Classifying fishing behavioral diversity using high-frequency movement data

Abstract

Effective management of social-ecological systems (SESs) requires an understanding of human behavior. In many SESs, there are hundreds of agents or more interacting with governance and regulatory institutions, driving management outcomes through collective behavior. Agents in these systems often display consistent behavioral characteristics over time that can help reduce the dimensionality of SES data by enabling the assignment of types. Typologies of resource-user behavior both enrich our knowledge of user cultures and provide critical information for management. Here, we develop a data-driven framework to identify resource-user typologies in SESs with high-dimensional data. To demonstrate policy applications, we apply the framework to a tightly coupled SES, commercial fishing. We leverage large fisheries-dependent datasets that include mandatory vessel logbooks, observer datasets, and high-resolution geospatial vessel tracking technologies. We first quantify vessel and behavioral characteristics using data that encode fishers’ spatial decisions and behaviors. We then use clustering to classify these characteristics into discrete fishing behavioral types (FBTs), determining that 3 types emerge in our case study. Finally, we investigate how a series of disturbances applied selection pressure on these FBTs, causing the disproportionate loss of one group. Our framework not only provides an efficient and unbiased method for identifying FBTs in near real time, but it can also improve management outcomes by enabling ex ante investigation of the consequences of disturbances such as policy actions.

A Robust Métier-Based Approach to Classifying Fishing Practices Within Commercial Fisheries

“…Our feature selection method, however, retains readability and interpretability as it preserves the original variable values in a reduced space. As noted by Jain and Dubes (1988), "validation of clustering structures is the most difficult and frustrating part of cluster analysis, " but is necessary for ensuring the clusters accurately reflect the "real-world" data (O'Farrell et al, 2019). We note that suitable computational internal validation methods are available in the machine learning literature but have been given only a fraction of attention in métier analysis.…”

“…Effective fisheries management requires an understanding of human behavior (Hilborn, 2007;Davie and Lordan, 2011;Fulton et al, 2011;O'Farrell et al, 2019). Evidence has shown that when this is ignored it can lead to unexpected and undesirable outcomes from management decisionmaking (Fulton et al, 2011).…”

“…In recent years, there has been a greater focus on understanding and classifying fisher behavior based on socio-demographic and psychological characteristics (e.g., Nielsen and Christensen, 2006;Abernathy, 2010;Boonstra and Hentati-Sundberg, 2016). Improved prediction of the behavioral response of fishers to management measures and policy can be obtained through understanding both their short-and long-term drivers including: motivations (e.g., market price), moral and social norms, values, perceptions, personal goals, and emotions (Boonstra and Hentati-Sundberg, 2016;O'Farrell et al, 2019). These drivers often manifest a diversity of individual fishing practices and operations within a specific fleet or fishery (Boonstra and Hentati-Sundberg, 2016).…”

“…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 (but also 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).…”

Expanding conservation culturomics and iEcology from terrestrial to aquatic realms

“…Furthermore, historical data from digitized texts, photographs, or ship logs can provide insights into the distribution or abundance of species at times when scientific sampling data are limited [ 51 ]. Culturomics can shed light on the behavior and preferences of fishers [ 52 ], for example, by assessing regional characteristics of recreational fisheries based on YouTube videos or discussion forums ( Fig 1A ) [ 7 , 9 , 12 ] or by analyzing internet search frequency to explore global trends in fishing interest and seasonality [ 5 ]. These approaches can also be used to monitor the effectiveness of fisheries management initiatives by tracking attitudes and compliance of fishers before, during, and after implementation of regulations and to anticipate reactions.…”