Fisheries science JSFS , tập 78, số 5, 2012 9

Fish Sci (2012) 78:965–975 DOI 10.1007/s12562-012-0525-1 ORIGINAL ARTICLE Fisheries Improving the effectiveness of escape windows in directed Norway lobster Nephrops norvegicus trawl fisheries Niels Madsen • Rene´ Holst • Rikke Petri Frandsen Ludvig A Krag • Received: 14 December 2011 / Accepted: 22 May 2012 / Published online: 30 June 2012 Ó The Japanese Society of Fisheries Science 2012 Abstract A substantial improvement in the bycatch selectivity of Norway lobster Nephrops norvegicus trawls is required, particularly with respect to cod Gadus morhua, whose stocks are at low levels in several areas Conventional escape windows are not adequate to properly release cod and other bycatch species caught in the trawls To address this issue, we developed a novel sorting box concept consisting of a four-panel section with a window on the top in order to improve the escape of cod and other bycatch species through an escape window while retaining the target catch of Norway lobster The concept was tested on a commercial trawler in Kattegat and Skagerrak Two different window mesh sizes and two different sorting box heights were tested using a traditional codend cover and a dual codend cover We observed greatly reduced bycatches of both cod and other fish species compared to a standard codend The reduction in bycatch decreased with decreasing mesh size and increasing height of the sorting box Escape of Norway lobster through the escape window was limited A modified version of the sorting box concept was implemented in the Kattegat fishery from 2009 onwards Keywords Cod Á Fishery management Á Norway lobster Á Plaice Á Selectivity Á Trawl Á Bycatch N Madsen (&) Á R P Frandsen Á L A Krag DTU Aqua, National Institute of Aquatic Resources, North Sea Science Park, P.O Box 101, 9850 Hirtshals, Denmark e-mail: nm@aqua.dtu.dk R Holst Institute of Regional Health Service Research, University of Southern Denmark, J.B Winsløwsvej 9b, 5000 Odense C, Denmark Introduction Norway lobster Nephrops norvegicus is a major target species in several areas in the North East Atlantic, and relatively small codend mesh sizes are used compared to the codends used in whitefish fisheries Consequently, Norway lobster fisheries are characterized by a relatively high bycatch of juvenile fish species and high discard rates This problem is rather universal among North East Atlantic Norway lobster fisheries, and trawls with improved selectivity properties have recently been tested in Portuguese waters [1], the Bay of Biscay [2], the North Sea [3–8], the Irish Sea [9], Icelandic waters [10], and the Kattegat and Skagerrak [11–15] Bycatch of Atlantic cod Gadus morhua is a particular problem, since stocks have decreased in several areas [16, 17] In the Kattegat, the cod stock is at a critically low level [14], and measures have been taken to rebuild it, including designating seasonally protected areas where only very selective fishing gear is allowed [14] The use of a sorting grid is an option in the Norway lobster fishery under current legislation in Skagerrak and Kattegat [12–14] and has also been tested recently in other Norway lobster fisheries [2, 3, 5, 8] While sorting grids are very effective at allowing cod to escape [12–14], they are more difficult to handle onboard the small vessels that typically operate in this area, and fish and debris can block the grid Furthermore, losses of Norway lobster, particularly the larger and more valuable individuals, have been observed [13] In general, Danish vessels in Kattegat and Skagerrak have not used the Norway lobster grids that have been permitted by the legislation since 2005, even though the use of these grids allows unlimited days at sea, whereas there have been severe restrictions on using less selective gear The squaremesh escape window (henceforth window) is one of the 123 966 most widely used selective devices in European fisheries A 120 mm window was implemented in the Kattegat and Skagerrak fisheries beginning in 2005 [11], but it did not produce a marked improvement in selectivity for cod [13] A first step in developing a new window to improve the selectivity of gear, and thereby reduce cod bycatches in the Norway lobster fishery without incurring major losses of the target species, was tested by Madsen et al [17] The results demonstrated very high reductions in cod bycatch without any apparent increased losses of Norway lobster compared to values quoted in the literature [17] The possibility that some Norway lobster escaped through the window could not be excluded from these experiments alone [17] A better estimate of possible escape through the window can be achieved by using a window cover which only collects fish that escape from the window [17] This paper presents the second stage in our research: developing and testing a window design that can be used commercially, and is simple to install and control in a variety of trawl designs We developed a flexible solution that allows the selectivity of a window to be adjusted according to a particular need We assessed the effects of different window mesh sizes and heights of the sorting box We compared our results against a standard codend, as a baseline, to assess the full effect of the new design concept Materials and methods Development of the sorting box concept The basic objective of the sorting box concept is to improve and control the gear’s selectivity for cod and other bycatch species without influencing its selectivity and catch efficiency for Norway lobster The initial development of this novel concept is described in more detail in Madsen et al [17] Here we discuss the further development and testing of the concept, focusing in particular on its commercial use The main ideas behind the sorting box concept are (1) to move the window as far back as possible to improve the release of cod but prevent the accumulating Norway lobster catch from coming into contact with the window; (2) to place the window in a relatively narrow four-panel section in order to promote stable performance and control over the up/down orientation; (3) to place the window in a relatively narrow section to reduce the escape route length; (4) to use larger mesh sizes in the window than those currently in use; and (5) to allow very quick adjustment of the selectivity so that changes that are needed to protect cod stocks can be made rapidly The sorting box developed and used in these sea trials is shown in Figs and The mesh size in the window was 123 Fish Sci (2012) 78:965–975 increased from about 290 mm (as used in the design that we tested previously [17]) to a nominal 370 mm because observations showed that the smaller mesh prevented the largest cod escaping This is an important consideration in the Kattegat area, where the specific aim is to protect cod in its spawning grounds An optional smaller nominal 150 mm mesh size was also tested, which would be sufficiently large to release cod below the MLS but to retain more cod and other relevant commercial bycatch species for use during periods when the landing of cod was permitted The net in the window was made of a coated polyamide thread produced by Carlsen Net (http://www carlsen-net.dk) The coating makes the netting stiffer and ensures a more stable opening, and has been very effective in other square mesh window applications The coated twine was white, which should reduce the visual contrast between the meshes in the window and the light from above [18] The window was fastened to the codend on each side with a zipper that was the same length as the box section (about m) (Fig 2) and was joined to the forward and aft ends of the codend with thread This means of attachment allows the net selectivity to be quickly adjusted by replacing the window with one with a different mesh size, and takes only about 10–15 The remainder of the codend was made of nominal 90 mm double-twine (4 mm) PE netting, which was chosen because it is widely used commercially and is stronger than the single twine netting used in our earlier prototypes [17] The sorting box was tested and adjusted in the Hirthals (Denmark) flume tank prior to the sea trials, and inspected and measured after the sea trials at a speed of 1.8 knots The sorting box shape was found to be more unstable (less stretched) when made from stiff double twine, than from the single twine used in the initial experiments [17] This problem was solved by adding leaded rope to each of the lower selvages of the four-panel section Different weights were attached to the two sorting boxes tested to assess the effect of the height of the sorting box The two sorting boxes were termed the ‘‘low’’ and the ‘‘high’’ sorting boxes, and the effect is illustrated in Fig The low and high sorting boxes had 5.8 and 6.8 kg of leaded rope (about kg/m) added, respectively, along the lower two selvages in the box section Test codends Three final versions of the sorting box were tested: (1) a low sorting box with a nominal 370 mm window; (2) a high sorting box with a nominal 370 mm window; and (3) a high sorting box with a nominal 150 mm window A conventional 90 mm two-panel standard diamond mesh codend was used as a comparison to the sorting boxes This codend was on one side of a twin trawl rig in Fish Sci (2012) 78:965–975 967 Fig Illustration of the low sorting box, with the high sorting configuration indicated by the hatched line The high configuration is obtained by increasing the amount of leaded rope Fig The low sorting box without any catch, which was tested at about 1.8 knots in the flume tank some separate experiments conducted prior to this test of the sorting boxes; the same vessel was used in the same areas and the same methodology was employed for the measurements The codend was made from the same type of netting as the sorting box trawls: mm double-PE netting, 92 open meshes in circumference and m long (stretched) It was attached to a m long diamond elongation made of the same netting as the codends that was the same number of meshes in circumference This was done to facilitate the handling of the codend, and furthermore the codend section had about the same length as the sorting box codends The 90 mm mesh is the minimum legal mesh size, and is the preferred mesh size of most of the Kattegat and Skagerrak fleet Codend covers Data on the selectivities of the various codends were obtained using the covered codend methodology The single covered codend methodology [19] was used to test the standard codend and the low sorting box The cover was made of nominal 40 mm (inside mesh opening) netting with a combination of kites, chains, and floats to keep the covers from touching or blocking the meshes of the test codends [19] 123 968 Fish Sci (2012) 78:965–975 Fig Illustration of the dual cover To measure the numbers of Norway lobster and fish escaping through the escape window, we developed a novel dual codend cover (Fig 3) which was used to test both of the high sorting boxes The cover is divided into two collecting bags; the upper one collects escapees through the escape window, while the lower one collects fish escaping from the rest of the sorting box system A 40 mm horizontal separator net panel is attached to the upper selvage of the sorting