André E. Punt

Fisheries stock assessment and decision analysis: the Bayesian approach

The Bayesian approach to stock assessment determines the probabilities of alternative hypotheses using information for the stock in question and from inferences for other stocks/species. These probabilities are essential if the consequences of alternative management actions are to be evaluated through a decision analysis. Using the Bayesian approach to stock assessment and decision analysis it becomes possible to admit the full range of uncertainty and use the collective historical experience of fisheries science when estimating the consequences of proposed management actions. Recent advances in computing algorithms and power have allowed methods based on the Bayesian approach to be used even for fairly complex stock assessment models and to be within the reach of most stock assessment scientists. However, to avoid coming to ill-founded conclusions, care must be taken when selecting prior distributions. In particular, selection of priors designed to be noninformative with respect to quantities of interest to management is problematic. The arguments of the paper are illustrated using New Zealands western stock of hoki, Macruronus novaezelandiae (Merlucciidae) and the Bering--Chukchi--Beaufort Seas stock of bowhead whales as examples

Standardization of catch and effort data in a spatially-structured shark fishery

Abstract The methods used to develop catch rate based indices of relative abundance for the school shark Galeorhinus galeus resource off southern Australia are outlined. These methods are based on fitting generalized linear models to catch and effort data for several regions in this fishery. This is to take account of the multi-gear nature of the fishery and the spatial structure of the trends in catch rate. The data on whether or not the catch rate is zero and the catch rate given that it is non-zero are analysed separately and then combined to provide indices of abundance. The former analysis is based on assuming the data are Bernoulli random variables. Given the uncertainty about the appropriate error-model to assume when fitting generalized linear models to catch and effort data, four alternative error-models — log-normal, log-gamma, Poisson, and negative binomial — were explored when modelling the non-zero catch rates.

On implementing maximum economic yield in commercial fisheries

Economists have long argued that a fishery that maximizes its economic potential usually will also satisfy its conservation objectives. Recently, maximum economic yield (MEY) has been identified as a primary management objective for Australian fisheries and is under consideration elsewhere. However, first attempts at estimating MEY as an actual management target for a real fishery (rather than a conceptual or theoretical exercise) have highlighted some substantial complexities generally unconsidered by fisheries economists. Here, we highlight some of the main issues encountered in our experience and their implications for estimating and transitioning to MEY. Using a bioeconomic model of an Australian fishery for which MEY is the management target, we note that unconstrained optimization may result in effort trajectories that would not be acceptable to industry or managers. Different assumptions regarding appropriate constraints result in different outcomes, each of which may be considered a valid MEY. Similarly, alternative treatments of prices and costs may result in differing estimates of MEY and their associated effort trajectories. To develop an implementable management strategy in an adaptive management framework, a set of assumptions must be agreed among scientists, economists, and industry and managers, indicating that operationalizing MEY is not simply a matter of estimating the numbers but requires strong industry commitment and involvement.

Information flow among fishing vessels modelled using a Bayesian network

Abstract Reaction of fishers is an essential source of uncertainty in implementing fishery management decisions. Provided they realistically capture fisher behaviour, models of fishing vessel dynamics provide the basis for evaluating the impact of proposed management strategies. Information flow among vessels has not been a major focus of such models however, although it might play a critical role in how a fleet responds to changes to management restrictions or levels of a resource. Such a response might then modify subsequent exploitation of the resource. In this paper, a spatially-explicit model of vessel fishing behaviour is developed for a line fishery on the Great Barrier Reef, Australia. Vessel behaviour is conditioned on past catch and effort data at a spatial resolution of 6×6 nautical mile grid cells. For each vessel, the probability of fishing a particular grid cell is determined from past income per unit effort experienced at that location, and the cost of steaming to it. The probability distribution across all possible grid cells represents a particular vessel’s perspective. This perspective is modified by information conveyed by other vessels using Bayesian-network information propagation. The information conveyed is the effort distribution of other vessels and is equivalent to a vessel ‘watching’ where other vessels fish. We compare the behaviours that vessels display when they act independently with those they display when they ‘watch’ each other, and show the effect that such information flow can have on a resource. Information flow among fishing vessels can be shown to have an effect on the dynamics and resource exploitation of a simulated fishery.

