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Record W3130891768 · doi:10.31230/osf.io/9tdhv

Marine protected area network design features that support resilient human-ocean systems: Applications for British Columbia, Canada

2018· preprint· en· W3130891768 on OpenAlexafffundabout
Jenn M. Burt, Phillip Akins, Erin Latham, Martina Beck, Anne K. Salomon, Natalie C. Ban

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsTula FoundationUniversity of VictoriaSimon Fraser University
FundersFisheries and Oceans CanadaHakai InstituteNatural Sciences and Engineering Research Council of CanadaMassachusetts Department of Fish and GameEuropean CommissionSocial Sciences and Humanities Research Council of CanadaGreat Barrier Reef Marine Park AuthorityNational Oceanic and Atmospheric AdministrationCalifornia Department of Fish and GameWorld Wildlife Fund
KeywordsMarine protected areaCorporate governanceBusinessProcess (computing)Environmental resource managementSocial network analysisEnvironmental planningGeographyEcologyPolitical scienceComputer scienceEnvironmental scienceHabitat

Abstract

fetched live from OpenAlex

In this report, we synthesize the overarching principles and general guidelines that underpin the establishment of marine protected area (MPA) networks designed to meet ecological, governance, social and cultural objectives, based on the peer-reviewed literature. These guidelines are supported by scientific research, institutional experience and global case studies, and take a social-ecological systems approach to marine conservation. Information reviewed in this report suggests that the design of MPAs and MPA networks require the simultaneous consideration of ecological features and processes, governance arrangements, economic costs and benefits, as well as social and cultural values. Planners, managers and decision-makers can use the guidelines synthesized in this report to support the process of MPA network design in their local contexts. We discuss how several of the design guidelines apply to the Pacific region of British Columbia (B.C.), Canada, given the federal and provincial governments have committed to establishing a bioregional network of MPAs.In this report we reviewed and synthesized:› Ecological principles and guidelines for MPA network design, with discussion and recommendations on how each of these principles could be applied in B.C.;› Species-specific movement and larval duration estimates for a selection of marine species of ecological, economic, cultural and conservation importance in B.C., with recommendations on how this can inform guidelines on the size and spacing of MPA networks in B.C.;› Overarching principles from global literature on good governance of MPAs and MPA networks;› Design goals and strategies for achieving different social objectives in MPA and MPA network planning; and› Opportunities and challenges for integrating local knowledge systems (focus on Traditional Ecological Knowledge) into marine planning and MPA design.Lastly, we assessed relevant B.C. policy documents using the ecological and good governance guideline frame- works.According to our synthesis of the literature, successful establishment and effective management of MPA networks depend on legitimate and effective governance arrangements that can accommodate ecological criteria while considering the perspectives and input of local resource users and stakeholders. Furthermore, policy makers should specify MPA objectives as this will guide design priorities, assessment and monitoring, and ensure that trade-o s are transparent.Overall, the principles and guidelines synthesized in this report support an approach to MPA design that incorporates biodiversity and ecosystem resilience objectives while recognizing human uses and values. Our compendium of information is most relevant to MPA planning processes in B.C., but can be applied and adapted to MPA and MPA network design in any other region.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.364

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0040.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.215
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2018
Admission routes3
Has abstractyes

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