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Articles
https://doi.org/10.1038/s41893-021-00817-0
1CORDIO East Africa, Mombasa, Kenya. 2IUCN Coral Specialist Group, Mombasa, Kenya. 3IUCN Grouper and Wrasse Specialist Group, Mombasa, Kenya.
4School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia. 5Oceanographic Research
Institute, Durban, South Africa. 6Centre for Applied Marine Sciences, Bangor University, Bangor, UK. 7Cooperative Institute for Marine and Atmospheric
Studies, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA. 8Ocean Chemistry and Ecosystems Division, NOAA
Atlantic Oceanographic and Meteorological Laboratory, Miami, FL, USA. 9AIDE, Moroni, Comoros. 10Administração Nacional das Áreas de Conservação
(ANAC), Maputo, Mozambique. 11Kenya Marine and Fisheries Research Institute, Mombasa, Kenya. 12Marine Parks and Reserves Unit (MPRU), Dar
es Salaam, Tanzania. 13Moheli Marine Park, Nioumachoua, Comoros. 14Seychelles National Parks Authority, Victoria, Seychelles. 15Centre National de
Recherches Océanographiques (CNRO), Nosy Be, Madagascar. 16Institute of Marine Science (IMS), Zanzibar, Tanzania. 17National Institute of Fisheries
Research, Maputo, Mozambique. ✉e-mail: dobura@cordioea.net
The collapse of an ecosystem signifies its functional ‘extinc-
tion’, when the characteristics and functions that define it are
transformed1,2. The risk of collapse in multiple ecosystems has
increased in the Anthropocene, as human impacts have changed
fundamental aspects of biosphere functioning3. Of particular con-
cern is where ecosystem collapse results in permanent loss of evo-
lutionary history through raising the risk of species extinction, loss
of ecological functions critical to ecosystem resilience and recovery
and loss of ecosystem services vital for peoples’ livelihoods, income
and wellbeing. Coral reef ecosystems are among the most biodiverse
and societally important ecosystems globally but up to 50% of the
world’s coral reefs are already degraded4 with 14% loss within the
last decade5 and the weight of evidence suggests that increasing
local stressors (fishing, pollution, coral diseases and cyclones) and
global stressors (warming and acidification) and their cumulative
and synergistic interactions6, give a window of only several decades
before collapse of these flagship ecosystems7.
The status of reefs at global scales is based on one key indicator, live
coral cover, that is both conceptually straightforward and accessible
to measure, making it a leading indicator of ecosystem health in the
ocean8. However, while live coral cover provides a basic measure of
the presence and status of the coral reef ecosystem engineers, it lacks
information on composition of the coral community, algae, other
invertebrates and fish9,10. All these groups contribute to a reef’s prop-
erties, ecological functioning and potential services to people; these
attributes may vary across all scales from local to regional and global.
Transition of coral reefs to alternative ecological states, and possible
ultimate collapse, depends on the status and trends in many of these
components and functions2,11. Many studies have assessed live coral
cover trends at regional scales, for example in the Indian Ocean12,13, the
East Asian–West/Central Pacific14 and the Caribbean15. Shifting com-
position within reef coral communities has been shown at regional
scales, for example the Great Barrier Reef16 and the Western Indian
Ocean (WIO)17. These studies shed light on drivers of decline, status
of reefs and management options. However, differences in methods
and datasets, and in interpretation of results, limit the ability to syn-
thesize regional findings to support coherent policy across regions
and to global levels, as well as to inform decision-making at smaller
scales, particularly within individual countries. Whilst documenting
the impacts of anthropogenic pressures on biological assemblages,
present coral reef assessment and monitoring methods need a unify-
ing framework to address the risk of complete ecosystem collapse18.
With a focus on coral reefs, a recent study called for ‘bridg[ing] the gap
between the theory and practice of assessing the risk of ecosystem col-
lapse, under the emerging framework for the International Union for
Conservation of Nature (IUCN) Red List of Ecosystems, by rigorously
defining both the initial and collapsed states, identifying the major
driver[s] of change and establishing quantitative collapse thresholds’16.
The Red List of Ecosystems (RLE) was designed to provide a
uniform, easily understood classification of the risk of ecosystem
Vulnerability to collapse of coral reef ecosystems
in the Western Indian Ocean
David Obura 1,2 ✉ , Mishal Gudka1, Melita Samoilys1,3, Kennedy Osuka 1, James Mbugua1,
David A. Keith 4, Sean Porter5, Ronan Roche 6, Ruben van Hooidonk 7,8, Said Ahamada9,
Armindo Araman10, Juliet Karisa11, John Komakoma12, Mouchtadi Madi13, Isabelle Ravinia14,
Haja Razafindrainibe15, Saleh Yahya16 and Francisco Zivane17
Ecosystems worldwide are under increasing threat. We applied a standardized method for assessing the risk of ecosystem col-
lapse, the International Union for Conservation of Nature (IUCN) Red List of Ecosystems, to coral reefs in the Western Indian
Ocean (WIO), covering 11,919 km2 of reef (~5% of the global total). Our approach combined indicators of change in historic
ecosystem extent, ecosystem functioning (hard corals, fleshy algae, herbivores and piscivores) and projected sea temperature
warming. We show that WIO coral reefs are vulnerable to collapse at the regional level, while in 11 nested ecoregions they range
from critically endangered (islands, driven by future warming) to vulnerable (continental coast and northern Seychelles, driven
principally by fishing pressure). Responses to avoid coral reef collapse must include ecosystem-based management of reefs
and adjacent systems combined with mitigating and adapting to climate change. Our approach can be replicated across coral
reefs globally to help countries and other actors meet conservation and sustainability targets set under multiple global conven-
tions—including the Convention on Biological Diversity’s post-2020 global biodiversity framework and the United Nations’
Sustainable Development Goals.
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collapse across all ecosystems and across multiple scales19,20.
Defining ecosystem collapse is operationalized for the RLE by set-
ting thresholds of collapse for key variables describing the ecosys-
tem (Table 1 and Supplementary Information 2.6). The RLE enables
integration of multiple variables of varying coverage and quality
across different ecosystem components and has direct application
to policy21. Building on the Red List of Threatened Species, the RLE
integrates multiple variables under five broad criteria, producing
a standard output comprising an ordered set of unthreatened to
threatened categories, from least concern to collapsed (Fig. 1). This
study applies the RLE to coral reefs in the WIO using as primary
data the global Millennium Coral Reef layer, an extensive regional
dataset on coral reefs recently compiled from multiple data con-
tributors that includes hard coral, fleshy algae and fish abundance
data5,13, as well as projected sea surface temperatures22.
