Selection & Evolution A2 Level Chapter 17 - Cambridge Assessment PDF

A2 LEVEL Chapter 17 Selection & Evolution Chapter Outline 3 Parts 1. Variation 2. Natural Selection 3. Evolution and Speciation Updated on 12/8/21 by Beh SJ @behlogy  Chapter Outline Part I: Variation Phenotype results from interaction of genotype and...

A2 LEVEL Chapter 17 Selection & Evolution Chapter Outline 3 Parts 1. Variation 2. Natural Selection 3. Evolution and Speciation Updated on 12/8/21 by Beh SJ @behlogy  Chapter Outline Part I: Variation Phenotype results from interaction of genotype and environment Continuous vs Discontinuous Variation Examples on how the environment influences phenotype P5: std dev, std error and error bars, t-test Updated on 12/8/21 by Beh SJ @behlogy  Chapter Outline Part II: Natural Selection Importance of genetic variation in selection Stabilising, disruptive and directional selection Examples for evolution by natural selection Antibiotic resistance in bacteria Industrial melanism in peppered moth Sickle cell anaemia The Hardy-Weinberg principle Genetic drift and founder effect Selective breeding / Artificial selection Milk yield of dairy cattle Disease resistance in varieties of wheat and rice Incorporation of mutant alleles for gibberellin synthesis into dwarf varieties Inbreeding and hybridisation of maize Updated on 12/8/21 by Beh SJ @behlogy Chapter Outline Part III: Evolution and Speciation Molecular evidence for evolution Allopatric vs sympatric speciation Extinction Updated on 12/8/21 by Beh SJ @behlogy  A2 LEVEL Chapter 17 Selection & Evolution Part 1: Variation  Chapter Outline Part I: Variation Phenotype results from interaction of genotype and environment Continuous vs Discontinuous Variation Examples on how the environment influences phenotype P5: std dev, std error and error bars, t-test Updated on 12/8/21 by Beh SJ @behlogy Variation Variation = presence of different characteristics Phenotype results from interaction of genotype and environment Phenotypic variation = Genetic variation + Environmental variation VP = VG + VE Two types of variation: 1. Discontinuous variation 2. Continuous variation Updated on 12/8/21 by Beh SJ @behlogy Continuous variation vs Discontinuous variation Feature Discontinuous Variation Continuous Variation Type of distribution Discontinuous distribution Normal distribution Number of genes One / few genes (Monogenic) Many genes (Polygenic) controlling phenotype Effect of diff alleles at Large Small single gene locus Diff genes have diff effects Genes have an additive effect Type of data Qualitative Quantitative No. of categories / Discrete categories, no Range of phenotypes, many intermediates intermediates intermediates Effect of environment Environment has effect Little or none on phenotype Helps smooth the curve Albinism, sickle cell anaemia, Examples Height, mass haemophilia, Huntington’s disease Updated on 12/8/21 by Beh SJ @behlogy Genetic Variation Phenotype results from interaction of genotype and environment Main source of genetic variations: 1) Meiosis and fertilization (refer to Chap 16) Crossing over @ Prophase I Independent assortment @ Metaphase I Random fertilization / mating 2) Mutations! Primary source of variations Results in new alleles Updated on 12/8/21 by Beh SJ @behlogy How the environment influences phenotype Phenotype results from interaction of genotype and environment Environmental factors that can influence phenotypes: Nutrients / diet Water availability Light intensity Disease / parasites Temperature Chemicals / mutagens Lifestyle and culture etc. Environment effect usually greater on polygenes → Polygenes = many genes controlling one trait → Phenotypes affected by environment often show continuous variation Updated on 12/8/21 by Beh SJ @behlogy How the environment influences phenotype How does the environment influence phenotype? The environment may…. 1. Limit / modify gene expression Size / mass / height 2. Trigger / switch on gene Examples: a) Low temp and change in animal colour b) High temp and gender in croc / curly wing in Drosophila c) UV light and melanin production d) Wavelength of light and plant growth 3. Induce mutation which affects phenotype Updated on 12/8/21 by Beh SJ @behlogy a) Low temp and change in animal colour Dark pigmentation in Himalayan rabbits → Controlled by both genotype and environment At low temp: Allele for dark pigment expressed Forming dark tips at ears, paws, nose & tail → Coldest parts of rabbit Updated on 12/8/21 by Beh SJ @behlogy b) High temp and gender in crocs / curly wing in Drosophila Gender of crocodiles