Australian Government Assistance to the Tsunami Relief Effort: Assessing impacts to near-shore

Timothy Skewes, Yimin Ye, Charis Burridge

October 2005

AusAID

Seychelles

Enquiries should be addressed to: Timothy Skewes CSIRO Marine and Atmospheric Research PO Box 120 Cleveland QLD 4163 Australia 233 Middle Street Cleveland Queensland Australia Telephone (07) 3826 7200 Int +61 7 3826 7200 Facsimile (07) 3826 7222 Int +61 7 3826 7222 Web site: http://www.marine.csiro.au

Important Notice

© Copyright Commonwealth Scientific and Industrial Research Organisation (‘CSIRO’) Australia 2005 All rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO. The results and analyses contained in this Report are based on a number of technical, circumstantial or otherwise specified assumptions and parameters. The user must make its own assessment of the suitability for its use of the information or material contained in or generated from the Report. To the extent permitted by law, CSIRO excludes all liability to any party for expenses, losses, damages and costs arising directly or indirectly from using this Report.

Use of this Report The use of this Report is subject to the terms on which it was prepared by CSIRO. In particular, the Report may only be used for the following purposes. • this Report may be copied for distribution within the Client’s organisation; • the information in this Report may be used by the entity for which it was prepared (“the Client”), or by the Client’s contractors and agents, for the Client’s internal business operations (but not licensing to third parties); • extracts of the Report distributed for these purposes must clearly note that the extract is part of a larger Report prepared by CSIRO for the Client. The Report must not be used as a means of endorsement without the prior written consent of CSIRO. The name, trade mark or logo of CSIRO must not be used without the prior written consent of CSIRO.

Acknowledgements Jan Robinson and Riaz Aumeeruddy of the Seychelles Fishing Authority were instrumental in defining the project objectives, making the data available, participating in fieldwork and in the interpretation of the results. Thanks to Maria Cedras (SFA) and Stephane Morgan (RV L`Amitie) for their assistance in the field. This study was funded by AusAID through its Tsunami Relief Fund. Thanks to Jill Bell of AusAID for facilitating this project.

National Library of Australia Cataloguing-in-Publication entry: Skewes, Timothy. Australian government assistance to the Seychelles Tsunami Relief Effort : assisting impacts to near-shore fisheries.

Bibliography. Includes index. ISBN 1 921061 29 4. ISBN 1 921061 30 8 (pdf).

1. Fisheries - Seychelles. 2. Indian Tsunami, 2004. I. Ye, Yimin. II. Burridge, Charis. III. CSIRO. Marine and Atmospheric Research. IV. Australian Agency for International Development. V. Title.

338.372709696 Table of Contents

Executive Summary...... 6 Introduction...... 7 Inshore finfish ...... 7 Small boat survey database...... 8 Sites sampled...... 9 Effort data recorded...... 10 Boat type and fishing gear...... 11 Catch ...... 14 Assessing the impact of tsunami on stocks...... 17 Changes in catch per unit effort ...... 18 Changes in monthly fishing effort...... 26 Statistical analysis ...... 30 Discussion ...... 34 Shallow reef benthic fisheries...... 35 Methods...... 35 Analysis ...... 35 Results...... 37 Discussion ...... 44 References ...... 45 Appendices ...... 46

Seychelles tsunami fisheries impacts 3 Figures Figure 1. Seychelles CAS database; number of sample days per month for all sites...... 8 Figure 2. Plot of average sample days per month (for months with samples) for each year 1985 to 2005...... 9 Figure 3. Total number of sample days for each site for the period 1985 to 2005...... 9 Figure 4. Proportion of total yearly sampling effort (days) for each landing site for 1985 to 2005...... 10 Figure 5. Number of records for EXISTING, LANDED and SAMPLED at all sites sampled for each year 1985 to 2005...... 11 Figure 6. Plot of the proportion of EXISTING boats that were recorded as LANDED (Proportion LANDED) and the proportion of LANDED boats that were sampled (Proportion SAMPLED) for each year for all sites...... 11 Figure 7. Number of records for EXISTING, LANDED and SAMPLED for each vessel-gear combination (that had more than zero records) at all sites sampled between 1985 and 2005. ... 12 Figure 8. Plot of the proportion of EXISTING boats that were recorded as LANDED (Proportion LANDED) and the proportion of LANDED boats that were sampled (Proportion SAMPLED for each boat/gear combination for all sites and years...... 13 Figure 9. Plot of the proportion of overall LANDED records for each vessel-gear combination at all sites sampled for each year...... 13 Figure 10. Total yearly catch in samples for each boat type (All data except 1996)...... 15 Figure 11. Total yearly catch in samples for Foot and Pir only (same as Figure 10) (All data except 1996)...... 15 Figure 12. Total yearly catch in samples for each gear type (All data except 1996)...... 16 Figure 13. Total catches in sampled vessels of the 18 fish /species groups recorded in the CAS monitoring program for 1985 to 2005...... 16 Figure 14. Annual catches of the 18 fish species/species groups recorded in the CAS monitoring program...... 17 Figure 15. Raw CPUEs in each month from 1985 to 2005 for the seven selected species (S1=,S2=…)...... 19 Figure 16. Monthly mean CPUEs from April 1985 to March 2005 for the 7 selected fish species (S1=, S2=…). Red marks post-tsunami...... 23 Figure 17. Monthly total fishing effort in sampled vessels from April 1985 to March 2005 targeting the seven selected fish species (For Species S1, S2, S3, S5 and S15, efforts was measured by man*hours of fishing, and for Species S8 and S9, in traps*hours). Red marks post-tsunami...... 27 Figure 18. Map of Mahe Island, a central granitic island of the Mahe Plateau with the location of 12 sites resampled after the tsunami in April 2005, and location 8 additional sites from the 2004 sea cucumber survey used in the mixed model GLM...... 36 Figure 19. Photo of sand covering seagrass at a previously high density seagrass site adjacent to Seychelles International airport...... 37 Figure 20. Photo of massive Porites showing discolouration and “Orange Patch Disease” coral disease at a site adjacent to Seychelles international airport...... 38 Figure 21. Average density of sea cucumber species surveyed at repeated sites in 2004 (pre- tsunami) and 2005 (post-tsunami)...... 39 Figure 22. Density of sea cucumbers (number per ha) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.) ...... 41 Figure 23. Density of sea urchins (number per Ha) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (error bars are 1 s.e.)...... 42 Figure 24. Cover of sand (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.).. 42 Figure 25. Cover of loose rubble (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.)...... 42 Figure 26. Cover of consolidated rubble (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.) ...... 43 Figure 27. Cover of live hard coral (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.)...... 43 Figure 28. Cover of soft coral (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.).. 43

Seychelles tsunami fisheries impacts 4

Tables Table 1. Missing data due to corrupt data...... 8 Table 2. Vessel types in the CAS database and their abbreviation used in the text...... 12 Table 3. Gear types in the CAS database and their abbreviation used in the text...... 12 Table 4. Species categories used for the small boat survey of the artisanal fisheries catch assessment survey...... 14 Table 5 Contributions of fish species and gear types to the total sampled catches (t) of the SFA monitoring program...... 18 Table 6 Results of GLM ANOVA for the seven selected fish species and all years (* denotes significant result)...... 31 Table 7. Coefficients of model variables and their relationship to base (Intercept, pretsunami, month 1) values for the seven selected fish species and all years. (* denotes significant result)...... 32 Table 8. Results of GLM ANOVA for the seven selected species, 2001-2005. (* denotes significant result)...... 33 Table 9. Coefficients of model variables and their relationship to base (Intercept, pretsunami, month 1) values for the seven selected fish species and with data from 2001-2005. (* denotes significant result)...... 33 Table 10. Average values for sea cucumber and sea urchin density and substrate cover and for 12 sites sampled around Mahe Island before (March and November 2004) and after (April 2005) the Boxing Day tsunami. Standard errors are in brackets. Also shown are the results of repeated measures t tests on transformed values (* denotes significant result, or marginally significant in the case of sea cucumber density)...... 40 Table 11. Results of mixed model GLM for selected variables for sites sampled around Mahe Island before (2004 - 20 sites) and after (April 2005 - 12 sites) the Boxing Day tsunami. Model in the form Variable=Impact Stratum Impact*Stratum (* denotes significant result, or near significant in the case of holothurian density)...... 41

Seychelles tsunami fisheries impacts 5 Executive Summary This study did not detect any decrease in fishery abundance for any species/species group in the artisinal inshore and shallow benthic fisheries in the three months after the Boxing Day tsunami compared to recent baseline data. In fact, sea cucumber density increased by 38% and rabbitfish (Siganus spp.) catch per unit effort (CPUE) increased by 68% following the tsunami. There could, however, be longer time scale changes to nearshore shallow fisheries that require assessment, such as recruitment related impacts that may cause fluctuations in future fishery species densities and abundances. The repeated benthic surveys did show changes to shallow nearshore habitats, including a decrease in the cover of sand (decreased by 38 %) and an increase in the cover of loose and consolidated rubble (more than double). These changes could certainly be related to scouring caused by tsunami related currents. Fortunately, we did not observe any decrease in the cover of hard or soft . The negative impacts of the tsunami appear relatively minor at this stage. Certainly, there were no large decreases in abundance for any of the fishery species examined here in the first three months after the tsunami, and the environmental changes should be reversed over time by the natural movement of soft sediments by ambient currents. This result is consistent with information provided by several post-tsunami assessments by other international and local agencies. The artisanal fisheries Catch Assessment Survey (CAS) operated by the Seychelles Fishing Authority (SFA) proved to be useful for monitoring the effects of environmental changes on fishery populations; however, some finer scale sampling of catch composition and population parameters is recommended to assess potential long term effects on fishery populations. The benthic survey sites used for the repeated measures analysis proved to be suitable for detecting changes in invertebrate populations and their habitats, and there exists a network of such sites throughout the Mahé Plateau and the Amirantes that could be included in a longer term monitoring strategy for benthic fisheries and fishery habitats.

Seychelles tsunami fisheries impacts 6

Introduction The 2004 Boxing Day earthquake triggered massive tsunamis which caused devastation on land and ruined many coastal communities in countries surrounding the . The tsunamis and associated water currents caused extensive damage to the shallow coastal environment as well as to marine life. Damage to coral reefs and their associated fisheries has been recorded as severe in countries close to the tsunami, such as (Allen, 2005) and Thailand (Comley et al., 2005), to more moderate in countries where the tsunami wave was smaller, such as Malaysia (Lee et al., 2005) and Sri Lanka (Rajasuriya et al., 2005). Initial surveys in the Seychelles indicated the impact ranged from very low on Mahé Island to high in some areas around the north-eastern Islands (Adam and Engelhardt, 2005; Obura and Abdulla, 2005). In the Seychelles, the waves caused widespread damage to coastal habitats and infrastructure. The potential consequences for fisheries was a key concern for local communities and the Government, as fishing and related activities are a very important economic sector and contribute approximately 40% to the national gross income. While the inshore artisanal fisheries are small in comparison to the industrialised tuna fisheries, they constitute an important source of income and employment for local populations. The per capita consumption of fish in Seychelles is one of the highest in the world and the artisanal fisheries contribute significantly to the protein requirements of the country, as well as providing for the supply of high quality marine produce to the tourism industry. There may be direct and indirect impacts of the tsunami on the fish stocks that support these fisheries. For example, shallow benthic resources such as sea cucumber could be affected by damage to coral reefs, and inshore1 finfish populations reportedly suffered high mortality due to stranding at the time of the impact. Assessing of the impacts of the tsunami on the inshore and shallow coastal reef fisheries was identified as an important information gap by the Seychelles government; it was listed as a top priority by the Seychelles Fishing Authority (SFA) and also as a significant information gap by the Seychelles Ministry of Environment and Natural Resources and by the UNEP. This report presents the results of an assessment of tsunami impacts on two important sectors of the artisanal inshore fishery: the inshore finfish fishery, which includes the artisanal trap fishery, and the shallow (mostly reef associated) benthic communities, including sea cucumbers.

