Counting coral reef fishes: Interaction between fish life-history traits and transect design
Post on 05-Sep-2016
Article history:Received 14 July 2009Received in revised form 26 February 2010Accepted 2 March 2010
Journal of Experimental Marine Biology and Ecology 387 (2010) 1523
Contents lists available at ScienceDirect
Journal of Experimental Ma
.e l1. Introduction
Understanding patterns of abundance, structure and diversity ofpopulations and communities is a major goal in ecology and requiressampling procedures which can produce spatial and temporalcomparable samples (e.g. Caughley, 1977). With the emergence ofmacro-ecology and at a time of global environmental threats thisrequirement is becoming an increasing concern as the data availablefor large-scale analyses and global assessments generally come from avariety of sources and involve a range of sampling methods.Understanding the characteristics of each census method and howdemographic estimates differ amongst methods is a rst but crucial
one of the most widely used methods to survey animals (seeSupplementary material I). Visual censuses are used for insects, birds,reptiles, mammals, invertebrates, and shes, notably in coral reefswhere underwater visual census (UVC) are the main methods used forthe long term monitoring of emblematic ecosystems such as the GreatBarrier Reef (see Australian Institute of Marine Sciences Long TermMonitoringProgramHalford andThompson, 1994), the assessmentoflarge-scale sheries such as the Pacic island reef sheries (seePROCFISH program of the South Pacic Commission) or for theworldwide monitoring of coral reefs (see the Global Coral ReefMonitoring Network GCRMN). On reefs (which encompass coralreefs butmore andmore temperate and coldwater reefs are sampled bystep to assess and correct the potential biasesinformation from different sources in large in
Visual censuses are non-destructive, lowmentable in monitoring programs, and as suc
Corresponding author.E-mail address: email@example.com (M. Ku
0022-0981/$ see front matter 2010 Elsevier B.V. Aldoi:10.1016/j.jembe.2010.03.003 2010 Elsevier B.V. All rights reserved.Density estimateFishTransectUnderwater visual censusUnderwater visual censuses are the most commonly used methods to estimate the density of reef shpopulations and assemblages. One basic assumption is that the observer will always detect sh in the sameway from one sampling unit to the next, implying that, on average, the spatial distribution pattern of shabundance or occurrence remains the same from one transect to the next (H0). The present work tested H0using data from 730 transects covering two regions (New Caledonia and French Polynesia), 604 species and504000 sh. Within transect variations in reef sh abundance and occurrence were studied according to sitefactors (region, reef type), life-history traits (adult size, home range, schooling behaviour, color, pattern,swimming speed, level in the water column, inquisitiveness, crypticity), and observations characteristics(distance of observation, size of the observed shes, number of shes within an observation, observeridentity). Two general trends were detected: 1 at the start of transects, both sh occurrence andabundance were higher than the values expected under H0; 2 a similar trend was also observed at the endof transects, but at a much lower magnitude. These effects were observed with varying degrees of magnitudefor all regions, reef types and observers, varied signicantly according to three life-history traits (size, homerange, and behaviour), but were not inuenced by species richness or abundance. These results indicate thatdatasets gathered from transects of various lengths cannot be pooled without correction. They also shed lighton some of the known differences between transects and point counts.involved when mergingternational databases.-cost, and easily imple-h have been and still are
UVC) these meththe technique prcounting sh witand Brown, 1996Supplementary mpoint countswagiven radius (BoSamoilys and Calbicki).
l rights reserved.a r t i c l e i n f o a b s t r a c tCounting coral reef shes: Interaction betransect design
Michel Kulbicki a,, Nathaniel Cornuet b, Laurent ViglGrard Moutham b, Pascale Chabanet d
a IRD, Universit de Perpignan, 52, Avenue Paul Alduy, 66860, Perpignan, Franceb IRD, B.P. A5, 98845, Nouma, New Caledoniac LIVE, University of New Caledonia, B.P. R4, 98851, Nouma Cedex, New Caledoniad IRD, B.P. 172, 97492, Ste Clotilde Cedex, La Runion, France
j ourna l homepage: wwween sh life-history traits and
a b, Laurent Wantiez c,
rine Biology and Ecology
sev ie r.com/ locate / jembeods were initiated by Brock (1954). Transect counts,oposed by Brock, is still the most used and consists ofhin a corridor of a given length and width (see Cappoand Edgar et al., 2004 for a quick review see alsoaterial I). The other type of visual census in use is
here the observer will census in a circle or half-circle ofhnsack and Bannerot, 1986;Watson andQuinn, 1997;rlos, 2000; McNeil et al., 2008). There have been
numerous articles describing the biases of these techniques and manysolutions have been proposed to correct them (e.g. Harmelin-Vivienet al., 1985; Jennings and Polunin, 1995; Watson et al., 1995; Cheal andThompson, 1997; Kulbicki, 1998; Kulbicki and Sarramgna, 1999;Samoilys and Carlos, 2000; McNeil et al., 2008; see also Supplementarymaterial I). A basic assumption of all these methods is that the observerwill record sh in the same way from one sampling unit to the next.This implies that, on average, an observer should detect sh with thesame accuracy at the start, middle and end of a transect (H0). In otherwords, this means that the same proportion of sh present should beobserved on any portion of the transect and not that the density of thesh should remain constant within the transect. If H0 is not respected,then combining results from different transect types within an analysisor using sub-units of a transect as pseudo-replicates would lead to
16 M. Kulbicki et al. / Journal of Experimental Marine Biology and Ecology 387 (2010) 1523biased results unless a correcting method is applied.It is likely that because many organisms react to the presence of an
observer, their detection will not in fact remain homogeneous withina transect (H1). In particular, organisms attracted to an observer willprobably be found in higher densities at the start of transects, whereasspecies which are frightened by an observer may increase in densityalong the transect, as they will often return as they gradually get usedto the presence of the observer. Thus factors such as transect lengthand width are likely to affect diversity and density estimates. Theamplitude of such phenomena has not been investigated to ourknowledge. In particular, H0 has never been tested formally and theeffect of transect length on demographic estimates has never beenevaluated. Yet, from the known behaviour of coral reef shes(Kulbicki, 1998) it is expected that animal movements could besufciently important as to induce important biases in diversity anddensity estimates from visual census techniques, and these will needto be addressed prior to using data from different sources for large-scale and/or global assessments.
