An investigation into the factors effecting the colonisation of … · 2013-05-15 · 1 Abstract This research aimed to identify factors which affect the colonisation of bog pools

This dissertation is submitted in part fulfilment of the requirements for the BSc (Honours) in Countryside Management

of the Royal Agricultural College, Cirencester.

2012

An investigation into the factors effecting the colonisation

of created bog pools on Exmoor.

Michelle Katherine Easton

1

Abstract

This research aimed to identify factors which affect the colonisation of bog pools created via the

mire restoration works on Exmoor, Somerset. Water chemistry- pH and conductivity, total nitrate

levels, peat depth, age of pool and area relating to wave action were the factors considered.

Thirty pools were surveyed with measurements for each of the above factors being taken and

vegetative cover recorded in percentages according to the Domin scale. Water chemistry was

identified as statistically significant, pH for the data set as a whole with p= 0.0147 and those

plant species identified as minerotrophic with p= 0.0132. Conductivity was statistically

significant for species in NVC communities M1 – M4 with p=0.0482, as well as those species

identified as acid plants p=0.0055. Peat depth was also statistically significant for minerotrophic

and acid species with p= 0.0139 and p=0.0045 respectively. No significance was found for total

nitrate levels, but this is considered to highlight the need for more water nutrient level tests

including phosphorus and mineral nutrients, which was found to be the main limitation of the

research.

2

Limitations

There are a number of limitations to this research;

The timing of the academic year meant that the surveys were carried out in the winter, this is not

a problem for sphagnum species which are visible year round, but vascular plant species such as

Sundews Drosera sp. will not have been present at the time of the surveys. Therefore these

results cannot be used as an indication of total diversity of the pools, but are instead

representational of the process of succession from open water to NVC bog pool communities.

The time restraints also limited the number of pools surveyed, ideally two or more sites from

each year would have been sampled but carrying out more was simply not possible in the time

allowed.

The accurate identification of species was given high priority and training was received from Dr

D. Smith of the mires on the moors project. However the surveyors (the author and a fellow

student) had very little previous experience with mire species or sphagnum mosses so although

great care and time was taken to identify species accurately some errors may have been made, or

some similar looking species missed at some locations.

Water nutrient content was not found to be significant even thought this is known to be a major

limiting factor for sphagnum growth.

3

Contents

Chapter 1. Aims & Hypothesis 5

Chapter 2. The Classification of Mire Habitats 6

Chapter 3 Environmental Services 7

3.1 Carbon 7

3.2 Water Supply 7

3.3 Water Storage & Flood Mitigation 8

3.4 Archaeology 8

Chapter 4. Exmoor 10

4.1 Exmoor National Park 10

4.2 The Formation of Exmoor 10

4.3 Mires Restoration Project 11

Chapter 5 Bog Pool Ecology 12

5.1 Creation & Importance of bog pools 12

5.2 Bog Pool Vegetation 12

5.3 Factors which effect the colonisation of Sphagnum species 13

Chapter 6 Methodology 14

6.1 Sites 14

6.2 Sampling Technique 14

6.3 Survey Technique 14

6.4 Laboratory analysis 15

Chapter 7 Results 29

Chapter 8 Discussion 32

8.1 Nutrient Availability 32

8.2 Water Chemistry 32

8.3 Pool Size 32

8.4 Peat Depth 33

8.5 Sequence of colonisation 34

Chapter 9 Conclusion 35

References 36

4

List of figures

Figure 1: Soil horizons showing formation of Iron pan. 10

Figure 2: Site location overview map 1. 16


Figure 4 Site location overview map 3. 18


Figure 6: Survey site location- Acklands. 20

Figure 7: Survey site location- Aldermans Barrow. 21

Figure 8: Survey site location- Black Pitts. 22

Figure 9: Survey site location- Comerslade. 23

Figure 10: Survey site location- Exehead. 24

Figure 11: Survey site location- Hangley Cleave. 25

Figure 12: Survey site location- Hommer Common. 26

Figure 13: Survey site location- North Twitchen. 27

Figure 14: Survey site location- Squallacombe. 28

Figure 15: Graph showing relationship between pH & total cover. 29

Figure 16: Graph showing relationship between conductivity and 29

NVC community species.

Figure 17: Graph showing relationship between peat depth & acid and 30

minerotrophic species.

Figure 18: Graph showing relationship between conductivity and acid 30

Species.

Figure 19: Graph showing relationship between pH & minerotrophic species 31

Figure 20: Survey photograph 2, showing wave action on pool surface. 33

List of Tables

Table 1: Sequence of bog pool colonisation. 5

Table 2: Site & Year of Restoration. 14

Table 3: Domin Scale. 15

Appendices

Appendices 1: Survey form.

Appendices 2: Survey site photographs

Appendices 3: Results.

Appendices 4: Multiple regression analysis results.

Appendices 5: Plant categories species lists.

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Chapter 1- Aims & Hypothesis

1.1 Aims

This research aims to identify factors which affect the speed of colonisation, and types of

vegetation which colonise the bog pools created by the mire restoration activities on Exmoor.

Attention is solely focused on the pool vegetation with transitional / marginal vegetation being

discounted, and particular attention is paid to sphagnum species as they are dominant in the NVC

communities which are present within an undisturbed mire habitat.

A general trend for the colonisation of the pools has been observed by the mires-on-the-moors

personnel (personal communication with Dr. D. Smith 2011), as outlined in table 1 below.

Year Observation

1 Green Algal blooms form

2 Sphagnum communities begin to colonise, these tend to be M2 or M1 if nutrient levels

are higher.

4/5 Floating vegetation mat is thick enough for hummock forming sphagnum species and

bog cotton grasses to begin colonising.

Table 1: Sequence of bog pool colonisation.

1.2 Hypothesis

Four hypotheses will be tested by this research.

a) Water nutrient content will affect sphagnum colonization of the bog pools.

b) Water pH and conductivity will affect which species colonise the bog pools.

c) Pool size will affect the colonisation of sphagnum species in the bog pools.

d) Peat depth will affect which species colonise the bog pools.

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Chapter 2- The Classification of Mires

The term mire refers to wetland habitats which are peat forming. The waterlogged, acidic soil

conditions limit decomposition allowing organic matter to accumulate in the form of peat

(Lindsay 1995). Mires are the lower latitude version of northern tundra ecosystems formed by

oceanic conditions, as opposed to the permafrost waterlogging which forms true tundra (Lindsay

1995). As such Britain’s mires support many specialised species at the southernmost edge of

their range (Lindsay 1995).

There have been a great variety of classification systems developed for mires, these tend to be

based on hydrological characteristics due to the habitats low floristic variety. The two main types

of mire habitats are bogs and fens.

The term fen describes a peat forming habitat which derives at least some of its nutrient and

water supply from the groundwater table, thus termed minerotrophic (Lindsay 1995). This is a

transitional habitat eventually developing into mature woodland or ombrotrophic bog (SNH

2011).

The term ombrotrophic or ombrogenous bog refers to mire habitats which receive water and

nutrient inputs from precipitation alone (Lindsay 1995). The vegetation communities are

separated from the ground water and mineral soils below by a layer of peat, making them

nutrient poor with low primary productivity (Allaby 2005). These are ancient climax habitats

thus being stable biotopes with some examples dating back 10,000 years (Lindsey 1995).

