Assessment of Cold-Water Coral Predictive Habitat Models

Assessment of Cold-Water Coral

Predictive Habitat Models

Author: Gerard Summers

Supervisor: Professor Andy Wheeler

Thesis summited as part of GG6514 Research Project for M.Sc. Applied Coastal

and Marine Management

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Table of Contents

1. Abstract .............................................................................................................................. 4

2. Introduction ........................................................................................................................ 5

3. Literature Review............................................................................................................... 8

3.1. The Porcupine Bank Canyon....................................................................................... 8

3.2. Cold Water Corals: Their Current Distribution and Natural Habitat .......................... 9

3.3. Ecological & Economic Value of Cold Water Corals............................................... 15

3.4. Threats to Cold Water Corals and their Ecosystems ................................................. 18

3.5. Conservation Efforts ................................................................................................. 21

3.6. Predictive Habitat Modelling .................................................................................... 23

4. Materials and Methods ..................................................................................................... 27

4.1. Study Site .................................................................................................................. 27

4.2. ROV-Video Data Collection ..................................................................................... 28

4.3. Seabed Classification ................................................................................................ 29

4.4. Environmental Parameters ........................................................................................ 33

4.5. ROV-Borne Multibeam Echosounder (MBES) ........................................................ 34

4.6. Analysis of Catalogue Data ....................................................................................... 35

4.7. CTD Processing......................................................................................................... 36

4.8. Davies and Guinotte (2011) Model Assessment ....................................................... 37

5. Results .............................................................................................................................. 39

5.1. Video Catalogue Classification ................................................................................. 39


5.3. Analysis of Catalogue Data ....................................................................................... 49

5.4. Davies and Guinotte (2011) Analysis ....................................................................... 54

5.4.1. INSS MBES ....................................................................................................... 54

5.4.2. ROV MBES ....................................................................................................... 56

6. Discussion ........................................................................................................................ 57

6.1. Video Catalogue Data ............................................................................................... 57

6.2. Sedimentary Regime ................................................................................................. 60

6.3. UNEP CWC Presence Data ...................................................................................... 62

6.4. Davies and Guinotte (2011) Analysis ....................................................................... 63


7. Conclusions ...................................................................................................................... 69

8. Acknowledgements .......................................................................................................... 70

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9. References ........................................................................................................................ 71

9.1. Books & Papers ......................................................................................................... 71

9.2. Websites .................................................................................................................... 84

10. Appendices .................................................................................................................... 85

10.1. Appendix I ............................................................................................................. 85

10.2. Appendix II ............................................................................................................ 88

10.3 Appendix III .......................................................................................................... 93

10.4 Appendix IV ........................................................................................................ 140

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1. Abstract

In a world of bleached reefs, declining fisheries, and enigmatic marine environments,

predictive habitat models provide the most effective and reliable method of estimating the

extent of Vulnerable Marine Ecosystems, such as coldwater coral (CWC) reefs (Ross and

Howell, 2012). It is of paramount importance to ensure that the accuracy of these regional and

global scale models is augmented with meticulously attained field data (Guinotte and Davies,

2014). CWC habitat models are an area of science that seeks to delineate the best potential

habitat for some of the most obscure and poorly understood organisms in the marine

environment (Dolan et al. 2008). The principal aim of this project is to take a GIS based

approach at appraising existing CWC predictive habitat models (Davies and Guinotte, 2011).

With a special emphasis on acquiring thorough video catalogue data from the upper Porcupine

Bank canyon, accompanied with CTD data of the hydrographic conditions within the canyon.

The aim is to help develop a holistic approach with specific focus on scleractinia. This model

will seek to examine the criteria within a predictive habitat model established by Davies and

Guinotte (2011), which sought to utilise CTD and coral presence data from UNEP to create a

model that was applicable at a global scale and the upscaling of which gave it a 30 arc second

resolution. Here the research will focus on a combination of CTD, and video data collected on

the Grainuile in tandem with coral presence data that is provided by UNEP, and bathymetric

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data provided by the INSS online portal. Thus, generating an exhaustive set of environmental

conditions under within which CWCs will be found.

2. Introduction

Despite its significance to the quality of life worldwide today, most of the sea floor is mapped

at a lower resolution than terrestrial records or event he surfaces of other planets, asteroids, and

moons (Becker et al. 2009; Sandwell et al. 2006). Quantitative spatial information of the abiotic

and biotic components of the environment are needed to build a map of the benthic habitat.

With the advent and development of submarine multibeam echo-sounders, digital mapping,

and advancements in ROV technology the true topography of the seafloor can be appreciated

(Brown et al. 2011). A substantial effort had been made through the 10-year Census of Marine

Life Project to document the potential biodiversity of the world’s oceans, with nearly 2 million

new species being identified (Costello et al. 2010).

However, there is a lack of biological information to supplement this due to the time-

consuming nature of the collection and cataloguing of specimens, this is exacerbated within

marine habitats due to the practical difficulties of sampling deeper waters. (Przeslawski et al.

2011). Thus, many of Earth’s benthic species have yet to be identified, with studies proposing

that four fifths of Earth’s total species have yet to be discovered (Bouchet, 2006). As a result,

the true contribution of these organisms within the benthic environment are not fully

appreciated and their economic and biological value compromised (Aanesen et al. 2015).

Efforts need to be made into creating predictive habitat models that will expedite the process

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and allow for real time habitat assessment (Robert et al. 2016). Considering that Ireland’s

marine territory comprises 10 times the area of its landmass, a territory that contains significant

CWC presence and projected coral habitat, it is therefore imperative that resources be

designated for the protection, exploration, and maintenance of CWC habitat (Frank et al. 2011).

In 2014, the existence of a CWCs habitat occurring in Irish waters along the Porcupine Bank

was discovered, here CWCs were thought to colonise near vertical bedrock, with estimates

doubling the coverage of cold water corals within Irish waters, approximately 500km2

(Wheeler et al. 2015). Similar CWC habitats have been observed previous to this research with

rich benthic assemblages and faunal associations, creating promising picture for CWC

occurrence in the Porcupine Bank Canyon and in Irish waters (Huvenne et al. 2011). Figure

1.0 displays Ireland’s marine domain.

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The chief aim of this project is to assess the validity and accuracy of the most widely accepted

habitat model (Davies and Guinotte 2011) and apply it on a local scale survey to test the

limitations and assess its application within a marine setting that is common along the margins

of the Porcupine Bank.

On a wider scale, this model is meant to improve CWC habitat models globally, in the hopes

that it may contribute to global CWC reservation through correct representations of their

distributions.

This is hoped to be achieved through the following objectives:

To collect substantial CTD data of the upper Porcupine Bank Canyon.

To gather and catalogue ROV video data of the upper Porcupine Bank Canyon.

Figure 1.0 Real Map of Ireland, location of the Porcupine Bank Canyon outlined

in black (courtesy of the Geological Survey of Ireland and the Marine Institute).

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To create a GIS model that incorporates a wide range of data.

To assess the accuracy of the UNEP CWC presence data within the upper Porcupine Bank.

To ascertain the total coverage of CWCs within the Porcupine Bank canyon.

3. Literature Review

3.1. The Porcupine Bank Canyon

Topography of the seafloor can often affect current speed as current velocities increase when

flow encounters steep topography, in canyons this has been linked with higher intensity internal

waves than surrounding slopes (Quaresma et al. 2007). Creating a current above the seafloor

and increasing turbulence and sediment resuspension, generating nepheloid layers and turbidity

flows within the canyon (Arzola et al. 2008). These currents have been detected about the

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Porcupine Bank, potentially altering nutrient flow (White et al. 2005; White et al. 2007). Thus,

canyons are an important thoroughfare for shelf slope particulate exchange, creating a

heterogeneous environment that contains a wide variety of niches for biological communities

(Garcia et al. 2008; Morris et al. 2013). The presence of these factors enables areas of

significant biodiversity to occur within these canyons (Vetter et al. 2010). It is this process that

is thought to have driven surface productivity over the Porcupine and Rockall Banks (White et

al. 2005). A visual of the Porcupine Bank Canyon is provided in Figure 1.1 in Appendix I.

The Porcupine Bank Canyon lies 490km west of Co. Kerry and is 48km long and 29km wide,

it slopes into the Porcupine Seabight to the east and the Rockall Trough in the west, it is the

largest of the canyons occurring along the Porcupine Bank (NPWS 2016). It consists of a main

canyon head that cuts deep into the bank (beginning at 500m bsl) with a series of sub canyons

that branch off into the continental shelf, the channel itself widens towards the continental slope

where the thalweg occurs (approximately 2800m bsl) (Dorschel et al. 2011). This canyon runs

parallel to a major antecedent fracture to the west in the oceanic crust (the Charlie-Gibbs

Fracture), leading some studies to postulate that the canyon itself may be underlain by a major

fault which influences the canyon’s size and orientation (Dorschel et al. 2011). Large CWC

mounds occur all along the flanks of the Porcupine Bank, these correspond with higher

observed surface productivity, driven by increased nutrient values (White and Dorschel 2010).

3.2. Cold Water Corals: Their Current Distribution and

Natural Habitat

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CWCs occur as azooxanthallae filter feeders, i.e. lack symbiotic algae, and are of the anthozoan

orders Scleractinia (Hard Corals), Octocorallia (Soft corals), Anthipatharia (Black corals), and

the hydrozoan family Stylasteridae (Hydrocorals) (Roberts, Wheeler & Freiwald, 2006).

