Considering recreational catch and harvest in fisheries management at the bio‐regional scale

Abstract The Gulf of Carpentaria in northern Australia supports several commercial fisheries and a largely tourist‐based recreational fishery. The results of a survey of 427 recreational fishing parties visiting the main town, Karumba, between March and September 2006 were examined using the bootstrap method to estimate confidence intervals for mean tourist catch and harvest of grunter, Pomadasys kaakan (Cuvier), and other recreational target species. Tourist anglers harvested between 99.8 and 117 t of P. kaakan and 32.6–38.2 t of blue salmon, Eleutheronema tetradactylum (Shaw), during the survey period. Resident recreational anglers harvested an additional 15–35 t of P. kaakan, but very little E. tetradactylum. In comparison, commercial harvest was 19 t of P. kaakan and 64 t of E. tetradactylum in the whole of the Queensland section of the Gulf of Carpentaria. The results underscore the need for appropriately collected recreational fishing data to support integrated fisheries management at the bioregional scale, and in the case of angling‐based tourist destinations, underpin a diversification of the tourist product.     

Integrating recreational fisheries data into stock assessment: implications for model performance and subsequent harvest strategies

Understanding the impacts of recreational fishing on commercially fished stocks is becoming increasingly relevant for fisheries managers. However, data from recreational fisheries are not commonly included in stock assessments of commercially fished stocks. Simulation models of two assessment methods employed in Australia's Commonwealth fisheries were used to explore how recreational fishery data can be included, and the likely consequences for management. In a data‐poor management strategy for blue eye trevalla, Hyperoglyphe antarctica (Carmichael), temporal trends in recreational catch most affected management outcomes. In a data‐rich age‐structured stock assessment for striped marlin, Kajikia audax (Philippi), estimates of stock status were biased when recreational catches were large or when the recreational fishery targeted different size classes than the commercial fishery and these data were not integrated into the assessment. Including data from recreational fishing can change perceptions of stock status and impact recommendations for harvest strategies and management action. An understanding of recreational fishery dynamics should be prioritised for some species.     

Measuring uncertainty in fisheries stock assessment: the delta method,bootstrap, and MCMC

Fisheries management depends on reliable quantification of uncertainty for decision‐making. We evaluate which uncertainty method can be expected to perform best for fisheries stock assessment. The method should generate confidence intervals that are neither too narrow nor too wide, in order to cover the true value of estimated quantities with a probability matching the claimed confidence level. This simulation study compares the performance of the delta method, the bootstrap, and Markov chain Monte Carlo (MCMC). A statistical catch‐at‐age model is fitted to 1000 simulated datasets, with varying recruitment and observation noise. Six reference points are estimated, and confidence intervals are constructed across a range of significance levels. Overall, the delta method and MCMC performed considerably better than the bootstrap, and MCMC was the most reliable method in terms of worst‐case performance, for our relatively data‐rich scenario and catch‐at‐age model, which was not subject to substantial model misspecification. All three methods generated too narrow confidence intervals, underestimating the true uncertainty. Bias correction improved the bootstrap performance, but not enough to match the performance of the delta method and MCMC. We recommend using MCMC as the default method for quantifying uncertainty in fisheries stock assessment, although the delta method is the fastest to apply, and the bootstrap is useful to diagnose estimator bias.     

Nineteenth century narratives reveal historic catch rates for Australian snapper (Pagrus auratus)

