Wariness of reef fish to passive diver presence with varying dive gear type across a coral reef depth gradient

Abstract

Both active and passive human interactions with reef fish communities are increasingly recognized to cause fish behavioural changes. However, few studies have considered how these behavioural adaptations impact standard reef survey techniques, particularly across natural gradients of interest to ecologists and reef managers. Here we measure fish abundance, biomass and minimum approach distance using stereo-video surveys to compare the effects of bubble-producing open-circuit scuba vs near-silent closed-circuit rebreathers. Surveys extended across a shallow to upper-mesophotic gradient on the fringing reefs of Utila, Honduras, to explore how the effects of diver gear choice vary with depth. For most fish families we recorded similar abundances and biomass with the two diving techniques, suggesting that open-circuit transects are generally appropriate for surveying western Atlantic reefs similar to Utila with regular tourist diving but no spearfishing. Despite no overall significant difference in fish abundance or biomass, we identified several fish families (Labridae, Pomacentridae, Scaridae) that allowed closed-circuit rebreather divers to approach more closely than open-circuit divers. In addition, smaller fish generally allowed divers to approach more closely than larger fish, and in most cases divers could approach fish more closely on mesophotic than shallow reefs. Despite these significant differences in approach distances, their magnitude suggest they are unlikely to affect reef fish detectability during normal fish surveys for most families. Our findings highlight the importance of considering variation in fish behavioural adaptations along natural gradients such as depth, which otherwise has the potential to cause biases when surveying by traditional monitoring programmes.

Full text

Wariness of reef fish to passive diver
presence with varying dive gear type
across a coral reef depth gradient
dominic a. andradi-brown
1,2
, erika gress
2
, jack h. laverick
1,2
, margaux a. a. monfared
2,3
,
alex david rogers
1†
and dan a. exton
2†
1
Department of Zoology, University of Oxford, The Tinbergen Building, South Parks Road, Oxford OX1 3PS, UK,
2
Operation
Wallacea, Wallace House, Old Bolingbroke, Spilsby PE23 4EX, UK,
3
School of Biological Sciences, University of Portsmouth,
University House, Winston Churchill Avenue, Portsmouth PO1 2UP, UK
†These authors have contributed equally and are joint last authors.
Both active and passive human interactions with reef fish communities are increasingly recognized to cause fish behavioural
changes. However, few studies have considered how these behavioural adaptations impact standard reef survey techniques,
particularly across natural gradients of interest to ecologists and reef managers. Here we measure fish abundance, biomass
and minimum approach distance using stereo-video surveys to compare the effects of bubble-producing open-circuit scuba vs
near-silent closed-circuit rebreathers. Surveys extended across a shallow to upper-mesophotic gradient on the fringing reefs of
Utila, Honduras, to explore how the effects of diver gear choice vary with depth. For most fish families we recorded similar
abundances and biomass with the two diving techniques, suggesting that open-circuit transects are generally appropriate for
surveying western Atlantic reefs similar to Utila with regular tourist diving but no spearfishing. Despite no overall significant
difference in fish abundance or biomass, we identified several fish families (Labridae, Pomacentridae, Scaridae) that allowed
closed-circuit rebreather divers to approach more closely than open-circuit divers. In addition, smaller fish generally allowed
divers to approach more closely than larger fish, and in most cases divers could approach fish more closely on mesophotic than
shallow reefs. Despite these significant differences in approach distances, their magnitude suggest they are unlikely to affect
reef fish detectability during normal fish surveys for most families. Our findings highlight the importance of considering vari-
ation in fish behavioural adaptations along natural gradients such as depth, which otherwise has the potential to cause biases
when surveying by traditional monitoring programmes.
Keywords: Fish wariness, mesophotic coral ecosystem, closed-circuit rebreather, flight initiation distance, passive diver presence, DOV,
stereo-video system, minimum approach distance, Honduras
Submitted 17 June 2016; accepted 6 June 2017
INTRODUCTION
Many studies have assessed the effectiveness of marine pro-
tected areas, including different management forms such as
no-take zones and partial protection, generally finding that
marine protected areas are effective in maintaining fish
density and biomass (Sciberras et al., 2013). However, assess-
ments rarely consider whether varying fish behaviours across
the study area may bias the results of their survey techniques
(Kulbicki, 1998, Feary et al., 2011). Fish behaviour is known to
be impacted by previous exposure to humans (Januchowski-
Hartley et al., 2015), yet locations with direct human interac-
tions with the marine environment tend to be those reef
managers are most interested in assessing. Even on a local
scale, the exposure of fish communities to these effects can
be highly variable along natural gradients such as depth.
With much recent interest in the threats faced by mesophotic
coral ecosystems (MCEs; reefs 30– 150 m depth) (Andradi-
Brown et al., 2016a), and whether they act as refuges from
fishing (Bejarano et al., 2014; Lindfield et al., 2016), gaining
a better understanding of fish behavioural survey biases
across depth gradients is crucial.
While bias in some form is an unavoidable symptom of all
survey methods, stakeholders rely heavily on data pertaining
to fish populations and their responses to management inter-
ventions to inform decision-making. On tropical coral reefs
this tends to involve baseline fish community data collected
using underwater visual census (UVC) by surveyors in the
water (English et al., 1997; Sale, 1997; Mapstone & Ayling,
1998). In many cases, to conduct UVC open-circuit (OC)
scuba divers swim along a fixed-length transect recording
individuals of all (or target) fish species, additionally estimat-
ing lengths in some cases (English et al., 1997). As a result of
concerns about repeatability between surveyors because of
observer bias (Thompson & Mapstone, 1997), video surveys
have begun to replace in-water observations for many
surveys (Mallet & Pelletier, 2014). While the use of video
removes many errors associated with in-water data collection,
the differing response behaviours of reef fish to diver presence
Corresponding author:
D.A. Andradi-Brown
Email: dominic.andradi-brown@zoo.ox.ac.uk
1
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raises new concerns (Chapman et al., 1974; Cole, 1994;
Watson & Harvey, 2007). The operation and bubble release
of traditional OC scuba generates high levels of noise at the
low frequencies fish are most sensitive to (Radford et al.,
2005). This has led to the suggestion fish may be able to
audibly detect diver presence before divers can visually iden-
tify fish (Radford et al., 2005). This potentially could allow
fish to avoid areas of reef with divers present and so remain
undetected, or aggregate around divers in the water from a
larger reef area enhancing fish abundance and biomass
estimates.
