Download The Effects of Coral Bleaching on Coral Reef Fisheries

The Effects of Coral Bleaching on Coral Reef
Fish, Fisheries, and Ecosystem Services in the
Western Indian Ocean
Tim R McClanahan
The Effects of Coral Bleaching on Coral Reef
Fish, Fisheries, and Ecosystem Services in the
Western Indian Ocean
Principal Investigator:
Tim R McClanahan,
Wildlife Conservation Society
Coral Reef Conservation
Kibaki Flats no.12
Bamburi, Kenyatta Beach
P.O. Box 99470 - 80107
Mombasa, Kenya
Tel: +254 41 548 6549
Fax: +254 41 475157
Email: [email protected]
Co-Investigators:
Seychelles - J. Robinson J and J.P. Bijoux
Reunion - H. Bruggemann, L. Bigot, P. Chabanet,
M. Guillamme
Kenya - J. Maina, N.A. Muthiga, J. Omukoto, A. Wamukota
Tanzania - A.T. Kamukuru, C. Muhando, Mohammed
Nur Mohammed, Saleh Yahya
Madagascar - H. Randriamahazo
International - A.T. Ateweberhan, K. Brown, J. Cinner,
S. Clark, T.M. Daw, A.J. Edwards, R. Galzin, K. Garpe,
N.A.J. Graham, S. Jennings, Y. Letourneur, U. Lindahl,
M.A. MacNeil, N.V.C Polunin, M. Pratchett,
M.D. Spalding, S. Stead, M.C. Öhman, C. Ruiz
Sebastian, C.R.C. Sheppard, V. Venus, S.K Wilson
This publication is available electronically at the following website: http://www.wiomsa.org
© 2009 Western Indian Ocean Marine Science Association (WIOMSA)
Published by: The Western Indian Ocean Marine Science Association (WIOMSA)
Mizingani Street, House No. 13644/10
P.O. Box 3298, Zanzibar
`United Republic of Tanzania
Tel: +255 24 2233472/2234597
Fax: +255 24 2233852
E-mail: [email protected]
All right reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted in any form or by any means, electronic,
mechanical, photocopying, recording or otherwise, without the prior permission
of the publisher and contact with the author.
Citation:
Tim R. McClanahan, 2009. The Effects of Coral Bleaching on Coral Reef Fish,
Fisheries, and Ecosystem Services in the Western Indian Ocean. MASMA
Final Technical Report. WIOMSA Book Series No. 9, viii + 52pp.
Disclaimer:
ISSN:
0856-7972
Front cover photos: Tim R. McClanahan
Design & Layout:
Gordon Arara
Printed by:
Linotypesetters Ltd.
ii
Abstract
The project evaluated the effects of coral bleaching and mortality on the coral reef communities,
fish, and fisheries in the western Indian Ocean through a combination of field studies before
and after the coral bleaching event in 1998, and the development of a historical database on
benthic and fish communities. Investigators repeated fish community surveys in Kenya,
Tanzania, Seychelles, Mauritius, Maldives, and Reunion before 1998 to determine the changes
and the effects of coral mortality, management, and remoteness on the changes in fish
communities. A database of all historical published and unpublished studies on benthic and
fish abundance and production in the western Indian Ocean produced a meta-analysis of the
spatial and temporal patterns of change. In addition, the proposal undertook a survey of fishing
communities in five countries (Kenya, Tanzania, Madagascar, Mauritius, and Seychelles) to
determine their dependence and likely responses to disturbance on coral reef resources. Finally,
the project providing support for coral reef and fish landing monitoring program of the Wildlife
Conservation Society in Kenya, which has been investigating the effects of changes in reef
communities on reef ecology and fisheries. The combined results of these field studies resulted
in a development and testing of conceptual and ecological simulation models to determine the
ability of models to predict the observed changes and to develop scenarios for the effects of
climate change on reef ecology and fisheries under different management scenarios in the
western Indian Ocean. The work involved ~35 author collaborations and a large number of
associated field assistants. At the time of this report, the study had produced 29 peer-reviewed
publications, four were in review, two book chapters, and six reports were produced along with
some graduate theses.
The study found high spatial variability in the extent and effect of the 1998 coral bleaching. Where
the impacts were strongest in the northern Indian Ocean there were reductions in fish
proportional to the loss of coral and most of the loss was with coral-dependent and small-bodied
species. Fisheries closures did not appear to fair better than fished areas. It was difficult to detect
an effect on the fishery as the large-bodied species were not heavily impacted and we expect there
will be lag effect that could take more than 10 years before this effect is evident. We found that
Mauritius, eastern Madagascar, and southern Tanzania had low environmental susceptibility and
consequently are not predicted to have strong warm-water impacts on their reefs in the future.
These sites were differentiated by their adaptive capacity, and the high adaptive capacity in
Mauritius can increase the chances for successful self-initiated recovery and protective
management. In contrast, it is expected that Madagascar will require donor support to build
adaptive capacity as a prerequisite to preservation efforts. The Seychelles and Kenya had high
environmental susceptibility, but were also differentiated by levels of adaptive capacity. High
adaptive capacity in the Seychelles can be used to develop alternatives to coral reef resource
dependence and efforts to reduce the effects of climate change. Simulation models suggest that
protecting coral reef herbivores and coral herbivores can be achieved by restricting the use of
spear guns and beach seines.
iii
Acknowledgements
This research project received a number of sources of support and collaborations. These included
the Western Indian Ocean Marine Science Association, Leverhulme Trust, Eppley, McBean, and
Tiffany Foundation, World Bank Targeted Research Group on Coral Bleaching, EU Erasmus
Mundus Programme, and Fisheries Society of the British Isles. Thanks to ANGAP - Madagascar,
Kenya Wildlife Services, Mauritius Oceanographic Institute, University of Mauritius, Tanga
Coastal Zone Development and Conservation Programme, University of Dar es Salaam Institute
of Marine Science, WCS Madagascar, Seychelles Fisheries Authority, Seychelles Centre for
Marine Research and Technology - Marine Parks Authority, and Nature Seychelles. We are grateful
for the assistance of a number of collaborators that helped with fieldwork, including C. Abunge, A.
Noor-Jahan Calloo, E. Camille, H. Confiance, N. Dhuny, S. Hamed, N. Jiddawi, J. Kabamba, H.
Kalambo, A. Kamukuru, J. Kawaka, J. Mariara, R. Mdedemi, R. Moothien-Pillay, E. Moses, J.P.
Speville, B. Ruchaia, P. Oomah, R. Persand and S.K. Wilson.
iv
Table of Contents
Page
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of Figures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Ecological context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Social context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Ecological studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Socioeconomic studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Item1: Meta-analysis of historical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Relationships between ENSO disturbance and coral diversity . . . . . . . . . . . . . . . . . . . . . 10
Item 2: Pre- and post-bleaching fish surveys. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Item 3: Long-term fishery dependent surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Fish catch monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Gear use studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Item 4: Modelling of fishery effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Item 5: Socioeconomic studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Effects of Coral Bleaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Item 1: Meta-analysis of historical data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Relationship between diversity and change in coral cover . . . . . . . . . . . . . . . . . . . . . . . . 19
Item 2: Pre- and post-bleaching fish surveys. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Item 3: Long-term fishery dependent surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Catch trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Gear-use studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Item 4: Modelling of fishery effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Item 5: Socioeconomic studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
v
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Item 1: Meta-analysis of historical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Temperature environments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Coral diversity and climate change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Item 2: Pre- and post-bleaching fish surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Item 3: Long-term fishery dependent surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Time-dynamics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Gear use considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Item 4: Modelling of fishery effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Item 5: Socioeconomic studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Recommendations for management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Regional management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Local-scale management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Recommendations for collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Appendix 1 Bibliography of papers produced by the project . . . . . . . . . . . . . . . . . . . . . . . . . 50
Published or in press . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Journal articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Book chapters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
In review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
In preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Reports and gray literature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
vi
List of Figures
Page
Figure 1. Coral reef ecosystem model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Figure 2. Historical variability in SST and changes in coral cover . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Figure 3. Temperature groups in the Western Indian Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Figure 4. Coral decline in the Western Indian Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Figure 5. Coral diversity-mortality relationship in the western Indian Ocean . . . . . . . . . . . . 20
Figure 6. Changes in coral reef fish in western Indian Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Figure 7. Trends in mean CPUE in Kenya. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Figure 8. Relationship between fish coral associations and gear . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Figure 9. Relationship between fish functional group and gear . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Figure 10. Simulation model output for the five main gear types . . . . . . . . . . . . . . . . . . . . . . . . . 24
Figure 11. Simulation model output on the grazer functional groups . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Figure 12. Adaptive capacity for 29 areas in five countries in the Western Indian Ocean . . . . 25
Figure 13. Model of social adaptive capacity and environmental susceptibility. . . . . . . . . . 26
vii
viii
Introduction
Perhaps the most tightly linked and publicized known consequence of climate change at the
current time is coral bleaching. The increase in sea surface water temperature is periodically
pushing corals over their thermal temperature limits and causing them to reject or change their
symbiotic algae, thus rendering the white skeleton visible, and causing mortality. Mass coral
bleaching events occurred in all coral reef regions of the world in 1998, resulting in 16% mortality
of the world´s reefs (Wilkinson 2000). Subsequent events occurred in the Pacific in 2002 and these
events are expected to increase in frequency and severity (Hoegh-Guldberg 1999, Sheppard 2003).
The greatest disturbance to date occurred in the Indian Ocean during the 1998 El Nino
(Wilkinson et al. 1999, Goreau et al. 2000); large areas experienced 50 to 99% mortality of living
corals (Goreau et al. 2000). The environmental causes and ecological, and social consequences of
this disturbance were the impetus for this research project and associated studies.
Ecological context
In response to these losses, large scale changes in ecosystem structure are predicted (Walther et al.
2002), but the dynamics of phase shifts and similar phenomena such as cascades are very poorly
known (Pinnegar et al. 2000). Coral reef scientists commonly evoke three competing hypotheses
of the effects of coral loss on the benthos and fisheries.
1) The loss of coral cover will increase space for fast-growing turf algae and that the associated
increase in organic production will result in elevated abundance of herbivores and perhaps
other trophic groups influenced by this increased production (Hart et al. 1996, Hatcher 1997).
However, this may negatively affect fish that prey on invertebrates associated with live coral
species (Kokita and Nakazono 2001, Spalding & Jarvis 2002).
2) Reduced live coral cover leads to bioerosion of calcareous reef structures (Sheppard et al. 2002),
structures already threatened by acidification through greenhouse gas build-up (Kleypas et al.
1999). This breakdown of structural complexity is significant as reef habitat provides a) niches
for various species to coexist (Hixon 1991), b) suitable substrate for reproductive activities and
larval settlement (Doherty & Williams 1988) and c) shelter for fish to escape predation (Hixon
1991, Graham et al. 2003). Species diversity, density and biomass of the fish community are
positively related to habitat complexity and live substrate cover (Ohman et al. 1998, Steele 1999),
loss of which will lead to a consequent loss of fish production (Sano et al. 1987, Sano 2000).
3) The loss of coral will open up space for colonization by fleshy algae (McClanahan et al. 2001a;
Williams et al. 2001) resulting in lower benthic production and palatable algae, thus reducing
the abundance and diversity of fish (McClanahan et al. 2001b, 2002a).
It is likely that all of these processes occur with a loss of coral cover, but empirical field studies are
required to test whether these changes are, in fact, the outcome of coral loss and which process
dominates. The time frame to see the full effects and succession of these scenarios will be
extended. These longer-term responses of coral reef fish communities to bleaching-mediated loss
of live coral and habitat structure were, until recently, scarcely understood.
To date studies of the effects of bleaching on major ecosystem components such as fish have been
limited to small spatial scale assessments of limited numbers of species (Booth & Beretta 2002,
Lindahl et al. 2001), or have been conducted too soon post bleaching to make longer term
inferences (McClanahan et al. 2002b; Sheppard et al. 2002; Spalding and Jarvis 2002). Similarly,
short-term effects of coral bleaching on fisheries catch rates and associated socio-economics are
considered small, but longer-term assessments have yet to be made (Pet-Soede 2000; Grandcourt &
Cesar 2003). Study of long-term effects of bleaching on fish assemblages require pre- and postbleaching fish and benthic data in regions that have been subject to heavy bleaching with control
sites that have not. This study took advantage of the existing data on coral cover and fish collected
before 1998 to evaluate the effects of the 1998 ENSO on these parameter approximately 7 years later.
1
Social context
Coral reefs are a vital resource to many nations of the Indian Ocean, many of which are
experiencing booming human coastal populations. Across the region the two most important
socio-economic reef-based activities are fisheries and tourism. Reef fisheries for local subsistence
fishermen represent, in many cases, their only livelihood. Degradation of coral reefs, subsequently
affecting fisheries, is thus expected to adversely affect many local fishing communities (Grandcourt
& Cesar 2003). Tourism is often heavily dependent on the reef as the main attraction and so
degradation to the resource may negatively affect overall tourism and reef related activities (Cesar
2000). Studies of tourist behavior have indicated that the abundance, size and diversity of reefassociated fishes are more valued by divers than the condition of the reef itself (Williams & Polunin
2000). Greater understanding is necessary to develop management and mitigation plans.
A contemporary challenge for management of biodiversity is characterizing sustainable
socioecological systems by coupling attributes of the environment, biological diversity, ecology,
and social organization (Berkes & Folke 1998; Adger 2000). This perennial challenge is further
heightened by environmental susceptibility to climate change, where the effects of local scale
resource extraction often interact with larger-scale disturbances caused by global climate change
(Clark et al. 2001; McClanahan et al. 2006a; 2008). Integrating these factors is becoming
increasingly urgent for coral reefs where large-scale and acute warm-water events have caused
widespread environmental stress, bleaching, and mortality of corals (Wilkinson 2004), particularly
in the Indian Ocean (McClanahan et al. 2007a). Environmental factors create conditions for coral
bleaching and mortality (Coles & Brown 2003) and these have been modeled, tested with field
observations, and used to predict the susceptibility of sites in the western Indian Ocean (Maina et
al. 2008). This is leading to a better understanding of management needs and activities based on
susceptibility to climate change (West & Salm 2003; Wooldridge & Done 2004; Baettig et al. 2007;
McClanahan et al. 2007b; 2008).
An increasingly critical aspect of conservation planning and action is understanding and
incorporating the heterogeneity in peoples' ability to cope with or adapt to changes in coral reefs
and fishery resources resulting from environmental change or management interventions
(Christie et al. 2005; Folke 2006; Adger 2006). The common assumption among conservation
biologists is that fisheries closures benefit people by improving or maintaining fish catch.
However, these net benefits may not be realized unless resource extraction is already beyond some
maximum sustained yield (Sladek-Nowlis & Roberts 1999), may not be equitably distributed
among people, and benefits may not be perceived by potential beneficiaries (Berkes 2004;
McClanahan et al. 2005). Additionally, people with low adaptive capacity may not be able to
tolerate the hiatus in resources across recovery times, adapt to changes in regulations, or take
advantage of the opportunities created by conservation.
Simultaneously studying environmental, ecological, and social systems is difficult, as each of
these systems is complex and hierarchically organized such that there is considerable
interdependence both within and between systems (Odum 1988). Knowing what, how much, and
which part of the hierarchy of the three systems to compare is challenged by both theory and the
trans-disciplinary nature of the investigations. Studying the foundation of systems is a good
starting place as the hierarchies are built on and depend on these foundations. Key environmental
parameters in the oceanographic environment are water temperature, light, and currents. In coral
reef ecosystems, coral-algal relationships are the foundation of the ecology, productivity, and
architectural complexity that support many fish and invertebrates utilized by people. Fish provide
the main link between the ecology of coral reefs and coastal households. The fundamental unit of
social organization is the household, where individuals produce and share resources, and actions
that destabilize the household are likely to meet with considerable resistance (Jentoft et al. 1998).
