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Irina Chemshirova Impact of trawling on benthic marine organisms off the Greenlandic shelf, 200 to 600 meters depth Abstract Mobile fishing gear directly impacts the seabed by both removing target organisms and reducing the habitat complexity of the benthos. This is due to the extensive contact gear has with the seabed. Camera surveys have been used in order to quantify the effect of trawling for the Northern shrimp (Pandalus borealis) on the benthos. Soft substrata have been found to be more sensitive to trawling. This is likely to be due to the frequency at which they are exploited. The diversity of hard substrata has been found to increase with trawling intensity. It is possible that hard substrata is only moderately disturbed hence increasing in diversity. The sensitivity of Vulnerable Marine Organisms (main habitat builders) was highlighted by their rapid decline with trawling intensity. Overall trawling affects the benthic organisms negatively. Their responses should be taken into consideration when fisheries management plans are developed. Introduction The marine benthos is composed of very diverse bottom-dwelling organisms (Poore & Wilson, 1993). This is especially interesting since the benthic zone offers a dark, cold habitat, lacking in resources (Snelgrove, Blackburn & Hutchings, 1998). Nevertheless Appeltans et al., (2012) estimate that there could be 687, 255 benthic species worldwide. Many of them are key in a range of ecological processes, from nutrient cycling to bioturbation (destabilising sediment by mixing it, regulating the amount of oxygen present) (Andersen & Kristensen, 1992, Solan et al., 2004). The benthos has also been shown to contribute to the upwelling of iron, a nutrient vital for phytoplankton productivity (Johnson, Chavez & Friederich, 1999). However, the benthic ecosystems have been put under increasing pressure by pollution, ocean acidification, and habitat degradation (Widdicombe & Spicer, 2008, Thrush & Dayton, 2002b, Burd, 2002). Fishing, more specifically fishing activities that have contact the seabed such as trawling. It is one of the main factors contributing to habitat degradation. Trawling for the Northern shrimp, Pandalus borealis is an extensive 1 Trawling Impact on the Greenlandic Benthos practice in the West Greenland. The main type of trawl used there is the otter trawl. It consists of two trawl doors (can weigh several tonnes) which keep the net open and are dragged along the seabed at an angle. It is estimated that these can penetrate up to 15cm into soft sediment even if they have metal shoes attached which aim to limit this (Jennings & Kaiser, 1998). As trawling technology has advanced, the repercussions for the benthic habitat have grown (Hall, 1999). The damage to the benthos increases with depth at which the gear is operated, its weight, the speed it is towed and the amount of contact with the seabed. For example, gear designed for deep sea trawling is heavier, thus it will be towed for a longer time period at a slower speed. Therefore the contact with the seabed will be prolonged. This will ultimately lead to greater damage of the benthos (Halpern et al., 2007, Thrush & Dayton, 2002b). Any fishing gear which has such contact with the seabed will cause disturbance of the benthos. Natural disruption is essential in order to avoid the dominance of one species. However it is important that it does not exceed the re-colonisation capacity of the community. Areas which are subjected to frequent natural disturbance are likely to be more resilient to fishing disturbance (Thrush & Dayton, 2002b). Natural disturbance events decrease in frequency with depth. Therefore community vulnerability increases with depth (Collie, Escanero & Valentine, 2000). Trawling disturbance has both long-term and short-term effects on the benthos. The most obvious short-term effect is the removal of organisms in the form of bycatch i.e. the non-target organisms and the target organism (P. borealis). Furthermore removal of organisms may have an effect on the ecosystem’s function. This usually occurs when a species is still present but in such low density that it can no longer adequately perform its function (Thrush & Dayton, 2002a). Indirect mortality, through contact with fishing gear has shown to be more damaging than bycatch (Jenkins, Beukers-Stewart & Brand, 2001). Ball, Fox & Munday (2000) investigated the effects of a demersal lobster fishery in the northwest of the Irish Sea. They found that diversity metrics (such as the Shannon Index and species richness) drop rapidly 24 hours after fishing, this was more apparent in their shallower sites. 