Relationship Between Kittiwake Productivity, Weather & Cliff Orientation

An analysis of the relationship between Kittiwake(Rissa tridactyla) productivity, prevailing wind direction, speed and
rainfall and the orientation of thecliff.
Page 1 of 37 Abigail Ferrar
Abstract
Black-Legged Kittiwakes (Rissa tridactyla) are colonially nesting gulls (Laridae)
which nest on small ledges on sea cliffs. Cliffs along North Yorkshire coast has the largest
UK population of kittiwakes. Kittiwake productivity has been observed by the RSPB in 2009
to 2015. Kittiwake productivity is monitored on a weekly basis. Productivity is the amount of
chicks that have fledged from the colony over the amount of nests. Climate conditions affect
nesting marine birds in a variety of ways, indirectly. Weather has a direct effect which can
cause long or short term effects. Effects wind and precipitation have on populations has been
analysed on kittiwakes feeding biology but have not for their productivity. Here it is shown
that wind and precipitation has no significant difference to kittiwake productivity. Kittiwake
productivity has decreased at Bempton/Flamborough and increased in Filey this year.
Pearson’s correlation shows that wind and precipitation have a correlation with kittiwake
productivity, with no significant difference overall. Weather used are averages over kittiwake
breeding season. Weekly averages compared to weekly kittiwake productivity could help
analyse this further. These results are also the starting point for the RSPB to take record
weather data whilst taking observations of nests to see if the wide variety of weather
variables can affect the productivity. Population models can help to develop a better
understanding of the indirect and long term effects weather is having on the kittiwake colony
along the food chain.
1. Introduction
Black-Legged Kittiwakes (Rissa tridactyla) are colonially nesting, long-lived gulls
(Laridae) with a widespread distribution around the British coast (Aiebischer and Coulson
1990). They feed from the surface or just below the surface on fish and invertebrates. They
have occasionally been known to feed on bird eggs, small mammals and earth worms (del

Hoyo et al 1996). Kittiwakes start to arrive at breeding locations from January and breed
between May and August on small ledges along sea cliffs. They typically lay clutches
between 1 and 3 eggs, however the 3rd is usually an insurance for if one of the other eggs fails
to hatch. Occasionally 3 chicks do survive, but studies have shown this is because they have
nested next to an additional ledge for the chicks to move freely or adults have more room to
feed its chicks (Coulson and Thomas 1985). These eggs are incubated by both of the adults
for 24 to 28 days, once hatched, the chicks start to fledge from34 days old but can be as old
as 58 days. They do not return to the nesting location until they are between 3 and 5 years old
to breed (Hatch et al 2009).
Due to their chosen nesting location, they have a higher fledging success than most
other gulls due to lack of predation (Coulson and Porter 1984, Coulson and Thomas 1985).
In 1996, Cadiou and Monnat found that kittiwakes left chicks longer in the middle of the
rearing period than at the start. The closer the chick got to fledging, the longer the adults
would leave them. However they found that the chick was never left unattended before it was
6 days old, they determined this was probably due to it not being able to control
thermoregulation.
In 1988 Thomas and Coulson studied the reproductive success on kittiwakes nesting
along North Shields, Tyne and Wear in North East England over a 31 year period. The
population dynamics and biology of the kittiwake have changed throughout this study period.
They consider that two factors had contributed to these changes, those that operated in the
colony and those effective in the marine environment in which the birds feed. These have
been studied separately. Thomas and Coulson believed that climatic conditions had no
relationship with the kittiwake population dynamics, however stated more data is needed to
determine the environmental factors responsible for these changes.

A climatic condition that affects marine birds that has been studied the most since
Thomas and Coulson 1988, is El Niño-Southern Oscillation (ENSO). ENSO is the cycle of
cold and warm sea surface temperatures of the tropical eastern and central Pacific Ocean.
With low air pressure in the east and high air pressure in the west Pacific (Trenberth et al
2007). It has the most dramatic effect causing regular total breeding failure and high
mortality for marine birds found in the Southern Oceans. Less studies have been carried out
in the North Hemisphere climatic conditions, the North Atlantic Oscillation (NAO) (Sadvik
et al 2003). It is the fluctuations in the different of atmospheric pressure at sea level between
the Azores high and Icelandic low. It controls the strength and direction of westerly winds
and storm tracks (Cook et al 1998). However, it has less catastrophic effects on marine bird
life history (Sadvik et al 2003).
In 2005, Sadvik et al tested how NAO affected the adult mortality and prey
availability on five North Atlantic marine birds, including the kittiwake. They could not find
clear evidence that climate had an effect on adult survival of kittiwakes but did for the other
species of birds, all were of the Auk family. They conclude that to have a full understanding
of any climate related response requires a factor to be identified that causes it, like weather.
Climate is an indirect cause of mortality that travels through the food web, having
long term effects can occur over 100s of years and help change the demography and life
history characteristics. If climate changes, it may take many years for the effect on marine
bird populations to be apparent. Whereas weather has more direct and short term effects i.e.
where a bird decides to locate its nest and when the bird starts to breed. (Durant et al 2004,
Sadvik et al 2005, Schreiber 2001 and Thompson and Ollason 2001).
As the kittiwake, like most other marine birds, have a low clutch size and some
breeding pairs do not lay every year with a long life expectancy they cannot adjust their

clutch size when food supply is low. Due to this in response to environmental fluctuations,
they have less flexibility for adjusting clutch sizes than a terrestrial bird would. It can take
several years for a climate variations to show an apparent effect on population size
(Thompson and Ollason 2001). To understand this complexed relationship between climate
conditions and marine bird populations it has been shown that a deep ecological study of the
food web and its different relationships with the environment needs to be conducted (Durant
et al 2004).
In 1990, Aebischer et al analysed the parallel long-term trends across four marine
trophic levels and weather between 1955 and 1987. They study phytoplankton, zooplankton,
herring, breeding variables of kittiwakes and westerly weather of the North Sea. They found
that they all showed similar patterns in their long-term trends. Each level was lagged by the
respect of the level below it. They determines that the signal of weather is strong enough to
be evident at all of the tropic levels of the marine ecosystem.
The complexities of multi trophic interactions have been an important area of
research, understanding the impact of climate change on the population dynamics need to be
studied further (Crick 2004). Understanding the evolutionary implications and determining
the effects of weather on marine birds due to short term changes requires long term
monitoring of the marine bird (Schreiber 2002). The aim of this study is to see if there is a
relationship between average wind directions (True Degrees North), wind speed (knots) and
rainfall amount (mm) on kittiwake productivity along the North Yorkshire Coast using data
from 2009 to 2015.