Despite these known effects, few UVC studies acknowledge
the bias that OC scuba is likely to cause to their results
(Dickens et al., 2011), instead most biases are assumed to be
consistent across survey sites and thus mitigated. However,
in areas with regular spearfishing, flight initiation distance
(FID; the minimum distance a diver can approach a fish
before it flees) of many fish families have repeatedly been
found to be higher than in protected areas (Gotanda et al.,
2009; Januchowski-Hartley et al., 2011,2014). Therefore,
despite standardized methodologies, varying fishing pressure
may bias survey results and artificially inflate the apparent
effectiveness of marine protected areas (Lindfield et al.,
2014a). For example, Lindfield et al.(2014a) used diver-
operated stereo-video systems to assess how close they could
approach fish when surveying using OC vs CCR. They
observed that fish species targeted by spearfishers avoid OC
divers, and this was significant enough to reduce abundance
and biomass estimates compared with surveys conducted in
the same location using CCR. Even within protected areas,
other more subtle effects may significantly bias results, for
example fish habituation to the presence of divers in areas
with intensive dive tourism (Titus et al., 2015), which can
be enhanced when divers feed fish populations (Cole, 1994),
or the presence of fish ontogenetic migrations, with more
mature and thus larger individuals found at greater depths
(Grol et al., 2014). Many fish families exhibit greater FID in
larger individuals than smaller individuals (Gotanda et al.,
2009; Januchowski-Hartley et al., 2011), suggesting for
species with well-defined ontogenetic migrations, divers
might be able to approach individuals more closely on
shallow reefs than deeper reefs.
The effects of recreational dive tourism are highly depth
biased, being typically limited to a maximum depth of 30 m,
and in reality generally much shallower because of training
limitations and no-decompression limits. This subsequently
skews the opportunity for fish habituation to OC divers
towards shallower reefs. Even when surveying across depth
gradients, the increased distance for bubbles to travel to the
surface when completing deeper transects is likely to produce
greater total noise (Radford et al., 2005), and thus impacts
on fish behaviour, than shallower transects, leading to unin-
tended bias in the data. Recent advances have made technical
diving more accessible, including the emergence of commer-
cially available Closed-Circuit Rebreathers (CCR). CCR
systems recycle gas rather than releasing bubbles into the
water column, see Sieber & Pyle (2010) for a detailed system
overview, and are therefore significantly quieter than their
OC counterparts. When it comes to their impact on fish behav-
iour, fish can detect the sounds of OC divers .200 m away
over a range of typical background noise levels, while CCR
divers can only be detected between 0.3 – 15.9 m away depend-
ing on background underwater noise (Radford et al., 2005).
It therefore seems likely that some patterns detected in fish
surveys using OC divers across depth gradients may be influ-
enced by fish behavioural biases that change with depth. Yet
no studies have directly investigated how detection bias in
OC scuba changes across the depth gradient. To investigate
this question we conducted fish community assessments
across a shallow to upper-mesophotic reef gradient at three
sites in the Bay Islands Marine Park, Utila, Honduras using
OC scuba and CCR. By conducting surveys using CCR, fish
disturbance effects caused by bubbles and sounds of OC
scuba regulators were absent, reducing these biases on fish
community assessment. We investigated whether depth inter-
acted with the differences observed between the two methods.
As the shallow reefs of Utila are heavily dived by recreational
divers, mesophotic fish populations are likely to be less habi-
tuated to diver presence than shallow reef fish, suggesting that
individuals could be more wary of OC divers on MCEs than
shallow reefs.
MATERIALS AND METHODS
Study sites
Surveys were conducted on the south shore of Utila, Bay
Islands, Honduras (Figure 1). Utila is located 29 km off
the Caribbean coast of Honduras, forming the southern
extent of the Mesoamerican Barrier Reef (Harborne et al.,
2001). While Utila is within the Bay Islands Marine Park,
fishing is allowed on the reefs, with the majority of fishing,
including at our study sites, carried out by handlines targeting
Lutjanidae and Serranidae (Gobert et al.,2005; Box & Canty,
2011). Historically fishers carried spears to opportunistically
shoot large fish they encountered, however, spearfishing has
been banned from the island’s fringing reefs since 2004
(Kramer et al., 2015). Recently tourism has replaced fishing
as the dominant source of income (Cronk & Steadman,
2002; Doiron & Weissenberger, 2014), primarily consisting
of recreational diving (Doiron & Weissenberger, 2014), with
.10 dive centres operational on Utila and tens of thousands
of recreational dives completed annually. Therefore, while
our study sites are fished at a relatively low level, they are
very heavily dived by recreational divers.
Fig. 1. Map of Utila with the three survey sites marked. (1) Little Bight, (2)
Coral View and (3) Rocky Point. Inset map indicates location of Utila
relative to the western Atlantic region.
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Shallow reefs on the south shore of Utila are a spur and
groove system, which transition into a mesophotic patch
reef system at 30– 40 m depth. Therefore shallow reefs
exist as a continuous reef system with 92% hard substratum
cover, while MCEs are broken by areas of sand, so only have
20% hard substratum cover (Andradi-Brown et al., 2016b).
Reef fish abundance and biomass patterns across this shallow
to upper-MCE depth gradient on the south shore of Utila have
previously been studied, finding that for the majority of
species both abundance and biomass decline with increased
depth (Andradi-Brown et al., 2016b).