2 Irina Chemshirova The long-term effects of trawling have been compared with those of terrestrial forest clearing (Watling & Norse, 1998). Trawling reduces the habitat complexity of the benthos. This occurs because the substrate is homogenised i.e. becomes more uniform. Habitat-building organisms such as corals are quite sensitive to disturbance. Upright organisms (a major feature of a complex habitat) provide better feeding opportunities for suspension feeders. It has been shown that current speeds increase dramatically only a few centimetres from the sea floor. Hence suspension feeders are likely to encounter more food in a more complex habitat (Caddy, 1973). A complex habitat also provides a refuge for vulnerable species or organisms in vulnerable life stages i.e. juveniles. For example Atlantic cod recruitment rates have been found to increase with more heterogeneous habitat (Kaiser et al., 2000). The species composition of the community may also alter with time. Kaiser et al. (2000) have found scavengers to dominate areas which have been subject to extreme fishing disturbance. The composition of the benthos often differs with substrate. Therefore communities are likely to react differently to disturbance. Collie, Escanero and Valentine (2000) discovered that those on gravel substrate are more sensitive to trawling than those on sandy and muddy terrains. Quantifying the observed effects of trawling is especially challenging because data is often incomplete. Frequently findings are more specific to local areas and cannot be applied to wide geographic ranges. Garcia, Ragnarsson and Eiríksson (2006) state that the effects of fishing are much more severe at the beginning of the exploitation of an area. Thus a community is already modified before being investigated. Therefore the full impact of the disturbance is not apparent. The aim of this investigation is to quantify the effect of trawling on the diversity observed in still images taken of the Greenlandic benthos. 3 Trawling Impact on the Greenlandic Benthos Methods This was addressed by photographing the seabed at locations which have experienced varied fishing disturbance off the West coast of Greenland (Figure 1). Credit: Chris Yesson Figure 1: Map showing the locations surveyed along the west Greenlandic coast and the level of trawling there. Camera surveys The surveys were carried out aboard M/T Paamiut. The cruises surveyed the areas between Aasiaat and Nuuk in 2011 and then between Nuuk and Qaqortoq in 2012 (Figure 1). The camera equipment was provided by the Greenland Climate Research Centre, Greenland Institute of Natural Resources (GINR). It consisted of: a camera (DSC-10000 Digital Ocean Imaging Systems (DOIS), USA) placed in waterproof housing that can withstand up to 2, 000 meters depth; a flash unit (200W-S Remote Head Strobe Model3831, DOIS, USA) tape was applied to the flash in order to improve image quality by reducing backscatter; a battery unit for the flash and a flash trigger. The flash was operated by adding a weight to the trigger. Therefore when the weight 4 Irina Chemshirova hits the sea bottom the flash is triggered and thus the seafloor is exposed allowing for an image to be taken. All of this equipment was mounted on a frame (see Figure 2). The frame was reinforced with additional weight at the bottom. This overcomes currents, which otherwise tend to drag the apparatus. Rulers were also added to the frame in order to give a concept of scale to the organisms in the images. For detailed camera settings see Appendix 1. Flash trigger Flash battery Camera in housing Flash Additional weight Trigger weight Credit: Kirsty Kemp Figure 2: Camera apparatus