box (Fig 3) To reduce the risk of influencing the performance of the sorting box, some slack was introduced into the separator net panel to ensure that it did not limit the movement of the sorting box at increased speed Furthermore, it was placed in a position where it could not be detected by fish looking through the window from the inside of the sorting box, and where it was unlikely to influence the water flow inside the sorting box The bottom panel of the sorting box was able to move freely with additional weight attached The top panel in front of the window was blinded to ensure that the only way to reach the upper cover was through the window Floats were attached to the lower collecting bag in the area where most of the escaping fish were expected to make their exit, to ensure that this part was clear of the codend The dual cover was tested and adjusted in a flume tank prior to the sea trials, and inspected after the sea trials Sea trials and data collection Sea trails were carried out in the Kattegat and Skagerrak, and general hauling information is provided in Table A commercial stern trawler (RS30, Mette Amalie, 386 kW, 20 m in length) was used to conduct sea trials in August 2007 It was rigged for twin trawling with two identical, combined fish and Norway lobster trawls that were fishing simultaneously The trawls had a circumference of 460 meshes in the fishing circle and a nominal mesh opening of 100 mm No codend extension was attached to the trawl since this could have closed the sorting box The sorting boxes were attached directly to the tapered end section of the trawl, where it had a circumference of 100 open meshes A three-warp towing system with a 550 kg chain clump and two 194 cm Welle otter boards was used to tow 123 the gear The low and high sorting boxes with the 370 mm mesh size were tested together on each side of the twin trawl rig The 150 mm high sorting box was tested on one side of the twin trawl rig The other side of the twin trawl rig was used for another experiment to be described elsewhere To obtain the weight of the total catch, the entire codend and cover fraction was weighed using a crane scale on deck, and then the weight of the netting was subtracted Overall length measurements of cod, plaice Pleuronectes platessa, and Norway lobster were taken, as these are the most important commercial species in this fishery All cod were measured, although only subsamples of plaice were measured for a few very large catches Norway lobster were caught in higher numbers and often subsampled Other commercial fish species caught by the sorting box were measured because they have some influence on the economics of this fishery For species where there is a stipulated MLS, only individuals above the MLS were measured, but very few individuals smaller than the MLS were observed Only cod, plaice, and Norway lobster were measured in the standard codend Fish were measured to the nearest cm A subsample of the Norway lobster catch was taken when catches were large and measured to the nearest mm with an electronic caliper The midpoints of the length classes of fish and Norway lobster were used in the subsequent analysis Mesh sizes were measured with the OMEGA gauge [20] The meshes of the sorting box codends (N = 200), the standard codend (N = 300), and windows (N = 50) were measured in wet conditions Statistical modeling and analysis Statistical models were constructed for the catch data for cod, plaice, and Norway lobster The analysis followed Frandsen et al [17] and Madsen et al [13], and the approach is briefly recapped here Individuals entering the codend were potentially exposed to two selective [21] mechanisms due to the actions of the sorting box window and the codend The following composite effective selection curve applies: Fish Sci (2012) 78:965–975 Table Operational conditions during the sea trials, as indicated by average values 969 Codend Cover No hauls Haul duration (h) Depth (m) Speed (knt) Codend catch (kg) Single 11 3.27 ± 0.18 64.4 ± 33.8 2.8 ± 0.28 147 ± 219 370 mm, high sorting box September 2007 Dual 11 3.27 ± 0.18 64.4 ± 33.8 2.8 ± 0.28 219 ± 114 150 mm, high sorting box Dual 10 3.35 ± 0.41 72.0 ± 30.5 2.7 ± 0.17 402 ± 199 Single 16 3.58 ± 0.79 52.0 ± 31.9 2.6 ± 0.11 222 ± 129 August 2007 370 mm, low sorting box Average per haul with the standard deviation (SD) SB sorting box August 2007 90 mm, standard codend uð‘Þ ¼ ½1 À c Á ð1 À rwindow ð‘Þފ Á rcodend ð‘Þ; where rwindow ð‘Þ and rcodend ð‘Þ denote the proportion of the cod (of length ‘) retained by the window and codend, respectively, given that they have been in contact with the window/codend, and c denotes the proportion of the fish that enter the codend and make contact with the window We used logistic curves to model rwindow ð‘Þ and rcodend ð‘Þ The proportions of the total catch retained in each compartment when testing the low sorting box with a traditional single cover are described by: ucodend ð‘Þ ¼ ½1 À c Á ð1 À rwindow ð‘Þފ Á rcodend ð‘Þ; ucover ð‘Þ ¼ À ½1 À c Á ð1 À rwindow ð‘Þފ Á rcodend ð‘Þ: For the three compartments when testing the high sorting boxes with a dual cover, these proportions are: ucodend ð‘Þ ¼ ½1 À c Á ð1 À rwindow ð‘Þފ Á rcodend ð‘Þ; uwindow cover ð‘Þ ¼ c Á ð1 À rwindow ð‘ÞÞ; ucodend cover ð‘Þ ¼ ½1 À c Á ð1 À rwindow ð‘Þފ Á ð1 À rcodend ð‘ÞÞ: For cod in all of the sorting boxes and plaice in the 150 mm high sorting box, the composite selection curve provided the best fit because of the considerable numbers that escaped through both the window and the codend In the standard codend, the traditionally used logistic function [22] was used for all species This was also the case for Norway lobster in all of the sorting boxes where escape through the window was limited and the goodness of fit for individual hauls, as assessed by deviance residuals and the deviance statistic [23], suggested that the logistic function generally provided a good fit The catch data for plaice in the two sorting boxes with a 370 mm window suggested a bell-shaped effective retention curve This reflects the product of codend selection and increasing escape through the window with increase in plaice length This window mesh size is too large in relation to the length of the plaice to detect any increase in retention with increasing fish length Comparing the normal, log-normal, and gamma curves [14], the normal curve ! ð‘ À ‘0 Þ2 r ð‘Þ ¼ x Á exp À 2r2 provided the best fit in terms of least deviance, where, ‘0 , r, and x are the modal length, the spread, and the modal value, respectively; see Frandsen et al [14] for further details The selectivity model fitting process failed to converge for several of the hauls for plaice and cod Consequently, the use of Fryer’s model of between-haul variation [24] would have been limited to a subset of the data for cod and plaice, thus leading to a loss of efficiency and potentially biased estimates Therefore, instead, we utilized all data for these two species by fitting the SELECT method to the set of data from all hauls stacked into a single data set [14, 25] Standard errors for the parameter estimates were subsequently adjusted by the REP [23, 25], which allowed the extra variation due to sampling from replicate hauls to be accommodated For Norway lobster, it was possible to fit selection curves for individual hauls, except for one haul of the standard codend where only four individuals were caught Residual maximum likelihood (REML) estimates were obtained using a fixed and random effects model, taking the between-haul variation into account [22, 24] We used Wald tests to analyze differences between parameters of the codends, with significant differences corresponding to nonoverlapping 95 % confidence limits Results Experiments with 370 mm windows The average codend and window mesh sizes (with standard deviations) of the low sorting box were 95.7 mm (2.7) and 367.1 mm (5.7), respectively For the high sorting box, the 123 970 Fish Sci (2012) 78:965–975 codend mesh measured 95.2 mm (2.5) and the window mesh 365.6 mm (6.9) The mesh size of the standard codend was 94.9 mm (3.4) Sorting box heights were measured following the sea trials and were found to be around 55 cm (low sorting box) and 75 cm (high sorting box) at a speed of 1.8 knots, which is the maximum speed for the flume tank The flume tank test showed that the codend and the escape window were totally clear of the dual cover and the separator net panel There was some slack in the separator net panel that made movements of the sorting box possible upon increasing the speed A total of 11 paired hauls were conducted with the two 370 mm sorting boxes The total codend catch of the low sorting box was lower than that for the high sorting box (Table 1) Table provides detailed information on the recorded total catches and retention and the escape of cod, plaice, and Norway lobster above and below the MLS The total retention rate of cod was very low (8.9 %) in the low sorting box and low (23.7 %) in the high sorting box compared to that in the standard codend (58.2 %) The proportion of cod above the MLS retained in the standard codend (84.2 %) was high compared to those in the low (10.4 %) and high (34.1 %) sorting boxes The dual cover showed that very few cod above the MLS escaped through the codend of the high sorting box, and that the main escape route was through the window Table Recorded catches, divided into total numbers or percentages that entered, were retained, and escaped through the codend and window The total retention of plaice above MLS was very low in the low sorting box (8.3 %) compared to that in the standard codend, where almost all of the plaice were retained The retention was higher (29.5 %) in the high sorting box The dual cover of the high sorting box showed that plaice above the MLS only escaped through the window Only 29.5 % of the plaice below the MLS escaped through the window Most Norway lobster above the MLS were retained in the standard codend (82.3 %), and slightly more were retained in the high sorting box (86.4 %), whereas fewer (72.0 %) were retained in the low sorting box The dual cover of the high sorting box showed that 2.0 % of the Norway lobster caught escaped through the window The numbers (or proportions) of escapees for other commercial species above the MLS from the sorting boxes are indicated in Table Saithe Pollachius virens were caught in higher numbers than the other species Very few saithe were retained at either codend, with only a minor difference (2.3 %) observed between the low sorting box and the high sorting box Catches of other species were lower (Table 3) Fewer haddock Melanogrammus aeglefinus were caught in the low sorting box and none were retained, whereas 11 % were retained in the high sorting box and all escapes were through the window Only a few witch flounder Glyptocephalus cynoglossus were retained in the low sorting box, whereas more than half were Cod Plaice Norway lobster UMLS MLS Tot UMLS MLS Tot UMLS MLS Tot Entered (no.) 1156 1942 3098 2323 205 2528 24601 4087 28688 Retained (no.) Retained (%) 75 6.5 202 10.4 276 8.9 121 5.2 17 8.3 139 5.5 9840 40.0 2943 72.0 12795 44.6 Entered (no.) 1743 1880 3623 1622 210 1832 20768 3918 24685 Retained (no.) 216 641 859 144 62 205 