The present article uses data from a large database on coral reefshes to test whether animal detectability remains, on average,constant within transects (H0). Specically, we investigate how reefsh observations are distributed along transects and examine anydeparture from H0 according to intrinsic (e.g. species, sh size,behaviour) and extrinsic (e.g. reef type, region, observer) factors.Finally, we discuss our results in order to explain some of the reporteddifferences amongst the most frequently used visual census methodsand highlight the consequences that these may have for large-scaleassessments of ecosystem status and functioning.
2. Materials and methods
2.1. Collection methods
The study encompasses 730 line transects performed in two regionsof the South Pacic Ocean, NewCaledonia and the Tuamotu archipelago(French Polynesia) (Table 1). These regions are characterized bydifferent levels of regional diversity, being higher in New Caledonia
Table 1Number of transects performed by each diver (nos. 1, 2, 5, and 7) according to habitatand region.
Habitat Diver no. Region Total
New Caledonia Tuamotu
Barrier 1 115 1152 65 117 1825 54 115 1697 115 115
Total barrier 119 462 581Fringing 2 84 84
5 65 65Total fringing 149 149Total 268 462 730than in the Tuamotu (Kulbicki, 2007), with 729 and 391 easilyobservable coral reef sh species respectively. The inner slopes ofbarrier reefs were sampled in both regions, and fringing reefs weresampled only in New Caledonia. Counts were performed by four diversin the Tuamotu (divers 1, 2, 5, and 7) but only two of those (divers 2 and5) collected data inNewCaledonia. All diverswere highly accomplishedreef sh counters, each with more than 10 years experience in UVC.
Line transects were performed as described by Labrosse et al.(2001). Briey, sh were recorded along a 50 m tape by two divers,one on each side of the tape. The tape was divided into 5 sections of10 m each. For each sh sighting the observer identied the speciesand recorded the size (TL in cm) and number of shes (school size) aswell as the perpendicular distance of the sh from the transect line.For schools, the average size and distance to both the nearest andfurthest shes of the group were recorded. Distances were recordedby 1 m class till 5 m, 2 m class from 6 to 10 m and 5 m class beyond10 m. Depth, substrate composition (ne sediment, gravel, debris,small and large blocks, rock, coral), algae and live coral were recordedfor each section by the MSA method (Clua et al., 2006). Transectsperformed in New Caledonia were set parallel to the reef slope(constant depth), whereas in French Polynesia the transects were laidperpendicular to the reef slope (increasing depth).
2.2. Data analysis
Two variables were analyzed in this study, occurrence andabundance. We dened an occurrence as a record of a sh or schoolof shes and abundance as the number of sh recorded in thatoccurrence. Both variables are a measure of sh abundance butoccurrence is insensitive to large schools. Occurrence is also a verygood proxy for species richness, with a very high correlation betweenthe two variables (r=0.95; N=3650, pb103). As a consequenceresults on species richness will not be presented as they are extremelysimilar to those given by occurrence. In order to have comparablemetrics for all transects, both abundance and occurrence werestandardized by transect totals and expressed in % prior to statisticalanalysis. To test if % abundance or occurrence changedwithin transect,the potential effect of the environment on these variables had to beremoved. This was performed in two steps. First, the relationshipbetween % abundance or occurrence and environmental variables (i.e.depth, substrate, algae, coral) was evaluated using a GLM. Second,environment-detrended % abundance and occurrence were calculatedby adding the mean value predicted by the GLM to model residuals.Unless otherwise stated, % abundance and % occurrence will referhereafter to detrended values.
With detrended values, testing H0 becomes equivalent to testing if%occurrence or % abundance remains constant within the transect. Totest (H0) both variables were analyzed by repeatedmeasureMANOVA(Zar, 1984) with transect section as a 5-level repeated factor. BecauseH0 predicts 20% of the observations on each section of a 5-sectiontransect (i.e. 100/5), observed % occurrence and abundance werecompared to theoretical 20% using t-tests. In order to investigate theeffects of species richness, sh total abundance, diver, region, and reeftype on sh distribution patterns, each of these factors was entered inseparate RM-MANOVAs. As distance sampling yields the distance of allthe observations to the transect line, the effect of transect width wasalso investigated by truncating the data by 1 m increments between 1and 10 m transect width. Species richness and sh abundancedistributions were calculated using each transect as a single observa-tion. The quartiles of these distributions were then used to grouptransects into four species richness or four sh abundance categoriesprior to the analysis of those two factors by RM-MANOVA. Due to anincomplete sampling design (Table 1), only the data from the barrierreefs of the Tuamotu were used to test for a diver effect. For similarreasons, only the barrier reef data fromdivers 2 and 5were used to test
for a region effect, and only the data fromNew Caledonia were used to
test for a reef type effect. For each species, sh distribution proleswere built from the calculation of average % occurrence at each of theve consecutive transect sections. Distribution proles were thenclustered by the k-meansmethod (Legendre and Legendre, 1998). TheCalinski criterion (Caliski andHarabasz, 1974)was used to determinethe best k-means partition (i.e., optimal k). Once the characteristicdistribution proles were identied, we used 2 tests to compare thetraits of species that followed each of those proles. Several traitsincluding sh size, school size, home range, level in water column,swimming speed, behaviour, color, patterns and crypticity (IRDdatabase, FISHEYE, Labrosse et al., 1999) were included in theanalysis (Table 2). To avoid biases due to low sample size, only speciesrecorded by divers 2 and 5 in every habitat and region and with morethan 20 occurrences were included in this analysis. These represented230 species out of the 604 species recorded along the 730 transects.
