse, an issue that is of significance to abalone fisheries worldwide. Statistical analyses of fishery and survey data were undertaken to infer functional relationships between catch, effort, recruitment, adult density, and extent of aggregation. Catch rates were hyperstable, an observation consistent with the targeting of large aggregations. Statistical analysis of recruitment showed a significant year effect, implying an environmental signal, and suggested that aggregation size impacts fertilization success. Aggregation size grew under closure, suggesting an impact of fishing on this demographic feature. Aggregative behaviour appears to be critical for subpopulation sustainability. Fishery collapse may be triggered by depletion of larger abalone aggregations by heavy fishing, resulting in declines in fertilized egg production. When heavy fishing coincides with unfavourable environmental conditions, recruitment may be insufficient to sustain the subpopulation. If aggregation is similarly critical for other subpopulations, management strategies could be adapted according to the extent of aggregation in each subpopulation.

Although active targeting of abalone aggregations is documented for various species, its impact on large aggregations is poorly understood. As large aggregations make the greatest contribution to reproductive success, yet are vulnerable to exploitation, it is important to understand how targeted fishing impacts aggregation structure. If observed postfishing patterns are equally likely to have occurred in response to more random, nontargeted fishing, then changes in aggregation patterns cannot be directly attributed to aggregation-based targeting behaviour. The effect of targeted fishing on greenlip abalone (Haliotis laevigata) aggregations was verified by simulating three different levels of "random" fishing behaviour to generate postfishing aggregation frequency distributions. Comparison of the output with postfishing survey distributions suggested that observed aggregation patterns could not have resulted from random search behaviour. The aggregation survey data can therefore be used as a valid basis on which to quantify both fishing behaviour in terms of aggregation-specific catch patterns and the response of aggregations to fishing.

One of the endeavours to address the shortage of catch per unit effort (CPUE) information from the Indonesian Indian Ocean tuna fishery is the collation of a large amount of catch and effort data collected by Indonesian Fisheries High School students (“FHS data”). This paper attempts to investigate spatial-temporal patterns of catch and effort of the FHS data for the main tuna species caught by the fishery: bigeye tuna (Thunnus obesus - BET), yellowfin tuna (T. albacares - YFT), albacore (T. alalunga - ALB) and southern bluefin tuna (T.maccoyii - SBT). Reported sets occurred in the Eastern Indian Ocean, north and south of 20°S. Recorded effort from the FHS data set was concentrated within the only known SBT spawning ground. However, within this data set, SBT were recorded in the lowest catch proportion relative to BET, YFT and ALB. The catch composition data suggested that YFT and ALB were predominantly targeted by the fishery, with ALB and SBT most predominantly recorded south of 20°S, whereas BET and YFT were mostly recorded north of 20°S. Unfortunately, there was no strong information on targeting practices reported by this data set, limiting any attempts to understand the factors that influenced those results. As the sampling predominantly occurred in between July and December, the data are not representative of fishing activities throughout the entire year, and any seasonal patterns from the FHS data set are biased. In addition, the FHS data set is prone to observation error and uncertainty in terms of fish identification and fishing location. Therefore, the FHS data set needs to be interpreted with caution.