Size selection of European flounder (Platichthys flesus)
in the demersal Baltic cod trawl fishery –
Theoretical investigations which improve multispecies selection
Master thesis
by
Ulrike Luschtinetz
2012
First supervisor: Prof. Dr. C. Möllmann
Institute for Hydrobiology and Fisheries Science, University of Hamburg,
Second supervisor: Dr. D. Stepputtis
Thünen Institute of Baltic Sea Fisheries Rostock
Table of Content
Table of Content
Table of Content ...... II Abstract ...... 1 Zusammenfassung ...... 2 1. Introduction ...... 3 1.1 Working hypotheses ...... 3
1.2 By catch and discard ...... 5
1.3 Species and size selectivity ...... 7
1.4 European Flounder (Platichthys flesus) ...... 10
1.4.1 General information ...... 10
1.4.2 Economical importance and fishery ...... 12
1.5 Demersal trawl fishery in the Baltic Sea...... 15
1.5.1 Bottom trawl ...... 15
1.5.2 Regulations ...... 16
2. Material and Methods...... 20 2.1 Influence of flounder morphology on selectivity – the method in general ...... 20
2.2 Influence of flounder morphology on selectivity – the method in detail ...... 21
2.2.1 Data sampling ...... 21
2.2.2 Measurement of flounder cross sections ...... 22
2.2.3 Definition of shape models ...... 23
2.2.4 Fall through experiments ...... 25
2.2.5 Simulation of mesh penetration and selection of a penetration model ...... 27
2.2.6 Prediction of selectivity parameter (Design guide) ...... 30
2.3 Comparison of simulated and experimental data ...... 32
2.4 Prediction of selectivity parameters for T90 meshes ...... 33
2.5 Influence of twine characteristics on flounder selectivity ...... 35
2.5.1 Data sampling ...... 35
2.5.2 Analysis ...... 36 II
Table of Content
3. Results ...... 37 3.1 Influence of flounder morphology on selectivity...... 37
3.1.1 Data sampling ...... 37
3.1.2 Shape model ...... 38
3.1.3 Penetration model ...... 42
3.1.4 Prediction of selectivity parameters ...... 44
3.2 Comparison of simulated and experimental selection data for T0 meshes ...... 52
3.3 Analyzing of T90 meshes ...... 53
3.4 Influence of twine characteristics on flounder selectivity ...... 58
4. Discussion ...... 60 4.1 Methodology ...... 61
4.1.1 FISHSELECT ...... 61
4.1.2 Cover codend method...... 62
4.2 Morphological description of flounder ...... 63
4.3 Square and diamond meshes (BACOMA) ...... 65
4.4 Rectangular meshes ...... 66
4.5 Hexagonal meshes (T90) ...... 66
4.6 Influence of twine characteristics on flounder selectivity ...... 67
4.7 Behavioral aspects ...... 68
4.8 Multispecies approach and outlook ...... 69
5. References ...... 70 6. Indices of tables and figures ...... 77 6.1 Index of tables ...... 77
6.2 Index of figures ...... 79
7. List of Abbrevations ...... 82 8. Acknowledgement ...... 83 9. Declaration of Authorship ...... 84 10. Appendix ...... 85
III
Abstract
Abstract
The European flounder (Platichthys flesus) is the most abundant and the most widely distributed flatfish species the Baltic Sea. Flounder are caught by a direct flounder fishery, as well as by catch in the demersal cod trawl fishery. Therefore, it is quite important to optimize the codend selection of cod (Gadus morhua), and even to improve the flatfish selectivity.
In this study, the theoretical selection potential of Baltic flounder was analyzed for different mesh types, using the FISHSELECT method. For the first time, this method was applied to a Baltic flatfish species. Measured morphological data, systematic fall trough experiments for defined mesh types, and simulations were used to predict selectivity parameters, L50 and selection range (SR), for defined meshes. Diamond, rectangular, and hexagonal mesh types were analyzed. The specific selectivity parameters were predicted and illustrated for defined mesh parameter, such as mesh opening and opening angle. The diamond meshes of the current legal BACOMA codend, with a mesh opening of 105 mm, have flounder L50 values between 11.10 cm and 23.41 cm. The simulated selectivity parameters correspond with experimental data (125 mm mesh opening), obtained at sea. The square meshes of the BACOMA escapement panel, with a mesh opening of 120 mm, have a predicted flounder L50 value of 21.01 cm.
Furthermore, underwater records of hexagonal mesh shapes of a T90 codend were analyzed with the FISHSELECT software. Selectivity parameter, L50 and SR, were predicted based on the observed mesh shapes. The values of predicted L50 values are below minimum landing size of flounder in Germany (25 cm).
Additional, the influence of twine characteristics on flounder selection was investigated. A significant influence of twine diameter and number of twine (single or double) was shown for T90 codends. An increase of these parameters results in decreasing L50 values.
The determined selectivity data of flounder for defined meshes may be used as a primary basis for decisions regarding mesh and codend developments. Further analyses are essential, especially for additional species, to achieve the long term aim of an improved multi species selection.
1
Zusammenfassung
Zusammenfassung
Die Europäische Flunder (Platichthys flesus) ist die häufigste und die am weitesten verbreitete Plattfischart in der Ostsee. Sie werden durch gezielte Flunderfischerei, als auch durch Beifänge in der demersalen Dorsch Schleppnetzfischerei, gefangen. Deshalb ist es wichtig, die Steert Selektion nicht nur für Dorsch (Gadus morhua) zu optimieren, sondern den Fokus auch auf eine Optimierung der Plattfischselektion zu richten.
In dieser Arbeit wurde das theoretische Selektionspotential von Flundern für verschiedene Maschenarten, durch Anwendung der FISHSELECT Methode, analysiert. Erstmalig wurde die Methode für eine Ostsee Plattfischart angewandt. Durch gemessene morphologische Daten der Flundern, praktizierte systematische Durchpass Versuche durch verschiedene Maschentypen, sowie auf diesen Daten basierten Simulationen, konnten Selektivitäts parameter, wie L50 oder Selektionsspanne (SR) für definierte Maschen berechnet werden. Untersucht wurden rhombische, rechteckige und hexagonale Maschentypen. Die spezifischen Selektivitätsparameter wurden für definierte Maschenparameter, wie Maschenöffnung und Öffnungswinkel, dargestellt. Für die rhombischen Maschen des Standard BACOMA Steertes, mit einer aktuellen minimalen Maschenöffnung von 105 mm, wurden L50 Werte zwischen 11.10 cm und 23.41 cm ermittelt. Die simulierten Selektivitätsparameter stimmen mit experimentell ermittelten Daten (125 mm Maschen öffnung) überein. Für die Quadratmaschen im BACOMA Fluchtfenster, mit einer legalen Maschenöffnung von 120 mm, wurden für Flundern ein L50 von 21.01 cm ermittelt.
Zusätzlich wurden Unterwasservideos von hexagonalen Maschen eines T90 Steertes ausgewertet und mit dem Programm FISHSELECT analysiert. Basierend auf den beobachteten Maschenformen wurden Selektivitätsparameter, L50 und SR, für Flunder simuliert. Die Werte der ermittelten Selektivitätsparameter für Flunder sind kleiner als ihre legale minimale Anlandelänge in Deutschland (25 cm).
Außerdem wurde der Einfluss von Netzgarn Parametern auf die Flunderselektion untersucht. Ein signifikanter Einfluss von Garnstärke und Garnanzahl (doppelt oder einfach) wurde bei T90 Steerten festgestellt, wobei eine Erhöhung beider Parameter zu einer Abnahme der L50 Werte führt. Die ermittelten Selektivitäts Daten von Flundern für definierte Maschen können als erste Grundlage für Entscheidungen für künftige Maschen und Steertentwicklungen herangezogen werden. Für das langfristige Ziel, eine verbesserte Mehrartenselektion, sind Untersuchungen weiterer Spezies notwendig. 2
Introduction
1. Introduction
1.1 Working hypotheses
The main target species in the Baltic Sea bottom trawl fishery is cod (Gadus morhua). In this fishery high amounts of not marketable fish were caught, which were thrown over board back in the Sea. They are named discards. So far, European regulations focused mainly on optimizing of the selection of cod. The selection of other fish species were only considered marginally. This results in especially high by catch rates of flatfish, such as plaice (Pleuronectes platessa) and flounder (Platichthys flesus). The aim of this study is the investigation of the selection potential of flounder for defined codends. Analysis and simulation tools as FISHSELECT (Herrmann et al. 2009) were used. Results of these analyses could be an important step for implementing a multi species selection approach. The long term aim is the reduction of cod catches with individual sizes below the current minimum landing size (MLS). The current minimum landing size of cod is 38 cm in Germany. Furthermore, by catches of undersized flatfish, such as flounder, should be minimized. For flounder, the current MLS in Germany is 25 cm (Kornilovs 2006). An improvement of net selectivity could result in a more sustainable fishery and an optimization of the working processes on board.
The specific selectivity of flounder was investigated for defined mesh shapes and sizes. The analyzing method FISHSELECT (Herrmann et al. 2009) was used in this study. This method uses morphological properties of the flounder to predict selectivity parameters of defined mesh shapes and sizes. The long term aim is the identification of optimal mesh characteristics for flounder selection. The analyzing process with the FISHSELECT method contains following steps (see also chapter 2.1 and 2.2) (Herrmann et al. 2009):
1. Parameterization of the morphometric properties (different cross sections) of the fishes with a Morphometer. Afterwards, digitalization of morphometric cross sections and length dependent parameterization of the cross sections with mathematical models (named shape models) with FISHSELECT.
2. Investigation of ability of fishes to pass through defined meshes (different mesh shapes and sizes), due to their individual gravity (fall trough trials).
3
Introduction
3. Finding a penetration model, that has the highest degree of agreement between simulated penetration of meshes and experimental fall through trials. The several tested penetration models include different compressions of the fish body.
4. Prediction of selectivity parameters for a broad range of mesh types. A virtual fish population and the best penetration model are the basis for predictions of specific selectivity properties (L50, SR) of different mesh shapes and sizes. So called design guides present information of selectivity parameter.
The penetration model (point 3.) is the basis to investigate an optimal mesh regarding the multi species approach. With information of the penetration model of flounder and – in a second step – with cod (and perhaps additional information about behaviors from observations with underwater cameras) it could be possible to develop different mesh types/ codend constructions, which have a high selectivity for both fish species. Possible are different codend designs, for example combination of different net materials, that have different mesh properties. Modified codends should be tested, for example with simulation programs or field trials. However, this study focuses on the theoretical investigations of size selectivity of flounder.
4
Introduction
1.2 By-catch and discard
One main aim of fishery regulations is the reduction of by catch and discard. By catch is defined as that amount of the catch, that is not the target species (Wileman et al. 1996). Discard is the amount of the catch, that consists of species and/ or sizes, that are not retained for sale and therefore rejected at sea (Wileman et al. 1996).
Discards are individuals caught below minimum landing size or of low or no market value (Feekings et al. 2012). Discarding also occurs when the catch is damaged or high graded or even the species quota is reached (Feekings et al. 2012). Discard rates depend on several factors, for example fleet, boots, individual hauls, used fishing gear, and also fish population structures. Proportion of discards depends on fishing metier and type of fishing activity (Ulleweit et al. 2010). Furthermore, discard rates vary within single fishing metier (Ulleweit et al. 2010).
Survival rates differ between different caught and discarded species. It is assumed that round fishes, such as cod, have lower survival rates compared to flatfish species, for example flounder, which are more robust. In contrast, survival rates of for example undersized flounder, which were discarded during the summer time, are assumed to be low (Gabriel et al. 2000). Reasons for varying survival rates are combinations of different factors such as fishing gears and vessel (gill nets or trawl, stern trawler or side trawler), trawling time, pressure on the fish during trawling, filling of the codend, depth of the trawl and towing velocity (ICES WKFLABA 2012). Furthermore, survival rates depend on handling time on board, fish species and its condition, as well as sanding of the gills (Gabriel et al. 2000).
High level of discard in the European Union can be explained by the use of insufficient fishing techniques regarding selective properties (Feekings et al. 2012). Mesh sizes and fishing area have a significant effect on fish selectivity, for example for plaice in the North Sea (Madsen et al. 2012). Therefore, regulations of mesh sizes are important to minimize discard. An additional attempt to solve discard problems is the reform of the Common Fishery Policy (CFP), which will aim to implement a discard ban (Anonymous 2009).
5
Introduction
In mixed species demersal trawl fisheries are reported the most complex discard problems, which result in high discards (Catchpole et al. 2005; Petter Johnsen and Eliasen 2011). Discard rates of flounder are high and heterogeneous in the demersal cod trawl fishery (Gardmark, et al. 2006). The estimated amount of discarded flounder is 5 – 10 times higher than the landed and reported portion of flounder (ICES WGBFAS 2012). The portion of discarded flounder is unsteady and fluctuates between the years, because flounder discard depends, amongst others, on flounder sizes and market prices. In the last years, the price for flounder declined and the amount of discard increased (ICES WGBFAS 2012). Similar to discard of flounder, the total landings of flounder vary and depend on market prices, economic state, and also sizes of the flounder. As a consequence, the total landings of flounder may not be used as an indicator for the development of the flounder stock.
A general procedure of investigation or estimation of discard data does not exist (ICES WGBFAS 2012). Discard rates are not available for all fleets, but some countries have initiated discard sampling programs, for example the data collection framework (DCF) developed by the European Commission in 2008. Scientists sample the commercial fishery and determine, amongst others, data of landings, effort, discard and also biological parameters. German scientists focused on fish species cod, herring (Clupea harengus), sprat (Sprattus sprattus), and also flounder.
6
Introduction
1.3 Species and size selectivity
The selection of fish by a fishing gear is defined as the process that causes the catch of the gear to have a different composition to that of the fish population in the geographical area in which the gear is being used (Wileman et al. 1996). The selection process can be described with a contact selection curve, which describes the probability that a fish of a given species and length is captured, given that it contacted the gear (Millar and Fryer 1999). The selectivity of a fishing gear is defined as a measurement of the selection process (Wileman et al. 1996). In general, selectivity could be classified in species selectivity and for each species in size selectivity (Wileman et al. 1996).
Selectivity depends on several factors (Lozán 1985; Wileman et al. 1996), for example on environmental conditions. Known factors are for example sea state, sea bed type, water depths, season, water temperature, and light level (Wileman et al. 1996). Furthermore, different types of vessels could influence selectivity (Bohl 1981; Tschernij and Holst 1999; Madsen 2007). Motion, handling procedure, engine power or trawling speed are relevant aspects which differ between different vessels (Wileman et al. 1996).
Different types of used gears or net constructions affect selectivity. One of the most important factors, which influence the selectivity, is the used codend of a gear. Codends could be modified by extensions or attachments. The selectivity depends on used meshes of the codend and the meshes have defined characteristics, for example mesh shape, mesh size and twine characteristics (Wileman et al. 1996). The performances of the codend and especially the meshes are relevant for the selection process, because mesh morphometrics could change especially during the trawling process while filling up the catch in the codend.
The accumulation in the codend, meaning catch size, as well as catch composition influence potential selectivity (Wileman et al. 1996). Aspects about fish species, behavior and condition, as well as morphological factors as size, shape and surface, and also compressibility of the fish has an additional influence on codend selection.
Restrictions of codend attachments, circumferences, codend extensions, mesh regulations (size, shape, and twine), and vessel types are included in the fishing legislations in the Baltic Sea.
7
Introduction
The selection process can be described by a contact selection curve, r(l), that is defined as the probability that a fish of length l is retained given that it entered the gear (Equation 1, Equation 2, Equation 3) (Wileman et al. 1996).
Different selection curves could be applied, for example logistic selection curve, probability curves (normal probability), Gompertz (log log) or Richards curve (Wileman et al. 1996; Frandsen 2011). The selection curve, which fits best with retention data is chosen. A maximum likelihood function is used (Wileman et al. 1996) to estimate the best fit of the curve. In general, high R square values, high p values, and low AIC values (Akaike 1974) indicate best fits (Frandsen 2011).