depend on In fruit flies with the curly wing temperature of eggs! mutation… Temp of 32-34oC= Males Temp of 19oC = straight wings Below 32oC / above 34oC = Females Temp of 25oC = curly wings (thermosensitive period) Updated on 12/8/21 by Beh SJ @behlogy c) UV light and melanin production After a few hours of exposure to UV radiation: Melanocytes produce melanin in skin → Causing skin to tan / form dark spots / freckles → Protecting cells from DNA damage Updated on 12/8/21 by Beh SJ @behlogy d) Wavelength of light and plant growth Red and blue light are most effective for plant growth Blue light = helps with seed germination Red light = helps flowers bloom, but leaves will have stretched and elongated appearance Updated on 12/8/21 by Beh SJ @behlogy  P5 Statistics in Biology  P5 Maths Skills Required! σ𝑥 Mean, 𝑥 = Median = (70+72) / 2 = 71 𝑛 Median Mode Range Interquartile range Mean = 710/10 = 71 Mode = 64 and 81 Range = 81-62 = 19 Standard deviation, s Standard error, SM 95% Confidence Interval = ± 2SM Updated on 12/8/21 by Beh SJ @behlogy  P5 Standard Deviation, s To show the spread of data about the mean, 𝑥 , in a sample that is normally distributed Indicates reliability of data If s = small value → Data is less scattered, more consistent and reliable If s = large value → Data is widely spread, results are less reliable Other functions: To calculate standard error, P/S: it’s different from what you learn in maths! and put error bars of a graph In maths, you learn standard deviation of a population, whereas in bio, we learn s.d. of a sample To calculate t-test value Updated on 12/8/21 by Beh SJ @behlogy  P5 Standard Deviation, s E.g. The student measured the petal length of 12 flowers in the woodlands. These were the results in mm: 2.5 4 9 1.75 3 7 2 3 6 8 1.5 6.5 The student then measured petal lengths of another 10 flowers in the garden. These were the results in mm: 2 2.5 2.5 2.5 2.5 3 2.5 2.5 1 2.5 a) State the null hypothesis for this experiment. b) Find the mean, 𝑥 and the standard deviation, s for both these sets of data. c) Determine whether the 2 sets of data are significantly different by: 1. plotting a bar chart with error bars 2. conducting a t-test Updated on 12/8/21 by Beh SJ @behlogy  σ𝑥 𝑥= = 54.24 / 12 = 4.52 𝑛 𝒙 𝒙-𝒙 (𝒙 - 𝒙 ) 2 2.5 4 9 1.75 3 7 2 3 6 8 1.5 6.5 σ(𝒙 - 𝒙 ) 2 σ(𝒙 − 𝒙 ) 2 𝑠= 𝑛 −1 Updated on 12/8/21 by Beh SJ @behlogy  P5 Standard Error, SM To show how close the mean of sample calculated is from the true mean of the population SM shows the reliability of the mean Used to put error bars on graphs Small value of standard error shows: → The sample mean value is closer to the actual mean →Mean is more reliable SM value is between 0 and 1 Updated on 12/8/21 by Beh SJ @behlogy  P5 Standard Error, SM E.g. The student measured the petal length of 12 flowers in the woodlands and 10 flowers in the garden. Standard deviation calculated from previous example: woodlands: s = 2.638 garden: s = 0.530 Calculate the standard error for both sets of data. woodlands: SM = 2.638/ 12 = 0.761 garden: SM = 0.530/ 10 = 0.168 → now we can plot a bar chart with error bars! Updated on 12/8/21 by Beh SJ @behlogy  P5 Bar Charts with Error Bars Requires: 1) Mean, 𝑥 = value for y coordinates 2) Standard error, SM 3) Error bars = lines on bar charts To draw error bars, we use upper and lower limits of a 95% confidence interval, that is mean ± 2 SM Function of error bars: → To see if there is a significant difference between two means Updated on 12/8/21 by Beh SJ @behlogy  P5 Upper & Lower Limits in Normal Distribution’s Curve 95% confidence interval = Interval where 95% of the sample’s data lies around the mean Upper & lower limits of 95% confidence interval →Min and max limit of the 95% interval →Is calculated by mean ± 2SM Updated on 12/8/21 by Beh SJ @behlogy  P5 Bar Charts with Error Bars E.g. The student measured the petal length of 12 flowers in the woodlands and 10 flowers in the garden. Woodlands: 𝑥 = 4.520 SM = 0.761 2SM = 1.522 Gardens: 𝑥 = 2.350 SM = 0.168 2SM = 0.336 Updated on 12/8/21 by Beh SJ @behlogy  P5 Error Bars Results Interpretation Error bars overlap - The two means are not significantly different - Null hypothesis is accepted → double check with t-test Error bars don’t overlap - The two means are significantly different - Null hypothesis is rejected Updated on 12/8/21 by Beh SJ @behlogy  P5 P5: Student’s t -test  P5 t-test To test whether data from 2 samples Are significantly different Requirements: Continuous / interval data Data is normally distributed Standard deviations are approx. the same Two samples have