2. Methods
2.1 Productivity observations
For this study the number of chicks fledged from the colony (productivity) was used
over the amount of eggs to how many chicks fledged ratio (breeding success). Productivity
monitoring for kittiwakes have been completed continuously at 18 sites (Fig 3 and 4) between
Flamborough Head and Filey for the last 6 years. These sites were chosen by the RSPB
following the guidelines and methodologies set out in ‘Marine bird monitoring handbook for
Britain and Ireland’ (Walsh et al 1995). I collected data from one plot at Bempton Cliff
Nature Reserve in 2015 (Fig 1); the other plots were collected by RSPB staff or volunteers.
We all followed the same methodology from Walsh et al. Productivity of kittiwakes is
calculated using the amount of chicks fledged divided by the number of completed nests each
year.
Figure 1: - Photograph of plot that I monitoredknown as Bartlett Nearat Bempton Cliffs RSPB reserve
(Photograph taken by Abigail Ferrar)

Firstly I needed to identify the location of kittiwake nests on my plot at end of May
2015. I was able to do this by having a photograph of the plot and marking what I thought
were kittiwake incubating, a complete nest that was currently unattended, a site holding a bird
with trace of test or a complete nest with a standing kittiwake. I found that there were 50
nests located on the plot and labelled them accordingly. Once the nests had been located and
labelled I returned to the site every 5 to 7 days to record the reproduction status until all the
kittiwakes had fledged from their parents nests. I recorded what I saw using codes; I, c/1, c/2,
c/3, c/0 or c/x (Refer to table 1 for descriptions of the codes). Codes c/1, c/2 and c/3 were
used when eggs were completely visible, sometimes it was hard to tell if there were more
than 1 so c/1+ was used to prevent confusion on calculating productivity. Once the chicks
had hatched, further codes were used to assess the age and size category (Table 2) whilst also
counting how many were present (Table 1).
Table 1: - Table showing the meaning of the codes usedwhen identifying a nest andits contents (Source Walsh et al
1995)
Code Meaning
I Sitting still, apparently incubating adult
c/1 c/2 c/3 Clutch of 1, 2 or 3 eggs
c/0 Empty well-built nest with adult attendance
c/x Well-built nest with adult standing, contents unknown
b/1 b/2 b/3 Number of chicks in nest

Table 2: - Table showing the codes and the description of the age and size category usedwhen recording chicks
(Source Walsh et al 1995)
Code Description
a Chick completely downy (Small)
b Downy chick, but black tips to upper wing-coverts just visible (S)
c Clear grey/black pattern visible on upper side of wing, but still some down on upper
wing, and mainly downy elsewhere (M/S)
d No down on upper side of wings, some down elsewhere (M/L)
e No down visible, wing tips at least equal to length of tail (L)
f Wing tips 1-2cm longer than tail (Fledgable)
ff Wing tips 3-4cm longer than tail (Fully fledged)
2.2 Productivity calculations
Kittiwake productivity was calculated using the Marine bird Monitoring Handbook
methodology (Walsh et al 1995). Productivity is the estimate of the entire population of the
breeding populations. As only a sample of the entire colony along the cliff face has been
monitored the number of chicks that have been considered as fledged from each plot is
divided by the peak amount of nests (AON) counted on the plots. The mean ± standard error
(SE) are calculated for each individual plot figures to given an estimate of colony
productivity.
2.3 Wind direction, wind speed and rainfall calculations
The RSPB do not collect local weather whilst collecting their data, the only source of
this data is off the met office. Wind direction, wind speed (Met office 2006 [b]) and rainfall
(Met office 2006 [a]) is only available upon request, I contacted the met office and explained
what I needed the information for. They responded by giving me a link to the Centre for
Environmental Data Archival (CEDA) as they store hourly data from every weather station

around the UK which is part of the Met Office Integrated Data Archive System (MIDAS).
Once I was able to find the folder that contained the information I needed, I extracted the
appropriate years in text files and convert them into excel. All three variables are collected
every hour, 365 days a year. As kittiwakes only breed from late May to early August, I only
needed data from this period. Following the procedure from Walsh et al, I used the last two
weeks of May to the first two weeks of August and calculated the average for the kittiwake
breeding season (Walsh et al 1995).
The met office stated that wind direction is only recorded at specific stations as it’s
not a variable that is provided daily at stations and only 6 months of hourly data is stored.
Using the search engine provided from the CEDA website I was able to find the nearest
weather station that recorded and collected wind data from 2009 to 2015. This station,
Bridlington MRSC, like all weather stations, has a unique source identifier (373). All data
that did not have this code as its source identifier was removed from the spreadsheet. Fig 2
shows how far Bridlington MRSC is from the first (Lighthouse) and last plot (Filey 10 a)
found along the coastline. It is 11.13miles from the last point and 4.63 miles from the first
point.