Mainly, the logistic selection curve is used, which is a symmetric function and described by two parameters (Fig. 1) (Wileman et al. 1996; Millar and Fryer 1999). Parameter L50 describes the length at which the fish has 50 % probability to be retained (Equation 4). The selection range (SR) describes the difference between the length for 75 % retention and 25 % retention (SR = L75 – L25) (Equation 5) (Wileman et al. 1996). The general aim is optimizing the catch and therefore minimization of loss of marketable fish and the reduction of caught undersized specimen (with lengths below minimum landing size). A steep selection curve has a small selection range and indicates well defined selection.
exp a + bL = Equation 1 1 + exp +
+ = = Equation 2 1 −
0.5 + = = 1 = 0 Equation 3 1 − 0.5
− = Equation 4
2 3 2.197 = − = ≈ Equation 5
8
Introduction
Fig. 1: Schematic illustration of a selectivity curve: Fitted logistic selection curve (black line) with selectivity parameters L50 and SR (L75 – L25) (red lines). Dots represent experimental retention data per length class. Portions of discard (area shaded red), landings (area shaded green) and lost marketable catch (area shaded grey) are illustrated. The minimum landing size of flounder (250 mm) is shown (black vertical line).
In addition to L50 and SR, other selectivity factors were used for calculations. Selection factor (SF) and selection ratio (SFA) are used to compare selectivity properties of codends with different mesh sizes. The selection factor (SF) is calculated by L50 divided by mesh size (MS) (Equation 6). For calculation of the selection factor may used the same unit for L50 and mesh size. The SFA is calculated by selection range divided by mesh size (Equation 7). A back transformation in standard selectivity parameter, L50 and SR, is possible (Equation 6, Equation 7).
or Equation 6 = 50 = ∗
or Equation 7 = = ∗
9
Introduction
1.4 European Flounder (Platichthys flesus)
1.4.1 General information
The European flounder (Platichthys flesus) is a demersal flatfish with a common length of 25 30 cm (Muus and Nielsen 1999). The flattened body has a characteristically oval form and a rough surface, especially at the lateral organ (Fig. 2). Often, the upper side is colored brown, in contrast the blind side is colored white (Muus and Nielsen 1999). Flounder are widely distributed, for example in the Atlantic Ocean, at the coast of Norway, in the Mediterranean Sea, and also in the brackish Baltic Sea (Muus and Nielsen 1999). The salinity level in the Baltic Sea decreases from west to east. In general, these high differences in salinity level limit distributions of marine species, but flounder can tolerate low salinity levels and even fresh water (Nissling et al. 2002). Flounder migrate into less saline waters and closer to the shore in shallower water than other flatfishes. Therefore, flounder is the most widely distributed flatfish in the Baltic Sea. Flounder occur everywhere, except the Bothnian Bay (SD31) and the eastern part of Gulf of Finland (SD32) (ICES WGBFAS 2012).
Fig. 2: European flounder (left) and their distribution (right, map: www.ecomare.nl)
The stock structure of flounder is very complex in the Baltic Sea. Different approaches were used for stock identification (ICES WGBFAS 2012). Amongst others, migration patterns (Aro 1989), spawning behavior (Nissling et al. 2002), and microsatellite/ genetic analyses (Florin and Hoglund 2008) were used. The second workshop on flounder assessment in the Baltic Sea identified flounder stocks based on egg buoyancy. They identified two flounder spawning groups with in total 11 different sub populations (Tab. 1) (ICES WKFLABA 2010).
10
Introduction
Shallow water spawners, with 6 populations, spawn in shallow coastal waters and their eggs develop near the bottom. Therefore, they are named demersal flounder stocks (Tab. 1). Mainly, they are located in the central and northern part of the Baltic Sea (ICES WGBFAS 2012). The deep water spawners, with 5 populations, release their free floating eggs in deeper water and their eggs develop in the free water column. Therefore, they are named pelagic flounder stocks (Tab. 1). They are located in the central and western Baltic Sea, for example in the Arkona basin or in the Bornholm deep (depths around 40 – 80 m) (ICES WGBFAS 2012).
Tab. 1: Flounder populations with numbers of stock, stock names, and ICES SD in the Baltic Sea (ICES WGBFAS 2012)
Number of stocks Stockname ICES SD Belt Sea 22 Öresund 23 5 pelagic Southern Baltic 24+ 25 Bay of Gdansk 26 Eastern Gotland 28 (26, 29) Swedish eastcoast 27 Latvian coast + Gulf of Riga + Hiumaa 28E+ 29SE Gotland Island 28 (27E) 6 demersal Åland 29, 30 Finnish coast of Gulf of Finland 32 Estonian coast of Gulf of Finland 32
Flounder spawning period start in the second half of February and end in May (ICES WGBFAS 2012). After spawning, feeding migration starts to coastal shallow waters, especially in southern areas near the German and Polish coast (ICES WGBFAS 2012). Flounder migrate to their spawning grounds in late autumn or winter time (ICES WGBFAS 2012).
The metamorphosis to a flatfish starts at average larvae sizes of 10 mm. One eye turns from one side to the other, often to the right site, which becomes the upper side of the fish (Muus and Nielsen 1999; vTI OSF 2011). About 70 % of the flounder are right eyed (ICES WGBFAS 2012). Adult flounders bury themselves in sandy grounds at daytime and are migrate in shallow water areas for feeding at night time (vTI OSF 2011). Their main prey items are mussels, worms, crabs, or little fishes (vTI OSF 2011).
11
Introduction
1.4.2 Economical importance and fishery
The flounder is one of the most important demersal species and the most commercial important flatfish in the Baltic Sea, followed by plaice (Pleuronectes platessa), dab (Limanda limanda) and turbot (Scophthalmus maximus) (Fig. 3).
In 2011, 2193 t of flounder were landed in Germany (BLE 2011). In 2010, the total consumption of flounder was 1 627 t in Germany (vTI OSF 2011). The average sale price for flounder was 0.59 euro per kilogram in Germany in 2011, whereas the average sale price for plaice was around 1.03 euro per kilogram (BLE 2011).
For flat fish species flounder are reported highest landings in the Baltic Sea (Fig. 3) (ICES WGBFAS 2012). Landings of flounder decreased between 1975 and 1990. In the early 1990ies, flounder landings increased rapidly and have reached a relative high and stable level since 1993 (Fig. 3). Landings of plaice, dab, and turbot have been relatively constant and low over the last years (Fig. 3). For these species, the landings were well below 5000 t per year during the last 30 years.
Flounder Plaice Dab Turbot Landings [t] 0 5000 10000 15000 20000
1975 1980 1985 1990 1995 2000 2005 2010 Year
Fig. 3: Landings (in t) of flounder, plaice, dab, and turbot in ICES subdivisions 22 - 32, years 1970 - 2010 (Data (ICES WGBFAS 2012)
12
Introduction
Flounder were landed by all littoral states of the Baltic Sea. However, landings are heterogeneous per country and ICES subdivision (SD), and indicate high spatial differences in ICES SD22 – SD32, for example in 2011 (Fig. 4). High flounder catches are reported in ICES SD24 – SD26 (Fig. 4). Poland, Denmark, and Germany landed highest portion of flounder in this area (SD24 and SD25), that results in 50 % of the total Baltic flounder catch (Fig. 5) (ICES WGBFAS 2012).
Fig. 4: Total landings of flounder (in t) in ICES subdivision 22 – 32 in 2011 (left); ICES subdivisions 21 – 32 in the Baltic Sea (right) (Data: (ICES WGBFAS 2012))
SD 24 SD 25 SD other
Total landings [1000 t]
0 5 10 15 1975 1980 1985 1990 1995 2000 2005 2010 Year
Fig. 5: Total landings of flounder in ICES subdivision 24, subdivision 25, and flounder landings of other SD for years 1975 – 2011 (Data: (ICES WGBFAS 2012))
13
Introduction
High catches of flounder, especially in SD24 and SD25, are caused by a direct flounder trawl fishery. Direct trawling fishery is practiced especially in Germany and Poland (SD24 and SD25), whereas also indirect methods as gill nets were used in Poland (ICES WGBFAS 2012). In Germany, directed flounder fishery occurs mainly during the feeding period in the 3rdand 4th quarter of the year (Kornilovs 2006; ICES WGBFAS 2012) and when cod is protected (Madsen 2007). Furthermore, flounder were also caught as by catches in cod fishery and were reported from Germany, Denmark, Russia, Lithuania, and Latvia (ICES WGBFAS 2012). Main season of flounder by catch is the winter and the total catch of flounder depends on the catch depth and area. Catch rates of a single fishing vessel vary and could be up to two tons flounder (ICES WGBFAS 2012).
For flounder, a general assessment is not available in the Baltic Sea (vTI OSF 2011). Therefore, no reference points or total allowable catches (TAC) are defined. Reasons are limited databases and obscurities in stock classifications (vTI OSF 2011). Since years, ICES working groups are working on a general assessment for Baltic flounder. Assessments are quite important for estimations of stock development, management advices, and predictions based on different analyses (for example virtual population analysis (VPA)). ICES working groups, such as the workshop on alternative assessment strategies for flounder in the Baltic Sea (WKFLABA), are working on age reading exchange programs, discard samplings, and analyses which are required for analytical assessments of this stock in future (ICES WKFLABA 2012).
14
Introduction
1.5 Demersal trawl fishery in the Baltic Sea
The most commonly used fishing gear is the bottom trawl in the Baltic demersal trawl fishery (see chapter 1.5.1). Regulations of used gears and codends, and also fishing periods and areas (see chapter 1.5.2), should minimize by catches, which is a high problem in the mixed demersal fishery in the Baltic Sea (see chaper1.2). Therefore, it is quite important to understand and investigate species and size selectivity (see chapter 1.3) of gears.
1.5.1 Bottom trawl
The demersal otter trawl is one of the most important fishing gears in the demersal trawl fishery (Fig. 7). Main target species are demersal fish species such as cod, plaice, flounder, and whiting (Merlangius merlangus). The demersal trawl is towed behind a vessel with an average speed of about 3 4 knots.
The following description and numbers therein refer to Fig. 6. Two steely or wooden otter boards (2) are fixed between the warps (1), and bridles (or jager) (3) to open the net horizontally. They get in contact with the bottom and whirl up sediment as a mud cloud. Both, the mud cloud and the bridles, have a stimulating or herding effect and cause fish to aggregate in front of the net. Often, the weighted ground rope (5) is modified with rubber bobbins or rubber discs. Their function is the protection of the net when it bounces over obstructions and avoiding catching invertebrates. The weighted ground rope (5) and the headline (6) with several floaters hold the net vertically open. Fish is coming over the front part (4) and the net belly (7) into the codend (8), where the catch accumulates.
Fig. 6: Schematic drawing of a demersl otter trawl (figure by vTI-OSF). Numbers (1-8) are described in the text.
15
Introduction
Fig. 7: Schematic drawing of a demersal otter trawl (figure by vTI-OSF): front view (left) and side view (right)
The performance of the net depends on several factors, amongst others, the length of the warps, towing speed, and the angle of the fixed otter boards (FAO 2012). Theoretical, it is possible to switch from a demersal to a pelagic trawl, while changing these parameters. Therefore, the demersal otter trawl is flexible to use. One of the main advantages is the high catch efficiency, due to the use of bridles, and otter boards, and their herding effect of the fish (FAO 2012).
1.5.2 Regulations
Restrictions regarding fishing effort and fishing methods should minimize by catches and therefore prevent overfishing. Regulations include restrictions of fishing areas, periods, fishing methods, and fishing equipment in the Baltic Sea.
In the European Union, the fishery regulations are fixed in the fishery rules of the International Baltic Sea Fishery Commission (IBSFC) (ICES WGBFAS 2012). The minimum landing size of flounder is 25 cm in SD22 – SD25 (fishery rule 8). The minimum mesh opening is restricted to 120 mm for nets like trawl nets, gillnets, and Danish seines in SD22 – SD27 (fishery rule 10.1) (ICES WGBFAS 2012). The European Union specified the two current legal codends in the Baltic demersal trawl fishery (EU regulation no. 686/2010):
i) BACOMA 120/105 mm ii) T90 120 mm 16
Introduction i) BACOMA 120/105 mm
The BACOMA codend is named after the project “Improving Technical Management in Baltic Cod Fishery”. The standard BACOMA codend consists of rhombic meshes with a minimum mesh opening of 105 mm (Fig. 8). The twine diameter has a maximum of 6 mm for single twine and a maximum of 4 mm for double twine (Fig. 9). A panel of knotless twine (ultra cross) is implemented in the upper aft part of the codend. This escapement panel is named BACOMA window and has a minimum mesh opening of 120 mm and a minimum length of 5.5 m (Fig. 8). The twine has a minimum thickness of 5 mm (Fig. 9) (EU regulation no. 686/2010).
Fig. 8: Schematic drawing of a BACOMA 120/105 mm codend (drawing by W. Rehme, vTI-OSF)
Fig. 9: Pictures of net material of BACOMA codend: T0 single twine 6 mm (left), T0 double 4 mm (middle) and square meshes of BACOMA escapement panel with twine diameter of 5 mm (right) 17
Introduction ii) T90 120 mm
A T90 codend consists of meshes, which were turned by 90 degree compared to standard BACOMA meshes (T0) (Fig. 10). Minimum mesh opening is 120 mm nominal. The twine has a maximum diameter of 6 mm for single twine or maximum 4 mm for double twine (Fig. 11). Codend and tunnels have lengths of 50 meshes (Fig. 10) (EU regulation no. 686/2010).
Fig. 10: Schematic drawing of a T90 codend (drawing by W. Rehme, vTI-OSF)
Fig. 11: Pictures of net material of T90 codends: single twine with 6 mm twine diameter (left) and double twine with 4 mm twine diameter (right)
18
Introduction
Furthermore, fishing periods and areas are restricted to limit fishing effort. In Germany it is prohibited to catch cod in defined areas during two periods of the year (Tab. 2). During these periods it is not allowed to fish cod with vessels bigger or equal 8 m and defined gears, such as trawling nets, purse seines, or gill nets with mesh sizes of 90 mm or bigger (LALLF 2012).
For flounder main fishing season is from June to February (ICES WGBFAS 2012), whereas during spawning season of flounder fishing of flounder is banned between February and May (Tab. 2) (EU regulation 1237/2010). Turbot landings are forbidden between June and July (Tab. 2) (EU regulation 1237/2010). But nevertheless, during these periods it is allowed to land a flounder or turbot proportion of 10 % of the caught total fresh weight. The codend of the trawl must have a minimum mesh opening of 105 mm (EU regulation 1237/2010).
In Germany, several national restrictions regarding the flatfish fishery were established, for example in Mecklenburg Vorpommern and Schleswig Holstein (ICES WGBFAS 2012). Until 2007, there was a prohibition for landing all female flounders in 12 sm zone from 1st February to 30th April (ICES WGBFAS 2012). In Poland, a closed season is in force from 15th February to 15th May (ICES WGBFAS 2012).
In general, high grading is forbidden for species with catch quota, as cod, herring, plaice, salmon and sprat in the Baltic Sea (EU regulation 1237/2010). Therefore, every caught quoted species have to be landed in the Baltic Sea. Nevertheless, high grading is not forbidden for flounder.
Tab. 2: Periods and areas in the Baltic Sea, where fishing of cod, flounder, and turbot is banned (modified after EU regulation 1237/2010). Species Area Period of ban Cod SD 22, 23, and 24 1st April – 30th April SD 25, 26, and 28 (SD28.2 is excluded) 1st July – 31th August Flounder SD 26, 27, 28, and 29 south of 59° 30’ N 15th February – 15th May SD 32 15th February – 31th May Turbot SD 25, 26, and 28 1st June – 31th July south of 56° 50’ N
19
Material and Methods
2. Material and Methods
2.1 Influence of flounder morphology on selectivity – the method in general
The simulation tool and software FISHSELECT (Herrmann et al. 2009) is used to predict selectivity properties, L50 and SR, of defined meshes and fish species. Analyses and simulations of this program can provide the basis for improving size and species selectivity. The procedure of FISHSELECT includes the following steps (Herrmann et al. 2009):
1. Parameterization of the morphometric properties (different cross sections) of the fishes with a Morphometer. Afterwards, digitalization of morphometric cross sections with a scanner and length depended parameterization of the cross sections with mathematical models (named shape models) with FISHSELECT (see chapter 2.2.2). 2. Investigation, whether fishes pass through defined meshes or not (different mesh shapes and sizes), due to their individual gravity. These experiments are named fall trough trials (see chapter 2.2.3). 3. Determination of different compressions of the fish bodies in penetration models. With simulations, which include combinations of information of shape models and fall troughs, the best penetration model was investigated. The best penetration model has highest degree of agreement (DA) between the simulated compression and the experimental fall through trials (see chapter 2.2.5). 4. Prediction of selectivity parameters, L50 and SR, for a broad range of mesh types. A virtual fish population is used, which is created based on real measured fish data. And the best penetration model is the basis for predictions of specific selectivity properties (L50, SR) of different mesh shapes and sizes. So called, design guides present information of L50 and SR values (see chapter 2.2.6).
