Abstract
Abstract Managing the footprint of highly mobile fishing fleets is increasingly important due to continuing declines in fish populations. However, social‐ecological drivers for fisher behaviour remain poorly understood for many fleets globally. Using the Sri Lankan fleet as a case study, we explored the role of social, environmental and policy drivers of effort distribution and illegal fishing. We used semi‐structured interviews and participatory mapping with 95 fishers, combined with explanatory modelling (GLM) and multivariate statistics, including principal component analysis (PCA). Our findings highlighted the broad footprint (~3,800,000 km2) of this fleet, with fishing effort expended in high seas (53.9%), domestic (40.9%) and, illegally, in foreign waters (5.2%). Twenty‐six per cent of fishers directly admitted to fishing illegally in foreign waters during interviews, whereas 62% of fishers indicated doing so during participatory mapping. GLMs explained underlying decisions of where to fish (36% of the total deviance in effort distribution) as a function of social variables (14%), notably distance from landing sites (13%), and environmental variables (11%), notably sea surface temperature (10%). Multivariate analysis revealed that individual fisher characteristics associated with illegal fishing, such as a level of reliance on sharks, vary across the fleet. The analysis of qualitative data suggested that the influence of interpersonal and community social networks and perceptions of higher catch value, particularly of sharks, may be important. Our approach demonstrated the utility of mixed methods research, including the collection of qualitative data, for creating a detailed understanding of spatial behaviour, including decisions of whether to fish illegally. Results highlighted the importance of adopting a social‐ecological lens to investigate drivers for human behaviour and non‐compliance with rules. We advocate for a nuanced approach to monitoring and managing of fleets, including investigating localised social drivers for illegal fishing and enhancing regional transparency in fleet monitoring. A free Plain Language Summary can be found within the Supporting Information of this article.
Highlights
As a consequence of depleted coastal fish populations, many fleets are expanding beyond national Exclusive Economic Zones (EEZs) to high seas areas (Sumaila et al, 2015; Tickler et al, 2018)
Multivariate analysis revealed that individual fisher characteristics associated with illegal fishing, such as a level of reliance on sharks, vary across the fleet
As a consequence of depleted coastal fish populations, many fleets are expanding beyond national Exclusive Economic Zones (EEZs) to high seas areas (Sumaila et al, 2015; Tickler et al, 2018)
Summary
As a consequence of depleted coastal fish populations, many fleets are expanding beyond national Exclusive Economic Zones (EEZs) to high seas areas (Sumaila et al, 2015; Tickler et al, 2018). Identifying which social factors are of importance on a fleet- specific basis can be difficult and time-consuming, leading them often to be poorly considered in understanding of spatial and compliance behaviour of fleets (Kaplan et al, 2010; van Putten et al, 2012) This can contribute to unintended feedback behaviours, including the displacement of fishing effort to more vulnerable areas, or non-compliance with spatial management policies, such as MPAs, due to confusion or a lack of alternatives (Castrejón & Charles, 2020; Mizrahi et al, 2019). We combined participatory and qualitative data collection methods with geospatial statistics, in order to map and understand the spatial distribution and compliance of the Sri Lankan offshore fishing fleet This fleet is known to operate over a large ocean area and is suspected of relatively high levels of illegal fishing in foreign EEZs (FEEZs, hereafter referred to as ‘non-compliance’). We compare our results with existing knowledge of behaviours for this fleet and discuss the importance of our findings within the context of national and regional policy and management
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