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Keywords:

  • sinking speed of longlines;
  • marine conservation;
  • incidental catch;
  • bycatch;
  • dynamic simulation;
  • numerical analysis

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGEMENT
  8. REFERENCES
  1. Sinking speed is one of the main factors affecting the performance of longlines. Demersal and pelagic longlines need to sink quickly, to prevent bycatch of marine animals and bait loss to seabirds. The incidental catch or bycatch of seabirds by longlining is a problem from the environmental conservation point of view. Thus, it is necessary to develop techniques to reduce the incidental catch or bycatch by longlining.
  2. This study focuses on the sinking speed of longlines. To determine the significant factors that may influence the sinking speed, the study considers the various parameters for longlining such as bait properties, rope materials, rope thickness, anchor weights, shooting speeds, shooting methods, and the ratio between the depth and mainline length.
  3. The results of the numerical analysis were verified by comparing them with observations from field experiments. The results suggest that the sinking speed can be increased and the bycatch during the sinking process can be reduced when baits have elliptical shapes with a small projected area relative to the sinking orientation, as well as when high-density and thicker materials are used. Heavier anchors also help to increase the sinking speed of the anchor part, excluding other parts such as the middle part of a mainline. If the water is shallow compared with the line's length, the anchor's weight has a smaller influence on the speed of sinking in the middle of the line. In this case, the factors that influence the sinking speed of a longline 10, 30, and 50 m from the stern are rope material, rope thickness, and shooting speed.
  4. These results may be used to design techniques for bycatch prevention. Based on these results, accounting for these important factors when designing a longline is expected to increase the fishing efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGEMENT
  8. REFERENCES

Longlining is a passive fishing method in which the fish come voluntarily to the gear as with traps, gill nets, and other types of hook gear (Brandt, 1984). Longlining consumes little fuel, produces little damage to fishing grounds, and provides better quality fish than other fishing methods such as trawling and gill netting. Longlining also provides high selectivity through appropriate selection of the hook's size and shape. However, longlining also has disadvantages such as the incidental catch or bycatch of marine animals such as seabirds, turtles, and others. The bycatch of seabirds and turtles occurs during the setting process of longlines. Additional information is needed to reduce the longlining bycatch. One type of information relates to the foraging behaviour of seabirds and turtles. Another can be obtained from analysis of the longline's sinking speed (Smith, 2001; Robertson et al., 2010). Some research has been carried out into the efficacy of streamers or tori lines in preventing bycatch of seabirds during the longline setting process (Smith, 2001; Melvin and Walker, 2008). The present study deals with the sinking speed of the longline, which is the main factor that determines the performance of the gear, and determines the degree of bait loss (and consequently fishing efficiency) and seabird bycatch during the longline setting period. Research that employs numerical methods to investigate the sinking speed of longlines is relatively scarce; however, the problem of seabird bycatch by longlining has been well studied, as has the behaviour of longlines in order to increase fishing efficiency.

Smith (2001) and Robertson et al. (2003) performed longline sinking experiments to investigate the factors that prevent seabird bycatch. Satani and Uozumi (1998) and O'Toole and Molloy (2000) measured line sinking rates of pelagic longlines, while Løkkeborg (1998) investigated the sinking rate of demersal longlines. Lee et al. (2005) evaluated the shape of pelagic longlines as a function of current characteristics. Miyamoto et al. (2006) conducted experiments using an ultrasonic positioning system and ORBCOMM buoy to measure the behaviour of longlines. Mizuno et al. (1999) conducted experiments and derived a mathematical model using micro-bathythermographs to estimate the underwater shape of tuna longlines; however, this mathematical model can only be used with data from micro-bathythermographs. Smith (2001) and Robertson et al. (2003) evaluated the sinking speed of longlines at depths of 20 m to prevent bycatch only. Mizuno et al. (1999) and Lee et al. (2005) assessed the behaviour of pelagic longlines. The sinking speed is dependent on the rope material density (i.e. the main material in the longline) and the rigging (anchor weights, snoods, stoppers, hooks, and baits). The hydrodynamic characteristics (i.e. resistance coefficient) of baits have not been dealt with by earlier research. Lee et al. (2011) carried out analyses to define the hydrodynamic characteristics of baits in connection with various sinking orientations. The sinking speed of a longline is also influenced by shooting speeds and shooting methods employed. Longlines, however, are generally designed and constructed to suit the behaviour of the target species, the depth of their habitats, oceanic factors, and weather conditions (Fridman, 1986; Prado, 1990; Bjordal and Løkkeborg, 1996). The primary objective here is to investigate which factor is the most significant affecting the sinking speed of longlines from the sea surface to the sea bottom at distances of 10, 30, and 50 m from the stern. This study also shows how numerical methods can be applied for the analysis of the sinking speed of longlines in connection with the above-mentioned parameters. The results provide useful information that may help reduce the bycatch and increase the potential fishing efficiency during the sinking phase (i.e. the setting process) of longlines.