The method and simulation tool FISHSELECT enables different possibilities for the analysis of research questions regarding selectivity.
20
Material and Methods
2.2 Influence of flounder morphology on selectivity – the method in detail
2.2.1 Data sampling
The data sampling was conducted on a research cruise of the FRV Solea (German research vessel, 42 m length overall, 950 kW) from 19/03/2010 – 01/04/2010 (619thcruise) in the southwestern Baltic Sea. The experiments were conducted to investigate the potential selectivity of flounder. The FISHELECT methodology (Herrmann et al. 2009) was used and applied to several Baltic Sea species, such as cod, whiting, plaice, dab, and turbot. The data of flounder were used for further analyses.
Fresh fish was continuously available during the cruise due to the ongoing fishing experiments. Fishes were selected to cover a wide size range. For the FISHSELECT method it is important that the fish is not affected by factors such as dehydration, depressurization or rigor mortis (Sistiaga et al. 2011). To minimize these factors, the selected fishes were transferred into tanks onboard, which were filled with fresh sea water. In these tanks, fish were kept alive until they were used for the experiments. At least five fishes were used for the experiments at the same time. Each fish were killed in a solution of anaesthetization (ethylene glycol mono phenyl ether). Then each fish was labeled with a number on its tail and the total length (in mm) and the weight of each fish (in g) were measured.
Furthermore, different transverse cross sections of the fishes were measured and it was tested whether each fish could pass defined meshes or not (Herrmann et al. 2009). Both experimental designs are described in the following chapters.
21
Material and Methods
2.2.2 Measurement of flounder cross-sections
It is known from previous investigations that few cross sections (usually 2) of the fish are enough to determine physically whether a fish pass through meshes or not (Herrmann et al. 2008). The numbers and positions of the cross sections vary and depend on fish species. Those specific cross sections for the fish species investigated were identified prior to full measurement sequence. For Baltic flounder, two transverse cross sections were identified to be critical for mesh penetration. These cross sections were used for further investigations. The first cross section (CS1) was measured at the highest point of the head of the flounder, whereas the second cross section (CS2) was measured at the maximum width of the flounder ignoring the fins (Fig. 12). Consequently, 234 cross sections were measured for 117 flounder. These transverse cross sections were measured with a mechanical sensing tool, named Morphometer (Fig. 12) (Herrmann et al. 2009). It is composed of many little flexible metal sticks. The sticks were used to measure and form the contours of transverse cross sections of the flounder. When the sticks are at the right position, they were fixed and approximate the fish shape.
CS2 CS1
Fig. 12: Flounder with positions of both cross-sections (CS1 and CS2), measurement tool Morphometer (right)
22
Material and Methods
2.2.3 Definition of shape models
The measured cross sections were digitized using a flatbed scanner. The scanned pictures were converted into digital descriptions with the image analyze function of FISHSELECT. Each digitalized cross section was represented by around 120 digital points (Fig. 13). Afterwards, different parametric models were fitted to the digitalized cross sections, while using the least square fitting method of FISHSELECT (Tab. 13).
In total, ten parametric models were tested to find the best performing model for each cross section. Each model is implemented in the FISHSELECT software and is described by different numbers of parameter (c1 – c4). The models named F _Flex_Drope (c1, c2, c3), F_Soft_Hexagonal (c1, c2), F_Shoe3 (c1, c2, c3), F_Kotelet (c1, c2, c3), F_Shoe4 (c1, c2), F_Flex_Ellipse2 (c1, c2, c3), F_Flex_Ellipse3 (c1, c2, c3), Half_Ellipse (c1, c2), Sym_Trapetz (c1, c2, c3), and Asym_Trapetz (c1, c2, c3, c4).
Two criteria were used to choose the best model:
i) The coefficient of determination (R square) is the percentage of explained variability and ranged between 0 and 1. High values indicate better fit. ii) The Akaike Information Criterion (AIC value) (Akaike 1974), where lower values indicate better fit. The AIC value considers the complexity of the model, while models with few parameters are preferred.
Each model was applied to both cross sections of each fish. Calculated R square and AIC values were averaged for all fish and compared for each model and cross section. The best performing model for each cross section was used for further simulations (description of best models in chapter 10).
The parameters of each model describe the cross sections of flounder. The model parameters of each fish were related to the lengths of the fishes, and were used for creating virtual populations (see chapter 2.2.6.3).
23
Material and Methods
Fig. 13: Digitalized cross-sections (CS) are represented by around 120 digital dots (left: CS1, right: CS2).
SHAPE CHART SHAPE CHART 100 100
90 90
80 80
70 70
60 60
50 50
40 40
30 30
20 20
10 10
0 0
-10 -10
-20 -20
-30 -30
-40 -40
-50 -50
-60 -60
-70 -70
-80 -80
-90 -90
-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 100
Fig. 14: Models (red dots) were fitted to digitalized cross-sections (green dots), left: model F_Flex_Drope is adapted to flounder CS1; right: model Asym_Trapetz is adapted to flounder CS2
Fig. 15: Models (red dots) were fitted to digitalized cross-sections (green dots), picture above: model F_Flex_Drope is adapted to flounder CS1; picture below: model Asym_Trapetz is adapted to flounder CS2 24
Material and Methods
2.2.4 Fall-through experiments
It was tested whether or not a fish passes through defined meshes (Herrmann et al. 2009). Forms of meshes with different sizes and shapes were cut in 5 mm solid nylon plates, which were placed in a construction to keep them horizontally (Fig. 16). The fish was held on its tail. It was tested whether the fish could pass the mesh due to its own gravity (Fig. 16). The best position of the fish in relation to the mesh shape was assumed, while changing the fish position.
Four different mesh shapes (square, diamond, hexagonal, and rectangular) were tested in the experiments. Different parameters were used to describe the mesh shapes (see chapter 2.2.4.1). In total, 191 different mesh templates were tested and 22 347 fall through trials were practiced for 117 measured flounders. The result is a matrix which shows whether a fish pass through the mesh or not (matrix with Y and N). These experimental fall through trials are the baseline for the determination of the penetration model, which is used for further predictions.
Fig. 16: Fall-trough trials: construction with nylon plates (left), fish were held on its tail (middle) and example of a mesh shape with a labeled flatfish (right)
25
Material and Methods
2.2.4.1 Parametric description of mesh shapes
Four general mesh types (square, diamond, hexagonal, and rectangular) were used for fall trough trials. Each mesh type is defined by specific parameters (Fig. 17).
The diamond shape is defined by the mesh size (m) and the opening angle (OA), which defines the opening of the mesh. The bar length is defined by half of the mesh size (m/2) (Fig. 17 a).
The mesh opening of a symmetric hexagonal mesh is also defined by the opening angle (OA) and two mesh bar lengths (parameter b and k) (Fig. 17 b).
The rectangular mesh is defined by two mesh bar lengths (parameter a and b), with the assumption of a right angle. Its mesh opening is defined by a squareness factor (SFA = 100 * (a/b)) (Herrmann et al. 2009) (Fig. 17 c).
Consequently, a square mesh is a special case of each mesh type. A square mesh is a diamond mesh with OA = 90°, a hexagonal mesh with OA = 180° or, a rectangular mesh with SFA = 100 % (Herrmann et al. 2009).
Fig. 17: Three tested geometrical shapes: diamond (a), hexagonal (b), and rectangular (c), are defined by different parameter: opening angle (OA), mesh size (m), bar lengths (a, b, k) (Sistiaga et al. 2011)
26
Material and Methods
2.2.5 Simulation of mesh-penetration and selection of a penetration model
The shape of a fish has some flexibility. It can be compressed or deformed while escaping through meshes. It is important to include information about the possible morphological change or compression during the escape process for the investigation of the real cross section of the fish. The compression which is used in FISHSELECT is the ability of the fish to deform its cross section shape during penetration a stiff mesh under the pull of gravity (Herrmann et al. 2009). The aim is the identification of the best penetration model, which describes the real compression of the fish. Information about real compressions of fish species are included in the conducted fall trough trials. While fish shapes were changed due to mesh penetration, they can pass meshes.
A fish can be compressed in three ways: dorsal, lateral, and vertical. The potential of compressibility of a fish species depends mainly on its specific morphology. Its compressibility depends especially on the bone structure or distribution and changing of soft tissues changes along the body (Herrmann et al. 2009). For a round fish, like cod, the highest deformation is expected in the ventral region (Fig. 18). Due to its bony structure, the dorsal and the head region is expected to have a lower deformation capability. It can be assumed that compression of the left and right body part of the cod are nearly equal (Herrmann et al. 2009). The compression differs between round fishes and flatfishes. The high deformation of the flatfish body depends on its morphometric. The flounder body is deformable, especially due to its flexible lateral fins.
COD FLOUNDER
Fig. 18: Schematic compressions of fish bodies, for example for cod (left) and flounder (right), black line represent the original transverse cross-section, the dashed line represents the compressed cross-sections
27
Material and Methods
In FISHSELECT, different types of penetration models can be used for simulation. Each model type is described by different parameter and assumes a specific compression of the fish. For flounder a penetration model is used, which include different deformation rates of the width and high (Fig. 18). The model is named STIFF_VER_CUT_COMP in FISHSELECT and is described by two parameters (CutW, CompH). Parameter CutW represents the cut in weight and parameter CompH represents the compression in height of flounder.
The model was applied with different parameter values for each cross section. Parameter values of CutW ranged between 0.0 and 0.48, in steps of 0.03 (result in 17 values). For parameter CompH, the values ranged between 0.7 and 1.0, in steps of 0.03 (result in 11 values). Consequently, in total were tested 187 (= 17 * 11) models for each cross section. Both cross sections were tested individually.
Fall trough trials were simulated for each combination of parameter of the penetration model. The result matrices were compared with the result matrices of the experimentally conducted fall trough trials. The results of the conducted fall trough trials include information about morphological aspects (cross sections) and the real compression of the fish bodies.
After comparison, the model results are listed due to their similarity with the experimental fall trough results. The degree of agreement (DA) presents the agreement of the assumed penetration model and the experimental fall trough trials. The DA is calculated by using the similarity (S) and dissimilarity (D) between simulated and experimental results (Equation 8). The value of DA ranges between 0 and 100 %, whereas 100 % indicates optimal agreement with the experimental fall trough trials.
= Equation 8 +
28
Material and Methods
After the preliminary results, a refined run of the simulation was done with more precise parameter for model optimization. Therefore, a refined small scaled parameter range is used for penetration models of both cross sections. For CS1, the values of parameter CutW ranged between 0.4 and 0.5 in steps of 0.03 (result in 5 values), and the values of parameter CompH ranged between 0.91 and 0.88 in steps of 0.1 (result in 4 values). Consequently, 20 models were tested for CS1. For CS2, the values of parameter CutW ranged between 0.04 and 0.10 in steps of 0.01 (result in 7 values), and the values of parameter CompH ranged between 0.83 and 0.68 in steps of 0.01 (result in 16 values). Consequently, 112 models were tested for CS2.
Afterwards, a comparative run with penetration models of both cross sections followed. The aim is the determination of the influence of both cross sections of the penetration of the mesh. Therefore, combined models of CS1 and CS2 were tested against the experimental fall through trials. In total, 2240 (= 20 * 112) models were tested. The DA value is used to identify the best performing penetration model. The penetration model with the highest DA is used for the predictions of selectivity parameters in further analyses (see chapter 2.2.5).
29
Material and Methods
2.2.6 Prediction of selectivity parameter (Design guide)
The best penetration model is used for further simulations and predictions, especially the prediction of selectivity parameters for defined meshes (Herrmann et al. 2009). In this study, the selectivity parameter L50 and SR are used. To estimate the selectivity parameter the following information are important (Herrmann et al. 2009):
i) Parameters of defined mesh types are defined for different mesh shapes and mesh sizes. Defined mesh types are for example diamond, hexagonal, or rectangular meshes. With defined mesh parameter were created single mesh lists. ii) A virtual fish population with a suitable size structure was created. iii) Fall trough trials were simulated for each individual fish of the virtual population. It was tested whether fish would pass different meshes, which are defined in the mesh lists.
For the investigation of the selective properties for specific meshes, the type and size of the meshes have to be defined. A variety of different meshes (included in a mesh list) were created by changing the mesh parameters.
2.2.6.1 Mesh list diamond
Diamond meshes are defined by two parameters (see chapter 2.2.4.1). The mesh list includes diamond meshes, which mesh sizes ranged between 70 and 170 mm, in steps of 5 mm. This results in 20 different mesh sizes. The opening angles of the diamond meshes vary between 10 and 90 degrees for each mesh size and were categorized in steps of 5 degree. This results in 17 different opening angles for each mesh size. In total, 357 different diamond meshes were tested.
2.2.6.2 Mesh list rectangular
The rectangular mesh is described by two parameters, the bar lengths a and b, and the mesh opening is defined by a squareness factor (SFA = 100 * a/b) (see chapter 2.2.4.1). The mesh list includes meshes with SFA values between 10 and 100%, in steps of 5 %. The mesh openings of the rectangular meshes ranged between 60 and 160 mm, in steps of 5 mm. 30
Material and Methods
2.2.6.3 Virtual flounder population
The properties of the virtual population were defined by using empirically establishes relationships between parameter of fish length and cross section shape, and their variations (Herrmann et al. 2009). A virtual population of 2000 individuals was generated randomly from a uniform size structure with the FISHSELECT tool (Herrmann et al. 2009), based on flounders measured onboard the FRV Solea. The lengths of fishes of the virtual population ranged between 30 and 600 mm, to cover a wide size range of flounder.
2.2.6.4 Multi simulation
It was tested whether or not the fishes of the virtual population (2000 flounder) could escape through meshes, which were defined in mesh lists. Each of the 2000 flounders was tested to pass through meshes of the diamond list, which consider 357 meshes. This results in 646000 simulated escapement trials. Results of the simulated escapement trials were used to calculate mesh specific selectivity parameters.
2.2.6.5 Usage of design guide
Selectivity parameters, L50 and SR, were predicted using the built in function in FISHSELECT and by assuming (Herrmann et al. 2009):
i) a logistic selection curve and ii) treating the simulated penetration data for each mesh as covered codend size selection data (Wileman et al. 1996).
The predicted L50 and SR values were visualized in a design guide. A design guide is an isoplot and presents L50 or SR values for defined mesh parameter of a defined mesh type (for example L50 values for diamond meshes with different mesh openings and opening angles). The design guides were creating using R, the statistical environment for computation and graphics (R Development Core Team 2012).
31
Material and Methods
2.3 Comparison of simulated and experimental data
The results of the simulated L50 data should be verified regarding their consistency with experimental data. Simulated and experimental results of selectivity for diamond meshes with a mesh size of 125 mm were compared.
The experimental data sampling was conducted during the 636th cruise of the FRV Solea in the western and southern Baltic Sea between 18th March and 7th April 2011. On this cruise different codends were tested and 43 hauls were conducted. 4 hauls were conducted with a T0 codend (twine: single 4 mm, and mesh opening of 125 mm). The cover codend method was applied (Wileman et al. 1996). The catch data of the codend and cover were analyzed separately. The flounder catch data from the individual hauls were pooled and analyzed. A logistic selection curve was applied using the SELNET software, developed by Bent Herrmann. Detailed information about SELNET are described by several studies (Sistiaga et al. 2010).
The selectivity properties based on these experimental data were compared to simulated selectivity properties. The simulated data were produced for flounder and a diamond mesh with a mesh opening of 125 mm and compared regarding their determined and predicted L50 and SR to verify the conformity between both.
