Trade‐offs in transitions between indigenous and commercial fishing sectors: the Torres Strait tropical rock lobster fishery

A bio‐economic model was developed to evaluate aspects of proposed quota‐based constraints vs the current effort control regulations for the tropical rock lobster, Panulirus ornatus, Fabricius, fishery in the Torres Strait (Australia/Papua New Guinea). The analysis integrates across biological, economic and social considerations. Model performance indicators have been chosen to reflect higher level policy objectives. The model simulation results indicate important trade‐offs. There is lower overall fleet total profit (across all the subfleets), lower fishery total value added and lower total employment if the fishery is quota‐constrained. This is due to an assumed rationalisation driven by incentives and current utilisation of capacity. The simulated re‐allocation of quota from the commercial non‐indigenous fleet allowing for greater potential indigenous fisher participation results in predicted increases in indigenous employment and would meet social objectives; however, due to limited capacity in the indigenous fleet, the simulated predicted lower catches led to lower total fishery profits and decreased total fishery value added within the supply chain. Investment in capacity could potentially offset this result.     

Theories and behavioural drivers underlying fleet dynamics models

In the domain of decision‐support tools for the management of marine fish resources, considerable attention has been paid to the development of models explaining how fish stocks change over space and time. In most models, fishing effort is assumed to be exogenous and determined by factors such as management. Increasingly, there has been a call for bio‐economic models to also account for the dynamics of fishing fleets, recognizing that fishers respond to changing environmental, institutional and economic conditions. A growing literature has sought to explicitly model the endogenous determinants of the capacity of fishing fleets, the intensity of its use and its temporal and spatial allocation across fishing opportunities. We review this literature, focusing on empirical applications of the behavioural models that have been put forward to explain and predict observed fleet dynamics. We find that although economic factors are usually included as a dominant driver in most studies, this is often based on the use of proxy variables for the key economic drivers, for which adequate data are lacking. Also, while many studies acknowledge that social and social–psychological factors play a significant role in explaining observed fishing behaviour, their inclusion in fishing fleet dynamic models is still very limited. Progress in this domain can only be achieved via the development of multidisciplinary research programmes focusing on applied quantitative analysis of the drivers of fishing fleet dynamics.     

Risk sensitivity and the behaviour of fishing vessels

Risk sensitivity is an important component of fisher behaviour, yet its impact on fisher decision‐making and utility is poorly understood. Here, we incorporate various forms of risk in a model of location choice by fishing vessels targeting broadbill swordfish, Xiphias gladius, and evaluate the importance of risk sensitivity for predicting location, perceived utility and profit over a fishing season. We consider short‐term, or trip‐level, risk as natural, social or endogenous. Longer‐term, or seasonal, risk includes accumulated profit as a state variable. When considering only short‐term risk, endogenous and social risks are predicted to have the largest impact on profit, while natural risk also is predicted to impact on location choice. When longer‐term (seasonal) risk was considered, the impacts of short‐term (trip‐level) risks on profit were reduced. Model results were somewhat sensitive to assumed input parameters. The model allows users to quantify the extent of risk interaction and the relative sensitivity of perceived utility, location choice and profit. By providing a utility function capturing the fisher decision‐making process, the model provides a platform for the consideration of simultaneous forms of risk under different circumstances.     

Can catch share fisheries better track management targets?

Fisheries management based on catch shares – divisions of annual fleet‐wide quotas among individuals or groups – has been strongly supported for their economic benefits, but biological consequences have not been rigorously quantified. We used a global meta‐analysis of 345 stocks to assess whether fisheries under catch shares were more likely to track management targets set for sustainable harvest than fisheries managed only by fleet‐wide quota caps or effort controls. We examined three ratios: catch‐to‐quota, current exploitation rate to target exploitation rate and current biomass to target biomass. For each, we calculated the mean response, variation around the target and the frequency of undesirable outcomes with respect to these targets. Regional effects were stronger than any other explanatory variable we examined. After accounting for region, we found the effects of catch shares primarily on catch‐to‐quota ratios: these ratios were less variable over time than in other fisheries. Over‐exploitation occurred in only 9% of stocks under catch shares compared to 13% of stocks under fleet‐wide quota caps. Additionally, over‐exploitation occurred in 41% of stocks under effort controls, suggesting a substantial benefit of quota caps alone. In contrast, there was no evidence for a response in the biomass of exploited populations because of either fleet‐wide quota caps or individual catch shares. Thus, for many fisheries, management controls improve under catch shares in terms of reduced variation in catch around quota targets, but ecological benefits in terms of increased biomass may not be realized by catch shares alone.     