WIO coral reefs are at risk of collapse
WIO coral reefs, covering 11,919 km2 and comprising about 5% of
the global total (Fig. 1 and Supplementary Table 1), are vulnerable
(VU) to ecosystem collapse. On the basis of available data to param-
etrize a coral reef ecosystem model (Fig. 2a), we assessed four of
five criteria of the RLE over a 50-yr time span: decline in ecosys-
tem extent (criterion A), vulnerability due to restricted geographic
distribution (criterion B) and ecosystem disruption resulting from
decline in the quality of abiotic (criterion C) and biotic factors (cri-
terion D) (Table 1). Criterion E was not evaluated as a quantitative
model could not be applied. Two criteria (C and D) returned a result
of VU (Table 2) on the basis of future warming using a likely path-
way for global greenhouse gas emissions (criterion C, representative
concentration pathway RCP 6.0) and biotic disruption on the basis
of reduction in piscivorous fishes indicative of fishing pressure (cri-
terion D). The other two criteria (A and B) returned a result of least
concern (LC). The RLE assigns the most threatened result (VU) as
the final status20.
At a finer geographic scale, there was considerable variation in
risk of ecosystem collapse among 11 coral reef ecoregions within
the WIO (Table 2). The highest levels of risk were scored for seven
ecoregions (four critically endangered (CR) and three endangered
(EN)) due to future warming, in the island ecoregions spread across
Madagascar, the Comoros, the outer Seychelles and the Mascarene
Islands (Mauritius and Reunion) (Fig. 1). The remaining four ecore-
gions were assessed as VU. Of these, reefs in the large continental
ecoregions (northern Tanzania–Kenya and northern Mozambique–
southern Tanzania) were VU on the basis of declining populations
of piscivorous fishes (Supplementary Table 15), whereas reefs in the
northern Seychelles and Delagoa (southern Mozambique–north-
ern South Africa) were VU due to decline in reef areal extent and
in Delagoa also due to limited geographic distribution of reefs
(Table 2).
Climate vulnerability
The dominant threat to coral reef ecosystems in the WIO is future
increase in thermal stress, as indicated in the seven island ecore-
gions rated CR and EN, over the next 50 years. Earlier onset of
catastrophic heat stress in island than in mainland locations is
largely consistent with other analyses7,22. Current trends in carbon
emissions are more consistent with RCP 6.0 than higher or lower
emissions pathways23. The results for RCP 6.0 provided a closer fit
to observed bleaching in response to thermal stress among ecore-
gions in recent years (Supplementary Information 5.2). Interpreting
thermal stress to corals from projected temperatures must be done
with caution as variance of temperature within the large grid cells
in climate models is very attenuated compared to that of empirical
observations, affecting calculations of exceedance of thermal thresh-
olds22. A possible illustration of this is that our analysis showed
a result of LC in all ecoregions for RCP 2.6 over the next 50 years
(Supplementary Table 6) despite empirical records of up to 30%
coral declines in the 1998 and substantial mortality in the 2016 mass
coral bleaching events13,24. On the basis of these considerations, we
selected emissions scenario RCP 6.0 as the basis for criterion C and
for interpreting the comparative risk among ecoregions to warming
temperatures to identify policy and management options.
Ecological integrity and biotic collapse
Ecological integrity is complex and includes functional, compo-
sitional, structural and spatial components25. Developing a con-
ceptual ecosystem model as required for the RLE (Supplementary
Information 2.5) provides an explicit hypothesis of ecological
integrity and, by extension, collapse. Arguably, as one of the most
diverse, complex and variable ecosystems in the world, coral reefs
present challenges to specifying a realistic model. Percentage coral
cover is a primary measure for coral reef health10 but is insufficient
for describing integrity9,16. Our data did not include taxonomic or
functional subclasses of corals, thus this analysis could not distin-
guish shifting composition of coral that has occurred in the WIO17,
as has happened elsewhere9,16. Inclusion of coral composition would
show decline in coral functional diversity within the coral com-
partment, potentially raising the risk level in this initial step in our
analysis (Supplementary Tables 2 and 18) and thus a higher collapse
risk overall.
Table 1 | Criteria and thresholds of collapse applied to the WIO
coral reef ecosystem (RLE) assessment
Criterion Criterion details and
standard RLE thresholds Coral reef indicators,
collapse thresholds and
key references
A—decline
in ecosystem
extent
A1—historical decline,
past 50yr
Decline: VU>30%;
EN>50%; CR>80
Percentage of coral cover
≤10% (ref. 73)
B—restricted
geographic
distribution
B1—area of ecosystem
(km2); VU≤50,000;
EN≤20,000; CR≤2,000
Area (km2)
B2—area of ecosystem
(number of 10×10km2
grid cells); VU≤50;
EN≤20; CR≤2
Number of grid cells
C—abiotic
disruption C2a—future decline, 50yr
Combination of relative
severity of disruption over
extent of ecosystem
Thermal stress (DHW)
calculated from sea
surface temperature
in global climate
projections22
Exceedance of DHW12
>2yr per decade using
RCP6.0 (refs. 22,23,77)
D—biotic
disruption D1—historical decline,
past 50yr
Recent coral reef
monitoring data (mean
values, 2013–2019)
compared to baseline
estimates 50years ago
Percentage of hard coral
cover—5%
Algae–coral ratio—0.83
Parrotfish abundance—10%
initial84
Grouper abundance—20%
initial85,29
E—quantitative
model NE due to absence of an applicable quantitative model
Standard thresholds are set by the RLE protocol, with coral reef-specific ones derived from the
literature. See Methods and Supplementary Information for full details and references for each
criterion. VU, vulnerable; EN, endangered; CR, critically endangered; NE, not evaluated; DHW,
degree heating weeks; RCP, representative concentration pathways.