3.1. Within-transect variation in average sh distribution
A total of 604 species, 62677 records (occurrence) and 504000 different amongst divers (RM-MANOVA, Wilk's test, pb103).
These differences were however small, NeumanKeuls post-hoc testsonly showing signicant differences between divers 1 and 2 atsections 4 and divers 1 and 5 at section 5 (Fig. 3A). Although the four
Fig. 1. Observed % occurrence and abundance according to sections along transects.Error bars are 95% condence intervals. The dashed line represents the expected valueunder H0 (no change in the average proportion of sh observed within a transect).
Fig. 2. % occurrence according to transect width and section. The dashed line representsthe expected value under H0. Results for % abundance are extremely similar and not
17M. Kulbicki et al. / Journal of Experimental Marine Biology and Ecology 387 (2010) 1523shes (abundance) were observed along the 730 transects surveyedby four divers on the fringing and barrier reefs of New Caledonia andthe Tuamotu. For both occurrence and abundance, the null hypothesisH0 was rejected (RM-MANOVAs,Wilk's test, pb103), with rst andlast transect sections signicantly different from intermediates(NewmanKeuls test, pb103), and signicantly above 20%, thevalue expected under H0 (Fig. 1). These results were not affected bythe number of species or sh recorded on transects as RM-MANOVAsperformed on both % occurrence and % abundance distributionproles revealed non-signicant species richness (p=0.19) and shabundance (p=0.56) effects. Changing the width of the transects(Fig. 2) had very little effect on the % occurrence or % abundance foreach section. In particular the % occurrence and % abundance were thehighest for section 1 then for section 5 for all transect widths, whereassections 2, 3, 4 were never signicantly different from one anotherand were below the expected 20% of H0.
3.2. Effects of divers, regions and reef types
Data collected by four divers on the barrier reefs of the Tuamoturevealed that % occurrence distribution proles were signicantly
Table 2Denition of the traits utilized in this study to characterize the species havingcharacteristic distribution proles along visual census transects (IRD database, Labrosseet al., 1999).
Life-history trait Categories
Home range 1 territorial: species that live their entire adult lifewithina smallterritory (usually less than 100 m), 2 sedentary: specieswhichwill live in a restricted range (often several hundred m), 3average home range: specieswhichwill spendmost of their timeon the same reef for an extended period (more than a week), 4large home range: species which will swim from one reef toanother within a restricted period (less than a week)
Level in the watercolumn
1 bottom dwelling: species which always stay on the bottom,2 above bottom: species which swim within the rst meterabove the bottom, 3 high in the water column: speciesswimming at least one meter above the bottom
School size 1 solitary species; 2 species living in pairs; 3 speciesmaking small schools (on average 320 shes); 4 speciesmaking medium size schools (2050 shes); 5 speciesmaking large schools ( N50 shes)
Swimming speed 1motionless or stationary; 2 slow; 3 medium; 4fastInquisitiveness 1shy; 2 neutral; 3 inquisitiveColor 1bright;2 dull;3 camouaged;4 silveryPatterns 1uniform; 2 stripes or bands; 3 dots; 4 compositeCryptic 1 yes; 2 nodivers did not record sh exactly in the same way along transects, allthe divers noted however a signicantly higher proportion ofobservations at the rst section, and 3 out of the 4 divers recordedan increase in occurrence at section 5. The greatest departure from theexpected 20% was observed for all divers at the rst section (t-tests,pb103 for all divers) and two divers showed % occurrence notsignicantly different from 20% at the last section (t-tests, p=0.57 fordiver 2 and p=0.89 for diver 7). Results for % abundance were similarwith an identical degree of signicance (pb103) and a signicantincrease at both the start (section 1) and end (section 5) of transects(Fig. 3B).
Barrier reef data collected by divers 2 and 5 showed that %occurrence proles were signicantly different between the regions ofNew Caledonia and Tuamotu when the divers were consideredseparately (RM-MANOVA, Wilk's test, p=0.0012). Post-hoc testsindicated that the signicant regional effect consisted of a higher shoccurrence in the rst section of the transect in French Polynesia thanshown.
18 M. Kulbicki et al. / Journal of Experimental Marine Biology and Ecology 387 (2010) 1523in New Caledonia for both divers (Fig. 4A and B) and a higher shoccurrence in French Polynesia in the last section of transect for diver5. There was no difference amongst divers within a region(SectionDiverRegion p=0.64). The same analysis performed on% abundance indicated less differences amongst regions, the onlydifference being a signicantly lower value on the last section fordiver 5 in French Polynesia (Fig. 4B and D). For both variableshowever, the differences between sections remained highly signi-cant (pb103) with section 1 always higher than others and section 5higher than sections 2, 3 and 4 for diver 5 in both New Caledonia.
The effects of reef type on sh distribution proles were tested forthe barrier and fringing reefs of New Caledonia that were surveyed bydivers 2 and 5. % occurrence was not signicantly different betweenreef types and this result was consistent among the divers (Fig. 5A;RM-MANOVA, p=0.15 for reef type and p=0.52 for reef typediver).There was however a small but signicant diver effect (RM-MANOVA,p=0.023), the only signicant difference being for diver 2 whoobserved lower occurrence on section 1 than diver 5 for fringing reefs(NeumanKeuls test, pb0.037). A similar analysis on % abundanceindicated no signicant difference between reefs (RM-MANOVA,p=0.10), divers (p=0.13) and diversreefs interaction (p=0.34).As shown above, the differences between sections were highlysignicant (pb103) for both % occurrence and abundance, with thelargest differences observed at sections 1 and 5.