Cluster analysis of linear model coefficients under contiguity constraints for identifying spatial and temporal fishing effort patterns

For fisheries management purposes, it is essential to take into account spatial and seasonal characteristics of fishing activities to allow a reliable assessment of fishing impact on resource. This paper presents a novel technique for describing spatial and temporal patterns in fishing effort. The spatial and seasonal fishing activity patterns of the French trawler fleet in the Celtic Sea during the period 1991–1998 were analysed by modelling fishing effort (fishing time) with generalised linear models. The linear model for fishing effort included fixed effects for both spatial (statistical rectangles) and temporal units (months). In addition, spatial correlations in any given month were modelled by an exponentially decreasing function. Temporal correlations were included using the previous month's fishing effort for a given spatial unit as predictor. A method based on cluster analysis of estimated model coefficients of spatial or temporal fixed effects is proposed for identifying groups of similar spatial and temporal units. A contiguity constraint is imposed in the clustering algorithm, ensuring that only neighbouring spatial units or consecutive temporal units are grouped. The cluster analysis identified 22 spatial and 9 temporal groups. Winter and spring months stood out as being more variable than the remaining months. Spatial groups were of varying size, and generally larger offshore. The proposed method is generic and could for example be used to analyse temporal and spatial patterns in catch or catch rate data.     

A predator–prey model for evaluating the spatial behaviour of fishing fleets: a case study of the red grouper Epinephelus morio fishery of Yucatan,Mexico

A predator–prey model was applied to the red grouper Epinephelus morio fishery from Yucatan, Mexico, with the aim of understanding the spatial behaviour of a mid-sized fishing fleet. The heterogeneity of the environment was considered, and different habitat quality levels were determined to address environmental differences. The behaviour of the fleet was also evaluated based on a simulation of restricted fishing areas (RFA). The model was applied utilising 5 years of information on catch and fishing effort (1973–1977). Four levels of habitat quality were defined (low, medium, high and exceptional). The total costs and revenue for a fishing expedition in the areas of exceptional habitat quality were higher due to their location farthest from the base port. The fleet was most efficient during the winter at locations in the eastern continental shelf, with the number of these areas clearly decreasing in the autumn. The model simulation of the RFA showed a total revenue reduction of 7–27 % in scenarios of RFA for adult protection. When the RFA was implemented for the protection of juvenile fish, the total revenue was not reduced, indicating that this type of RFA might be a feasible management strategy for a sustainable fishery.     

Spatio‐temporal distribution of albacore (Thunnus alalunga) catches in the northeastern Atlantic: relationship with the thermal environment

When the spring seasonal warming starts, North Atlantic albacore (Thunnus alalunga) juveniles and pre‐adults perform a trophic migration to the northeastern Atlantic, to the Bay of Biscay and to the southeast of Ireland. During this migration, they are exploited by Spanish trolling and baitboat fleets. The present study analyzes the relationship between the albacore spatio‐temporal distribution and the thermal environment. For this approach, several analyses have been performed on a database including fishing logbooks and sea surface temperature (SST) images, covering the period between 1987 and 2003. SST values and the SST gradients at the catch locations have been statistically compared to broader surrounding areas to test whether the thermal environment determines the spatial distribution of albacore. General additive models (GAM) have been used also to evaluate the relative importance of environmental variables and fleet behaviour. The results obtained show that, although juvenile albacore catch locations are affected by fleet dynamics, there is a close spatial and temporal relationship with the seasonal evolution of a statistically significant preferential SST window (16–18°C). However, differences have been identified between the relationship of albacore with SST within the Bay of Biscay in July and August (higher temperature). Such differences are found also in the spatial distribution of the catch locations; these reflect clearly the presence of two groups, differentiated after the third week of the fishing campaign at the end of June. The analysis undertaken relating the distribution of North Atlantic albacore juveniles with thermal gradients did not provide any evidence of a relationship between these catch locations and the nearby occurrence of thermal gradients.     