MATERIALS AND METHODS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGEMENT
  8. REFERENCES

Physical modelling of longlines

In this study a longline was modelled as a flexible structure and was divided into a set of mass points and springs. As shown in Figure 1, the spring connects adjacent mass points, including buoys, additional rigging, and rope connections.

image

Figure 1. Mass–spring model of demersal longline and forces acting on an element of rope (FB includes the forces of buoyancy and gravity, FL is the lift force, FD is the drag force, r is the position vector between adjacent mass points, U is the speed vector, and α is the angle of attack).

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The motion of the longline can be described by

  • display math(1)

where m is the mass of mass points, Δm is the added mass, inline image is the acceleration vector, fint represents the internal force between the mass points, and fext represents the external forces applied to the mass points. The mass added to the mass points was obtained from the following equation:

  • display math(2)

where ρsw is the density of seawater, VN is the volume of the mass points and Km is the added mass coefficient. The Km value of the floats and sinkers, regarded as spheres, was set to 1.5 (Takaki et al., 2004; Wakaba and Balachandar, 2007; Lee CW et al., 2008; Lee JH et al., 2008, 2011). Cylindrical structures, such as ropes, were described by the following equation:

  • display math(3)

where α is the angle of attack.

Internal forces

The internal force is the force applied to the springs that connect the mass points. It was assumed that internal forces applied to the rope were parallel to the direction of tension. The change in length of the spring was assumed to be linearly proportional to the magnitude of the applied force.

The internal forces between the mass points were described by:

  • display math(4)

where ki is the stiffness of the line, ni is the unit vector along the spring, ri is the position vector between the adjacent mass points, |ri| is the magnitude of the position vector and li0 is the original length of the spring. The unit vector, ni, may be obtained by dividing the vector ri by its magnitude, |ri|. The force, fint, is proportional to the elongation of the spring, and ni(|ri| − li0) is the displacement in three-dimensional space.

The stiffness of the line, ki, can be described by:

  • display math(5)

where E is the modulus of elasticity and A is the effective area (i.e. cross-sectional area) of the material.

The cross-sectional area (i.e. effective cross-sectional area) of a rope is approximately 60% of the apparent cross-sectional area. Under the same tensile load, the elongation of a cable is greater than the elongation of a solid bar that is made of the same material and has the same metallic cross-sectional area because the wires in a cable tighten and behave as fibres in a rope. Thus, the effective elastic modulus of a cable is less than the modulus of the original material (Gere and Goodno, 2009). In the present work, the effective modulus of the rope was assumed to be 65% of the base material's modulus.

External forces

External forces represent the interaction between the points and the environment (Lee JH et al., 2008). The forces acting on the bar are shown in Figure 1. External forces acting on the mass points include the drag force (FD), the lift force (FL), and the buoyancy (the sinking force, FB). The external forces are the sum of the individual forces and can be written as:

  • display math(6)

The current was assumed to be steady and uniform, and inertial force was neglected. The drag and the lift forces on the mass points affect the shape of a structure under water, and can be described as:

  • display math(7)
  • display math(8)

where CD is the drag coefficient, ρsw is the density of seawater, Ap is the projected area of the structure and U is the magnitude of the resultant velocity vector, U. The magnitude of U was obtained by subtracting the velocity vector of the mass point from the current velocity vector. The vector nv is the unit vector for drag and acts in the direction opposite to that of the resultant velocity vector. The quantity CL is the lift coefficient and nL is the direction of the lift force. The drag and the lift coefficients, excluding the anchor, that were used in this study were adopted from a previous investigation (Robert and John, 1973; Lee et al., 2005).

For each element, the direction of lift forces was obtained from the vector product U × (U × r) and the unit vector, nL, for a particular direction:

  • display math(9)

where |U × (U × r)| is the magnitude of the vector.

