Not to be cited without prior reference to the authors
CM 2008/G:08
The impact of commercial fishing on the determination of habitat
associations of sea scallops
Stephen J. Smith1 , Jerry Black1 , Brian J. Todd2 , Vladimir E. Kostylev2 and Mark J.
Lundy1
1
Population Ecology Division, Fisheries and Oceans Canada
2
Geoscience Centre Atlantic, Natural Resources Canada
Bedford Institute of Oceanography
P.O. 1006
Dartmouth, Nova Scotia B2Y 4A2
Abstract
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 upon surficial geology maps from a
multibeam bottom mapping and geology groundtruth project completed in 2004 in this area
have been used for the 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 upon satellite vessel
monitoring data suggests that either the habitat is being changed by fishing or the use of
abundance as an indicator of the strength of preference or association is misleading when
the population is being depleted by the fishery. We were able to track these changes because
we had survey data from the beginning of this fishery. These results could have implications
on the interpretation of species habitat associations from areas where data is only available
from periods when the population had been exploited over a long time.
Keywords: Seabed mapping, multibeam bathymetry, Bayesian methods
Contact author:
Stephen J. Smith, Population Ecology Division, Department of Fisheries and Oceans,
Bedford Institute of Oceanography, 1 Challenger Drive, Dartmouth, Nova Scotia, B2Y 4A2,
Canada [tel: +1 902 426 3317, fax: +1 902 426 1862, e-mail: [email protected]]
2
Introduction
Periodic surveys are a common method of monitoring fish/shellfish populations (Gunderson
1993). Many of these surveys were designed with minimal information about the distribution of the target species and due to 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. In the case of 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 had come
from multibeam mapping of Browns Bank off of the south Atlantic coast of Nova Scotia
(Fig. 1). Starting in 2002, a joint industry/government funded multibeam project for an
area known as Scallop Fishing Area (SFA) 29 was initiated (Fig. 1). The scallop fishery in
this area started in 2001 and from 2001 to 2004 either a random (2001) or a stratified random
(management subareas) survey had 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. Details on the multibeam program and the 2005 survey are available in Smith
Smith (2006).
The initial results from using this surficial bottom type map for survey design were
encouraging with respect to increasing the precision of the stratified mean or total numbers
of scallop over the previous designs. However, this increase in precision has diminished
for the more recent surveys in 2006 and 2007 suggesting that the relationships between
the distribution of scallops and the bottom type strata were becoming weaker with time
(unpublished data and Smith et al. 2008). By their nature, scallop beds are predictable with
respect to location and as a fishery matures, these beds become well known to the fishermen
and targeted by them (Smith and Rago 2004). Given that habitat association is often
determined by what conditions are most associated with the higher abundance or biomasses
of a species (e.g., Perry and Smith 1994 Methratta and Link 2006), there is the likelihood
here of the fishery having an impact on detecting habitat associations when measured in
this way. Spatial information on fishing effort is available for this fishery since 2002 in the
form of Vessel Monitoring System (VMS) positions. Given that geo-referenced survey and
commercial fishing data are available from the beginning of the SFA 29 scallop fishery, there
is a unique opportunity here to investigate for a concurrent diminishment in the strength of
habitat association with the increasing impact of the fishery over time.
Materials and Methods
Survey
Research surveys have been conducted over the major scallop fishing grounds in scallop
fishing area 29 using commercial fishing vessels after the fishing season since 2001. The
3
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) were sampled on each
tow. Sampling and measurements were conducted as per standard scallop research survey
protocols (Smith and Lundy 2002).
Catches in the lined gear were used to estimate the abundance of scallops with shell
height less than 80 mm while the catches from the unlined gear were used to estimate the
abundance of scallops with shell heights greater than or equal to 80 mm. Catches of scallops
with shell heights less than 40 mm are thought to give qualitative indications of abundance
only, due to uncertainties about catchability of the small animals.