12648 3268 15922 Retained (%) 12.4 34.1 23.7 8.9 29.5 11.2 60.9 83.4 64.5 370 mm, low sorting box 370 mm, high sorting box Escaped codend (%) 29.8 2.1 15.4 61.7 0.0 54.6 36.9 15.2 33.5 Escaped window (%) 57.8 63.8 60.9 29.5 70.5 34.2 2.2 1.5 2.0 Entered (no.) 1599 2141 3740 333 213 546 6172 1371 7543 Retained (no.) 168 1355 1522 69 179 248 4438 1185 5627 Retained (%) 10.5 63.3 40.7 20.7 84.0 45.4 71.9 86.4 74.6 Escaped codend (%) 52.2 2.2 23.6 65.2 13.6 45.1 24.8 11.6 22.4 Escaped window (%) 37.3 34.5 35.7 14.1 2.3 9.5 3.3 2.0 3.0 Entered (no.) 444 333 777 2984 263 3247 22711 4626 27337 Retained (no.) Retained (%) 181 40.8 280 84.2 452 58.2 830 27.8 262 99.6 1042 32.1 14830 65.3 3807 82.3 18644 68.2 150 mm, high sorting box 90 mm, standard codend MLS cod = 30 cm, plaice = 27 cm, Norway lobster = 40 mm; UMLS under minimum landing size 123 Fish Sci (2012) 78:965–975 Table Recorded catches of other commercial species at or above the MLS, divided into total numbers or percentages that entered, were retained, and escaped through the codend and window 971 Saithe Haddock Witch flounder Brill Hake Entered (no.) 455 23 49 47 Retained (%) 5.1 0.0 4.1 38.3 NA Entered (no.) 578 35 41 42 Retained (%) 7.4 11.4 53.7 38.1 NA Escaped codend (%) 0.0 0.0 0.0 11.9 NA Escaped window (%) 92.6 88.6 46.3 50.0 NA Entered (no.) 208 35 60 23 14 Retained (%) 63.9 45.7 96.7 100.0 64.3 Escaped codend (%) 0.0 0.0 0.0 0.0 0.0 Escaped window (%) 36.1 54.3 3.3 0.0 35.7 370 mm, low sorting box 370 mm, high sorting box 150 mm, high sorting box MLS saithe = 30 cm, haddock = 27 cm, witch flounder = 28 cm, brill = 30 cm, hake = 30 cm retained in the high sorting box, where all escapes were through the window The total retention of brill Scophthalmus rhombus was the same between the low sorting box and the high sorting box, and most escapes from the high sorting box were through the window Experiments with the 150 mm window The codend mesh size of the sorting box with the 150 mm window was the same as that of the high sorting box with the 370 mm window The window mesh size (with standard deviation) was 148.6 ± 4.8 mm A total of ten hauls were performed (Table 1) Very few cod below the MLS were retained (10.5 %), with most escaping through the codend meshes (Table 2) About 30 % more cod above the MLS were retained than in the large mesh (370 mm window) high sorting box, with most escapes being through the window More than twice as many plaice below the MLS were retained compared to the large mesh high sorting box The retention of plaice above the MLS was high compared to those for the two large mesh sorting boxes, and very few plaice escaped through the window Most of the escaping plaice that were below the MLS left through the codend Compared to the large mesh high sorting box, % more Norway lobster above the MLS and 11 % below the MLS were retained The escape rates through the window were 2.0 and 3.3 % for Norway lobster above and below the MLS, respectively Fewer individuals escaped through the codend compared to the large-mesh high sorting box The numbers (or proportions) of escapees for other commercial species are shown in Table Many more individuals were retained than for the other sorting box codends: almost all witch flounder and brill, almost twothirds of the saithe, and almost half of the haddock Saithe, haddock, witch flounder, and hake escaped only through the window Selectivity estimates The selectivity parameters for cod, plaice, and Norway lobster are shown in Table 4, and the resulting selection curves are depicted in Fig The estimated proportion of cod that came into contact with the window of the sorting box (c) was high (89 %) for the 370 mm low sorting box The probability of window contact was statistical significantly lower (61 %) in the 370 mm high sorting box, and statistical significantly lower still in the 150 mm high sorting box (43 %) The estimated selectivity parameters for cod in all codends (‘50%;codend , SRcodend) did not differ statistically significantly These parameters were similar for both of the high sorting boxes, whereas the high standard errors for the low sorting box indicated a less precise estimate The resulting selection curves are compared in Fig The selection curves for cod in the tested sorting box codends are clearly shifted to the right of that for the standard codend, and there is a marked effect of the 370 mm window size, particularly for the low sorting box, which shows a very low retention, even for larger cod The difference between the selection curves for cod in the two 370 mm sorting boxes increases with fish length up to around 50 cm and then decreases However, there were not many observations of fish above 80 cm The retention of the 370 mm low sorting box rose steeply for fish above 80 cm The modal top of the selection curve for plaice (r) for the 370 mm low sorting box is shifted to the left compared with that for the 370 mm high sorting box, and the peak (x) is about half the height (Table 4; Fig 4) The model estimates that 14 % of the plaice made contact with the 123 972 Table Estimated selectivity parameters, with the standard error given in parentheses Fish Sci (2012) 78:965–975 Cod Parameter Plaice Estimate Parameter Norway lobster Estimate Parameter Estimate 370 mm, low sorting box ‘50%;codend (cm) 18.1 (4.95) ‘0 (cm) 26.6 (0.591) ‘50% (mm) 20.7 (5.2) SRcodend (cm) 20.1 (8.92) r (cm) 3.96 (0.319) SR (cm) 32.5 (8.5) ‘50%;window 101.2 (10.1) x 0.285 (0.047) DF 17 SRwindow (cm) 15.7 (12.1) DF 157 c 0.890 (0.020) DEV 188 DF 414 DEV 417 370 mm, high sorting box ‘50%;codend (cm) 23.3 (0.559) ‘0 (cm) 30.5 (0.842) ‘50% (mm) 26.3 (2.9) SRcodend (cm) 8.23 (0.581) r (cm) 5.62 (0.477) SR (cm) 15.8 (1.2) ‘50%;window 106.1 (21.3) x 0.706 (0.121) DF 17 ‘50% (mm) 26.7 (3.7) SRwindow (cm) 23.4 (11.6) DF 35 c 0.611 (0.012) DEV 143 DF 853 DEV 1301 ‘50%;codend (cm) 24.9 (0.916) 150 mm, high sorting box ‘50%;codend (cm) 24.2 (0.662) SRcodend (cm) 7.79 (0.610) SRcodend (cm) 7.27 (1.27) SR (cm) 15.2 (1.4) ‘50%;window 52.3 (1.26) ‘50%;window 29.6 (2.32) DF 15 SRwindow (cm) 6.95 (1.75) SRwindow (cm) 2.34 (1.94) c DF 0.431 (0.017) 1013 c DF 0.139 (0.039) 259 DEV 1230 DEV 306 90 mm, standard codend DF degrees of freedom, DEV deviance ‘50% (cm) 23.9 (0.63) ‘50% (cm) 21.8 (0.12) ‘50% (mm) 32.7 (2.23) SR (cm) 14.5 (1.4) SR (cm) 3.3 (0.17) SR (mm) 18.0 (1.7) DF 288 DF 611 DF 25 DEV 334 DEV 319 Fig Selectivity curves for the tested codends window (c) in the 150 mm low sorting box Most observations of plaice related to fish 10–40 cm long There were no statistically significant differences in the selectivity parameters of Norway lobster between any of 123 the four codends, and the resulting selection curves are shown in Fig However, the standard errors were relatively large (Table 4) This can largely be explained by the high variation that was observed between hauls Very few Fish Sci (2012) 78:965–975 Norway lobster were observed outside the range 25– 50 mm, and a substantial part of the selection curve was therefore not covered Inspection of the 95 % confidence limits around the selection curves indicated that there was no statistically significant difference between any of the codends within the range of observations Discussion There was no statistically significant difference between the selectivity parameters for Norway lobster between the codends However, these parameters must be regarded with caution since there was a high variation between the hauls, as indicated by relatively large variances In general, considerable variation in the results between experiments for Norway lobster is common [26] The use of a dual cover is important in order to enable the identification of the very small number of escapes through the window, as observed in the present experiment The dual cover indicated a very low escape (2–3 %) of Norway lobster through the window in the high sorting box, but this may also be the case through the top panel of a codend without a window [27] Because variances are high, and escape rates through the window are likely low, it is impossible to detect any significant potential loss of Norway lobster from the low sorting box Genuinely low losses seem possible since fewer individuals above the MLS were retained than for the other codends Further investigation of this issue is important for reducing losses of Norway lobster as much as possible, and thus maximizing the economic viability of the Norway lobster fishery Caution is required when comparing the selectivities of the high and low sorting boxes Although it was possible to measure the difference in height in a flume tank, the situation may be different during sea trials In the flume tank, the height was measured at a water speed (i.e., 1.8 knt) that is lower than is usual during commercial fishing activity (around 2.5 knt), and without a catch in the trawl The considerable differences in catch rates, which were highest in the high sorting boxes, could likely influence the height and the water flow inside the sorting box, causing additional differences The height of the sorting box will also depend on the height of the trawl at the aft end to which the sorting box is attached Our main theory is that the height of the sorting box was actually lower than measured in the flume tank, which particularly influences the selectivity of the window in the low sorting box This is supported by the very high proportion of escaping cod and plaice and a tendency (albeit not a statistically significant one) for lower retention of Norway lobster, which are more likely to make contact with the window A lower height and geometry and a narrower section will likely change the water flow inside 973 the sorting box section, which could be decreased or could increase the flow through the window The dual cover did not influence the performance of the high sorting box in the flume tank test It is not likely that increasing the speed would change the performance of the side panel in a significant way The very high escapes of saithe and haddock (around 90 %) through the window of the large-mesh high sorting box suggest that it window penetration was not a problem Saithe are relatively large, strong swimmers, and show very active net escape behavior, as haddock [13], suggesting that the height (distance) is a driving factor The escape of cod was considerable in both of the high sorting boxes, considering that little or no effect of using an escape window has been detected in the past [11, 13] The contact probability in the low sorting box is very high, with close to 90 % of the cod entering the codend coming into contact with the window These findings suggest that the height is a very important influence on the escape of fish through a window as it affects the escape distance, and a narrow section will increase escape panic However, the height will also influence species other than cod, leading to economic losses if these are landed This experiment, as well as other experiments [13, 27], shows that most commercial species above the MLS will be retained in a standard 90 mm codend, and most that escape through the window will be lost This issue deserves continued investigation on order to optimize the design further In addition, it is important to try to keep the performance