Inshore finfish fishery The Seychelles artisanal fisheries have landed an average of approximately 4500 t annually since 1985 (Robinson and Shroff, 2004). Although constituting only about 10% of Seychelles’ total fish landings, the artisanal fisheries sector has a fundamental influence on local communities and the economy due to its operational features. It is a diverse sector using several different types of boats and gear and targets over 100 fish species. These fish species are mostly demersal and semi-pelagic species. As many of these species are reef associated, they may be particularly vulnerable to changes in shallow reef habitats. This study focused on several important inshore finfish fisheries and used fishery dependent data collected by the artisanal fisheries Catch Assessment Survey (CAS) to assess the impacts of the tsunami. The CAS data collection system, which was established in 1985, is a stratified catch, effort and species composition data collection system. The survey is stratified geographically and by boat and gear type. Comprising four boat type surveys, the system also includes data collection from the main export companies.

1 Inshore fisheries are defined as those operating close (generally within 10 nautical miles) to the main granitic islands of Seychelles, within sector 1 of the 15 fisheries sectors delimitated by SFA for statistical purposes.

Seychelles tsunami fisheries impacts 7 The inshore trap fisheries were identified as those most at risk from tsunami impacts and there were reports of localised juvenile and adult mortality of the target species (Siganus spp.; rabbitfish) due to stranding by the tsunami (Jude Bijoux, pers comm.). The offshore fisheries mostly target deeper demersal (red snappers, e.g. Lutjanus sebae; groupers, e.g. Epinephalus chlorostigma, jobfish, e.g. Aprion virescens) and semi pelagic (trevally, barracuda) species. Since it was recognised that these offshore fisheries resources were less likely to have suffered tsunami impacts, SFA identified the small boat survey database as the most appropriate for this assessment. Databases for the remaining three boat type surveys, namely the whaler handline survey, the schooner survey and the sport fishery survey, were not included as a large proportion of the fishing effort for these fisheries occurs offshore.

Small boat survey database The CAS database contains catch, effort and species composition data from April 1985 to March 2005. Overall there are records from 26,122 sampling days at various landing sites throughout the Seychelles main Islands and the database contains 57,807 individual catch, effort and species composition records. There are some gaps in the database, notably in late 1986, mid 1991, and almost all of 1996 (Figure 1). A list of missing months was supplied by SFA and is listed in Table 1. The number of sample days per month (for months with sampling) varied from 2 days to 175 days of site sampling (Figure 1, Figure 2), with an average for the entire period of 120.4 sample days per month. Sampling effort increased from a low of 77.7/month in 1985 to a peak of 150/month in 1990 and thereafter slowly declined to an average of 116/month by 2005 (Figure 2).

Table 1. Missing data due to corrupt data. Year Missing months 1986 August, October, November, December 1989 October 1991 April 1992 February. April 1994 November 1996 Only January data available

200

180

160

140

120

100

80

Sample daysSample per month 60

40

20

0 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05

Figure 1. Seychelles CAS database; number of sample days per month for all sites.

Seychelles tsunami fisheries impacts 8

160

140

120

100

80

60

40 Average samples per month per samples Average

20

0 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Figure 2. Plot of average sample days per month (for months with samples) for each year 1985 to 2005.

Sites sampled Catch data was collected by trained observers from 54 recognized landing sites (although 5 sites had zero sample days in the current database (Appendix 1)) from the main islands of Mahé, Praslin and La Digue. However, not all landing sites were sampled equally, with sampling effort allocated according to the number of boats existing at each site (Figure 3). Sample allocation differed over time (Figure 4, Appendix 2), most likely in response to changes in the number of vessels existing at each site. The top 11 sites accounted for 66% of total daily samples, but the ranking did change somewhat from year to year (Figure 4).

3500 Strata name NW MAHENE MAHE E MAHE W MAHE NE PRASLIN NW PRASLIN LA DIGUE 3000

2500

2000

1500

1000 Total number of samples for each site

500

0 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 Site Number

Figure 3. Total number of sample days for each site for the period 1985 to 2005.

Seychelles tsunami fisheries impacts 9 11 12 16 13 14 15 16 17 18 14 19 20 21 22 23 24 12 25 26 27 28 10 29 30 31 32 33 34 8 35 36 37 38 39 40 6 41 42 43 44 45 46 4 47 48 49 50 Proportion of totalProportion yearly samplingeffort (%) 51 52 2 53 54 55 56 57 58 0 59 60 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 61 62 63 Figure 4. Proportion of total yearly sampling effort (days) for each landing site for 1985 to 2005.

Effort data recorded Vessels recorded in the database are characterised by their activity and sampled status as follows: EXISTING. Existing boats are all boats which are not under repair and which are (potentially) engaged in full or part time fishing. This number is later used as a raising factor for monthly catch estimates for all sites, even those not sampled. LANDED. The number of boats landing catch at that site on that day (a subset of EXISTING). This is an estimate of the fishing effort on that day. SAMPLED. Number of Boats sampled at the site (a subset of LANDED). The total records of boats observed as EXISTING, LANDED and SAMPLED increased from 1985 to 1989 and then decreased sharply by 1991, and remained relatively consistent thereafter (Figure 5). The change in 1991 corresponds to the implementation of the whaler handline survey, following which the records for most types of whaler vessel were no longer included in the small boat database. However, the proportion of boats that were observed fishing (LANDED) as a proportion of existing boats was reasonably consistent over the study period, with an overall average of 26.7% (Figure 5, Figure 6). The proportion of LANDED boats that were also SAMPLED was also consistent over the study period with an overall average of 90.9%.

Seychelles tsunami fisheries impacts 10

25000 Existing Landed Sampled 20000

15000

10000 Number of records

5000

0 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Year

Figure 5. Number of records for EXISTING, LANDED and SAMPLED at all sites sampled for each year 1985 to 2005.

100

90

80

70

60 Proportion LANDED 50 Proportion SAMPLED

40

Proportion of total (%) 30

20

10

0

5 6 1 2 3 8 9 0 1 5 l 9 9 0 ta 98 98 987 99 99 994 99 00 00 1 1 1 1988 1989 1990 19 1 1 1 1995 1996 1997 19 1 2 2 2002 2003 2004 20 To Sample year

Figure 6. Plot of the proportion of EXISTING boats that were recorded as LANDED (Proportion LANDED) and the proportion of LANDED boats that were sampled (Proportion SAMPLED) for each year for all sites.

Boat type and fishing gear There are four vessel types identified in the small boat survey: fishing by foot (essentially no vessel); vessels with inboard motors, which are generally slow but more sea worthy and suitable for extended trips; vessels with outboard motors (>15hp), which are generally smaller but faster and suitable for day trips; and pirogues, which are an older style vessel that may have a small outboard motor (<15 hp) (Table 2); and nine gear or gear combinations (Table 3). There was considerable variation in the total number of records for each vessel-gear combination, with OB-LHP and OB-FIXS making up almost half the records at 29.2% and 16.7% of all EXISTING, and 26.7% and 23.9% of LANDED respectively (Figure 7).

Seychelles tsunami fisheries impacts 11

Table 2. Vessel types in the CAS database and their abbreviation used in the text. Vessel type Prefix Pirogue PIR Inboard IB Outboard OB Fishermen on foot FOOT

Table 3. Gear types in the CAS database and their abbreviation used in the text. Gear type Prefix Handlines LHP Traps FIX Static Trap FIXS Active Trap FIXA Handlines & Traps LHP FIX Encircling Gillnets GNC Set Gillnets GNS Beach Seine BS Harpoon HAR

80000

Existing 70000 Landed Sampled 60000

50000

40000

30000 Number of records

20000

10000

0

S P S C S C S P H IXA IXS N N N _B F F LH PFIX _L B _ _ _G _FIXA _FIXS _G _G T_FIXS T_HAR IB_FIX IB O B B B PIR_BS R R IR IR IR_ O O O O O OB_GN OB_LHP PI PI P P P _LH FO FO OB_LHPFIX PIR

Figure 7. Number of records for EXISTING, LANDED and SAMPLED for each vessel-gear combination (that had more than zero records) at all sites sampled between 1985 and 2005.

The proportion of EXISTING boats that were observed fishing (LANDED) varied between boat-gear types, ranging from 10.3% (PIR-LHP) to 40.6% (IB-FIXS) (Figure 8). The proportion of active (LANDED) boats that were SAMPLED was generally high but also varied by boat-gear combination and ranged from 63.3% (IB-LHP) to 100% (several boat-gear categories) (Figure 8)

Seychelles tsunami fisheries impacts 12

100

90

80

70

60

50 Proportion LANDED Proportion SAMPLED 40

Proportion of total (%) 30

20

10

0

P S S C S IX H B X N N HP BS FIXA FI G G L PFIX _ Total B_ _ _ _ H IR _FIXA IB_FIXS IB_L O B B B B_ L P IR IR_FIXS R_GNC O O O OB_ O _ I PIR_LHP_LHPF B P P P PIR_GNS R FOOT_FIXSFOOT_HAR O PI Boat_gear combination

Figure 8. Plot of the proportion of EXISTING boats that were recorded as LANDED (Proportion LANDED) and the proportion of LANDED boats that were sampled (Proportion SAMPLED for each boat/gear combination for all sites and years.

The relative effort by different boat-gear combinations observed in the small boat survey database also varied over the years, with a high proportion of total effort being IB-LHP in the late 1980s then dropping to zero by 1991 (Figure 9), corresponding to incorporation of the IB- LHP combination in the whaler handline survey. Also, the two dominant vessel-gear combinations show opposing trends with OB-LHP declining and OB-FIXS increasing (Figure 9).

FOOT_FIXS 45.0 FOOT_HAR 40.0 IB_FIXS IB_LHP 35.0 OB_BS

30.0 OB_FIXA OB_FIXS 25.0 OB_GNC OB_GNS 20.0 OB_LHP 15.0 OB_LHPFIX Proportion of total records total of Proportion PIR_BS 10.0 PIR_FIXA

5.0 PIR_FIXS PIR_GNC 0.0 PIR_GNS 6 3 4 88 9 0 0 05 1985 1986 1987 19 1989 1990 1991 1992 1993 1994 1995 19 1997 1998 1999 2000 2001 2002 20 20 20 PIR_LHP Year PIR_LHPFIX

Figure 9. Plot of the proportion of overall LANDED records for each vessel-gear combination at all sites sampled for each year.

Seychelles tsunami fisheries impacts 13 Table 4. Species categories used for the small boat survey of the artisanal fisheries catch assessment survey. Code Species group Scientific Name Common Name S1 Carangue Balo gymnostethus Bludger trevally S2 Other carangue eg Caranx spp. Other trevallies S3 Maquereau doux Rastrelliger kanagurta Indian Mackerel S4 Other maquereau eg Caesio spp. Fusiliers S5 Bonite Euthynnus affinis Kawakawa (bonito) S6 Other pelagic eg Katsuwonus pelamis Skipjack Tuna S7 Becune eg Sphyraena spp. Barracudas S8 Cordonier eg Siganus spp. Rabbitfish (Spinefoot) S9 Other trap fish eg Acanthurus spp. Surgeonfishes S10 Red snappers Lutjanus app. Snappers S11 Bourgeois Lutjanus sebae Emperor Red Snapper S12 Job Aprion virescens Jobfish S13 Maconde Epinephelus Chlorostigma Brown Spotted Grouper S14 Other vielle eg Cephalopholis spp. Groupers S15 Capitaine eg Lethrinus spp. Emperors S16 Sharks and rays eg Carcharhinus spp. Sharks and rays S17 Octopus Octopus vulgaris Octopus S18 Others

Catch Of the four vessel types incorporated in the small boat survey, OB has contributed the largest portion of catch consistently since 1985 (Figure 10). The IB boats produced a large amount of catch between 1985 and 1991, but diminished its contribution to almost zero in the small boat survey after these vessels were incorporated in the whaler handline survey. The other two types of boats constituted a very limited contribution to the small boat fisheries (Figure 10, Figure 11). Fishing gears show an even greater diversity. The most important fishing gears in terms of catch are LHP, FIXS and GNC, followed by LHPFIX and FIXA (Figure 12). LHP had an extremely large catch between 1985 and 1991 due mainly to the contribution of IB. But, after 1991, LHP, FIXS and GNC had a similar, even contribution to the fishery.