Stock assessment of school shark, Galeorhinus galeus, based on a spatially explicit population dynamics model

The school shark (Galeorhinus galeus) resource off southern Australia is assessed by use of an assessment approach that takes account of the spatial structure of the population. The population dynamics model underlying the assessment considers the spatial as well as the age-specific characteristics of school shark. It allows for a series of fisheries (each based on a different gear type), explicitly models the pupping/recruitment process, and allows for multiple stocks. The values for the parameters of this model are determined by fitting it to catch-rate data and information from tagging studies. The point estimates of the pup production at the start of 1997 range from 12% to 18% of the pre-exploitation equilibrium size, depending on the specifications of the assessment. Allowing for spatial structure and incorporating tag release–recapture data lead to reduced uncertainty compared with earlier assessments. The status of the resource, as reflected by the ratio of present to virgin pup production and total (1+) biomass, is sensitive to the assumed level of movement between the stocks in New Zealand and those in Australia, with lower values corresponding to higher levels of movement.

Estimating the size-transition matrix for Tasmanian rock lobster, Jasus edwardsii

Assessment of the southern rock lobster (Jasus edwardsii) resource in Tasmania is based on a size-structured population dynamics model. One of the most important inputs to this model is the set of matrices that represent the season-specific probabilities of a lobster growing from one size-class to another. These matrices are estimated from tag–recapture data within a maximum-likelihood estimation framework. Measures of precision are determined from the asymptotic variance–covariance matrix. Various alternative models are contrasted for one site in the south-east of Tasmania, and a best model is selected by the likelihood ratio test. The growth model used is based on a generalization of the von Bertalanffy growth equation. Growth rates differ markedly among regions around Tasmania, being slowest in the south and fastest in the north. Growth of legal-size males is noticeably faster than that of legal-size females. It is shown that ignoring the effects of selectivity can lead to biased estimates of growth rate. An extension to the method is presented and applied that estimates size-specific selectivity in an attempt to eliminate this bias.

Review of progress in the introduction of management strategy evaluation (MSE) approaches in Australia's South East Fishery

The MSE approach provides a simulation-based framework within which harvest strategies, stock assessment methods, performance indicators and research programmes can be compared. This approach has been used in the Australian South East Fishery (SEF) to assess harvest strategies for the over-exploited eastern gemfish resource and to compare different levels of discard monitoring for blue grenadier. The main challenges to use of the MSE approach in the SEF are poorly specified management objectives and the lack of quantitative stock assessments on which to build operating models for many of the species.

Reconciling Approaches to the Assessment and Management of Data-Poor Species and Fisheries with Australia's Harvest Strategy Policy

Abstract There is an increasing expectation for decision makers to use robust scientific advice on the status of exploited fish stocks. For example, Australia has recently implemented a harvest strategy policy for federally managed fisheries based on limit and target biomass reference points. In common with most fisheries jurisdictions, however, Australia has many data-poor species and fisheries for which biomass estimates are unavailable. Consequently, the challenge for those tasked with providing management advice for Australian fisheries has been reconciling the need to achieve specific risk-related sustainability objectives with the reality of the available data and assessments for data-poor species and fisheries. Some general recommendations regarding how to achieve this balance are drawn using case studies from two multispecies trawl fisheries. The lack of data on which to base quantitative stock assessments using population dynamics models does not preclude the development of objective harvest control rules. Evaluation of harvest control rules using technical procedures (e.g., the management strategy evaluation approach) is ideal, but implementation before rigorous testing is sometimes a necessary reality. Information from data-rich species and fisheries can be used to inform “assessments” for data-poor species and thereby develop appropriate control rules. This can be done through formal methods, such as the “Robin Hood” approach (in which assessments from data-rich species are used to inform assessments of data-poor species), or less formally by grouping species into “baskets” and basing management decisions on one appropriate member of the group. Stakeholder knowledge and buy-in to the process of developing appropriate harvest strategies are essential when species or fisheries are data poor. Use of this information, however, needs to be constrained by policy decisions, such as prespecified performance standards. There will always be a trade-off between the cost of data collection and the value of a fishery; in this article, we highlight that this trade-off does not have to be a major impediment to the development of realistic and sufficiently precautionary control rules for the management of data-poor species and fisheries.