Scleractinia are the focal part of this study as they are among the most important ecosystem

engineering CWC for the benthic environment (Jones, Lawton & Shachak, 1997), the

significance of this group of corals is such that they build 3 dimensional frameworks that

support highly diverse fauna (Roberts, Wheeler & Freiwald, 2006). Such corals can be found

as colonial and solitary species within depths of a few metres to > 5000m on continental

margins, seamounts and mid ocean ridges (Roberts et al. 2009). Although some species can

live in muddy environments, most tend to live on hard substrates with enhanced currents to

ensure sufficient food supply (Huvenne et al. 2011). Although, CWCs were initially thought

to have a limited distribution, they are now found to be over 5000 species that occur in every

ocean across the globe (Roberts et al. 2009; Wagner et al. 2012). They most commonly occur

in continental slope settings, on deep shelves and along the flanks of oceanic banks and

seamounts (Roberts et al. 2009). Continental slopes offer a variety of topographic irregularities

that provide suitable substrate for CWC larvae to settle and colonise (Cordes et al. 2016).

Figure 1.2 below illustrates UNEPs global coral presence data.

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Species of Scleractinia that are significant to the Porcupine Bank include Lophelia pertusa,

Madrepora occulata, and Desmophyllum dionthus, with Lophelia pertusa and Madrepora

Occulata commonly found at depths of 50 to >2000m in the North Atlantic, with depths if

occurrence becoming progressively shallower towards the poles (Wheeler et al., 2007; Roberts

et al. 2009). Figure 1.3 below shows a photographic example of a live L. pertusa specimen,

taken at the Porcupine Bank Canyon. Open slope CWC mounds can be found in the Atlantic

along the Rockall and Porcupine Banks, and these do not occur by happenstance and have been

found to correlate with large scale water mass boundaries (White and Dorschel 2010, Mienis

Figure 1.2 Global Coral Presence (Freiwald et al. 2017)

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et al. 2007). L. pertusa tend to be the dominant framework forming corals and thrive due to the

complex interplay of chemical, physical, and biological factors occurs (De Clippele et al.

2017). These are yet to be fully quantified and a definitive model for their preferred habitat has

yet to be realised (Thiem et al. 2006). CWCs occur within water temperatures ranging from 4

to 12°C (Roberts, Wheeler & Freiwald, 2006), with water densities ranging from 27.35-27.65

kg m-3 (Dullo et al. 2008), and occurring in areas of fast currents with high productivity

(Wheeler et al. 2011). CWCs, unlike their tropical counterparts, feed on zooplankton and

therefore require an environment that can provide strong currents that can supply nutrients,

these currents can also aid in the dispersal of eggs and disposal of waste material (Foley, van

Rensburg and Armstrong, 2010). This also prevents excess sedimentation and enables CWCs

to feed effectively (De Mol et al. 2002).

Figure 1.3: L. pertusa, image taken on dive 9

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CWCs establish themselves when a coral larvae colonises an area of hard substrate, all CWC

are colonies that consist of animals named polyps (Wheeler et al. 2011). Once growth has been

initiated, the success of the coral colony largely relies on the aforementioned environmental

controls. Scleractinia are ecosystem engineers that need to outgrow sedimentation and other

destructive forces in order to maintain their 3-dimensional structure that enables their continued

growth as it acts as a foundation for juvenile coral specimens to establish themselves upon in

the future (Maier et al. 2011). If favourable conditions persist over a sufficient period of time

then new coral can grow on the skeletal remains, potentially creating mounds that can be to

350m off the sea floor and extend out over several square kilometres (Mullineaux and Mills,

2006). Coral mounds are made through succession of coral reef development, sedimentation

and erosion, coral skeletal matter may be dissolved over time and will often form a cement to

hold the different elements of the framework together (Wheeler et al. 2011). Whether these

mounds sustain contemporary reefs determines whether they are described as active mounds

(supports live coral specimens) or retired mounds (the framework is dead) (Roberts et al. 2009).

The topography of the seabed has a strong influence on the morphology of the overlying corals

(Wheeler et al. 2011). Corals can occur as a thin veneer along a suited substrate as seen in

several sites along the Gulf of Mexico (Hübscher et al. 2010). Figure 1.4 below displays a

series of antipatharian and scleractinian coral specimens occurring on top of a large dropstone.

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Historically the CWCs occurring on the NE Atlantic continental margin remain the most

intensively studied (Van Oevelen et al. 2009; Frank et al. 2011; Wheeler et al. 2011; Dorschel

et al. 2009). Observed influences on CWCs vary depending on both the temporal scale being

examined, with second scale turbulences and diffusion to month long tidal regimes and major

oceanographic phenomenon (Navas et al. 2014). Relevant spatial scales vary from cm size

currents flow through and across coral, which affects local biochemical fluxes and skeletal

morphologies to currents that flow across entire reef complexes (Navas et al. 2017). It is clear

that a series of water mass characteristics interact to drive marine ecosystem structure and

Figure 1.4: Antipatharia and Scleractinia Specimens taken

from dive 12.

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functioning in deep water settings, however the data and models to analyse this drive are

lacking (Flögel et al. 2014). On the Porcupine Bank, a large dome shaped cold water mass has

been observed, this is occurring due to the presence of deep water mixing regime that occurs

over the bank (Mazzini et al. 2011; White et al. 2005; White et al. 2007). Previous studies

indicate that this mixing regime is generated from the interaction of the East North Atlantic

Water (ENAW), a warm saline water mass, and deep winter currents, a distinction thermocline

has been observed at approximately 600m depth (Mazzini et al. 2011).

CWCs can also be affected by long term global changes in climate. Challenger Mound, for

example, occurs within the Belgica Mound Provenance of the Porcupine Seabight, and contains

mounds that appear to have been initiated at least 2.4 ma. Challenger mound also displays

pronounced hiatuses in growth, occurring from 1.7-1.0 ma, with of growth reinitiating shortly

afterwards (Kano et al. 2007). These have been linked with glacial cycles, with a significant

loss in production causing the elimination of most of the fauna within the reef during

glaciations, then during interglacial periods, the mounds continue their previous growth

(Roberts, Wheeler & Freiwald, 2006). As a result, CWC mounds forming in the NE Atlantic

are generated as a sequences of interglacial coral reef framework overlain by glacial hemiplegic

deposits (Roberts and Cairns 2014). Thus, highlighting the profound influence that the

chemical and physical conditions can have on CWC mound evolution, as it is continuously

influenced by the interaction of the hydrodynamic and sedimentary regimes, which is coupled

very closely with the global climate (Henry and Roberts, 2007).

3.3. Ecological & Economic Value of Cold Water Corals

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In order to determine economic value of CWC corals, ecosystem goods and services provided

by CWCs must first be established (Foley, Rensburg and Armstrong 2008). This includes any

direct or indirect use values; direct use refers to any actual or planned use of a service by an

individual (Bolt et al. 2005). Direct use values are based on stocks or assets, such as coral and

fish biomass and genetic raw materials embodied in organisms, which are sold in markets and

services derived from a CWC area (Foley, Rensburg, and Armstrong 2008). These goods or

services can include value of contracts for commercial seafood products, coral for jewellery,

and organisms and raw materials extracted from CWC sites which are used as inputs by

manufacturing and pharmaceutical industries. CWC coral offer indirect use values, in the form

of environmental regulating services such as temperature regulation, regulation of atmospheric

greenhouse gases, and adsorption of waste and pollutants (Armstrong et al. 2012). Most

importantly, it supports ocean life by the cycling of nutrients and provides a habitat for a vast

array of species (Jobstvogt et al. 2014).

Authogenic ecosystem engineers such as trees or corals generate or alter their habitat through

their presence (Miller et al. 2012). Ecosystem engineers may ameliorate the environmental

conditions, they can transform the biological interactions within a habitat (Cathalot et al. 2015).

CWCs are archetypal authogenic engineers and they provide habitat complexity in otherwise

homogeneous, sedimented environments, augmenting the biodiversity within the area

(Bongiorni et al. 2010). Food web analysis of CWCs in the Logachev Mound complex suggests

that CWCs have a much greater production rate of organic carbon than the surrounding soft

substrate environments, a significant find given the scarcity of food within this part of the ocean

(Van Oevelen et al. 2009).

CWC mounds are hotspots of biodiversity for benthic habitats, an example of this can be seen

in a study of CWCs within the Porcupine Seabight, which yielded 349 species including 10

undescribed species from a total of 7 box cores (Henry and Roberts, 2007). Morris et al. (2013)

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conducted studies that yielded 855 coral colonies per 100m ROV transect, including 31

individual coral species. Highest taxonomic richness was observed within the L. pertusa reef

habitat (Morris et al. 2013). CWCs are not only significant in terms of local species richness

(α-diversity), their macro habitat heterogeneity ensures that they are also important centres of

spatial turnover in β-diversity (Henry and Davies 2009). Nonetheless, within certain areas of

the world, the role of CWCs in fish species distribution remains unclear when concerning the

influence of multiple drivers occurring over various spatial scales, with studies suggesting that

CWCs role is species specific and not universal across all fauna (Milligan et al. 2016).