There are two main types of ombrotrophic bog in Britain as identified by Goode and Ratcliffe

(1977) raised bog and blanket bog, both of which are globally rare. Raised bogs have a typically

dome shaped cross section, forming over uniform clay sub-straits, dried up lake sediments or

estuarine sites. In the U.K. they are confined to the oceanic climate of western areas (Allaby

2005). Blanket bogs lie across the ground surface (Mitsch 2007) thus are able to colonise flat to

moderately sloping areas, they are again confined to oceanic climates with high humidity (Allaby

2005).

Blanket bog is the most common on Exmoor, with over 30 square kilometres present. Although

at this point it is important to note that not all the mire areas on Exmoor are ombrotrophic, some

are spring driven poor fen habitats (JNCC 2008).

7

Chapter 3- Environmental Services

Interest and research into the functioning of Peatlands has grown in recent years due to increased

recognition of the environmental services they provide, these include; carbon storage and

sequestration, storage and cleaning of water and the preservation of archaeology and

environmental information within the peat layers.

3.1 The Carbon cycle

With predicted climate change, carbon and other greenhouse gas cycles have been the focus of

intense research. The identification of carbon stores and the processes affecting them is a major

part of this.

Peatlands are both a store and potential source of greenhouse gasses such as carbon dioxide and

methane (Lamers 1999). Estimates put the northern peatlands carbon store at 450 Gt, this

amounts to 30% of the global soil carbon pool (Gorham 1991).

Both CO2 and Methane play an important internal role within peatland systems (Lamers 1999).

As long as a mire system is healthy, which requires the correct conditions for healthy sphagnum

growth (Lindsey 1995), it will continue to absorb and lock up CO2 from the atmosphere via peat

accumulation (Anderson and Mitch 2007). If the peat is degraded, drained or cut it will dry out

allowing normal decomposition, there by releasing the carbon stored within the peat (Lamers

1999).

In addition to degradation due to man’s actions is the impact of climate change, predictions

forecast warmer dryer summers and wetter winters. These changes to rainfall distribution could

have a devastating effect on mire communities (Peatland Portal 2012). Total water inputs must

match or exceed the losses experienced through evaporation or seepage for a mire to remain

intact (Lindsay 1995). Backeus (1998) identified that precipitation distribution over the year, not

total precipitation is the most important factor in healthy sphagnum growth, and that it was

moisture conditions in the august of the previous year which had the most significant impacts. If

the peatlands cannot adapt to climate changes the unique conditions which contain the carbon in

a stored state will be interrupted, hence a greenhouse gas store could become a source, further

compounding the problems faced due to climate change. (Lamers 1999 & Peat Portal 2012).

Despite this long-term uncertainty peatland restoration and creation projects are still thought to

be one of the best opportunities for carbon sequestration (Mitsch 2007). This is a major driver in

the restoration of peatlands across Europe (Peat Portal 2012).

3.2 Water Supply

The IUCN (2010) inquiry on the UK’s peatlands states that 70% of the UK’s drinking water

comes from surface water sources. The majority of this is from upland areas dominated by

peatlands. The quality of water released by peatlands is naturally very high as they filter

8

impurities and pollutants out from the rain water (Lindsay 1995); however over the last 30 years

the levels of Dissolved Organic Carbon (DOC) have doubled (IUCN 2010).

This mobilization of carbon into the fluvial system shows the degradation of peatland as a

terrestrial carbon store (Wheeler & Shaw 1995). It also has secondary environmental impacts

including the reduction of light levels in streams, due to the brown discolouration caused by the

presence of DOC, reducing biodiversity. DOC also mobilises metals and other pollutants and

must be removed from drinking water as once chlorinated it causes the formation of carcinogenic

substances (Woddington & Price, 2000). This has led to vastly increased costs to the water

companies (IUCN 2010) and is another major driver for the large investment put into peatland

restoration schemes (Wallage et al 2010).

3.3 Water Storage & Flood Mitigation

Some literature such as Lindsey (1995) refers to peatlands as a ‘sponge’ with the absorbent effect

of the mosses and peat slowing runoff rates in storm conditions and thus reducing erosion. This

idea states that the water from storm events is absorbed and slowly released in dryer periods,

stabilising river levels, aiding biodiversity and reducing flood risks downstream.

However the IUCN (2010) inquiry of peatlands finds this view erroneous, stating that intact

peatlands have a consistently high water table (usually within 5cm of the surface) and therefore

little extra hold capacity to adsorbed rainfall in storm events. The velocity of runoff is shown to

be affected by surface features; velocity is lower over cotton grass or sphagnum compared to

bare peat, but outflow stream are still flashy by nature.

However the IUCN still found that restoring degraded peatlands, those which have been drained

for agriculture or forestry purposes, will improve water holding capacity and reduce runoff rates

and erosion. This is because degraded peatlands have a lowered water table which can create a

hydrophobic surface layer of peat reducing infiltration and increasing surface run off. This leads

to shrinkage and cracking within the peat bed and aids the formation of macro-pores, greatly

increasing the hydro conductivity of the peat, again increasing run off rates and erosion. The

blocking of ditches has been found to locally influence run off rates but it is still not clear how

far downstream these benefits reach (IUCN 2010). Worrall, Armstrong and Holden (2007)

investigated the impacts of blocking drains in the Whitendale catchment in the Forest of Bowland

Northern England, and found that the water tables were increased and stabilised, overall this

increased storage capacity and it was found that flow rates were reduced once the works had

been completed.

3.4 Archaeology

Intact peatland is also of great archaeological importance as many artefacts, that under normal

conditions would decompose, are preserved. Signs of historical land use such a field patterns

which existed before the peat formed are also protected beneath peat deposits (Lindsay 1995).

9

Peatlands also hold a valuable paleoecology record detailing not only their own development,

but also changes in vegetation and the environment on a larger scale through pollen rain records,

atmospheric pollution and other deposits from events such as volcanic eruptions.

10

Chapter 4- Exmoor

4.1 Exmoor National Park

Exmoor National Park (ENP) was designated in 1954, located in south west England it covers an

area of 267 square miles. Exmoor’s rich landscape has been shaped by natural and human factors

over thousands of years resulting in a diversity of habitats including moorland, woodland valleys

and farmland, the archaeological resources present are also substantial.

4.2 The Formation of Exmoor

Although the ice sheets of the last glacial period, which ended some 10,000years ago, didn’t

cover Exmoor many of the topographic features such as large rolling hills and deep, steep sided

valleys were carved by the resulting melt waters (REF). It is this variety of features which gives

rise to Exmoor’s' varied habitats. But it is the upland, moorland and mire areas which are the

focus of this paper.

After the ice age broadleaved woodlands slowly spread northwards across the UK covering most

of the land mass including Exmoor. This remained the case until the Bronze Age clearances

which began around 2000 BC (Exmoor National Park, 2011 (a)). At this time the climate was

much warmer making the upland areas suitable for settlement and farming. The settlers cleared

the woodlands using them for fuel and materials, the cleared land being turned over to small

scale agriculture and livestock grazing. This maintained an open landscape dominated by grasses

and small shrubs (Everything Exmoor, 2011).

By the end of the Bronze Age, around 1000BC, the climate had cooled; this coupled with the

loss of the trees had a massive impacted on the soils. A process known as podzolisation occurred,

for this to take place the soil must initially be free draining and have an accumulation of acid rich

organic material at the surface, it is this build-up of acids which mobilises the iron in the soil.

Through the podzolisation the surface horizons become very acidic and weathered, with the top

soil growing pale and ashen as the nutrients are depleted, being washed down into the sub soil. A

thin hard iron pan forms in the sub soil, beneath which are a further series of orangey horizons

with sharply defined boundaries. (Atherdan, 1992), Figure 2 by Conway (1994) shows a good

example of this kind soil formation.