Figure 2: - Map showing the location of Bridlington MRSC (1), Lighthouse Plot (2) and Filey Plot 10(a) (3). The
distance between points 1 and 2 is 4.63miles. The distances between 1 and 3 is 11.13 miles. The distances between 2
and 3 is 12.49 miles (Createdon OS Maps).
Wind direction has been measured in the direction in which the wind blows in True
Degrees North (East = 090, South = 180, West = 270 and North = 360). Wind speed is
measured in Knots. If no wind was blowing at the time of measurement, wind direction and
speed were both set at a zero value. Rainfall data is described as the rainfall accumulation and
precipitation amount (mm).
2.4 GPS and Cliff Direction
Each of the site location is known to the RSPB; however, the GPS co-ordinates of the
locations were unknown. Each plot was visited and the GPS co-ordinates (Table 3) and the
direction the cliff faced were recorded using a Ricoh Calio 500SE device. These GPS
readings were used to highlight the locations on maps. Each plot was visited and the
approximate direction, as some of the plots were unsafe to reach, the cliff faced was recorded
using a compass. The degrees of Magnetic North were recorded from this however as the Met

Office has used True Degrees North to measure the wind direction, this needed to be convert
to match. Using the latest OS map of the area, I was able to determine the degrees differences
from Magnetic North to true north. This was 2° to the west, which is negative of the magnetic
north. 2° was subtracting from the figures I had collected from the plots to find the True
North.
Table 3: - GPS co-ordinates of all the plots found along the North Yorkshire Coast – Listed in
alphabetical order
Plot Co-ordinates
Back of Breil Nook N 54. 12782 W 0. 09641
Back of Newcombe N 54. 12984 W 0.09960
Bartlett Nab Far N 54. 15120 W 0. 17215
Bartlett Nab Near N 54. 15089 W 0. 17057
Breil Nook North N 54. 12806 W 0. 09641
Breil Nook South N 54. 12789 W 0. 09641
Filey 1 N 54. 21884 W 0. 27263
Filey 10(a) N 54. 22944 W 0. 31301
Filey 7 N 54. 22491 W 0. 29559
Filey 8 N 54. 22675 W 0. 30318
Filey 9 N 54. 22793 W 0. 31142
Grandstand North Far Edge N 54. 14995 W 0. 16824
Grandstand North Low N 54. 149987 W 0. 16824
Grandstand North Mid N 54. 149987 W 0. 16824
Grandstand North Near N 54. 14947 W 0. 16787
Grandstand North Near Edge N54. 1955 W 0. 16787
Jubilee Far N 54. 15233 W 0. 17862
Lighthouse N 54. 12737 W 0. 09885
Newcombe N 54. 12966 W 0. 10080
Old Dor N 54. 14473 W 0.15701
Saddle from Breil N 54. 12791 W 0. 09698
Saddle Nook 1 N 54. 12804 W 0. 09721
Saddle Nook 2 N 54. 12813 W 0. 09703
Swineshaw N 54. 12737 W 0. 09641
Figure 2 shows an edited map from the RSPB of the plots found along the North
Yorkshire coast, plot 1 starting at Bempton Cliffs reserve and plot 20 being at Flamborough
Lighthouse. Plots 12 and 13 were removed from this study as data had been collected this
year. Figure 3 shows a map created in ARC GIS of the plots found north of Filey.

Figure 3: - Plots for kittiwake monitoring along the North Yorkshire coast from Flamborough Head to Bempton
(Edited map from RSPB 2015 report)

2.5. Statistical Methods
The correlation between the breeding season (May – Aug) averages of wind speed,
wind direction and rainfall and each plot between 2009 and 2015 were tested using
Spearman’s Rank correlation. This is justified because weather variables could have an
influence on the productivity of kittiwakes at these plots. Correlation between the weather
variables were also tested against the entire colony at Bempton and Flamborough and the
entire colony at Filey for the same time period. All of these tests were performed on R (R
Core Team 2015). The codes that were used for these tests can be found in Appendix II.
3. Results
3.1 Productivity
Kittiwake productivity for the Bempton to Flamborough colony for 2009 to 2015 can
be seen in Figure 5. The graph shows a fluctuation in productivity since 2009. Error bars
Figure 4: - Plots for kittiwake monitoring along the North Yorkshire coast from Filey (Map created in ARC GIS)

represent the standard deviation and standard error to give an estimate for the rest of the cliff
face. Kittiwake productivity for the Filey colony for 2012 to 2015 can be seen in Figure 6.
The shows the productivity has increased since 2009.
Figure 5: - Bempton to Flamborough kittiwake colony productivity from 2009 - 2015. Error bars
maximum is standard deviation, error bars minimum is standard error.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
2009 2010 2011 2012 2013 2014 2015
KIttiwakeProductivity
Year of observation

Figure 6: - Filey kittiwake colony productivity from 2009 - 2015.Error bars maximum is standard
deviation, error bars minimum is standard error.
3.2 Wind direction (True Degrees North)
3.2.1 Colony overall
The kittiwake productivity of the Bempton and Flamborough colony shows to have a
positive correlation with wind direction, however there is no significant difference between
them (r = 0.21, p = 0.65, n = 29). Filey kittiwake colony shows a higher correlation with wind
direction than that at Bempton and Flamborough, but also has no significance difference (r =
0.79, p = 0.21, n = 19).
3.2.2 Bempton to Flamborough colony
Most plots showed to have a positive correlation with wind direction but showed no
significant difference (Table 4). Breil South and Newcombe did show to have a weak
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
2012 2013 2014 2015
KIttiwakeProductivity
Year of Observation

negative correlation with wind direction, however it shows to have no significant difference
(p = 0.83, p = 0.76, respectively).
Breil Nook South and North are located facing each other, they are located
within a large “u” bend within the cliff (158°, 278° respectively).. Fig 5 shows the
productivity of both plots. It shows that every year the data has been collected both plots have
had a similar productivity. As previously stated, there is a weak negative correlation at Breil
Nook South with no significant difference (r = -0.1, p = 0.83, n = 7). Breil Nook North shows
to have a weak positive correlation with no significant difference (r = 0.27, p = 0.55, n = 7).
Table 4: - All plots in Bempton and Flamborough, the direction the plot faces, correlation coefficient (r),
significant difference (p) and how many years the plot had been observed (n)
Plot Cliff Direction r p n
Jubilee Far 68 0.28 0.54 7
Bartlett Nab Near 23 0.51 0.31 7
Bartlett Nab Far 58 0.20 0.67 6
Grandstand North Near 78 0.35 0.5 7
Grandstand North Near Edge 78 0.37 0.41 6
Grandstand North Far Edge 108 0.03 0.96 5
Grandstand North Mid 108 0.14 0.77 7
Grandstand North Low 108 0.34 0.51 6
Old Dor 53 0.12 0.8 7
Newcombe 268 -0.14 0.76 7
Back of Newcombe 148 0.42 0.41 6
Saddle Nook 1 298 0.34 0.45 7
Saddle Nook 2 268 0.4 0.43 6
Saddle from Breil 138 0.77 0.08 6
Breil Nook North 158 0.27 0.55 7