CWCs can provide nursery grounds and living space for many different juvenile and adult

species, many commercial fish species take advantage of this commodity and as such CWC

habitats can be vital for fish stock maintenance (Huvenne et al. 2016). Henry et al. (2013) used

video surveys to evaluate spawning grounds for the shark species Galeus melastomus within

the Mingulay Reef Complex off Western Scotland. The findings indicated a greater abundance

of this species was concentrated around the reefs within the area, and individuals were also

noted to be larger than average, indicating that the reef may also provide a feeding grounds for

this species (Henry et al. 2016). Such sharks are valuable species for recreational angling and

their habitat use in coral ecosystems suggest possible environmental synergies, whereby one

ecosystem function is enhanced by another (Bennett et al. 2009).

Tropical corals have gained a prolific status within the conservation community with a wide

range of valuation studies being conducted, identifying these habitats as biomes with the

highest valued ecosystem services in aggregate, emphasising their importance (Sheppard

2013). Their deep-sea counterparts, cold-water corals (CWCs) have been subjected to one

valuation effort, which was largely inconclusive (Glenn et al. 2010). This is surprising given

that 65% of all corals occur below depths of 50m (Lindner et al. 2008).

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3.4. Threats to Cold Water Corals and their Ecosystems

Currently, the global environment is .in a state of flux with atmospheric and oceanic

temperatures on the rise worldwide causing change that is novel within the realms of human

history (Harsch et al. 2017). As such ecologists recognise the importance of the monitoring

and predicting of this change in order to help curb the catastrophic effects of global warming

(Evans 2012). Coldwater corals (CWCs) are an important reservoir for biodiversity and provide

a refuge and a spawning ground for a whole host of flora and fauna (Ballion et al. 2012), they

also represent a vast store of carbon in the form of calcium carbonate (Foley, van Rensburg

and Armstrong, 2010). Deep water marine environments are characterised by low fecundity,

low productivity, older age at maturity and a high longevity of the species adapted to the

environment, as a result, CWC ecosystems are regarded as being one of the most sensitive deep

marine ecosystems to change (Fabri et al. 2014). CWCs occur within a narrow range of

parameters including temperature, pH, and salinity ranges, consequently, these organisms have

a very low tolerance to environmental change (Goffredo and Dubinsky, 2016).

Current research indicates that ocean acidification and increasing temperatures are threatening

these habitats, with studies indicating that 70% of all scleractinian corals to be exposed to

corrosive waters and temperature increases by the end of this century, this may cause a

significant decrease in their extent and continued growth (Büscher, Form and Riebesell, 2017).

Such changes in the environmental parameters can cause a destabilisation of established CWC

reef systems and an extreme loss in biodiversity. Tittensor et al. (2010) model the suitability

of potential habitats for CWCs today versus what the projected chemistry of the oceans in 2099

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under the IPCC, IS92a, and S650 scenarios, all of which exhibited a pronounced reduction in

suitable coral habitat. This depletion of coral habitat is not uniform as seamounts were found

to be consistent refuges for CWCs due to higher levels of aragonite saturation (Tittensor et al.

2010).

CWC reefs are vital ecosystems for many types of commercial fish causing significant

concentrations to occur around the reef, currently shallow continental shelf fish stocks are in

decline and fisheries are moving into deeper regions (Husebø et al. 2002). Bottom trawling

remains one of the most destructive impacts on CWC habitats, as heavy metal frames are

involved within the practice and as they are dragged along the sea floor they can destroy exiting

coral habitat (Armstrong et al. 2014). When considering that a normal fishing excursion would

last over 10 days and cover 100km2, trawling within the marine environment has the potential

to damage a large portion of CWC habitat, this is illustrated in Figure 1.5 below. (Davies et al.

2007). Such fishing practices can often lead to sediment becoming re-suspended in the water

column, this is detrimental to CWCs as they are suspension feeders and their ability to take in

nutrients can be hampered by the sediment being re-deposited on top of them (D’Onghia et al.

2016). This effect has been observed to impact a much greater area than that of the physical

destruction caused by the trawling equipment. In areas of complicated bottom topography (such

as the Ionian Sea), which remain inaccessible to trawling, threats may come from the use of

other gears such as long line fishing (Sampaio et al. 2012). It is believed that bottom long lines

sweep the sea bed (when gear is hauled up) in a comparable way to demersal trawling

(Kilpatrick et al. 2011).

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Lastras et al. (2016) conducted a comprehensive video survey on the impacts of bottom

trawling practices on corals within La Fonera Submarine Canyon Head in the Northern

Mediterranean Sea, the survey revealed extensive trawling damage and sea litter. Surveys

conducted studying the differences between unfished and fished seamount habitats in the

Chatham Rise off the coast of New Zealand, this zone is subject to extensive trawling practices

primarily for the exploitation of orange roughy and oreos (Clark and O’Driscoll 2003). Clark

and Rowden (2009) revealed a marked difference in the invertebrate assemblages between

them, with fished seamounts displaying a depleted coral habitat relative to the unfished zones,

the fished zones also exhibited signs of damage created by trawling. L. Pertusa have a skeletal

extension rate of 6 to 26 mm/yr, this had been derived from studies that indirectly examined

the stable isotopes (Mortensen & Rapp, 1998; Mikkelsen et al. 2008; Rogers 1999), and studies

observing corals that were grown on artificial substrate (Gass and Roberts, 2006). Thus, CWC

recovery rate from any form of damage to reef or mound structures is slow, it is often estimated

to take tens of thousands of years for a reef to fully recover, thus CWCs are sensitive to

destructive anthropogenic influences, fulfilling all the requirements to be labelled vulnerable

organisms (Andrews et al. 2005; Foley, van Rensburg and Armstrong, 2010).

Figure 1.5 Coral debris created from demersal trawling, trawl marks are

evident, resulting in complete CWC habitat destruction (Rossi et al. 2017).

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Similar parallels can be drawn from the recent development of the hydrocarbon industry as

deeper reservoirs are required to sate the globe’s current demand for oil and gas resources,

raising concerns amongst conservationists that CWCs could be smothered by the discharge

(Rogers 1999). While studies have shown that CWCs can adapt reasonably well to an

environment with active near-bed sediment fluxes, exposure to fine sediments and drill

cuttings, they are still measured with a marked drop in skeletal growth (Larsson et al. 2013).

White et al. (2012) conducted surveys on coral communities in the Gulf of Mexico 3-4 months

after the Deepwater Horizon oil spill had occurred, 10 healthy coral communities were

observed at >20km from the initial spill site, however one site at 11km presented with

widespread signs of stress. 46% of 43 corals imaged at the site exhibited evidence of impact

on more than half the colony, manifesting in tissue loss, sclerite enlargement, excess mucous

production, bleached ophiuroids, and covering by brown flocculent material (White et al.

2012).

3.5. Conservation Efforts

One of the biggest challenges with deep marine habitats is raising awareness of the hidden

vulnerability and diversity of these corals, as CWC are seen as a novelty (Huvenne et al. 2016).

A dichotomy exists between the need to preserve biologically diverse habitat and maintaining

economically lucrative fish stocks, as CWC habitats have been found to support large

populations of economically viable fish stocks (Henry et al. 2013). Understanding the

processes that drive species distribution and the scale at which these processes operate is of

vital importance, particularly when the species is of a high conservation or commercial value

(Milligan et al. 2016). CWCs have been found to be vital to species specific faunal

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assemblages, and are now recognised as being threatened at the levels of species, habitat (e.g.

trawling damage) and ecosystem (e.g. ocean acidification) (Huvenne et al. 2016). Efforts

focusing their conservation through the designation of protected areas (MPAs), where activities

such as bottom fishing with towed nets and dredges being restricted (Roberts and Cairns 2014).

These MPAs have the potential to protect both CWC habitat and fish stock and are the most

suitable solution with especially when dealing with areas where enforcement and compliance

costs and would outstrip any potential economic output (Foley et al. 2010). MPAs can be

enforced on a wide range of levels from complete closure of the area, restriction of fishing gear

used, to zoning approaches where different activities are banned from different zones (Foley et

al. 2010). Implementation of MPAs in shallow sea environments have shown widescale

improvement with increased abundance and lifespan of individual species and a greater

connectivity within the environments displayed, it is believed that enforcing MPAs in deeper

marine environments will have a similar effect and aid in the recovery of CWC ecosystems

(Davies et al. 2007).

Huvenne et al. (2016) conducted a study 13 year after a deep fisheries closure and MPA at

approximately 1000m depth had been imposed at the Darwin Mounds in the NE Atlantic in

1998, this was an area that had been subjected to bottom trawling practices. Results show that

areas that largely avoided disturbance showed similar levels of coral presence as had been

found in 1998, however areas in the eastern portion of the Darwin Mounds maintained a

desolated habitat with no recolonization and lack of growth of the corals (Huvenne et al. 2016).