Video surveys of fish communities
Fish community surveys were conducted along 50 m long by
5 m wide transects using a diver operated stereo-video system
(DOV), made up of two Canon HFS21 video cameras (see
Andradi-Brown et al., 2016b). Cameras were separated by
0.75 m with convergence angles of 888888. By recording with
two cameras simultaneously, DOV allows post-dive analysis
of fish abundance and accurate measurements of body
length and distance from camera, all within automatically
defined transect boundaries (Harvey et al., 2001,2004). Four
replicate transects separated by 10 m were conducted follow-
ing the reef contour at four depths (5, 15, 25, 40 m) at three
sites, giving 48 unique transect locations across all sites and
depths (Figure 1). Each transect was surveyed twice, with
each survey taking place on different days, once using OC
scuba equipment and once using CCR, giving 96 transect
surveys conducted in total. The order of whether to survey a
transect by CCR or OC first was randomized for each individ-
ual transect. In no cases were all four transects at the same
depth at a site surveyed by the same dive gear type within
the same day. All survey sites had fixed/marked start locations
for each transect allowing the same area of reef to be surveyed
by both methods. All transects were conducted during
daytime between the hours of 8 am and 4 pm during July –
September 2015. Transects were surveyed by a DOV operator
using OC or CCR followed by a second diver laying a transect
tape to measure distance. To minimize disturbance to the fish
community prior to recording, a 60 m transect tape was used,
with the cameras set recording and synchronized with a hand
torch before swimming 10 m along the reef with the cameras
filming directly downwards below the diver. After the DOV
operator had swum 10 m, the transect diver signalled for
them to lift the cameras and begin the transect proper, signal-
ling again once the full 50 m had been swum. Cameras were
held to film looking forward along the reef following the
depth contour. Each transect took 3 min to film. CCR
surveys were conducted using a mixture of Hollis Prism 2
(Hollis, San Leandro, CA, USA), rEvo X micro (rEvo
rebreathers, Bruges, Belgium), or Sentinel (Vobster Marine
Systems, Somerset, UK) rebreathers.
Video analysis
Transect videos were blinded to survey method and analysed
with EventMeasure software v3.51 (SeaGIS, Melbourne,
Australia). Transect boundaries were defined as 2.5 m either
side of the transect giving a 5 ×50 m survey area for each
transect. All fish were identified to species level or the
lowest taxonomic level possible if not identifiable to species.
If visible on both cameras, fish length and distance from
cameras was recorded using the built in measurement tools
in EventMeasure. Measurements were taken when each indi-
vidual fish was at its closest to the DOV system, thus recording
the minimum approach distance (MAD; the minimum dis-
tance the DOV operator could approach the fish before it
moved away). Where MADs were below the minimum dis-
tance needed to appear simultaneously on both cameras, we
recorded the fish at the closest point while visible on both
cameras. The exact minimum distance for a fish to appear
on both cameras was variable based on how far from the
centre of the transect the fish was located. For a fish central
to the transect, the minimum MAD that could be recorded
is 50 cm. All visible fish in front of the cameras, regardless
of distance away, and within the 5 m transect width were
recorded at the point they were closest to the DOV. Fish
lengths were converted to biomass using length-weight para-
meters for each species from Fishbase (Froese & Pauly,
2016). Where fish appeared on the transect but were never
visible on both cameras simultaneously, fish lengths and
MAD were unable to be recorded and thus these were only
included in abundance and biomass analysis. To estimate
biomass for these fish, they were first allocated the mean
length recorded for that species from other individuals
recorded on the transect and this estimate then converted to
biomass. Raw data is available from figshare (http://dx.doi.
org/10.6084/m9.figshare.5072329). Data analyses were con-
ducted at the family level. However, because of the historical
large bodied grouper fishery around Utila we split the family
Serranidae into the two sub-families: Epinephelinae and
Serraninae. In addition, because of the differing ecological
roles on Caribbean reefs, we considered the Scaridae sub-
family separately from all other members of the Labridae
family during analysis.
Abundance and biomass analysis
As a result of the non-normal nature of fish abundance data
we used permutational multivariate analyses of variance
(PERMANOVA) because PERMANOVA has fewer assump-
tions about the distribution of the data (Anderson et al.,
2008). A PERMANOVA was run for each fish family indi-
vidually based on a Bray– Curtis dissimilarity matrix con-
structed with the square rooted abundance data or biomass
data, testing for differences between sites, depth and the site:
depth interaction. Bray– Curtis was used so common absences
of fish families from transects do not influence the results
(Clarke et al., 2006), because we had many transects where a
fish family was not recorded by either CCR or OC.
However, Bray– Curtis dissimilarities are undefined when
transects contain no individuals at all. To address this, we
included an additional dummy family given an abundance
or biomass of 1 for all transects alongside the family of interest
in all PERMANOVAs (Clarke et al., 2006). We restricted ana-
lysis to fish families that appeared on .50 transect surveys
(out of 96 total) across all sites and depths. All
PERMANOVAs were conducted in R (R Core Team, 2013)
using the ‘adonis’ function in the package vegan (Oksanen
et al., 2013) and run for 99,999 permutations.
Minimum Approach Distance (MAD) analysis
To investigate MAD for each fish family we used analysis of
covariance (ANCOVA). Differences in MAD between
dive gear and depth effects on reef fish 3
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methods are most likely to affect the detection of fish furthest
away from the divers. To identify whether differences in MAD
potentially could alter fish detection ability over the distances
visually surveyed by DOV, we calculated the 90th quantile of
MAD for each fish family by transect for families that were
detected on .50 transects. To meet normality and homogen-
eity assumptions we natural log-transformed raw MAD data.
We tested the effects of site (categorical), depth (continuous)
and method (categorical) on MAD. For significant explana-
tory variables we used the model to generate predictions
based on altering the significant explanatory variable, and
then back-transformed log(MAD) predictions to see the
effect on MAD.
