CareyDissertation2014-HardbackPrint

Steven Carey 486940
Dissertation Tutor – Prof Tim Stott 6031OUTDOR
The Impact of Weather on
Red Squirrel (Sciurus
vulgaris) Activity at Formby
Point
By Steven Carey
Dissertation Tutor: Prof Tim Stott
Liverpool John Moores University
Outdoor Education BSc (Hons)
2014

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The Impact of Weather on Red Squirrel (Sciurus vulgaris) Activity at Formby Point.
By Steven Carey.
Submitted in partial requirement for the award of BSc (Hons) in Outdoor Education.
21st
March 2014

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Acknowledgements
The Author would like to thank various people who have contributed to the success of this
dissertation, without their help this project would not have been made possible.
I would like to firstly thank my supervisor, Professor Tim Stott of Liverpool John Moores
University, for providing me with the support, guidance and wisdom for this project.
I would like to thank Andrew Brockbank, the Head Ranger of National Trust Formby, for
granting the permission to gather red squirrel and meteorological data at the National Trust
Formby site.
I would like to thank Rachel Miler of the Lancashire Wildlife Trust; Rachel granted me
access to historical data of squirrel activity at the National Trust Formby site and shared the
practical field methodology used to gather this data.
I would also like to thank Gerald Rice of Liverpool John Moores University for proof reading
this dissertation and for providing academic support.
Abstract
The red squirrel (Sciurus vulgaris) is a threatened species that lives within the coniferous tree
canopy at Formby Point. The impact of weather on red squirrel activity is relatively unknown
and is a concern as activity is required for the squirrels’ survival. Amid the high activity
mornings in Spring and Autumn, the effects of weather on red squirrel activity were studied
at the National Trust Formby site. The Lancashire Wildlife Trust supplied activity data from
Spring 2010 to Spring 2013, while 12, 1000 metre distance-time transects were conducted in
November 2013. Meteorological data from Spring 2010 to Autumn 2013 were compared with
squirrel activity.
Synthesising the November 2013 meteorological and activity data suggested that mean wind
speed, gust speed, wind direction, temperature, wind-chill, dew point and cloud cover, all
have a significant impact on red squirrel activity. The most significant weather element was
found to be mean wind speed; activity was observed to be 0 during wind speeds of 1.2 m/s,
contrasting to the activity measure ranging from 9 to 22 during days of negligible wind.
There was not enough precipitation during the 12 surveys to provide sufficient evidence to
suggest whether or not rain impacts upon activity. It was finally discovered that pressure,
humidity and visibility were not considered to impact on red squirrel activity. Other

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influences which could affect red squirrel activity in the future, such as global warming and
coastal erosion, were also discussed, as it is likely that they are alternative determinants on
red squirrel activity and morality.

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List of Figures
Figure 1: Squirrel activity per hour between Civil Twilight hours (Tonkin, 1983, p. 102). .....6
Figure 2: The location of The National Trust Formby site (Ordnance Survey, 2013). .............9
Figure 3: The location of The National Trust Formby site (Ordnance Survey, 2013). .............9
Figure 4: The effects of the poxvirus in 2008 (Miller, 2013, p.1). ..........................................10
Figure 5: Map displaying the route of transects 1 and 2, adapted from (Ordnance Survey,
2013). .......................................................................................................................................11
Figure 6: Height of weather station devices. ...........................................................................15
Figure 7: Weather Station distance from the trees...................................................................16
Figure 8: Wind profile in a forest canopy (Crockford and Hui, 2007, p. 6)............................17
Figure 9: Calculating tree height (Calkins and Yule, 1927, p.16). ..........................................18
Figure 10: Monoculture woodland at Formby Point................................................................20
Figure 11: The distance from Crosby Weather Station to Formby Point (Ordnance Survey,
2013). .......................................................................................................................................25
Figure 12: Relationship between red squirrel activity and wind speed. ..................................33
Figure 13: Relationship between the average wind speed for the day at Crosby and red
squirrel activity. .......................................................................................................................33
Figure 14: Relationship between red squirrel activity and wind speed at a 2 metre profile and
a 21 metre profile within the tree canopy. ...............................................................................35
Figure 15: Relationship between red squirrel activity and gust speed.....................................36
Figure 16: Relationship between the mean gust speed for the day at Crosby and red squirrel
activity......................................................................................................................................37
Figure 17: Transect 1 rose diagram. ........................................................................................39
Figure 18: Transect 2 rose diagram. ........................................................................................40
Figure 19: Relationship between red squirrel activity and temperature. .................................41
Figure 20: Relationship between the average temperature for the day at Crosby and red
Figure 21: Relationship between red squirrel activity and temperature, during negligible wind
speeds.......................................................................................................................................42
Figure 22: Relationship between red squirrel activity and wind-chill.....................................44
Figure 23: Relationship between the average wind-chill, for the day, at Crosby and red
Figure 24: Relationship between red squirrel activity and cloud cover...................................46
Figure 25: Relationship between red squirrel activity and dew point. ....................................47

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Table 1: Behaviour during periods of activity, adapted from (Tonkin, 1983)...........................5
Table 2: Comparison of Survey Methods (Gurnell et al. 2009, p4). .......................................12
Table 3: Visual count assumptions, adapted from (Gurnell et al. 2007). ................................12
Table 4: Visual count limitations, adapted from (Gurnell et al. 2004)....................................12
Table 5: Okta classification, adapted from (The Met Office, 2010)........................................14
Table 6: instrumentation list. ...................................................................................................21
Table 7: Collection Type and Permission................................................................................26
Table 8: Critical values for Pearson correlation coefficient (Weathingtion, Cunningham and
Pittenger, 2012, p. 219)............................................................................................................27
Table 9: Critical values for Spearman’s rank correlation coefficient (Rees, 1995, p. 248).....28
Table 10: Primary red squirrel activity synthesised with meteorological data, which produced
a significant correlation............................................................................................................31
Table 11: Primary red squirrel data compared with Crosby weather station...........................32
Table 12: Values used to calculate the wind speed at a profile of 21 metres within the tree
canopy......................................................................................................................................34
Table 13: Activity for given wind direction ............................................................................38
Table 14: Temperature, where wind speed is considered to be negligible. .............................42
Table 15: The transects where fine precipitation was observed. .............................................48
Table 16: The primary data, which produced no significant correlation between weather and
activity......................................................................................................................................50
Table 17: Displaying the secondary data, which produced no significant correlation. ...........51
Table 18: A map showing the 1 kilometre distance from the foredune to the far edge of the
woodland (Ordnance Survey, 2013). .......................................................................................53

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List of Nomenclatures
𝐴 𝑡 = Vertical cross-sectional area of a tree m2
.
𝐶 𝑑 = Drag Coefficient of a Corsican pine (Pinus nigra ssp. laricio) ≅ 0.32 (Mayhead, 1973).
𝑑 = Zero-plane displacement height m.
𝑑𝑓 = Degrees of Freedom.
𝐹 = Force Kg.
ℎ = Height of the tree canopy.
𝑘 = Von Karman’s constant ≅ 0.41 (Vachon and Prairie, 2013).
𝑛 = Sample size.
𝜌 = Density of Air ≅ 1.25 kg m-3
.
𝜌 𝑤 = Wind pressure.
𝜎 = Standard Deviation.
r = Pearson Correlation Coefficient.
T = Temperature °C.
𝜏 𝑡 = Sheer stress of the tree.
𝑢(𝑧) = Wind Speed at given height(𝑧).
𝑢∗ = Friction Velocity.
𝑉 = Wind Speed ms-1
.
WC = Wind-chill °C.
𝑥 and 𝑦 = Respective data values.
𝑥𝑖 = Data values.
𝑧 = Given height in tree canopy.
𝑧0 = Roughness length m.

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Contents
Acknowledgements................................................................................................................... II
Abstract.....................................................................................................................................II
List of Figures..........................................................................................................................IV
List of Nomenclatures..............................................................................................................VI
1.0 Introduction..........................................................................................................................1
1.1 Status: A Threatened Species...........................................................................................1
1.2 Habitat and Dietary Requirements...................................................................................1
1.3 Red Squirrel Activity.......................................................................................................1
1.4 The Reason for Population Decline .................................................................................1
1.4.1 Disease......................................................................................................................1
1.4.2 Interference of Grey Squirrels upon the Red population..........................................2
1.4.3 Environmental Change and Forest Defragmentation................................................2
1.4.4 Competition for Resources .......................................................................................3
1.5 Scientific Justification......................................................................................................3
2.0 Aim Objectives and Hypothesis...........................................................................................4
2.1 Aim ..................................................................................................................................4
2.2 Objectives ........................................................................................................................4
2.3 Null-Hypothesis...............................................................................................................4
3.0 Literature Review.................................................................................................................4
3.1 Introduction......................................................................................................................4
3.2 Key Literature Discussing the Impacts of Weather upon Activity..................................4
3.3 Key Literature on Red Squirrel Field Surveys.................................................................7
3.4 Key Literature Discussing Meteorological data Gathering .............................................7
3.5 Key Literature Discussing Wind Equations.....................................................................8
4.0 Site Description....................................................................................................................8
4.1 Previous research at Formby Point ................................................................................10
5.0 Methodology......................................................................................................................10
5.1 Squirrel Activity.............................................................................................................10
5.1.1 Primary Squirrel Data .............................................................................................10
5.1.2 Secondary Squirrel Data .........................................................................................13
5.2 Meteorological Data.......................................................................................................13
5.2.1 Primary Meteorological Data..................................................................................13
5.2.2 Instrumentation List................................................................................................21
5.2.3 Secondary Meteorological Data..............................................................................24

