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.

A maximum-likelihood method for estimating natural mortality and catchability coefficient from catch-and-effort data

A simple stochastic model of a fish population subject to natural and fishing mortalities is described. The fishing effort is assumed to vary over different periods but to be constant within each period. A maximum-likelihood approach is developed for estimating natural mortality (M) and the catchability coefficient (q) simultaneously from catch-and-effort data. If there is not enough contrast in the data to provide reliable estimates of both M and q, as is often the case in practice, the method can be used to obtain the best possible values of q for a range of possible values of M. These techniques are illustrated with tiger prawn (Penaeus semisulcatus) data from the Northern Prawn Fishery of Australia.

Quota and Licensing Systems in the VIII Division European Anchovy

Paper presented at 12th Annual Conference of EAERE 2003 Bilbao (Spain)

Estimating relative abundance from catch and effort data, using neural networks

We develop and test a method to estimate relative abundance from catch and effort data using neural networks. Most stock assessment models use time series of relative abundance as their major source of information on abundance levels. These time series of relative abundance are frequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models (GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundance of the population. However, GLMs are restricted in the types of relationships between the CPUE and the explanatory variables. An alternative approach is to use structural models based on scientific understanding to develop complex non-linear relationships between CPUE and the explanatory variables. Unfortunately, the scientific understanding required to develop these models may not be available. In contrast to structural models, neural networks uses the data to estimate the structure of the non-linear relationship between CPUE and the explanatory variables. Therefore neural networks may provide a better alternative when the structure of the relationship is uncertain. We use simulated data based on a habitat based-method to test the neural network approach and to compare it to the GLM approach. Cross validation and simulation tests show that the neural network performed better than nominal effort and the GLM approach. However, the improvement over GLMs is not substantial. We applied the neural network model to CPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.

The relationship between catch and effort and its effect on predicted yields of sardine fisheries in Lake Kariba

There is an unusual relationship between catch per unit effort and effort in the Lake Kariba sardine (Limnothrissa miodon) fishery. This is apparently a results of ecological changes in the lake following the decline of the Salvinia mats that existed there until 1973. Predictive models based on the entire data set (1974-89) are of limited value because they are influenced by the rapid decline in catch per unit effort that took place from 1974 to 1978. A model based on the 1980-89 data indicates that the current catch could be increased substantially. Some empirical models and features of the sardine's biology suggest that it is a realistic model.

Application of the Leslie Model to Commercial Catch and Effort of the Slipper Lobster, Scyllarides squammosus, Fishery in the Northwestern Hawaiian Islands

Commercial catch and effort data were fit to the Leslie model to estimate preexploitation abundance and the catchability coefficient of slipper lobster, Scyllarides squammosus, in the Northwestern Hawaiian Islands (NWHI). A single vessel fished for 34 consecutive days in the vicinity of Laysan Island and caught 126,127 total slipper lobster in 36,170 trap hauls. Adjusted catch of legal slipper lobster dropped from a high of 3.70 to 1.16 lobster per trap haul. Preexploitation abundance at Laysan Island was an estimated 204,000 legal slipper lobster, which was extrapolated to yield an estimate of 1.2 X 106 to 3.8 X 106 lobster for the entire NWHI slipper lobster fishery.

The relationship between multi-species catch and effort: among fishery comparisons

The control of fishing mortality via fishing effort remains fundamental to most fisheries management strategies even at the local community or co-management level. Decisions to support such strategies require knowledge of the underlying response of the catch to changes in effort. Even under adaptive management strategies, imprecise knowledge of the response is likely to help accelerate the adaptive learning process. Data and institutional capacity requirements to employ multi-species biomass dynamics and age-structured models invariably render their use impractical particularly in less developed regions of the world. Surplus production models fitted to catch and effort data aggregated across all species offer viable alternatives. The current paper seeks models of this type that best describe the multi-species catch–effort responses in floodplain-rivers, lakes and reservoirs and reef-based fisheries based upon among fishery comparisons, building on earlier work. Three alternative surplus production models were fitted to estimates of catch per unit area (CPUA) and fisher density for 258 fisheries in Africa, Asia and South America. In all cases examined, the best or equal best fitting model was the Fox type, explaining up to 90% of the variation in CPUA. For lake and reservoir fisheries in Africa and Asia, the Schaefer and an asymptotic model fitted equally well. The Fox model estimates of fisher density (fishers km−2) at maximum yield (iMY) for floodplain-rivers, African lakes and reservoirs and reef-based fisheries are 13.7 (95% CI [11.8, 16.4]); 27.8 (95% CI [17.5, 66.7]) and 643 (95% CI [459,1075]), respectively and compare well with earlier estimates. Corresponding estimates of maximum yield are also given. The significantly higher value of iMY for reef-based fisheries compared to estimates for rivers and lakes reflects the use of a different measure of fisher density based upon human population size estimates. The models predict that maximum yield is achieved at a higher fishing intensity in Asian lakes compared to those in Africa. This may reflect the common practice in Asia of stocking lakes to augment natural recruitment. Because of the equilibrium assumptions underlying the models, all the estimates of maximum yield and corresponding levels of effort should be treated with caution.

