Using data-limited approaches to assess data-rich Indian Ocean bigeye tuna:Data quantity evaluation and critical information for management implications

The majority of fishery stocks in the world are data limited,which limits formal stock assessments.Identifying the impacts of input data on stock assessment is critical for improving stock assessment and developing precautionary management strategies.We compare catch advice obtained from applications of various datalimited methods(DLMs)with forecasted catch advice from existing data-rich stock assessment models for the Indian Ocean bigeye tuna(Thunnus obesus).Our goal was to evaluate the consistency of catch advice derived from data-rich methods and data-limited approaches when only a subset of data is available.The Stock Synthesis(SS)results were treated as benchmarks for comparison because they reflect the most comprehensive and best possible scientific information of the stock.This study indicated that although the DLMs examined appeared robust for the Indian Ocean bigeye tuna,the implied catch advice differed between data-limited approaches and the current assessment,due to different data inputs and model assumptions.Most DLMs tended to provide more optimistic catch advice compared with the SS,which was mostly influenced by historical catches,current abundance and depletion estimates,and natural mortality,but was less sensitive to life-history parameters(particularly those related to growth).This study highlights the utility of DLMs and their implications on catch advice for the management of tuna stocks.

Effect of calibration data series length on performance and optimal parameters of hydrological model

In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.