Bright spots among the world’s coral reefs

Abstract

Ongoing declines in the structure and function of the world's coral reefs require novel approaches to sustain these ecosystems and the millions of people who depend on them. A presently unexplored approach that draws on theory and practice in human health and rural development is to systematically identify and learn from the 'outliers'-places where ecosystems are substantially better ('bright spots') or worse ('dark spots') than expected, given the environmental conditions and socioeconomic drivers they are exposed to. Here we compile data from more than 2,500 reefs worldwide and develop a Bayesian hierarchical model to generate expectations of how standing stocks of reef fish biomass are related to 18 socioeconomic drivers and environmental conditions. We identify 15 bright spots and 35 dark spots among our global survey of coral reefs, defined as sites that have biomass levels more than two standard deviations from expectations. Importantly, bright spots are not simply comprised of remote areas with low fishing pressure; they include localities where human populations and use of ecosystem resources is high, potentially providing insights into how communities have successfully confronted strong drivers of change. Conversely, dark spots are not necessarily the sites with the lowest absolute biomass and even include some remote, uninhabited locations often considered near pristine. We surveyed local experts about social, institutional, and environmental conditions at these sites to reveal that bright spots are characterized by strong sociocultural institutions such as customary taboos and marine tenure, high levels of local engagement in management, high dependence on marine resources, and beneficial environmental conditions such as deep-water refuges. Alternatively, dark spots are characterized by intensive capture and storage technology and a recent history of environmental shocks. Our results suggest that investments in strengthening fisheries governance, particularly aspects such as participation and property rights, could facilitate innovative conservation actions that help communities defy expectations of global reef degradation.

Full text

00 MONTH 2016 | VOL 000 | NATURE | 1
LETTER doi:10.1038/nature18607
Bright spots among the world’s coral reefs
Joshua E. Cinner1, Cindy Huchery1, M. Aaron MacNeil1,2,3, Nicholas A.J. Graham1,4, Tim R. McClanahan5, Joseph Maina5,6,7,
Eva Maire1,8, John N. Kittinger9,10, Christina C. Hicks1,4,9, Camilo Mora11, Edward H. Allison12, Stephanie D’Agata5,7,13,
Andrew Hoey1, David A. Feary14, Larry Crowder9, Ivor D. Williams15, Michel Kulbicki16, Laurent Vigliola13, Laurent Wantiez17,
Graham Edgar18, Rick D. Stuart-Smith18, Stuart A. Sandin19, Alison L. Green20, Marah J. Hardt21, Maria Beger6,
Alan Friedlander22,23, Stuart J. Campbell5, Katherine E. Holmes5, Shaun K. Wilson24,25, Eran Brokovich26, Andrew J. Brooks27,
Juan J. Cruz-Motta28, David J. Booth29, Pascale Chabanet30, Charlie Gough31, Mark Tupper32, Sebastian C. A. Ferse33,
U. Rashid Sumaila34 & David Mouillot1,8
Ongoing declines in the structure and function of the world’s coral
reefs
1,2
require novel approaches to sustain these ecosystems and the
millions of people who depend on them
3
. A presently unexplored
approach that draws on theory and practice in human health and
rural development4,5 is to systematically identify and learn from
the ‘outliers’—places where ecosystems are substantially better
(‘bright spots’) or worse (‘dark spots’) than expected, given the
environmental conditions and socioeconomic drivers they are
exposed to. Here we compile data from more than 2,500 reefs
worldwide and develop a Bayesian hierarchical model to generate
expectations of how standing stocks of reef fish biomass are related
to 18 socioeconomic drivers and environmental conditions. We
identify 15 bright spots and 35 dark spots among our global survey
of coral reefs, defined as sites that have biomass levels more than
two standard deviations from expectations. Importantly, bright
spots are not simply comprised of remote areas with low fishing
pressure; they include localities where human populations and use
of ecosystem resources is high, potentially providing insights into
how communities have successfully confronted strong drivers of
change. Conversely, dark spots are not necessarily the sites with the
lowest absolute biomass and even include some remote, uninhabited
locations often considered near pristine6. We surveyed local
experts about social, institutional, and environmental conditions
at these sites to reveal that bright spots are characterized by strong
sociocultural institutions such as customary taboos and marine
tenure, high levels of local engagement in management, high
dependence on marine resources, and beneficial environmental
conditions such as deep-water refuges. Alternatively, dark spots
are characterized by intensive capture and storage technology and
a recent history of environmental shocks. Our results suggest that
investments in strengthening fisheries governance, particularly
aspects such as participation and property rights, could facilitate
innovative conservation actions that help communities defy
expectations of global reef degradation.
Despite substantial international conservation efforts, diversity and
abundance continue to decline within many of the world’s ecosystems1,7.
Most conservation approaches aim to identify and protect places of
high ecological integrity under minimal threat
8
. Yet, with escalating
social and environmental drivers of change, conservation actions are
also needed where people and nature coexist, especially where human
effects are already severe
9
. Here, we highlight an approach for imple-
menting conservation in coupled human–natural systems focused on
identifying and learning from outliers—places that are performing
substantially better than expected, given the socioeconomic and envi-
ronmental conditions they are exposed to. By their very nature, outliers
deviate from expectations, and consequently can provide novel insights
into confronting complex problems where conventional solutions have
failed. This type of positive deviance, or bright spot analysis has been
used in fields such as business, health, and human development to
uncover local actions and governance systems that work in the con-
text of widespread failure
10,11
, and holds much promise in informing
conservation.
