Fishers as foragers: Individual variation among small-scale fishing vessels as revealed by novel tracking technology
Introduction
Understanding the spatial ecology of fisheries and the foraging behavior of those individuals engaged in harvest is critical for estimating their impacts on marine species and evaluating different management scenarios (Watson et al., 2004; Anticamara et al., 2011). Knowledge of how people operate in a fishery system can provide insight into how that system works (Salas and Gaertner, 2004) and many researchers have argued that an incomplete or inaccurate understanding of fisher behavior has contributed to the collapse of many fisheries worldwide (Hilborn, 1985; Wilen et al., 2002; Branch et al., 2006). Substantial fisheries research has focused on long-term entry and investment decisions, such as which species to target and which fishing gear to use (Gordon, 1954; Costello et al., 2008). But after gear and target species have been selected, fishers must subsequently decide when and where to fish. These short-term choices impact the households and communities of which they are a part, and the marine ecosystems in which they are embedded (Eales and Wilen, 1986; Salas et al., 2004). High-resolution, georeferenced observations of fishing processes can provide useful information about fisher behavior and the spatiotemporal dynamics of the species they target (Defeo and Castilla, 1998; Bertrand et al., 2007), and aid in the development of targeted strategies designed to ensure their sustainability.
Small-scale fisheries (SSF) employ > 90 % of the world’s capture fishers (Kolding et al., 2014) and provide livelihoods and food security for hundreds of millions of individuals around the world (FAO, 2018). In recent years, the sector has drawn increased attention from scholars, resource managers, and policy makers as its substantial contributions to global fishery landings, direct human consumption, and the international seafood trade have come to light (Chuenpagdee, 2011; Crona et al., 2015). Despite the critical role SSFs play in supporting coastal economies and human well-being, many nearshore coastal ecosystems are declining due to pollution, overfishing, and habitat loss (Jackson et al., 2001). In regions where management is weak and/or focused primarily on the industrial sector, SSF operations are often structured by local rules-in-use that may differ substantially from formal laws and regulations (Cinti et al., 2010). Effective management of such systems requires an understanding of fishers, their behaviors, and the diverse factors that influence their decisions (Naranjo-Madrigal et al., 2015). This need is especially acute across tropical and semi-tropical coastal regions where small-scale fisheries are often characterized by a diversity of gear types and target species, large spatial and temporal variation in landings, dispersed local landing sites, and uncertain resource access (Naranjo Madrigal and Salas Márquez, 2014). When fishing effort is applied to a multi-species resource, fishers make decisions regarding target species and fishing areas daily (Cabrera and Defeo, 2001). Yet, behavior and decision-making within such systems and their impacts on local livelihoods and resources remain poorly understood.
Fishing is an, “uncertain and competitive activity,” (Salas and Gaertner, 2004) in which strategies and tactics are influenced by fishers’ perceptions, preferences, abilities, and relationships. Short-term harvesting operations are influenced by variable environmental conditions (Salas et al., 2004) as well as changes in resource abundance, distribution (Shester, 2010), and market price (Defeo and Castilla, 1998). Within fisheries and fleet dynamics literatures, several bioeconomic (e.g., Random Utility Models; see Eales and Wilen, 1986; Holland and Sutinen, 1999) and ecological (e.g., the theory of Ideal Free Distribution; see Gillis et al., 1993; Gillis, 2003) frameworks have been used assess the relative importance of such drivers. Such approaches are based upon the assumptions that fishers have accurate knowledge concerning the distribution of target resources, can move between locations without constraint, and are driven by a desire to maximize profits. Debate continues concerning whether such models can explain decision making within small-scale fisheries where individual variability is thought to be more pronounced (Abernethy et al., 2007; Wallace et al., 2016). With small boat sizes and limited capital investment, small-scale fishers are often limited by weather conditions, the price of fuel, permits and equipment, and access to information (Cabrera and Defeo, 2001; Salas et al., 2004; Abernethy et al., 2007; Naranjo-Madrigal et al., 2015) in addition to social and cultural factors unique to specific local contexts (Béné and Tewfik, 2001; Frawley et al., 2019a). Indeed, recent research concerning both the small-scale (Wallace et al., 2016) and industrial (Girardin et al., 2017; Bourdaud et al., 2018) sectors has found that individual fishing habits and traditions may be more influential when selecting fishing grounds than economic opportunism.
In mixed fisheries, it is hypothesized that fishers attempt to maximize revenue, rather than catch volume (Girardin et al., 2017). While specialist fishers concentrate on a specific area, species, or fishing method, generalist fishers exploit multiple species using multiple gear types (Smith and McKelvey, 1986). Though generalists may sacrifice some degree of efficiency, particularly during resource booms, their flexibility is believed to mitigate the risk and income fluctuations associated with environmental and economic variability (Kasperski and Holland, 2013; Finkbeiner, 2015; Frawley et al., 2020). Likewise, it has been suggested that individual fishers differ in their willingness to accept risk and uncertainty. Decisions regarding the allocation of fishing effort are likely influenced by these risk profiles as certain species and habitats are more intrinsically variable than others (Girardin et al., 2017). Some fishers opt to explore new resources and unfamiliar fishing grounds; others prefer to exploit resources that have already been discovered, forgoing occasional high rewards for steady, but lower, economic returns (Allen and McGlade, 1986; Shester, 2010). Though agent-based modeling simulations capable of accommodating such variability are increasingly applied to small-scale fisheries and other natural resource systems, there is a recognized need to ground related theoretical insight with empirical data (Lindkvist et al., 2019).