Seychelles tsunami fisheries impacts 14

500

450

400

350

300 FOOT IB 250 OB 200 PIR

150 Total catch samples (t) Total in 100

50

0 1985 1990 1995 2000 2005

Figure 10. Total yearly catch in samples for each boat type (All data except 1996).

25

20

15 FOOT PIR 10 Total catch samples (t) Total in 5

0 1985 1990 1995 2000 2005

Figure 11. Total yearly catch in samples for Foot and Pir only (same as Figure 10) (All data except 1996).

Seychelles tsunami fisheries impacts 15 600 BS FIXA 500 FIXS GNC 400 GNS HAR LHP 300 LHPFIX

200 Total catch samples (t) Total in

100

0 1985 1990 1995 2000 2005

Figure 12. Total yearly catch in samples for each gear type (All data except 1996).

Species or species groups recorded The overall artisanal fishery catches over 100 species, of which the small boat survey protocol captures 18 species/species groups (Table 4 - for full details see Appendix 3). The largest contributors to catch for the entire period (1985 to 2005) were bludger trevally (S1), Indian mackerel (S3) and Siganus spp. (rabbitfish, S8) (Figure 13), although S1 were very prevalent in the early catch samples due to high recorded catch rates in the IB LHP vessel category (Figure 14). The small boat survey prior to 1991 was not well designed to sample catch and effort for whaler vessels and yielded high catch rates. This situation was rectified through the implementation of the whaler handline survey in 1991.

S1, Bludger trevally S3, Indian Mackerel S8, Rabbitfish S9, Other trap fish S2, Other carangids S15, Lethrinids S5, Bonito S12, Jobfish S4, Other mackeral S10, Red snappers S7, Barracuda S11, Red Emperor S17, Octopus S13, Brow n Spotted Grouper S6, Other pelagic S18, Others S14, Other grouper S16, Sharks and rays Figure 13. Total catches in sampled vessels of the 18 fish species/species groups recorded in the CAS monitoring program for 1985 to 2005.

Seychelles tsunami fisheries impacts 16

1990 1995 2000 S16 S17 S18 300

250

200

150

100

50

0 S11 S12 S13 S14 S15 300

250

200

150

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50

0 S6 S7 S8 S9 S10 300 Catch 250

200

150

100

50

0 S1 S2 S3 S4 S5 300

250

200

150

100

50

0 1990 1995 2000 1990 1995 2000 1990 1995 2000 Year

Figure 14. Annual catches of the 18 fish species/species groups recorded in the CAS monitoring program.

Assessing the impact of tsunami on fish stocks The Seychelles small boat fishery survey employs 4 types of fishing boats, 8 types of fishing gear and captures18 fish species/species groups. Different boats and gears are employed for different targets and under different circumstances. Each species behaves differently and plays a unique role in the marine ecosystem. To study the impacts of the tsunami. therefore, each boat and gear combination should be examined separately for each species. This means that we have potentially 576 different combinations. To detect possible impacts of the tsunami on different fish stocks, long time series data is required. Over the period of 1985- 2005, many of the boat-gear combinations and species did not have a satisfactory time series of data as most gear types catch only one or two main species (Table 5). We therefore selected boat-gear and species combinations for analysis based on their contributions to the total catch. For species, those contributing more than 5% to the total catch were selected and for gear, only three types that had more than 6% catches were chosen (BOLD in Table 5). With these selection criteria, only one gear type was dominant for each species/species group. For boat types, only OB was included as FOOT and PIR throughout the study period, and IB after 1991, had negligible catches.

Seychelles tsunami fisheries impacts 17 Table 5 Contributions of fish species and gear types to the total sampled catches (t) of the SFA monitoring program.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 Total %

BS 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 5 1

FIXA 0 0 0 0 0 0 0 10 1 0 0 0 0 0 1 0 0 0 11 2 FIXS 0 0 0 0 0 0 0 30 33 120006 0 1 17515

GNC 1 0 47 18 0 0 0 0 0 0 0 0 0 0 0 0 0 1 68 14

GNS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 3 1 HAR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 6 1

LHP 134 36 00 28 6 11 0 0 14 8 25 6 4 19 2 1 1 295 60

LHPFIX 4 2 0 0 2 0 0 6 7 1 1 2 1 0 3 0 0 0 29 6

Total 138 39 49 20 30 6 11 46 41 16 10 26 7 5 30 5 8 5 494 100

% 28 8 10 4 6 1 2 98 325116 1 2 1 100

Changes in catch per unit effort A stock abundance index suitable for analysis of fisheries dependent data can be defined as catch per unit effort (CPUE). Fishing effort measures the capacity of a unit boat-gear combination to catch fish within a certain period of operational time. So, effort units should be varied with gear to present a proportional relationship between CPUE and fish stock abundance. We selected three types of gear for analysis. The appropriate effort measures for these gear types are as follows: for LHP and GNC, effort was measured by MAN*HOURS of fishing, and for FIXS effort expressed as number of lift*traps. CPUE was then calculated for individual records for the seven major species/species groups and grouped by month (Figure 15). The plot of individual CPUE figures against time provides information about the general distribution of data points and outliers. The months that have missing values were clearly demonstrated for example, January-March 1985, September, November and December 1986, March 1987, February-December 1996, etc. It appears that each species/species group has outliers, but in different years, and often clumped into a single month (Figure 15). This information could form the basis for systematic data checking. Preliminary perusal of the raw data also suggests that if these are data-entry errors, the catch data are more likely to erroneous rather than fishing effort data. In any case, the more recent data for the period leading up to and just after the tsunami appeared to be reasonably well behaved,

Seychelles tsunami fisheries impacts 18

2 4 6 8 10 12 2 4 6 8 10 12 2 4 6 8 10 12 1985 1986 1987 1988 1989 1990 400

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Figure 15. Raw CPUEs in each month from 1985 to 2005 for the seven selected species (S1=,S2=…).

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Figure 15 (cont)

Seychelles tsunami fisheries impacts 19 2 4 6 8 10 12 2 4 6 8 10 12 2 4 6 8 10 12 1985 1986 1987 1988 1989 1990

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Figure 15 (cont)

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Figure 15 (cont)

Seychelles tsunami fisheries impacts 20

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Figure 15 (cont)

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Seychelles tsunami fisheries impacts 21 2 4 6 8 10 12 2 4 6 8 10 12 2 4 6 8 10 12 1985 1986 1987 1988 1989 1990

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Figure 15 (cont)

We then calculated monthly mean CPUE values for each species/species group for the period from April 1985 to March 2005 and plotted them against time to assess trends (Figure 16). For Species S1 (bludger trevally), monthly CPUE seems quite stable over the time period, with a slightly increased mean and enlarged variability after 1997. Species S2 (other carangids) had a trend very similar to S1, but with a clearer increase in CPUE and its variability after 1997. Both Species S1 and S2 were caught with LHP. Their similar trends may be related to the development of this gear over time and associated increases in efficiency. The CPUE of Species S3 (Indian mackerel) increased from 1985 to 1995, but dropped in 1996 after which another general increasing trend was obvious (Figure 16). Species S3 was caught with GNC. The increasing trend in CPUE within the two time periods was in contrast with the overall decrease between the two periods. It should be noted that there were significant gaps in the data between 1996 and 1999. Species S5 (bonito) had the most stable CPUEs over time, with a slightly increased variation after the late 1990s (Figure 16). Species S8 (siganids) and S9 (other trap ), both predominantly caught by static traps (FIXS) had very similar patterns, gradually increasing from 1985 to 1991 together with higher variability (Figure 16). After 1991, CPUEs of both species groups fluctuated somewhat, but with a general decreasing trend from 1991 to 2001, and then another steady increase from 2002 to 2005. The decreasing trend in CPUE for the S8 (siganids) from 1991 to 2001 reinforces an earlier analysis of the CAS data to determine the impact of the 1998 coral mortality (Grandcourt and Cesar, 2003). Species S15 (lethrinids) exhibited a constant increasing trend in CPUE over the entire time period from 1985-2005 (Figure 16). As usual, variation also becomes larger when the value of CPUE increases. Based on the time series of CPUE we constructed for the seven major species in the OB fishery, it is possible to visually compare the CPUEs after the tsunami (last three values) with

Seychelles tsunami fisheries impacts 22 the previous ones for any changes. The CPUEs in January, February and March in 2005 were within the normal range of variation and did not show any clear declines in comparison with those seen in 2004 (Figure 16).

S1 CPUE 0 102030405060

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Figure 16. Monthly mean CPUEs from April 1985 to March 2005 for the 7 selected fish species (S1=, S2=…). Red marks post-tsunami.

S2 CPUE 0 10203040

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Figure 16 (cont)

Seychelles tsunami fisheries impacts 23 S3 CPUE 0 20 40 60 80 100 120 140

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Figure 16 (cont)

S5 CPUE 02468

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Figure 16 (cont)

Seychelles tsunami fisheries impacts 24

S8 CPUE 0 5 10 15 20

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Figure 16 (cont)

S9 CPUE 24681012

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Figure 16 (cont)

Seychelles tsunami fisheries impacts 25 S15 CPUE 2468

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Figure 16 (cont)

Changes in monthly fishing effort Given the extensive damages to vessels and gear reported to SFA by fishers, a loss of fishing capacity immediately after the tsunami may be expected. Moreover, fishers might not have gone out to sea because of anticipated further tsunami strikes. This kind of impact can be identified by fishing effort analysis. In this section, we calculated the monthly total fishing effort for the seven selected species. For S1, it seems there were two periods divided by1991 (Figure 17). The monthly total effort was higher in years before 1991 than that after 1991, for reasons discussed earlier. Typically, over the last five years, monthly effort was decreasing. However, the total effort in January-March in 2005 was not obviously different from the previous trend. Species S2 had a relatively lower monthly effort than Species S1 in general (Figure 17). However, its temporal pattern was similar to Species S1. Two time periods can be identified with a decreasing trend over the last five years. No unusual changes can be found following the tsunami. For S3, monthly total effort varied greatly, but the most noticeable is the increase observed after 2000 (Figure 17). This trend is very similar to that seen in CPUE over the last 5 years (Figure 16). The monthly effort values in January to March 2005 were actually higher than those of the first quarter of 2004. The monthly effort targeting S5 decreased up to 1996 and remained nearly unchanged afterward, particularly so over the last five years. Both S8 and S9 had very variable monthly total efforts and similar temporal patterns. Although there were differences between the two species groups in detail, both had a higher effort after 1997 than the years of 1985-1996 (Figure 17). For Species S15, an overall decreasing trend was clear if 1985 and 1986 were excluded, although with strong small-scale fluctuations (Figure 17). The monthly efforts in January- March 2005 were on the high end of an increasing trend over the last three years.

Seychelles tsunami fisheries impacts 26

In summary, the monthly fishing effort expended on each of the seven selected species did not demonstrate any unusual changes following the 2004 Boxing Day tsunami. This may suggest that, while some damage to vessels and gear loss was reported, fishing activity of the artisanal fishery in Seychelles was not significantly influenced by the natural disaster.

S1 Effort 0 500 1000 1500

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Figure 17. Monthly total fishing effort in sampled vessels from April 1985 to March 2005 targeting the seven selected fish species (For Species S1, S2, S3, S5 and S15, efforts was measured by man*hours of fishing, and for Species S8 and S9, in traps*hours). Red marks post-tsunami.

