Estimation of tow distance and spatial heterogeneity using data from inclinometer sensors: an example using a clam survey dredge

Fisheries Research 55 (2002) 49–61

Estimation of tow distance and spatial heterogeneity using data from inclinometer sensors: an example using a clam survey dredge James R. Weinberga,*, Paul J. Ragoa, W. Waldo Wakefieldb, Charles Keitha a

b

Northeast Fisheries Science Center, National Marine Fisheries Service, 166 Water Street, Woods Hole, MA 02543, USA Northwest Fisheries Science Center, Hatfield Marine Science Center, 2030 S. Marine Science Dr., Newport, OR 97365, USA Received 6 October 2000; received in revised form 20 February 2001; accepted 5 March 2001

Abstract A major clam fishery in the USA is based on the Atlantic surfclam (Spisula solidissima) and ocean quahog (Arctica islandica). For stock assessment, the National Marine Fisheries Service (NMFS) has conducted surveys of these species with a standardized hydraulic clam dredge since 1980. The sampling procedure has been to tow the dredge for 5 min at 0.77 m/s (i.e., 1.5 kn), which gives a nominal tow distance of 232 m. This indirect calculation does not include any sampling that may take place when the dredge is being set out or retrieved. To get a direct estimate of distance sampled, we placed sensors and a camera on the dredge to monitor bottom contact and towing speed. A total of 70 tows was made in May 1997 across a range of depths, typical of those made during clam surveys, and at a range of tow speeds and scopes (i.e., ratio of tow line length to bottom depth). For each tow, we counted the number of clams captured and estimated the distance sampled based directly on the sensor data. Actual distance sampled per tow was often 1.5–3 times greater than the nominal distance (timed tow), and tow distance increased with water depth. This occurred because the dredge sampled the bottom when it was being set out and hauled back to the ship. Consideration of actual tow distance changed conclusions regarding the effect of scope on catch rate of surfclams. In addition, data on tow distance and catch per tow allowed us to examine the spatial heterogeneity of the two clam species using a negative binomial model. Our findings suggest that estimating tow distances directly can improve estimates of biomass in clam resource surveys. Published by Elsevier Science B.V. Keywords: Tow distance; Clam dredge; Atlantic surfclam; Ocean quahog; Survey; Spatial heterogeneity

1. Introduction Measuring the performance of survey gear is an important research topic (e.g., Godø and Enga˚s, 1989; Godø et al., 1990; Koeller, 1991; Enga˚s, 1994; Enga˚s et al., 1997; Lauth et al., 1998) because results from fishery surveys are used to monitor resources and

*

Corresponding author. Tel.: þ508-495-2000; fax: þ508-495-2393. E-mail address: [email protected] (J.R. Weinberg). 0165-7836/02/$ – see front matter. Published by Elsevier Science B.V. PII: S 0 1 6 5 - 7 8 3 6 ( 0 1 ) 0 0 2 9 2 - 2

establish catch quotas. The Northeast Fisheries Science Center (NEFSC) of the National Marine Fisheries Service (NMFS) has carried out clam surveys along the Atlantic coast of the United States since 1963. The targets of the clam survey are the Atlantic surfclam (Spisula solidissima, Dillwyn 1817) and ocean quahog (Arctica islandica, Linne 1767). A multimillion dollar fishery is based on these clams. The species differ in their life histories and geographical distributions (Ropes, 1980; Thompson et al., 1980; Murawski et al., 1982; Murawski and Serchuk, 1989; Weinberg, 1998, 1999).

