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What is Big BRUVver up to? Methods and uses of baited underwater video


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Baited Remote Underwater Video Stations (BRUVS) is a popular technique to assess mobile nektonic and demersal assemblages, particularly for fish communities. The benefits of using BRUVS have been well documented, with their non-destructive and non-extractive nature, ease to replicate, relatively-cheap personnel costs, and low risk to personnel often cited. However, there is a wide variability in the set-up, experimental design, and implementation of this method. We performed a literature review of 161 peer-reviewed studies from all continents published from 1950 to 2016 to describe how BRUVS has been used by quantitatively assessing 24 variables, including camera set-up and orientation, soak time, bait quantity, type and preparation method, habitat and depth deployed in, and number of replicates used. Such information is critical to gauge the comparability of the results obtained across BRUVS studies. Generally, there was a wide variety in the location, deployment method, bait used, and for the purpose that BRUVS was deployed. In some studies, the methods were adequately described so that they included information on the 24 variables analysed, but there were 34 % of studies which failed to report three or more variables. We present a protocol for what minimal information to include in methods sections and urge authors to include all relevant information to ensure replicability and allow adequate comparisons to be made across studies.
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1 23
Reviews in Fish Biology and Fisheries
ISSN 0960-3166
Rev Fish Biol Fisheries
DOI 10.1007/s11160-016-9450-1
What is Big BRUVver up to? Methods and
uses of baited underwater video
Sasha K.Whitmarsh, Peter
G.Fairweather & Charlie Huveneers
1 23
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What is Big BRUVver up to? Methods and uses of baited
underwater video
Sasha K. Whitmarsh .Peter G. Fairweather .Charlie Huveneers
Received: 19 April 2016 / Accepted: 22 September 2016
ÓSpringer International Publishing Switzerland 2016
Abstract Baited Remote Underwater Video Sta-
tions (BRUVS) is a popular technique to assess
mobile nektonic and demersal assemblages, particu-
larly for fish communities. The benefits of using
BRUVS have been well documented, with their non-
destructive and non-extractive nature, ease to repli-
cate, relatively-cheap personnel costs, and low risk to
personnel often cited. However, there is a wide
variability in the set-up, experimental design, and
implementation of this method. We performed a
literature review of 161 peer-reviewed studies from
all continents published from 1950 to 2016 to
describe how BRUVS has beenusedbyquantita-
tively assessing 24 variables, including camera set-up
and orientation, soak time, bait quantity, type and
preparation method, habitat and depth deployed in,
and number of replicates used. Such information is
critical to gauge the comparability of the results
obtained across BRUVS studies. Generally, there
was a wide variety in the location, deployment
method, bait used, and for the purpose that BRUVS
was deployed. In some studies, the methods were
adequately described so that they included
information on the 24 variables analysed, but there
were 34 % of studies which failed to report three or
more variables. We present a protocol for what
minimal information to include in methods sections
and urge authors to include all relevant information
to ensure replicability and allow adequate compar-
isons to be made across studies.
Keywords BRUVS Fish assemblages Nekton
Non-destructive Behaviour Methodology
Information about marine ecosystems is becoming
increasingly sought after as the understanding of their
importance in ecosystem services, global processes,
and economies increases (Costanza et al. 1997). For
many of these services, fish and other nekton are
particularly important and have been the main focus of
several studies (e.g. Holmlund and Hammer 1999;
Worm et al. 2006). Such studies have highlighted the
need for methods which are capable of sampling a
large portion of the population or community, are non-
extractive, and allow for simultaneous counts of
multiple taxa. There is also a growing desire for more
behavioural data about fish species, along with less
destructive methods suitable for protected areas, and
for methods that are cheap, repeatable, and compara-
ble. Baited underwater video (for the purpose of this
Electronic supplementary material The online version of
this article (doi:10.1007/s11160-016-9450-1) contains supple-
mentary material, which is available to authorized users.
S. K. Whitmarsh (&)P. G. Fairweather C. Huveneers
School of Biological Sciences, Flinders University,
GPO Box 2100, Adelaide, SA 5001, Australia
Rev Fish Biol Fisheries
DOI 10.1007/s11160-016-9450-1
Author's personal copy
review referred to as Baited Remote Underwater
Video Stations or BRUVS) is a popular technique to
assess mobile nektonic and demersal assemblages,
particularly for fish communities and fits the above
BRUVS have been compared to many other
commonly-used techniques for assessing fish assem-
blages with the most common comparison being
between BRUVS and Underwater Visual Census
(UVC) (e.g. Stobart et al. 2007; Colton and Swearer
2010; Lowry et al. 2012) or Diver Operated Video
(DOV) (Watson et al. 2005; Langlois et al. 2010;
Watson et al. 2010). Other comparisons include:
BRUVS versus baited traps (Harvey et al. 2012a;
Wakefield et al. 2013; Langlois et al. 2015); versus
angling (Willis et al. 2000; Langlois et al. 2012a;
Gardner and Struthers 2013); versus trawling (Cappo
et al. 2004); versus seine netting (Whitmarsh 2012);
versus longline surveys (Brooks et al. 2011; Santana-
Garcon et al. 2014a; McLean et al. 2015); and versus
Automated Underwater Vehicles (AUV) and towed
video (Seiler 2013). These studies show that BRUVS
are a useful tool with many benefits compared to more
traditional techniques. Nevertheless, as each study has
aims that vary, the appropriate method to use should
be selected on a case-by-case basis (see Murphy and
Jenkins (2010) or Mallet and Pelletier (2014) for a
review of the benefits and biases of these methods in
relation to BRUVS).
Over the last 15 years, as the available technology
improved and the aims of studies using this equipment
have broadened, the methods used when deploying
BRUVS have progressively increased in variety.
Factors that can vary from study to study include:
the number and orientation of cameras; soak time (i.e.
the amount of time the unit is left underwater);
habitat(s) sampled; depth ranges of deployments; and
the number of replicates used. The bait used can differ
in terms of type, quantity, and preparation method. The
type of video metric (i.e. how fish and other nekton are
counted or measured) can also be different across
studies. Standardisation in the use of BRUVS has
previously been attempted (Cappo et al. 2007) to allow
for a better comparison across studies, but modified or
novel approaches to this technology are continually
arising, increasing variability in methods used. We
propose that authors should ensure that they provide
enough information to allow comparisons between the
different BRUVS set-ups used, instead of attempting
to reach a level of standardisation that might not be
achievable. The overall purpose of this literature
review is to explore how and in which ways different
studies have used BRUVS. We hope to highlight: the
need for a comprehensive and descriptive method
section; aspects which could be further investigated to
improve the informational output of BRUVS; other
unexplored applications of BRUVS; and ultimately
suggest a protocol of information that authors should
routinely include in the methods section.
Searches of the peer-reviewed literature were con-
ducted up to 18/07/2016 using the keywords ‘‘baited
and video’’ or ‘‘BRUVS’’, within Google Scholar,
Scopus, Proquest (Aquatic Sciences and Fisheries
Abstracts), and Biological Abstracts for the time
period between 1950 and the search date. Searches
returned between 59 and 497 hits across the various
databases, with additional (10,000?, mostly irrele-
vant) hits from Google Scholar for the ‘‘baited and
video’’ search term. Papers were included in the
analysis if bait was used in one or more replicates and
if video footage was used rather than still images. A
total of 161 studies were found (Online Resource 1),
from which 24 variables of the study were extracted
(Table 1). The purpose and novelty of the studies were
also assessed.
Results and discussion
A comparison of methods used in baited video
Description of the study: when and where
Studies using baited videos began in the mid-nineties
(Ellis and DeMartini 1995) and have increased over
time (Fig. 1a), with 33 studies published in 2015 and
13 (plus three in press) in the first half of 2016. The
year 2007 appeared to be a breakthrough year for
BRUVS studies going from 1 in 2006 to 8. This
increase may in part be due to a workshop on baited
video held at a national conference in Australia in
2006. The increase in BRUVS studies over time is
likely to be due to an increased exposure of the method
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and its benefits, advances in technology, and the trend
towards more affordable electronic equipment.
We found that BRUVS studies had been performed
in all continents as well as within international waters
(Fig. 1b). Oceania was by far the most popular (70 %)
region for studies to be conducted in, with Australia
alone contributing 61 %. Other continents had gener-
ally fewer studies conducted there, especially Africa
(6), Asia (6), and South America (3) along with
Antarctica (3). Geographically, temperate areas were
the main focus areas at 42 %; however, tropical and
sub-tropical areas were still well-represented at 26 and
35 %, respectively. Polar areas received less attention
and were only investigated in 2 % of studies. The
majority of the studies included in this review were
exclusively in marine ecosystems (94 %), while 4 %
were in estuarine and only 2 % in freshwater ecosys-
tems (e.g. Ebner and Morgan 2013; Ebner et al. 2015).
The most common habitat in which BRUVS were
deployed was reef areas, with coral and rocky reefs
Table 1 A list of the 24 variables included in this review of baited studies that also will act as a protocol for factors to include in
method sections
Variable Examples # of studies reported in
(% out of 161)
When and where
Year published 1996, 2006, 2016 100
Location study was conducted in Adelaide, South Australia, Australia 100
Geographical area Temperate, tropical, polar 100
Aquatic realm Marine, estuarine, freshwater 100
Habitat type Seagrass, rocky reef 97
About the video system
Name of systems BRUVS 96
Orientation of camera(s) Horizontal, vertical (to substrate) 99
Number and type of cameras 1 or 2, GoPro Hero 3?, Panasonic HandyCam 99
Type of length measurement Fork length using stereo-BRUVS 85
Max range visible 3 m, to bait bag 46
Soak time 30, 60 min 98
Distance between reps 250, 500 m 65
About the bait
Type Sardines, Sardinops sagax 94
Quantity 500, 1000 g 84
Preparation method Crushed, whole, chopped 84
Deployment method Mesh bag, perforated PVC bait container 82
About the deployment
Minimum depth 3, 10 m 85
Maximum depth 50, 25 m 86
Variation in depth (range) 47, 15 m 82
About the sampling design and analysis
Number of replicates 3, 6 93
Video metric used MaxN,T1st, etc. 99
Software used EventMeasure, VLC etc. 54
Taxa included Teleost, Chondrichthyes, Cephalopoda, Crustacea 96
% to species level 75 % able to be identified to species level 55
In addition to those listed we also suggest including the time of day the study was conducted and any additional items added to the
system such as lights or current meters
Rev Fish Biol Fisheries
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together accounting for 43 % of the habitats studied
(Fig. 1c). Studies in multiple habitats (23 %) also
commonly used reef habitats as one of their sampled
areas. Rocky reefs were more commonly sampled than
coral reefs, which may be due to the prevalence of
rocky reef areas within the temperate regions of
Australia, where a large proportion of BRUVS studies
are conducted. Pelagic (7 %; e.g. Rees et al. 2015) and
deep-water (12 %; e.g. Collins et al. 2002) habitats
were also studied. Seagrass and ‘other’ habitats were
less common with only 2 % for seagrass (e.g. Whit-
marsh et al. 2014) and 9 % for the ‘other’ category,
which included soft sediments (e.g. Howarth et al.