of the sorting box stable We foresee several ways in which this could be achieved: (1) the use of four-panel sections throughout; (2) the use of single twine with the same diameter as that used in the first design [17]; (3) the insertion of a frame made of plastic or nylon into each end of the box section to keep the opening fixed; (4) optimizing the opening in the aft part of the trawl to which the sorting box section is attached and avoiding codend extensions; and (5) using square meshes instead of diamond meshes in the box section with a fixed mesh opening, of a size that makes it possible to retain Norway lobster more selectively [27, 28] All of our experiments were conducted during the summer and during the day, but cod may react differently at night [29], or when the water temperature is lower and swimming performance is reduced [30, 31] Furthermore, it is important to assess if escapes occur when the trawl is hauled back, since this may cause additional mortality compared to escapes during towing [32, 33] Underwater observations of fish behavior in relation to the gear would be valuable, but several previous attempts to this have failed because of heavy mud clouds which cause limited visibility in this fishery The escape rate of plaice was very high with the 370 mm windows, similar to those seen in our initial prototype trials [17] The vertical distribution of plaice is 123 1138 learning more about its properties To understand the texture and physical characteristics of fish-meat gel, its rheological properties must be investigated [3] Rheological properties are mainly governed by molecular mass and molecular conformation [4] These factors are influenced by concentration, temperature, pH, and ionic strength, among others With increasing concentration, the hydrodynamic domains of the protein molecules come into contact with each other, and these interactions between the suspended proteins are of paramount importance [4] Heating decreases the viscosity due to an increase in the kinetic energy, and thus thermal denaturation contributes to the rheology of the protein system [4] The pH and ionic strength of macromolecular systems affect its rheological properties by altering the electrostatic charge [5] Differences in the functional properties of different gels may also derive from inherent factors (protein structure, molecular mass, and amino acid composition) [6, 7] These differences can be investigated by measuring the rheological behavior of the gels In fish-meat gel, water accounts for approximately 75 % of the entire material To understand its rheological properties, not only the protein itself, but also its concentration must be known Furthermore, the relationship between the water and protein, and how this relationship affects the gel structure and the state of the network chain must also be elucidated In the present study, to clarify the water retention mechanism at a molecular level in fish-meat protein gels with various salinities, the number of network chains, the molecular weight between the cross-linking points, the specific surface area, and the rheological properties were measured The number of network chains and the molecular weight between the cross-linking points were analyzed on the basis of the rubber elasticity theory; For example, because kamaboko exhibits entropy elasticity, Takagi [8] measured the stress–strain behavior at tension and compression sides of kamaboko (without added starch) and compared the stress– strain curve with that of an ideal rubber, and Niwa [9] reported a similar experiment that considered the decrease in the sectional area with pulling In addition, Hamada [10] examined the factors for temperature changes in kamaboko when it is stretched, and reported that the stress–strain curve of kamaboko was similar to that of an ideal rubber, but only when assuming that the elasticity of kamaboko is not just entropy elasticity but also energy elasticity However, at approximately 25 °C, it has been reported that entropy elasticity contributes more to the elasticity of kamaboko, and that its elasticity is basically entropy elasticity Therefore, in the present study, the viscoelasticity of kamaboko was considered to be rubber elasticity In fact, the myofibrillar proteins actomyosin (AM) and myosin (M), which are the major proteins responsible for gelation, play an important role in determining the texture and 123 Fish Sci (2012) 78:1137–1146 processing characteristics of meat products The myofibrillar proteins are generally extracted in an intermediate- or highionic-strength buffer, and are therefore referred to as saltsoluble proteins They constitute approximately 55–60 % of the total muscle protein or 10 % of the weight of skeletal muscle [7] The heat-induced gelation of M results in the formation of a three-dimensional (3D) network structure that holds water in a less mobile state [11] It has been suggested that the rheological and physical properties of M gels are more dependent upon molecular size and less influenced by the amino acid composition or distribution [12] During network formation, water retention is enhanced, which influences the yield, texture, and cohesion of the final product, and also determines the gelling capacity of the myofibrillar proteins [13] However, the aggregation of the protein can also affect the physical properties of the network, and when heated, it just coheres rather than forming a gel, thus not reflecting the elasticity but only the hardness of the gel It also interacts with M directly or indirectly and thus influences the M gel, so consideration of the characterization of kamaboko as a rubber is not very scientific Therefore, to correctly and clearly understand the gel structure, the salt-soluble protein was extracted and used to prepare a simple network Consequently, in this study, AM and M gels were also prepared with different water contents to determine their structures and compare their characteristics with those of the fish-meat protein gels Materials and methods Fish-meat gel preparation Sample gels were prepared from frozen surimi (SA grade) of Alaska pollack Theragra chalcogramma, which was stored at -20 °C and then partially thawed at °C for one night Approximately 200 g surimi was cut into small pieces by chopping with a cutter for The chopped material was mixed for with enough NaCl and distilled water to create final formulations with 76, 77, 78, 79, 80, and 81 % water content and with salinities of and 10 % These materials defined as sols were maintained at temperature below °C To obtain the cooked gels, the sol was placed into polyvinyl chloride casings (d = 10-2 m) and subjected to heating at 80 °C for 30 in a water bath These gels were cooled in ice water for 10 and then stored at °C until analysis M and AM gel preparation Alaska pollack M was prepared [14] as follows: First, frozen surimi was cut into small pieces, and a fivefold volume of 50 mM phosphate buffer (pH 7.0) was used to Fish Sci (2012) 78:1137–1146 wash the material three times, with the pellets collected each time using a gauze Then, the pellets were mixed with a threefold volume of 0.45 M KCl, 17.6 mM Na2HPO4, 49 mM KH2PO4, 10 mM Na4P2O7 (pH 6.4) for to extract the M Next, the mixture was subjected to centrifugation at 100009g for 15 to remove the precipitates The supernatant was then added to 10.3 times the volume of cold distilled water The precipitate was again collected by centrifugation at 100009g for 10 and then washed with a fivefold volume of 0.03 M KCl and 10 mM Trismaleate (pH 7.5) The precipitate was obtained after centrifugation at 100009g for 10 Then the precipitate was washed again, and AM was obtained after centrifugation at 100009g for 10 AM was then dissociated into actin and M using M KCl, 80 mM Tris-maleate (pH 7.5), 0.6 M KCl, 50 mM Na4P2O7 (pH 7.5), M MgCl2, 0.1 M ethylenediamine tetraacetic acid (EDTA), 50 mM 2-mercaptoethanol, 0.6 M KCl, 10 mM Tris-maleate (pH 7.5) with gentle stirring for one night, followed by addition of mM adenosine triphosphate (ATP) and the removal of the actin by ultracentrifugation at 1000009g for h The supernatant was diluted with a sixfold volume of 0.05 M NaCl, 20 mM phosphate buffer (pH 7.0), and the precipitated M was collected by centrifugation at 100009g for 10 The AM and M were mixed with NaCl and distilled water to prepare solutions with final concentrations of 89, 90, 91, 92, and 93 % moisture and salinities of and 10 % The solutions were then heated at 80 °C for 30 in a water bath to obtain the cooked gels These gels were cooled in ice water for 10 and then stored at °C until analysis SDS-PAGE To confirm that the AM and M were indeed isolated, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was carried out on a slab gel electrophoresis device (KS-8001; Oriental Instruments LTD, Japan) using commercially supplied PhastGel The thickness of the gel was approximately mm The sample buffer used for electrophoresis was composed of 25 % 0.5 M Tris–HCl (pH 6.8), 20 % glycerin, % sodium dodecyl sulfate (SDS), 0.01 % bromophenol blue (BPB), and 10 % 2-mercaptoethanol in 100 ml distilled water Soluble protein were mixed (1:1, v/v) with the sample buffer Immediately following the electrophoresis run at 20–25 mA, the gels were removed from the unit and placed in a development unit for staining The staining solution was composed of 0.25 % CBB R-250 in 50 % methanol and 10 % acetic acid in distilled water After 30–60 min, the gels were then treated with a destaining solution composed of 30 % methanol and 10 % acetic acid in distilled water The destaining solution was changed every 15–20 until 1139 several bands were observed These bands were used to confirm whether AM and M were extracted Salt-soluble proteins Muscle protein can be classified on the basis of its solubility in salt The protein forming the elasticity of kamaboko is the myofibril protein, which is a salt-soluble protein The Lowry method [15] was used to determine the protein concentration Protein solutions of 2.5, 5, and 10 % salinity were prepared, and the concentrations were measured by absorbance (ABS) at 750 nm In this study, a spectrophotometer (U-3300; Hitachi Ltd., Tokyo Japan) was used to confirm the relationship between the salinity and the quantity of salt-soluble protein Water was used as the reference Stress-relaxation measurements Kamaboko gels were heated at each temperature (15, 25, 35, and 45 °C), and the temperature was maintained during the texture measurement using a rheometer (Tensipressor, TTP-50BXII; Taketomo Elect.) The gels were cut into pieces (length 1.0 10-2 m), and 7–10 pieces were placed into a tray for each measurement A cylindrical plunger with diameter of 1.2 10-2 mm was used, and the plunge rate was 10-3 m/s The compression time for the measurement was 60 s, and the strain was 0.1 Dynamic viscoelasticity measurements An RS 50 Haake Rheostress instrument was used to characterize the rheological behavior of kamaboko, AM, and M gels The two main parameters determined in a dynamic rheological test are the storage modulus, G0 , and the loss modulus, G’’ All the dynamic rheological measurements were obtained at frequency of Hz, the temperature was increased at a rate of °C/min, and the thickness of the