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5.3 Permission to use Primary and Secondary Data ............................................................26
5.4 Statistical Analysis.........................................................................................................26
5.4.1 Pearson Correlation Coefficient (r).........................................................................26
5.3.2 Spearman’s Rank Correlation Coefficient (𝒓𝒔)......................................................28
5.3.3 Population Standard Deviation (𝝈) ........................................................................29
6.0 Results and Discussion ......................................................................................................30
6.1 Introduction....................................................................................................................30
6.2 Significant Results .........................................................................................................31
6.3 Wind Speed....................................................................................................................33
6.4 Gust Speed .....................................................................................................................36
6.5 Wind Direction...............................................................................................................38
6.6 Temperature...................................................................................................................41
6.7 Wind-chill ......................................................................................................................44
6.8 Cloud Cover...................................................................................................................46
6.9 Dew Point.......................................................................................................................47
6.10 Precipitation.................................................................................................................48
6.11 Pressure........................................................................................................................49
6.12 Humidity ......................................................................................................................49
6.13 Visibility ......................................................................................................................49
6.14 Future Predictions of Weather and its Impact on Red Squirrel Activity.....................52
7.0 Conclusion .........................................................................................................................54
7.1 Further Research ............................................................................................................55
8.0 References..........................................................................................................................56
9.0 Appendices.........................................................................................................................63
Appendix 1, Primary Squirrel Data .....................................................................................63
Appendix 2, Secondary Squirrel Data .................................................................................86
Appendix 3, Primary Meteorological Data..........................................................................87
Appendix 4, Secondary Meteorological Data......................................................................99
Appendix 5, Tree Angle Data from 15m away..................................................................100
Appendix 6, Risk Assessment............................................................................................101

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1.0 Introduction
1.1 Status: A Threatened Species
Rice-Oxley (2001) notes that red squirrels (Sciurus vulgaris) are fully protected under the
Wildlife and Countryside Act 1981(Great Britain). Natural England (2011) considers the red
squirrel to be a threatened species and believes that if no action is taken mainland red
squirrels will become extinct within the next 20 to 30 years.
1.2 Habitat and Dietary Requirements
Red squirrels mostly forage and live within coniferous trees where gnawed cone axes are
often scattered on the forest floor (Bang and Dahlstrøm, 2006). Red squirrels are primarily
seed eaters although they have been known to consume buds, fungi, berries, bird’s eggs and
even tree sap (Rice-Oxley, 2001). Oddie et al. (2005) suggests that the best time to observe
red squirrels is during the autumn while they gather and bury food to survive the non-
hibernating winter.
1.3 Red Squirrel Activity
Activity is important for red squirrels as it allows them to meet their dietary requirements. It
has been discovered by Tonkin (1983) that red squirrel activity often influences foraging time
which has an impact upon the squirrel’s body weight. Red squirrels often lose body weight
during winter; this is considered true even when foraging time is experienced (Tonkin, 1983).
If red squirrels fail to expose themselves to enough foraging time they may not acquire
enough energy to survive the winter season.
1.4 The Reason for Population Decline
A number of hypotheses have been suggested that attempt to explain why the native red
squirrel population has declined. These hypotheses include: disease, environmental change
and forest defragmentation, interference by grey squirrels upon the red population and
competition for resources (Skelcher, 1997).
1.4.1 Disease
Keymer (1983) discovered that the parapox virus and coccidiosis (Eimeria spp. Infection)
causes death in red squirrels at a more frequent rate than in grey squirrels. The parapox virus
was not recorded in Britain until 1994 but was previously reported in North America in grey
squirrels (Sainsbury and Gurnell, 1995; Duff, Scott and Keymer, 1996). It is a unanimous

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opinion that grey squirrels carry the virus, infecting red squirrels (Sainsbury et al. 2000), and
therefore grey squirrels hold a degree of responsibility for the population decline.
1.4.2 Interference of Grey Squirrels upon the Red population
As well as carrying the parapox virus grey squirrels could be interfering with reds. Skelcher
(1997) and Wauters and Gurnell (1999) suggest that there are three methods of interference
discussed in the literature: the first is that grey squirrels are being aggressive towards red
squirrels, another is that red squirrels often avoid grey squirrels and the final view is that grey
squirrels affect the mating behaviour of red squirrels.
In early research, Middleton (1930) and Shorten (1954) observed aggressive behaviour in
cohabitation where grey squirrels would chase and kill red squirrels. However observations
of squirrels at feeders at Formby Point showed that red squirrels avoid greys by dispersing
during the presence of grey squirrels and that interspecific aggression is often rare (Lello and
Shuttleworth, 1998). More recently it has been accepted that interspecific aggression is non-
existent or at least less common than intraspecific aggression (Gurnell, 1987; Gurnell et al.
2004).
Skelcher (1997) suggests that the possibility of grey squirrels interfering in courtship is
unlikely. However, interactions have been observed in cohabitation during mating chases,
which could disrupt opportunities for the red squirrel to reproduce (Bertram and Moltu,
1987). It has more recently been observed that interspecific interactions between male and
female species were none aggressive and that same sex interactions were aggressive
suggesting that a sexual interspecific interaction could be present (Wauters and Gurnell,
1999).
1.4.3 Environmental Change and Forest Defragmentation
Forest defragmentation is often caused by anthropogenic activity, separating the forest habitat
into smaller patches (Skelcher, 1997; Wauters, 1997). Fragmented populations of red
squirrels experience higher chances of inbreeding and genetic drift due to a reduced
population (Trizio et al. 2005). Squirrels found to be in a fragmented habitat often disperse
over a great distance and live within multinuclear home ranges, they often move from one
location to another, causing increased exposure to predation (Wauters, 1997; Verbeylen,
Bruyn and Matthysen, 2003).
Seeds produced by the woodland environment are the most important food source for
squirrels (Moller, 1983; Gurnell, 1983; Holm, 1987; Wauters and Dhondt, 1987). Red

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squirrels thrive in coniferous woodland as their physiology enables them to access seed on
fine twigs, proportionally they have longer legs and are therefore are more agile than grey
squirrels (Holm, 1987). It is generally accepted that the harvesting of such coniferous
woodland in Scotland, during the 1800s, contributed to the decline in population (Ritchie,
1920; Shorten, 1954; Gurnell, 1987).
1.4.4 Competition for Resources
Despite red squirrels having the advantage of agility in coniferous woodland, grey squirrels
have a physiological advantage which provides them with a higher energy carrying capacity.
Grey squirrels are often more than twice the red squirrels body weight, where grey squirrels
can increase their fat reserves each autumn by approximately twice as much as the red
squirrel, providing them with more winter fat (Kenward and Tonkin, 1983). These
advantages allow greys to reproduce in years where reds do not and consequently greys are
more likely to increase in population than reds (Skelcher, 1997).
1.5 Scientific Justification
Formby Point is one of the few places in England that is not fully colonised by grey squirrels
(Oddie et al. 2005). The red squirrels at this site could be considered vulnerable for particular
reasons. During an unstructured observational pilot survey it was recognised that red squirrel
activity appeared low when wind speed was high. It has also been observed that squirrel
species can display alterations in body weight due to the effects of weather (Short and Duke,
1971; Wauters and Dhondt, 1989). As the site is on the coast it often receives adverse
weather conditions, this is especially true for wind which is considered to occur due to the
change in pressure between the land and sea (Barry and Chorley, 2009). Adverse weather
could be reducing activity; where activity is often considered to be of high importance for the
red squirrels body weight, enabling it to survive through the winter (Tonkin, 1983). Brownsea
Island and The Isle of White are also coastal environments which support red squirrels;
however, Formby Point is one of the most Southerly environments on the United Kingdom
mainland which supports the red squirrel. As Formby Point is connected to the mainland the
grey squirrel could have a higher chance of colonisation, it is therefore reasonable to suggest
that the red squirrel could be more vulnerable to the parapox virus. Due to the vulnerability of
the red squirrel and its elated vulnerability according to geographical location; this project
will investigate the impact of weather on red squirrel activity at Formby Point.

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2.0 Aim Objectives and Hypothesis
2.1 Aim
To discover if there is a relationship between weather and red squirrel activity at Formby
Point.
2.2 Objectives
1. Investigate red squirrel activity at Formby Point in a range of weather conditions.
2. Analyse the weather at the time of investigation of the red squirrel activity at Formby
Point.
3. Synthesise red squirrel activity and meteorological data in order to investigate if
weather impacts on squirrel activity.
2.3 Null-Hypothesis
Red squirrel activity will not be affected by weather conditions.
3.0 Literature Review
3.1 Introduction
There is a paucity of academic literature surrounding the impact that weather may have upon
red squirrel activity. The purpose of this literature review is to provide a platform to help
construct a coherent discussion that remains central to the main subject and attempts to
answer all relevant questions. The relevant impact of weather on red squirrels that has been
outlined within contemporary literature will be discussed along with relevant field surveys
and meteorological methodologies.
3.2 Key Literature Discussing the Impacts of Weather upon Activity
The Lancashire Wildlife Trust (2013) produced an annual report which has contributed
towards ideas for the methodology of this project; it mentions the impact of weather on red
squirrel activity in the Sefton Coastal area where Formby Point is situated. Visual transects
and hair tube surveys were used to survey the population and calculate the winter survival by
comparing data from Autumn and Spring. However, there are several omissions in The
Lancashire Wildlife Trust (2013) report, the reference to weather affecting squirrel activity is
an assumption that cold weather may have affected the results, although no meteorological
data was collected or compared to squirrel activity.

