Data-limited fisheries: Incorporating expert knowledge into stock assessment
- University of Helsinki
Stock assessment is a critical step in fisheries management, since it directly estimates reference points that help determine whether a population’s size is acceptable and subsequently, to set harvest levels. Therefore, many international agreements require that all exploited aquatic populations are assessed quantitatively. However, for the majority of the worlds’ harvested fish stocks, data is lacking. Such fisheries are often referred to as data-poor or data-limited and are a major challenge for stock assessment scientists and fisheries managers, since the traditional approaches to stock assessment cannot be implemented. The necessity to assess the status of all fisheries, led to the development of models tailored to data-limited situations. In this thesis, I first introduced the characteristics of data-limited fisheries, and then described the various quantitative indicators and models developed to assess them, some of which are widely used in real assessment schemes. I reviewed the approaches by their input requirements and their biological realism. Compared to the models used to assess data-rich stocks, models tailored to data-limited stock assessment contain a large degree of uncertainty and therefore, I recommended further exploration of the existing datalimited approaches. This thesis continued by focusing on a particular group of data-limited assessment methods, which are based on stock reduction analysis. Although such models can cope with low data availability, at the same time, they are particularly sensitive to the misspecification of relative stock status (expressed as the current biomass level relative to virgin biomass), a critical input requirement. However, stock status is unavailable for the majority of data-limited stocks. Therefore, I explored different sources of information used to estimate stock status under such circumstances. First, I considered the use of fisheries experts’ opinion and presented a method to elicit expert knowledge using a novel, user-friendly on-line application. To evaluate the experts’ ability to predict stock status, I compared the elicited distributions to stock statuses derived from data-rich models. In this work, I explored the performance of experts with different levels of experience in stock assessment, since scientific expertise is not evenly distributed throughout the world, and quantified how well they performed relative to each other. The results indicated that the true stock status is the most significant factor accounting for bias in expert opinions, followed by their experience level. Nevertheless, expert opinions are often used to inform management decisions and this thesis revealed that for data-limited stock assessment, expert elicited stock status priors potentially can be highly biased, leading to highly biased harvest recommendation levels. A way to overcome this issue is by calibrating expert judgment. To achieve this, my coauthors and I developed a hierarchical Bayesian model for expert calibration. The model’s main assumption is that experts’ biases vary as a function of the true value of the parameter, as identified in the expert elicitation experiment. Experts’ bias function was explicitly modeled, following the supra-Bayesian approach, using Gaussian processes to construct the prior, and the results of the expert elicitation experiment were used as calibration data to infer the posterior. The constructed models were tested both with simulated data and with the expert elicitation results. The tested models for expert judgment calibration, substantially improved stock status predictions compared to those that were uncalibrated and in comparison to vague uniform guesses, thereby demonstrating the value of calibration in minimizing expert bias. In the last article included in this thesis, uncalibrated and calibrated expert opinion derived stock status priors were compared to productivity and susceptibility (PSA) vulnerability scores and catch trend-derived (Boosted regression trees; BRTs) stock status priors. Furthermore, the performance of each of these methods was evaluated and compared to a commonly used prior that assumes a stock is at B40% (i.e. 40% of the virgin biomass). First, I evaluated the degree of bias in estimating true stock status and then, the effect of bias on the estimation procedure of overfishing limits (OFLs) in the specific assessment models for ten data-rich stocks. All, with the exception of fisheries experts with no experience in stock assessment, provided more accurate priors about stock status than the B40% rule. Experts with experience in stock assessment produced particularly informative and accurate priors, exemplifying their important role in the assessment procedure. Based on the performance evaluation and the data requirements for constructing a stock status prior, I recommended a procedure for selecting the most appropriate prior(s).
|Myöntöpäivämäärä||28 toukokuuta 2019|
|Tila||Julkaistu - 28 toukokuuta 2019|
|OKM-julkaisutyyppi||G5 Tohtorinväitöskirja (artikkeli)|