Breil Nook South 278 -0.1 0.83 7
Back of Breil Nook 318 0.51 0.38 5
Swineshaw 138 0.49 0.4 5
Lighthouse 180 0.13 0.77 7
Figure 7: - Scatter plot comparing the correlation between wind direction at Breil South and North. Red dots
being Breil South and blue dots being Breil North.
3.2.3 Filey colony
Filey plot 1 did not have enough finite observations to analyse with correlation. Filey
Plot 8 showed a strong negative correlation with wind direction showing no significant
difference (r = -0.95, p =0.20, n = 3). All other plots showed positive correlation with no
significant difference (Table 5). Plots 9 and 10a are opposite each other on the cliff face and
are located on a “u” bend. Fig 8 shows these plots against wind direction. The plot shows that

productivity does differ throughout the years, a part from 2012 where no productivity was
recorded at both plots. Both plots have a weak positive correlation with no significant
difference.
Table 5: - All plots found at Filey, the direction the plot faces, correlation coefficient (r), significant
difference (p) for wind direction and how many years the plot had been observed (n).
Filey Plot 1 51 - - 2
Filey Plot 7 349 0.74 0.26 4
Filey Plot 8 335 -0.95 0.2 3
Filey Plot 9 325 0.4 0.6 4
Filey Plot 10(a) 55 0.48 0.52 4
Figure 8: - Kittiwake productivity of Filey Plot 9 and Filey Plot 10a plotted against wind direction (true
degrees north) for each observation year. Orange is Filey 9. Grey is Filey10 (a). Half orange/half grey dot
symbolises that in 2012 there was no productivity for both plots

3.3 Wind Speed (knots)
3.3.1. Colony Overall
positive correlation with wind speed, however there is no significant difference between them
(r = 0.15, p = 0.74, n = 29). Filey kittiwake colony shows a weak negative correlation with
wind speed, but also has no significance difference (r = -0.16, p = 0.83, n = 19).
All results show to have no significant difference between the plot and wind speed
(knots) (Table 6). Newcombe and Back of Newcombe are located behind one another due to
this they face opposite directions (268°, 148°, respectively). Fig 9 shows that both plot sites
have a weak positive correlation with no significant difference (r = 0.01, p = 0.83, n = 7, r =
0.10, p = 0.84, n = 6, respectively). As shown on Fig 9, the productivity of each plot has been
similar each year of observation. It shows that that 2012 had the highest average wind speed
than the other years, but the productivity was almost the same for both plots and similar
productivity for 2014 which had a lower wind speed average.

Table 6: - All plots in Bempton and Flamborough, the direction the cliff faces, the correlation coefficient
(r) and significant difference (p) with wind speed (knots) and how many years the plot had been observed
(n)
Jubilee Far 68 0.21 0.66 7
Bartlett Nab Near 23 0.26 0.62 7
Grandstand North Near 78 -0.15 0.75 7
Grandstand North Far Edge 108 -0.07 0.91 5
Grandstand North Low 108 -0.31 0.55 6
Old Dor 53 0.43 0.34 7
Newcombe 268 0.10 0.83 7
Back of Newcombe 148 0.10 0.84 6
Saddle Nook 1 298 -0.31 0.49 7
Saddle Nook 2 268 0.01 0.98 6
Saddle from Breil 138 -0.27 0.61 6
Breil Nook South 278 0.32 0.49 7
Back of Breil Nook 318 -0.12 0.85 5
Swineshaw 138 0.37 0.54 5
Lighthouse 180 -0.22 0.63 7

Figure 9: - Newcombe and Back of Newcombe plotted against wind speed (knots) for every year of
observation. Green is Newcombe. Purple is Back of Newcombe.
3.3.3 Filey Colony
Filey plot 7 and 8 showed to have a weak and strong (r = 0.49) positive
correlation (r = 0.92) with wind speed with no significant difference. Whereas plots 9 and 10a
showed to have a strong negative correlations with no significant difference (Table 7). Fig 10
shows these plots against wind speed. Shows that each year the productivity does differ, a
part from 2012 where no productivity was recorded at both plots. Filey plot 10 has a slightly
higher negative correlation with wind speed, which can be seen in the graph as the
productivity is lower than at Filey plot 9. There is no significant difference between the two
plots and wind speed. (r = -0.71, p = 0.29, n = 4, r = -0.88, p = 0.12, n = 4, respectively).

Table 7: - All plots found at Filey, the direction the plot faces, correlation coefficient (r) and significant
difference (p) for wind speed (knots) and how many years the plot had been observed (n).
Filey 1 51 - - 2
Filey 7 349 0.49 0.51 4
Filey 8 335 0.92 0.26 3
Filey 9 325 -0.71 0.29 4
Filey 10(a) 55 -0.88 0.12 4
Figure 10: - Kittiwake productivity of Filey Plot 9 and Filey Plot 10a plotted against wind speed (knots)
for each observation year. Yellow is Filey 9. Black is Filey10 (a). Half yellow/half black dot symbolises
that in 2012 there was no productivity for both plots

3.3 Precipitation amount (mm)
3.3.1. Colony Overall
weak negative correlation with precipitation amount, however there is no significant
difference between them (r = -0.22, p = 0.63, n = 29). Filey kittiwake colony however shows
to have a very week positive correlation with precipitation amount, but also has no
significance difference (r = 0.06, p = 0.94, n = 19).
Majority of the plots along Bempton and Flamborough showed to have a negative
correlation with precipitation amount, lower the amount of precipitation, the higher the
productivity. However they all showed to have no significant difference (Table 8). 3 plots
found at Bempton Cliffs all face the same direction however showed difference correlations.
Grandstand North Far Edge, Grandstand North Mid and Grandstand North Low all face 108°
(Table 7). Far Edge (FE) and Low (L) both show to have a negative correlation with
precipitation amount (r = -0.54, r = -0.56, respectively), whereas Mid (M) shows to have very
weak positive correlation (r = 0.04). All three locations show to have no significant difference
(p = 0.25 (FE), p = 0.94 (M), p = 0.25 (L)).
Fig 11 shows all 3 plots against precipitation amount (mm). The graph shows that
productivity has differed for each plot for the same year. FE shows to have higher
productivity and L shows to have the lowest throughout the years.