Snapper (Pagrus auratus) is widely distributed throughout subtropical and temperate southern oceans and forms a significant recreational and commercial fishery in Queensland, Australia. Using data from government reports, media sources, popular publications and a government fisheries survey carried out in 1910, we compiled information on individual snapper fishing trips that took place prior to the commencement of fisherywide organized data collection, from 1871 to 1939. In addition to extracting all available quantitative data, we translated qualitative information into bounded estimates and used multiple imputation to handle missing values, forming 287 records for which catch rate (snapper fisher^?1 h^?1) could be derived. Uncertainty was handled through a parametric maximum likelihood framework (a transformed trivariate Gaussian), which facilitated statistical comparisons between data sources. No statistically significant differences in catch rates were found among media sources and the government fisheries survey. Catch rates remained stable throughout the time series, averaging 3.75 snapper fisher^?1 h^?1 (95% confidence interval, 3.42–4.09) as the fishery expanded into new grounds. In comparison, a contemporary (1993–2002) south‐east Queensland charter fishery produced an average catch rate of 0.4 snapper fisher^?1 h^?1 (95% confidence interval, 0.31–0.58). These data illustrate the productivity of a fishery during its earliest years of development and represent the earliest catch rate data globally for this species. By adopting a formalized approach to address issues common to many historical records – missing data, a lack of quantitative information and reporting bias – our analysis demonstrates the potential for historical narratives to contribute to contemporary fisheries management.     

What makes fisheries data informative?

Informative data in fisheries stock assessment are those that lead to accurate estimates of abundance and reference points. In practice, the accuracy of estimated abundance is unknown and it is often unclear which features of the data make them informative or uninformative. Neither is it obvious which model assumptions will improve estimation performance, given a particular data set. In this simulation study, 10 hypotheses are addressed using multiple scenarios, estimation models, and reference points. The simulated data scenarios all share the same biological and fleet characteristics, but vary in terms of the fishing history. The estimation models are based on a common statistical catch‐at‐age framework, but estimate different parameters and have different parts of the data available to them. Among the findings is that a ‘one‐way trip’ scenario, where harvest rate gradually increases while abundance decreases, proved no less informative than a contrasted catch history. Models that excluded either abundance index or catch at age performed surprisingly well, compared to models that included both data types. Natural mortality rate, M, was estimated with some reliability when age‐composition data were available from before major catches were removed. Stock‐recruitment steepness, h, was estimated with some reliability when abundance‐index or age‐composition data were available from years of very low abundance. Understanding what makes fisheries data informative or uninformative enables scientists to identify fisheries for which stock assessment models are likely to be biased or imprecise. Managers can also benefit from guidelines on how to distribute funding and manpower among different data collection programmes to gather the most information.     

Are stock assessment methods too complicated?

This critical review argues that several methods for the estimation and prediction of numbers‐at‐age, fishing mortality coefficients F, and recruitment for a stock of fish are too hard to explain to customers (the fishing industry, managers, etc.) and do not pay enough attention to weaknesses in the supporting data, assumptions and theory. The review is linked to North Sea demersal stocks. First, weaknesses in the various types of data used in North Sea assessments are summarized, i.e. total landings, discards, commercial and research vessel abundance indices, age‐length keys and natural mortality (M). A list of features that an ideal assessment should have is put forward as a basis for comparing different methods. The importance of independence and weighting when combining different types of data in an assessment is stressed. Assessment methods considered are Virtual Population Analysis, ad hoc tuning, extended survivors analysis (XSA), year‐class curves, catch‐at‐age modelling, and state‐space models fitted by Kalman filter or Bayesian methods. Year‐class curves (not to be confused with ‘catch‐curves’) are the favoured method because of their applicability to data sets separately, their visual appeal, simple statistical basis, minimal assumptions, the availability of confidence limits, and the ease with which estimates can be combined from different data sets after separate analyses. They do not estimate absolute stock numbers or F but neither do other methods unless M is accurately known, as is seldom true.     

Estimating fisheries reference points from catch and resilience

This study presents a Monte Carlo method (CMSY) for estimating fisheries reference points from catch, resilience and qualitative stock status information on data‐limited stocks. It also presents a Bayesian state‐space implementation of the Schaefer production model (BSM), fitted to catch and biomass or catch‐per‐unit‐of‐effort (CPUE) data. Special emphasis was given to derive informative priors for productivity, unexploited stock size, catchability and biomass from population dynamics theory. Both models gave good predictions of the maximum intrinsic rate of population increase r, unexploited stock size k and maximum sustainable yield MSY when validated against simulated data with known parameter values. CMSY provided, in addition, reasonable predictions of relative biomass and exploitation rate. Both models were evaluated against 128 real stocks, where estimates of biomass were available from full stock assessments. BSM estimates of r, k and MSY were used as benchmarks for the respective CMSY estimates and were not significantly different in 76% of the stocks. A similar test against 28 data‐limited stocks, where CPUE instead of biomass was available, showed that BSM and CMSY estimates of r, k and MSY were not significantly different in 89% of the stocks. Both CMSY and BSM combine the production model with a simple stock–recruitment model, accounting for reduced recruitment at severely depleted stock sizes.     

