Thailand Quantification of Macrofauna

7/28/2019 Thailand Quantification of Macrofauna

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Quantification of variability in marcofauna

of pre-tsunami sedimentary tropical shores

of SW Thailand

By James Fay

MSci Marine Biology

2012


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Abstract

This report aimed to assess the variability of macrofaunal species at the sites: LSon = Laem

Son; TND = Thung Nang Dam; KRa = Ko Ra; Kb = Krabi. Off of the coast of SW Thailand

where it then quantified this variability by analysing the data using MDS plots, CLUSTER

plots of abundances of the macrofauna at each station first with singleton results and then

without singleton results. This showed that the removal of singletons decreased the similarity

between the sites most likely due to the removal of a species that was common at another site.

It also showed that from the MDS plots the site Kb was particularly dissimilar to the other

three sites TND, LSon and KRa. The results then looked at the feeding mechanisms of the

species to find a small variation between the sites which was attributed to each site having a

different habitat. This was also seen in the last result which showed the sites were variable

with regards to having vegetation present as only some sites had vegetation present. This was

identified to be sea grass. However further analysis showed that the habitats varied even more

due to physical factors where internal waves were present and the possibility of patchiness of

the vegetation. Furthermore different levels of nutrient inputs influenced the productivity of

each site causing more variation. These contributing factors lead to unique habitats at each

site which was found to be the main cause in the variation as the habitat influences the

species present at the site.


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Introduction

The importance of quantifying variability of an ecological system is an important study

which is needed to understand processes which are occurring that cause various patterns to

arise. By quantifying this variability it is possible to construct models which can predict

patterns and responses of various anthropogenic and natural inputs to a system. From these

models it is also therefore possible to form new hypotheses (Ysebaert & Herman, 2002). By

quantifying variations it is possible to construct food webs by looking at the interactions

between different sites. This also makes it possible to implement management protocols in

order to maintain and sustain ecosystemsbiodiversitys which are of growing interest with

the potential to discover new patterns (Landres et al. 1999 & Barrio Frojan et al. 2005).

Within this study the area to be studied is a series of sites located off of the coast of SW-

Thailand within the Andaman Sea shown by (figure 1). The reason for choosing this site for

quantifying variability is because there is minimal previous work been carried out on this area

before (Barrio Frojan et al. 2006). Where understanding the variability of a system is

important in understanding its processes and makes it a scientifically important site (Ysebaert

& Herman 2002). Furthermore it is an important location for this study to be carried out

seeing as it is an important fishing ground and undergoes a biannual monsoon season creating

a unique ecosystem through large seasonal variations (Buranapratheprat et al. 2002 &

Nielsen et al. 2004). Other reasons for studying the SW Thailand coast is due to its strong

tidal actions which makes suspended particulate matter available causing high productivity

and a site of biological interest (Umezawa et al. 2009).


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Figure 1. Map to show study area where data was collected at the stations, LSon, KRa, TND

and Kb where LSon = Laem Son; TND = Thung Nang Dam; KRa = Ko Ra; Kb = Krabi

(Barrio Frojan et al. 2006).

From (figure 1) you can see the locations of the sites where the data was collected the sites

are spread over 200km of coastline and are geographically separated as a result of this. Along

the coastline mangroves and sea grass are present which provide sediment stability and help

dampen the effect of tsunamis. All of the sites within the study have a semi-diurnal tidal

cycle (Barrio Frojan et al. 2006).

The reason for quantifying macrofauna off of the coast of Thailand is because they are a very

important part of marine food webs. Where monitoring them is important for monitoring a

system (Ysebaert & Herman, 2002). Macrofauna also make good indicator species of changes

to a system due to their constant presence within the system due to various macrofaunal

species having sedentary lifestyles. Therefore by studying the macrofauna of Thailand it is a

good indicator towards changes occurring such as pollution and will also give an idea what


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will be happening to the ecosystem as a whole which will aid in management (Ababio et al.

1999). Macrofauna are also have important ecological effects on the environment through

their sediment interactions due to some species having burrowing lifestyles as burrows

increase microbial productivity (Kristensen and Kostka 2004). Furthermore macrofauna can

also be classified into bio-turbators and bio-stabilisers where they either weaken or strengthen

the sediment respectively via burrowing or foraging motions, this has a major impact

ecologically as it influences the habitat they live in (Volkenborn et al. 2009). For these

reasons this study will concentrate on the variability of macrofauna as they are of important

ecological significance.