The Iron pan is impermeable to water so limits drainage. This coupled with the wetter, cooler

climate at the end of the Bronze Age caused the soil above the iron pan to become water logged,

creating the conditions for peat formation to occur. Over the next few thousand years the process

of peat accumulation continued, resulting in the peat bogs we see today (Atherden 1992).

Evidence of these pre-historic settlements and the field systems they created are preserved

beneath the peat layers, but one unique floristic feature of Exmoor is the presence of greater

woodruff, a woodland plant found growing on the open moorland where it is a remnant of the

ancient forests which once covered the hill tops.

11

Figure 1; Image showing formation of iron pan in soil horizons (Conway 1994).

4.3 The Exmoor Mires Restoration Project

The UK is home to 20% of the global resource of blanket bog, with Exmoor being a premier

location. However in 1818 the moor was brought by John Knight who set about improving the

mires for agricultural use. This involved excavating drainage ditches and ploughing up the

peatland in order to break the iron pan and improve drainage, these kinds of practices continued

well into the 20th

century (Smith, 2008).

The effect of the ditches is to lower the water levels and increase runoff from the surrounding

area causing the peat to dry. This results in a vegetation shift from sphagnum dominated

communities such as M1 & M2, towards moorland communities dominated by heathers and

purple moor grass (Rodwell 1991).

The various stages of the Exmoor Restoration Projects have aimed to restore the natural

hydrology of the mires by blocking the ditches with wood, peat or grass bale dams. The project

originally ran from 2006 to 2010, with an earlier pilot scheme in 2000. It has involved a number

of organisations including the Environment Agency, Exmoor National Park, Natural England

and South West Water. Over this time the project carried out restoration work on 12 sites,

blocking over 50 ditches resulting in the re-flooding of 300 hectares. A further possible 150 sites

covering 2000 hectares have since been identified, these are being addressed by the next phase of

the project; Mires-On-The-Moors 2010-2015, funded by south west water via bids made to

OFWAT the water regulator (Exmoor National Park 2011).

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Chapter 5- Bog pool Ecology

5.1 Creation & Importance of bog Pools

Some of the dams built to block the drainage ditches result in the formation of pools and these

are hotspots for biodiversity, adding to species richness (Fountaine et al 2007) and providing

foraging sites for amphibians (Mazerolle 2005) and rest areas for migrating birds (Desrochers

2001).

Studies have highlighted the importance of these microhabitat features for the overall diversity of

the mire habitats. Glaser (1992) found that the number of wet to dry micro-topographic gradients

such as pools, hollows and hummocks present on raised bogs in eastern North American was a

key factor in the richness of vascular plants present. Vitt et al (1995) showed a general

correlation between bryophyte and habitat diversity in peatlands of western Canada.

5.2 Bog pool Vegetation

As part of the British Plant Communities series Rodwell et al (1991) identified 38 mire

communities, however only two of these are specific to bog pools.

M1 Sphagnum - Ariculum is characterized by floating mats and wet carpets of sphagna with

scattered vascular plants growing on or through them. It is found on ombrogenous base poor

mires mostly in western Britain. Constant species are; Common cotton grass Eriphorum

angustifolium, Menyanthes trifoliate, Sphagnum auriculatum, Sphagnum cuspidatum.

M2 Sphagnum cuspidatum - recurvum is characterized by large soft wet mats of S. cuspidatum

and/or recurvum. Other sphagna species are sometimes present, when on a highly patterned pool

/ hollow mire surface these other species indicate the drier pool edges. But this is more

commonly found as a less defined extensive lawn with vascular plants scattered throughout but

with low total cover. Constant species are; Erica tetralix, Eriophorum angustifolium, Dorsera

rotundifolia, Sphagnum cuspidatum / recurvum.

These National vegetation classifications show that sphagna species are dominant or at least co-

dominant in bog pool communities which is unusual amongst bryophytes. Sphagna cover large

areas of peatland in the northern hemisphere (Daniels & Eddy 1985). The success of sphagna lies

with the ability to utilise the nutrients present and store water very effectively. Sphagnum species

can absorb mineral cations from rain water and exchange it with hydrogen ions allowing them to

thrive in nutrient poor environments. This action acidifies the environment making it suitable

only for acid tolerant species (Daniels & Eddy 1985).

The anatomy and growth form of Sphagna have adapted to retain large amounts of water. They

have an erect stem with regularly branching clusters or fascicles, the stems can grow to indefinite

length but the fascicle branches are strictly limited (Daniels & Eddy 1985). The growth pattern

of the sphagna will commonly vary depending on moisture levels, with denser forms appearing

in dry areas as the dense mates and hummocks hold water more effectively. This is important as

Sphagna have no roots or internal water transport tissues, yet they can hold up to 20 times the

13

dry weight in water. This is due to the very large hyaline cells located inside the branching

leaves; these are dead at maturity with thick bands of supportive material and store water. A

second type of cell is the slender photosynthetic cells which fit between the hyalines. Nearly all

sphagna can survive short periods of drying out, during which the empty hyaline cells turn white

helping to reflect heat and reduce evaporation.

5.3 Factors effecting the colonisation of Sphagnum species

Under ideal conditions S. cuspidatum will colonise the entire water layer providing a floating

mat to support other species (Eiseltova 2010). However the colonization of S. cuspidatum on

larger bodies of water is hampered by wave action, making the location and growth of sheltering

vascular plants important for Sphagnum formation (Eiseltova 2010 & Waterman 1926). Common

Cottongrass Eriophorum angustifolium is one such sheltering plant. It is an important constant

species in both NVC bog pool communities as well as other mire communities and has been

found to be an major influencing factor in the spread of vegetation in eroded gully systems in

Dark peat National Park, South Pennines. The research carried out by Crowe et al (2008) shows

that colonisation of E. angustifolium increased surface roughness, slowing down water flow and

increasing peat particle deposition, thus aiding revegetation.

S. cuspidatum mats float due to trapped oxygen bubbles giving them buoyance, these bubbles are

produced by the process of photosynthesis. Therefore in order to establish buoyancy at the start

of the growing season the conditions within the water column must allow photosynthesis to take

place (Wheeler & Shaw 1995). Light levels are a major part of this, in clear waters sphagnums

have been found growing at a depth of 9m, however in most peatlands, especially those under

restoration, the discolouration of the water by DOC limits sphagnum growth to water around

50cm deep (Eiseltova 2010). These shallower waters also put the sphagnum closer to the

sediments at bottom of the pool which is a major source of carbon dioxide for photosynthesis

(Eiseltova 2010).

As mentioned above poor fen habitats also exist on Exmoor, these are spring driven, peat

forming habitats also dominated by Sphagnum species (JNCC 2008). Although poor fen waters

are acidic, around pH5 (JNCC 2008), they are also minerotrophic in nature and contain higher

levels of mineral nutrients than purely ombrotrophic systems (Mitsch & Gosselink 2007). This

allows different plant assemblages to grow including species like Juncus effuses Soft rush and

Juncus actiflorus jointed rush.

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Chapter 6- Methodology

6.1 The Sites

With the help of Dr. D. Smith, head of the mires on the moors project, nine sites where bog pools

have been created due to ditch blocking were identified. All of the surveyed pools have remained

intact since creation with no leaking or repaired pools being used.

The sites are outlined in table one and locations shown of maps 1 – 3. None of the sites have had

fertilisers applied and all were and still are used for rough grazing, although grazing levels are

very low due to the areas being under conservation management. A series of maps follows the

first, figure 321 shows the locations of the restored sites on an OS map background. The rest,

figures 2-321show the location of the ditches, ditch blocks and surveyed pools using the 2010

aerial photographs as a background, this is currently the most recent aerial footage available

therefore pools created by restorations carried out in 2011 are not visible.