Effects of sampling errors on abundance estimates from virtual population analysis for walleye pollock in northern waters of Sea of Japan

ABSTRACT:   Effects of sampling errors on abundance estimates from virtual population analysis (VPA) were quantified with the bootstrap method for stock of walleye pollock in the the Sea of Japan. In the bootstrap method, individual fish measurements were resampled. A total of 1000 bootstrap samples were produced for each year from 1991 to 2001. The coefficients of variation (CV) of catch at age in 2001 ranged 6.1–33.1%. The CV of an abundance estimate in 2001 ranged 9.0–35.7%. Abundance estimates of the oldest age and the latest year, which had larger CVs than the other estimates, were sensitive to sampling errors. Effects of sampling errors became smaller when the catch at age had been accumulated over a few years. Although VPA includes various types of errors, only the sampling errors have room for improvement in reality. Quantifying the effect of sampling error on VPA estimates is essential for sound and efficient stock management, and is emphasized in this study.     

Explaining recreational angling catch rates of Eurasian perch,Perca fluviatilis: the role of natural and fishing‐related environmental factors

Angling catch records are frequently used to reveal fish population developments. It is therefore important to understand the determinants of angling catches. This study focused on angler‐related, biotic and abiotic factors influencing catchability of Eurasian perch, Perca fluviatilis L. A multi‐lake (21 lakes) study based on angling diaries collected in Mecklenburg‐Vorpommern, Germany (2006/2007), found that angler‐related factors such as fishing experience, species preference and bait/lure type had a large impact on perch catch rates. Additionally, environmental conditions (nutritional status and water transparency) affected either the size or the number of perch caught by anglers. Catch rates varied seasonally, which was confirmed by an experimental fishery on a gravel pit (2008). This portion of the study showed that altered food availabilities in the course of the year caused food limitation in perch, which in turn facilitated high catch rates and female‐biased exploitation in autumn. It is concluded that both angler‐related and abiotic factors interact affecting perch catch rates and size of perch captured in recreational angling.     

Estimating stock depletion level from patterns of catch history

The degree to which a stock is depleted is one of the most important quantities in fisheries management because it is used to quantify the success of management and to inform management responses. However, stock depletion is extremely difficult to estimate, particularly with limited data. Using the RAM Legacy database, we developed a boosted regression tree (BRT) model to correlate depletion with a range of predictors calculated from catch data, making the model usable for many fisheries worldwide. The most important predictors were found to be catch trends obtained from linear regressions of scaled catch on time, including regression coefficients for the whole catch time series, the subseries before and after the maximum catch, and in recent years. Eight predictors explain about 80% of variation in depletion. There is a correlation of .5 between measured levels of depletion and the predictions of the BRT model. Predictions are less biased when the stock is fished down below half of the carrying capacity. The BRT model outperforms comparable existing catch‐based depletion estimators and could be used to provide priors for depletion for data‐poor stock assessment methods, or used more directly to provide estimates of the probability that depletion is below a given threshold value.     

A simple method for estimating MSY from catch and resilience

The Law of the Sea requires that fish stocks are maintained at levels that can produce the maximum sustainable yield (MSY). However, for most fish stocks, no estimates of MSY are currently available. Here, we present a new method for estimating MSY from catch data, resilience of the respective species, and simple assumptions about relative stock sizes at the first and final year of the catch data time series. We compare our results with 146 MSY estimates derived from full stock assessments and find excellent agreement. We present principles for fisheries management of data‐poor stocks, based only on information about catches and MSY.     