To obtain the coefficient of drag, the angle of attack (α) between the velocity vector U and the position vector r was determined. The angle of attack was obtained from the following equation:

  • display math(10)

The force of gravity and the buoyancy of the structures (FB) can be described as

  • display math(11)

where ρi is the density of the material, ρsw is the density of seawater, VN is the volume of the elements, and g is the acceleration of gravity.

Methods of calculation

After substituting the internal and external forces in Equation (1), the governing equation was transformed into the following non-linear second-order differential equation in the time domain:

  • display math(12)

where M is the total mass and inline image is the acceleration. Equation (12) was transformed into two first-order differential equations:

  • display math(13)
  • display math(14)

In this study, the structure was described as a stiff system of equations, which were solved using the Newmark- β (Newmark, 1959) method.

Full-scale model bait experiments in a flume tank

The drag coefficients of the baits were derived from experiments that used model baits of different shapes, sizes, and sinking orientations. The experiment with model baits was performed in the flume tank under still water conditions (length of observation window was 21.3 m, width was 8 m, and depth of water was 2.7 m) at the North Sea Centre in Hirtshals, Denmark.

In Norway, bait is available as frozen blocks and must be thawed properly before it is cut. Bait (i.e. mackerel, herring, and saury) is commonly used in 20–40 g portions (Prado, 1990; Bjordal and Løkkeborg, 1996), and the specific gravity of bait is slightly greater than 1.0 (Johnston et al., 1994). In the theoretical analyses, model baits were based on the aforementioned weights and densities and were designed to simulate the shape and density of real baits. As shown in Figure 2, fillet and elliptical baits tested in the theoretical analyses corresponded to frozen and thawed baits used in fishing. The baits used in the experiments were made of wood. Lead was added to model the specific gravity, as can be seen from the data in Table 1. Thin twine was used to attach the sinker (as specified in Table 2) to the model baits, as shown in Figure 3.

image

Figure 2. Schematic presentation of baits and their corresponding orientations during sinking: (a) fillet shape; (b) elliptic shape (A, B, C: sinking orientation of baits).

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Table 1. Details of full scale model baits used in the experiments
Shape and sizeLength (cm)Width (cm)Height (cm)Weight of bait (g)Density of bait (g cm–3)
  1. F-: fillet shape, E-: elliptic shape, -L,-M,-S: size of bait (large, middle, small)

F-L4.931.8291.11
F-M4.22.81.8231.12
F-S3.321.8131.12
E-L5.553.41.8341.11
E-M4.6531.8251.11
E-S3.652.251.8151.11
Table 2. Specifications of the twine and the weights for the experiments
 Length (cm)Diameter (cm)Weight (g)
Twine100.20.228
Sinker3.30.88.9
image

Figure 3. Bait setup for flume tank: (a) fillet shaped bait with sinking orientation ‘A’; (b) elliptical bait with sinking orientation ‘C’.

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The mean sinking speed of the model baits was calculated by measuring the sinking time and water depth. The mean sinking times for different experimental conditions (i.e. bait, sinker, and twine, or sinker and twine) and depths were measured under stationary conditions throughout the sinking process. The flume tank measured height was 2.7 m. Video recordings revealed that in still water the oscillation of a bait with orientation A was minor and could be ignored. The results were based on full-scale tests; thus, similar oscillations may occur in practical conditions.

The drag coefficient of a bait as a function of bait shape, size, and sinking orientation was calculated. The total drag force (bait, sinker, and twine) was calculated using Equation (15), and the drag forces on the sinker and twine were calculated from Equations (16), (17), and (18). The drag force of the bait was calculated by subtracting the drag forces due to the sinker and twine from the total drag force, as shown in Equation (18). Finally, the drag coefficients of the baits were calculated from Equation (19). Equation (15) is valid only when the sinking speed is constant. The resulting set of equations was:

  • display math(15)
  • display math(16)
  • display math(17)
  • display math(18)
  • display math(19)

where the subscripts T, S, R, B in the equations denote Total, Sinker, Rope, and Bait respectively, ρw is the density of water, Rt is the total drag force (sinker, twine, and bait), m(S + R + B) is the total mass (sinker, twine, and bait), V(S + R + B) is the total volume (sinker, twine, and bait), R(S + R) is the resistance of the sinker and twine, A(S + R) is the projected area of the sinker and twine, U(S + R) is the sinking speed of the sinker and twine without the bait, C(S + R) is the resulting coefficient for the resistance of the sinker and twine, AP(B) is the projected area of the baits, U(S + R + B) is the sinking speed of the bait, including the sinker and twine, RB is the resistance of the bait and CD(B) is the drag coefficient of the bait, g is the gravitational acceleration.