3.3. Effects of sh traits
K-mean clustering analysis determined 3 groups of species withdifferent % occurrence distribution proles (Fig. 6). All three proles
Fig. 3. Comparison of average % occurrence and % abundance distribution prolesamongst divers for barrier reefs in the Tuamotu. Error bars indicate 95% CondenceIntervals. The dashed line represents the expected value under H0.were different from each another (RM-ANOVA, pb103) and at leastone of their sections had an average different from H0. Cluster 1grouped 136 species. It had the attest distribution prole. Howeverthe proportion of occurrences detected on the rst section wassignicantly higher than H0 (Fig. 6A; pb103), the other sectionswere not different fromH0. Cluster 2 grouped only 30 species and wascharacterized by a higher proportion of occurrence in the last section(values above H0, pb103) and a proportion of occurrence in the rstsection in accordance with H0. Cluster 3 grouped 64 species and wascharacterized by a very high proportion of occurrence in the rstsection (pb103). The % abundance of the 3 clusters followed similartrends to those for occurrence (Fig. 6B) with however somedifferences. In particular all 3 clusters had values signicantly above20% for section 1 and only cluster 2 had values signicantly larger than20% for section 5.
Species belonging to these clusters had different characteristics(Fig. 7; Table 3). Species from cluster 1 were characterized by a lowerproportion of species having a large adult size, a lower proportion ofspecies having a wide home range, and a larger proportion of specieshaving a neutral behaviour (Table 3). Furthermore, sh from cluster 1were often observed closer to the transect line andwere usually of smallsize (510 cm; Fig. 7A). The major families and genera represented incluster 1 were small Epinephelinae, Chaetodontidae, Centropyge, thesmaller Pomacentridae (Chromis, Chrysiptera, Pomacentrus), Mullidae,Nemipteridae, small Labridae (Stethojulis, Halichoeres, Thalassoma),small Acanthuridae (Ctenochaetus, Zebrasoma, sedentary Acanthurus),small tomediumsize Scaridae, and Siganidae. Species fromcluster 2 and3 displayed traits opposite to those of cluster 1. Theywere characterizedby a large adult size and a large home range (Table 3). However, speciesfrom cluster 2 showed the highest proportion of shy species whilespecies from cluster 3 showed the highest proportion of speciesattracted to an observer (Table 3). Underwater, sh from cluster 2were ofmediumsize andwere generally observed at amediumdistancefrom the transect line (Fig. 7B) while sh in cluster 3 were larger andobserved at a greater distance (Fig. 7C). There were not enough speciesin cluster 2 to have sufcient numbers within a family. However,Lethrinus and large Pomacentridae were included in this cluster. Themajor taxa for cluster 3 were large Serranidae, Lutjanus, the largestChaetodon, Abudefduf, the largest Labridae (Bodianus, Cheilinus) andScaridae. Other traits had similar values amongst clusters, in particularno signicantdifference could be found for school size, bodypattern andswimming speed. Color was marginally signicant (p=0.05) withspecies of cluster 2 being more frequently brightly colored or silvery.
The results of this study show very clearly that two major effectswere detected. The most important effect was an increase in theproportion of species and shes observed at the start of transects. Thesecond effect was an increase in the proportion of species and shes atthe end of transects, but this increase was much lower than theincrease at the start of transects. Themagnitude of these effects variedwith sh size, distance of observation, some life-history traits (speciessize, home range, behaviour) and the regions of observation (NewCaledonia and French Polynesia). These effects were consistentamongst observers but their magnitude changed signicantly fromone observer to another. The following discussion explores some ofthe potential consequences of these ndings for the use of visualcensuses, in particular when comparing data from various methods ofvisual censuses.
Underwater censuses of shes are performed either usingtransects or point counts. The results of these methods are at timespooled into the same survey or end up in the same database. Thequestion then arises whether these two methods will yield anydifference in the estimates of population parameters or population
structure. Although the present article was neither conceived nor
19M. Kulbicki et al. / Journal of Experimental Marine Biology and Ecology 387 (2010) 1523suited to compare these methods, it may shed some light on theknown differences existing between them. Bohnsack and Bannerot(1986) set the standards for underwater point counts and one of theirmajor recommendations was that observers should wait severalminutes after reaching the bottom before starting to record shes. Thesame recommendation was given for transect counts (Harmelin-
Fig. 4. Average % occurrence (A and B) and % abundance (C and D) distribution proles for transects surveyed by divers 2 (A and C) and 5 (B and D) in the barrier reefs of NewCaledonia (diamonds) and Tuamotu regions (squares). Error bars indicate 95% Condence Intervals. The dashed line represents the expected value under H0.
Fig. 5.Average % occurrence distribution proles for transects surveyedbydivers 2 (A) and5 (B) in the fringing and barrier reefs of NewCaledonia. Error bars indicate 95% CondenceIntervals. The dashed line represents the expected value under H0.
Fig. 6. Average % occurrence distribution proles for the 3 clusters of speciesdetermined by a k-mean clustering analysis. Error bars indicate 95% CondenceIntervals. The dashed line represents the expected value under H0.
20 M. Kulbicki et al. / Journal of Experimental Marine Biology and Ecology 387 (2010) 1523Vivien et al., 1985). These recommendations were based on thegeneral awareness that sh are initially attracted by the presence ofdivers, but no precise measure of this behaviour had been undertaken.Since then several authors have investigated the differences betweenpoint counts and transects (e.g. Samoilys and Carlos, 2000; Colvocor-esses and Acosta, 2007). The general nding is that point counts tendto yield higher diversity and density estimates than transects, but noexplanation was available for the reason behind these differences. Inmany ways counts made on the rst section of our transects can beassimilated to point counts, even though there are certainly somedifferences. The higher proportion of sh at the start of transects (thiswill be referred hereafter as the rst section effect) observed duringthis study conrms that point counts will yield higher estimates.