This highlights the need for a proactive approach to CWC conservation as the slow growth rate

for CWCs exhibits the lack of resilience that these organisms have to drastic and invasive

changes to their environment (van Rensburg and Armstrong, 2010). Armstrong et al. (2014)

postulates that many of the protected areas delineated for coral reef are designated based on the

fact that coral habitats are unique zones within the marine environment, with most areas being

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chosen due to limited impact on fisheries, rather than maintaining an emphasis of conservation

for the species. It is therefore imperative to prioritise CWC and to integrate a comprehensive

policy of conservation that is inclusive, whose processes are accessible for stakeholders in a

marine zone (Stillman et al. 2016). Ross and Howell (2012) utilised predictive habitat

modelling to assess the suitability of predictive habitat modelling when delineating Marine

Protected Areas for listed coral species, concluding that these models enabled prompt

assessment of potential habitats. Predictive habitat models have been employed in the

assessment of spatial closures for CWCs on the landings of economic fish species in the Hectat

Strait, in British Columbia, here estimations show that if 99% of coral reef habitat were closed

off for protection, this would reduce landing values to 63% (Largasse et al. 2015). These

models also provide a simple and straightforward means that allow local stakeholders to

visualise and comprehend the significance and extent of habitats, enabling more effective

management and inclusion of natural resources (Stillman et al. 2016).

3.6. Predictive Habitat Modelling

Predictive habitat modelling has proven itself a valuable tool in quantifying, evaluation

governing, and conservation of natural resources and is the basis of many studies on a wide

variety of species ranging from those native to tropical rainforest climates to cetacean research

(Hansen et al. 2015; Lambert et al. 2014). Predictive habitat models have also played a vital

role in prioritising areas for conservation and scientific research (Ross and Howell 2012).

Therefore, it is critical to ensure that these models are made to the highest quality possible

However, it is clear from the research that much of the data theoretical and that field

observations and surveys are needed to ground truth this data and ensure increased accuracy of

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these models (Davies et al. 2008; Davies and Guinotte 2011; Guinotte and Davies 2014). This

is limited by the high costs associated with sampling and surveying the deep sea, making it

economically infeasible to maintain detailed survey data of all the world’s oceans (Davies and

Guinotte 2011).

Ecological Niche Assessment has been an essential part of establishing predictive habitat

models and has been extensively applied to CWC habitat prediction, establishing global and

regional CWC habitat models (Davies et al. 2008; Dolan et al. 2008; Yesson et al. 2012;

Guinan et al. 2009). Executed through the intense scrutiny of presence records and their

relationship with the physical parameters occurring within the environment (Mc Nyset 2005).

Ecological Niche Assessment relies on understanding the factors influencing species

distribution, and this can be done over multiple scales (Guinan et al. 2009). GIS based

techniques and statistical methods (Ecological Niche Factor Analysis) have been included in

these assessments in past studies (Zhao et al. 2006; Hirzel et al. 2006). A common component

within these models is the incorporation of georeferenced presence data in combination with

environmental variables that ultimately culminate in a map which delineates areas of potential

species coverage (Guinan et al. 2009). Presence data can be derived from museum records

(Lim et al. 2003), atlas collections (Peterson 2001), and field samples (De Clippele et al. 2017).

Environmental variables are obtained as those variables that may influences the ecological

niche within which the species occupies and are not variables that would change with time.

Historically Ecological Niche Factor Analysis (ENFA) was one of the most successful methods

of predictive habitat modelling for terrestrial and marine habitats (Dolan et al. 2008). ENFA

employs a suite of variables, Eco Geographic Variables (EGVs), and relates them to species

distribution data to create a habitat suitability model (Freiwald and Roberts, 2005). Regardless

the quality of the input data that is used remains the controlling feature within a habitat

suitability model, with challenges arising from poor data sampling techniques often caused by

25

a lack of concern for experimental design (Guinan et al. 2009). Many studies involve the

manual weighting of EGVs based off independent variables that are specific to the area, and as

such are only suited to best fit the specimens limited within that survey site (De Clippele et al.

2017). Success applications of the technique have been applied to CWC modelling using

presence only data mostly with global distributions (Davies et al. 2008).

Maxent (Maximum Entropy Models) based models that use presence only data now have

surpassed the traditional ENFA based approach and have produced models that give a more

exact position of CWC habitat, a comparison that has recently come to light (Davies et al.

2011; Tittensor et al. 2009; Ross and Howell 2012). Davies and Guinotte (2011) displays the

difference in resolution between a previous model created through Biomapper utilising ENFA

presence based analysis and a Maxent model utilising presence only data (Davies et al. 2008),

Figure 1.6 below displays resolution improvement between the 2 models.

26

UNEP CWC presence database represents the most complete record of CWC distribution

across the globe, and has been utilised by many studies as parameters for CWC occurrence

globally (Yesson et al. 2015; Davies and Guinotte 2011). Yet some of the coral positions that

are included within this data are taken from data that has yet to be groundtruthed (A., J.,

Figure 1.6: Davies and Guinotte (2011) show the increase in resolution between the current

model, Map (a) and previous global distribution models, Map (b) (Davies et al. 2008).

27

Wheeler, Personal Communication, 31st August 2017). suggesting possible political factors

interfering with the authenticity of the dataset and the accuracy of predictive habitat models

that include this data within their parameters (Dorschel et al. 2010).

4. Materials and Methods

4.1. Study Site

The Porcupine Bank is dominated by the East North Atlantic Water (ENAW), which originates

from the Bay of Biscay and enters the Rockall Trough from the south east and is carried

northwards by the Continental Slope Current (Pollard et al. 1996; New, Smith and Wright

2001). The ENAW ranges between 200-700m (White and Dorschel 2010) and is underlain by

the Mediterranean Outflow Water (MOW), which enters the Rockall Trough from the south

according to CTD and boundary layer data collected in the north of the Porcupine Bank (Mienis

et al. 2007). A steep pycnocline exists between the MOW and the ENAW (Morris et al. 2013).

The MOW extends to 1200m in depth where is interacts with the Labrador Sea Water, this has

a maximum salinity at 1800m in depth and its signature ends at 1900m, hereafter there are

many water masses that occur in small amounts (Van Rooji 2004). Bottom current, salinity and

temperature profiles of this area show a clear semi diurnal cyclicity, with salinity ranging from

35.2-35.4%, temperatures ranging from 7-9°C, and current speeds ranging between 10-20ms-

1, sometimes reaching speeds of 30ms-1 (Mienis et al. 2007). Current speeds from a deeper

reference site (2500m bsl) reached less than 10ms-1 (Mienis et al. 2007). Sedimentological

28

studies also confirm the increase in bottom current speeds towards the coral carbonate mound

range (Øvrebrø et al. 2006).

4.2. ROV-Video Data Collection

During the CoCoHaCa ROV survey (2017) on the ILV Grainuile, video data was captured via

composite video with the Holland 1 ROV (cruise number RH17002). Positioning and

navigation were acquired through the deployment of a USBL (Sonardyne Ranger 2) and RDI

Workhorse DVL. The altimeter on the ROV recorded and logged the height of the ROV from

the seabed. Video and photographic data was captured through a digital stills camera mounted

on the ROV facing towards the seabed. This data was recorded in transects that ran roughly

west to east over the eastern wall of the Porcupine Bank Canyon, with the ROV maintaining a

2m distance from the sea bed or mound surface. The positions of these dives are outlined in

Figure 1.7 Appendix I. The ROV roughly began at the foot of an incline and gradually

continued up the slope and terminated at higher ground. This data was used to ground truth the

pre-existing coral presence data provided by UNEP and to help validate the models that have

been presented in previous studies (Davies et al. 2011). Figure 1.8 shows a sideview of the

ROV Holland I.

29

4.3. Seabed Classification

A georeferenced video catalogue of the seabed surface had been created using a seabed

classification scheme that had been adopted from antecedent research that assesses ROV dive

based facies classification (Lim, Wheeler and Arnaubec 2017; Dorschel et al. 2007;).

Processing of the video data required a manual classification scheme to be employed, as neither

a supervised nor an unsupervised classification scheme could be employed as a consistent

height could not be maintained by the ROV off of the seabed. The facies classifiers chosen

were: “Live Coral Framework”, “Dead Coral Framework”, “Soft Corals”, “Hard Coral

Individuals”, “Hemipelagic sediment” and “Bedrock”. This seabed classification scheme was

Figure 1.8: ROV Holland I recovering onto deck

(Marine Institute)

30

complemented by USBL navigation data that headed the top of the video display, allowing for

time capture measurements of seabed change to be noted. These observations were noted in

excel, sea bed facies changes were noted immediately, and were merged with OFFOPS GPS

coordinates by matching the times of the observed facies changes with the corresponding

coordinates, allowing for quantification of the CWCs and their habitat. A test of the

classification scheme was run over dives 1 and 2 to amend any previous classes and calibrate

the classes. An example of the classification scheme can be seen in Table 1.0 below. Hard coral

facies classes were the facies used to validate the model as they create the 3 dimensional

framework that is central to CWC importance.

Class Facies

1 Live Coral Framework (LCF)

2 Dead Coral Framework (DCF)

3 Soft Coral

4 Hard Coral Individuals

5 Sediment

6 Coral Debris

7 Bedrock

8 Miscellaneous

Table 1.0: Seabed Facies classes

31

LCF facies was used to label coral reef that had living coral specimens growing on top of a 3

dimensonal framework, i.e. that come together to form an active reef or mound. Dead Coral

Framework consisted of dead CWC skeleton which had not been further colonised by new

CWC, video analysis determined that there are no living parts. Soft coral faceis were designated

for seabed dominated by antipatharians. Hard Coral facies was reserved for those individual

coral specimens that are in the initial stages of growth and have not come together to form one

cohesive framework. Sediment facies was designated for those habitats that were dominated

by hemipelagic sediment. Coral debris facies was designated for carbonate rubble, that

included recognisable biogenic material (i.e. coral rubble and shell fragments) and sediment.