Verification of the numerical model by full-scale longline experiments

Full-scale longline experiments were designed to verify the numerical method that was developed to analyse the sinking speed of longlines rigged with baits. The experiments were conducted at the Trondheim fjord by R/V Gunnerus on 22 May 2008 and 10 June 2008. The R/V Gunnerus was equipped with an underwater positioning system (HiPAP 500, Kongsberg, Horten, Norway) on the hull, in order to track the location of transponders during sinking. The longlines were set as the vessel steamed at approximately 3 knots along the desired course. The longlines were rigged with snoods, hooks and baits, as shown in Figure 4 and recorded in Table 3. For full rigged gear, the distance between the snoods was 1.8 m, and 256 snoods were used in the experiment. The anchor used in the experiment weighed 6 kg, and the diameter of the buoy was 31 cm. Two buoys were attached to the buoylines at each end of the set. During both experiments, depth sensors (TDR, Vemco, Canada), and transponders (MST 319/N, Kongsberg, Horten, Norway) were attached to the anchors. The bait used in the experiment was herring, and each fish was cut into four pieces. The weight of the bait was approximately 25 g. The bait was cut into an elliptical shape, as illustrated in Figure 4. The depth of water at the experimental sites was approximately 500 m.

image

Figure 4. Schematic presentation of longlines with snoods and baited hooks for full-scale sea experiment.

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Table 3. Specifications of the longline used in the experiments
PartMaterialDensity (g cm–3)Diameter (mm)Length (m)Weight (kg)Buoyancy (N)Number (EA)
MainlinePA1.146462.6   
BuoylinePA1.144.5, 8530, 600   
SnoodPA1.141.290.4  256
AnchorSteel   6  
Buoy  310  145.24 (2 at each end)
Hook    0.004 256
BaitHerring   0.025 256
Position of the sensorAt each anchor

Simulation cases for the analysis of the sinking speed of longline

The longline selected for simulations is a common type of Norwegian demersal longline (Table 4) with a snood spacing of 1.5 m and two buoys at each end of the line, as illustrated in Figure 5. The simulations were performed using a water depth of 700 m and a shooting speed of 6 kn. The simulation conditions are described in Table 5. The simulations were performed with a four times grouping method in order to reduce the calculation load.

Table 4. Specifications of the longline used for the simulations
PartMaterialDensity (g cm–3)Diameter (mm)Length (m)Weight (kg)Buoyancy (N)Number (EA)
MainlinePA1.1473000   
BuoylinePA1.1471000   
SnoodPA1.141.0240.4  2000
AnchorSteel   12 (in air)  
Buoy  310  145.24 (2 at each end)
Hook    0.004 2000
image

Figure 5. Schematic drawing of the simulated longline. No. 1: first shot anchor. No. 6: second shot anchor.

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Table 5. Specifications of simulation for the longline
Total number of mass points (EA)Calculation interval (s)Maximum iteration number per time step
12020.00025

Positions of measurement points for the sinking speed analysis

Because different parts of longlines are likely to sink with different speeds, six measurement points, approximately 600 m apart, were positioned along the longline, as shown in Figure 5. In all simulation scenarios, the sinking speeds of the longlines were measured at these six different positions. Furthermore, the sinking speeds of the longlines at distances of 10, 30, and 50 m from the stern were analysed at the measurement position No. 3, which is in the middle part of the mainline and reflects the characteristics of mainline sinking speed. This specific distance was chosen because more than 98% of seabird attacks were shown to occur within 10 to 50 m of the stern (Melvin et al., 2001). The sinking speeds of other measurement positions can be estimated from the tendency of the global sinking speed.

Simulation cases

The simulations in this study describe six different cases: the simulations deal with the effects of bait properties, rope thickness and material, anchor weights, shooting speeds, and shooting methods on the sinking speed of a longline.