samples was 10-3 m A plate and plate sensor system (PP35) was used for the measurement compartment, and it was heated to 10–80 °C to measure the storage modulus, G0 To prevent evaporation, deionized water was spread over the outer edges of the sample Specific surface area It is thought that the molecule-specific surface area of the fish-meat protein gel reflects the area that exists between the cross-linking points of the gel Therefore, the specific surface area as determined by the Brunauer–Emmett– Teller (BET) method [16] can be used to evaluate the structure of protein gel Consequently, by measuring the specific surface area of the gel where water molecules are 123 1140 adsorbed, the relationships between the moisture or salt content and the molecule-specific surface area can be examined at a molecular level The sample gels were prepared as described above Gels with different moisture contents were dried for weeks below 30 °C and then powdered The powdered samples were passed through a stainless-steel sieve (JIS Z 8801) to select particles with diameter of 100–106 lm The specific surface area was measured by the BET method with a Kinoshita-type surface area apparatus (KR-300; Kinoshita RKC Instrument Co., Ltd.) Approximately 0.1–0.2 g dried and graded powder was placed in a flask, and the flask was then connected to the device An oil diffusion pump was used to degas the system until a vacuum of 1.33 10-2 to 1.33 10-3 Pa was reached In addition, the sample was heated and vacuated to remove the water as vapor Next, only the flask with samples was soaked in a Dewar bottle filled with liquid nitrogen Then, first, only helium gas was led into the flask to evaluate the dead surface area of the system, because helium with boiling point of ca K, lower than that (ca 77 K) of nitrogen, shows no adsorption on the sample surface, and the resulting change in pressure could be read on the burette Second, only argon gas, with boiling point ca 87 K higher than that of nitrogen, was substituted for the helium to adsorb on the sample surface, and the pressure was again read on the burette The quantity of adsorbed gas was calculated by subtracting the amount of helium adsorbed from that of argon adsorbed In addition, because the measurement was carried out at 20 °C, the time for the gas to be adsorbed by the sample was considered to be the adsorption equilibrium time, and was within Statistical analysis Statistical analysis was performed using Excel and Statistical Analysis System software (SAS Institute Inc., Cary, NC) Significant difference were defined at p \ 0.05 Comparison of means was carried out by Duncan’s multiple-range test Results Salt-soluble proteins Based on a standard curve made using bovine serum albumin, the quantity of salt-soluble protein per 10 g sol was calculated from the ABS of the sample The quantity of salt-soluble protein in the fish gel with salt content of % was 102 mg, and in the gel with salt content of 10 % was 109 mg (p \ 0.05) The gel with salt content of 2.5 % contained 76 mg salt-soluble protein per 10 g sol (Table 1) 123 Fish Sci (2012) 78:1137–1146 Table Quantity of salt-soluble protein in the fish at different salt concentrations Salt-soluble protein (mg/sol 10 g) 76 ± 1.37z Salt concentration (%) 2.5 y 102 ± 1.02 5.0 109 ± 1.21x 10.0 Different letters in the same column indicate significant differences (p \ 0.05) Fig SDS-PAGE of myofibrillar protein (a myosin, b actomyosin, m standard proteins, Lane loaded with 10 lg protein on 12 % SDSPAGE, MHC myosin heavy chain, AC actin) As can be seen from these results, the amount of saltsoluble protein increased remarkably when the salt content increased to %, but did not increase noticeably when the salt content exceeded % Muscle M, the main component of the myofibrillar proteins, is composed of two heavy chains of approximately 220 kDa each, and four light chains of approximately 20 kDa each [17] M’s heavy chain is composed of a long a-helix tail and a globular head The two heavy chains are woven together to form a tail and two pearshaped heads [18] Actin is about 42 kDa From the results of the SDS-PAGE analysis, it was confirmed that actin and M were isolated from the surimi (Fig 1) As with the fishmeat gel, the quantity of M and AM in the corresponding gels with different salt contents did not change significantly At salt contents of and 10 %, the M and AM concentrations were 0.12 and 0.15, and 0.26 and 0.33 mg/ml, respectively (Fig 2) However, the results did indicate that, at the same salt concentrations, the amount of dissolved M was less than that of dissolved AM Number of network chains The fish paste was affected by changes in temperature, with the result that the elastic modulus changed, and thus it was Fish Sci (2012) 78:1137–1146 1141 20 1.0 y = 1.35x + 0.26 R = 0.99 15 a a b E0 (×10 N/m ) 0.8 ABS 0.6 0.4 b 10 c a c d a a b c c c b b 0.2 0 0.1 0.2 0.3 0.4 10 0.5 20 c (mg/ml) where R is the gas constant, m is the number of chains per unit volume (mol/l), T is the absolute temperature, Lu is the length of the sample before the transformation (m), L is the length of the sample after transformation (m), and P = E0 is the elastic modulus E0 was obtained as E0 = p0/e0, where p0 is the initial stress and e0 is the strain The values for the elastic modulus of the gels as determined from static viscoelasticity measurement using gels with and 10 % salt contents are shown in Figs and 4, respectively From Figs and 4, it can be seen that, when the moisture content and central temperature of the gel increased, the elastic modulus decreased (p \ 0.05) The relationship between the number of network chains and the moisture content for the gels with and 10 % salt content at different temperatures is shown in Figs and 6, respectively As the moisture in the gel increased, the number of network chains decreased (p \ 0.05) Therefore, because the concentration of the protein decreased with an increase in the moisture content, the network density that supports the gel structure decreased At water content of approximately 79 %, the number of network chains in the fish-meat gel with 10 % salt content decreased to a noticeably small value (p \ 0.05) In other words, moisture of approximately 79 % serves as a boundary that prevents sufficient formation of a network structure In addition, the 50 25 a 20 a a 15 10 a a b assumed to exhibit entropy elasticity Therefore, based on the temperature dependence of the elasticity E0, the number of chains per unit volume of the sample was determined By the rubber elastic theory [19], it was found that at each temperature, n o2 P ¼ vRT ðL=Lu Þ2 ÀðLu =LÞ ð1Þ 40 Fig Elastic modulus at different temperatures of % salt fish gel Filled diamonds 75 %, open squares 77 %, filled triangles 79 %, open circles 80 % Different letters indicate significant differences (p \ 0.05) E0(×10 N/m2) Fig Standard curve of bovini albumin Filled circles standard, filled square % M salted, filled diamond 10 % M salted, open square % AM salted, open diamond 10 % AM salted 30 Temperature (ºC) b c c b b b b c c d d c d d 10 20 30 40 50 Temperature ( ºC) Fig Elastic modulus at different temperatures of 10 % salt fish gel Filled diamonds 75 %, open squares 77 %, filled triangles 78 %, open circles 79 %, filled squares 80 % Different letters indicate significant differences (p \ 0.05) temperature influence was important as well: as the temperature increased, the change in the number of network chains decreased Molecular weight between the cross-linking points The average molecular weight between the cross-linking points can be determined from the storage modulus when the network size of the gel has been estimated [20] The storage modulus results from the vibration of the polymer chains and structures forming aggregation Therefore, in this study, it was assumed that the entangled points in the 123 1142 Fish Sci (2012) 78:1137–1146 Table Minimum storage elastic modulus and temperature for each water content in % salt gel a b Water content (%) Storage elastic modulus (9103 N/m2) Temperature (°C) b 76 5.05 ± 0.03a 58.8 ± 1.8 c 77 4.19 ± 0.07d 50.6 ± 2.3 c 78 79 4.75 ± 0.11b 4.53 ± 0.07c 56.6 ± 2.1 61.2 ± 1.2 80 3.11 ± 0.09e 64.1 ± 1.5 81 2.40 ± 0.04f 55.8 ± 1.0 a a ν (× 10 mol/l) a a b d c c c b b Different letters in the same column indicate significant differences (p \ 0.05) 74 76 78 80 Moisture (%) Table Minimum storage elastic modulus and temperature for each water content in 10 % salt gel Fig Relationship between the moisture and the number of network chains at different temperatures in % salt fish gel Filled diamonds 15 °C, open squares 25 °C, filled triangles 35 °C, open circles 45 °C Different letters indicate significant differences (p \ 0.05) Water content (%) Storage elastic modulus (9103 N/m2) Temperature (°C) 76 5.73 ± 0.10c 60.6 ± 1.1 77 7.25 ± 0.08a 57.7 ± 1.1 78 79 6.82 ± 0.09b 4.75 ± 0.15d 67.2 ± 1.5 55.2 ± 1.7 80 3.87 ± 0.16e 63.5 ± 1.2 a Different letters in the same column indicate significant differences (p \ 0.05) a a b ν (×10 mol/l) a a b b b c b b c d d d d 74 76 78 c c c 80 Moisture (%) Fig Relationship between the moisture and the number of network chains at different temperatures in 10 % salt fish gel Filled diamonds 15 °C, open squares 25 °C, filled triangles 35 °C, open circles 45 °C Different letters indicate significant differences (p \ 0.05) fish-meat protein gel behave like cross-linking points, and that the elasticity is due to the storage modulus, which can be determined from dynamic viscoelasticity measurements Thus, the molecular weight between the cross-linking points could be estimated from the theoretical formula for the rubber-like elasticity in amorphous polymers, as follows: G0 ¼ qu1=3 RT Me ð2Þ 123 where q is the density (kg/m3), u is the volume fraction of the polymer, R is the gas constant, T is the absolute temperature, and Me is the molecular weight The results of the dynamic viscoelasticity measurements are presented in Tables and (p \ 0.05) From the data in the tables, it can be seen that the storage modulus of the protein gel decreased when the temperature reached 50–60 °C It is thought that, as the surimi was heated, the AM in the fishmeat underwent enzyme digestion, resulting in the deterioration of the gel, because at this temperature, the activity of the enzyme protease is at its highest level Based on the values in these tables, the average molecular weights between the cross-linking points for the gels with different moisture contents were calculated and are shown in Fig As the moisture increased, the average molecular weight between the cross-linking points of the gel also increased When the moisture content increased above 78–79 %, the average molecular weight between the cross-linking points increased remarkably (p \ 0.05) In the same manner, the average molecular weight between the cross-linking points of the M and AM gels with various moisture contents were also calculated (Figs 8, 9) It can be seen from the figures that, as the moisture increased, the average molecular weight between Fish Sci (2012) 78:1137–1146 1143 1E11 a Me (×10 ) 1E10 a c e c c b a b a 76 77 78 c c 79 80 81 89 90 91 92 93 Moisture (%) Moisture (%) Fig Relationship between the moisture and the molecular weight between