32
Material and Methods
2.4 Prediction of selectivity parameters for T90 meshes
The shapes of diamond meshes are rather defined under fishing conditions and only the mesh opening angle is changing. It is more complex to describe the shape of a T90 mesh during fishing. Information about T90 codends are rarely, especially information of under water records. Analyzing of underwater records enables the investigation of real mesh performances of the codend. The knowledge about real mesh performance could amplify results of theoretical analyses.
In this study, mesh shapes of a T90 codend were extracted from underwater records. Afterwards, the meshes were digitalized. Knowledge of defined mesh shapes is used to predict selectivity properties of T90 codend. Furthermore, information were investigated about opportunities of a fish passing through observed meshes or not.
The data collection process was conducted onboard the FRV Walther Herwig III (353rd cruise) in the Baltic Sea between 01st April and 14th April 2012. A standard T90 codend (120 mm, polytit compact single 4 mm twine) was applied. An underwater video camera was installed on a remotely operated towed vehicle (ROTV). The ROTV was directly guided along the net and meanwhile underwater videos were recorded.
Underwater videos were converted into single images for further analyses with the FISHSELECT software. The underwater images were analyzed regarding the mesh performance of the codend. Two different parts of the codend were analyzed separately, because the mesh performance differs at different parts of the codend. The parts were classified:
i) Beginning of the net with narrow, more closer meshes and (front part) ii) Terminal part of the net with wider open meshes (end part)
Mesh shapes were extracted and digitalized of the front part (20 meshes) and of the end part (15 meshes) with the FISHSELECT software. The procedure of mesh shape description is similar to the description of fish shapes (chapter 2.2.2). The mesh shapes were described by two models (diamond and hexagonal) using the FISHSELECT method. The model fitting follows also the same procedure as for fish shapes (chapter 2.2.3). Best fitted model were determined.
33
Material and Methods
Furthermore, different selectivity properties for different model descriptions of T90 mesh shapes were investigated to validate differences in model approximations of the mesh shapes. Two models, which were approximations of the mesh shapes, were adapted to the mesh shapes. Furthermore, a model is used, which includes exact information of the digitalized dots of the mesh shapes and is named non parametric model. The non parametric model describes the digitalized mesh shape of the codend. Consequently, three models describe the mesh shapes of the under water records:
i) Nonparametric shapes of the meshes ii) Diamond shape and iii) Hexagonal shape description.
Three mesh lists were created. Each mesh list includes information of each mesh adapted to the model. Selectivity parameter, L50 and SR, were predicted and compared for each mesh and model. Thereby, the non parametric model is used as a reference model, which is compared with the approximations of the models diamond and hexagonal. The differences between each model and the reference model were calculated by using the sum of squares (SS) method (Equation 9).
= − ̅ Equation 9
The conformity between the approximations and the baseline were investigated to show the differences between calculated properties of the different approximations.
34
Material and Methods
2.5 Influence of twine characteristics on flounder selectivity
The codend is one of the most important factors, which influence size selectivity. Its selectivity depends on codend characteristics, as used material, mesh shapes, and sizes, and also twine characteristics. The influence of defined twine characteristics on flounder selectivity were investigated in this study. Tested twine parameter were:
i) Number of twine (single or double), ii) Twine diameter, and iii) Direction of the netting (T0 or T90).
2.5.1 Data sampling
Data sampling was conducted onboard the FRV Solea (42 m length overall, 950 kW) in the Baltic Sea between 18th March and 7th April 2011 (636th cruise). In total, 12 different codends with modified twine characteristics were tested during this cruise. The same netting material was used for T0 and T90 direction. For both directions, codends with single twine and nominal twine diameter of 4, 6, and 8 mm were tested. Consequently, for each direction were tested 3 codends. Furthermore, codends with double twine and nominal twine diameter of 3, 4, and 6 mm were tested. This results in additional 3 codends for each direction. Consequently, 12 different codends were tested (with in total 43 hauls):
• T0, single twine (nominal twine diameter 4, 6, and, 8 mm) • T90, single twine (nominal twine diameter 4, 6, and, 8 mm) • T0, double twine (nominal twine diameter 3, 4, and, 6 mm) • T90, double twine (nominal twine diameter 3, 4, and, 6 mm)
All tested codends were constructed of polytit COMPACT netting (EuroREd S.L., Callosa de Segura, http://www.euroned.org) and have 50 meshes in the circumference of the codend.
The cover codend method (Wileman et al. 1996) was applied. Each tested codend was encased by a second codend, named cover, which has small mesh sizes of 80 mm. The calculated selectivity based on entire fish population, which enters the codend. Escaped fishes of the codend were caught with the cover. Both catches were analysed to calculate selectivity parameter. 35
Material and Methods
2.5.2 Analysis
The same analyze method, developed and applied for round fish and plaice by Herrmann (Herrmann et al. 2012b), was used for flounder. Analyses were done with the software SELNET, which is a flexible analyze tool for investigating selectivity of gears, developed by Bent Herrmann and used in several studies (for example (Sistiaga et al. 2010; Wienbeck et al. 2011; Herrmann et al. 2012a)). The SELNET software is used to analyze size selectivity and catch data of towed fishing gears, and is used for simulations and predictions. Single hauls or groups of hauls could be analyzed. The analyses include two steps (further information (Herrmann et al. 2012b)):
i) The individual hauls were analyzed by applying the logistic curve (Wileman et al. 1996) to investigate selectivity parameters, L50 and SR and corresponding SF and SFA values. ii) The results were used of all individual hauls simultaneously for SF and SFA, and the covariance matrix for these. Further the information of parameter twine diameter, number of twine, and direction were implemented.
The influence of the twine parameters were tested in a model, which include parameter terms and interaction terms. The full model is described by Herrmann (Herrmann et al. 2012b). Each possible combination of the parameters were tested with the Fryer method (Fryer 1991), a fixed random model. The Fryer method is used for pairwise analysis. And it considers between haul variation in the size selection process without any fixed effects.
The fixed parameters are: twine diameter, number of twine (double and single) and the direction of the netting (T0 and T90). Random parameters are the variations between the hauls. The models based on selectivity factors SF and SFA, which include information about defined mesh sizes. The mesh sizes differ for the tested codends. Information about exact twine diameter were investigated by (Herrmann et al. 2012b). For SF and SFA, in each case, were used 9 different parameters, which result in 218 possible combinations. Consequently, 262144 different potential Fryer models were tested against each other. The full model description is described in by Herrmann (Herrmann et al. 2012b). All tested combinations of the model are ranked, based on following criteria:
i) Significance of parameter ii) AIC value (Akaike 1974) Best model hast significant parameters and lowest AIC. 36
Results
3. Results
3.1 Influence of flounder morphology on selectivity
3.1.1 Data sampling
In total, 117 flounder were sampled and analyzed. All flounder were handpicked from the fishing operation. The selected fishes should cover a wide size range. The lengths of the sampled flounder ranged between 15.5 cm and 64.1 cm (Fig. 19). Their weights ranged between 38 and 1417 g (Fig. 20). Relation between weights and lengths of measured flounder is quite high (R square = 0.98).
8 7 6 5 4
Number 3 2 1 0 150 175 200 225 250 275 300 325 350 375 400 425 450 475 500 Length [mm]
Fig. 19: Length distribution of measured flounder
1600 y = 4E-06x3.1867 1400 R² = 0.9808 1200
1000
800
Weight Weight [g] 600
400
200
0 150 175 200 225 250 275 300 325 350 375 400 425 450 475 500 Length [mm]
Fig. 20: Length-weight relation of measured flounder
37
Results
3.1.2 Shape model
The shapes of 117 flounders were identified while measuring two transverse cross sections of each flounder with the Morphometer. After scanning and digitalizing of the measured cross sections, ten models were tested for each cross section (CS1 and CS2) to investigate the model which describes the cross section of the flounder best. Each model is applied to each cross section and fish. The fit between model and cross section is given by fit statistics (R square and AIC values). For each model the averaged R square and AIC value of all fishes of one cross section were calculated and compared (Tab. 3, Fig. 6). The model named F_Flex_Drope (mean AIC: 471.565; mean R square: 0.979) performed best for cross section one (Tab. 3). For cross section two (CS2) the model named Asym_Trapetz performed best (mean AIC: 575.5311; mean R square: 0.9917) (Fig. 6). Therefore, these two models were used for further analyses.
Tab. 3: Summary of fit statistics of model results to describe cross-section 1, grey shaded line represent best model
CS1 Model Mean AIC MeanR F_Flex_Drope 471.5652 0.9791 F_Soft_Hexagonal 570.3277 0.9592 F_Shoe3 503.7614 0.9747 F_Kotelet 534.5465 0.9681 F_Shoe4 542.5109 0.9662 F_Flex_Ellipse2 499.2340 0.9751 F_Flex_Ellipse3 517.6831 0.9714 Half_Ellipse 535.3755 0.9675 Sym_Trapetz 523.1918 0.9711 Asym_Trapetz 480.5939 0.9789
Tab. 4: Summary of fit statistics of model results to describe cross-section 2, grey shaded line represent best model CS2 Model Mean AIC Mean R F_Flex_Drope 612.2218 0.9881 F_Soft_Hexagonal 831.8617 0.9637 F_Shoe3 686.5860 0.9828 F_Kotelet 769.8844 0.9732 F_Shoe4 779.9436 0.9712 F_Flex_Ellipse2 656.0635 0.9850 F_Flex_Ellipse3 700.4350 0.9809 Half_Ellipse 778.4606 0.9720 Sym_Trapetz 663.0076 0.9853 Asym_Trapetz 575.5311 0.9917
38
Results
F_Flex_Drope F_Soft_Hexagonal
F_Shoe3 F_Kotelet
F_Shoe4 F_Flex_Ellipse2
F_Flex_Ellipse3 Half_Ellipse
Sym_Trapetz Asym_Trapetz
Fig. 21: Ten different shape models were adapted to cross-section one (CS1) of a single flounder
39
Results
Parameters of both models were plotted against the lengths of the flounders (Fig. 22). Power functions (x = a * lb) of flounder lengths were fitted to these data and 95 % confidence levels were illustrated.
The fit statistics for each parameter of the model were calculated. Power functions were applied (x = a * lb), and coefficients a and b, and further R square values were calculated for both cross sections (Tab. 5, Tab. 6).
For CS1, with model Flex_Drope, highest R square values were investigated for parameter c1 (R square = 0.862) and c2 (R square = 0.919), which represent the height and the bottom width of the model (Tab. 5). Lowest R square value was calculated for parameter c3 (R square = 0.222), the top width of the model (Tab. 5).
Fig. 22: Values of each parameter are plotted against fish lengths, 95 % confidence limits are illustrated
Tab. 5: Fit statistics of model Flex_Drope for cross-section 1 Power function (x = a * lb) Parameter R square Coefficient a Coefficient b Parameter vs. fish length c1 0.862 0.161 0.98 c2 0.919 0.085 0.99 c3 0.222 0.804 0.31
40
Results
For CS2, with model Asym_Trapetz, highest R square values were investigated for parameter c2 (R square = 0.951) and parameter c1 (R square = 0.784) (Tab. 6). Lower R square values were investigated for parameter c3 (R square = 0.563) and c4 (R square = 0.578) (Tab. 6).
Fig. 23: Model Asym_Trapetz: Values of each parameter are plotted against fish lengths, 95 % confidence limits are illustrated
Tab. 6: Fit statistics of model Asym_Trapetz for cross-section 2 Power function (x = a * lb) Parameter R square Coefficient a Coefficient b Parameter vs. fish length c1 0.784 0.042 1.11 c2 0.951 0.394 1.02 c3 0.563 0.027 1.28 c4 0.578 0.326 0.86
41
Results
3.1.3 Penetration model
Fish specific compressions, while passing the mesh, should be investigated and the information should be implemented in models which were used for further calculations. The possible compressions of each fish were defined while testing several penetration models for each cross section (see chapter 2.2.5). For the first run, in total 187 penetration models for each cross section (CutW 17 * CompH 11) were tested. In the second refined run 20 models (CutW 5 * CompH 4) were tested for CS1. For CS2, in total 112 models were tested (CutW 7 * CompH). First, the defined penetration models were conducted for each individually cross section.
Afterwards, a combined simulation followed with in total 2240 (= 20 * 112) models, where both cross sections were included. The best penetration model was selected based on the highest value of degree of agreement (DA). This value takes into account the similarity (S) and dissimilarity (D) between penetration models and experimental fall through trials (see chapter 2.2.5).
A comparison of values of penetration model parameter, similarity, dissimilarity and degree of agreement of the first and refined simulation indicate minor differences between both (Tab. 7, Tab. 8). For flounder, the degree of agreement (DA) of the first (coarse) combined simulation was 96.715 %, whereas the degree of agreement was 96.818 % for the combined refined simulation. High DA values indicate high conformity between the penetration model simulation and experimental results (fall through results). Therefore, this best penetration model with defined parameter is used for further analyses.
42
Results
Tab. 7: Results of first (coarse) run of penetration models for cross-section 1 (CS1) and cross-section 2 (CS2) and penetration model parameter (CutW and CompH) Cross Section CutW CompH Similarity Dissimilarity Degree of Agreement CS1 0.00 1.00 19184 3163 89.585 % CS2 0.09 0.79 21359 988 95.579 % Combined CS1 0.48 0.91 21613 734 96.715 % CS2 0.06 0.70
Tab. 8: Results of second (refined) run of penetration models for cross-section 1 (CS1) and cross-section 2 (CS2) and penetration model parameter (CutW and CompH) Cross Section CutW CompH Similarity Dissimilarity Degree of Agreement CS1 0.40 0.91 18026 4321 80.664 % CS2 0.08 0.78 21363 984 95.597 % Combined CS1 0.50 0.91 21636 711 96.818 % CS2 0.08 0.68
43
Results
3.1.4 Prediction of selectivity parameters
The best penetration model and the created virtual population of 2000 flounder were used to predict selectivity parameters for different mesh configurations, based on for example diamond, hexagonal, and rectangular mesh shapes. Selectivity parameters, L50 and SR, were calculated based on simulated retention data for different mesh characteristics, such as opening angles and mesh openings of defined meshes
3.1.4.1 Analyses of diamond meshes (T0)
For flounder, the selectivity parameters differ for diamond meshes and depend on mesh opening and opening angle (Fig. 24, Fig. 25). The mesh openings of the diamond meshes ranged between 70 and 170 mm and the opening angle ranged between 0 and 90° (Fig. 9). Minor differences of L50 values are between opening angles of 40 and 60 degrees for one mesh opening (Fig. 24). This enables relative stable selectivity despite changes in opening angles. High differences of the L50 values are between opening angle 0 and 40 degrees for one specific mesh opening. These changes of L50 with mesh opening could result in high variability of selectivity of the codend during towing.
Commonly BACOMA codends are used in the Baltic cod fishery. BACOMA condends consist of two types of diamond meshes. The main part of the codend is made of diamond meshes with a mesh opening of 105 mm. Additional an escapement window is implemented in the upper end part of the codend. It composed of square meshes (which are a special case of the diamond mesh) and have mesh openings of 120 mm.
