1. Variation
BIOLOGY AS UNIT 2

3. Interspecific variation
Where one species differs from another.

4. Intraspecific variation
Where members of the same species differ from each other.

6. Causes of variation Mutation
Meiosis
Fusion of gametes
Environmental influences
Use page 125 of the text book to complete page 4 of your booklet now.

7. Causes of variation
Mutation : sudden changes to genes and chromosomes

8. Albino black bird
The effect of a mutation!

9. Meiosis : The type of cell division that forms gametes and mixes genetic material.

10. Fusion of gametes :
Random Fertilisation
occurs.
Offspring inherit some
characteristics of each
parent. This adds extra
variety to the offspring
2 parents can produce!

11. Environmental
influences:

12. Egs of Environmental Factors that can affect plant growth: 1) Climate conditions such as temperature, amount of rainfall, light intensity. 2) Soil conditions such as pH, concentration of mineral ions present eg Nitrate conc. Note: A plant might have the genes to grow tall but if it doesn’t get the light, water and nitrates it needs, it will never get to be Tall. It’s genes will not be expressed.

13. In many cases it is the combined effect of genetic differences and the environment that leads to variation…..
….however, it is sometimes very difficult to distinguish between (a) the effects of the genes on an organism and (b) the effects of the environment on an organism……
…..as they combine together to produce differences between individuals. So it is often difficult to draw reliable conclusions.

14. There are two main types of variation:
Discontinuous Variation – These are very clear cut alternatives of a given trait and there are no half-way stages
e.g. Smooth or Wrinkled pea plant seeds
Tall or Dwarf pea plants
Male or Female
Tongue Roller or Non-Roller
DISCONTINUOUS VARIATION FEATURES ARE
USUALLY ONLY CONTROLLED BY JUST ONE
GENE!

15. Measuring variation Tongue rolling (draw a simple bar
chart for the results
in the class)

16. (B) Continuous Variation -
Within a species, there is a range of intermediate phenotypes,
or measurements from one extreme to the other. Often there
is no separation into sharply distinct categories (no discrete
categories exist).
Typical examples include:
Human heights
Human weights
Length of feet IQ
Natural hair shade
Nose shape
Pigmentation of skin and eyes
Cow milk yields.

17. Measuring Variation
For example, Human heights.

18. If the heights of lots of humans of a given age is plotted graphically, a FREQUENCY DISTRIBUTION CURVE is obtained. It is important that a LARGE ENOUGH SAMPLE OF INDIVIDUALS ARE MEASURED in the investigation/experiment so that the RESULTS DATA IS REPRESENTATIVE (this means that the results DO reflect the true pattern or trend in the wider population in general). To ensure that the data is VALID (ie scientifically truthful) the humans measured will all need to be the same age and the same gender. This means that we are CONTROLLING THE OTHER VARIABLES that could otherwise interfere with and distort the results trend obtained.

19. HEIGHTS OF 1,000 MALE STUDENTS AGED 18-23 YEARS IN CMS:
THE RESULTS SHOW
A NORMAL
DISTRIBUTION
CURVE

20. Conclusion – Most adult male humans have heights close to the average or mean value, with fewer individuals being either very tall or very short. In the investigation, there was a RANGE of results from 159cm (smallest height) to 185cm tall (largest height).
The normal distribution curve pattern obtained can be
explained as follows:
THIS PATTERN IS TYPICAL OF POLYGENIC INHERITANCE IN WHICH SEVERAL GENES INTERACT TOGETHER IN COMPLEX WAYS TO CONTROL THE CONTINUOUSLY VARIABLE CHARACTERISTIC OF THE ORGANISM.

21. Measuring variation
Tongue rolling
Finger length

23. Other examples
of variation

24. Peppered moth

25. WORK TO DO:
Finish Tally chart and Bar chart started in class.
Do Q1 and Q2 from p120 Collins A2 textbook (about 4 sketch graphs).
Look at the summary info in the next 3 slides!