Multibeam mapping
Multibeam bathymetric data were collected over Scallop Fishing Area 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 in the
starboard pontoon. This system produces 60 beams arrayed over an arc of 150◦ and operates
by ensonifying a narrow strip of sea floor across track and detecting the bottom echoes. The
width of 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 ensonification 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 about 5.0 km2 hr−1 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 is 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 Kontinentalsokkel
Undersrkelser (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 (Gordon Jr. et al. 2000). The system included forward
4
and 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 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). This categorization is presented here as a 1:50 000 scale map (Fig. 2, Todd 2008).
SFA 29 is located within the glaciated continental shelf of Atlantic Canada. Here, bedrock is
overlain by widespread ice-contact sediment (till), glaciomarine silt, and postglacial sand and
gravel. Ice-contact sediment consists of a heterogeneous mixture of clay, sand, gravel and
boulders of varying size and shape. Glaciomarine silt is poorly sorted clayey and sandy silt
with some gravel. Postglacial sand and gravel consists of well-sorted sand, grading to rounded
and subrounded gravel. Mapped boundaries between sediment types are approximate. Based
on the coordinates of the Towcam images, the bottom type was put into one of the following
5 geophysical categories:
1. Pg - Postglacial 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;
4. Dg Igneous bedrock mantled with discontinuous sediments;
5. C-Om - Metamorphic bedrock (granite) mantled with discontinuous sediments.
VMS Data
Vessel monitoring system (VMS) positional information were obtained for licensed vessels
occurring within the SFA 29 fishing area for each fishing season for the years 2002–20071 .
The VMS data provide hourly positional information for each vessel without an indication
of vessel activity. While the reported speed of a vessel is available from a few vessel’s
instrumentation, in this analysis the vessel speed is inferred from successive observations
and assigned to the 1st observation of successive pairs. The data were further restricted
to low speed observations (greater than 0 kt and less than 4 kt) as an indicator of when
fishing activity may have occurred. Fishing set duration in the scallop fishery is often in the
order of 20 minutes so that the start and end of fishing events are not captured within this
data. Each position was classified into surficial geology strata based on the available surficial
geology polygons derived from the 1:50 000 scale map (Fig. 2).
To characterize the rough spatial distribution of relative effort, the individual observations
were aggregated into 1/400th degree cells (approx. 0.25 km latitude). Aggregate statistics for
1
Data from 2001 were not in the VMS database.
5
number of observations, distance, duration, speed, and observations within surficial geology
classified regimes by year, were calculated for each cell. The VMS data aggregated into these
cells were associated with the location of each of the survey tows for 2002 to 2007 by the
nearest Euclidean distance. Survey tows that were more than 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.
A comparison of annual trends of effort measured by VMS and by commercial fishing
logs (thousands of hours) suggest similar trends over time (Fig. 3).
Model
Various kinds of standard statistical distributions have been suggested for fisheries survey
data but all have difficulty in dealing with the large proportion of zeroes catches and the
few large catches (see Smith 1990). Zero-augmented distributions such as the ∆-lognormal
(Pennington 1983 Smith 1988), ∆-gamma (Stefansson 1996) have been used with limited
success (Myers and Pepin 1990 Syrjala 2000). These models assume that the process for
generating zero catches is entirely different from that for the non-zero catches.
In the same vein, zero-inflated forms of standard discrete distributions such as the Poisson, negative binomial or binomial which already include zero observations have been developed (Tu 2002). In these cases, some zero catches are expected depending upon the
magnitude of the mean and may reflect an absence of species in a tow from a suitable habitat because of sampling error. Zeroes catches beyond what was expected given the mean
may reflect areas of unsuitable or unused suitable habitat due to low densities of the target
species (Martin et al. 2005).
The zero-inflated negative binomial distribution was chosen here because the negative
binomial component can accommodate overdispersion in count data relative to the Poisson
distribution. The zero-inflated negative binomial distribution consists of two components, a
mixture distribution for expected and excess zeroes and a negative binomial distribution for
catches greater than zero. The parameter p refers to the probability that an observation,
zero or otherwise comes from a negative binomial distribution, while 1 − p is the probability
that the observation came from a point mass at zero.
