Abstract

Developing a typology of heterogeneous fishing practices through the use of metier analysis is a useful step in understanding the dynamics of fishing fleets and enabling effective implementation of management outcomes. We develop a non-hierarchical clustering method framework to quantitatively categorize categorise individual fishing events to a particular metier based on corresponding catch composition, gear configuration and spatial and temporal references. Our method clustering framework has several innovations over predecessors including: i) introducing alternative methods for encoding and transforming fisheries data;new methods of encoding and transforming fisheries data; (ii) variable (feature) selection methods; (iii) complementary metrics and methods for internal metier validation; and (iv) use of a network science method to model and analyse analyse fishing practices. To demonstrate applicability, we apply this framework to the strengths of this method, we apply it to the Australian Eastern Tuna and Billfish Fishery (ETBF), an Australiana multispecies pelagic longline fishery with a diversity of fishing practices. We identified a total of seven stable metiers within the ETBF. While each metier was, characterised characterised by a single predominant target species, and they were differentiated more by seasonal and temporal references (e.g. time of set, month, latitude) than gear configuration (e.g. hooks per basket) or target species. By collapsing a large amount of high-dimensional operational data into a relatively uniform and limited number of components, decision-makers can more easily evaluate the likely consequences of management and design policies that target particular homogenous fishing practices. a particular metier.

Highlights

  • Effective fisheries management requires an understanding of human behavior (Hilborn, 2007; Davie and Lordan, 2011; Fulton et al, 2011; O’Farrell et al, 2019)

  • We use the term “métiers” to reflect these units of fishing practices, which have been defined by the European Commission (EC) Data Collection Framework as “a group of fishing operations targeting a similar species, using similar gear, during the same period of the year and/or within the same areas and which are characterized by a similar exploitation pattern” (EU, 2016)

  • Most studies that use non-hierarchical clustering frameworks (Duarte et al, 2009; Castro et al, 2010, 2011; Winker et al, 2013) determine the final number of valid métiers based on the maximum average silhouette width across clusters. While this facilitates a more objective selection of the most appropriate number of clusters compared to simple visual inspection (Rousseeuw, 1987), we examined a range of values close to the proposed k guided by the maximum average silhouette width through the use of an internal validation method taken from the machine learning literature (Hennig, 2007)

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Summary

Introduction

Effective fisheries management requires an understanding of human behavior (Hilborn, 2007; Davie and Lordan, 2011; Fulton et al, 2011; O’Farrell et al, 2019). Developing a typology of these individual fishing practices and operations allows large amounts of high-dimensional data from a fleet or fishery to be collapsed into a relatively uniform and limited number of components for analysis (O’Farrell et al, 2019) These components, clusters, or units have been termed “métiers” (Marchal and Horwood, 1996; Moore et al, 2019), “fishing strategies” (He et al, 1997; Holley and Marchal, 2004; Nielsen and Christensen, 2006; Abernathy, 2010), “fishing tactics” (Pelletier and Ferraris, 2000; Maynou et al, 2011), “fishing modes” (Purcell et al, 2018), or “fishing styles” (Boonstra and Hentati-Sundberg, 2016) and reflect similarities at the fishing event (and trip or vessel) level in inter alia, species targeted, type of gear utilized, as well as the location and time of fishing (Nielsen and Christensen, 2006; Ziegler, 2012). We use the term “métiers” to reflect these units of fishing practices, which have been defined by the European Commission (EC) Data Collection Framework as “a group of fishing operations targeting a similar (assemblage of) species, using similar gear, during the same period of the year and/or within the same areas and which are characterized by a similar exploitation pattern” (EU, 2016)

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