The aims of this study is therefore to look at the variability of macrofaunal species between

the sites Kb, LSon, KRa and TND with an emphasis on polychaete species where it is then to

identify any variabilitys and differences found between these sites using statistical analysis.

The aim is to then quantify the variability in order to understand why these sites differ from

each other in order to understand any patterns that may occur. To hypothesis the sites will be

varied from one another as they are located within different areas and will have different

processes acting upon them. On the other hand the stations within the sites will be less varied

as they will have the same processes acting upon them.

Methods

The data was collected from the methods of (Barrio Frojan et al. 2006).

This data was then analysed using Primer where MDS plots and CLUSTER plots were

constructed to look at the similarities between each station of the study area. The data was

also re-analysed after removing species that only appeared once at a station so the more

abundant species could be compared more closely. Furthermore as polychaetes were the

primary subject of the study and also most abundantly found the results were also analysed

without the other species found so the stations variability can be compared with respect to

just polychaetes. Whilst constructing graphs for where singleton species had been removed

for polychaete and crustacean species the stress level of the graphs were too high and

therefore not reliable enough to be used as a result. In addition to looking at the variability of

species found. The feeding mechanisms of each species found were identified and compared

using MDS and CLUSTER plots and a stacked bar graph to look at breakdown of feeding

types at each station which would reveal variations attributed to feeding. Lastly the seabed


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habitat was identified for each station and compared comparatively within a table in order to

identify variation at a habitat level.

Results

After analysing the results collected off the coast of Thailand the graphs showed the

following variations between the different sites of macrofaunal species:

Figure 2. MDS plot of polychaete and crustacean species abundances at each site including

singleton species with similarity rings in (%) to show how similar each site are from each

other.

From (figure 2) the variation between different sites is very high as they are only 24% similar

and there is a larger variation between Kb and the other sites TND, LSon and KRa, therefore

a larger difference between them. However the stations at each site were more closely related

as they have a higher similarity shown by the 40% similarity ring. This shows that there is

still a high amount of variation within each site of the study area. As a result when

quantifying the variation there are site specific and station specific variables that need to be

explained.

Speci es_Ab undance

Transform: Log(X+1)

Resemblance: S17 Bray Curtis similarity

SiteTND(N)

TND(S)

KRa(N)

KRa(S)LSon1

LSon2

Kb1a

Kb1b

Kb2a

Kb2b

Similarity24

40

60

TND(N)_A3TND(N)_B1

TND(N)_B3TND(N)_B4

TND(N)_C1

TND(N)_C3

TND(S)_A1

TND(S)_A2

TND(S)_B1

TND(S)_B3TND(S)_B4

TND(S)_C2

KRa(N)_A1

KRa(N)_A3

KRa(N)_B1KRa(N)_C1

KRa(N)_C3KRa(N)_C4

KRa(S)_A2KRa(S)_A3KRa(S)_B2

KRa(S)_B3

KRa(S)_C1KRa(S)_C4

LSon1_A4

LSon1_B2LSon1)_B3

LSon1_C1

LSon1_C3

LSon1_C4

LSon2_A1

LSon2_B1LSon2_B4

LSon2_C1

LSon2_C2

LSon2_C3

Kb1a_A1

Kb1a_A2Kb1a_B1

Kb1a_B2

Kb1a_C1Kb1a_C2

Kb1b_A3

Kb1b_A4Kb1b_B3

Kb1b_B4

Kb1b_C3

Kb1b_C4

Kb2a_A1

Kb2a_A2

Kb2a_B1

Kb2a_B2Kb2a_C1

Kb2a_C2Kb2b_A3

Kb2b_A4

Kb2b_B3

Kb2b_B4

Kb2b_C3

Kb2b_C4

2D Stress: 0.24


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Figure 3. MDS plot of just polychaetes at each station including singleton species with

similarity rings in (%) to show how similar each site are from each other.

Within (figure 3) where crustacean species have been removed when comparing to (figure 2)

it shows a similar pattern of variation between sites at 24% similarity and also between

stations at 40% similarity. However it has also increased similarity between some stations for

example station LSon2_A1 is now more similar to its surrounding stations, yet it has also

decreased similarity between other LSon stations. This indicates that there is a strong

crustacean presence at LSon which is causing variation between the sites and stations.