Bedrock facies was for exposed bedrock that occurred along the sea floor, including only rock

that consisted of the underlying bedrock, excluding regolith (such as hemipelagic sediment and

dropstones). Miscellaneous was used to indicate the areas where the seabed had no longer been

in sight of the ROV. All facies were designated once they achieved cell coverage (video frame

coverage) of >50%. Any changes in the seabed were immediately documented and the position

noted. Examples of facies 1-7 can be seen in Figure 1.9 below.

32

(A)

AA

AA

AA

A(

A

(B)

(C) (D) (E) (F)

Figure 1.9: Examples of the predominant Seabed Facies: (A) Bedrock, (B) Coral debris,

(C) Antipatharians (soft coral), (D) Live Coral Framework, (E) Hemipelagic sediment, &

(F) Dead Coral Framework.

(C) (D)

(E) (F)

33

4.4. Environmental Parameters

A CTD profile had been recorded at the start and end of each dive to create a record of the

environmental conditions of the water column and to help identify possible water mass

changes. CTDs 1-39 and CTDs 44-49 were taken through the ROVs on board CTD sensor,

with the depths ranging from 560-1254m water depth. CTDs 40-43 were collected using a

Seabird SBE 911 profiler, which included sensors for temperature, conductivity and dissolved

oxygen, as well as a transmissometer. The CTD was deployed without the use of a rosette to

ensure speed of capture. They were taken at depths ranging from 1180m to 1794m. CTD

collection occurred at the beginning and end of each dive, with the downcast occurring as the

ROV was lowered into the water, an upcast was taken upon its retrieval.

CTD data was imported into ArcGIS to assess any potential correlations with the coral presence

data and to eliminate any parameters that have no effect on the distribution of CWCs within

the area. All ROV CTDs were taken at 2m away from the seabed, all SBE CTDs were taken at

depths of 10m off the seabed to avoid damaging the equipment. CTD raw data files were

processed through the SBE Data Processing software, they were first converted to *cnv. files,

and in order to enter them into ArcGIS software effectively they were bin averaged to 1m. The

*cnv. files were imported into excel and the data was separated into tables, then plotted using

Grapher 8 in order to recognise different water masses within the vertical column. CTD

Locations are included in Figure 2.0 in Appendix I. A picture of the Seabird SBE 911 CTD

profiler can be seen in Figure 2.1 below.

34

4.5. ROV-Borne Multibeam Echosounder (MBES)

ROV mounted MBES data was collected over the Porcupine Bank Canyon as part of the

QuERCI I Survey (2015), this was conducted on board the RV Celtic Explorer, with the

Holland I ROV. A high-resolution Kongsberg EM2040 MBES was mounted on the front of

the ROV and this acquired data at a frequency of 400 kHz. The survey travelled at a speed of

2 knots with the ROV maintaining a distance of 30m off of the seabed. A sound velocity probe

had been integrated with the MBES and the positioning and attitude of the ROV were procured

by using a Kongsberg HAINS internal navigation system, ultra-short baseline (USBL) system

(Sonardyne Ranger 2) and doppler velocity log (DVL). These combined with the dGPS on the

Figure 2.1: CTD Profiler, without Rosette

35

ship produced a MBES grid that was imported into Arc Map 10.4.1 at a 10cm grid size. The

loss in temporal resolution with regards to this dataset is offset by the longevity presented by

CWCs and their slow growth relative to other marine organisms (Fabri et al. 2014).

INSS bathymetry data of the Porcupine Bank Canyon was used as a lower resolution dataset

that included all the coral presence data, a second dataset was acquired to apply the weights of

the 2 models to the entire Porcupine Bank to discern any further areas of CWC coverage.

4.6. Analysis of Catalogue Data

The video data catalogue was imported into ArcMap 10.4.1 and was then inserted into a map

that had bathymetry collected from both the ROV-borne MBES (10m Cell size) and a lower

resolution dataset (35m cell size). Acquisition of the lower resolution bathymetry was done

through NOAA website through the multibeam portal. This data was provided by the INSS

through hull mounted multibeam surveys of the Porcupine Bank, these surveys are the most

complete view of the Irish seabed and allowed for a comprehensive study of the Porcupine

Bank Canyon. As the ROV-borne MBES had been collected in UTM Zone 28, the bathymetry

taken provided by the INSS was converted to this projection using the project raster tool in

ArcMap. Slope (Degrees) and aspect profiles for both datasets were then generated through the

Arc Toolbox Spatial Analyst Tools.

Once the video catalogue data had been acquired for the various dives they were synchronised

with the navigation from the ROV and filtered so that it only displayed the location data for

the corals. The high spatial resolution data was utilised for dives 8, 9a, 13, 14, 15, 16, 17, 18,

18a, 19 and 20 as they are the only dives to occur completely within the higher resolution

dataset area. The lower resolution data was used for all dives within the survey (e.g. Dives 1-

36

20) to ensure that there was one uniform dataset for the entire catalogue. The location data from

the ROV had been acquired through OFOBS software, however dives 6, 9, 12, 13, 14, 15, 17,

18, and 18a were missing and so the raw navigation files were pulled from the ROV and were

processed. Consequently, these dives were recorded in UTM Zone 28, whereas the other dives

were recorded in WGS 1984, to ensure the highest accuracy possible and to simplify the

measurements, both datasets were exported as *shp. files and converted to UTM Zone 28 using

the project tool. Once correctly georeferenced, slope and depth values were obtained using the

extract values tool.

4.7. CTD Processing

CTD data was plotted on ArcGIS, by extracting the data as to only include the data for the

maximum and minimum coral depth values. This data was further broken down into 4 separate

depth zones, that correspond with the preferred depth zones seen in Davies and Guinotte

(2011). They also correspond with the maximum and minimum values observed in the hard-

coral presence data. Graphs depicting the environmental conditions within each CTD can be

found in Figures 2.2 to 6.8 in Appendix II.

Averaging of the data allowed for effective interpolation to ensure effective averaging of the

data and prevent overloading the model with data. However, this averaging has also assumed

much of the coverage within the model area and thus reducing the spatial accuracy and the

originality of the data. Interpolation was done at a resolution of 80m, this was the most

precision resolution achievable with the data whilst keeping a uniform resolution across all

depth zones. Salinity, dissolved oxygen, and temperature were determined from the literature

to be the most significant criteria and were included in this process (Flögel et al. 2014; Dullo

37

et al. 2008; Davies et al. 2008). The raster interpolate toolset was used to interpolate the data

to create a raster dataset for each parameter. In order to avoid any further averaging of the data,

the Nearest Neighbour interpolation tool was implemented to generate this raster dataset.

4.8. Davies and Guinotte (2011) Model Assessment

These criteria were then weighted, based on the values stated within Davies and Guinotte

(2011). As L. pertusa were the dominant CWC observed within the survey site, and they are

the most important framework their values were the ones included within the recreation of the

model (De Clippele et al. 2017). However, certain criteria (Aragonite Saturation and Turbidity)

had to be excluded from the construction of the model as the equipment used within the survey

was not capable of measuring their parameters. Any values that occurred outside the constraints

of the data sets attained were excluded also, as they did not apply to the survey area.

Frequency analysis, as previous mentioned, was applied to the depth and slope values through

the reclassify tool was used to create individual raster files. These were subsequently combined

and weighted, through the map algebra tool, according to observed trends to create idealised

terrain parameters for CWCs within the area. Slope was weighted with greater significance, as

seen within the literature (Davies and Guinotte 2011). The equation below was employed to

combine the depth and slope raster data sets for both the INSS and the ROV MBES data:

(𝑆𝑙𝑜𝑝𝑒 ∗ .75) + (𝐷𝑒𝑝𝑡ℎ ∗ 25)

The values for CTD were initially weighted through reclassify the layers based off the

frequency data for each environmental criterion within the study, which can be seen in the

38

appendices (Davies and Guinotte 2011). This was done for depth ranges 1100-900m, 900-

600m, and 600-0m which were determined through distribution analysis in terms of depth. The

Map Algebra formula used to combine these 3 layers was as follows:

((1100𝑚 − 900𝑚)𝐸𝑣 ∗ .4) + ((900 − 600)𝐸𝑣 ∗ .267) + ((600 − 0)𝐸𝑣 ∗ .333)

(Ev= Environmental variable)

Once this had been done, the physical and the chemical parameters were combined both

through map algebra to create an overall model from both the MBES and INSS data. Both

models were compared with a previous model for CWCs by applying the same criteria stated

within the study (Davies and Guinotte 2011). The values for hard corals were then

superimposed on this model and values were extracted to view where these points occurred

within the model and how this compares with a model created using the frequency distribution

of the CWC presence data. The tables for the weights assigned can be seen in Table 1.1-1.6. in

the Appendices.

Graphs displaying the distribution of L. pertusa for some of the model criteria are included

Below in Figure 6.9.

39

5. Results

5.1. Video Catalogue Classification

The video data had been captured over a series of 20 randomly plotted dives which amassed to

approximately 56 hours of video data which amounted to 53 km of video transects, which were

run between depths of 568m to 1426m in depth. Dive1-12 were taken initially, going from

Figure 6.9: Environmental Variables for L. pertusa

(Davies and Guinotte 2011).

