Foundations of Biology Whats the BIG IDEA EVOLUTION

Foundations of Biology!

What’s the BIG IDEA? EVOLUTION INTERACTION ENERGY INFORMATION ORGANIZATION

What’s the BIG IDEA? EVOLUTION – the core theme! Populations of organisms change over time INTERACTION Organisms must interact with each other and the environment ENERGY Life requires transfer and transformation of energy & matter INFORMATION Organisms must express and pass on genetic information ORGANIZATION Organisms maintain order & link structure & function

Organization – life is ordered! l From atoms to ecosystems, the structure determines the function l l Molecules: enzymes must perfectly match their substrates; order of bases in DNA sets the code Cells: nerve cells have many branches to better send and receive signals Organs: the stomach has thick muscle to allow churning Ecosystems: rainforest canopy vs mid-level vs floor (different “ways of life” at each level)

Organization – life is ordered! l Cells are basic units of life…all life functions happen at the cellular level l Have specific structures to do specific jobs (organelles) § Ex: mitochondria – curvy surface to make more ATP Prokaryotic = simple, non-nucleus cell; bacteria l Eukaryotic = fancier cell with nucleus & the fancy organelles; all other cells l

Organization – life is ordered! …apparent at ALL levels! Does this cost energy or give off energy to maintain?

Organization – life is ordered! Form fits function The way something is put together determines how it works What is an example of this on a human?

Organization – the hierarchy… Atom Molecule Organelle Cell Tissue Organ System Organism Population Community Ecosystem

Organization – Life maintains homeostasis Maintain internal environment What sorts of things must be kept stable within you? Once the value is set, why doesn’t it just stay there?

Information l DNA holds code for all types of life functions l l All organisms use this, and it is same structure Proteins made with same process in all life l DNA copied into RNA which builds proteins ALL organisms share these …why is that significant?

Information l Genetic code passed to offspring l l By combining sexually – meiosis By exact copying – mitosis & asexual reproduction Why is it so important to pass on these codes? Do all organisms do this?

Information Life reproduces itself life comes from life …applies to cells as well as whole organisms So where did the first life come from?

Recap… l l Predict 4 characteristics that the first cells would have had. Defend your choices.

Information l Organisms send and receive signals l l Internal – hormones, nerve impulses, … Exterior – sounds, sights, etc Why is it so important to pass on these signals? Do all organisms do this?

Information - Signaling Must take in info and respond accordingly What is an example of your body doing this? Does this happen at the cell level?

Energy – at all levels! l All organisms make ATP by breaking down glucose… which is made by the green things l l l Ecosystem: sun autotrophs heterotrophs Chloroplasts: light + CO 2 glucose Mitochondria: glucose ATP + CO 2 Why is it so important to transform energy? Do all organisms do this?

Energy – at all levels! need energy to do work …at ALL levels! What kind of work does “life” do?

Interaction l Life depends on the environment l l l Abiotic factors: light, water, O 2, etc Ex: plants use light & CO 2 & water to make sugar Life depends on other living things l l Biotic factors: symbiosis, parasitism, predation, etc Ex: flowers needs bees for pollination What is the payoff for this interaction? What is the risk?

Evolution – the core theme!!! l Descent with modification l l Populations of organisms share common ancestors, but make changes to genes over long periods of time Natural selection l l Survival of the fittest: genes that give better traits move on to next generation (reproductive success) Depends on variation (what nature selects from) Why can’t you personally evolve?

Evolution – unity in diversity l l l ALL organisms share certain traits (like glycolysis, DNA, ATP, cell structures, …) Many species exist…all with varied traits 3 Domains… 6 Kingdoms …what are they called?

Evolution – unity in diversity l 3 Domains l Archaea – extreme-habitat prokaryotes l Bacteria – “common bacteria”; prokaryotes l Eukarya – all eukaryotes (animals, plants, fungi, protists)

The End

Science as a Process Chapter 1 – Part 2

Scientific Method

It starts with a question, then… l You make a hypothesis l l l Tentative explanation based on previous knowledge Must be testable Can be eliminated, but not confirmed with certainty

Does the amount of protein affect growth rate of mice? l What would a proper hypothesis for this question be?

