2043
The impact of commercial fishing on the determination of
habitat associations for sea scallops (Placopecten magellanicus,
Gmelin)
Stephen J. Smith, Jerry Black, Brian J. Todd, Vladimir E. Kostylev, and Mark J. Lundy
Smith, S. J., Black, J., Todd, B. J., Kostylev, V. E. and Lundy, M. J. 2009. The impact of commercial fishing on the determination of habitat
associations for sea scallops (Placopecten magellanicus, Gmelin). – ICES Journal of Marine Science, 66: 2043– 2051.
The sea scallop (Placopecten magellanicus) population off southwestern Nova Scotia in Scallop Fishing Area 29 has been monitored by
an annual drag survey since the fishery started there in 2001. A new stratification scheme based on surficial geology maps from a
multibeam bottom mapping and geology ground-truth project completed in 2004 in the area have been used for survey design
since 2005. Survey data from before 2005 have been post-stratified using the new strata. The efficiency of the design with respect
to variance reduction appears to have diminished over time suggesting that the association between scallop abundance and
bottom type may not have been as strong or constant as first assumed. Modelling of the association between scallop abundance
and bottom type and depth using a Bayesian hierarchical approach confirms this diminishing relationship. Comparison of the
results from the model with spatial measures of fishing effort based on satellite vessel monitoring data suggests that increasing exploitation may be masking the relationships as scallop beds are targeted and fished down. These results could have implications on the
interpretation of species habitat associations from areas where data are only available from periods when the populations have been
exploited over a long time. In these cases, the spatial distribution of fishing effort may be a better indicator of species habitat associations than the estimates from surveys.
Keywords: Bayesian methods, multibeam bathymetry, seabed mapping.
Received 31 December 2008; accepted 10 June 2009; advance access publication 10 July 2009.
S. J. Smith, J. Black, and M. J. Lundy: Population Ecology Division, Department of Fisheries and Oceans, Bedford Institute of Oceanography, 1
Challenger Drive, Dartmouth, NS, Canada B2Y 4A2. B. J. Todd and V. E. Kostylev: Geoscience Centre Atlantic, Natural Resources Canada,
Bedford Institute of Oceanography, 1 Challenger Drive, Dartmouth, NS, Canada B2Y 4A2. Correspondence to S. J. Smith: tel: þ1 902 426 3317;
fax: þ1 902 426 1862; e-mail: [email protected].
Introduction
Periodic surveys are a common method for monitoring fish and
shellfish populations (Gunderson, 1993). Many of these surveys
were designed with minimal information about the distribution
of the target species, and because of logistical considerations,
survey designs rarely change over time. For those surveys that
use a sample survey or finite population design (Thompson,
2002), the stratified random design is the most commonly used.
Strata are usually designed for broad distribution patterns and
may also incorporate management area boundaries.
After a number of years of conducting surveys, additional
information may become available on underlying causes for distribution patterns. For sea scallop (Placopecten magellanicus),
bottom type has been identified as a determining factor for distribution (Thouzeau et al., 1991; Kostylev et al., 2003). The results of
the latter study were from multibeam mapping of Browns Bank in
the Atlantic ocean off southern Nova Scotia (Figure 1 inset).
Starting in 2002, a joint industry/government-funded multibeam
project for an area known as Scallop Fishing Area (SFA) 29 was
initiated (Figure 1 inset). The scallop fishery in that area started
in 2001 and from 2001 to 2004 either a random (2001) or a stratified random (management subareas for 2002–2004) survey had
# 2009
been used to monitor the stock status of the scallops. In 2005,
preliminary surficial sediment maps became available to redesign
the surveys using bottom type. The initial results from using this
surficial bottom-type map for the survey design in 2005 were
encouraging with respect to increasing the precision of the stratified mean or total numbers of scallop over the previous design.
Recently, a new interpretation of the bottom-type data for SFA
29 became available which also incorporates sidescan sonar and
seismic information not analysed before. Potential improvements
from using this new version of the bottom-type map to design the
scallop survey need to be evaluated.
The success of any survey design lies in the continuing strong
relationship between the variables used for stratification and the
variables being measured. By their nature, the location of scallop
beds are predictable, and as a fishery progresses, these beds
become well known to the fishers and targeted by them (Smith
and Rago, 2004). Given that habitat association is often determined by what conditions are most associated with the greater
abundance or biomass of a species (e.g. Perry and Smith, 1994;
Methratta and Link, 2006a), there is the likelihood of the fishery
having an impact on detecting habitat associations when measured
in this way. In this study, we model the relationships between
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2044
S. J. Smith et al.
Figure 1. SFA 29 study area with geophysical bottom types defined as follows.
