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Introduction

Overview

Since the 1980s, fisheries economists have employed spatial models in a variety of fisheries to better understand and explain the factors that influence the spatial behavior and fishery participation choices that fishers make when fishing. This is important for predicting how fishers may respond to, for example, marine protection areas (MPAs), climate-related species range shifts, changes in fishing costs or fish prices, fish size differences, or the implementation of various management actions such as catch share policies.

The Spatial Economics Toolbox for Fisheries (FishSET) is a set of statistical programming and data management tools developed to achieve the following goals:

• Standardize data management and organization.
• Provide easily accessible tools to enable location choice models to provide input to the management of key fisheries.
• Organize statistical code so that predictions of fisher behavior developed by the field’s leading innovators can be incorporated and transparent to all users.

Modeling fisher behavior is important for designing effective fishery management policy. Failing to account for fisher behavior can lead to unanticipated consequences or the failure of the management policy (Abbott and Haynie 2012, Hilborn 2007, Peterson 2000). Discrete choice random utility maximization (RUM) models, which utilize explanatory variables at the level of individual vessels to predict aggregate effort levels in alternative fishing locations, have become more widely used in fisheries (Eales and Wilen 1986, Raphael 2017, Depalle, Thebaud, and Sanchirico 2020). These models assume that fishers choose, from a discrete set of fishing locations, a location to maximize expected utility, which is modeled as a function of expected revenue, distance from the fisher’s current location, other information about the vessel or fisher, and/or other information about the potential fishing locations (e.g. depth, temperature, spatial quota limits). Commonly used RUM modeling techniques have been conditional and multinomial logit models, nested logit models, and the mixed logit model (Train 2002). Many model variants and formulations have been developed by economists in fisheries and other fields, with innovations motivated by the policy question being analyzed, the nature and extent of available data, and the statistical background and computing power of the analysts which has increased greatly with time.