This study examined all three aspects of the socioecological environment of the coral reef and
examined their interactions and consequences for future scenarios.
2
Objectives
The project aims to elucidate what happens to the coral reef fish assemblage in response to coral
bleaching in the medium- to long-term (7-8 years) and thus derives implications for fisheries,
tourism, and biodiversity conservation. The study will achieve this by four methods; repeat
detailed benthic and fish surveys at four locations across the Indian Ocean, continuation of a
long-term fishery dependant survey of east African reefs, a meta-analysis at the scale of the western
Indian Ocean and modelling of the three scenarios outlined above within the context of the
western Indian Ocean. Specifically, we will test the following aims and hypotheses:
1.
The change in fish communities is greater on reefs with a history of bleaching and coral
mortality than those without this history
2.
Loss of live coral through bleaching is associated with a reduction in the abundance of
animals that are strict corallivores and specialists otherwise dependent on the live cover.
3.
Increased turf algal cover is associated with increases in herbivorous fish and other grazers on
reefs, whereas fleshy algal dominance has an overall negative effect.
4.
Structural complexity of dead reefs is reduced post bleaching.
5.
Smaller prey fish species are more affected by a reduction in structural complexity than larger
predatory species.
6.
If structural complexity has declined markedly, a reduction occurs in the density, diversity,
and biomass of the associated fish assemblage, including those targeted by fisheries.
7.
Areas heavily affected by fishing are more affected by the bleaching event than those
protected from fishing.
8.
Fisheries production is reduced on reefs heavily affected by bleaching.
9.
Implement socioeconomic monitoring at twelve coral reef study sites in the four countries that
will form a baseline for regular socioeconomic monitoring at these sites.
10. Integrate this socioeconomic data with existing and proposed ecological data on marine
protected areas, gear, dive, and ornamental collection restrictions, and coral bleaching to
inform and improve coral reef management in East Africa.
11. Examine the longer-term socioeconomic effects from the 1998 coral bleaching event
12. Use this information in conjunction with socioeconomic and ecological data to help
understand and improve responses to coral bleaching in East Africa and the Western Indian
Ocean.
13. Use this information in conjunction with existing WCS ecological data and SocMon-based
research in Indonesia and Papua New Guinea to conduct a highly detailed meta-analysis of
how socioeconomic factors influence the use and management of coral reefs throughout the
Indo-Pacific.
14. To influence the plans and activities of marine resource managers in the region by informing
them of the results of the various studies.
15. To help maintain the system of dialogue and exchange of information between stakeholders
and end-users in order to promote longer-term conservation, governance, management and
policy goals for coral reef ecosystems.
3
Literature Review
Ecological studies
Bleaching is a stress response common among scleractinian and alcyonarian corals, clams and
anemones, that causes the pigmented symbiotic micro-algae (zooxanthallae) to be expelled, leaving
the animal tissue pale or white (Hoegh-Guldberg 1999). Persistent low levels of natural or
“background” bleaching occur on all coral reefs (Williams & Bunkley-Williams 1990) contributing to
natural attrition and turnover among corals. However, “mass-bleaching” where multiple species
exhibit unusually high incidences of bleaching tends to reflect extreme environmental stresses (Glynn
1991). Mass bleaching may result from high or low water temperatures, excessive ultraviolet radiation,
aerial exposure, reduced salinity, high sedimentation, pollutants, or toxins (Williams & BunkleyWilliams 1990; Glynn 1991, Smith and Buddemeier 1992; Hoegh-Guldberg 1999; Brown 1997).
Recent and severe mass-bleaching events have primarily resulted from positive thermal anomalies,
linked to global climate change (Glynn 1991; Hoegh-Guldberg 1999). In the most extreme example,
mass-bleaching in 1998 resulted from severe El Niño conditions (Wilkinson 1998; Stone et al. 1999),
combined with the Indian Ocean dipole (Saji et al. 1999), which dramatically increased sea-surface
temperatures throughout the tropical Pacific, Indian and Atlantic Oceans (Goreau et al. 2000).
Throughout 1998, mass bleaching occurred on an unprecedented geographic scale (Goreau et al.
2000) and effectively “destroyed” 16% of the coral reefs around the world (Wilkinson 2000a).
The 1998 global mass-bleaching was the most devastating and widespread bleaching event ever
recorded, and contributed greatly to increased acceptance of global climate change as both a real
phenomenon and a significant threat to entire ecosystems (Walther et al. 2002). The significance of
this event was highlighted by the death of coral colonies that had survived 100-1,000 years of
environmental fluctuations and climate change (Hodgson 1999; Goreau et al. 2000). Effects of the
1998 bleaching were nonetheless spatially variable, both at very large (geographic scales,
McClanahan et al. 2007a) and very small scales (e.g., between adjacent coral colonies, Marshall &
Baird 2000). Coral bleaching was particularly severe on coral reefs in the Indian Ocean, where coral
cover declined by an average of 46% following the 1998-bleaching event (Hoegh-Guldberg 2004). In
contrast, coral bleaching killed only 3% of corals in the southwest Pacific (Papua New Guinea and
Australia's Great Barrier Reef), although there were isolated incidences of very high (up to 90%) coral
mortality (Berkelmans & Oliver 1999). Since the 1998 global mass-bleaching event, there have been
only isolated instances of mass-bleaching (Berkelmans et al. 2004; McClanahan et al. 2007b; Penin
et al. 2007), but further mass-bleaching on the scale of the 1998 event is inevitable given sustained
and ongoing climate change (Sheppard 2003; Hoegh-Guldberg 2004; Donner et al. 2005).
Coral reef fishes strongly associate with conspicuous features of habitat structure (e.g., Jones &
Syms 1998; Munday and Jones 1998), but there are opposing schools of thought on the critical
aspects of coral reef habitats. Certain authors (Jones 1988; Munday 2000; Chabanet et al. 2004;
Jones et al. 2004; Holbrook et al. 2000, 2002, 2006) consider that live coral cover has a major
influence on the distribution and abundance of coral reef fishes. Accordingly, major changes in
the abundance, diversity or composition of fishes have been related to extensive coral loss
(Williams 1986, Sano et al. 1987; Jones et al. 2004; Munday et al. 2004a; Pratchett et al. 2006).
Alternatively, there is considerable correlative evidence linking the abundances and diversity of
coral reef fishes with spatial and temporal variation in topographic complexity (Luckhurst &
Luckhurst 1978; McClanahan 1994; Jennings et al. 1996; Öhman and Rajasuriya 1998; Lawson et
al. 1999; Gratwicke & Speight 2005; Garpe et al. 2006; Wilson et al. 2007), and some authors
perceive that live coral is largely irrelevant, except in providing habitat diversity and topographic
complexity (Sale 1991; Lindahl et al 2001; Garpe et al. 2006). Ultimately, both coral cover and
topographic complexity may be critical elements of coral reef habitats, though they may influence
different components of reef fish assemblages. For example, coral cover is important for specialist
fishes that depend on corals for food or shelter (Williams 1986; Pratchett et al. 2006), whereas
topographic complexity plays a key role in enhancing diversity of coral reef fishes (Lindahl et al.
2001; Graham et al. 2006).
4
The relative importance of coral cover versus topographic complexity is critical to understanding
the effects of habitat perturbations in coral reef ecosystems (Wilson et al. 2006). Comparative
studies on the effects of habitat perturbations on coral reef fishes have differentiated between
disturbances that affect live coral cover versus topographic complexity (Sano et al. 1987; Wilson et
al. 2006). Disturbances are separated into; i) biological disturbances (e.g., climate-induced coral
bleaching, outbreaks of A. planci, and coral disease), which kill corals without immediately
compromising the integrity of coral skeletons (Sano et al. 1987; Garpe et al. 2006), and ii) physical
disturbances (e.g., severe tropical storms, and tsunamis), which break-down, displace and/ or
overturn entire coral colonies, simultaneously reducing both live coral cover and structural
complexity (Cheal et al. 2002; Halford et al. 2004; Baird et al. 2005). Biological disturbances can
have severe effects, but these effects appear to be limited to fishes that are highly dependent on
corals for food or shelter (Williams 1986). In contrast, physical disturbances that cause a loss of
topographic complexity can have much more wide-ranging effects (Sano et al. 1987), as many
coral reef fishes that do not depend on live corals are nonetheless dependent on topographic
complexity provided by healthy coral growth (Carpenter et al. 1981, Glynn 2006).
Climate change is rapidly emerging as the single greatest threat to coral reef fishes (Wilson et al.
2006; Munday et al. 2007; Pratchett et al. 2007). The effects of coral bleaching and coral loss on
coral reef fishes may not always be immediate apparent, nor become manifest through short-term
declines in adult abundance. For example, reduced availability of preferred coral prey can have
significant, but sub-lethal effects on coral feeding fishes (Kokita & Nakazono 2001; Berumen et al.
2005). Pratchett et al. (2004) showed that there was no short-term decline in the abundance of an
obligate coral-feeding butterflyfish (Chaetodon lunulatus) despite a 55% decline in coral cover
caused by mass bleaching. However, C. lunulatus did exhibit significant declines in physiological
condition (Pratchett et al. 2004), which contributed to reduced survivorship and eventual
population declines (Pratchett et al. 2006). Similarly, reductions in live coral may limit settlement
and recruitment for fishes that are otherwise unaffected by coral depletion (Booth & Beretta 2002;
Feary et al. 2007). Limitations to population replenishment will undoubtedly reduce species
abundance, but these effects may not be immediately apparent (Feary et al. 2007).
Several studies have documented extensive declines in the abundance and diversity of coral reef
fishes several years (>3 years) after climate-induced coral bleaching (Garpe et al. 2006; Graham et
al. 2006). These studies attribute changes in reef fish assemblages to the delayed effect of
structural collapse of dead corals, which reduces overall topographic complexity of coral reef
habitats (see also Sano et al. 1987). Many different fishes declined in abundance, including
herbivorous fishes (Graham et al. 2006) that might be expected to benefit from the increased
abundance of algae following extensive coral loss. However, delayed effects of coral bleaching on
reef fishes may also be due to either; i) a lack of recruitment by fishes that need coral at settlement
(Jones et al. 2004); ii) reductions in survivorship and reproductive output of fishes that lead to
gradual declines in population size (Pratchett et al. 2004); or iii) secondary declines in the
abundance of piscivorous fishes due to declines in coral-dependent prey fishes. Consequently,
loss of live coral may be as important as declining topographic complexity in protracted effects of
coral bleaching on coral reef fishes (Jones et al. 2004). Existing studies have never attempted to
separate these effects, though there is substantial correlative and indirect evidence that both live
coral and topographic complexity are important attributes of coral reef habitats and strongly affect
communities of coral reef fishes (Carpenter et al. 1981; Graham et al. 2006). Longer term effects of
coral bleaching on coral reef fishes are poorly understood, mostly because there are few studies
that have measured changes in the reef fish assemblages >3 years post bleaching (Jones et al. 2004;
Graham et al. 2006), and no studies have sampled >10 years post-bleaching.
The important question is what proportion of fishes actually depend on live corals for their longterm persistence. A wide range of associations exist between reef fishes and live corals, from
species that depend on live coral for food and habitat (Munday 2002; Pratchett 2005), to species
that are rarely associated with live coral and are characteristic of sites with low coral cover (Sano
5
et al. 1984). Current estimates of the proportion of coral reef fishes with apparent and direct
reliance on live corals are 9-11% (Munday et al. 2007; Jones et al. 2004). Regardless, Jones et al.
(2004) showed that 75% of coral reef fishes exhibit declines in abundance following extensive
(90%) coral mortality, which suggests that importance of corals for coral reef fishes may have been
grossly under estimated.
The strong linkage between coral reef fishes and underlying habitat clearly makes them
susceptible to climate-induced coral bleaching. However, provided bleaching is sporadic and
does not cause 100% mortality of critical coral species, even highly susceptible fish populations
may have the potential to persist and recover. For example, Holbrook et al. (2006) show that
dramatic declines in fish abundance and diversity may not occur until coral cover is reduced to
less than 10% (see also Wilson et al. 2006). Recovery of fish communities will be expected,
provided there are refuge adult populations and recovery of critical aspects of the biological and/
or physical structure of these habitats (Halford et al. 2004). Recovery of fish populations is reliant
upon many factors, such as the continual supply of fish recruits (Doherty & Williams 1988) and
the ability of fishes to reclaim space occupied prior to temporary disruptions in habitat structure.
Following major habitat perturbations, and in the absence of any further disturbances, it may take
as little as 5 years for coral cover to return to pre-disturbance levels (Halford et al. 2004; Gardner et
al. 2005). The time taken for coral cover to increase depends on the severity and extent of coral
loss, which dictates the ability of surviving corals to recover, reproduce and reseed affected areas
(Hughes & Connell 1999; Golbuu et al. 2007). Importantly, isolated reefs are more sensitive to
declines in viability of local populations, whereas well-connected reefs, such as those along
continental margins or large archipelagos, are more likely to be reseeded by larvae from nearby
and relatively unaffected populations (Ayre & Hughes 2004). Recovery will be much faster if at
least some coral colonies survive the bleaching event, as often occurs during mass-bleaching
(Baird & Marshall 2002), because growth of surviving corals leads to more rapid increases in coral
cover compared to settlement and subsequent growth of new individuals (Connell et al. 1997),
while local populations of mature corals are the most reliable source of new recruits (Hughes et al.
2000).
Coral mortality following bleaching events provides space on the reef that is rapidly colonized by
turf forming algae (Diaz-Pulido & McCook 2002). On reefs where herbivorous fish and/or
urchins are abundant, algal assemblages may remain as cropped turf forms following coral
bleaching and coral loss (Aronson et al. 2002; Arthur et al. 2005). However, where grazing pressure
is low or if coral mortality is extensive, local stocks of turf feeding species may be unable to counter
increased algal abundance and macroalgal blooms can develop (Ostrander et al. 2000; Aronson &
Precht 2006), bringing about a phase shift from coral- to macroalgal-dominated reefs. Once
established, macroalgae may persist, as many of the fish species traditionally expected to target
macroalgae feed almost exclusively on epilithic turf forming algae and avoid larger fleshy thalli
(Bellwood et al. 2006; Ledlie et al. 2007). Many herbivorous fishes are important in preventing
dominance of macroalgae in coral reef habitats, thereby preventing phase-shifts (Bellwood et al.
2004; Ledlie et al. 2007). However, it has been argued that different fishes are needed to facilitate
regeneration once macroaglae are established (Bellwood et al. 2006). Many faster growing corals,
such as branching Acropora and Pocillopora are however, highly susceptible to coral bleaching
(Marshall & Baird 2000, Loya et al. 2001) and may be unable to persist as bleaching becomes more
frequent and more severe. Consequently, coral communities are likely to become dominated by
bleaching-resistant species (Hughes et al. 2003; McClanahan et al. 2007), most of which are less
structurally complex and rarely used by coral-dwelling or coral-feeding species (Bellwood et al.
2006; Munday et al. 2007).
6
Socioeconomic studies
Worldwide, the goods and services that coral reefs provide are estimated to be worth almost
USD$30B/year (Cesar et al. 2003). Sustained and ongoing climate induced coral bleaching will
significantly diminish the value of these goods and services, resulting in serious consequences for
people and even countries dependent on coral reefs (Lesser 2004). Coral bleaching has the
potential to affect fisheries, the livelihoods and health of fisheries-dependent communities, reefrelated and coastal tourism, and important ecosystem services provided by reefs (Westmacott et al.
2000; Cesar et al. 2003; McClanahan et al. 2002, Graham et al. 2007). To date, empirical studies
have focused largely on the effects of bleaching on fisheries and tourism, which are the most
obvious immediate effects (but see Sheppard et al. 2005).
