An Investigation of Optimum Methods and Unit Sizes for the Visual Estimation of Abundances of

74 Pages·2011·20.15 MB·English

RESEARCH PUBLICATION No. 47
An Investigation of Optimum
Methods and Unit Sizes
for the Visual Estimation
of Abundances of Some
Coral Reef Organisms
B D Mapstone
Departmentof Marine Biology
James Cook University
AM Ayling
Sea Research
A REPORT TO THE GREAT BARRIER REEF MARINE PARK AUTHORITY
©GreatBarrierReefMarine ParkAuthority 1998
ISSN 1037-1508
ISBN 0642 230439
Published March 1998
by the GreatBarrierReefMarine Park Authority
Theopinionsexpressed in thisdocumentare not necessarilythoseof
theGreatDarrierReefMarine ParkAuthority.
Accuracyincalculations, figures, tables,names, quotations, referencesetc.
isthecompleteresponsibilityoftheauthors.
National Library of Australia Cataloguing-in-Publication data:
Mapstone, Bruce D. (Bruce David).
An investigation ofoptimum methods and unitsizes for the
visual estimation ofabundances ofsome coral reef
organisms.
Bibliography.
ISBN 0642 23043 9.
1. Coral reefbiology-Research -Queensland -Great
BarrierReef-Methodology. 2. Marine organisms -Research
-Queensland-Great BarrierReef-Methodology. 3.
Environmental sampling- Queensland -Great Barrier Reef.
4. Environmental monitoring -Queensland -Great Barrier
Reef. 1.Ayling, Tony, 1947-. II. Great Barrier Reef
Marine ParkAuthority (Australia). III. Title. (Series:
Research publication (Great Barrier ReefMarine Park
Authority (Australia» ;no. 47).
577.8809943
GREAT BARRIER REEF
MARINE PARKAUTHORITY
PO Box 1379
Townsville Qld 4810
Telephone [phone]
CONTENTS
EXECUTIVESUMMARY v
Acknowledgments iii
INTRODUCTION 1
MATERIALS AND METHODS 5
Location and Time ofField Work 5
OrganismsCounted 6
Sizes and Allocation ofSampling Units 6
Transect Sizes 6
AllocationofSampling Units 6
Counting Procedures 7
Different Methods ofSurvey 8
Data Handlingand Analysis 9
Characteristics oftheData 9
Biases in Estimates ofPopulation Density 10
PreliminOlY MlIltivariateAnalyses 10
Univariate Analyses 12
Site andPass Effects 13
Effects ofTransect Length and Width 14
Analyses ofAuxiliary Variables 15
EffectsofMigration ofFish Across Transect Borders 15
Variations in COllnting Times 15
Effects ofDepth ofTransects 16
Effects ofTime ofDay on EstimatedDensity 16
Effects ofObserver and Methods ofSurveying Large Transects 17
Precision and Cost-Efficiency ofSampling 17
Precisionfrom Transects ofDifferent Dimensions 17
Cost-efficiency ofTransects ofDifferent Dimensions 17
RESULTS 19
Characteristics ofthe Data 19
Biases in Estimates ofPopulation Density 20
PreliminatyAnalyses -MlIltivariate Data 20 .