To demonstrate this approach, we compiled data from 2,514 coral
reefs in 46 countries, states, and territories (hereafter ‘nations/states’)
and developed a Bayesian hierarchical model to generate expected con-
ditions of how standing reef fish biomass (a key indicator of resource
availability and ecosystem functions
12
) was related to 18 key environ-
mental variables and socioeconomic drivers (Fig. 1; Extended Data
Tables 1–4; Extended Data Figs 1–3; Methods). Drawing on a broad
body of theoretical and empirical research in the social sciences
13–15
and ecology
2,6,16
on coupled human–natural systems, we quantified
how reef fish biomass (Fig. 1a) was related to distal social drivers such
as markets, affluence, governance, and population (Fig. 1b, c), while
controlling for well-known environmental conditions such as depth,
1Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland 4811, Australia. 2Australian Institute of Marine Science, PMB 3 Townsville
MC, Townsville, Queensland 4810, Australia. 3Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia B3H 3J5 Canada. 4Lancaster Environment Centre, Lancaster
University, Lancaster LA1 4YQ, UK. 5Wildlife Conservation Society, Global Marine Program, Bronx, New York 10460, USA. 6Australian Research Council Centre of Excellence for Environmental
Decisions, Centre for Biodiversity and Conservation Science, University of Queensland, Brisbane St Lucia, Queensland 4074, Australia. 7Department of Environmental Sciences, Macquarie
University, North Ryde, New South Wales 2109, Australia. 8MARBEC, UMR 9190, IRD-CNRS-UM-IFREMER, Université Montpellier, 34095 Montpellier Cedex, France. 9Center for Ocean Solutions,
Stanford University, California 94305, USA. 10Conservation International Hawaii, Betty and Gordon Moore Center for Science and Oceans, 7192 Kalaniana‘ole Hwy, Suite G230, Honolulu,
Hawaii 96825, USA. 11Department of Geography, University of Hawaii at Manoa, Honolulu, Hawaii 96822, USA. 12School of Marine and Environmental Affairs, University of Washington, Seattle,
Washington 98102 USA. 13Institut de Recherche pour le Développement, UMR IRD-UR-CNRS ENTROPIE, Laboratoire d’Excellence LABEX CORAIL, BP A5, 98848 Nouméa Cedex, New Caledonia.
14Ecology & Evolution Group, School of Life Sciences, University Park, University of Nottingham, Nottingham NG7 2RD, UK. 15Coral Reef Ecosystems Division, NOAA Pacific Islands Fisheries
Science Center, Honolulu, Hawaii 96818, USA. 16UMR Entropie, Labex Corail, –IRD, Université de Perpignan, 66000 Perpignan, France. 17EA4243 LIVE, University of New Caledonia, BPR4 98851
Nouméa Cedex, New Caledonia. 18Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania 7001, Australia. 19Scripps Institution of Oceanography, University of
California, San Diego, La Jolla, California 92093, USA. 20The Nature Conservancy, Brisbane, Queensland 4101, Australia. 21Future of Fish, 7315 Wisconsin Ave, Suite 1000W, Bethesda, Maryland
20814, USA. 22Fisheries Ecology Research Lab, Department of Biology, University of Hawaii, Honolulu, Hawaii 96822, USA. 23National Geographic Society, Pristine Seas Program, 1145 17th
Street NW, Washington DC 20036-4688, USA. 24Department of Parks and Wildlife, Kensington, Perth, Western Australia 6151, Australia. 25Oceans Institute, University of Western Australia,
Crawley, Western Australia 6009, Australia. 26The Israeli Society of Ecology and Environmental Sciences, Kehilat New York 19 Tel Aviv, Israel. 27Marine Science Institute, University of California,
Santa Barbara, California 93106-6150, USA. 28Departamento de Ciencias Marinas., Recinto Universitario de Mayaguez, Universidad de Puerto Rico, San Juan 00680, Puerto Rico. 29School of Life
Sciences, University of Technology, Sydney, New South Wales 2007, Australia. 30UMR ENTROPIE, Laboratoire d’Excellence LABEX CORAIL, Institut de Recherche pour le Développement, CS 41095,
97495 Sainte Clotilde, La Réunion. 31Blue Ventures Conservation, 39-41 North Road, London N7 9DP, UK. 32Coastal Resources Association, St. Joseph St., Brgy. Nonoc, Surigao City, Surigao del
Norte 8400, Philippines. 33Leibniz Centre for Tropical Marine Ecology (ZMT), Fahrenheitstrasse 6, D-28359 Bremen, Germany. 34Fisheries Economics Research Unit, University of British Columbia,
2202 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada.
© 2016 Macmillan Publishers Limited. All rights reserved

2 | NATURE | VOL 000 | 00 MONTH 2016
Letter
reSeArCH
habitat, and productivity (Fig. 1d) (Extended Data Table 1; Methods).
In contrast to many global studies of reef systems that are focused on
demonstrating the severity of human effects
6
, our examination seeks
to uncover potential policy levers by highlighting the relative role of
specific social drivers. A key finding from our global analysis is that
our metric of potential interactions with urban centres, called market
–1.0 –0.5 0.0 0.5 1.0
High compliance reserve
Local population growth
Low compliance reserve
Fishing restricted
Openly shed
Nearest settlement gravity
Market gravity
b
a
–1.0 –0.5 0.0 0.5 1.0
Human development index
Voice and accountability
Tourism
Population size
Reef sh landings
c
–1.0 –0.5 0.0 0.5 1.0
Productivity
Depth
Reef slope
Reef crest
Reef lagoon
Reef at
d
3.2
4.0
4.8
5.6
6.4
7.2
8.0
8.8
Biomass (log[kg ha–1])
Standardized effect size
Figure 1 | Global patterns and drivers of reef fish biomass. a, Reef fish
biomass among 918 study sites. Points vary in size and colour proportional
to the amount of fish biomass. b–d, Standardized effect size of local-
scale social drivers, nation/state-scale social drivers, and environmental
covariates, respectively. Parameter estimates are Bayesian posterior median
values, 95% uncertainty intervals (UI; thin lines), and 50% UI (thick lines).
Black dots indicate that the 95% UI does not overlap 0; grey closed circles
indicates that 75% of the posterior distribution lies to one side of 0; and
grey open circles indicate that the 50% UI overlaps 0.
b
100 250 500 1,000 2,500
–2
0
2
Deviation from expected (s.d.)
Dark spots
Bright spots
a
Reef sh biomass
(kg ha–1)
7.
8.
9. 15.
11.
12.
6.
5. Mauritius 2. Kenya
16.
1.
14.
3.
4.
13.
10.
17.
3. Madagascar
10. Australia
4. Seychelles
13. Northwestern Hawaiian Islands
7. Indonesia
1. Tanzania
14. Hawaii
16. Jamaica
17. Venezuela
8. Commonwealth
of the Northern
Mariana
Islands
9. Papua New Guinea
15. Pacic Remote
Island Areas
11. Solomon Islands 6. British Indian Ocean Territory
12. Kiribati
5.
2.