Coral bleaching has the potential to affect reef and reef-related fisheries through a number of
mechanisms: i) coral mortality or structural loss may cause a decline in the abundance of reefrelated fishes (i.e. those that depend on coral for settlement, feeding, dwelling, or are associated
with the reef structure) leading to reduced catches of these fishes (Westmacott et al. 2000; Graham
et al. 2007); ii) highly mobile predatory species, though not directly dependent on reef structures,
may decline in abundance following declines in abundance of prey species (Westmacott et al.
2000); iii) declines in reef fish biodiversity may lead to a reduction in energy transfer to higher
trophic levels, which may mean that reefs will support a reduced biomass of higher order
predators (Munday et al. 2007); iv) many fishes can be negatively affected by a high abundance of
late-successional algae (McClanahan et al. 2002) that may dominate after coral mortality; v) coral
mortality may cause a reduction in abundance of some small-bodied ornamental coral-feeding
and coral-dwelling fishes, which are valued by the aquarium trade (Wilson et al. 2006); and vi)
macro-algae habitats attract some invertebrates (McClanahan et al. 2001), such that the
abundance of some invertebrate feeders may increase (Pratchett et al. 2007). These changes in the
relative abundance of targeted species are likely to significantly affect fisheries yields, species
composition of catches, and the economic value of coral reef fisheries (Westmacott et al. 2000).
Most likely, catch composition will change even if there is no decline in catch rates, which may
reduce the total value of landings (Westmacott et al. 2000).
Despite potential effects of climate-induced coral bleaching on coral reef fisheries, no studies
have actually shown that total catch, catch composition, or value of fisheries have been affected by
severe mass-bleaching (McClanahan et al. 2002; Grandcourt & Cesar 2003). However, detecting
definitive effects of coral bleaching on fisheries yields may be very difficult, because the
confounding effects of overfishing in many locations outweigh any effects that loss of live coral
may have on temporal trends in fisheries yields. For example, in Mombasa, Kenya, Westmacott et
al. (2000) found a decline in catch while effort remained the same, but concluded that bleaching
had no detectable effect because the rate of decline in the fishery was consistent for several years
prior to the bleaching. In a longer-term study of Kenyan fisheries (1994-2001), McClanahan et al.
(2002) found that overall Siganidae catches were reduced by 8% after the bleaching event and that
catch per fisherman per day decreased 20-30% for Siganidae and Scaridae, but attributed most of
this change to a 17% rise in fishing effort over the study period. Likewise, in the Seychelles fishery,
low abundance and yield of herbivorous Siganidae post-bleaching was attributed to a prebleaching trend associated with fishing effort (Grandcourt & Cesar 2003). These results reinforce
the importance of examining long-term trends, fishing effort and catch composition rather than
simply documenting changes in focus parameters immediately before and immediately after
punctuated disturbances (Hughes & Connell 1999).
Failure to detect significant effects of climate-induced coral bleaching on coral reef fisheries may
also be due to the fact that many fisheries mainly target fishes that are not dependent on coral
(e.g., Acanthuridae, Siganidae, Scaridae, and various planktonic species) (Bellwood 1988; Goreau
et al. 2000). In artisanal fisheries in both Kenya and Papua New Guinea, coral-dependent fishes
made up only a very small proportion of the species and individuals caught. Despite major
7
differences in fishing intensity (Cinner & McClanahan 2006), the overwhelming majority of fish
caught in both these fisheries were associated with the reef structure, but not explicitly reliant on
live corals. There were also significant landings of fishes that are not really dependent on coral
reefs, but may be caught in the proximity of coral reefs (e.g., Carangidae and Scombridae). These
data suggest that artisanal fisheries may be relatively unaffected by coral loss caused by climateinduced coral bleaching, but there could be major consequences associated with the longer term
declines in topographic complexity (Graham et al. 2007) However the significant lag between the
coral loss and its potential effect on most target fish populations might mask the effects of
bleaching on fisheries' catches. Changes in fishing locations or habitats, coincidental with
bleaching (Westmacott et al. 2000), or simultaneous/subsequent disturbances such as cyclones,
increased sedimentation, or freshwater runoff (Jones et al. 2004; Munday 2004), may also make it
difficult to establish the relative importance of different disturbances. Consequently, effects of
climate-induced coral bleaching on coral reef fisheries are likely to be difficult to detect, as they
are highly protracted, and potentially masked by a wide range of other factors.
Economic costs of coral bleaching on coral reef fisheries may be most apparent in niche fisheries
targeting mainly coral-dependent fishes (e.g., aquarium fisheries). The international marine
ornamental fish trade is currently worth US$90-300 million per annum (Sadovy & Vincent 2002),
and mostly targets small coral reef fishes such as the Pomacentridae, Chaetodontidae,
Monocanthidae, and Apogonidae (Kolm & Berglund 2003; Tissot & Hallacher 2003; Lunn &
Moreau 2004). Many of these fishes (especially butterflyfishes) are coral-dependent and thus
highly susceptible to climate-induced coral bleaching. However, it is unclear if or how this fishery
might be affected by significant declines in wild populations of coral-dependent fishes.
Importantly, aquarium fisheries may opportunistically exploit individuals from a range of targeted
species, such that overall catch rates are insensitive to fluctuations in relative abundance of
different species. However, the most specialised of coral reef fishes are often difficult to maintain
in aquaria (Allen et al. 1998; Michael 2004), and generally not targeted for the ornamental trade
(Allen et al. 1998). Nonetheless, species most susceptible to bleaching are also in high demand for
the aquarium trade. Climate-induced coral bleaching might be expected to effect total value if not
the overall catches of aquarium fishes. The recent disappearance of several coral-dependent
fishes from aquarium catches (e.g., Oxymonocanthus longirostris) has been directly attributed to
declines in local stocks following the 1998 mass-bleaching event (Dulvy et al. 2003).
In contrast to the rather inconclusive studies concerning effects of bleaching on coral reef fisheries,
several studies have been able to detect and project the effects of coral bleaching on reef-based
tourism (Westmacott et al. 2000; Andersson 2007). Bleaching has been found to negatively affect
tourism and tourism-related industries by reducing the attractiveness of particular locations or
activities for tourists, resulting in a change of either destination or activity and subsequent loss of
reef-related revenue (Westmacott et al. 2000; Uyarra et al. 2005). Tourist perceptions of changes in
coral reef habitats after bleaching are often limited (except where there has been extensive
degradation and extensive loss of fishes), but knowledge of recent bleaching may influence where
tourists choose to dive (including snorkelling) on both small spatial scales (i.e. a particular dive site)
and large spatial scales (i.e. a particular country or region). In Kenya and Tanzania, >75% of divers
avoided dive sites known to have bleached (Westmacott et al. 2000; Andersson 2007). Where user
fees exist for access to marine protected areas and areas under customary ownership, changes in
specific dive site selection may affect revenue generation used for reef management and
community development. On a larger scale, bleaching may reduce national and regional
economies by influencing the choices of destinations. For example, 19% of Zanzibar, Tanzania and
30% of Mombasa, Kenya tourists would change their holiday destination as a result of bleaching
(Westmacott et al. 2000), 80% of tourists visiting Bonaire would be unwilling to return at equivalent
cost in the event of coral bleaching (Uyarra et al. 2005), and there was 5-10% decline in the number
of tourists visiting Palau subsequent to the 1998 coral bleaching (Graham et al. 2000).
8
The recreational value of diving in Kenya and Tanzania was no different in 1999 (after widespread
bleaching) compared to 1996 (before the bleaching), suggesting that visitors were still eager to visit
and view coral reef habitats (Westmacott et al. 2000). However, the divers in 1999 were less
experienced than divers in 1996, suggesting that more experienced divers may have chosen to dive
in alternate regions that experienced less bleaching. In 1999, estimates of economic losses from
diver satisfaction ranged from US$1.6-4.8 million in Zanzibar (Ngazy et al. 2002). A ‘willingness to
pay' survey of tourists in the Philippines found that individual divers were prepared to pay an
average of US$202 more to dive a ‘pristine' reef compared to a bleached or degraded reef, while
snorkellers were willing to pay and additional $US25 (Cesar 2000).
Overall economic losses attributable to recent climate-induced coral bleaching are staggering,
ranging from tens of millions of dollars for a single country to billions of dollars for the Indian
Ocean (Westmacott et al. 2000). For example, changes in destination choice and decreases in
consumer surplus (i.e. the value of the dive experience to the diver less the costs of the vacation)
resulted in total losses to the Palau tourism industry as high as $US750,000 over the two years
following bleaching (Graham et al. 2000). In the Philippines, recent bleaching is predicted to result
in economic losses ranging from $US6 million to as high as $US27 million, depending on the time
required for reefs to recover (Cesar 2000). The overall economic damages from the 1998 coral
bleaching event in the Indian Ocean (over a 20 year time frame with a 10% discount rate) could be
over $US8 billion; $US1.4 billion loss of food production/fisheries, $US3.5 billion loss of tourism
revenue, $US2.2 billion loss of coastal protection, and $US1.2 in other services (Wilkinson 1999;
Westmacott et al. 2000).
9
Methodology
Item 1. Meta-analysis of historical data
The above field studies will facilitate a process of accumulating data from field studies that
describe the abundance and production of main components of the coral reefs in this region. We
propose to develop a database of field data on benthic and fish abundance from our studies, other
peer-reviewed published studies, and also a number of unpublished consultant and “in-house”
government, academic theses and expeditions, and NGO reports (i.e. CRCP, GCRMN, CORDIO,
and ReefCheck). With these data we performed a meta-analysis of the factors that influenced coral
mortality in time and space.
Change in coral cover was collated for 36 major reef areas of the Western Indian Ocean region
(WIO) by compiling published and unpublished data across the 1998-bleaching disturbance. We
present survey data immediately before and after the event to the extent possible from the data
presentations. In most cases, data from the same site were used for ‘before' and ‘after'. Where
several ‘before' or ‘after' data were presented for a reef without specific site descriptions, only the
pooled means were considered. If only one cover (either ‘before' or ‘after') and mortality were
given, the remaining cover was obtained from back calculation. When a range of cover data was
given for a site, then the mid-point was taken. Occasionally, rate of change in cover was given and
cover was calculated from the rate of change by accounting for the elapsed time (e.g. South Africa).
When only a single data point was given for ‘before', it was matched by the average of the 'after'
data (e.g. northern Kenya). For a single region (Gulf of Kutch, India) only mortality data was
presented, hence, no cover was presented for ‘before' or ‘after' cover.
Absolute change in cover for each reef area was calculated by subtracting the cover after 1998 from
the cover before 1998. Absolute change in cover was positively correlated with the cover before
1998 (linear regression analysis: R2 = 0.44; p = 0.0002). Therefore, it was necessary to normalize for
the initial cover, and we used the relative cover by dividing the absolute change by the cover before
1998. The procedure reduces the variation due to coral community structure.
For each central point of an area where cover data were used, corresponding monthly mean
JCOMM-SST data (Nov 1981-Dec 1997) were downloaded from the internet
(http://iri.Columbia.edu/climate/monitoring/ipb) and a number of SST statistics calculated. JCOMMSST has a lower resolution (1ox1o) than NOAA-SST (50x50km; http://www.osdpd.noaa.gov/
PSB/EPS/SST) but we chose it over NOAA-SST because it corrects satellite observations with in
situ measurements (Reynolds et al. 2005) and it better predicted coral bleaching in an earlier study.
The following SST statistics were calculated for each square for the whole time series: mean,
median, minimum, maximum, standard deviation, skewness, kurtosis and bimodality. We
conducted Principal Components Analysis on the different SST variables. After removing the
variables that showed high autocorrelation with analysis of inverse correlation, only SD, skewness,
kurtosis and one biomodality index (amplitude of the smaller peak; AM) remained and were used
in the final PCA. AM has inverse relationship with the degree of bimodality, therefore, we used the
-1
inverse of AM (AM ).
Relationships between ENSO disturbance and coral diversity
We examined the relationship between the disturbance across the 1998 ENSO and the diversity of
coral species to determine the risks of proposed conservation and management implementation.
By using coral species/generic richness data for 42 western Indian Ocean countries/regions we
investigated the relationship between taxonomic richness and coral mortality due to the 1998
ENSO in order to identify areas of high/low diversity with high/low mild impacts of climate
change as a means to prioritize conservation actions. The numbers of genera of scleractinian
corals were obtained for 42 countries/regions in WIO from published and unpublished literature.
10
The diversity data set is based on many reports but we updated (Sheppard 1998) as it summarizes
most of the older records. We included only region or country based records; records from
localized areas (e.g. cities) were not used. Where duplicate diversity estimates were encountered,
the largest value was used, which was often the most recent one. The relationship between
diversity and change in cover was investigated with correlation analysis and a scatter plot was
constructed in order to identify areas of high and low diversity with higher and lower change in
cover (mortality) in 1998. Values were scaled between 0 and 1 relative to the minimum and
maximum according to the following formula:
Item 2: Pre- and post-bleaching fish surveys
We identified all field studies that had comprehensively surveyed reef fish assemblages and
associated benthic composition and structure from the western Indian Ocean region from 1990 to
before the 1998 coral bleaching event (majority 1994-95). This resulted in eight separate largescale studies (across seven countries). Original investigators returned to their study locations in
2005 to repeat the surveys, using field protocols identical to those used in the original surveys. The
protocols were standardized within, rather than among study locations as it is more robust to
quantify effect sizes in this way and then standardize when comparing among studies. Where the
original investigator could not return, an experienced surveyor from the team repeated the work.
An associated field study workshop for the project, which involved many of the researchers from
the region, found experienced observer bias to be a very small component of the variation in fish
counts. All reef surveys were conducted on the reef flat or shallow reef slope. The abundance of all
diurnally active, non-cryptic, reef-associated fish was assessed during each survey, however
methods varied among study locations from point counts of differing dimensions to belt transects
of differing dimensions. Replication also varied from 3 to 16. This resulted in a survey area per site
2
2
of ~200m to ~2500m . Benthic quantification also varied in spatial scale and from visual estimates
to line intercept transects, but the results are expected to be comparable. Estimates of change in
live coral cover were calculated and plotted on a map by country and management strategy and at
a more aggregated level with 95% confidence limits. Measures of structural complexity also varied,
however they were strongly correlated and these correlation coefficients were used to standardize
them to a common scale. The relationship between percent change in coral cover and percent
change in structural complexity was assessed by correlation analysis. The presence of variation in
field methods is routine in meta-analytical studies, and thus the choice of effect size calculation
and variance weighting is integral to the comparability of study results.
Statistical analysis
Meta-analysis frequently employs a unitless effect size metrics to standardize the information
present among accumulated studies. The potential to observe changes in a before and after
comparison can be greatly influenced by initial values at a given location; sites with larger initial
values have a greater scope to reveal change than those with low values. To achieve a comparable
metric at all locations and to account for initial values, we calculated effect sizes as the percent
change between the mid 1990s and 2005;
% difference = [(Aa,i - Ab,i) / Ab,i ] x 100
(1)
where Ab and Aa were mean values at sites in the mid 1990's and 2005 respectively. It was not
necessary to account for study duration as this was effectively standardized in the study design. We
calculated individual effect sizes for change in coral cover, structural complexity, fish species
richness, and fish density in four functional groups for which data were available at the majority of
sites (obligate corallivores, herbivores, planktivores, and mixed-diet fishes assigned using regional
fish identification guides, published literature and http://www.fishbase.org), for four size classes
11
of fish species (maximum attainable size <20 cm, 21-40 cm, 41-60 cm, and >60 cm) and for the
same four functional groups listed above within the <20 cm maximum attainable size category.
Herbivores include all those species that feed on algae and or detrital aggregates from the epilithic
algal matrix. Because percent-change losses have a strongly right-tailed distribution, i.e. a
maximum potential decline of 100%, but a potentially limitless increase, we transformed all of the
DT values to be balanced around zero:
DT=loge(1+[D/101]).