2015) and restricted habitats such as intertidal rock
pools (e.g. Harasti et al. 2014). The prevalence of use
in reef habitats is most likely a factor of increased
visibility through the water column compared to some
other benthic (soft bottom) habitats. Reef areas are
often home to commercially-targeted fish species and
are prime areas for tourism such as snorkelling and
diving, which makes these areas of high commercial
interest. Ecologically, reef areas support a wide range
of species and usually have high biodiversity (Mal-
colm et al. 2007) leading them to be targeted by
researchers and managers.
Variables relating to the video system
The terminology surrounding BRUVS was widely
variable, with the name of the unit falling into more
1995 2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 In
# of studies
Africa Antarcca Asia Europe North
Oceania South
# of studies
Rocky Reef Coral Reef Seagrass Pelagic Deepwater Mulple Other Unspecified
# of studies
Habitat type
# of studies
Name of unit
Fig. 1 a The frequency of
BRUVS studies published
by year until 18/07/2016.
bThe continent or
geographical realm in which
each study was conducted.
cThe habitat type in which
BRUVS were deployed for
the 161 studies assessed.
The ‘Multiple’ category was
used where more than one
habitat type was studied and
included some of the other
habitat categories listed
(except for pelagic and
deep-water), as well as some
included in the ‘Other’
category, such as bare sand.
‘Deep-water ([100 m)’
habitats included shelf
slope, soft sediments and
hard substrates. dFrequency
of the name given by the
study’s authors for the
baited video unit from 161
studies assessed
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than 17 categories (Fig. 1d). Three very similar unit
names dominated the literature: BRUV, BRUVS and
BRUVs; all acronyms standing for Baited Remote
Underwater Video Stations or Systems (Fig. 1d).
BRUV(S/s) as an acronym appears to have first been
published by Cappo et al. (2001). Other common
names include Baited Underwater Video (BUV) and
more general names such as baited landers or baited
video. Some authors have developed individual names
for their systems such as DeepBRUVS (Marouchos
et al. 2011) and BotCam (Merritt et al. 2011).
Generally, having multiple names can be a problem
because it leads to confusion and allows for ambiguity
about the method. Multiple names also make literature
searches more difficult and may confuse non-special-
ists. The name BRUVS has been trademarked by
AIMS, but it is not linked to any patent of the design
and AIMS does not enforce the use of the trademarked
name in peer-reviewed publications (M. Cappo, pers.
comm.). Since variations on BRUV(S/s) are the most
commonly published names for this method, we urge
that a standard form of this name be chosen and used.
We are recommending BRUVS as this name has most
prevalent use (Fig. 1d).
The orientation of the camera(s) is an important
aspect to consider when setting up the BRUVS
arrangement. The majority (85 %) of BRUVS set-ups
used a horizontal camera arrangement, while 14 %
had a vertical orientation pointing down towards the
seafloor; the remaining (1 %) studies did not specify
the camera orientation. The orientation of the camera
can affect the number of organisms that can be
observed or reliably identified. For example, Lan-
glois et al. (2006) showed that a horizontal set-up
recorded 14 species versus four for a vertical set-up,
with some species appearing shy of entering the
vertical field of view, most likely due to the
perceived confined space under the camera. A major
benefit of vertical set-ups, however, is the ability to
measure fish size with single cameras using the
known fixed height above the substrate and a ruler to
measure fish. Vertical BRUVS were used first in the
early 2000s (e.g. Willis and Babcock 2000) but have
had limited use across the years, with one third of
vertical set-ups occurring in deep habitats. The
prevalence of horizontal BRUVS is likely because
of the increased field of view (depending on water
clarity) and the ease of identification of many fish
species from a side-on perspective.
BRUVS are predominantly used with one (single)
or two (stereo) cameras. Single-BRUVS consist of one
camera usually mounted directly behind or above the
bait arm. Stereo-BRUVS consist of two cameras
mounted at specific angles (usually 7°–8°) to each side
of the bait of the arm and are calibrated to allow for
accurate fish measurements (see Harvey et al. 2002a
for more details). Single-BRUVS are smaller, lighter,
cheaper, and take less time to set-up (prior to and
during field work) than stereo-BRUVS. Stereo-
BRUVS take up more boat space, require more
specialised gear for retrieval and may be a limiting
factor for replicate numbers when using smaller
vessels or make field costs higher by requiring more
days in the field than single systems. The calibration of
stereo-BRUVS also adds to preparation and analysis
time. Based on our literature search, the majority of
studies (60 %) used a single camera compared to only
36 % using stereo-BRUVS and four studies using a
combination of both systems and one failing to specify
the number of cameras used. It is likely that the
prevalence of single-BRUVS is the result of their ease
of use, affordability, and space constraints. Overall,
the question of whether to use single- or stereo-
BRUVS may come down to a number of factors (e.g.
money, space, and time) but ultimately should be
decided depending on whether there is a need for
accurate length measurements to fulfil the proposed
aims of the study.
Length measurements can be used to estimate
biomass, gain an understanding of population and
recuitment dynamics, and estimate fecundity (Ricker
1975). It can also be particularly useful in protected
areas, where there is an expectation that fishing
influences the size of fishes and that there will be a
different size distribution in protected areas compared
to unprotected areas (e.g. Watson et al. 2009). There
were 65 studies that either used stereo-BRUVS or
mentioned length as a variable for their study. Out of
those 65, 38 % failed to present or use any of the
estimated length data. Typically, studies which did
present the length data were evaluating the use of
length data under a range of circumstances, e.g. for
precision (Merritt et al. 2011), with new technology
(Letessier et al. 2015) or over different soak times
(Misa et al. 2016), comparing lengths or biomass
between protected and unprotected areas, or across
different methods (e.g. Langlois et al. 2015), habitats
(e.g. Fitzpatrick et al. 2012), or other impacts (e.g.
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seasons (McIlwain et al. 2011). Where length data
were presented, it was most often measured using
stereo-BRUVS (41 studies) and within those, 15
studies presented fork length data. There were 20
studies that estimated fish length using single cameras,
most often using a reference object or ruler within the
field of view (e.g. known length of bait bag) to gauge
fish length. Stereo-BRUVS (or single-BRUVS with
the ability to accurately measure fishes (see below))
are necessary to answer specific questions where fish
size is a critical variable, but where this information is
not required and in 38 % of studies not even presented,
we believe that sampling effort and the additional
cameras required for stereo could be better spent on
increasing replication.
One of the other benefits of using stereo-BRUVS
over single-BRUVS is the ability to accurately mea-
sure maximum visibility. Such knowledge can be used
to improve comparisons between studies where visi-
bility is different. It can also be used to standardise the
maximum distance up to which fishes are counted, but
such standardisation requires a longer video process-
ing time as once a distance threshold has been set, it
would be necessary to ensure that only fishes within
that distance are counted. Although a visibility
measurement can be informative for any study, it
was only mentioned in 36 % of all studies (Fig. 2a).
While it is possible to restrict the distance within
which fish are counted on single-BRUVS, it can be
based on a subjective distance and commonly involves
constricting the analysed field of view to quite small
areas (e.g. only to the bait bag).
Studies have compared the accuracy of stereo-
BRUVS versus single-BRUVS and shown that the
accuracy of fish length measurement using single-
BRUVS deteriorated with distance from the measur-
ing scale (±2 m) and angle of view ([50°), while
stereo gave a good estimate of length at a variety of
angles and distances within 7 m (Harvey et al. 2002b).
Some work has been done more recently to improve
the accuracy of measurements taken from single-
BRUVS, such as the development of mirrored surfaces
allowing for a more exact positioning of fish in vertical
set-ups, leading to more accurate measurements
(Trobbiani and Venerus 2015). It is also possible to
obtain accurate length data using on a known ratio of
eye to head height predetermined for each fish species
(Richardson et al. 2015). This method could be
especially useful for targeted studies that are focusing
on a few species only, as the proportion of eye size to
head height has to be calculated for each fish species
prior to BRUVS deployments. Developments such as
these continue to improve BRUVS as a method and
make it more accessible by providing ways to gain
additional accurate information from single-BRUVS.
Soak time differed greatly across studies (Fig. 2b),
with an apparent trimodal distribution. Peaks occurred
around 30, 60, and[90 min. Few studies used times of
less than 30 min, but 17 % of studies ran for more than
90 min, which often involved the use of additional
power sources or extended batteries (e.g. Jamieson
et al. 2006). Four studies (Gladstone et al. 2012;
Santana-Garcon et al. 2014c; Harasti et al. 2015; Misa
et al. 2016) specifically compared different soak times
and found 60–90 min to be optimal for an estuarine
environment (Gladstone et al. 2012), 120 min optimal
for pelagic habitats (Santana-Garcon et al. 2014c), and
30 min was found to be sufficient in rocky reef
habitats (Harasti et al. 2015). Misa et al. (2016) found
shorter soak times (15 min) sufficient for snap-shot
abundance estimates of Hawaiian bottomfish assem-
blages. Furthermore, some studies have included pilot
studies of longer soak times to determine species and
abundance accumulation curves, which justified a
shorter time to be used subsequently in the main study
(e.g. Unsworth et al. 2014).
Due to the variable and complex nature of currents
(and hence bait plume modelling) and fish behaviour,
the distance between replicates is often a contentious
issue among BRUVS experts. There is considerable
variation across the studies assessed in terms of the
minimum distance between replicates (Fig. 2c). Very
few studies (only 2) had distances greater than 550 m,
while 9 % (15 studies) had distances less than 150 m,
with the minimum specified distance being 25 m (e.g.
Colefax et al. 2016). Thirty-five percent of studies
failed to mention the distance between replicates.
There have been no studies investigating the impacts
of replicate spacing on the assemblages observed.