mounted on frame The survey station locations were chosen based on the fishing impact using a qGIS map (Figure 1). This map was developed based on logbook data for 1986-2010 provided by the Greenland Fishery and Licence Control. The fishing data consists of trawling effort for five year periods between 1986 and 2010. Upon arriving at the location the camera apparatus was deployed using a winch wire, off a platform located at the starboard side of the vessel (Figure 3). The following was recorded at each sampling station – Start and Finish Latitude and Longitude, Depth1 , Wire Out2 and Time. On each camera drop the sea bottom was 1 seabed depth directly under the vessel, recorded by the ship’s sounding system 5 Trawling Impact on the Greenlandic Benthos detected by monitoring the wire for loss of tension. After detecting bottom the frame was raised up by 10-20 meters. It was lowered again after 1 minute to allow for settling of any disturbed sediment and to allow for the ship to drift. This ensured that different area was captured with each camera drop. The camera apparatus was lowered a total of ten times per station, thus giving a total of ten images of the seafloor. The area of the seafloor recorded in each image is 0.3m2(Kemp, 2011). Credit: Julius Nielsen Figure 3: Deployment of camera Image processing The images collected on the cruises were then processed at the Institute of Zoology, London. The images from the 2012 cruise were processed by Poppy Simon as part of an MSci project (Simon, 2013). The identifications of organisms made by her served as a guide to the processing of the 2011 images (Appendix 2). A combination of identification guides and expert websites were used to confirm the classification of the organisms (Picton & National Museums of Northern Ireland, 2013, Telnes, 2013, Gibson, Hextall & Rogers, 2001, Hayward, Nelson-Smith & Shields, 2001, Sars, 1899). All of this was used to compile an independent guide specific to the study system (Appendix 3). The initial aim for the 2011 processing was to identify 2 the amount of winch wire used when deploying the camera to the seabed 6 Irina Chemshirova organisms to Family level. This proved too time-consuming, except for Starfish and Polychaete worms. The identifications were further confirmed by experts at Marine Ecological Surveys in Bath whilst partaking in a marine taxonomy course. Substrate was classified as hard or soft for each station (Figure 4). A station was categorised as being hard substrate when rocks, pebbles and shells were present (Figure 4A). Stations classed as soft substrate mainly consisted of mud and sand (Figure 4B). Figure 4: Substrate classification. A-hard, B-soft. Not all images were of sufficient quality for processing i.e. they were blurry. Therefore some were excluded from the analysis as the identifications made could be unreliable. In order to control for image quality, each image was given a rating of high, medium or low (Appendix 4). The best available images were chosen from each station (low quality images were not included in the analysis). A total of five images were used per station in order 7 Trawling Impact on the Greenlandic Benthos to maximise the quality of the images used and maintain consistency throughout the stations. Analysis Fishing impact data was available as start and end trawl times and locations for all Greenlandic shrimp trawl vessels. As many trawls do not proceed in a straight line, the most representative measure of impact was found to be trawl duration rather than distance. The trawling impact was measured in cumulative minutes trawled aggregated over a grid of 3.5 x 3.5 km, for both 5 year and 25 year periods (pers. comm. Yesson, 2013). The trawling impact was treated as a continuous explanatory variable. A Shapiro-Wilks normality test was carried out on the data for the total trawling impact (25 year period). It showed that it was not distributed normally (W=0.78 p<0.001). The data was then log-transformed but it still failed the normality test. Therefore a Box-Cox transformation was applied using the following formula (Box & Cox, 1964): The value of λ was calculated to be 0.18. This was also carried out