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Our analysis was strengthened, however, by including three
further biotic compartments (Supplementary Information 2.5)—
fleshy algae cover (Fig. 2d) and abundance in two trophic groups
of fishes (herbivores and piscivores) (Fig. 2e,f). However, as with
coral cover, inconsistencies in the quality and quantity of data gen-
erated for these variables over the last 30 years constrained our
analysis. To minimize gaps in data coverage, we aggregated mul-
tiple algal components into a single indicator (combining mac-
roalgae with low- and high-canopy growth forms of turf algae), as
these were classified variably by monitoring programmes across
the region (Supplementary Information 6.1). This may conflate
contrasting positive and negative roles of algae with respect to cor-
als26,27 but allowed us to assess algae cover in all ecoregions in which
coral cover was assessed (Supplementary Table 15). The risk level
for algae influenced the result for criterion D for only one ecore-
gion (northern Tanzania–Kenya), raising the risk level by one step
(Supplementary Table 15). We addressed trade-offs stemming from
limited consistency and coverage of fish data by using only one (sub)
family in each functional group (parrotfish (Scarini) for herbivores
and groupers (Epinephelidae) for piscivores), enabling coverage of
six and seven ecoregions, respectively (Supplementary Table 9).
To improve future data series, the monitoring network is adopt-
ing new standards to enhance data quality, addressing the above
limitations8,10. This will enable additional subcomp onents of the eco-
system to be assessed (for example, low and high turfs, macroalgae,
different trophic levels of consumers and so on), allowing more
powerful and informative threat diagnoses and risk assessments.
Application of the RLE at smaller geographic scales will reduce
the variety of data sources and potentially enable inclusion of
finer-resolution variables for subcompartments of coral, algae, her-
bivores and piscivores, and potentially enable analysis of additional
compartments, such as sea urchins or other invertebrate grazers.
This would enable closer matching of the ecosystem model (Fig. 2a
and Supplementary Fig. 2) to nuanced interactions in the reef com-
munity, such as the differential effects of algal subcompartments
on corals and of different functional groups among herbivores
and piscivores.
Stepping towards ecosystem collapse. The RLE enables discrimi-
nation of signals of collapse from multiple biotic compartments
within criterion D but an important question is whether critical
status of any one compartment should drive the rating of the entire
ecosystem. In this study, the dominant biotic signal of ecosystem
collapse, decline in piscivore populations, was assessed as EN–CR
in four of the seven ecoregions with sufficient data (Fig. 3). Grouper
are vulnerable to the loss of coral structure28 and their life histories
make them especially vulnerable to fishing29. By contrast parrotfish
may respond in opposite ways to these threats, with documentation
of positive responses to coral degradation in Tanzania30, the Chagos
Archipelago31 and, in the Seychelles, masking fishing effects32.
60° E30° E 50° E40° E
10° N
0°
10° S
20° S
30° S
Criterion B—least concern
CR
VU
VU
VU
LC
LC LC
LC
LC
LC
LC
Criterion D—vulnerable
EN
VU
VU
VU
VU
VU
VU
NT
LC
LC
DD
Criterion A—least concern
VU
VU
VU
LC LC
LC
LC
LC
LC
DD
LC
Criterion C—vulnerable
CR
CR
CR
CR
EN
EN
EN
LC
LC LC
LC
0 400 800 1,200
N
1,600200 km
Somalia
Kenya
Tanzania
Mozambique
S. Africa
Madagascar
Reunion
Mauritius
Comoros
Mayotte
Overall—vulnerable
VU
EN
CR
VU
VU
VU
EN
EN
CR
CR
CR
Seychelles
1
7
6
3
2
4
11
9
10
8
5
Ecoregion
1. Northern Tanzania–Kenya
2. Northern Mozambique–southern Tanzania
3. Comoros
4. Western Madagascar
5. Northern Madagascar
6. Seychelles, outer
7. Seychelles, northern
8. Mascarene Islands
9. Eastern Madagascar
10. Southern Madagascar
11. Delagoa
Critically endangered (CR)
Endangered (EN)
Vulnerable (VU)
Near threatened (NT)
Least concern (LC)
Data deficient (DD)
Collapsed (CO)
Not evaluated (NE)
Threatened categories
Fig. 1 | Coral reefs in the WIO and 11 of its ecoregions were evaluated using the RLE. The overall risk level for each ecoregion is shown (left) and for each
criterion assessed: A, B, C and D (panels in upper right, see also Table 2). The outcome for the regional scale assessment (that is, the whole WIO) is given
at the top of each panel. Coral reefs in the Somali ecoregion were not evaluated (NE). The ecoregion names and RLE categories hierarchy and colour codes
used throughout the study are shown in the lower right.
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Hence the lower risk levels we found for parrotfish. Coral cover was
assessed LC in all four ecoregions where groupers were EN–CR.
Standard RLE practice has been to assign the maximum risk level
across alternative indicators within each criterion20,33, however, we
questioned whether the entire reef systems across the five countries
and territories (Kenya, Tanzania, northern Mozambique, Mauritius
and Reunion) within these ecoregions should be rated EN–CR
on the basis of one fish group. More generally, in complex eco-
systems with multiple biotic compartments, interactions between
them of different strengths and functional redundancies34, it is not
clear that in all cases where any one compartment is at high risk,
the whole ecosystem should be at that risk level (Supplementary
Information 2.6).
To resolve this issue we developed a structured algorithm for
assessing ecological integrity and risk of collapse based on hierarchi-
cal interactions between ecosystem compartments (Supplementary
Information 2.6 and Supplementary Table 2) and tested it against two
alternatives with less structure (Supplementary Information 6.1.4
and 6.3.1). The algorithm started with assigning the coral risk level,
then the risk level was increased incrementally if risk was higher in
the algae, then herbivore, then piscivore compartments. In the exam-
ples in the prior paragraph (where coral was LC and piscivores were
EN–CR), this resulted in final risk levels in three ecoregions rising
to VU, in each case stepped up twice by higher risk levels in any two
of the algae, herbivore and piscivore compartments (Supplementary
Table 15). This final status reflects the importance of piscivores
in the top-down control of prey populations with direct and indi-
rect impacts on reef ecology (Supplementary Information 6.1)
but avoids undue inflation of overall risk where other compart-
ments are in a good state.