Distance between replicates is often used as a proxy
for independence, with the hope that fish cannot swim
or are not swimming between replicates. Such inde-
pendence requirements avoid over-inflation of abun-
dance by ensuring individuals are not double-counted
on more than one replicate. The reasoning for different
distances between BRUVS vary but are often based on
hypothetical distances that fish may be able to swim
between BRUVS within a given time frame (e.g. Ellis
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and DeMartini 1995). This can then lead to soak time
becoming a factor for the appropriate minimum
distance between replicates. However, based on the
literature reviewed, we only found a weak positive
correlation between soak time and distance between
replicates (Fig. 3a; Pearson correlation p=0.243,
2-tailed probability =0.020). It is likely that the ideal
distance between BRUVS will be variable and
dependent on a number of factors including current
speed and direction, influence of tides, time of day,
and bait used. Fish behaviour is also likely to play a
strong role in the assemblages observed and the
>1 <2 2 - 4 4.1 - 6 6.1 - 8 8.1 - 10 >10 Unspecified
# of studies
Maximum range visible (m)
0-29 30 31-59 60 61-90 >90 Other Unspecified
# of studies
Soak me (min)
≤150 151-250 251-350 351-450 451-550 ≥550 N/A Unspecified
# of studies
Minimum spacings between replicates (m)
Mesh/cage bag Canister Container No vessel Unspecified
# of studies
How bait was deployed
Fig. 2 a The maximum range visible from when viewing the
BRUVS footage. bThe soak time for each of the 161 studies
assessed that used a form of BRUVS. Studies in the ‘Other’
category included studies with multiple soak times and those
which took periodic video clips over a larger time frame. cThe
minimum space between replicates when being deployed within
the field. N/A refers to studies which only had one replicate.
dDeployment method used for the bait. Containers were usually
PVC pipe with holes to allow for plume dispersal, Canisters
were used for timed releases often in deep-water habitats, and
No vessel means no container was used and so the bait was
physically attached to a section of the BRUVS
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recommended distance between replicates. Swimming
speed, guild, schooling nature, shyness, interactions
with other species, apparent hunger, and individual
‘personality’ (or behavioural syndrome, Sih et al.
2004) are aspects of behaviour that may affect fish
assemblages and also whether fish are likely to be
moving between replicates. There is, however, some
evidence that even fish considered to be mobile might
not move between replicates. For example, large
more-mobile species such as smooth rays, Dasyatis
brevicaudata, were only seen on a single replicate (out
of 6) spaced 100 m apart (S Whitmarsh, unpublished
data). While a greater distance between replicates is
likely to reduce the chances of double-counting
individuals, this may not always be possible. An
example of this may be when investigating small
isolated habitats (such as wrecks), where it may not be
possible to space out replicates while still ensuring that
the BRUVS are close enough to the target habitat.
There is also a risk of spacing replicates too far apart
and still expecting them to function as a replicate.
Such an issue is more likely to occur in heterogeneous
habitats. For example, if 6 replicates were spaced
500 m apart and arranged in a line (e.g. along a depth
contour), the first and last replicate would be 3 km
apart. This is far enough for other factors to have
changed (e.g. wave exposure, current speed, wind
direction, habitat). Without further studies investigat-
ing the impacts of spacing, we cannot recommend an
optimal approach but we urge authors to carefully
consider a distance that is logical based on the focus of
the study, report the distance used, and explore the
data collected to identify potential species that may
have been double-counted.
Variables relating to the bait
The use of bait compared to unbaited systems has been
specifically investigated by four studies (Harvey et al.
2007; Bernard and Go
¨tz 2012; Dorman et al. 2012;
Hannah and Blume 2014). Bait increased the similar-
ity between replicates providing better statistical
power (Harvey et al. 2007; Bernard and Go
¨tz 2012;
Dorman et al. 2012). Bait also increased the number of
0-29 30 31-59 60 61-90 >90
Soak me (min)
Minimum distance (m)
0-29 30 31-59 60 61-90 >90
Soak me (min)
Bait amount (g)
Lower limit (m)
Upper limit (m)
Fig. 3 Soak time plotted against athe minimum distance
between replicates and bthe bait quantity, where the size of each
dot represents the # of studies in each combination, as shown in
legend. cThe lower and upper depth limits in which the BRUVS
were deployed for the 161 studies that could be assessed.
Excluding the 10 pelagic studies which were conducted mid-
water and the 26 studies which failed to specify an upper (1),
lower (4) or both limits (21)
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predatory and scavenging species, while not affecting
the numbers of herbivorous and omnivorous fishes
seen, and baited replicates were better able to detect
changes between habitat types (Harvey et al. 2007;
Bernard and Go
¨tz 2012; Dorman et al. 2012). Hannah
and Blume (2014) showed that bait increased the
abundance of deep-water demersal fishes by 47 % and
lured the fish closer to the unit allowing for more
accurate length measurements and species identifica-
tion. Overall, these studies conclude that the benefits
of using bait in marine environments appear to
outweigh any perceived costs. There has been con-
cern, however, about the ability of bait to attract fish
from a large area potentially leading to inflated
densities (Taylor et al. 2013). Variability in currents,
winds, and turbidity across replicate deployments can
lead to large changes in plume dispersal and signif-
icantly alter interpretations (Taylor et al. 2013).
Studies rarely considered this factor and few studies
implemented current measuring devices, such as
current meters or drogues (Taylor et al. 2013).
Bait choice is often a well-discussed issue for all
methods that require its use (e.g. longline fishing;
Løkkeborg et al. 2014) and has also been investigated
for BRUVS, with a number of studies specifically
looking at the effects of bait type. Dorman et al. (2012)
and Wraith et al. (2013) each investigated three
different bait types. Dorman et al. (2012) compared
sardines, cat food, and a vegetable mix with unbaited
controls and found similar assemblages between these
three bait types. Cat food, however, depleted rapidly
and did not always last for the 60-minute deployment
time. The vegetable mix was costlier, harder to use
(due to having to mix the bait) and caused obscuration
of the field of view, and consequently was not
recommended by the authors. Wraith et al. (2013)
compared amongst three marine baits, chopped sar-
dine, chopped abalone viscera and crushed urchin, and
found urchin to record significantly less fish abun-
dance and species richness, and increased time of first
arrival compared to the other two bait types. The bait
type used also affected the feeding guilds observed
with sardines attracting more generalist carnivores,
zooplanktivores, and macroinvertebrate carnivores,
and being potentially more consistent at attracting
herbivores than the other two types. Overall, the
authors recommended using oily fish such as sardines.
Walsh et al. (2016) also investigated three bait types,
sardines, mussels and a locally available alternative to
sardines (Australian salmon). Walsh et al. (2016)
found similar results between the two fish species,
while the mussels attracted more omnivorous species,
but had a lower overall species diversity. Based on our
literature search, the most common bait type was the
Australian sardine, Sardinops sagax, although other
species or sub-species of sardines were also commonly
used. Sardines accounted for over 56 % of the bait
types used (Fig. 4) and were often also included as part
of the mixed bait types and in some of the veg-
etable mixes used. Bait type was always marine-based
(with the vegetable mixes containing fish oils), with
the exception of chicken, which was used in specific
studies to attract Nautilis spp., pig carcasses for
attraction in the deep sea, silverside meat, and dough
(‘Other’ category, Fig. 4; Online Resource 1). The
prevalence of sardines used in the collective literature
appears to be supported by the above studies that
compared bait types. Sardines are often said to be good
as bait due to their oiliness, low cost, ready accessi-
bility, and persistence within the bait bag (Dorman
et al. 2012; Wraith et al. 2013). Although sardines can
be easily accessed in some temperate regions such as
Chile or Australia, in areas where sardines may be
difficult to acquire such as in the tropics, we recom-
mend using a similar oily fish that is readily accessible
in that region, as per Walsh et al. (2016).
Various quantities of bait have been used when
deploying baited video ranging from 50 g to over 2 kg
(Fig. 4). The most common bait quantity was within the
801–1000 g category (with 27 % of studies; Fig. 4),
with a majority of these using approximately 1000 g
(Online Resource 1); 501–800 g was the next most
popular category with 20 % of studies using a quantity
within this range (most commonly 800 g; Online
Resource 1). Bait quantity was thus more varied across
studies than the type or preparation method used
(Fig. 4). Only one study tested whether varying the
quantity of bait affected the observed fish assemblages
(Hardinge et al. 2013). There were no significant
differences in fish diversity between 200, 1000, or
2000 g of bait but there were some individual species
differences, with the moray eel, Gymnothorax wood-
wardi, being significantly more abundant with 2000 g
of bait than with 200 g (Hardinge et al. 2013). The lack
of differences in fish diversity may have been caused by
the limited bait depletion (i.e. low bait predation)
leading to fish being equally attracted to the baited
video throughout the deployment regardless of the
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quantity of bait used (Harvey et al. 2007). There is,
however, a need to spatially replicate the study in areas
likely to have high bait depletion, such as those with
high fish abundance or areas with different water
temperatures that may affect the foraging rates of fishes.
There was a variable positive correlation between bait
quantity and soak time (Fig. 3b; Pearson correlation
p=0.221, 2-tailed probability =0.015), which only
suggests a weak trend for the deployments with longer
soak times ([90 min) to also use more bait ([1000 g).
Overall, there appeared to be little consideration of the
appropriate quantity of bait to choose and this remains
an area for improvement and future research.
Despite the copious literature available on BRUVS,
there appears to be no studies investigating the effects of
the preparation method for the bait. Many authors
assume that crushing the bait (particularly sardines)
enables a more even plume dispersal (e.g. Watson et al.
2009), but it may be worthwhile for a future study to test
this hypothesis. From the literature analysed, bait was
prepared in a variety of ways, with crushing being the
most common method (55 % of studies). Chopped and
whole-bait preparations were less common at 15 and
12 %, respectively, while 16 % of studies did not specify
the way the bait was prepared (Table 1).
Deployment method for the bait also varied across
studies (Fig. 2d) with a majority of studies (55 %)
using some form of mesh bag in which bait was likely
to be somewhat accessible to taxa for feeding. The use
of a perforated container (usually PVC) was the next
most common method (19 %), which served to
disperse the bait plume but restricted the access to
taxa for feeding on the bait. Some studies (2 %) in the
deep-water habitat choose a timed-release method
using canisters to enable fresh bait to be released
periodically. Six percent of studies used no form of
vessel for bait deployment. The remaining 18 % of
studies did not specify the bait deployment method. It
is possible that the delivery method for the bait may
influence assemblages observed as the ability to
physically feed on the bait (more likely with a mesh
bag) may lengthen the amount of time individuals
remain around the BRUVS and hence inflate MaxN,or
attract/deter other species. This area of study has not
been investigated, but warrants future investigation.
Variables relating to the deployment
The depths at which baited video are deployed varied
from very shallow (0.5 m) to deep-water (8074 m).
Very shallow baited video studies were uncommon,
with only 14 % of studies having the shallowest
deployment depth class of less than 10 m (Fig. 3c).