For both mesh openings, predicted L50 and SR values are illustrated in the isoplot. Square meshes with a mesh opening of 120 mm have a predicted L50 of 21.01 cm for flounder. For diamond meshes with 105 mm mesh opening ranged the predicted L50 between 11.10 cm and 23.41 cm. The predicted maximum L50 values are simulated at an optimal opening angle of 45 degree. The predicted values of L50 are smaller than 25 cm, which is the current minimum landing size of flounder in Germany, for both mesh types. Selection ranges indicate a relative homogenous pattern for each mesh opening and opening angle (Fig. 25). The predicted SR ranged between 1.06 cm and 1.82 cm for diamond meshes with 105 mm mesh opening. The predicted SR is 1.71 cm for diamond meshes with 120 mm mesh opening. 44
Results
15
80
0 2 1 2
4 2 6 25 2 60 7 2 8 2
9 2 1 0 2 3 3 3
3 4 5 6 7
3 3 3 3 3
40 openingangle[degree]
19 17 18 1 16 20 5 1 14 1 123 22 23 110
80 100 120 140 160 mesh opening [mm]
Fig. 24: Design guide for diamond meshes for flounder: isolines represent L50 (in cm) values for specific mesh openings (in degree) and opening angles (in mm); L50 values of a square mesh of BACOMA window (opening angle 90 degree, mesh opening 120 mm) (red dot), SR values of standard BACOMA meshes (mesh opening 105 mm) (green line)
0 . 1 1.0
1 . 1.9 80 1 1.9
1 1.8 . 5 1.8
0 . 1.9 60 1 1.8 1.7 2.0 1.92.0 2.2 4 . 5 2 . 40 2 1.8 openingangle[degree] .7 1 1 2 . . 2.5 0 1.1 2 2.4 2.3 20 1.2 2.0 2.1 1 1.4 1.6 1.9 0 .0 1.3 .9 1.1 1.3 1.2 1.71.8
80 100 120 140 160 mesh opening [mm] Fig. 25: Design guide for diamond meshes for flounder: isolines represent values of selection range (SR in cm) for specific mesh openings (in mm) and opening angles (in degree), SR values of a square mesh of BACOMA window (opening angle 90 degree, mesh opening 120 mm) (red dot), SR values of standard BACOMA meshes (mesh opening 105 mm) (green line)
45
Results
Tab. 9: Predicted L50 values (in cm) for flounder: diamond mesh with defined opening angle (oa in degree) and mesh opening (in mm); cells shaded green correspond to meshes in the lower panel in BACOMA codend (105 mm); red shaded cell correspond to meshes in the escapement window of current legal BACOMA codend (square mesh with mesh opening of 120 mm)
170 18.10 24.47 29.01 32.65 34.67 35.90 36.66 36.76 36.55 35.86 35.29 34.39 33.71 32.85 31.74 30.56 29.58 32.28
165 17.44 23.70 28.32 31.68 33.75 35.08 35.56 35.67 35.41 34.92 34.23 33.69 32.85 31.84 30.78 29.85 28.71 31.38
160 16.73 23.08 27.60 30.69 32.91 34.02 34.54 34.73 34.37 34.09 33.41 32.69 31.81 30.81 30.00 29.07 27.73 30.49
155 16.16 22.30 26.69 29.80 31.77 32.94 33.61 33.80 33.42 33.01 32.42 31.71 30.73 30.08 29.22 27.87 27.27 29.58
150 15.78 21.71 26.04 28.69 30.92 31.95 32.64 32.71 32.39 32.00 31.36 30.53 30.03 29.20 27.98 27.39 26.31 28.68
145 15.34 20.98 25.35 28.03 29.69 30.99 31.32 31.64 31.31 30.90 30.31 29.70 29.10 27.98 27.40 26.52 25.51 27.77
140 14.69 20.32 24.27 27.36 28.88 29.82 30.41 30.51 30.16 29.80 29.30 28.86 27.92 27.39 26.56 25.65 24.60 26.85
135 14.24 19.65 23.41 26.42 28.06 28.92 29.34 29.47 29.22 28.99 28.27 27.73 27.30 26.49 25.68 24.56 23.66 25.97
130 13.74 18.92 22.65 25.53 27.39 28.07 28.36 28.60 28.34 27.92 27.51 27.05 26.31 25.57 24.60 23.72 22.89 25.13
125 13.38 18.15 22.00 24.50 26.38 27.37 27.51 27.48 27.34 27.20 26.67 26.08 25.23 24.53 23.66 22.89 21.95 24.25
120 12.80 17.47 21.21 23.54 25.32 26.32 26.80 26.76 26.44 26.08 25.44 24.82 24.27 23.54 22.77 21.89 21.01 23.32
esh opening in in mm opening esh 115 m 12.20 16.73 20.12 22.74 24.30 25.13 25.52 25.63 25.29 24.85 24.37 23.82 23.17 22.59 21.68 20.93 20.24 22.31
110 11.62 16.03 19.34 21.77 23.26 24.11 24.53 24.40 24.21 23.79 23.29 22.83 22.24 21.48 20.87 20.17 19.45 21.38
105 11.10 15.29 18.34 20.83 22.15 23.11 23.32 23.41 23.07 22.77 22.33 21.71 21.10 20.58 19.96 19.24 18.46 20.40
100 10.52 14.46 17.76 19.79 21.13 22.00 22.27 22.21 22.00 21.68 21.19 20.75 20.38 19.66 18.91 18.26 17.53 19.44
95 10.06 13.90 16.90 18.70 20.31 20.99 21.19 21.08 20.88 20.64 20.30 19.84 19.31 18.60 18.07 17.35 16.85 18.53
90 9.53 13.26 16.13 17.91 18.91 19.81 20.10 20.07 19.86 19.52 19.17 18.70 18.26 17.60 17.01 16.47 15.97 17.55
85 9.13 12.65 15.08 17.09 18.08 18.76 18.98 18.87 18.70 18.46 18.10 17.65 17.15 16.76 16.28 15.77 15.39 16.64
80 8.60 11.74 14.22 16.17 17.08 17.66 18.01 17.94 17.68 17.34 16.96 16.71 16.33 15.89 15.54 15.11 14.42 15.73
75 8.18 11.00 13.33 15.09 16.20 16.62 16.74 16.73 16.63 16.33 16.15 15.87 15.57 15.16 14.63 14.00 13.37 14.80
70 7.74 10.34 12.75 14.16 15.29 15.94 15.97 15.91 15.82 15.62 15.39 14.96 14.55 14.09 13.55 13.04 12.46 13.98
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 OA Mean
46
Results
Tab. 10: Predicted SR values (in cm) for flounder: diamond mesh with defined opening angle (oa in degree) and mesh opening (in mm); cells shaded green correspond to meshes in the lower panel in BACOMA codend (105 mm); red shaded cell correspond to meshes in the escapement window of current legal BACOMA codend (square mesh with mesh opening of 120 mm)
1.59 1.68 2.28 2.54 2.77 2.44 2.27 2.57 2.44 2.21 2.16 1.94 2.14 2.14 2.18 2.10 1.96 2.20 170
1.35 1.84 2.20 2.83 2.52 2.50 2.53 2.36 2.30 1.96 1.95 2.14 2.20 2.16 1.98 2.14 1.96 2.17 165
1.15 1.82 1.97 2.63 2.52 2.48 2.29 2.12 2.02 1.97 2.07 2.13 2.17 1.95 2.18 2.05 1.70 2.07 160
1.33 1.79 2.07 2.27 2.37 2.39 2.40 2.28 2.27 2.12 2.09 2.07 1.99 2.07 2.02 1.74 1.90 2.07 155
1.44 1.81 2.05 2.34 2.64 2.39 2.27 2.13 2.10 2.25 1.90 2.00 2.12 2.07 1.74 1.84 2.00 2.06 150
1.49 1.44 1.78 2.18 2.53 2.23 2.27 2.36 1.87 1.84 2.11 2.06 2.03 1.74 1.74 2.06 1.80 1.97 145
1.43 1.54 1.69 1.94 2.22 2.51 2.23 2.14 2.19 2.23 2.03 2.12 1.74 1.84 1.88 1.99 1.60 1.96 140
1.33 1.57 1.66 2.11 2.11 2.36 2.31 2.24 2.06 2.00 1.79 1.74 1.90 1.96 1.96 1.63 1.59 1.90 135
1.05 1.39 1.69 1.98 1.81 2.01 2.00 2.07 1.90 1.70 1.76 1.84 2.00 1.84 1.60 1.58 1.85 1.77 130
1.18 1.19 1.65 1.75 1.85 1.77 1.69 1.72 1.67 1.80 1.97 1.86 1.77 1.52 1.61 1.81 1.82 1.68 125
1.32 1.36 1.45 1.70 1.73 1.89 1.74 1.89 1.92 1.97 1.90 1.58 1.66 1.67 1.83 1.84 1.71 1.72 120
1.36 1.07 1.29 1.68 1.79 1.80 1.60 1.76 1.82 1.65 1.62 1.67 1.78 1.85 1.81 1.70 1.44 1.63 115
0.99 1.19 1.53 1.79 1.76 1.65 1.60 1.68 1.54 1.65 1.84 1.74 1.77 1.91 1.70 1.49 1.45 1.60 110
1.06 1.27 1.13 1.39 1.72 1.78 1.80 1.66 1.74 1.70 1.82 1.78 1.73 1.48 1.53 1.46 1.23 1.55 105
0.91 1.31 1.30 1.54 1.74 1.85 1.94 1.82 1.79 1.79 1.76 1.67 1.31 1.52 1.30 1.27 1.33 1.54 100
95 1.02 1.11 1.24 1.27 1.38 1.61 1.76 1.67 1.58 1.47 1.45 1.41 1.32 1.34 1.28 1.26 1.26 1.38
90 0.93 1.13 1.00 1.02 1.26 1.33 1.17 1.41 1.49 1.56 1.44 1.27 1.24 1.44 1.29 1.09 0.93 1.24
85 0.87 1.20 1.26 1.21 1.15 1.16 1.27 1.21 1.24 1.23 1.30 1.34 1.24 1.18 0.92 0.96 1.19 1.17
80 0.81 0.96 1.22 0.87 1.01 1.20 1.31 1.29 1.37 1.19 1.28 1.19 1.02 0.90 1.16 1.27 1.03 1.12
75 0.65 0.79 1.11 1.19 0.79 1.09 1.22 1.15 1.16 1.06 0.98 0.87 1.12 1.26 1.08 0.88 0.86 1.02
70 0.79 0.90 1.07 0.93 1.09 0.90 0.93 0.83 0.89 1.15 1.19 1.25 0.98 0.89 0.90 0.73 0.85 0.96
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 OA Mean
47
Results
3.1.4.2 Analyses of rectangular meshes
The flounder selectivity parameters depend on rectangular mesh opening and squareness factor (Tab. 11, Tab. 12). The predicted selectivity parameter, L50 and SR, are illustrated in isoplots (Fig. 26, Fig. 27).
The mesh opening range between 70 and 160 mm and the squareness factor between 10 and 100 %. Minor differences of the L50 values are between squareness factor 20 and 100 % for each defined mesh opening. The L50 values are relatively stable, by changing the squareness factor. High differences in L50 values are shown for squareness factors below 20 %. The predicted L50 value is 21.01 cm for a rectangular mesh with a squareness factor of 100 %, which is a square mesh.
48
Results
100 1 3
1 1
4 5
6
80 1
7
1
8
1
9 1 0 21 60 2
2 3 1 2 2 2 4 2 5 40 2
squareness factor [%] factor squareness 6 7 2 2 8 2 9 2 1 3 20 30 1314 12
80 100 120 140 160 mesh opening [mm]
Fig. 26: Design guide for rectangular meshes for flounder: isolines represent L50 values (cm) for specific mesh openings (mm) and squareness factors; L50 of a square mesh of BACOMA window (squareness factor = 100 %, mesh opening 120 mm) (red dot)
100 1.7 1.8 0.9
80 1.0 1.6 2.0 1.3 1.5 2.0 1.71.8 1.0 3 . 1 1.6 60 0.9 1.4
1 1 1.8 1
. . 5 . 6 7 1.7
1 1.1 1.0 40 .
squareness factor [%] factor squareness 1 1.0 1.8 2.0 1.8 1.9 2.1 1.1 1.7 1.7 2.0 20 1.2 1.91.8 1.8 2.23 1.41.5 2.1 2.645 1.87
80 100 120 140 160 mesh opening [mm]
Fig. 27: Design guide for rectangular meshes for flounder: isolines represent SR values (cm) for specific mesh openings (mm) and squareness factors (%), SR of a square mesh of BACOMA window (squareness factor = 100 %, mesh opening 120 mm) (red dot)
49
Results
Tab. 11: Predicted L50 values (in cm) for flounder: rectangular mesh with defined squareness factors (SFA in %) and mesh openings (in mm); red shaded cell corresponds to meshes in the escapement window of current legal BACOMA codend (square mesh with mesh opening of 120 mm)
20.22 29.01 32.79 32.21 31.31 30.66 30.28 29.78 29.43 29.02 28.84 28.50 28.19 27.89 27.73 27.70 27.76 27.68 27.73 28.78 160
19.64 27.95 31.83 31.03 30.46 29.93 29.38 28.89 28.21 27.87 27.70 27.64 27.50 27.39 27.30 27.27 27.26 27.22 27.21 27.98 155
18.95 27.19 30.73 30.33 29.53 29.02 28.18 27.75 27.50 27.34 27.22 27.02 26.87 26.56 26.34 26.31 26.37 26.25 26.28 27.14 150
18.32 26.18 29.98 29.32 28.52 27.84 27.47 27.22 26.84 26.37 26.28 26.16 25.95 25.80 25.56 25.47 25.38 25.32 25.47 26.29 145
17.62 25.38 28.99 28.14 27.50 27.31 26.81 26.28 25.98 25.59 25.35 25.17 24.95 24.82 24.66 24.63 24.56 24.47 24.60 25.41 140
16.92 24.46 27.76 27.42 26.81 26.23 25.77 25.32 24.92 24.63 24.40 24.21 24.18 23.98 23.72 23.63 23.69 23.51 23.63 24.48 135
16.23 23.52 27.15 26.40 25.87 25.26 24.73 24.37 23.98 23.60 23.51 23.39 23.26 23.11 22.98 22.92 22.92 22.83 22.89 23.63 130
15.67 22.64 26.05 25.41 24.85 24.21 23.79 23.51 23.11 22.86 22.74 22.56 22.47 22.24 22.09 22.00 22.00 21.86 21.95 22.74 125
15.07 21.71 24.91 24.50 23.76 23.39 22.90 22.66 22.21 21.95 21.62 21.48 21.31 21.19 21.04 21.04 21.04 20.93 21.01 21.77 120
14.40 20.75 23.92 23.48 22.96 22.50 21.86 21.48 21.13 20.78 20.81 20.72 20.61 20.41 20.28 20.28 20.31 20.25 20.28 20.91 115
mesh opening in in mm opening mesh 13.86 20.03 22.83 22.53 21.74 21.31 20.93 20.72 20.45 20.22 19.97 19.83 19.76 19.66 19.49 19.45 19.49 19.34 19.45 20.06 110
13.31 18.94 21.74 21.31 20.75 20.53 20.06 19.69 19.45 19.20 18.95 18.77 18.67 18.60 18.46 18.42 18.43 18.32 18.46 19.06 105
12.56 18.04 20.69 20.47 19.91 19.59 18.99 18.70 18.49 18.29 18.13 18.04 17.94 17.71 17.58 17.52 17.59 17.43 17.58 18.17 100
95 11.93 17.17 19.77 19.49 18.81 18.43 18.10 17.91 17.52 17.29 17.18 17.10 16.99 16.94 16.82 16.82 16.82 16.74 16.82 17.30
90 11.25 16.10 18.56 18.32 17.91 17.50 17.13 16.99 16.76 16.47 16.44 16.33 16.25 16.18 16.00 15.97 15.94 15.92 15.94 16.42
85 10.54 15.26 17.61 17.29 16.94 16.60 16.33 16.15 15.87 15.79 15.72 15.64 15.57 15.52 15.42 15.39 15.44 15.39 15.39 15.68
9.96 80 14.40 16.65 16.41 16.12 15.84 15.59 15.49 15.34 15.03 14.81 14.70 14.68 14.60 14.48 14.48 14.48 14.32 14.48 14.83
9.41 75 13.55 15.79 15.62 15.39 15.08 14.65 14.45 14.26 14.00 13.86 13.86 13.75 13.61 13.43 13.41 13.44 13.31 13.41 13.91
8.76 70 12.65 14.89 14.68 14.29 13.94 13.72 13.35 13.07 12.97 12.78 12.71 12.68 12.62 12.52 12.49 12.49 12.46 12.49 12.92
L50 SFA 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% Mean 100% 50
Results
Tab. 12: Predicted SR values (in cm) for flounder: rectangular mesh with defined squareness factors (SFA in %) and mesh openings (in mm); red shaded cell corresponds to square meshes in the escapement window of the current legal BACOMA codend (120 mm)
20.22 29.01 32.79 32.21 31.31 30.66 30.28 29.78 29.43 29.02 28.84 28.50 28.19 27.89 27.73 27.70 27.76 27.68 27.73 28.78 160
19.64 27.95 31.83 31.03 30.46 29.93 29.38 28.89 28.21 27.87 27.70 27.64 27.50 27.39 27.30 27.27 27.26 27.22 27.21 27.98 155
18.95 27.19 30.73 30.33 29.53 29.02 28.18 27.75 27.50 27.34 27.22 27.02 26.87 26.56 26.34 26.31 26.37 26.25 26.28 27.14 150
18.32 26.18 29.98 29.32 28.52 27.84 27.47 27.22 26.84 26.37 26.28 26.16 25.95 25.80 25.56 25.47 25.38 25.32 25.47 26.29 145
17.62 25.38 28.99 28.14 27.50 27.31 26.81 26.28 25.98 25.59 25.35 25.17 24.95 24.82 24.66 24.63 24.56 24.47 24.60 25.41 140
16.92 24.46 27.76 27.42 26.81 26.23 25.77 25.32 24.92 24.63 24.40 24.21 24.18 23.98 23.72 23.63 23.69 23.51 23.63 24.48 135
16.23 23.52 27.15 26.40 25.87 25.26 24.73 24.37 23.98 23.60 23.51 23.39 23.26 23.11 22.98 22.92 22.92 22.83 22.89 23.63 130
15.67 22.64 26.05 25.41 24.85 24.21 23.79 23.51 23.11 22.86 22.74 22.56 22.47 22.24 22.09 22.00 22.00 21.86 21.95 22.74 125
15.07 21.71 24.91 24.50 23.76 23.39 22.90 22.66 22.21 21.95 21.62 21.48 21.31 21.19 21.04 21.04 21.04 20.93 21.01 21.77 120
14.40 20.75 23.92 23.48 22.96 22.50 21.86 21.48 21.13 20.78 20.81 20.72 20.61 20.41 20.28 20.28 20.31 20.25 20.28 20.91 115
mesh opening in in mm opening mesh 13.86 20.03 22.83 22.53 21.74 21.31 20.93 20.72 20.45 20.22 19.97 19.83 19.76 19.66 19.49 19.45 19.49 19.34 19.45 20.06 110
13.31 18.94 21.74 21.31 20.75 20.53 20.06 19.69 19.45 19.20 18.95 18.77 18.67 18.60 18.46 18.42 18.43 18.32 18.46 19.06 105
12.56 18.04 20.69 20.47 19.91 19.59 18.99 18.70 18.49 18.29 18.13 18.04 17.94 17.71 17.58 17.52 17.59 17.43 17.58 18.17 100
95 11.93 17.17 19.77 19.49 18.81 18.43 18.10 17.91 17.52 17.29 17.18 17.10 16.99 16.94 16.82 16.82 16.82 16.74 16.82 17.30
90 11.25 16.10 18.56 18.32 17.91 17.50 17.13 16.99 16.76 16.47 16.44 16.33 16.25 16.18 16.00 15.97 15.94 15.92 15.94 16.42
85 10.54 15.26 17.61 17.29 16.94 16.60 16.33 16.15 15.87 15.79 15.72 15.64 15.57 15.52 15.42 15.39 15.44 15.39 15.39 15.68
9.96 80 14.40 16.65 16.41 16.12 15.84 15.59 15.49 15.34 15.03 14.81 14.70 14.68 14.60 14.48 14.48 14.48 14.32 14.48 14.83
9.41 75 13.55 15.79 15.62 15.39 15.08 14.65 14.45 14.26 14.00 13.86 13.86 13.75 13.61 13.43 13.41 13.44 13.31 13.41 13.91
8.76 70 12.65 14.89 14.68 14.29 13.94 13.72 13.35 13.07 12.97 12.78 12.71 12.68 12.62 12.52 12.49 12.49 12.46 12.49 12.92
L50 SFA 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% Mean 100%
51
Results
3.2 Comparison of simulated and experimental selection data for T0 meshes
Flounder specific retention data were simulated for different types of diamond meshes. Simulated selection retention data for flounder should be tested compared with experimental data from sea trials. The retention data of diamond meshes with a mesh opening of 125 mm were tested against experimental data from sea trials. The sea trials were conducted with a T0 single 4 mm net with a mesh opening of 125 mm. In total 4 hauls were conducted with this net after the cover codend method (Wileman et al. 1996).