Speci es_Ab undance

Transform: Log(X+1)


SiteTND(N)

TND(S)

KRa(N)KRa(S)

LSon1

LSon2

Kb1a

Kb1b

Kb2a

Kb2b

Similarity24

40

60

TND(N)_A3TND(N)_B1

TND(N)_B3TND(N)_B4

TND(N)_C1

TND(N)_C3

TND(S)_A1

TND(S)_A2

TND(S)_B1

TND(S)_B3TND(S)_B4

TND(S)_C2

KRa(N)_A1

KRa(N)_A3

KRa(N)_B1

KRa(N)_C1

KRa(N)_C3KRa(N)_C4

KRa(S)_A2KRa(S)_A3KRa(S)_B2

KRa(S)_B3

KRa(S)_C1KRa(S)_C4

LSon1_A4

LSon1_B2LSon1)_B3

LSon1_C1

LSon1_C3LSon1_C4

LSon2_A1

LSon2_B1LSon2_B4

LSon2_C1

LSon2_C2

LSon2_C3

Kb1a_A1

Kb1a_A2

Kb1a_B1

Kb1a_B2Kb1a_C1

Kb1a_C2

Kb1b_A3

Kb1b_A4

Kb1b_B3

Kb1b_B4

Kb1b_C3

Kb1b_C4

Kb2a_A1

Kb2a_A2Kb2a_B1

Kb2a_B2Kb2a_C1

Kb2a_C2

Kb2b_A3Kb2b_A4

Kb2b_B3

Kb2b_B4

Kb2b_C3

Kb2b_C4

2D Stress: 0.23


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Figure 5. CLUSTER plot of site total abundances of polychaetes with singleton species with

a 50% slice.

The similarity between each site shown in (figure 5) also shows the differences between the

Northern, Central and Southern sites as shown in (figure 4). The TND and KRa Central sites

are more closely related together, the Southern Krabi sites are more closely related to each

other and the Northern LSon sites. From the above figures it shows that there are clear site

specific variables causing variation.

Adundance

Group average

Kb1b

Kb1a

Kb2a

Kb2b

KRa(N)

KRa(S)

KRa

TND(N)

TND(S)

TND

LSon2

LSon1

Samples

100 80 60 40 20

Similarity

Transform: Log(X+1)



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Figure 6. MDS plot of polychaete abundances at each station without singleton species with

similarity rings in (%) to show how similar each site are from each other.

By removing species that only appeared once at a site or station the species that form themajority of a sites population can be compared and as a result can be more closely compared.

from (figure 6) looking at polychaete species by removing the singletons it has decreased the

similarity between sites as the Northern, Central and Southern sites have decreased similarity

between each other. Furthermore comparing between stations at each site their similarity has

also decreased. This shows that the addition of singleton species causes less variation

between sites and stations, but also that the majority of the population present at each site is

more varied between each other which by removing the singleton results have revealed this

result.

AbundanceTransform: Log(X+1)


Site

TND(N)

TND(S)

KRa(N)

KRa(S)

LSon1

LSon2

Krabi 1a

Krabi 1b

Krabi 2a

Krabi 2b

Similarity

20

50

2D Stress: 0.22


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Figure 7. The percentage of each feeding type found at each site shown comparatively

against other stations (Fauchald & Jumars, 1979).

Analysing the feeding type (figure 7) shows that there is some variation with feeding type at

each site as the Central cites TND and KRa have a higher percentage of deposit feeders than

the other stations. However they vary from each other as TND has a larger percentage of

carnivores than KRa. Yet KRa has a higher percentage of surface deposit feeders than TND.

When looking at the LSon Northern sites has the largest percentage of carnivores present and

has low percentages of other feeding type species. It also has a small percentage of filter

feeders present as well. And lastly the Krabi Southern sites have a consistent carnivore

percentage but have a high percentage of surface deposit feeders and deposit feeders. From

these variations in feeding type between each site this could explain some of the variation

between the sites. However as they are still very similar as shown by (figure 8) it cannot

account for all of the variation and there are other factors causing the variation between the

sites.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

TND(N) TND(S) TND KRa(N) KRa(S) KRa LSon2 LSon1 Kb1a Kb1b Kb2a Kb2b

surface deposit feeder carnivore deposit feeder filter feeder carrion feeder


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Figure 8. MDS plot to show how similar the variation in feeding types between stations is

using similarity rings in (%) to show how similar each site are from each other (Fauchald &

Jumars, 1979).