As individual fish body size has been show to alter fish
behavioural responses to divers, we also analysed our data
using an ANCOVA without grouping fish family data by tran-
sect. This allowed four explanatory variables: site and survey
method (categorical), and depth and fish length (continuous),
and was done for all fish families with ≥30 individual MAD
and fish length observations for both CCR and OC (giving a
minimum total number of observations of 60). We also
tested for interactions between survey method, depth and
fish length on MAD. ANCOVA models were fitted in base
R using the stats package (R Core Team, 2013). The function
‘step’ was used to simplify models starting with the full model
with all interactions, and iteratively removing one variable or
interaction at a time from the model starting with the most
complex. If removing a variable or interaction resulted in a
lower model Akaike information criterion (AIC) the variable
or interaction was dropped, if not it was replaced and
another variable or interaction tested. Where significant
two-way interactions were identified we examined them
while controlling for the other explanatory variables by fol-
lowing a partial correlation approach (Brown & Hendrix,
2014). This involved plotting relationships between the
centred residuals from linear models of the variables of inter-
est with the other explanatory variables. During model check-
ing two Lachnolaimus maximus records were removed as they
had an undue influence on the fitted model. Lachnolaimus
maximus is a large-bodied Labridae species and these two
records, the only times we recorded this species, were both
of individual fish substantially larger than any other
Labridae in our dataset. With these two individuals removed
the remaining 379 Labridae MAD and length measurements
met the model assumptions.
RESULTS
Abundance
Median fish abundance was greater for CCR transects than
OC at all surveyed depths (Figure 2), however there was no
significant effect of survey method on total fish abundance
per transect (PERMANOVA, Pseudo-F¼2.17, P¼0.09) or
total fish biomass per transect (PERMANOVA, Pseudo-F¼
0.01, P¼0.96). However, we found abundance differences
between CCR and OC when considering specific fish families.
Method effects were recorded based on abundance for
Acanthuridae and Tetraodontidae, and based on biomass for
Lutjanidae and Tetraodontidae. In addition we found signifi-
cant method:depth interactions based on abundance and
biomass for Tetraodontidae across the shallow to mesophotic
depth gradient (Table 1). Acanthuridae median abundance
per transect was similar between methods (Figure 3A), but
shallow (5 m) OC transects detected several large schools of
Acanthurus coeruleus increasing the mean abundance esti-
mates from 1.83 +0.58 per 250 m
2
for CCR to 6.67 +5.05
per 250 m
2
for OC (mean +SE). Tetraodontidae displayed
similar abundance recorded by both methods in the shallows,
but greater abundance recorded by CCR than OC at 25
(14.92 +3.55 vs 5.83 +5.21 per 250 m
2
) and 40 m
(25.67 +13.09 vs 4.17 +3.92 per 250 m
2
)(Figure 3B).
Tetraodontidae biomass also showed the same pattern
(Figure 3D). Greater Lutjanidae biomass was recorded by
CCR in the shallows than OC, with 637 +279 vs 463 +
297 g per 250 m
2
at 5 m while there was less difference
between CCR and OC (139 +38 vs 309 +110 g per
250 m
2
)at40m(Figure 3C). The majority of fish families
tested did not show any effect of survey method on abundance
or biomass, though many exhibited effects of depth and survey
site on abundance (Table 1). No difference in abundance or
biomass between the two methods was detected for
Haemulidae, Labridae, Lutjanidae, Pomacentridae or
Scaridae (Table 1).
Minimum Approach Distance (MAD)
To evaluate whether fish are likely to be missed from transects
we examined the 90th quantile of MAD for each fish family by
transect. Only Scaridae showed any effect of method, with indi-
viduals fleeing more readily from OC than CCR divers
(Table 2). However, Labridae, Lutjantidae, Pomacentridae
and Scaridae all showed effects of depth on the 90th quantile
of MAD, with fish allowing divers to more closely approach
at 40 m compared with the shallows (Table 2). Despite these
broad patterns when aggregating data across whole transects,
when considering individual fish, differences in MAD in rela-
tion to survey method, depth and individual fish length were
observed. Table 3 shows analysis results for individual fish,
along with model predictions of MAD. MAD was greater for
Fig. 2. Comparison of the two survey methods across the depth gradient for
total fish abundance per transect for all sites. The solid black line represents
the median, with the box indicating the upper and lower quartiles, and
whiskers representing the maximum or minimum observed value that is
within 1.5 times the interquartile range of the upper or lower quartile,
respectively. Open circles represent data points that fall outside the mean +
1.5 times the interquartile range.
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OC for Labridae, Pomacentridae and Scaridae, meaning indi-
vidual fish could be more closely approached 23%, 6% and
14% respectively by divers when using CCR (Table 3). The sub-
family Serraninae showed the opposite pattern, with divers able
to approach 17% closer on OC than CCR (Table 3).
Interactions between method and depth were also found for
Labridae, with divers on average able to approach fish more
closely on CCR (229.3 +48.2 cm) than OC (264.2 +
34.9 cm) at 5 m. However, this difference decreased as depth
increased, with little difference between CCR (194.9 +
44.6 cm) and OC (168.0 +34.7 cm) at 40 m (Figure 4D).
Many fish families did not show a difference in MAD
between the two survey methods, including Acanthuridae,
Hamulidae, Lutjanidae and Tetraodontidae (Table 3).
Many families were observed to have higher MADs for
larger individuals, including Acanthuridae, Labridae,
Pomacentridae and Scaridae, meaning larger fish allowed
divers to approach them less closely than smaller fish
(Table 3). In some cases these differences were large, for
example, we predict on shallow reefs using OC a juvenile
Acanthuridae 9.9 cm long would have a MAD of 205.9 cm,
while a mature individual 23.2 cm long would have a MAD
of 290.6 cm giving .0.8 m difference. Haemulidae also
showed a significant depth and length interaction, with
greater effects of fish length on MAD at mesophotic depths,
and less effect in the shallows (Figure 4A), suggesting that
larger individuals are more tolerant of allowing divers closer
on shallow reefs than they are at mesophotic depths.