Length-Based Reference Points for Data-Limited Situations: Applications and Restrictions

Abstract Current fisheries management policies generally require an assessment of stock status, which is a difficult task when population and fisheries data are limited. Three simple metrics based on catch length compositions (i.e., that reflect exclusive take of mature individuals, Pmat; that consist primarily of fish of optimal size, the size at which the highest yield from a cohort occurs, Popt; and that demonstrate the conservation of large, mature individuals, Pmega) can be used to monitor population status relative to exploitation. The metrics (collectively referred to as Px) were intended to avoid growth and recruitment overfishing, but there was no quantitative linkage to stock status and calculation of future sustainable catches. We attempt to make this connection by exploring the relationship of Px measures to fishing mortality and spawning biomass (SB). The relationships are compared specifically to the current target reference point (0.4 times the virgin, or unfished, SB [SB0]) and limit reference point (0.25SB0) used for the U.S. West Coast groundfish fishery by using simulations based on a deterministic age-structured population dynamics model. Sensitivity to fishery selectivity, life history traits, and recruitment compensation (steepness) is explored. Each Px measure showed a wide range of possible values depending on fishery selectivity, steepness, and the ratio of the length at maturity (Lmat) to the optimal fishing length (Lopt). Although the values of Px may be compatible with sustainable fishing, these values are not always sufficient to ensure stock protection from overfishing. Moreover, values for Px cannot be interpreted adequately without knowledge of the selectivity pattern. A new measure, Pobj (the sum of Pmat, Popt, and Pmega), is introduced to distinguish selectivity patterns and construct a decision tree for development of stock status indicators. Heuristic indicator values are presented to demonstrate the utility of this approach. Although several caveats remain, this approach builds on the recommendations of previous literature by giving further guidance related to interpreting catch length composition data under variable fishery conditions without collecting additional information. It also provides a link to developing harvest control rules that inform proactive fisheries management under data-limited conditions.

Stock assessment of the blue grenadier Macruronus novaezelandiae resource off south-eastern Australia

The fishery can be divided into two subfisheries (‘spawning’ and ‘non-spawning’). Commercial catch rates for the ‘non-spawning’ subfishery declined from the late 1980s to 1997, whereas those for the ‘spawning’ subfishery exhibit no obvious temporal trend. An ‘Integrated Analysis’ assessment, of the feasibility of reconciling these differing trends, uses catch (landed and discarded), catch rate, length-at-age, and catch-at-age data and estimates of absolute abundance based on the egg-production method. It emphasizes uncertainty due to model assumptions and the data included in the assessment. Use of the discard data allows more precise estimation of the magnitude of recent recruitments. Spawning biomass is estimated to have declined from a peak in 1989–91 to 1999 although fishing mortality has consistently been <6%for each subfishery. One main reason for the reduction in population size is the weakness of year-classes spawned from 1988 to 1993. Differences in catch rates between the two subfisheries can therefore be explained by interactions between the components of the population harvested by the two ‘subfisheries’, and the trends in year-class strength. A risk analysis is used to evaluate the consequences of different future levels of harvest for different assessment assumptions. Overall, the spawning biomass is predicted to increase over the next five to ten years as a result of the strong 1994 and 1995 year-classes, although the extent of this increase remains uncertain.