Null Hypothesis Assumption that the observed difference between two samples is purely accidental - not due to the effect the independent variable has on the dependent variable Example… Ho = Adding more water to daisies will have no effect on how tall they grow. OR… for some types of experiments, the null will be “the observed data & expected data are the same”

Null Hypothesis If experiment is does not have an independent variable the null will more likely be written as… “there will be no difference between the expected data and the observed data” Example: when a red-eyed female fruit fly is crossed with a white-eyed male fruit fly, what are the results? (white is recessive, so we expect all babies to be red) Null: All offspring will have red eyes.

Does the amount of protein affect growth rate of mice? l What would a proper NULL hypothesis for this question be?

Experimental Design l Large sample size Replicated many times l Control Group l l The “baseline”…what results compared against Not present in “comparative” investigations Controlled Variables l Remain the same between all groups, so that they are NOT factors in the experiment

Experimental Design Variables l Independent Variable (“I” set up beforehand) ~ is the only variable that is changed between experimental groups ~ example: color of light on plants l Dependent Variable (“Data” collected “During” experiment) ~ is the effect of the independent variable ~ it is what you measure as you experiment ~ ex: height plants grow

Does the amount of protein affect growth rate of mice? l What would the control for this experiment be? l What are the… IV l DV l CV l

Experimental Design l l l Bob wants to see which fertilizer will make his tomato plants produce the largest # of tomatoes. He uses the same variety of tomato, same amt & type of soil, same amt of water & plants them in same area. Group 1 gets no fertilizer, group 2 gets Brand Q, group 3 gets Brand R, group 4 gets Brand S. Identify: l l l IV DV CV control group experimental groups

Experimental Design l Sue wants to see if plant food makes rose bushes produce more flowers. l Identify: l l l l IV DV CVs Control group Null hypothesis On her data table, what would the labels be at the top? (which one goes where? ) What would the labels on each axis of a graph be?

Data Tables Independent Variable (unit) Dependent Variable (unit)

Data Tables Table 1. Height of Sunflowers when Grown in Varying Colors of Light Color of Light White (daylight) Red Blue Green Sunflower Height (cm) 7 6 8 3

Lab – Oreo Claim I. Purpose: Do double-stuff Oreos really have double the stuff? II. Background l What do you already know? III. Hypothesis l l HO = HA = IV. Procedure l l l IV=? DV=? CV=? Normally Simple labeled sketch of procedure For this one easier to describe in words?

Do Double-Stuff Oreos really have the double the stuff? l Regular (x 2) l l l … … … DS … … …

Analyzing Data l Standard Deviation l l Standard Error of the Mean l l How spread out is the data? How likely is it that our “mean” is a good value? Chi-square test & t-Test l How close to our “expected” is close enough?

Analyzing Data l Standard Deviation: l How spread out is the data? l how far on average any data point is from the mean the smaller the SD, the closer the scores are to mean when SD is large, the scores are more widely spread l l

Analyzing Data l Standard Deviation: l l Moving a SD away from mean in either direction lets us estimate what % of points are within that section In science we almost always go with the 95% confidence level… this is equal to +/-2 SD units

Analyzing Data l Standard Deviation: To calculate, you need to know… l l l Individual data points (x) Mean of data points ( ) Sample size (n)

Analyzing Data l Standard Deviation: Steps… 1. Calculate mean 2. Fill out chart with calculations (subtract & square) 3. Add up last column 4. Divide that sum by (n-1) 5. Take square root of that number

Analyzing Data l Standard Error of the Mean l How likely is it that our “mean” is a good value? l the higher the sample size, the more sure we can be to calculate this, simple divide the standard deviation value (s) by the square-root of the sample size. l l l Yes, you have already done most of the work by now! In science we almost always use 2 x. SEM to get to the 95% confidence level

Graph – DRY MIX Graph 1. Height of Sunflowers when Grown in Different Colors of Light Height of Sunflowers (cm) Color of Light

Which Graph to Use? l Check the x-axis (the IV) l l If it is numbers…make it a LINE graph If it is words…make it a BAR graph

Which Graph to Use? l Bar Graph l For discrete data…words on the X!

Which Graph to Use? l Modified Bar Graph l Includes error bars (see SEM)

Which Graph to Use? l Line Graph/Scatter-Plot l l Line of best fit (regression line) For continuous data…#s on the X!

Analyzing Data l Standard Deviation l l Standard Error of the Mean l l How spread out is the data? How likely is it that our “mean” is a good value? Chi-square test & t-Test l How close to our “expected” is close enough?

