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Modeling the Functional Network for Spatial Navigation in the Human Brain

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The value of spatial information in MPA network design.

Christopher Costello¹, Andrew Rassweiler, David Siegel

¹Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106, USA. costello@bren.ucsb.edu

Proceedings of the National Academy of Sciences of the United States of America

|February 24, 2010

Summary

Improved spatial data significantly boosts fishery value by enabling targeted fishing strategies. Utilizing all available information, even incomplete, is crucial for maximizing economic returns in fisheries management.

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Area of Science:

Fisheries science
Marine ecology
Oceanography
Fisheries economics

Background:

Spatial fisheries management integrates ecological, oceanographic, and economic data.
Advances allow for spatially explicit policies like marine protected areas (MPAs).
Sparse data limits the effectiveness of spatial policies, necessitating broader approaches.

Purpose of the Study:

To develop a framework for analyzing the value of spatial information in fisheries management.
To assess the economic impact of improved spatial data on US Pacific coast fisheries.

Main Methods:

Developed a general analytical framework for the value of information in spatial fisheries management.
Applied the framework to simulate outcomes for several US Pacific coast fisheries.

Main Results:

Improved spatial information can increase fishery value by over 10% in simulations.
The optimal management approach shifts from uniform effort to spatially targeted strategies, with some areas intensively fished and others closed.
Utilizing all available data, even incomplete, can double fishery value compared to using incorrect assumptions.

Conclusions:

Spatial information is valuable and can significantly alter effective fisheries management strategies.
Targeted spatial management, informed by data, offers substantial economic benefits.
Maximizing fishery value requires incorporating all available spatial data, however imperfect.