for the five year periods of fishing impact. A log transformation was deemed more suitable for the normal distribution of the data as the value of λ was -0.02. Only two five-year periods were analysed in more depth, 19861990 and 2006-2010. This is because they make it possible to determine if the system is recovering from fishing disturbance. From the image dataset, Station 201149 was excluded from further analysis. It was identified as an outlier due to the fact that it was the shallowest station at 68m. This was further confirmed when diagnostic plots were carried out on the initial regression 8 analysis (Figure 5). Irina Chemshirova 2 Residuals vs Leverage 0 -1 -2 Standardized residuals 1 201106 201248 201149 Cook's distance 0.00 0.01 0.02 0.03 0.04 0.05 0.06 Leverage lm(shannon49 ~ fishing.trans.allno49) Figure 5: Diagnostic plot showing which data points have the most influence on the model. The station in itself was unique in terms of the large density of brittle stars observed (Figure 6). 9 Trawling Impact on the Greenlandic Benthos An assessment of completeness of sampling was made examining species accumulation curves for our sampling stations (Ugland, Gray & Ellingsen, 2003). The Shannon-Weiner Index measures both the richness and evenness of the community. This explanatory variable was continuous. A high value means that the community is both species rich and is not dominated by any one particular taxon (Shannon, 1948). The number of individuals and number of brittle stars were calculated in order to compare with the diversity indices. These are discrete count data, however they were treated as continuous variables in the analysis. This data was log transformed in the regression analysis. The same calculations were carried out on a subset of the taxa. These were deemed Vulnerable Marine Organisms (VMOs) based on a report by FAO on management of deep sea fisheries (Food and Agriculture Organisation of the United Nations (FAO), 2009). This allows us to compare how organisms thought to be sensitive to disturbance respond to trawling. All of these were treated as the response variable in further analysis. As both variables were continuous a linear regression was used to further analyse the data. It allowed us to determine if there is a linear relationship, between trawling impact and diversity. A 3d scatter plot was also generated in order to determine if there were any stations which could be classified as recovering from fishing. This returned too few stations in order to pursue further analysis. All analysis was performed using the statistics software R (R Core Team, 2013) using the packages vegan, MASS and scatterplot3d. 10 Irina Chemshirova Results A total of 80 stations from both years were analysed. In total of 44 taxa identified, with 41 in the analysis set. Three additional taxa were found in low quality images. A Shapiro – Wilks test was performed on the total trawling impact data. After box-cox transformation the data set still failed the test (W=0.95, p<0.01). Nevertheless the data was more normally distributed than before so the transformation was used. The species accumulation curves (Figure 7) show that the sampling effort accounts for most of the species present. The jacknife estimate predicted that there 20 0 10 Species 30 40 should be 45 species (SE=2), whilst we found 41. 0 20 40 60 80 Sites Figure 7: Shows that the species number is reaching the asymptote with increased sampling effort. The linear regressions showed that diversity metrics generally declined with increased trawling (Figure 8, Appendix 5, and Table 1). Notable exception is the Shannon index for hard substrata sites as it increases with greater trawling disturbance (Figure 8). When examining the diversity metrics for the VMOs the decline with trawling is more apparent (Appendix 6 & 7, Table 2). Recovery sites were identified as ones with no fishing impact in the last 5 year period (2006-2010) but high levels of fishing in the first 5 year period (1986-1991). The 3D scatter plot produced showed that there are very few recovery sites (Figure 9). Brittle stars also decline with trawling (Appendix 8, Table 3). 