the cross-linking points of fish gels Filled diamonds % salted, open squares 10 % salted Different letters indicate significant differences (p \ 0.05) c c 1E8 75 b 1E9 b d e Me b Fig Relationship between the moisture and the molecular weight between the cross-linking points of actomyosin gels Filled diamonds % salted, open squares 10 % salted Different letters indicate significant differences (p \ 0.05) water retention ability, which indicates that the other proteins not contribute to the network 1E10 Specific surface area a 1E9 Me a 1E8 b bc c b b 1E7 P=V ðP0 À PÞ ¼ 1=ðVm  CÞ þ ðC À 1Þ=ðVm  C Þ Â P=P0 ð3Þ c 89 90 91 The BET theory explains the physical adsorption of gas molecules onto a solid surface and serves as the basis for an important analysis technique for the measurement of the specific surface area of a material The quantity of gas adsorbed as a monolayer on the sample surface can be calculated on the basis of the equation 92 93 Moisture (%) Fig Relationship between the moisture and the molecular weight between the cross-linking points of myosin gels Filled triangles % salted, filled circles 10 % salted Different letters indicate significant differences (p \ 0.05) the cross-linking points of the M gel also increased (p \ 0.05) Specifically, at water content exceeding 91 % and salinity of %, the molecular weight between the cross-linking points increased remarkably This result also indicates that the region within the gel structure increased in size A similar phenomenon was also observed for the AM gel, but when the moisture content neared 92 %, the molecular weight between the cross-linking points decreased, which may be due to the lack of gel formation resulting from low-density cross-linking of AM In addition, compared with the fish-meat gel, the M gel had higher where P0 is the saturation pressure of the adsorbate, Vm is the gas quantity adsorbed by the monolayer, and C is the BET constant Then, the specific surface area S can be calculated from the results of the amount of gas adsorbed by g of a sample as S ¼ Vm  NA  A=V0 ð4Þ where S is the specific surface area, NA is Avogadro’s number, and V0 is the molar volume of the adsorbent gas The results of the calculation of the specific surface area in the and 10 % salt content gels are shown in Fig 10 For the gel with salt content of 10 %, the specific surface area did increase when the moisture content increased (p \ 0.05) In particular, when the moisture content increased from 77 to 78 %, the specific surface area increased by a factor of approximately 1.5 However, the specific surface area of fish-meat gel with 80 % water content tended to decrease with increasing water content It 123 1144 Fish Sci (2012) 78:1137–1146 10 Table Relationship between the salt concentration and specific surface area of the fish-meat gel b a a a -1 S (m g ) b a a c a c Change of specific surface 2.5 About 77 % times 10 About 78 % 1.5 times b c 75 80 85 90 95 Moisture (%) Fig 10 Relationship between the water-holding ability and the specific surface area of the gel Filled circles 10 %, filled triangles % salted fish gels, open circles 10 % salted myosin gels, open triangles % salted myosin gels Different letters indicate significant differences (p \ 0.05) is possible that, as the moisture content increases, the formation of the gel does not sufficiently take place, so that the amount of aggregation is high, and the surface available for the adsorption of water molecules does not grow It was also observed that the specific surface areas of the M increased as the moisture content increased (Fig 10) (p \ 0.05) The change in the specific surface area corresponds to the change in the molecular weight between the cross-linking points of the gels, indicating that, when the moisture content increases, the structure of gels changes Discussion The results show that the quantity of salt-soluble protein did not increase noticeably when the salt content exceeded % According to the literature [21], when the salt content rises, the elasticity of kamaboko begins to deteriorate, and thus kamaboko cannot be formed The water molecules are attracted to the salt when the salt content is high, and thus the protein cannot be dissolved because there is no longer enough water This phenomenon in which the solubility of the protein deteriorates because of a high salt concentration is called salting-out The fact that the quantity of salt-soluble protein did not increase when the salt content was 10 % is thought to be an indication that salting-out occurred According to the elastic modulus results, in the lowmoisture gel, since small amounts of water settled into the protein network, it can be expected that the structure of the network chain was strong In addition, because the 123 Water content at which the specific surface increased sharply b bc 70 Salt concentration (%) elasticity of kamaboko is basically due to entropy elasticity [8, 9], the volume increases with temperature, and the gel expands and becomes flexible, and thus the temperature influence on the elastic modulus will be greater On the other hand, for a gel having high moisture content, the distance between the protein molecules is large, and a network structure cannot be sufficiently formed In other words, because the gel becomes flexible, it is thought that the overall temperature influence on the elastic modulus is small, even at higher temperatures From the results of the number of network chains, as the water content of the fishmeat gel increased, the number of network chains decreased These results show a tendency similar to that observed for the experiment on a gel with salt content of 2.5 % [22] However, a change was recognized when the moisture content was 76–77 %, and at high salt concentration, a change was also seen when the moisture content was high When the salt content increases, the quantity of dissolved protein increases, which then leads to an increase in the degree of entanglement As the water content increased, the average molecular weight between the cross-linking points increased Kamaboko is a strong gel, because it is supported by a network structure that is formed by a strong combination of S–S and hydrophobic bonds as a result of heating [21], and even if the moisture increases, these covalent bonds cannot be broken easily by heating energy [23–25]; therefore, the network structure also cannot be readily broken When gelation occurs, the water is maintained in the gaps created by the 3D network structure It is estimated that the average molecular weight between the cross-linking points increased because the concentration of the protein decreased, then the network grew in size to retain all the water As a result, we considered that the distance between the cross-linking points increased, and then a relative new network structure formed When the moisture increased to 78 %, the specific surface area increased by approximately a factor of 1.5 It is interesting to note that, in the former study [22] on fishmeat protein gel with salt content of 2.5 %, as the moisture content of the gel increased from 77 to 79 %, the specific surface area increased about times (Table 4) In addition, when the salt content was increased, a sudden increase of the specific surface area also appeared at high moisture Fish Sci (2012) 78:1137–1146 From these results, it was concluded that, when the water content exceeds a certain level, the structure of the network chain of the fish-meat protein gel changes Kamaboko is regarded as an aggregation of network chains and an aggregate of the protein molecules If the number of network chains increases and water enters the network, then as a result, it is thought the water hydrates the protein and therefore the hydrophobic bonds disappear [26], and the distance between the cross-linking points increases or the molecular chain is divided When the salt content is high, the amount of dissolved myofibril protein increases [21], and the cross-linking becomes dense, making it difficult for water to enter the network, and thus the increase in the specific surface area may be limited to a factor of approximately 1.5 In addition, beyond a certain moisture content, the specific surface area suddenly increased, because the number of network chains decreased, and the average molecular weight between the cross-linking points increased in the fish-meat gel; this also suggests the occurrence of cleavage of the network chains which are formed through molecular aggregation It is also possible that hydrophobic interactions [27] between macromolecules that are nonpolar or have weak polarity such as proteins enter into the water solution, contributing to the water structuring In the gel, it is estimated that there is considerably more of this structured water than can be accounted for by a monolayer of adsorbed water molecules Furthermore, the characteristics of the fish-meat gel were compared with gels of AM and M, which make up the network structure of the fish-meat gel, to more scientifically elucidate the changes in the gel constitution with changes in moisture and salt concentration Independent of whether fish-meat, AM, or M gel was used, the same trends in the molecular weight between the cross-linking points and the specific surface area with increasing moisture content were shown The specific surface area is thought to reflect the area that exists between the cross-linking points of the gel; therefore, based on the changes in the specific surface area and the molecular weight between the crosslinking points of the gels, it is concluded that the size of the network enlarges, and the network chains are cleaved as the moisture content increases To predict the texture of food at any condition of salt concentration and water content, it is important to understand more about the relationship between the function of the gel structure and the rheological parameters, because the rheological properties of food considerably influence its taste Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited 1145 References Noguchi S (1992) Science of foods and water (Shyokuhin to mizu no kagaku) (in Japanese) Saiwai Shobo, Tokyo, p 180, 226 Goto S, Isemura T (1964) Studies of the hydration and the structure of water and their roles in protein structure Bull Chem Soc Jpn 37:1693–1697 Benjakul S, Visessanguan W, Ishizaki S, Tanaka M (2001) Differences in gelation characteristics of natural actomyosin from two species of bigeye snapper, Priacanthus tayenus and Priacanthus macracanthus J Food Sci 66:1311–1318 Mitchell JR, Ledward D (1985) Functional properties of food macromolecules Elsevier Applied Science 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Introduction to physical polymer science (koubunshi no buturigaku) (in Japanese) Shokabo, Tokyo, pp 91–114 20 Onogi S (1982) Rheology for chemists (kagakusya no tameno reoroji) (in Japanese) Kagakudojin, Kyoto, pp 119–157 21 Okada M (2000) Science of Kamaboko (Kamaboko no kagaku) (in Japanese) Seizando-Shoten, Tokyo, pp 52, 55–56 22 Ogawa H (2005) New information for the gel structure from a dynamic viscoelastic measurement In: Itoh Y et al (eds) Ashi formation of Kamaboko II (Kamaboko no ashi keisei II) (in Japanese) Kouseisha, Tokyo, pp 64–65 123 1146 23 Hamada