Table 8: - All plots in Bempton and Flamborough, the direction the cliff faces, the correlation coefficient
(r) and significant difference (p) with precipitation amount (mm) and how many years the plot had been
observed (n)
Jubilee Far 68 -0.06 0.90 7
Bartlett Nab Near 23 -0.36 0.49 7
Grandstand North Near 78 -0.32 0.49 7
Grandstand North Far Edge 108 -0.54 0.35 5
Grandstand North Low 108 -0.56 0.25 6
Old Dor 53 -0.24 0.61 7
Newcombe 268 0.18 0.70 7
Back of Newcombe 148 -0.21 0.69 6
Saddle Nook 1 298 -0.56 0.19 7
Saddle Nook 2 268 -0.38 0.45 6
Saddle from Breil 138 -0.62 0.19 6
Breil Nook South 278 -0.11 0.82 7
Back of Breil Nook 318 -0.81 0.10 5
Swineshaw 138 0.01 0.99 5
Lighthouse 180 -0.10 0.84 7

Figure 11: - Grandstand Mid (Green), Grandstand Far Edge (Blue) and Grandstand Low (Red) kittiwake
productivity against average precipitation amount (mm) for each observation year.
3.4.3 Filey colony
Filey plot 7 and 8 showed to have a weak negative correlation (r = -0.15) and a weak
negative positive correlation (r = 0.19) with precipitation amount with no significant
difference (p = 0.85, p = 0.88, respectively) (Table 9). Plot 12 shows to have a very weak
positive correlation (r = 0.05) whereas plot 10a had a weak negative correlation (r = -0.28).
Both plots showed to have no significant difference (p = 0.95, p = 0.72, respectively). Fig 11
shows these plots against precipitation amount (mm). Shows that each year the productivity
does differ, a part from 2012 where no productivity was recorded at both plots. Both plots
demonstrate that in 2014 there was a slight increase in productivity despite there being a
higher precipitation amount (mm). In 2015 there was a decrease in productivity, with a fall in
precipitation amount (mm).

Table 9: - All plots found at Filey, the direction the plot faces, correlation coefficient (r) and significant
difference (p) for rainfall amount (precipitation mm) and how many years the plot had been observed (n).
Filey 1 51 - - 2
Filey 7 349 -0.15 0.85 4
Filey 8 335 0.19 0.88 3
Filey 9 325 0.05 0.95 4
Filey 10(a) 55 -0.28 0.72 4
Figure 12: - Kittiwake productivity of Filey Plot 9 and Filey Plot 10a plotted against precipitation amount
(mm) for each observation year. Green is Filey 9. Blue is Filey10 (a). Half blue/half green dot symbolises
that in 2012 there was no productivity for both plots. 2013 and 2015 for Filey 10a had the same
productivity and amount of precipitation.

4. Discussion
Although the data shows that the three weather variables have no significant
difference and each show a different correlation (positive or negative) despite the location or
cliff direction, there were some limitation effecting this result. Wind direction, wind speed
and precipitation amount within this study were an average of the breeding season months
(May, June, July and August), this does not show a great representative of productivities
relationship with weather as the data varied. This was less noticeable with wind direction,
however wind speed and precipitation amount were affected by this limitation. Some of the
weeks within the breeding season had an average wind speed of 12 knots but there were more
weeks with lower wind speeds making the overall average low. Precipitation amount had
similar results, there were more weeks with no precipitation than large amounts of
precipitation. Comparing the weekly kittiwake observations, rather than the productivity,
could enhance this study further to see if there is a relationship between weekly average
weather variables and when chicks were no longer observed at the nests. Strong gusts of wind
and passing storm that fill up the nests causing small chicks to become chilled have short
term effects on seabirds. The RSPB could record the weather on the day of observation to
further analyse the relationship between the weather and productivity.
As this study only looked at the direct effects of weather on kittiwake productivity,
other factors could be the reason for the changing productivity at each of the plots. Indirect
effects, like prey availability. These effects on kittiwake productivity are more complexed as
they effect the kittiwake’s food chain and not the kittiwakes themselves. Temperature is an
example of an indirect effect on kittiwakes and their prey. A certain temperature may be
ambient for the kittiwake and its prey, however the prey’s source of food may not be suited to
this temperature (Durant et al 2004). Temperature can also have an effect on chick growth
and increase mortality of small chicks. Hot and cold temperatures can have an effect on these

small chicks as they are unable to control their own thermoregulation (Burger and Gochfeld
1990). However there has been few studies on what is a fatal temperatures for eggs or chicks
(Schreiber 2002).
This year, some of the nests that were observed as unattended small chicks. With the
weeks following these nests were observed as being inhabited or the small chicks were dead.
As these chicks were so young, the lack of thermoregulation and no adult present to do so for
them, could have caused mortality, effecting the productivity of that plot.
A population model can be used to help determine what indirect effects weather is
having on kittiwake populations. These types of models can help establish a better
understanding of the complexities of the interaction between different components of the
kittiwake’s demography including prey availability and migration (Crick 2003). In 2003,
Jenouvrier et al used a Leslie matrix population model to understand the southern fulmar
(Fulmarus glacialoides) population dynamics and environmental stochasticity that affect the
survival and breeding performances.
Some of the plots have been monitored less than others, skewing the data further.
However plots that had only been monitored once over this study period have been removed.
Also, Filey plot 9 and 10 (a) were effected by a landslides early into 2012s breeding season
causing all of the kittiwake nests to fail and the adults not having a chance to relay in the
breeding season causing a 0 productivity. As the correlation could have been higher/lower if
there was productivity.
Overall during breeding season, kittiwake productivity is not directly affected by the
average wind direction, speed or the amount of precipitation. Further studies will be needed
to establish if weekly weather has an effect on chick mortality. Or if there are any indirect
effects the weather is having on the productivity and overall populations of the kittiwakes.