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Several hydraulic clam dredges were developed by NMFS for clam surveys in the 1960s and 1970s (Standley and Parker, 1967; Parker, 1971). In the late 1970s, a new dredge with a better design was built to sample a larger area, to collect a greater number of large clams per sample, and to better standardize gear performance. This new dredge was first used in a quantitative clam survey in 1980 and it is similar to hydraulic dredges used by the clam industry. It has been deployed from the R/V Delaware II (48 m in length). A detailed description of this dredge, which is still in use, is given in Smolowitz and Nulk (1982). Some of its major features include a weight of 3.2 t, a cutting blade width of 1.52 m, and a submersible hydraulic pump mounted on the dredge to maintain constant water pressure at all depths sampled. The pump shoots water through nozzles into the sediment in advance of the cutting blade and cage. The jets of water loosen the sediment and cause the clams to ‘‘float’’ in a slurry of sediment and water. The clams are captured in the dredge cage when the cutting blade at the cage entrance passes under them. Standard sampling protocol during NMFS clam surveys has been to tow the dredge with a 2:1 scope (i.e., ratio of tow line length to bottom depth) at a speed of 0.77 m/s (1.5 kn) for 5 min. Towing at this speed and duration gives a nominal tow distance of 232 m. Estimates of tow distance before 1997 were made with a Doppler speed log on the R/V Delaware II for the 5 min timed towing period. The 5 min period begins when the dredge is on the bottom and the 2:1 scope has been established. Swept area biomass estimates in early ocean quahog stock assessments assumed that tow distances were close to the nominal value, 232 m (Murawski and Serchuk, 1983; NEFSC, 1986; Weinberg, 1993). That traditional method for estimating tow distance did not take into account possible sampling by the dredge while it was being set out and hauled in. Because of the central role this dredge plays in NMFS clam surveys and in the resulting biomass estimates, the present study was conducted to obtain a direct estimate of distance sampled per tow, and to determine the effect of scope and towing velocity on the catch rate of clams. Sensors were utilized to monitor towing velocity, angle of contact between the dredge and the sea floor, electrical power to the hydraulic pump, and ship’s position. The sensor data

were also used to examine the spatial heterogeneity of clams, another factor that potentially affects catch rate. We used a modified negative binomial model (Bissell, 1972) in which the expected catch is a function of average density and distance towed.

2. Methods 2.1. Experiments Data were collected with the R/V Delaware II in May 1997 to examine two factors that could affect tow distance and clam catch: towing velocity and scope. Sampling was done at two locations (Fig. 1) and a randomized blocks design (Hicks, 1982) was used at each location. The first experiment took place in 24 m of water off the coast of New Jersey (408000 N, 738510 W) and involved surfclams; the second involved ocean quahogs in 56 m of water off Long Island, New York (408190 N, 728190 W) (Fig. 1). Each experiment had five blocks (Fig. 1), with 16 squares per block (Table 1). A square size of 0.42 km2 was selected for the surfclam experiment based on our assumptions about the ability of the ship and pilot to place a 230 m long tow within a prescribed square. During the surfclam experiment, we recognized that tows could be made within smaller squares. Therefore, the area per square was reduced to 0.14 km2 in the ocean

Fig. 1. Study sites off New Jersey and Long Island, New York. Each experiment had five blocks (labeled A–J), whose location and size are indicated.

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Table 1 Description of two randomized block experiments carried out with the R/V Delaware II Target species Surfclam Ocean quahog

Date of experiment 17–20 May 1997 21–22 May 1997

Number of blocks 5 5

Squares per block 16 16

quahog experiment in an attempt to increase the power of the experiment by reducing variance in catch caused by spatial heterogeneity. The vessel moved from square to square, taking one tow per square at the designated scope and velocity (speed over bottom, as opposed to speed through the water) (Table 1). The number of clams captured in each tow was recorded and the tow distance was estimated directly from sensors (see Section 2.2). The dredge was lined with a 2:5 cm  5:1 cm mesh, as it is during surveys. Small clams pass through the mesh and out of the dredge (Weinberg, 1999; NEFSC, 2000a). We assume that the mesh selectivity applied equally across treatments. In some tows, the desired scope and/or velocity were not achieved. When this occurred, the data were not used and the tow was repeated in the same block but in a randomly chosen, unsampled square. For the surfclam experiment, treatments included two scopes (2:1, 3:1) and four velocities (0.77, 0.98, 1.13, 1.29 m/s). The highest velocity was abandoned for the ocean quahog experiment because vessel pilots had difficulty achieving and maintaining that velocity during the allotted time. Total number of tows analyzed for catch and distance in the surfclam and ocean quahog experiments were 40 and 30, respectively. In the experimental design an attempt was made to keep target tow distance equivalent across all velocities by varying towing duration. Thus, for towing velocities of 0.77, 0.98, 1.13, and 1.29 m/s, we designated tow durations of 5, 4, 3.5, and 3 min, respectively. Each pairwise combination of velocity and duration results in a target tow distance of approximately 231 m (0.125 nmi). Target tow distances do not include the periods when the dredge is set out and hauled back. Furthermore, target distances do not account for times during the tow when the dredge may not be sampling if it has risen off the bottom.