We also detected interactions between fish length and
survey method for Labridae and Pomacentridae (Table 3),
with smaller Labridae appearing more wary of CCR divers
than OC divers which reversed for larger individuals, which
were more wary of OC divers (Figure 4C). However, the
opposite patterns was observed for Pomacentridae
(Figure 4B). For Haemulidae we detected a Length:Depth
interaction, with larger fish being more wary on deeper reefs
than smaller fish, yet less difference based on body size in
the shallows (Figure 4A).
DISCUSSION
Of the seven fish families encountered on more than 50 tran-
sect surveys, only three showed variations in overall abun-
dance or biomass between OC and CCR, and we found no
difference in total fish abundance or biomass between
the two methods. While we did detect differences in fish
Table 1. PERMANOVA results based on Bray– Curtis dissimilarity
matrix for difference in fish abundance and biomass recorded for each
fish family by OC and CCR.
Abundance Biomass
DF Pseudo-Fp(perm) Pseudo-Fp( perm)
Acanthuridae
Site 2 4.69 <0.001 4.47 <0.001
Method 1 3.79 0.045 1.93 0.151
Depth 1 32.22 <0.001 23.99 <0.001
Method:Depth 1 0.70 0.434 1.09 0.301
Residuals 90
Total 95
Haemulidae
Site 2 0.33 0.190 0.34 0.223
Method 1 3.06 0.079 3.23 0.064
Depth 1 0.78 0.385 0.21 0.745
Method:Depth 1 0.91 0.345 0.86 0.367
Residuals 90
Total 95
Labridae
Site 2 12.10 0.001 5.21 0.006
Method 1 0.15 0.834 0.15 0.917
Depth 1 13.99 <0.001 9.06 <0.001
Method:Depth 1 0.09 0.897 0.23 0.845
Residuals 90
Total 95
Lutjanidae
Site 2 0.87 0.244 1.46 0.154
Method 1 2.84 0.091 4.10 0.031
Depth 1 1.27 0.259 0.56 0.508
Method:Depth 1 0.08 0.847 0.39 0.615
Residuals 90
Total 95
Pomacentridae
Site 2 14.34 <0.001 9.87 <0.001
Method 1 0.31 0.691 0.63 0.544
Depth 1 80.69 <0.001 49.73 <0.001
Method:Depth 1 1.96 0.147 2.49 0.077
Residuals 90
Total 95
Scaridae
Site 2 8.62 <0.001 6.85 <0.001
Method 1 0.16 0.773 0.33 0.724
Depth 1 26.32 <0.001 16.92 <0.001
Method:Depth 1 0.38 0.586 0.24 0.820
Residuals 90
Total 95
Tetraodontidae
Site 2 11.85 <0.001 10.97 <0.001
Method 1 11.84 <0.001 8.66 0.001
Depth 1 7.20 0.005 5.66 0.011
Method:Depth 1 14.47 <0.001 13.76 <0.001
Residuals 90
Total 95
Only fish families recorded on .50 out of the 96 transect surveys are
shown.
Fig. 3. Fish family abundance or biomass recorded by CCR (light grey) and
OC (dark grey) across the depth gradient. Fish families are (A)
Acanthuridae abundance, (B) Tetraodontidae abundance, (C) Lutjanidae
biomass and (D) Tetraodontidae biomass. The solid black line represents the
median, with the box indicating the upper and lower quartiles and whiskers
representing the maximum or minimum observed value that is within 1.5
times the interquartile range of the upper or lower quartile respectively.
dive gear and depth effects on reef fish 5
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behaviour between OC scuba and CCR techniques, particu-
larly Minimum Approach Distance (MAD) for Labridae,
Pomacentridae and Scaridae and Serraninae, only Scaridae
showed differences between methods for 90th quantile of
MAD (representing fish remaining furthest away from the
cameras). Despite this, none of these differences in MAD
appear to be of sufficient magnitude to affect detectability
during typical reef monitoring programmes. This suggests
generally OC scuba surveys are appropriate for reef fish com-
munity monitoring on Utila.
Detectability variation between OC and CCR
Of the differences in abundance and biomass we identified
between techniques: Tetraodontidae abundance and biomass
was similar on shallow reefs between techniques, but greater
abundance and biomass was recorded by CCR than OC on
MCEs. Lutjanidae biomass was higher when surveyed with
CCR than OC. In contrast Acanthuridae abundance was
similar between methods on MCEs, yet OC recorded more
Acanthuridae individuals in the shallows. To our knowledge
only two studies have previously compared fish abundance
and biomass surveys conducted by OC and CCR, both of
which were located in the Indo-Pacific region and focused
on comparing fish surveys at the same depth between areas
with differing levels of spearfishing (Lindfield et al., 2014a;
Gray et al., 2016). One conducted in Micronesia demonstrated
a clear negative effect of OC on recorded fish biomass in areas
with spearfishing (Lindfield et al., 2014a). The other, con-
ducted in Hawaii found no overall effect across a gradient in
fishing pressure, but that at the most heavily spearfished
site, fish biomass of key species was lower when surveyed by
OC (Gray et al., 2016). Therefore we believe this study is the
first identifying that differences in fish community surveys
between OC and CCR can be depth specific.
We found few differences in abundance or biomass
between OC and CCR for most fish families. This lack of
major differences between survey methods for many fish fam-
ilies’ abundance and biomass was surprising, as it is in con-
trast to previous studies which demonstrate effects of diver
presence in the water, e.g. Watson & Harvey (2007).