, metamorphic bedrock; Dg, igneous bedrock; Gm,
glaciomarine silt; Ic1, ice-contact sediment (till); Pg, post-glacial sand and gravel. Inset shows the location of SFA 29 study area off the south
coast of Nova Scotia, Canada. Fishery management subareas A– E are also shown.
survey catches, bottom type, and depth for the SFA 29 survey data
from 2001 to 2007 to determine if there have been any changes that
may be due to the scallop fishery. The potential impact of the
scallop fishery on these relationships was assessed using spatial
information on fishing effort in the form of vessel monitoring
system (VMS) position data available since 2002.
Material and Methods
Multibeam mapping
Multibeam bathymetric data were collected over SFA 29 by the
Canadian Hydrographic Service using the Canadian Coast
Guard Ship “Frederick G. Creed”, a SWATH (Small Waterplane
Area Twin Hull) vessel. The ship was equipped with a Simrad
Subsea EM1000 multibeam bathymetric survey system (95 kHz)
with the transducer mounted on the starboard pontoon. This
system produces 60 beams arrayed over an arc of 1508 and operates
by insonifying a narrow strip of sea floor across track and detecting
the bottom echoes. The width of the sea floor imaged on each
survey line was five to six times the water depth. Line spacing
was about three to four times water depth to provide insonification overlap between adjacent lines. The differential global positioning system was used for navigation, providing positional
accuracy of +3 m. Survey speeds averaged 14 knots resulting in
an average data collection rate of 5.0 km2 h21 in water depths
of 20 –70 m. The data were adjusted for tidal variation using
tidal predictions from the Canadian Hydrographic Service.
In addition to bathymetric data, multibeam sonar technology
records the intensity of echoes returned from the seabed.
Multibeam backscatter intensity data are a measure of the reflectivity of the sea floor. High values represent hard seabeds such
as rock outcrops and gravel, whereas low values represent soft
seabeds such as muddy sand and mud. Backscatter intensity
images provide an approximation for the distribution of bottom
types by emphasizing differences in acoustic response from different types of sea-floor sediment.
To complement the multibeam sonar survey, high-resolution
geophysical profiles were collected in the study area (Todd et al.,
2004). The systems deployed included a Huntec deep-tow
seismic (DTS) boomer to show geological structure below the
seabed and a Simrad MS992 sidescan sonar (120 and 330 kHz)
to show the distribution and morphological details of large-scale
features and bottom types.
Sea-floor sediment samples were collected using a 0.75-m3
Institutt for Kontinentalsokkelundersøkelser (IKU) grab sampler.
In regions of soft sediment, the grab sampler is able to penetrate
the sea floor up to 0.5 m and preserve the integrity of the layering
within the surficial sediments. Grain size descriptions based on the
samples adhere to the Wentworth size class scheme for clastic sediments (Wentworth, 1922).
High-resolution sea-floor imagery was obtained using
Campod, an instrumented tripod equipped with video and still
cameras (Gorden et al., 2000). The system included forwardand downward-looking video cameras and a downward-looking
35-mm still camera. Campod was allowed to drift across the
seabed and was placed, stationary, on the sea floor at locations
of interest.
A geophysical categorization of bottom type was developed
based on the interpretation of geophysical data (multibeam
sonar bathymetry and backscatter strength, seismic reflection profiles, sidescan sonograms) and geological data (sea floor photographs and video, sediment samples; Todd et al., 2009). SFA 29
2045
Impact of commercial fishing on the determination of habitat associations
is located within the glaciated continental shelf of Atlantic Canada.
Here, bedrock is overlain by widespread ice-contact sediment
(till), glaciomarine silt, and post-glacial sand and gravel. The following five geophysical categories were identified (see also
Figure 1). Mapped boundaries between sediment types are
approximate.
1. Pg: Post-glacial sand and gravel: well-sorted sand, grading to
rounded, and subrounded gravels;
2. Gm: Glaciomarine silt: acoustically stratified, poorly sorted
clayey and sandy silt with some gravel; sometimes embedded
with underlying ice-contact sediment Ic1;
3. Ic1: Ice-contact sediment (till): unconsolidated drift probably
consisting of heterogeneous mixture of clay, sand, gravel, and
boulders varying in size and shape;
pairs. The data were further restricted to low-speed observations
(.0 knot and ,4 knots) as an indicator of when fishing activity
may have occurred. Fishing set duration in the scallop fishery is
often in the order of 20 min so that the start and the end of
fishing events are not captured within these data. Each position
was classified into surficial geology strata based on the available
surficial geology polygons derived from Figure 1.
To characterize the spatial distribution of relative effort, the
individual observations were aggregated into 1/400th degree
cells (0.25 km latitude). The VMS data aggregated into these
cells were associated with the location of each of the survey tows
for 2002– 2007 by the nearest Euclidean distance. Survey tows
that were .800 m (distance of a survey standard tow) from any
VMS cells were assumed to have an observation of zero fishing
effort associated with them.