Area of each square (km2) 0.42 0.14

Experimental treatments Scopes

Velocities (m/s)

2:1, 3:1 2:1, 3:1

0.77, 0.98, 1.13, 1.29 0.77, 0.98, 1.13

Number of tows 40 30

2.2. Sensors Sensors were utilized to monitor dredge performance, allowing for direct estimates of distance sampled per tow. Velocity of the R/V Delaware II was monitored with a Trimble Centurion PCODE GPS receiver. The angle of the dredge was monitored with a self-contained, battery-powered inclinometer (Fig. 2) which recorded the pitch angle of the dredge. The pendulum inclinometer was linked to an Onset Computer Optic StowAwayTM temperature logger that was modified to log angle rather than temperature. A custom built optical link was used for offloading data with an Onset Optic ShuttleTM data transporter. The pocket-sized Optic Shuttle provided a convenient means to readout and relaunch the data logger, and to download the data from each tow into the shipboard computer. The components were housed in schedule40 stainless steel pressure housings, 10 cm in diameter and 33 cm in length. The inclinometer was mounted on the cage of the dredge. A black and white video camera system (Fig. 2) was mounted at the top of the dredge cage, providing an oblique composite view of the manifold and nozzles, the blade, the port runner, and the area of sea floor between the nozzles and cutting blade. When the video camera was used, it recorded the entire sequence from deployment to dredging and recovery of the dredge. The camera provided a clear view of the sequence of sea floor landing and liftoff of the dredge. The video camera footage was used to verify that the sensor data were interpreted correctly. The dredge blade has a maximum penetration depth of 20 cm into the sediment. Based on the underwater videos taken while the dredge was in operation, the blade does not need to be completely buried in the sediment for the dredge to catch clams. Even 10 cm of blade penetration is adequate to capture clams. Given

52 J.R. Weinberg et al. / Fisheries Research 55 (2002) 49–61 Fig. 2. (A) hydraulic clam dredge secured on launch and recovery ramp aboard the RV Delaware II; the power cable is visible at its attachment point atop the dredge; (B) forward half of the dredge, showing the large submersible pump (center, forward) and smaller inclinometer sensor (left side, steel case); (C) view from beneath the dredge, showing manifold with 11 nozzles, and manifold pressure sensor (above nozzles); (D) video camera system used to make in situ observations of the dredge and sea floor; shown are Deep Sea Power and B&W video camera and light, rechargeable battery packs, Hi8 camcorder, and pressure housing with attachment harness.

J.R. Weinberg et al. / Fisheries Research 55 (2002) 49–61

the distance between the blade and the rear of the dredge and assuming that the dredge pivots from its rear, a 10 cm blade depth translates into a critical angle for the dredge of 2.318 relative to the sea floor. Hence, the dredge was considered to be sampling whenever the angle of the dredge was <2.318. Path length (distance sampled) for tow i, Li, was computed as X dij aij pij ; (1) Li ¼ j

where j is the jth second during the tow, d the distance traveled by the ship relative to ground, and a and p are [0,1] indicator variables that determine whether or not the dredge is sampling the bottom at time j. The angle of the dredge affects the estimate of Li according to  1 if dredge < 2:31 ; aij ¼ (2) 0 otherwise: The other indicator variable reflects whether sufficient electrical power has been provided to the submersible pump for proper clam sampling. It is defined as  1 if amps > 50; (3) pij ¼ 0 otherwise:

swept (a). In this notation, the negative binomial model is  K   Ci K Dai NBðCi jai Þ ¼ Dai þ K Dai þ K  Ci  Y K þm 1  ; (4) m m¼1 where Ci is the catch in tow i and K the dispersion parameter of the negative binomial model (Pielou, 1977; Welch and Ishida, 1993). The expected mean and variance of catch under this model are EðCi Þ ¼ Dai ;

VðCi Þ ¼ Dai þ

ðDai Þ2 : K

(5)