!α
α
Pr (yi = 0| xi , zi ) = 1 − p (xi ) + p (xi )
λ(zi ) + α
Γ (yi + α)
Pr (yi = r| xi , zi ) = p (xi )
Γ (α) yi !
λ(zi )yi αα
×
, r = 1, 2, 3, . . .
(λ(zi ) + α)yi +α
(1)
The α parameter is the dispersion parameter for the negative binomial distribution.
The relationship between the p parameter and covariates xi was modelled through the
use of a logit link, while the mean parameter λ of the negative binomial (for yi > 0) was
modelled with a log link.
6
logit (p (xi )) = β0 + β1 x1i ,
log (λ (zi )) = γ0 + γ1 zi .
Bayesian models were used with parameter estimates for the negative binomial and the
zero-inflated negative binomial obtained using the Winbugs package (Lunn et al. 2000). For
each model, a total of 35000 samples were taken from the posterior distribution of which the
first 15000 were 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 (Spiegelhalter et al. 2002).
Results
The proportion of non-zero catches by bottom type indicate that 0.75 and higher of the
tows in Gm, Ic1 and Pg bottoms will result in catches of scallops (Table 1a). The presence
of scallops in tows on Dg and C-Om bottom types were more variable and lower. 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 four to five
years of the series (Table 1b). There is also evidence for a decreasing linear relationship
between log numbers of scallops and log depth in Pg and Ic1 bottom types for at least for
the first four years (Fig. 4). This relationship appears to disappear in the data between 2005
and 2007.
Negative binomial models were fitted to the survey catch data by year to explore for
relationships with bottom type and depth. Models were initially screened by differences in
DIC greater than five and then parameter estimates were investigated to determine significant
effects. Only in the case of bottom type and catch numbers for 2005 was a relationship found
and in this case, the model picked up on the lower means for C-Om and Ic1 relative to Pg
(Table 2).
Comparison of the DIC values for any of the zero-inflated negative binomial models
(Table 3) with those for the negative binomial (Table 2) indicate that in all cases the former
type of model fit the data much better than the latter type. Bottom type was not a significant
effect for the fit of the zero-inflated model to the 2001 but depth was significant for mean
number (Table 3). However, bottom type was significant for the proportion of observations
from a negative binomial and depth was significant for mean numbers for catches in 2002
to 2004. Bottom type only picked differences between mean numbers in 2005 similar to the
negative binomial model but neither depth nor bottom type with respect to either mean
number or proportion of observations from a negative binomial distribution were significant
for 2006 or 2007. Parameter estimates for the models for the 2002 to 2004 data indicate that
the main effects picked up with respect to the proportion of data from the negative binomial
distribution were the lower proportions for C-Om relative to the Pg types of bottom (Table 4).
Classification of VMS signals where speed of the vessels was calculated to be greater
than zero and less than four kts (indicating fishing in our study) to bottom type being fished
suggests that Ic1 and Pg bottom types are the most important with respect to the scallop
7
fishery (Table 5). For the case of Pg bottom type, the percent of cumulative fishing activity
ranged from 34.6 to 54.25 percent exceeding the 19.85 percent of the area of SFA 29 made
up by this bottom type.
Within each aggregation cell, the cumulative number of VMS signals was calculated and
associated with survey tows for each year. That is, tows coincident with VMS cells in the
2002 survey were only associated with 2002 VMS records because the 2001 data was not
available. However, for survey data from 2003 onwards, the VMS cells contained data from
the current and previous years for which there data available.
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 (Fig. 5).
Discussion
Most information used to determine habitat associations of marine animals comes 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 three to four 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. In particular, this
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.
The geophysical features used in this study are 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 is a range of
finer structure that may be present in more than geophysical feature (Tremblay et al. 2008).
Tremblay et al. (2008) found that scallops were more often than not associated with cobblegravel types of bottom. The main sediment size feature identified for the Pg category in their
study was higher percentages of cobble-gravel and boulder-cobble-gravel sediments than for
the other geophysical categories. Therefore, the strength of the association between scallops
and geophysical categories may reflect the relative compositions of sediment size features
within them.