(2)
This transformation scales to a common floor and ceiling on potential declines and increases,
where a maximum potential decline has a value of -4.6 and a maximum increase +4.6. The
transformation approximately normalizes the error distribution and stabilizes its variance. Raw
data were available for many of the original studies, allowing us to estimate average effect-sizes at
some locations. Because data were collected from the same sites but not the same transects, we
estimated effect-size means and variances at these sites using non-parametric bootstrapping of
the before and after observations (R=9999) with (1) and (2), by randomly matching before-after
pairs at each iteration. This generated sample means and expected variance ranges for many, but
not all, of the study locations.
We evaluated evidence for a regional relationship between reef fish and coral cover using an areavariance weighting scheme implemented in a Bayesian meta-analysis framework. The use of area
surveyed as a weighting scheme in coral reef meta-analyses has become widespread because
actual variance will depend on individual measurement size and replication. The Bayesian
approach allowed us to model the hierarchical structure of the data, estimate the magnitude of
regional-scale effects, and to specify a level of uncertainty about individual study estimates. By
sharing information among studies, this approach maximized the strength of inferences made
across the entire range of meta-data used, allowing us to make probability statements about the
likelihood of reef fish declines given potential future changes in coral conditions.
Item 3: Long-term fishery dependent surveys
Fish catch monitoring
As outlined above the loss of coral cover can have a number of possible effects on coral reef fish
and fisheries and consequences for fisheries, but unless fish catches are carefully monitored across
these bleaching events the opportunity to evaluate the different outcomes cannot be tested. Marine
fish catch data for many of the Indian Ocean countries is not well collected and country-level
statistics are often based on very little confirmed catch data and assumptions and calculations that
will only approximate the actual catch. Further, these data often combine catches from many
different ecosystems and habitats, such that it is difficult to distinguish catch from coral reefs,
mangroves, and pelagic ecosystems. As a response to this problem in Kenya, the Wildlife
Conservation Society has developed an independent fish-catch monitoring program in the coral
reef lagoons of southern Kenya (McClanahan & Mangi 2000, 2001, 2004). Catch are monitored by
family and gear types for three to six days per month in 10 sites with different types of management,
including closed-area and gear management. Measures of effort in terms of numbers of fishermen
and boats are collected to determine the effort and fish prices are also monitored in order to
evaluate the economics of this artisanal fishery.
Gear use studies
Coral reef ecosystems are complex systems and therefore difficult to manage from an ecosystem
perspective when social conditions do not facilitate the use of fisheries closures. Suggestions for
management based on an understanding of coral reef ecology are a useful first step but can be
challenging to implement due to complex and challenging socioeconomic considerations
(McClanahan et al. 2008). Finding appropriate ways to implement management that can be
12
adopted by fishing cultures and that then have desired ecological consequences is perhaps one of
the greatest challenges to implementing ecosystem-based management of coral reefs.
Management of fishing gear may be one the most likely ways to achieve ecosystem management
in areas without closures as gear are selective in their catch and are also one of the management
options that is most accepted by fishers and managers (McClanahan et al. 2005). Consequently,
we examined the potential for managing gear such that the ecological processes are maintained
and recovery after disturbances, such as coral mortality, are enhanced.
The study examines fishing gear and their catches in two countries that represent a low to moderate
level of fishing, namely Papua New Guinea (PNG), and moderate to high level fishing, namely
Kenya (Cinner & McClanahan 2006; McClanahan et al. 2008). We evaluated the species and
functional group selectivity of the gear in these two countries and use this to calibrate the ecosystem
model. Catch data is based on six fishing sites in PNG that were studied and described by Cinner &
McClanahan (2006) and ten fish landing sites in Kenya (McClanahan 2008). Landing sites were
selected to represent a wide range of social, economic, and demographic conditions rather than a
random or haphazard selection. The fisheries were, however, typical tropical artisanal fisheries
where catch was derived from shallow-water coral reef and seagrass ecosystems. The analyses of the
catch data were based on 4205 and 2154 fish specimens from Kenya from PNG, respectively.
The methods to sample fish catch in the two countries were slightly different. In both countries we
opportunistically examined fish landings at all times of the day and night by approaching and
asking permission from fishers as they returned from fishing activities and the whole catch was
measured whether the catch was for market or home use. The length, abundance, and taxonomic
composition of catch were recorded. In PNG, we photographed the fish with a digital camera
(SonyTM DSC P-1, 3.3 megapixel) using the methods of Cinner & McClanahan (2006) and
recorded the gear used to capture each fish. When multiple gears were used in a single trip, we
separated the catch by gear type. In Kenya, catches were identified, counted and measured to the
nearest centimeter using a fixed marker rule on a flat board. For large catches a sub sample was
measured. Where possible the entire catch was sampled, but where this was not possible the main
sampling focus was to ensure that each gear used at each site was sampled, each species landed
was recorded, and each size class for each species was measured.
In both countries, each fish was identified to genus and species (Lieske & Myers 1994; Randall et
al. 1997). Although a variety of fishing gears and techniques were used throughout PNG, three
main gear types were widespread and used in sufficient numbers to be useful for management and
comparable, namely line fishing, gill nets, and spearguns. In Kenya, these three gears were also
commonly used along with beach seines and traps. The infrequency of use of other fishing
methods (including weirs, poisons, bombs, and derris root) did not allow for sufficient data to
make comparisons.
We used expert opinion to group species into the following functional groups, based on their diet:
piscivore, macro piscivore-invertivore, plankivores, macro and micro invertivores, grazer (microturfs), macrograzers (seagrass and large erect algae), scrapervator (parrotfish that remove coralline
algae and calcium carbonate while grazing), and detritivores. We also classified a few species as a
“key-species” if an ecologist had identified this species as being particularly important for
ecological processes, i.e. batfish, red-lined triggerfish (McClanahan 2000; Bellwood et al. 2006).
We used ordination plots generated by correspondence analyses to examine how nation and gear
were related to the above functional guilds and species.
To examine how specific fishing gear selectively target cohorts of fish that are: 1) differentially
susceptible to bleaching; and 2) from select functional groups, we used: a) plots of how each gear
targeted fish of differing levels of coral association; b) ordination plots generated by
correspondence analyses to examine how location and gear were related to coral association and
guild of captured species; and c) logistic regression models to examine whether the coral
association of fish could be predicted by gear type, location, country or fishing intensity.
13
Item 4: Modelling of fishery effects
Given the multiple scenarios of the effects of coral bleaching and mortality on reef ecology, fish,
and fisheries there is a need to formalize, quantify, and test possible scenarios with ecological
models based on a holistic view of the ecosystem and realistic field data. These models can
assist researchers in seeing possible outcomes at different time scales and based on more
variables that are commonly considered by field biologists and single-species models (HoeghGuldberg 1999; Sheppard 2003). Extractive fisheries, both large- and small-scale, drive human
use of coral reefs. Having established the ecological and social vulnerability of a site under the
preceding objectives, managers will still need to understand how different fisheries
management approaches will combine with climate change factors, ecosystem function, and
socio-economic context. It is broadly agreed that the protection of coral reef grazers will be
imporant for maintaining the recovery of coral reefs after grazing (Hughes et al. 2003; Bellwood
et al. 2007; Mumby et al. 2007). Thus, this research initiative refinement a coral reef ecosystemfisheries simulation model to undertake fishing experiments on reefs to determine those types
of fishing gear and levels of fishing that would protect coral reef grazers.
We developed a coral reef simulation model to specifically run scenarios for fisheries management
and climate change. The model is an expansion and improvement on a model published in 1995
(McClanahan 1995), based on the inputs from a World Bank Targeted Research Group on
Bleaching workshop held in December 2006 that used expert advice to reorganize the model and
improve its calibration. The model was developed specifically with the objective of testing the effects
of coral bleaching and mortality on the larger ecology and fisheries production by quantifiyin the
sensitively of benthic and fish groups to bleaching and coral mortality (Pratchett et al. 2008). The
model has been given the acronym CAFFEE (Coral-Algae-Fish-Fisheries Ecosystem Energetics)
and is a system-dynamic model of a coral reef ecosystem based on the transfer of energy and
interactions between functional groups and developed using the STELLA programming language.
The model integrates 17 functionally distinct groups across four trophic levels, including six primary
producers, eight primary consumers, two secondary consumers and one tertiary consumer (Fig. 1).
Fisheries
POM
pelagic
Zooplankton
Piscivores
Planktivores
DOM
pelagic
Bacterio-&
Phytoplankton
DOM
reef
Detritivores
Invertivores
Grazers
Scrapevators
Corallivores
Algivore
Inverts
Corallivore
Inverts
Detritivores
Inverts
POM
reef
Turf
Algae
Fleshy
Algae
Calsifying
Algae
Massive
Corals
Branching
Corals
LIGHT
Figure 1. Systems diagram showing the main component and flows of the current coral reef ecosystem model.
14
Five types of fishery with distinct gear-selective catch ratios are also integrated into the model.
The model has been calibrated using parameters obtained from existing literature or derived
using datasets from collaborating scientists. The main parameters used to model interactions
between functional groups include production, consumption, respiration, excretion, and
assimilation rates, as well as group-specific rates of capture by different fisheries. Other features
of the model include: resource competition and density-dependent consumption; a detrituscycle of benthic and pelagic DOM and POM; and trophodynamics based on consumption,
assimilation, and production efficiencies.
The model applies increasing levels of fishing effort to the various fish components to
determine critical fisheries variables such as Maximum Sustainable Yield or Catch Per Unit
Effort for different gear and rates of removal of key functional groups. These simulations further
allow predicting the effects on the ecosystem of resource extraction, and are thus helpful in
identifying conditions leading to ecosystem events such as decreasing functional diversity or
overall productivity, and phase shifts. The gear and catch data can be calibrated with gear and
catch data from landing sites and has been applied using data collected in PNG and Kenya. The
species list of catch and the reef's sensitivity to bleaching and coral mortality has been compiled
(Pratchett et al. 2008) and will be integrated into the fish functional groups such that the
impacts of the fishing on these groups as well as the impacts of the reduction or loss of these
groups on the ecosystem will be modelled. In addition, based on the compiled data on coral
mortality rates that will be derived from field data compilations , scenario testing was applied to
reefs with different mortality, fishing intensity and catch selection properties. These included
examining recovery rates after coral mortality based on catch rates, selection, and gear use. The
Adaptive Capacity of the communities will also be modelled based on their reliance on coral
reef fisheries and modelled changes.
Item 5. Socioeconomic studies
Data were collected from 24 human communities around 27 coral reef sites in the western
Indian Ocean spanning five regions: southern Kenya, Tanzania, granitic Seychelles, Mauritius,
and Madagascar. We employed the social adaptive capacity index developed and described in
more detail in McClanahan et al. (2008). To develop this indicator, we conducted household
surveys and key informant interviews in 24 sites (Table 1). We used eight indicators of AC that
were derived from the socioeconomic surveys: 1) recognition of causal agents influencing
marine resources (measured by content organizing responses to open-ended questions about
what could effect the number of fish in the sea); 2) capacity to anticipate change and to develop
strategies to respond (measured by organizing responses according to content to open ended
questions relating to a hypothetical 50% decline in fish catch); 3) occupational mobility
(indicated as whether the respondent changed jobs in the past five years and preferred their
current occupation); 4) occupational multiplicity (the total number of person-jobs in the
household); 5) social capital (measured as the total number of community groups the
respondent belonged to, Pretty and Ward 2001); 6) material assets (a material style of life
indicator measured by factor analyzing whether respondents had 15 material possessions such
as vehicle, electricity and the type of walls, roof, and floor, McClanahan et al. 2008a): 7)
technology (measured as the diversity of fishing gears used); and 8) infrastructure (measured by
factor analyzing 20 infrastructure items such as hard top road, medical clinic, Pollnac 1998).
We used the Analytic Hierarchy Process (AHP) to determine the weightings for each indicator
(Saaty 1980). AHP provides a framework to derive ratios from simple pair-wise comparisons and
produces a continuous response variable (Forman & Gass 2001). Ten social scientists
individually conducted pair-wise comparisons of all eight indicators. Bray-Curtis similarity
indices between researchers' weightings ranged from 73-92% (mean 80%). We used an average
of the scientists' weightings to calculate AC for each community (see McClanahan et al. 2008a
for the indicator weightings). Both climate change and the capacity household, community, and
15
Table 1. Indicators used to calculate adaptive capacity index
Indicator
Measurement
Recognition of causality and
human agency in marine
resources (Tompkins 2005)
Whether interviewee suggested factors which affect fish
populations and/or interventions to improve fish populations
Capacity to anticipate change
and develop response strategies
(Brooks & Adger 2005)
Stated response of fishers to a hypothetical 50% decline in
catches
Occupational mobility
(Allison & Ellis 2001)
Changes of employment within last 5 years, whether forced or
voluntary, and whether new occupation preferred.
Wealth (Pollnac & Crawford
2000)
Principal component of presence of 15 material assets:
vehicle, electricity, television, gas or electric stove, fan, piped
water, refrigerator, radio, video player, and the type of walls,
roof, and floors
Occupational multiplicity
(Allison & Ellis 2001)
Total number of person-occupations per household (squareroot transformed)
Social capital
(Pretty & Ward 2001)
Whether the interviewee is a member of community
organizations
Technology (IPCC 2007)
Number of different gears used by fishing households
(square-root transformed)
Infrastructure (Pollnac 1998)
Principal component of presence of 20 infrastructure items in
the community. Infrastructure items adapted from Pollnac
(1998) are as follows: hospital, medical clinic, doctor, dentist,
primary school, secondary school, piped water, sewer, sewage
treatment, septic tanks, electricity service, phone service, food
market, pharmacy, hotel, restaurant, petrol station, public
transportation, paved road, banking facilities.
national-level organization. Adaptive Capacity can be characterized at each of these scales, but
we considered the household and community scales to be most appropriate for our analysis.
First, because national and local governments play a of communities to adapt to it are multiscale issues, with the latter incorporating individual, relatively minor role in determining
capacity at these sites; second, because of the distinctness of rural coastal communities; and
third, because practical initiatives to increase Adaptive Capacity typically focus on the
community scale (Smit & Wandel 2006). Thus, our indicators of Adaptive Capacity mainly focus
on the household and community scale, although national-level differences in development
and government investment are reflected in the material assets and local infrastructure
indicators. Each indicator was normalized then combined as a weighted score to provide a scale
of adaptive capacity that also ranged from 0-1. We plotted the communities' mean Adaptive
Capacity against the predicted susceptibility of adjacent reefs to bleaching (Environmental
Susceptibility; Maina et al. 2008) and examined how differing conservation actions may be
appropriate across nations and sites in the WIO.
16
Effects of Coral Bleaching
Results
Item 1. Meta-analysis of historical data
Average coral cover based on 36 locations declined by ~40% across the 1998 bleaching event,
with the highest mortality in the central and northern regions of WIO, with the exception of the
Red Sea and Gulf of Aden (Fig. 2).
A
B
SD-SST
Rel Change (%)
0.5 - 0.75
0 - 10
0.75 - 1
10 -25
1 - 1.25
25 - 50
1.25 - 1.5
50 - 75
1.5 - 1.75
75 - 100
1.75 - 2
Figure 2. Spatial variations in relative changes in coral cover across the 1998 bleaching event (A), and
historical variability in SST represented by standard deviation (SD) (B). There was significant regional
variation (ANOVA: F = 2.41; p = 0.0001) in relative change (decline) in coral cover. The most severely
affected reef areas were southern India, Sri Lanka, central atolls of the Maldives and Granite Seychelles.