Fifty-one percent of studies did not extend past 50 m
in depth. Only nine studies sampled exclusively within
the shallowest range (B5 m), while there were 14
Sardines Other fish Mix Vegetable mix Other
# of studies
Bait type
Fig. 4 Bait type and quantity (g) for 161 studies that used a
form of BRUVS. ‘Vegetable mix’ was composed of varying
amounts of falafel mixed with fish oils. ‘Mix’ bait was
composed of multiple components, usually fish and squid. The
‘Other’ bait type category includes baits such as commercial fish
feeds and chicken. ‘Sardines’ were usually Sardinops sagax or
S. neopilchardus. The unknown bait types are excluded. The
‘Other’ quantity category included those studies with variable
amounts of bait, unknown quantities and those studies which
only specified a whole number of fish
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studies that sampled exclusively in the deepest depth
range ([100 m). This shows a wide range of use for
BRUVS and its applicability to a broad range of
depths. The studies assessed had a narrow variation in
depths sampled with 42 % sampling within a range of
20 m or less (Fig. 5a) compared to 15 % spanning a
0-10 11-20 21-50 51-100 >100 Unspecified
# of studies
Depth variaon (m)
One 2-3 4-6 7-9 10-20 >20 Strafied Unspecified
# of studies
# of replicates
MaxN MaxN per me
T1st MeanCount Species specific Other
# of studies
Metric used
# of studies
# of 'unspecified' categories (out of 24)
Fig. 5 a The variation (range) in metres between the lower and
upper depths of the demersal BRUVS deployments for the 161
studies assessed (excluding the pelagic studies, as these did not
have a normal depth variation; see pelagic BRUVS section).
bThe number of replicates taken in each of the 161 studies
assessed. Where a range was given, the upper value was used to
assign the category here. Stratified indicates a sampling design
which spans across a large area that conducted enough replicates
to get good spatial coverage across particular strata such as
habitat or depth (e.g. Moore et al. 2010). cThe metric used to
assess the video footage from the 161 studies assessed. Studies
were counted in more than one category where more than a
single metric was used. Species-specific metrics included
identifying individuals through the use of colouration or
patterns. T1st is the time of first arrival for each species. ‘Other’
included metrics involving assessing behaviour of individuals,
bait loss, habitat coverage, and abundance metrics other than
MaxN.dThe number of categories (out of the 24 reviewed here)
in which the methodology of that study was ‘unspecified’ (i.e.
not stated explicitly; N=161 studies)
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range greater than 100 m. Failure to specify either a
lower or upper depth limit resulted in 29 studies
(18 %) having an unknown depth range.
Variables relating to sampling design and analysis
The number of replicates used when deploying
BRUVS was variable (Fig. 5b). Thirty-two percent
of the studies reviewed used four to six replicates at
each location. We were unable to determine the
number of replicates used in 9 % of the reviewed
studies, while 9 % were unreplicated (all of which
were in deep-water or ‘other’ habitats). These studies
are likely to be un-replicated due to the large cost
involved in sampling at such great depths. Some
studies (8 %) also chose a stratified sampling design.
This design type involves the deployment of BRUVS
across a large area while ensuring that replicates are
representative of all strata, e.g., habitats or depths,
represented within that area (e.g. Moore et al. 2010).
Images from BRUVS videos can be measured in a
variety of ways, depending on the aims of the study.
The most common metric recorded was MaxN (the
maximum number of a particular species seen in any
one video frame across the duration of the video
record). MaxN was used in some form, either over a set
time period or across the whole video, in 81 % of the
reviewed studies. MaxN can be used in conjunction
with other metrics such as time of first arrival (T1st)or
time first fed (Fig. 5c). However, this was done in less
than 20 % of cases. MaxN can be modified slightly to
include the maximum number seen over a set time
period, e.g. 30 s, or can be estimated at specific
intervals, e.g. every 5 min. Some studies (10 %)
focused upon species-specific metrics such as identi-
fying individual sharks (e.g. Bond et al. 2012; Ryan
et al. 2015)orNautilis spp. based on skin or shell
colouration patterns (e.g. Dunstan et al. 2011). The
‘Other’ category includes observation of specific
behaviour (e.g. Bailey et al. 2007), total species
counts (e.g. Craig et al. 2011), and residence time (i.e.
how long animals stayed at the bait; e.g. Smale et al.
2007) metrics.
MaxN can be used to assess the relative abundance
of organisms. It is often considered a conservative
estimate as more individual organisms may be present
around the BRUVS but remain uncounted because
they do not appear in the field of view at the same time.
This relative abundance measure can be used to assess
and compare spatio-temporal differences in aquatic
assemblages. Saturation may, however, occur when a
high number of individuals obscure the field of view to
the point that additional individuals cannot be seen
(Schobernd et al. 2014; Stobart et al. 2015). Such
saturation can result in the inability to detect differ-
ences between locations when fish abundance is high
(Stobart et al. 2015) and results in MaxN being non-
linearly related to true abundance (Schobernd et al.
Recently, MeanCount (used in 2 % of studies;
Fig. 5c) has been suggested as an alternative to MaxN
that can be linearly related to true abundance
(Schobernd et al. 2014). MeanCount uses either
systematically or randomly selected individual frames
from across the video which are subsequently counted
and then the mean is calculated. As the entirety of the
video is not viewed, MeanCount has a tendency to
over-inflate zero observations and is less precise than
MaxN (Campbell et al. 2015; Stobart et al. 2015).
T1st is a measure of how fast species are first
observed in the field of view. In some cases, there has
been a negative correlation shown between T1st and
MaxN, meaning that if a species where to arrive
quickly to the bait, the species is often highly abundant
(Stobart et al. 2015). T1st can also be used to infer the
distance a species may have travelled to get to the
BRUVS. However, as T1st is influenced by both the
distance the fishes are away from the BRUVS as well
as the behavioural response to the bait used (i.e. how
attracted they are to the bait) which can vary between
species, it can be difficult to disentangle what T1st is
really showing.
The main types of software that were used to obtain
the above metrics could be classified into 5 groups
(Fig. 6a): specialised software for viewing BRUVS
videos such as EventMeasure (;
used in 34 % of studies) and the AIMS BRUVS soft-
ware (no longer available; 12 %), generic media
players or photo viewers (e.g. VLC, Adobe Photoshop;
8 %), software designed for measuring objects within
photos (e.g. Visual Measurement System; 8 %), and
other programs for further specific purposes (e.g.
Hotspotter for identifying Nautilis spp.; 3 %). Pho-
toMeasure and EventMeasure were combined as a
single category as PhotoMeasure has been superseded
by the newer versions of EventMeasure. The use of a
specialised software program designed for the viewing
of BRUVS videos allows for considerable time-saving
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when processing videos. A high proportion of studies
did not specify the software used for video analysis
(46 %).
Purpose of studies: what is BRUVS used for?
BRUVS has been used for answering a wide variety of
scientific questions (Fig. 6b). The most frequent
reason (34 %) for deploying BRUVS was in relation
to assessing the effects of marine protected areas
(MPAs; e.g. Bornt et al. 2015; Coleman et al. 2015).
The large number of studies using BRUVS to study
MPAs is likely related to the non-destructive and non-
extractive nature of BRUVS, making it a suitable al-
ternative to more traditional methods. Studies that
looked at particular species or behaviours (24 %; e.g.
Denny et al. 2004; Gutteridge et al. 2011) and those
which assessed changes in fish assemblages along a
gradient or between habitats (25 %; e.g. Gomelyuk
2009; Langlois et al. 2012b) were the second- and
third-most common study aims. Method comparisons
both within BRUVS (e.g. different soak times; Glad-
stone et al. 2012) and between BRUVS and other
methods (e.g. BRUVS vs. longlines; Brooks et al.
2011), were also popular with 19 % of studies
choosing to focus upon within-BRUVS method com-
parisons and 18 % on comparisons with other meth-
ods. This perhaps reflects a view in many minds that
BRUVS is still developing and their use needs
justification. There were also studies that investigated
day-to-day (Birt et al. 2012) or day-to-night (e.g.
Svane et al. 2008) variation and variability in night-
time (e.g. Fitzpatrick et al. 2013) assemblages, which
accounted for 5 % of the total.
A majority of studies using BRUVS had a particular
focus on fish assemblages, these being the nektonic
organisms that most frequently come to the bait.
However, a number of other organisms are alsoattracted
to the baited units or can be seen by happenstance,
particularly cephalopods and crustaceans, along with
other mobile invertebrates, cetaceans, pinnipeds and
aquatic birds (e.g. Whitmarsh et al. 2014). In our review,
there were only 11 % of studies which counted all
nektonic species seen on their videos, compared to 64 %
that assessed teleost assemblages, and 60 % that
assessed Chondrichthyes (Fig. 7a). An additional 7 %
Media player/Adobe AIMS BRUVS soware Other photo measuring
EventMeasure/PhotoMeasure Other Unspecified
# of studies
Soware used for video analysis
Changes along a
gradient or between
Marine protected
Other fishing related Night and/or day Method comparison
within BRUVS
Method comparison
with another method
# of studies
Focus of study
Fig. 6 a The software used to assess the videos. ‘‘Other photo
measuring software’ includes programs designed for measuring
objects within photos (excluding PhotoMeasure which was
combined with EventMeasure due to the dual function of
EventMeasure in recent versions of the software) such as Visual
Measurement System. The ‘Other’ category includes programs
for more specific purposes such as Hotspotter which is used to
identifying Nautilis spp. bThe focus of the study for each of the
161 studies analysed. Studies were counted in more than one
column where they covered more than a single focus. The
‘Other’ category includes those which did not fit in any other
category including artificial versus natural reef assessments and
other sorts of impacts
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of studies assessed a single or multiple specific fish
species. Only six of the 161 studies analysed in this
review had a focus on non-fish species (two on the
cephalopod Nautilis (Dunstan et al. 2011;Barordetal.
2014), two on crustaceans (stone crabs in the Lithodidae
family, Collins et al. 2002; and other decapod crus-
taceans, Jamieson et al. 2009), one on reptiles (three
species of sea snakes, the olive sea snake, Aipysurus
laevis, the spine-bellied sea snake, Lapemis curtus,and
the ornate sea snake, Hydrophis ocellatus,Udyawer
et al. 2014), and one on buccinid gastropods (Aguzzi
et al. 2012), withthree out of these six being from deep-
sea habitats. Aside from the traditionally teleost-
focussed studies, in recent years studies focussing
exclusively on chondrichthyans have begun to be
published such as White et al. (2013), Rizzari et al.
(2014) and Ryan et al. (2015).
We were able to determine the percentage of taxa
putatively identified to species level in 65 % of studies
(Fig. 7b). Ten percent of all studies were able to
identify all taxa to species level while only 2 % of
studies had greater than 30 % unable to be identified to
All Teleost Chondrichthyes Cephalopoda Crustacea Single or
specific fish
Other Unspecified
# of studies
Phyla Idenfied
0 <5 <10 <20 <30 <40 Targeted Unspecified
# of studies
% of taxa not Idenfied to species level
Fig. 7 a The phyla that were identified from each of the 161
studies in this review. All includes those studies which counted
any mobile taxa able to be consistently recognised. Crustacea
were most commonly decapods. The Other category included
sea snakes, echinoderms, and one study in which no biota were
identified (only the habitat type). bThe percentage of taxa
unable to be identified down the species level for each study
assessed (n=160). The targeted category specifies those
studies which focused on only a single or few specific species.