26. In discontinuous variation:
There is a strong genetic factor
Often due to the alleles of a single gene.
Environmental factors play either: no role (e.g. ABO blood groups) or a small role (e.g. tall and dwarf pea plants)

27. Measuring variation
Tongue rolling
Finger length

29. Continuous and Discontinuous Variation
Two discrete types –
Discontinuous variation
Tall pea plants
Not all tall plants are the same height – their local environments were all
slightly different, producing continuous variation within the tall plants.
frequency
Short pea plants
Height in cm

30. In continuous variation:
There is a strong environmental factor
Also, many genes are involved – polygenic inheritance.
e.g. many genes contribute to height in humans, but a lack of food will lead to stunted growth regardless of the persons genotype.

31. HEIGHTS OF 1,000 MALE STUDENTS AGED 18-23 YEARS IN CMS:
THE RESULTS SHOW
A NORMAL
DISTRIBUTION
CURVE

32. The Mean and Standard Deviation

33. HEIGHTS OF 1,000 MALE STUDENTS AGED 18-23 YEARS IN CMS:
THE RESULTS SHOW
A NORMAL
DISTRIBUTION
CURVE

34. If the heights of lots of humans of a given age is plotted graphically, a FREQUENCY DISTRIBUTION CURVE is obtained. It is important that a LARGE ENOUGH SAMPLE OF INDIVIDUALS ARE MEASURED in the investigation/experiment so that the RESULTS DATA IS REPRESENTATIVE (this means that the results DO reflect the true pattern or trend in the wider population in general. To ensure that the data is VALID (ie scientifically truthful) we need to CONTROL THE OTHER VARIABLES in the investigation that could otherwise interfere with and distort the results trend obtained. Therefore the humans measured for their height will all need to be the same age and the same gender.

35. Conclusion – Most adult male humans have heights close to the average or mean value, with fewer individuals being either very tall or very short. In the investigation, there was a RANGE of results from 159cm (smallest height) to 185cm tall (largest height). The MEAN was calculated as 172cm.
The normal distribution curve (roughly symmetrical) pattern obtained can be explained as follows:
THIS PATTERN IS TYPICAL OF POLYGENIC INHERITANCE IN WHICH SEVERAL GENES INTERACT TOGETHER IN COMPLEX WAYS TO CONTROL THE CONTINUOUSLY VARIABLE CHARACTERISTIC OF THE ORGANISM.

36. Normal distribution curve (sketch this onto HB p5)
Mean
68% of the
measurements are within 1 sd either side of the mean.
Mean

37. A graph curve shape depends on:
Variation in Ivy Leaves
This shape of graph shows a normal distribution.
A graph curve shape depends on:
The standard deviation and
The mean
Frequency
Width
Frequency
Width

38. Variation in Ivy Leaves
Frequency
Mean -1 sd
68% +1 sd
-1.96 sd
95%
+1.96 sd

39. Variation in Ivy Leaves
Frequency
Smaller standard deviation (sd)
- data is more tightly clumped around the mean value. The data is MORE RELIABLE!
Larger standard deviation (sd)
- data more widely spread either side of the mean value. The data is LESS RELIABLE!
Size Class

40. (Put onto HB p5)
Data with SMALL standard deviation is MORE RELIABLE. The data has a smaller spread either side of the mean value (measurements are tightly clumped)
Frequency
Data with LARGE standard deviation is LESS RELIABLE. The data has a wider spread either side off the mean value) (measurements are more spread out from the mean).
Width
The Standard Deviation (SD) indicates the spread of the data around the mean.
The mean is the average of all the data values.

41. Once the standard deviation value has been calculated, we can represent it in tables, in bar charts and in graphs:
Example:
If the mean number of moss plants growing on the sunny side of a wall was 30 and the standard deviation value was 3,
then the data is shown as the mean + 1sd (30+3 = 33) and the mean – 1sd (30-3 = 27).
We know from this, that 68% of the raw data values for the numbers of moss plants found in this experiment, growing on the brightly lit wall, were spread between 27 and 33 plants.