Amazon fisheries. I. - Variations in the relative abundance of tambaqui ( Colossoma macropomum Cuvier, 1818) based on catch and effort data of the gill-net fisheries.

Adjusted mean catches which are calculated discounting the effect of average fishing effort increase the further the lake is from Manaus, Brazil. -from Author

Catch and effort from a recreational trolling fishery in a large lake

Over recent decades recreational fisheries have grown substantially throughout the world. Despite this increase, catches from recreational fisheries have often been ignored in fisheries management, although this is now being remedied. Monitoring recreational fisheries can be expensive, and the primary means used for monitoring is angler (creel) surveys, typically funded from sales of fishing licences. The studies presented in this thesis examine different approaches to monitoring recreational trolling fisheries’ catch and effort, where fishing licenses are not required and there are no reporting requirements. I present results from a complemented roving/mail-in survey undertaken during 2013-2014 to estimate recreational effort and catch of Atlantic salmon (Salmo salar) and brown trout (S. trutta) in the largest lake in the European Union, Lake Vänern, Sweden. I also evaluate different angler catch reporting methods (mail-in, tournament reports and face-to-face interviews) and compare catch rates within and among spring and fall fishing periods. In addition, mail-in survey data are examined for recall bias.   I estimate that 28.7 tonnes of salmon and trout combined were harvested by the recreational trolling fishery in 2014, more than the commercial and subsistence fisheries combined. Seasonal differences in both recreational effort and catch were observed. Effort, in boat hours, was significantly higher in spring than in fall. Catch rates of trout were higher in fall than in spring, but there were no seasonal differences in catches of salmon. Harvest per boat day did not differ significantly among catch reporting methods, indicating that all three methods could be useful for managers interested in harvest rates. In contrast, total and released catch per boat day differed among reporting methods, with tournament anglers catching more fish in total. Finally, there was little evidence for recall bias in mail-in surveys, indicating that mail-in surveys are useful for collecting unbiased catch data. My study is the most comprehensive angler survey to date for Lake Vänern, and my results should be of immediate use to local fisheries managers and should also be of interest to researchers and managers interested in estimating catch and effort for fisheries at large spatial scales.

Standardized geo-referenced catch, fishing effort and length-frequency data for the Indian Ocean Bigeye Tuna, Thunnus obesus (1952-2014)

Geo-referenced catch and fishing effort data of the bigeye tuna fisheries in the Indian Ocean over 1952-2014 were analysed and standardized to facilitate population dynamics modelling studies. During this sixty-two years historical period of exploitation, many changes occurred both in the fishing techniques and the monitoring of activity. This study includes a series of processing steps used for standardization of spatial resolution, conversion and standardization of catch and effort units, raising of geo-referenced catch into nominal catch level, screening and correction of outliers, and detection of major catchability changes over long time series of fishing data, i.e., the Japanese longline fleet operating in the tropical Indian Ocean. A total of thirty fisheries were finally determined from longline, purse seine and other-gears data sets, from which 10 longline and four purse seine fisheries represented 96% of the whole historical catch. The geo-referenced records consists of catch, fishing effort and associated length frequency samples of all fisheries.

Effects of fish density distribution and effort distribution on catchability

The effects of fish density distribution and effort distribution on the overall catchability coefficient are examined. Emphasis is also on how aggregation and effort distribution interact to affect overall catch rate [catch per unit effort (cpue)]. In particular, it is proposed to evaluate three indices, the catchability index, the knowledge parameter, and the aggregation index, to describe the effectiveness of targeting and the effects on overall catchability in the stock area. Analytical expressions are provided so that these indices can easily be calculated. The average of the cpue calculated from small units where fishing is random is a better index for measuring the stock abundance. The overall cpue, the ratio of lumped catch and effort, together with the average cpue, can be used to assess the effectiveness of targeting. The proposed methods are applied to the commercial catch and effort data from the Australian northern prawn fishery. The indices are obtained assuming a power law for the effort distribution as an approximation of targeting during the fishing operation. Targeting increased catchability in some areas by 10%, which may have important implications on management advice.