Fisher behaviour within the framework of Optimal Foraging Theory

Fishers and exploited fish/shellfish populations can be considered analogous to animal predator-prey systems, in which fishers are predators competing for a particular predator-prey resource. However, whilst substantial research has been conducted on the exploited ‘prey’ in fisheries, the behaviours and population dynamics of the ‘predators’ (fishers) have received less attention (van Putten et al., 2012). This imbalance in fisheries systems leads us to understand only half of a coupled system. A number of authors have demonstrated Optimal Foraging Theory (OFT) (MacArthur and Pianka, 1966) to be a suitable framework for investigating fisher behaviour (Begossi, 1992; Begossi et al., 2009; de Oliveira and Begossi, 2011; Lee et al., 2014; Sosis, 2002). Optimal foraging theory states that

individuals aim to maximise their net energy intake over time (analogous to catches or profit for a fisher); there are several models under the umbrella of OFT relevant to modelling fishers (Figure 1.1).

Figure 1.1 Optimal Foraging Models. Blue shaded boxes indicate those explored further during the PhD

An extension to OFT incorporates the Ideal Free Distribution (IFD; Fretwell and Lucas, 1969), which predicts that foragers will distribute themselves proportionally to the amount of resources in an area, with each forager receiving equal benefits. Therefore, more foragers will be present in resource-rich patches, but the overall return rate will be equal between foragers. The IFD has been demonstrated to offer a good estimation of the distribution of fishers moving between distinct

37 foraging sites (Gillis, 2001, 2003; Voges et al., 2005) but significant deviations from this prediction also have been found (Abernethy et al., 2007).

Other theoretical models relevant to fisheries fall under the framework of OFT: Patch Choice Models determine where to search for food items; Marginal Value Theorem determines how long to search for items; and Central Place Theorem predicts foraging levels given the distance travelled (Charnov, 1976; MacArthur and Pianka, 1966; Orians and Pearson, 1979; Figure 1.1). Marginal Value Theorem (MVT; Charnov, 1976) has been used to predict how long fishers should stay in a fishing ground, with some success, although fishers have been shown to operate sub-optimally, staying longer than MVT predicts is economically optimal (Begossi, 1992). De Oliveira and Begossi (2011) have also

demonstrated that the predictions of the Central Place Foraging Theorem (CPF) hold true; time searching inside a patch, and optimal load size (i.e. catch size) increases with distance, as the forager tries to compensate for the increased costs of travelling further. Prey Choice Models determine when a forager should change their target prey species. In single-species fisheries there may not be an alternative prey or target species; however, it could also be considered analogous to sourcing alternative income. The level of off sector pluriactivity has been shown to be a strong determinant in predicting responses to management (Gelcich et al., 2005) but the ability to change income source will vary between individuals within and between fisheries (Cambiè et al., 2017).

Under the framework of OFT, according to the MVT, a forager can be expected to use a resource until the energetic cost exceeds the gain (MacArthur and Pianka, 1966); similarly, a fisher could be expected to operate in an area until the perceived benefits of moving to a different location out-weigh the costs. This could be in relation to returning to a patch on subsequent trips, or in moving between patches during a fishing trip. Indeed de Oliveira and Begossi (2011) found that fishers returned more often to grounds where the return rate of the previous trip was higher than the average return for the environment. Griffen (2009) also showed that the use of simple patch leaving rules, based on decision rules according to current consumption rates (c.f. the theory of MVT which has an unrealistic assumption of ideal knowledge of alternate consumption rates), allowed crabs to distribute approximately according to ideal free expectations. The return rate at which a forager decides to change location can be termed the ‘giving up threshold’ (GUT). In fisheries this could represent a resource density below which it is not economically viable to continue fishing. The GUT may vary between individuals, and may depend on a range of variables, such as economic strategy or spatial preferences, and average stock status across all grounds (i.e. how depleted the resources are).

38 With regards to predicting fishing behaviour, OFT and associated models are, however, subject to some unrealistic assumptions, namely: foragers have ideal knowledge of resource levels in each patch; foragers are able to move equally between all patches; and foragers have equal competitive abilities. In reality, this would not be the case; fishers may know estimates of resource densities, but cannot know exact values; larger vessels may have greater potential to travel further and more quickly between patches; and larger vessels may out-compete smaller vessels (Rijnsdorp et al., 2008).

Fishers are also often assumed to be perfectly informed rational profit-maximisers (profit

maximisation can be considered analogous to optimal foraging), who value future profits less than current profits (Holland, 2008). Indeed an unwillingness or inability to accept short term costs in favour of long term benefits may have contributed to the difficulty in reducing overfishing

(Beddington et al., 2007). Nonetheless, in reality, there are likely to be violations to the assumptions of profit maximisation behaviour, but these violations are not well understood in fisheries

(Abernethy et al., 2007; Christensen and Raakjær, 2006; Holland, 2008). The economic drivers for each fisher may be influenced by additional social factors, such as safety, comfort and time (Bene and Tewfik, 2001; Cabrera and Defeo, 2001; Salas and Gaertner, 2004). The response of fishers to management actions may be influenced by these social factors (Abernethy et al., 2007). Christensen

& Raakjær (2006) found that less than 10% of fishers had a strategy based strongly on profit maximisation. Leisure time is often not considered in fisheries economic models, but can be an important trade-off with fishing for longer and achieving higher profits (Abernethy et al., 2007).

Yield- or income-targeting behaviour (Simon, 1955) and loss aversion (Kahneman and Tversky, 1979) could also lead to deviations from profit maximisation; fishers have been shown to exhibit satisficing behaviour, in which profit maximisation is no longer the objective function once a certain level of need or satisfaction has been met (Christensen and Raakjær, 2006; Jager et al., 2000; Salas and Gaertner, 2004). Béné (1996) defined a fisher’s strategy to be “the set of decision criteria that link a given fishing behaviour with the objective(s) and constraint(s) that have stimulated such behaviour”.

To successfully predict the fleet-wide responses to management options we must understand the individual differences in competitive drive and ability of the fishers, as well as their differing

economic expectations, incentives and drivers (Bene and Tewfik, 2001; Gelcich et al., 2005). A better understanding of the relative importance of driving factors and motivations per individual would allow insights into how economic strategies differ both within and between fisheries.

Modelling fishermen with an assumption that they act with perfect economic rationality, i.e. always behaving in a way that maximises their income, also implies that they are able to consider all possible options and outcomes and weigh them up before making a decision (i.e. perfect rationality)

39 (Holland, 2008). In practice, fishers may use simple ‘rules of thumb’ to decide where to fish, because the time it would take to rationally decide between all possible fishing options would be

uneconomical, and they may be better off using a simple rule or ‘hunch’ to maximise the available fishing time, rather than computing the perfect choice (Gatewood, 1983; Holland, 2008; Tversky and Kahneman, 1974). Fishers also tend to be risk averse and habitual, and are likely to choose the same location to fish out of habit and inertia to change (Eggert and Martinsson, 2004).