References
[1.] Aebischer, N. J., & Coulson, J. C. (1990). Survival of the kittiwake in relation to sex, year, breeding
experience and position in the colony. The Journal of Animal Ecology,1063-1071.
[2.] Aebischer, N., Coulson, J., and Colebrook, J. (1990). Parallel long-term trends across four marine trophic
levels and weather. Nature, 347, 753–5
[3.] Cadiou, B. & Monnat , J,Y. (1996) Parental attendance and squatting in the Kittiwake Rissa tridactyla
during the rearing period,Bird Study, 43:2, 164-171
[4.] Cook, E. R., D'Arrigo R, D, & Briffa,K, R. (1998). A reconstruction ofthe North Atlantic Oscillation
using tree-ring chronologiesfrom North America and Europe. Holocene, 8, 9-17
[5.] Coulson, J. C., & Porter, J. M. (1985). Reproductive success ofthe kittiwake Rissa tridactyla: the roles of
clutch size, chick growth rates and parental quality. Ibis, 127(4), 450-466.
[6.] Coulson, J. C., & Thomas, C. S. (1985). Changes in the biology of the kittiwake Rissa tridactyla: a 31-
year study of a breeding colony. The Journal of Animal Ecology,9-26.
[7.] Crick, H. Q. (2004). The impact of climate change on birds. Ibis, 146(s1), 48-56.
[8.] del Hoyo, J., Elliot, A. and Sargatal, J. (1996). Handbook of the Birds of the World. Volume 3: Hoatzin to
Auks. Lynx Edicions, Barcelona.
[9.] Durant, J. M., Stenseth,N. C., Anker-Nilssen, T., Harris, M. P., Thompson, P. M., & Wanless,S. (2004).
Marine birds and climate fluctuation in the North Atlantic. Marine ecosystems and climate variation:the
North Atlantic,95-105.
[10.] Hatch, S. A., Robertson, G. J., & Baird, P. H. (2009). Black-legged kittiwake (Rissa tridactyla). The
Birds of North America. Ithaca: Cornell Lab of Ornithology.
[11.] Jenouvrier, S., Barbraud, C. and Weimerskirch, H. (2003), Effects of climate variability on the
temporal population dynamics of southern fulmars. Journal of Animal Ecology, 72: 576–587.
[12.] Met Office (2006) [a]: UK Daily rainfall Data, Part of the Met Office Integrated Data Archive System
(MIDAS). NCAS British Atmospheric Data Centre, [Date Accessed1st August 2015].
http://catalogue.ceda.ac.uk/uuid/c732716511d3442f05cdeccbe99b8f90
[13.] Met Office (2006) [b]: UK Mean Wind Data, Part of the Met Office Integrated Data Archive System
(MIDAS). NCAS British Atmospheric Data Centre, [Date Accessed1st August 2015].
http://catalogue.ceda.ac.uk/uuid/a1f65a362c26c9fa667d98c431a1ad38

[14.] R Core Team (2015). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL https://www.R-study.org/ .
[15.] SANDVIK, H., ERIKSTAD, K. E., BARRETT, R. T. & YOCCOZ, N. G. (2005), The effect of climate
on adult survival in five species of North Atlantic marine birds. Journal of Animal Ecology, 74: 817–83
[16.] Schreiber, E. A. (2001). Climate and weather effects on marine birds. Biology of marine birds, 179-
207.
[17.] Thomas, C. S., & Coulson, J. C. (1988). Reproductive success ofkittiwake gulls, Rissa
tridactyla. Reproductive success: Studies of individual variation in contrasting breeding systems, 251-262.
[18.] Thompson, P. and Ollason, J. (2001). Lagged effects of ocean climate change on fulmar population
dynamics. Nature, 413, 417–20.
[19.] Trenberth, K.E., P.D. Jones, P. Ambenje, R. Bojariu, D. Easterling, A. Klein Tank, D. Parker, F.
Rahimzadeh, J.A. Renwick, M. Rusticucci, B. Soden & P. Zhai, (2007) Observations: Surface and
Atmospheric Climate Change. In: Climate Change 2007:The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
Cambridge University Press, Cambridge, United Kingdom
[20.] Walsh, P. M., Halley, D. J., Harris, M. P., Del Nevo, A., Sim, I. M. W., & Tasker, M. L. (1995).
Marine bird monitoring handbook forBritain and Ireland: a compilation of methods for survey and
monitoring of breeding marine birds. JNCC/RSPB/ITE/Marine bird Group.
WORD COUNT = 4124

Appendix 1 – R Code
kittiwake <- read.table(file.choose(), na.strings=c("", "NA"), sep="t", header=T)
attach(kittiwake)
library(mgcv)
row.names(kittiwake)[1] <- 2009 # Renamed the rows so graphs could be
labeled
row.names(kittiwake)[2] <- 2010
row.names(kittiwake)
###################### WIND DIRECTION ####################
wind_dir.1 <- cor.test(wind_dir, back_briel, use = "complete") # Spearmans rank
correlation to check for a relationship between wind direction and a plot site - this
code is also used for wind speed and precipitation
wind_dir.1
wind_dir.2 <- cor.test(wind_dir, back_newcombe, use = "complete")
wind_dir.2
wind_dir.3 <- cor.test(wind_dir, bartlett_far, use = "complete")
wind_dir.3
wind_dir.4 <- cor.test(wind_dir, bartlett_near, use = "complete")
wind_dir.4
wind_dir.5 <- cor.test(wind_dir, breil_north, use = "complete")
wind_dir.5
wind_dir.6 <- cor.test(wind_dir, briel_south, use = "complete")
wind_dir.6
wind_dir.7 <- cor.test(wind_dir, grandstand_far, use = "complete")
wind_dir.7
wind_dir.8 <- cor.test(wind_dir, grandstand_low, use = "complete")
wind_dir.8
wind_dir.9 <- cor.test(wind_dir, grandstand_mid, use = "complete")
wind_dir.9
wind_dir.10 <- cor.test(wind_dir, grandstand_near, use = "complete")
wind_dir.10