Large 7imlsects - Univariate Analyses 22
Site Variation andEffects ofPass 22
Effects ofTransect Length and Width 26
Small Transects - Univariate Analyses 30
Analyses ofAuxiliary Variables 34
Migration ofFish Across Transect Margins 34
EstimatedDensitiesAfterCorrectionfor Migration 36
Variations in COllnting Times 38
Effects ofthe Depth ofTransects 39
Effects ofthe Time-of-Day ofSampling 42
Effects ofObserver and Methods ofSurveying LargeTransects 42
Comparisons ofObservers 42
Comparisons ofCOllnting Methods 44
Precision and Cost-Efficiency ofSampling 44
Precisionfrom Transects ofDifferent Dimensions 44
Cost-efficiency ofnansects ofDifferent Dimensions 51
DISCUSSION 55
Characteristics ofthe Data and Analytical Methods 55
Sources ofBias 57
Trallsect Dimellsiolls 57
Search Illtellsity 58
Depth alldTime ofDay 59
ObserversalldCoulltillg Methods 59
Precision and Cost Efficiency 60
OptimumTransectDimensions 61
Suggested Procedurefor Routine Survey 63
Concluding Remarks 64
REFERENCES 66
TABLES
Table 1: Site Descriptions 5
Table 2: (M)ANOVA Models II
Table 3: Taxa Counted 19
Table 4: MANOVA ofEstimated Densities 21
Table5: Summary ofANOVA ofDensities by Taxon- LargeTransects 23
Table6: Details ofANOVA ofDensities by Taxon -Large Transects 24
Table 7: ANOVA ofAverage Densities - Lethrinids & Lutjanids 28
Table 8: Summary ofANOVAofDensities by Taxon -Small Transects 31
Table 9: Details ofANOVA ofDensities by Taxon -Small Transects 32
Table 10: ANOVA ofAverage Densities -C. talboti & L. dimidiatlls 33
Table 11: Migration Across Transect Boundaries -Chaetodons & Coral Trout 34
Table 12: ANOVA ofDensities Adjusted for Migration 36
Table 13: Search Intensities by Transect Size 38
Table 14: Depths ofTransects 41
Table 15: Density Differences Between Shallow & Deep Half-transects 41
Table 16: Rank Frequencies ofCounts by Observers I &2 42
Table 17: MANOVA ofPrecision -Small & Large Transects 44
Table 18: ANOVA ofPrecision by Taxon -Small & LargeTransects 46
Table 19: MANOVA ofCost-efficiency -Small & LargeTransects 52
Table 20: ANOVA ofCost-efficiency byTaxon 52
II
FIGURES
Figurc 1: T. derasa - Effects ofSite'Transect Width on Density 22
Figure2: A. pla/lci, C. baro/lessa - Effects ofSite'Pass on Density 26
Figurc3: Effects ofTransect Width on Density, by Taxon 27
Figurc4: Effects ofTransect Length on Density, by Taxon 29
Figure S: T. derasa, C. trifasciatlls -Effects ofTransect Length'Width on
Density 30
Figure6: C. talboti, L. dimidiatlls -Effects ofTransect Width on Density 33
Figurc7: Effects ofMigration to/from Transects on Density -Chaetodons &
Coral Trout 35
Figurc 8: Effects ofTransectSizeon Density Adjusted for Migration 37
Figurc9: Observed and Expected Times to SurveyTransects 39
Figure 10: Effects ofCounting Method on Density, by Taxon 43
Figurc 11: Effects ofDiver Activity on Density, by Taxon 45
Figurc 12: Effects ofTransect Length on Prccision, by Taxon 48
Figure 13: Effects ofTransect Width on Precision, by Taxon 50
Figurc 14: T. derasa - Effccts ofTransect Length'Width on Precision 51
Figurc IS: Effects ofTransect Width on Cost-efficiency, by Taxon 53
Acknowledgmcnts
We wish to thank the following people, all ofwhom contributed substantially to the project:
Howard Choat, for useful discussions and provision ofthe facilities of the Department ofMarine
Biology, James Cook University; Ros Priest and Brian Cohen for assistance with both the
organisation and execution offield work, and the tedious data entry; Greg Andrew, Linda Axe,
Richard Birdsey, and Steve Neale for field assistance and data entry; Steve Hillman and Jamie
Oliver for project management at the GBRMPA end; and (especially) Gina Mercer and Lucy
Mercer Mapstone for their support and tolerance during the preparation ofthis report. We also
thank the three reviewers ofthe draft final report - Peter Doherty, Helene Marsh, and Tony
Underwood - whose comments were ofgreat assistance. Any persistentdeficiencies, however, are
ours entirely. The work was funded by aconsultancy agreement from the Great BarrierReef
Marine Park Authority (to JCU and Sea Research) and a National Research Fellowship (to BDM).
iii
EXECUTIVE SUMMARY
Surveys ofabundances ofdemersal biota provide the empirical bases for assessing the biological
status ofthe Great BarrierReef, testing the success or failure ofmanagement strategies,
investigating anthropogenic impacts on the reef, and contextualising fundamental research. The
accuracy and precision ofthe data that arise from surveys will be at least partly a function ofthe
survey methodsl. It is important, therefore, to thoroughly document the sampling characteristics of
those methods. We sought to examine the relative biases and characteristics ofprecision of
sampling several species ofbenthic organisms and demersal fish by visual surveys ofbelt (or strip)
transects ofa range ofsizes. We also examined the biases arising from several procedures for
counting organisms within transects.