However, Watson & Harvey (2007) used fish point counts
from a static camera system with an OC diver either present
or absent. Habituation to OC divers has been recorded in
reef fish on Utilan fringing reefs, though habituated fish still
exhibited diminished behaviours compared with surveys
without divers present (Titus et al., 2015). Previous studies
have also found few differences when comparing fish commu-
nities surveyed by OC and semi-closed rebreathers (Cole et al.,
2007), though unlike CCR semi-closed rebreathers produce
bubbles. Our lack of differences in observed abundance or
biomass between survey techniques for many fish families
fits with results of Lindfield et al.(2014a), who found that
their non-spearfished family control, Chaetodontidae, did
not show a significant difference in biomass between survey
techniques, and also similar results between methods within
protected areas. The lack of difference in abundance or
biomass based on survey method is further supported by
our analysis of the 90th quantile of MAD for seven fish fam-
ilies. We found only one family, Scaridae, which had a lower
90
th
quantile of MAD for CCR than OC. This suggests that
regardless of technique, fish with the greatest MAD are still
similar between methods, and within the detection range of
both techniques.
While our abundance and biomass results and 90th quan-
tile of MAD suggest that choice of dive technique has limited
effect on broad fish community results, our MAD results show
that some fish families did in fact exhibit differences in their
responses to the two dive techniques. MADs were lower for
CCR surveyed fish than OC for several families (Labridae,
Pomacentridae, Scaridae). Only the sub-family Serraninae
had smaller MAD for OC transects than CCR transects.
This leads us to conclude that differences between OC and
CCR do cause detectable effects on fish surveys, most likely
through driving active avoidance or attraction behavioural
responses in some key fish families. However, despite these
differences being detectable, they are unlikely to undermine
the data collection and the reliability of either dive gear
Table 2. ANCOVA model results for 90th quantile per transect of log
Minimum Approach Distance (MAD) for fish families following simplifi-
cation based on model AIC.
Family/term DF MS FP Effect on MAD (cm)
Acanthuridae
Site 2 0.45 2.91 0.066
Depth 1 0.10 0.64 0.427
Method 1 0.61 3.94 0.054
Residuals 43 0.15
Haemulidae
Site 2 0.00 0.04 0.961
Depth 1 0.19 2.11 0.154
Method 1 0.18 2.00 0.164
Residuals 45 0.09
Labridae
Site 2 1.05 7.03 0.002 CV: 263.7, LB: 285.6, RP:
395.5
Depth 1 2.53 16.91 <0.001 5 m: 388.4, 40 m: 226.5
Method 1 0.26 1.76 0.190
Residuals 64 0.15
Lutjanidae
Site 2 0.13 1.06 0.354
Depth 1 0.72 5.72 0.021 5 m: 416.8, 40 m: 307.3
Method 1 0.20 1.56 0.217
Residuals 52 0.13
Pomacentridae
Site 2 0.64 10.29 <0.001 CV: 309.7, LB: 358.2, RP:
415.3
Depth 1 2.29 36.67 <0.001 5 m: 417.2, 40 m: 279.3
Method 1 0.01 0.15 0.699
Residuals 75 0.06
Scaridae
Site 2 0.71 6.45 0.003 CV: 312.7, LB: 334.0, RP:
423.0
Depth 1 1.12 10.22 0.002 5 m: 404.8, 40 m: 300.6
Method 1 0.48 4.38 0.040 OC: 386.2, CCR: 339.3
Residuals 73 0.11
Tetraodontidae
Site 2 0.22 2.09 0.133
Depth 1 0.01 0.11 0.744
Method 1 0.04 0.35 0.555
Residuals 60 0.11
Only fish families that were recorded on .50 transects. Effect on MAD
column shows the mean 90th quantile MAD value (in cm) across all trans-
ects within a category for variables that were found to be significant, allow-
ing direction and approximate magnitude of effects to be seen. Sites were:
Coral View (CV), Little Bight (LB) and Rocky Point (RP), while methods
were: open circuit (OC) and closed-circuit rebreather (CCR).
6 dominic a. andradi-brown et al.
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when conducting surveys. Other studies, for example, Feary
et al.(2011) have reached analogous conclusions when com-
paring FID in areas with fishing and those without. They
found that while active spearfishing increases mean FID,
this increased FID does not extend beyond the range at
which the fish can be detected (Feary et al., 2011).
Table 3. ANCOVA model results for log Minimum Approach Distance (MAD) for fish families following simplification based on model AIC.
Family (no. individuals)/term DF MS FP Predicted MAD (cm)
Acanthuridae (78)
Site 2 1.20 7.48 0.001 CV: 204.1, LB: 263.4, RP: 327.7
Depth 1 0.03 0.21 0.646
Method 1 0.37 2.31 0.133
Length 1 1.18 7.36 0.008 10% (9.9 cm): 205.9, 90% (23.2 cm): 290.6
Depth:Length 1 0.43 2.66 0.107
Residuals 71 0.16
Haemulidae (118)
Depth 1 1.52 14.84 <0.001 5 m: 269.5, 40 m: 205.9
Length 1 0.00 0.03 0.876
Depth:Length 1 0.43 4.23 0.042
Residuals 114 0.10
Labridae (373)
Site 2 0.42 3.97 0.020 CV: 184.3, LB: 184.6, RP: 212.1
Depth 1 5.58 52.28 <0.001 5 m: 199.3, 40 m: 151.5
Method 1 1.79 16.82 <0.001 OC: 208.9, CCR: 184.3
Length 1 3.51 32.92 <0.001 10% (3.0 cm): 202.2, 90% (13.1 cm): 219.9
Depth:Method 1 0.52 4.85 0.028
Depth:Length 1 0.03 0.31 0.580
Method:Length 1 0.76 7.12 0.008
Depth:Method:Length 1 0.71 6.68 0.010
Residuals 363 0.11
Lutjanidae (113)
Site 2 0.91 9.58 <0.001 CV: 321.0, LB: 278.7, RP: 286.0
Depth 1 1.01 10.65 0.001 5 m: 347.3, 40 m: 263.7
Length 1 0.06 0.67 0.414
Residuals 108 0.09
Pomacentridae (759)
Site 2 0.55 4.71 0.009 CV: 228.3, LB: 227.3, RP: 254.1
Depth 1 2.31 19.81 <0.001 5 m: 240.4, 40 m: 200.6
Method 1 1.56 13.37 <0.001 OC: 249.6, CCR: 228.3
Length 1 1.62 13.93 <0.001 10% (3.0 cm): 248.2, 90% (10.5 cm): 251.0
Depth:Length 1 0.41 3.52 0.061
Method:Length 1 1.33 11.41 0.001
Residuals 751 0.12
Scaridae (293)
Site 2 0.44 3.50 0.032 CV: 222.0, LB: 220.1, RP: 241.4
Depth 1 0.70 5.58 0.019 5 m: 232.7, 40 m: 197.5