There are several potential reasons behind this rst section effect.This pattern is found for most species, but it is more pronounced forspecies with a larger size, larger home range, which swim above thebottom and are attracted by divers (cluster 3). A number of these
Fig. 7. Spatial distribution of shes within transects according to sh size (4classes:b10 cm, 1020 cm, 2030 cm,N30 cm), distance from transect line (m), %abundance and cluster (as dened in Fig.6).species belong to important food sh species such as Lutjanidae, largeLabridae and some Acanthuridae or Lethrinidae. If counts wererestricted to the initial section, then there would be a good chancethat such sh species would be over-estimated. Interestingly Samoilysand Carlos (2000) found that point counts gave higher estimates thantransects for Acanthuridae, Lutjanidae and Lethrinidae, which couldbe a reection of the rst section effect we observed. More recentlyColvocoresses and Acosta (2007), comparing underwater visualcensuses from transects and point counts, found that point countsyieldedmuch higher density estimates for nearly all species (from 2 to6 times higher, depending on species). It is however difcult tocompare their results with this study as the species are different.
There is no similarity between the species belonging to our cluster3 and the families with the highest probability of detection estimatedin Tanzania by McNeil et al. (2008). This suggests that the rstsection effect is not related to sh detectability. This is conrmed bythe absence of variation in the level of therst section effect as transectwidth increases (Fig. 2), detection of sh decreasing rapidly withtransect width (Kulbicki, 1998). The absence of effect on the detectionproles for a number of traits such as color, shape, swimming speed orpattern further conrms this hypothesis. This suggests that the biases
Table 3Biological and ecological traits which differ signicantly amongst clusters. The numbersare the frequencies of species within each cluster which have the corresponding trait.P values are given for Chi2 testing if the proportion for a given class changes accordingto cluster. NS: not signicant.
Cluster-1 Cluster-2 Cluster-3 All species Chi
Sizeb8 cm 12.5 6.7 3.1 9.1 NS815 cm 34.6 40.0 18.8 30.9 NS1530 cm 29.4 16.7 37.5 30.0 NS3050 cm 18.4 20.0 21.9 19.6 NSN50 cm 5.1 16.6 18.7 10.4 0.011
BehaviourAttracted 14.7 13.3 25.0 17.4 NSNeutral 75.0 56.7 50.0 65.7 NSShy 10.3 30.0 25.0 17.0 0.011
Home rangeTerritorial 14.7 20.0 12.5 14.8 NSSedentary 53.7 30.0 37.5 46.1 NSLarge HR 25.7 33.3 31.3 28.3 NSVery Large HR 5.9 16.7 18.8 10.9 0.021introduced by the rst section effect are not linked to the capacity ofthe observers to detect sh, but more to the behaviour of the sh.Similar results have been obtained for birds (Lee and Marsden, 2008)with up to 58% difference between counts with andwithout a settlingtime. It has already been indicated that the behaviour of shestowards the observer can induce major biases in underwater visualcensuses (e.g. Kulbicki, 1998; Colvocoresses and Acosta, 2007). Mostpoint count procedures take into account the inquisitive nature ofmany sh species and recommend to start recording the sh only afterseveral minutes of presence. The way the transects were conducted inthis study, the time required to count sh along a transect was onaverage around 60 min (from 15 to 20 min on transects with very lowdensities, up to a maximum of 150 min), with a longer period usuallyspent on the rst section (but unfortunately the time spent on eachsection was not recorded). It is therefore a good initiative to staymotionless during several minutes before starting to count, but it isprobably not going to entirely remove this rst section effect,especially for sh species belonging to the third cluster. This isconrmed by Colvocoresses and Acosta (2007) who, despite allowingsome time before counting the sh, still found higher estimates ofdensities for point counts for 19 out of 20 species tested. The rstsection effect in our study seems very general as it was recorded by allobservers, in both regions and on both reef types. It was also common
21M. Kulbicki et al. / Journal of Experimental Marine Biology and Ecology 387 (2010) 1523to both occurrence and abundance. However the magnitude of thiseffect varied with observer and with region. A major consequence isthat it is probably inaccurate to compare or to use within the sameanalysis, data from point counts and transects, or to pool data fromtransects of various lengths, without correcting for this bias. Such acorrection is probably difcultwithout a specic calibration procedureto formally compare the methods (Colvocoresses and Acosta, 2007).Two other minor points should be noted. First, these results suggestthat splitting a transect into sub-transects and using these as pseudo-replicates will probably result in biased results. Second, a number ofexperimental designs place several transects very close one afteranother. These transects should not be counted without a majorinterruption between one another, otherwise the rst transect is likelyto yield higher estimates compared to the following ones.
A second effect which was detected during this study is theincrease in observations in the last section of the transect. This effect isalso detected for longer transects (100 m transects are also availablein our database and display the same type of increase in the lastsection a brief summary is available as Supplementary material II).The most likely reason for this phenomena is that observers tend torecord objects beyond the limit of the area to be surveyed. This biashas been coined edge effect or boundary effect (see Andrew andMapstone, 1987, for a review; Tessier et al., 2005; Tessier andChabanet, 2006). One may invoke several reasons for this. The rstreason is that in our study there was no physical indication of theboundaries of the transect, as for most transect procedures we knowfor reef shes. As a result the observer does not know exactly wherethe transect ends when at some distance from the end and maytherefore record sh which are not part of the transect. This is aproblem linked to the estimation of distances by observers. Under-water there have been several studies which have tackled this issue(Nolan and Taylor, 1980; Bohnsack and Bannerot, 1986; Tresher andGunn, 1986; Harvey et al., 2004). These studies indicate that diverswill estimate distances with a 1015% error. If divers systematicallyover-estimated the distance at which the transect ends by 15%, itwould almost entirely take into account this edge effect as the latteris on average 18% for occurrence and 20% for abundance. The secondreason is basically a human factor, most observers feeling, usuallynot consciously, that the more, the better and will add, at the lastminute, species which they did not observe earlier on the transect andwhich are at the limit of the area to be surveyed. This behaviour isprobably translated into the characteristics of the species for whichthis edge effect is the most important (species from cluster 2) andwhich are shy (species staying away from the observer), medium size,schooling and fast swimming species. Such species may not attract theattention of the observer for most of the transect but could berecorded in an increased spurt of attention the observer will usuallygive before ending the transect count. It should be noted that thewidth of the transect had no effect on the magnitude of both the rstsection and this edge effects which suggests that visibility hardlyintervenes.