11 1.5 1.0 Substrata 0.5 Shannon Index 2.0 2.5 Trawling Impact on the Greenlandic Benthos 0.0 hard soft both A 0 10 20 30 40 50 B 2.5 1.5 2.0 2 0.5 Substrata hard soft both 0.0 0.0 hard soft both 0 1.0 Shannon Index 1.5 1.0 Substrata 0.5 Shannon Index 2.0 2.5 Total Trawling Impact 4 6 8 10 0 2 4 6 8 10 C log(Trawling Impact 2006-2010)(mins) log(Trawling Impact 1986-1990)(mins) Figure 8: A: Showing the relationship between Shannon diversity and trawling impact. Diversity decreases as a function of trawling impact for both substrata (Table 1). B: Showing the relationship between trawling impact for 1986 to 1990 and Shannon diversity index. Diversity is declining with increased trawling for both substrata (Table 1). C: Showing the relationship for the latest trawling period (2006-2010) and Shannon diversity. Increased trawling has a negative impact on diversity for both substrata (Table 1). 12 Irina Chemshirova Table 1: Calculated linear regression statistics on the relationship between trawling impact and diversity measures for all the taxa recorded (•p<0.1, *p<0.05, **p<0.01, ***p<0.001). Response Slope variable estimate d.f. R2 t Shannon Index Total Trawling (Figure 8A) Substrata Hard 0.009* 42 0.13 2.50 Soft -0.03** 34 0.26 -3.42 Both -0.01* 78 0.06 -2.31 Trawling Period 1986-1990 (Figure 8B) Substrata Hard 0.06*** 42 0.32 4.40 Soft -0.02 34 0.02 -0.76 Both -0.02 78 0.03 -1.46 Trawling Period 2006-2010 (Figure 8C) Substrata Hard 0.04* 42 0.20 3.24 Soft -0.05 34 0.06 -1.47 Both -0.04* 78 0.08 -2.59 No. individuals (Appendix 5) Total Trawling Substrata Hard -0.02** 42 0.14 -2.63 Soft -0.05 34 0.05 -1.39 Both -0.06*** 78 0.22 -4.60 Trawling Period 1986-1990 Substrata Hard -0.03 42 0.03 -1.21 Soft -0.12 34 0.08 -1.67 Both -0.23*** 78 0.27 -5.40 Trawling Period 1986-1990 Substrata 13 Trawling Impact on the Greenlandic Benthos Hard -0.08** 42 0.20 -3.25 Soft -0.17* 34 0.14 -2.35 Both -0.27*** 78 0.42 -7.56 14 Irina Chemshirova Table 2: Linear regression statistics on the relationship between trawling impact and diversity measures for Vulnerable Marine Organisms (•p<0.1, *p<0.05, **p<0.01, ***p<0.001). Response Slope variable d.f. R2 t estimate Shannon Index (Appendix 6) Total Trawling Substrata Hard -0.004 42 0.04 -1.38 Soft -0.04* 34 0.13 -2.26 Both -0.02*** 78 0.20 -4.46 Trawling Period 1986-1990 Substrata Hard 0.003 42 0.001 0.22 Soft -0.06** 34 0.19 -2.84 Both -0.06*** 78 0.24 -4.96 Trawling Period 2006-2010 Substrata Hard 0.0003 42 10-6 0.02 Soft -0.07** 34 0.25 -3.39 Both -0.07*** 78 0.31 -5.97 No. individuals (Appendix 7) Total Trawling Substrata Hard -0.009 42 0.04 -1.24 Soft -0.07*** 34 0.23 -3.17 Both -0.07*** 78 0.24 -4.90 Trawling Period 1986-1990 Substrata Hard -0.06• 42 0.08 -1.89 Soft -0.19** 34 0.23 -2.84 Both -0.07** 78 0.23 -3.17 Trawling Period 2006-2010 Substrata Hard -0.06• 42 0.09 -1.99 15 Trawling Impact on the Greenlandic Benthos Soft -0.20** 34 0.22 -3.07 Both -0.32*** 78 0.44 -7.75 Table 3: Linear regression statistics on the relationship between trawling impact and the number of brittle stars found at each station (•p<0.1, *p<0.05, **p<0.01, ***p<0.001). Response Slope variable estimate d.f. R2 t Total Trawling (Appendix 8) Substrata Hard -0.03* 42 0.14 -2.62 Soft -0.04* 34 0.13 -2.21 Both -0.07*** 78 0.28 -5.44 Trawling Period 1986-1990 Substrata Hard -0.12* 42 0.11 -2.32 Soft -0.14* 34 0.15 -2.46 Both -0.26*** 78 0.35 -6.44 Trawling Period 2006-2010 Substrata Hard -0.11* 42 0.12 -2.40 Soft -0.16* 34 0.17 -2.65 Both -0.28*** 78 0.42 -7.50 16 10 8 0.5 6 4 0.0 2 log(Trawling Impact 2006-2010) (mins) 1.5 12 1.0 Shannon Index 2.0 2.5 3.0 Irina Chemshirova 0 0 2 4 6 8 10 12 log(Trawling Impact 1986-1990) (mins) Figure 9: Showing the potential recovery stations in red, based on the relationship between the first and last fishing period and their respective Shannon diversity. 