S2 Effort 0 200 400 600 800 1000

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Figure 17 (cont)

Seychelles tsunami fisheries impacts 27

S3 Effort 0 200 400 600 800

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Figure 17 (cont)

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Figure 17 (cont)

Seychelles tsunami fisheries impacts 28

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Figure 17 (cont)

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Figure 17 (cont)

Seychelles tsunami fisheries impacts 29 S15 Effort 0 200 400 600 800 1000 1200 1400

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Figure 17 (cont)

Statistical analysis To detect any changes in CPUE associated with the tsunami, a General Linear Model (GLM) was used to compare before and after data together with any other factors or covariates, such as year and month. However, the fishery monitoring surveys carried out by SFA are limited to catch landings and effort expended by different types of boats and fishing gear and no other covariates are included in the survey. GLM modeling, therefore, was limited to fishery-related factor variables, basically year and month. Due to the fact that only three months of data were available after the tsunami, the year and tsunami factors cannot be used together because they can mean the same thing for the single after-tsunami year. Consequently, we explore two methods: one using the tsunami factor and the other using the year factor. The only difference between these two methods is in interpretation. With the tsunami factor, it is straightforward in that if the tsunami factor is significant, then the impact of tsunami on CPUE is statistically significant, and the extent of the impact can be estimated from the coefficient of the factor. However, with the year-factor model, the significance of the year factor does not mean the impact of tsunami is statistically significant. The statistic test and its degree can only be obtained by the statistics of the 2004 year coefficient. With regards to the month factor, we can choose to include only the three months of January, February and March, because these are the months for which we have data after the tsunami, or, in contrast, all twelve months could be included when data are available. If there is a clear monthly pattern, the use of all months should have some advantages. But, the use of only three months would not fundamentally change the results as the partial pattern should still remain. Due to the fact that we have only three months of data for the year after tsunami, we decided to employ the first model. The second method was also explored but the results did not differ much from those of the first model. Therefore, only the results of the first method are presented here.

Seychelles tsunami fisheries impacts 30

The following GLM model was used in this study: CPUEt_impact,month=constant +t_impact+month+t_impact*month where CPUE is the catch per unit effort in a month and “t_impact” is a factor for before or after the tsunami. For the months before the tsunami a dummy variable of 0 was assigned and for those after the tsunami 1 was assigned. Month is a factor variable related to the 12 calendar months each year, but trimmed to the first three months. The above model was applied separately to each of the seven selected fish species. The interaction term did not help much in explaining the variation of the CPUE data for all of the species. This is because the monthly pattern in the small boat fisheries is quite weak. The results are presented in Table 6. It can be seen that neither t_impact nor month factor was consistently significant in the GLM model, meaning that CPUE did not show clear patterns for any of the seven species, which was the conclusion derived by visual observation of the monthly CPUE data (Figure 16). The impact factor was significant for Species S3 (Indian mackerel), S8 (rabbitfishes) and S15 (emperors), which indicates that the mean CPUE changed after the tsunami (Table 6). From the coefficient estimates we can see the changes were all positive (Table 7). The CPUE for the three months after the tsunami was almost double for S3, increased by 1.56 kg/trap for S8 and by 1.15 kg/man.hour for S15, compared to the average for the first three months for the years 1985 to 2004 and after taking account of the monthly pattern (Table 7).

Table 6 Results of GLM ANOVA for the seven selected fish species and all years (* denotes significant result). Species Effect Df Sum of Sq Mean Sq F value Pr(F) S1 t_impact 1 3.70 3.72 0.04 0.85 month 2 3763.40 1881.70 17.85 0.00 Residuals 1509 159038.30 105.39 S2 t_impact 1 68.60 68.64 0.30 0.58 month 2 2093.70 1046.85 4.65 0.01 Residuals 1400 315147.10 225.11 S3 t_impact 1 28597.00 28597.39 23.29 0.00* month 2 5584.00 2792.06 2.27 0.10 Residuals 1040 1276784.00 1227.68 S5 t_impact 1 0.90 0.90 0.17 0.68 month 2 82.86 41.43 7.71 0.00 Residuals 1155 6203.07 5.37 S8 t_impact 1 359.40 359.40 5.31 0.02* month 2 107.70 53.84 0.80 0.45 Residuals 2961 200352.50 67.66 S9 t_impact 1 48.30 48.27 1.17 0.28 month 2 442.70 221.36 5.38 0.00 Residuals 3030 124690.20 41.15 S15 t_impact 1 116.88 116.88 15.06 0.00* month 2 6.95 3.48 0.45 0.64 Residuals 2577 19994.94 7.76

Seychelles tsunami fisheries impacts 31 Table 7. Coefficients of model variables and their relationship to base (Intercept, pretsunami, month 1) values for the seven selected fish species and all years. (* denotes significant result). Value Value SE t value Pr(>|t|) S1 (Intercept) 6.6902 0.2694 24.8295 <0.0001* t_impact -0.1439 1.4786 -0.0973 0.9225 month.L 2.1908 0.4445 4.9286 <0.0001* month.Q 1.6127 0.4736 3.4052 0.0007* S2 (Intercept) 4.1052 0.4107 9.9945 <0.0001* t_impact 1.0711 2.1222 0.5047 0.6138 month.L 1.7358 0.6749 2.5718 0.0102* month.Q 1.2809 0.7227 1.7722 0.0766 S3 (Intercept) 22.2208 1.1324 19.6224 <0.0001* t_impact 20.5328 4.0876 5.0232 <0.0001* month.L -3.2193 1.8771 -1.715 0.0866 month.Q 2.6552 1.9171 1.385 0.1664 S5 (Intercept) 1.7148 0.0704 24.3545 <0.0001* t_impact 0.0754 0.31 0.2433 0.8079 month.L 0.068 0.1146 0.5938 0.5528 month.Q -0.4798 0.1229 -3.9059 0.0001* S8 (Intercept) 4.0275 0.1566 25.7233 <0.0001* t_impact 1.5616 0.6569 2.3774 0.0175* month.L -0.3022 0.2595 -1.1645 0.2443 month.Q 0.11 0.2672 0.4118 0.6805 S9 (Intercept) 4.0537 0.1196 33.9059 <0.0001* t_impact 0.6296 0.5888 1.0692 0.2850 month.L 0.4769 0.2029 2.3499 0.0188* month.Q 0.4953 0.2026 2.444 0.0146* S15 (Intercept) 1.8347 0.0558 32.8597 <0.0001* t_impact 1.1502 0.2991 3.8455 0.0001* month.L 0.0663 0.0945 0.7012 0.4832 month.Q 0.0617 0.0955 0.6456 0.5186

In the above analysis, all years (1985 to 2005) were included. This approach may not be justified when there exists a general trend over the study period. Such long-term trends do appear to exist in the CPUE data of the seven selected species (Figure 16). To avoid this kind of contamination, we applied the GLM model to just the last five years, i.e. from 2001- 2005, inclusive (Table 8). Surprisingly, month factor was not significant for any species, and the tsunami factor was significant only for S8 (rabbitfishes). A check on the coefficient estimate shows that CPUE for S8 increased by 1.68 kg/trap after the tsunami (Table 9), similar to the result derived from the analysis of data from all years (Table 7).

Seychelles tsunami fisheries impacts 32

Table 8. Results of GLM ANOVA for the seven selected species, 2001-2005. (* denotes significant result) Df Sum of Sq Mean Sq F value Pr(F) S1 t_impact 1 116.59 116.59 0.95 0.33 month 2 544.27 272.14 2.22 0.11 Residuals 291 35619.72 122.40 S2 t_impact 1 212.21 212.21 1.00 0.32 month 2 989.20 494.60 2.33 0.10 Residuals 298 63174.38 211.99 S3 t_impact 1 1909.40 1909.44 1.14 0.29 month 2 8310.60 4155.29 2.47 0.09 Residuals 237 398049.40 1679.53 S5 t_impact 1 1.48 1.48 0.26 0.61 month 2 14.75 7.37 1.31 0.27 Residuals 178 999.68 5.62 S8 t_impact 1 358.75 358.75 9.34 0.00* month 2 0.63 0.32 0.01 0.99 Residuals 702 26958.63 38.40 S9 t_impact 1 52.05 52.05 1.37 0.24 month 2 83.45 41.72 1.10 0.33 Residuals 638 24208.93 37.95 S15 t_impact 1 0.21 0.21 0.02 0.89 month 2 2.63 1.31 0.11 0.90 Residuals 428 5137.18 12.00 Table 9. Coefficients of model variables and their relationship to base (Intercept, pretsunami, month 1) values for the seven selected fish species and with data from 2001-2005. (* denotes significant result) Value SE t value Pr(>|t|) S1 (Intercept) 8.6878 0.7172 12.1132 <0.0001* t_impact -2.0109 1.7415 -1.1547 0.2492 month.L 1.2081 1.098 1.1003 0.2721 month.Q 2.1669 1.1672 1.8565 0.0644 S2 (Intercept) 7.501 0.9263 8.0976 <0.0001* t_impact -2.5936 2.2296 -1.1632 0.2457 month.L 1.7336 1.4173 1.2232 0.2222 month.Q 2.8364 1.5216 1.8641 0.0633 S3 (Intercept) 36.3263 3.2505 11.1757 <0.0001* t_impact 6.6937 5.7484 1.1644 0.2454 month.L -4.0669 4.8129 -0.845 0.399 month.Q -8.9027 4.7278 -1.8831 0.0609 S5 (Intercept) 1.6312 0.2142 7.6148 <0.0001* t_impact 0.1902 0.3767 0.5049 0.6142 month.L 0.3384 0.3056 1.1072 0.2697 month.Q 0.3669 0.3044 1.2054 0.2297 S8 (Intercept) 3.8812 0.2676 14.5014 <0.0001* t_impact 1.6779 0.5522 3.0385 0.0025* month.L 0.0484 0.4022 0.1202 0.9043 month.Q 0.0165 0.4094 0.0402 0.9679 S9 (Intercept) 5.382 0.2708 19.8721 <0.0001* t_impact -0.7182 0.6175 -1.1631 0.2452 month.L 0.5561 0.4203 1.323 0.1863 month.Q 0.2793 0.4231 0.6601 0.5094 S15 (Intercept) 2.9397 0.1874 15.6841 <0.0001* t_impact 0.0477 0.4121 0.1158 0.9078 month.L 0.0119 0.2883 0.0413 0.9671 month.Q 0.1353 0.2904 0.4658 0.6416

Seychelles tsunami fisheries impacts 33 Discussion The CPUE values calculated from the Seychelles small boat survey database show large inter-year and intra-year variations over the study period of 1985-2005. Although irregular patterns exist over years, seasonal patterns seem very weak. This indicates that fish density does not exhibit seasonal variation however, small boat fishing grounds do change during the SE Trades, with greater effort in shallow lagoons (Yan Robinson, pers comm.) therefore fishermen may maintain their catch rates by targetting optimal fishing areas. The GLM modeling based on CPUE data of the seven selected fish species did not detect any negative changes after the 2004 Boxing Day tsunami. Also, no clear reduction in monthly total fishing effort could be found following the tsunami. This suggests the small boat fisheries were not greatly hindered by tsunami impacts due to any physical damages. In fact, most vessel damages and losses occurred in the whaler and schooner fleets which were moored in the inner harbour of Port Victoria, an area which suffered significant impact. Many small boats moored near district landing sites escaped damages and outboard engines are usually removed when the boat is moored. In terms of gear, many static fish traps were deployed in deeper areas at the time of the tsunami, and spare fish traps kept by fishers may have compensated for any losses. Therefore, we conclude that the 2004 tsunami did not cause major impacts to short term fish stock abundances and fishing activity of the small boat fleets. CPUE estimates are mainly used for detecting changes in stock abundance as CPUE is assumed to be roughly proportional to abundance. Fish abundance is unlikely to change in the short term if the tsunami did not cause large mortality to adult fish. The potential environmental damage caused by the tsunami is more likely translated into fish habitat loss or an increase in natural mortality of fish at early life stages. This kind of impact may manifest itself on longer time scales. Therefore, a monitoring programme should be implemented to detect long term effects on fishery resources. The long-term effects on impacted fishery populations may be complex, since there are many processes that influence productivity of the target populations, such as fishing effort and practices, coral bleaching mortality, habitat degradation and natural variability in recruitment. For the fast growing species, such as rabbitfish, observations of year class strength over the next five years may reveal a signal related to the tsunami. The collection of ancillary information such as length frequency and age data from the examination of fish otoliths would compliment the analysis of fishery data. SFA has a detailed database of damage to fishing infrastructure (and therefore effort). However, some reported damages may have been exaggerated (especially for lost traps) as compensation is being provided. While there was a period of several weeks immediately after the tsunami when there were low amounts of fish in the market, the beginning of the year often marks a downturn in fish availability. SFA estimates indicate that overall fishing effort in most artisanal fisheries quickly returned to around 80-90 % of effort recorded in the same period of the previous year probably as a result of relatively rapid repairs to minor damages and due to the considerable latent effort in most fisheries. While most species did not exhibit significant changes in CPUE, this study was limited to simple methods by the following factors: 1. Only three months of data were available after the tsunami. This does not only diminish the likelihood that the potential impact was reflected in the data, but also limits the GLM capability to detect any changes because the contrast between the pre- and post- tsunami CPUEs was not balanced. 2. Missing records. There are no records at all for some months. This breaks the time series and prevents the use of time-series related methods. For serious studies, data imputation should be carried out before any analysis. However, this is not an easy task and requires some serious effort input. It is certainly beyond the scope of this project.