The spatial distribution of numerical abundance or biomass of a species may not the best
metric to be used to investigate habitat associations. Using MacCall’s (1990) basin model,
Van Horne (1983) suggests that because of density-dependent habitat suitability processes
high densities of animals could also indicate overflow of the population into less suitable
habitats. In the case of scallops and other bivalves, veliger larvae preparing to settle may
not be able to escape the feeding currents of the adults in the same area (Troost et al. 2008).
As a result, areas of high density of adults possibly reflecting suitable habitat will not be
preceived as suitable habitat for the settlement of larvae.
8
Spatial patterns in growth parameters may offer another means to evaluate habitat suitability. Preliminary investigation of the growth of scallops in SFA 29 indicate that there are
differences between bottom types (Smith et al. 2008). However, density-dependent habitat
suitability may also have an effect on growth in areas where high densities of scallops are
competing for food.
Acknowledgements
We would like to thank Scott Hayward for his contributions to processing the geophysical
bottom data for our analysis and Jessica Sameoto for preparing Figure 2. 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.
9
References
Brooks, S., and Gelman, A. 1998. General methods for monitoring convergence of iterative
simulations. J. Comput. Graph. Stat. 7: 434–455.
Gordon Jr., D., Kenchington, E., Gilkinson, K., Steeves, D.M.G., Chin-Yee, M., Vass, W.,
Bentham, K., and Boudreau, P. 2000. Canadian imaging and sampling technology for
studying marine benthic habitat and biological communities. ICES CM 2000/T:07 .
Gunderson, D.R. 1993. Surveys of Fisheries Resources. John Wiley & Sons, New York, NY.
Kostylev, V.E., Courtney, R.C., Robert, G., and Todd, B.J. 2003. Stock evaluation of giant
scallop (Placopecten magellanicus) using high-resolution acoustics for seabed mapping.
Fish. Res. 60: 479–492.
Lunn, D.J., Thomas, A., Best, N., and Spiegelhalter, D. 2000. WinBUGS –a Bayesian modelling framework: concepts, structure, and extensibility. Stat. and Comput. 10: 325–337.
MacCall, A.D. 1990. Dynamic geography of marine fish populations. University of Washington Press, Seattle.
Martin, T.G., Wintle, B.A., Rhodes, J.R., Kuhnert, P.M., Field, S.A., Low-Choy, S.J., Tyre,
A.J., and Possingham, H.P. 2005. Zero tolerance ecology: improving ecological inference
by modelling the source of zero observations. Ecol. Let. 8: 1235–1246.
Methratta, E.T., and Link, J.S. 2006. Associations between surficial sediments and groundfish distributions in the Gulf of MaineGeorges Bank region. N. Amer. J. Fish. Manage.
26: 473–489.
Myers, R.A., and Pepin, P. 1990. The robustness of lognormal-based estimators of abundance.
Biom. 46: 1185–1192.
Pennington, M. 1983. Efficient estimators of abundance, for fish and plankton surveys. Biom.
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. Can. J. Fish. Aquat. Sci. 51:
589–602.
Smith, S.J. 1988. Evaluating the efficiency of the ∆-distribution mean estimator. Biom. 44:
485–493.
Smith, S.J. 1990. Use of statistical models for the estimation of abundance from groundfish
trawl surveys. Can. J. Fish. Aquat. Sci. 47: 894–903.
Smith, S.J. (Editor) 2006. Presentation and Review of the Benthic Mapping Project in
Scallop Fishing Area 29, Southwest Nova Scotia; 16 February 2006. DFO Can. Sci.
Advis. Sec. Proceed. Ser. 2006/047: vi+42pp.
10
Smith, S.J., and Lundy, M.J. 2002. Scallop Production Area 4 in the Bay of Fundy: Stock
status and forecast. DFO Can. Sci. Adv. Sec. Res. Doc. 2002/018: 86 pp.
Smith, S.J., Lundy, M.J., Tremblay, J.M., Frail, C., and Rowe, S. 2008. Scallop fishing area
29: Stock status and update for 2008. DFO Can. Sci. Adv. Sec. Res. Doc. 2008/033:
50 pp.