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Less recent but contemporary literature discusses the impact of weather on red squirrels in
more detail. Tonkin (1983) and Pulliainen and Jussila (1995) focus on different aspects of
weather and how aspects impact upon squirrel activity. Tonkin (1983) discusses the impact of
cloud cover, temperature, rainfall, wind speed, pressure and snowfall and came to the
conclusion that cloud cover, snowstorms, high winds and pressure affect red squirrel activity.
However these observations were contradictory to the results of Pulliainen and Jussila (1995)
who stated that wind and cloud cover did not affect activity; this contradiction could be due
to alternative disturbances or combined meteorological elements affecting squirrel activity.
Pulliainen and Jussila (1995) include no meteorological values within their study and
therefore the statement which suggested that weather had no impact could have been an
assumption. However, it is unanimous between both Tonkin (1983) and Pulliainen and Jussila
(1995) that temperature has little effect on squirrel activity. Tonkin (1983) included more
weather elements; however there are omissions in the study, humidity and visibility were not
measured and could prove to have a profound effect upon activity. Wind direction also went
unmeasured, where it has been observed by Tittensor (1970) that wind direction may have a
direct effect upon squirrel activity. Tonkin (1983) conducted the study in a single woodland
area where further research in a number of locations could have provided different results or
could have strengthened Tonkin’s argument.
Tonkin (1983) assigned a category of behaviour while carrying out squirrel field surveys
which contributed to ideas for the methodology of this research project (see Table 1), another
contribution was the mean level of activity per month during each hour between central
twilight hours, as this provided the basis for the time that the study should take place (see
Figure 1, p14).
Table 1: Behaviour during periods of activity, adapted from (Tonkin, 1983).
Category Explanation
Foraging Includes, searching, handling and food
storage.
Travelling Excludes foraging and social behaviour and
involves the squirrel in the process of moving
from one location to another.
Exclusive interactions Communications between squirrels.
Grooming
Dozing Squirrel sleeping outside of their drey.
Other Any other observed behaviour.

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Figure 1: Squirrel activity per hour between Civil Twilight hours (Tonkin, 1983, p. 102).

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3.3 Key Literature on Red Squirrel Field Surveys
Various literature sources such as Gurnell and Pepper (1994), Gurnell et al. (2009), Gurnell
et al. (2007) and Gurnell et al. (2004) discuss research field methodologies for red squirrel
observations, all of which evaluate methods of monitoring red squirrels in Britain, the articles
unanimously include drey counts, feeding transects, hair tube surveys and visual surveys
which appear to be commonly accepted methods used to monitor red squirrels. Each of these
literature sources have provided ideas for the methodology used in this research project, these
ideas could lead to evidence that provides coherent support for the discussion and essentially
answer the main question. Gurnell et al. (2004) evaluates the accuracy and precision of each
methodology based upon relative accuracy and precision of previous studies. Gurnell and
Pepper (1994) discuss a broader range of squirrel research methodologies including radio-
tracking, trapping and the observation of nest boxes and proposed that unsuitable weather
such as heavy rain, strong winds, or cold temperatures should be avoided as it is unlikely that
squirrels will be active. Gurnell et al. (2007) developed field study protocols and
recommended the design of a survey for monitoring programs in the United Kingdom and
investigated new survey monitoring methods, which fit within the field study protocols.
Visual surveys were one of the main methods of monitoring discussed by Gurnell et al.
(2007) with a justification that the differences between red and grey squirrels can be achieved
using this monitoring method. However, Gurnell et al. (2009) argued that red and grey
squirrels are not easy to distinguish as the colour of each species can vary significantly.
Gurnell and Pepper (1994), Gurnell et al. (2009), Gurnell et al. (2007) and Gurnell et al.
(2004) omitted to describe the practice and the physical process of observing without
disturbing the red squirrels. Further research could be conducted to discover the best practice
for carrying out the surveys without disturbance, as over disturbance could produce moral
and ethical issues where squirrel activity and behaviour could be affected due to human
interaction.
3.4 Key Literature Discussing Meteorological data Gathering
The UK Meteorological Office (2010) describes the way measurements are carried out using
weather stations in the United Kingdom. The World Meteorological Organisation (2008)
discussed agreed international meteorological data generation standards. Both sources agree
on a standard height of 10m for the measurement of wind speed and direction. The World
Meteorological Organisation (2008) suggested that the global standard height of a rain gauge
can vary between 0.5 and 1.5m whereas The UK Meteorological Office (2010) has

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standardised 1.5m from the ground for weather recording in the United Kingdom. There are
omissions within both documents as no information is provided for gathering weather in the
forest environment. However, Crockford and Hui (2007) address these omissions by
discussing the weather at different heights in different types of woodland.
Henderson-Sellers et al. (1981) outline the strengths and weaknesses of different cloud cover
measurement methods and argue that there is no single accepted method to measure cloud
cover. However, The UK Meteorological Office (2010) suggest that the main method used
is the Okta measurement system, which is often used for research in contemporary literature
as used by Kyba et al. (2012), Mittermaier (2012), Yamanda et al. (2013), Kirchgäßner
(2010) and Crooker and Mittermaier (2012).
3.5 Key Literature Discussing Wind Equations
Oliver and Mayhead (1974) analyse the wind profiles within the canopy of a woodland
summarising equations to calculate the wind speed at given heights within the canopy,
however, further equations which are needed to calculate variables nested within the equation
are omitted. Within more contemporary literature Crockford and Hui (2007) note the
equation and include a range of equations used to calculate the zero-plane displacement
height and the roughness length. However the equation needed to calculate the friction
velocity is omitted. Reible (1999) notes the friction velocity equation which was omitted by
Crockford and Hui (2007).
Another equation that is to be used in the methodology is the calculation of wind-chill.
Osczevski and Bluestein (2005) note various wind-chill equations that are used with varying
units. This text also discusses the theories and concepts of wind-chill.
4.0 Site Description
The area for this investigation is on the coast at Formby Point, North of Liverpool (see Figure
2, p.17). Owned by the National Trust, it is a Site of Special Scientific Interest (see Figure 3,
p.17) and is one of the only locations in England which has a red squirrel population (Rice-
Oxley, 2001). The first of the 400 hectare coniferous woodland was planted near Lifeboat
Road in 1795, the majority was later planted in 1885 (York and York, 2008). Many of the
pine trees are over 100 years old and therefore may begin to die, potentially diminishing the
red squirrel habitat (Cornish, 2002). However, the National Trust has an active woodland
management policy to maintain the woodland at Formby (Cornish, 2002).

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Figure 3: The location of The National Trust Formby site (Ordnance Survey, 2013).
National Trust
Formby Point
Lifeboat Road
Figure 2: The location of The National Trust Formby site (Ordnance Survey, 2013).

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4.1 Previous research at Formby Point
Ongoing monitoring of the red squirrel population is already carried out by wildlife officers
at Formby Point. During 2008 there were a high number of fatalities due to the Parapox virus,
leading to a decrease in population (Miler, 2013). In 2009 the virus reduced the population to
15% of the recorded population in 2002 (see Figure 4).
5.0 Methodology
Quantitatively monitoring squirrel activity and generating timely weather data will be used to
synthesis and compare weather with red squirrel activity.
5.1 Squirrel Activity
Squirrel activity is often considered to be highest in the Autumn months shortly after sunrise
(Oddie, et al. 2005; Tonkin, 1983). Squirrels were monitored during this time period for both
the primary and secondary data. The secondary data also included the high activity period
experienced during Spring (Tonkin, 1983).
5.1.1 Primary Squirrel Data
In November 2013 an objectivist and manageable yet generalised data sample of squirrel
activity was gathered at Formby Point using visual surveys as discussed by Gurnell and
Pepper (1994), Gurnell et al. (2009), Gurnell et al. (2007) and Gurnell et al. (2004). Two
transects were conducted to provide representative cluster samples, the transect points were
surveyed six times producing generalised data (see Figure 5, p.19). Each 1000m transect was
conducted using standardised Time-Area counts as proposed by Gurnell and Pepper (1994).
The researcher waited at each stopping point and observed for five minutes, the researcher
then observed while walking 100m, in two minutes, between consecutive stopping points (see
Figure 5, p.19). This visual survey method, when used in less dense woodland, such as that at
Figure 4: The effects of the poxvirus in 2008 (Miller, 2013, p.1).

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Formby Point, is an effective way of estimating activity and can be used to distinguish
between red and grey squirrels (Gurnell et al. 2007) (see Table 2, p.20). A Global Positioning
System was used to record the grid reference of each stopping point. Data for squirrel
behaviour was also gathered; squirrel behaviour was split into categories (see Table 1, p.13).
Each transect was conducted 30 minutes after sunrise as the activity in November has been
observed to be higher in the morning by Tonkin, (1983) (see Figure 1, p.14). This accounted
for the change in activity throughout the day, providing a more accurate representation of the
impact of weather. With higher activity recorded in the period just after sunrise, it is likely
that a more representable synthesis between activity and weather was observed.
Start point of
Transect 2
End point of
Transect 1
Start point of
Transect 1
End point of
Transect 2
Weather
Station
SD 27949 08230
SD 27920 08324
SD 27898 08420
SD 27893 08510
SD 27911 08580
SD 27967 08617
SD 28046 08691
SD 27894 08792
SD 27884 08796
SD 27752 08903
SD 27843 08848
SD 28099 08220
SD 28147 08087
SD 28053 08047
SD 27970 07964
SD 28042 07856SD 27930 07801
SD 27899 07758
SD 27891 07594
SD 27868 07668
SD 27798 07580
SD 28043 07934
Figure 5: Map displaying the route of transects 1 and 2, adapted from (Ordnance Survey, 2013).