Figure 2 | Bright and dark spots among the world’s coral reefs. a, Each
site’s deviation from expected biomass (y axis) along a gradient of nation/
state mean biomass (x axis). The 50 sites with biomass values > 2 standard
deviations above or below expected values were considered bright (yellow)
and dark (black) spots, respectively. Each grey vertical line represents a
nation/state; those with bright or dark spots are labelled and numbered.
There can be multiple bright or dark spots in each nation/state. b, Map
highlighting bright and dark spots with large circles, and other sites in
small circles. Numbers correspond to panel a.
© 2016 Macmillan Publishers Limited. All rights reserved

00 MONTH 2016 | VOL 000 | NATURE | 3
Letter reSeArCH
gravity17 (Methods), more so than local or national population
pressure, management, environmental conditions, or national socioec-
onomic context, had the strongest relationship with reef fish biomass
(Fig. 1). Specifically, we found that reef fish biomass decreased as the
size and accessibility of markets increased (Extended Data Fig. 1b).
Somewhat counter-intuitively, fish biomass was higher in places with
high local human population growth rates, probably reflecting human
migration to areas of better environmental quality18—a phenomenon
that could result in increased degradation at these sites over time. We
found a strong positive, but less certain relationship (that is, a high
standardized effect size, but only > 75% of the posterior distribution
above zero) with the Human Development Index, meaning that reefs
tended to be in better condition in wealthier nations/states (Fig. 1c).
Our analysis also confirmed the role that marine reserves can play in
sustaining biomass on coral reefs, but only when compliance is high
(Fig. 1b), reinforcing the importance of fostering compliance for
reserves to be successful.
Next, we identified 15 bright spots and 35 dark spots among the
world’s coral reefs, defined as sites with biomass levels more than two
standard deviations higher or lower than expectations from our global
model, respectively (Fig. 2; Methods; Extended Data Table 5). Rather
than simply identifying places in the best or worst condition, our bright
spots approach reveals the places that most strongly defy expectations.
Using them to inform the conservation discourse will certainly chal-
lenge established ideas of where and how conservation efforts should
be focused. For example, remote places far from human impacts are
conventionally considered near-pristine areas of high conservation
value6, yet most of the bright spots we identified occur in fished, pop-
ulated areas (Extended Data Table 5), some with biomass values below
the global average. Alternatively, some remote places such as parts of
the northwest Hawaiian Islands underperform (that is, were identified
as dark spots).
Detailed analysis of why bright spots can evade the fate of similar
areas facing equivalent stresses will require a new research agenda
gathering detailed site-level information on social and institutional
conditions, technological innovations, external influences, and
ecological processes
19
that are simply not available in a global-scale
analysis. As a hypothesis-generating exploration to begin uncovering
why bright and dark spots may diverge from expectations, we sur-
veyed data providers who sampled the sites and other experts with
first-hand knowledge about the presence or absence of ten key social
and environmental conditions at the 15 bright spots, 35 dark spots,
and 14 average sites with biomass values closest to model expecta-
tions (see Methods and Supplementary Information for details). Our
initial exploration revealed that bright spots were more likely to have
high levels of local engagement in the management process, high
dependence on coastal resources, and the presence of sociocultural
governance institutions such as customary tenure or taboos (Fig. 3;
Methods). For example, in one bright spot, Karkar Island, Papua New
Guinea, resource use is restricted through an adaptive rotational har-
vest system based on ecological feedbacks, marine tenure that allows
for the exclusion of fishers from outside the local village, and initiation
rights that limit individuals’ entry into certain fisheries20. Bright spots
were also generally proximate to deep water, which may help provide
a refuge from disturbance for corals and fish21 (Fig. 3; Extended Data
Fig. 4). Conversely, dark spots were distinguished by having fishing
technologies allowing for more intensive exploitation, such as fish
freezers and potentially destructive netting, as well as a recent history
of environmental shocks (for example, coral bleaching or cyclone;
Fig. 3). The latter is particularly worrisome in the context of climate
change, which is likely to lead to increased coral bleaching and more
intense cyclones22.
Our global analyses highlight two novel opportunities to inform
coral reef governance. The first is to use bright spots as agents of
change to expand the conservation discourse from the current focus
on protecting places under minimal threat
8
, towards harnessing les-
sons from places that have successfully confronted numerous or severe
stressors. Our bright spots approach can be used to inform the types of
investments and governance structures that may help to create more
sustainable pathways for impacted coral reefs. Specifically, our initial
investigation highlights how investments that strengthen fisheries
governance, particularly issues such as participation and property
rights, could help communities to innovate in ways that allow them
to defy expectations. Conversely, the more typical efforts to provide
capture and storage infrastructure, particularly where there are envi-
ronmental shocks and local-scale governance is weak, may lead to
Figure 3 | Differences in key social and environmental conditions between bright spots, dark spots, and ‘average’ sites. a, Social and institutional
conditions; b, external- or donor-driven projects; c, technologies; d, environmental conditions. P values are determined using Fisher’s exact test.
Intensive netting includes beach seine nets, surround gill nets, and muro-ami.
0%
20%
40%
60%
80%
100%
Bright
n = 15
Average
n = 14
Dark
n = 35
External projects
0%
20%
40%
60%
80%
100%
Bright
n = 10
Average
n = 11
Dark
n = 34
Bright
n = 12
Average
n = 11
Dark
n = 35
Bright
n = 15
Average
n = 14
Dark
n = 35
Deep water refuge
P = 0.025
Mangroves
P = 0.665
Environmental shock
P = 0.027
0%
20%
40%
60%
80%
100%
Bright
n = 13
Average
n = 13
Dark
n = 35
Bright
n = 15
Average
n = 12
Dark
n = 35
Bright
n = 14
Average
n = 13
Dark
n = 35
Intensive netting
P = 0.012
Motorised vessels
P = 0.227
Freezers
P < 0.001
0%
20%
40%
60%
80%
100%
Bright
n = 15
Average
n = 14
Dark
n = 35
Bright
n = 10
Average
n = 7
Dark
n = 32
Bright
n = 10
Average
n = 9
Dark
n = 32
Taboos/tenure
P < 0.001
Engagement
P < 0.001
Dependence
P = 0.002 P = 0.78
ab
c
d
Present
Absent
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4 | NATURE | VOL 000 | 00 MONTH 2016
Letter
reSeArCH
social– ecological traps23 that reinforce resource degradation beyond
expectations. Effectively harnessing the potential to learn from both
bright and dark spots will require scientists to increase research efforts
in these places, NGOs to catalyse lessons from other areas, donors to
start investing in novel solutions, and policy makers to ensure that
governance structures foster flexible learning and experimentation.