To account for differences among B blocks ðb ¼ 1; . . . ; BÞ, Eq. (4) was modified by substituting Db for D, Ci, b for Ci, and ai, b for ai. The log-likelihood function for the general model is LLðCi;b jK; Db ; ai;b Þ " !# Ib B X X logðKÞ logðDb ai;b þ KÞ ¼K i¼1

b¼1

" # Ib B X X þ Ci;b ðlogðDb ai;b Þ logðDb ai;b þ KÞÞ b¼1

2.3. Data analysis and modeling Analyses of variance (ANOVA) were performed on clam catch per tow and tow length (i.e., distance) using the PROC GLM procedure in SAS (Statistical Analysis Systems Institute, 1985). Data used in the analyses were transformed to natural logarithms because this transformation reduced heterogeneity of variance among treatment combinations. The data transformation did not change the conclusions from the analyses. Of the 70 experimental tows analyzed, eight tows had incomplete sensor data and their tow distances could not be estimated directly. For these tows, distance was assumed equal to the average of those tows which had complete sensor data and were from the same treatment combination. Spatial heterogeneity of both clam species was examined further with a modified negative binomial model. To account for variable tow distance , we applied Bissell’s (1972) modification of the negative binomial equation in which the mean value m is written as the product of average density (D) and total area

53

þ

B X b¼1

"

i¼1 Ci;b Ib X X i¼1 j¼1

logðK þ j 1Þ

Ib X

# logðCi;b !Þ :

i¼1

(6) Estimates of Db and K were obtained numerically with FORTRAN using the IMSL optimization subroutine DCPOL. Likelihood ratio tests and Akaike’s Information Criterion (AIC; Akaike, 1973; White and Bennetts, 1996) were used to determine the best statistical model for each species. The AIC, a function of the likelihood (L) and the number of parameters in the statistical model, was computed as 2 logðLÞ þ 2ðNo: estimated parametersÞ:

(7)

Due to the small sample size within blocks (n ¼ 8 for the surfclam experiment and n ¼ 6 for the ocean quahog experiment), a separate K for each block in an experiment could not be reliably estimated. We, therefore, assumed a common K for all blocks, and focused only on estimating relative density differences among blocks. The estimates correspond to relative

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density, rather than density, because dredge efficiency was not included in the analysis. Furthermore, the data used in these analyses were restricted to tows for which swept area estimates were available (i.e., treatment means were not substituted for missing cells in the design). Predicted mean catches per tow and variances for selected AIC models for each species were computed by using Eq. (5). Predicted catches were compared to the observed catches per tow in the experiment; predicted variances were averaged by block and compared to the actual sample variances by block.

3. Results Based on the sensor data, estimated distances during the 5 min timed portion of the tows were very close to the target distance of 230 m (Table 2). Estimates of total tow distances, based on sensor data, were much greater than 230 m and varied among experimental treatments (Table 2; Fig. 3). The timed part of the tow accounted for only 46% of the total distance in the surfclam study and only 32% in the ocean quahog study (Table 2). Thus, the majority of the bottom sampling in these tows took place during set out and haul back periods for the dredge. In both the surfclam and ocean quahog experiments, total tow distance was significantly greater when the 3:1 scope was used, compared with the 2:1 scope (Table 3, ANOVA 1 and 2; Fig. 3). This difference was due to the increased time required to set out and retrieve the dredge with the 3:1 scope. In the surfclam experiment, carried out at 25 m depth, total mean tow

Fig. 3. Tow distance (arithmetic mean and 95% confidence intervals) as a function of scope and station depth. Dashed line shows the target tow distance, 231 m (0.125 nmi.).

distance increased from 435 m with the 2:1 scope to 556 m with 3:1 scope (Fig. 3). Depth had an effect on total distance sampled. For example, with a 2:1 scope the average total distance per tow at 25 m depth was 435 m compared to 565 m at 56 m depth. The added time deploying and retrieving the gear in deeper water contributed to this 30% increase in distance.

Table 2 Partitioning the total observed tow distance into two parts: the timed portion and othera Source

Tow distance (m) Surfclam experiment

Timed tow Other Total

Ocean quahog experiment

Mean

S.D.

N

Mean

S.D.

N

227.5 265.5 493.0

13.2 87.9 83.6

34 34 34

232.7 491.2 723.9

12.6 159.2 160.7

28 28 28

a ‘‘Other’’ includes the time when the dredge is set out and hauled back to the ship. Only tows with complete sensor data were used in the calculations. Summary statistics include the arithmetic mean, sample standard deviation, and sample size (i.e., number of tows).

