Elsevier

Fisheries Research

Volume 238, June 2021, 105896
Fisheries Research

Fishers as foragers: Individual variation among small-scale fishing vessels as revealed by novel tracking technology

https://doi.org/10.1016/j.fishres.2021.105896Get rights and content

Highlights

  • Recent advances in VTS technology enable novel small-scale fisheries investigations.

  • Significant variation in foraging behavior exists within these social-ecological systems.

  • Recognizing and accounting for behavioral heterogeneity can improve fisheries management.

Abstract

Effective fisheries management requires an understanding of fisher behavior. Though vessel tracking systems are increasingly used to describe the movements and activities of industrial fishing fleets, their use has been limited within the small-scale fisheries employing the vast majority of the world’s capture fishers. Here we combine novel vessel tracking technology with logbook data to conduct a high-resolution analysis of behavior and decision-making within a small-scale fishery. Our results indicate significant heterogeneity in fisher behavior, catch composition, and profits within a small-scale fleet operating in the central Gulf of California, even amongst fishing vessels using similar gear types. The weekly home ranges (75 % Kernel Utilization Distribution) occupied by fishers spanned 1.5–1121.8 km2 across 13 vessels, while weekly profits ranged from -1810 to 26,160 pesos. Differences in the spatial interactions and catch profiles of observed vessels revealed the existence of behavioral associations linked with distinct fishing strategies. After identifying and describing the contextual factors driving such heterogeneity among vessels using hook and line fishing gear, we interpret emergent patterns and processes using insights from foraging theory and marine social science. In illustrating the applications and opportunities presented by recent advances in vessel tracking technology, we argue for the use of spatially explicit analytical approaches in assessing behavioral diversity within small-scale fisheries and in designing robust and equitable management strategies.

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

References (114)

  • P. Etnoyer et al.

    Sea-surface temperature gradients across blue whale and sea turtle foraging trajectories off the Baja California Peninsula, Mexico

    Deep Sea Res. Part 2 Top. Stud. Oceanogr

    (2006)
  • E.M. Finkbeiner

    The role of diversification in dynamic small-scale fisheries: lessons from Baja California sur, Mexico

    Global Environ. Chang.

    (2015)
  • J.T. Finn et al.

    Applying network methods to acoustic telemetry data: modeling the movements of tropical marine fishes

    Ecol. Modell.

    (2014)
  • B. Gonzalez-Mon et al.

    Spatial diversification as a mechanism to adapt to environmental changes in small-scale fisheries

    Environ. Sci. Policy

    (2021)
  • D.W. Macdonald et al.

    The evaluation of home range size and configuration using radio tracking data

  • H. Naranjo-Madrigal et al.

    Understanding socio-ecological drivers of spatial allocation choice in a multi-species artisanal fishery: a Bayesian network modeling approach

    Mar. Policy

    (2015)
  • C.J. Robinson et al.

    Prolonged decline of jumbo squid (Dosidicus gigas) landings in the Gulf of California is associated with chronically low wind stress and decreased chlorophyll a after El Niño 2009–2010

    Fish. Res.

    (2016)
  • J.N. Sanchirico et al.

    Bioeconomics of spatial exploitation in a patchy environment

    J. Environ. Econ. Manage.

    (1999)
  • K.E. Abernethy et al.

    Why do fishers fish where they fish? Using the ideal free distribution to understand the behaviour of artisanal reef fishers

    Can. J. Fish. Aquat. Sci.

    (2007)
  • P.M. Allen et al.

    Dynamics of discovery and exploitation: the case of the Scotian Shelf groundfish fisheries

    Can. J. Fish. Aquat. Sci.

    (1986)
  • R.O. Amoroso et al.

    Bottom trawl fishing footprints on the world’s continental shelves

    Proc. Natl. Acad. Sci. U.S.A.

    (2018)
  • I. Azuz-Adeath et al.

    Governance and socioeconomics of the Gulf of California Large marine ecosystem

    Environ. Dev.

    (2017)
  • A. Bakun

    Fronts and eddies as key structures in the habitat of marine fish larvae: opportunity, adaptive response and competitive advantage

    Sci. Mar.