Northern Arabian/Persian Gulf, Gulf of Oman, Chagos, Kenya, southern Tanzania and southern
Seychelles were heavily affected. Southern Arabian/Persian Gulf, Arabian Sea (Socotra and Gulf of
Kutch), southern Maldives, northern Tanzania, northern and central Mozambique and Aldabra suffered
intermediate impacts. The Red Sea, Mayotte, Comoros, southern Mozambique, South Africa,
Madagascar, Reunion, Mauritius and Rodrigues were the least affected.
WIO reefs could be categorized into 3 major groups of varying mortality based on their SST
properties (Fig. 3). Locations with narrow unimodal SST distributions and high kurtosis (red
circles) had low SD SST and very high mortality. With the flattening of the SST distributions at
kurtosis ≥ -1.2, decrease in kurtosis was accompanied by increase in SD SST and decrease in
mortality. Within these areas of unimodal SST distributions, variation in SD and was associated
with that of skewness. For example, in Socotra and to a lesser extent the southern Maldives, the
SD increased with a decrease in skewness and mortality became lower than could be expected
for the kurtosis. At the lower end of the kurtosis, areas of weak bimodality (blue circles) were
separated from those with strong bimodal SST distributions (yellow circles). Areas with flat
unimodal or weak bimodal SST distributions and moderate SD SSTs had the lowest mortality
while those with strong bimodal SST distributions and large SD SSTs had high mortality.
17
3.0
A
Arabian/Persian
2.5
B
Gulf
PCA 2 20.83 %)
2.0
1.5
Gulf of Oman
1.0
N. Red Sea
0.5
0.0
-0.5
SST groups
Granite Seychelles
Mayotte
-2
-1
0
1
PCA 1 (66.54 %)
Sri Lanka
2
:1
:2
3
:3
Figure 3. The three major SST groups obtained from Principal Component Analysis (A) and their spatial
distribution in the western Indian Ocean (B). The first PC axis separates areas of unimodal (red circles)
from those of bimodal SST distributions. SD (eigenvector = -0.39), kurtosis (0.59), skewness (0.50) and
bimodality (-0.49) had similar contributions to the axis. The 2nd PC axis separates the bimodal groups into
those with large SD and stronger bimodality (yellow circles) and those with weaker to moderate SD and
bimodality (blue circles) mainly based on SD (0.82) followed by bimodality (0.56). Inset: SST frequency
distribution histograms of representative WIO reef areas. Sri Lanka: right skewed narrow and high mortality;
Granite Seychelles: left skewed narrow and high mortality; Mayotte: left skewed, weak bimodality and low
mortality; Northern Red Sea: symmetrical, intermediate bimodality and low mortality; Gulf of Oman: left
skewed, strong bimodality; Arabian/Persian Gulf: symmetrical, strong bimodality.
Mortality varied with the size of the standard deviation (SD SST; Fig. 4) related to changes in the
kurtosis, skewness, and the degree of bimodality of the SST frequency distributions (Fig. 4).
Mortality decreased as the SST distribution became flatter, wider, and bimodal and the SD
increased up to ~2.3 SD, but increased as the SD increased further and distributions became
wider and strongly bimodal. The study identified the low kurtosis areas with flat and weak
bimodal SST distributions and moderate SD SSTs as the most resistant. These are mostly
situated in high retention areas (e.g. the triangle between southern Tanzania, northern
Mozambique, and northern Madagascar), or on leeward sides of islands (e.g. inner Zanzibar)
and the subtropics (e.g. South Africa and northern Red Sea).
In order to determine the Significant regional variation was observed in both absolute and
relative changes in cover (p < 0.05). The most severely affected reef areas were southern India, Sri
Lanka, central atolls of the Maldives and Granite Seychelles. Northern Arabian/Persian Gulf,
Gulf of Oman, Chagos, Kenya, southern Tanzania and southern Seychelles were heavily
impacted. Southern Arabian/Persian Gulf, Arabian Sea (Socotra and Gulf of Kutch), southern
Maldives, northern Tanzania, northern and central Mozambique and Aldabra suffered
intermediate impacts. The Red Sea, Mayotte, Comoros, southern Mozambique, South Africa,
Madagascar, Reunion, Mauritius and Rodrigues were the least impacted. Absolute change in
cover significantly increased as a function of the cover before 1998. Subsequent analyses on
relationships between cover and environmental variables were done on the relative cover.
18
120
A
100
Relationship between diversity and
change in coral cover
Group 1
Group 2
Group 3
80
A scatter plot of relative taxonomic richness
by relative change in coral cover across
1998 found there was not a significant
relationship between taxonomic richness
and absolute or relative change across 1998
(p >0.05) (Fig. 5). North-western Australia,
Gulf of Oman, southern Kenya, Mafia and
Mnazi Bay (Tanzania), Lakshadweep
(India), Sri Lanka, Maldives, Seychelles and
Chagos were areas of high diversity that
suffered high mortality in 1998. High
diversity areas that had lower mortality were
northwestern Madagascar, ThailandMergui, Mayotte, Mozambique, Rodrigues,
Socotra, Songo Songo (central Tanzania),
southern Red Sea and Zanzibar (northern
Tanzania).
60
40
20
0
0
1
2
3
SD-SST
4
5
6
120
B
100
80
60
40
Maldives - S
20
Gulf of Kutch
0
-1.6
-1.4
-1.2
-1.0
-0.8 -0.6
Item 2: Pre- and post-bleaching fish
surveys
Socotra
-0.4
-0.2
0.0
2.6
2.8
Kurtosis-SST
100
C
80
60
Seychelles - S
Tanzania - S
40
20
0
1.4
1.6
1.8
2.0
2.2
2.4
-1
µ
Bimodality (Ln(A )
Figure 4. Relative change (decline) in coral cover
(%) in the Western Indian Ocean as a function of
SST variability (SD SST). Change in cover declined
with SD between 0.5-2.3 (R2=0.58; p<0.0001). The
four Arabian/Persian/Oman Gulf outliers were not
included in the analysis. The fit of the model
improved considerably after excluding the two
outliers (Tanzania and Seychelles) (R2 = 0.43; p =
0.0006).
19
It is clear that the impacts of the 1998
bleaching event were highly variable across
the region, and provides a continuum
against which to test secondar y
consequences, such as the effects of coral
loss on f ish assemblages. Recent
developments in assessing the effects of
coral disturbance on fish have highlighted
the importance of eroding structural
complexity in driving responses, which, as
erosion of coral structures can take some
time, explains the much smaller impacts on
fish shortly after coral mortality. Structural
complexity was quantified at 50 of our 66
sites we studied. Importantly, there was a
strong correlation between loss in coral
cover and loss in structural complexity
across the region (r = 0.77, P<0.001). The
strong collinearity in the two measures
precludes independent assessment of
variables, and therefore the effects of
changing coral cover on fish identified in
the Bayesian meta-analyses are likely to
result from a combination of loss in coral
cover and structural complexity.
1.2
1.0
Species richness
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Relative change in cover
Figure 5. Scatterplot of coral diversity-mortality relationship in the western Indian Ocean. Values are
standardized from 0 to 1 relative to the lowest and highest values. High and low areas are separated at a
conventional 50% cut level (dashed lines).
Coral loss predicted declines in reef-fish species richness, and abundance of obligate corallivores,
planktivores and fishes <20 cm throughout the western Indian Ocean (Fig. 6). We tested five
possible trajectory descriptors in each case, but only found evidence for linear fits between coral
decline and change in groupings of the fish community. Trends in species richness were
significant, but weak, and largely driven by the Seychelles and Mafia Island. There was substantial
evidence for a 1:1 relationship between changes in obligate corallivore abundance and percent
coral cover. The linear relationship was consistent for declines and increases in coral cover. From
these results we estimate, given any future 50% decline in coral cover, there is a 76% probability of
equivalent declines in obligate corallivores at any given site in the western Indian Ocean. The
relationship between change in diurnal planktivore abundance and coral cover was relatively
strong; given a future 50% decline in coral cover, we estimate a 68% probability of observing
declines in planktivore abundance. We found no relationship between a loss of coral and change
in abundance of herbivore and mixed diet feeder groups.
20
6
Fished
Protected
in Species Richness
4
-25
-20
-15
-10
-5
0
5
10
30
Fished
20
Protected
10
in Corallivores
0
-10
-20
-30
-40
-25
-20
-15
-10
-5
0
Coral depletion index
5
10
Figure 6. The relationship between coral depletion and changes in coral reef fish for the strongest
significant variables in western Indian Ocean study sites.
When species were grouped by their maximum attainable size, a clear trend was apparent for
species <20 cm, but no relationship was observed for 21-40 cm, 41-60 cm or >60 cm groupings.
Given a future 50% decline in coral cover, we estimate a 52% probability of observing declines
in the abundance of fish species with maximum body lengths <20cm. Within this size class,
planktivores make up a considerable portion of the abundance (44%), however herbivores
and mixed diet feeders also contribute substantially (28% and 20% respectively), and
corallivores have limited input (8%). Analyses of trophic groups within the <20cm size
category highlights that, along with obligate corallivores and planktivores, there was also
evidence of declines in herbivores.
We only found weak evidence for differences between NTAs and fished areas for change in
diurnal planktivore abundance and small-bodied herbivore abundance (<20cm). In both cases
the negative relationship between fish abundance and coral decline was greater for the NTAs.
Importantly, irrespective of body size and trophic categorization, NTAs provided no clear
benefits for any of the fish groups in terms of their change in response to coral decline.
21
Item 3: Long-term fishery dependent surveys
Catch trends
Ten-year trends in CPUE in Kenya showed no overall trend but rather a rapid response to
changes in management in sites near the Mombasa Marine Park (Kenyatta Beach) and south
coast sites where the elimination of beach seines beginning in 2000 and ending in 2004 was
associated with a rise in CPUE (Fig. 7). The catch fisher-1 day-1 in 1996, at the start of the study,
was 4.3 kg fisher-1day-1 in Kenyatta and 3.9 kg fisher-1 day-1 in the South coast. These values
declined for four years to 2.6 kg fisher-1day-1 and 2.5 kg fisher-1day-1 for South coast and Kenyatta
beach landing sites in 2000, respectively. Following the removal of beach seines from Mwaepe
in 1997 and Mgwani in 1999 the decline in CPUE in the South coast halted. A marked turning
point occurred in 2001 for the South coast with CPUE increasing initially to 3.5 kg fisher-1day-1,
dropping slightly in 2002 before steadily increasing to 3.9 kg fisher-1day-1 in 2005 after the final
seine net removal. Kenyatta followed a similar pattern increasing to 3.6 kg fisher-1day-1 in 2002
with a reduced use of seine nets and then stabilizing at 4.0 kg fisher-1day-1 in 2005. North coast
landing sites that were monitored as controls for the South coast changes, maintained a CPUE
yield of approximately 2.3 kg fisher-1day-1. While CPUE dropped slightly in 2002, there was no
significant change in CPUE between 2001 and 2005 (Fig. 7; F4,10=0.45, P=0.77).
Trend in catch over time
Kenyatta
5
North coast
South coast
-1-1
CPUE, kg fisher day
-1
4
3
2
1
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Time, years
McClanahan, Hicks & Darling 2008 Ecological Applications
Figure 7. Ten-year trends in mean CPUE (± standard error) for three management groups studied in
Kenya. The Kenyatta landing sites is next to a closure, the Mombasa Marine National Park. South coast
sites slowly eliminated beach seine use during the study period and north coast sites were maintained as
controls for the beach seine reduction.
22
Dimension 2; Eigenvalue: 0.014 (17.0% of Inertia)
0.4
Spear
0.2
Beach siene
Low
0.0
Medium
Net
Trap
Line
Line
-0.2
Net
Spear
-0.4
-0.6
Coral Association
-0.8
High
Kenya
PNG
-1.0
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
Dimension 1; Eigenvalue: 0.071 (82.96% of Inertia)
Figure 8. Ordination plot generated by correspondence analysis, representing the relationship between
the strength of the coral associations of the specimen caught and the type of gear used for their capture.
Gear-use studies
Species that are highly susceptible to bleaching comprised <6% of the catch from any specific
gear (Fig. 8). However, line, net, and spear guns in the moderately exploited PNG fishery
targeted a slightly higher proportion of highly susceptible fish than the same gears in the heavily
exploited Kenyan fishery (Fig. 8). There was considerable variation in how different gears
targeted the proportion and number of species that are moderately susceptible to bleaching.
More than 40% of the catch from all gears was comprised of species moderately susceptible to
bleaching, but spear guns in both PNG and Kenya and traps in Kenya targeted a majority of fish
that were moderately susceptible to bleaching. Line fishing in both countries targeted >58% of
fish with low susceptibility to bleaching, and beach seining in Kenya targeted 52% of fish with
low bleaching susceptibility. In PNG, spear fishing targeted the highest number of species,
while net fishing targeted the highest number of species in Kenya.
Gears were strongly associated with the functional and key species groups in both countries and
there are clear distinctions between the gear and also the nation (Fig. 9). PNG fishing gear
separate with spears catching (micro) grazers, scrapevators, key species, detritivores, and
planktivores. Higher macro-grazers (i.e. browsers) catch is what distinguished Kenyan spears,
traps, and beach seines. Hook and line in both Kenya and PNG captured more carnivores but
Kenya had fewer planktivores in the catch. Set or gill nets also caught more carnivores and the
catch of piscivores and macro-invertivores was most common in PNG where as Kenyan nets
caught more micro-invertivores and some grazers.
23
1
Fish Guilds
Dimension 2; Eigenvalue: 0.109 (33.42% of Inertia)
0.8
NET
Kenya
PNG
0.6
Line
Line
0.4
Pisc-Invert-Macro
Planktivore
0.2
Piscivore
Detritivore
Invert-Macro
Invert-Macro
0.0
SPEAR
Beach siene
Net
-0.2
Key Species
Scrapervator
-0.4
Trap
Spear
Grazer
Grazer-Macro
-0.6
-0.8-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Dimension 1; Eigenvalue: 0.160 (49.29% of Inertia)
Figure 9. Ordination plot generated by correspondence analysis, representing the relationship between the
specimen caught and the type of gear used for their capture by country.
Trap
Annual Catch - Kenya
Bsiene
Sgun
Net
Line
70
80
90
Catch (x 1,000 kg/km2yr)
50
40
30
20
10
0
0
10
20
30
40
50
60
2
Fishing effort (#men/km )
Figure 10. Simulation results for a model output based on the Kenyan calibration of the five main gear types.
24
100
Item 4: Modelling of fishery effects
Model run using the calibration for the Kenyan catch for each gear separately showed various total
catch with effort responses for the five gears (Fig. 10). Maximum yields and efforts increase from
traps, to spear guns, to beach seines, to net and line. Most gears were associated with a decline in
the fisheries beyond the maximum catch, which for this simulation calibration was found between
-2
12 and 22 fishers km . It should be appreciated that the CPUE are so low at the high ends of effort
-1
(<10 grams day ) that few fishers could afford to fish. Model outputs calibrated for all gear and
pooling all grazers together into a single functional group indicates that spear guns, traps, Kenyan
gill nets, and beach seines reduce grazers to extinction and lines and PNG nets will not (Fig. 11).
GRAZERS
100%
TrapK
BsieneK
SgunPNG
NetPNG
NetK
SgunK
LineK
% of pre-f ishing stock
80%
60%
40%
20%
0%
0
10
20
30
40
50
60
70
80
90
100
2
Fishing effort (#men/km )
Figure 11. Effects of all of the common gear types on the grazer functional groups. Grazers are considered
important for recovery of corals after disturbances.