J.R. Weinberg et al. / Fisheries Research 55 (2002) 49–61

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Table 3 Results from two ANOVAs examining the response variable: ln(tow distance), in the surfclam and ocean quahog experiments, respectivelya Source

Response variable: ln(tow distance) Surfclam experiment d.f.

Block (B) Scope (S) Velocity (V) SV BS BV BSV Pooled error

4 1 3 3 4 12 12 28

Mean square 0.015 0.631 0.016 0.020 0.010 0.007 0.008 0.008

Ocean quahog experiment F-value

Significance *

1.7 73.1 1.9 2.3 – – – –

ns

***

ns ns – – – –

d.f.

Mean square

F-value

Significance

4 1 2 2 4 8 8 20

0.003 1.326 0.006 0.007 0.011 0.005 0.003 0.005

0.6 222.2 1.1 1.2 – – – –

ns ***

ns ns – – – –

a Experimental factors included block, scope, and velocity. F-values are based on the pooled error term (Hicks, 1982), which is the sum from three sources: B  S, B  V, and B  S  V. * p > 0:05. *** p < 0:001.

catch per tow to a standard distance of 463 m (i.e., 0.25 nmi). This distance was chosen because it was within the range of many of the actual tow distances in the experiments (Fig. 3). Another ANOVA was then performed to see whether standardized catch per tow was affected by either scope or velocity. In contrast with the previous ANOVA which indicated that scope had a large effect on catch rate of surfclams, scope was not now statistically significant (Table 4, ANOVA 2).

Scope (S) had a significant effect on unadjusted (for tow distance) catch of surfclams per tow (Table 4, ANOVA 1). Arithmetic mean catches increased from 149 individuals per tow with the 2:1 scope to 245 individuals per tow with the 3:1 scope (Fig. 4). Other factors such as block, towing velocity (V), and the S  V interaction did not have a statistically significant effect on unadjusted surfclam catch per tow. Because tow distance was computed independently from clam catch, distance was used to standardize Table 4 Results from two ANOVAs examining surfclam catch per towa Source

ANOVA d.f.

Block (B) Scope (S) Velocity (V) SV BV BSV Pooled error BS a

4 1 3 3 12 12 28 4

Response variable: ln(unadjusted catch)

Response variable: ln(catch adjusted for tow distance)

Mean square

Mean square

F-value

Significance

0.256 0.418 0.297 0.007 0.361 0.184 0.327 0.652

0.8 1.3 0.9 0.0 – – – –

ns ns ns ns – – – –

0.220 2.061 0.235 0.008 0.324 0.201 0.316 0.633

F-value 0.7 6.5 0.7 0.0 – – – –

Significance **

ns *

ns ns – – – –

In the first ANOVA, the response variable was ln(unadjusted catch per tow), in numbers. In the second ANOVA, the response variable was ln(surfclam catch per tow), standardized to a tow distance of 463 m (0.25 nmi). Experimental factors included block, scope, and velocity. All F-values are based on the pooled error term (Hicks, 1982), which is the sum from three sources: B  S, B  V, and B  S  V. * p < 0:05. ** p > 0:05.

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Fig. 4. Number of surfclams captured per tow (arithmetic mean and 95% confidence intervals) as a function of scope. Left: unadjusted (raw) catch per tow. This was not standardized for tow distance. Right: catch per tow was standardized to a tow distance of 463 m (i.e., 0.25 nmi).

The ANOVA, conducted on the unadjusted (for distance) ocean quahog catch per tow data, indicated that only the block effect was statistically significant (Table 5). Another ANOVA (not shown) was also performed on the catch per tow data after standardizing for tow distance, and the block effect was still the only significant effect. Tows in block F captured about 500–1200 ocean quahogs per 463 m (i.e., 0.25 nmi), whereas in block J catches ranged from only about 100–220 individuals (Fig. 5). Intermediate catches

Fig. 5. Number of surfclams and ocean quahogs captured per standardized tow, by experimental block. Catch per tow was standardized to a tow distance of 463 m (0.25 nmi). Vertical lines show the range. For each species, blocks were ordered according to arithmetic mean catch of clams per tow.

were made in blocks G–I. In contrast with the ocean quahog data, there were no clear differences in catches of surfclams among Blocks A–E (Fig. 5). This occurred even though the area covered by the surfclam blocks (A–E) was greater than that covered by ocean quahog blocks (F–J) (Fig. 1, Table 1).