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Assumptions were made when carrying out the visual surveys (see Table 3) (Gurnell et al.
2007). Not only that but practical problems and limitations, such as disturbance and limited
view or observation, that are appropriate to this methodology arose (see Table 4) (Gurnell et
al. 2004).
Table 3: Visual count assumptions, adapted from (Gurnell et al. 2007).
Assumption 1 Squirrels on transects will not be missed in the count; however it is easy to
miss squirrels in the canopy especially in dense coniferous woodland.
Assumption 2 Squirrels will not be counted more than once as the squirrels do not move in
relation to the observer, there is the possibility some squirrels may move
through the forest and be counted twice.
Table 4: Visual count limitations, adapted from (Gurnell et al. 2004).
Limitation Solution to mitigate the problem
The probability of viewing a
squirrel is often low.
The researcher carried out multiple surveys on the same
transects.
A good sighting is needed to
distinguish between Red and
Grey squirrels.
The researcher identified the species on multiple
distinguishing features including, hair colour, fur length on
ears and size of the body.
Squirrels may react to the
presence of the observer and
hide.
The researcher moved quietly and dressed in mute coloured
clothing to avoid detection.
In order to minimise the risk of the process used to gather the primary data a risk assessment
was created (see Appendix 6).
Table 2: Comparison of Survey Methods (Gurnell et al. 2009, p4).
:

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5.1.2 Secondary Squirrel Data
The Lancashire Wildlife Trust collected secondary data; this was completed by a range of
staff and volunteers, between 2008 and 2013. The staff and volunteers used the same
methodology to gather the primary data as outlined in section 5.1.1 on p17-19. The transects
were conducted in Spring and Autumn each year. The number of transects conducted each
year ranged from 1, in Spring 2010, to 8, in Spring 2013. The results from 2008 and 2009
have been omitted, as a significantly low amount of activity was observed and dead squirrels
were included, this was likely to be due to the parapox outbreak in 2008 (see Figure 4, p.18).
Limitations of the Secondary Squirrel Data
As the data have been gathered by a multitude of staff and volunteers the data could be
exposed to subjectivity or slight variation in observation techniques or methods.
5.2 Meteorological Data
As with the squirrel data, the meteorological data are homogeneous. They do, however,
contain more categories, so a stratified random sample methodology was applied. The
meteorological data was more generalised, nevertheless it remains manageable; multiple
elements of meteorological data were generated in five minute intervals. Meteorological data
gathered consists of data that may impact upon squirrel activity, as discussed by Tonkin
(1983), Tittensor (1970) and Pulliainen and Jussila (1995). These include air pressure, rain,
temperature, wind speed and cloud cover. Humidity was also included, as the literature
review has demonstrated that it has most likely been omitted from the literature. Because of
this there is a strong uncertainty as to whether or not humidity has an impact on squirrel
activity.
5.2.1 Primary Meteorological Data
All weather elements, apart from cloud cover, were measured using a Maplin USB Wireless
Touchscreen Weather Station (see Figure 6, p.23 and Figure 7, p.24), which was positioned
near both transects (see Figure 5, p.19). The weather station comprised of a number of
meteorological measuring instruments and required extra equipment to calibrate them (see
Table 6, pp.29-32).
Cloud cover was estimated using the Okta observation system, which is the standard
observation system within the United Kingdom (The UK Meteorological Office, 2010). The
Okta system is an estimate of the total amount of cloud in the sky, measured in eighths (see
Table 5, p.22) (The World Meteorological Organisation, 2008).

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The variables measured were:-
 Wind Speed (ms-1
).
 Wind Direction.
 Gust Speed (ms-1
).
 Temperature (°C).
 Humidity (%).
 Relative Pressure (hPa).
 Absolute Pressure (hPa).
 Cloud Cover (Okta).
 Dew Point (°C).
Table 5: Okta classification, adapted from (The Met Office, 2010).
Oktas Classification
0 No clouds visible in the sky.
1 One eighth of the total sky or less is covered in cloud.
2 Two eighths of the total sky or less is covered in cloud.
3 Three eighths of the total sky or less is covered in cloud.
4 Four eighths of the total sky or less is covered in cloud.
5 Five eighths of the total sky or less is covered in cloud.
6 Six eighths of the total sky or less is covered in cloud.
7 Seven eighths of the total sky or less is covered in cloud.
8 The sky is totally covered by cloud.
9 Sky is obscured by other meteorological phenomena.

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1.5m from the
ground
2m from the ground
Figure 6: Height of weather station devices.

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5.2.1.2 Wind
The wind data generated from the weather station was at a height of two metres (see Figure 6
p.22), which is within the forest sub-canopy. While observing red squirrel activity, it was
noticed that the majority of the activity was within the tree canopy at around two meters from
the top of the canopy. This notion of activity has been supported by King (1997). The wind
speed in the sub-canopy is often different to that at the top of the canopy, this has been
discussed by Crockford and Hui (2007) (see Figure 8, p25).
Figure 7: Weather Station distance from the trees.
Weather station situated at
least 10m distance from trees

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(1)
Corckford and Hui (2007, p. 6) noted that the wind speed in the canopy (𝑢(𝑧)) can be
calculated, at height (𝑧), using
𝑢(𝑧) =
𝑢∗
𝑘
ln(
𝑧 − 𝑑
𝑧0
)
Where:-
𝑢∗= Friction Velocity.
𝑘 = Von Karman’s constant ≅ 0.41 (Vachon and Prairie, 2013).
𝑑 = Zero-plane displacement height m.
𝑧0 = Roughness length m.
In order to calculate 𝑢(𝑧) the equation needs to be nested with other equations, 𝑑, 𝑧0 and 𝑢∗
which all require further calculation. As noted by Crockford and Hui (2007), Hicks et.al.
(1975, p. 65) generated a large sample of wind data within coniferous woodland and analysed
it, discovering that
Height of average
observed red squirrel
activity (z)
Figure 8: Wind profile in a forest canopy (Crockford and Hui, 2007, p. 6).

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(2)
(3)
(4)
𝑑 = 0.8ℎ
𝑧0 = 0.3(ℎ − 𝑑)
Where:-
ℎ = height of the tree canopy.
The height was calculated by using trigonometry; where the distance from the tree (15
metres) is the adjacent, the height is the opposite and the angle was as measured using an
abney level (see appendix 5). The difference in angle from the perpendicular must be
calculated depending on the ground and height of the researcher; this results in using the
trigonomic equation twice (see Figure 9).
As noted by Reible (1999, p. 262) calculating 𝑢∗ involved further nesting of equations:-
𝑢∗ = √
𝜏 𝑡
𝜌
Where:-
𝜏 𝑡 = Sheer stress of the tree hPa.
𝜌 = Density of Air ≅ 1.25 kg m-3
.
Figure 9: Calculating tree height (Calkins and Yule, 1927, p.16).

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(7)
(5)
(6)
Calculating 𝜏 𝑡:-
𝜏 𝑡 =
𝐹
𝐴 𝑡
Where:-
𝐹 = Force Kg.
𝐴 𝑡 = Vertical cross-sectional area of a tree m2
.
Calculating 𝐹:-
𝐹 = 𝐴 𝑡 × 𝜌 𝑤 × 𝐶 𝑑
Where:-
𝜌 𝑤 = Wind pressure.
𝐶 𝑑 = drag coefficient of a Corsican pine (Pinus nigra ssp. laricio), which is considered to be
0.32 by Mayhead (1973).The drag coefficient was calculated by analysing the wind tunnel
data of a range of coniferous trees. A monoculture environment of Corsican pine trees
surrounded the weather station (see Figure 10, p.28).
Calculating 𝜌 𝑤:-
𝜌 𝑤 = 𝜌 × 𝑉2
× 𝐶 𝑑
Where:-
𝑉 = Wind speed ms-1
.
When nesting equation (6) with equation (5), equation (5) is represented as:-
𝜏 𝑡 = 𝜌 × 𝐶 𝑑
By using these equations it has been made possible to gain an estimate of the wind speed
within the tree canopy where the red squirrels are often observed.

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5.2.2.2 Wind-chill
Wind-chill is the cooling effect combining low temperature and wind on warm blooded
species, the temperature felt is a perceived air temperature (Gross, 2010). Wind-chill is often
expressed equivalently to temperature (Barry and Chorley, 2009), where the equivalent
temperature is commonly considered to be subjective between humans (Neima and Shacham,
1995). Even though squirrels have a different physiology, it is reasonable to suggest that the
same factors of wind, through convection and temperature, cool red squirrels. It is reasonable
to argue that squirrels will experience a similar thermodynamic effect, due to the heat flow of
the zeroth law of thermodynamics (Atkins, 2010), this is especially reasonable to suggest, as
squirrels have a similar body temperature between 37-40°C, as noted by King (1997). The
wind-chill that squirrels experience is therefore expressed without suggesting the perceived
cooling temperature; instead it is represented with the human equivalent.
Osczevski and Bluestein (2005, pp. 1457) suggests a wind-chill (𝑊𝐶) equation which is
commonly used in Europe as it requires metric data
𝑊𝐶 = 13.12 + 0.6215𝑇 − 11.37𝑉0.16
+ 0.3965𝑇𝑉0.16
Where:
T= Temperature °C.
(8)
Figure 10: Monoculture woodland at Formby Point.

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Limitations of the Wind-chill Equation
The wind-chill equation is erroneous where wind speed is less than 1 kilometre per hour, as
the equation does not comply with the zeroth law of thermo dynamics (Atkins, 2010).
It is also reasonable to suggest that wind-chill can only occur when wind is experienced.
5.2.2 Instrumentation List
An instrumentation list was created (see Table 6) to display the equipment that was used for
the study.
Table 6: instrumentation list.
Name of
Equipment
Image of equipment Description and
use
Thermo-
Hydro
pressure
sensor with
Measures the
Temperature,
Barometric Pressure
Humidity and calculates
relative humidity. The
device was positioned
1.5m from the ground.
Cup
Anemometer.
Consists of 3
hemispherical cups,
which catch the wind
and measure its speed in
meters per second. This
was situated 2m from
the ground.

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Tipping
Bucket Rain
Gauge.
Calibrated to measure
minimum of 0.3 mm of
rainfall during 5 minute
intervals of rain. This
was situated 1.5m from
the ground.
Wind Vane. Measures the wind
direction relative to
North. This was situated
at a height of 2m.
3m tape
Measure.
Used to measure the
height of the devices
from the ground.

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Tripod. Used to fix the weather
station at a certain
height.
Silva
Expedition 4
Compass
Used to calibrate the
direction of the Wind
Vane.
Round Spirit
Level
Used to measure the
lateral direction of the
wind crossbar so that it
can be positioned
perpendicular to the
ground.
Garmin
foretrex 401
Global
Positioning
System.
Used to gain an accurate
grid reference of the
weather station location,
this is the same Global
Positioning Unit used to
measure the grid
reference at each
stopping point on the
transects.