Estimates ofpopulation densities ofmost poritid corals, chaetodontid and lethrinid fishes, and
coral trout were affected significantly by the width oftransect surveyed. Estimated densities were
greater for narrow (4m wide) transects than for wider transects (12m & 20m wide). Estimated
densities ofchaetodons and poritid corals were also affected by transect length (20m, 60m, 100m),
with estimates declining as transect length increased. Counts ofsmall fishes within small transects
(1-4m x 10-30m) were generally unaffected by the length or width oftransects. The results were
consistent oversix sites on two reefs, which represented a diversity ofhabitat and exposure
conditions.
The effccts oftransect size on estimated density were not caused by observers failing to count or
over-counting fishes which migrated into or out ofthe transects whilst they were being surveyed.
Nordid the results arise because oftrends in abundances with either the depth ofsubstratum
surveyed or the time ofday when counts were taken. The most likely explanation for changes in
bias with changing transect size was systematic variation in the intensity with which transects were
searched. Larger transects were searched far less intensively (time taken per unit area) than small
transects. It is likely, therefore, that organisms were more likely to be overlooked in large
transects than in small transects.
The method by which transects were counted did not affect estimated densities, although estimates
differed systematically between two observers. Thorough training and periodic re-calibration of
observers will be essential for the utility ofdata stemming from ongoing monitoring programmes.
The precision ofestimates from shorter and, for some taxa, narrower transects was consistently
poorer than from longer (and wider) transects. Precision ofestimates varies with the number of
replicates surveyed, and so it is useful to consider the comparativecosts (time taken) ofsampling
sufficient transects ofeach size to achieve astandard precision. We refer to this measure as cost
efficiency. Cost-efficiency varied greatly among sites for transects ofall sizes, and there was no
consistent relationship between cost-efficiency and transect size for most taxa.
We recommend the use of50m x 5m transects for the survey ofmany large, mobile fishes and
discrete benthic organisms. Transects ofthis size are likely to provide the least biased estimates of
density with the optimum balance ofcost-efficiency and logistic convenience. Forsurvey of
several small fishes, we recommend transects of20m x 2.5m, although there is little empirical
reason to favour one transect size over the others we considered. It is important to note, however,
that application ofthe methods we recommend to taxa other than those we considered should be
preceded by thorough verification that the methods are appropriate for those other taxa.
We describe a set ofprocedures by which these transects can besurveyed efficiently by two-three
divers and suggest that these methods provide asound, well documented methodological basis for
the further development ofquantitative reef-wide monitoring.
I Provided that thesampling design is appropriate for theobjectives being pursued.
v
'"troductio" Page I
INTRODUCTION
Increasing concern about the ethical and economic trade-offs between exploitation and
conservation ofnatural biological resources in recent years has placed greater pressure on
environmental managers. The Great BarrierReefis a particularly topical case, both within
Australia and world-wide. The gazetting ofthe Great BarrierReefas a multi-use marine park
explicitly demanded the conservation ofthe biological characteristics ofthe Great BarrierReefin
the context ofongoing recreational use and commercial development (GBR Marine Park Act,
1975). As managers ofthe GBRMP, therefore, the Great BarrierReefMarine Park Authority
(GBRMPA) is faced with balancing the interests ofan array ofcommercial activities (e.g. tourist
industries; scale, crustacean and sessile invertebrate fisheries; commercial shipping), recreational
users, Aboriginal users, and research groups, whilst endeavouring to ensure that the bio-physical
system is conserved. Management in this contexteffectivelyentails the regulation ofhuman use,
ego by zoning areas fordifferent allowable uses, rather than intervention in natural bio-physical
processes perse (Hendee etai, 1990). The success ofsuch regulatory managenient practices
should be assessed with reference to two variables: i) status ofthe ecosystem that is to be
conserved; and ii) thedegree to which human use is facilitated to the satisfaction ofusers.
Monitoring thestatus and dynamics ofthe natural system, and ofhuman lise ofthe system,
therefore, provides the feedback necessary to assess thesuccess or failure ofmanagement
strategies. Thequality and quantity of information derived from such monitoring is crucial to the
evolution ofprudent andjustifiable management (Hendee et ai, 1990).