4. Dg: igneous bedrock mantled with discontinuous sediments;
Statistical model
5.
The model used here for evaluating the relationship between
scallop catches in the survey and bottom type has a hierarchical
structure. The initial stage assigns a probability p(xi) to the event
that a survey tow is made in habitat suitable for scallops.
Conditional on the tow being in scallop habitat, the number of
scallops caught including zero scallops is then modelled using a
standard probability distribution. Here, we use the negative binomial distribution to account for potential overdispersion. That is,
for scallop catch yi in tow i,
: metamorphic bedrock granite mantled with discontinuous sediments.
Survey
Research surveys using commercial fishing vessels have been conducted over the major scallop fishing grounds in SFA 29 following
the fishing season every year since 2001. The vessels used nine
miracle drags with 75 – 78 mm inside diameter rings knitted
together with steel washers and with offshore chafing rubbers.
Note that steel washers were not used for the 2001 survey. Drag
number 1 was lined with 38 mm polypropylene mesh to retain
the smaller scallops. The catch in the two end drags (numbers 1
and 9) was sampled on each tow. A standard tow was defined as
covering 4400 m2. Sampling and measurements were conducted
as per standard scallop research survey protocols (Smith and
Lundy, 2002).
As noted earlier, the survey design has undergone a number of
changes since its inception. In 2001, 120 tows were allocated at
random over the whole SFA 29 area. Strata corresponding to the
management subareas (A–E, Figure 1) were defined for the
2002–2004 surveys, and tows were randomly allocated within
each subarea. A preliminary interpretation of the surficial
geology was used to define strata in terms of sediment type for
the 2005– 2007 surveys (for details see Smith, 2006).
In this study, we modelled the distribution of scallops with shell
heights of 40 mm. Catches in the lined gear were used to estimate the abundance of scallops with shell height from 40 to
79 mm, whereas the catches from the unlined gear were used to
estimate the abundance of scallops with shell heights 80 mm.
Catches of scallops with shell heights ,40 mm are thought to
give qualitative indications of abundance only, because of uncertainties about catchability of the small animals.
VMS data
VMS positional information were obtained for licensed vessels
operating within SFA 29 for each fishing season for the years
2002–2007 (data from 2001 were not in the VMS database). The
VMS data provide hourly positional information for each vessel
without an indication of vessel activity. Although the reported
speed of a vessel is available from a few vessels’ instrumentation,
in this analysis, the vessel speed is inferred from the successive
observations and assigned to the first observation of successive
Pr yi ¼ 0j xi ; zi ¼ ð1 pðxi ÞÞ þ pðxi Þ
G yi þ a
Pr yi ¼ rj xi ; zi ¼ pðxi Þ
GðaÞyi !
a
a
;
lðzi Þ þ a
ð1Þ
lðzi Þyi aa
;
ðlðzi Þ þ aÞyi þa
r ¼ 1; 2; 3; . . .
The a parameter is the dispersion parameter for the negative
binomial distribution. The total probability for the event of zero
scallops being caught is simply the sum of the probability of tow
i being on unsuitable bottom, 1 2 p(xi) and the probability of
Table 1. Comparison of SFA 29 scallop survey design efficiency for
three different stratification schemes.
Stratified Design
Geological strata
Year
2001
2002
2003
2004
2005
2006
2007
Subarea
11.34
5.57
17.50
22.67
–
–
–
Surficial
46.80
28.15
9.85
11.67
20.06
28.42
23.96
Geophysical
46.39
19.16
28.04
21.95
22.35
25.11
13.28
Efficiency was measured as the percentage decrease in the stratified variance
of the mean relative to the variance of the mean from a simple random
sample design (Smith and Gavaris, 1993). Subarea refers to stratification by
management subarea. Surficial strata are defined in the sediment map given
in Smith et al. (2007). Before 2005, management subareas were used to
design the survey, whereas the surficial sediment map was used from 2005
to 2007. Catch data were post-stratified for the geophysical strata using
domain estimators (Särndal et al., 1992).
2046
S. J. Smith et al.
the tow being on suitable bottom (p(xi)) times the probability of
obtaining yi ¼ 0 from a negative binomial distribution. The probability of tow i being on suitable or unsuitable bottom may be
modified by covariates (xi) such as depth or bottom type. The
mean parameter for the negative binomial distribution, l(zi)
may also be a function of covariates measured at tow i denoted
here as zi. The relationship between the probability p(xi) and
Table 2. Results from the annual research survey of SFA 29 by
bottom type.