Case 1 (bait shapes and sinking orientations)

To determine how the bait properties influence the sinking speed of a longline, the simulations were performed using the results from the model bait experiments. The simulations used large sized baits, which is the most commonly used bait size in commercial longlining (Table 6). The sinking orientation of each one of the baits was set to reflect the orientation that is commonly used in commercial longlining with ‘A’ sinking orientation of fillet shaped bait and ‘C’ sinking orientation of elliptical bait. The simulations analyse quantitatively the influence of bait on the sinking speed of a longline as a function of depth.

Table 6. Details of baits used in the simulations
Shape and sizeLength (cm)Width (cm)Height (cm)Weight of bait (g)
  1. F-: fillet shape, E-: elliptic shape, -L: size of bait (large)

F-L4.931.829
E-L5.553.41.834
Case 2 (rope thickness and material)

To investigate the influence of rope properties, such as the thickness and density, on the sinking speeds of longlines, simulations were carried out with different materials, such as scanline, polyamid, and polyester. The thickness of the ropes varied from 5.5 to 8 mm in steps of 0.5 mm. The weights of the swivel and stopper also changed with the rope's thickness. The simulations also reflect the changes to the weight of the swivel and stopper.

Case 3 (anchor weight)

To investigate how different anchor weights influence the sinking speed of longlines in commercial demersal longlining, six different anchor weights (10 to 50 kg in 10 kg intervals) were used for the simulations. The influence of water depth and length of mainlines on the sinking speeds were considered as well.

Case 4 (shooting speed)

To measure the influence of shooting speed on sinking speed, simulations were performed for shooting speeds ranging from 6 to 10 kn, in steps of 2 kn. A fishing vessel shoots a longline as fast as possible, depending on sea conditions. The simulations show how the shooting speed can influence the sinking speed. The analysis of sinking and shooting speeds will be discussed in terms of economics and fishing efficiency later in this study. In this study, the shooting speed was defined as the line's shooting speed from the vessel; thus, the shooting speed was not equal to the vessel speed.

Case 5 (shooting methods)

The simulations were carried out for different shooting methods: (1) ‘horizontal shooting’, where a vessel shoots the first buoyline and mainline up to the end of the last buoyline; and (2) ‘vertical shooting’, where a vessel drops the whole coil of the last buoyline into the water after shooting the mainline and dropping the last anchor (Figure 6). Simulation results show which shooting method increases the sinking speed, thereby improving the efficiency of the fishing process.

image

Figure 6. The various longline shooting methods.

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RESULTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGEMENT
  8. REFERENCES

Full-scale model bait experiments in a flume tank

The results of model bait experiments suggested that the drag coefficient and the size and sinking orientation of bait were correlated. The drag coefficients of fillet-shaped baits ranged from 1.735 to 0.763, while the coefficients of elliptical baits ranged from 1.483 to 0.62. The baits used in the experiment were consistent with practical conditions, but the processing of bait led to non-uniform sizes. Thus, three different sizes of baits were used in the experiments. For large baits, varying the sinking orientation resulted in drag coefficients of 1.735 and 0.863 for fillet-shaped baits, while the coefficients of elliptical baits ranged from 1.483 to 0.663. In commercial longlining, fillet-shaped baits are typically used with sinking orientation ‘A’, as defined in Figure 2. Based on the experiments, the resistance coefficient of this configuration was 1.735. Elliptical baits typically sink according to the orientation ‘C’, and the corresponding resistance coefficient was 0.663.

Experimental results suggested that fillet-shaped baits generally possess a higher hydrodynamic resistance coefficient than elliptical baits. The results revealed that fillet-shaped baits exhibit higher resistance during sinking than the elliptical baits. Table 7 lists the drag coefficients of baits for various shapes, sizes, and sinking orientations.

Table 7. Drag coefficients of baits with different shapes, sizes and orientations
 F-LσF-MσF-SσE-LσE-MσE-Sσ
  1. F-: fillet shape, E-: elliptic shape, -L: large size of bait, -M: medium size of bait, -S: small size of bait.