Management responsibilities in the GBR region are manifest at a variety ofscales ofspaceand
time. Impact assessment and issucs ofreefuse are typically addressed at relatively local scales
(within reefs) and overshort times (one to five years). Zoning ofthe GBR and general
managcment strategies, however, extend to very large spatial scales (reefs, regions) and are
operativeover long times (5 years - decades). To adequatcly assess the responses ofreefbiota to
these multiple activities requires sound empirical knowledge ofspatial and temporal patterns in the
distribution and abundance oforganisms on the GBR under 'normal' conditions, and ofthe
resilience ofpopulations to perturbation. This information is most efficiently provided by
carefully planned quantitativedescriptive studies over a range ofspatial and temporal scales - i.e.,
via a sound monitoring programme- supplementcd with the manipulative experimental studies that
are the province offundamental research.
The information derived from monitoring is, in part, likely to be ofa general, descriptive nature
intended to provide an empirical context within which to make decisions about the management of
specific issues or perturbations, and within which the results oflocal fundamental research can be
better interpreted. It is important therefore, that field sampling is designed carefully to provide
context-information for a variety ofissues, as well as providing empirical tests ofthe effects of
management strategies on biota (Alcala 1988, Craik 1981, Crimp 1986, GBRMPA 1978, 1979,
Hendee etat 1990, Russ 1984, 1989). In most cases, management decisions will be made on the
basis ofexisting information, such as that delivered by general monitoring, or afterrelatively sh0I1
term studies. Typically it will be desirable to integrate information from different sources in a
coherent way. The strongest and most general basis for coherent integration among studies is
sound empirical knowledge about the samplingand analytical procedures from which each piece
ofinfomlation was derived (Andrew & Mapstone 1987, Green 1979). Thechoice ofsampling
methods for general monitoring, therefore, should be related to methods used widely in other
studies, and the documentation oftheircharacteristics will be an importantcomponent in the
development ofmonitoring programmes.
The implementation ofa general monitoring programme oversuch a range ofscales will be
expensive and logistically constrained. It is critical, therefore, that sampling methods arecost
effective. That is, methods should bechosen that i) provide reliabledata ii) will maximise the
potential to identify changes or patterns in the abundance ofbiota; iii) are logistically feasible to
Page2 OplimultlSampli"8 Ullits
repeatedly implement in a wide range ofsituations; and iv) arecheap, so that maximum flexibility
for thedesign ofmonitoring can be retained, within what will always be limited budgets.
In addition to theabove, mainly logistic considerations, the final design ofageneral monitoring
programme must be based on considerationsofthe biology oftheorganisms ofinterest, the ways
in which we perceiveand count them, and the scalesat which natural fluctuations in abundances
occurand management actions are likely (Andrew & Mapstone 1987, Green 1979, Resh 1979).
Particularly important biological considerations include:
• the spatial and temporal characteristics ofdispersion ofthe organisms;
• the rates and ranges ofmovement ofthe organisms, particularly with respect to the size
ofsampling units and the time taken to count them;
• the spatial and temporal scales at which abundances vary most.
Important methodological considerations are:
• thesize and shapeofunit within which organisms are counted;
• the rates and ways in which theunits are surveyed by an observer;
Critical aspects ofthedesign ofa monitoring programme are:
• the numbers ofunits used;
• the arrangement ofsampling units at spatial and temporal scales greater than those of
the sampling units.
Interactions among the above biological, methodological, and design factors will dctermine the
degree to which real patterns in abundances are reflected in ourdata, the precision ofestimates
obtained from asampling programme, and the resolving powerofstatistical tests based on those
estimates. The most effectiveallocation ofavailableresources rests on a thorough understanding
ofthese factors.
Thus, an early step in the development ofa monitoring programmeshould be theexamination of
the relationships between methodology and small-scale biological features, resulting in the choice
ofsampling unit and method ofsurvey that provides an optimum balance between reducing the
costs oflocal sampling and maximising the precision ofestimates for each subjectspecies orgroup
oforganism. Itshould be verified, as far as possible, that the chosen sampling unit has adequate
samplingcharacteristicsover the range ofenvironmental conditions (e.g. habitat, population
density) within which it will be used. Non-destructive methods are generally preferred to methods
that alter the environment being monitored. Subsequently, the optimum sampling method is used
to estimate the variation in abundances at a range ofscales, and theseestimates are then employed
to decide where best to allocate limited resources to answeraspecificquestion.