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Abney Level Used to measure the
angle from the ground to
the height of a tree from
15 metres away.
30 metre tap
measure
Used to measure 15
metres along the ground
from a tree in order to
calculate its height when
used in conjunction with
the abney level.
5.2.3 Secondary Meteorological Data
The secondary meteorological data was generated at Crosby near the National Trust Formby
site (see Figure 11, p.33). The historical mean weather values for each day have been
provided by TuTiempo (2013). The data dates back to 1984; however, data is only needed
from 2010 to 2013 as the relevant squirrel monitoring occurred during this time period. The
weather station generated various weather element data, including those in the primary
meteorological data section:-
 Mean Temperature (°C).
 Maximum Temperature (°C).
 Minimum Temperature (°C).
 Mean Sea Level Pressure (hPa).
 Humidity (%).
 Precipitation Amount (mm).
 Mean Visibility (Km).
 Mean Wind Speed (Kmh-1
).
 Gust Speed (Kmh-1
).

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Limitations of the meteorological Secondary Data
As the data generated has a large interval of one day, it may not be an accurate representation
of the weather during the specific time period when the transects were conducted. However,
it has been argued by Barry and Chorley (2009) that weather over a short period often follows
the trend in weather for the day and therefore the data could provide further evidence which
may synthesise with red squirrel activity.
The location of the weather station is five kilometres from the study site and therefore the
meteorological data may have a slight variation to the weather at Formby Point.
Crosby Weather
Station
National Trust
Formby Site
Figure 11: The distance from Crosby Weather Station to Formby Point (Ordnance Survey, 2013).

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5.3 Permission to use Primary and Secondary Data
Permission was granted for both primary and secondary data collection (see Table 9).
Table 7: Collection Type and Permission.
Type of Data Data Collected From Permission
Primary At Formby Point by
conducting squirrel survey
transects (see Figure 5, p.19).
Granted from Andrew
Brockbank, the Formby Point
National Trust Head Ranger.
Primary Weather station situated at
SD 28130805 (see Figure 5,
p.19).
Granted from Andrew
Brockbank, the Formby Point
National Trust Head Ranger.
Secondary At Formby Point from
previous squirrel survey
transects since 2003 however
access is limited as data also
currently being used by a
PhD student.
Data to be contributed by
Rachel Miller of the
Lancashire Wildlife Trust.
Secondary Crosby Weather Station. The data is published for use
by TuTiempo (2013).
5.4 Statistical Analysis
A number of methods were used to statistically analyse the data, these include the Pearson
correlation coefficient, Spearman’s rank correlation coefficient and standard deviation.
5.4.1 Pearson Correlation Coefficient (r)
The Pearson correlation coefficient (r) is often used to test the strength in relationship
between sets of data which have a linear relationship. The equation used to calculate Pearson
correlation coefficient was noted by Rees (1995, p. 190).
𝑟 =
∑ 𝑥𝑦 −
∑ 𝑥 ∑ 𝑦
𝑛
√[∑ 𝑥2−
(∑ 𝑥)2
𝑛
][∑ 𝑦2−
(∑ 𝑦)2
𝑛
]
Where:
𝑛 = Sample size.
𝑥 and 𝑦 = Respective data values
(9)

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The correlation is considered to be positive or negative once interpreted, where:
+1 = a perfect positive correlation.
0 = no correlation.
-1 = a perfect negative correlation.
The Pearson correlation coefficient was used to determine whether correlations between
weather and red squirrel activity are significant. This was achieved by matching the degrees
of freedom (𝑑𝑓 = 𝑛 − 2), with its given correlation coefficient (see Table 7). A correlation is
considered significant if it has significance of at least 95% (Rees, 1995; Zar, 1972).
Table 8: Critical values for Pearson correlation coefficient (Weathingtion, Cunningham and Pittenger, 2012, p. 219).

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5.3.2 Spearman’s Rank Correlation Coefficient (𝒓 𝒔)
The methodology used to test for significance with Pearson correlation coefficient is similar
to Spearman’s rank correlation coefficient (𝒓 𝒔). Spearman’s rank correlation coefficient tests
the strength in relationship between sets of data, which have a monotonic relationship. The
equation used to calculate Spearman’s rank correlation coefficient was noted by Rees (1995,
p. 166)
𝑟𝑠 = 1 −
6 ∑ 𝑑2
𝑛3−𝑛
Where:
𝑑 = the differences in rank of each individual sample.
The Spearman’s rank correlation coefficient was used to determine whether correlations
between weather and red squirrel activity are significant. This was achieved by matching the
sample size with its given correlation coefficient (see Table 8). A correlation is considered
significant if it has significance of at least 95% (Rees, 1995; Zar, 1972).
(10)
Table 9: Critical values for Spearman’s rank correlation coefficient
(Rees, 1995, p. 248).

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5.3.3 Population Standard Deviation (𝝈)
Standard deviation (𝜎) was used to measure the amount data spreads from the mean, it
measures the amount of dispersion from a graphs tend line (Bassett et al. 2000). The higher
the value of standard deviation the more the data deviates from the mean. The equation to
calculate a populations standard deviation has been noted by Rees (1995, p. 31)
𝜎 = √
∑(𝑥 𝑖−𝜇)2
𝑛−1
Where:
𝑥𝑖 = data values.
𝜇 = mean of the values.
(11)

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6.0 Results and Discussion
6.1 Introduction
The data presented in this project suggest that weather can impact on red squirrel activity and
therefore does not support the null-hypothesis. The synthesising of meteorological data and
red squirrel observations has produced strong correlations with wind speed, gust speed, wind-
chill, wind direction, temperature, dew point and cloud cover. However, pressure, humidity,
precipitation and visibility yielded no significant correlations. The secondary activity data
produced no correlations between activity and weather, most likely due to the number of
observers used for the transects. Each observer may have conducted the observation of the
transects with a subjective methodology, which could have affected any correlations.

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6.2 Significant Results
Data presented within Table 10 are considered to have a significant correlation, where the
significance is greater than 95%, synthesising the primary squirrel data (see appendix 1) and
primary meteorological data (see appendix 3). A wind speed data set in kilometres per hour
was included, as this was required for the calculation of wind-chill, this unit of wind speed
was also used to compare to the Crosby wind speed which was generated in kilometres per
hour.
Table 10: Primary red squirrel activity synthesised with meteorological data, which produced a significant
correlation.
Transect
Date
dd/mm/yy
Transect
Number
Red
Squirrel
Activity
Mean
Wind
Speed
ms-1
Mean
Wind
Speed
Kmh-1
Mean
Gust
Speed
ms-1
Mean
Tempera
-ture °C
Mean
Wind
-chill
°C
Mean
Cloud
Cover
Okta
Mean
Dew
Point
°C
08/11/13 1 9 0.0 0.0 0.2 7.9 7.9 7 6.0
09/11/13 1 6 0.1 0.4 0.2 5.8 9.0 1 4.5
11/11/13 1 5 0.4 1.4 0.9 10.0 11.5 8 9.3
12/11/13 1 5 0.5 1.8 0.9 10.1 11.3 1 7.9
14/11/13 1 0 1.2 4.3 3.7 7.7 7.4 8 5.1
15/11/13 1 8 0.1 0.4 0.4 9.2 12.3 8 6.9
19/11/13 2 15 0.1 0.4 0.2 1.2 4.6 1 -1.4
22/11/13 2 15 0.0 0.0 0.0 1.1 1.1 1 -1.8
23/11/13 2 22 0.0 0.0 0.0 1.1 1.1 1 -1.8
27/11/13 2 3 0.4 1.4 0.9 9.8 11.3 8 8.9
28/11/13 2 10 0.0 0.0 0.1 9.6 9.6 7 8.8
29/11/13 2 2 1.3 4.7 2.2 9.8 9.6 7 8.1
Mean 0.3 1.2 0.8 6.9 8.1 5 5.0
Standard
Deviation
0.4 1.6 1.1 3.6 3.7 3 4.1
Pearson
Correlation
Coefficient
(Linear
Relationship)
N/A N/A N/A -0.810 -0.772 -0.610 -0.792
Spearman’s
Rank
Correlation
Coefficient
(Monotonic
Relationship)
-0.890 -0.890 -0.947 N/A N/A N/A N/A
Significance 99% 99% 99% 99% 99% 95% 99%

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No significant correlations were found when the Crosby Weather Station data (see appendix
4) and secondary squirrel data (see appendix 2) were synthesised. This is likely due to the
range of people used to monitor the red squirrels, which exposes the collected data to
subjectivity and variation within the observational methods. This called into question the
synthesising of the secondary data and has encouraged presentation and discussion of data
that is more coherent with the literature.
Significant correlations were discovered between the Crosby Weather Station data and
primary squirrel survey data, where the significance is greater than 95% (see Table 11).
Table 11: Primary red squirrel data compared with Crosby weather station.
Transect
Date
dd/mm/yy
Red
Squirrel
Activity
Mean
Temperature
°C
Maximum
Temperature
°C
Minimum
Temperature
°C
Wind
Speed
Kmh-1
Gust
Speed
Kmh-1
Wind
-chill
°C
08/11/13 9 8.2 8.9 6.4 6.4 12.4 4.8
09/11/13 6 7.4 11.0 4.6 6.8 11.8 3.6
11/11/13 5 9.8 12.4 6.2 5.4 9.3 7.2
12/11/13 5 10.2 11.6 9.4 7.2 8.8 7.1
14/11/13 0 8.5 10.4 6.5 12.6 17.5 3.7
15/11/13 8 8.2 10.3 3.6 3.6 6.7 6.0
19/11/13 15 4.6 6.8 1.7 6.9 12.9 0.0
22/11/13 15 4.3 7.8 1.2 2.9 4.6 1.7
23/11/13 22 2.4 6.6 -2.1 1.7 3.1 0.7
27/11/13 3 9.0 9.5 8.3 6.4 8.8 5.8
28/11/13 10 8.7 9.9 8.0 2.7 5.1 7.2
29/11/13 2 8.5 9.6 7.6 11.6 14.9 3.9
Mean 7.5 9.6 5.1 6.2 9.7 4.3
Standard
Deviation
2.3 1.7 3.3 3.2 4.2 2.3
Pearson
Correlation
Coefficient
-0.873 -0.773 -0.843 N/A N/A -0.617
Spearman’s
Rank
Correlation
Coefficient
N/A N/A N/A -0.733 -0.613 N/A
Significance 99% 99% 99% 98% 95% 95%

