The instantaneous total mortality rate(Z) of a fish population is one of the important parameters in fisheries stock assessment. The estimation of Z is crucial to fish population dynamics analysis,abundance and catch forecast,and fisheries management. A catch curve-based method for estimating time-based Z and its change trend from catch per unit effort(CPUE) data of multiple cohorts is developed. Unlike the traditional catch-curve method,the method developed here does not need the assumption of constant Z throughout the time,but the Z values in n continuous years are assumed constant,and then the Z values in different n continuous years are estimated using the age-based CPUE data within these years. The results of the simulation analyses show that the trends of the estimated time-based Z are consistent with the trends of the true Z,and the estimated rates of change from this approach are close to the true change rates(the relative differences between the change rates of the estimated Z and the true Z are smaller than 10%). Variations of both Z and recruitment can affect the estimates of Z value and the trend of Z. The most appropriate value of n can be different given the effects of different factors. Therefore,the appropriate value of n for different fisheries should be determined through a simulation analysis as we demonstrated in this study. Further analyses suggested that selectivity and age estimation are also two factors that can affect the estimated Z values if there is error in either of them,but the estimated change rates of Z are still close to the true change rates. We also applied this approach to the Atlantic cod(G adus morhua) fishery of eastern Newfoundland and Labrador from 1983 to 1997,and obtained reasonable estimates of time-based Z.
We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, China. We used logbooks from five commercial fishing boats and data in government's monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude), and another included just two variables, month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore, month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month, but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively, indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September, with relative estimate errors (REE) of <18%. However, the yields in October and November were significantly underestimated, with REEs of 36% and 27%, respectively.
We evaluated the effect of various error sources in fishery harvest/effort data on the maximum sustainable yield （MSY） and corresponding fishing effort （EMsv） using Monte Carlo simulation analyses. A high coefficient of variation （CV） of the catch and effort values biased the estimates of MSY and EMsv. Thus, the state of the fisheries resource and its exploitation was overestimated. We compared the effect using three surplus production models, Hilborn-Waters （H-W）, Schnute, and Prager models. The estimates generated using the H-W model were significantly affected by the CV. The Schnute model was least affected by errors in the underlying data. The CVof the catch data had a greater impact on the assessment than the CV of the fishing effort. Similarly, the changes in CV had a greater impact on the estimated maximum sustainable yield （MSY） than on the corresponding estimate of fishing effort （EMsY）. We discuss the likely effect of these biases on management efforts and provide suggestions for the improvement of fishery evaluations.