Global fishing capacity and fishing effort from 1950 to 2012

Global marine wild‐capture landings have remained relatively stable for >20 years; however, there is a lack of credible fishing capacity and effort information required to assess the sustainability and efficiency of the global fleet. As such, we estimated global fishing capacity and effort from 1950 to 2012 using a relatively comprehensive database developed by the FAO, supplemented by other data sources. Using random sampling techniques, we estimated the uncertainty surrounding many of our estimates enabling the identification of deficiencies and limitations. Global fishing capacity and effort increased rapidly from the late 1970s through to around 2010 before stabilizing. The Asian fleet is more than an order of magnitude larger than any other region in both capacity and effort, and continues to increase. Most other regions have stabilized, and there have been considerable declines in Europe and, to a lesser extent, in North America. Developed nations, as a whole, have decreased in both measures in the recent years and are responsible for the stabilization of the global trend. Developing and undeveloped countries are still increasing with the former having the largest fleet and showing the greatest relative increase with the socioeconomic impacts of reversing these trends likely to be high. The efficiency of the global fleet, in terms of watt days of fishing effort per tonnage of wild marine catch, is now less than in 1950 despite the considerable technological advances, and expansion throughout the world's oceans, that has occurred during this period of time.     

Does ‘race to fish’ behaviour emerge in an individual transferable quota fishery when the total allowable catch becomes non‐binding?

Successful individual transferable quota (ITQ) management requires a binding (constraining) total allowable catch (TAC). A non‐binding TAC may result in a shift back towards open access conditions, where fishers increasingly compete (‘race’) to catch their share of the total harvest. This process was examined by comparing fishing fleet behaviour and profitability in the Tasmanian southern rock lobster (Jasus edwardsii) fishery (TSRLF), Australia. Between 2008 and 2010, the TSRLF had a non‐binding TAC and effectively reverted to a regulated, limited‐entry fishery. Fishers' uncertainty about future profitability and their ability to take their allocated catch weakened the security characteristic of the ITQ allocation. The low quota lease price contributed to an increase in fleet capacity, while the more limited reduction in quota asset value proved an investment barrier, hindering the autonomous adjustment of quota towards the most efficient fishers. In the TSRLF, catch rates vary more than beach price and are therefore more important for determining daily revenue (i.e., price x catch rate) than market price. Consequently, fishers concentrated effort during times of higher catch rates rather than high market demand. This increased rent dissipation as fishers engaged in competitive race to fish to be the first to exploit the stock and obtain higher catch rates. The history of this fishery emphasizes the need for a constraining TAC in all ITQ fisheries, not only for stock management, but also to manage the security of the ITQ allocation and prevent unanticipated and undesirable changes in fisher behaviour and fishery profitability.     

MEY = MSY

It has been generally accepted for more than half a century that the fishing sector stood to gain from managing fisheries at the effort level producing maximum economic yield (MEY) rather than at the higher effort level producing maximum sustainable yield (MSY). However, the acceptance is built on evaluating only the revenue and cost structure for the fishing fleet, not for the overall fishing sector including processing, distribution and marketing of fish products. Considering these links of the fish value chain moves the MEY-level closer to, but slightly below the MSY-level. For society as a whole, this means that MSY is the more appropriate target reference level.     

