Impact of trawling on benthic marine organisms off … · Irina Chemshirova 1 Impact of trawling on benthic marine organisms off the Greenlandic shelf, 200 to 600 meters depth Abstract

Irina Chemshirova

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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

Trawling Impact on the Greenlandic Benthos

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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.

Irina Chemshirova

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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.


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Methods

This was addressed by photographing the seabed at locations which have experienced

varied fishing disturbance off the West coast of Greenland (Figure 1).

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

Figure 1: Map showing the locations surveyed along the west Greenlandic

coast and the level of trawling there.

Credit: Chris Yesson

Irina Chemshirova

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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.

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

Flash

battery

Camera in

housing Flash

Trigger weight

Additional

weight

Flash

trigger

Credit: Kirsty Kemp


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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).

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

Credit: Julius Nielsen

Irina Chemshirova

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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


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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, 1986-

1990 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 analysis (Figure 5).

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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).

0.00 0.01 0.02 0.03 0.04 0.05 0.06

-2-1

01

2

Leverage

Sta

nd

ard

ize

d r

esid

ua

ls

lm(shannon49 ~ fishing.trans.allno49)

Cook's distance

Residuals vs Leverage

201149

201248

201106


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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.

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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

should be 45 species (SE=2), whilst we found 41.

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).

0 20 40 60 80

01

020

30

40

Sites

Sp

ecie

s


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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).

0 10 20 30 40 50

0.0

0.5

1.0

1.5

2.0

2.5

Total Trawling Impact

Sh

ann

on

In

de

x

Substrata

hard

softboth

0 2 4 6 8 10

0.0

0.5

1.0

1.5

2.0

2.5

log(Trawling Impact 1986-1990)(mins)

Sh

an

non

Ind

ex

Substrata

hard

softboth

0 2 4 6 8 10

0.0

0.5

1.0

1.5

2.0

2.5


Sh

an

non

Ind

ex

Substrata

hard

softboth

A

C B

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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

variable

Slope

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


Substrata


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Hard -0.08** 42 0.20 -3.25

Soft -0.17* 34 0.14 -2.35

Both -0.27*** 78 0.42 -7.56

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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

variable

Slope

estimate

d.f. R2 t

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


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


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


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


Substrata

Hard -0.06• 42 0.09 -1.99


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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

variable

Slope

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


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


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

Irina Chemshirova

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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.

0 2 4 6 8 10 12

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0

2

4

6

8

10

12

log(Trawling Impact 1986-1990) (mins)

log(T

raw

lin

g Im

pa

ct 20

06-2

01

0)

(min

s)

Sha

nn

on In

de

x


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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).

Irina Chemshirova

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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.


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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.

We highlight the need for consistent taxonomic level identification (i.e. all

individuals counted to be recorded at only order or family level).

This study may aid in the outlining of sustainable management plans for the

Greenlandic shrimp fishery. If they prove successful, they can be implemented on a

wider scale. Furthermore these findings may serve as a basis of creating a policy

framework which may benefit in increasing the nation’s economic and environmental

capital.

Acknowledgements:

Many thanks to Andrew Croft Memorial Fund and Percy Sladen Memorial Fund for

making my participation in the field collection of this project possible.

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Appendix 1: Detailed Camera settings used for image capture

Shutter speed 1/100 sec

Distance bottom

frame to camera lens

80 cm

Focus distance 70 cm (manual)

F-stop 11

Programme Manual (M)

WB Flash

ISO 100

(Kemp, 2011)


26

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

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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


28

Appendix 4: Image quality classification

An image classified as high quality

An image classified as medium quality

Irina Chemshirova

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An image classified as low quality


30

Appendix 5: Linear Regression Graphs - Number of individuals vs Trawling Impact

0 10 20 30 40 50

12

34

56

7


log

(No

. o

f in

div

idu

als

)

Substrate type

hardsoft

0 2 4 6 8 10

12

34

56

7


log

(No

. o

f in

div

idu

als

)

Substrate type

hard

soft

0 2 4 6 8 10

12

34

56

7


log

(No

. o

f in

div

idu

als

)

Substrate type

hard

soft

Irina Chemshirova

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Appendix 6: Linear Regression Graphs VMO Shannon vs Trawling Impact

0 10 20 30 40 50

0.0

0.5

1.0

1.5


Sh

an

no

n In

de

x V

MO

s

Substrate type

hard

soft

0 2 4 6 8 10

0.0

0.5

1.0

1.5


Sh

an

no

n In

de

x V

MO

s

Substrate ty pe

hard

sof t

0 2 4 6 8 10

0.0

0.5

1.0

1.5


Sh

an

no

n In

de

x V

MO

s

Substrate ty pe

hard

sof t


32

Appendix 7: Linear Regression Graphs VMO Number of individuals vs Trawling Impact

0 10 20 30 40 50

01

23

45

6


log

(No

. o

f V

MO

in

div

idu

als

)

Substrate type

hard

soft

0 2 4 6 8 10

01

23

45

6


log

(No

. o

f V

MO

in

div

idu

als

)

Substrate type

hard

soft

0 2 4 6 8 10

01

23

45

6


log

(No

. V

MO

of in

div

idu

als

)

Substrate type

hard

soft

Irina Chemshirova

33

Appendix 8: Linear Regression Graphs Number of Brittle stars vs Trawling Impact

0 2 4 6 8 10

01

23

45

6


log(N

o. of B

rittle

sta

rs)

Substrate ty pe

hard

sof t

0 2 4 6 8 10

01

23

45

6


log(N

o. of B

rittle

sta

rs)

Substrate ty pe

hard

sof t

0 10 20 30 40 50

01

23

45

6


log(N

o. of B

rittle

sta

rs)

Substrate type

hard

soft

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Impact of trawling on benthic marine organisms off … · Irina Chemshirova 1 Impact of trawling on benthic marine organisms off the Greenlandic shelf, 200 to 600 meters depth Abstract