Bottom type
Dg
Gm
Year
Proportion of non-zero catches
2001
0.80
0.63
1.00
2002
0.46
0.83
1.00
2003
0.57
0.80
1.00
2004
0.50
0.63
1.00
2005
0.75
0.80
0.91
2006
0.40
0.67
1.00
2007
0.60
0.83
1.00
Mean number of scallops from non-zero catches
2001
141.03
244.62
221.00
2002
122.83
375.98
288.58
2003
1 067.40
175.50
294.81
2004
208.33
134.84
109.82
2005
54.77
201.85
249.77
2006
43.00
9.60
146.48
2007
331.02
260.90
85.47
Ic1
Pg
0.81
0.75
0.88
0.88
0.80
0.87
0.88
0.80
0.89
0.96
0.85
0.88
0.83
0.93
287.81
447.54
446.31
197.90
218.74
152.57
287.76
519.14
602.62
666.03
436.06
592.58
185.55
216.13
Bottom types defined as follows:
, metamorphic bedrock; Dg, igneous
bedrock; Gm, glaciomarine silt; Ic1, ice-contact sediment (till); Pg,
post-glacial sand and gravel.
covariates xi was modelled through the use of a logit link,
whereas the mean parameter l(zi) was modelled as a function of
covariates zi with a log link.
logitð pðxi ÞÞ ¼ b0 þ b1 x1i ;
logðlðzi ÞÞ ¼ g0 þ g1 zi :
The above model is known as a zero-inflated negative binomial
(Tu, 2002) and has been used in many ecological applications
relating habitat to the abundance of a species in particular
locations (e.g. Martin et al., 2005). This type of model differs
from the zero-augmented distributions, such as the D-lognormal
(Pennington, 1983; Smith, 1988) and D-gamma (Stefansson,
1996), that assume that the process for generating zero catches is
entirely different from that for the non-zero catches.
Parameter estimates and model inference were developed
here using a Bayesian model for the zero-inflated negative
binomial and the WinBUGS computer package (Lunn et al.,
2000). For each model, a total of 20 000 samples for each of two
chains was taken from the posterior distribution with the first
5 000 discarded for burn-in. Convergence to the posterior was
evaluated using the Brooks –Gelman test (Brooks and Gelman,
1998). Models were screened using the deviance information
criterion (DIC; Spiegelhalter et al., 2002). A decrease in the DIC
of 5 or greater was taken to indicate that there was strong evidence
that the associated model provided an adequate fit to the data.
Results
Scallop catches
The survey design using surficial strata provided gains in efficiency
(precision) over the previous management subarea stratified
Figure 2. Natural log of non-zero catches of scallops with shell height 40 mm vs. depth from SFA 29 scallop surveys by year and bottom
type. Bottom types are defined in Figure 1.
2047
Impact of commercial fishing on the determination of habitat associations
design when it was introduced in 2005 (Table 1). Since then the
design has performed poorly, resulting in lower efficiency for the
stratified means relative to simple random sampling in both
2006 and 2007. Except 2003, the new geophysical map appears
to provide an improved design but the efficiency in 2007 is only
a little more than half what it was in 2006. Note that the highest
efficiencies for both bottom-type-based designs were in the
first year of the survey and fishery. Given that the geophysicalbased design appears to be an improvement over the surficial
sediment-based design, all analyses will use the geophysical-based
design.
Using the proportion of non-zero catches by bottom type as a
rough indicator of habitat suitable for scallops, the results indicate
that 0.75 and higher of the survey tows in Gm, Ic1 and Pg bottoms
resulted in catches of scallops (Table 2). The presence of scallops
was more variable and lower in survey tows on Dg and
bottom types. The results were similar for the mean number of
scallops in the non-zero tows with the mean being generally
higher for tows in Ic1 and Pg bottom types, especially in the
first 4 –5 years of the series (Table 2).
There is also evidence for a decreasing linear relationship
between log numbers of scallops and depth in Pg and Ic1
bottom types, for data from 2001 to 2004 (Figure 2). With the
possible exception of the Gm bottom type, sample sizes were too
small to detect this relationship in the other bottom types. For
Pg and Ic1, this relationship between log numbers and depth
appears to disappear in the data between 2005 and 2007.
Bottom type was not a significant effect for the fit of the
zero-inflated model for the 2001 data but depth was significant
for mean number (Table 3). Bottom type was significant for the
p(xi) parameter for 2002–2004 and depth was significant for the
l(zi) parameter for catches in 2001, 2002, and 2004. Slope estimates for depth were similar for 2001– 2004 (Table 4). In 2005,
the mean parameter l(zi) was related to bottom type alone,
whereas p(xi) was not.
Parameter estimates for bottom type for the 2002– 2004 data
indicate that the main effects picked up were the lower probabilities of catching scallops in tows in
type bottom relative to
Pg (Table 4). All other types of bottom were not significantly
different from Pg. In 2005, the bottom-type part of the model
simply picked up the large differences in mean catches between
the Pg bottom type and all the other bottom types. Mean
numbers by bottom type have become more similar in the last
two years of the survey series.