Site Year of Restoration

Acklands 2009

Aldermans Barrow 2008

Black Pitts 2007

Black Pitts Pilot

restoration

2000

Black Pitts Old pools 1950’s

Commerslade 2011

Exe Head 2007

Hangley Cleave 2008

Hommer Common 2011

North Twitchen 2009

Squallacombe 2007

Table 2: Sites and year of restoration.

6.2 Sampling technique

The pools were chosen to be representative of the site, with the inclusion of the largest and

smallest pools. Some sites such as Black Pitts had higher sampling rates due to the number and

diversity of the ditches and pools present. Others such as Hangley Cleave had very few pools

present on the site. In addition Black Pitts has 3 pools of particular interest; the first is the only

pool to have remained intact from the early ditch blocking trials in 2000; this is survey number 1.

The other 2 are pools which are visible on aerial photographs from the 1940’s; these are surveys

4 and 5.

6.3 Survey Technique

Once a suitable pool had been identified, the location of the forming ditch block was logged in

the GPS handset, this data was used to create the following set of maps. The feeding water

source was then identified. A photograph of each pool was taken with a small sign displaying the

survey number included to aid identification later. Due to possible disturbance caused by later

activities two water samples were collected at this point in the survey, they were taken from

arm’s length away from the edge, or as far as was safely possible.

The pool itself was used as the transect in order to avoid including over flow or seasonally wet

areas, care was taken to identify each pools edge. If grass or sedges are present they must form a

15

floating mat to be considered in the pool. Size measurements were taken from this ‘true’ edge,

so some survey areas are smaller than the areas of water shown in the survey photographs. Some

pools were formed where two channels met and were clearly T shaped, in order to avoid

recording falsely large pool sizes these were recorded with two measurements, one for each

section.

Vegetation cover was recorded as percentage cover according to the Domin scale as shown in

table 3.

Value % Cover

1 <4% few individuals

2 <4% several individuals

3 <4% Many individuals

4 4-10%

5 11-25%

6 26-33%

7 34-50%

8 51-75%

9 76-90%

10 91-100%

Table 3: Domin Scale.

Any new species appearing in the 20 – 30cm along the edge were discounted as being

transitional wet – dry features rather than true pool cover. Peat depth was recorded by pushing a

pole into the ground next to the survey site. All the information was recorded on the survey form

designed by the author see appendices 2.

6.4 Laboratory Analysis

The water samples were frozen until all the surveys were completed so that all lab work was

carried out at the same time. Firstly pH and conductivity (µs) were recorded; two readings for

each were taken with the mean of these being the final result. The samples were then filtered and

put through a flow injection analyser (FOSS 5 STAR 5000) to identify ammonium NH4 and

nitrate NO3 & NO2 levels, the mean of the two results was calculated and then the scores for

NH4 and NO3 & NO2 added together to give the total nitrates available to plants.

16

Figure 2; Site locations overview map 1.

17

Figure 3; Site location overview map 2.

18


19


20

Figure 6; Survey site locations- Acklands

21

Figure 7; Survey site locations- Aldermans Barrow

22

Figure 8; Survey site locations- Black Pitts.

23

Figure 9; Survey site locations - Commerslade.

24

Figure 10; Survey site locations- Exe Head.

25

Figure 11; Survey site locations- Hangley Cleave.

26

Figure 12; Survey site locations- Hommer Common.

27

Figure 13; Survey site locations- North Twitchen.

28

Figure 14; Survey site locations- Squallacombe.

29

Chapter 7- Results

The data collected is shown in Appendices 3, the cover scores are percentages selected according

to the Domin scale. Statistical analysis was carried out using multiple regression analysis with

stepwise deletion, full results of which are in appendices 4 with the results grouped in a number

of ways;

Analysis of the data group as a whole showed water pH to be statistically significant as

P=0.0147, figure 2 shows pH plotted against the total cover scores.

Figure 15; Graph showing relationship between water pH and vegetation cover.

The data group was then altered to only include plant species found in NVC communities M1,

M2, M3 and M4 which are the bog pool and mire communities. The results showed that

conductivity is statistically significant with P= 0.0482, as illustrated in figure 3

Figure 16; Graph showing the link between conductivity and cover in NVC community species.

0

5

10

15

20

25

30

35

40

45

50

0 1 2 3 4 5 6 7 8

Cover

Sco

re

pH

pH

0

5

10

15

20

25

30

35

40

45

50

0 50 100 150 200

Cover

Sco

re

Conductivity

NVC & Conductivity

30

The plant species were then separated according to preferred conditions; this was done using a

list supplied by Dr. D. Smith, see appendices 5 for details. The majority of the species present in

this data set fell into two main categories, acid and minerotrophic. These were put through the

statistical analysis with the results showing;

Peat depth is statistically significant for both the acid and minerotrophic groups as shown in

figure 4, with P= 0.0045 for the acid species and P= 0.0139 for the minerotrophic species.

Figure 17; Graph showing the significance of peat depth for both acid and minerotrophic species.

There is also a relationship between conductivity and the acid species as shown in figure 5, this

was found to be statistically significant with P= 0.0055.

Figure 18; Graph showing the relationship between conductivity and acid species.

0

5

10

15

20

25

30

0.00 0.50 1.00 1.50 2.00

Cover

Sco

re

Peat Depth

Habitat Indicator Species & Peat Depth

Acid

Minerotrophic

0

5

10

15

20

25

30

0 50 100 150 200

Cover

Sco

re

Conductivity

Conductivity & Acid Species

31

A further relationship was found for pH in the minerotrophic species group as shown in figure 6,

with p= 0.0132.

Figure 19; Graph showing relationship between pH & minerotrophic species group.

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8

Cover

Sco

re

pH

pH & Minerotrophic Species

32

Chapter 8- Discussion

8.1 Nutrient Availability

It is surprising that no relationship has been found between Nitrogen levels and colonisation as

this is a well-documented relationship. Research carried out by Gunnarsson & Rydin (2000)

showed N to be a limiting factor for sphagnum growth, it also showed that even low influxes of

nitrogen reduced the biomass production of lawn and hummock sphagnum communities. But

from these results the null hypothesis must be accepted a) Water nutrient content has no effect on

which species colonise the bog pools.

However other nutrients may be an influencing factor, the significance of peat depths suggests

that mineral nutrients play an important role on Exmoor. Indeed the pools surveyed varied from

purely rain fed ombrotrophic mires to spring / stream driven, poor fen communities, this is a

gradient which is not well understood. Water samples often yield very similar pH and

conductivity readings and yet pools can support strikingly different vegetation communities

(Hayati & Proctor 1991). Due to this it is suggested any further research include tests for mineral

nutrients, Hayati & Proctors (1991) research into the limiting nutrients in acid mire vegetation

could be a suitable starting point.

8.2 Water Chemistry

The chemistry of bog water is known to be a major influencing factor in sphagnum moss

distributions (Daniels & Eddy,1985), Literature pH and conductivity levels for bog waters are

between 3.3 – 5.5pH and below 80 ucm, transitional waters have a pH between 4.5 – 6.0

(Thames River 2009).

It is therefore not surprising that these two factors repeatedly have a significant effect on the

colonisation of the bog pools surveyed. The data set as a whole found pH to be a statistically

significant factor, with a 1 Standard Deviation (SD) of change in pH causing a 0.466 SD of

change in cover score. While conductivity showed statistical significance for both the NVC and

Acid category data sets with 1 SD of changing causing 0.5679 SD & 0.458 SD of change in

cover score respectively. From these results we can accept hypothesis b) Water pH and

conductivity will affect which species colonise the bog pools.