Table 5 ANOVA results from the ocean quahog experiment to examine the effect of block, scope, and velocity on ln(unadjusted number of ocean quahogs per tow)a Source Block (B) Scope (S) Velocity (V) SV BS BV BSV Pooled error a

d.f. 4 1 2 2 4 8 8 20

Mean square 1.762 0.024 0.100 0.028 0.004 0.155 0.053 0.084

F-value

Significance

20.9 0.3 1.2 0.3 – – – –

***

F-values are based on the pooled error term from sources B  S, B  V, and B  S  V (Hicks, 1982). p > 0:05. *** p < 0:001. *

ns* ns ns – – – –

J.R. Weinberg et al. / Fisheries Research 55 (2002) 49–61

Results from ANOVAs highlighted the importance of swept area as a determinant of catch, but the presence of a significant block effect for ocean quahogs also suggested important differences between species in fine-scale distribution patterns. To examine this further, we applied a negative binomial statistical model to a nested series of catch models (Table 6). Models SC1 and Q1 represent the full model (all blocks are different) for surfclams and ocean quahogs, respectively, and SC5 and Q5 represent the most reduced models in which no differences in density among blocks were assumed. The goal was to select a model for each species that explained a large amount of variation in the data with few parameters. Each likelihood ratio test (LRT) for a species was based on a comparison between the full model (SC1 or Q1) and a specific reduced model. In the case of surfclams, none of the LRTs were significant, implying that the most parsimonious model (i.e., SC5, with two parameters) might be the best. However, the three parameter model, SC4, was superior overall because it had the smallest AIC. Both of these models suggested that variation between blocks played little role in determining surfclam catch per tow. For the ocean quahog data, both the LRT results and AIC suggested that the five parameter model, Q2, was best. It was chosen because it was the most parsimonious model that did not have a significant LRT, and because it had the lowest AIC. Q2 is relatively complex, with separate density parameters for four out of five blocks. Model Q2 implies that, with the exception of Blocks G and H, relative density was different in each block (Fig. 5). The agreement between the observed and predicted catches using the negative binomial model was high (r ¼ 0:87, p < 0:0001, Fig. 6). Moreover, the average of the predicted standard deviations of catch per tow for each block compared favorably with the sample standard deviations (r ¼ 0:77, p ¼ 0:0085, Fig. 7). These results reinforce the analyses of variance because they show that, for each species, the catch was affected by average area swept per tow. While area swept was an important determinant of catch per tow, an even larger effect was due to differences in average relative density between species. Based on the most reduced models (SC5, Q5) which do not contain terms for block differences, estimates of overall average relative density for surfclams

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and ocean quahogs were 0.260 and 0.498 m 2 (Table 6), respectively. There were also large differences among blocks in average relative density of ocean quahogs. For example, based on model Q2 (Table 6), which contains separate block terms, the ratio of highest to lowest block averages for ocean quahogs was 461% (i.e., 1.070/0.232). In contrast, model SC4 for surfclams (Table 6) suggests an increase in density of only about 58% between the pooled averages for blocks A, E and blocks B–D. The joint effect of density and area swept on the predicted variance of catch is illustrated in Fig. 8 with the ‘‘best’’ models SC4 and Q2. Within each block, the predicted variance in catch was positively related to area swept. However, greater changes in the predicted variance were associated with sampling in different blocks. The magnitudes of the area differences and catches among blocks provide insight into the spatial heterogeneity of the two species. The block sizes for the surfclam experiments were 6.72 km2 compared with 2.24 km2 for the ocean quahog experiment. Therefore, on average the minimum distance pffiffiffiffiffiffiffiffiffi between pffiffiffiffiffiffiffiffiffi tows within a block was about two ð 6:72= 2:24Þ times greater for surfclams than for ocean quahogs. Moreover, the distance between the northernmost and southernmost blocks within the surfclam experiments was 15.4 km compared to only 6.8 km for ocean quahogs. Thus, ocean quahogs exhibited a nearly 5fold variation in density over a distance of 6.8 km in contrast to only a 58% variation in average density over a range of 15.4 km in the surfclam experiment. In summary, differences in the variance in catch of surfclams and ocean quahogs were driven primarily by differences in density between the two species, by differences in clam density among blocks and finally, by differences in area swept per tow.