Estimation of northern pike population sizes via mark–recapture monitoring

Abstract Estimates of population size are needed in populations targeted by recreational fisheries, to determine their conservational status and to formulate fishing regulations. Mark–recapture monitoring is a promising tool for this because of the increased popularity of catch‐and‐release angling. Northern pike, Esox lucius L., population sizes were estimated in four lakes based on experimental mark–recapture fishing using a hierarchical Bayesian model that also estimated natural mortality. The approach was compared with output from traditional Petersen’s population size estimates. Despite intensive fishing with multiple gears, the population size estimates suggested that a substantial proportion of individuals remained unobserved by fishing. Petersen’s estimates depended on the assumed rate of natural mortality, and no mortality rate scenario matched with the Bayesian estimates. Consequently, the number of individuals caught by recreational anglers is a poor proxy of population size for which estimations should be carried out using a modelling approach that also estimates natural mortality.     

Effect of catch-and-release angling on growth of largemouth bass, Micropterus salmoides

Catch‐and‐release angling is popular in many parts of the world and plays an increasingly important role in management of recreational fisheries. Although the magnitude of catch‐and‐release mortality is well documented for many species, potential sublethal effects have been little studied. An experiment was conducted to assess directly the effects of catch‐and‐release angling on growth of largemouth bass, Micropterus salmoides Lacépède. Angling mortality was 0.00 ± 0.092% for largemouth bass caught on plastic grubs. There was no difference (P = 0.57) in weight gain between caught and uncaught fish over a 40‐day angling and recovery period. Although catch‐and‐release angling appears to have no effect on largemouth bass growth, previous studies documented sublethal effects on growth and reproduction in other species, suggesting that the occurrence and magnitude of sublethal effects vary among species.     

From scientific obscurity to conservation priority: Research on angler catch rates is the catalyst for saving the hump‐backed mahseer Tor remadevii from extinction

1. The mahseer (Tor spp.) fishes of South and Southeast Asia are iconic megafaunal species that are highly valued by recreational anglers. Knowledge on their populations is limited owing to the challenges associated with sampling these large‐bodied fishes (>50 kg) in remote monsoonal rivers.
2. Despite its global iconic status among recreational anglers, the hump‐backed mahseer of the Cauvery River (South India) lacked a valid scientific name and was on a trajectory towards extinction until its rapidly declining population status was established by analyses of angler catch records.
3. Angling records from 1998 to 2012 showed that mahseer catch rates had increased in this period. The resulting publication in Aquatic Conservation (AQC) highlighted the positive role of catch‐and‐release angling in providing information on data‐poor species. However, further analyses showed that these catches comprised not one but two distinct phenotypes.
4. Before 1993, all mahseer captured were hump‐backed; since then, a blue‐fin phenotype appeared in catches and subsequently dominated them. These results triggered further studies indicating that the hump‐backed mahseer was the endemic Tor remadevii and that the blue‐fin was the invasive Tor khudree, introduced in 1976 and then stocked periodically from hatcheries.
5. The initial AQC publication successfully demonstrated the high value of organized angling as a monitoring tool for data‐poor fishes and its application to assessing the temporal population patterns of large‐bodied fishes in monsoonal rivers. It was also the catalyst for initiating subsequent studies on T. remadevii that, together, enabled its recent assessment as ‘Critically Endangered’ on the International Union for Conservation of Nature (IUCN) Red List. In the absence of the AQC paper, and the subsequent studies that it triggered, it is highly probable that the species would have remained on a trajectory towards rapid extinction. Instead, the first major steps to safeguarding its future have been taken.