Indeed, bright spots may have much to offer in terms of how to crea-
tively confront drivers of change and prioritize conservation actions.
Likewise, dark spots can help identify development strategies to avoid.
Critically, the bright spots we identified span the development spec-
trum from low to high income (for example, Solomon Islands and
territories of the USA, respectively; Fig. 2), showing that lessons about
effective reef management can emerge from diverse places.
A second opportunity stems from a renewed focus on managing the
socioeconomic drivers that shape reef conditions. Many social drivers
are amenable to governance interventions, and our comprehensive
analysis (Fig. 1) suggests that an increased policy focus on social drivers
such as markets and development could result in improvements to reef
fish biomass. For example, given the important influence of markets in
our analysis, reef managers, donor organizations, conservation groups,
and coastal communities could improve sustainability by developing
interventions that dampen the negative influence of markets on reef sys-
tems. A portfolio of market interventions, including eco- labelling and
sustainable harvesting certifications, fisheries improvement projects,
and value chain interventions have been developed within large-scale
industrial fisheries to condition access to markets based on sustainable
harvesting
24,25
. Although there is considerable scope for adapting these
interventions to artisanal coral reef fisheries in both local and regional
markets, effectively dampening the negative influence of markets may
also require developing novel interventions that address the range of
ways in which markets can lead to over exploitation. Existing research
suggests that markets create incentives for overexploitation not only by
affecting price and price variability for reef products26, but also by influ-
encing people’s behaviour
27,28
, including their willingness to cooperate
in the collective management of natural resources29.
The long-term viability of coral reefs will ultimately depend on inter-
national action to reduce carbon emissions22. However, fisheries remain
a pervasive source of reef degradation, and effective local-level fisheries
governance is crucial to sustaining ecological processes that give reefs
the best chance of coping with global environmental change30. Seeking
out and learning from bright spots is a novel approach to conserva-
tion that may offer insights into confronting the complex governance
problems facing coupled human–natural systems such as coral reefs.
Online Content Methods, along with any additional Extended Data display items and
Source Data, are available in the online version of the paper; references unique to
these sections appear only in the online paper.
Received 5 January; accepted 27 May 2016.
Published online 15 June 2016.
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Supplementary Information is available in the online version of the paper.
Acknowledgements The ARC Centre of Excellence for Coral Reef Studies,
Stanford University, and University of Montpellier funded working group
meetings. This work was supported by J.E.C.’s Pew Fellowship in Marine
Conservation and ARC Australian Research Fellowship. Thanks to M. Barnes for
constructive comments.
Author Contributions J.E.C. conceived of the study with support from M.A.M.,
N.A.J.G., T.R.M., J.K., C.Hu., D.M., C.M., E.H.A., and C.C.Hi.; C.Hu. managed the
database; M.A.M., J.E.C., and D.M. developed and implemented the analyses;
J.E.C. led the manuscript with M.A.M. and N.A.J.G. All other authors contributed
data and made substantive contributions to the text.
Author Information This is the Social-Ecological Research Frontiers (SERF)
working group contribution no. 11. Reprints and permissions information
is available at www.nature.com/reprints. The authors declare no competing
financial interests. Readers are welcome to comment on the online version of
the paper. Correspondence and requests for materials should be addressed to
J.E.C. (Joshua.cinner@jcu.edu.au).
Reviewer Information Nature thanks S. Qian, B. Walker and the other
anonymous reviewer(s) for their contribution to the peer review of this work.
© 2016 Macmillan Publishers Limited. All rights reserved

Letter reSeArCH
METHODS
No statistical methods were used to predetermine sample size.
Scales of data. Our data were organized at three spatial scales:
(i) Reef (n = 2,514). e smallest scale, which had an average of 2.4 surveys/
transects.
(ii) Site (a cluster of reefs; n = 918). We clustered reefs together that were with-
in 4 km of each other, and used the centroid of these clusters to estimate
site-level social and site-level environmental covariates (Extended Data
Table 1). To make these clusters, we rst estimated the linear distance
between all reefs, then used a hierarchical analysis with the complete-
linkage clustering technique based on the maximum distance between
reefs. We set the cut-o at 4 km to select mutually exclusive sites where reefs
cannot be more distant than 4 km. e choice of 4 km was informed by a
3-year study of the spatial movement patterns of artisanal coral reef shers,
corresponding to the highest density of shing activities on reefs based on
GPS-derived eort density maps of artisanal coral reef shing activities31.
is clustering analysis was carried out using the R functions hclust and
cutree, resulting in an average of 2.7 reefs per site.
(iii) Nation/state (nation, state, or territory; n = 46). A larger scale in our analysis
was nation/state, which are jurisdictions that generally correspond to indi-
vidual nations (but could also include states, territories, overseas regions, or
extremely remote areas within a state such as the northwest Hawaiian Islands;
Extended Data Table 2), within which sites and reefs were nested for analysis.
Estimating biomass. Reef fish biomass can reflect a broad selection of reef fish
functioning and benthic conditions
12,32–34
, and is a key metric of resource availabil-
ity for reef fisheries. Reef fish biomass estimates were based on instantaneous visual
counts from 6,088 surveys collected from 2,514 reefs. All surveys used standard
belt-transects, distance sampling, or point-counts, and were conducted between
2004 and 2013. Where data from multiple years were available from a single reef,
we included only data from the year closest to 2010. Within each sur vey area, reef
associated fishes were identified to species level, abundance counted, and total
length (TL) estimated, with the exception of one data provider who measured
biomass at the family level. To make estimates of biomass from these transect-level
data comparable among studies, we:
(iv) Retained families that were consistently studied and were above a mini-
mum size cut-o. us, we retained counts of > 10-cm diurnally active,
non-cryptic reef sh that are resident on the reef (20 families, 774 species),
excluding sharks and semi-pelagic species. We also excluded three groups
of shes that are strongly associated with coral habitat conditions and are
rarely targets for sheries (Anthiinae, Chaetodontidae, and Cirrhitidae).