We further tested this algorithm by applying it to prior RLE
applications in the Meso-American reef33 and Colombia35. The
resulting level of risk was the same as reported in those studies
because in both cases the coral compartments were at the high-
est levels of risk, either equal to, or higher than, other ecosystem
compartments assessed. The value of this algorithm in facilitating
greater standardization and consistency among studies will become
clearer with repeated application of the RLE across scales, in coral
reef regions where the importance of different biotic compartments
may vary and where data availability also varies, enabling more or
a b
c
f e d
Coral
Bleaching
Coral
Fleshy algae
Nutrients/
sediments
Algae
Fish
Fish
Connectivity
Spatial reef system
Rugosity
/habitat
Warming SST Coastal
development
Drivers and pressures
Turf and macro Herbivores
piscivores
Rivers/
sediments Fishing
A1,D1: coral
cover
D1: algae cover D1: abundance
B1/2: coral reef extent
C2: SST/DHW
Fig. 2 | Coral reef ecosystem model applied in assessing the risk of collapse for WIO coral reefs. a, The coral reef model; further details are provided
in Supplementary Information 2.5 and Supplementary Fig. 2. b, A healthy coral community in 5m depth in Mafia Island, Tanzania, with diverse and
abundant coral. c, Bleaching and mortality among coral genera due to thermal stress in 2016, Mayotte, Comoro Archipelago. d, A reef surface dominated
by Sargassum macroalgae, Songosongo, Tanzania. e, A school of the parrotfish Hipposcarus harid, St Brandons Island, Mauritius. f, The grouper Epinephelus
tukula, a dominant piscivore and highly vulnerable to fishing, northern Mozambique. Image credits: b–e, D. Obura; f, M. Samoilys.
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less detailed parametrization of the reef ecosystem model. This
algorithm may also be appropriate to other ecosystems which have
a defining biogenic architectural compartment such as forests on
land36 and other marine ecosystems such as oyster reefs, mangroves,
seagrasses and kelp forests.
Regional and global comparisons
All three prior applications of the RLE to coral reefs have been in the
Caribbean. The first application assessed Caribbean reefs, compris-
ing 6.7% of the world’s coral reefs as one region19, with an EN–CR
result. This higher risk level than WIO reefs is consistent with the
Table 2 | Risk of collapse of WIO coral reef ecosystems in 11 ecoregions, across criteria A–D of the RLE
Region A B C D Overall
WIO region LC LC VU VU VU (C2a, D1a)
Ecoregions
1 Northern Tanzania–Kenya LC LC LC VU VU (D1a)
2 Northern Mozambique–southern Tanzania LC LC LC VU VU (D1a)
3 Comoros LC LC CR VU CR (C2a)
4 Western Madagascar LC LC EN EN EN (C2a, D1a)
5 Northern Madagascar LC LC EN LC EN (C2a)
6 Seychelles, outer VU LC EN VU EN (C2a)
7 Seychelles, northern VU LC LC VU VU (A1, D1a)
8 Mascarene Islands LC VU CR NT CR (C2a)
9 Eastern Madagascar LC VU CR LC CR (C2a)
10 Southern Madagascar DD EN CR DD CR (C2a)
11 Delagoa VU VU LC VU VU (A1, B1a(iii)b, B2, D1a)
The overall result lists the final risk level and in parenthesis are the criteria and subcriteria on which it is based. DD, data deficient; see text for criteria codes. For details behind these results and the
subcriteria coding see Supplementary Information 3–6.
Percentage of iterations
Groupers
LC NT VU EN CR
Percentage of iterations
Parrotfish
Percentage of iterations
Algae
0
20
40
60
80
100
Percentage of iterations
Coral
a
b
c
d
LC
LC LC LC LC LC LC LC LCVU
LC LC LC LC LC
VU VUDD
VUDD
NT NT NT NT NT NT LCVU VU NTDD DD DD DD DD
EN
–CR
–CR
–CR–CR–CR
EN EN EN VU VU EN DD DD NT
DD DD
NT
0
20
40
30
10
60
50
80
70
100
90
0
20
40
60
80
100
0
20
40
30
10
60
50
80
70
100
90
Northern Tanzania–Kenya
Northern Mozambique–southern
Tanzania
Comoros
Western Madagascar
Northern Madagascar
Seychelles, outer
Seychelles, northern
Mascarene Islands
Eastern Madagascar
Southern Madagascar
Delagoa
WIO region
Northern Tanzania–Kenya
Northern Mozambique–southern
Tanzania
Comoros
Western Madagascar
Northern Madagascar
Seychelles, outer
Seychelles, northern
Mascarene Islands
Eastern Madagascar
Southern Madagascar
Delagoa
WIO region
Fig. 3 | Risk levels of biotic disruption for each compartment in the reef model for criterion D of the RLE. a–d, Coral cover (a), algae–coral ratio
(b), herbivorous fish (parrotfish) abundance (c) and piscivorous fish (grouper) abundance (d) for each ecoregion and the WIO as a whole (Supplementary
Tables 12–14). The y axis shows the percentage of iterations returning each risk level of 750 iterations of randomly selected initial values from a defined
range for each compartment (Supplementary Tables 7, 8 and 10). The letters at the base of each column show the risk level assigned to each ecoregion by
compartment. The final risk level determined for each ecoregion is shown in Table 2.
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literature that Caribbean reefs have experienced greater decline than
those in the Indo-Pacific due to a variety of intrinsic factors (for
example, coral and algal dynamics) and extrinsic factors (land-based
impacts and connectivity)5,13,15,37. The second application assessed
the Meso-American Barrier Reef33, also an ecoregion and one of the
healthiest coral reef regions in the Caribbean15, as CR on the basis
of both coral and piscivore compartments in a quantitative model.
This contrasts with corals being LC to VU and piscivores NT (near
threatened) to EN–CR in the WIO (Supplementary Table 15).
The third application, simultaneous with this study, focused on
Colombian Caribbean coral reefs35 and also used a spatially hierar-
chical approach, although extending from a scale comparable to our
ecoregions down to smaller reef areas. At the larger (national) level,
reefs were VU, while in the two nested subregions (‘continental’ and
‘oceanic’) reefs were VU and EN, respectively.