     

Combining fishers’ knowledge and cost‐effective monitoring tools in the management of marine recreational fisheries: A case study of the squid and cuttlefish fishery of the Ría of Vigo (NW Spain)

A new methodology based in the use of fishers’ knowledge and cost‐effective tools to obtain information about marine recreational fisheries (MRF) is presented. The squid and cuttlefish fishery of the Ría of Vigo (NW Spain) was selected because it is managed in a data‐poor environment. In‐depth interviews (57) were conducted with fishers, collecting ecological and socio‐economic information. A cartography of fishing grounds based on their knowledge was obtained, while the intensity of effort and catches was mapped by the monitoring of two vessels with low‐cost GPS data loggers. The 102 shore anglers and 248 recreational boats catch 8 t/year of European squid Loligo vulgaris and 11 t/year of common cuttlefish Sepia officinalis (11% of total catches on these species in the area). Shore anglers fish from 11 ports, while boat fishers use 14 fishing grounds (covering 30 km²). Most of the catches (86%) are landed by boats, and their CPUE is higher in the outer part of the Ría of Vigo. The use of fishers’ knowledge and cost‐effective monitoring is encouraged to obtain information for the management of MRF. Given the economic contribution of MRF (260,000 €/year in direct expenses), this activity should be considered in the regulations.     

An evaluation of the Leslie—DeLury method and of a weighted method for estimating the size of a closed population

A modification of the Leslie—DeLury method for estimating the size of a closed population is presented, and the precision and biases of both the Leslie—DeLury method and the modified method are examined. The modification involves an iterative procedure based on weighted linear regression of the catch-per-unit-effort upon the corrected-cumulative-catch. The standard deviations of the predicted initial population sizes obtained using this weighted method are found to be on the average, sixteen percent smaller than those obtained using the conventional corrected Leslie—DeLury method. Both of these methods have small positive biases which are approximately equal on the average, to one-fifth of the standard deviation of the predicted initial population size. Hence, the weighted method is on the average, both more precise and less biased.The conclusions stated above are based on the results of a simulation model. Simulations were done within the following ranges of parameter values: 2000 N₁ 12000, 1 N_c 15, 0.5 (1 − Exp(−Mf̄q̄)) 0.9, 0.0 < S_f/f̄ 1.0, 20 M 8,

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where N₁ is the initial population size, is the mean effort expended per sampling interval, S_f is the standard deviation of the efforts, M is the number of sampling intervals, is the mean catchability, S_q is the standard deviation of the catch-ability, and N_c is clump size for aggregated catches. The mean catchability is treated as a constant rather than a parameter because it can be set to any specified value by the appropriate selection of the units of measurement of effort.The suggested weighting is developed for catch data which are obtained by random sampling, without replacement, from a closed population (a population in which there is no immigration, no emigration, no natural mortality, and no recruitment) for which there is no interference among the units of effort, and for which the measurement errors in the catches and efforts are negligible. The catchability and the effort are treated as random variables. Aggregation of the catches is allowed by dividing the population among a number of clumps (each of the same size) which are subject to capture.The weights are computed using the equation (

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is the effort expended in interval i, C_i is the catch from interval i, and where the coefficients a and b are obtained from the regression of the squared residuals using a quadratic model with no constant term (( ). The predicted catches, , are obtained from the weighted regression of the catch-per-unit-effort upon the corrected-cumulative-catch (done in the previous iteration). The initial estimates of the predicted catch are obtained using the conected Leslie—DeLury estimate. Five to ten iterations are generally needed to obtain stable estimates.     

Modelling the oceanic habitats of two pelagic species using recreational fisheries data

Defining the oceanic habitats of migratory marine species is important for both single species and ecosystem‐based fisheries management, particularly when the distribution of these habitats vary temporally. This can be achieved using species distribution models that include physical environmental predictors. In the present study, species distribution models that describe the seasonal habitats of two pelagic fish (dolphinfish, Coryphaena hippurus and yellowtail kingfish, Seriola lalandi), are developed using 19 yr of presence‐only data from a recreational angler‐based catch‐and‐release fishing programme. A Poisson point process model within a generalized additive modelling framework was used to determine the species distributions off the east coast of Australia as a function of several oceanographic covariates. This modelling framework uses presence‐only data to determine the intensity of fish (fish km^?2), rather than a probability of fish presence. Sea surface temperature (SST), sea level anomaly, SST frontal index and eddy kinetic energy were significant environmental predictors for both dolphinfish and kingfish distributions. Models for both species indicate a greater fish intensity off the east Australian coast during summer and autumn in response to the regional oceanography, namely shelf incursions by the East Australian Current. This study provides a framework for using presence‐only recreational fisheries data to create species distribution models that can contribute to the future dynamic spatial management of pelagic fisheries.     