Analyzing Data l Standard Deviation: l How spread out is the data? l how far on average any data point is from the mean the smaller the SD, the closer the scores are to mean when SD is large, the scores are more widely spread l l

Analyzing Data l Standard Error of the Mean l How likely is it that our “mean” is a good value? l the higher the sample size, the more sure we can be to calculate this, simple divide the standard deviation value (s) by the square-root of the sample size. l l Yes, you have already done most of the work by now!

Analyzing Data l t-Test l How close to our “expected” is close enough? l l Used to compare two data sets Null hypothesis will say the data sets are the same…use this to reject/accept that! Plug in #s you already know…get t-value use table to see if there is too much difference between the sets to say that they are the same

Analyzing Data l l Chi-square test How close to our “expected” is close enough? l Will be taught later!

The End

Fake Quiz! Answer on scratch paper (or in your notes). Don’t look at your notes! Answer everything!

Bob wants to see if having earthworms in the soil will make his pepper plants produce more peppers. He sets up 4 planting pots with soil, pepper plant seedlings, & water. To the 4 pots he adds 0, 2, 4, and 6 earthworms. He allows the plants to grow for 2 months, counting the total # of peppers produced during that time. 1. What is the IV? 2. What is the DV? 3. List any 3 controlled variables that will be needed. 4. Which is the control group? 5. What is the null hypothesis for this experiment? 6. Which “characteristics of life” are involved in this scenario? (Be able to defend your choices!)

Test Review! l l l 1. Calculating slope of line 2. Test format - discuss 3. Practice – free response ques: experiment 4. Practice – FRQ: graphing/stats 5. Check your list!

Calculate the Slope: What is the rate between 2 and 6 minutes?

Calculate the Slope: What is the rate between 1 and 5 seconds?

Test Format l l l Mostly multiple-choice (about 60 pts) Some numerical response (about 10 pts) Free Response question (about 30 pts)

Numerical Response l l Are griddable on real AP Exam…write-in on ours Units not required if it is the griddable kind Do not have to show work unless specifically told to do so! Will be these: slope…mean…SD…SEx

Free Response – an example… A student wondered whether radish seeds require a certain number of hours of light in order to sprout successfully. l Identify the IV and DV. Describe measures taken to allow for controlled variables. l Write a null hypothesis for this experiment. Write an alternate hypothesis for this experiment. (Label each. )

Free Response – an example… A student wondered whether radish seeds require a certain number of hours of light in order to sprout successfully. l Design a controlled experiment to test the null hypothesis. Describe data that would cause you to reject your null hypothesis. Defend your answer.

Lab – Mealworm Growth l I. Purpose: Does temperature affect how mealworms grow? l Limitations/requirements: 1 week of growth Will measure individual MWs before & after Temps: Cold (~10)…Medium (~22)…Hot (~28) l l l May want to pick only TWO, but can do more Will be doing SD, SEM, t-Test fast

Lab – Mealworm Growth l I. Purpose: Does temperature affect how mealworms grow? l Follow the lab format…green paper! What value will you actually measure? (DV) What variables do you need to control (CVs) Is there a control group? If so, what? What will data table(s) look like? l l fast

Notes about the Lab Write-up… l Please go get your lab notebook and look through the mealworm lab

Notes about the Lab Write-up… l l MAJOR ISSUES: The final data table should be a summary showing the IV and DV (Temp vs Growth) l All stats tests and graph are related to the AVERAGE growth, not individual readings l The graph needs to be stretched high enough that data can actually be compared well

Notes about the Lab Write-up… l l MAJOR ISSUES: The conclusion must state stats proof as to why you are accepting/rejecting null l l Based on t-test or error bars… not “common sense” “Since the t-value of 1. 321 was lower than the critical cut-off value of 2. 776, the null hypothesis must be accepted” Then can discuss why this may be skewed…hot = pupa The sample size was ridiculously tiny for all grps l l Error bars are actually supposed to be 2 x. SEM!!! Would there be any way for your values to not be equal?

Notes about the Lab Write-up… l l l MINOR ISSUES: The “ I. Purpose” is a question Think ahead as to what data you’ll be recording …can make much more concise data recordings (doesn’t have to be a table, but they are efficient!) Due to tiny sample size, should’ve borrowed data Include units in all measures (DTs and graphs) Must include data summary (sentences) l “the mean was higher in the hot than cold”…