Not shown is one study which assessed habitat only and as such
this variable was not applicable
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species level and none greater than 40 %. Generally,
species that could not be identified to species level
were small, cryptic or rare species, which is likely to
result in a bias against such species. Visibility may
also affect how well species are able to be identified.
The type of organism targeted for the study can also
affect rates of identification. For example, fish species
can generally be reliably identified from video
footage, but other smaller mobile animals e.g. crus-
taceans, echinoderms, and cephalopods can be more
difficult to identify. Despite this, since these types of
animals are generally less well-studied, any informa-
tion gathered about them can be useful.
A possibility for assisting newcomers to BRUVS
and improving the ease of identification for existing
persons could be for more routine and shared image
archives. Mentions of image archives are not promi-
nent within the published literature, but archives are
likely to exist for many BRUVS teams and the sharing
and concatenation of such archives would assist in
ensuring the accuracy of identified species.
We also categorised each study as either standard or
novel to highlight any unusual uses of BRUVS. For the
purpose of this literature review, we define the
standard use of BRUVS as follows (anything that
did not fit into this category was considered ‘novel’):
Daytime deployment on the seafloor, in subtidal,
shallow (\120 m deep) habitat, single or stereo
(2 cameras maximum) camera(s) facing towards
the bait bag either horizontal or downwards, a
single bait arm with a mesh bait bag attached,
single use bait, and with a video length of no
more than 90 min.
Out of the 161 studies, 110 (68 %) were considered
standard and 51 (32 %) novel. The main novel
developments for BRUVS were the extensions into
pelagic habitats, modification for deep-water deploy-
ments, and night-time uses.
Overall, only 6 % of the studies analysed had
detailed method sections that stated all of the 24 main
variables in this literature review. However, 60 % of
the studies were only missing values for 2 or less of
these categories (Fig. 5d; Table 1). The most com-
monly unreported variables included the maximum
visible range (reported in only 46 % of studies), the
software used for analysis (54 %), the number of
species able to be identified to species level (55 %)
and the distance between replicates (65 %; Table 1).
Novel method development of BRUVS
Pelagic deployments
The use of BRUVS in the pelagic environment is a
relatively recent development, with only two studies
published using this method up to and including
2012 (Heagney et al. 2007; Robbins et al. 2011).
Since 2012, it has increased in popularity with an
additional nine studies using BRUVS in the pelagic
environment (Letessier et al. 2013; Santana-Garcon
et al. 2014a,b,c,d; Anderson and Santana-Garcon
2015; Bouchet and Meeuwig 2015; Rees et al. 2015;
Scott et al. 2015). This method involves changing
the focus of BRUVS from the traditional demersal
setting to suspending the unit within the water
column to better sample pelagic fishes. The pelagic
BRUVS are horizontally set up and usually allowed
to float at a specific depth below the surface (e.g.
10 m; Heagney et al. 2007), as opposed to resting
on or near the seafloor in standard use, although
some studies set a specific distance above the
substrate (e.g. 10 m above the bottom; Santana-
Garcon et al. 2014a). Other major modifications
include the use of additional floats, ropes, and
weights to allow for a stable mid-water deployment.
Recently, developments have been made to allow
for a drifting pelagic set-up (Bouchet and Meeuwig
2015) that can cover broad stretches of ocean space
that in that study had an average transect length of
4.9 km during a 165 min deployment.
Bait plume dispersal has been highlighted as a
major factor that could affect the fish assemblages
observed via pelagic BRUVS in particular due to the
sparse and heterogeneous nature of fish assemblages
within this environment (Heagney et al. 2007).
Heagney et al. (2007) recommended the addition of
a current meter to assist in determining the likely
plume dispersal. Taylor et al. (2013) also recom-
mended this or similar current-measuring devices to
be used for benthic deployments. Furthermore, an
increased soak time (Letessier et al. 2013) and
replication (minimum of 8 in tropical environments)
is needed to account for the highly heterogeneous
distribution of pelagic species (Santana-Garcon et al.
2014c). There has also been evidence for additional
attractants to be used alongside traditional bait in the
pelagic environment, such as those based on sound
(recordings of bait fish) and sight (metallic reflectors;
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Rees et al. 2015). Rees et al. (2015) compared these
different attractant methods and found that the com-
bination of all three attractants was more effective at
attracting consistent numbers of fish than the individ-
ual components alone.
Six of the 11 studies using pelagic BRUVS
(55 %) were focussed on developing and assessing
the validity of the method, while, of the others, two
looked at behaviour of a particular species (Robbins
et al. 2011; Santana-Garcon et al. 2014b), one
looked at the the impacts of artificial reefs (Scott
et al. 2015), another used the data specifically to
demonstrate a novel statistical analysis technique
(Anderson and Santana-Garcon 2015) and one
focussed upon using BRUVS to determine the
effects of MPAs on pelagic species (Santana-Garcon
et al. 2014d).
Deep-water deployments
While the use of still photography in the deep sea
has occurred since the 1960s (Gage and Tyler 1991),
the use of BRUVS in deep-water habitats has only
begun in the last 14 years, with the first published
articles appearing in 2002 (e.g. Collins et al. 2002;
Yau et al. 2002). There are numerous challenges to
using BRUVS within the deep sea that are not
present in shallower environments, such as increased
pressure resulting in the need for sturdier housing
for the cameras, reduced light resulting in the need
for external lighting sources (and consequently
powered by batteries), reduced diversity and abun-
dance resulting in a need for longer soak time and
potentially more replication being necessary, which
is also compounded by the long descent time from
the surface. There are also depths where ropes and
surface floats become impractical leading to the
need for remote release mechanisms to allow gear
recovery. Some deep-water studies also used larger
baits, such as pig carcasses, and leave them out for
extended periods (days-months; Anderson and Bell
2014). The additional cost for the these features
along with increased general field costs associated
with working in deep-water habitats means that
sampling becomes very expensive, which could be a
reason why there is little to no replication with
deep-water studies (70 % with none or unknown)
and also why 60 % have a soak time longer than
90 min (e.g. Bailey et al. 2007).
Night-time deployments
The optimisation of BRUVS for use specifically at
night has begun recently with studies such as Fitz-
patrick et al. (2013), although the use of BRUVS in
deep-water habitats has occurred for a longer period
and has some of the same challenges (e.g. use of
lights). To observe the impact on fish assemblages,
Fitzpatrick et al. (2013) examined three different light
colours (red, white, and blue) in a range of habitats
both inside and outside protected areas. They found
that each light affected fish assemblages differently
and suggested that this was most likely due to
differences in fish behaviour or physiology towards
different light sources. The wavelength of red light
(620–630 nm), like that of infrared (\700 nm), is
below the spectrum that fish are sensitive to but is
rapidly attenuated in the water column compared to
white and blue light, which can be seen for a greater
distance but may attract or disturb some species.
Fitzpatrick et al. (2013) found that red light sampled
the highest abundance of fish of the three light colours
and was particularly good at sampling non-commer-
cial species; however, it illuminated the smallest area
due to the attenuation of red light in seawater. White
and blue light sampled similar fish assemblages but
had higher abundances of some commercially-tar-
geted species such as snapper, Chrysophrys auratus,
compared to red, and also illuminated a greater area.
The authors recommended further studies into the
impacts of light colour on fish assemblages. These
results are somewhat different from those found from
another study by Harvey et al. (2012b), where white
light sampled a greater number of individual fish
compared to red but was not able to distinguish
between six different benthic habitat types as well as
the red light could.
Another study used infrared light to assess noctur-
nal fish assemblages and compared these results to
those from UVC (Bassett and Montgomery 2011). A
higher abundance of olfactory specialists, species
which rely heavily on sense of smell (e.g. yellow
moray eels, northern conger eels, southern bastard
cod) were observed from infrared BRUVS compared
to UVC, and these species consistently arrived at the
bait quicker than non-olfactory specialists. Studies
have also used BRUVS to compare assemblages
between day and night (e.g. Svane et al. 2008; Svane
and Barnett 2008), and found that BRUVS can
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effectively discern changes between day-time and
night-time behaviours, such as an increased consump-
tion of bait at night.
Other innovations
Other novel uses of BRUVS include the development
of ‘miniBRUVS’ for use in rockpool environments
(Harasti et al. 2014), which is also the only intertidal
use of BRUVS that has occurred so far. This devel-
opment was successfully used to assess the abundance
and distribution of a threatened and otherwise hard-to-
study rockpool-specialist fish, the black rock-cod,
Epinephelus daemelii.
Optimisation versus standardisation: developing
a protocol for reporting methods
Optimisation is the trialling of different variables to
ensure the best use of resources (time, effort and
money) to deliver benefits (e.g. detect increased
abundance or diversity or maximise ability to dis-
criminate between factors). There have been several
studies that have focussed on the optimisation of
BRUVS (e.g. Gladstone et al. 2012; Harasti et al.
2015), with all studies falling into the method
development within BRUVS (19 %) considered as
working towards optimisation. However, few studies
have compared method optimisation between loca-
tions or habitats. Different areas even within similar
habitat types, such as temperate reefs, still seem to
display different values for each optimal scenario,
such as seen in a study by Harasti et al. (2015), which
showed that, in eastern Australia, the MaxN for many
reef species occurred within 12.5 min making a soak
time of 30 min quite practical. In contrast, MaxN in
South Australia took longer to be reached (30–40 min;
Whitmarsh et al. unpublished data) meaning that a
soak time of 60 min is more applicable. Both studies
used similar methods with the exception that Harasti
et al. (2015) used slightly more bait at 1000 g
compared to 800 g. Generally, we urge caution when
assuming optimal scenarios still apply in different
areas or habitats and advise authors to conduct their
own pilot studies if possible.
In general, it is easy to deviate from the ‘standard’
use of BRUVS to tailor to specific objectives such as
studies of Nautilis sp. using chicken as bait with a soak
time of 12 h (e.g. Barord et al. 2014) or modifying the
system to work in small rock-pool environments (e.g.
Harasti et al. 2014). There is, however, no consensus
about whether it is better to tailor the method to each
specific scenario being tested or to strive towards
standardisation to better enable valid comparisons
across studies. The goal of standardisation of BRUVS
as a method may be worthwhile but is ultimately, we
believe, unachievable and may in fact negatively
impact novel developments and methodological
breakthroughs. Currently, if comparisons amongst
studies are attempted, some authors fail to specify
enough details in their paper’s methods section for the
differences to be accurately accounted for. We suggest
a standard protocol of what information to be included
within the literature (Table 1), rather than a standard
protocol for use.