Within (figure 8) it shows the similarity of each site with regards to each feeding type found

at each site for polychaete species which shows that the sites are very similar with similarity

between all sites above 80% as shown by (figure 9). It also shows there is still a larger

difference between the Southern Krabi sites and the other Northern LSon and Central TND

and KRa sites. This could account for some of the large species variations between Kb and

the other three sites TND, KRa and LSon.

Abundance

Transform: Log(X+1)


TND(N)

TND(S)

TND

KRa(N)KRa(S)

KRa

LSon2

LSon1

Kb1a

Kb1b

Kb2a

Kb2b

Similarity85

9095

TND(N)

TND(S)

TNDKRa(N)

KRa(S)

KRa

LSon2

LSon1

Kb1a

Kb1b

Kb2a

Kb2b

2D Stress: 0.11


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Figure 9. CLUSTER plot showing the similarity of different feeding types at each station

(Fauchald & Jumars, 1979).

From (figure 9) it further reinforces the high similarity shown by (figure 8) and that there is a

site variation with regards to feeding type which could account for some of the variation of

the study area of Thailand. It also shows that the variation between the Northern, Central,Southern site is the greatest. Also as the similarity of the feeding types is very high it means

that the feeding type does not explain all of the variation between the sites and is therefore

only a contributing factor.

Abundance

Group average

Kb1a

Kb1b

Kb2b

TND(N)

TND(S)

TND

KRa(S)

Kb2a

LSon2

LSon1

KRa(N)

KRa

Samples

100 95 90 85 80

Similarity

Transform: Log(X+1)



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Station Habitat Sand (%)

TND (N) Vegetated 71

TND (S) Vegetated 62

KRa (N) Non-Vegetated 60KRa (S) Non-Vegetated 64

LSon1 Non-Vegetated 55

LSon2 Vegetated 41

Kb1a Vegetated 60

Kb1b Non-Vegetated 93

Kb2a Vegetated 60

Kb2b Non-Vegetated 56Table 1. Habitat state and sand coverage at each station (Barrio Frojan et al. 2006).

Within (Table 1) it shows what type of habitat is present at each site and the sand coverage of

the seabed. From which you can see that the habitat state is variable as Vegetative state is

regardless of sand coverage for example KRa (N) is not vegetated but Kb2a is vegetated

where both have a sand coverage of 60%. However as the habitat state is variable between

sites this could explain some variation in species found. The habitat state variation is most

likely due to some other variable acting upon the system. Also when comparing the

vegetative state against feeding type. Sites that have vegetation have a larger percentage of

the more dominant feeding type and seem to account for a larger percentage of the feeding

types present at a station.

To summarise the results there is a large difference between the sites particularly between Kb

and the other three sites TND, KRa and LSon. Also by removing the crustaceans it decreasesthe variation as and the sites become more similar. But removing the singletons increases

variation between the sites. The stations within each site are also more similar to each other

than compared to other station from other sites. There is also a variation of feeding type

between the sites but are still very similar. And lastly the vegetative state varies from site to

site with different coverages of sand. Furthermore the most common species of polychaetes

to be found are from the families: Nereididae, Spionidae, Capitellidae and Paraonidae (Barrio

Frojan et al. 2006).


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Discussion

Analysing the results there are many different potential causes to the large variation between

each site seen within the results which could be attributed to physical, chemical or biological

factors. Furthermore there may be some anthropogenic influences that could cause these

variations. In addition interactions between all these may produce further variations.

Firstly possible variations that may be quantified by physical processes could be the presence

of solitons caused when the waves hit the Nicobar Islands causing internal waves which

allow the potential for high productivity. This causes turbulence and shear which has a

potential of mixing and exposure to strong wave action which is documented on nearby

beaches of Phuket Island (Dexter, 1996). However due being on the other side of Phuket

island Krabi is sheltered from this and therefore is not exposed to these unlike LSon, TND

and KRa. This therefore allows for some potential variation between these stations.