Seychelles tsunami fisheries impacts 34

Shallow reef benthic fisheries There had been no assessment to date of possible impacts to benthic invertebrate fisheries, such as sea cucumbers. SFA, with assistance from the FAO, had recently (March and November 2004) completed a project to determine the distribution and abundance of commercial sea cucumbers in the Seychelles, to form the basis for a sustainable management strategy for this fishery (Aumeeruddy et al., 2005). As part of this project, the SFA carried out a large-scale stock survey that included the shallow fringing reefs of the main Islands of the Mahé Plateau. Besides sea cucumber density, the survey also conducted counts of benthic macro-invertebrates and semi-quantitative estimates of percent cover of important benthic components, such as the substrate characteristics and cover of macro-biotic fauna and flora. The project surveyed 38 sites on and adjacent to coral reefs around Mahé, Praslin and La Digue, and 4 sites at Ile Mamelles. These data represent a baseline for a repeated measures study to investigate changes in sea cucumber density and their related habitats. Existing sites could be re-surveyed using a multidisciplinary survey protocol that would be tailored to produce a repeated measures assessment of changes to the shallow benthic communities caused by the tsunami. The main advantage of a repeated measures design is that it controls for between-site differences. This reduces the error variance in the longitudinal comparison and increases the economy and power of the study.

Methods Twelve sites were re-sampled close to Mahé Island by SFA and CSIRO scientists on April 19th and 20th, 2005; 6 sites on the northwest side of Mahé Island and 6 sites on the north eastern side (Figure 18). All sites were previously surveyed during the sea cucumber stock surveys carried out in 2004. All were shallow (<20 m) and part of the fringing reef system of Mahé Island. Field work was undertaken by a team of divers operating from a dinghy and locating sample sites using a portable Global Positioning System (GPS). At each site, two divers swam along a 100 m transect and recorded resource and habitat information 4 m either side of the transect line. Sea cucumbers and other benthic fauna of commercial or ecological interest were counted, and where possible, returned to the dinghy and measured (weight and length). At each site, the substrate was described in terms of the percentage of sand (<2 cm dia.), rubble (2 cm – 30 cm), boulders (>30 cm), consolidated rubble, pavement and live hard and soft coral. The growth forms and dominant taxa of the live coral component and the percentage cover of all other conspicuous biota such as seagrass and algae were also recorded. This is the same sampling protocol used during the full scale sea cucumber survey carried out in 2004 (Aumeeruddy et al., 2005). A full list of variables recorded is included in Appendix 4.

Analysis Survey data were entered into a database for checking and verification. Counts of benthic invertebrates (e.g. sea cucumbers and sea urchins) and cover of environmental variables were standardized to density (individuals/ha) and absolute cover estimates respectively. The density estimates for sea cucumber and sea urchins for each site and year (2004 and 2005) were used to calculate overall and stratum averages and variance values for the 12 repeated sites for each sample year. Paired site density estimates were analyzed to investigate the changes in average abundance between the 2004 and 2005 surveys using paired tests on raw and transformed (log and square root) data. As well as looking at overall changes, we compared the sites between each stratum (west and east) to investigate any spatial patterns in changes.

Seychelles tsunami fisheries impacts 35 #Ñ Ste Anne #Ñ Beau Vallon #Ñ Marine Park #Ñ Ñ Bay Ñ #Ñ Ñ #Ñ Ñ #Ñ Ñ #Ñ Ñ

#Ñ Ñ #Ñ Ñ

#Ñ Mahe Island N #Ñ

# Repeat sites Ñ Pre-tsunami only Reefs Marine parks

0510Kilometers

M ah e

P s la e te t au n a r i

m

A

0100200300Kilometers

Figure 18. Map of Mahe Island, a central granitic island of the Mahe Plateau with the location of 12 sites resampled after the tsunami in April 2005, and location 8 additional sites from the 2004 sea cucumber survey used in the mixed model GLM. Mixed model GLM A Mixed Model General Linear Model (GLM) was used to analyse selected species counts and environmental variables. The analysis was done in SAS and using the MIXED Procedure. A mixed model GLM is a generalisation of the standard linear model (GLM), the generalisation being that the data are permitted to exhibit correlation and non-constant variability. This allows for the fact that the data were collected in a repeated measure design. In addition to the 12 repeated sites, we also included 8 sites that were only sampled during 2004 to increase the level of statistical power in the between-stratum comparison and associated interaction terms. After investigating the distribution and variance patterns of each variable, the data was transformed thus: 1. Sea cucumber and sea urchin density; log transformation with the addition of half the minimum positive number for the entire distribution 2. Environmental % cover estimates (e.g. live coral, soft coral, rubble); square root transformation after the addition of 0.1 3. Sand % cover; arcsine of the square root transformation.

Seychelles tsunami fisheries impacts 36

Results Diver observations We did not observe any major signs of direct tsunami related damage during the survey, such as turned over coral heads or terrigenous debris, as has been reported on damaged reefs in other areas (see introductory section). However, we did observe some sand covering at a high seagrass density area adjacent to the airport (Figure 19). This is an area that has been documented as having a high tsunami wave impact, with a total water level fluctuation of over 8 m with associated strong water currents (Jackson et al., 2005). Also, the massive corals that are found in deeper water at this site did appear to be degraded in that they were covered by a thin layer of sediment, were generally discoloured, and were affected by “orange-patch” coral disease (Engelhardt, 2004) (Figure 20). However, it is not clear that theses impacts were due soley to the tsunami, as similar impacts had been recorded previously due to reclamation works.

Figure 19. Photo of sand covering seagrass at a previously high density seagrass site adjacent to Seychelles International airport.

Seychelles tsunami fisheries impacts 37 Figure 20. Photo of massive Porites coral showing discolouration and “Orange Patch Disease” coral disease at a site adjacent to Seychelles international airport.

Repeated measures analysis The results of the repeated measures comparison (Table 10) and the mixed model analysis (Table 11) indicated that for sea cucumbers, there was a marginally significant increase in density after the tsunami of +38% (Figure 22). There was also a significant difference in the density of sea cucumbers between the two strata (Stratum treatment in Table 11), with the west stratum having a higher density. The increase in density was also greater in the western stratum (Figure 22). However, the interaction term of the mixed model was not significant (Table 11) indicating no significant differences in the changes in each stratum between sampling years. Change comparisons for individual species of sea cucumber were all statistically non- significant. Population compositions for the two periods were reasonably similar (Figure 21), although there were more Bohadschia argus, B. subrubra and Pearsonothuria graeffei in the post tsunami population and less B. marmorata. Sea urchin density was comparatively high, mostly being made up of the large black varieties, Echinothrix diadema and Diadema setosum, with densities of up to 3375/ha recorded at sites in Beau Vallon Bay. Although there was an increase in the density of sea urchins between the two sampling years (Figure 23), it was not statistically significant (Table 10).The two stratum had very different results, with the western stratum showing a large increase in average density and the eastern stratum a large decrease, although neither change was statistically significant. This indicates that the sea urchin populations are quite variable over time and spatially, making them difficult to monitor using a small number of repeated measures sites.

Seychelles tsunami fisheries impacts 38

160 H. fuscogilva H. scabra var versicolor 140 H. scabra Pentard 120 H. nobilis A. miliaris A. mauritiana 100 B. marmorata S. hermanni 80 P. greaffei H. edulis 60 A. echinites S. chloronotus

Average density (No. per ha) per (No. density Average 40 B. subrubra H. atra 20 B. argus T. ananas 0 Actinopyga white belly Pre-tsunami Post-tsunami

Figure 21. Average density of sea cucumber species surveyed at repeated sites in 2004 (pre-tsunami) and 2005 (post-tsunami).

Substrate components showed considerable change between the pre-tsunami and post- tsunami sampling periods. Sand cover decreased by 38% (Figure 24) while cover of rubble (Figure 25) and consolidated rubble (Figure 26) more than doubled. The changes were all statistically significant (Table 10, Table 11) and consistent over the two strata (non significant Impact*Stratum interaction term, Table 11), although the eastern stratum did show a larger increase in loose rubble than the western stratum (Table 10, Figure 25) Average cover of live coral cover increased slightly between the two sampling periods (Figure 27), though the change was not statistically significant overall (Table 10, Table 11). However, the increase in the eastern stratum was large and statistically significant, due almost entirely to one site in the Ste Anne Marine Park where our transect line crossed a small area with a very high density of live coral that was not surveyed the previous year. This illustrates a constraint in this type of sample methodology, in that transects are not exactly fixed and that some variation will occur due to random nature of the transect direction. However, significance tests on paired change parameters will still be a legitimate reflection of overall changes because inter-site variation will be taken into account in the analysis. Soft coral cover was consistent over the two years (Figure 28) but also extremely spatially variable, resulting in large standard errors and non-significant results even for the large differences between the two strata, given that most of the soft coral was found in the western stratum (Table 11, Figure 28).

Seychelles tsunami fisheries impacts 39 Table 10. Average values for sea cucumber and sea urchin density and substrate cover and for 12 sites sampled around Mahe Island before (March and November 2004) and after (April 2005) the Boxing Day tsunami. Standard errors are in brackets. Also shown are the results of repeated measures t tests on transformed values (* denotes significant result, or marginally significant in the case of sea cucumber density). Strata Variable Pre-tsunami Post-tsunami Transformation P All Sea cucumbers 108.33 149.31 Log 0.051* (35.96) (34.33) Urchins 518.75 770.14 Log 0.253 (270.26) (313.73) Sand 72.17 44.58 Arcsin Sqrt 0.012* (10.72) (10.42) Rubble 7.67 17.25 Sqrt 0.020* (2.82) (3.32) Cons rub 4.92 13.92 Sqrt 0.021* (2.54) (3.08) Live coral 3.00 3.96 Sqrt 0.168 (1.08) (1.28) Soft coral 4.33 3.92 Sqrt 0.182 (4.15) (3.29) West Sea cucumbers 120.83 193.75 Log 0.124 (41.54) (49.03) Urchins 593.75 1427.08 Log 0.086 (386.54) (506.83) Sand 72.83 46.17 Arcsin Sqrt 0.059 (14.81) (13.49) Rubble 9.33 15.50 Sqrt 0.180 (4.48) (3.25) Cons rub 4.17 16.67 Sqrt 0.070 (2.01) (4.94) Live coral 3.50 3.42 Sqrt 0.815 (1.60) (1.46) Soft coral 8.67 7.58 Sqrt 0.530 (8.27) (6.49) East Sea cucumbers 95.83 104.86 Log 0.272 (62.47) (44.64) Urchins 443.75 113.19 Log 0.885 (411.95) (59.81) Sand 71.50 43.00 Arcsin Sqrt 0.136 (16.92) (17.16) Rubble 6.00 19.00 Sqrt 0.088 (3.70) (6.06) Cons rub 5.67 11.17 Sqrt 0.211 (4.91) (3.79) Live coral 2.50 4.50 Sqrt 0.024* (1.57) (2.23) Soft coral 0.00 0.25 Sqrt 0.189 (0.17)

Seychelles tsunami fisheries impacts 40

Table 11. Results of mixed model GLM for selected variables for sites sampled around Mahe Island before (2004 - 20 sites) and after (April 2005 - 12 sites) the Boxing Day tsunami. Model in the form Variable=Impact Stratum Impact*Stratum (* denotes significant result, or near significant in the case of holothurian density). Num Den Variable Treatment F P(F) d.f. d.f. Sea cucumber density Impact 1 10 4.89 0.051* Stratum 1 18 5.18 0.035* Impact*Stratum 1 10 0.04 0.843 Urchins Impact 1 10 0.99 0.344 Stratum 1 18 7.40 0.014* Impact*Stratum 1 10 2.32 0.159 Sand Impact 1 10 9.18 0.013* Stratum 1 18 0.60 0.447 Impact*Stratum 1 10 0.14 0.717 Rubble Impact 1 10 6.20 0.032* Stratum 1 18 0.79 0.385 Impact*Stratum 1 10 0.37 0.557 Consolidated Rubble Impact 1 10 13.02 0.005* Stratum 1 18 0.41 0.530 Impact*Stratum 1 10 0.19 0.668 Live coral Impact 1 10 2.15 0.173 Stratum 1 18 0.42 0.527 Impact*Stratum 1 10 1.40 0.263 Soft coral Impact 1 10 2.06 0.182 Stratum 1 18 2.78 0.113 Impact*Stratum 1 10 0.02 0.901

300 Pre-tsunami 250 Post-tsunami

200

150

100

50

Sea cucumber density (No. per ha) per (No. density cucumber Sea 0 All Wes t Eas t Stratum Figure 22. Density of sea cucumbers (number per ha) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.)