Future directions
We have identified some gaps in the current knowl-
edge base such as the effects of distance between
replicates, bait amount, preparation, and deployment
method, continued lack of studies accounting for
plume effects and using current meters, further
impacts of light colours on nocturnal or deep fish
assemblages, appropriate soak times under a range of
habitats and conditions, and the appropriate numbers
of replicates to account for the variable nature of fish
One key aspect of method deployment not often
covered in the literature is the effect of bait preparation
on fish assemblages observed. Although it is unlikely
that large differences in assemblages would be
observed from using chopped versus crushed sardines,
it is reasonable to assume that some differences may
result from a comparison of whole versus crushed
sardines, if there is any increased areal coverage of
plume dispersal coming from crushed bait.
Future research using BRUVS could focus on
gaining additional data from the video metrics in
addition to MaxN. For example, behavioural data
could enhance our knowledge of how species
interact with themselves, other species, and bait,
while oceanographic data (e.g. temperature, salinity)
through attachment of sensors to the unit would
provide a way to investigate the influence that these
factors have on fish and other nekton. A more
formal description of habitat features seen from the
images and better use of fish arrival or departure
Rev Fish Biol Fisheries
Author's personal copy
times and hence length of stays could also increase
our knowledge of fish assemblages. There is also
scope to increase the use of BRUVS outside of reef
areas, with some studies showing that it is an
effective method for soft-bottom (Gladstone et al.
2012), seagrass (Whitmarsh et al. 2014), pelagic
(Rees et al. 2015), and deep-water (D’Onghia et al.
2015a,b) environments.
The other major area for potential growth in
BRUVS is to focus on other nektonic species rather
than fishes. Combinations of different unit designs and
bait may enable BRUVS to be tailored to any number
of mobile species including cephalopods, marine
birds, marine mammals, marine reptiles, crustaceans,
and other benthic mega-invertebrates (e.g. sea stars,
sea cucumbers and large gastropods).
Overall, BRUVS is a widely-used method for
assessing nektonic assemblages and their behaviour.
This review shows the robust and flexible nature of
BRUVS and its widely applicable uses from cata-
loguing the behaviour of particular species to
broader changes in mobile communities within a
wide variety of depths and habitats. Its use over the
last two decades has led to further developments to
the method, including the introduction of stereo-
BRUVS, pelagic BRUVS, and night-time BRUVS.
Several studies have also focused on optimising or
standardising the use of BRUVS. To enable more
accurate comparisons across studies while still
allowing novel and specialised use, we recommend
a protocol that authors can follow to allow sufficient
detail to be included in methods sections.
Acknowledgments We thank two anonymous reviewers for
their comments which improved our manuscript.
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... Este es un método de observación pasivo que puede ser utilizado de forma alternativa a los métodos de monitoreo previamente mencionados, pues se trata de una herramienta bastante flexible que puede ser utilizado en diferentes zonas geográficas, hábitats y profundidades (White et al., 2013). Si bien es cierto que su uso no se encuentra restringido a peces, estos han sido su principal taxa de estudio (Whitmarsh et al., 2017), gracias a su facilidad para monitorear desde especies pequeñas y costeras, hasta especies grandes y móviles que tienden a evitar a los buzos durante sus transectos (Brooks et al., 2011). ...
... Los VSRC (de aquí en adelante BRUVS) comenzaron a usarse en el año de 1995, donde se utilizaron por primera vez para evaluar las abundancias de una especie de pargo (Pristipomoides filamentosus) (Ellis y DeMartini, 1995) , y su uso ha ido aumentando con el tiempo, especialmente después de 2007. Lo anterior se debe al reconocimiento de los beneficios del método, avances de los equipos electrónicos y su accesibilidad económica (Whitmarsh et al., 2017). ...
... Si bien es cierto que las BRUVS han sido bastante utilizados para el estudio de las comunidades de peces, existen algunos problemas respecto a su estandarización debido a las modificaciones se tienen que realizar para estudiar diferentes tipos de hábitats, especies y sitios (Whitmarsh et al., 2017). Por tal motivo, se recomienda mencionar las características empleadas en el diseño experimental, las cuales comprenden 24 variables, que incluyen aspectos como la localización, tipo de hábitat, orientación de la cámara, tipo de carnada, profundidad, softwares utilizados para el análisis, entre otros (Whitmarsh et al., 2017) ...
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Realizar un seguimiento continuo de las comunidades marinas es importante para su manejo y conservación. Desafortunadamente, debido a las dificultades metodológicas, los esfuerzos de monitoreo en el ambiente pelágico han sido pocos en comparación con los del ambiente bentónico. Es por ello que se han implementado nuevas tecnologías para facilitar el monitoreo en estos hábitats, tal y como son las cámaras remotas cebadas (BRUVS por sus siglas en inglés). El presente estudio tiene como objetivo utilizar esta herramienta para comparar las comunidades de peces pelágicos en dos regiones y sus localidades: el Suroeste del Golfo de California (SGC) y el Archipiélago de Revillagigedo (AR); haciendo énfasis en el nivel trófico promedio y el efecto de la profundidad de fondo. Para cada sitio se identificaron las especies y sus abundancias con base al número máximo de individuos en un data frame por hora (MaxNhr). Para cada localidad se calcularon los índices de diversidad (H, D, J, Δ+) y se realizaron análisis de ordenación. Para conocer si existían diferencias en la composición de especies se realizó un análisis PERMANOVA, y un análisis SIMPER para conocer las especies responsables de dichas diferencias. Para comprobar si existían diferencias significativas en el Nivel Trófico por región y localidades, se realizó una prueba de Kruskall-Wallis utilizando el valor trófico de cada especie, obtenido de FishBase, en función de su abundancia relativa y gremio trófico. En cuanto a la profundidad de fondo, las cámaras fueron clasificadas en someras (≤30 metros) o profundas (>30 metros). Finalmente se aplicó un análisis PERMANOVA para comprobar las diferencias estadísticas, y se distinguió la aportación de cada gremio trófico y preferencia de hábitat en función de la abundancia relativa. Se lograron colocar y analizar correctamente 140 cámaras (SGC = 29, AR =111), lo que corresponde a 244.1 horas efectivas de grabación. Se identificaron un total de 60 especies (SGC = 41; AR = 30) y se contabilizaron 8,507 individuos. Las curvas de acumulación y estimadores de diversidad indicaron un buen esfuerzo de muestreo para el AR, pero pobre para el SGC. Con excepción del índice J a nivel localidad, y el índice D a nivel región, no se encontraron diferencias significativas entre los índices de diversidad. En contraste, los análisis de ordenación y PERMANOVA si encontraron diferencias entre regiones y localidades. De manera similar el Nivel Trófico fue mayor en el AR debido a una mayor cantidad de carnívoros superiores. Con respecto a la profundidad, el análisis PERMANOVA encontró diferencias significativas entre los ensambles. Mientras que los ambientes someros tuvieron más abundancia de peces arrecifales y carnívoros inferiores, los ensambles profundos presentaron una proporción similar de carnívoros superiores e inferiores, dominados por especies pelágicas. Los resultados anteriores indican que la diversidad de peces pelágicos es similar entre ambas regiones, pero difieren en cuanto a uniformidad y composición, lo que deja en evidencia la importancia de las áreas naturales protegidas para la conservación de los depredadores tope.
... In the last decades, new techniques have been developed to study marine ecosystems (Barnett et al., 2010) amidst growing threats, such as habitat degradation, pollution, overfishing and climate change (Brautigam et al., 2015;Simpfendorfer et al., 2011). Particular emphasis has been put on non-lethal, non-extractive methods that can detect temporal and spatial fluctuations in populations associated with natural and anthropogenic impacts (Barley et al., 2017;Willis et al., 2000;Whitmarsh et al., 2017). One such method is the Baited Remote Underwater Video Stations (BRUVS). ...
... One such method is the Baited Remote Underwater Video Stations (BRUVS). The use of BRUVS has become increasingly popular worldwide for assessing marine diversity and estimating relative abundances (Whitmarsh et al., 2017). This technique is less labour intensive and produces less biased estimates of species richness and relative abundance than underwater visual censuses with SCUBA or Diver Operated Video (Brooks et al., 2011;Harvey et al., 2002;Watson et al., 2010). ...
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Introduction: Video techniques are used worldwide to study marine communities. As elsewhere, the use of remote underwater videos has recently increased in Brazil and there is a need for information about their advantages , disadvantages, and reliability in tropical habitats. Objective: To evaluate the use of baited remote underwater video stations (BRUVS) in fish diversity research in a tropical habitat. Methods: We used baited video stations to record the fishes and their relationship with habitat type, underwater visibility and depth, in 79 random sites in the Metropolitan Region of Recife, Northeastern Brazil (11 days in November 2017). Results: We recorded 3 286 individuals (65 taxa, 29 families) along a 25 km section of the shoreline, 10.2 to 28.6 m depth. The Clupeidae dominated numerically, followed by Haemulidae, Carangidae, and Lutjanidae; by species, Haemulon aurolineatum, Opisthonema oglinum, Haemulon steindachneri, Lutjanus synagris and Caranx crysos. The highest mean number of species was detected over sediment close to shipwrecks, but we found no differences among the mean number of individuals between habitat types. More species and individuals were observed at a depth of 20-25 m depth. The highest mean number of species was in 2-3 m of visibility, and the highest number of individuals within 4-5 m. Conclusions: Video recording seemed to be a valid method, and indicated that-besides being relatively diverse-the local fish community is dominated by a few species of small and medium-sized mesopredators, and a few top predators.
... Baited Remote Underwater Stereo-Video systems (stereo-BRUVs) are becoming increasingly popular to assess the effects of NTRs on different dimensions of fish assemblages, especially fisheries target species, including changes in the functional diversity Coleman et al., 2015;Whitmarsh et al., 2017). The typical stereo-BRUV setup consists of two cameras installed in a baited structure that is laid in the seabed or in the water column, allowing for accurate measurements of organisms through photogrammetry (Harvey and Shortis, 1996). ...
... The typical stereo-BRUV setup consists of two cameras installed in a baited structure that is laid in the seabed or in the water column, allowing for accurate measurements of organisms through photogrammetry (Harvey and Shortis, 1996). Stereo-BRUVs sample a wide range of species, including herbivores (Harvey et al., 2021), and can be applied across a wide variety of habitats and depths (Whitmarsh et al., 2017). In addition, as a remote sensing technique, it detects large and mobile animals that usually avoid divers and active fishing gears (Cappo et al., 2006;Goetze et al., 2015). ...