Furthermore LSon is more exposed as it is not as sheltered as TND or KRa which are more

sheltered by the island Ko Phra Thong (Nielsen et al. 2004). Furthermore Krabi is within a

mangrove area which reduces water currents making it a sheltered area which is also similar

for TND which has allowed the growth of sea grass and vegetation which may quantify for

variation between the sites (Chansang and Poovachiranon 1994 & Cochard et al. 2008). Due

to the sheltered nature of Krabi compared to the other stations which are more exposed via

the internal waves this has implications towards larval dispersal as the larvae will not disperse

as well compared to the other stations and therefore species at Krabi may be different

compared to the other stations as they become more isolated (Beu and Kitamura, 1998). This

could explain why the other sites are more similar compared to Krabi.

Chemical processes could also quantify some of the variation between the sites as Thailand

has a large amount of pig, poultry farm, fish ponds and paddy fields which causes a high

amount of nutrients to run-off into the estuaries of Thailand which pollute the surrounding

areas (Buranapratheprat et al. 2002). Where also industrialisation and use of pesticides has

caused an increase in mercury contamination within the Andaman sea (Cheevaporn and

Menasveta, 2003). Through different levels of nutrient inputs from different estuaries can

cause variation between the sites through different levels of productivity. The different levels

of nutrients available to the sites are also different if vegetation is present as the bioavailable

carbon is much higher where vegetation is present compared to sites where no vegetation is

present. As a result of sites with vegetation present more nutrients are available and therefore


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have a higher productivity allowing a larger carrying capacity for that site which will allow

more species to exist and as a result have a higher diversity. This higher diversity will cause

variation between the sites and could be a possible reason for the large difference seen

between the sites (Duarte et al. 2005). Furthermore because Krabi is more sheltered it could

cause eutrophication through high residence times and poor tidal flushing (Cheevaporn and

Menasveta, 2003). In addition to that there is also a large amount of sewage discharge from

Phuket which would fuel eutrophication (Reopanichkul et al. 2010). In addition due to the

other sites being more exposed to wave action and in a tsunami vulnerable area they are

prone to backwash where sediment is transported via tsunamis and causes large amounts of

Suspended particulate matter to become available (Sugawara et al. 2009).

In addition potential biological influences that could cause the large variations observed

could be due to different predators present at different site which would cause different food

webs to emerge and as a result cause different species to occur at different sites causing

variation between the sites. This would arise through a variation of habitat caused by the

above physical and chemical factors which cause different habitats to occur. For example the

sheltered nature of Krabi surrounded by mangroves and some of the stations vegetated by sea

grass provide a more complex habitat allowing prey items to hide more efficiently as a result

predators adapted to hunting in vegetation would arise here. This would also be similar atTND but because it is more exposed a different predator adapted to hunting in more exposed

conditions would arise here. Then compared to the sites like LSon and KRa which are

exposed and low in vegetation predators adapted to catching prey items that dont have the

option of hiding in vegetation would be found here as the habitat complexity would be lower.

Through different levels of habitat complexity i.e. vegetated with sea grasses or non-

vegetated will cause different predators which causes variations between the sites (Beukers &

Jones, 1997).

In addition the variation in habitat makes different niche environments available where

different species will arise suited to their surrounding environment for example the presence

of vegetation at the sites which is known to be sea grass (Barrio Frojan et al. 2006). The

presence of sea grass provides a three dimensional habitat for macrofauna as they can feed

amongst the sea grass and not just on the sediment as a result it provides a larger habitable

environment for species to live in which will increase the diversity of the site containing sea

grass. On the other hand the sites without sea grass will be restricted to feeding on the

sediment and therefore be less diverse as the habitable environment is less without sea grass


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(Ansari etal. 1991). Which as shown within the results there are sites with sea grass and

without sea grass present. Through the presence of different habitats this therefore causes

variation between the sites. Furthermore from the seabed coverage of sand all sites had sand

present which would indicate that the sea grass is most likely patchy which could explain the

variation between the stations of sites that contained sea grass (Wiens, 1997). Furthermore

patchiness also creates two habitats within the same area which would increase diversity

further and allow new species that can utilise both the sea grass habitat and the exposed

seabed environment which would cause greater variation between the sites depending on the

patchiness of sea grass (Wiens, 1997). This could account for the differences between the

Northern, Central and Southern areas. Where the Northern site is non-vegetated and exposed.

