Parsing human and biophysical drivers of coral reef regimes

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  • Jean-Baptiste Jouffray
    Stockholm University
  • Lisa Wedding
    Stanford University
  • Albert V. Norstrom
    Stockholm University
  • Mary Donovan
    University of Hawaii at Manoa, Hawaii
  • Gareth Williams
  • Larry Crowder
    Stanford University
  • Ashley Erickson
  • Alan M. Friedlander
    University of Hawaii at Manoa, Hawaii
  • Nicholas A.J. Graham
    Lancaster University
  • Jamison M. Gove
    Pacific Islands Fisheries Science Center, Hawaii
  • Carrie Kappel
  • John Kittinger
  • Joey Lecky
  • Kirsten Oleson
  • Kimberly Selkoe
  • Crow White
  • Ivor Williams
    NOAA Pacific Islands Fisheries Science Center, Honolulu, Hawaii 96818, USA.
  • Magnus Nystrom
    Stockholm University
Coral reefs worldwide face unprecedented cumulative anthropogenic effects of interacting local human pressures, global climate change and distal social processes. Reefs are also bound by the natural biophysical environment within which they exist. In this context, a key challenge for effective management is understanding how anthropogenic and biophysical conditions interact to drive distinct coral reef configurations. Here, we use machine learning to conduct explanatory predictions on reef ecosystems defined by both fish and benthic communities. Drawing on the most spatially extensive dataset available across the Hawaiian archipelago—20 anthropogenic and biophysical predictors over 620 survey sites—we model the occurrence of four distinct reef regimes and provide a novel approach to quantify the relative influence of human and environmental variables in shaping reef ecosystems. Our findings highlight the nuances of what underpins different coral reef regimes, the overwhelming importance of biophysical predictors and how a reef's natural setting may either expand or narrow the opportunity space for management interventions. The methods developed through this study can help inform reef practitioners and hold promises for replication across a broad range of ecosystems.
Original languageEnglish
Article number20182544
JournalProceedings of the Royal Society B: Biological Sciences
Volume286
Issue number1896
Early online date13 Feb 2019
DOIs
Publication statusPublished - Feb 2019

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