Families included are: Acanthuridae, Balistidae, Diodontidae, Ephippidae,
Haemulidae, Kyphosidae, Labridae, Lethrinidae, Lutjanidae, Monacanthi-
dae, Mullidae, Nemipteridae, Pinguipedidae, Pomacanthidae, Serranidae,
Siganidae, Sparidae, Synodontidae, Tetraodontidae and Zanclidae. We
calculated the total biomass of sh on each reef using standard published
species-level length–weight relationship parameters or those available on
FishBase35. When length–weight relationship parameters were not available
for a species, we used the parameters for a closely related species or genus.
(v) Directly accounted for depth and habitat as covariates in the model (see
Environmental conditions section below).
(vi) Accounted for any potential bias among data providers (capturing informa-
tion on both inter-observer dierences, and census methods) by including
each data provider as a random eect in our model.
Biomass means, medians, and standard deviations were calculated at the reef-
scale. All reported log values are the natural log.
Social drivers
Local population growth. We created a 100 km buffer around each site and used
this to calculate human population within the buffer in 2000 and 2010 based
on the Socioeconomic Data and Application Centre (SEDAC) gridded popula-
tion of the world database
36
. Population growth was the proportional difference
between the population in 2000 and 2010. We chose a 100 km buffer as a reasonable
range at which many key human impacts from population (for example, land-use
and nutrients) might affect reefs37.
Management. For each site, we determined if it was unfished, that is, whether it
fell within the borders of a no-take marine reserve (we asked data providers to
further classify whether the reserve had high or low levels of compliance);
restricted—whether there were active restrictions on gears (for example, bans on
the use of nets, spear guns, or traps) or fishing effort (which could have included
areas inside marine parks that were not necessarily no take); or fished, that is, reg-
ularly fished without effective restrictions. To determine these classifications, we
used the expert opinion of the data providers, and triangulated this with a global
database of marine reserve boundaries38.
Gravity. We adapted the economic geography concept of ‘gravity’
17,39–41
, also called
interactance
42
, to examine potential interactions between reefs and: (i) major urban
centres/markets (defined as provincial capital cities, major population centres,
landmark cities, national capitals, and ports); and (ii) the nearest human settle-
ments. This application of the gravity concept infers that potential interactions
increase with population size, but decay exponentially with the effective distance
between two points. Thus, we gathered data on both population estimates and a
surrogate for distance: travel time.
Population estimations. We gathered population estimates for: (i) the nearest major
markets (which includes national capitals, provincial capitals, major population
centres, ports, and landmark cities) using the World Cities base map from ESRI;
and (ii) the nearest human settlement within a 500 km radius using LandScan 2011
database. The different data sets were required because the latter is available in
raster format while the former is available as point data. We chose a 500 km radius
from the nearest settlement as the maximum distance any non-market fishing
activities for fresh reef fish are likely to occur.
Travel time calculation. Travel time was computed using a cost–distance algorithm
that computes the least ‘cost’ (in minutes) of travelling between two locations on
a regular raster grid. In our case, the two locations were either the centroid of the
site (that is, reef cluster) and the nearest settlement, or the centroid of the site and
the major market. The cost (that is, time) of travelling between the two locations
was determined by using a raster grid of land cover and road networks with the
cells containing values that represent the time required to travel across them43:
• Tree cover, broadleaved, deciduous and evergreen, closed; regularly ooded
tree cover, shrub, or herbaceous cover (fresh, saline, & brackish water) = speed
of 1 k m h−1
• Tree cover, broadleaved, deciduous, open (open = 15–40% tree cover) = speed
of 1.25 k m h−1
• Tree cover, needle-leaved, deciduous and evergreen, mixed leaf type; shrub
cover, closed-open, deciduous and evergreen; herbaceous cover, closed-open;
cultivated and managed areas; mosaic: cropland/tree cover/other natural veg-
etation, cropland/shrub or grass cover = spe ed of 1.5 km h−1
• Mosaic: tree cover/other natural vegetation; tree cover, burnt = speed of
1.25 km h−1
• Sparse herbaceous or sparse shrub cover = speed of 2.5 km h−1
• Water = speed of 20 km h−1
• Roads = speed of 60 km h−1
• Track = speed of 30 km h−1
• Articial surfaces and associated areas = speed of 30 km h−1
• Missing values = speed of 1.4 km h−1
We termed this raster grid a friction-surface (with the time required to travel
across different types of surfaces analogous to different levels of friction). To
develop the friction-surface, we used global data sets of road networks, land cover,
and shorelines:
• Road network data was extracted from the Vector Map Level 0 (VMap0) from
the National Imagery and Mapping Agency’s (NIMA) Digital Chart of the
World (DCW). We converted vector data from VMap0 to 1 km resolution
raster.
• Land cover data were extracted from the Global Land Cover 2000 (ref. 44).
• To dene the shorelines, we used the GSHHS (Global Self-consistent,
Hierarchical, High-resolution Shoreline) database version 2.2.2.
These three friction components (road networks, land cover, and water bodies)
were combined into a single friction surface with a Behrmann map projection.
We calculated our cost-distance models in R
45
using the accCost function of the
gdistance package. The function uses Dijkstra’s algorithm to calculate least-cost
distance between two cells on the grid and the associated distance taking into
account obstacles and the local friction of the landscape46. Travel time estimates
over a particular surface could be affected by the infrastructure (for example, road
quality) and types of technology used (for example, types of boats). These types
of data were not available at a global scale but could be important modifications
in more localized studies.
Gravity computation. To compute the gravity to the nearest market, we calculated
the population of the nearest major market and divided that by the squared travel
time between the market and the site. Although other exponents can be used
47
, we
used the squared distance (or in our case, travel time), which is relatively common
in geography and economics. This decay function could be influenced by local
considerations, such as infrastructure quality (for example, roads), the types of
transport technology (that is, vessels being used), and fuel prices, which were not
© 2016 Macmillan Publishers Limited. All rights reserved

Letter
reSeArCH
available in a comparable format for this global analysis, but could be important
considerations in more localized adaptations of this study.
To determine the gravity of the nearest settlement, we located the nearest pop-
ulated pixel within 500 km, determined the population of that pixel, and divided
that by the squared travel time between that cell and the reef site.
As is standard practice in many agricultural economics studies
48
, an assumption
in our study is that the nearest major capital or landmark city represents a market.