4. Discussion The ability to estimate tow distance directly with sensors is valuable for interpreting experiments on catch rate and in estimating resource biomass (Godø and Enga˚ s, 1989; Enga˚ s et al., 1997). For the present experiments, distance sampled varied greatly between treatments and with depth. All observed tow distances, regardless of treatment, were well above the nominal

58 Table 6 Parameter estimates of relative clam density by block, the dispersion coefficient (K), likelihood ratio tests (LRT), and AIC from surfclam (SC) and ocean quahog (Q) modelsa Hypothesis

Number of parameters

Parameter estimates

–Log-likelihood

Block identifier

LRT

d.f.

Probability

AIC

K parameter

A

B

C

D

E

SC1 SC2 SC3 SC4 SC5

A, B, C, D, E A, B ¼ C ¼ D, E A, B ¼ C ¼ D ¼ E A¼E, B ¼ C ¼ D A¼B¼C¼D¼E

6 4 3 3 2

0.378 0.380 0.379 0.337 0.260 F

0.211 0.213 0.235 0.213 0.260 G

0.205 0.213 0.235 0.213 0.260 H

0.222 0.213 0.235 0.213 0.260 I

0.300 0.298 0.235 0.337 0.260 J

4.717 4.679 4.371 4.636 3.806

197.63 197.68 199.12 198.08 201.61

– 0.1 2.9 0.9 7.9

– 2 3 3 4

– 0.95 0.39 0.82 0.09

407.3 403.4 404.2 402.2 407.2

Q1 Q2 Q3 Q4 Q5

F, G, H, I, J F, G ¼ H, I, J F, G ¼ H ¼ J, I F, G ¼ H ¼ I ¼ J F¼G¼H¼I¼J

6 5 4 3 2

1.072 1.070 1.078 1.108 0.498

0.349 0.365 0.323 0.375 0.498

0.387 0.365 0.323 0.375 0.498

0.566 0.572 0.563 0.375 0.498

0.238 0.232 0.323 0.375 0.498

11.147 11.096 8.565 6.096 2.960

177.71 177.85 181.24 186.17 197.59

– 0.3 7.1 16.9 39.8

– 1 2 3 4

– 0.59 0.03 <0.01 <0.01

367.4 365.7 370.5 378.4 399.2

a Only one value of K was estimated for each model. Relative to the other models, SC1 and Q1 are ‘‘full’’ models. Each LRT examines whether there is a significant loss of fit going from the ‘‘full’’ model to any other model with fewer parameters. A high probability for the test statistic indicates no difference in fit between the two models. ‘‘AIC’’: Akaike’s Information Criteria.

J.R. Weinberg et al. / Fisheries Research 55 (2002) 49–61

Model

J.R. Weinberg et al. / Fisheries Research 55 (2002) 49–61

Fig. 6. Predicted and observed, unadjusted catch per tow by species. Predicted values were based on the ‘‘best’’ negative binomial models SC4 and Q2 (Table 6) for surfclams () and ocean quahogs (), respectively. The diagonal line corresponds to a hypothetical perfect fit between the model and the observations.

distance corresponding to a 5 min timed tow. This occurred because the clam dredge was also sampling while being set out and hauled in. During these periods, video footage with the in situ camera confirmed that the dredge was moving forward, and clams were hydraulically excavated from the sediment and captured in the dredge. The time to set out and haul back the dredge could make up a large fraction of the actual sampling time, especially in deep water or with a 3:1 scope, because the duration of the timed part of the tow was relatively short (5 min). Winch speed

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Fig. 7. Predicted and observed standard deviation of unadjusted catch per tow for surfclams () and ocean quahogs (), by block. Letters refer to experimental blocks (see Fig. 1). The diagonal line corresponds to a hypothetical perfect fit between the model and the observations.

should affect tow distance because it controls dredge retrieval time. In the surfclam experiment, better estimates of distance sampled by the dredge changed the conclusions about the effect of scope on catch rate. Catch rate appeared to increase with the 3:1 scope, and this result would have raised a question as to whether to modify the scope in future surveys; however, after standardizing for distance based on sensor data, reanalysis of the catch data suggested no significant difference in catch between the two levels of scope. Although no scope

Fig. 8. Predicted variance in unadjusted catch per tow as a function of area swept, by block. Surfclams were caught in blocks A–E (left). Ocean quahogs were caught in blocks F–J (right). Each line is made by connecting the points from the same block. Some lines overlap.