17 Trawling Impact on the Greenlandic Benthos Discussion The number of taxa we found was consistent with similar studies. Mac Donald et. al. (2010) discovered 51 taxa at 900m depth. They also took physical samples at their study sites, which would have allowed them to sample more of the infaunal community. There is evidence of trawling impacting the diversity of the benthos. All of the diversity metrics show that soft substrata sites have been more severely affected. Kaiser et al. (2006) have also found these communities are quite vulnerable and their recovery may take years. The soft substrata are more frequently disturbed by trawling activities. Therefore they are not given enough time to recover before they are disturbed again (Kaiser et al., 2006). Furthermore the soft sediments are more prone to resuspension. This may lead to the smothering of the benthos and anaerobic conditions which often hinder the settlement of the larvae of many organisms (Jones, 1992). It is also possible that the extent of the diversity of the soft-bottom sites has been under-recorded due to the burrowing behaviour of many of the organisms there. Simpson and Watling (2006) recorded burrow density and size. Whilst we recorded the number of visible burrows and animal trails we did not perform any statistical analysis on them. It would be interesting to develop this further and measure how trawling is impacting this infaunal habitat structure. The hard substrata sites on the other hand, have responded positively to trawling when considering Shannon’s Index. This kind of substrata is less frequently disturbed and his community has low levels of natural disturbance due to its depth. Therefore trawling could be increasing the diversity by acting as an intermediate disturbance. Thus removing the some of the slow growing species which are better at competing for resources and giving a chance for the rapidly colonising species to settle instead (Blanchard et al., 2004). This is further confirmed by Figure 9. It shows that intermediate amount of trawling (in brown, regardless of substrata) has the similar diversity to sites which haven’t been trawled for a very long period of time (in red). However when considering the number of individuals found at hard substrata stations a decline was observed. This was consistent with findings by Freese et al. (1999). Therefore it could be that the Shannon Index is not suitable for measuring the diversity with our current level of classification (i.e. most are identified to variable taxonomic levels, none to species). 18 Irina Chemshirova Most studies have reported that trawling has no long term effects on soft substrata (Simpson & Watling, 2006, Sparks-McConkey & Watling, 2001, Kaiser et al., 1998). However all of them have simulated trawling disturbance. They state that the level of disturbance they subjected the benthos to is not as intensive as commercial trawling. Therefore it is possible that the long term effects we have seen here are due to the extensive and frequent exploitation of soft-bottom sites. Smith, Papadopoulou & Diliberto, (2000) have found that recovery is much slower when surveying sites which are being trawled commercially. They also state that the four months recovery period is not enough to counter the negative effects of trawling. The declines of Vulnerable Marine Organisms were more evident across both substrata. These taxa are more sensitive to disturbance (MacDonald et al., 1996). Also they are usually long-lived and reach a relatively large size when undisturbed. Therefore larger individuals are usually more susceptible to trawling damage as there is a greater chance of impact with the fishing gear (Bergman & van Santbrink, 2000). This coincides with the findings of McConnaughey, Mier & Dew (2000), where there is greater diversity and numbers of sedentary taxa (i.e. soft corals, sponges, ascidians, bryozoans) in untrawled areas. Simpson & Watling (2006) and Prena et al. (1999) also found that trawling affects sponges and corals to a greater extent than other organisms. Trawling has been shown to generally cause a shift in the community from habitat-building organisms (like the VMOs) to mobile and burrowing organisms which are more capable in dealing with continuous trawling pressure (De Juan, Demestre & Sanchez, 2011). Furthermore some models estimate that sponges and corals may take up to several decades to recover from trawling damage. This is mainly due to their slow growth and limited dispersal (Rooper et al., 2011, McConnaughey, Mier & Dew, 