The traits which inuenced the average distribution proles, i.e.size, home range and behaviour, are correlated to one another. Inparticular most territorial species are small, do not move much andare not affected by observers. For such species (which belong mostlyto cluster 1) the average distribution proles tend to be atter than forthe other species. On the other hand many species with a large homerange tend to be large and often swim above the bottom. Theiraverage distribution prole tends to have the largest departure fromH0 (species from cluster 3). As already indicated by Kulbicki (1998)the detection of many species will depend on their behaviour. Thismay generate considerable biases in the densities and biomassestimates as indicated for instance by Jennings and Polunin (1995)for Lethrinidae, by Willis et al. (2001) for Pagrus auratus (Sparidae),and by Edgar et al. (2004) for Monacanthidae. These authors found
important underestimates with visual censuses compared to othermethods. Similarly Kulbicki (1988) and Kulbicki et al. (2000a)indicate that some families such as Lutjanidae, Serranidae orLethrinidae tend to be better estimated with shing gear than withvisual surveys, and comparisons between trawl catch and visualsurveys (Kulbicki andWantiez, 1990) also indicate that sh behaviouris an important part in their detection and therefore their estimates byvisual censuses.
There were signicant differences amongst observers in theaverage distribution proles. These differences originated mainlyfrom the end of the transect (Section 5). Biases due to observers arevery common, even amongst trained observers as in the present case(e.g. St-John et al., 1990; Thompson and Mapston, 1997; Edgar et al;2004;Williams et al., 2006a; McClanahan et al., 2007). Such biases arevery difcult to correct for in the absence of a reference or standard(see Halford and Thompson, 1994 for standards; Thompson andMapston, 1997). However in the present case all divers providedsimilar average distribution proles, i.e. with a rst section effectand an edge effect, the difference between divers stemming fromthe magnitude of these effects. This pattern was also consistent withreef type and region which suggests that this average distributionprole is common to most situations on reefs.
The number of species or the number of sh on a transect is knownto inuence the quality of the sampling, the accuracy decreasing witheither species richness or abundance (e.g. for shes Harmelin-Vivienet al., 1985; Fowler, 1987; Bellwood and Alcala, 1988; Lincoln Smith,1988; Greene and Alevizon, 1989; Cheal and Thompson, 1997; formammals Caughley, 1977). In the present study neither speciesrichness nor abundance levels inuenced average distributionproles. This suggests that average distribution proles are indepen-dent of the accuracy of the census. This absence of effect on theaverage distribution proles also suggests that observation durationmay not be a key factor in the observed phenomena. Indeed ifobservation duration intervened signicantly, one would expect tohave different average distribution proles in areas with highabundances (and therefore which require a long time to count)compared to areas with low abundances (which are quicklycensused). This does not mean that census duration is not animportant issue, several studies suggesting that a slow census willincrease the chances of over-estimating densities (e.g. Bohnsack andBannerot, 1986, St-John et al., 1990; Watson et al., 1995; Watson andQuinn, 1997; De Girolamo and Mazzoldi, 2001). This nding suggeststhat the differences between point counts and transects areunaffected by abundance, diversity or reef type which opens theway for methods to correct such differences. The following paragraphbrings however some limits to this consideration.
The difference observed between regions was due to differentproportions of species. In particular the proportion of species ofcluster 3 (which generates the highest rst section effect) was largerin the Tuamotu than in New Caledonia (respectively 46% in theTuamotu and 22% in New Caledonia for occurrence; 29% and 13% forabundance). However, shing, a factor, which was not possible to testin the present situation, is very likely to be important. In the Tuamotushing pressure was very low due to low human population densities(b1 person/km of reef), whereas in New Caledonia shing pressurewas much higher due to large human populations (N30 persons/kmof reef). Changes with shing pressure in sh behaviour towards theobserver have been documented by Kulbicki (1998) and arerecognized as a major factor in sh detectability (McNeil et al.,2008). As a consequence, the magnitude of the effects documented inthis article is very likely to vary according to shing pressure. Thiscould have implications when comparing MPAs with shed areas,even if the same methodology is applied as already indicated byKulbicki (1998).
A large number of scientists argue that as long as the bias is similarfrom one transect to the next there is no concern in comparing
between transects. For instance, if small sh are constantly
strongly underestimate the densities and diversities of small species
correct for the numerous known biases linked to visual censuses of
given survey (unless the transects are extremely long, e.g. N200 m).
22 M. Kulbicki et al. / Journal of Experimental Marine Biology and Ecology 387 (2010) 1523With the emergence of large international databases the presentstudy clearly shows that before mixing the results from several UVCmethods it will be mandatory to standardize the estimates.
The authors wish to thank Mrs. Suzie Mills for correcting theEnglish and the anonymous reviewers for their constructive com-ments. [ST]
Appendix A. Supplementary data
Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.jembe.2010.03.003.
Ackerman, J.L., Bellwood, D.R., 2000. Reef sh assemblages: a re-evaluation usingenclosed rotenone stations. Marine Ecology Progress Series 206, 227237.