The RLE method provides a consistent result across the above
studies and the present one; however, differences in selection of
variables, thresholds and how they are parametrized introduce
uncertainties in comparisons among them, even within the same
ecosystem type. More broadly, the RLE has been critiqued on con-
sistency in identification and definition of ecosystem units, the
meaning of ‘collapse’ for an ecosystem, and specifics of the catego-
ries and criteria used38. Many of these critiques have been addressed
over time2,39 and growing acceptance of the RLE is shown by its
application globally40, recent calls for studies of ecosystem col-
lapse16, exploration of its use in multiple policy domains21,40 and
its potential as an indicator in the monitoring frameworks of the
Convention on Biological Diversity (CBD) and the Sustainable
Development Goals18,39,41.
To further strengthen applicability of the RLE to coral reefs glob-
ally and to support national commitments under these conventions,
we developed an approach that further standardizes application of
the RLE to coral reefs regions, in five ways: (1) we used a consistent
biogeographic and ecosystem framework, based on the Marine
Ecoregions of the World, a global ecosystem typology developed for
the RLE42 and the established Global Coral Reef Monitoring Network
(GCRMN) regional structure5,10, all based on the Millennium Coral
Reef layer maintained by the World Conservation Monitoring
Centre (UNEP-WCMC); (2) we used globally consistent real-time
datasets from: (i) collaborative networks on reef status compiled
through the GCRMN as the global aggregator of Essential Ocean
Variables for coral reefs5,8,10 and (ii) on projected thermal stress22;
(3) we formulated a general ecosystem model applicable to (1) and
(2) with scope for additional compartments if relevant and if data
availability allows (Supplementary Information 2.5 and Fig. 2a);
(4) we developed a structured algorithm for assessing risk of biotic
disruption on the basis of ordered interactions affecting coral reef
ecosystem integrity (Supplementary Information 2.6, 6.1.4 and 6.3).
This allows for differences in interactions among key compartments
that may vary geographically, as well as data gaps that are inevitable
given the resources and capacities available in most coral reef coun-
tries5,10; and (5) we generated a Git-based repository and R code
for all steps of the analysis (Supplementary Information 3–6) to
facilitate tailored application in other regions.
Management and policy implications
Uncertainty in the climate trajectory that will eventuate, and variance
at many scales in how corals and reefs respond to warming5,43 and
other threats, mean that varied policy and management responses
(Table 3) need to be considered44. These cover a spectrum of actions
from addressing climate mitigation and adaptation to address-
ing local threats. The multiple criteria and broad evidence-base of
the RLE enable structured consideration among these21. In those
ecoregions on the East African mainland coast less threatened by
future warming, local management actions will have greater scope
to maintain or improve reef health, particularly those focused on
Table 3 | Portfolio of policy and management responses to address the main drivers of risk of collapse of WIO coral reefs
Risk level and critical factor Ecoregions and specific
indicators of risk Range of policy and management responses to alleviate critical risk factors
Climate, EN–CR (C2a, SST warming) • Comoros, Mascarene
Islands, eastern Madagascar
and southern Madagascar
(CR)
• Northern Madagascar (EN)
• Commit to strong climate change mitigation, through Paris Agreement/
NDCs and national implementation of emission reductions and adaptation
plans relevant to coral reefs.
• Use scenarios in policy and management planning, to consider higher and
lower risk levels to maintain future options.
• Establish climate adaptation plans, to for example:
◦ optimize benefit flows (on 20–30yr time frames) until coral reefs
transition to an alternative state;
◦ develop ecosystem and resource use policies anticipating potential
alternative states of reefs, to maximize biodiversity and benefits after
a transition;
◦ identify and develop ‘climate smart’ fisheries with reduced ecosystem
impacts and more secure livelihood benefits;
◦ identify alternative livelihood options and diversified income streams
in coral reef landscapes.
• Identify and protect climate refugia and connectivity nodes through MPAs
and OECMs.
• Invest in local (co)management (OECMs) to reduce synergistic threats,
to maximize climate resilience and buy time for adaptation.
• Improve management of species and pressures that disrupt ecosystem
processes, such as fisheries, land-based impacts to coral reefs, direct
damage from tourism and so on.
• Develop guidance and best practices on enhancing recovery of reefs
through alleviating pressures, understanding of role of herbivory,
assisted restoration efforts and so on.
Climate with biotic disruption, EN–VU • Seychelles, outer
(climate, EN; coral, VU)
• Western Madagascar
(climate, EN; herbivores and
piscivores, VU)
Biotic disruption, VU (D1a) • Northern Tanzania–Kenya,
northern Mozambique–
southern Tanzania
(piscivores, VU)
• Seychelles, northern
(coral and piscivores, VU)
• Delagoa (coral, algae
and herbivores, A and
B1/B2, VU)
• Algae not a key driver of
higher threat alone but in
synergy with other factors
(northern Tanzania–Kenya,
Delagoa)
Given the broad scale of this assessment at ecoregional levels, multiple responses across climate- and ecosystem-focused actions will probably be required within any country. MPA, marine protected
areas; NDC, nationally determined contribution; OECM, other effective conservation measures. Summary is based on ref. 21. The policy and management options in the right hand column are ordered from
those focused on climate change responses (top) to ecosystem resilience focus (bottom).
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alleviating fishing pressure and promoting coral recovery after ther-
mal stress events. Some of these ecoregions (for example, northern
Mozambique–southern Tanzania) show strong levels of larval sup-
ply to more vulnerable ecoregions45 and may play a key role in the
recovery of corals from mass mortalities through larval connectivity,
so managing them as central nodes in a connectivity network will
be an important element of resilience-based management across the
entire region. In addition, protecting climate refugia—reefs demon-
strating lesser impact from thermal stress events, whether on scales
from hundreds of metres to hundreds of kilometres—must be a key
component for extending protection7 through marine protected
areas or other effective conservation measures.
However, even for the ecoregions threatened by warming, it
will be important to reduce local reef threats and reef vulnerability
to address three ‘no regrets’ objectives: (1) to maintain ecosystem
function and resilience to buy time for coral populations to poten-
tially adapt to warmer conditions through compositional shifts and/
or genetic changes, (2) to sustain the valuable economic and liveli-
hood benefits that coral reefs provide on a daily basis for as long as
possible into the future44 and (3) as part of broader integrated and
ecosystem-based management of coastal and marine ecosystems
that can facilitate positive ecosystem transitions forced by a chang-
ing climate46.