2000). In many studies, brittle stars are shown to be more numerous in trawled areas and to actively scavenge on the remains of damaged benthic organisms (Groenewold & Fonds, 2000). However we found that they are strongly decreasing in numbers across all substrata. This could be due to the scavenging assemblages being quite transient (24 to 48 hours) and form shortly after a trawling event has occurred (Bergmann et al., 2002). Therefore our current trawling data is not sufficient to detect these abundance changes as it does not overlap with our camera data. 19 Trawling Impact on the Greenlandic Benthos Soft substrata organisms may be more susceptible to trawling than previously thought. This can be attributed to the frequency at which they face disturbance. The overall effect of trawling on hard substrata is not entirely clear. It is likely that with increased trawling intensity the diversity levels would be similar to those we found for soft substrata. 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Yesson, C.(2013) How the fishing data was prepared for analysis Chemshirova, I. 24 Irina Chemshirova Appendix 1: Detailed Camera settings used for image capture Shutter speed 1/100 sec Distance bottom 80 cm frame to camera lens Focus distance 70 cm (manual) F-stop 11 Programme Manual (M) WB Flash ISO 100 (Kemp, 2011) 25 Trawling Impact on the Greenlandic Benthos Appendix 2: Taxa found in the surveys from 2011 and 2012. Taxa designated as VMOs have been highlighted. Taxa Total Soft Coral 197 Sea fans/pens 3 Anemones 117 Zoanthids 906 Hydroids 584 Stylasterina 1479 Asteriidae 4 Pterasteridae 5 Echinasteridae 15 Solasteridae 5 Goniasteridae 9 Astropectinidae 2 Starfish (other) 1 Brittle stars 5461 Sea urchins 50 Sea cucumbers 142 Crinoids 45 Encrusting Sponges 460 Massive Sponges 1646 Arborescent Sponges 229 Sabellidae 713 26 Irina Chemshirova Eunicidae 636 Serpulidae 299 Aphroditidae 0 Polynoidae 1 Decapoda 51 Amphipoda 0 Isopoda 76 Sea Spiders 12 Gastropoda 39 Chitons 43 Bivalves 208 Scaphopoda 8 Sepioida 0 Octopoda 2 Terebratulida 161 Erect Bryozoans 944 Encrusting Bryozans 1203 Soft Bryozoans 142 Ascidians 3978 Rajiformes 1 Scorpaeniformes 1 Peciformes 4 Pleunectiformes 3 27 Trawling Impact on the Greenlandic Benthos Appendix 4: Image quality classification An image classified as high quality An image classified as medium quality 28 Irina Chemshirova An image classified as low quality 29 Trawling Impact on the Greenlandic Benthos 4 3 2 log(No. of individuals) 5 6 7 Appendix 5: Linear Regression Graphs - Number of individuals vs Trawling Impact Substrate type 1 hard soft 0 10 20 30 40 50 0 2 6 5 4 3 Substrate type hard soft 1 hard soft 2 log(No. of individuals) 6 5 4 3 2 Substrate type 1 log(No. of individuals) 7 7 Total Trawling Impact 4 6 8 0 10 2 4 6 8 10 log(Trawling Impact 2006-2010)(mins) log(Trawling Impact 1986-1990)(mins) 30 0.5 1.0 Shannon Index VMOs 1.5 Irina Chemshirova Appendix 6: Linear Regression Graphs VMO Shannon vs Trawling Impact Substrate type 0.0 hard soft 0 10 20 30 40 50 1.5 1.0 Substrate ty pe hard sof t 0.0 hard sof t 0.5 Shannon Index VMOs 1.5 1.0 0.5 Substrate ty pe 0.0 Shannon Index VMOs Total Trawling Impact 0 2 4 6 8 0 10 2 4 6 8 10 log(Trawling Impact 2006-2010)(mins) log(Trawling Impact 1986-1990)(mins) 31 Trawling Impact on the Greenlandic Benthos 4 3 2 1 log(No. of VMO individuals) 5 6 Appendix 7: Linear Regression Graphs VMO Number of individuals vs Trawling Impact Substrate type 0 hard soft 0 10 20 30 40 50 hard soft 0 2 4 6 8 6 5 4 3 2 1 Substrate type hard soft 0 log(No. VMO of individuals) 6 5 4 3 2 1 Substrate type 0 log(No. of VMO individuals) Total Trawling Impact 0 10 2 4 6 8 10 log(Trawling Impact 2006-2010)(mins) log(Trawling Impact 1986-1990)(mins) 32 1 2 3 4 log(No. of Brittle stars) 5 6 Irina Chemshirova Appendix 8: Linear Regression Graphs Number of Brittle stars vs Trawling Impact Substrate type 0 hard soft 0 10 20 30 40 50 4 3 2 Substrate ty pe 1 hard sof t 5 log(No. of Brittle stars) 6 6 5 4 3 2 1 Substrate ty pe hard sof t 0 0 log(No. of Brittle stars) Total Trawling Impact 0 2 4 6 8 10 0 log(Trawling Impact 1986-1990)(mins) 2 4 6 8 10 log(Trawling Impact 2006-2010)(mins) 33