Shepperson, J. L., van der Vaart, E., Murray, L. G., Bell, E., Mackinson, S., and Kaiser, M. J.
All work completed by JS, with supervisory input from EV, LGM, EB, SM and MJK.
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6.1. Abstract
Human behaviour is an area of considerable uncertainty in fisheries management; failing to account for the behavioural response of fishermen can lead to unintended consequences of management.
Individual-based models (IBMs) could help to address some of the knowledge gaps in our understanding of fisher behaviour, and help both managers and fishers better predict and
understand the potential consequences of different management scenarios. Nevertheless, a lack of comprehensively validated fishery IBMs may have hindered their application in fisheries
management. In particular, models are often built with little consideration given to alternative possible submodels of fishing behaviour. By contrasting alternative decision models, or ‘theories’ of behaviour, more robust models could be developed.
The primary objectives were to design an IBM of the Isle of Man scallop fishery, and then use it to develop and test different submodels, or theories, for patch choice behaviour by fishing vessels.
Approximate Bayesian Computation was used to select for models that generated outputs closest to real fishery values in vessel monitoring system and logbook data.
Using simple foraging decision rules, parameterised using data collected directly from fishermen, it was possible to build an IBM that could reproduce patterns seen in the Isle of Man scallop fishery with reasonable similarity. The model was able to reproduce realistic values for the extent of fishing, average trip CPUE, average fishing hours per trip, average steaming hours per trip, average fuel used, average landings, and total landings across the fishing season. The development process increased our understanding of fishing behaviour in the Isle of Man scallop fishery, and provided insights into how to predict fishing behaviour in a model environment. In particular, it highlighted the importance of including a random component of fishing behaviour (e.g. to account for gut feeling), rather than using only fully informed behaviour.
Predicting responses to management by modelling fishers under the assumption that they act in an economically rational manner, or as optimal foragers, may overestimate the capacity of the fleet to compensate for restrictions such as closed areas, and may underestimate the economic impact that a management measure may have on the fishery.
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6.2. Introduction
Human behaviour is an area of considerable uncertainty in fisheries management (Fulton et al., 2011). Failing to account for the behavioural response of fishermen to management can lead to unintended consequences of management, and even produce negative environmental, economic, or social effects (Hilborn et al., 2004; Pascoe and Mardle, 2005). An inability to foresee (or failure to consider) the displacement of effort following management can lead to unintended consequences (Dinmore et al., 2003). To implement effective fisheries management we should be confident that fishers will respond to management actions as intended, but to do this we need a good
understanding of fisher behaviour and how to predict it (Bacalso et al., 2013; Charles, 1995; Gordon, 1953; Hallwass et al., 2013; Hilborn, 2007; Marchal et al., 2007; Murray et al., 2011; Salas and Gaertner, 2004; Wilen et al., 2002).
6.2.1. Individual-based modelling could be a good platform to better understand fishing activity
Individual-based models (IBMs) could help to address some of the knowledge gaps in our understanding of fisher behaviour, and allow us to create simulation tools that could help both managers and fishers better predict and understand the potential consequences of different management scenarios (Evans, 2012; Grimm and Railsback, 2005). IBMs view systems as having properties that arise from the behaviours and interactions of the individuals that make up the system (Grimm and Railsback, 2005). This makes it relevant for modelling fishing behaviour, as it is the decisions made by, and behaviours of, individual fishermen that drive the spatial patterns seen in the system (Plaganyi et al., 2014; Hilborn, 2007).
With advances in computing power, IBMs present an opportunity to model complex systems with more realism than previously possible (van der Vaart et al., 2015). Increasingly complex models can, however, be criticised as being ‘black boxes’, that are too complex to really understand and
communicate (Topping et al., 2010). The structure of a model is a compromise between realism, complexity, and efficiency (Evans, 2012; Evans et al., 2013); an IBM must capture all of the processes and heterogeneity required to understand the system, but must also not be overly computationally demanding, or so complex that parameter uncertainty renders it too complex for application.
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6.2.2. Open, simple, realistic model development
Optimal Foraging Theory (OFT) (MacArthur and Pianka, 1966) has been demonstrated as a suitable framework for investigating fisher behaviour (Begossi, 1992; Begossi et al., 2009; de Oliveira and Begossi, 2011; Lee et al., 2014; Sosis, 2002). OFT states that individuals aim to maximise their net energy intake over time (analogous to catches or profit for a fisher), and is therefore comparable to assuming fishers follow profit maximisation behaviour (Holland, 2008). Modelling fishers under the framework of OFT provides a relatively simple, established model of patch selection behaviours on which to base a model. Nevertheless, the questionnaire surveys (Chapter 2) and analysis of VMS and logbook data (Chapter 3) suggested that there may be violations to some of the assumptions of OFT;
namely that all fishers do not have equal abilities, fishers may not have complete knowledge of catch rates in the system, and importantly, not all fishers may be true profit maximisers (Chapter 2,
Chapter 3). An IBM provides a more flexible framework within which to account for deviations from such theory (Grimm and Railsback, 2005).
To parameterise an IBM, a detailed understanding of the behaviours in the system is required.
Collecting data directly from fishers can be termed fishers knowledge (FK), and can provide useful and reliable information on a fishery system (O’Donnell et al., 2012; Shepperson et al., 2014, 2016;
Teixeira et al., 2013). Using data collected directly from fishers may help to make a model more realistic, for example providing boundary conditions such as a maximum distance a vessel is able or willing to travel. In addition, it may help to keep the model simpler, allowing redundant processes to be excluded, for example, fishers consistently stated they were able to fish very close to one
another, therefore no displacement competition between vessels needed to be modelled (Chapter 3), whereas this has been shown to be important in other fishery systems (Rijnsdorp, 2000).
Understanding more about fishing strategies through interviewing fishers also highlighted a potential need to include individual variability in capabilities and objectives / requirements, and suggested some fishers may not be true profit maximisers (Chapter 2).
6.2.3. Pattern Oriented Modelling In IBMs
IBMs are often developed using pattern oriented modelling (POM), which is essentially a protocol to build and evaluate IBMs (Grimm et al., 2005; Grimm and Railsback, 2012). The first stage of POM uses patterns in the real system to determine the entities and processes needed in the model; the second stage of POM considers how to find realistic representations of these processes, using alternative submodels of different complexity or structure to represent each process (Grimm and