The flounder retention data of all hauls were pooled and analyzed using a logistic selection curve. The retention as well as the selectivity parameter, L50 and SR, were calculated and compared (Fig. 28) (Tab. 13). The simulated selection curve show high conformity with the experimental results, because the differences between the predicted and experimental
L50 (SSL50 = 0.0108) and SR (SSSR = 0.0928) are minor (Tab. 13).
Tab. 13: Comparison of simulated and experimental selectivity parameter for flounder Experimental Simulated Sum of squares (SS) L50 25.0412 25.1451 0.0108 SR 3.1398 3.4444 0.0928
simulated data experimental data Retention rate 0.0 0.2 0.4 0.6 0.8 1.0
50 100 150 200 250 300 350 400 450 500 550 600 Length [mm]
Fig. 28: Comparison of simulated and experimental selection curves for flounder (diamond mesh with a mesh opening of 125 mm)
52
Results
3.3 Analyzing of T90 meshes
In total, 35 mesh shapes were extracted from images of underwater records for prediction of selectivity parameters of T90 meshes (Fig. 29). The mesh samples were extracted of two parts (front and end part) of the T90 single 4 mm codend with mesh opening of 120 mm (Fig. 29). The same procedure as for the flounder shape model selection was applied with the software FISHSELECT (chapter 2.2.1). Two shape models, diamond and hexagonal model, were tested to identify the best performed model for the T90 mesh (Fig. 29).
1b
1a 1c
2b
2a 2c
Fig. 29: Picture of the digitalized meshes of front part (1a) and the end part (2b) of the codend, and fitted hexagonal (1b, 2b) and diamond models (1c, 2c) for one single mesh 53
Results
The hexagonal model indicate best fit with highest R square (R squarefront = 0.94, R squareend = 0.95) and lowest AIC value (AICfront = 242.86, AICend = 271.51) for both parts of the net in comparison with the diamond model (R squarefront = 0.94, R squareend = 0.95;
AICfront = 242.86, AICend = 271.51) (Tab. 14). The hexagonal model has the highest R square values in the end part of the codend (R squareend = 0.95), but the difference between both parts are minor (R squareend R squarefront = 0.008) (Tab. 14).
Tab. 14: Fit characteristics (mean values of AIC and R square) of two applied shape- models (diamond, hexagonal) for end and front part of the codend
Fit characteristics Localisation of meshes Number of meshes Shape model AIC mean R2 mean Front part 20 Diamond 270.918 0.911 Front part 20 Hexagonal 242.865 0.942 End part 15 Diamond 304.296 0.919 End part 15 Hexagonal 271.512 0.950
Selectivity parameters were predicted for both models, the diamond and hexagonal shape model, based on the penetration model and virtual population of 2000 flounders. Additional selectivity parameters were predicted for a non parametric model, which include the digitalized dots of the flounder cross section shape. This model is used as a reference model to validate the performance of the diamond and hexagonal model.
Mean selective properties, L50 and SR, were calculated for all three models (Tab. 15). The nonparametric model has the lowest L50 and SR value (L50 = 22.6, SR =2.54). The hexagonal model has a close L50 value, but a wider selection range. In comparison the diamond model has a smaller selection range and higher L50 value as the hexagonal model (Tab. 15). The predicted L50 values of all three mesh descriptions are smaller than the current minimum landing size of flounder (25 cm in Germany).
Tab. 15: Predicted mean selectivity parameter, L50 and SR, for 35 meshes of the underwater records, described by non parametric model, diamond model or hexagonal model Non parametric Diamond Hexagonal L50 22.603 24.166 23.301 SR 2.537 2.678 2.745
54
Results
Predicted L50 and SR values were calculated and compared for each mesh and model (non parametric, hexagonal and diamond) (Tab. 16, Tab. 17). The non parametric model is used as baseline for the sum of squares method to validate differences between the models. The diamond model has lower sum of squares (SSL50 = 134.50, SSSR = 1.95) as the hexagonal model (SSL50 = 48.97, SSSR = 1.34). The hexagonal model indicates best fit with the nonparametric model (Tab. 16). Predicted L50 values ranged between 18.18 cm and 26.48 cm for the hexagonal model. Predicted L50 values ranged between 21.86 cm and 27.70 cm for the diamond model (Tab. 16).
Mean predicted L50 for meshes located in the front part of the codend differ between 22.95 cm (non parametric model) and 23.83 cm (diamond model). The mean predicted L50 for the hexagonal model is close to the diamond model with 23.68 cm. In comparison to meshes of the end part of the codend mean predicted L50 values are smaller for the non parametric (22.40 cm) and hexagonal model (21.98 cm), whereas the diamond model as a higher predicted mean value (24.91 cm).
55
Results
Tab. 16: Predicted L50 values for three mesh descriptions (non parametric (nonpar), diamond, hexagonal) and their sum of squares (SS) for each extracted mesh shape (mesh ID) of the underwater records Location Mesh ID Nonpar Diamond Hexagonal SS Diamond SS hexagonal Front 1 24.63 25.53 25.61 0.8100 0.96040 Front 2 24.18 24.75 24.85 0.3249 0.44890 Front 3 23.41 24.40 24.34 0.9801 0.86490 Front 4 23.28 24.40 24.30 1.2544 1.04040 Front 5 23.48 24.37 24.24 0.7921 0.57760 Front 6 23.37 24.01 24.01 0.4096 0.40960 Front 7 23.62 24.40 24.24 0.6084 0.38440 Front 8 21.83 23.01 23.05 1.3924 1.48840 Front 9 21.22 22.65 22.30 2.0449 1.16640 Front 10 22.12 23.07 22.94 0.9025 0.67240 Front 11 21.74 22.56 21.77 0.6724 0.00090 Front 12 22.20 22.86 22.61 0.4356 0.16810 Front 13 25.78 26.38 26.22 0.36000 0.19360 Front 14 21.48 21.86 22.00 0.14440 0.27040 Front 15 20.93 22.59 22.24 2.75560 1.71610 Front 16 21.60 22.59 22.63 0.98010 1.06090 Front 17 21.86 23.47 22.74 2.59210 0.77440 Front 18 23.88 25.00 25.01 1.25440 1.27690 Front 19 26.72 26.72 26.48 0.00000 0.05760 Front 20 21.74 22.06 21.83 0.10240 0.00810 Mean 1 20 22.95 23.83 23.67 End 21 25.28 27.42 24.82 4.57960 0.21160 End 22 23.42 26.10 23.11 7.18240 0.09610 End 23 22.83 24.05 18.18 1.48840 21.6225 End 24 23.17 24.98 23.02 3.27610 0.02250 End 25 23.32 24.59 23.08 1.61290 0.05760 End 26 23.01 25.41 22.60 5.76000 0.16810 End 27 20.61 22.21 24.02 2.56000 11.6281 End 28 22.80 25.26 22.40 6.05160 0.16000 End 29 20.69 22.89 20.37 4.84000 0.10240 End 30 20.24 22.51 19.76 5.15290 0.23040 End 31 21.39 24.27 21.13 8.29440 0.06760 End 32 20.69 22.80 20.13 4.45210 0.31360 End 33 22.99 26.75 22.80 14.1376 0.03610 End 34 20.50 26.75 19.76 39.0625 0.54760 End 35 25.01 27.70 24.60 7.23610 0.16810 Mean 22.40 24.91 21.98
Sum SS 134.5029 48.9727
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Results
Tab. 17: Predicted selection range values for three mesh descriptions (non parametric (nonpar), diamond, hexagonal) and their sum of squares (SS) for each extracted mesh shape (mesh ID) of the underwater records
Location Mesh ID Nonpar Diamond Hexagonal SS Diamond SS hexagonal Front 1 1.62 2.02 1.64 0.1600 0.0004 Front 2 1.79 1.75 1.72 0.0016 0.0049 Front 3 1.76 1.73 1.54 0.0009 0.0484 Front 4 1.64 1.65 1.50 0.0001 0.0196 Front 5 1.72 1.75 1.58 0.0009 0.0196 Front 6 1.43 1.71 1.50 0.0784 0.0049 Front 7 1.29 1.70 1.55 0.1681 0.0676 Front 8 1.83 1.69 1.41 0.0196 0.1764 Front 9 1.71 1.84 1.84 0.0169 0.0169 Front 10 1.65 1.74 1.57 0.0081 0.0064 Front 11 1.88 1.77 1.90 0.0121 0.0004 Front 12 1.69 1.84 1.66 0.0225 0.0009 Front 13 1.80 1.92 1.64 0.0144 0.0256 Front 14 1.67 1.65 1.43 0.0004 0.0576 Front 15 2.08 1.82 1.95 0.0676 0.0169 Front 16 1.75 1.74 1.48 0.0001 0.0729 Front 17 1.82 1.56 1.81 0.0676 0.0001 Front 18 1.55 1.86 1.81 0.0961 0.0676 Front 19 1.62 2.10 1.71 0.2304 0.0081 Front 20 1.54 1.71 1.66 0.0289 0.0144 Mean 1 20 1.69 1.78 1.65 End 21 1.89 1.77 1.63 0.0144 0.0676 End 22 1.81 2.06 1.83 0.0625 0.0004 End 23 1.87 1.60 1.17 0.0729 0.4900 End 24 1.75 1.70 1.82 0.0025 0.0049 End 25 1.82 1.62 1.84 0.0400 0.0004 End 26 1.82 1.81 1.90 0.0001 0.0064 End 27 1.54 1.79 1.69 0.0625 0.0225 End 28 1.87 1.75 1.87 0.0144 0.0000 End 29 1.63 1.75 1.38 0.0144 0.0625 End 30 1.44 1.92 1.49 0.2304 0.0025 End 31 1.84 1.69 1.77 0.0225 0.0049 End 32 1.63 1.83 1.49 0.0400 0.0196 End 33 1.85 1.97 1.87 0.0144 0.0004 End 34 1.37 1.97 1.49 0.3600 0.0144 End 35 1.71 1.75 1.60 0.0016 0.0121 Mean 21 35 1.72 1.80 1.66
Sum 1.9473 1.3382
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Results
3.4 Influence of twine characteristics on flounder selectivity
The influence of twine characteristics on flounder selectivity was tested while using Fryer models, which were implemented in the SELNET program. All tested submodels were listed due to their AIC values. Best model has significant parameter and lowest AIC value. Back transformation of SF and SFA in commonly used selectivity parameters is possible. The model, which explain SF and therefore also L50, depends on direction, twine diameter and number of twines. The model of SFA, and therefore SR, depends on direction and twine diameter. Best models were illustrated below:
SF = + INTERCEPT_SF + DIRECTION*TWINEDIAMETER_SF + DIRECTION*DOUBLE*TWINEDIAMETER*TWINEDIAMETER_SF SFA = + INTERCEPT_SFA + DIRECTION*TWINEDIAMETER_SFA
Based on the models, prediction of SF (L50) and SFA (SR) values are possible for defined twine characteristics (Fig. 30, Tab. 18). Flounder selectivity depends on twine diameter and number of twines, especially for T90 codends. While increasing twine diameter, the selectivity for flounder decreased. Using double T90 nettings with increasing twine diameter, the effect is also a decrease in selectivity for flounder. For double twine the predicted L50 values ranged between 19.42 cm (6 mm twine diameter) and 22.48 cm (2 mm twine diameter) for T90 netting. For T90 netting, highest L50 value (22.68 cm) for flounder selectivity was investigated by using single twine of 2 mm twine diameter. The influence of twine diameter and double/ single twine is less for T0 codends.