8.3 Pool Size

As discussed above wave action is known to be a major factor in sphagnum colonisation in both

natural (Waterman 1926) and restored bog pools (Eiseltova 2008). It was therefore hypothesised

that such a relationship would exist on Exmoor, but no statistically significant difference in

vegetation colonisation was found. Therefore the null hypothesis must be accepted C) Pool size

has no effect on the colonisation of sphagnum species in the bog pools.

There are a number of possible reasons for these results including; the pools sampled did not

cover a wide enough range of sizes; many of the pools were sheltered by the ditch sides, the dam

33

itself or vascular vegetation so wind action many have been reduced; or perhaps the pools need

more time to colonise before the effect of wave action is fully ascertainable.

It is therefore considered that any further research should continue to investigate the effects of

wave action for these reasons, and attention is drawn to the photograph below showing survey

number 2. This pool was the largest surveyed at 10 x 25m and at the time of the survey the wave

action was clearly visible, with the waves stopping at the nearside edge as they hit the submerged

S. cuspidatum, the sheltering action of the rush in the centre also visible.

Figure 20; Survey picture 2 showing wave action.

8.4 Peat Depth

Peat depth was also found to be statistically significant for the acid and minerotrophic categories,

with an increase of 1 SD of peat depth causing a 0.7819 SD increase in cover score for acid

species, and a 0.6704 SD decreasing in cover score for minerotrophic species as shown in figure

4. This means hypothesis d) Peat depth will affect which species colonise the bog pools can be

accepted.

It is likely that this is due to the intrusion of mineral rich spring water through thinner peat layers

which gives rise to poor fen development rather than ombrotrophic conditions. Research by

O’Reilly (2008) into sphagnum as management indicators, found peat depth to be significantly

correlated with sphagnum species richness, and with seven individual sphagnum species. This is

34

something which could be included in any further research in order to create a model to predict

which species are likely to colonise an area according to peat depth, thus helping to target

restoration.

8.5 Sequence of Colonisation

There is very little literature on the sequence of colonisation found in created bog pools, but the

results obtained follow the expected sequence outlined above. It was expected that the growth of

algae in the early stages of pool vegetation development would be linked to higher nutrient

levels, due to disturbance. However this is not shown to be the case with no link between algae

growth and Nitrogen levels being observed. This again raises the question of what other nutrients

are present, especially phosphorous, and it is suggested at this nutrient also be included in future

investigations.

35

Chapter 9 Conclusions

Water chemistry was found to be a significant factor in determining which species

colonised the bog pools, with pH being significant for the data set as a whole, and

conductivity being significant for the NVC climax communities, and those plants classed

as acid loving.

Peat depth was found to be a significant factor in determining which of the two main

plant categories (acid or minerotrophic) were present. This is due to the peat layer

separating the vegetative surface from mineral rich ground waters.

Wave action relating to pool size was not found to be a major limiting factor on Exmoor.

However due to the importance of this factor in literature, and the possible reasons

outlined above, it is still recommended that it be included in any further research.

The sequence of colonisation observed by the Mires-On-The-Moors staff was found to be

representative by the data set although limitations of the study were highlighted by the

lack of correlation between algae growth and nutrient levels.

Area relating to wave action was observed to be affecting the pools in the field, however

this was not shown in the survey results. It is considered here that the pools need longer

to become established before the influence of wave action is full ascertainable.

36

Appendices

Appendices 1: Survey form.

EXMOOR BOG POOL SURVEY

SURVEY NO. DATE: / /

SITE SURVEYOR BLOCK

NO.

WATER

SOURCE (tick)

OMBROTROPHIC STREAM

FED

SPRING

FED

pH

CONDUCTIVITY

POOLSIZE DIAGRAME (inclu. Direction of flow)

M X M

VEGITATION

SPECIES % COVER

Sphagnums -

S. cuspidatum

S. denticulatum (articulatum)

S. fallax (recurvum)

S. palustre

S. papillosum

Rushes

Soft rush Juncus effuses (round stem, flowers half way down stem, indicates poor fen)

Jointed rush Juncus actiflorus (flatter stem, can feel joints, flowers at end, indicates proper fen-

higher nutrient level)

Bulbous rush Juncus bulbosus (small, fine & green, forms floating mats)

Sedges

37

Bog cotton grass Eriophorum angustifolium (broader leaf, red, acid bog, wet/ pool sp.)

Hares tail cotton grass Eirophorum vaginatum (finer leaved species)

Grasses

Total cover

Other species

Water crowfoot Ranunculus tripartitus

Pond weed potamogetons

Notes & observations:

38

Appendices 2: Survey site photographs

Survey 1

Survey 2

Survey 3

39

Survey 4

Survey 5

Survey 6

40

Survey 7

Survey 8

Survey 9

41

Survey 10

Survey 11

Survey 12

42

Survey 13

Survey 14

Survey 15

43

Survey 16

Survey 17

Survey 18

44

Survey 19

Survey 20

Survey 21

45

Survey 22

Survey 23

Survey 24

46

Survey 25

Survey 26

Survey 27

47

Survey 28

Survey 29

Survey 30

48

Appendices 3: Results.

Nitrates Results

Results Adjusted to over 0.05

Sample No.

Sum NO3 & NO2-N

NH4-N

mg/L mg/L

Sample No.