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6.3 Wind Speed
The correlation between wind speed and red squirrel activity (see Table 10, p.39) provides
evidence to suggest that wind speed has an impact on red squirrel activity, as the synthesis of
wind speed and activity display a significance of 99% for the primary data and 98% for the
secondary meteorological data. The strong negative exponential correlations (see Figures 12
& 13) indicate that, when the wind speed increases, red squirrel activity rapidly decreases
towards 0. A close inspection of the primary wind speed graph indicated that squirrel activity
is almost 0 when wind speed within the woodland is greater than 3 km/h.
0
5
10
15
20
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0
RedSquirrelActivity
Average Wind Speed during each Day (Kmh-1)
𝒚 = 𝟑𝟗𝟖. 𝟑𝒆−𝟎.𝟐𝟑𝒙
𝒓 𝒔 = −𝟎. 𝟕𝟕𝟑
0
2
4
6
8
10
12
14
16
18
20
22
0 1 2 3 4
RedSquirrelActivity
Wind Speed (Kmh-1)
𝒚 = 𝟏𝟖. 𝟐𝟖𝒆−𝟏.𝟔𝟔𝒙
𝒓 𝒔 = −𝟎. 𝟖𝟗𝟎
Figure 12: Relationship between red squirrel activity and wind speed.
Figure 13: Relationship between the average wind speed for the day at Crosby and red squirrel activity.

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Upon further examination of the Crosby wind speed graph (see Figure 13, p.41) it is apparent
that higher wind speeds occur than those of the on-site weather station. The higher values
generated by the Crosby Weather Station are most likely due to the wind being subject to less
friction (Barry and Chorley, 2009), as the weather station is not within the sub canopy.
The wind speed was calculated at a profile height of 21 m from the forest floor, or 2 m from
the top of the woodland canopy (see appendix 5), in order to discover the wind speed in the
location where the majority of the squirrel activity was observed (see Table 12). It has also
been supported by Oddie et al. (2005) that red squirrels often operate within the top section
of the canopy. The relationship between wind speed at 21 m and red squirrel activity was
plotted on a graph (see Figure, 14, p43). The correlation suggests that wind speed within the
canopy reduces squirrel activity by a slightly reduced rate than wind speed within the sub
canopy. The canopy wind speed is most probably reduced due to the increased friction within
the canopy and it is this slower wind speed which is most likely to be directly affecting the
red squirrel activity.
Table 12: Values used to calculate the wind speed at a profile of 21 metres within the tree canopy.
(V2
) Wind
speed squared
(m s-1
)2
(𝝆 𝒘)
Wind
pressure
(𝝉 𝒕)
Sheer
stress
(𝒖∗)
Friction
velocity
(𝒖(𝒛)) wind
speed at height
21m (m s-1
)
(%) Decrease
in wind at 21m
in canopy
0.00 0 0 0 0.00 0.00
0.01 0.004 0.00128 0.02921187 0.09 16.58
0.16 0.064 0.02048 0.116847479 0.34 16.58
0.25 0.1 0.032 0.146059349 0.43 16.58
1.44 0.576 0.18432 0.350542437 1.03 16.58
0.01 0.004 0.00128 0.02921187 0.09 16.58
0.01 0.004 0.00128 0.02921187 0.09 16.58
0.00 0 0 0 0.00 0.00
0.00 0 0 0 0.00 0.00
0.16 0.064 0.02048 0.116847479 0.34 16.58
0.00 0 0 0 0.00 0.00
1.69 0.676 0.21632 0.379754307 1.12 16.58
Mean 0.29
Standard
Deviation
0.37
Pearson
Correlation
Coefficient
-0.714
Significance 99%

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Pulliainen and Jussila (1995) argue that wind did not affect their study of red squirrel
mobility; however no meteorological data were included by Pulliainen and Jussila (1995)
which suggests that their argument was an assumption. Nevertheless, the findings within this
project coherently represent what is frequently found within the literature. Tonkin (1983) and
Tittensor (1970) suggest that red squirrel activity is compromised in high winds. The
inhibition of activity during high winds could be occurring due to the red squirrels’ reliance
on agility to access the seed on the fine twigs within coniferous woodland (Holm, 1987). An
increase in wind speed could be compromising red squirrel agility and therefore limits the
squirrels’ activity within the coniferous environment at Formby Point.
0
2
4
6
8
10
12
14
16
18
20
22
0 0.2 0.4 0.6 0.8 1 1.2
RedSquirrelActivity
Wind Speed (ms^-1)
Wind speed at 21m
profile
Wind speed at 2m
profile
𝒚 = 𝟏𝟖. 𝟐𝟖𝒆−𝟔.𝟔𝟓𝒙
r = -0.890
𝒚 = 𝟏𝟖. 𝟐𝟖𝒆−𝟓.𝟗𝟕𝒙
r = -0.890
Figure 14: Relationship between red squirrel activity and wind speed at a 2 metre profile and a 21 metre profile within
the tree canopy.

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6.4 Gust Speed
The gust speed for the on-site meteorological data and the meteorological Crosby data have a
similar correlation to that of wind speed, when synthesised with red squirrel activity (see
Tables 10, p.39 & 11, p.40). As with wind speed the on-site gust speed graph and Crosby gust
speed graph both display strong negative exponential correlations tending towards an activity
level of 0 with a significance of 99% and 95% respectively (see Figures 15 and 16 p.45).
However the gust speed threshold, where activity is considered to be 0, is much higher than
that of mean wind speed. Where activity is 0, gust speed is approximately 7 km/h at the on-
site weather station whereas mean wind speed is closer to 2.5 km/h. The lower tolerance
towards gust speed is most likely due to the wind speed values being a mean result which
includes the higher wind values displayed by gust speed. This is emphasised when analysing
the mean and standard deviation of gust speed against wind speed, as the gust speed values
are both greater.
0
2
4
6
8
10
12
14
16
18
20
22
0.0 2.0 4.0 6.0 8.0 10.0 12.0
RedSquirrelActivity
Gust Speed (Kmh-1)
𝒚 = 𝟑𝟐. 𝟎𝟓𝒆−𝟎.𝟖𝟗𝒙
𝒓 𝒔 = 0.947
Figure 15: Relationship between red squirrel activity and gust speed.

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Similar to wind speed, gust speed is found to be consistent with Tonkin’s (1983)
observations, where red squirrel activity reduces in high winds. This would further support a
hypothesis which suggests that activity is often limited in high winds as red squirrel agility
becomes compromised.
0
5
10
15
20
0.0 5.0 10.0 15.0 20.0
RedSquirrelActivity
Gust Speed During each Day (Kmh-1)
𝒚 = 𝟔𝟏𝟏. 𝟕𝒆−𝟎.𝟏𝟔𝒙
𝒓 𝒔 = −𝟎. 𝟔𝟏𝟑
Figure 16: Relationship between the mean gust speed for the day at Crosby and red squirrel activity.

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6.5 Wind Direction
A Table was created to display the relationship between wind direction and red squirrel
activity (see Table 13) along with a rose diagram for transect 1 (see Figure 17, p.47) and
transect 2 (see Figure 18, p.48). However, wind direction has a degree of limitation because
secondary wind direction is not included in this analysis, as the Crosby Weather Station does
not generate directional wind data (TuTiempo, 2013). The wind direction outside of the forest
is therefore omitted from this analysis. Wind direction is also limited to a small range of wind
directions, as no data was generated from the North, North East or North West. If more time
had been available it could have been possible to generate more data, yielding a broader
range of wind directions and providing a better understanding of its effect upon red squirrel
activity.
Despite the limitations, wind direction appears to have an impact on red squirrel activity
which seems to be consistent with the literature. Upon observation of the rose diagrams for
each transect, it is apparent that activity is high when the wind blows from the South and low
when wind comes from the West. The notion that activity is significantly higher during
Southerly winds as opposed to Westerly winds is supported by Tittensor (1970). Temperature
could be the critical factor in relation to wind as suggested by Lampio (1967) who argued that
the slightest wind can reduce activity in cold conditions. This is further supported by the
unanimous argument that Southerly winds tend to be warmer than Westerly winds, due to air
mass sources (Barry and Chorley, 2009; Langmuir, 2013).
Table 13: Activity for given wind direction
Wind
Direction
W E SE S SW
Mean
Activity for
given Wind
Direction
(Transect 1)
4 5 6 9 N/A
Mean
Activity for
given Wind
Direction
(Transect 2)
5 N/A 19 N/A 15

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Activity
Figure 17: Transect 1 rose diagram.

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Activity
Figure 18: Transect 2 rose diagram.

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6.6 Temperature
The correlations between temperature and activity for both the primary and secondary
meteorological data have a significance of 99% (see Tables 10, p.39 & 11, p.40). The graphs
presented strong negative linear correlations, where temperature increased as squirrel activity
decreased (see Figures 19 & 20). The trend-lines display that when temperature is 9°C at
Crosby, or 13°C within the woodland, then red squirrel activity is often considered to be 0.
The mean temperature within the forest is higher than that of Crosby; this is most likely due
to the microscale insulating properties of a forest as discussed by Barry and Chorley (2009).
0
5
10
15
20
0.0 2.0 4.0 6.0 8.0 10.0 12.0
RedSquirrelActivity
Temperature (°C)
𝒚 + 𝟏. 𝟑𝟗𝒙 = 𝟏𝟖
𝒓 = −𝟎. 𝟖𝟏𝟎
0
5
10
15
20
0.0 2.0 4.0 6.0 8.0 10.0
RedSquirrelActivity
Average Temperature for Each Day (°C)
𝒚 + 𝟐. 𝟑𝒙 = 𝟐𝟓. 𝟔
𝒓 = −𝟎. 𝟖𝟕𝟑
Figure 19: Relationship between red squirrel activity and temperature.
Figure 20: Relationship between the average temperature for the day at Crosby and red squirrel activity.