Andrew, N.L., Mapstone, B.D., 1987. Sampling and the description of spatial pattern inmarine ecology. Oceanography and Marine Biology Annual Review 25, 3990.
Bellwood, D.R., Alcala, A.C., 1988. The effect of a minimum length specication on visualestimates of density and biomass of coral reef shes. Coral reefs 7, 2327.
Bohnsack, J.A., Bannerot, S.P., 1986. A stationnary visual census technique forquantitavely assessing community structure of coral reef shes. NOOA Technicalreport NMFS 41, 15.
Brock, V.E., 1954. A preliminary report on a method of estimating reef sh populations.Journal of Wildlife Management 18, 297308.
Caliski, T., Harabasz, J., 1974. A dendrite method for cluster analysis. Communicationsin Statistics Simulation and Computation 3 (1), 127.
Cappo, M., Brown, I.W., 1996. Evaluation of sampling methods for reef sh population.CRC reef research technical report, Townsville Australia. 6.
Caughley, G., 1977. Analysis of Vertebrate Populations. Wiley, London.Cheal, A.J., Thompson, A.A., 1997. Comparing visual counts of coral reef sh:
implications of transect width and species selection. Marine Ecology ProgressSeries 158, 241248.
Christensen, M.S., Winterbottom, R., 1980. A correction factor for, and its application to,visual censuses of littoral sh. South African Journal of Zoology 16, 7379.sh. The present study demonstrates that the detected proportion ofthe true population or assemblage within a transect is not evenlydistributed, contrary to what it theoretically should be. This shedslight on some of the differences observed between point counts andtransects, but it does not advocate one method above another. It alsopoints to the need of using a constant length for the transects within a(Christensen and Winterbottom, 1980; Kulbicki, 1990; Ackerman andBellwood, 2000; Kulbicki et al., 2000b; Willis, 2001; Williams et al.,2006b) but visual censuses give a better estimate of large mobilespecies. There is therefore no best method, neither is it possible tounderestimated with the same bias, then it is possible to comparedensities of these shes across a number of situations. This is true,however unfortunately biases are not necessarily constant from onetransect to the next. An example may illustrate this. Consider acomparison between an MPA and a non protected area. In MPAs share usually diver friendlywhereas in unprotected areas they tend tobe diver shy. This will result in a higher detected proportion of thetrue density in the MPA compared to the unprotected area. Fish tendto be smaller outsideMPAs, which again will affect their detection, thedetected proportion of small sh being lower than for large sh. Thecompounded effect of these two biases could result in a signicantlylarger proportion of sh present in the MPA compared to the nonprotected area and therefore overestimate the MPA effect. Similarexamples could be given comparing shallow with deep reefs as shbehaviour and sh size will change as depth increases.
Numerous studies show that visual censuses do not capture anunbiased image of sh assemblages. For instance comparisons betweenrotenone poisoning and visual censuses show that visual censusesClua, E., Legendre, P., Vigliola, L., Magron, F., Kulbicki, M., Sarramgna, S., Labrosse, P.,Galzin, R., 2006. Medium scale approach (MSA) for improved assessment of coralreef sh habitat. Journal of Experimental Marine Biology and Ecology 333, 219230.
Colvocoresses, J., Acosta, A., 2007. A large-scale eld comparison of strip transect andstationary point count methods for conducting length-based underwater visualsurveys of reef sh populations. Fisheries Research 85, 130141.
De Girolamo, M., Mazzoldi, C., 2001. The application of visual census on Mediterraneanrocky habitats. Marine Environmental Research 51, 116.
Edgar, G.J., Barrett, N.S., Morton, A.J., 2004. Biases associated with the use of underwatervisual census techniques to quantify the density and size-structure of shpopulations. Journal of Experimental Marine Biology and Ecology 308, 269290.
Fowler, A.J., 1987. The development of sampling strategies for population studies ofcoral reef shes. A case study. Coral Reefs 6, 4958.
Greene, L.E., Alevizon, W.S., 1989. Comparative accuracies of visual assessmentmethods for coral reef shes. Bulletin of Marine Science 44, 899912.
Halford, A.R., Thompson, A.A., 1994. Visual census surveys of reef sh. Long-termMonitoring of the Great Barrier Reef. Standard Operational Procedure. N 3.Australian Institute of Marine Science, Townsville Australia. 22 pp.
Harmelin-Vivien, M., Harmelin, J.G., Chauvet, C., Duval, C., Galzin, R., Lejeune, P.,Barnab, G., Blanc, F., Chevalier, R., Duclerc, J., Lasserre, G., 1985. Evaluation visuelledes peuplements et populations de poissons: mthodes et problmes. RevueEcologie (Terre Vie) 40, 467539.
Harvey, E., Fletcher, F., Shortis, M.R., Kendrick, G.A., 2004. A comparison of underwaterdistance estimates made by scuba divers and a stereo video system. Implicationsfor underwater visual census of reef sh abundance. Marine and FreshwaterResearch 55, 573580.
Jennings, S., Polunin, N.V.C., 1995. Biased underwater visual census biomass estimatesfor target species in tropical sheries. Journal of Fish Biology 47, 733736.
Kulbicki, M., 1988. Correlation between catch data from bottom longlines and shcensus in the south-west lagoon of New Caledonia. Proc. 6th Int. Coral Reef Symp, 2,pp. 305312. Townsville 812 Aug. 1988.
Kulbicki, M., 1990. Comparisons between rotenone poisoning and visual counts fordensity and biomass estimates of coral reef sh populations. Proc. Cong. 1990International Society for Reef Studies, pp. 105112. Noumea 1418 Nov. 1990.
Kulbicki, M., 1998. How the acquired behaviour of commercial reef shes may inuencethe results obtained from visual censuses. Journal of Experimental Marine Biologyand Ecology 222, 1130.
