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Predicting Coral Recruitment in Palau’s Complex Reef Archipelago

Overview of attention for article published in PLOS ONE, November 2012
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Title
Predicting Coral Recruitment in Palau’s Complex Reef Archipelago
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0050998
Pubmed ID
Authors

Yimnang Golbuu, Eric Wolanski, Jacques Wasai Idechong, Steven Victor, Adelle Lukes Isechal, Noelle Wenty Oldiais, David Idip, Robert H. Richmond, Robert van Woesik

Abstract

Reproduction and recruitment are key processes that replenish marine populations. Here we use the Palau archipelago, in the western Pacific Ocean, as a case study to examine scales of connectivity and to determine whether an oceanographic model, incorporating the complex reef architecture, is a useful predictor of coral recruitment. We tested the hypothesis that the reefs with the highest retention also had the highest densities of juvenile coral density from 80 field sites. Field comparisons showed a significant correlation between the densities of juvenile Acropora colonies and total larval recruitment derived from the model (i.e., calculated as the sum of the densities of larvae that self-seeded and recruited from the other reefs in the archipelago). Long-distance larval imports may be too infrequent to sustain coral populations, but are critical for recovery in times of extreme local stress.

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Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 <1%
United States 1 <1%
Australia 1 <1%
Taiwan 1 <1%
Unknown 101 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 29%
Researcher 16 15%
Student > Master 15 14%
Student > Doctoral Student 8 8%
Student > Bachelor 6 6%
Other 14 13%
Unknown 16 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 38%
Environmental Science 23 22%
Earth and Planetary Sciences 11 10%
Biochemistry, Genetics and Molecular Biology 2 2%
Social Sciences 2 2%
Other 4 4%
Unknown 23 22%