Assuming that a range ofsampling methods are feasible within an envelope ofcost and logistic
considerations, two main empirical features have been considered to distinguish which sampling
units and/or methods are most desirable: i) accuracy or relative bias; and ii) precision. The
accuracy ofan estimate refers to its degree ofdeparture from the tlUe population value (Andrew &
Mapstone 1987, Lincoln ela/1982, Sokal & Rohlf 1981, Underwood 1981). Bias refers to the
consistencyofinaccuracy - ie theextent to which repeated estimates tend to differ from the 'truth'
in thesamedirection. Both accuracy and bias will be extremelydifficult to asses in absolute terms
for field studies because we generally always haveonly estimatesofthe parameterof interest and,
therefore, can never besure ofits true value. The relative bias ofa numberofestimatescan be
inferred, however, ifit can be assumed that all are biased in the samedirection - i.e., ifit is
probable that all are either overestimatesor underestimates. In general, sampling units that
provide data with the smallest bias will be preferred. The precision ofan estimate refers to the
expected variation in repeated estimates ofthe same population (Andrew & Mapstone 1987,
Cochran 1963, Cochran & Cox 1957, Elliot 1977, Lincoln ela/ 1982, Sokal & Rohlf 1981), and is
often expressed as the (unit-less) ratio ofstandarderrorto mean. The precision ofan estimate is
independent ofits bias, and is by definition a relative property ofaestimate unrelated to any
absolute 'truth'. Hence, comparisonsofprecision are straightforward. Methods which provide
betterprecision for the samesamplingeffort are preferred.
l1ltroducti01l Page3
Several authors have found thateitheror both ofthese properties vary greatly with sampling
method (e.g. Fowler 1987, Gray and Bell 1985, Lincoln Smith 1988, 1989, Samoilys & Carlos
1992), and/or the sizeofsampling units (see review by Andrew & Mapstone 1987, Downing &
Anderson 1985, Downing & Cyr 1985), and may vary among habitats, times, sites etc. for a given
sampling unit (Short & Bayliss 1985). These and other authors have raised the spectreofspurious
inferences arising from inappropriate choice ofsampling methods, and have emphasised the need
to quantify the characteristics ofprecision and bias associated with chosen methods (Bros &
Cowell 1987, Green 1979, Downing 1979, Pringle 1985).
The samplingcharacteristics ofseveral methods ofvisual survey have been examined previously
(e.g. hectare counts ofPfectropOllllls spp. (Ayling 1983a, Ayling &Ayling 1984a, GBRMPA
1979); timed visual surveys and/or belt transects for estimatingabundances offish (Bohnsack &
Banerot 1983, DeMartini & Roberts 1982, GBRMPA 1978, 1979, Kimmel 1985, Samoilys &
Carlos 1992,Sanderson & Salonsky 1980); manta-tow, and strip-transectcounts ofholothurians
oversand (Harriott 1984); manta-tow surveys ofhard corals and Acanthasterpfanci (Fernandez,
1990, Fernandezetaf 1990, Kenchington 1978), line-intercept methods, point, and quadrat
methods (Weinberg 1981), and video methods (AIMS, Mapstone in progress) for estimating
abundance ofsessile fauna. Whilst the manta tow technique has received considerablescrutiny,
therecontinues to besome uncertainty overthe strengths and weaknesses ofmethods for the
survey ofbenthic biotaand demersal fishes on the GBR (Ayling 1983a, Ayling & Ayling 1984a,
Crimp 1986, 1987, GBRMPA 1978, 1979, 1986),). In most cases, the relative bias ofsurvey
methods have been the primary characteristic of interest, in many cases based on adesire to
adequately characterise the assemblage structure ofthe sampled areas (e.g. GBRMPA 1978.
Russell etaf 1978, Sale & Douglas 1981). Considerations ofthe precision ofestimates of
abundances or the utility ofmethods in the context ofthe likely design characteristics of
monitoring programmes, however, have received less attention.