    (2006)
  • A. Bakun et al.

    Issues of ecosystem-based management of forage fisheries in “open” non-stationary ecosystems: the example of the sardine fishery in the Gulf of California

    Rev. Fish. Biol. Fisher

    (2010)
  • J. Barry et al.

    Foraging specialisms influence space use and movement patterns of the European eel Anguilla anguilla

    Hydrobiologia

    (2016)
  • C. Béné

    Effects of market constraints, the remuneration system, and resource dynamics on the spatial distribution of fishing effort

    Can. J. Fish. Aquat. Sci.

    (1996)
  • C. Béné et al.

    Fishing effort allocation and fishermen’s decision-making process in a multi-species small-scale fishery: analysis of the conch and lobster fishery in Turks and Caicos Islands

    Hum. Ecol.

    (2001)
  • S. Bertrand et al.

    Scale-invariant movements of fishermen: the same foraging strategy as natural predators

    Ecol. Appl.

    (2007)
  • S. Bertrand et al.

    Local depletion by a fishery can affect seabird foraging

    J. Appl. Ecol.

    (2012)
  • D.I. Bolnick et al.

    Measuring individualälevel resource specialization

    Ecology

    (2002)
  • D.I. Bolnick et al.

    The ecology of individuals: incidence and implications of individual specialization

    Am. Nat.

    (2003)
  • P. Bourdaud et al.

    Improving the interpretation of fishing effort and pressures in mixed fisheries using spatial overlap metrics

    Can. J. Fish. Aquat. Sci.

    (2018)
  • T.A. Branch et al.

    Fleet dynamics and fishermen behavior: lessons for fisheries managers

    Can. J. Fish. Aquat. Sci.

    (2006)
  • J.L. Cabrera et al.

    Daily bioeconomic analysis in a multispecific artisanal fishery in Yucatan, Mexico

    Aquat. Living Resour.

    (2001)
  • M.A. Carvajal et al.

    The Gulf of California: natural resource concerns and the pursuit of a vision

  • G.A. Casselberry et al.

    Network analysis reveals multispecies spatial associations in the shark community of a Caribbean marine protected area

    Mar. Ecol. Prog. Ser.

    (2020)
  • C. Chaboud

    Risques et incertitudes dans les pêches: le point de vue de l’économiste

  • R. Chuenpagdee

    World Small-scale Fisheries: Contemporary Visions

    (2011)
  • M.A. Cisneros-Mata

    The importance of fisheries in the Gulf of California and ecosystem-based sustainable co-management for conservation

    The Gulf of California: biodiversity and Conservation

    (2010)
  • A. Clauset et al.

    Finding community structure in very large networks

    Phys. Rev.

    (2004)
  • A. Costa et al.

    Generalisation within specialization: inter-individual diet variation in the only specialized salamander in the world

    Sci. Rep.

    (2015)
  • C. Costello et al.

    Can catch shares prevent fisheries collapse?

    Science

    (2008)
  • G. Csardi et al.

    The igraph software package for complex network research

    InterJ. l Complex Syst.

    (2006)
  • T.K. Davies et al.

    Modelling the spatial behaviour of a tropical tuna purse seine fleet

    PLoS One

    (2014)
  • F.J. de la Cruz-González et al.

    Análisis socioeconómico de las pesquerías de camarón y calamar gigante en el noroeste de México

    Interciencia

    (2007)
  • E.N. de Souza et al.

    Improving fishing pattern detection from satellite AIS using data mining and machine learning

    PLoS One

    (2016)
  • E.P. Durrenberger

    Fisheries management models: assumptions and realities or, why shrimpers in Mississippi are not firms

    Hum. Organ.

    (1997)
  • J. Eales et al.

    An examination of fishing location choice in the pink shrimp fishery

    Mar. Resour. Econ.

    (1986)
  • ESRI

    ArcGIS

    (2018)
  • FAO

    The State of World Fisheries and Aquaculture-meeting Sustainable Development Goals

    (2018)
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