M, Inamasu Y (1983) Influences of temperature and water content on the viscoelasticity of Kamaboko Nippon Suisan Gakkaishi 49:1897–1902 24 Hamada M, Inamasu Y (1984) Influences of temperature and starch on the viscoelasticity of Kamaboko Nippon Suisan Gakkaishi 50:537–540 25 Niwa E, Wang TT, Kanoh S, Nakayama T (1987) Temperature dependence of elasticity of Kamaboko Nippon Suisan Gakkaishi 53:2255–2257 123 Fish Sci (2012) 78:1137–1146 26 Niwa E (1975) Role of hydrophobic bonding in gelation of fish flesh paste Nippon Suisan Gakkaishi 41:907–910 27 Niwa E, Matsubara Y, Hamada I (1982) Hydrogen and other polar bonding in fish flesh gel and setting gel Nippon Suisan Gakkaishi 48:667–670 Fish Sci (2012) 78:1147–1152 DOI 10.1007/s12562-012-0533-1 ORIGINAL ARTICLE Social Science Estimating the economic damage caused by jellyfish to fisheries in Korea Do-Hoon Kim • Ju-Nam Seo • Won-Duk Yoon Young-Sang Suh • Received: 26 March 2012 / Accepted: 21 June 2012 / Published online: 31 July 2012 Ó The Japanese Society of Fisheries Science 2012 Abstract Jellyfish cause a range of problems: they sting beach vacationers in the summer, block nuclear power plant intakes (thus disrupting the supply of electricity), decrease fishery catches, cause fishing activities to be delayed, decrease product value, and damage fishing gear This study gauged the types and amount of damage directly caused by jellyfish Using jellyfish monitoring data, the decreases in catch and product value for each fishery type as well as the estimated damage to each fishery type caused by jellyfish were evaluated The results showed that the decrease in catch ranged between 6.5 and 33.7 %, and the decrease in product value ranged between 6.8 and 25.3 %, depending on fishery type The annual direct damage caused by jellyfish was estimated to be between US$ 68.2 million and US$ 204.6 million This corresponds to a minimum of 2.1 % and a maximum of 25 % of the annual production value, demonstrating that jellyfish cause considerable damage to the fishing industry Keywords Economic damage Á Fisheries Á Fisheries policy Á Fishery resources Á Gill-net fishery Á Jellyfish D.-H Kim Á J.-N Seo (&) Technology Management Center, National Fisheries Research and Development Institute, Busan 619-902, Republic of Korea e-mail: bada79@nfrdi.go.kr W.-D Yoon Á Y.-S Suh Fishery and Ocean Information Division, National Fisheries Research and Development Institute, Busan 619-902, Republic of Korea Introduction Due to their biological characteristics, the number of jellyfish increases exponentially in a favorable environment In particular, increases in species diversity and individual size can be expected, as well as continual mass outbreaks [1–4] A mass outbreak of jellyfish with a high adaptability and reproductive rate can devastate fishery resources and marine ecosystems [5–8] Mass outbreaks of jellyfish since the 1960s, caused by global warming, environmental pollution, the construction of marine structures, and deteriorating marine resources, have had a huge influence worldwide, triggering significant social and economic problems [9–11] There was a case in which a fisherman in China died due to skin contact [12], and Aurelia aurita appeared on a large scale in the late 1990s in Korea In the 2000s, oversized Nemopilema nomurai appeared in large quantities, causing economic damage and stress to fishermen and beach vacationers alike Among the various forms of damage caused by jellyfish, it has been reported that damage to the fishing industry has been relatively high, with estimated economic losses in the millions of dollars worldwide [13, 14] Outbreaks of large numbers of jellyfish in Korea have caused substantial damage to the fishing industry, including trawl net, stow net, long bag set net, drift gill net, and set net fisheries Types of damage include damage to fishing gear caused by trapping so many jellyfish in the net that the net breaks, as well as the loss of value that results when fish are stung by jellyfish Catching a large quantity of jellyfish by fishing gear causes tidal resistance and delays fishing operations As each incident caused by jellyfish is generally recorded individually, it is difficult to estimate the overall economic damage to the fishing industry caused by jellyfish However, it is critically important to analyze the damage 123 1148 Table Regions in which jellyfish were monitored, fishery types, and parameters monitored Fish Sci (2012) 78:1147–1152 Region Fishery type Contents Busan Trap fishery, gill net fishery, set net fishery, lift net fishery, jigging fishery, aquaculture, etc Incheon, Gyeonggi Trap fishery, long bag set net fishery, stow net fishery, jigging fishery, aquaculture, etc Gangwon Jigging fishery, gill net fishery, set net fishery, etc Appearance of jellyfish, area and numbers, appearance time and date, water depth and temperature, rate of fishing gear destruction (%), rate of catch reduction (%), rate of product value reduction (%), time delay caused to fishing activities, area of skin contact, etc Jeju Jigging fishery, gill net fishery, etc Chungnam Stow net fishery, gill net fishery, etc Jeonbuk Gill net fishery, long bag set net fishery, stow net fishery, set net fishery, jigging fishery, aquaculture, etc Jeonnam Gill net fishery, long bag set net fishery, stow net fishery, set net fishery, jigging fishery, aquaculture, etc Gyeongbuk Ulsan Set net fishery, gill net fishery, jigging fishery, trap fishery, etc Gyeongnam Gill net fishery, trap fishery, set net fishery, aquaculture, etc by fishery type and to estimate the economic effects more specifically so that effective countermeasures and a fit-forpurpose fisheries management policy can be established With this in mind, in the study described in this paper, we attempted to analyze the types and amount of damage caused by jellyfish to fisheries using the data collected from the Jellyfish Monitoring Information Center in Korea Materials and methods Analytical data To determine the damage caused to the fishing industry by jellyfish, it is necessary to monitor the appearance of jellyfish periodically and to investigate the types of damage caused by jellyfish by fishery type Since 2006, Korea has operated a Jellyfish Monitoring Information Center, which operates a periodic monitoring system that supports the analysis of outbreaks of large numbers of jellyfish, and well as the damage they cause Two hundred sixty-six fishermen from various fisheries and 87 public officers from 11 local governments and 12 local ocean and fisheries agencies were selected to perform periodic monitoring and to send information to the Jellyfish Monitoring Information Center at the National Fisheries Research and Development Institute for collection and analysis Specifically, sampling was performed at related fisheries in every region for monitoring For example, the coastal regions of Busan, Incheon, Gyeonggi, Gangwon, Jeju, Chungnam, Jeonbuk, Ulsan, and Gyengnam were selected, with the various fishery types represented as much as 123 possible The location, date, and time of jellyfish occurrence as well as the depth and temperature of the water at that location were recorded The rate of damage to fishing gear, the rate of loss of fish, the loss of freshness, the delay caused by jellyfish to fishing activities, and areas of skin contact with jellyfish were examined, as shown in Table On July 1, 2006, the collection and entry of data supplied by nationwide monitoring staff into the database was initiated The area in which jellyfish appeared was analyzed on a weekly basis to determine their appearance characteristics This study analyzed the types and amount of damage caused by jellyfish using the five-year period from 2006 to 2010, in order to obtain well-examined and organized data on the damage caused by jellyfish to the fishing industry Analytical method To analyze the damage, damage rates per fishery type were determined using regional jellyfish monitoring data Damage to the fishing industry can be divided into direct damage (such as reduced catches and fish value depreciation) and indirect damage (such as fishing gear damage, delays to activities, and skin trouble) However, it is not possible to accurately estimate the economic loss resulting from fishing gear damage or delays to activities, so there are limitations on the extent to which additional information can be used Therefore, we only considered direct damage in our estimation of the economic loss caused by jellyfish With regard to direct damage by fishery type, it was found that most fishery types experienced damage from jellyfish, including stow net, gill net, trap, jigging, set net, Fish Sci (2012) 78:1147–1152 1149 Table Rates of decrease in average annual catch and product value (as caused by jellyfish) for the stow net fishery (for the period 2006–2010) Average Catch (tons) Production value (million US$) Rate of decrease in catch (%) Rate of decrease in product value (%) 74,456 184.8 26.8 19.6 lift net, long bag set net, and round haul net fisheries, as well as aquaculture However, only seven fishery types were considered when estimating economic loss due to jellyfish: stow net, gill net, trap, jigging, lift net, set net, and long bag set net fisheries, as statistical data on resources were available for these fishery types In most coastal fisheries, various species of the same type are caught Individuals of the target species were counted to generate data on fishing gear and activities The stow net fishery mainly caught anchovy and yellow croaker, while the gill net fishery targeted anchovy, flat fish, croaker, and blue crab The trap fishery targeted common conger, octopus, common octopus, and crab, while the jigging fishery focused on squid, hair tail, anchovy, and blue crab The set net fishery caught mackerel, jack mackerel, anchovy, herring, and squid, while the lift net fishery caught anchovy Meanwhile, the long bag set net fishery mainly caught anchovy and shrimp To estimate the economic damage caused to the seven fishery types considered here, the rate of decrease in the average annual catch, the rate of decrease in product value based on the amount of product and its value were checked by applying the weighted average method to the five-year sampling data from the Jellyfish Monitoring Information Center (2006–2010) For example, for the stow net fishery, the average annual rate of decrease in catch was 26.8 %, while the rate of decrease in product value was estimated to be 19.6 %, as shown in Table The damage caused by the decrease in catch and the damage due to the decrease in value were then calculated using annual catch data for each fishery type from the fisheries production statistics Specifically, the damage due to catch decrease (CD) was calculated using Eq [i.e., by multiplying the average catch (AC) by the catch reduction (CR) and then by the average market price (AP)]: CD ¼ AC  CR  AP: FD ¼ CD þ PD: ð2Þ The final direct damage (FD) per fishery type was estimated using Eq [i.e., by adding the catch damage ð3Þ The final damage per fishery type was estimated by considering two scenarios: jellyfish occurrence scenario (jellyfish appear for a period of two months) and jellyfish occurrence scenario (for six months) The final estimate