Smith, S.J., and Rago, P. 2004. Biological reference points for sea scallops (Placopecten
magellanicus): the benefits and costs of being nearly sessile. Can. J. Fish. Aquat. Sci.
61: 1338–1354.
Spiegelhalter, D., Best, N., Carlin, B., and der Linde, A.V. 2002. Bayesian measures of model
complexity and fit (with discussion). J. Roy. Stat. Soc. Ser. B. 64: 583–640.
Stefansson, G. 1996. Analysis of ground fish survey abundance data: combining the glm and
delta approaches. ICES J. Mar. Sci. 53: 577–588.
Syrjala, S. 2000. Critique on the use of the delta distribution for the analysis of trawl survey
data. ICES J. Mar. Sci. 57: 831–842.
Thompson, S.K. 2002. Sampling. Second edition, John Wiley & Sons, New York, N.Y., 343 p.
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). Mar. Ecol. Prog. Ser. 74: 205–218.
Todd, B., Rainey, W., Girouard, P., Valentine, P., Chapman, C., Middleton, G., Boyce, A.,
Uyesugi, M., Kostylev, V., Rumbolt, S., Fromm, S., Hynes, S., and Spencer, P. 2004.
Expedition report CCGS Hudson 2003-054: German Bank, Gulf of Maine. Technical
report, Geological Survey of Canada, Open File 4728: 126 p.
Todd, B.J. 2008. Surficial geology and sun-illuminated seafloor topography, German Bank
area, Scotian Shelf. Geological Survey of Canada, scale 1:50 000.
Tremblay, M.J., Smith, S.J., Todd, B.J., and Clement1, P.M. 2008. Associations of lobsters
(homarus americanus) off southwestern Nova Scotia with bottom type from images and
geophysical maps. ICES CM 2008/G:04 .
Troost, K., Veldhuizen, R., Stamhuis, E.J., and Wolff, W.J. 2008. Can bivalve veligers escape
feeding currents of adult bivalves? J. Expt. Mar. Bio. Ecol. 358: 185–196.
Tu, W. 2002. Zero-inflated data. In Encyclopedia of Envirometrics, Edited by A. El-Shaarawi
and W. Peiegorsch, John Wiley and Sons. 3: 2387–2390.
Van Horne, B. 1983. Density as a misleading indicator of habitat quality. J. Wildl. Manage.
47: 893–901.
Wentworth, C. 1922. A scale of grade and class terms for clastic sediments. J. Geol. 30:
377–392.
11
Table 1. Results from annual research survey of Scallop Fishing Area (SFA) 29 by bottom type.
a) Proportion of tows in survey where 1 or more scallops were caught. b) Mean of the non-zero
catches of scallops (all sizes). Bottom types defined as follows. C-Om = Metamorphic bedrock
(granite), Dg = Igneous bedrock, Gm = Glaciomarine silt, Ic1 = Ice-contact sediment (till), Pg =
Postglacial sand and gravel.
Bottom type
Year
C-Om
Dg
Gm
Ic1
Pg
a) Proportion of non-zero catches.
2001
0.80
0.63
1.00
0.81
0.80
2002
0.46
0.83
1.00
0.75
0.89
2003
0.57
0.80
1.00
0.88
0.96
2004
0.50
0.63
1.00
0.88
0.85
2005
0.75
0.80
0.91
0.80
0.88
2006
0.40
0.67
1.00
0.87
0.83
2007
0.60
0.83
1.00
0.88
0.93
b) Mean number of scallops from non-zero catches.
2001
141.03
244.62
221.00
287.81
519.14
2002
122.83
375.98
288.58
447.54
602.62
2003
1067.40
175.50
294.81
446.31
666.03
2004
208.33
134.84
109.82
197.90
436.06
2005
54.77
201.85
249.77
218.74
592.58
2006
43.00
9.60
146.48
152.57
185.55
2007
331.02
260.90
85.47
287.76
216.13
12
Table 2. Results from fitting the negative binomial distribution to the survey numbers caught (all
sizes). Model entries for the negative binomial refer to the parameter λ. Models were screened
using the Deviance information criterion (DIC).