Infrastructure
0.5
Gear Diversity
0.4
Social Capital
0.3
Occupational
Multiplicity
0.2
Mat. Style Life
0.1
Occupational
Moblity
Beombre
SZ MS
Le Morne
SZ
Roche Caman
KY KY
Anse Vobert
MS
Ponte aux Piments
TZ
Shela
TZ
Grand Anse
TZ
Mpenda
MS
Blue Bay
TZ
Nyamanzi
Mazizni
MD MS
Stone Town
St Martin
TZ
Dar es Salaam
KY KY
Pointe des Lascas
Buyu
KY
Tampolo
MD
Vipingo
MD MD KY TZ
Bamburi
Tanjona
Mayungu
Kuruwtu
Mtangata
NW Madagascar
0
Ambodiatry Area
Weighted Indicator Scores and total adaptive capacity
0.6
MS
SZ
SZ
Response to
Decline
Human agency
Figure 12. Weighted contribution of 8 indicators of adaptive capacity for 29 areas in 5 countries in the
Western Indian Ocean ranked according to their overall adaptive capacity score (MD Madagascar, KY
Kenya, TZ Tanzania, MS Mauritius, SZ Seychelles).
25
Item 5. Socioeconomic studies
Our socioeconomic survey provided an Adaptive Capacity index for 29 communities based on
eight quantitative indicators (Fig. 12). Sites in Seychelles and Mauritius all had high Adaptive
Capacity, but the countries differed considerably in their susceptibility to coral bleaching.
Mauritian sites fell into the low and Seychelles into the high Environmental Susceptibility
categories. Sites in Madagascar, Tanzania, and Kenya all showed low to moderate Adaptive
Capacity, but highly variable Environmental Susceptibility (Fig. 13).
Environmental susceptibility
A
Relief and
reorganisation
Adapt and
transform
Kenya
Tanzania
Seychelles
Mauratius
East Madagascar
West Madagascar
Protect and
preserve and
Capacity
building
Social adaptive capacity
Environmental susceptibility
B
Social adaptive capacity
Figure 13. A, Theoretical model indicating gradients of social adaptive capacity against
environmental susceptibility to produce four quadrants of differing conservation priorities. B, Case
study from the western Indian Ocean spanning 5 countries:
Kenya,
Tanzania
Seychelles,
Mauritius,
Northeast Madagascar and
Northwest Madagascar.
26
Discussion
The Western Indian Ocean exhibited considerable variability in the oceanographic response to
the 1998 ENSO, coral mortality, and effects on the fish, fisheries and ecosystem services. This
has produced a fascinating and complex response that indicates the considerable heterogeneity
of the region something that may hold the key to understanding its vulnerability and resilience
to climate change. Below are a series of discussion concerning the key foci of this research
followed by a set of conclusions and recommendations.
Item 1. Meta-analysis of historical data
Temperature environments
The Indian Ocean contains a variety of water temperature environments, but there appear to be
three large groupings of these environments, unimodal environments with narrow variance,
those with larger variance, and those with bimodal distributions. Each are associated with
different regions. The area around the Island of Madagascar is one of the more heterogeneous
environments, which protects the East African region from the influence of the East African
Coastal Current System and creates a high retention shadow area with a stable environment
(McClanahan et al. 2007). Some reefs in this region were the least affected during the 1998
ENSO and others have recovered fast and the region still supports the typical Indo-Pacific coral
communities characterized by a high relative cover of susceptible taxa, e.g. Acropora (Obura
2005; McClanahan et al 2007). These areas in the equatorial current shadow of Madagascar
have SSTs approaching the weak bimodal distribution (e.g. Mayotte/Comoros and NW
Madagascar). Additionally, the area north of Mayotte/Comoros had the lowest observed degree
heating weeks/months in 1998 and has the lowest SST rise in East Africa and the cool and stable
environments provide a potential refugia from climate change (Maina et al. 2008). In contrast,
the strong seasonality in the Gulf region creates SST distributions that are discontinuous
around the mean/median SST values and have two peaks at both extremes. It is an extreme case
of a bimodal distribution, and is less resistant to extreme events.
The extreme variations in the Gulf are probably insufficiently stable to produce adaptation.
Stable and continuous environmental variations produce greater effects than larger and
more sporadic ones (Benedetti-Cecchi 2003). It is less clear as to how the large variation in
SSTs in the Gulf could have the potential for thermal protection but tolerance thresholds of
corals in this region, with already high maximum SSTs, could easily be exceeded by small
anomalies. The Gulf region suffered more frequent severe mass mortalities than any other
region in the WIO prior to 1998 (Wilson et al. 2000). One source of deviation from univariate
predictions can be found in complex island-archipelagic areas, notably some locations in
group 2 (blue circles), including Tanzania, southern Seychelles, and Aldabra-Cosmoledo
islands, where average mortality was higher than expected for their SST properties. Here we
suggest that the creation of local conditions around islands produces patterns and
measurement weaknesses that cannot be well predicted from the moderate spatial
resolution temperature data we used (Maina et al. 2008).
The frequency of extreme anomalous events is expected to increase with the rise in mean
temperatures and this has lead scientists to question the resilience of coral reefs in the face of
the repeated and increasing severity of bleaching events (Hoegh-Guldberg 1999; Sheppard
2003; McWilliams et al. 2005) Whether corals have the capacity to adapt and cope with the
level of SST rise is keenly debated. However, it is unlikely that coral and symbiont species will
undergo genetic adaptations fast enough to match the current level of SST rise unless the rise is
slowed by climate mitigation strategies. SST properties that are a function of latitude and
coastal geomorphology interacting with currents are sufficiently stable over recent geologic
time to contribute to resistance to climate change by some combination of the above three
27
properties. Endogenous genetic variation and acclimation will produce taxa-specific thermal
tolerances while community change is expected to produce symbiotic and coral host
community associations with properties of resistance to the thermal environments. Therefore,
already existing endogenous genetic variation or adaptation, change in community structure,
and acclimation facilitated by background SST variability remain the most likely factors
expected to create resilience to climate change. The reefs and coral communities that survived
the severe disturbance due to the 1998 ENSO event will probably survive better future events.
Those that survived as a result of protection due to their environmental properties will
maintain existing gene pool and community structure. Those communities that resist or
tolerate and recover faster will have some capacity for adaptation by fixation of resistant and
tolerant genes. Both communities have the potential to provide larval seed to facilitate the
recovery of other heavily damaged areas and deserve increased efforts at management in the
framework of resilience to climate change.
The findings are useful for predicting future effects from ENSO and other similar time-scale
climatic disturbances and for evaluating ecological risk associated with conservation and
management implementation programs. Identifying and conserving areas that have the
environmental/ecological capacity to resist and tolerate climatic disturbances is a high priority
for mitigating climate change effects in the near future, as bleaching events are expected to
increase in scale and severity (Donner et al. 2007).
Coral diversity and climate change
Ecological and climatic processes that are associated with reef area and latitude largely regulate
the distribution of taxonomic richness in WIO. The relationship with latitude in particular
suggests that extreme environments, probably through reduced origination and increased
extinction rates, could have historically shaped patterns of taxonomic richness. The degree of
impact on WIO coral reefs from climate change is predicted to be directly negatively related with
the historical SST variability (McClanahan et al. 2007b; Maina et al. 2008) Older studies had
indicated that central Indian Ocean Islands and areas near the Indian subcontinent contained
the highest coral diversity. These are areas of low historical SST variability that suffered one of the
highest mortalities (Wilkinson 1998) and face high threats of biodiversity loss from climate change
impacts. The areas in the south that suffered lower mortality in 1998 (McClanahan et al. 2007a;
Maina et al. 2008) have more diverse reef and non-reef habitats than areas in the northern part of
WIO (Richmond 2002; Keesting & Irvine 2005).
The ultimate effects of any natural or human-induced disturbance on biodiversity will be
determined by the functional characteristics of the affected taxa that operate in different
contexts, including the role of dominant taxa, key-stone species, engineering species and the
overall species interactions and influence of environmental change (Loreau 2000; Hooper et al.
2005). While it is clear that species richness is not entirely an ecological synonym for
biodiversity, we used species richness due to the unavailability of data on species composition
at the ocean-basin scale of this study. By combining changes in coral cover and species richness,
however, a more holistic effect could be captured and the potential for predictive capacity
increased because change in cover will follow compositional effects of at least the dominant
taxa. The dominant taxa are often the most susceptible to increased temperatures (e.g. Acropora,
Montipora and Pocillopora) (McClanahan et al. 2007). Although climate change is likely to result
in community change, without or with only minimal change in species richness, given the
narrow thermal threshold of corals, it is expected to bring about change by eliminating taxa of
both low and high ecological importance (Glynn 1996; Hoegh-Guldberg 1999).
28
Item 2: Pre- and post-bleaching fish surveys
We have identified spatially variable declines in coral cover, reef structural complexity, fish
species richness and the abundance of various feeding and size groups of reef fish across the
Indian Ocean following the 1998 bleaching event. These changes are substantial for some
groups, and indicate little insurance offered by current small-scale no-take areas (NTA)
management across the region. The spatial patterns present in our data provide important
information for future conservation planning and generic lessons for managing whole coral reef
ecosystems in a changing climate.
There was little difference in the decline of coral cover between NTAs and fished areas across
the Indian Ocean, with some evidence for greater declines within NTAs. This result is likely due
to NTAs often being sited in areas where the cover of Acropora and other thermally sensitive
and branching coral species is high (McClanahan 2008). Our analysis also indicated little
difference between NTAs and fished areas for those fish groups that declined in response to
coral loss. The only indication of a differential response was the greater decline in NTAs for
planktivores and small-bodied herbivores. Large, remote and pristine areas seem to be resilient
to a wide range of disturbances (Sandin et al. 2008), which has led to calls to assess the
effectiveness of NTAs in conserving coral reefs through climate disturbance (Knowlton and
Jackson 2008). One clear difference to these remote areas is that NTAs on reefs are typically
small and surrounded by much larger areas that are exploited and often degraded (McClanahan
et al. 2007). As we did not have repeat temporal data since the initial coral loss in 1998, we can
not explicitly infer recovery rates from our data, however the NTAs we studied are not preventing
declines in coral and fish groups following coral bleaching and it seems likely that, over this
time scale, recovery rates are no different between NTAs and fished areas, as has been shown for
some of the NTAs where temporal data was available (McClanahan 2008).
We detected declines in fish species richness across the western Indian Ocean in response to
loss of live coral cover. Although only a small proportion of species are heavily coral dependent,
most species are reliant on the reef structure at some stage in their life history, and change in
species richness was likely due to loss in the physical structure of the reef, rather than live coral
(Graham et al. 2006; Wilson et al. 2006). The variability in loss of structural complexity may
explain why the trend for species richness was not stronger, with locations such as Chagos,
where recovery of coral has been rapid, potentially retaining structural complexity in the
interim. Although this was the most likely driver of the region-wide decline, some studies have
highlighted that live coral can be an important settlement cue for various settling larval fish
(Jones et al. 2004) and the nature of this relationship is an important area for future research.
Although previous studies have identified obligate corallivores as a functional group vulnerable
to declines in coral cover (Wilson et al. 2006; Pratchett et al. 2008), this is the first study to
demonstrate declines over such a large spatial scale. We have also identified a 1:1 linear
relationship between coral loss and obligate corallivore decline, suggesting their survival on the
reef is tightly linked to coral cover. The diurnal planktivores in the study were largely smallbodied species from the damselfish family that are often closely associated with the reef matrix
(Munday and Jones 1998; Feary et al. 2007). Their decline is most likely due to predation
vulnerability, linked to loss of coral and structural collapse (Graham et al. 2006). Planktivores
and corallivores showed the strongest relationships of all groups to declining coral cover and
are likely to be the groups most threatened from the predicted ongoing decline in global reef
health (Wilson et al. 2006; Pratchett et al. 2008).
Although herbivores are hypothesized to increase in abundance following coral decline due to
a greater availability of algal resources, previous studies have reported high variation in this
relationship and have often been conducted shortly after disturbances, limiting their ability to
detect demographic changes (Wilson et al. 2006). Here we tested this hypothesis across large
spatial and temporal scales where the assemblage had a moderate time to respond. Herbivores
29
are thought to be a key functional group, responsible for the resilience of reef systems by
controlling algal growth (Bellwood et al. 2004; Mumby et al. 2006) and ultimately allowing
settlement of new coral recruits (Hughes et al. 2007). However, our data show that the
proliferation of algae following extensive coral mortality was unlikely to be controlled by a
corresponding increase in herbivorous fish abundance. Changes to size structure and biomass
of herbivore stocks cannot be ruled out and may initially encourage increased consumption
and control of algae. However, studies from Seychelles suggest such changes may be indicative
of future declines in herbivore abundance and biomass due to a loss of refuge from predators,
leading to reduced recruitment to adult size classes (Graham et al. 2007).
The mixed-diet group also showed no response to declining coral cover. This group of fish
includes species from families such as Lethrinidae, Mullidae, Lutjanidae, and Labridae, many
of which are habitat generalists, foraging and recruiting to non-coral reef habitats such as
seagrass. Species in these groups also tend to forage over fairly large spatial scales, indicating a
lack of reliance on specific habitat types. Due to this decoupling of reliance on reef habitat and
the potential benefits they may glean from increased food resources, this may be the group that
will be sustained in the long term, although a large amount of variation can be expected at the
species level (Wilson et al. 2006), leading to changes in community composition.
Small-bodied fish are known to be more reliant on the reef matrix, inhabit narrower niches, and be
more vulnerable to predation (Munday & Jones 1998). Our analyses highlight the vulnerability of
small-bodies species to coral and structural complexity loss. Within this size category, obligate
corallivore and planktivore groups showed strong declines. Interestingly, there was also a reduction
is abundance of small-bodied herbivores. Although herbivore abundance may not be declining
overall, the reduction of these small-bodied species is of concern as they perform important
functional roles on coral reefs (Dorenbosch et al. 2005). Small mixed-diet feeders again showed no
trend, demonstrating the resistance of species with generalist life history traits to coral loss.
Our analyses highlight great geographic variation in the impact of coral bleaching across the
region, with the Seychelles suffering the greatest in terms of coral loss and associated effects on
fish, and the Mascarene Islands (Réunion and Mauritius) suffering the least. These trends could
be due to several factors: prevailing currents and variation in temperatures have been identified
as key determinants of coral mortality in the region, likely reducing mortality in the Mascarene
Islands in particular (McClanahan et al. 2007) and well-connected reef systems are expected to
contain the pockets of refugia required for landscape-scale recovery (Nystrom & Folke 2001).
This is evident when comparing recovery of the well-connected mainland reefs of Kenya and
Tanzania and the geographically extensive Chagos and Maldives to the geographically small
and isolated inner Seychelles. The inner Seychelles is a shallow continental shelf basin, with
most fringing reefs extending to only 7-9m depth. This 'bathtub effect' likely led to extensive
mortality in 1998 and precluded any depth refuge below which corals could survive. Where live
coral extends to 40-50m depth, such as in the atolls of Chagos or the islands of Réunion and
Mauritius, a depth refuge of broodstock may encourage faster recovery of corals at shallower
depths (Sheppard & Obura 2005). Finally, the atolls surveyed in Chagos are uninhabited and off
limits to reef fishing. The lack of multiple anthropogenic stresses that most other reef systems
endure may have helped promote recovery from the disturbance (Sandin et al. 2008).
30
Item 3: Long-term fishery dependent surveys
Time-dynamics
Our study and previous studies of the effects of bleaching on coral reef fisheries have not been
able to detect immediate or strong effects on catches and concluded that aspects such as fishing
effort and management are more important than the effects of coral mortality (McClanahan et al.
2002; Grandcourt & Cesar 2003; Graham et al. 2007). The larg-scale survey above makes the
reasons for these patterns more clear as the effects of coral bleaching on the fish in the first ten
years after the disturbance is mostly restricted to small-bodied and corallivorous species, both of
which are not a large part of the catch. There is evidence that smaller size classes of fish will be
affected and this could lead to a lag effect in the catches (Graham et al. 2007). Consequently, it is
fortunate that there are not strong immediate effects but very concerning that there could be
delayed effects that are expected to be more apparent as the frequency of climate disturbances
increases to a frequency where recruitment could be a sever limitation (Sheppard 2003).