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The effects of fishing have been documented across coral reefs worldwide. No-take marine reserves do not only act as a conservation tool but also allow an opportunity to study impacts of fishing, by acting as control sites. In addition, well-planned and well-managed no-take marine reserves (NTRs) provide conservation benefits that are essential to marine biodiversity and ecosystem-based management. The Abrolhos Marine National Park, off the tropical Brazilian coast, protects part of the largest coral reef system in the South Atlantic. To investigate the effects of fishing on reef fish richness, abundance, biomass, and functional diversity of the fish assemblage, we compared sites across two protection levels considering the variation in habitats (Fringing Reefs—Protected; Pinnacles Reefs—Protected; and Coastal Reefs—Open Access), using Baited Remote Underwater Stereo-Video systems (stereo-BRUVs). We adjusted generalized additive mixed models of fish assemblage characteristics with protection levels and environmental variables, such as topographic complexity (mean relief and relief variation), visibility, and benthic cover percentage. Inside NTRs, we found higher total biomass and biomass of fishery target species and carnivores, specifically for the Carcharhinidae (sharks) and Epinephelidae (groupers) families, indicating direct fisheries effects on these groups. In contrast, the ecological parameters of non-target fish were positively correlated with habitat characteristics, including mean relief and variance of relief. Moreover, fish functional diversity was higher within NTRs, demonstrating an even distribution of functional entities. The presence of large mobile predators and the overall higher biomass of carnivores inside the NTR indicate the effect of fishing exclusion. Our results point to the value of NTRs to study the effects of fishing and achieve biodiversity conservation and suggest the importance of using remote sampling methods to assess large mobile predators.
... All species of fish are manually identified with specialist video analysis software with built-in species libraries, and the abundance of each species is estimated by recording the "MaxN" value for each (e.g., Whitmarsh et al., 2017). As previously noted, MaxN is not useful for the DL application, but the species labels associated with MaxN are used. ...
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Machine-assisted object detection and classification of fish species from Baited Remote Underwater Video Station (BRUVS) surveys using deep learning algorithms presents an opportunity for optimising analysis time and rapid reporting of marine ecosystem statuses. Training object detection algorithms for BRUVS analysis presents significant challenges: the model requires training datasets with bounding boxes already applied identifying the location of all fish individuals in a scene, and it requires training datasets identifying species with labels. In both cases, substantial volumes of data are required and this is currently a manual, labour-intensive process, resulting in a paucity of the labelled data currently required for training object detection models for species detection. Here, we present a “machine-assisted” approach for i) a generalised model to automate the application of bounding boxes to any underwater environment containing fish and ii) fish detection and classification to species identification level, up to 12 target species. A catch-all “fish” classification is applied to fish individuals that remain unidentified due to a lack of available training and validation data. Machine-assisted bounding box annotation was shown to detect and label fish on out-of-sample datasets with a recall between 0.70 and 0.89 and automated labelling of 12 targeted species with an F1 score of 0.79. On average, 12% of fish were given a bounding box with species labels and 88% of fish were located and given a fish label and identified for manual labelling. Taking a combined, machine-assisted approach presents a significant advancement towards the applied use of deep learning for fish species detection in fish analysis and workflows and has potential for future fish ecologist uptake if integrated into video analysis software. Manual labelling and classification effort is still required, and a community effort to address the limitation presented by a severe paucity of training data would improve automation accuracy and encourage increased uptake.
... The use of video equipment to assess species occurrence and abundance has become popular among fish researchers, due in large part to the widespread availability of affordable so-called sport action cameras in recent years (Letessier et al., 2015). This has resulted in a marked increase in the use of baited remote underwater video stations (BRUVS) in fish population studies (Whitmarsh et al., 2017), with the added benefit that the technique is non-invasive and does not require the species under consideration to physically engage with the equipment in order to be 'captured' (Cappo et al., 2003). However, with the notable exception of species that are suited to the photographic identification of individuals, abundance estimates derived from BRUVS require careful interpretation (Sherman et al., 2018). ...
Sets of baited hooks decrease in fishing efficacy over time as catch accumulates and bait is lost, and this complicates the quantification of fishing effort for species abundance calculations. Although hook-timers facilitate a more accurate estimation of fishing time, they cannot provide taxonomic information in the case of escapees or bait loss, nor can they provide information on animals that approach the gear but do not physically engage with it. To overcome these limitations, the present study attached sport action cameras to baited drumlines during a catch-and-release survey of sharks in the shallow, coastal waters of the eastern Caicos Bank, Turks and Caicos Islands. Overall, the true fishing time was found to be appreciably less than the apparent fishing time and more sharks approached or interacted with the gear than were successfully captured by it. Furthermore, shark-gear interactions varied by species while the amount of bait loss differed between deployment areas. Taken together, these findings suggest that baited-hook surveys underestimate shark abundance and that the magnitude of this error varies by species and habitat type.
... Due to the logistically challenging and remote environment of the current study, in depths beyond scientific diving limits and with strong currents, stereo-BRUVs were a logistically feasible sampling method. However, baited methods have a range of recognised biases that could influence the findings of any study (Cappo et al., 2007;Whitmarsh et al., 2017). Although potential biases have likely influenced the current study in a relatively consistent fashion, as the method was used consistently across it, we recommend that future studies in such environments should endeavour to use complementary non-baited sample data (e.g. ...
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No-take marine reserves are often located in remote locations far away from human activity, limiting perceived impact on extractive users but also reducing their use for investigating impacts of fishing. This study aimed to establish a benchmark in the distribution of fished species across the Ningaloo Marine Park – Commonwealth (NMP-Commonwealth), and adjacent comparable habitats within the Ningaloo Marine Park - State (NMP-State), in Western Australia to test if there was evidence of an effect of recreational fishing, as no commercial fishing is allowed within either marine park. We also examined whether the remote location of the newly established (2018) No-take Zone (NTZ), in NMP-Commonwealth, limits its use for studying the effects of fishing. Throughout the NMP-Commonwealth and NMP-State, where recreational fishing is permitted, we expected the abundance of recreationally fished fish species to increase with increasing distance to the nearest boat ramp, as a proxy of recreational fishing effort. Conversely, we did not expect the abundance of non-fished species and overall species richness to vary in response to the proxy for human activity. Distance to the nearest boat ramp was found to be a strong predictor of fished species abundance, indicating that the effect of recreational fishing can be detected across the NMP-Commonwealth. The effect of the NTZ on fished species abundance was weakly positive, but this difference across the NTZ is expected to increase over time. Habitat composition predictors were only found to influence species richness and non-fished species abundance. This study suggests a clear footprint of recreational fishing across the NMP-Commonwealth and as a result the new NTZ, despite its remote location, can act as a control in future studies of recreational fishing effects.
... We then analysed the accuracy of the model using common performance metrics (Fig. 1). We focussed on the most widely used measure of abundance in videos, MaxN, the maximum number of fish visible in a video in any one frame (Ellis and DeMartini 1995;Whitmarsh et al. 2016;Langlois et al. 2020). ...
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Efficacious monitoring of fish stocks is critical for efficient management. Multibeam acoustic cameras, that use sound-reflectance to generate moving pictures, provide an important alternative to traditional video-based methods that are inoperable in turbid waters. However, acoustic cameras, like standard video monitoring methods, produce large volumes of imagery from which it is time consuming and costly to extract data manually. Deep learning, a form of machine learning, can be used to automate the processing and analysis of acoustic data. We used convolutional neural networks (CNNs) to detect and count fish in a publicly available dual-frequency identification sonar (DIDSON) dataset. We compared three types of detections, direct acoustic, acoustic shadows, and a combination of direct and shadows. The deep learning model was highly reliable at detecting fish to obtain abundance data using acoustic data. Model accuracy for counts-per-image was improved by the inclusion of shadows (F1 scores, a measure of the model accuracy: direct 0.79, shadow 0.88, combined 0.90). Model accuracy for MaxN per video was high for all three types of detections (F1 scores: direct 0.90, shadow 0.90, combined 0.91). Our results demonstrate that CNNs are a powerful tool for automating underwater acoustic data analysis. Given this promise, we suggest broadening the scope of testing to include a wider range of fish shapes, sizes, and abundances, with a view to automating species (or ‘morphospecies’) identification and counts.
... Whether they aim to measure management effectiveness or human impacts, various ecological indicators have been proposed (Meehan et al., 2020). However, most ecological indicators are based on surveys conducted with fishing gears, underwater visual censuses (UVCs), remote underwater videos (RUVs) and baited remote underwater videos (BRUVs), which (1) are selective and lead to biased estimates of biodiversity (Whitmarsh et al., 2017;Costello et al., 2017); (2) require highly advanced taxonomic expertise (Thomsen and Willerslev, 2015); or (3) are destructive or invasive (scientific trawling). The bias can be particularly striking when considering small species such as cryptobenthic fishes, hidden in the substrate, that cannot be correctly monitored with most fishing gears, UVCs or BRUVs (Smith-Vaniz et al., 2006;Alzate et al., 2014) while constituting the building blocks of reef ecosystems (Brandl et al., 2019). ...
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In a context of marine biodiversity erosion, the need to better understand the effects of overfishing stands out. New genetic techniques such as environmental DNA (eDNA) metabarcoding have emerged and allow the detection of a wider range of species compared to conventional methods, but still fall short of providing reliable abundance estimations and subsequent ecological indicators. In this paper, we propose a combination of metabarcoding and quantitative polymerase chain reaction to obtain the quantity of eDNA molecules per species. This method was used inside and outside six no-take Mediterranean marine reserves to measure the effect of the protection on fish species and build a new indicator. Even if the total quantity of fish eDNA molecules was not different between the inside and outside of the reserves, we detected that cryptobenthic fish eDNA was significantly associated to the outside of reserves. Based on this observation, we propose a novel ecological indicator, the Demerso-pelagic to Benthic fish eDNA Ratio (DeBRa), taking advantage of the eDNA capacity to detect cryptobenthic reef fishes which are often missed by classical surveys. The DeBRa was significantly higher inside reserves, reflecting a higher relative quantity of eDNA molecules belonging to pelagic and demersal fishes under protection against fishing, therefore it appears to be a reliable eDNA-based indicator of human pressure. Furthermore, the DeBRa was not sensitive to habitat or environmental variations and does not require a complete reference database of eDNA sequences since it can rely on sequences assigned at the genus or family scale if possible and necessary.
... BRUVS are used to document a wide variety of marine organisms from the tropics to the polar regions, with well-established protocols on system design, sampling methodology and data processing (Langlois et al., 2020;Whitmarsh et al., 2017). The non-destructive nature of video sampling, with minimal disturbance to marine life and the seabed, is particularly useful for monitoring vulnerable species and habitats, for example, endangered species in protected areas (Espinoza et al., 2020) or biogenic habitats (Orfanidis et al., 2021). ...
