The County for Wales (2007). The Bosherston lakes sit

The Bosherston Lakes are a man-made
lake system comprised of four lakes (Figure 1); Eastern Arm, Central Arm,
Central Lake and Western arm (Husband, Cassidy and Stimpson,
2009; Giddings, 2011). Stackpole Stream and Cheriton Stream
feed the lake system (Husband, Cassidy and Stimpson,
2009; Giddings, 2011). It is now known that the Western arm
and Central Lake are fed by a groundwater spring system (Giddings, 2011).

















lakes fall under particular interest due to their geographical position, the
surrounding geology and biological rarity (Pembrokeshire
Coast National Park, 2011; Countryside
County for Wales (2007).
The Bosherston lakes sit above a Limestone bedrock, in where 96% of water is
estimated to be lost to (Giddings, 2011). Home to unique flora and fauna
including Chara spp., salinity levels
within the lake system are of much interest (Holman et al., 2009; Haycock & Hinton, 2010).
Conductivity is often recorded when looking at salinity levels (Williams and Sherwood, 1994; Kim et al., 2013). Bosherston lakes are most likely to
succumb to climatic changes that are predicted for the future (Holman et al.,
2009). Salinity levels are thought to increase by two scenarios; sea level rise
and increased global temperatures thus resulting in lower lake levels (Holman
et al., 2009). An increase in global sea levels (Church et al., 2013) has not
yet reached a point where water from the sea, continually passes the Broadhaven
Dam (Holman et al., 2009). Therefore it can be hypothesized that any increases
in salinity levels within Bosherston Lakes at present, can be linked with water
level. This leads to the following hypotheses;  

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Water levels are seen to effect salinity levels within four Bosherton Lakes.

Conductivity levels, thus salinity levels do not decrease when increased levels
of water can be seen in four Bosherton Lakes.