Reporting on international and national policies on biodiver-
sity47, climate48 and people’s dependence on nature4, has relied solely
on mean percentage hard coral cover as a primary indicator of
coral reef status. Current consultations on new ecosystem targets
for the CBD strongly recommend separate measures of area and
integrity for quantifying ecosystem health18,25, to guide actions to
protect or restore ecosystems effectively. The RLE is well suited for
this purpose, as ecosystem area is addressed in criteria A and B,
and ecosystem integrity in criteria C and D, such that it synthesizes
additional indicators beyond coral cover into a single composite
index. As an indicator in the proposed monitoring framework for
the post-2020 global biodiversity framework, the RLE can support
assessment of the ecosystem component of biodiversity and thereby
also benefits supplied to people4,18,49. Extending studies of ecosys-
tem collapse, such as the RLE for coral reefs, to global levels can
strengthen application of global policies for coral reef conservation
and sustainability16,50.
While coral reefs are distributed globally, the regional scale
provides a spatial scope where reef function and connectiv-
ity match scales of ocean governance processes51. Applying the
RLE at this scale supports both intra- and inter-regional com-
parisons, informing policy and action across scales. The WIO
region is the same as that of the Nairobi Convention, one of the
ten UNEP Regional Seas that contain coral reefs. At this scale,
and within nested ecoregional analyses, this analysis can support
coherent intra- and inter-regional policy processes. However, to
inform management at national and smaller scales, the ecore-
gional scale applied here is too broad. Including more localized
and improved data to address more aspects of the reef model (Fig.
2a and Supplementary Fig. 2) enabling greater disaggregation of
biotic compartments and setting analysis within national policy
frameworks, can guide management down to local scales21 adding
to the wide variety of detailed studies already contributing to reef
management at these scales.
Methods
We assessed the risk of ecosystem collapse of coral reefs at a regional level for
the WIO as well as in 11 ecoregions within it (Supplementary Table 1 and Fig.
1), applying the IUCN RLE methodology20,52. The coral reef ecosystems assessed
correspond to distinctive reef areas based on global53,54 and regional55 analyses, and
level 4 in the IUCN Global Ecosystem Typology42 (Supplementary Information
2.1 and Table 1). We de veloped a conceptual ecosystem model to structure the
assessment on the basis of recent syntheses of coral reef status and resilience
(Supplementary Information 2.5), focused on the primary interactions between hard
corals, fleshy algae and two trophic groups of fish, herbivores and piscivores (Fig . 2a).
On the basis of the literature, we identified fishing (extraction) and climate change
(increasing thermal stress) as the two dominant pressures on coral reefs of the WIO
(Supplementary Information 2.4). Following the RLE guidelines we evaluated all
criteria, focussing on these two pressures, although there were insufficient data to
evaluate criterion E. The Supplement ary Information contains full details of the
methods, including a synthesis of data limitations (Supplementary Information 7.1).
Coral reef ecosystem model. The RLE requires a cause–effect conceptual
model to be developed for an ecosystem52. The coral reef ecosystem model we
developed (Fig. 2a) is based on key interactions on coral reefs and builds on
earlier coral reef applications of the RLE (Supplementary Information 2.5 and
6.1). It involves corals, fleshy algae and functional interactions of herbivorous
and piscivorous fish, and the influence of external pressures19,33. The model
incorporates understanding of coral reef community dynamics and transitions
between states56–59 and reef resilience dynamics58,60,61. Corals are recognized as
the ecosystem engineers, affected by competitive interactions with fleshy algae
and cascading effects of top-down consumers through the trophic ecology of
multiple taxonomic groups. The algae community is the primary ‘alternate’ space
occupier on coral reefs competing with corals62, here represented by turf, macro
and calcareous algae summed together. Herbivorous fishes (here represented
by parrotfish) have strong mediating effects on algae and corals63–65, while
piscivorous fishes (represented by groupers) play a key functional role in nutrient
cycling, biomass production66,67, transfer of energy and material68. These comprise
the four main compartments in our coral reef ecosystem model and correspond to
available and consistent data across the whole region for parametrizing the model
(Supplementary Information 6.1)13.
Aspects of the ecosystem model that we could not include in the assessment
were direct data on fishing pressure on coral reefs—data were not available among
countries and at regional levels, and we determined that direct abundance data for
groupers, which are sensitive to fishing pressure (Criterion D), provided a more
reliable metric than indirect measures based on human population or market
proximity69. Sedimentation and eutrophication pressure were not assessed; although
indices and proxies can be derived for these from remotely sensed water-leaving
radiances70,71, it is difficult to parametrize thresholds at local scales for reef collapse
for WIO reefs72 and data were not available for the required 50 yr (Supplementary
Information 2.4). However, these variables may be more appropriate at finer scales
within countries where datasets may be available to enable filling such gaps.
RLE criteria. The RLE evaluates risk in five broad criteria: reduction in geographic
distribution of an ecosystem (criterion A), risks associated with small size
or restricted geographic distribution (criterion B), risks from environmental
degradation or abiotic factors (criterion C), risks from biotic disruption or
changes among ecosystem compartments (criterion D) and quantitative ecosystem
dynamics modelling (criterion E). All criteria must be evaluated, returning a result
of NE if analysis is not possible (Fig. 1) or a threatened or unthreatened status from
the highest risk identified among the criteria evaluated.
Criterion A—reduction in geographic distribution of coral reefs. Decline
in the extent of an ecosystem is a direct measure of its disruption and collapse
(Supplementary Information 3). Coral reefs combine two features—the
geomorphological biogenic substratum and dominance of hard corals that build
the reef and provide habitat for diverse ecological interactions. Given the lack of
data on change in the geographic extent of coral-dominated habitat over time,
we developed a proxy indicator representing the extent of functioning coral reef.
The literature on coral reefs is converging on a value of 10% coral cover as a
threshold below which insufficient calcification and carbonate deposition occurs
for the maintenance of a coral reef ecosystem73. Site-based coral cover data used
in criterion D were used to identify the proportion of sites within an ecoregion
currently below the critical coral cover threshold for reef accretion. In this
criterion, 10% coral cover relates to reef accretion in terms of the maintenance
of the substratum for potential coral colonization, whereas in criterion D a lower
threshold of 5% coral cover is used as a limit for collapse in relation to recovery
of the coral population (Table 1 and Supplementary Information 3.1). We
evaluated recent decline over 50 yr (criterion A1) but could not evaluate future
(A2a and A2b) or longer term historical (A3) declines.
