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.     

CPUE standardisation and the construction of indices of stock abundance in a spatially varying fishery using general linear models

Construction of annual indices of stock abundance based on catch and effort data remains central to many fisheries’ assessments. While the use of more advanced statistical methods has helped catch rates to be standardised against many explanatory variables, the changing spatial characteristics of most fisheries data sets provide additional challenges for constructing reliable indices of stock abundance. After reviewing the use of general linear models to construct indices of annual stock abundance, potential biases which can arise due to the unequal and changing nature of the spatial distribution of fishing effort are examined and illustrated through the analysis of simulated data. Finally, some options are suggested for modelling catch rates in unfished strata and for accounting for the uncertainties in the stock and fishery dynamics which arise in the interpretation of spatially varying catch rate data.     

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.     

Modelling South Pacific jack mackerel spatial population dynamics and fisheries

Since the 1970s, South Pacific jack mackerel (Trachurus murphyi) is one of the world's most important commercial exploited fish stock. The peak in the catch was achieved in the 1990s, after which the catch for all fleets steadily decreased due to strong fishing mortality and potentially unfavourable environmental conditions. An application of the ecosystem and fish population model SEAPODYM was developed for this species in the South Pacific Ocean to determine the extent of environmental and fisheries drivers on the stock dynamics. We combined publicly available fishing data, acoustic biomass estimates and expert knowledge to optimise fish population dynamics parameters (habitats, movements, natural and fishing mortality). Despite a large proportion of missing catch over the simulation period, the model provides realistic distributions of biomass, a good fit‐to‐data and is in agreement with the literature. The feeding habitat is predicted to be delineated by water temperature between 15°C for the first cohorts and 8.5°C for the oldest and dissolved oxygen concentration above 1.8 ml/L. Optimal spawning temperature is estimated to 15.57°C (S.E.: 0.75°C). The core habitat is predicted off Central Chile which is also the main fishing ground. There are other areas of higher fish concentration east of New Zealand, in the eastern part of the southern convergence and off Peru and northern Chile. However, there is a clear continuity between these different large sub‐populations. Fishing is predicted to have by far the highest impact, a result that should be reinforced if all fishing mortality could be included.     

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.