Fishing patterns
As noted earlier, VMS signals were classified as indicating fishing
activity when the speed of the vessels was calculated to be .0
and ,4 knots. A comparison of annual trends of effort measured
by VMS and by commercial fishing logs (thousands of hours)
suggests similar trends over time validating our use of these data
as a proxy for fishing effort (Figure 3).
Classification of VMS signals by bottom type being fished
suggests that Ic1 and Pg bottom types are the most important
with respect to the scallop fishery (Table 5). For the case of Pg
bottom type, the percentage of cumulative fishing activity
ranged from 34.6 to 54.25, exceeding the percentage of the area
of SFA 29 associated with this bottom type.
Within each aggregation cell, the cumulative numbers of VMS
signals were calculated and associated with survey tows for each
year. That is, survey tows coincident with VMS cells in 2002
Table 3. Results from fitting the zero-inflated negative binomial
distribution to the numbers of scallops caught in the annual
survey.
Model
Survey Year
2001
2002
2003
2004
2005
2006
2007
logit (p(xi))
No covariate
No covariate
þBottom type
No covariate
No covariate
No covariate
þBottom type
þBottom type
No covariate
No covariate
þBottom type
þBottom type
No covariate
No covariate
þBottom type
þBottom type
No covariate
No covariate
No covariate
þBottom type
No covariate
No covariate
No covariate
þBottom type
No covariate
No covariate
No covariate
þBottom type
No covariate
log(l(zi))
No covariate
þBottom type
No covariate
þDepth
No covariate
þBottom type
No covariate
þDepth
No covariate
þBottom type
No covariate
þDepth
No covariate
þBottom type
No covariate
þDepth
þBottom type þ Depth
No covariate
þBottom type
No covariate
þDepth
No covariate
þBottom type
No covariate
þDepth
No covariate
þBottom type
No covariate
þDepth
DIC
778.16
778.74
780.58
770.32*
1 002.45
1 001.58
994.28
979.10*
903.18
901.66
894.62*
893.88*
774.23
768.51
769.61
762.89*
767.07
818.96
805.94*
818.83
818.78
685.10
680.95*
684.80
686.51
774.01
779.35
769.12*
769.22*
Covariates added to the model are indicated for the logit function for the
probability of the survey tow being on suitable habitat for scallops (p(xi))
and the log function for the mean (l(zi)) of the negative binomial
distribution. Models were screened using the DIC. Models with the smallest
DIC for each survey are indicated with an asterisk.
were only associated with 2002 VMS records because the 2001
data were not available. However, for survey data from 2003 on,
the cells contained VMS data from the current and previous
years. As fishing has progressed from the first years of this
fishery, fishing effort as measured by the cumulative VMS data
has become more concentrated in the shallower areas, especially
for the Ic1 and Pg bottom types (Figure 4).
If the impact of the fishery is behind the diminishment of the
relationship between scallop survey catches and bottom type and
depth, then it may be possible that these relationships could be
detected using only those survey tows which occurred in areas of
low fishing effort. For example, arbitrarily choosing tows where
the cumulative VMS signals were ,30, the negative relationship
between log numbers and depth appears to be more evident for
2002–2006 for tows made on Pg type bottom and up to 2004
for Ic1 type bottom (Figure 5). Note that the higher catches
(marked as .400 scallops tow21 with the dotted line on
Figure 5) in the shallower depths are eliminated by 2006/2007
which in turn eliminates the relationship between scallop catches
and depths. Zero-inflated negative binomial models were fitted
to these data with separate slopes for tows by bottom type and
by different definitions of low and high fishing effort using the
2048
S. J. Smith et al.
Table 4. Posterior median parameter estimates and limits for 95% credible regions from zero-inflated negative binomial model.
Depth (m)
a
Year
Estimates for a and g1
2001
2002
2003
2004
2005
2006
2007
g1
0.025
0.50
0.975
0.025
0.50
0.975
0.601
0.588
0.504
0.597
0.545
0.733
0.415
0.843
0.806
0.748
0.902
0.773
1.041
0.669
1.120
1.052
1.030
1.228
1.036
1.413
0.919
20.041
20.048
20.040
20.041
–
–
–
20.026
20.034
20.023
20.025
–
–
–
20.009
20.018
20.001
20.007
–
–
–
Survey year
Parameter estimates
Credible region
Parameter estimates for bottom type contrasts
Pg
0.025
0.5
0.975
– Pg
0.025
0.5
0.975
Dg–Pg
0.025
0.5
0.975
Gm –Pg
0.025
0.5
0.975
Ic1– Pg
0.025
0.5
0.975
2002
2003
2004
2005
1.194
2.218
3.814
24.302
22.395
20.875
22.660
20.015
14.880
20.118
6.991
22.540
22.796
21.102
0.096
2.128
3.920
12.230
211.770
23.674
20.919
27.740
20.873
17.790
23.595
5.879
22.180
28.980
21.585
8.193
0.872
1.897
3.400
23.787
21.906
20.218
23.363
21.314
0.721
20.451
6.720
22.420
21.398
0.360
3.874
6.059
6.383
6.752
23.407
22.292
20.722
21.969
20.995
20.377
21.531
20.828
20.047
21.521
20.990
20.420
Models were screened using the DIC.