  2. A: A sinking orientation of baits, B: B sinking orientation of baits, C: C sinking orientation of baits.

  3. σ: Standard deviation

A1.740.041.430.021.270.061.480.011.380.011.020.02
B1.410.021.250.020.930.031.280.051.020.040.760.02
C0.860.010.810.010.760.010.660.020.650.020.620.01

Verification of the numerical model by full-scale longline experiments

The sinking speeds obtained from the numerical model can be compared with the experimental results. The sinking profiles of the experimental and simulated longlines are shown in Figure 6 which provides a comparison of the sinking profiles of the first and second anchors from the experiment performed on 10 June 2008. The sinking profile of the longline revealed that the first anchor displayed unusual sinking behaviour at a depth of approximately 200 m. During the shooting process, the connection between the mainline and the second anchor became immobilized on the bait shooter. Therefore, the longline was drawn tight until the second anchor was thrown into the water. Thus, the sinking profile of the first anchor at a depth between 150 and 200 m displayed a sudden drop (Figure 7(a)). The simulations were not developed to consider these types of situations; thus, the simulated results did not agree with the sinking profile of the first anchor. However, the simulated results agreed with the experimental sinking profile of the second anchor (Figure 7(b)). If the simulation could be adjusted to reflect the immobilization of the anchor on the bait shooter during the shooting process, the simulation would have provided reasonable sinking speeds.

image

Figure 7. Longline sinking profiles with bait (10 June 2008) and simulated profiles at the (a) first and (b) second anchor (TDR: temperature depth recorder, Sim: simulation, MST 319/N: transponder).

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Simulation cases for the analysis of longline's sinking speed

Case 1 (bait shapes, sizes, and sinking orientations)

Simulation of the longline rigged without baits was performed to determine the effect of baits on the sinking speed (Table 8). The sinking speed of longline with different shaped baits (i.e. fillet and elliptic), large size and different sinking orientations was calculated using the simulation (Table 8). The results in Table 8 show that the sinking speeds of the longline are indeed affected by the shape and the orientation of baits and that the baits produced additional resistance forces during the sinking phase. However, the sinking speed at the measurement positions 2, 3, 4, and 5 did not vary much during the sinking process at the different depths, even though the sinking speed increased with depth (Figure 8). Figure 9 illustrates the development of the shape of a 3000 m longline during the sinking process down to a depth of 700 m. The anchors and buoylines were included in the calculations, but they are not shown in the plot. The lowest black line represents the shape of the longline without baits. When the anchor at position No. 1 hit the bottom, the middle point of the longline reached a depth of 350 m and the second anchor was still sinking. The sinking speeds of longline without baits and the one with ‘elliptic large size C orientation’ did not differ much, even though at 10 m behind the stern the sinking speed of a longline rigged with ‘fillet large size A orientation’ was 13.4% lower than that of ‘elliptic large size C orientation’ (Figure 10).

Table 8. Mean sinking speed without bait at the different measurement positions*
Measurement position*Sinking speed (m s–1) from simulation
Without baitFillet – large sizeElliptic – large size
  • *

    These measurement positions on the longline are shown diagrammatically on Figure 5.

10.4910.4300.439
20.2460.2060.234
30.2270.1980.217
40.2260.1980.217
50.2400.2040.229
60.5180.4360.463
image

Figure 8. Mean sinking speed of the longline without bait as a function of depth. Different curves represent different positions along the longline.

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image

Figure 9. Development of the mainline during the sinking process due to the effect of baits. The development of the longline was measured at the middle point of the mainline at a depth of 350 m. WB: without bait; FLA: fillet large size with A sinking orientation; ELC: elliptic large size with C sinking orientation.

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image

Figure 10. Sinking speed of the longline at position No. 3, for different bait types, 10, 30, and 50 m from the stern.

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Case 2 (rope's thickness and material)

The sinking speed of the longline increases with thickness because, for increasing rope diameter, the weight of rope in the water is increasing faster than the resistance of the rope, owing to the changing angle of attack (Figure 11(a)). The sinking speeds of longlines made of different materials and with thicknesses ranging from 5.5 mm to 8 mm in steps of 0.5 mm were measured at measurement position No. 3, for 10, 30, and 50 m from the stern (Figure 11(b)). In the case of Scanline, the mean sinking speed (m s–1) varied from 0.382 to 0.530 at 10 m from the stern, 0.264 to 0.372 at 30 m from the stern, and 0.156 to 0.218 at 50 m from the stern (Figure 11(b) top). In the case of Polyamid, the mean sinking speed varied from 0.405 to 0.560 at 10 m from the stern, 0.282 to 0.393 at 30 m from the stern, and 0.165 to 0.231 at 50 m from the stern (Figure 11(b) middle). In the case of Polyester, the mean sinking speed varied from 0.531 to 0.723 at 10 m from the stern, 0.370 to 0.506 at 30 m from the stern, and 0.297 to 0.231 at 50 m from the stern (Figure 11(b) bottom). The mean sinking speed of longlines at the measurement position No. 3 for a given distance from the stern was found to be increase with increasing density and thickness of materials.

image

Figure 11. Mean sinking speeds of longlines (a) that are made from different materials and have different diameters (diameters ranging from 5.5 mm to 8 mm in steps of 0.5 mm), (b) at measurement position No. 3, for 10, 30, and 50 m from the stern.