Method 1 1.00 7.96 0.005 OC: 254.1, CCR: 222.0
Length 1 3.98 31.84 <0.001 10% (7.0 cm): 223.3, 90% (26.6 cm): 300.3
Residuals 287 0.13
Serraninae (92)
Method 1 1.04 10.59 0.002 OC: 156.8, CCR: 195.7
Length 1 0.09 0.90 0.344
Method:Length 1 0.27 2.70 0.104
Residuals 66 0.10
Tetraodontidae (296)
Depth 1 1.00 11.53 <0.001 5 m: 200.7, 40 m: 170.8
Method 1 0.18 2.07 0.151
Length 1 0.18 2.10 0.148
Method:Length 1 0.29 3.31 0.070
Residuals 291 0.09
Only fish families that ≥30 individual fish MAD measurements were made for both open circuit and closed circuit are shown (therefore ≥60 MAD
measurements per family). Predicted MAD shows the prediction from the fitted model for MAD when altering the variable of interest while holding
all other variables constant. All MAD predictions are reported in cm. For site effects, the prediction was based on assuming 15 m depth, CCR
surveys and the mean fish length for the family recorded within our dataset. Depth effect predictions used 5 and 40 m depths and are based on:
Coral View, open-circuit diving and mean family fish length. Method effect predictions are based on: Coral View, 15 m depth and mean family fish
length. Length predictions are based on Coral View, 15 m depth and open-circuit scuba, the presented length predictions represent the 10 and 90% quan-
tiles of recorded fish lengths for the family (these fish body lengths are indicated in brackets below). Sites were Coral View (CV), Little Bight (LB) and
Rocky Point (RP).
dive gear and depth effects on reef fish 7
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Several possible explanations are likely for the fish families
for which we did observe different abundances or biomass
between techniques. However, caution is required in inter-
preting the results, as surveys were limited to three sites and
reef structure changes across the shallow to upper-MCE
depth gradient (Andradi-Brown et al., 2016b), making inter-
preting Depth:Method interactions harder. Higher abun-
dances recorded for Acanthuridae by OC are likely to be
caused by shallow OC transects encountering several large
schools of Atlantic blue tang (Acanthurus coeruleus) that
were not encountered when the transects were conducted by
CCR. While we cannot rule out attraction effects of OC
divers to these schools, we did not observe any other fish fam-
ilies attracted to OC on shallow reefs.
Tetraodontidae abundance patterns are harder to explain,
but most individuals recorded belonged to one species, the
Caribbean sharp-nose pufferfish (Canthigaster rostrata). As
these are a small-bodied species, habituation effects to the
sounds of shallow OC divers could explain the observed
differences in abundance and biomass between techniques.
If this were the case, it is not clear why similar abundance pat-
terns were not detected in many other reef fish families. For
example, our Lutjanidae results suggest that in the shallows
individuals are not habituated to diver presence, with higher
biomass recorded by CCR than OC, with less difference at
depth (Figure 3C). Tetraodontidae were, however, detected
on the most transects of all fish families tested, and are one
of the most abundant fish detected on Utila across both
shallow reefs and upper-MCEs (Andradi-Brown et al.,
2016b). This high number of transects combined with large
numbers of individuals recorded meant we had greater
power to detect differences in abundance between techniques
and across the depth gradient in Tetraodontidae than
many other fish families we tested. However, our MAD
results for Tetraodontidae run counter to this explanation,
as we found no differences in MAD between OC and CCR,
and MAD declined with increased depth, therefore MCE
Tetraodontidae allowed divers to approach more closely
Fig. 4. Visualization of MAD (A) Depth:Length interactions for Haemulidae, (B) Method:Length interaction for Pomacentridae, (C) Method:Length interaction
for Labridae and (D) Depth:Method interaction for Labridae. Minimum approach distance and fish length and depth have been standardized for the effects of site
and depth/fish length then centred.
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than shallow reef ones. This is the reverse pattern expected if
this species was habituated to the presence of OC divers on
shallow reefs. MAD measurements with DOV are most effect-
ive for larger fish species that can be observed with both
cameras in the stereo-video pair in order to measure distance.
DOV has also been shown to be poorer compared with some
other survey techniques at detecting smaller-bodied indivi-
duals such as Tetraodontidae (Andradi-Brown et al., 2016c).
Therefore caution is required when interpreting MAD
values for small-bodied species that do not respond to
divers by swimming away, but instead hide in the reef struc-
ture. If the majority of disturbed fish hide before we observe
them on both cameras then we are unable to record this
high MAD distance, potentially biasing our comparison.
As many in-water diver surveys of shallow-reef communities
are conducted by OC (English et al., 1997), while those on
MCEs are generally conducted by CCR (Bejarano et al., 2014;
Pinheiro et al., 2016), many researchers have been cautious of
making comparisons across the depth gradient between data-
sets collected with different techniques. While caution is
required when analysing data that compounds depth and
dive gear, our results suggest broad comparisons between
shallow reef OC surveys and MCE CCR surveys on Utila are
likely to still give informative patterns relating to biological
changes in fish communities, rather than be primarily driven
by differences between fish detectability caused by dive gear.
