Variation BIOLOGY AS UNIT 2

Interspecific variation Where one species differs from another.

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

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.

Causes of variation Mutation : sudden changes to genes and chromosomes

Albino black bird The effect of a mutation!

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

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

Environmental influences:

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.

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.

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!

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

(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.

Measuring Variation For example, Human heights.

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.

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

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.

Measuring variation Tongue rolling Finger length

Other examples of variation

Peppered moth

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!

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)

Measuring variation Tongue rolling Finger length

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

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.

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

The Mean and Standard Deviation

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

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.

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.

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

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

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

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

(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.

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.

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

Use of standard deviation on graphs Shady Bright

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

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

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?

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?

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

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

Calculating the mean and standard deviation using an Excel spreadsheet Click start, All programs, MS office 2007, Microsoft office excel 2007. 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

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

Colorimeter data from HB p5 mean sd 0.97 0.98 0.99 0.007071 Class A 0.88 0.93 1.03 1.08 0.079057 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.

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

Leaf Width of a Sycamore tree 95 120 114 98 122 89 92 105 99 84 76 75 116 Mean Sd 98.85 15.85 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.