Table 5. Percentage of VMS signals (0 , speed , 4 knots) by
bottom type in the SFA 29 scallop fishery.
Bottom type
Year
2002
2.08
2003
1.36
2004
4.58
2005
6.91
2006
5.08
2007
3.79
Area by bottom type
2002 –2004
2.90
2005 þ
9.38
Figure 3. Comparison of annual trends in scallop fishing effort in
SFA 29 measured by 1000s of hours fished reported in commercial
fishing logbooks and total number of VMS signals from vessels whose
speed was .0 and ,4 knots.
Dg
14.69
5.96
4.09
9.85
5.09
2.99
Gm
2.07
4.14
3.93
2.33
3.54
3.36
Ic1
38.79
53.94
33.16
31.84
39.95
40.05
Pg
42.37
34.60
54.25
49.07
46.34
49.81
12.57
10.25
7.30
6.19
63.77
54.32
13.43
19.85
The percentages of the area associated for each of the bottom types are
given at the bottom of the table. A portion of SFA 29 was closed to fishing
, metamorphic bedrock;
before 2005. Bottom types defined as follows:
Dg, igneous bedrock; Gm, glaciomarine silt; Ic1, ice-contact sediment (till);
Pg, post-glacial sand and gravel.
Discussion
VMS data (Table 6). The results suggest that we can still detect the
relationship between scallop numbers and depth for those tows
where fishing effort was ,20 cumulative VMS signals, but over
time, areas with such low levels of cumulative effort become
quite rare.
The geophysical features used in this study were characterized
according to their glacial origins and post-glacial processes. A
comparison of this kind of characterization with that by determining sediment size from underwater photographs indicates that
there are ranges of finer structure that may be present in more
Impact of commercial fishing on the determination of habitat associations
2049
Figure 4. The distribution of the cumulative number of VMS signals associated with survey stations in each year in SFA 29 with respect to
depth by survey year and bottom type. Bottom types are defined in Figure 1.
Figure 5. Natural log of non-zero catches of scallops with shell height 40 mm vs. depth from SFA 29 scallop surveys by year for bottom
types Pg and Ic1. Only catches where associated cumulative number of VMS signals 30. Bottom types are defined in Figure 1. The horizontal
dotted line indicates 400 scallops per survey tow.
than one geophysical feature (Tremblay et al., 2009). Similar to
other studies of this species (Thouzeau et al., 1991; Kostylev
et al., 2003), Tremblay et al. (2009) found that scallops were
more often than not associated with cobble types of bottom.
Higher percentages of cobble and gravel sediments were identified
in their study for the Pg category compared with the other
2050
S. J. Smith et al.
Table 6. DIC values for models fitted to survey catches made on bottom types Ic1 (ice-contact sediment—till) and Pg (post-glacial sand
and gravel) in the SFA 29 survey.
All tows
Year
2001
2002
2003
2004
2005
2006
2007
Number of tows
82
95
70
70
72
75
71
Number of tows VMS 5 0
–
42
22
18
12
11
10
Null
645.69
800.58
686.21
628.59
644.97
566.25
578.78
1Depth
639.12
786.45
683.84
623.61
646.59
563.69
580.93
Tows where VMS signals
50
–
769.75
679.47
617.31
635.40
565.39
580.73
10
–
782.99
672.91
617.37
626.28
561.45
576.28
20
–
782.18
686.59
613.72
629.16
561.74
575.76
30
–
787.75
688.32
615.39
632.76
566.42
576.28
Null model and model with depth fit to all tows. Models including depth fit to tows where VMS cumulative signals (0 , speed , 4 kn) were equal to zero,
less than or equal to 10, 20, or 30. VMS records were not available for 2001.
categories. Therefore, the strength of the association between scallops and geophysical categories may reflect the relative compositions of sediment size features within them.
Most information used to determine habitat associations of
marine animals come from areas where fisheries for the study
species or other species have been in operation for decades or
longer. For sedentary species like scallops, specific areas and
even year classes can be reliably targeted year after year. We have
characterized habitat association according to spatial patterns in
abundance. Associations that were evident in the first 3 –4 years
of the fishery were no longer picked up in the data in the last
three years coincident with increasing cumulative fishing activity
as measured by the VMS data. The increasing concentration of
fishing in the shallower depths may have eliminated evidence for
the relationship between numbers of scallops and depth. In
addition, the mean number per tow of scallops by bottom type
have become more similar over time as the higher density areas
have been fished down.
