Risk sensitivity and the behaviour of fishing vessels

Dowling, Natalie; Wilcox, Chris; Mangel, Marc

doi:10.1111/faf.12064

Cited by 16 publications

(11 citation statements)

References 33 publications

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“…Social identity (29), risk aversion (30), and other factors may have contributed to delaying fishers' responses to changing stock levels (31,32). Time spent fishing was therefore treated as a "stock" in the system dynamics sense (33): that is, a quantity that only gradually responds to changes in input.…”

Section: Modelmentioning

confidence: 99%

An empirical model of the Baltic Sea reveals the importance of social dynamics for ecological regime shifts

Lade

Niiranen

Hentati‐Sundberg

et al. 2015

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

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Regime shifts triggered by human activities and environmental changes have led to significant ecological and socioeconomic consequences in marine and terrestrial ecosystems worldwide. Ecological processes and feedbacks associated with regime shifts have received considerable attention, but human individual and collective behavior is rarely treated as an integrated component of such shifts. Here, we used generalized modeling to develop a coupled social-ecological model that integrated rich social and ecological data to investigate the role of social dynamics in the 1980s Baltic Sea cod boom and collapse. We showed that psychological, economic, and regulatory aspects of fisher decision making, in addition to ecological interactions, contributed both to the temporary persistence of the cod boom and to its subsequent collapse. These features of the social-ecological system also would have limited the effectiveness of stronger fishery regulations. Our results provide quantitative, empirical evidence that incorporating social dynamics into models of natural resources is critical for understanding how resources can be managed sustainably. We also show that generalized modeling, which is well-suited to collaborative model development and does not require detailed specification of causal relationships between system variables, can help tackle the complexities involved in creating and analyzing socialecological models.social-ecological systems | fisheries | generalized modeling | human decision making | feedback analysis

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Section: Modelmentioning

confidence: 99%

An empirical model of the Baltic Sea reveals the importance of social dynamics for ecological regime shifts

Lade

Niiranen

Hentati‐Sundberg

et al. 2015

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

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“…2) representing the fishers' perception of the state of the fishery. We assume that this perception lags behind the actual state of the fishery, for example due to social identity (Sønvisen, 2014) or risk aversion (Dowling, Wilcox, & Mangel, 2015).…”

Section: Conceptual Modelmentioning

confidence: 99%

Generalized modeling of empirical social‐ecological systems

Lade

Niiranen

2017

Natural Resource Modeling

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Modeling social-ecological systems is difficult due to the complexity of ecosystems and of individual and collective human behavior. Key components of the social-ecological system are often over-simplified or omitted. Generalized modeling is a dynamical systems approach that can overcome some of these challenges. It can rigorously analyze qualitative system dynamics such as regime shifts despite incomplete knowledge of the model's constituent processes. Here, we review generalized modeling and use a recent study on the Baltic Sea cod fishery's boom and collapse to demonstrate its application to modeling the dynamics of empirical social-ecological systems. These empirical applications demand new methods of analysis suited to larger, more complicated generalized models. Generalized modeling is a promising tool for rapidly developing mathematically rigorous, process-based understanding of a social-ecological system's dynamics despite limited knowledge of the system.Comment: This is the authors' accepted manuscript. The final version of the paper including copy-editing can be accessed at http://onlinelibrary.wiley.com/doi/10.1111/nrm.12129/ful

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“…While studies have predicted shifts in tuna distributions (Lehodey et al 2010, Dueri et al 2014, Lehodey et al 2015, including poleward shifts for temperate species (Hobday 2010, Dell et al 2015, the distribution of fishing effort is ultimately determined by the fishers. Their response will likely depend on multiple factors, including access to traditional fishing areas (Girardin et al 2017), the movement of target species (Michael et al 2015), fishing strategy (Michael et al 2017), and perceived risk (Dowling et al 2015). Although bycatch of albatross was estimated to be relatively low in the legal superfleets, shifts in the distribution and timing of effort, as well as in the distribution of the birds, could result in different bycatch patterns in the future.…”

Section: Improving Model Fitmentioning

confidence: 99%

Illegal fishing bycatch overshadows climate as a driver of albatross population decline

Michael¹,

Thomson²,

Barbraud³

et al. 2017

Mar. Ecol. Prog. Ser.

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Effective management of albatross populations requires understanding the impacts of environmental factors on albatross demographics. An integrated modelling approach incorporating multiple data sources can further the understanding of albatross demographics by incorporating error from all components of modeling, and help distinguish between variability related to one factor (e.g. environment) from that of another factor (e.g. density dependence). We applied such an integrated, spatially-explicit population model to quantify the impact of environmental conditions (sea surface temperature, SST), fisheries, and density dependence on a black-browed albatross Thalassarche melanophris population breeding on Kerguelen Island, southern Indian Ocean for the period 1950 to 2011. The model was structured by sex, age-class, and breeding stage, with a 5° × 5° spatial scale and monthly temporal scale. All parameters were estimated within a maximum likelihood framework. This includes estimation of seabird bycatch rates of each of 5 fishing super-fleets, grouped by gear type and reported bycatch rates: (1) Japanese pelagic longline, (2) other pelagic longline, (3) legal demersal longline, (4) trawl, and (5) illegal, unreported, and unregulated (IUU) demersal longline. A decline in the Kerguelen black-browed albatross population occurred between the mid-1990s through the mid-2000s. Our analysis attributes the majority of modelled bycatch to the IUU demersal longline super-fleet operating near Kerguelen Island for this period. Including SST during the incubation period indicated that warm SST favors high breeding success. These results indicate that effective management requires an integrated understanding of the impacts of the environment as well as illegal and legal fishing activities on vulnerable populations. KEY WORDS: Demographics • Integrated population model • Illegal, unreported, and unregulated (IUU) fishing • Black-browed albatross • Thalassarche melanophris • Kerguelen Island Resale or republication not permitted without written consent of the publisher This authors' personal copy may not be publicly or systematically copied or distributed, or posted on the Open Web, except with written permission of the copyright holder(s). It may be distributed to interested individuals on request.

show abstract

Risk sensitivity and the behaviour of fishing vessels

Cited by 16 publications

References 33 publications

An empirical model of the Baltic Sea reveals the importance of social dynamics for ecological regime shifts

An empirical model of the Baltic Sea reveals the importance of social dynamics for ecological regime shifts

Generalized modeling of empirical social‐ecological systems

Illegal fishing bycatch overshadows climate as a driver of albatross population decline

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