Hypothesis

For my investigation we studied a 550m stretch of beach between Lochranza and Catacol on the Isle of Arran, as shown on the OS map (page 2). On this beach it consisted of many granite and phylite pebbles. Phylite could have ended up here from erosion from the cliffs by abrasion. The granite however is not located in this area of the island and could only be transported by the River Eason, joining the River Charmadale and also the River Catacol. I think more likely though that the granite, which ended up on the beach we are studying, came from the River Catacol. When the granite is deposited at the river mouth they are all different sizes and we say that they are ‘unsorted’. This granite could have been transported round to the location where we studied by Longshore Drift travelling from the Southwest and transporting sediment along the coast towards the Northeast sorting the granite as it moved. There is evidence that longshore drift is acting and travelling from the Southwest direction. (1) It could have produced the spit located in Lochranza Bay and (2) the most dominant prevailing winds travel from that direction. For my investigation I am going to use Longshore Drift to produce 2 hypotheses:

> Granite pebbles will become smaller in size towards Lochranza.

> Granite pebbles will become more sorted towards Lochranza.

Development of a Strategy

To prove that this hypothesis is true, I needed primary and secondary data to help me. I was informed of what granite looked like so I knew what I was looking for (secondary). Geology maps were given to me also to be able to locate where the granite had come from (secondary). Another source of data was that a couple of days before we had studied river basins. This gave me some background knowledge to put towards my data collection and analysis theories. For the data collection we are going to walk along the beach and study the average size of the pebbles on that part of the beach. We will record the data in table and then analyse it, using standard deviation and spearmans rank correlation coefficient ( methods of numerical analysis) and present the data using different techniques, i.e. Scatter-graphs

Before I undergo the data collection process I have to foresee a risk assessment as shown below: –

Risk

Effect

Consequences

Precautions

Drowning

Very Low

Very High

* Taking Care

* Teacher Precautions

Twisting Ankle

Moderate

Moderate/high

Proper footwear

Exposure/

Hypothermia

Low

High

Correct Clothing

Data Collection

To gather the data we did an interrupted belt transept. We started at the end of the beach closest to Lochranza. We measured out 50m with a tape measure and took footsteps to see how many it would take for 50m so we did not have to measure it out everytime, although this is not very accurate. We started at 0m just below the strand line of the beach and continued along the strandline so that we were not criss-crossing along the beach. We laid out 4m rulers to mark out the 1m Quadrat. In the quadrates we were only interested in Granite because it was the granite that had been transported to this beach by longshore drift. With the Granite pebbles we had to study what the length of the B-axis was. We did this using a set of callipers. In every quadrat we took 2 granite pebbles which were nearest to each corner and two nearest the middle. This enabled us to get a representative sample. Also this method is non-biased to give fair results. This quadrat analysis was done every 50m to extend over the beach. As we took the data we filled it into a results table. As shown below:

Distance from start (m)

0

3.5

3.1

4.1

3.1

4.1

2.5

2.4

2.0

3.2

3.3

50

3.2

3.1

2.5

2.2

2.0

2.3

3.1

2.2

2.3

2.8

100

2.6

3.5

1.5

1.5

1.5

1.1

1.8

3.4

3.8

2.4

150

2.5

2.0

1.8

3.1

2.2

3.5

2.6

2.5

4.0

2.5

200

5.8

8.3

4.6

3.6

3.1

3.5

6.3

5.0

2.9

5.2

250

8.9

9.2

8.5

8.6

8.7

6.8

8.2

6.5

10.7

8.9

300

7.2

8.5

7.3

3.5

8.2

6.0

6.8

12.5

3.9

6.7

350

8.7

8.9

7.0

6.2

9.2

5.9

6.0

11.2

9.9

10.2

400

4.1

4.6

9.4

8.6

15.0

6.5

11.0

8.5

15.1

8.6

450

7.1

5.6

5.0

7.5

5.5

4.1

7.5

6.5

6.6

7.6

500

7.5

10.2

10.8

6.8

8.9

11.0

9.8

10.1

9.5

5.2

Data Presentation and Analysis

When back to the field study centre we attempted to interpret the results we had collected. I calculated the mean pebble size for each sample and from this calculated the standard deviation.

The results are displayed on the next page.

Mean b-axis and Standard deviation results:

Distance From Start (m)

Mean b-axis (cm)

Standard Deviation

0

3.13

0.68

50

2.57

0.4196

100

2.31

0.928

150

2.68

0.6297

200

4.83

1.59

250

8.45

1.035

300

7.11

2.42

350

8.32

1.82

400

9.14

3.76

450

6.30

1.20

500

8.98

1.89

Also I have drawn 2 scatter-graphs with the data above. The mean b-axis graph is on page 5 and the standard deviation graph is on page 6.

Analysis of the graphs – After studying the graphs I can safely say that they both show a positive correlation, showing the relationship between distance and size. As the distance of the samples increased so did the size of the pebbles. On the standard deviation graph, at 50m the standard deviation was 0.4196cm at 250m it was 1.035cm and at 400m it was 3.76cm. There were although a few variations at the end of our results. At 450m the standard deviation was 1.20cm and at 500m it was 1.89cm. This can be explained by the reason that some pebbles in the current process of being transported by longshore drift. So we had studied a section of the beach where some pebbles are still in transit. Therefore we may have been unlucky enough to have measured these in our study.

After working out now that there is a positive correlation between distance up the beach and size of the pebbles, I wanted to find out how strong the correlation was and I did this by using the method of Spearmans Rank Correlation Coefficient. I worked this out in the table below.

Distance from start (m)

Distance Rank

Mean b-axis (cm)

Mean b-axis rank

d

D

0

11

3.13

8

3

9

50

10

2.57

10

0

0

100

9

2.31

11

-2

4

150

8

2.68

9

-1

1

200

7

4.83

7

0

0

250

6

8.45

3

3

9

300

5

7.11

5

0

0

350

4

8.32

4

0

0

400

3

9.14

1

2

4

450

2

6.30

6

-4

16

500

1

8.98

2

-1

1

TOTAL d = 44 (6?44) (1331 – 11) = 0.2

1 – 0.2 = 0.8

The worked out spearmans rank came out at 0.8. This value doesn’t mean much on its own. It must be looked up on a Spearmans Rank significance Table. This table shows that for the number of samples we did, (11) it is 0.1%. 0/1% on this chart means that the relationship is 99% significant and that it has not occurred by chance.

Summary

After returning to my hypotheses and my findings, including standard deviation and spearmans rank, I can draw this essay to a conclusion. It is most obvious that my hypotheses are true. The further travelled away from Lochranza up the beach into the oncoming direction of longshore drift the size of the pebbles b-axis increased significantly in size from an average of 7.13cm at 0m to 9.98 at 500m, and also the more sorted they become as you can see from the graph on page 5.

I believe that my results were reliable. Although there could have been things that could have been done to have made them more reliable. One for example could have been where we used footsteps to mark out 50m rather than measure every 50m with the tape measure. This would have made the results more accurate because we may have been taking results from slightly incorrect places. Also to make the results more reliable we could have gone further up the beach and taken more results.

When taking the data there were several limitations that I had to take into consideration. One was that we only took 10 pebbles from each quadrat and if we had taken more of a range then we may have got more accurate results. This could not be done because of the limitation of time. Time also is a limitation that affected the amount of quadrats that we studied. We could have done more but the time as well as the tide coming in (affected by time) determined how many we could do. If we could have done more samples it would have improved it.