Ideally we would have used a global database of all local and regional markets
for coral reef fish, but this type of database is not available at a global scale. As a
sensitivity analysis to help justify our assumption that capital and landmark cities
were a reasonable proxy for reef fish markets, we tested a series of candidate models
that predicted biomass based on: (1) cumulative gravity of all cities within 500 km;
(2) gravity of the nearest city; (3) travel time to the nearest city; (4) population of
the nearest city; (5) gravity to the nearest human population above 40 people km
−2
(assumed to be a small peri-urban area and potential local market); (6) the travel
time between the reef and a small peri-urban area; (7) the population size of the
small peri-urban population; (8) gravity to the nearest human population above
75 people km−2 (assumed to be a large peri-urban area and potential market);
(9) the travel time between the reef and this large peri-urban population; (10) the
population size of this large peri-urban population; and (11) the total population
size within a 500 km radius. Model selection revealed that the best two models
were gravity of the nearest city and gravity of all cities within 500km (with a 3
AIC value difference between them; Extended Data Table 3). Importantly, when
looking at the individual components of gravity models, the travel time compo-
nents all had a much lower AIC value than the population components, which is
broadly consistent with previous systematic review studies
49
. Similarly, travel time
to the nearest city had a lower AIC score than any aspect of either the peri-urban
or urban measures. This suggests our use of capital and landmark cities is likely
to better capture exploitation drivers from markets rather than simple popula-
tion pressures. This may be because market dynamics are difficult to capture by
population threshold estimates; for example some small provincial capitals where
fish markets are located have very low population densities, while some larger
population centres may not have a market. Downscaled regional or local analyses
could attempt to use more detailed knowledge about fish markets, but we used the
best proxy available at a global scale.
Human Development Index (HDI). HDI is a summary measure of human devel-
opment encompassing: a long and healthy life, being knowledgeable, and having
a decent standard of living. In cases where HDI values were not available specific
to the State (for example, Florida and Hawaii), we used the national (for example,
USA) HDI value.
Population size. For each nation/state, we determined the size of the human pop-
ulation. Data were derived mainly from census reports, the CIA fact book, and
Wikipedia.
Tourism. We examined tourist arrivals relative to the nation/state population
size (above). Tourism arrivals were gathered primarily from the World Tourism
Organization’s Compendium of Tourism Statistics.
National reef fish landings. Catch data were obtained from the Sea Around Us
Project (SAUP) catch database (http://www.seaaroundus.org), except for Florida,
which was not reported separately in the database. We identified 200 reef fish
species and taxon groups in the SAUP catch database50. Note that reef-associated
pelagics such as scombrids and carangids normally form part of reef fish catches.
However, we chose not to include these species because they are also targeted and
caught in large amounts by large-scale, non-reef operations.
Voice and accountability. This metric, from the World Bank survey on governance,
reflects the perceptions of the extent to which a country’s citizens are able to par-
ticipate in selecting their government, as well as freedom of expression, freedom of
association, and a free media. In cases where governance values were not available
specific to the nation/state (for example, Florida and Hawaii), we used national
(for example, USA) values.
Environmental drivers
Depth. The depth of reef surveys were grouped into the following categories: < 4 m,
4–10 m, > 10 m to account for broad differences in reef fish community structure
attributable to a number of inter-linked depth- related factors. Categories were
necessary to standardise methods used by data providers and were determined by
pre-existing categories used by several data providers.
Habitat. We included the following habitat categories:
(i) Slope. e reef slope habitat is typically on the ocean side of a reef, where the
reef slopes down into deeper water.
(ii) Crest. e reef crest habitat is the section that joins a reef slope to the reef at.
e zone is typied by high wave energy (that is, where the waves break). It
is also typied by a change in the angle of the reef from an inclined slope to
a horizontal reef at.
(iii) Flat. e reef at habitat is typically horizontal and extends back from the
reef crest for 10’s to 100’s of metres;
(iv) Lagoon/back reef. Lagoon reef habitats are where the continuous reef at
breaks up into more patchy reef environments sheltered from wave energy.
ese habitats can be behind barrier/fringing reefs or within atolls. Back
reef habitats are similar broken habitats where the wave energy does not
typically reach the reefs and thus forms a less continuous ‘lagoon style’ reef
habitat. Due to minimal representation among our sample, we excluded
other less prevalent habitat types, such as channels and banks. To verify
the sites’ habitat information, we used the Millennium Coral Reef Map-
ping Project (MCRMP) hierarchical data51, Google Earth, and site depth
information.
Productivity. We examined ocean productivity for each of our sites in mg of C per
m
2
per day (http://www.science.oregonstate.edu/ocean.productivity/). Using the
monthly data for years 2005 to 2010 (in hdf format), we imported and converted
those data into ArcGIS. We then calculated yearly average and finally an average
for all these years. We used a 100 km buffer around each of our sites and examined
the average productivity within that radius. Note that ocean productivity esti-
mates are less accurate for near-shore environments, but we used the best available
data.
Analyses. We first looked for collinearity among our covariates using bivariate
correlations and variance inflation factor estimates (Extended Data Fig. 2 and
Extended Data Table 4). This led to the exclusion of several covariates (not
described above): (i) geographic basin (tropical Atlantic, western Indo-Pacific,
central Indo-Pacific, or eastern Indo-Pacific); (ii) gross domestic product (purchas-
ing power parity); (iii) rule of law (World Bank governance index); (iv) control of
corruption (World Bank governance index); and (v) sedimentation. Additionally,
we removed an index of climate stress, developed by Maina et al.52, which incor-
porated 11 different environmental conditions, such as the mean and variability
of sea surface temperature due to repeated lack of convergence for this parameter
in the model, likely indicative of unidentified multicollinearity. All other covar-
iates had correlation coefficients 0.7 or less and variance inflation factor scores
less than 5 (indicating multicollinearity was not a serious concern). Care must be
taken in causal attribution of covariates that were significant in our model, but
demonstrated collinearity with candidate covariates that were removed during the
aforementioned process. Importantly, the covariate that exhibited the largest effect
size in our model, market gravity, was not strongly collinear with other candidate
covariates.
To quantify the multi-scale social, environmental, and economic factors affect-
ing reef fish biomass we adopted a Bayesian hierarchical modelling approach that
explicitly recognized the three scales of spatial organization: reef (j), site (k), and
nation/state (s).