Gear use considerations
Fish species with strong coral associations represent a small proportion of catch in PNG and Kenya.
Species within this category are often small bodied, and feed, dwell or settle into live coral (Wilson et
al. 2006). As most reef fisheries target larger species and individuals preferentially (Jennings &
Polunin 1997), these small coral associated fish are likely to be incidental and or opportunistic
components of the catch. The slightly higher proportion of highly reef associated fish caught in PNG
may reflect a less degraded environment in those reefs compared to Kenya, associated with lower
fishing pressures (Cinner & McClanahan 2006a, b) and milder bleaching stress (Wilkinson 2004).
Although species with strong coral affiliations are not a major component of these fisheries, fish
species with medium level coral associations, that are reliant on the reef structure for habitat,
represent approximately 50% of fishers catch in both Kenya and PNG. These species are not
likely to be immediately effected by bleaching events or other disturbances that result in high
coral mortality (Pratchett et al. 2008), however the erosion of reef structures and subsequent loss
of structural complexity can have detrimental effects on their abundance and size structure
(Graham et al. 2006, 2007). Conservation of these fish is paramount, as fish reliant on reef
structure include species responsible for preventing algal dominance on reefs.
Our analyses indicate clear differences in the susceptibility of reef-associated fish to different
gear types. Spears and traps generally targeted more reef associated species than lines and nets,
implying gear management may be an appropriate policy for reefs in many countries where
climate change is predicted to degrade the environment in coming decades (Donner et al.
2005). The gears that target most reef associated fish also target key functional groups of fish on
reefs, suggesting modification or banning of certain gears may be an appropriate adaptive
management response to climate variation and change.
Perhaps the most pertinent point is that spears, nets and traps target a greater proportion of
herbivorous and key species than line fishing. Feeding activities of grazers and scrapevators are
critical to the resilience of coral reefs (Bellwood et al. 2004; Mumby et al 2007); hence
identification of fishing gears that preferentially target them is of great importance (McClanahan
& Cinner 2008). Kenyan spear fishers also caught the highest proportion of grazing fish that feed
on macroalgae and seagrass, and although these represent a small number of species within coral
reef fish communities (Bellwood et al. 2006; Ledlie et al. 2006) these fish have the potential to
reverse phase shifts on reefs (Hughes et al. 2007). Furthermore, spear fishing, trapping and netting
capture a greater proportion of key species that have been shown to play essential roles in
predation and herbivory (McClanahan 1995; Power et al. 1995; Bellwood et al. 2003, 2006; Fox &
Bellwood 2008). Thus, these fishing gears are likely to compound the impacts of coral bleaching
and potentially retard the ability of reefs to recover.
31
Line fishing still targets a substantial proportion of fish associated with coral however in both
PNG and Kenya line fishing typically targets piscivores and macro-invertebrate feeders. The
greater proportion of piscivore and macro-invertebrate feeders in the line fishery of PNG
compared to Kenya is likely indicative of the lighter fishing pressure in PNG as piscivores often
decline rapidly with increasing fishing pressure (Jennings & Polunin1997). Piscivores can be
important in structuring coral reef fish assemblages (Graham et al. 2003; Mumby et al. 2006;
Sandin et al. 2008) and so, although line fishers are targeting fewer fish that associate with coral,
they may impact the trophic structure of the ecosystem (McClanahan & Cinner 2008).
Net fishers and beachseine fishers targeted the least amount of fish associated with the reef,
although there was still a fairly substantial proportion of the catch from the medium reef
associated category. Both nets and beachseine targeted a fairly diverse suite of trophic groups of
fish, but there was a fairly high proportion of herbivorous fish in the catch including grazers of
macroalgae and key species in Kenya. One of the principal reasons why these gears target a
smaller proportion of reef associated species is likely due to the areas the gears are deployed.
Nets and beachseines are often used in seagrass beds and other soft-bottom areas to avoid
damage to the nets. Although our analyses suggest these may be suitable gears to encourage on
reefs likely to become degraded through coral bleaching, previous studies have highlighted
potential conflicts with other gears. Indeed, beachseine nets in particular are destructive to
habitat, target a large proportion of juveniles and conflict with other gears and user groups
(McClanahan & Mangi 2004). The removal of this gear may be one of the most effective
management interventions to increase overall yield (McClanahan et al. 2008). There are clear
trade-offs to be made between minimizing gear conflict, reducing overfishing, maintaining yield
and protecting species vulnerable to coral bleaching and important for reef recovery processes.
Our analyses highlight that line fishing may be preferential over spear and trap fishing on reefs
in a changing climate. Changes to regulations of spear and trap fishing may be more
appropriate than phasing them out in favor of the other gears. Although spear fishing may be
highly selective in developed countries (Frisch et al. 2008), it is less likely to be in poorer
societies. Spear fishing is however, a fishing technique that can adapt to changes in resource
availability. Similarly, trap fishing is a passive and largely non-destructive gear and various
initiatives relating to mesh size, shape of traps and soak times may be a more successful strategy
than phasing traps out (Robichaud et al. 1999; Munro et al. 2003).
Item 4: Modelling of fishery effects
Field data on fish catches by gear in the two countries indicates that there are both strong
association with the functional groups of the caught fish and the type of gear and also the
nation. We expected that the national effect would be present due to the different levels of
fishing intensity and its history in the two countries, Kenya having higher fisher numbers and
intensity of fishing than PNG. The main difference between Kenya and PNG is the high
amounts of grazers, particularly macrograzers, in the catch by spearguns, beach seines, and set
nets in Kenya. Many of these macrograzers feed in and on seagrass and erect algae, so the
difference is probably due to there being more and greater use of the seagrass ecosystems in the
Kenyan lagoons or to greater fishing down of the food web in Kenya (McClanahan et al. 2008).
Scraping parrotfish are uncommon in the Kenyan catch and may results from a degraded coral
reef ecosystem where reef grazing is dominated by sea urchins rather than scraping parrotfish
(McClanahan 1995). Without more research to estimate effort, it's history and the use of
seagrass ecosystems by fishers, these two explanations are difficult to distinguish.
Spear guns are largely catching herbivores of various kinds in both countries, but also a
considerably high diversity of other species, as found in catch-biodiversity studies (McClanahan
& Cinner 2008). Shifting yields for the different gear suggests that fishers are likely to switch to
more effective gear as yields drop, and this may lead to greater use of beach seines and spearguns,
and associated impacts on grazers (Pauly et al. 1989; McClanahan et al. 2008). Catching fish
32
higher in the food web may not result in the loss of grazers and possible consequences for corals,
but it is also associated with lower yields, although they are more stable and less likely to collapse.
This indicates the types of trade offs that are required in managing the fishery.
To avoid a collapse and possible degradation of reefs associated with low grazing that gear
effective at catching grazers should be avoided. In Kenya, this has not occurred because
grazing sea urchins have largely occupied the niche of fish grazers and resulted in greater
stability of the ecosystem over time (McClanahan 2008). In Kenya, the catch has also been
stable as most catch is now derived from seagrass ecosystems that appear less likely or possibly
slow to collapse or be dominated by sea urchins (Heck and Valentine 2007; McClanahan et al.
2008). Where seagrass and sea urchins are not available to buffer overfishing effects, we may
expect to see collapses or phase shifts in coral reef ecosystems at lower levels of effort, where
the most effective or destructive gear are not controlled. Regardless, we suggest that one can
use knowledge of the gear effects to develop a conceptual basis for adaptive ecosystem-based
management of the coral reef ecosystem where knowledge of functional groups and their
effects on reef processes can be utilized to develop a feedback between the state of the reef
ecosystem and gear use choices (McClanahan & Cinner 2008).
Item 5. Socioeconomic studies
Our framework suggests that development of Adaptive Capacity is a priority throughout the
studied western Indian Ocean countries. Conservation strategies at sites with low Environmental
Susceptibility should focus on integrated conservation and development with, for example,
investments in income generation and livelihood diversification. In high Environmental
Susceptibility sites it is essential that development strategies do not make local communities or
industries more dependent on reef-based resources that are at risk. We find that the current
conservation strategies in these countries are not aligned with the approaches suggested by our
framework. For example, Kenyan reefs are susceptible to bleaching, suggesting that they are
unlikely to sustain a high-quality tourist experience. Yet Kenya has a moderately large marine
protected area fisheries closure system (8.6% of its reef area; Table 2) that is highly dependent on
tourism. Therefore, the sustainability of this protection strategy under climate change scenarios is
questionable. In Tanzania, some sites generally have higher Adaptive Capacity and lower
Environmental Susceptibility, suggesting that investment in more protection could be effective.
However, Tanzania currently lacks an effective system of large fisheries closures, protecting only 66
km2 (1.9%) of its reefs from fishing. Most sites in Madagascar have low Environmental
Susceptibility and consequently are expected to fare better than reefs in Tanzania and Kenya, yet
currently only 10.4 km2 (0.5%) of their reef area is protected (Table 2). The Madagascar
government's commitment to triple the amount of protected areas is critical to regional
conservation, but since Madagascar had extremely low overall levels of Adaptive Capacity, this
must be accompanied by investing in community development efforts such that local people can
cope and comply with, and benefit from protected areas.
Table 2. Percentage of coral reefs in Kenya, Tanzania, Madagascar, Mauritius, and the Seychelles
protectesd by no-take fishing closure
Madagascar
Mauritius
Seychelles
2230
870
1690
630
3580
No take area (NTA)(km )
10.4
8.5
255.7
54.3
66.0
NTA as % of reef area
0.5%
0.9
15.1
8.6
1.9
Area of coral reef (km2)a
2 b,c
(Spalding et al. 2001), b (Gell & Roberts 2003), c (Wells 2006)
33
Kenya
Tanzania
Seychelles sites fell into the high Environmental Susceptibility, high Adaptive Capacity
quadrant, suggesting a poor prognosis for their reefs, which will likely require active ecosystem
management programs to recover from past coral bleaching episodes and prepare for future
climatic change. The Seychelles, where reefs within and outside of parks have been, and we
predict will continue to be, severely affected by climate-induced coral bleaching (Graham et
al. 2007) has embraced a preservationist approach and protects 255.7 km2, over 15%, of its
reefs from fishing, the highest amount and proportion of the five countries. Mauritian sites fell
into the low Environmental Susceptibility, moderate Adaptive Capacity quadrant, where our
framework suggests that a protectionist conservation policy, for example large marine
protected areas, would meet conservation goals that local communities could cope with and
potentially support. Mauritius, where reef preservation would provide the greatest long-term
benefits, protects only 8.5 km2, less than 1%, of its reefs, from fishing (the smallest area of any
country we studied). These findings suggest strategies at odds with current conservation action
in these countries. In higher Adaptive Capacity countries, economic development strategies
that lessen dependence on coral reef resources will reduce the vulnerability of their
economies and livelihoods to climate change. In Mauritius and Seychelles these strategies
include tourism, offshore fisheries, and services based on information technology.
34
Conclusions and Recommendations
The 1998 bleaching event had, and is still having, extensive impacts across the western Indian
Ocean. Although ocean-scale coral reef integrity has been lost, it is positive to see that effects
were spatially variable and that in some locations the indirect effects on fish assemblages and
likely implications for human society have been small. Geography seems to be a key
determinant in the ability of reefs to absorb and recover from such large-scale disturbances and
this should be considered for other regions likely to suffer similar large-scale disturbances in the
future. Marine biogeographic information is useful in strengthening the scientific basis for
systematic planning of marine conservation (Lourie & Vincent 2004) but its application in coral
reef research could be limited mainly by the different spatial scales in species and mortality
distributions. Risk of extinction of coral taxa is considered based on their geographical ranges,
and local and regional endemics (Goreau et al. 2000; Roberts et al. 2002; McClanahan et al.
2007a) and those with small population sizes and high susceptibility to bleaching
(McClanahan et al. 2007a) are most vulnerable. Most extant WIO coral species have a wide
distribution, with only a few having restricted distributions (Sheppard 1998; Veron 2000; Veron
1995). Therefore, the threat related to endemism is limited. Based on the 1998 ENSO
responses, areas of relatively high endemism in the Arabian Sea and southern India/Sri Lanka
(Roberts et al. 2002; Sheppard 1998) could be at higher risk. Other high endemism areas in the
south (e.g. Mascarene) and the high diversity areas on the edges of these endemic centers
suffered lower mortality in 1998 and could be at a lower risk.
There were areas of both high and low diversity that suffered severe and mild mortality impacts
during the 1998 ENSO. High diversity areas of Zanzibar and Songo Songo (Tanzania), southern
Mozambique, Comoros, Mayotte, northern Madagascar, Nicobar-Andaman, southern Red
Sea, Gulf of Aden were less affected and should be priority areas for conservation planning.
High diversity areas of the central Indian Ocean Islands, Kenya, Mafia and Mnazi Bay
(Tanzania), areas near the Indian subcontinent and NW Australia suffered among the highest
mortalities and will probably face high threats of biodiversity loss. By combining
biogeographic and ecological information in taxonomic richness distribution and the
predicted habitat loss due to climate change, areas that are expected to face significant impacts
from future climate change have been identified.
Although there was no evidence that existing NTAs are promoting recovery of coral, these
NTAs are still supporting a greater biomass of fishery stocks (Graham et al. 2007; McClanahan
et al. 2007), indicating long-term fisheries management should not be compromised. There is,
however, a need for new NTAs, incorporated into existing networks that protect source reefs
resilient to large-scale disturbance, and areas likely to retain their physical structure. This will
help sustain the upstream spawning stocks of corals and specialized fish species required for
landscape-scale recovery. Such management is likely to be unsuccessful in isolation, and
improved management of entire reef systems, reducing the stresses and pressures to areas
outside NTAs will be necessary to maximize the capacity for systems to recover from large
scale and ongoing disturbance.
Recommendations for management
Regional management
A key finding and recommendation of this research is the importance of large-scale
biogeographic planning as a means to prepare for climate change and aligning the
environmental susceptibility of the reefs with the appropriate management. Above we have
identified areas what will and will not suffer the most change from climate change and this
needs to be a basis for regional planning and management priorities. All regions will require
management that reduces the detrimental impacts of climate change, but the prescriptions
35
for each region are different. The current application of our novel framework to the WIO
reveals that current conservation strategies are poorly prepared for climate change. We
suggest that this could be improved by a regional approach to coral reef management that
integrates development and conservation based on likely long-term outcomes. Our
framework provides a basis for understanding the local context and then prioritizing
pragmatic actions at the appropriate scale to manage social-ecological systems in the face of
environmental change.
Incorporating Adaptive Capacity and Environmental Susceptibility into conservation
planning will represent a significant shift in how many resource managers and donors
approach conservation issues. We predict that the current emphasis on the creation of
closures to build ecological resilience and minimize climate change impacts will only work
socially and ecologically in a limited region where high Adaptive Capacity and low
Environmental Susceptibility intersect. Other areas may need to focus on enhancing Adaptive
Capacity, which will require large investments in economic alternatives to reef-based
livelihoods and social and physical infrastructure. These investments move beyond the
formulaic 'participation' or compensation approaches common in many protected areas
(Peters 1998). Conservation policies based on integrated analysis of Environmental
Susceptibility and Adaptive Capacity are more likely to result in actions that enhance the
ability of reef ecosystems and local communities who depend on them to cope with both the
expected and unexpected impacts of climate change.
Our framework is applicable to a wide range of social-ecological systems and stressors. The
intensity of data collection and analysis required for our case study was high because adaptive
capacity issues arising at the household and community scales are most relevant for this topic
and region. However, metrics of adaptive capacity have been developed at a range of scales,
using widely available secondary data (Yohe & Tol 2002; Tompkins & Adger 2005). Thus,
depending on the particular topic under investigation, our framework may be applicable to
situations where less intensive data collection and post-processing are required. Likewise, mapbased Environmental Susceptibility models are being increasingly developed at a range of
scales (Aragão et al. 2007; Baettig et al. 2007). Our framework could also be extended to consider
additional axes such as local impacts on ecosystems, the strength of governance systems, or the
ability of ecosystems to provide goods and services.