As the fishery develops over time, evidence for relationships
between scallop numbers and depth can be observed when
only survey tows coincident with low or no fishing effort were
used in the analysis (Table 6, Figure 5). However, all areas of
suitable scallop habitat eventually become exploited, making it
unlikely that the habitat relationships noted in the early period
of the fishery will be picked up later on. Perhaps the relationships between habitat and abundance may become evident
again after a large recruitment event assuming that settlement
of larvae corresponds to species’ habitat preferences. To date,
no such large year class has successfully recruited to the
scallop fishery in this area.
The determination of associations between benthic habitat
and fish and invertebrate species is a basic requirement for
spatial management (e.g. Williams and Bax, 2001; Holmes
et al., 2008). The potential impact of fishing activity on disturbing
or modifying the bottom is usually acknowledged in studies for
these associations (e.g. Methratta and Link, 2006a), but not the
effect identified here of masking the relationship between abundance and bottom type. Methratta and Link (2006b) reported
only being able to identify weak relationships between biomass
and substratum type for 24 demersal finfish species from trawl
survey data in the Gulf of Maine and Georges Bank area. Of
the 24 species studied, 19 exhibited declines in biomass over
the 35-year survey time-series in response to fishery exploitation.
Perhaps the declines in biomass of these species may have
masked any of the relationships between biomass and substratum
type.
Smith et al. (2007) investigated the spatial distribution of catch
rate and fishing effort from commercial fishing logs for the SFA 29
scallop fishery. They observed that catch rates had become more
similar throughout the area over time, whereas the spatial distribution of the density of fishing effort appeared to match that of
scallop abundance as predicted by the ideal-free distribution
theory (Fretwell and Lucas, 1970; Gillis et al., 1993). In the
current study, the spatial distribution of effort exhibited higher
concentrations of fishing on the Pg bottom type and in the shallower depths (Table 5, Figure 4) both of which had been identified
as having the higher densities of scallops in the survey data
(Table 4). This finding suggests that although fishing may mask
the relationship between abundance and habitat type, the spatial
distribution of fishing effort may in itself be a good indicator for
habitat associations for exploited species.
Acknowledgements
We thank Scott Hayward for his contributions to processing the
geophysical bottom data for our analysis and Jessica Sameoto for
preparing Figure 1. Our thanks also go to the Captains and
crews of the scallop fishing vessels used to conduct the annual
surveys in Scallop Fishing Area 29. Two anonymous referees provided very useful comments on the previous draft that helped us to
improve our paper.
References
Brooks, S., and Gelman, A. 1998. General methods for monitoring
convergence of iterative simulations. Journal of Computational
and Graphical Statistics, 7: 434 –455.
Fretwell, S. D., and Lucas, H. L. 1970. On territorial behaviour
and other factors inuencing the habitat distribution in
birds. I. theoretical developments. Acta Biotheoretica, 19: 16– 36.
Gillis, D. M., Peterman, R. M., and Tyler, A. V. 1993. Movement
dynamics in a fishery: application of the ideal free distribution to
spatial allocation of effort. Canadian Journal of Fisheries and
Aquatic Sciences, 50: 323– 333.
Gordon, D., Kenchington, E., Gilkinson, K., Steeves, D. M. G.,
Chin-Yee, M., Vass, W., Bentham, K., et al. 2000. Canadian
imaging and sampling technology for studying marine benthic
habitat and biological communities. ICES Document CM 2000/
T: 07. 11 pp.
Gunderson, D. R. 1993. Surveys of Fisheries Resources. John Wiley &
Sons, New York, NY.
Holmes, K. W., Niel, K. P. V., Radford, B., Kendrick, G. A., and Grove,
S. L. 2008. Modelling distribution of marine benthos from hydroacoustics and underwater video. Continental Shelf Research, 28:
1800– 1810.
Impact of commercial fishing on the determination of habitat associations
Kostylev, V. E., Courtney, R. C., Robert, G., and Todd, B. J. 2003. Stock
evaluation of giant scallop (Placopecten magellanicus) using highresolution acoustics for seabed mapping. Fisheries Research, 60:
479– 492.
Lunn, D. J., Thomas, A., Best, N., and Spiegelhalter, D. 2000.
WinBUGS—a Bayesian modelling framework: concepts, structure,
and extensibility. Statistics and Computing, 10: 325– 337.
Martin, T. G., Wintle, B. A., Rhodes, J. R., Kuhnert, P. M., Field, S. A.,
Low-Choy, S. J., Tyre, A. J., et al. 2005. Zero tolerance ecology:
improving ecological inference by modelling the source of zero
observations. Ecology Letters, 8: 1235 –1246.
