Data from: Identifying multiple coral reef regimes and their drivers across the Hawaiian archipelago

DOI

Loss of coral reef resilience can lead to dramatic changes in benthic structure, often called regime shifts, which significantly alter ecosystem processes and functioning. In the face of global change and increasing direct human impacts, there is an urgent need to anticipate and prevent undesirable regime shifts and, conversely, to reverse shifts in already degraded reef systems. Such challenges require a better understanding of the human and natural drivers that support or undermine different reef regimes. The Hawaiian archipelago extends across a wide gradient of natural and anthropogenic conditions and provides us a unique opportunity to investigate the relationships between multiple reef regimes, their dynamics and potential drivers. We applied a combination of exploratory ordination methods and inferential statistics to one of the most comprehensive coral reef datasets available in order to detect, visualize and define potential multiple ecosystem regimes. This study demonstrates the existence of three distinct reef regimes dominated by hard corals, turf algae or macroalgae. Results from boosted regression trees show nonlinear patterns among predictors that help to explain the occurrence of these regimes, and highlight herbivore biomass as the key driver in addition to effluent, latitude and depth.

Identifier
DOI https://doi.org/10.5061/dryad.rg832
Source https://nbn-resolving.org/urn:nbn:nl:ui:13-ql-ot70
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:87251
Provenance
Creator Jouffray, Jean-Baptiste; Nyström, Magnus; Norström, Albert V.; Williams, Ivor D.; Wedding, Lisa M.; Kittinger, John N.; Williams, Gareth J.
Publisher Data Archiving and Networked Services (DANS)
Publication Year 2014
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Representation
Resource Type Dataset
Discipline Life Sciences;Medicine