Variation in the population density of adult sea-trout, Salmo trutta, in 67 rivers in England and Wales

Abstract– Analysis of annual rod and commercial catches of adult seatrout demonstrated strong positive relationships between large-scale spatial variance (S²_s, variation in catches between rivers for each year) and spatial mean density (x?_s, mean catch per year), and also between temporal variance (S²_t, variation in catches between years for each river) and temporal mean density (x?_t, mean catch for each river). Both relationships were described by a power function, s²=ax^-b. For spatial variability, there were no significant differences between power functions for both rod and commercial catches from the North West, Welsh and South West regions. As the power b was not significantly different from two, relative spatial variability (measured by coefficients of variation (CV)) was fairly constant between years. Significantly higher values of b were obtained for the Wessex (GM b= 2.233) and North East (GM b=2.824) regions, and therefore the increase in CV with mean annual catch was significant but slight for Wessex rivers and marked for North East rivers. For temporal variability, there were no significant differences between power functions for both rod and commercial catches from all 5 regions and therefore a common power function was fitted to the data from all 67 rivers. As the power b was significantly less than two (GM b= 1.729), relative temporal variability (measured by CV) decreased significantly with increasing mean catch per river. Some limitations and implications of this analysis are discussed. Similar results from different regions for both rod and commercial catches suggest that such data do reflect adult population density, in spite of the different methods used to catch the sea-trout, variations in fishing effort and failures to report catches. The analysis of temporal variability provides a basis for classifying the major sea-trout rivers according to their mean annual catch and their relative variability in catches between years (using the CV).     

The application of recreational fishing survey data for ecological research,a case study from Western Port,Australia

There is a world‐wide need for information on the ecology and habitat dependencies of important fish species. Recreational fishing surveys represent a potential source of data to increase our knowledge of fish distribution and habitat relationships. Here, a case study is provided on two key recreational species, King George Whiting Sillaginodes punctatus (Cuvier) and Snapper Chrysophrys auratus (Forster). The data came from a recreational fishing survey where 10,978 interviews were conducted from 1998 to 2013 in Western Port, Victoria, Australia. Spatial mapping of catch rate in relation to depth and habitat showed that S. punctatus distribution was related to seagrass cover while C. auratus distribution was related to deeper reef habitat. Juveniles of both species were more abundant in the south‐eastern section of the bay, where water quality is affected by catchment inputs. Overall, the study showed that the inclusion of spatial and habitat information in the design of recreational fishing surveys can significantly increase understanding of the ecology of key species.     

Following the chain to elusive anglers

Obtaining reliable estimates of important parameters from recreational fisheries is problematic but critical for stock assessment and effective resource management. Sampling methodologies based on traditional design‐based sampling theory, is inadequate in obtaining representative catch and effort data, social or demographical characterization, or fisher behaviour from small hard‐to‐reach components within recreational fisheries (e.g. specialized sport fisheries) that may account for the majority of the catch for some species. A model‐based approach to sampling is necessary. Researchers in other disciplines including epidemiology and social sciences routinely survey rare or ‘hidden’ populations within the general community by penetration of social networks rather than by interception of individuals. We encourage fisheries researchers to rethink survey designs and consider the social elements of recreational fishing. Employing chain‐referral methods, such as respondent‐driven sampling (RDS), may be a statistically robust and cost‐effective option for sampling elusive sub‐elements within recreational fisheries. Chain‐referral sampling methodology is outlined and an example of a complemented ‘RDS‐recapture’ survey design is introduced as a cost‐effective application to estimating total catch in recreational fisheries.