order to discourage the null hypothesis it is hopeful that a result will show
findings to have a significant difference, <0.05. 2: Data Analysis: 2.1 Methodology Recorded data was arranged by the following way: an average of each month was collected and further arranged into seasons. Seasons can be defined as: Winter (December, January, February), Spring (March, April, May), Summer (June, July, August) and Autumn (September, October, November). It was chosen to collect data with the same format (collecting averages) for each site, to minimise as much bias as possible. In order to successfully discourage the null hypothesis a simple regression was performed. Using this statistical test allowed a forecast to be made for both conductivity and water levels from 2018-2020.       2.2 Analysis: It can be seen that a negative correlation is present in three sites (Figure 2, 3 & 4).  The Central arm (Figure 5) shows a positive correlation for 2005 and 2007. It can be suggested that a large discharge of nitrate may have run-off from surrounding fields at this time as pollutants such as nitrate and phosphates increase conductivity (Morrison et al., 2001). By showing years and seasons, a trend can be seen (Figure 2-5) in all four lakes displaying the link between water levels, precipitation and evapotranspiration levels e.g. the water budget (Villagra et al., 2017; Perrow and Davy, 2002). Across the four lakes, water levels are shown to drop in autumn 2006, being more apparent in the Western arm (Figure 3) and the Central Arm (Figure 5). The Central arm (Figure 5 & 6) can be seen to have the highest peak in conductivity, in winter 2005. The Western arm (Figure 3) can be seen to display the largest variance in water levels (m), with the Central arm (Figure 5) showing the largest variance in conductivity levels (µ/cm). It can be observed that the Central arm shows the highest levels in both water (m) and conductivity (µ/cm) (Figure 6 & 7), in contrast to this the Central Lake records the lowest. It can be identified that a trend suggesting a decline in water levels can be seen across the four lakes (Figure 7), with an incline in conductivity levels (µ/cm) being noticed (Figure 8), including the Stackpole Stream which feeds the lakes.                                                                                                                                                                                           2.3 Results Autumn 2006, presents a decline in water levels across all four sites, with each site recording; Central Lake; 0.12m, Eastern arm; 0.5m Western arm; 0.35m and Central arm; 1.05m. For the Central Lake, Eastern arm and Western arm these recordings provide the lowest found between 2005 and 2009, however the Central arm is recorded to drop to 0.87m in summer 2009. The Western arm displayed the largest variance in water levels, with records as low as 0.35m and highs of 1.99m. The Central arm provides the highest records of both water (m) and conductivity levels (µ/cm) with spring 2006 recording 2.13m and winter 2005 recording 630 (µ/cm). The Central arm can also be seen to show the largest variance in conductivity levels with records ranging from 163.33 (µ/cm) – 630 (µ/cm).   2.4 Statistics: A simple linear regression was carried out to present data that determines salinity levels (recorded by conductivity levels), are affected by water levels. A significant regression equation was found for each site recording water level; Central Lake (F(4,14)= 6.709, p< 0.003, with an R2 of 0.65, Western arm (F(4,14)= 6.065, p< 0.004, with an R2 of 0.63, Eastern arm (F(4,14)= 4.889, p< 0.011, with an R2 of 0.58 and Central arm (F(4,14)= 5.094, p< 0.009, with an R2 of 0.59. Conductivity levels also show a significant regression equation for each site; Central Lake (F(4,39)= 30.623, p<1.46E-11, with an R2 of 0.75, Western arm (F(4,39)= 47.528, p<1.73E-14, with an R2 of 0.82, Eastern arm (F(4,39)= 42.224, p<1.13E-13, with an R2 of 0.81 and Central arm (F(4,39)= 3.918, p<0.009, with an R2 of 0.28. With that in mind, a forecast could be made for future years and seasons based on the simple linear regression conducted (Figure 9 & 10).                                                             3: Discussion: By performing a simple linear regression, results have shown that the null hypothesis can be discredited as all p values are < 0.05.  With the null hypothesis being discredited it can be concluded that Bosherston Lakes are likely to increase in conductivity levels, thus salinity levels, even without sea water crossing the Broadhaven dam. Rising temperatures (Holman et al, 2009; Kirtman et al., 2013), thus resulting in lower water levels can provide an understanding for this result. Records show that July 2006 showed record breaking temperatures in the UK (Prior and Beswick, 2007). This event is clearly seen within the data presented with all four lakes showing, in some cases, a dramatic drop in water levels for autumn 2006. This event is not only seen at Bosherston Lakes, with two catchments in southern England also experiencing lowest water levels recorded since 1997 (Darling et al., 2012). Forecasts were made for both water levels (m) and conductivity (µ/cm). In both scenarios a clear negative correlation can be seen across all four lakes, however results show a decline for both water level and conductivity. This goes against the trend seen for conductivity within data recorded. It can therefore be suggested that modelling forecasts should not be done using a simple regression forecast but use similar models such as ECHAM5 model (a GCM model) as seen in other investigations (Ye et al., 2011). It was hypothesized that a decrease in water levels will see an increase in salinity levels. A global comparison can be made with data recorded here. The incline trend of conductivity, supporting salinity levels, can also be seen in other lake systems induced by anthropogenic climatic changes (Jeppesen et al., 2015). Increases in salinity are seen to alter salt-sensitive communities (Jeppesen et al., 2015; Ballot et al., 2009). It can be predicted that chara spp. are likely to disappear based on results found here and the following evidence: Filamentous algae (another algal species found in Bosherston Lakes), more tolerant to increased nutrients such as phosphorus and nitrates, can become the dominant species (Hurford, Schneider and Cowx, 2010) and an increase in hydraulic residence time, thus resulting in higher salinity can result to ionic imbalances in salt-sensitive species (Jeppesen et al., 2015). It is important to document that Limestone, the bedrock in which the four lakes sit upon, can effect conductivity levels due to minerals entering the groundwater system (Duba et al., 1978). However this does not change the fact an incline trend is apparent. 4: Conclusion: The null hypothesis was discredited by comparing conductivity levels with water levels. Water levels are seen to show a decline over a five year period. Conductivity levels display an incline over a 10 and 31 year period. UK climatic events are recorded within the data and are easily seen.  Trends can be compared globally with anthropogenic climatic changes altering many lake systems around the world by effecting both water levels and salinity levels. Salt sensitive species found in Bosherston Lakes are likely to be pushed to the upper end of their salinity and nutrient tolerances.                         Student Number: 928471. Word Count exl figure captions & References: 1488. 5: References: Ballot, A., Kotut, K., Novelo, E. and Krienitz, L. (2009). Changes of phytoplankton communities in Lakes Naivasha and Oloidien, examples of degradation and salinization of lakes in the Kenyan Rift Valley. Hydrobiologia, 632(1), pp.359-363. Church, J.A., P.U. Clark, A. Cazenave, J.M. Gregory, S. Jevrejeva, A. Levermann, M.A. Merrifield, G.A. Milne, R.S. Nerem, P.D. Nunn, A.J. Payne, W.T. Pfeffer, D. Stammer and A.S. Unnikrishnan, 2013: Sea Level Change. In: Climate Change 2013: The Physical Science Basis. 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