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It was initially believed that wind speed could indirectly skew the synthesised correlation
between activity and temperature. However a graph displaying the correlation between red
squirrel activity and temperature (see Figure 21), where wind speed is considered to be
negligible (see Table 14), also displays a strong negative linear correlation and is supported
by a significance of 95%. This provides evidence to suggest that red squirrel activity
decreases as temperature increases, even with the impact of wind, within the woodland at
Formby Point.
Table 14: Temperature, where wind speed is considered to be negligible.
Transect
date
dd/mm/yyyy
Red
Squirrel
Activity
Mean
Temperature
(°C)
Mean
Wind
Speed
(ms-1
)
08/11/2013 9 7.9 0.0
15/11/2013 8 9.2 0.1
09/11/2013 6 5.8 0.1
23/11/2013 22 1.1 0.0
22/11/2013 15 1.1 0.0
19/11/2013 15 1.2 0.1
28/11/2013 10 9.6 0.0
Mean 5.1 0.0
Standard
Deviation
3.6 0.0
Pearson
correlation
coefficient
-0.782
Significance 95%
0
5
10
15
20
25
0 2 4 6 8 10 12
RedSquirrelActivity
Temperature (°C)
Figure 21: Relationship between red squirrel activity and temperature, during negligible wind speeds.

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Tonkin (1987) suggested that red squirrel activity was not affected by temperature during a
study in Cumbria. However, Tonkin (1987) goes on to mention that Purroy and Rey (1974)
and Shorten (1962) describe a heat dispersing posture, which red squirrels have been known
to adopt, called spread-eagle. This implies that red squirrels thermo-regulate in order to keep
their body temperature between 37°C-40°C (King, 1997). Even though the monitoring
occurred during a cold part of the day, it could be argued that red squirrels become too hot
whilst in high activity due to exertion. The squirrels could become less active due to their
need for thermoregulation, when morning temperatures are slightly warmer than usual. This
could be further supported by suggesting that the high activity rate observed after sunrise by
Tonkin (1983) requires less thermo regulation, which could suggest why red squirrels take
advantage of the morning time period, with the exception of warm mornings as suggested by
the results of this study.

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6.7 Wind-chill
Wind-chill is frequently considered to be the perceived air temperature according to wind
speed (Gross, 2010). The primary wind-chill graph (see Figure 22) has limitations, as the
equation used to calculate wind-chill is considered to be erroneous where wind speed is less
than 1 kilometre per hour; 7 of the 12 samples gave readings less than 1 kilometre per hour
(see Table 10, p.39). The on-site wind-chill graph should therefore be observed with caution.
However, it does provide a negative linear correlation with significance of 99%.
It could be argued that squirrels are not subject to the error present within the equation, as
they have a different physiology to humans. During a wind-chill study it was suggested that
cattle may not experience a change in air temperature when wind speed is less than 16
kilometres per hour (Ames and Insley, 1975). This is most likely due to the cattle having a
different physiology. As with the cattle, humans and red squirrels have a different
physiology; therefore wind-chill could be impacting squirrel activity when wind speed is less
than 1 kilometre per hour.
0
5
10
15
20
25
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0
RedSquirrelActivity
Wind-chill (°C)
𝒚 + 𝟏. 𝟐𝟕𝒙 = 𝟏𝟖. 𝟓𝟒
𝒓 = -0.772
Figure 22: Relationship between red squirrel activity and wind-chill.

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The data from the Crosby Weather Station displayed no wind speed values that were less than
1 kilometre per hour. This increase in speed was most likely due to the weather station being
situated outside of the woodland where there was less friction (Barry and Chorely, 2009;
Crockford and Hui, 2007). Therefor the data is not considered erroneous when used with the
wind-chill equation. The Crosby wind-chill graph (see Figure 23) displays a negative linear
correlation with a significance of 95%, where wind-chill increases as activity decreases, both
of the wind-chill graphs significantly support the impact of wind-chill on activity.
As previously mentioned, within the wind direction section of this discussion, the
combination of temperature and wind has been known to impact upon red squirrel activity
(Lampio, 1967). The wind-chill temperature could be impacting the red squirrels in a similar
manner to temperature as an increase in wind could cause the red squirrels to perceive a
lower air temperature (Gross, 2010). The lower air temperature could be creating an
environment where more efficient thermo-regulation can take place and therefore encourages
red squirrel activity. This further supports the idea that activity reduces in warmer
temperatures due to the red squirrels thermo-regulation needs.
0
5
10
15
20
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
RedSquirrelActivity
Windchill (°C)
𝒚 + 𝟏. 𝟔𝒙
= 𝟏𝟓. 𝟏
𝒓 = −𝟎. 𝟔𝟏𝟕
Figure 23: Relationship between the average wind-chill, for the day, at Crosby and red squirrel activity.

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6.8 Cloud Cover
It is worth noting that cloud cover data is subjective to the limitations of the accuracy of the
observer (The UK Meteorological Office, 2010). Nevertheless the cloud cover graph (see
Figure 24) displays a 95% significant negative linear correlation, where red squirrel activity
increases as cloud cover decreases. When the sky is covered in cloud the average activity was
approximately 5, if there was no cloud cover the activity was considered to be almost 14.
The reduction in activity due to cloud cover is supported within literature. Tonkin (1983)
suggested that cloud cover reduces the onset of first light, causing the sun to appear to rise at
a later time, which corresponds to the onset of red squirrel activity. The same could be true
for the red squirrels at Formby.
0
5
10
15
20
0 1 2 3 4 5 6 7 8
RedSquirrelActivity
Cloud Cover (Okta)
𝒚 + 𝟏. 𝟏𝒙
= 𝟏𝟑. 𝟖
𝒓 = −𝟎. 𝟔𝟏𝟎
Figure 24: Relationship between red squirrel activity and cloud cover.

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6.9 Dew Point
Dew point was not included in the secondary meteorological data, as no data was generated
by the Crosby Weather Station. The dew point graph (see Figure 25) demonstrated a strong
negative correlation with a significance of 99% (see Table 10, p.39), suggesting that red
squirrel activity reduces as dew point increases. The equation of the line of best fit on the dew
point graph depicts that, when squirrel activity (𝑦) is 0, the dew point (𝑥) is considered to be
11.8.
Dew point is the temperature of the air where condensation of water vapour begins (Halonen
et al. 2010). The humidity in the air therefore changes at the same rate as the dew point
(Vincent et al. 2007). As the temperature increases so does the carrying capacity of water
vapour in air, therefore the potential for increased humidity increases. It has been argued by
Elliott and Angell (2012) that, humidity directly affects cloud cover and visibility. The
reduction in activity when dew point increases could be a reflection of what was found in the
cloud cover section; this supports Tonkins (1983) suggestion that the sun appears to rise at a
later time, which reduces the onset of activity.
0
5
10
15
20
-2 0 2 4 6 8 10
RedSquirrelActivity
Dew Point (°C)
𝒚 + 𝟏. 𝟐𝒙
= 𝟏𝟒. 𝟐
𝒓 = −𝟎. 𝟕𝟗𝟐
Figure 25: Relationship between red squirrel activity and dew point.

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6.10 Precipitation
Crosby Weather Station displays average precipitation data for each day; however the data
supplied may not have any bearing on this project, as the on-site weather station did not
generate any rain data suggesting that no rain was observed during the transects. However,
fine rain, which was considered to be too sensitive for the weather station to read, was
observed by the researcher. This occurred on the 11th
, 12th
and 27th
of November, during the
transects where squirrel activity was 5, 5 and 3 respectively, the activity was considered to be
low when compared to the mean of 8 (see Table 15).
Table 15: The transects where fine precipitation was observed.
The number of samples where drizzle was observed is low and therefore the data should be
considered to be insufficient to conduct meaningful analysis. If more time had been available,
then precipitation could have been fully compared and synthesised with activity data, since it
is likely that more samples with precipitation would have been generated. The literature
suggests that red squirrel activity is not impacted upon when precipitation occurs (Tonkin,
1983; Tittensor, 1970; Pulliainen and Jussila, 1995 and Purroy and Rey, 1974). Remaining
coherent with the literature, it is reasonable to argue that the impact of precipitation on
activity is negligible even though the data in Table 15 suggests otherwise.
Transect
date
dd/mm/yyyy
Total
Red
Squirrel
Activity
Observed
Drizzle
During
Transect
08/11/2013 9
09/11/2013 6
11/11/2013 5 Yes
12/11/2013 5 Yes
14/11/2013 0
15/11/2013 8
19/11/2013 15
22/11/2013 15
23/11/2013 22
27/11/2013 3 Yes
28/11/2013 10
29/11/2013 2
Mean 8

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6.11 Pressure
The correlation between red squirrel activity and pressure, including sea level pressure,
relative pressure and absolute pressure, is considered to be insignificant with a significance of
less than 95% (see Table 16, p.58 and 17, p.59). There is little evidence to suggest a
correlation between red squirrel activity and pressure; however, Lampio (1967) briefly
suggests that pressure impacts upon red squirrel activity. It is reasonable to argue that there is
still a level of uncertainty, as there is very little evidence to suggest whether there is or is not
a correlation. If more time had been available this element could have been studied to a
greater depth in order to come to a stronger conclusion. Nevertheless, given the low
significance it is justifiable to suggest that pressure does not impact on red squirrel activity.
6.12 Humidity
Similar to pressure, the correlation between humidity and red squirrel activity was considered
to be insignificant with a significance of less than 95% (see Table 16, p.58 and 17, p.59).
Humidity has not been found within the literature and therefore it is reasonable to suggest
that it may not have an impact on the red squirrels at Formby Point. However, if more time
had been available then humidity could have been studied to a greater depth and therefore a
stronger conclusion could have been found for humidity.
6.13 Visibility
As with pressure and humidity, the correlation between visibility and red squirrel activity is
considered to be insignificant with a significance of less than 95% (see Table 17, p.59).
However the visibility was only measured at Crosby and therefore the visibility could have
been different within the canopy at Formby Point. Visibility has not been found within the
literature and therefore it is uncertain as to whether visibility may or may not have an impact
on the red squirrels at Formby Point. However, visibility has been compared to cloud cover
on the Okta scale (The UK Meteorological Office, 2010), and therefore could delay the onset
of red squirrel activity as argued by Tonkin (1983).