Seychelles tsunami fisheries impacts 41 2500 Pre-tsunami Post-tsunami 2000

1500

1000

500 Sea urchin density (No. per ha) per (No. density urchin Sea 0 All West East Stratum

Figure 23. Density of sea urchins (number per Ha) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (error bars are 1 s.e.)

Pre-tsunami 100 Post-tsunami 90 80 70 60 50 40 30 20 Sand cover (% of bottom) of (% cover Sand 10 0 All Wes t Eas t Stratum Figure 24. Cover of sand (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.)

30 Pre-tsunami 25 Post-tsunami

20

15

10

Rubble (% of bottom) Rubbleof (% 5

0 All West East Stratum Figure 25. Cover of loose rubble (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.)

Seychelles tsunami fisheries impacts 42

25 Pre-tsunami Post-tsunami 20

15

10

5

Consolidated rubble (% of bottom) of (% rubble Consolidated 0 All West East Stratum Figure 26. Cover of consolidated rubble (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.)

8 Pre-tsunami 7 Post-tsunami 6

5

4

3

2

Hard coral (% of bottom) 1

0 All Wes t Eas t Stratum Figure 27. Cover of live hard coral (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.)

18 Pre-tsunami 16 Post-tsunami 14 12 10 8 6 4 Soft coral (% of bottom) of (% coral Soft 2 0 All West East Stratum Figure 28. Cover of soft coral (% of bottom) at repeated measures sites before and after the tsunami (n=12), and separately for sites in the east (n=6) and west (n=6) strata. (Error bars are 1 s.e.)

Seychelles tsunami fisheries impacts 43 Discussion The results of the benthic survey indicate that the tsunami did not have a large negative impact on an important shallow reef benthic resource (sea cucumbers) around the main Island of Mahé. The tsunami would not be expected to cause much direct damage to the sea cucumbers, and the movement of sediment and stirring of the environment may in fact have promoted the bacteria and detritus that the sea cucumbers depend on for food. These general observations showing a lack of direct tsunami related damage to the fringing reef is similar to previous assessments of tsunami damage in the area carried out soon after the tsunami (Adam and Engelhardt, 2005; Obura and Abdulla, 2005). However, the large changes to relative sand and rubble cover observed at the repeated sites may be related to large water movements associated with the Boxing Day tsunami. Sand cover was reduced substantially and loose rubble cover was over double the pre-tsunami value, as was consolidated rubble. These changes could be related to movements of loose sediments during the tsunami event, with finer particles being moved to deeper water, as has been observed on heavily impacted reefs in Indonesia and Malaysia (Allan, 2005; Lee et al., 2005). This is not surprising given the scale of the water movements that occurred during the Seychelles tsunami event (Jackson et al., 2005). Live hard coral and soft coral cover did not change significantly at the repeated sites after the tsunami, and the increasing trend may indicate the continued recovery of live coral in the Seychelles after the catastrophic mortality resulting from the 1998 coral bleaching event (Engelhardt et al., 2002). This lack of impact is somewhat surprising given the observed changes in sand and rubble cover and the reported large scale movement of water caused by the tsunami. It seems that, at Mahé Island at least, the water movements were lower than the threshold value for breaking attached coral structures, and the movement of fine sediment away from the shallow reefs did not undermine the coral structures to an extent where wide-scale damage was caused. However, there are some indications that there may have been more extensive damage to shallow coral reefs around the granitic Islands in the northeast of the Mahé Plateau, such as Praslin and La Digue (Obura and Abdulla, 2005). Certainly we would expect that tsunami related impacts would be more prevalent in the northeast region, as the wave would have lost some energy as it travelled over the Mahé Plateau (Figure 18). As there are suitable baseline sites around the north-eastern islands; sampling could be repeated in that area to quantify changes. Although the number of sites in this study was quite small, and the power for detecting changes is consequently limited, we were still able to produce useful outcomes due to the added power of the repeated measures design and the scale of the changes observed. The changes do not appear to be deleterious at this stage. Certainly there were no large decreases in sea cucumber density or live coral cover in the three months after the tsunami, and the environmental changes may be reversed over time by natural movement of soft sediments by ambient currents. Of course, there may be longer time scale changes that may have to be assessed. There are suitable benthic sample sites on shallow reefs throughout the Seychelles Plateau and the Amirantes that could be included in a longer term monitoring strategy.

Seychelles tsunami fisheries impacts 44

References Adam, P.A., and Engelhardt U. (2005) Seychelles Reefs Post the Tsunami. Marine Conservation News, Vol 3:1, January 2005 Allen, G.R. (2005) Tsunami Underwater: Marine Effects of the S.E. Asian Tsunami at Weh Island , Indonesia. GOLDEN DOLPHIN Video CD Magazine Issue No 23: July/September 2005. Aumeeruddy, R., Skewes, T., Dorizo, J., Carocci, F., Coeur de Lion, F., Harris, A., Henriette, C., Cedras, M. (2005) Resource assessment and management of the Seychelles sea cucumber fishery. FAO Project Number: TCP/SEY/2902 (A). October 2005 Comley, J., O'Farrell, S., Hamylton, S., Ingwersen, C., and Walker, R. (2005) The Impact of the December 2004 Indian Ocean Tsunami on the Resources of Mu Ko Surin Marine National Park, Thailand. Coral Cay Conservation, London, UK. 26 p. Engelhardt, U., Russell, M. and Wendling, B. (2002) Coral communities around the Seychelles Islands 1998-2002. In: Coral reef degradation in the Indian Ocean: status report 2002. Eds Olof Linden et al., Sweden: CORDIO, p. 212-231 Engelhardt, U. (2004) The status of scleractinian coral and reef associated fish communities 6 years after the1998 mass coral bleaching event. Seychelles Marine Ecosystem Management Project (SEYMEMP) - Coral Reef Study. Final Report - March 2004 Grandcourt, E. M. & Cesar, H.S.J. (2003) The bio-economic impact of mass coral mortality on the coastal reef fisheries of the Seychelles. Fisheries Research, 60: 539-550. Jackson, L.E., Barrie, J.V., Forbes, D.L. , Shaw, J., Manson, G.K., Schmidt, M. (2005) Effects of the 26 December 2004 Indian Ocean tsunami in the republic of Seychelles. Report of the Canada-UNESCO Indian Ocean Tsunami Expedition,19 January – 5 February 2005. Natural Resources Canada, Open File 4539 Lee, Y.L. , Affendi, Y.A. Tajuddin, B.H. Yusuf, Y.B. Kee Alfan A.A. and Anuar E. A. (2005) A Post-Tsunami Assessment of Coastal Living Resources of Langkawi Archipelago, Peninsular Malaysia. NAGA, WorldFish Center Newsletter Vol. 28 No. 1 & 2:17-22 Obura, D., and Abdulla, A., (2005) Assessment of Tsunami Impacts on the Marine Environment of the Seychelles. IUCN/UNEP Report. Rajasuriya, A., Karunarathna, C., Tamelander, J., Perera, N., Fernando, M. (2005) Rapid Assessment of Tsunami Damage to Coral Reefs in Sri Lanka. Interim Report No. 1. http://www.nara.ac.lk/RAP/ Robinson, J. and Shroff, J. (2004) The fisheries sector in the Seychelles: an overview, with an emphasis on artisinal fisheries. SMDJ Seychelles Medical and Dental Journal, Special Issue, 7(1).

Seychelles tsunami fisheries impacts 45

Appendices Appendix 1. Number of sample days at each site for each year. SITE 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Total 10 0 11 50 67 103 118 176 183 128 136 180 152 181 15 144 116 125 101 128 116 96 84 21 2420 12 27 22 45 35 8 16 10 11 8 9 12 1 10 1 1 3 219 13 32 36 52 62 29 25 16 16 16 13 16 1 21 26 21 20 22 15 31 44 10 524 14 39 40 72 74 81 85 56 61 42 25 25 3 59 46 47 46 66 69 68 49 16 1069 15 0 16 38 37 69 71 50 12 11 15 1 2 1 12 25 22 15 16 18 14 17 3 449 17 1 2 2 4 8 2 1 14 6 18 17 13 29 41 9 167 18 1 2 11 14 7 15 6 8 2 2 2 7 21 3 101 19 2 2 2 7 1 2 16 20 65 88 164 181 200 203 129 103 134 159 211 21 217 120 71 113 199 204 165 135 38 2920 21 2 2 14 19 5 9 3 3 1 8 13 3 1 1 5 11 3 103 22 1 1 1 5 7 49 63 49 59 91 15 341 23 21 10 1 5 11 15 8 2 13 57 72 74 18 307 24 1 4 2 7 25 1 1 1 3 26 2 2 1 5 27 16 12 13 21 24 14 4 27 36 31 28 2 58 62 71 76 97 100 80 53 17 842 28 40 41 45 43 35 68 21 23 17 21 1 4 3 1 3 3 4 11 33 2 419 29 3 7 10 37 42 34 39 37 46 44 11 310 30 37 37 62 81 93 108 78 67 61 44 109 13 114 78 69 58 86 89 71 70 16 1441 31 6 6 7 1 1 11 4 8 5 11 14 17 22 17 19 29 25 29 51 15 298 32 4 15 11 4 2 17 6 6 8 9 10 2 2 2 2 6 13 4 123 33 29 33 51 60 152 183 125 159 140 163 146 11 112 65 98 63 88 82 70 77 18 1925 34 4 7 3 1 12 11 11 13 15 23 3 34 27 27 24 18 17 27 9 6 292 35 19 19 49 59 29 26 32 46 29 44 37 6 24 30 23 15 26 20 16 30 4 583 36 5 3 10 5 3 13 15 17 1 11 18 13 15 13 13 12 16 2 185 37 32 46 58 15 7 12 25 18 16 16 32 1 13 20 13 10 13 14 12 20 3 396 38 3 3 6 39 43 55 91 117 181 200 123 156 176 178 187 17 89 93 93 75 112 92 94 74 17 2263 40 15 13 9 37 10 16 7 15 28 6 28 1 51 89 72 62 38 33 21 14 7 572 41 27 42 76 94 98 80 62 69 27 39 26 3 21 18 20 20 21 20 23 35 7 828 42 1 5 19 2 23 18 30 33 33 25 2 14 14 16 16 20 17 16 13 2 319 43 2 2 1 1 7 12 32 13 27 30 2 11 17 19 17 17 13 17 18 4 262 44 3 15 17 21 24 28 15 17 31 37 38 3 36 37 33 23 17 28 23 30 5 481 45 0 46 2 2 1 10 3 2 6 1 4 6 5 12 10 10 3 3 3 2 85 47 9 19 20 22 18 18 13 18 17 12 12 3 11 10 21 19 20 32 27 33 4 358 SITE 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Total 48 0 49 3 10 8 1 1 10 6 3 8 5 18 1 11 11 16 15 9 15 17 18 2 188 50 1 4 1 7 8 15 6 6 4 1 9 4 3 1 2 72 51 31 33 43 53 47 73 49 53 72 71 73 6 72 79 72 55 62 70 69 67 17 1167 52 5 5 6 6 4 11 5 6 11 11 9 1 10 10 11 11 8 15 12 18 6 181 53 2 2 1 1 8 2 2 6 5 4 1 3 4 41 54 3 11 14 55 2 3 1 14 14 18 23 22 25 3 25 22 26 20 21 20 24 19 10 312 56 33 33 54 52 59 93 47 53 68 65 74 6 65 69 67 49 63 47 64 62 18 1141 57 2 3 17 12 3 19 17 15 17 13 18 2 20 15 17 11 18 17 17 20 7 280 58 8 8 12 22 21 31 23 29 38 38 40 3 33 39 39 30 35 35 41 41 4 570 59 8 8 4 14 11 18 25 19 21 2 19 18 12 11 14 11 18 12 3 248 60 0 61 20 20 48 61 54 79 43 61 98 81 96 7 77 81 80 61 65 69 67 68 17 1253 62 1 1 63 2 3 1 4 3 2 15 Total 699 827 1255 1337 1451 1799 1176 1337 1446 1399 1606 142 1492 1436 1388 1212 1478 1431 1409 1454 348 26122