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Case 3 (anchor weight)

The sinking speed of the line close to the anchor increases with increasing anchor weight (Figure 12). As expected, the sinking speed at the anchor (position 1) increases with increasing anchor weight, and the speed decreases as the line sinks deeper owing to the resistance of the rest of the line. At the middle position (No. 3), the speed increases slightly with increasing anchor weight up to a depth of 500 m, and then speed increases quickly down to a depth of 1000 m owing to the increased influence of the sinking anchor (Figure 12). The vertical speed starts to decrease as the anchor settles on the bottom. As the anchor sinks, the angle of attack of the water flow to the mainline is reduced. Consequently, the total resistance of the line is reduced along with its sinking speed. On the other hand, if the water is shallow compared with the line length, the anchor weight has a smaller influence on the speed of sinking in the middle of the line because the anchor reaches the bottom in a short time. In commercial fishing, the ratio between water depth and longline length is usually below one third. Therefore, the use of heavy anchors does not greatly affect the sinking speed in the middle part of the longlines, and heavy anchors do not improve the fishing efficiency by moving the bait to the bottom in a shorter time.

image

Figure 12. Influence of the anchor weight and the ratio of water depth to length of the mainline on the mean sinking speed of the longline.

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The mean sinking speed of longlines at the measurement position No. 3 for 10, 30, and 50 m from the stern does not show much difference when the anchor's weight is increased. The mean sinking speed varied from 0.522 to 0.506 at 10 m from the stern, ranged from 0.366 to 0.349 at 30 m from the stern, and ranged from 0.215 to 0.203 at 50 m from the stern.

Case 4 (shooting speed)

Simulations of longlines with three different shooting speeds are plotted at the same elapsed time after shooting the gear in Figure 13. The mean sinking speeds of the longlines as a function of depth at the anchor positions are plotted in Figure 14(a). The sinking speed of longlines with a 10 kn shooting speed at the first and second anchor locations increases by 3.5% and 20% in comparison with the 6 kn shooting speed simulation. The sinking speed at the middle part of the mainline does not vary greatly for different shooting speeds. However, the mean sinking speed of a longline at the measurement position No. 3 for 10 m from the stern increases with increased shooting speed (Figure 14(b)). At a distance of 10 m from the stern, the sinking speed of longlines with a 10 kn shooting speed was 19.2% higher than the sinking speed of longlines with a shooting speed of 8 kn, and was 57.7% higher than the sinking speed of longlines with a shooting speed of 6 kn. Thus, a fast shooting speed is beneficial because it appears to reduce the bycatch close to the stern, even if this comes at the expense of higher fuel consumption.

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Figure 13. Results of longline simulations for shooting speeds of 6, 8, and 10 kn.

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Figure 14. Mean sinking speed of the longline (a) at the anchor positions, (b) at the measurement position No. 3, for 10, 30, and 50 m from the stern, for different shooting speeds.

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Case 5 (shooting methods)

For the vertical shooting method, the sinking speed close to the last anchor (position No. 6) is 18% faster than the sinking speed obtained with the horizontal shooting method (Figure 15). At position No. 5, the sinking speed obtained with the vertical shooting method is 3% faster than the sinking speed obtained with the horizontal method. The sinking speeds at other locations (i.e. position No. 1, 2, 3, and 4) were not greatly affected by the choice of shooting method. However, the sinking speed was influenced by the length of the longline and by the water depth. If the longline length is shorter than the water depth, the shooting method will influence all parts of the longline, as mentioned above for Case 3 (anchor weight). The simulation was carried out based on the length of longlines and depths that are found in real situations. Based on these results, the sinking speed of the longline close to the second anchor will increase if the vertical shooting method is used without entangling the buoylines. Naturally, the vertical shooting method can be applied to the first anchor if needed. The mean sinking speeds of the longline were not different when different shooting methods were employed.

