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. 2019 Feb 13;286(1896):20182544.
doi: 10.1098/rspb.2018.2544.

Parsing human and biophysical drivers of coral reef regimes

Jean-Baptiste Jouffray  1   2 , Lisa M Wedding  3 , Albert V Norström  1 , Mary K Donovan  4 , Gareth J Williams  5 , Larry B Crowder  6 , Ashley L Erickson  3 , Alan M Friedlander  7 , Nicholas A J Graham  8 , Jamison M Gove  9 , Carrie V Kappel  10 , John N Kittinger  11   12 , Joey Lecky  13 , Kirsten L L Oleson  13 , Kimberly A Selkoe  10 , Crow White  14 , Ivor D Williams  9 , Magnus Nyström  1
Affiliations

Parsing human and biophysical drivers of coral reef regimes

Jean-Baptiste Jouffray et al. Proc Biol Sci. .

Abstract

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.

Keywords: Hawai‘i; boosted regression trees; ecology; interactions; management; regime shift.

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Conflict of interest statement

We declare we have no competing interests.

Figures

Figure 1.
Figure 1.
Map of the study area showing the location of 620 sites across the main Hawaiian Islands (Hawai‘i, USA), categorized into four distinct reef regimes. Key characteristics of each regime are provided below the respective icons. Explore an interactive version of the map at https://stanford.maps.arcgis.com/apps/StoryMapBasic/index.html?appid=b50b97f3cadb4c919a85bb6e4dd654cd.
Figure 2.
Figure 2.
(a) Relative influence of anthropogenic (dark grey) and biophysical (light grey) predictor variables used to model the occurrence of each reef regime. The ‘asterisks’ mark variables with an influence above what could be expected by chance (greater than 5%, indicated by the dotted line). The signs + and − display the general direction of the relationship, when discernible. (b) Distribution of the four regimes along a continuum of anthropogenic versus biophysical relative contribution, calculated by considering only the variables with a relative influence greater than 5%. SST, sea surface temperature; max, maximum monthly climatological mean; STD, standard deviation of the long-term mean; anomaly, frequency of anomalies. (Online version in colour.)
Figure 3.
Figure 3.
Partial dependency plots with 95% confidence intervals for the five most influential variables predicting the occurrence of four distinct reef regimes (a–d). The graphs show the effect of a given predictor on the probability of occurrence of the regime while keeping all other variables at their mean. Relative influence of each predictor is reported between parentheses. Grey tick marks across the top of each plot indicate observed data points. SST, sea surface temperature; max, maximum monthly climatological mean; STD, standard deviation of the long-term mean; anomaly, frequency of anomalies. (Online version in colour.)
See this image and copyright information in PMC

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