Year
2001
Model
1
1+Bottom
1+Depth
2002
1
1+Bottom
1+Depth
1032.45
1031.23
1029.56
2003
1
1+Bottom
1+Depth
917.48
919.97
916.42
2004
1
1+Bottom
1+Depth
798.78
798.77
798.74
2005
1
1+Bottom
1+Bottom+Depth
838.90
833.63
835.60
2006
1
1+Bottom
1+Depth
710.76
707.83
711.82
2007
1
1+Bottom
1+Depth
783.62
790.53
783.24
13
DIC
802.47
806.68
802.24
Table 3. Results from fitting the zero-inflated negative binomial distribution to the survey numbers caught (all sizes). Model entries for the zero-inflated form the model is coded as (model for
proportion of observations from negative binomial, model for parameter λ). Models were screened
using the Deviance information criterion (DIC).
Year
2001
Model
(1,1)
(1,1+Bottom)
(1+Bottom,1)
(1,1+Depth)
2002
(1,1)
(1,1+Bottom)
(1+Bottom,1)
(1+Bottom,1+Depth)
1002.45
1001.58
994.28
979.10
2003
(1,1)
(1,1+Bottom)
(1+Bottom,1)
(1+Bottom,1+Depth)
903.18
901.66
894.62
893.88
2004
(1,1)
(1,1+Bottom)
(1+Bottom,1)
(1+Bottom,1+Depth)
774.23
768.51
769.61
762.89
2005
(1,1)
(1,1+Bottom)
(1+Bottom,1)
(1,1+Depth)
818.96
805.94
818.83
818.78
2006
(1,1)
(1,1+Bottom)
(1+Bottom,1)
(1,1+Depth)
685.10
680.95
684.80
686.51
2007
(1,1)
(1,1+Bottom)
(1+Bottom,1)
(1,1+Depth)
774.01
779.35
769.12
769.22
14
DIC
778.16
778.74
780.58
770.32
15
Year
2001
2002
2003
2004
2005
2006
2007
Pg
0.50
2.218
3.920
1.897
0.025
1.194
2.128
0.872
3.814
12.230
3.400
0.975
Year
2001
2002
2003
2004
2005
2006
2007
C-Om-Pg
0.50
−2.395
−3.674
−1.906
0.025
−4.302
−11.770
−3.787
0.025
0.601
0.588
0.504
0.597
0.545
0.733
0.415
0.975
p0.975 =
14.880
17.790
0.721
p0.975 =
p0.975 =
p0.975 =
0.025
−0.041
−0.048
−0.040
−0.041
Bottom type
Dg-Pg
0.50
p0.50 = 0.807
−0.015
−0.873
−1.314
p0.50 = 0.859
p0.50 = 0.838
p0.50 = 0.892
0.975
1.120
1.052
1.030
1.228
1.036
1.413
0.919
0.975
0.025
p0.025 = 0.717
−0.875
−2.660
−0.919
−7.740
−0.218
−3.363
p0.025 = 0.777
p0.025 = 0.752
p0.025 = 0.807
α
0.50
0.843
0.806
0.748
0.902
0.773
1.041
0.669
0.878
−0.118
−3.595
−0.451
0.925
0.907
0.995
0.025
6.991
5.879
6.720
Gm-Pg
0.50
Depth, m
γ1
0.50
−0.026
−0.034
−0.023
−0.025
22.540
22.180
22.420
0.975
0.975
−0.009
−0.018
−0.001
−0.007
−2.796
−8.980
−1.398
0.025
−1.102
−1.585
0.360
Ic1-Pg
0.50
0.096
8.193
3.874
0.975
Table 4. Posterior median parameter estimates and limits for 95 percent credible regions from zero-inflated negative binomial model.
Models were screened using the Deviance information criterion (DIC).