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Table 16: The primary data, which produced no significant correlation between weather and activity.
Transect date
(dd:mm:yyyy)
Total Red
Squirrel
Activity
Relative
Pressure
(Hpa)
Absolute
Pressure
(Hpa)
Humidity
(%)
08/11/2013 9 1006.1 1014.9 88
09/11/2013 6 1006.8 1015.6 91
11/11/2013 5 1017.9 1014.8 96
12/11/2013 5 1028.5 1014.7 86
14/11/2013 0 1026.5 1015.2 83
15/11/2013 8 1037.8 1014.2 85
19/11/2013 15 1032.3 1016.1 83
22/11/2013 15 1031.9 1015.7 81
23/11/2013 22 1031.7 1015.5 81
27/11/2013 3 1038.5 1022.3 94
28/11/2013 10 1039.7 1013.9 95
29/11/2013 2 1028.3 1014.1 89
Mean 1027.2 1015.6 88
Standard Deviation 10.9 2.1 5
Pearson Correlation
Coefficient
0.180 -0.112 -0.496
Spearman's Rank
Correlation
Coefficient
0.287 0.204 -0.437
Significance <95% <95% <95%

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Table 17: Displaying the secondary data, which produced no significant correlation.
Transect
Date
dd/mm/yyyy
Red Squirrel
Activity
Mean
sea level
Pressure
(hPa)
Mean
Humidity
(%)
Precipitation
Amount
(mm)
Mean
Visibility
(Km)
08/11/2013 9 1002.6 84 0.0 17.4
09/11/2013 6 1002.6 80 11.4 15.1
11/11/2013 5 1017.4 92 1.3 13.4
12/11/2013 5 1026.3 82 0.5 11.9
14/11/2013 0 1024.6 78 1.0 10.5
15/11/2013 8 1033.4 88 4.6 12.1
19/11/2013 15 1014.6 77 6.1 21.4
22/11/2013 15 1022.0 84 0.0 18.2
23/11/2013 22 1027.2 89 0.0 13.0
27/11/2013 3 1035.1 94 0.3 12.9
28/11/2013 10 1035.3 92 0.0 16.1
29/11/2013 2 1026.4 85 0.8 10.6
Mean 1022.3 85 2.2 14.4
Standard
Deviation
10.7 5 3.4 3.2
Pearson
Correlation
Coefficient
-0.059 0.034 -0.033 0.571
Significance <95% <95% <95% <95%

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6.14 Future Predictions of Weather and its Impact on Red Squirrel Activity
Research suggests that weather is likely to become more adverse due to the effects of global
warming (Bullock, Haddow and Haddow, 2009; Parry et al, 2007; Oxley, 2012), for example
it is likely that storms will become more frequent (Emanuel, 2005; Elsner, Kossin and Jagger,
2008; Knutson and Tuleya, 2004) and wind speed may increase (Latham and Smith, 1990).
This study and the literature, supports the view that red squirrel activity often decreases
during adverse weather conditions. An increase in frequency of adverse weather could be
fatal for the red squirrel population at Formby Point, as red squirrels are considered to be
dependent upon their ability to forage during their periods of activity (Tonkin, 1983).
An alternative determinant could be responsible for the mortality of the red squirrel
population at Formby point. The majority of the coniferous woodland was planted in 1795
and 1885 (York and York, 2008). Many of the Corsican pine trees are over 100 years old and
are likely to be approaching the end of their lifespan (Cornish, 2002). The red squirrel habitat
could therefore be beginning to deplete. The red squirrel population at Formby Point is
arguably dependent upon this habitat; the loss of this habitat will most likely result in the
mortality of the red squirrel population at Formby Point. However, the National Trust have an
active woodland management policy to maintain this habitat (Cornish, 2002). It is worth
noting that the researcher has recently observed, and has been involved in, the replenishment
of this habitat.

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Another factor which could lead to the mortality of the population is sand dune erosion. Pye
and Neil (1994) argue that the Sefton Coast sand dunes are eroding at 3 metres per year.
However Mathews (2014), who is a countryside ranger for National Trust Formby, disputes
this figure and claims the erosion rate is currently 4 metres per year, and he noted that a
recent storm surge in December 2013 eroded the dunes by 8 metres. It could be argued that
the erosion rate may continue to increase, due to the predicted rise in sea level and increased
frequency of storm surges, as a result of global warming (Houghton, 1997). The foredunes
are just over 1 kilometre from the far boundary of the woodland (see Figure 26). If the sand
dunes continue to erode at a rate of 4 metres per year, then the woodland will likely become
encroached by the sea within 250 years, turning the red squirrel habitat into a sunken forest.
However, the erosion process could be significantly reduced once the sea level reaches the
forest, as Smith (1976) discovered that riverbanks with a root system often significantly
reinforce bank materials. The root system of the forest could retain the sand.
1 Km
Table 18: A map showing the 1 kilometre distance from the foredune to the far edge of the
woodland (Ordnance Survey, 2013).

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7.0 Conclusion
The aim and objectives of this project have been met. This project has investigated red
squirrel activity within a multitude of weather conditions and has synthesised the
meteorological and activity data. The results have confirmed that weather conditions most
likely have an impact upon red squirrel activity at the National Trust Formby site.
One of the more significant findings of this investigation is that wind elements of the
meteorological data, including mean wind speed and gust speed, were found to have the
highest impact on red squirrel activity. Activity was reduced at an exponential rate compared
to the linear rates of other weather elements. It has also been discovered that wind direction
frequently has further impact upon activity.
It was also found that temperature, wind-chill, dew point and cloud cover impact on activity.
Where each of the weather elements increased, the activity decreased at a linear rate. It is not
surprising that temperature; wind chill and dew point have similar correlations with activity
as they are considered to be forms of temperature. Wind-chill is considered to be the
perceived air temperature due to wind speed (Gross, 2010) and dew point is the temperature
at which water condenses at a given humidity (Halonen et al. 2010). Furthermore, dew point
could be considered to be the onset of cloud cover, and therefore related to cloud cover
(Elliott and Angell, 2012). It is therefore likely that both temperature and cloud cover
elements impact red squirrel activity at a linear rate, where activity reduces as temperature
increases and the onset of activity reduces with increased cloud cover.
There was not enough precipitation to provide sufficient evidence to suggest whether or not
rain impacts upon activity, rain was observed during three of the mornings where transects
were conducted, however the rain was too fine for the on-site weather station to generate
data.
Pressure, humidity and visibility did not provide evidence to suggest that they have an impact
upon activity, as no significant correlations were found. There was also very little evidence
within the literature to suggest an impact of these elements upon red squirrel activity;
however it could be argued that visibility may have reduced the onset of activity in a similar
way to cloud cover.
This research has proved to be significant as it could encourage further research into the
impact of weather upon the threatened red squirrel, which could lead to more appropriate

Steven Carey 486940
55
management and protection for the red squirrel. It could be argued that weather is an
important consideration for the management of the red squirrel as the weather elements could
become increasingly adverse and therefore further disrupt red squirrel activity. However
future management strategies and considerations could prove to be complex and will likely
require further investigation.
7.1 Further Research
It has been discussed by Lampio (1967) that, weather has a more predictable impact on red
squirrel activity than single weather elements; this study has explored the combination of
elements in the form of wind-chill and dew point. An additional study could be conducted to
explore, in more detail, further combinations of weather. This could provide research beyond
the fundamental relationship between single weather elements and the impact on red squirrel
activity, which could potentially provide an array of more predictable weather patterns as
described by Lampio (1967).
It has been found that red squirrel activity is often considered to change during different
times of the year (Tonkin, 1983). As this study was limited to the Autumn period, another
research project could be conducted to investigate if weather impacts on activity during the
Spring, Summer or Winter periods.
How about tagging squirrels with small radio-transmitters ? I know these are used on birds
and probably on a range of other species – perhaps find a reference or 2 ?
Tree-top mounted weather station (s) ?

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8.0 References
Ames, D. and Insley, L. (1975) ‘Wind-Chill Effect for Cattle and Sheep’, Journal of Animal
Science, 40(1), pp. 161-165.
Atkins, P. (2010) The Laws of Thermodynamics A Very Short Introduction. New York:
Oxford University Press.
Bang, P. and Dahlstrøm, P. (2006) Animal Tracks and Sign. New York: Oxford University
Press.
Barry, R. and Chorley, R. (2009) Atmosphere, Weather and Climate. 9th edn. London:
Routledge.
Bassett, E., Bremner, J., Jolliffe, I., Jones, B. Morgan, B. and North, P. (2000) Statistics
Problems and Solutions. 2nd edn. London: World Scientific Publishing.
Bertram, B. and Moltu, D. (1987) The Reintroduction of Red Squirrels into Regent’s Park,
London: Report of The Zoological Society of London.
Bullock, J., Haddow, G. and Haddow, K. (2009) Global Warming, Natural Hazards, and
Emergency Management, United States of America: CRC Press.
Crockford, A. and Hui, S. (2007) Wind Profiles and Forests Validation of Wind Resource
Assessment Methodologies Including the Effects of Forests. MSc thesis. Technical University
of Denmark [Online]. Available at: http://www.visiondag.dtu.dk/upload/institutter/ mek/fm
/eksamensprojekter/crockford%26hui2007.pdf (Accessed: 16 January 2014).
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