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effect was detected here, the mechanism whereby the catch rate could have been affected by scope would involve the time of contact and depth of the cutting blade in relation to the sediment. Sensor data have been incorporated into recent NMFS stock assessments for surfclams and ocean quahogs in the Exclusive Economic Zone (EEZ) (NEFSC, 1998a,b, 2000a,b). For example, when sensors were used to estimate tow distance directly, the average weight of surfclam meat (not corrected for dredge efficiency) per 277 m (0.15 nmi) tow in the northern New Jersey region was estimated at 4.9 kg in 1999 (NEFSC, 2000a). This is 39% lower than the meat weight estimate, for the same distance, derived from the traditional method based on Doppler readings (NEFSC, 2000a). Sensor data also affected the most recent ocean quahog assessment (NEFSC, 2000b). For example, in 1999 a total of 18 random tows were taken in relatively deep water (73–92 m) off Georges Bank to determine how much of the resource was present at that depth range. The estimate of distance towed averaged 406 m (0.22 nmi) based on sensor (i.e., inclinometer and velocity) data, compared to 222 m (0.12 nmi) based on the traditional Doppler method. This increase in the estimate of distance sampled, based on sensors, factored directly into the calculation of a lower biomass in that area (NEFSC, 2000b). In the scope/velocity experiment, the large differences in ocean quahog abundance over small spatial scales (blocks) suggest fine-scale patchiness. From the available data, it is not possible to infer whether the greater spatial heterogeneity of ocean quahogs compared to surfclams is characteristic of these species, or simply reflected conditions at the experimental sites. Additional investigation of this is warranted. It was important to carry out the scope/velocity experiment because the effect of these factors on catch was not well understood. In 1979 when the new clam dredge was being tested, an attempt was made to study the effect of scope, speed, and tow duration on catch, but those data were inconclusive because the dredge, which was then lined with small 2.5 cm mesh, filled rapidly and then ‘‘acted as a plow’’ (Smolowitz and Nulk, 1982). Meyer et al. (1981) studied the performance of an earlier model of the NMFS hydraulic clam dredge, which was smaller and did not have a submersible pump, and concluded that dredge effi-

ciency was sensitive to speed of towing and scope; supporting data were not presented in their paper. We did not detect a significant velocity effect, although velocity could have affected catch rate by changing the effect of the hydraulic dredge on the bottom sediments. When the water jets are off, the dredge acts much like a shovel; it collects sediment and clams until it is full, and then it no longer samples properly (Medcof and Caddy, 1971; Smolowitz and Nulk, 1982). The water jets loosen the sediment and reduce the engine rpm (revolutions per minute) needed to tow the dredge (Smolowitz and Nulk, 1982). If the towing velocity is too slow, <1 kn, the jets will excavate a hole. In that case, clams might pass under the blade assembly and reduce the catch. When the towing velocity is too fast, the jets would not have enough time to loosen the substrate and the dredge would act like a shovel, as described earlier. A number of studies have demonstrated that the behavior of survey gear is complex and that this has important implications for survey abundance estimates (e.g., Godø and Enga˚ s, 1989; Godø et al., 1990; Koeller, 1991; Lauth et al., 1998). The present study suggests that distance sampled by the clam dredge exceeds the nominal distance because the dredge samples the bottom while being set out and retrieved. Experimental studies of commercial scallop dredges equipped with inclinometer sensors indicate that, like the clam dredge, sampling is not restricted to the timed portion of the tow (NEFSC, 1999). Additional research on distance sampled by dredges and other gear commonly used in fishery surveys is warranted.

Acknowledgements The inclinometers and in situ video system were designed, built, and kindly provided to us by S. McEntire, C. Rose, and R. Lauth at the NOAA NMFS Alaska Fisheries Science Center in Seattle, WA. This work was made possible by the hard work and cooperation of the Captain and Crew of the R/V Delaware II, T. Azarovitz, V. Nordahl, J. Galbraith, R. Clifford, B. Lake, and P. Twohig. We are grateful to H. Malcolm and J. Johnson for computer support at sea, and to S. Murawski and F. Serchuk for reviewing versions of the manuscript.

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