NO3 & NO2 mean NH4

Mean2

Total N

1a 0.083 0.250

1 0.083 0.105 0.250 0.238 0.343

1b 0.127 0.226

0.127

0.226 2a -0.005 0.179

2 0 0 0.179 0.363 0.363

2b -0.006 0.548

0

0.548 3a -0.006 0.095

3 0 0 0.095 0.875 0.875

3b -0.007 0.080

0

0.080 4a -0.004 0.068

4 0 0 0.068 0 0

4b -0.007 0.026

0

0.000

5a -0.006 -

0.032

5 0 0 0.000 0 0

5b -0.006 -

0.036

0

0.000

6a -0.008 -

0.030

6 0 0 0.000 0 0

6b -0.006 -

0.031

0

0.000

7a -0.006 -

0.017

7 0 0 0.000 0 0

7b -0.008 -

0.048

0

0.000 8a -0.007 0.159

8 0 0 0.000 0 0

8b -0.005 0.054

0

0.054 10a 0.011 0.002

10 0 0 0.000 0 0

10b 0.018 -

0.012

0

0.000

11a -0.008 -

0.027

11 0 0 0.000 0 0

11b -0.007 -

0.014

0

0.000

12a -0.007 -

0.035

12 0 0 0.000 0 0

12b 0.002 -

0.024

0

0.000

13a 0.019 -

0.022

13 0 0 0.000 0 0

13b -0.002 -

0.042

0

0.000 14a 0.010 -

14 0 0 0.000 0 0

49

0.025

14b 0.058 0.018

0.058

0.000

15a 0.158 -

0.038

15 0.158 0.158 0.000 0 0.158

15b 0.159 -

0.038

0.159

0.000

16a 0.109 -

0.038

16 0.109 0.156 0.000 0 0.156

16b 0.204 -

0.040

0.204

0.000

17a -0.006 -

0.047

17 0 0 0.000 0 0

17b -0.006 -

0.096

0

0.000

18a 0.270 -

0.043

18 0.27 0.367 0.000 0 0.367

18b 0.465 -

0.034

0.465

0.000

19a 0.485 -

0.031

19 0.485 0.327 0.000 0 0.327

19b 0.179 -

0.028

0.17

0.000

20a 0.178 -

0.042

20 0.178 0.218 0.000 0 0.218

20b 0.259 -

0.031

0.259

0.000

21a 0.225 -

0.030

21 0.225 0.217 0.000 0 0.217

21b 0.209 -

0.023

0.209

0.000

22a -0.004 -

0.052

22 0 0 0.000 0 0

22b 0.004 -

0.044

0

0.000 23a 0.001 0.002

23 0 0 0.000 0 0

23b 0.003 0.004

0

0.000 24a 0.001 0.041

24 0 0 0.000 0.07 0.07

24b 0.015 0.140

0

0.140

25a 0.020 -

0.026

25 0 0 0.000 0 0

25b 0.040 -

0.031

0

0.000 26a 0.020 0.006

26 0 0 0.000 0 0

26b 0.010 -

0.001

0

0.000

27a 0.001 -

0.027

27 0 0 0.000 0 0

50

27b 0.002 -

0.002

0

0.000 28a 0.003 0.005

28 0 0 0.000 0 0

28b 0.002 -

0.017

0

0.000

29a -0.003 -

0.015

29 0 0 0.000 0 0

29b 0.005 0.017

0

0.000 30a 0.005 0.011

30 0 0 0.000 0 0

30b 0.010 0.020

0

0.000

51

Appendices 4: Multiple regression analysis results.

1. Multiple Regression Analysis, - All data

n = 30 variable = 7

Var mean SD

1 Date 2004.5 14.9637

2 Peat Depth 0.6527 0.3632

3pH 5.4073 1.3035

4Conductivity 60.35 33.0971

5 Area 20.1927 44.9117

6 Nitrates 0.4365 1.8165

7 Cover 23.0333 9.9567

Correlation matrix

Date PD pH Con Area Nit Cover

1 -0.7031 0.0968 -0.1041 0.0617 0.0546 -0.0626

-0.7031 1 -0.0635 -0.0415 0.3533 -0.1053 -0.0952

0.0968 -0.0635 1 -0.129 0.0395 -0.7579 0.4109

-0.1041 -0.0415 -0.129 1 0.0006 -0.364 0.2655

0.0617 0.3533 0.0395 0.0006 1 -0.0472 -0.0614

0.0546 -0.1053 -0.7579 -0.364 -0.0472 1 -0.4469

-0.0626 -0.0952 0.4109 0.2655 -0.0614 -0.4469 1

Tables of output PCor = Partial correlation coefficient

PSReg = Partial standardised regression coefficient

PReg = Partial regression coefficient

SE = Standard error of partial regression coefficient

p = alpha (α) probability of Type I error of PReg

Stepwise removal of the least effective independent variable

Run 0

var PCor PSReg PReg SE t P

1Date -0.1695 -0.2407 -0.1601 0.1941 -0.8251 0.4182

2PD -0.1462 -0.2357 -6.4617 9.1198 -0.7085 0.4861

3pH 0.2193 0.429 3.2769 3.0405 1.0778 0.2928

4Con 0.1998 0.274 0.0824 0.0843 0.9777 0.3389

5Area 0.0173 0.0181 0.004 0.0483 0.083 0.9346

6Nit -0.016 -0.0329 -0.1803 2.3474 -0.0768 0.9395

Const = 325.5447

R = 0.363 R Sq = 0.1318

52

Analysis of variance

df SSq MSq F p

Reg 6 1043.6282 173.938 2.1845 0.0819

Res 23 1831.3385 79.6234

Tot 29 2874.9667

Smallest Partial Correlation Coefficient is from variable 6

Run 1

var PCor PSReg PReg SE t p

1Date -0.1703 -0.2357 -0.1568 0.1852 -0.8465 0.406

2PD -0.1534 -0.225 -6.1691 8.112 -0.7605 0.4547

3pH 0.4739 0.4564 3.4861 1.3221 2.6367 0.0147

4Con 0.3182 0.2905 0.0874 0.0532 1.6441 0.1138

5Area 0.0142 0.0145 0.0032 0.0461 0.0695 0.9452

Const = 317.2 R = 0.5825 R Sq = 0.3394


df SSq MSq F p

Reg 5 1674.7953 334.9591 6.6982 0.0005

Res 24 1200.1713 50.0071

Tot 29 2874.9667


Run 2 (Area removed)


1Date -0.1855 -0.2265 -0.1507 0.1596 -0.9439 0.3546

2PD -0.1761 -0.2133 -5.8487 6.5401 -0.8943 0.3801

3pH 0.4749 0.457 3.4908 1.2938 2.698 0.0126

4Con 0.3219 0.292 0.0878 0.0517 1.6999 0.1021

Const = 304.7225

R = 0.592 R Sq = 0.3504


df SSq MSq F p

Reg 4 1701.8897 425.4724 9.0674 0.0001

Res 25 1173.077 46.9231

Tot 29 2874.9667


Run 3 (PD removed)


53

1Date -0.086 -0.0741 -0.0493 0.1119 -0.4403 0.6635

3pH 0.4707 0.459 3.5061 1.2887 2.7206 0.0117

4Con 0.3455 0.317 0.0954 0.0508 1.8774 0.0722

Const = 97.0992

R = 0.5653 R Sq = 0.3196


df SSq MSq F p

Reg 3 1625.2785 541.7595 11.2714 <0.0001

Res 26 1249.6882 48.0649

Tot 29 2874.9667


Run 4 (Date removed)


3 0.4657 0.4527 3.4581 1.2648 2.7341 0.0111

4 0.3523 0.3239 0.0974 0.0498 1.956 0.0613

Const = -1.546

R = 0.5604 R Sq = 0.314


df SSq MSq F p

Reg 2 1611.0874 805.5437 17.2087 <0.0001

Res 27 1263.8792 46.8103

Tot 29 2874.9667


Run 5 (Conductivity removed)