Table 5. Percent of vessel monitoring system (VMS) pings (0<speed<4 kts) by bottom type in
Scallop Fishing Area (SFA) 29 scallop fishery. Final row labelled Area refers to the percent of
the area associated with each of the bottom types. Bottom types defined as follows. C-Om=
Metamorphic bedrock (granite), Dg = Igneous bedrock, Gm = Glaciomarine silt, Ic1 = Ice-contact
sediment (till), Pg = Postglacial sand and gravel.
Year
2002
2003
2004
2005
2006
2007
Area
C-Om
2.08
1.36
4.58
6.91
5.08
3.79
9.38
Bottom type
Gm
2.07
4.14
3.93
2.33
3.54
3.36
6.19
Dg
14.69
5.96
4.09
9.85
5.09
2.99
10.25
16
Ic1
38.79
53.94
33.16
31.84
39.95
40.05
54.32
Pg
42.37
34.60
54.25
49.07
46.34
49.81
19.85
Fig. 1. Location of Scallop Fishing Area (SFA 29) 29 study area off of the south coast of Nova
Scotia, Canada.
17
Fig. 2. Scallop Fishing Area (SFA) 29 study area with geophysical map. Bottom types defined as
follows. C-Om = Metamorphic bedrock (granite), Dg = Igneous bedrock, Gm = Glaciomarine silt,
Ic1 = Ice-contact sediment (till), Pg = Postglacial sand and gravel. map
18
15
20
5
Effort, VMS pings
Effort (1000s h)
10
30
VMS, 0<speed<4 kts
0
0
10
Commercial logs
2002
2003
2004
2005
2006
2007
Year
Fig. 3. Comparison of annual trends in scallop fishing effort in Scallop Fishing Area (SFA 29) 29
measured by 1000s of hours fished reported in commercial fishing log books and total number of
vessel monitoring system (VMS) pings from vessels whose speed was greater than 0 and less than
4 kts.
19
3.0
3.5
4.0
4.5
5.0
3.0
3.5
4.0
4.5
5.0
3.0
3.5
4.0
4.5
5.0
Pg
2001
Pg
2002
Pg
2003
Pg
2004
Pg
2005
Pg
2006
Pg
2007
Ic1
2001
Ic1
2002
Ic1
2003
Ic1
2004
Ic1
2005
Ic1
2006
Ic1
2007
8
6
4
2
0
8
Ln(total numbers per tow)
6
4
2
0
Gm
2001
Gm
2002
Gm
2003
Gm
2004
Gm
2005
Gm
2006
Gm
2007
Dg
2001
Dg
2002
Dg
2003
Dg
2004
Dg
2005
Dg
2006
Dg
2007
8
6
4
2
0
8
6
4
2
0
C_Om
2001
C_Om
2002
C_Om
2003
C_Om
2004
C_Om
2005
C_Om
2006
C_Om
2007
8
6
4
2
0
3.0
3.5
4.0
4.5
5.0
3.0
3.5
4.0
4.5
5.0
3.0
Ln(Depth, m)
Fig. 4.
20
3.5
4.0
4.5
5.0
3.0
3.5
4.0
4.5
5.0
3.0
3.5
4.0
4.5
5.0
3.0
3.5
4.0
4.5
5.0
3.0
3.5
4.0
Pg
2002
Pg
2003
Pg
2004
Pg
2005
Pg
2006
Pg
2007
Ic1
2002
Ic1
2003
Ic1
2004
Ic1
2005
Ic1
2006
Ic1
2007
4.5
5.0
60
40
20
0
Number of cumulative VMS Hits
60
40
20
0
Gm
2002
Gm
2003
Gm
2004
Gm
2005
Gm
2006
Gm
2007
Dg
2002
Dg
2003
Dg
2004
Dg
2005
Dg
2006
Dg
2007
60
40
20
0
60
40
20
0
C_Om
2002
C_Om
2003
C_Om
2004
C_Om
2005
C_Om
2006
C_Om
2007
60
40
20
0
3.0
3.5
4.0
4.5
5.0
3.0
3.5
4.0
4.5
5.0
Ln(Depth, m)
Fig. 5.
21
3.0
3.5
4.0
4.5
5.0