Kulbicki, M., 2007. Biogeography of reef shes of the French territories in the SouthPacic. Cybium 31 (2), 275288.
Kulbicki, M., Sarramgna, S., 1999. Comparison of density estimates derived from striptransect and distance sampling for underwater visual censuses: a case study ofChaetodontidae and Pomacanthidae. Aquatice Living Resources 12, 315325.
Kulbicki, M., Wantiez, L., 1990. Comparison between sh bycatch from shrimptrawlnet and visual censuses in St. Vincent Bay, New Caledonia. Fishery Bulletin88, 667675.
Kulbicki, M., Labrosse, P., Letourneur, Y., 2000a. Stock assessment of commercial shesin the northern New Caledonian lagoon 2 lagoon bottom and near reef shes.Aquatic Living Resource 13, 7790.
Kulbicki, M., Bozec, Y.M., Ferraris, J., 2000b. Comparaison des structures de peuple-ments obtenus par rotnone et comptages en plonge dans deux baies de NouvelleCaldonie. Rencontres Franaises Ichtyologie (RIF, Paris. March 2000.
Labrosse, P., Letourneur, Y., Kulbicki, M., Magron, F., 1999. FISHEYE A new database onthe biology and ecology of lagoonal and reef shes of the South Pacic. Exemple ofits use on the ecology of commercial herbivorous shes Proc. 5th Indo-Pacic FishConf, pp. 657672. Nouma) SFI and IRD Paris.
Labrosse, P., Kulbicki, M., Ferraris, J., 2001. Comptage visuel de poissons en plonge:conditions d'utilisation et de mise en uvre. South Pacic Comission, Nouma.
Lee, D.C., Marsden, S.J., 2008. Adjusting count period strategies to improve the accuracyof forest bird abundance estimates from point transect distance sampling surveys.Ibis 150, 315325.
Legendre, P., Legendre, L., 1998. Numerical Ecology2nd ed. Elsevier, Amsterdam.English.
Lincoln Smith, M.P., 1988. Effects of observer swimming speed on sample counts oftemperate rocky reef sh assemblages. Marine Ecology Progress Series 43, 223231.
McClanahan, T.R., Graham, N.A.J., Maina, J., Chabanet, P., Bruggemann, J.H., Polunin, N.V.C.,2007. Inuence of instantaneous variation on estimates of coral reef sh populationsand communities. Marine Ecology Progress Series 340, 221234.
McNeil, M.A., Tyler, E.H.M., Fonnesbeck, C.J., Rushton, S.P., Polunin, N.V.C., Conroy, M.J.,2008. Accounting for detectability in reef-sh biodiversity estimates. MarineEcology Progress Series 367, 249260.
Nolan, R.S., Taylor Jr., L.R., 1980. An evaluation of transects methods for sh census onHawaiian reefs. Ms Hawaiian Cooperative Fishery Unit University of Hawaii. 26 pp.
Samoilys, M.A., Carlos, G., 2000. Determining methods of underwater visual census forestimating the abundance of coral reef shes. Environmental Biology of Fishes 57,289304.
St-John, J., Russ Lad, G.R., Gladstone, W., 1990. Accuracy and bias of visual estimates ofnumbers, size structure and biomass of a coral reef sh. Marine Ecology ProgressSeries 64, 253262.
Tessier, E., Chabanet, P., 2006. Using video for estimating sh post larvae abundanceafter mass installation on articial reefs. 10th International Coral Reef Symposium,5, pp. 18071811. Japon.
Tessier, E., Chabanet, P., Pothin, K., Soria, M., Lasserre, G., 2005. Visual census of tropicalsh assemblages on articial reef: slate versus video recording techniques. Journalof Experimental Marine Biology and Ecology 315 (1), 1730.
Thompson, A.A., Mapston, B.D., 1997. Observer effect and training in underwater visualsurveys of reef shes. Marine Ecology Progress Series 154, 5363.
Tresher, R.E., Gunn, J.S., 1986. Comparative analysis of visual census techniques for higlymobile, reef associated piscivores (Carangidae). Environmental Biology of shes 17,93116.
Watson, R.A., Quinn II, T.J., 1997. Performance of transect and point count underwatervisual census methods. Ecological Modelling 104, 103112.
Watson, R.A., Carlos, G.M., Samoilys, M.A., 1995. Bias introduced by the non-randommovement of sh in visual transect surveys. Ecological modelling 77, 205214.
Williams, I.D., Walsh, W.J., Tissot, B.N., Hallacher, L.E., 2006a. Impact of observers'experience level on counts of shes in underwater visual surveys. Marine EcologyProgress Series 310, 185191.
Williams, J.T., Wantiez, L., Chauvet, R., Galzin, R., Harmelin-Vivien, M., Jobet, E., Juncker,M., Mou-Tham, G., Planes, S., Sasal, P., 2006b. Checklist of the shoreshes of WallisIslands (Wallis and Futuna French Territories, South-Central Pacic). Cybium 30,247260.
Willis, T.J., 2001. Visual census methods underestimate density and diversity of crypticreef shes. Journal of Fish Biology 59, 14081411.
Willis, T.J., Millar, R.B., Babcock, R.C., 2001. Detection of spatial variability in relativedensity of shes: comparison of visual census, angling, and baited underwatervideo. Marine Ecology Progress Series 198, 249260.
Zar, J.H., 1984. Biostatistical Analysis, 2nd ed. Prentice-Hall, Englewood Cliffs, N.J.
23M. Kulbicki et al. / Journal of Experimental Marine Biology and Ecology 387 (2010) 1523
Counting coral reef fishes: Interaction between fish life-history traits and transect designIntroductionMaterials and methodsCollection methodsData analysis
ResultsWithin-transect variation in average fish distributionEffects of divers, regions and reef typesEffects of fish traits