40

North to South. Dives 13-20 were taken with return journey northwards. A map of the resulting

catalogue data can be observed in Figure 1.8 in Appendix I. CWC cover was not constant and

significant portions of each dive consisted of sediment cover tends to generally dominate the

seabed facies for the canyon. CWC presence was first noted in dives 1 and 2, with coral

presence occurring at the beginning of both dives. Figure 7.0 below illustrates a close up of the

catalogue data for these dives. These areas of CWC presence are made up significant, elongate

carbonate mound structures with a rough north east to south west alignment. The main

scleractinians observed throughout the survey were Lophelia pertusa and Madrepora occulata.

41

After dive 2 CWC were largely absent, with no hard-coral facies observed between dives 3 and

5, with soft coral colonies being the only CWCs identified. This can be observed in Figure 7.1

Fig 7.0: Seabed Facies Cover for Dive 1 & 2

superimposed over the INSS Slope Data.

42

below shows the lack of CWC presence within these dives. Antipatharia typically occurred at

greater depths than the Scleractinia, with small colonies of antipatharia often preceding large

CWC mounds.

43

Figure 7.1: Seabed facies data for dives 1 to 6.

44

Hard Coral seabed facies had not been noted again until dive 6. These occurred as sporadic

reefs that did not constitute established mound structures. Large carbonate mounds began

reappearing at dive 9. This dive had the most established CWC reefs and consisted of a large

L. pertusa specimens, see Figure 7.2 below.

45

Figure 7.2: Seabed facies plotted over slope data.

46

LCF was more common along topographic spurs and less common in gullies as is evident in

Figure 7.2 above. Soft Corals tended to occur in large numbers along the wall of the escarpment

to starting at dive 20. Coral presence within the flat areas between mounds was largely absent,

with exception noted at dives 1, 10, 11 and 12. Sedimentary features such as sand ripples and

scouring had been observed in dives 1-6 and dives 10-12, an example of the sediment rippling

is shown below in Figure 7.4.

Fig 7.4: Taken from Dive 2, clear asymmetrical rippling.

47

Dives 1-6 and dives 10-12 also contained dropstones that ranged anywhere from boulders to

cobbles some of which were angular and had a low sphericity. Scouring was evident around

these dropstones, displayed in Figure 7.5 below.

Total lack of CWC coverage was noted at dive 10 with CWC presence returning at dive 11.

The coverage to the south of the mapping area can be seen in Figure 7.6 below:

Figure 7.5: Taken from Dive 10, showing a clear gouge

mark developed from scouring around the dropstones

occurring Orientation is roughly North East to South West.

48

Figure 7.6: Illustrating seabed cover facies for dives

10-12 superimposed over the INSS slope data.

49


CTD data shows with highest temperatures at the surface (13-12°C) and minimum

temperatures occurring at approximately 3°C (CTD 41) at depths of 1794m. A noticeable layer

of relatively stable temperatures and salinities at a water depth of between 200 to 700m. Therein

after temperature and salinity decreased rapidly with depth, with dives under 1100m reaching

3˚C. In contrast, dissolved oxygen levels increase with increase in depth. The vertical profile

of each CTD can be seen in Figures 2.2 to 6.8 in Appendix II. Downcasts that were recorded

as the ROV descended through the water column often achieved depths that were greater than

the corresponding upcasts that were recorded as the ROV ascended through the water column,

with CTD 9 (Upcast) being the shallowest full CTD at 568m and CTD 41 (Downcast) being

the deepest full CTD at 1794m. Oxygen level increased slightly with depth.

5.3. Analysis of Catalogue Data

Maximum and minimum depth value range for hard coral occurrence was 1105-596m for the

INSS MBES. Frequency analysis of the hard coral presence data through the INSS bathymetry

raster dataset showed that greater than 85% of hard coral distribution occurred within depths

of 600-900m. The mean depth for hard coral occurrence was 798m. Frequency table for the

INSS hard coral depths can be viewed in table 1.7 below.

50

Hard Coral INSS Depth Distribution

Range(m) Frequency %Total

500 to 600 90 0.44

600 to 700 5729 27.83

700 to 800 7524 36.54

800 to 900 4311 20.94

900 to 1000 2416 11.73

1000 to 1100 519 2.52

1100 to 1200 0 0

Extraction of the slope values from the INSS slope data for the hard coral locations showed

that approximately 94% of all hard coral locations had been noted within slopes of 0 to 0.52

radians. With corals displaying a maximum value of 0.89 Radians and a minimum value of

0.025908 Radians. The mean derived from this data was .31 Radians. Table 1.8 below displays

the percentage frequency for the slope data extracted from the INSS MBES.

Table 1.7: INSS depth reclass

frequency distributions.

51

Hard Corals INSS Slope Distribution

Range (Radians) Frequency % of Total

0 to 0.175 5365 26.05760357

0.175 to 0.35 7443 36.15037156

0.35 to 0.52 5400 26.22759726

0.52 to 0.7 1261 6.124629657

0.7 to 0.87 1079 5.24066249

0.87 to 1.05 41 0.199135461

1.05 to 1.22 0 0

1.22 to 1.4 0 0

1.4 to 1.5707965 0 0

Depth values for hard corals extracted from ROV MBES bathymetry data show frequency

distributions of 63% for depths ranging between 600 to 900m depth for hard corals. The

corresponding maximum depth and minimum depth values occur between 1095-659m depth.

The mean depth value for this data set was 839m. The full data depth distribution can be seen

in Table 1.9 below:

Table 1.8: Hard Coral Frequency

Distribution for INSS Bathymetry Data

52

Hard Coral Depth Distribution ROV MBES

Range Frequency % of Total

1300-1200 0 0

1200-1100 0 0

1100-1000 491 7.35

1000-900 1826 27.32

900-800 1698 25.41

800-700 1597 23.90

700-600 1071 16.02

The ROV MBES slope raster showed that 72% of hard coral distribution occurred within values

of 0.5 to 1.2 Radians. With 28% hard coral locations occurring within the range 0 to 0.5

Radians. The maximum and minimum slope values for the ROV MBES slope are 1.154984

Table 2.0: Depth Distributions for Hard

Corals derived from ROV MBES.

53

and 0.111014 respectively. The distribution frequency values for this data is shown in Table

2.1 below:

Hard Corals ROV MBES Slope

Distribution

Range (Radians) Frequncy

% of

Total

0 0 0

0.17 41 0.7442367

0.35 333 6.0446542

0.52 1113 20.203304

0.7 1465 26.592848

0.87 1136 20.620802

1.047 279 5.06444

1.22 1142 20.729715

1.4 0 0

1.57 0 0

Figure 7.7 below illustrates the distribution of hard corals with regards to the aspect of the

terrain derived from the INSS MBES data and the ROV MBES data. Hard corals show a

distribution of 64% for those slopes that dip in a west to north west direction in the INSS data

set.

Table 2.1: Illustrating ROV

MBES Hard Coral Frequency

54

5.4. Davies and Guinotte (2011) Analysis

5.4.1. INSS MBES

This included values for CWC depth, as CWC depth frequency values were included up to a

maximum depth of 2252m and a minimum depth of 453m for the INSS data. Similar constraints

prevented full inclusion of the slope weight values as the maximum slope observed included

occurs at 1.13 Radians in the INSS data. Thus, observations for any values beyond these ranges

could not be made.

Figure 7.7: Aspect for the INSS MBES (A) and the ROV MBES (B).

(A) (B)

55

The terrain data shows moderate initial success within the INSS bathymetry dataset as

considering that the final terrain component of the model had 66% of the total hard coral

presence data occurring within areas that were projected to have a probability of 61% to 90%

of hard coral occurrence. 25% of the hard coral presence data occurred within areas that were

projected to have 40 to 60% probability of hard coral occurrence. However, within 90-100%

probability bracket, only 8% of the total Hard Coral presence data occurs within.

For the final model, displayed in Figure 7.8 in Appendix IV, including the variables derived

from the CTD data, 71% of all CWC data occurred within the 41-62% range. 15% of all hard

coral presence values occurred within the 83% to 100% range. The remaining 13% occurred

within probability range of 20-41%. The results of this analysis Is included in Table 2.2 below:

Davies Probability Analysis

Probability Frequency % of Total

0 0 0

21 0 0

42 703 13

63 3842 71

83 816 15

100 28 1

Table 2.2: Probability frequencies for

Davies and Guinotte (2011) final

model.

56

5.4.2. ROV MBES

The maximum and minimum values for the depth data acquired from the ROV MBES were

2043m and 563m respectively. The average depth of hard corals was 839m water depth. The

maximum value for Radians in the ROV MBES was 1.46 Radians. With an average slope of

.71 radians. The model itself can be viewed in Figure 7.9 Appendix IV.

ROV MBES showed similar numbers displayed within the INSS multibeam data with regards

to the final model with 62% of hard coral presence data occurring within probability range of

41-62%. 18% of hard coral presence data occurs within the probability range of 21%-42%. All

values can be seen in Table 2.3 below:

Davies and Guinotte (2011) ROV MBES

Weighted

Value

% Probability

Range Frequency

% of

Total

20 0 0 0

22.5 0 to 21 0 0

25 21 to 42 974 18

27.5 42 to 63 3355 62

30 63 to 83 937 17

32 83 to 100 123 3

Table 2.3: ROV MBES final model

57

6. Discussion

6.1. Video Catalogue Data

Initial findings within the data show that there are 2 main habitat types, Type (1), CWC occur

on carbonate mounds that occur on gentle slopes along the sides of the canyon, here L. pertusa

tends to dominate. This is predominately found to the North and South of the area with the

corals occurring along elongated carbonate mounds that are roughly aligned North East to

South West. These habitats can be found in Figure 8.0 below.

