David Tomberlin

Endangered seabird habitat management as a partially observable Markov decision process.

Abstract The marbled murrelet (Brachyramphus marmoratus) is an endangered seabird that nests in coastal forests from Alaska to California. The value of these forests for human use, coupled with the difficulty of determining whether a forest stand is occupied by nesting marbled murrelets, poses a dilemma for land managers. Should they implement a costly survey to gather information on whether a potential nest site is occupied, or should they allow human use, effectively assuming the site is unoccupied? This article demonstrates the application of the partially observable Markov decision process (POMDP) as a framework for addressing this question. The analysis yields a policy in which the optimal action is a function of the decision-makers subjective probability that a potential nest site is occupied by marbled murrelets. By incorporating stochastic state dynamics and the choice of whether to invest in learning, the POMDP provides a formal representation of adaptive management when active learning is possible. JEL Classification Codes: Q22, Q23

Hierarchical Analysis of Production Efficiency in a Coastal Trawl Fishery

We present, pedagogically, the Bayesian approach to composed error models under alternative, hierarchical characterizations; demonstrate, briefly, the Bayesian approach to model comparison using recent advances in Markov Chain Monte Carlo (MCMC) methods; and illustrate, empirically, the value of these techniques to natural resource economics and coastal fisheries management, in particular. The Bayesian approach to fisheries efficiency analysis is interesting for at least three reasons. First, it is a robust and highly flexible alternative to commonly applied, frequentist procedures, which dominate the literature. Second, the Bayesian approach is extremely simple to implement, requiring only a modest addition to most natural-resource economist tool-kits. Third, despite its attractions, applications of Bayesian methodology in coastal fisheries management are few.

Estimating Fishing Vessel Capacity: A Comparison of Nonparametric Frontier Approaches

Abstract Fishing capacity has been an important national and international topic for over a decade. Led by the Food and Agriculture Organization of the United Nations (FAO), an international effort was undertaken in 1998 to define and measure fishing capacity, during which three methods to measure fishing capacity were identified–data envelopment analysis (DEA), stochastic production frontiers (SPF), and the peak-to-peak approach. Most estimates of capacity have been carried out using DEA. This study introduces “order-m” frontiers and the free disposal hull (FDH) as additional methods to estimate fishing capacity, and compares capacity estimates for a group of fishing vessels based on the DEA, FDH, and order-m models. Our results show a large difference between capacity estimates using DEA when compared to the other two methods. JEL Classification Codes: Q22, D24

Bayesian Ranking and Selection of Fishing Boat Efficiencies

The steadily accumulating literature on technical efficiency in fisheries attests to the importance of efficiency as an indicator of fleet condition and as an object of management concern. In this paper, we extend previous work by presenting a Bayesian hierarchical approach that yields both efficiency estimates and, as a byproduct of the estimation algorithm, probabilistic rankings of the relative technical efficiencies of fishing boats. The estimation algorithm is based on recent advances in Markov Chain Monte Carlo (MCMC) methods—Gibbs sampling, in particular—which have not been widely used in fisheries economics. We apply the method to a sample of 10,865 boat trips in the US Pacific hake (or whiting) fishery during 1987-2003. We uncover systematic differences between efficiency rankings based on sample mean efficiency estimates and those that exploit the full posterior distributions of boat efficiencies to estimate the probability that a given boat has the highest true mean efficiency.

Practical Precautionary Resource Management Using Robust Optimization

AbstractnUncertainties inherent in fisheries motivate a precautionary approach to management, meaning an approach specifically intended to avoid bad outcomes. Stochastic dynamic optimization models, which have been in the fisheries literature for decades, provide a framework for decision making when uncertain outcomes have known probabilities. However, most such models incorporate population dynamics models for which the parameters are assumed known. In this paper, we apply a robust optimization approach to capture a form of uncertainty nearly universal in fisheries, uncertainty regarding the values of model parameters. Our approach, developed by Nilim and El Ghaoui (Oper Res 53(5):780–798, 2005), establishes bounds on parameter values based on the available data and the degree of precaution that the decision maker chooses. To demonstrate the applicability of the method to fisheries management problems, we use a simple example, the Skeena River sockeye salmon fishery. We show that robust optimization offers a structured and computationally tractable approach to formulating precautionary harvest policies. Moreover, as better information about the resource becomes available, less conservative management is possible without reducing the level of precaution.n

Bayesian hierarchical estimationof technical efficiency in a fishery

This article presents a Bayesian hierarchical approach to estimating stochastic production frontiers in fisheries. Based on our application of this approach to the US West Coast hake fishery, we conclude that (1) panel models with hierarchical structure to allow for boat- and year-specific efficiency measures are preferable to simpler specifications, and (2) there appears to have been a progressive outward shift in the efficient frontier in the shore-based hake fishery during 1987–2003.

Trip-Level Analysis of Efficiency Changes in Oregon’s Deepwater Trawl Fishery

In 2003, an industry-financed, government-administered buyback of trawl fishing permits and vessels took place on the US West Coast, resulting in the retirement of about one-third of the limited-entry trawl fleet. The lack of cost data in this fishery precludes an analysis of how the buyback has affected profitability, but changes in technical efficiency can provide some insight into the program’s effects. This paper, the first of a planned series of analyses of the buyback’s effect on technical efficiency in the trawl fleet, applies stochastic frontier analysis to assess whether technical efficiency changed perceptibly after 2003. We adopt a hierarchical modeling approach estimated with Markov Chain Monte Carlo methods, and present results from both Cobb-Douglas and translog specifications. The analysis is limited to 13 boats active in Oregon’s deepwater ‘DTS’ fishery, which targets dover sole, thornyheads, and sablefish. The results suggest that the buyback has had little impact on trip-level technical efficiency in the study fishery. However, departures from the frontier are markedly bi-modal, indicating that a mixed-density approach to estimation may be more appropriate.