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Accurate knowledge on spatiotemporal distributions of marine species and their association with surrounding habitats is crucial to inform adaptive management actions responding to coastal degradation across the globe. Here, we investigate the potential use of environmental DNA (eDNA) to detect species–habitat associations in a patchy coastal area of the Baltic Sea. We directly compare species‐specific qPCR analysis of eDNA with baited remote underwater video systems (BRUVS), two non‐invasive methods widely used to monitor marine habitats. Four focal species (cod Gadus morhua, flounder Platichthys flesus, plaice Pleuronectes platessa, and goldsinny wrasse Ctenolabrus rupestris) were selected based on contrasting habitat associations (reef‐ vs. sand‐associated species), as well as differential levels of mobility and residency, to investigate whether these factors affected the detection of species–habitat associations from eDNA. To this end, a species‐specific qPCR assay for goldsinny wrasse is developed and made available herein. In addition, potential correlations between eDNA signals and abundance counts (MaxN) from videos were assessed. Results from Bayesian multilevel models revealed strong evidence for a sand association for sedentary flounder (98% posterior probability) and a reef association for highly resident wrasse (99% posterior probability) using eDNA, in agreement with BRUVS. However, contrary to BRUVS, eDNA sampling did not detect habitat associations for cod or plaice. We found a positive correlation between eDNA detection and MaxN for wrasse (posterior probability 95%), but not for the remaining species and explanatory power of all relationships was generally limited. Our results indicate that eDNA sampling can detect species–habitat associations on a fine spatial scale, yet this ability likely depends on the mobility and residency of the target organism, with associations for sedentary or resident species most likely to be detected. Combined sampling with conventional non‐invasive methods is advised to improve detection of habitat associations for mobile and transient species, or for species with low eDNA concentrations. There is a growing demand for low‐cost marine monitoring techniques capable of documenting species‐habitat associations (SHAs). In this comparative assessment with baited remote video, we show that eDNA can detect SHAs on a fine spatial scale, yet this ability likely depends on the mobility and residency levels of target organisms.
... Fauna was identified to species level. As a relative measure of abundance, the maximum number of individuals seen in any frame (MaxN; following [57]) during the 20 minute video (each sampling period) was calculated. As a measure of species richness, the maximum number of species seen over the full 20 minute recording (was calculated following [58]. ...
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Coral reefs face worldwide decline from threats such as climate change, destructive fishing practices, overfishing and pollution. Artificial reefs have shown potential as a method to mitigate localised habitat loss and biodiversity decline on degraded coral reefs. The health of coral reefs in Indonesia and their associated faunal populations have displayed a downward trend in recent decades, and community-managed non-government organisations have started using artificial reefs to restore local degraded reef habitats. In this study, we demonstrate how locally-managed NGOs and communities in north Bali, Indonesia have implemented artificial reef projects, and assess the associated benefits to biodiversity. Using Remote Underwater Video (RUV) over a 3 month period in north Bali, fish assemblages on two artificial reefs of different ages (new and mature) were compared to two nearby natural habitats: degraded sand flats and relatively healthy coral reefs. When compared with a nearby degraded sand habitat, both artificial reefs displayed a significantly higher number of species, which for the mature artificial reef was not statistically different to a nearby coral reef. Community structure was also compared, again showing similarity between artificial reefs and natural coral reefs, but differing in a few species, including specific damselfish and wrasse. This study is one of few which highlight the potential of artificial reef habitat enhancement in Indonesia, and suggests that these structures can provide ecologically equivalent mobile faunal communities to a natural reef on a localised scale. As such, well designed projects may be able to provide some local ecosystem services lost from degraded coral reefs, and become an important focus for coastal communities.
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Baited hooks used in angling may influence foraging behaviour and trophic interactions among reef fishes, which may be reinforced over successive generations. In contrast to other scavenging opportunities involving pulsed resources (e.g. food falls), there is an asymmetric susceptibility to becoming hooked for species and size classes. The resulting effect may provoke behavioural changes in reef fishes through cognitive or phenotypic selective processes. We investigated the effects of angling pressure on the feeding behaviour and community structure of reef-associated fishes using a novel underwater video approach. Fish community and behavioural responses to simulated angling baits were assessed at paired sites of contrasting angling pressure at Amity Point and Ballina, on the east coast of Australia from late 2013 to 2014. Bait introduction differentially influenced trophic interactions of reef-associated communities according to angling intensity regime. Locations with higher angling pressure correlated with increased feeding hesitation among potentially capture-susceptible species, most notably Acanthopagrus australis. The feeding hierarchy of less dominant feeders and their interactions also differed compared with those exhibited under lower angling intensity regimes. Specifically, smaller opportunistic feeders (e.g. Microcanthus strigatus) that could utilise angling bait for dietary supplementation without risk of being hooked were able to maintain higher feeding ranks in areas subject to high angling pressure due to reduced interference competition from larger species, e.g. A. australis. We discuss the potential for complex ecological effects to follow from high-intensity angling activities and highlight the potentially confounding effects of using bait as a focus for fish assemblage monitoring.
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Baited stereo-camera surveys of fish assemblages provide conservative estimates of abundance and length-frequency distributions. While underwater camera systems have numerous advantages over traditional fishing and diver surveys, limitations in sampling capacity, data processing time, and resultant data still exist. Previous studies have shown that shorter camera soak times can increase sampling efficiency and reduce per-sample data processing time without affecting overall data quality. Using data from stereo-video surveys of bottomfish in the main Hawaiian Islands, this study evaluates the effect of camera soak time on relative abundance metrics, fish length data, sampling efficiency, and power to detect differences in relative abundance and fish lengths. A soak time of 15 min was found to be the shortest duration able to capture bottomfish abundance and length metrics while 30 min generated data that did not significantly differ from the standard 40-min soak time. These shorter soak times allow for better survey efficiency and improved cost–benefit through increased levels of field sampling and reductions in video-processing time, while maintaining the power to detect differences in bottomfish relative abundance and lengths. The main drawback to shortening soak time was the concurrent reduction in the number of length measurements collected per species. An increased sample yield can alleviate this effect but only for bottomfish with a higher frequency of occurrence. Species-specific patterns in abundance were apparent in this study suggesting a strong influence of fish behavior on stereo-video abundance metrics. While a soak time of 15 to 30 min was found to be sufficient for effectively sampling bottomfish, the cost–benefit of employing a given soak time in future stereo-video surveys should be assessed based on the target species and survey goals.
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Estimating fish sizes from camera images is an important requirement of many fish monitoring programs, typically involving complex and expensive technology such as stereo-video. However, as a fish grows, the relative size of its eye typically decreases, providing a potential means of estimating fish size from a single image. We show that the ratio of head height to eye diameter is a good predictor of body length for 6 species of common New Zealand reef fish representing 6 different families. The regression equations describing such relationships can be used to estimate lengths of individual fish from single photographs or video frames, which in turn can be used to estimate the distance of each fish from the camera (by determining the proportion of the image frame occupied by an object of known length at known distances) in order to standardize the survey area. In a field test, lengths of 90% of 511 individual snapper Pagrus auratus recorded by unbaited video cameras could be estimated from their head height:eye diameter ratios. This method enables fish lengths to be estimated from single still or video images, allowing fish to be monitored with small inexpensive cameras. While this simple and cost-effective approach will increase the accessibility of video monitoring techniques, it will be best suited to areas where fish diversity is low enough to enable equations to be obtained for all common species, or where the focus is on a subset of species (e.g. harvested species).
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We present a novel system of drifting pelagic baited stereo-video cameras that operate in deep-water, topographically complex environments typically considered inaccessible for sampling. The instruments are portable, semi-autonomous and inexpensive, allowing the recording of high-definition video footage in near-real time and over broad stretches of ocean space. We illustrate their benefits and potential as non-extractive monitoring tools for offshore marine reserves with a pilot study conducted within the newly established Perth Canyon Commonwealth Marine Reserve, southwestern Australia (32 S, 115 E). Using occupancy and maximum entropy models, we predict the distribution of midwater fishes and sharks and show that their most suitable habitat encompasses a wider fraction of the canyon head than is covered by park boundaries. Our proof-of-concept study demonstrates that drifting pelagic stereo-video cameras can serve as appropriate field platforms for the construction of species distribution models with implications for ocean zoning and conservation planning efforts.
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Natural fluctuations in the abundance and length of targeted fish are often disrupted by acute environmental changes and anthropogenic impacts, particularly fishing pressure. Long-term assessments of targeted fish populations inside and outside areas closed to fishing are often necessary to elucidate these effects, yet few of these studies extend over long time periods. We assessed trends in the abundance and length of six targeted fish species in areas open and closed to fishing on seven occasions
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A stereo-video baited camera system (BotCam) has been developed as a fishery-independent tool to monitor and study deepwater fish species and their habitat. During testing, BotCam was deployed primarily in water depths between 100 and 300 m for an assessment of its use in monitoring and studying Hawaiian bottomfish species. Details of the video analyses and data from the pilot study with BotCam in Hawai'i are presented. Multibeam bathymetry and backscatter data were used to delineate bottomfish habitat strata, and a stratified random sampling design was used for BotCam deployment locations. Video data were analyzed to assess relative fish abundance and to measure f ish size composition. Results corroborate published depth ranges and zones of the target species, as well as their habitat preferences. The results indicate that BotCam is a promising tool for monitoring and studying demersal fish populations associated with deepwater habitats to a depth of 300 m, at mesohabitat scales. BotCam is a flexible, nonextractive, and economical means to better understand deepwater ecosystems and improve science-based ecosystem approaches to management.
Cool-temperate reef fish assemblages are often poorly described below 20 m because of depth limitations of conventional diver-based visual census. The recent development of baited remote underwater video systems (BRUVs) provide an alternative quantitative sampling method. Despite being used in warmer temperate and tropical waters and cool-temperate waters in Victoria, initial trials of vertical BRUVs in Tasmania, Australia, provided poor results. Our study explored possible reasons for this, including using horizontal BRUVs and various baits across a depth gradient. We examined the fish fauna in, and adjacent to, a small, but long-established, no-take marine reserve to assess the potential for BRUVs to enhance monitoring programs in exposed coastal environments. Significant differences in the fish assemblage were described relating to location, depth and bait type, with pilchards being an effective bait type. Fish abundance and species richness increased with depth. The research validated BRUVs for monitoring deep-reef systems in Tasmania, reinforced the importance of depth in structuring fish assemblages and identified the range of observable species in this region that may not be fully captured with diver-based surveys alone. Power analyses were conducted using the data generated here to inform the amount of replication needed to detect biologically meaningful differences in targeted fish assemblages in subsequent studies examining the response of no-take marine reserves to protection. This facilitates future assessments of the effectiveness of Tasmanian no-take reserves and allows for more broad-scale studies that can address a range of ecological and conservation questions.