Fishing effort redistribution in response to area closures

Spatial fishery closures will induce fishing effort to either move to open areas or to cease to fish. When designing a short- or long-term closed area management regime, the expected impact of that closure will depend upon how that effort is redistributed. We present a redistribution model based upon Ideal Free Distributions (IFDs) which is intermediate in complexity between analyses in which effort is distributed uniformly over open areas and models of full fleet dynamics. The IFD models incorporate the fundamentals of the decision process invoked by fishers facing relocation and the ensuing catch rates that result from the addition or removal of effort. Two classes of models were tested: an Availability model where catch rate declines were proportional to abundance; and an Abundance model where abundance declines at an exponential rate with the entry of displaced effort into an area. Results of these models were compared with uniform and proportional redistribution methods. The IFD-based methods included relative cost of relocation, thereby illustrating the importance of both catch rates and movement costs in designing closed area regulations. To demonstrate the methods, hypothetical area closures to United States pelagic longliners in the western Atlantic were examined and the impact of those closures on bycatch rates was evaluated. Guidance for selecting an appropriate model structure for a particular closed area problem is given.     

Analysing commercial catch and effort data from a Penaeid trawl fishery: A comparison of linear models, mixed models, and generalised estimating equations approaches

Statistical methods are often used to analyse commercial catch and effort data to provide standardised fishing effort and/or a relative index of fish abundance for input into stock assessment models. Achieving reliable results has proved difficult in Australia's Northern Prawn Fishery (NPF), due to a combination of such factors as the biological characteristics of the animals, some aspects of the fleet dynamics, and the changes in fishing technology. For this set of data, we compared four modelling approaches (linear models, mixed models, generalised estimating equations, and generalised linear models) with respect to the outcomes of the standardised fishing effort or the relative index of abundance. We also varied the number and form of vessel covariates in the models. Within a subset of data from this fishery, modelling correlation structures did not alter the conclusions from simpler statistical models. The random-effects models also yielded similar results. This is because the estimators are all consistent even if the correlation structure is mis-specified, and the data set is very large. However, the standard errors from different models differed, suggesting that different methods have different statistical efficiency. We suggest that there is value in modelling the variance function and the correlation structure, to make valid and efficient statistical inferences and gain insight into the data. We found that fishing power was separable from the indices of prawn abundance only when we offset the impact of vessel characteristics at assumed values from external sources. This may be due to the large degree of confounding within the data, and the extreme temporal changes in certain aspects of individual vessels, the fleet and the fleet dynamics.     

Quantifying fishing effort: a synthesis of current methods and their applications

The need to accurately quantify fishing effort has increased in recent years as fisheries have expanded around the world and many fish stocks and non‐target species are threatened with collapse. Quantification methods vary greatly among fisheries, and to date there has not been a comprehensive review of these methods. Here we review existing approaches to quantify fishing effort in small‐scale, recreational, industrial, and illegal, unreported and unregulated (IUU) fisheries. We present the strengths and limitations of existing methods, identifying the most robust methods and the critical knowledge gaps that must be addressed to improve our ability to quantify and map fishing effort. Although identifying the ‘best’ method ultimately depends on the intended application of the data, in general, quantification methods that are based on information on gear use and spatial distribution offer the best approaches to representing fishing effort on a broad scale. Integrating fisher’s knowledge and involving fishers in data collection and management decisions may be the most effective way to improve data quality and accessibility.     

Thirty years of fleet dynamics modelling using discrete‐choice models: What have we learned?

Anticipating fisher behaviour is necessary for successful fisheries management. Of the different concepts that have been developed to understand individual fisher behaviour, random utility models (RUMs) have attracted considerable attention in the past three decades, and more particularly so since the 2000s. This study aimed at summarizing and analysing the information gathered from RUMs used during the last three decades around the globe. A methodology has been developed to standardize information across different studies and compare RUM results. The studies selected focused on fishing effort allocation. Six types of fisher behaviour drivers were considered: the presence of other vessels in the same fishing area, tradition, expected revenue, species targeting, costs, and risk‐taking. Analyses were performed using three separate linear modelling approaches to assess the extent to which these different drivers impacted fisher behaviour in three fleet types: fleets fishing for demersal species using active gears, fleets fishing for demersal species using passive gears and fleets fishing for pelagic species. Fishers are attracted by higher expected revenue, tradition, species targeting and presence of others, but avoid choices involving large costs. Results also suggest that fishers fishing for demersal species using active gears are generally more influenced by past seasonal (long‐term) patterns than by the most recent (short‐term) information. Finally, the comparison of expected revenue with other fisher behaviour drivers highlights that demersal fishing vessels are risk‐averse and that tradition and species targeting influence fisher decisions more than expected revenue.     