Currently, few tools exist within SSFs to identify, monitor, and manage behavioral heterogeneity. Spatially explicit, high resolution time series provided by electronic vessel tracking systems are increasingly used to assess the dynamic footprint of large-scale and/or industrial fisheries worldwide (Amoroso et al., 2018; Kroodsma et al., 2018; McCauley et al., 2018). In recent years, vessel tracking technology has been used to estimate the environmental and economic drivers of global fisheries (Kroodsma et al., 2018), to quantify the proportion of fished habitat (Amoroso et al., 2018), to assess the effectiveness of marine protected areas (McCauley et al., 2016; White et al., 2020), and to predict hotspots of bycatch for threatened species (Queiroz et al., 2016; White et al., 2019). However, these emerging tools have not yet been applied to small-scale fisheries. Here we present an application of novel tracking technology, the Pelagic Data Systems (PDS) vessel tracking system (VTS), to examine the behavior of individual small-scale fishing vessels within a dynamic marine environment. We describe heterogeneity among vessels within a small-scale fishing fleet and attempt to identify local factors and conditions driving individual variation in spatial allocation of effort, movement ecology, catch composition and profit. When combined with traditional fisheries data sources, PDS technology provides the opportunity to apply insights from foraging theory and marine social science to improve our understanding of fishing behavior and local resource dynamics within data-poor SSF systems. By integrating a novel technology with environmental and catch data, we illustrate how knowledge of fisher behavior can improve scientific understanding of SSF as dynamic marine social-ecological systems and aid in the development of management strategies designed to ensure their sustainability.
Section snippets
Study system
Depending on the season, anywhere between 10,000 and 24,000 small-scale fishing boats operate in the Gulf of California, directly employing more than 56,000 people (Carvajal et al., 2004; Azuz-Adeath and Cortés-Ruiz, 2017). Though the Gulf represents Mexico’s chief source of fishery resources for national and international markets, inefficiency within the fisheries sector and the government at-large have led to a marked decline in many marine resources (Espinoza-Tenorio et al., 2011). Following
Collaborative fisheries research program (logbooks and vessel tracking devices)
We conducted fieldwork in Santa Rosalía between April and June of 2016. After initial scoping, we recruited thirteen small-scale fishing vessels to participate in the study. Efforts were made to choose vessels that were broadly representative of local SSF operations (in terms of vessel size, engine power, gear type etc.) but our sample was ultimately limited to those full-time fishers willing to engage in the research process and provide data documenting legal fishing operations. The total size
Results
We deployed tracking devices on 13 small-scale fishing boats operating out of the port of Santa Rosalía and monitored 435 fishing trips which took place over a 10-week study period (Table 1). Spatial data for Boat_L and logbook data for Boat_M were not included in the final analysis due to tracking device failure and incomplete reporting. Overall, the weekly home ranges (75 % KUD) occupied by observed vessels ranged from 1.5 to 1121.8 km2, while weekly profits varied from -1810 to +26,160
Discussion
Fishers are increasingly recognized as top predators within marine ecosystems (Bertrand et al., 2007; Watson et al., 2018), yet scientific understanding of the behaviors and decision-making processes influencing selection of fishing grounds and target species remains limited. This is particularly true within the context of data-poor SSF where the strategies and tactics individuals employ are likely informed by the diverse environments in which they operate, the constraints they may encounter,
Conclusion
Fisheries are complex and adaptive socio-ecological systems where the exploitation of marine resources is driven by individuals’ interactions with dynamic environmental and socioeconomic conditions. Yet, uncertainty surrounding how and why fishermen behave the way they do remains a major challenge in the design and implementation of sustainable fisheries management (Fulton et al., 2011; Hobday et al., 2011; Watson et al., 2018). Even where formal governance and management capacity exists,
CRediT authorship contribution statement
Timothy H. Frawley: Conceptualization, Methodology, Validation, Formal analysis, Data curation, Writing - original draft, Visualization, Writing - review & editing. Hannah E. Blondin: Methodology, Formal analysis, Visualization, Writing - review & editing. Timothy D. White: Methodology, Writing - review & editing. Rachel R. Carlson: Formal analysis, Visualization, Writing - review & editing. Brianna Villalon: Data curation, Visualization, Writing - review & editing. Larry B. Crowder:
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors would like to thank the members of El Programa de Pesca Pelágica de Santa Rosalía for the patience and generosity they displayed over the course of this and other investigations, support from the MAREA (DEB-1632648) and MAREA+ (BCS-2009821) reasearch groups funded by the US National Science Foundation Coupled Natural and Human Systems program, Melissa Garren and other members of the Pelagic Data Systems team for technical expertise and a willingness to engage, Eduardo Sartero
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