58
Results
25 24 23 22 21 T90D 20 T90S
L50 L50 [cm] 19 T0D 18 17 T0S 16 15 2345678 Twine diameter [mm]
Fig. 30: Predicted L50 values for defined twine diameter and different used codends with defined twine characteristics
Tab. 18: Predicted SF and SFA values (and back transformation of L50 values) for defined twines (single/ double and different thickness) for T0 and T90 meshes double T0 T90 twine SF SFA L50 SF SFA L50 2 1.950 0.266 23.402 1.873 0.241 22.477 3 1.950 0.266 23.402 1.822 0.229 21.864 3.5 1.950 0.266 23.402 1.793 0.223 21.520 4 1.950 0.266 23.402 1.762 0.216 21.150 4.5 1.950 0.266 23.402 1.730 0.210 20.755 5 1.950 0.266 23.402 1.695 0.204 20.335 5.5 1.950 0.266 23.402 1.657 0.198 19.890 6 1.950 0.266 23.402 1.618 0.192 19.420 single T0 T90 twine SF SFA L50 SF SFA L50 2 1.950 0.266 23.402 1.890 0.241 22.679 4 1.950 0.266 23.402 1.830 0.216 21.956 4.5 1.950 0.266 23.402 1.815 0.210 21.775 5 1.950 0.266 23.402 1.800 0.204 21.594 5.5 1.950 0.266 23.402 1.784 0.198 21.414 6 1.950 0.266 23.402 1.769 0.192 21.233 6.5 1.950 0.266 23.402 1.754 0.190 21.052 7 1.950 0.266 23.402 1.739 0.180 20.872 7.5 1.950 0.266 23.402 1.724 0.174 20.691 8 1.950 0.266 23.402 1.709 0.167 20.510
59
Discussion
4. Discussion
The demersal Baltic trawl fishery is a mixed species fishery with main target species cod (ICES WGBFAS 2012). Since years, scientists mainly have focused on an improvement of the selectivity of cod (Bohl 1981; Dahm and Gabriel 2001; Madsen et al. 2002; Herrmann et al. 2009; Wienbeck et al. 2011). Therefore, the selectivity of current legal codends (BACOMA 105/ 120 mm, and T90 120 mm) is mainly optimized for cod selection. Fishes with different morphological characteristics are potentially not optimal selected. The poor selection of several fish species results in high by catches and even discards. Discarding is a topic of high social, economical, and ecological importance. Therefore, several studies investigate reasons of discards and ways to reduce discards (Ulleweit et al. 2010; Feekings et al. 2012; Madsen et al. 2012).
Highly variable discard rates are reported for beam and bottom trawl fisheries (Ulleweit et al. 2010). Especially, the mixed species demersal trawl fisheries are complex, which result in high discard rates (Catchpole et al. 2005; Petter Johnsen and Eliasen 2011). Baltic flounder are caught by a direct fishery and also as by catch in the mixed demersal Baltic fishery. For Baltic flounder, a discard rate of 13 % was reported between 2006 and 2009, based on German DCF data (Probst et al. 2011). High portions of plaice discard were reported. High mortality rates are reported for discarded plaice and sole, which were caught by otter trawls in the North Sea (Beek et al. 1990; Millner et al. 1993). Coming regulations should aim to reduce unwanted by catch. The Common Fishery Policy (CFP) plans a discard ban for the Baltic Sea (Anonymous 2009). Therefore, it is quite important to investigate and optimize selectivity of gears to reduce the amount of unwanted by catch. Optimized selectivity should result in optimized catches, which mainly consist of target species with sizes above minimum landing sizes or marketable sizes. This results in a more sustainable fishery, due to protection of young year classes, and could help to avoid overfishing.
Exploitation patterns of fish populations are significantly related to the design of the codend of towed gears, the where catch accumulate and the fish is stimulated to escape through meshes (Mac Lennan 1992). The codend, as one of the main factors for selectivity, is defined by several parameters as material, mesh shapes, and mesh sizes (Herrmann and O’Neill 2006; Herrmann et al. 2007, 2012b; Wienbeck et al. 2011).
60
Discussion
Since years, scientists have tested new or modified codends by conducting experimental sea trials to optimize their selectivity (Bohl 1981; Gabriel et al. 2000; Paschen et al. 2001; Madsen et al. 2006; Wienbeck and Panten 2008; Krag et al. 2009; Wienbeck et al. 2011). Often, experimental sea have some limitations, such as number of parameter, which could tested at each time, or they are limited by less sea time and high costs. Therefore, during last years alternative methodologies were investigated to minimize high efforts and costs of conduction experimental sea trials. Examples are the FISHSELECT method or the PRESEMO software (Herrmann and O’Neill 2005; O’Neill and Herrmann 2007; Herrmann et al. 2009; Frandsen et al. 2010; Sistiaga et al. 2011; Krag et al. 2011).
4.1 Methodology
4.1.1 FISHSELECT
The methodology FISHSLECT (Herrmann et al. 2009) focus on the potential selectivity of fish species for defined meshes. The methodology is used to predict selectivity parameters, which are based on morphological fish data. Thereby, it is a sustainable and animal friendly method, because information about selectivity could be investigated without additional sea time. This minimizes costs and efforts. Furthermore, the method provides foundations for new or modified meshes or codends, which will be tested in experimental set ups at sea. On the other hand, FISHSELECT can be used for the interpretation of experimental results obtained from sea trials and provide the basis for a quick and well founded advice about technical regulations in different fisheries (Herrmann et al. 2009). The use of a Morphometer, which consists of flexible sticks, provides quite accurate shape approximations of the fish. Therefore, it is important that the measured transvers fish shapes are not affected by factors, such as dehydration, depression, or rigor mortis (Sistiaga 2010). The fish shape has to be presented as realistic as possible. Additional, it is important to cover a wide size range of measured fishes (Sistiaga 2010) to ensure the calculation of a sufficient virtual fish population. Furthermore, several assumptions are included in FISHSELECT (Herrmann et al. 2009). An optimal rotation of the fish to the mesh opening is assumed. Consequently, the L50 and SR values are optimal predicted values, which are theoretically possible. Furthermore, a stiff mesh shape is assumed, which is not deformable by the fish. But a possible deformation or compression of the fish is assumed during the escapement. 61
Discussion
The gravity of the fish, which is used for the fall trough trials, is assumed to represent the swimming ability of the fish during the mesh penetration (Herrmann et al. 2009; Krag et al. 2011). It is based on the assumption, that fishes with increasing size have more power to penetrate meshes (Efanov 1987). The increase of fish weight corresponds to an increase of its gravity. This assumption has to be validate in further experiments (Krag et al. 2011).
As a very flexible method, FISHSELECT is adaptable for several fish species, such as cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) in the North Sea (Herrmann et al. 2009; Sistiaga et al. 2011; Krag et al. 2011), red fish (Sebastes spp.) (Herrmann et al. 2012a) or other organism like Danish nephrops (Nephrops norvegicus) (Frandsen et al. 2010). In this study, the selectivity potential of Baltic flounder was analyzed with the FISHSELECT method. This is the first Baltic Sea species, which was investigated with this method.
4.1.2 Cover-codend method
Several sampling techniques are used to investigate the selectivity of defined codends. Common methods are the paired gear method and the cover codend method, especially for bottom trawling (Wileman et al. 1996). In this study, catch data were investigated by using the cover codend method (Wileman et al. 1996). The cover codend method considers the entire fish population which enters the codend to calculate the selectivity of the codend. Several opportunities are possible to adapt this method, for example splitting of covers, which enables the calculation of selectivity for individual compartments (for example (Frandsen et al. 2010)). In general, it could be heavy to handle the equipment for the cover codend method on small vessels (Frandsen et al. 2010). Concerns exist regarding the potential contact between codend and cover, which can block or influence the mesh openings of the codend and the escapement behaviors of fishes. Therefore, this method can result in bias (Madsen et al. 2006).
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Discussion
4.2 Morphological description of flounder
The FISHSELECT method is used to investigate the cross section of the flounder, which has highest effect on mesh selection. Furthermore, the compression of the fishes during the mesh penetration was investigated.
The 117 measured flounder cover a wide size range and therefore provide a good data base for further predictions. Both measured cross sections could be described by approximations of geometrical shapes, with F_Flex_Drope model for CS1 and Asym_Trapetz (asymmetric trapezoid) for CS2. The morphological descriptions of CS1 and CS2 explain 96.8 % of the results, which were experimentally conducted with the fall through trials in the laboratory. The mesh penetration affects deformation of the width and the height of the flounder. The mesh penetration is mainly effected by CS2 (95.6 %), the width of flounder. Consequently, the flounder is imitated to pass through meshes by its widest point of the flat body. These results are consistent with results of conducted plaice experiments in the North Sea (Herrmann et al. 2008). During the plaice experiments three cross sections were measured. The CS1 of plaice correspond to the position of CS1, as used for flounder in this study. The CS3 of plaice corresponds to the CS2 of flounder. Therefore, the CS3 of plaice were renamed to CS2 to compare both species (Tab. 20). For plaice, highest degree of agreement (DA) was found for CS1 and CS2 (96.2 %), which is quite similar to flounder results. As for flounder, the mesh penetration of plaice is mainly effected by CS2 (92.1 %).
Tab. 19: Degree of agreement (DA) between results of penetration models and experimental fall-through trials of flounder and plaice; results of single cross-sections (CS1 or CS2) and results of combined comparison (CS1 and CS2) CS DA Flounder DA Plaice CS1 80.7 % 78.2 % CS2 95.6 % 92.1 % CS1 + CS2 96.8% 96.2 %
Flounder cross sections were approximated by model F_Flex_Drope for CS1 and model Asym_Trapetz for CS2, whereas both cross sections of plaice were approximated by the model Asym_Trapetz. The F_Flex_Drope model is a current development of the FISHSELECT software and therefore was not available for plaice experiments in 2008 (Herrmann et al. 2008).
63
Discussion
The CS2 of both species are described by the model Asym_Trapetz. The parameters of the length based regressions for both species were calculated and compared (Tab.21, Tab. 22). For plaice and flounder, highest regression coefficients (R squares) were obtained for regression of the parameters of the height (c1), and of the width (c2), with fish individual lengths. The high R square values indicate a good description of the variation of individual fishes. The investigated shape model and penetration model could be used for predictions of selectivity parameter.
Tab. 20: Model Asym_Trapetz for CS2 (flounder) Power function (x = a * lb) R square Coefficient a Coefficient b Parameter vs. fish length cs1 0.784 0.042 1.11 cs2 0.951 0.394 1.02 cs3 0.563 0.027 1.28 cs4 0.578 0.326 0.86
Tab. 21: Model Asym_Trapetz for CS2 (plaice) Power function (x = a * lb) R square Coefficient a Coefficient b Parameter vs. fish length cs1 0.75 0.0191 1.20 cs2 0.91 0.8797 0.89 cs3 0.41 0.0939 1.00 cs4 0.61 0.1854 1.00
64
Discussion
4.3 Square and diamond meshes (BACOMA)
Simulated selectivity data were compared with experimental selectivity data, obtained at sea. This comparison indicates high conformity between simulated and experimental data and support simulated results. Selectivity parameters were predicted for defined diamond meshes. For square meshes with a mesh opening of 120 mm (BACOMA escape window), as a special case of the diamond mesh, a L50 of 21.01 cm was obtained. The predicted L50 value for the meshes of the current legal BACOMA escapement window is below the minimum landing size of flounder in Germany (25 cm). The predicted L50 values are lower for square meshes than for diamond meshes with opening angles smaller than 90 degree. Therefore, selection of flatfishes is not improved (low L50 values) by using square meshes. This result corresponds with results of further studies of for example winter flounder (Pseudopleuronecte samericanus), European plaice (Pleuronectes platessa), sole (Solea Solea) or American plaice (Hippoglossoides platessoides) (Simpson 1989; Walsh et al. 1992; Fonteyne and M’Rabet 1992; He 2007).
Whereas, square meshes improve the selectivity of round fish species, such as cod (Gadus morhua) (Halliday et al. 1999), haddock (Melanogrammus aeglefinus) (Robertson and Stewart 1988; Halliday et al. 1999) and whiting (Merlangius merlangus) (Robertson and Stewart 1988). Implementations of square meshes may reduce the by catch of small round fishes, like gadoids, but it has to be considered that it leads in higher catches of small flatfishes, due to its laterally compressed shapes (Simpson 1989; Walsh et al. 1992). This could be a problem for mixed fisheries like in the North Sea and Baltic Sea, where square mesh panels are used and high amounts of flatfishes were caught. It was assumed, that “square meshes retain their shapes under load, without the characteristic stretching seen in in diamond mesh codends” (Robertson and Stewart 1988).
Diamond meshes with an opening angle between approximately 40 and 60 degrees are more optimal for potential flatfish selection. An increase of diamond mesh openings improves selectivity of flounder. Especially, moderately stretched diamond mesh shapes indicate high selectivity (and high L50) for flatfishes, due to their characteristically laterally compressed body shapes. Additional studies have shown similar results, for example (Simpson 1989; Walsh et al. 1992).
65
Discussion
An increase of the mesh opening up to 120 mm was suggested for improved plaice selection in the North Sea (Wienbeck and Panten 2008). In the same way, an increase of diamond mesh openings up to 125 mm (for single twine) was suggested flounder in the Baltic Sea (Paschen et al. 2001; Richter and Rehme 2002).
4.4 Rectangular meshes
The flounder specific selectivity parameters were predicted for rectangular meshes with defined mesh openings and squareness factors. For each mesh opening, the predicted L50 values are relatively constant for squareness factors between 20 and 100 %. The predicted L50 values are quite low and show high differences for squareness factors below 20 %. Rectangular meshes enable relative stable selectivity parameter for flounder for defined mesh opening. Codends could be modified by using this knowledge to improve flatfish selection. For example, codends could be modified with grids, which consist of rectangular meshes. Grids were also used to optimize the size selectivity of Neprophs (Frandsen 2011).
4.5 Hexagonal meshes (T90)
Flounder specific selectivity parameters were predicted for hexagonal T90 codend meshes. The mesh shapes, extracted from underwater recordings, enable predictions for observed mesh shapes and selectivity performances. Predicted L50 values for flounder differ for both analyzed parts of the codend. The predicted L50 values are lower for meshes in the aft part of the codend, where the catch accumulates. The meshes are more open in the area in front of the catch. Therefore, for round fishes as Baltic cod, these more open T90 meshes indicate improved escape possibilities, while this results in poor selectivity of flatfishes, due to their flattened morphology. Summarized, square meshes and even T90 meshes do not improve selectivity of flounder in the aft part of the codend.
66
Discussion
4.6 Influence of twine characteristics on flounder selectivity
Selectivity is influenced by mesh and twine characteristics. In this study, the influence of different twine characteristics on flounder selectivity was investigated. The twine diameter and the number of twine have a significance influence on flounder selectivity for T90 codends. The predicted L50 values decrease with increasing twine diameter and number of twine. The highest size selectivity of flounder was found for T0 codends, whereas for T90 codends the calculated L50 values are lower.
These results correspond with studies, which were conducted for Baltic plaice (Herrmann et al. 2012b). The size selectivity of plaice is not improved by using T90 codends. As for flounder, the L50 of plaice is higher by using T0 codends (Herrmann et al. 2012b). The consistency between the results of plaice and flounder are explainable due to their similar morphological characteristics (same flattened bodies). Furthermore, they investigated the size selectivity of cod. Best selectivity was found for thin, single twine and a rotation of the netting by 90 degree (Herrmann et al. 2012b). The studies found an improved selectivity for thinner twines.
As an additional aspect, meshes could function as a visual barrier for the fishes. This visual barrier could influence their behavior and minimize their escapement attempts (Herrmann and O’Neill 2006). It is assumed, that this effect is higher for thicker twine as for thinner twine (Herrmann and O’Neill 2006). Additional, the size selectivity of cod depends also on the number of meshes in the circumference of the codend (Wienbeck et al. 2011; Herrmann et al. 2012b).
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Discussion
4.7 Behavioral aspects
The codend selectivity is mainly influenced by the characteristics of the codend, such as mesh types and mesh sizes, which are defined as technical measurements in fishery regulations.
The FISHSELECT method analyses the potential selectivity for defined meshes, and does not consider behavioral aspects. Nevertheless, information about fish behavior, their escapement attempts, and activities during the trawl process influence the size selectivity and are therefore quite important. The demersal species cod and flounder have different escape behaviors. Flounder stay close to the bottom, whereas cod escape mainly through upper parts of the codend. Therefore, escapement windows were implemented in upper aft panel of BACOMA codends to improve selectivity of round fishes, such as cod (Dahm and Gabriel 2001; Madsen et al. 2002). Commonly, cod has a more passive reaction than other round fishes, such as haddock (Sistiaga et al. 2011). The knowledge about behavioral aspects of cod and flounder were used to develop a selective flatfish trawl, tested in the Skagerrak and Baltic Sea (Madsen et al. 2006). Whereby, the upper part of the codend is reduced, which improves the escapement of cod (Madsen et al. 2006). The fish specific mortality rates of cod and even flounder varying between different areas and depths (Broadhurst et al. 2006).