Fisheries management scenarios: trade‐offs between economic and biological objectives

An age‐structured, population biology submodel and an economic submodel with vessel‐specific dynamics were applied to a demersal fishery in the North Aegean (NE Mediterranean) that consists of three main stocks: European hake Merluccius merluccius (L.), red mullet Mullus barbatus L. and striped red mullet Mullus surmuletus L. The Mefisto model is a bioeconomic simulation model through which the biological and economic submodels (disaggregated at the vessel level) are linked by means of a fishing mortality vector. Alternative management scenarios were built and tested based on input controls, and the performance of these strategies was examined against those of current policies. Three alternative management strategies were as follows: (1) reducing the coastal fisheries' fleet units; (2) limiting the effort level (days at sea) of the trawl fleets; and (3) changing the selectivity patterns of the trawl by increasing mesh size. The results show that for all three species, any of the three management measures (input controls) would be beneficial to both the stock and the fleets (over the medium and long terms) compared with the projections over time for the status quo. Improving the selectivity of the fishing gear proved to be more beneficial than limiting nominal effort, which was in turn more beneficial than decreasing coastal fleet size.     

Bioeconomic evaluation of fisheries enforcement effort using a multifleet simulation model

The economics of enforcement is traditionally modelled using rational agents assumed to act according to their expected economic utility. In this investigation we derive this expectation by implementing a stochastic simulation model to integrate across associated levels of uncertainty using Monte Carlo. The model is developed for the South Georgia toothfish longline fishery, in which legal catches are strictly managed according to the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) conservation measures. Assuming full compliance in the legal fishery, this investigation deals with the role of a fishing patrol vessel (FPV) in excluding illegal fishing vessels. Both legal and illegal fleets are dynamic. For the legal fleet, catch is set by a harvest control rule within the model framework, with vessels leaving if fishing becomes unprofitable. The illegal catch is determined by the number of vessels operating, which predicts catch on the basis of an estimated production function. The exit of illegal vessels is determined by the probability of detection by the FPV, which is a function of days spent on patrol, and estimated profits. The entry of illegal vessels occurs at a fixed mean rate, with the model integrating over uncertainty in this parameter. An obvious trade off exists in that higher levels of illegal activity lead to lower expected returns to the legal fleet. We thus provide a cost-benefit framework for investigating the trade-offs associated with decisions on enforcement in the fishery, and confirm that current enforcement effort levels are well justified.     

A mail survey to estimate the fishery dynamics of southern Florida's bonefish charter fleet

Abstract Bonefish, Albula vulpes (L.), support an economically important recreational fishery in southern Florida, USA that has received little scientific study and has never been adequately assessed. A mail survey of 322 captains that comprise the southern Florida bonefish charter fleet was conducted to acquire a baseline understanding of the primary fishery statistics. The response rate was 59% and a follow‐up telephone survey of non‐respondents indicated no non‐response bias. Experience in the fishery ranged from 3 to 61 years. The annual fishing effort was 30 875 boat days. The majority of the fleet fishing effort occurred in the northern Florida Keys and is presumed to reflect bonefish abundance. The instantaneous mortality rate of released fish was 0.11 year^?1. The majority of the respondents indicated that the bonefish stock had declined over the past decade.     

A multispecies approach to subsetting logbook data for purposes of estimating CPUE

An initial step in catch and effort analysis is determination of what subset of the data is relevant to the analysis. We propose an objective approach to subsetting trip records of catch and effort data when fishing locations are unknown; the species composition taken on a fishing trip is used to infer if that trip's fishing effort occurred in a habitat where the species of interest (the target species) is likely to occur. We use a logistic regression of multispecies presence–absence information to predict the probability that the target species would be present. A critical value of probability that best predicts target species presence and absence in the data set forms an objective basis for subsetting the trip records. We test this approach by applying it to a data set where individual fishing locations are known, and we show that the method is an effective substitute for information on individual fishing locations.