3 0.4109 0.4109 3.139 1.316 2.3853 0.0243

Const = 6.0595

R = 0.4109 R Sq = 0.1689


df SSq MSq F p

Reg 1 1181.4657 1181.4657 19.5341 0.0001

Res 28 1693.5009 60.4822

Tot 29 2874.9667


2. Multiple Regression Analysis - Acid plants n = 30 varaible = 7

54

Var mean SD

1 2004.5 14.9637

2 0.6527 0.3632

3 5.4073 1.3035

4 60.35 33.0971

5 20.1927 44.9117

6 0.1031 0.1935

7 9.3667 6.2394

Correlation matrix

1 -0.7031 0.0968 -0.1041 0.0617 0.0958 -0.3817

-0.7031 1 -0.0635 -0.0415 0.3533 0.055 0.5791

0.0968 -0.0635 1 -0.129 0.0395 0.2774 -0.0212

-0.1041 -0.0415 -0.129 1 0.0006 -0.1675 0.3944

0.0617 0.3533 0.0395 0.0006 1 0.2752 0.2107

0.0958 0.055 0.2774 -0.1675 0.2752 1 -0.0171

-0.3817 0.5791 -0.0212 0.3944 0.2107 -0.0171 1







Run 0

var PCor PSReg PReg SE t P

1date 0.1886 0.2141 0.0893 0.0969 0.9212 0.3669

2PD 0.5511 0.7819 13.4339 4.2411 3.1676 0.0045

3pH 0.0976 0.0703 0.3365 0.7153 0.4704 0.6427

4con 0.5401 0.458 0.0863 0.0281 3.0778 0.0055

5area -0.0949 -0.0815 -0.0113 0.0248 -0.4572 0.652

6nit -0.0013 -0.001 -0.0312 5.0318 -0.0062 0.9951

Const = -185.1202

R = 0.7302 R Sq = 0.5332


df SSq MSq F p

Reg 6 824.3545 137.3924 10.3739 <0.0001

Res 23 304.6122 13.244

55

Tot 29 1128.9667


Run 1

Var PCor PSReg PReg SE t p

1date 0.1887 0.214 0.0892 0.0948 0.9413 0.3563

2PD 0.5511 0.7819 13.4338 4.1518 3.2357 0.0037

3Ph 0.1006 0.0701 0.3353 0.6767 0.4956 0.6249

4con 0.5439 0.4581 0.0864 0.0272 3.1749 0.0042

5area -0.0978 -0.0818 -0.0114 0.0236 -0.4812 0.6349

Const = -185.0805

R = 0.7324 R Sq = 0.5364


df SSq MSq F p

Reg 5 826.8413 165.3683 13.1364 <0.0001

Res 24 302.1254 12.5886

Tot 29 1128.9667


Run 2


1date 0.1623 0.1619 0.0675 0.0821 0.8226 0.4188

2PD 0.5903 0.7158 12.2983 3.363 3.6569 0.0012

3pH 0.0953 0.0666 0.3186 0.6653 0.4789 0.6364

4cond 0.5377 0.4495 0.0847 0.0266 3.1888 0.0039

Const = -140.8543

R = 0.7438 R Sq = 0.5532


df SSq MSq F p

Reg 4 839.6773 209.9193 18.1409 <0.0001

Res 25 289.2894 11.5716

Tot 29 1128.9667


Run 3


1date 0.1664 0.1667 0.0695 0.0808 0.8605 0.3977

2PD 0.588 0.7146 12.277 3.3125 3.7063 0.001

4Con 0.5318 0.4413 0.0832 0.026 3.2019 0.0037

56

Const = -142.9752

R = 0.7374 R Sq = 0.5437


df SSq MSq F p

Reg 3 832.4699 277.49 24.3333 <0.0001

Res 26 296.4968 11.4037

Tot 29 1128.9667


Run 4


2PD 0.6485 0.5965 10.2476 2.315 4.4267 0.0002

4Con 0.5136 0.4191 0.079 0.0254 3.1104 0.0045

Const = -2.0897

R = 0.7572 R Sq = 0.5734


df SSq MSq F p

Reg 2 854.8891 427.4446 42.1085 <0.0001

Res 27 274.0776 10.151

Tot 29 1128.9667


Run 5

var PCor PSReg PReg SE T p

2PD 0.5791 0.5791 9.949 2.6471 3.7584 0.0008

Const = 2.8733

R = 0.5791 R Sq = 0.3353


df SSq MSq F p

Reg 1 653.7483 653.7483 38.519 <0.0001

Res 28 475.2184 16.9721

Tot 29 1128.9667


3. Multiple Regression Analysis- Minerotrophic plants. n = 30 variable = 7

57

Var mean SD

1 2004.5 14.9637

2 0.6527 0.3632

3 5.4073 1.3035

4 60.35 33.0971

5 20.1927 44.9117

6 0.3031 1.0931

7 11.2333 8.0545

Correlation matrix

1 -0.7031 0.0968 -0.1041 0.0617 0.0612 0.1466

-0.7031 1 -0.0635 -0.0415 0.3533 -0.1011 -0.4218

0.0968 -0.0635 1 -0.129 0.0395 -0.7361 0.5046

-0.1041 -0.0415 -0.129 1 0.0006 -0.3748 -0.0519

0.0617 0.3533 0.0395 0.0006 1 -0.0276 -0.1768

0.0612 -0.1011 -0.7361 -0.3748 -0.0276 1 -0.2606

0.1466 -0.4218 0.5046 -0.0519 -0.1768 -0.2606 1







Run 0


1Date -0.308 -0.3886 -0.2092 0.1347 -1.5527 0.1348

2PD -0.4368 -0.6594 -14.6242 6.2804 -2.3286 0.0295

3pH 0.3768 0.6213 3.839 1.9677 1.951 0.0639

4con 0.0194 0.0218 0.0053 0.057 0.093 0.9268

5area 0.0663 0.06 0.0108 0.0338 0.3185 0.7531

6nit 0.0987 0.1636 1.2058 2.5347 0.4757 0.639

Const = 418.4166

R = 0.617 R Sq = 0.3806


df SSq MSq F p

58

Reg 6 1160.7336 193.4556 6.1744 0.0006

Res 23 720.633 31.3319

Tot 29 1881.3667


Run 1


1date -0.3257 -0.3954 -0.2128 0.1261 -1.6876 0.105

2PD -0.4774 -0.6704 -14.8682 5.5868 -2.6613 0.0139

3pH 0.4805 0.6007 3.7115 1.3828 2.6841 0.0132

5con 0.0736 0.0645 0.0116 0.032 0.3616 0.721

6Area 0.1264 0.1397 1.0296 1.649 0.6244 0.5385

Const = 426.9773

R = 0.6931 R Sq = 0.4805


df SSq MSq F p

Reg 5 1304.0612 260.8122 10.8426 <0.0001

Res 24 577.3054 24.0544

Tot 29 1881.3667


Run 2


1Date -0.3294 -0.3564 -0.1918 0.11 -1.7443 0.0939

2PD -0.5175 -0.6192 -13.7337 4.5412 -3.0242 0.0059

3pH 0.4842 0.6067 3.7486 1.3548 2.767 0.0107

6nit 0.1312 0.1451 1.0695 1.6164 0.6617 0.5145

Const = 384.1457

R = 0.7134 R Sq = 0.5089


df SSq MSq F p

Reg 4 1342.1382 335.5345 15.5562 <0.0001

Res 25 539.2285 21.5691

Tot 29 1881.3667


Run 3


59

1date -0.3205 -0.348 -0.1873 0.1086 -1.7253 0.0968

2PD -0.5263 -0.6349 -14.0813 4.4617 -3.156 0.0041

3pH 0.5621 0.498 3.0773 0.888 3.4653 0.0019

Const = 379.277

R = 0.7458 R Sq = 0.5562


df SSq MSq F p

Reg 3 1403.1332 467.7111 25.4279 <0.0001

Res 26 478.2334 18.3936

Tot 29 1881.3667


Run 4


2PD -0.4524 -0.3913 -8.6796 3.2931 -2.6357 0.014

3pH 0.5281 0.4798 2.9646 0.9175 3.2313 0.0033

Const = 0.8677

R = 0.653 R Sq = 0.4264


df SSq MSq F p

Reg 2 1228.5768 614.2884 25.4075 <0.0001

Res 27 652.7899 24.1774

Tot 29 1881.3667


Run 5


3pH 0.5046 0.5046 3.118 1.0082 3.0928 0.0046

Const = -5.627

R = 0.5046 R Sq = 0.2546


df SSq MSq F p

Reg 1 949.3545 949.3545 28.521 <0.0001

Res 28 932.0122 33.2861

Tot 29 1881.3667


4. Multiple Regression Analysis – NVC

60

n = 30 varaible = 7

Var mean SD

1 1876.0333 503.7147

2 0.546 0.296

3 5.081 1.894

4 55.4667 36.1186

5 19.7427 45.0726

6 0.3031 1.0931

7 14.4667 9.475