The performances of meshes differ during the trawling process and even at different parts of the codend. In the aft part of the codend, meshes are more open, especially when the catch accumulates. Caught fishes block the meshes and prevent escape possibilities. Therefore, the main fraction of fish escapes through meshes, which are located in front of the catch accumulation (Robertson and Stewart 1988). Meshes in front of this area are normally more stretched and more closed (Robertson and Stewart 1988). General information about fish behavior could help to understand species selectivity but nevertheless, it requires information about the potential of size selectivity of the codend/ meshes to improve size selectivity.
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Discussion
4.8 Multispecies approach and outlook
Geographical locations, seasons, and mesh size categories are factors, which affect codend selectivity and are directly controllable by the fishery management. This study focused on predictions of flounder selectivity parameters for defined mesh types and sizes. Therefore, this study is a first step to understand the mesh specific selectivity potential of Baltic flounder, as one main target species in the demersal fishery in the Baltic Sea. The main target species and their fishing periods differ in the Baltic Sea. Preferred target species depend on seasonal closures, fish abundances, and market prices. The directed flounder fishery becomes more and more important in the Baltic Sea. Reasons are, amongst others, uneven distribution of cod stocks and limited cod quota. Commonly, three situations could be classified in the Baltic Sea (pers. comment John Much, fisherman from Heiligenhafen): i) Preferred target species are cod and flounder with sizes above minimum landing size. They were caught in the first quarter of the year, especially in the western Baltic Sea. ii) Target species are only flounder with sizes above minimum landing size, which is the case in the first quarter of the year and iii) Preferred target species are cod, whereas mainly catches of flounder are not preferred (time: after spring).
The used method in this study should be applied to additional Baltic Sea species, such as cod, whiting, or plaice. The prediction of selectivity parameter enables predictions of species specific selectivity and therefore, the information could be compared, especially with focus on flounder and cod selection. The results could be used for further simulations, for example with the software PRESEMO (Herrmann 2005; Herrmann and O’Neill 2005; O’Neill and Herrmann 2007).
Furthermore, this study is one important step for the long term aim of improving the multi species selection in the mixed demersal Baltic trawl fishery. The knowledge of flounder selectivity could be used to develop optimized selective codends, which minimize unwanted by catches. High selective codends enables a more sustainable fishery and could avoid overfishing.
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References
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Indices of tables and figures
6. Indices of tables and figures
6.1 Index of tables
Tab. 1: Flounder populations with numbers of stock, stock names, and ICES SD in the Baltic Sea (ICES WGBFAS 2012) ...... 11 Tab. 2: Periods and areas in the Baltic Sea, where fishing of cod, flounder, and turbot is banned (modified after EU regulation 1237/2010)...... 19 Tab. 3: Summary of fit statistics of model results to describe cross section 1, grey shaded line represent best model ...... 38 Tab. 4: Summary of fit statistics of model results to describe cross section 2, grey shaded line represent best model ...... 38 Tab. 5: Fit statistics of model Flex_Drope for cross section 1 ...... 40 Tab. 6: Fit statistics of model Asym_Trapetz for cross section 2 ...... 41 Tab. 7: Results of first (coarse) run of penetration models for cross section 1 (CS1) and cross section 2 (CS2) and penetration model parameter (CutW and CompH) ...... 43 Tab. 8: Results of second (refined) run of penetration models for cross section 1 (CS1) and cross section 2 (CS2) and penetration model parameter (CutW and CompH) ...... 43 Tab. 9: Predicted L50 values (in cm) for flounder: diamond mesh with defined opening angle (oa in degree) and mesh opening (in mm); cells shaded green correspond to meshes in the lower panel in BACOMA codend (105 mm); red shaded cell correspond to meshes in the escapement window of current legal BACOMA codend (square mesh with mesh opening of 120 mm) ...... 46 Tab. 10: Predicted SR values (in cm) for flounder: diamond mesh with defined opening angle (oa in degree) and mesh opening (in mm); cells shaded green correspond to meshes in the lower panel in BACOMA codend (105 mm); red shaded cell correspond to meshes in the escapement window of current legal BACOMA codend (square mesh with mesh opening of 120 mm) ...... 47 Tab. 11: Predicted L50 values (in cm) for flounder: rectangular mesh with defined squareness factors (SFA in %) and mesh openings (in mm); red shaded cell corresponds to meshes in the escapement window of current legal BACOMA codend (square mesh with mesh opening of 120 mm) ...... 50 Tab. 12: Predicted SR values (in cm) for flounder: rectangular mesh with defined squareness factors (SFA in %) and mesh openings (in mm); red shaded cell corresponds to
77
Indices of tables and figures square meshes in the escapement window of the current legal BACOMA codend (120 mm) ...... 51 Tab. 13: Comparison of simulated and experimental selectivity parameter for flounder ... 52 Tab. 14: Fit characteristics (mean values of AIC and R square) of two applied shape models (diamond, hexagonal) for end and front part of the codend...... 54 Tab. 15: Predicted mean selectivity parameter, L50 and SR, for 35 meshes of the underwater records, described by non parametric model, diamond model or hexagonal model ...... 54 Tab. 16: Predicted L50 values for three mesh descriptions (non parametric (nonpar), diamond, hexagonal) and their sum of squares (SS) for each extracted mesh shape (mesh ID) of the underwater records ...... 56 Tab. 17: Predicted selection range values for three mesh descriptions (non parametric (nonpar), diamond, hexagonal) and their sum of squares (SS) for each extracted mesh shape (mesh ID) of the underwater records ...... 57 Tab. 18: Predicted SF and SFA values (and back transformation of L50 values) for defined twines (single/ double and different thickness) for T0 and T90 meshes ...... 59 Tab. 19: Degree of agreement (DA) between results of penetration models and experimental fall through trials of flounder and plaice; results of single cross sections (CS1 or CS2) and results of combined comparison (CS1 and CS2) ...... 63 Tab. 20: Model Asym_Trapetz for CS2 (flounder) ...... 64 Tab. 21: Model Asym_Trapetz for CS2 (plaice) ...... 64
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Indices of tables and figures
6.2 Index of figures
Fig. 1: Schematic illustration of a selectivity curve: Fitted logistic selection curve (black line) with selectivity parameters L50 and SR (L75 – L25) (red lines). Dots represent experimental retention data per length class. Portions of discard (area shaded red), landings (area shaded green) and lost marketable catch (area shaded grey) are illustrated. The minimum landing size of flounder (250 mm) is shown (black vertical line)...... 9 Fig. 2: European flounder (left) and their distribution (right, map: www.ecomare.nl) ...... 10 Fig. 3: Landings (in t) of flounder, plaice, dab, and turbot in ICES subdivisions 22 32, years 1970 2010 (Data (ICES WGBFAS 2012)...... 12 Fig. 4: Total landings of flounder (in t) in ICES subdivision 22 – 32 in 2011 (left); ICES subdivisions 21 – 32 in the Baltic Sea (right) (Data: (ICES WGBFAS 2012)) ...... 13 Fig. 5: Total landings of flounder in ICES subdivision 24, subdivision 25, and flounder landings of other SD for years 1975 – 2011 (Data: (ICES WGBFAS 2012))...... 13 Fig. 6: Schematic drawing of a demersl otter trawl (figure by vTI OSF). Numbers (1 8) are described in the text...... 15 Fig. 7: Schematic drawing of a demersal otter trawl (figure by vTI OSF): front view (left) and side view (right) ...... 16 Fig. 8: Schematic drawing of a BACOMA 120/105 mm codend (drawing by W. Rehme, vTI OSF) ...... 17 Fig. 9: Pictures of net material of BACOMA codend: T0 single twine 6 mm (left), T0 double 4 mm (middle) and square meshes of BACOMA escapement panel with twine diameter of 5 mm (right) ...... 17 Fig. 10: Schematic drawing of a T90 codend (drawing by W. Rehme, vTI OSF)...... 18 Fig. 11: Pictures of net material of T90 codends: single twine with 6 mm twine diameter (left) and double twine with 4 mm twine diameter (right) ...... 18 Fig. 12: Flounder with positions of both cross sections (CS1 and CS2), measurement tool Morphometer (right) ...... 22 Fig. 13: Digitalized cross sections (CS) are represented by around 120 digital dots (left: CS1, right: CS2)...... 24 Fig. 14: Models (red dots) were fitted to digitalized cross sections (green dots), left: model F_Flex_Drope is adapted to flounder CS1; right: model Asym_Trapetz is adapted to flounder CS2 ...... 24
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Indices of tables and figures
Fig. 15: Models (red dots) were fitted to digitalized cross sections (green dots), picture above: model F_Flex_Drope is adapted to flounder CS1; picture below: model Asym_Trapetz is adapted to flounder CS2 ...... 24 Fig. 16: Fall trough trials: construction with nylon plates (left), fish were held on its tail (middle) and example of a mesh shape with a labeled flatfish (right) ...... 25 Fig. 17: Three tested geometrical shapes: diamond (a), hexagonal (b), and rectangular (c), are defined by different parameter: opening angle (OA), mesh size (m), bar lengths (a, b, k) (Sistiaga et al. 2011) ...... 26 Fig. 18: Schematic compressions of fish bodies, for example for cod (left) and flounder (right), black line represent the original transverse cross section, the dashed line represents the compressed cross sections ...... 27 Fig. 19: Length distribution of measured flounder ...... 37 Fig. 20: Length weight relation of measured flounder ...... 37 Fig. 21: Ten different shape models were adapted to cross section one (CS1) of a single flounder ...... 39 Fig. 22: Values of each parameter are plotted against fish lengths, 95 % confidence limits are illustrated ...... 40 Fig. 23: Model Asym_Trapetz: Values of each parameter are plotted against fish lengths, 95 % confidence limits are illustrated ...... 41 Fig. 24: Design guide for diamond meshes for flounder: isolines represent L50 (in cm) values for specific mesh openings (in degree) and opening angles (in mm); L50 values of a square mesh of BACOMA window (opening angle 90 degree, mesh opening 120 mm) (red dot), SR values of standard BACOMA meshes (mesh opening 105 mm) (green line) ...... 45 Fig. 25: Design guide for diamond meshes for flounder: isolines represent values of selection range (SR in cm) for specific mesh openings (in mm) and opening angles (in degree), SR values of a square mesh of BACOMA window (opening angle 90 degree, mesh opening 120 mm) (red dot), SR values of standard BACOMA meshes (mesh opening 105 mm) (green line) ...... 45 Fig. 26: Design guide for rectangular meshes for flounder: isolines represent L50 values (cm) for specific mesh openings (mm) and squareness factors; L50 of a square mesh of BACOMA window (squareness factor = 100 %, mesh opening 120 mm) (red dot) ...... 49 Fig. 27: Design guide for rectangular meshes for flounder: isolines represent SR values (cm) for specific mesh openings (mm) and squareness factors (%), SR of a square mesh of BACOMA window (squareness factor = 100 %, mesh opening 120 mm) (red dot) ...... 49
80
Fig. 28: Comparison of simulated and experimental selection curves for flounder (diamond mesh with a mesh opening of 125 mm) ...... 52 Fig. 29: Picture of the digitalized meshes of front part (1a) and the end part (2b) of the codend, and fitted hexagonal (1b, 2b) and diamond models (1c, 2c) for one single mesh . 53 Fig. 30: Predicted L50 values for defined twine diameter and different used codends with defined twine characteristics ...... 59 Fig. 31: Geometrical description of an asymmetric trapezoid, which is defined by 4 parameters: c1: height, c2: bottom width, c3: top width, and c4: top asymmetry...... 86
81
List of Abbrevations
7. List of Abbrevations
AIC Akaike Information Criterion (Akaike 1974) BACOMA or Legal codend in the Baltic Sea, T0 Abbreviation of the project name “Baltic Cod Management“ BLE Bundesanstalt für Landwirtschaft und Ernährung, Federal Office for Agriculture and Food CFP Common Fisheries Policy CS1 Cross Section 1 CS2 Cross Section 2 DCF Data Collection Framework FAO Food and Agriculture Organization of the United Nations FFS Fischereiforschungsschiff FISHSELECT Software and simulation tool developed by B. Herrmann FRV Fishery Research Vessel ICES International Council for the Exploration of the Sea ICES SD ICES Subdivision L25 Length at which 25 % of the fish are retained L50 Length at which 50 % oft he fish are retained L75 Length at which 75 % of the fish are retained LALLF Landesamt für Landwirtschaft, Lebensmittelsicherheit und Fischerei m Meshsize (in mm) MLS Minimum Landing Size OA Opening Angle (in degree) SELNET Computer software for analysis of gear selectivity data (SELection in trawl NETting) SF Selection Factor SFA Selection ratio SR Selection Range, difference between L75 and L25 SS Sum of Squares T90 Legal codend in the Baltic Sea, meshes are turned by 90 degree vTI OSF Thünen Institute of Baltic Sea Fisheries Rostock
82
Acknowledgement
8. Acknowledgement
Meinem Erstgutachter Prof. Dr. Christian Möllmann danke ich für die Bereitschaft meine Masterarbeit zu betreuen und zu begutachten. Dr. Daniel Stepputtis, meinem Zweitgutachter, danke ich für die Bereitstellung des Themas, die umfassende Betreuung meiner Arbeit und die vielen hilfreichen Gespräche. PD Dr. Hammer danke ich für die Möglichkeit meine Masterarbeit an dem Thünen Institut für Ostseefischerei anfertigen zu dürfen.
Especially, I would like to thank PhD Bent Herrmann for his extensive support, the many useful phone calls, and the guest friendship during my stay in Denmark.
Ein herzlicher Dank geht auch an Harald Wienbeck, der als Fahrtleiter die FFS SOLEA Forschungsreisen durchführte. Außerdem danke ich PhD Ludvig Ahm Krag, sowie Tomek Niemiro, Waldemar Moderhak und Kerstin Schuhmann, die bei der Datenerhebung auf den Fangtechnik Reisen mitwirkten und die gemeinsame Zeit unvergessen machen. Des Weiteren gilt der gesamten Crew der FFS SOLEA und der FFS Walther Herwig III mein Dank.
Für den großartigen Rückhalt während meines gesamten Studiums, insbesondere in der Zeit meiner Abschlussarbeit, danke ich von Herzen Andreas, meiner Familie, sowie meinen Freunden.
83
Declaration of Authorship
9. Declaration of Authorship
Hiermit bestätige ich, Ulrike Luschtinetz, dass die vorliegende Arbeit von mir selbständig verfasst wurde und ich keine anderen als die angegebenen Hilfsmittel – insbesondere keine im Quellenverzeichnis nicht benannten Internet–Quellen – benutzt habe und die Arbeit von mir vorher nicht einem anderen Prüfungsverfahren eingereicht wurde. Die eingereichte schriftliche Fassung entspricht der auf dem elektronischen Speichermedium. Ich bin damit einverstanden, dass die Masterarbeit veröffentlicht wird.
Hamburg, den 29. Oktober 2012
84
Appendix
10. Appendix
Shape model description
The transverse cross sections were described by shape models. The FISHSELECT software use a polar coordinate system, with polar coordinates ( , r) to describe the cross sections. is the angle and ranged between 0 and 360 degree. The radius, r, correspond to the angle. The points (x, y) are present in polar coordinates on the cross section surface in following way (Herrmann et al. 2009).
= + , where our representation returns the angle in the correct quadrant = ,
The parametric shape models, F_Flex_Drope and Asym_Trapetz (asymmetric trapezoid), are used in this study and are described by defined parameter. The F_Flex_Drope model is described by three parameter (c1, c2, and c3), whereas the Asym_Trapetz is described by four parameter (c1, c2, c3, and c4). Parametric descriptions ensure good descriptions of the cross sections. Parametric models with few parameters are preferred. The AIC values consider information about number of parameter, used in models.
85
Appendix
Asymmetric trapezoid
The model asymmetric trapezoid is described by 4 parameters (c1, c2, c3, and c4) (
Fig. 31) (Herrmann et al. 2008). The parameters are c1, the bottom width, c2, the top width, c3, the top width and a c4, as a distance.
× ∈ ; × × × × = ∈ ; × × × | | ∈ ; ∪ ; Where
= − , = , 2 + 2 − = , 2 − = − , −
c4 c3
c1
c2
Fig. 31: Geometrical description of an asymmetric trapezoid, which is defined by 4 parameters: c1: height, c2: bottom width, c3: top width, and c4: top asymmetry.
86