Figure 8.0: Dives 10-12 (A) and dives 1-5 (B) are plotted over the

slope (Radians) and make up Habitat Type 1, occurring on carbonate

mounds, CWCs occur on the steeper regions of these mounds.

58

The 2nd habitat type had been broken up into 2 sections: Type 2 (a), where CWC are found

along steep sided cliffs of exposed bedrock, here Antiparthia dominate; and Type 2 (b) where

cold water corals are found on carbonate mounds, similar to those seen in Type (1), along flat

areas along that occur on the tops of habitat Type 2 (a). These habitats are illustrated in Figures

8.1 & 8.2 below. Overall coral coverage is not constant and coral debris and sediment facies

make up the majority of dives 3, 4, 5 & 10. LCF was more common along spurs that occurred

along the sides of the Porcupine Bank Canyon, with DCF and coral debris occurring with the

gullies that lay in between. Coral debris tended to dominate in Habitat 2 (a), were it often

occurred at the bottom of cliff faces or down the sides in crevices.

59

Figure 8.1: Here the hard corals (red points) are

overlain on the INSS slope raster, 2 main CWC

habitats occur Habitat 2 (a) occurring as steep

escarpments that coral reef cling to, exposed

bedrock is most common, often occurring as

sheer cliffs, with soft corals attached. Habitat 2

(b) can be observed as the mounds that occur

within the flat areas that appear after the steep

cliff habitats observed in Habitat 2 (a).

Figure 8.2: Displaying CWC Habitat Type 1,

this occurs the NE and SE of the Porcupine

Bank Canyon, occurring over elongated

mounds, which are bound by vast tracts of sand

that have little to no biota, sea cucumbers and

sand eels dominate these environments. This

image also displays the limitations applied by

the use of coarse resolution environmental data

as dives 1 and 2 in the North and dives 11 and

12 in the South are excluded from the final

model.

60

Once this catalogue data had been plotted through ArcGIS and the terrain values extracted from

the INSS MBES a clear distinction was occurring between what had been observed in the high

resolution ROV based MBES and the lower resolution INSS multibeam bathymetry. As hard

corals were plotted with a lower slope angles than what was suggested by the INSS data.

Possible explanations for this variance within the data include the coarseness of the data within

the INSS data affecting the quality of the slope data produced, the low sample selection

included within the ROV MBES data set causing a truncated view of the overall coral presence

data.

6.2. Sedimentary Regime

The geometry of the sedimentary ripples found in Figure 7.2, are indicative of a high energy

bottom current influenced environment, with the cross ripple profile exhibiting a roughly

North-East South-West orientation. This coupled with the existence of a strong scouring regime

evident in the scour marks occurring around the drops stones as seen in Figure 7.3 above

indicate the intensity of these currents (Foubert et al. 2011). This may be a significant factor

with regards to the distributions of the corals within the canyon as they were found mainly

along the lee side of the mounds with respect to the current, or along cliff sides in Habitat 2

(A) that faced away from the current direction. Highlighting the possibility that CWCs within

this region prefer to be sheltered from the prevailing current. Where the sediment may be more

stable, and as a result better able to support CWC communities.

The presence of such large and angular dropstones alluding to deposition through a sudden

increase in environmental energy. Some areas within the canyon showed signs of corals being

stripped off of bedrock with their remains occurring at the foot of ledges. This may indicate

61

that a periodic hydrographic change may occur within the canyon, causing the build up of a

potential cold watermass at the head of the canyon. Over time this may become unstable and

cause a flush of water through the canyon, increase local current velocities surrounding the

corals and stripping surface of CWCs. This is a trend that occurs within the aspect data Lack

of consistent CTD data prevents from a definitive answer with regards to the hydrography of

the canyon. Suggesting that the hydrographic regime may be more complicated than previously

postulated (White et al. 2007; Mienis et al. 2007).

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6.3. UNEP CWC Presence Data

Dive 10 and it is clear that coral presence is denoted by the UNEP data, however sediment and

coral debris were the only seabed facies identified during this dive this is exhibited in Figure

8.1 in Appendix IV. Similar observations were noted at dive 12 were seabed facies was

dominated by dead coral framework or coral debris. Dense CWC coverage was found in areas

outside of the UNEP coral presence data, such as dive 9a. Some of the most densely populated

carbonate mounds were detected within these dives, with large specimens of L. pertusa and

antipatharia were identified, an example of these specimens can be seen in Figures 8.3 & 8.4.

Such data deficiency emphases the need for increased field data to complete this dataset. Albeit,

the presence data had confirmed many sites that were enclosed by the UNEP coral presence

data.

Figure 8.3: Example of L. pertusa taken from dive 8. Figure 8.4: Example of antipatharia taken from dive

8.

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6.4. Davies and Guinotte (2011) Analysis

Davies and Guinotte (2011) have presented a model that performed at a higher rate than the

model derived from the validation of the CWC location data. Once the topographical data had

been applied, the highest frequencies of CWC presence data occurring within the areas of

moderate probability. There were areas that had predicted CWC occurrence in the INSS based

model where where no coral had been detected, which can be seen in Figure 8.5 below.

64

Figure 8.5: Dive 10

65

However, the model broke down in resolution with the inclusion of the CTD data. The use of

Maxent software may be the most effective way of creating these models, unfortunately, due

to time constraints, this type of modelling could not be applied effectively create a replicate

of the original experiment. The use of the model criteria within GIS software provided a time

effective application of the model constraints whilst maintaining as much accuracy as

possible. The incorporation of the CTD data within the ROV MBES based model and the

INSS based model brought the coarseness of the data to a point where the model could not be

effectively applied to predict CWC habitat. The resulting probability frequencies displayed

within tables

The averaging of the data to allow for its interpolation may have reduced the effectiveness of

the contribution of the dataset to the overall modelling, as the paucity of the data was such that

it could not be ameliorated through GIS Spatial Analyst tools. Averaging of the data also

caused restricted the extent of the model over the survey area as it excluded outlying CTDs

from the interpolation raster. As a result, a significant portion of the areas to the north and south

of the survey site were left without any model coverage at all. This was something that could

not be avoided without further averaging of the data, which would have compromised the

resolution to the point where the data set was no longer reliable for the scale that the model

was to be applied over. This also prevents full inclusion of the hard coral presence data when

ascertaining the validity of the final model, as a total of 5389 hard coral presence points were

included, in comparison with 20589 (26%) for the entire survey area within the Porcupine Bank

Canyon.

This was clear during the interpolation process, when the only effective interpolation of the

data generated a raster data set with an 80m cell size, this is in stark contrast to the INSS

bathymetry data, at 30m spatial resolution and the ROV based MBES, at 10m spatial resolution.

Coarse spatial resolution has also impeded the model’s ability to predict coral habitat presence,

66

with habitat being predicted in areas with vast amounts of sediment cover or coral debris, with

no hard coral presence noted. Figure 8.6 exhibits the low resolution of the model with

comparison to the model including only the slope and depth data for the ROV MBES.

It is clearly evident from Figure 8.4 that the CTD data adds a coarseness to the data that

diminishes the spatial resolution of the data set, causing the model to break down. It is clear

that the model is only as effective as its lowest resolution data set.

Figure 8.6: Displaying ROV MBES with slope and

depth model in part (i) and the complete model with

combined CTD data and ROV MBES in part (ii).

(i) (ii)

67

Due to lack of consistency within the CTD data, effective interpolations of the information

collected could only be made in waters with depths between 1100-0m, as only a quarter of total

dives were below 1100m. This could have been avoided by potentially taking more CTD casts

and increasing the size of the overall dataset to nullify these effects. The clustering of the CTDs

observed in Figure 2.0 open up the data to bias and strong spatial correlation factors may limit

the final dataset, a uniform grid of video lines with regular CTD casts taken may help to

increase the spatial resolution of the data and eliminate any additional spatial biases from

creeping into the data.

Another factor that contributed to the coarseness of the CTD data may have due to the fact that

the original CTD casts were taken at irregular intervals and the resulting dataset had antecedent

inconsistent spatial resolution. This factor is clearly evident when considering the positions of

the CTDs within Habitat Type (1), here the dives are greater in length, with the upcasts and

downcasts occurring at an increased distance away from each other to the north and south of

the survey area. Averaging of the CTDs was required to effectively interpolate the data,

however, the paucity of the data prevented high spatial resolution analysis of the environmental

data. The contrast between the spatial resolution of the combined terrain data (the slope and

depth raster reclass) and the final model incorporating the CTD data underlines the impact that

this paucity has.

The presence data displayed mainly occurred within probabilities of 40-60% within both the

INSS and ROV MBES data models, suggesting that the resolution of MBES data may not have

had a significant impact on the validity of the data as the results were consistent for both

resolutions. The criteria taken from the model created by Davies and Guinotte (2011) for

MBES data may not accurately depict CWC probability at local scales. This can be seen as the

final model for the INSS MBES displays a distribution of 71% of hard corals within the INSS

final model data set were found to occur in zones with a probability of 42% to 63%.