In this study we investigated the methodological aspects ofestimating the abundances ofa number
ofreeforganisms. We were concerned primarily with theutility ofbelt (or strip) transects of
various sizes as sampling units for the estimation ofpopulation densities. The characteristics of
relative bias and precision ofsampling by belt transects have not been considered in thecontext of
a multi-species monitoring programme, although theirapplication to counting particularspecies
have been examined previously (Ayling 1983a, Ayling & Ayling 1984a, Fowler 1987, Mapstone
1988, Sale &Sharp 1983). The organisms with which we were concerned were relatively large,
discrete organisms amenable to rapid counting in the field by divers, which were ofinterest to the
GBRMPA as measures ofmanagement success (GBRMPA 1978), and which had been surveyed
by related methods in the past (e.g., Ayling I983b,c, Ayling &Ayling 1984b,c, 1985, 1986a,b,
Doherty 1987, Doherty & Williams, 1988, Fowler 1987, Mapstone 1988). Our intercst was in the
selection ofoptimum methods for the estimation ofpopulation densities in the framework offuture
monitoring programmes for the GBR, and we were not considering the utility of visual survey as a
means ofestimating population structure (see Ayling 1983b,c, Ayling & Ayling 1984b,c, 1985,
1986a,b, 1991, 1992a,b, Ayling et al 1991, Crimp 1987, GBRMPA 1978, 1979).
Thus, the project had two primary objectives.
1. Estimation ofrelative biases caused by sampling unit dimensions, diver activity, different
observers, and searching procedures.
2. Estimation ofthe characteristics ofprecision ofestimates from units ofdifferent size and
the relativecost-efficiency ofsampling with thoseunits. Relative cost-efficiency here
means theexpenditure required to obtain estimates with uniform precision.
Materials &Methods Page5
MATERIALS AND METHODS
Location and Time ofField Work
Field work was done at Davies Reef(18°50'S 14fl38'E) and Bowden Reef(19'0I'S 14fl56'E) in
the Central Section ofthe Great BarrierReefMarine Park between August 6 and September30
1989. Both reefs are mid-shelfreefs, approximately 75km from the coast. The work was done
during two trips to the reef(s). On the first trip (August 6-(4), two sites were sampled on the front
reefslope ofDavies Reef, whilst on the second trip (September 17-30), afurther two sites were
sampled on Davies Reefand two sites were sampled on Bowden Reef.
Thedefinition ofasite was necessarily arbitrary apriori, but we sought to minimise habitat
variation within sites whilst allowing the random allocation ofsamplingunits with little overlap.
Each site comprised, therefore, a band ofsuperficially homogeneous consolidated reef800-I000m
long, 25m wide from the 2m depth contour, and with a maximum depth of20m. In choosing
different sites, we sought to maximise variation among sites to ensure generality ofthe results of
the work. Hence, the six sites were chosen to represent a variety ofconditions in which shallow
reeforganisms might besurveyed routinely on mid-outer shelfreefs. Sitedetails are given in
Table 1.
The allocation oftransects in the field, however, was constrained such that transects did not span
major and conspicuous discontinuities ofhabitat, such as sand patches, deep passes in the reef
front, etc. Thus, some reefslopesites were lengthened by moving some transects along-shore to
avoid such discontinuities, and transects weredistributed among 10and 14 bommies at sites I and
3 respectively. From one to five transects were counted on each bommie. This fragmentation of
transect allocation was appropriate because: i) bommie fields are, by definition, fragmented
habitat; ii) it would be likely (and desirable) that in routine sampling several bommies would have
to be sampled to realise even moderate replication oftransects within that habitat; and iii) given
the above constraints, it was impossible and undesirable to fit all transects ofall sizes on a single
bommie.
Table 1: Locations, depth characteristics, and times ofsampling ofsites surveyed with transects
ofeach dimension. Depths are the minimum and maximum depths ofthe margins of
transects sampled, times are the earliest - latest times at which surveys were
begun/finished at each site. Site# gives the numberused in the text to refer to each site.
'Bommies' refer to large (><50m<l» patch reefs isolated from other reefby expanses of
sand. Sites I and 3 included 10 and 14 bommies respectively.
Reef Location/Habitat Depth (m) Dates Times Site #
Davies Windward reefslope 2 - 20 August 6-10 08:43 - 17:03 5
Windward reefslope 2 - 18 August 10-142 08:38 - 16:40 6
Leeward large 3 - 18 September 17-20 08:12- 16:52 3
bommies
Leeward reefslope I - 18 September20-23 08:17 - 16:53 4
Bowden Leeward large 2 - 10 September24-27 08:13-16:36 1
bommies
Leeward reefslope 2 - 12 September27-30 08:19- 16:43 2
2Asaresultofinjury,small fish weresampledatonlyonefront reefsiteonDaviesreefin thefirst field trip,and were
sampledat thesecond front reefsiteonthesecond trip.