In adopting the Bayesian approach we developed two models for inference:
a null model, consisting only of the hierarchical units of observation (that is,
intercepts-only) and a full model that included all of our covariates (drivers) of
interest. Covariates were entered into the model at the relevant scale, leading
to a hierarchical model whereby lower-level intercepts (averages) were placed
in the context of higher-level covariates in which they were nested. We used
the null model as a baseline against which we could ensure that our full model
performed better than a model with no covariate information. We did not
remove ‘non-significant’ covariates from the model because each covariate was
carefully considered for inclusion and could therefore reasonably be considered
as having an effect, even if small or uncertain; removing factors from the model
is equivalent to fixing parameter estimates at exactly zero—a highly-subjective
modelling decision after covariates have already been selected as potentially
important53.
The full model assumed the observed, reef-scale observations of fish biomass
(y
ijks
) were modelled using a non-central t distribution, allowing for fatter tails than
typical log-normal models of reef fish biomass32. We chose the non-central t after
having initially used a log-normal model because our model diagnostics suggested
that several model parameters had not converged. We ran a supplementary analysis
to support our use of the non-central t distribution with 3.5 degrees of freedom
(see Supplementary Information). Therefore our model was:
µτ∼(.)ytlog[ ]non central,,35
ijks
ijks reef
µββ=+X
ijks jks0reef reef
τ∼( )−
U0,100
reef
2
with Xreef representing the matrix of observed reef-scale covariates and βreef array
of estimated reef-scale parameters. The τ
reef
(and all subsequent τ values) were
assumed common across observations in the final model and were minimally
© 2016 Macmillan Publishers Limited. All rights reserved

Letter reSeArCH
informative
53
. Using a similar structure, the reef-scale intercepts (β
0jks
) were struc-
tured as a function of site-scale covariates (Xsit):
βµτ∼( )N,
jksjks
0sit
µγγ=+X
jksks0sit sit
τ∼( )−
U0,100
sit
2
with γ
sit
representing an array of site-scale parameters. Building upon the hier-
archy, the site-scale intercepts (γ
0ks
) were structured as a function of state-scale
covariates (Xsta):
γµτ∼( )N,
ks ks0sta
µγγ=+X
ks 0sta
sta
τ∼( )−
U0,100
sta
2
Finally, at the top scale of the analysis we allowed for a global (overall) estimate of
average log-biomass (γ0):
γ∼(.)N00,1000
0
The relationships between fish biomass and reef, site, and state-scale drivers
was carried out using the PyMC package54 for the Python programming language,
using a Metropolis-Hastings (MH) sampler run for 106 iterations, with a 900,000
iteration burn-in thinned by 10, leaving 10,000 samples in the posterior distribu-
tion of each parameter; these long burn-in times are often required with a com-
plex model using the MH algorithm. Convergence was monitored by examining
posterior chains and distributions for stability and by running multiple chains
from different starting points and checking for convergence using Gelman–Rubin
statistics
55
for parameters across multiple chains; all were at or close to 1, indicating
good convergence of parameters across multiple chains.
Overall model fit. We conducted posterior predictive checks for goodness of fit
(GoF) using Bayesian P values
43
(BpV), whereby fit was assessed by the discrep-
ancy between observed or simulated data and their expected values. To do this we
simulated new data (y
inew
) by sampling from the joint posterior of our model (θ )
and calculated the Freeman–Tukey measure of discrepancy for the observed (y
iobs
)
or simulated data, given their expected values (μi):
∑
θ(|)= (−)Dy yy
i
ii
2
yielding two arrays of median discrepancies D(yobs|θ ) and D(ynew|θ ) that were
then used to calculate a BpV for our model by recording the proportion of times
D(yobs|θ ) was greater than D(ynew|θ ) (Extended Data Fig. 3a). A BpV above 0.975
or under 0.025 provides substantial evidence for lack of model fit. Evaluated by
the deviance information criterion (DIC), the full model greatly outperformed a
null model that included no covariates (Δ DIC = 472).
To examine homoscedasticity, we checked residuals against fitted values. We
also checked the residuals against all covariates included in the model, and several
covariates that were not included in the model (primarily due to collinearity),
including: (i) Atoll, a binary metric of whether the reef was on an atoll or not;
(ii) control of corruption, perceptions of the extent to which public power is exer-
cised for private gain, including both petty and grand forms of corruption, as well
as ‘capture’ of the state by elites and private interests, derived from the World Bank
survey on governance; (iii) geographic basin, whether the site was in the tropi-
cal Atlantic, western Indo-Pacific, central Indo-Pacific, or eastern Indo-Pacific;
(iv) connectivity, we examined three measures based on the area of coral reef
within a 30 km, 100 km, and 600 km radius of the site; (v) sedimentation; (vi) coral
cover (which was only available for a subset of the sites); (vii) climate stress
52
; and
(viii) census method. The model residuals showed no patterns with these eight
additional covariates, suggesting they would not explain additional information
in our model.
Bright and dark spot estimates. Because the performance of site scale locations
are of substantial interest in uncovering novel solutions for reef conservation, we
defined bright and dark spots at the site scale. To this end, we defined bright (or
dark) spots as locations where expected site-scale intercepts (γ0ks) differed by more
than two standard deviations from their nation/state-scale expected value (μks),
given all the covariates present in the full hierarchical model:
µγ µγ=( −)>..( −)SS 2[sd ]
ks ks ks ks
spot
00
This, in effect, probabilistically identified the most deviant sites, given the
model, while shrinking sites towards their group-level means, thereby allowing
us to overcome potential bias due to low and varying sample sizes that can lead to
extreme values from chance alone. After an initial log-normal model formulation,
where we were not confident in model convergence, we employed a non-central
t distribution at the observation scale, which facilitated model convergence and
dampened any effects of potentially extreme reef-scale observations on the bright
and dark spot estimates. Further, we did not consider a site a bright or dark spot if
the group-level (that is, nation/state) mean included fewer than five sites.