Correlation matrix

1 0.5014 0.729 0.4175 0.1185 0.075 0.4148

0.5014 1 0.3761 0.0782 0.5621 -0.0207 0.2983

0.729 0.3761 1 0.2306 0.1078 -0.4572 0.4111

0.4175 0.0782 0.2306 1 0.0596 -0.3047 0.5217

0.1185 0.5621 0.1078 0.0596 1 -0.0247 0.085

0.075 -0.0207 -0.4572 -0.3047 -0.0247 1 -0.2811

0.4148 0.2983 0.4111 0.5217 0.085 -0.2811 1







Run 0

Variable PCor PSReg PReg SE t p

1 Date -0.1188 -0.3111 -0.0059 0.0102 -0.5739 0.5719

2 Peat depth 0.2535 0.3148 10.0774 8.0179 1.2569 0.222

3 pH 0.1929 0.4626 2.3143 2.4541 0.943 0.3559

4 Conductivity 0.3998 0.5679 0.149 0.0712 2.0918 0.0482

5 Area -0.1356 -0.1356 -0.0285 0.0434 -0.6564 0.5184

6Nitrates 0.0742 0.1299 1.1263 3.1561 0.3569 0.7246

Const = 0.1422 R = 0.5211 R Sq = 0.2715


df SSq MSq F p

61

Reg 6 1356.6502 226.1084 4.171 0.0056

Res 23 1246.8164 54.2094

Tot 29 2603.4667


Run 1


1 -0.1067 -0.1461 -0.0027 0.0052 -0.5259 0.604

2 0.2436 0.284 9.0907 7.3881 1.2305 0.231

3 0.2618 0.3094 1.5478 1.1649 1.3286 0.197

4 0.4916 0.4963 0.1302 0.0471 2.7653 0.011

5 -0.1228 -0.1203 -0.0253 0.0417 -0.6062 0.5503

Const = 0.0734

R = 0.5941 R Sq = 0.353


df SSq MSq F p

Reg 5 1546.7904 309.3581 7.0264 0.0004

Res 24 1056.6762 44.0282

Tot 29 2603.4667


Run 2


2 0.2203 0.2305 7.3785 6.5354 1.129 0.2701

3 0.2551 0.2296 1.1486 0.8708 1.3189 0.1996

4 0.4948 0.4565 0.1197 0.0421 2.8469 0.0089

5 -0.1009 -0.0965 -0.0203 0.04 -0.5071 0.6167

Const = -1.639

R = 0.5788 R Sq = 0.335


df SSq MSq F p

Reg 4 1506.964 376.741 8.5896 0.0002

Res 25 1096.5027 43.8601

Tot 29 2603.4667


Run 3


62

2 0.1992 0.1718 5.4995 5.306 1.0365 0.3099

3 0.2694 0.2422 1.2116 0.8495 1.4263 0.1662

4 0.49 0.4524 0.1187 0.0414 2.8663 0.0083

Const = -1.2749

R = 0.5685 R Sq = 0.3232


df SSq MSq F p

Reg 3 1480.1226 493.3742 11.4192 <0.0001

Res 26 1123.3441 43.2055

Tot 29 2603.4667


Run 4


3 0.3503 0.3072 1.5366 0.7906 1.9435 0.0628

4 0.4812 0.4508 0.1183 0.0415 2.8527 0.0084

Const = 0.099

R = 0.5709 R Sq = 0.3259


df SSq MSq F p

Reg 2 1486.2762 743.1381 17.96 <0.0001

Res 27 1117.1905 41.3774

Tot 29 2603.4667


Run 5


4 0.5217 0.5217 0.1369 0.0423 3.2357 0.0032

Const = 6.8758

R = 0.5217 R Sq = 0.2722


df SSq MSq F p

Reg 1 1358.2004 1358.2004 30.5393 <0.0001

Res 28 1245.2662 44.4738

Tot 29 2603.4667


63

Appendices 5: Plant categories species lists.

Agricultural Grassland Wood land Dry heath and acid

grassland notes:

Meso/ Minerotrophic mire Quadrat

Acid mire / wet heath

Calluna vulgaris Ling

Cardamine pratensis Cuckoo Flower

Cerastium fontanum Common Mouse-ear

Cirsium palustre Marsh Thistle

Drosera rotundifolia Sundew

Epilobium palustre Marsh Willowherb

Erica tetralix Cross-leaved Heath

Galium palustre Marsh Bedstraw

Galium saxatile Heath Bedstraw

Montia fontana Blinks

Narthecium ossifragum Bog Asphodel

Pedicularis sylvatica Lousewort

Plantago lanceolata Ribwort Plantain

Polygala serpyllifolia Milkwort

Potentilla erecta Tormentil

Potamogeton sp. Pondweed

Prunella vulgaris Selfheal

Ranunculus acris Meadow Buttercup

Ranunculus batrachium Water-crowfoot

Ranunculus flammula Lesser Spearwort

Ranunculus repens Creeping Buttercup

Rumex acetosa Common Sorrel

Sculletaria minor Lesser Skullcap

Stellaria ulignosa (alsine) Bog Stitchwort

Succisa pratensis Devil's Bit Scabious

Taraxacum officinale Dandelion

Trifolium repens White Clover

Vaccinium myrtillus Whortleberry

V. oxycoccus Cranberry

Viola palustris Marsh Violet

Wahlenbergia hederacea Ivy-leaved Bell-flower

Agrostis spp. Bent Grasses

Anthoxanthum odoratum Sweet Vernal

Cynosurus cristatus Crested Dog's-tail

Deschampsia flexuosa Wavy-hair grass

Festuca spp Fescue

Glyceria fluitans Floating sweet-grass

Holcus lanatus Yorkshire Fog

Holcus mollis Creeping Soft Grass

Molinia caerulea Purple Moor Grass

Nardus stricta Matt Grass

Poa annua Annual meadow grass

Poa trivialis L. Rough meadow grass

64

Trichophorum cespitosum Deer Grass

Juncus acutiflorus Sharp-flowered Rush

Juncus bulbosus Bulbous Rush

Juncus effusus Soft Rush

Juncus squarrosus Heath Rush

Luzula multiflora Heath Woodrush

Carex binervis Green-ribbed Sedge

Carex demissa Commom yellow sedge

Carex echinata Star Sedge

Carex flacca Glaucous Sedge

Carex flava Yellow Sedge

Carex nigra Common Sedge

Carex ovalis Oval Sedge

Carex panicea Carnation Sedge

Carex pulicaris Flea Sedge

Carex sp. Unknown Sedge

Eriophorum angustifolium Bog Cotton-grass

Eriophorum vaginatum Hare's tail

Aulacomnium palustre

Bryum pseudotriquetam Calliergonella cuspidatum Calliergonella stramineum Campylopus introflexus

Campylopus paradoxus Campylopus spp Dicranella heteromalla Dicranium scoparium

Hylocomium splendens Hypnum cupressiforme Isopterygium elegans Mnium hornum

Pleurozium schreberi Polytrichum alpestre Polytrichum commune Polytrichum formosum

Pseudoscleropodium purum Racomitrium lanuginosum Rhytidiadelphus squarrosus Rhytidiadelphus loreus

Thuiidium tamariscinum

Sphagnum acutifolia spp Sphagnum augustifolium

Sphagnum denticulatum Ex- Sphagnum auriculatum

Sphagnum capillifolium Sphagnum cuspidatum Sphagnum fimbriatum

Sphagnum molle Sphagnum palustre Sphagnum papillosum Sphagnum fallax Ex- Sphagnum recurvum

65

Sphagnum subsecundum Sphagnum subnitens

Sphagnum tenellum Sphagnum unknown

Liverwort Cladonia sp.(squammules)

Oreopteris limbosperma Mountain Fern

Blechnum spicant Hard Fern

Salix sp Willow sp

Open Water

Bare Peat

66

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