wind_dir.11 <- cor.test(wind_dir, grandstand, use = "complete")
wind_dir.11
wind_dir.12 <- cor.test(wind_dir, jubilee, use = "complete")
wind_dir.12
wind_dir.13 <- cor.test(wind_dir, lighthouse, use = "complete")
wind_dir.13
wind_dir.14 <- cor.test(wind_dir, newcombe, use = "complete")
wind_dir.14
wind_dir.15 <- cor.test(wind_dir, old_dor, use = "complete")
wind_dir.15
wind_dir.16 <- cor.test(wind_dir, saddle_briel, use = "complete")
wind_dir.16
wind_dir.17 <- cor.test(wind_dir, saddle_1, use = "complete")
wind_dir.17
wind_dir.18 <- cor.test(wind_dir, saddle_2, use = "complete")
wind_dir.18
wind_dir.19 <- cor.test(wind_dir, swineshaw, use = "complete")
wind_dir.19
wind_dir.20 <- cor.test(wind_dir, filey_1, use = "complete") #not enough data
wind_dir.20
wind_dir.21 <- cor.test(wind_dir, filey_7, use = "complete")
wind_dir.21
wind_dir.22
wind_dir.23
wind_dir.24<- cor.test(wind_dir, filey_10a, use = "complete")
wind_dir.24
wind_dir.25<- cor.test(wind_dir, flam, use = "complete") ### Overall colony at
Flamborough and Bempton
wind_dir.25
wind_dir.26<- cor.test(wind_dir, filey, use = "complete") ### overal colony at Filey
wind_dir.26

### Breil South & Briel North plotted on same graph ###
plot(wind_dir,briel_south, xlab="Wind Direction (True Degrees North)",
frame.plot=FALSE,
main="Kittiwake Productivity at facing cliffs vs Wind Direction",
ylab="Kittiwake Productivity", col="red", pch=16, family="serif",
ylim=c(0,1.5), xlim=c(180,230),
text(wind_dir, briel_south, row.names(kittiwake), cex=0.6, pos=1))
par(new=T)
plot(wind_dir,breil_north, ylab="", xlab="", axes=F, pch=16, col="blue",
ylim=c(0,1.5), xlim=c(180,230),
text(wind_dir, breil_north, row.names(kittiwake), cex=0.6, pos=1),
legend(230,1.5,
col=c(“red”, “blue”),
c(“Breil South”, “Briel North”),bty=”o”, cex=.8))
################## WIND SPEED ############################
wind_sp.1 <- cor.test(wind_sp, back_briel, use = "complete")
wind_sp.1
wind_sp.2 <- cor.test(wind_sp, back_newcombe, use = "complete")
wind_sp.2
wind_sp.3 <- cor.test(wind_sp, bartlett_far, use = "complete")
wind_sp.3
wind_sp.4 <- cor.test(wind_sp, bartlett_near, use = "complete")
wind_sp.4
wind_sp.5 <- cor.test(wind_sp, breil_north, use = "complete")
wind_sp.5
wind_sp.6 <- cor.test(wind_sp, briel_south, use = "complete")
wind_sp.6
wind_sp.7 <- cor.test(wind_sp, grandstand_far, use = "complete")
wind_sp.7
wind_sp.8 <- cor.test(wind_sp, grandstand_low, use = "complete")
wind_sp.8
wind_sp.9 <- cor.test(wind_sp, grandstand_mid, use = "complete")
wind_sp.9
wind_sp.10 <- cor.test(wind_sp, grandstand_near, use = "complete")
wind_sp.10

wind_sp.11 <- cor.test(wind_sp, grandstand, use = "complete")
wind_sp.11
wind_sp.12 <- cor.test(wind_sp, jubilee, use = "complete")
wind_sp.12
wind_sp.13 <- cor.test(wind_sp, lighthouse, use = "complete")
wind_sp.13
wind_sp.14 <- cor.test(wind_sp, newcombe, use = "complete")
wind_sp.14
wind_sp.15 <- cor.test(wind_sp, old_dor, use = "complete")
wind_sp.15
wind_sp.16 <- cor.test(wind_sp, saddle_briel, use = "complete")
wind_sp.16
wind_sp.17 <- cor.test(wind_sp, saddle_1, use = "complete")
wind_sp.17
wind_sp.18 <- cor.test(wind_sp, saddle_2, use = "complete")
wind_sp.18
wind_sp.19 <- cor.test(wind_sp, swineshaw, use = "complete")
wind_sp.19
wind_sp.20 <- cor.test(wind_sp, filey_1, use = "complete") #not enough data
wind_sp.20
wind_sp.21 <- cor.test(wind_sp, filey_7, use = "complete")
wind_sp.21
wind_sp.22
wind_sp.23
wind_sp.24<- cor.test(wind_sp, filey_10a, use = "complete") #not enough data
wind_sp.24
wind_sp.25<- cor.test(wind_sp, flam, use = "complete") ### Overall colony at
wind_sp.25
wind_sp.26<- cor.test(wind_sp, filey, use = "complete") ### overal colony at Filey
wind_sp.26