Criterion B—restricted geographic distribution. Limited geographic distribution
is a key determinant of ecosystem vulnerability, as any given major threat may
affect a large proportion of the overall ecosystem extent. We used the Millennium
Coral Reef layer74 maintained by the UNEP-WCMC to derive the extent of
occurrence (EOO, the minimum convex polygon within which all ecosystem
units in the ecoregion are located) and area of occupancy (AOO, the number of
10 × 10 km2 grid cells of which at least 1% of their area was coral reef) of coral reefs
and compare these to the standard RLE thresholds, to assess criteria B1 and B2
respectively (Table 1 and Supplementary Information 4.1). We were able to apply
two of the three possible subcriteria for B1 and B2: a(iii) ‘a measure of disruption
to biotic interactions appropriate to the characteristic biota of the ecosystem’ and
b ‘observed or inferred threatening processes that are likely to cause continuing
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declines in geographic distribution, environmental quality or biotic interactions
within the next 20 yr (Supplementary Information 2.2–2.4).
Criterion C—environmental degradation. Abiotic degradation reduces the
capacity of an ecosystem to sustain its characteristic biota. Sea surface temperature
(SST), supporting calculation of an index of thermal stress, was the only abiotic
variable with adequate temporal and spatial coverage to assess criterion C
and is the dominant environmental stress affecting coral reefs in the WIO
(Supplementary Information 2.4)75,76. Sedimentation and eutrophication (using
chlorophyll a as a proxy) were investigated but had insufficient historical time
series and no clear thresholds for collapse to enable their analysis (Supplementary
Information 5.1). Historical SST time series did not span the required 50 yr; thus,
we assessed criterion C2a, using SST projections 50 yr into the future22. We did not
assess hindcasted SST from the same climate models because historical changes in
coral cover provide a more direct measure of risk.
Future thermal stress was assessed using two critical thresholds for bleaching,
8 and 12 degree heating weeks (DHW) per annum77, across four greenhouse gas
emission scenarios (RCPs 2.6, 4.5, 6.0 and 8.5) (Supplementary Information 5.1).
A threshold of two major bleaching events per decade (that is, two annual
exceedances of the DHW threshold) was used as the threshold for ecosystem
collapse33, calculated using decades spanning the 50-yr period from 2020 (2015–
2024) to 2070 (2065–2074). Final analysis was based on the following critical
thresholds (see Supplementary Information 5.2 for more detail, as well as the
discussion in the main text):
DHW 12 is associated with more severe warming impacts to corals and less
likely to be within the adaptative capacity of corals to thermal stress, and
RCP 6.0 presents a more plausible scenario, provides greater differentiation
among ecoregions and matches conditions observed to date of coral bleaching (see
Supplementary Information 5.2 and the section Climate vulnerability).
Criterion D—biotic disruption. Disruption of biotic processes and interactions
leads to loss of function in an ecosystem and its potential collapse, particularly for
important processes and/or organisms playing key functional roles. We assessed
four main compartments in the ecosystem model (Supplementary Information
6.1 and Fig. 2a) with the following indicators: hard coral cover, fleshy algae–coral
cover ratio, parrotfish abundance and grouper abundance. Data were obtained
from a regional dataset13,24 generated through a collaborative process and globally
consistent methods established by the GCRMN10,78,79 and applying best practices
established for global biodiversity and ocean observing systems8,80,81.
Monitoring sites were spread unevenly across ten ecoregions
(Supplementary Fig. 3), with varying sample sizes for different variables due to
characteristics of each contributing monitoring programme (Supplementary
Tables 7–9). Given the consistency in survey sites in shallow fore reef and lagoon
patch reefs across the WIO13, we grouped all sites to represent coral reef habitats
as a whole (Supplementary Information 6.1). Data were sufficient to assess coral
cover and algae–coral ratio for ten of the 11 ecoregions but, for parrotfish and
grouper abundance, only for six and seven ecoregions, respectively (Supplementary
Information 6.1). Variables used included:
• percentage of hard coral cover;
• percentage of eshy algae cover, as the sum of turf algae, macroalgae and artic-
ulated calcareous algae (for example, Halimeda), when available. Although
functional characteristics of well-grazed, low-canopy turfs are very dierent
(and not detrimental to corals) from those of high-canopy turfs26 the data
supplied by contributors combines them under ‘algal turf’ following standard
GCRMN methods10,13 so their eects could not be separated. Further, data
gaps and historical decisions in the regional dataset compelled aggregation of
algae groups ‘harmful’ to corals (that is, the categories listed above, other than
coralline algae (Supplementary Information 6.1);
• abundance of parrotsh and abundance of groupers, as representatives of her-
bivorous and piscivorous sh, respectively. Although biomass data are oen
considered a more sensitive indicator32, much of the regional GCRMN survey
data13 do not include sh size, therefore biomass could not be calculated.
Several studies support abundance as an important sh metric in ecological
function (for example, refs. 82,83).
We evaluated criterion D1, for change over the last 50 yr, using data from
2013–2019 to estimate current conditions. Data were not available from 50 years
ago, so we extrapolated initial values from available historical data (Supplementary
Information 6.1.1): for coral and algae cover, based on sites known to be in
healthy condition before the 1998 mass coral bleaching event; and for fish
abundance, based on reference sites that are remote, well protected for at least
10 yr and/or uninhabited. This gave mean and variance estimates for initial values
(Supplementary Information 6.1.1) on the basis of which we randomly sampled
initial values to calculate relative severity of decline for all sites and repeated
this 750 times to derive an aggregate result (Supplementary Information 6.1.3).
Collapse thresholds for each indicator were set at 5% for hard coral cover, 0.83
for algae–coral ratio and 10% and 20% of initial population values for parrotfish
and grouper abundance, respectively (Table 1). These collapse thresholds were
based on different factors for each variable (Supplementary Information 6.1.2); for
corals and algae, on expectations of potential recovery of corals from low levels
and relative proportions of algae to coral cover that might affect coral recovery.
For the fish indicators the thresholds represent severe biotic disruption to the reef
ecosystem, on the basis of reef fish productivity–biomass relationships84 and stock
productivity modelling in tropical fisheries85, although for longer lived species such
as groupers, 30% is generally recommended29.