Analysing conditions at bright spots. For our preliminary exploration into why
bright and dark spots may diverge from expectations, we surveyed data providers
and other experts about key social, institutional, and environmental conditions
at the 15 bright spots, 35 dark spots, and 14 sites that performed most closely to
model specifications. Specifically, we developed an online survey (SI) using Survey
Monke y (http://www.surveymonkey.com) software, which we asked data providers
who sampled those sites to complete with input from local experts, where neces-
sary. Data providers generally filled in the survey in consultation with nationally
based field team members who had detailed local knowledge of the socioeconomic
and environmental conditions at each of the sites. Research on bright spots in
agricultural development
19
highlights several types of social and environmental
conditions that may lead to bright spots, which we adapted and developed proxies
for as the basis of our survey into why our bright and dark spots may diverge from
expectations. These include:
(i) Social and institutional conditions. We examined the presence of custom-
ary management institutions such as taboos and marine tenure institutions,
whether there was substantial engagement by local people in management
(specically dened as there being active engagement by local people in reef
management decisions; token involvement and consultation were not consid-
ered substantial engagement), and whether there were high levels of depend-
ence on marine resources (specically, whether a majority of local residents
depend on reef sh as a primary source of food or income). All social and
institutional conditions were converted to presence/absence data. Depend-
ence on resources and engagement were limited to sites that had adjacent
human populations. All other conditions were recorded regardless of whether
there is an adjacent community.
(ii) Technological use/innovation. We examined the presence of motorized ves-
sels, intensive capture equipment (such as beach seine nets, surround gill
nets, and muro-ami nets), and storage capacity (that is, freezers).
(iii) External inuences (such as donor-driven projects). We examined the pres-
ence of NGOs, shery development projects, development initiatives (such
as alternative livelihoods), and sheries improvement projects. All external
inuences were recorded as present/absent then summarized into a single
index of whether external projects were occurring at the site.
(iv) Environmental/ecological processes (for example, recruitment and con-
nectivity). We examined whether sites were within 5km of mangroves and
deep-water refuges, and whether there had been any major environmental
disturbances such as coral bleaching, tsunami, and cyclones within the past
5 years. All environmental conditions were recorded as present/absent.
As an exploratory analysis of associations between these conditions and
whether sites diverged more or less from expectations, we used two complemen-
tary approaches. The link between the presence/absence of the aforementioned
conditions and whether a site was bright, average, or dark was assessed using a
Fisher’s exac t test. Then we tested whether the mean deviation in fish biomass from
expected was similar between sites with presence or absence of the mechanisms
in question (that is, the presence or absence of marine tenure/taboos) using an
ANOVA assuming unequal variance. The two tests yielded similar results, but
provide slightly different ways to conceptualize the issue, the former is correlative
while the latter explains deviation from expectations based on conditions, so we
provide both (Fig. 3 and Extended Data Fig. 4). It is important to note that some
of these social and environmental conditions were significantly associated (that
is, Fisher’s exact probabilities < 0.05), and further research is required to uncover
how these and other conditions may make sites bright or dark.
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Letter reSeArCH
Extended Data Figure 1 | Marginal relationships between reef fish
biomass and social drivers. a, Local population growth; b, market
gravity; c, nearest settlement gravity; d, tourism; e, nation/state
population size; f, Human Development Index; g, high compliance
marine reserve (0 is fished baseline); h, restricted fishing (0 is fished
baseline); i, low-compliance marine reserve (0 is fished baseline); j, voice
and accountability; k, reef fish landings; l, ocean productivity; m, depth
(− 1 = 0–4 m, 0 = 4–10 m, 1 = > 10 m); n, reef flat (0 is reef slope baseline);
o, reef crest flat (0 is reef slope baseline); p, lagoon/back reef flat (0 is reef
slope baseline). All variables displayed on the x axis are standardized. Red
lines are the marginal trend line for each parameter as estimated by the full
model. Grey lines are 100 simulations of the marginal trend line sampled
from the posterior distributions of the intercept and parameter slope,
analogous to conventional confidence intervals. Two asterisks indicate that
95% of the posterior density is in either a positive or negative direction
(Fig. 1b–d); a single asterisk indicates that 75% of the posterior density is
in either a positive or negative direction.
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Letter
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Extended Data Figure 2 | Correlation plot of candidate continuous covariates before accounting for collinearity (Extended Data Table 4).
Collinearity between continuous and categorical covariates (including biogeographic region, habitat, protection status, and depth) were analysed using
box plots.
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Letter reSeArCH
Extended Data Figure 3 | Model fit statistics. Top, Bayesian P values (BpV)
for the full model indicating goodness of fit, based on posterior discrepancy.
Points are Freeman–Tukey differences between observed and expected
values, and simulated and expected values within the MCMC scheme
(n = 10,000). Plot shows no evidence for lack of fit between the model
and the data. Bottom, Posterior distribution for the degrees of freedom
parameter (ν ) in our supplementary analysis of candidate distributions. The
highest posterior density of 3.46, with 97.5% of the total posterior density
below 4 provides strong evidence in favour of a non-central t distribution
relative to a normal distribution and supports the use of 3.5 for ν .
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Letter
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Extended Data Figure 4 | Box plot of deviation from expected as a
function of the presence or absence of key social and environmental
conditions expected to produce bright spots. Boxes range from the first
to third quartile and whiskers extend to the highest value that is within
1.5× the inter-quartile range (that is, distance between the first and third
quartiles). Data beyond the end of the whiskers are outliers, which are
plotted as points.
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Letter reSeArCH
Extended Data Table 1 | Summary of social and environmental covariates
Further details can be found in the Methods. The smallest scale is the individual reef. Sites consist of clusters of reefs within 4km of each other. Nations/states generally correspond to countries, but
can also include or territories or states, particularly when geographically isolated (for example, Hawaii). Refs 36 and 50 are cited in this table.
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Extended Data Table 2 | List of nations/states covered in study and their respective average biomass (kg ha−1
± standard error)
In most cases, nation/state refers to an individual country, but can also include states (for example, Hawaii or Florida), territories (for example, British Indian Ocean Territory), or other jurisdictions.
We treated the northwestern Hawaiian islands and Farquhar as separate ‘nation/states’ from Hawaii and the Seychelles, respectively, because they are extremely isolated and have little or no human
population. In practical terms, this meant dierent values for a few nation/state scale indicators that ended up having relatively small eect sizes (Fig. 1b): population, tourism visitations, and in the
case of the northwestern Hawaiian islands, sh landings.
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Extended Data Table 3 | Model selection of potential gravity indicators and components
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Extended Data Table 4 | Variance inflation factor (VIF) scores for continuous data before and after removing variables due to collinearity
X = covariate removed.
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Extended Data Table 5 | List of bright and dark spot locations, population status, and protection status
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