Local-scale management
At the local scale there are specific environments that are more likely to survive climate
disturbances and these need to be identified and prioritized for management. These include
areas that are unlikely to change much over time, such as up-welling areas, but also areas that
have high resistance to disturbance, including areas with naturally high environmental
variability. Finally, areas with high recovery potential such as well connected reefs or reefs with
the grazer communities intact. These and other recommendations have been provided in
previous compilations (West & Salm 2003).
The main contribution of this study has been to identify fishing and practical management
methods that are most likely to reduce the impacts on coral-dependent fish and improve the
chances for recovery after disturbances. Research indicates that spearguns and some nets are
likely to have the largest impacts on these two groups of fishes and that efforts to reduce their
use will improve the chances for reduced effects and recovery.
36
Recommendations for collaboration
This project involved a large number of collaborators and was successful in their
coordination and production of scientific papers. Consequently, we have included a brief
background on the challenges and successes of this project from this perspective. This
project like many reflected an imbalance in the contributions of the various the project
collaborators in terms of data collection and the time it took to organize the data and share it
with the Principal Investigators (PIs). With some collaborators this process was slow, taking
over a year from collection to remittance of data, in the worst case, planned data collection
was never completed. Data accountability was also a concern for a project this size and future
collaborators should know that all data will be required for the PIs all line transect data, not
just an averaged value at a site. Accountability is also required for research budgets and
collaborators should be prepared for greater transparency of all transferred project funds.
The idea of a “collaborator contract” was suggested whereby all future collaborators would
have these standards outlined and be aware that major breaches of this contract would result in
their removal from the project. This would make collaboration and explanation of the situation
to WIOMSA (or the appropriate granting agency) more professional and less personal.
However, there was concern that this would simply create more paperwork and bureaucracy for
the PIs who are already time-limited. Choosing appropriate collaborators based on a history of
productivity and collaboration can greatly increase the chances of success.
The ecological group was very successful in its accomplishments despite a modest budget
and noted the project could not have been accomplished without matching funds from
various sources, including the Leverhulme Trust (UK). In instances where collaboration had
some shortcomings, the PI team made use of opportunities to adapt and include more sites
(ex. Mauritius, Chagos Archipelago). There needs to be flexibility in partnerships and
associated sites selection to accommodate problems that arise from changing commitments.
The socio-economic group was most concerned about the occasional poor overlap between
the social and ecological sampling sites. It was suggested that more organization in site
selection at the beginning of the project could have resulted in a higher sample size of
matched ecological and social sites. The timing mismatch between the ecological (first) and
social surveys (second) likely enhanced the problem. The idea of ecological and social surveys
occurring together was suggested as a solution if the necessary logistical challenges could be
overcome. More discussions with local biologists and social scientists in each of the study
countries could also make site selection more efficient. For example, identification of mainly
pelagic fishing sites from Seychelles officials would have removed these sites from the study
and allowed the survey team to focus on primarily coral reef fisheries.
Standardization of the questionnaires and how each question and survey team member
asked it was an issue. A longer and more extensive pilot study with all members of the survey
teams present would have been beneficial and should be incorporated into the timeline and
budget of future projects.
Creating a database prior to the major field collection and possibly during a pilot phase would
save time, errors, and potential incompatibility issues. The database and database entry training
skills should be made available to members of the survey team to enhance local capacity
building objectives and provide a better understanding of collection and entry issues. This
would provide a better understanding of how to structure questions and group answers. The
idea of the part-time hiring a professional database manager to create and support the database
issues was though to be an excellent addition that should be included in future budgets.
37
The first week surveys in a new country should be devoted to understanding the local fishery
to provide a baseline and general understanding of the fishery prior to beginning social
surveys. The identification of local gears, fishing grounds and terminology is critical to avoid
ambiguity during the first surveys. Interviews should be encouraged to inform team leaders of
new information in order to update the database. For example, if a new gear type is discovered
during interviews, it is important to note this, inform the team leader and then update the
database accordingly, as opposed to simple attempting to lump this new gear into a preexisting gear type on the survey form. Carrying a map and becoming familiar with local
distances and landmarks would also allow interviewers to judge if the information provided is
realistic and accurate. This will require more field time and should be included in the budget.
Mitigation of the above issues would require more time between project planning, pilot
studies, and the collection of field data. More extensive project planning could have
identified important questions to be addressed on the questionnaires and avoid the problem
of an evolving questionnaire during the field season. More time for a pilot study would also
have provided an opportunity for evaluation and refinement of the questionnaires before the
start of the major field season. This timeline and the required budget needs, including airfares
of PIs and team leaders to separate planning workshops, pilot studies and field seasons,
should be considered for future projects. All of these suggestions indicate the need for more
time and money but, despite these small problems, the good will of the investigators led to a
highly successful project.
38
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produced by the project
49
Appendix 1 Bibliography of papers produced by the project
Published or in press
Journal articles
1.
Cinner, J. E., T. R. McClanahan, N. A. J. Graham, M. S. Pratchett, & S. Wilson. 2009.
Gear-based fisheries management as a potential adaptive response to coral bleaching.
Journal of Applied Ecology. in press.
2. McClanahan, T. R., M. Ateweberhan, J. Omukoto, & L. Pearson. 2009. Recent seawater
temperature histories, status, and predictions for Madagascar's coral reefs. Marine
Ecology Progress Series. In press
3. Cinner, J. E., T. R. McClanahan, & A. Wamukota. 2009. Differences in livelihoods,
socioeconomic characteristics, and knowledge about the sea between fishers and nonfishers living near and far from marine parks on the Kenyan coast. Marine Policy in press.
4. McClanahan, T. R., J. E. Cinner, N. A. J. Graham, T. M. Daw, J. Maina, S. M. Stead, A.
Wamukota, K. Brown, V. Venus, & N. V. C. Polunin. 2009. Identifying reefs of hope and
hopeful actions: Contextualizing environmental, ecological, and social parameters to
effectively respond to climate change. Conservation Biology: DOI: 10.1111/j.15231739.2008.01154.x.
5. McClanahan, T. R., N. A. Muthiga, J. Maina, A. T. Kamukuru, & S. Yahya. 2009. Changes
in northern Tanzania coral reefs over a period of increased fisheries management and
climatic disturbance. Aquatic Conservation: Marine and Freshwater Ecosystems. DOI:
10.1002/aqc.1020
6. Cinner, J. E., T. R. McClanahan, & A. Wamukota. 2009. Differences in livelihoods,
McClanahan, T. R., E. Weil, & J. Maina. 2009. Strong relationship between coral
bleaching and growth anomalies in massive Porites. Global Change Biology DOI:
10.1111/j.1365-2486.2008.01799.x.
7.
Cinner, J., T. Daw, & T. R. McClanahan. 2009. Socioeconomic factors that affect
artisanal fishers' readiness to exit a declining fishery. Conservation Biology 23:124-130.
8. Cinner, J. E., T. R. McClanahan, T. M. Daw, N. A. J. Graham, J. Maina, S. K. Wilson, & T.
P. Hughes. 2009. Linking social and ecological systems to sustain coral reef fisheries.
Current Biology 19:206-212.
9. MacNeil, M. A., N. A. J. Graham, M. J. Conroy, C. J. Fonnesbeck, N. V. C. Polunin, S. P.
Rushton, P. Chabanet, & T. R. McClanahan. 2008. Detection heterogeneity in
underwater visual census data. Journal of Fish Biology 73:1748-1763.
10. Maina, J., T. R. McClanahan, & V. Venus. 2008. Meso-scale modelling of coral's
susceptibility to environmental stress using remotely sensed data: Reply to comments
by Dunne (2008). Ecological Modelling 218:192-194.
11. Maina, J., V. Venus, T. R. McClanahan, & M. Ateweberhan. 2008. Modelling
susceptibility of coral reefs to environmental stress using remote sensing data and GIS
models in the western Indian Ocean. Ecological Modelling 212:180-199.
12. McClanahan, T. R. 2008. Loving corals to death? Marine Pollution Bulletin 56:381.
13. McClanahan, T. R., C. Ruiz Sebastian, J. Cinner, J. Maina, S. Wilson, & N. Graham.
2008. Managing fishing gear to encourage ecosystem-based management of coral reefs
fisheries. in B. Riegl, editor. Proceedings of the 11th International Coral Reef
Symposium,, Ft. Lauderdale, Florida, 7-11 July 2008.
50
14. Graham, N. A. J., T. R. McClanahan, M. A. MacNeil, S. K. Wilson, N. Polunin, S.
Jennings, P. Chabanet, S. Clark, M. Spalding, Y. Letourner, L. Bigot, R. Galzin, M.
Öhman, K. C. Garpe, A. J. Edwards, & C. R. C. Sheppard. 2008. Climate warming and
the ocean-scale integrity of coral reef ecosystems. PLOS One 3:e30309.
doi:30310.31371/journal.pone.0003039.
15. McClanahan T.R, J. Cinner, J. Maina, N.A.J. Graham, T.M. Daw, S.M. Stead, A.
Wamukota, K. Brown, M. Ateweberhan, V. Venus, & N.V.C. Polunin. Conservation
action in a changing climate. Conservation Letters 1:53-59.
16. McClanahan, T. R. 2008. Response of the coral reef benthos and herbivory to fishery
closure management and the 1998 ENSO disturbance. Oecologia 155:169-177.
17. Pratchett, M, P. Munday, S. Wilson, N. Graham, J. Cinner, D. Bellwood, G. Jones, N.
Polunin, T. McClanahan. Effects of climate-inducted coral bleaching on coral-reef
fishers: ecological and economic consequences. Invited review in Oceanography and
Marine Biology Annual Review: 46: 251-296.
18. Maina, J., V. Venus, T. R. McClanahan, & M. Ateweberhan. 2008. Modelling
susceptibility of coral reefs to environmental stress using remote sensing data and GIS
models in the western Indian Ocean. Ecological Modelling 212:180-199.
19. McClanahan, T. R., M. Ateweberhan, & J. Omukoto. 2008. Long-term changes in coral
colony size distributions on Kenyan reefs under different management regimes and
across the 1998 bleaching event. Marine Biology 153:755-768.
20. Graham NAJ (2007) Ecological versatility and the decline of coral feeding fishes
following climate driven coral mortality. Marine Biology 153: 119-127
21. Graham NAJ, Wilson SK, Jennings S, Polunin NVC, Robinson J, Bijoux JP, Daw TM
(2007) Lag effects in the impacts of mass coral bleaching on coral reef fish, fisheries,
and ecosystems. Conservation Biology 21: 1291-1300
22. Ledlie MH, Graham NAJ, Bythell JC, Wilson SK, Jennings S, Polunin NVC, Hardcastle
J (2007) Phase shifts and the role of herbivory in the resilience of coral reefs. Coral
Reefs 26: 641-653
23. McClanahan TR, Ateweberhan M, Ruiz Sebastian C, Graham NAJ, Wilson SK,
Bruggemann JH, Guillaume MMM (2007) Predictability of coral bleaching from
synoptic satellite and in situ temperature observations. Coral Reefs 26: 695-701
24. McClanahan TR, Graham NAJ, Maina J, Chabanet P, Bruggemenn JH, Polunin NVC
(2007) The influence of instantaneous variation on estimates of coral reef fish
populations and communities. Marine Ecology Progress Series 340: 221-234
25. McClanahan TR, Anteweberhan M, Graham NAJ, Wilson SK, Ruiz Sebastian C,
Bruggemann JH, Guillaume MMM (2007) Western Indian Ocean coral communities:
bleaching responses and susceptibility to extinction. Marine Ecology Progress Series
337: 1-13
26. Wilson SK, Graham NAJ, Polunin NVC (2007) Appraisal of visual assessments of habitat
complexity and benthic composition on coral reefs. Marine Biology 151: 1069-1076
27. Graham NAJ, McClanahan TR, Letourneur Y, Galzin R (2007) Anthropogenic
stressors, inter-specific competition and ENSO effects on a Mauritian coral reef.
Environmental Biology of Fishes 78: 57-69
51
28. Graham NAJ, Wilson SK, Jennings S, Polunin NVC, Bijoux, JP, Robinson J (2006)
Dynamic fragility of oceanic coral reef ecosystems. Proceedings of the National Academy
of Sciences of the USA 103: 8425-8429
29. Garpe, K. C., S. A. S. Yahya, U. Lindahl, & M. C. Ohman. 2006. Long-term effects of the
1998 coral bleaching event on reef fish assemblages. Marine Ecology Progress Series
315:237-246.
Book chapters
1.
McClanahan, T. R., E. Weil, J. Cortes, A. Baird, & M. Ateweberhan. 2008. Consequences
of coral bleaching for sessile organisms. in M. van Oppen & J. M. Lough, editors. Coral
Bleaching: Patterns, Processes, Causes and Consequences. Springer Ecological
Studies, Berlin.
2.
Cinner, J., T. R. McClanahan, C. Abunge, & A. Wamukota. Livelihoods in fishing
communities along the north coast of Kenya. In J. Hoorweg & N. A. Muthiga, editors.
Coastal Ecology. African Studies Centre, Leiden, Netherlands. in press
In review
1.
Ruiz Sebastian, C., K. J. Sink, D. A. Cowan, & T. R. McClanahan. in review. Bleaching
response of corals and their Symbiodinium communities in southern African reefs.
Marine Biology in review.
2.
Ateweberhan, M., & T. R. McClanahan. in review. Marine reserves, herbivory, ecological
diversity, and the resilence of coral reefs to climate change. Ecology Letters in review.
3.
Ateweberhan, M., & M. T. R. in review. Linking biodiversity and climate change related
impacts on western Indian Ocean coral reefs. Marine Ecology Progress Series in review.
4.
Darling, E. S., T. R. McClanahan, & I. M. Cote. In review. Antagonistic interaction
between bleaching on coral communities. Conservation Letters. In review
Reports and gray literature
1. Cinner J. & M. Fuentes. Human Dimensions of Madagascar's Marine Protected Areas. In
Obura D, Tamelander J, & Linden O. “Ten years after bleaching consequences and
issues facing countries in the Indian Ocean. CORDIO/Sida-SAREC. Mombasa. In press
2. Cinner, J. (2007) The role of taboos in conserving coastal resources in Madagascar.
Invited paper in Traditional Marine Resource Management and Knowledge
Information Bulletin. 22: 15-23
3. Cinner J. & M. Fuentes 2006. A Baseline socioeconomic assessment of marine protected
areas in Madagascar. A report prepared for the Wildlife Conservation Society,
Madagascar.
4. Stead SM, Daw T, Graham NAJ, Gray TS, Polunin NVC, Robinson J, McClanahan TR
(2006) Trends in climate change, coastal governance, coral reef ecology and socioeconomic variation in the Seychelles. World Maritime Technology Conference,
IMAREST, London, 55-66.
5. McClanahan, T. R. 2007. Monitoring bleaching in the Western Indian Ocean - going
beyond some white corals. Reef Encounter 34:15-16.
6. Cinner, J. & T. McClanahan 2005. A socioeconomic assessment of fishing communities
along the north coast of Kenya. WCS working paper
52
For more information contact:
1. Western Indian Ocean Marine
Science Association
P. O. Box 3298
Zanzíbar, Tanzania
Tel: +255-24-2233472
Fax: +255-24-2233852
Website: www.wiomsa.org
E-mail:
[email protected]
2. Tim McClanahan
Wildlife Conservation Society
Coral Reef Conservation
Kibaki Flats no.12
Bamburi, Kenyatta Beach
P. O. Box 99470 - 80107
Mombasa, Kenya
Tel: +254 41 548 6549
Email: [email protected]