Seychelles tsunami fisheries impacts 47 Appendix 2. Proportion of sample days (% of total sample days) at each site for each year. SITE 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Total 10 11 7.2 8.1 8.2 8.8 12.1 10.2 10.9 10.2 12.4 10.9 11.3 10.6 9.7 8.1 9.0 8.3 8.7 8.1 6.8 5.8 6.0 9.3 12 3.9 2.7 3.6 2.6 0.6 0.9 0.9 0.8 0.6 0.6 0.7 0.7 0.7 0.1 0.1 0.2 0.8 13 4.6 4.4 4.1 4.6 2.0 1.4 1.4 1.2 1.1 0.9 1.0 0.7 1.4 1.8 1.5 1.7 1.5 1.0 2.2 3.0 2.9 2.0 14 5.6 4.8 5.7 5.5 5.6 4.7 4.8 4.6 2.9 1.8 1.6 2.1 4.0 3.2 3.4 3.8 4.5 4.8 4.8 3.4 4.6 4.1 15 16 5.4 4.5 5.5 5.3 3.4 0.7 0.9 1.1 0.1 0.1 0.7 0.8 1.7 1.6 1.2 1.1 1.3 1.0 1.2 0.9 1.7 17 0.1 0.2 0.2 0.3 0.4 0.2 0.1 1.0 0.4 1.5 1.2 0.9 2.1 2.8 2.6 0.6 18 0.1 0.2 0.9 1.0 0.5 0.8 0.5 0.6 0.1 0.2 0.1 0.5 1.4 0.9 0.4 19 0.3 0.2 0.1 0.4 0.1 0.1 0.1 20 9.3 10.6 13.1 13.5 13.8 11.3 11.0 7.7 9.3 11.4 13.1 14.8 14.5 8.4 5.1 9.3 13.5 14.3 11.7 9.3 10.9 11.2 21 0.3 0.2 1.0 1.1 0.4 0.7 0.2 0.2 0.1 0.5 0.9 0.2 0.1 0.1 0.4 0.8 0.9 0.4 22 0.1 0.1 0.1 0.3 0.5 4.0 4.3 3.4 4.2 6.3 4.3 1.3 23 3.0 1.2 0.1 0.3 0.9 1.1 0.6 0.1 0.8 3.8 5.0 5.3 1.5 1.2 24 0.1 0.2 0.2 0.0 25 0.1 0.1 0.1 0.0 26 0.3 0.2 0.1 0.0 27 2.3 1.5 1.0 1.6 1.7 0.8 0.3 2.0 2.5 2.2 1.7 1.4 3.9 4.3 5.1 6.3 6.6 7.0 5.7 3.6 4.9 3.2 28 5.7 5.0 3.6 3.2 2.4 3.8 1.8 1.7 1.2 1.5 0.1 0.3 0.2 0.1 0.2 0.2 0.3 0.8 2.3 0.6 1.6 29 0.4 0.8 0.8 2.6 3.0 2.8 2.6 2.6 3.3 3.0 3.2 1.2 30 5.3 4.5 4.9 6.1 6.4 6.0 6.6 5.0 4.2 3.1 6.8 9.2 7.6 5.4 5.0 4.8 5.8 6.2 5.0 4.8 4.6 5.5 31 0.9 0.7 0.6 0.1 0.1 0.6 0.3 0.6 0.3 0.8 0.9 1.1 1.5 1.2 1.6 2.0 1.7 2.1 3.5 4.3 1.1 32 0.6 1.8 0.9 0.3 0.1 0.9 0.5 0.4 0.6 0.6 0.6 0.1 0.2 0.1 0.1 0.4 0.9 1.1 0.5 33 4.1 4.0 4.1 4.5 10.5 10.2 10.6 11.9 9.7 11.7 9.1 7.7 7.5 4.5 7.1 5.2 6.0 5.7 5.0 5.3 5.2 7.4 34 0.6 0.8 0.2 0.1 0.7 0.9 0.8 0.9 1.1 1.4 2.1 2.3 1.9 1.9 2.0 1.2 1.2 1.9 0.6 1.7 1.1 35 2.7 2.3 3.9 4.4 2.0 1.4 2.7 3.4 2.0 3.1 2.3 4.2 1.6 2.1 1.7 1.2 1.8 1.4 1.1 2.1 1.1 2.2 36 0.7 0.4 0.6 0.4 0.2 0.9 1.1 1.1 0.7 0.7 1.3 0.9 1.2 0.9 0.9 0.9 1.1 0.6 0.7 37 4.6 5.6 4.6 1.1 0.5 0.7 2.1 1.3 1.1 1.1 2.0 0.7 0.9 1.4 0.9 0.8 0.9 1.0 0.9 1.4 0.9 1.5 38 0.4 0.4 0.0 39 6.2 6.7 7.3 8.8 12.5 11.1 10.5 11.7 12.2 12.7 11.6 12.0 6.0 6.5 6.7 6.2 7.6 6.4 6.7 5.1 4.9 8.7 40 2.1 1.6 0.7 2.8 0.7 0.9 0.6 1.1 1.9 0.4 1.7 0.7 3.4 6.2 5.2 5.1 2.6 2.3 1.5 1.0 2.0 2.2 41 3.9 5.1 6.1 7.0 6.8 4.4 5.3 5.2 1.9 2.8 1.6 2.1 1.4 1.3 1.4 1.7 1.4 1.4 1.6 2.4 2.0 3.2 42 0.1 0.6 1.5 0.1 1.3 1.5 2.2 2.3 2.4 1.6 1.4 0.9 1.0 1.2 1.3 1.4 1.2 1.1 0.9 0.6 1.2 43 0.3 0.2 0.1 0.1 0.4 1.0 2.4 0.9 1.9 1.9 1.4 0.7 1.2 1.4 1.4 1.2 0.9 1.2 1.2 1.1 1.0 44 0.4 1.8 1.4 1.6 1.7 1.6 1.3 1.3 2.1 2.6 2.4 2.1 2.4 2.6 2.4 1.9 1.2 2.0 1.6 2.1 1.4 1.8 45 46 0.3 0.2 0.1 0.6 0.3 0.1 0.4 0.1 0.2 0.4 0.3 0.9 0.8 0.7 0.2 0.2 0.2 0.6 0.3 47 1.3 2.3 1.6 1.6 1.2 1.0 1.1 1.3 1.2 0.9 0.7 2.1 0.7 0.7 1.5 1.6 1.4 2.2 1.9 2.3 1.1 1.4 48 49 0.4 1.2 0.6 0.1 0.1 0.6 0.5 0.2 0.6 0.4 1.1 0.7 0.7 0.8 1.2 1.2 0.6 1.0 1.2 1.2 0.6 0.7 50 0.1 0.5 0.1 0.5 0.4 1.3 0.4 0.4 0.3 0.1 0.6 0.3 0.2 0.1 0.1 0.3

Seychelles tsunami fisheries impacts 48

SITE 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Total 51 4.4 4.0 3.4 4.0 3.2 4.1 4.2 4.0 5.0 5.1 4.5 4.2 4.8 5.5 5.2 4.5 4.2 4.9 4.9 4.6 4.9 4.5 52 0.7 0.6 0.5 0.4 0.3 0.6 0.4 0.4 0.8 0.8 0.6 0.7 0.7 0.7 0.8 0.9 0.5 1.0 0.9 1.2 1.7 0.7 53 0.3 0.2 0.1 0.1 0.4 0.2 0.1 0.4 0.4 0.2 0.7 0.2 0.3 0.2 54 0.2 0.8 0.1 55 0.3 0.4 0.1 0.8 1.2 1.3 1.6 1.6 1.6 2.1 1.7 1.5 1.9 1.7 1.4 1.4 1.7 1.3 2.9 1.2 56 4.7 4.0 4.3 3.9 4.1 5.2 4.0 4.0 4.7 4.6 4.6 4.2 4.4 4.8 4.8 4.0 4.3 3.3 4.5 4.3 5.2 4.4 57 0.3 0.4 1.4 0.9 0.2 1.1 1.4 1.1 1.2 0.9 1.1 1.4 1.3 1.0 1.2 0.9 1.2 1.2 1.2 1.4 2.0 1.1 58 1.1 1.0 1.0 1.6 1.4 1.7 2.0 2.2 2.6 2.7 2.5 2.1 2.2 2.7 2.8 2.5 2.4 2.4 2.9 2.8 1.1 2.2 59 1.1 1.0 0.3 0.8 0.9 1.3 1.7 1.4 1.3 1.4 1.3 1.3 0.9 0.9 0.9 0.8 1.3 0.8 0.9 0.9 60 61 2.9 2.4 3.8 4.6 3.7 4.4 3.7 4.6 6.8 5.8 6.0 4.9 5.2 5.6 5.8 5.0 4.4 4.8 4.8 4.7 4.9 4.8 62 0.1 0.0 63 0.3 0.4 0.1 0.2 0.3 0.1 0.1

Seychelles tsunami fisheries impacts 49

Appendix 3. Species categories used for the small boat survey of the artisanal fisheries catch assessment survey. Code Species group Scientific Name Common Name Local Name (Kreol) S1 Carangue Balo Carangoides gymnostethus Bludger Karang Balo

S2 Other carangue Alectis indicus Indian Threadfin Karang plim Carangoides chrysophrys Longnose Trevally Karang Monik Carangoides fulvoguttatus Karang Plat Carangoides malabaricus Malabar Trevally Karang Monik Caranx ignobilis Karang Ledan Caranx melampygus Karang Ver Caranx sexfasciatus Bigeye Trevally Karang Ledan Egalatis bipinnulata Rainbow runner Galate Gnathanodon speciosus Golden Trevally Karang Saser Seriola rivoliana Almaco Jack Somon

S3 Maquereau doux Rastrelliger kanagurta Indian Mackerel Makro Dou

S4 Other maquereau Caesio caerulaurea Blue and Gold Fusilier Makro Kannal Caesio lunaris Lunar fusilier Makro Ble Caesio xanthonotus Yellowfin Fusilier Makro Zonn Decapterus lajang Mawan Decapterus macarellus Mackerel Scad Mawan Decapterus macrosoma Shortfin Scad Mawan Decapterus russellii Indian Scad Mawan Paracaesio xanthurus Yellowtail Blue Snapper Makro Zonn

S5 Bonite Euthynnus affinis Kawakawa Bonit Fol

S6 Other pelagic Acanthocybium solandri Wahoo Kingfish,Wahoo Euthynnus affinis Kawakawa Bonit Fol Istiophorus platypterus Sailfish Sailfish Katsuwonus pelamis Skipjack Tuna Ton Reye Thunnus albacares Yellowfin Tuna Ton Zonn Coryphaena hippurus Common dolphinfish Dorad Sarda orientalis Striped Bonito Brosadan Tetrapturus audax Striped Marlin Marlin Rey Makaira indica Black Marlin Espadron Makaira mazara Blue Marlin Espadron Gymnosarda unicolor Dog Tooth Tuna Ton Ledan