68


Once the CTD data had been plotted it was quite clear that a significant portion followed a

similar pattern in terms of temperature, density, and dissolved oxygen content with descent

along the water column that had been mentioned previously in the literature as being indicators

of certain water masses (White et al. 2007; Mazzini et al. 2011). Higher temperatures and

salinities observed at depths between 200 to 700m, here temperatures were relatively stable

with little differentiation between the values, this would coincide with the ENAW. A

thermocline occurs at 700m, coinciding with the Mediterranean Ouflow Water (MOW), here

temperatures and salinity begin to fall dramatically as oxygen increases with depth, and closer

to the influence of the Labrador Sea Water (Mienis et al. 2007). The nature of the CTD data

collection truncated the view of the data once they were incorporated into the GIS, as the lack

of depth with many of the upcasts resulted in extreme paucity in waters with a depth of 1100m

or greater. With only 7 CTD casts included in the averaged dataset.

However, due to technical problems CTD data files were corrupted with varying degrees of

severity (partial data had been recovered from CTDs 20, 23, 29, 30, 32, 37, 40, 41, 42 and 45).

Most of the data was recoverable for use in the model, however CTDs 23 & 40 were not

included as the depth values were incorrect for CTD 40 and the file for CTD 23 was corrupted

such that the data after a depth of 123m was beyond use, both files were not included in the

final dataset. CTD 22 was aborted after the ROV had been dispatched as the vessel was in the

wrong area. CTD 31 lacked data for the first 100m, however the rest of the data was recovered.

Due to technical errors the SBE 911 failed to record after the first dive, the CTD for the ROV

Holland was substituted for this for CTDs 40-43, the SBE 911 had been repaired for CTDs 40-

69

43, however the repair had failed after CTD 43. The CTDs show a strong clustering of the CTD

data is seen in towards the centre of the survey area, with the data becoming more sparsely

distributed in over Habitat Type (1), the north and south of the area. CTD coverage within the

Habitat Type (1) totals 17 CTDs, this is in contrast to the remaining 29 CTDs covering the

Habitat Type (2) at the centre of the survey site.

7. Conclusions

It is clear from the evidence below that the model provided by Davies and Guinotte (2011) is

one that requires much more field data to be applied at a local scale resolution. However, with

feasibility restrictions such as the substantial cost of gathering this data as well as the time

required to maintain a reliable database, local scale predictive habitat models may not be

possible with the current technology and extent of knowledge today. As seen throughout the

study, the UNEP CWC database is not an exhaustive list of CWC presence, yet is remains the

most extensive and reliable database for CWCs and is employed within many habitat models.

Such compromises are regularly made within CWC predictive habitat models as they are often

an amalgamation of datasets taken over wide temporal and spatial resolutions in order to

compensate for the paucity of datasets (Davies et al. 2008; Guinotte and Davies 2014; Davies

and Guinotte 2011). Thus, inconsistencies within the data may ultimately create a model that

has applications on global scale, where the coarseness of this data is complements the scale

onto which it is applied.

The marine habitat of the Porcupine Bank Canyon exhibits traits that suggest that its

complexity may not be realised through global scale analysis. A narrower focus on

70

environmental parameters within the survey site coupled with local specific CWC presence

data may be required for finer scale habitat modelling. Terrestrial habitat models have been

well proven or have access to meticulous and reliable data, much of which is open to the public

and can be collected throughout the calendar year on a variety of spatial and spectral

resolutions, enabling the most accurate predictions to be made (Becker et al. 2009). The

accessibility of the field within these environments varies between environments, however

oftentimes, instruments may be left in strategic areas to augment this remote sensing data with

fresh field survey data. Until cost effective methods of deep marine field surveying become

accessible to the wider scientific community the overall resolution of the data extracted from

the deep marine environments of the world will remain at a coarse resolution. With predictive

habitat modelling restricted to producing predictions at small scale resolutions.

8. Acknowledgements

I would like to commend a group of people, who have not only assisted in the execution of this

model, but also amplified its effectiveness and accuracy with their steadfast knowledge and

keen insight into the nature of CWC habitats. Firstly, I would like to thank my supervisor

Professor Andy Wheeler, without whom this model would not exist. I would also like to thank

Dr. Aaron Lim, whose patience and tenacity accelerated the analysis with his wealth of

knowledge on CWC studies and GIS experience. I would like to thank Helen Bradley of the

Geography Department, whose knowledge on GIS and predictive habitat suitability algorithms

assisted in correcting many of the mistakes and blunders generated by the author. Lastly, I

71

would like to thank Saoirse O’Neill, whose constant support and encouragement provided the

fuel to finish the project and to create it to the best possible standard.

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9.2. Websites

Marine.ie. (2017). Marine Institute - Real Map Of Ireland. [online] Available at:

https://www.marine.ie/Home/sites/default/files/MIFiles/Images/IrelandsMarineResources/Th

e%20Real%20Map%20of%20Ireland.jpg [Accessed 21 Sep. 2017].

https://www.marine.ie/Home/sites/default/files/MIFiles/Images/IrelandsMarineResources/The%20Real%20Map%20of%20Ireland.jpg

https://www.marine.ie/Home/sites/default/files/MIFiles/Images/IrelandsMarineResources/The%20Real%20Map%20of%20Ireland.jpg

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10. Appendices

10.1. Appendix I

Figure 1.1: Porcupine Bank overview

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Figure 1.8: Total ROV dive transects

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Figure 2.0 CTD locations

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10.2. Appendix II

Hard Coral Oxygen Frequency

Class Distribution % Frequency Weight

6.011261 0 0 1

6.056792 280 1.449950805 1

6.102322 62 0.321060535 1

6.147852 1049 5.432137124 2

6.193382 3057 15.83035576 6

6.262235 1989 10.29982911 4

6.300497 0 0 0

6.338759 1652 8.554709751 3

6.377022 3438 17.80332453 6

6.415284 1347 6.975299052 2

6.497307 0 0 1

6.541527 0 0 1

6.585747 0 0 1

6.629967 2777 14.38040495 6

6.681546 3660 18.95292838 8

Table 1.1: Frequency Analysis for Hard

Corals with regards Oxygen

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Hard Coral Depth Frequency INSS

Class Frequency %Total Classification factor

500 to 600 90 0.437126621 1

600 to 700 5729 27.82553791 6

700 to 800 7524 36.54378552 8

800 to 900 4311 20.93836515 5

900 to 1000 2416 11.73442129 2

1000 to 1100 519 2.520763514 1

1100 to 1200 0 0 0

Hard Coral Slope Frequency INSS

Class Frequency % of Total Classification Factor

0 to 10 5365 26.0576 6

10 to 20 7443 36.15037 8

20 to 30 5400 26.2276 6

30 to 40 1261 6.12463 2

40 to 50 1079 5.240662 2

50 to 60 41 0.199135 1

Table 1.2: Hard Coral Presence Data Depth Frequency

INSS Bathymetry Data

Table 1.3: Hard Coral Slope Frequency Data Frequency

INSS Slope Data

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Hard Coral Salinity

Class Distribution

%

Frequency Weight

34.178791 267 1.3826317 1

34.303 0 0 1

34.434178 0 0 1

34.689565 0 0 1

34.944952 0 0 1

35.086079 0 0 1

35.148529 13 0.0673191 1

35.210979 0 0 1

35.27343 6317 32.711926 3

35.33588 1289 6.6749521 1

35.419441 11425 59.163171 6

Table 1.4 Hard Coral Salinity Frequency

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Depth Range (m) Weight

2252 2050.55555 6

2050.5555 1849.1111 1

1849.1111 1647.666667 1

1647.6666 1446.222222 1

1446.2222 1244.777778 2

1244.7777 1043.333333 4

1043.3333 841.888889 6

841.88888 640.4444 6

640.44444 439 4

Table 1.5 CWC Depth Weight Classes

(Davies and Guinotte 2011)

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Slope Range (Radians) Weight

0 0.164159 4

0.164159 0.328319 8

0.328319 0.492478 6

0.492478 0.656638 4

0.656638 0.820797 4

0.820797 0.984957 3

0.984957 1.149116 2

1.149116 1.313276 2

1.313276 1.477435 1

Table 1.6 CWC Slope Weight Classes

(Davies and Guinotte 2011)

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10.3 Appendix III

Figure 2.2: CTD 2 water column data winch CTD downcast

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Figure 2.3: CTD 3 water column data upcast

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Figure 2.4: CTD 4 water column data downcast

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98


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100


101


Data

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Figure 4.0: CTD 20 water column data Downcast

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118


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129


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Figure 5.9: CTD 39 water column data upcast.

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Figure 6.0: CTD 41 water column data upcast.

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10.4 Appendix IV

Figure 7.8: Model from INSS Data

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Figure 7.9: ROV MBES final model

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Fig 8.1: Dives 10-12, occurring North to South, plotted over INSS

Bathymetry Data. Dive 10 Displays clear lack of coral presence

within zones previously designated by UNEP as confirmed coral

habitat. Dive 11 shows moderate CWC presence however there is a

marked amount of coral debris and sediment facies cover. Dive 12

shows strong CWC presence. (LCF is marked in yellow, sediment

facies is marked in black).

Assessment of Cold-Water Coral Predictive Habitat Models

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