Methratta, E. T., and Link, J. S. 2006a. Associations between surficial
sediments and ground-fish distributions in the Gulf of Maine—
Georges Bank region. North American Journal of Fisheries
Management, 26: 473– 489.
Methratta, E. T., and Link, J. S. 2006b. Seasonal variation in groundfish
habitat associations in the Gulf of Maine—Georges Bank region.
Marine Ecology Progress Series, 326: 245 – 256.
Pennington, M. 1983. Efficient estimators of abundance, for fish and
plankton surveys. Biometrics, 39: 281– 286.
Perry, R. I., and Smith, S. J. 1994. Identifying habitat associations of
marine fishes using survey data: an application to the Northwest
Atlantic. Canadian Journal of Fisheries and Aquatic Sciences, 51:
589– 602.
Särndal, C. E., Swenson, B., and Wretman, J. 1992. Model assisted
survey sampling. Springer, New York.
Smith, S. J. 1988. Evaluating the efficiency of the D-distribution mean
estimator. Biometrics, 44: 485– 493.
Smith, S. J., Rowe, S., Lundy, M., Tremblay, J. M., and Frail, C. 2007.
Scallop fishing area 29: stock status and update for 2007. DFO
Canadian Science Advisory Secretariat Research Document,
2007/029. iv þ 71 pp.
Smith, S. J. (Ed.) 2006. Presentation and Review of the Benthic
Mapping Project in Scallop Fishing Area 29, Southwest Nova
Scotia, 16 February 2006. DFO Canadian Science Advisory
Secretariat Proceedings Series, 2006/047. vi þ 42 pp.
Smith, S. J., and Gavaris, S. 1993. Improving the precision of fish
abundance estimates of Eastern Scotian Shelf cod from bottom
trawl surveys. North American Journal of Fisheries Management,
13: 35– 47.
Smith, S. J., and Lundy, M. J. 2002. Scallop production area 4 in the
Bay of Fundy: stock status and forecast. DFO Canadian Science
Advisory Secretariat Research Document, 2002/018. 89 pp.
2051
Smith, S. J., and Rago, P. 2004. Biological reference points for sea scallops (Placopecten magellanicus): the benefits and costs of being
nearly sessile. Canadian Journal of Fisheries and Aquatic
Sciences, 61: 1338– 1354.
Spiegelhalter, D., Best, N., Carlin, B., and der Linde, A. V. 2002.
Bayesian measures of model complexity and fit (with discussion).
Journal of the Royal Statistical Society, Series B, 64: 583 – 640.
Stefansson, G. 1996. Analysis of ground fish survey abundance data:
combining the glm and delta approaches. ICES Journal of
Marine Science, 53: 577 – 588.
Thompson, S. K. 2002. Sampling, 2nd edn. John Wiley & Sons,
New York, NY. 343 pp.
Thouzeau, G., Robert, G., and Smith, S. J. 1991. Spatial variability in
distribution and growth of juvenile and adult sea scallops
Placopecten magellanicus (Gmelin) on eastern Georges Bank
(Northwest Atlantic). Marine Ecology Progress Series, 74:
205– 218.
Todd, B. J., Kostylev, V. E., and Valentine, P. C. 2009. Shaded seafloor
relief, backscatter strength, surficial geology, and benthic habitat:
German Bank, Scotian Shelf, offshore Nova Scotia. Geological
Survey of Canada, File 6124, scale 1 : 100 000.
Todd, B. J., Rainey, W. A., Girouard, P. R., Valentine, P. C., Chapman,
C. B., Middleton, G. D., Boyce, A. A., et al. 2004. Expedition report
CCGS Hudson 2003-054: German Bank, Gulf of Maine. Technical
Report, Geological Survey of Canada, Open File 4728. 126 pp.
Tremblay, M. J., Smith, S. J., Todd, B. J., Clement, P. M., and
McKeown, D. L. 2009. Associations of lobsters (Homarus
americanus) off southwestern Nova Scotia with bottom type
from images and geophysical maps. ICES Journal of Marine
Science, in press.
Tu, W. 2002. Zero-inflated data. In Encyclopedia of Envirometrics, 3rd
edn, pp. 2387– 2390 Ed. by A. El-Shaarawi, and W. Peiegorsch.
John Wiley & Sons, New York.
Wentworth, C. 1922. A scale of grade and class terms for clastic sediments. Journal of Geology, 30: 377– 392.
Williams, A., and Bax, N. J. 2001. Delineating fish-habitat associations
for spatially based management: an example from the southeastern Australian continental shelf. Marine and Freshwater
Research, 52: 513– 536.
doi:10.1093/icesjms/fsp196