The Central sites are both vegetated and non-vegetated and semi-exposed and the Southern

Krabi site is both non-vegetated and vegetated but sheltered. With different habitats present at

each location different species will occur and cause variation between the sites and could be

the reason why the sites are so different from each other.

One potential cause of variation between the sites could be due to the presence or absence of

mangroves at a station as it is documented that mangroves are along the coast line (Barrio

Frojan et al. 2006) which provide a different habitat for species to thrive in. however near

Ranong the mangroves have been commercially exploited which is near the site LSon whichis an exposed area. The removal of the mangroves from this area may have caused variation

of LSon from the other sites by removing a habitat for prey items to hide amongst. This could

explain why LSon has a large proportion of carnivores present compared to the other sites

(Macintosh et al. 2002).

Another biological influence that could be a contributing factor towards the the large

variation between the sites is the feeding type of the species found at the sites. Due to the

different habitats different food sources are available and therefore different feeding methods

are used in order to obtain that food which causes variation as shown in the results. These

different feeding mechanisms arise as a result of the habitats being different which as a result

mean different species are present (Fauchald and Jumars, 1979 & Gutierrez and Iribarne,

2004).

Looking at the results section other potential reasons for the large variability between stations

could be due to the presence of crustaceans as well as polychaetes. As within the singleton

section where crustaceans have been added the sites become more dissimilar from each other


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but when removed become more similar to each other. This shows that basing the similarity

of a site based on just polychaetes is not enough to quantify the full extent of variability at a

site as when adding other classes of species the variation increases between the sites as

interactions between different species becomes more noticeable as species that are usually

associated with another species will occur near them. After taking into account all the species

that appear at each site there will be even more variation as it cascades through different

trophic levels (Southwood, 1977).

When the singleton species were removed it showed that the sites became more dissimilar

indicating that the singletons reduced variability. This is most likely due to the singletons

being present at more than one site and therefore by removing them it removes a common

factor between sites and therefore decreasing similarity. Also it was documented from

previous studies using the same dataset that only one species occurred at all stations and that

33% of the species found only occurred at one site therefore by removing singletons it would

mean that there would not be the same species present at every station and unlikely that 1

species would appear at multiple stations as a result removing singletons causes more

variability between the sites (Barrio Frojan et al. 2006). The possibility why the removal of a

singleton species may decrease similarity is because if at one site a species is abundant but at

another site it is a singleton, the presence of that singleton makes it more similar to the sitewhere the same species is abundant. By removing it removes the link between the sites and

makes them more dissimilar. On the other hand it would improve similarity if rare species

were removed that only occurred at one site as it would remove a difference between sites.

However as similarity decreased it would imply that singletons found and removed were

most likely present in more abundance at other sites rather than being rare. This has most

likely occurred due to larval dispersal where a few larvae have left the Krabi site and become

singletons at other sites or vice versa where larvae have been carried into Krabi this would

explain how you could have an abundant species at one site and then a singleton species at

another site (Beu & Kitamura, 1998).

Another source of potential variability is the genetics of species at each site. Where Krabi is

more isolated there is the potential for limitation in the gene pool as conditions are more

stable due to being sheltered. This would impact the site by reducing the fitness of individuals

and whilst having a large species diversity there may be a low genetic diversity as a result of

low larval dispersion. As a result at the other stations there may be smaller species diversity

due to more exposed conditions but a greater genetic diversity and therefore a higher


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individual fitness due to a greater larval dispersal potential (Bell and Okamura, 2005 &

Bulleri and Chapman, 2010). However this has not been tested and would be ideal for future

study in order to test this as a hypothesis. Another potential topic for future study would be to

look at the larval dispersal from each site as this data would reinforce or disprove the gene

pool limitation hypothesis. Furthermore it would also further investigate the presence of

singletons and their implications on variation between the sites.

To conclude the main variation between the sites can be attributed to different habitats at

each site which allow different species to occur as they occupy different niches created by the

habitat causing the variation. However the variation in habitats is caused by variation of

various contributing factors such as physical, chemical and biological interactions and

anthropogenic inputs which create these different habitats and allow the wide variation of

macrofaunal species to occur.

References

Ababio, T.K.G., Furstenberg, J.P., Baird, D., Vanreusel, A., 1999, Nematodes as indicators

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