##################### Plotting Wind Speed ###########################
###### All these codes are for a linear regression with graphically codes. Only thing that is
changed for each code is the variable plotted and the names of axis and titles.
plot(wind_sp,briel_south, xlab="Wind Speed (Knots)",
main="Kittiwake Productivity at facing cliffs vs Wind Speed",
ylim=c(0,1.5), xlim=c(7,9),
text(wind_sp, briel_south, row.names(kittiwake), cex=0.6, pos=1))
par(new=T)
plot(wind_sp,breil_north, ylab="", xlab="", axes=F, pch=16, col="blue",
ylim=c(0,1.5), xlim=c(7,9),
text(wind_sp, breil_north, row.names(kittiwake), cex=0.6, pos=1))
######################### PRECIPITATION ###############################
prcp_amt.1 <- cor.test(prcp_amt, back_briel, use = "complete")
prcp_amt.1
prcp_amt.2 <- cor.test(prcp_amt, back_newcombe, use = "complete")
prcp_amt.2
prcp_amt.3 <- cor.test(prcp_amt, bartlett_far, use = "complete")
prcp_amt.3
prcp_amt.4 <- cor.test(prcp_amt, bartlett_near, use = "complete")
prcp_amt.4
prcp_amt.5 <- cor.test(prcp_amt, breil_north, use = "complete")
prcp_amt.5
prcp_amt.6 <- cor.test(prcp_amt, briel_south, use = "complete")
prcp_amt.6
prcp_amt.7 <- cor.test(prcp_amt, grandstand_far, use = "complete")
prcp_amt.7
prcp_amt.8 <- cor.test(prcp_amt, grandstand_low, use = "complete")
prcp_amt.8
prcp_amt.9 <- cor.test(prcp_amt, grandstand_mid, use = "complete")
prcp_amt.9
prcp_amt.10 <- cor.test(prcp_amt, grandstand_near, use = "complete")
prcp_amt.10
prcp_amt.11 <- cor.test(prcp_amt, grandstand, use = "complete")
prcp_amt.11

prcp_amt.12 <- cor.test(prcp_amt, jubilee, use = "complete")
prcp_amt.12
prcp_amt.13 <- cor.test(prcp_amt, lighthouse, use = "complete")
prcp_amt.13
prcp_amt.14 <- cor.test(prcp_amt, newcombe, use = "complete")
prcp_amt.14
prcp_amt.15 <- cor.test(prcp_amt, old_dor, use = "complete")
prcp_amt.15
prcp_amt.16 <- cor.test(prcp_amt, saddle_briel, use = "complete")
prcp_amt.16
prcp_amt.17 <- cor.test(prcp_amt, saddle_1, use = "complete")
prcp_amt.17
prcp_amt.18 <- cor.test(prcp_amt, saddle_2, use = "complete")
prcp_amt.18
prcp_amt.19 <- cor.test(prcp_amt, swineshaw, use = "complete")
prcp_amt.19
prcp_amt.20 <- cor.test(prcp_amt, filey_1, use = "complete") #not enough data
prcp_amt.20
prcp_amt.21 <- cor.test(prcp_amt, filey_7, use = "complete")
prcp_amt.21
prcp_amt.22
prcp_amt.23
prcp_amt.24<- cor.test(prcp_amt, filey_10a, use = "complete")
prcp_amt.24
prcp_amt.25<- cor.test(prcp_amt, flam, use = "complete") ### Overall colony at
prcp_amt.25
prcp_amt.26<- cor.test(prcp_amt, filey, use = "complete") ### overal colony at Filey
prcp_amt.26
################# Plotting Precipitation ######################
###### All these codes are for a linear regression with graphically codes. Only thing that is
changed for each code is the variable plotted and the names of axis and titles.

plot(prcp_amt,briel_south, xlab="Rainfall (mm)",
main="Kittiwake Productivity at facing cliffs vs Average Yearly Rainfall ",
ylim=c(0,1.5), xlim=c(0,0.12),
text(prcp_amt, briel_south, row.names(kittiwake), cex=0.6, pos=1))
par(new=T)
plot(prcp_amt,breil_north, ylab="", xlab="", axes=F, pch=16, col="blue",
ylim=c(0,1.5), xlim=c(0,0.12),
text(prcp_amt, breil_north, row.names(kittiwake), cex=0.6, pos=1))
plot(prcp_amt,filey_9, xlab="Rainfall (mm)",
frame.plot=FALSE,
ylab="Kittiwake Productivity", col=507, pch=16, family="serif",
ylim=c(-0.1,1), xlim=c(0,0.12),
text(prcp_amt, filey_9, row.names(kittiwake), cex=0.6, pos=1))
par(new=T)
plot(prcp_amt,filey_10a, ylab="", xlab="", axes=F, pch=16, col=429,
ylim=c(-0.1,1), xlim=c(0,0.12),
text(prcp_amt, filey_10a, row.names(kittiwake), cex=0.6, pos=1))

Appendix 2 – Extra credit
Although my main duties on this placement were to collect data of kittiwake
productivity and use this to analysis the relationship it has with wind variables from 2009 –
2015, I also volunteered with the RSPB on my free days. I was located on one of the 6 view
points along the Bempton Cliffs nature reserve and inform people of the birds located on the
cliff and answer any questions they might have had about the birds themselves or surrounding
locations – i.e. Filey, Bridlington or Scarborough.
As it was the summer holidays, a lot of
families of all ages would visit the site. The RSPB
provided me with ecology of the birds and pictures
to help visitors identify them. Also I was provided
with a telescope with adjustable legs to help even
the smallest of visitor see the birds up close and
personal.
Some of the volunteers have been there for over 20 years. If I didn’t know the answer
to a question, I was able to ask someone that did. This helped me to expand my knowledge in
marine birds and built my confidence in speaking to a wide range of different people of all
ages. Seeing the older generation getting excited over seeing a puffin up close and personal
for the first time, made the cold and wet days’ worth while.
As well as being located at one of the viewpoints, I
was fortunate enough to take part in a “Puffin Patrol”
along the cliffs with one of the more experience
volunteers. A “Puffin Patrol” gives children and adults
alike a chance to take a tour of the cliffs with lots of
information about the location, the cliffs themselves and of
course the puffins. Unfortunately the few tours I took, we
did not see any puffins and despite of this the volunteers
were full of information about the other nesting birds.
Again enhancing my knowledge.
Being located at Bempton on my free days has been a great experience for myself.
Interacting with members of the public has encouraged me to find out more about these
beautiful birds. Seeing children getting excited and asking me a wide range of questions has
been the best experience of this placement!

Relationship Between Kittiwake Productivity, Weather & Cliff Orientation

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