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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
https://doi.org/10.1007/13836_...
Part of book or chapter of book . 2019 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
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Marine Conservation and Marine Protected Areas

Authors: Amanda Xuereb; Cassidy C. D’Aloia; Rémi M. Daigle; Marco Andrello; Alicia Dalongeville; Stéphanie Manel; David Mouillot; +5 Authors

Marine Conservation and Marine Protected Areas

Abstract

Marine protected areas are important tools for the conservation of marine biodiversity, providing refuge for harvested species and mitigating the negative impacts of human activities in marine ecosystems. However, delineating sites for protection within effective MPA networks is a formidable challenge. A primary objective of MPA planning is to optimize connectivity among reserve sites, such that immigration from distant sites or populations sustains biodiversity, both within MPAs and in adjacent unprotected areas. Additionally, as climate change further threatens marine biodiversity, adaptation to novel climatic and environmental conditions also has important consequences for the persistence of marine populations. Inferences from population genomics can provide valuable insight into the design of MPA networks, both for ensuring connectivity and preserving adaptive potential for future environmental change. However, genetic and genomic data are rarely used to inform marine spatial planning. Effective dissemination of primary research to practitioners will be key to the successful integration of these valuable data into MPA network designs.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    19
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Top 10%
Average
Top 10%
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