for the direct damage caused by jellyfish was derived by adding together the damage to all seven fishery types Results Results for rates of damage caused by jellyfish to each fishery type According to the results of analyzing the rates of damage caused by jellyfish to each fishery type, as shown in Table 3, the damage caused by jellyfish depended on the fishing method or the characteristics of the fishing gear In terms of the catch decrease rate, the long bag set net fishery presented the largest at 34 %, followed by the stow net fishery at 27 %, the set net fishery at 22 %, the trap fishery at 21.5 %, the gill net fishery at 20 %, the lift net fishery at 19 %, and the jigging fishery at 6.5 % In terms of the decrease in product value, the long bag set net fishery showed the highest value, followed by the stow net fishery, the set net fishery, the gill net fishery, and the trap fishery However, for fishing gear damage, the long Table Average annual damage rate by jellyfish for each fishery type (for the period 2006–2010) Fishery type Rate of catch decrease (%) Rate of product value decrease (%) Rate of fishing gear destruction (%) Stow net fishery 26.8 19.6 13.8 42 Gill net fishery 20.3 16.1 19.2 66 Trap fishery 21.5 14.3 5.0 50 6.5 6.8 0.3 16 Set net fishery 21.6 17.3 9.1 45 Lift net fishery 19.2 8.0 0.8 80 Long bag set net fishery 33.7 25.3 22.8 240 ð1Þ The damage caused by value decrease (PD) was calculated using Eq [i.e., by multiplying the average market price (AP) by the rate of reduction in product value (PR), and multiplying this by the value calculated by subtracting the catch reduction from the annual average catch (AC)]: PD ¼ AC  ð1 À CRÞ Â ðAP  PRÞ: (CD) to the product damage caused by the decrease in product value (PD)]: Jigging fishery Delay caused to fishing activities (min) 123 1150 Fish Sci (2012) 78:1147–1152 bag set net fishery suffered the most damage, followed by the gill net fishery There were also differences in the delay caused by jellyfish to fishing activities The long bag set net fishery incurred the longest delays, and the average delay ranged between 16 and 80 As such, the damage caused by jellyfish was substantial and diverse, and included decreases in catch and product value, fishing gear damage, and delays to fishing activities Results for the amounts of damage caused by jellyfish to each fishery type Table shows the amounts of damage caused by jellyfish to each fishery type, which were calculated using the catch decreases and product value decreases estimated above for each fishery type The damage level varied according to the declines in catch and product value The gill net fishery suffered the greatest damage due to a decline in catch, as they had higher catch levels than other fisheries, and both the average market price of their target species and the rate of decline in their catch caused by jellyfish were relatively high This was followed by the trap fishery, as it also had higher catch levels than the other fisheries, and the average market price of its target species was relatively high, as was the decline in its catch rate as a result of jellyfish The stow net fishery incurred damage to a value of US$ 49.5 million due to a decrease in catch, while the corresponding damage to the set net fishery was US$ 16.9 million In particular, the long bag set net fishery had relatively lower damage due to its small amount of catch and low average market prices Thus, the estimated damage was US$ 8.7 million The damage due to the product value decrease was lower than the damage due to the catch decrease However, the rankings of the product value decreases among the different fishery types were the same as the rankings of the catch decreases: the gill net fishery incurred the greatest damage (US$ 63.5 million), followed by the trap fishery, the stow net fishery, the set net fishery, the jigging fishery, the long bag set net fishery, and the lift net fishery In terms of potential annual damage, which was calculated by adding the catch decrease to the product value decrease, the gill net fishery presented the largest figure, followed by the trap fishery, the stow net fishery, the set net fishery, the jigging fishery, the long bag set net fishery, and the lift net fishery When the gross damage to each fishery type was compared with the average annual production, the stow net fishery was found to have incurred 41.1 % damage, the gill net 33.1 %, the trap fishery 32.7 %, the jigging fishery 12.9 %, the set net fishery 35.2 %, the lift net fishery 25.7 %, and the long bag set net fishery 50.5 % damage, which demonstrates the huge damage caused to each fishery type by jellyfish However, as jellyfish not damage fishing all year round, these figures may not reflect the actual damage Jellyfish appear in the coastal regions of Korea from late May to December, so the period of damage ranges from a minimum of two months (scenario 1) to a maximum of six months (scenario 2) [15] Accordingly, jellyfish cause damage to each fishery type for at least two months and at most six each year Table shows an analysis by scenario In scenario (damage lasting two months), the damage rate (in comparison to the gross production value) was 2.1–8.4 %, and the average damage rate among the seven fishery types was about 5.4 % In scenario (damage lasting six months, the damage rate was 6.4–25.2 %, and the average damage rate among the fishery types was about 16.1 % Thus, the direct damage to the fishing industry caused by jellyfish was estimated to be about US$ 68.2–204.6 million annually, depending on the period of jellyfish occurrence Table Average annual amount of damage caused by jellyfish to each fishery type (for the period 2006–2010) Fishery Stow net fishery Gill net fishery Trap fishery Catcha (ton) Market priceb (US$/ kg) 74,456 2.48 Rate of catch decrease (%) Rate of product value decrease (%) Damage from catch decrease (CD) (1,000 US$) Damage from product value decrease (PD) (1,000 US$) Total damage (FD) (1,000 US$) 26.8 19.6 49,494 26,496 75,990 4.43 20.3 16.1 100,439 63,488 163,926 69,175 4.70 21.5 14.3 69,848 36,469 106,317 111,655 Jigging fishery 63,360 2.27 6.5 6.8 9,366 9,161 18,527 Set net fishery 46,825 1.67 21.6 17.3 16,896 10,610 27,506 Lift net fishery 14,831 1.03 19.2 8.0 2,926 985 3,912 Long bag set net fishery 16,366 1.58 33.7 25.3 8,716 4,338 13,055 a Average catch (2006–2010) b Average market price (2006–2010) 123 Fish Sci (2012) 78:1147–1152 1151 Table Estimates of the amount of damage amount by fishery type Scenario (jelly appear for two months) Cost of damage (1,000 US$) Scenario (jellyfish appear for six months) Damage rate (%) Cost of damage (1,000 US$) Damage rate (%) Stow net fishery 12,665 6.8 37,995 20.5 Gill net fishery 27,321 5.5 81,963 16.5 Trap fishery 17,720 5.5 53,159 16.4 Jigging fishery 3,088 2.1 9,263 6.4 Set net fishery 4,584 5.9 13,753 17.8 Lift net fishery Long bag set net fishery Total 652 4.5 1,956 13.4 2,176 8.4 6,527 25.2 68,205 5.4 204,616 16.1 The cost of the damage in scenario was calculated by assuming a two-month period of damage caused by jellyfish The cost of the damage in scenario was calculated by assuming a six-month period of damage caused by jellyfish The damage rate is the percentage corresponding to the cost of the damage caused by jellyfish divided by the annual average production value per fishery type Discussion Jellyfish cause a range of problems: they sting beach vacationers in the summer, block nuclear power plant intakes (thus disrupting the supply of electricity), decrease fishery catches, cause delays to fishing activities, decrease product value, and damage fishing gear This study estimated the types of direct damage caused by jellyfish and the amount (cost) of this damage Using jellyfish monitoring data, the decreases in catch and product value were analyzed for each fishery type, and the damage to each fishery type was estimated The catch decrease was found to range between 6.5 and 33.7 % and the decrease in product value ranged between 6.8 and 25.3 % depending on the fishery type The annual direct economic damage was estimated to be US$ 68.2–204.6 million, depending on the period of jellyfish occurrence This corresponds to a minimum of 2.1 and a maximum of 25 % of the annual product value, demonstrating the considerable damage caused by jellyfish to the fishing industry However, this study only considered seven fishery types for which statistical data on production were available, and it excluded fishing gear damage or damage due to delays to fishing activities Thus, the actual damage will be higher than the values indicated above Indeed, it would increase even more if damage to beach vacationers and nuclear power plants as well as physical damage to fishermen were to be considered too To reduce this damage, it is imperative to: develop a jellyfish removal instrument that can be customized to each fishery type; monitor the occurrence or movement of jellyfish; and set and operate effective preventive measures In 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Plant J 30:5 29 5 39 8 Apse MP, Aharon GS, Snedden WA, Blumwald E ( 199 9) Salt tolerance conferred by overexpression of a vacuolar Na?/H? antiport in Arabidopsis Science 285:1256–1258 9 Nozaki K, Kuroda T, Mizushima T, Tsuchiya T ( 199 8) A new Na?/H? antiporter, NhaD, of Vibrio parahaemolyticus Biochim Biophys Acta 13 69: 213–220 10 Ottow EA, Polle A, Brosche M, Kangasjarvi J, Dibrov P, Zorb C, Teichmann... sp 6 in IMK medium were 0.80 2, 0.62 9, 0. 39 4, and 1. 09 divisions/day, respectively (Fig 1; Table 1 ), and those in f/2 medium were 0.86 6, 0.60 8, 0.35 3, and 0 .91 8 divisions/day, respectively (Fig 1; Table 1) Averaged growth rates of Ostreopsis spp strains growing in IMK, f/ 2, PES, and SWM3 media were 0.7 29 ± 0.27 8, 0.686 ± 0.24 5, 0.456 ± 0.10 7, and 0.311 ± 0.100 divisions/day, respectively (Table 2) From... 708 .92 5 79 708 .92 5 79 6464.4 0.5 792 0.03 29 Model 1-1 C * light 7 19. 95 5 79 708.82 577 6457.4 0.5 896 0.0337 Model 1-2 C * lunar 741.02 5 79 708.71 578 6434 .9 0.6 096 0.0350 Model 1-3 C * boat 725.88 5 79 708. 79 570 6465.5 0. 595 2 0.0340 Model 1-4 C * month 736.54 5 79 708.75 578 64 39. 2 0.6053 0.0347 Model 2-1 C * lunar ? light 748. 29 5 79 708.73 576 6432.1 0.6165 0.0355 Model 2-2 C * lunar ? boat 754 .93 5 79. .. 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PyNhaD homologous to AtSOS1 and named SynNhaP ), which is involved in exchange activity, is conserved in the SOS1type Na?/H? antiporter The conserved amino acid was also found in PySOS1 (Fig 1) In NhaD-type Na?/H? antiporters, Habibian et al [21] reported that the residues Ser 15 0, Asp 15 4, Asn 15 5, Asn 18 9, Asp 19 9, Thr 20 1, Thr 20 2, Ser 38 9, Asn 39 4, Ser 42 8, and Ser 431 of NhaD from Vibrio cholera (named... 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A marine benthic dinoflagellate, genus Ostreopsis, capable of synthesizing PTX and PTX analogs is widely distributed in coastal environments in tropical, subtropical, and temperate regions of the world [1 5, 19 22] They are 123 99 4 usually present as benthic cells on seaweed, sand, rocks, and invertebrates [1 5, 1 9, 20] and also as planktonic cells in the water column [1 5, 23] Blooms of toxic Ostreopsis