Aligning complex, adaptive systems theory and data-limited assessment strategies for improved fisheries management
Abstract/Contents
- Abstract
- By combining SCUBA fieldwork in southern Monterey Bay kelp forests with statistical modeling, this thesis addresses how to successfully implement, monitor, and assess the efficacy of ecosystem-based management* approaches, specifically applied to the California nearshore and U.S. West Coast fisheries. It explores the interaction between marine populations, with an emphasis on fish and fisheries management, and their broader environmental context -- including the social-ecological and management context -- with an aim to develop new tools for scientific understanding and improved management of these populations. Results include the characterization of how fish respond to coastal hydrodynamics on a local scale (e.g., ocean swell, water temperature, and dissolved oxygen) with insight into how to use this knowledge to inform future sampling in California's marine reserves, including adaptive sampling designs that incorporate effects of local hydrodynamic regimes. I then integrate local-scale SCUBA data on fish populations, collected on the scale of a single marine reserve, into fisheries models. In collaboration with NOAA Fisheries scientists, I propose a new analytical method that provides reference points to inform management. We call this method "reserve-based SPR." It is a practical, easy-to-implement, information-limited method (i.e., "data-poor method"). This work brings together conservation-based tools, specifically marine reserves, with fisheries management approaches. Data-poor methods like reserve-based SPR, although subject to many caveats, can often provide scientific guidance to reduce biological and management risks. To organize data-poor fisheries methods into one coherent landscape with case-study examples for managers to use as guidelines, I offer a new framework. This data-poor framework is designed to help managers and stakeholders consider and choose appropriate analytical methods for their specific context. No data-poor method is perfect but many produce "good enough" sustainable yield estimates or ecosystem indicators with bias that is risk-averse and precautionary for population sustainability. Collectively, this thesis is united in its theme of understanding uncertainty and complex, adaptive systems for applied science and management purposes. *NOAA Fisheries (2006) definition of "ecosystem-based management": An approach that takes major ecosystem components and services -- both structural and functional -- into account in managing fisheries. It values habitat, embraces a multispecies perspective, and is committed to understanding ecosystem processes. Its goal is to rebuild and sustain populations, species, biological communities, and marine ecosystems at high levels of productivity and biological diversity so as not to jeopardize a wide range of goods and services from marine ecosystems while providing food, revenue, and recreation for humans.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2012 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Honey, Kristen Taber |
|---|---|
| Associated with | Stanford University, Department of Environment and Resources. |
| Primary advisor | Micheli, Fiorenza |
| Primary advisor | Naylor, Rosamond |
| Thesis advisor | Micheli, Fiorenza |
| Thesis advisor | Naylor, Rosamond |
| Thesis advisor | Carr, Mark |
| Thesis advisor | Monismith, Stephen Gene |
| Advisor | Carr, Mark |
| Advisor | Monismith, Stephen Gene |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Kristen Taber Honey. |
|---|---|
| Note | Submitted to the Emmett Interdisciplinary Program in Environment and Resources. |
| Thesis | Ph.D. Stanford University 2012 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2012 by Kristen Taber Honey
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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