Utila however does not have a reef fish spearfishery, and the
MAD changes with depth we observed are unlikely to limit
detectability of fish. However, our MAD results reinforce the
need to account for differing fish behavioural responses
before comparing fish data collected by different dive techni-
ques in areas with spearfisheries. We anticipate that in other
locations where depth-restricted spearfishing occurs (e.g.
Lindfield et al.,2014b), comparisons between different dive
gears will highlight greater dive gear choice effects across the
depth gradient.
Changes in MAD with fish size and depth
We recorded greater MADs for larger individuals in the fam-
ilies Acanthuridae, Labridae, Lutjanidae, Pomacentridae,
Scaridae and Serraninae. These results are consistent with pre-
vious FID studies, which have found larger Acanthuridae and
Scaridae do not allow divers to approach as closely before
fleeing in areas with spearfisheries (Gotanda et al., 2009;
Januchowski-Hartley et al., 2011). In addition, studies of lion-
fish around Utila (which are culled by spearfishers as part of
an invasive species management programme) have found
that larger lionfish react to diver presence at greater distances
than smaller lionfish (Andradi-Brown et al., 2017). However,
spearfishing has been banned around Utila since 2004 for all
species except lionfish (Kramer et al., 2015), making it unlikely
that historical fisheries are driving our observed patterns.
Another explanation is proposed by Gotanda et al.(2009)
based on ecological processes unrelated to fishing pressure:
Generally as reproductive value increases it is predicted that
risk taking should decrease (Clark, 1994). As mortality rates
decline with increased size in marine fish (Sogard, 1997),
larger individuals would therefore be predicted to reduce
risk taking.
Changes in MAD were recorded for six out of eight families
based on the survey depth (Haemulidae, Labridae, Lutjanidae,
Pomacentridae, Scaridae and Tetraodontidae), with in all
cases lower MAD at greater depths. The drivers of this
pattern are not clear, and there could be several possible
explanations. These results do not fit with previous work
in the Bay Islands of Honduras, which suggest that reef
fish can habituate to diver presence (Titus et al., 2015),
and (despite shallow reef culling) that lionfish show no dif-
ference in diver response distance between shallow reefs and
MCEs (Andradi-Brown et al., 2017). With the majority of
diving on Utila limited to shallow reefs, habitation effects
in the shallows would be expected to lead to increasing
MAD with increasing depth. Other possible explanations
include the changing reef structure across the depth gradi-
ent. While data on structural complexity of the reefs at
each depth is not available, previous work at two of our
sites, combined with wider surveys at other sites around
Utila has indicated that hard substrata cover declines with
increased depth (Andradi-Brown et al., 2016b). This
decline in hard substrata, partially caused as the reefs shift
from a spur and groove shallow reef system to a MCE
patch reef system, is also associated with a decline in struc-
tural complexity. Fish on MCE patch reefs may be less likely
to flee from approaching divers, as this would require
moving away from the reef over a large area of open sand,
allowing divers to move closer to them than in the shallows
where a continuous reef exists. Another explanation could
bereducedlightlevelsondeeperreefsmakeitharderfor
fish to identify diver approaches. However, a study of reef
fish visual acuity across shallow to mesophotic reefs suggests
high plastic adaptive ability of fish visual systems to com-
pensate for lower light levels (Brokovich et al., 2010).
These adaptations were sufficient for a zooplanktivorous
species from the family Pomacentridae to show little
change in foraging behaviour across a shallow reef to
upper-MCE gradient (Brokovich et al., 2010). Natural
changes in reef structure with depth combined with fish
visual adaptive plasticity make it hard to disentangle
effects of depth from those of changing reef structure and
light levels, and require further research.
In addition to a general decline in MAD at increased
depth, in Haemulidae we detected weak Depth:Length inter-
actions. This suggests larger fish are more likely to flee on
MCEs than shallow reefs than would be expected from their
body size alone. Previous work has hypothesized that light
levels may interact with fish length in affecting FIDs
(Januchowski-Hartley et al., 2011), as generally there is an
improvement in fish vision with increasing body size
(Fernald, 1985). This could explain these results, as fish of all
body sizes are more likely to be able to detect divers approach-
ing in shallow sites with high light availability, whereas at
deeper depths with lower light levels it may be harder for
small fish to identify divers approaching than large fish.
This study highlights the importance of considering
changes in fish behaviour when conducting reef fish surveys
across depth gradients. While for many fish families we did
not detect differences in abundance between OC and CCR
surveys, we did identify family level differences in how close
divers could approach individual fish between the methods.
Some differences in approach distance varied with depth,
with the direction of response family specific. This study
highlights the need for reef fish community studies making
comparisons across natural gradients such as depth to be
aware of changes in fish behaviour that may affect their fish
detection results.
dive gear and depth effects on reef fish 9
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ACKNOWLEDGEMENTS
We wish to thank Faye-Marie Crooke, Sarah Laverty and
Richard Astley (Coral View Research Center) and Marı
´a
Arteaga and Suriel Duen
˜as (Bay Islands Conservation
Association) for fieldwork support. We also wish to thank
Luke Shepherd, Edd Stockdale and Georgina Wright for
assisting with transect filming. We thank Tom Letessier for
advice on univariate PERMANOVA, and two anonymous
reviewers who helped improve this manuscript.
FINANCIAL SUPPORT
All authors would like to express thanks to the fieldwork
funders: Royal Geographical Society Ralph Brown
Expedition Award, Zoological Society of London Erasmus
Darwin Barlow Expedition Grants, Operation Wallacea and
the University of Oxford Expeditions Council. DAAB is
jointly funded by a Fisheries Society of the British Isles PhD
studentship and by Operation Wallacea.
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a stereo-video system: implications for underwater visual census of reef
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Correspondence should be addressed to:
D. A. Andradi-Brown, Department of Zoology,
University of Oxford, The Tinbergen Building,
South Parks Road, Oxford OX1 3PS, UK
email: dominic.andradi-brown@zoo.ox.ac.uk
dive gear and depth effects on reef fish 11
https://doi.org/10.1017/S0025315417001278
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