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Figure 15. Simulations of the longline for horizontal and vertical shooting methods.

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DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGEMENT
  8. REFERENCES

In the present study, the mean sinking speed of a longline from the surface to the sea bottom and at 10, 30, and 50 m from the stern were calculated taking into consideration various factors such as gear construction and operational conditions.

The mean sinking speeds at different positions along the longline were derived, for various parameters, using the simulations. The sinking speeds were derived as a function of depth for different parameters such as bait shape, bait sinking orientation, bait size, thickness of rope, rope material, anchor weights, and shooting speed. When heavy anchors are applied to the longlines, the shooting speed needs to be increased to prevent the anchors from reaching the bottom before the shooting process is completed. Techniques to increase the sinking speed of longlines include the use of high-density materials, using thicker materials and a small projected area of elliptic bait with the ‘C’ sinking orientation. When thicker materials are used for longlines, the convenience of handling should be considered, even though the sinking speed will increase compared with the same longline made of thinner materials. Heavier anchors also help to increase the sinking speed of the anchor part, but do not have much influence on other parts (such as the middle part of the mainline). If the water depth is small compared with the line length, the anchor weight has a smaller influence on the sinking speed in the middle of the line. This implies that, in commercial longlining, heavier anchors do not exert much influence on the sinking speed of longlines. Melvin et al. (2001) proved that more than 98% of all seabird attacks occurred within 10–50 m of the stern. Melvin and Walker (2008) also showed that seabird bycatch mitigation in pelagic longline fisheries may be achieved by eliminating dives within 50 to 80 m of the stern when the gear is within 5 m of the surface. Furthermore, the foraging capabilities of seabird species vary. Some are only capable of surface feeding. Some have poor diving capability (to about 1 m) and others are proficient divers (to 20 m or more but are still largely reliant upon visual detectability from the surface so sink rate of baits remains critical) (Brother et al., 1999). From the results for this study, the sinking speeds of longline at 10, 30, and 50 m behind the stern were also calculated taking account of the various factors such as gear construction and operational conditions. Robertson et al. (2010) also described the size of bait effect on the sinking speed of the longline. Here, the results from this study were in agreement with those of Robertson et al. (2010) regarding the sinking orientation of bait. This phenomenon is dependent on the hydrodynamic characteristics of the bait. The sinking speed at 10 m astern of longline rigged with ‘elliptic large size C orientation’ was 13.4% faster than that of ‘fillet large size A orientation’. However, the sinking speed at 10 m astern of longline with ‘elliptic large size C orientation’ did not differ much from that without bait. Similar to the findings of Robertson et al. (2003), the heavier the longline, the faster the sinking speed. Smith (2001) showed that the anchor weight did not affect the middle part of the mainline, and this study found similar results. The sinking speed of a longline was determined by the shooting speed and shooting method. The shooting method (i.e. lining tube, funnel, etc.) has previously been studied, however, this study dealt with the shooting speeds and shooting methods in an operational aspect, in order to observe the influence on sinking speed. At a distance of 10 m from the stern, the sinking speed of longlines with a 10 kn shooting speed was 19.2% higher than the sinking speed of longlines with a shooting speed of 8 kn, and was 57.7% higher than the sinking speed of longlines with a shooting speed of 6 kn, even though the sinking speed of longline at the 30 m and 50 m distances from the stern did not differ. Higher shooting speeds can be helpful to reduce bycatch at the surface.

Many studies have been carried out with the aim of reducing bycatch, and a number of advances have been made, such as the introduction of seabird scaring lines (i.e. streamer lines), underwater setting devices (i.e. lining tube, funnel), or circle hooks. In this study, various parameters were investigated to determine the sinking speed. The results show various alternatives that would increase the sinking speed and thus reduce the bycatch. A possible way to increase the sinking speed of longlines is to use smaller weights on the mainline instead of heavier anchors. Based on these results, a combination of higher-density materials, small projected areas of elliptic bait to the sinking orientation, and small weights on the mainline appear to be the most effective ways to increase the sinking speed of longlines. Furthermore, a high shooting speed can be expected to reduce the bycatch close to the stern.

ACKNOWLEDGEMENT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGEMENT
  8. REFERENCES

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education Science and Technology (NRF-2012R1A1A1011106).

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGEMENT
  8. REFERENCES
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