42. Use of standard deviation on bar charts
What was the (a) Mean number of moss plants found on the shady wall?
(b) Sd value? (c) spread of the data that included 68% of the counts?
(d) Data with the most reliable mean?
Mean
number of moss
plants on
different
sides of
a wall. 40
Show 1 SD value up from the mean and show 1 SD value downwards from the mean 35 30
Shady side
Bright side

43. Use of standard deviation on graphs
Shady
Bright

44. Line graphs with sd bars
If the standard deviation bars don’t overlap at all then there IS a statistically significant difference between those 2 mean values.
If the standard deviation bars do overlap then there is NOT a statistically significant difference between those 2 mean values.
The bars show +/- 1 sd either side of the mean
MEAN VALUES

45. Line graphs with sd bars
If the standard deviation bars don’t overlap at all then there IS a statistically significant difference between those 2 mean values. (eg between values B and D)
If the standard deviation bars do overlap then there is NOT a statistically significant difference between those 2 mean values. (eg between values A and B)
The bars show +/- 1 sd either side of the mean D
MEAN VALUES C B A

46. Use of standard deviation on bar charts
What was the (a) Mean number of moss plants found on the shady wall?
(b) Sd value? (c) spread of the data that included 68% of the counts?
(d) Data with the most reliable mean?
Mean
number of moss
plants on
different
sides of
a wall. 40
Show 1 SD value up from the mean and show 1 SD value downwards from the mean 35 30
ANSWER:
There is no statistically significant difference between these 2 mean values
Shady side
Bright side
QUESTION: Is there a statistically significant difference between these 2 mean values?

47. Use of standard deviation on bar charts
What was the (a) Mean number of moss plants found on the shady wall?
(b) Sd value? (c) spread of the data that included 68% of the counts?
(d) Data with the most reliable mean?
Mean
number of moss
plants on
different
sides of
a wall. 40
Show 1 SD value up from the mean and show 1 SD value downwards from the mean 35 30
ANSWER:
There is a statistically significant difference between these 2 mean values
Shady side
Bright side
QUESTION: Is there a statistically significant difference between THESE 2 mean values?

48. How to calculate the mean and standard deviation of results data using an Excel spreadsheet

49. Sucrase/sucrose colorimeter readings at 60oC
Calculate the mean of each set of data.
Class A mean absorbance=
Class B mean absorbance=
Which data set is more reliable?
Why?
Class A
Class B
0.97
0.88
0.98
0.93
1.03
0.99
1.08

50. Calculating the mean and standard deviation using an Excel spreadsheet
Click start, All programs, MS office 2007, Microsoft office excel This opens your excel spreadsheet.
Then input the data from HB p5 into row 2 (for class A data) and row 3 (for class B data). Leave row 1 free at this stage.
Now enter the title ‘mean’ into the row 1 cell of the next blank column and ‘sd’ into the row 1 cell of the very next blank column on from the one with mean titled in it.
In row 2 under the heading mean, select the cell then click on the icon fx. Select the function Average, then ok. Now enter the cells you want to find the mean of ie A2:E2, then ok. The mean will automatically appear in the selected cell.
It is similar for sd. Click on row 2 under the heading sd, select the cell then from the fx menu choose STDEV, then ok. Enter the cells you want to calculate the sd of ie A2:E2 and the value for Standard Deviation will appear in the selected cell.
Repeat calculations of mean and STDEV for other row of data

51. Colorimeter data from HB p5
mean sd
0.97
0.98
0.99
0.88
0.93
1.03
1.08

52. Colorimeter data from HB p5
mean sd
0.97
0.98
0.99
Class A
0.88
0.93
1.03
1.08
Class B
Class A data set is more reliable than Class B data set, because Class A has a smaller standard deviation (sd) from the mean value. The data is less spread out either side of the mean.

53. Leaf Width of a Sycamore tree
TASK: Use Excel to calculate the MEAN and Sd of this data 95
120
114 98
122 89 92
105 99 84 76 75
116

54. Leaf Width of a Sycamore tree95
120
114 98
122 89 92
105 99 84 76 75
116
Mean Sd
There is quite a large sd here from the mean value, showing that there was a large spread of measurements around the mean. The data does not show very good reliability!
TASK 1: Plot a simple bar chart (smallest to largest values as bars), show the mean, show the sd of the data.
TASK 2: Do the Exam Questions on VARIATION carefully.