S7 Becune Sphyraena bleekeri Sawtooth Barracuda Bekin Vera Sphyraena forsteri Bigeye Barracuda Bekin Sphyraena jello Pickhandle Barracuda Bekin Karo Sphyraena obtusata Obtuse Barracuda Bekin Gomon

S8 Cordonier Siganus argenteus Streamlined Spinefoot Kordonnyen Soulfanm Siganus canaliculatus Whitespotted Spinefoot kordonnyen Brizan Siganus corallinus Bluespotted Spinefoot Kordonnyen Lafimen Siganus stellatus Brownspotted Spinefoot Kordonnyen Margrit Siganus sutor Shoemaker Spinefoot Kordonnyen Blan

S9 Other trap fish Abudefduf septemfasciatus Banded Sergeant Bweter Abudefduf sexfasciatus Scissor-tail Sergeant Bweter Karo Abudefduf sordidus Black-spot Sergeant Bweter Ros Acanthurus bleekeri Bleekers Surgeonfish Sirizyen Acanthurus dussumieri Eyestripe surgeonfish Sirizyen Acanthurus leucosternon Powderblue surgeonfish Sirizyen Acanthurus lineatus Lined surgeonfish Sirizyen Acanthurus mata Elongated surgeonfish Sirizyen Acanthurus nigrofuscus Brown surgeonfish Sirizyen Acanthurus thompsoni Thompson's surgeonfish Sirizyen Acanthurus xanthopterus Yellowtail Surgeonfish Sirizyen Bolbometopon muricatum Green Humphead Filanbaz Parrotfish Cetoscarus bicolor Bicolour parrotfish Kakatwa Cheilinus chlorourus Floral Wrasse Kalam Cheilinus trilobatus Tripletail Wrasse Kalam Chelinus fasciatus Red Breasted Wrasse Kalam Code Species group Scientific Name Common Name Local Name (Kreol) Chlororus sordidus Daisy parrotfish Kakatwa Ver Chlororus strongylocephalus Indian Ocean steephead Kakatwa parrotfish Cryptotomus spinidens Spinytooth parrotfish Kalam Diagramma pictum Painted Sweetlips Kaptenn di Por Plectorhynchus orientalis Oriental Sweetlips Vyey Sesil Halichoeres hortulanus Checkerboard Wrasse Tanmaren Halichoeres marginatus Dusky wrasse Tanmaren Halichoeres scapularis Zigzag Sandwrasse Tanmaren Halichoeres zeylonicus Goldstripe Wrasse Tanmaren Parupeneus barberinus Dash and Dot Goatfish Rouze Tas Parupeneus cinnabarinus Cinnabar Goatfish Rouze Lokal Parupeneus cylostomus Goldsaddle goatfish kapisen Parupeneus indicus Indian goatfish Kapisen Parupeneus macronema Longbarbel Goatfish Kapisen Gran Labarb Parupeneus pleurostigma Sidespot goatfish kapisen Parupeneus porphyreus Rosy Goatfish Rouze Rouz Pomacanthus maculosus Yellowbar Angelfish Nyas Pomacanthus semicirculatus Semicircle Angelfish Nyas Scarus caudofasciatus Redbarred parrotfish Kakatwa Scarus falcipinnis Sicklefin Parrotfish Kakatwa Ver Scarus frenatus Bridled parrotfish Kakatwa Ver Scarus ghobban Yellowscale Parrotfish Kakatwa Blan Scarus globiceps Globehead parrotfish Kakatwa Ver Scarus niger Dusky Parrotfish Kakatwa Brizan Scarus psittacus Common parrotfish Kakatwa Ver Scarus rubrioviolaceus Ember Parrotfish Kakatwa Hipposcarus harid Candelamoa Parrotfish Kakatwa Brino Naso hexacanthus Blacktongue Unicornfish Korn Blan Naso vlamingii Bignose unicornefish Korn Leptoscarus vaigiensis Marbled Parrotfish Marar

S10 Red snappers Lutjanus bohar Twospot Red Snapper Vara Vara Lutjanus coccineus Humphead Snapper Bordmar

S11 Bourgeois Lutjanus sebae Emperor Red Snapper Bourzwa

S12 Job Aphareus rutilans Red Smalltooth Job Zob Zonn Aprion virescens Green Jobfish Zob Gri Pristipomoides filamentosus Bluespotted Jobfish Batrikan, Kalkal

S13 Maconde Epinephelus Chlorostigma Brown Spotted Grouper Vyey Makonde

S14 Other vielle Cephalopholis argus Peacock Grouper Vyey Kwizinyen Cephalopholis miniata Vermilion Seabass Vyey Zannan Cephalopholis sonnerati Tomato Hind Msye Angar Epinephelus fasciatus Redbanded Grouper Madanm Dilo, Vyey Rouz Epinephelus faveatus Bigspot Grouper Vyey Sat Epinephelus flavocaeruleus Blue & Yellow Grouper Vyey Plat Epinephelus macrospilos Snubnose Grouper Vyey Sat Epinephelus morruha Contour Rockcod Tioffe Epinephelus multinotatus White Blotched Grouper Vyey Plat Epinephelus polyphekadion Marbled Grouper Vyey Mashata Epinephelus tukula Potato Grouper Vyey Tukula Plectropomus laevis Spotted Coral Trout Vyey Babonn Sesil Plectropomus maculatus Leopard Coral Grouper Vyey Zannannan Plectropomus punctatus Marbled Coral Grouper Vyey Babonn Fey Koko Variola louti Lyretail Grouper Krwasan Anyperodon leucogrammicus Slender Grouper Seval Dibwa

S15 Capitaine Gymnocranius griseus Grey Large-eye Bream Sousout Gymnocranius robinsoni Bluelined Large-eye Kaptenn Blan Bream Lethrinus conchyliatus Red Axel Emperor Gel de Ven Lethrinus crocineus Yellowtail Emperor Laskar Lethrinus elongatus Longface Emperor Gel long Lethrinus lentjan Redspot Emperor Zekler

Seychelles tsunami fisheries impacts 51 Code Species group Scientific Name Common Name Local Name (Kreol) Lethrinus mahsena Mahsena Emperor Madanm Beri Lethrinus nebulosus Spangled Emperor Kaptenn Rouz Lethrinus olivaceus Longface Emperor Bek Bek Lethrinus variegatus Variegated Emperor Baksou Lutjanus argentimaculatus Mangrove Red Snapper Karp Lutjanus bengalensis Bengal Snapper Madras Lutjanus gibbus Humpback Red Snapper Terez Lutjanus kasmira Bluelined Snapper Madras Lutjanus lutjanus Bigeye Snapper Madras Zonn

S16 Sharks and rays Aetobatus narinari Spotted eagle ray Lare Sousouri Carcharhinus albimarginatus Silvertip Shark Reken Waro Carcharhinus amblyrhnchos Grey Reef Shark Reken Bar Carcharhinus brevipinna Spinner Shark Reken nennen pwent Carcharhinus longimanus Oceanic Whitetip Shark Reken Kannal Carcharhinus melanopterus Blacktip Reef Shark Reken Nwanr Carcharhinus miliberti Reken Blan Carcharhinus sorrah Spot Tail shark Reken nennen pwent Carcharhinus tjujot Reken nennen pwent Galeocerdo cuvieri Tiger Shark Reken Demwazel Ginglymostoma Shorttail Nurse Shark Landormi brevicaudatum Ginglymostoma ferrugineum Tawny Nurse Shark Landormi Hamantura uarnak Honeycomb Stingray Lare Boukle Isurus Oxyrinchus Short fin Mako Moro Loxodon macrorhinus Sliteye Shark Reken Pisar Manta birostris Manta Ray Dyab De Mer Rhinobatos blochi Sand Shark La Guitar Rhynchobatus djiddensis Giant Guitarfish Reken Violon Sphyrna lewini Scalloped hammerhead Reken Marto Sphyrna zygaena Smooth hammerhead Reken Marto Taeniura lymma Bluespotted Ribbontail Lare Bannann ray Taeniura melanospilos Blotched Funtail Ray Lare Brizan

S17 Octopus Octopus vulgaris Octopus Zourit

S18 Others Abalistes stellatus Starry triggerfish Bours Etelis carbunculus Ruby Snapper Job la Flamm Etelis marshi Ruby Snapper Job la Flamm Geres oyena Common Silver-biddy Breton Herklotsichthys punctatus Sardine Herring Sardin Ordiner Herklotsichthys Blueline herring Sardin Ordiner quadrimaculatus Lethrinus harak Blackspot Emperor Ziblo Lutjanus fulviflama Black-Spot Snapper Ziblo Macolor niger Black and White snapper Kousoupa Sardinella melanura Blacktip sardinella Sardin Scolopsis frenatus Seychelles Moncle Batgren Bream

Seychelles tsunami fisheries impacts 52

Appendix 4. List of biota and classification codes collected during benthic diver surveys.

Category Level Measure Substratum % cover Soft sediment (<2cm dia) Mud (M) Sandy mud (SM) Muddy sand (MS) Sand (S) Rubble (2cm – 30cm) Boulders (>30cm dia) Consolidated rubble Hard substrate Limestone pavement Live coral Dead standing coral

Coral growth forms % cover Branching Digitate Massive Submassive Tabulate Foliose Encrusting

Fungids All Fungidae % cover

Soft coral All Alcyonacea % cover

Sponges All Porifera % cover

Crinoids All Crinoidea % cover

Hydroids All Hydroida % cover

Gorgonians and sea whips All Gorgonacea % cover

Seagrass All species % cover Halophila ovalis Halophila spinulosa Cymodocea serrulata Cymodocea rotundata Halodule uninervis Thalassodendron ciliatum Syringodium isoetifolium

Algae Dominant species, all classes % cover Sargassum Padina Halimeda Turbinaria ornata Caulerpa Green algae Brown algae Red algae

Holothurians All large species Counts Aspidochirotida Family Holothuriidae Holothuria scabra Sandfish Holothuria scabra versicolor Golden sandfish Holothuria nobilis Black teatfish Holothuria fuscogilva White teatfish Holothuria spp.1 Flowerfish, Pentad Holothuria atra Lollyfish Holothuria edulis Pinkfish Holothuria fuscopunctata Elephants trunk fish

Seychelles tsunami fisheries impacts 53 Holothuria leucospilota* Holothuria coluber Snakefish Holothuria hilla Holothuria martensis Deep water trawl grounds Holothuria ocellata Deep water trawl grounds Actinopyga miliaris Blackfish Actinopyga echinites Deep water redfish Actinopyga mauritiana Surf redfish Actinopyga lecanora Stonefish Bohadschia argus Leopardfish, tigerfish Bohadschia marmorata Brown sandfish Pearsonothuria graeffei Long stickyfish, flowerfish Bohadschia similus Chalkfish Family Stichopodidae Stichopus chloronotus Greenfish Stichopus hermanni Curryfish Stichopus vastus Curryfish Stichopus horrens Peanutfish, dragonfish Thelenota ananas Prickly redfish Thelenota anax Amberfish Order Apodida Family Synaptidae Euapta godeffroyi Synapta maculata Order Dendrochirotida Family Cucumaridae Pentacta anseps Deep water, muddy bottom Psuedocolochirus axiologus Deep water, muddy bottom

Clams All Tridacnids Counts Tridacna crocea Tridacna squamosa Tridacna maxima Tridacna derasa Hippopus hippopus

Lobsters All Palinurids Count Panulirus penicillatus Panulirus versicolor Panulirus longipes Ranina ranina (spanner )

Trochus Trochus niloticus Counts

Pearlshell Pinctada margaritifera Count

Starfish (Asteroids) Culcita sp Counts Acanthaster planci All other species

Urchins (Echinoids) Diadema sp. Counts Echinometra sp All other species

Seychelles tsunami fisheries impacts 54