Given that there are multiple compartments to the model, whether all of
them need to have crossed collapse thresholds for the system to be collapsed,
or just one or several, needs to be considered. Current RLE practice assigns the
highest risk category across indicators within and across criteria to the overall
ecosystem risk; however, in complex ecosystems with multiple compartments and
interactions of different hierarchy and strength, this may not provide the most
effective representation of risk. Further, with variation in data availability being
a real constraint, both within an assessment as here, or between assessments, the
inclusion or exclusion of compartments would influence results too strongly to
allow comparisons if the highest risk category across compartments is applied
(Supplementary Information 6.1.4).
On the basis of our ecosystem model and the compartments used (Fig. 2a),
we constructed an algorithm that considers each compartment in sequence and
relative risk levels from LC to CR. In this algorithm, percentage coral cover is
the ‘root variable’ for setting the base state of the ecosystem, then the following
interactions are considered in sequence—first competition with algae, then
top-down control of algae by parrotfish and finally apex predator interactions
by groupers. For each step in this sequence, the initial risk status may be raised a
single step in the sequence VU-NT-VU-EN-CR, on the basis of the following logic:
(1) If the risk status of the next compartment is the same as, or less than, that of
the prior compartment(s), the current risk status is conserved.
(2) If the risk status of the next compartment is higher than that of the prior
compartment(s), the current risk status is increased by one step, irrespective
of the gap in status between the two.
Thus, the coral risk status sets the initial risk level, then first algae–coral ratio,
then parrotfish then grouper status might increase the aggregate level of risk by
a single category at each step (Supplementary Table 2). We tested this algorithm
of biotic collapse (Supplementary Information 2.6) against two alternatives, each
incorporating less biological structure, to evaluate potential uncertainties and
their implications (Supplementary Information 6.1.4 and 6.3.1). On the basis of
these findings we selected the structured model as most appropriately reflecting
ecological interactions and stages in biotic collapse.
Criterion E—quantitative model. Criterion E was NE due to lack of a quantitative
model for WIO coral reef ecosystems.
Overall risk of collapse. Following standard RLE guidance20,52, overall risk of
collapse for each ecoregion was determined by selecting the highest risk level
among criteria A–D. We also assessed risk of collapse for the WIO region as a
whole, for each criterion, by weighting each ecoregion’s score by its area of coral
reefs (Supplementary Information 2.7,3.2,5.2 and 6.2).
Strengths and weaknesses. Data gaps for some threatening processes, lack
of genera information for hard coral, variation in contributed data for algae
and fish forcing compromises in how data were aggregated, varying spatial
coverage among reef variables, lack of disaggregation by reef zone, the
length and robustness of time series, and estimated thresholds for collapse,
influence confidence in some inferences about risk of collapse (Supplementary
Information 7.1 and Supplementary Table 19). Nonetheless, the RLE assessment
protocol requires a comprehensive and critical review of the key processes
and available data to diagnose those processes most important to ecosystem
viability, using multiple approaches. As a result, despite the limitations, this
RLE assessment of WIO coral reefs has produced five important advances: (1)
an up-to-date regional-scale analysis of reefs most at risk; (2) a diagnosis of
the dominant threats among these; (3) increased robustness and relevance of
decision-support for coral reef management and policy; (4) an updated coral
reef database compiled by the GCRMN regional network under the Coral Reef
Task Force (CRTF) of the Nairobi Convention, with an improved understanding
of data gaps and (5) introduced an assessment approach that can be adapted
to other coral reef regions globally, as well as other critical ecosystems, such as
mangroves and seagrass beds.
Reporting summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The study used existing and available data and did not involve any primary
data collection. Data on hard coral and algae cover as well as fish abundance
were compiled from multiple contributors (coral reef monitoring data collected
using standard methods defined by the GCRMN) as described in ref. 13. These
data are owned by the various data contributors (full list in Supplementary
Information 8.1) and permissions to access data would need to be sought from
individual contributors, which can be facilitated by the corresponding author.
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SST projection data were obtained from ref. 22 open access and coral reef extent
data was from Millennium Coral Reef layer as described in ref. 74 (http://www.
imars.usf.edu/MC/).
Code availability
Data processing, aggregation and analysis were undertaken in R with code saved in
GitHub. Each criterion was calculated using individual analytical flows developed
using R Markdown. Each code file had its own specific input data and used
standard R functions like tidyr, dplyr, plyr and ggplot for the various steps. For
criterion B, calculations of the AOO and EOO were done using a tool specifically
developed for the RLE, redlistr ((23)). These analytical workflows could be made
available from the corresponding author on request.
Received: 31 January 2021; Accepted: 25 October 2021;
Published online: 6 December 2021
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Acknowledgements
We thank contributors in the Western Indian Ocean GCRMN for access to data for this
study and the IUCN Red List of Ecosystems Unit for initial training and scoping. This
study was supported by the Norwegian Agency for Development Cooperation (NORAD)
for the project ‘Innovating and sharing knowledge for coastal resilience in Eastern Africa’
at CORDIO East Africa (to D.O., M.G., M.S., K.O. and J.M.). The scientific results and
conclusions, as well as any views or opinions expressed herein, are those of the author(s)
and do not necessarily reflect the views of NOAA or the Department of Commerce.
Author contributions
D.O. was responsible for overall leadership, coordination and manuscript preparation.
D.O. and M.S. undertook fundraising. M.G. took leadership on analysis and coding.
D.O., M.G., M.S., K.O., J.M., D.A.K., S.P. and R.R. were involved in methodology and
conceptual development, including how to use data and inputs. D.O., M.G., M.S., K.O.,
S.P., S.A., J. Karisa, M.M. and S.Y. contributed coral reef monitoring data. D.O., M.G.,
M.S., K.O., J.M., S.P., R.R. and R.v.H. were involved in primary analysis. D.O., M.G., M.S.,
K.O., J.M., D.A.K., S.P., R.R., R.v.H., S.A., A.A., J. Karisa, J. Komakoma, M.M., I.R., H.R.,
S.Y. and F.Z. contributed other data and tools, including considering interpretation and
workshops, manuscript writing and editing.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41893-021-00817-0.
Correspondence and requests for materials should be addressed to David Obura.
Peer review information Nature Sustainability thanks J. Duffy, Andres Etter, Peter
Mumby and the other, anonymous, reviewer(s) for their contribution to the peer review
of this work.
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