Monday, January 22, 2018

Teaching about Adaptive Evolution: Battle of the Beaks

One of the important topics we cover in my BI111 (Biological Diversity & Evolution) class is the idea that Natural Selection leads to adaptive evolution. One way in which I teach this us this class (total enrolment ~800), is by a role-playing game I call the "Battle of the Beaks"

 The exercise is based specifically around the drought-related events described in of Boag & Grant (1984) Ecological Monographs 54:463-489. For this exercise I have purchased a number of pliers of various sizes from needle-nose through to pipe-wrenches (mostly from garage sales and/or dollar stores), as well as peanuts and walnuts* I start off by creating two teams of students with different sized-pliers. Typically each team is made up of 3 individuals (1 to be the "baby finch", who is to be "fed" by the "adult  finches").

 There are buckets of peanuts* at each of the classroom, and students have to run to the buckets, pick up peanuts, race back to the starting point, crush the shells to release the contents (one team member (the "baby") counts the number of nuts cracked). This is a race, to see how different beak teams perform in 2 minutes. This is meant to simulate a good year in the Galapagos. 

Next, to simulate what happens during a drought (when there is less food available, and what is left are primarily harder nuts), I break out the walnuts, and we re-do the competition between the teams. Now, the larger beaks have the advantage**. I then use this to talk about adaptive evolution, and specialization. Overall it is a fun and easy exercise that I highly recommend.

*This may be an issue for you if need to worry about allergies. This year was the first year I moved away from peanuts & walnuts and instead went to 2 types of beans (pinto & lima) + ping-pong balls (as the "large" seed).  Instead of crushing the seeds, the students could use their plier "beaks" to pick up the beads. The small beads will be easier to pick up, while the larger beads/marbles could only be picked up with the larger pliers. Alternatively you could marbles/beads of different sizes, or types of pasta. 

**Sometimes the results are not what you expect if you get a particularly competitive (or the opposite) team, so be prepared if the results are not what you might have been expecting!

Monday, January 8, 2018

Teaching Hardy-Weinberg & Population Genetics using playing cards

In-Class Population Genetics Exercise (note that this exercise will take more than one class to complete: at the end of 1st class, remind students to hold onto their cards).
To perform this exercise, you will need: i) a class of students; ii) sufficient playing cards so that each student can obtain 2 cards each (remove joker & instruction cards); iii) one distinct set of playing cards (that will be distributed among the sets); iv) everyone has a personal response system (PRS), i.e. an iClicker.

When cards are initially handed out to the students, DO NOT shuffle them, as we want initial population be in a state of non-H-W-equilibrium.

These cards represent (initially) a one gene, two-allele system. Hearts & Diamonds represent the red allele (r), while spades & clubs represent the black (B) allele. We initially treat B as dominant over r. (A real-life equivalent is the B & r alleles at the K-locus for coat colour in Cocker Spaniels). Each student represents a diploid individual, who is hermaphroditic (capable of mating with anyone else in the population).

Initial census of the class:
(For all calculations, I am assuming that the class is comprised of N=270 individuals –see accompanying excel file for other values).
Total starting population size of 270, most of which will be BB or rr.
Use clicker to 1st count phenotypes (Black or red) in population
Use clicker to then count genotypes.
Ask students to calculate p & q in starting population (should be p~0.5, q~0.5).

Simulation of Random Mating:
In this exercise, the point is to establish a population which is stable, and in which Hardy-Weinberg equilibrium becomes established

A new organism will replace each individual organism.
Ask students to turn to someone nearby, and to randomly exchange one of their two playing cards. This represents a reproductive event.

Using clicker, ask students what they think has happened to the frequencies of p & q (same as before, increase in p/decrease in q, increase in q/decrease in p, decrease in p/decrease in q, increase in p, increase in q).
Explain why p & q don’t change (no loss or gain of cards)

Ask students what they think will happen to phenotypes (same as before, increase in BLACK/decrease in RED, increase in RED/decrease in BLACK). Assuming that there will be more heterozygotes, we should see increase in Black phenotypes, and a decrease in Red phenotypes.
Poll students using iClicker to determine the distribution of RED & BLACK phenotypes in the population. Compare to initial distribution.

Ask students what they think will happen to genotypes (same as before, increase in BB & Br decrease in rr; decrease in BB increase in Br decrease in rr; decrease in BB, increase in Br decrease in rr, increase in BB decrease in Br increase in rr). Assuming that there will be more heterozygotes (due to random mating), we should see increase in Br phenotypes, and a decrease in BB & rr genotypes.

Poll students using iClicker to determine the distribution of BB, Br & rr phenotypes in the population.

At this point, discuss concept of H-W equilibrium: what is a model, why is it useful, what are its limitations (i.e. assumptions: sexually reproducing organism, reasonable large population, mating is random, no migration into or out of the population, no mutations, no selection

Use H-W formula to calculate predicted genotypes & phenotypes in the population. Compare these values to observations made in class.
Discuss why values may not match (mating was not fully random, finite population means no fractional individuals possible).

Get class to repeat random mating exercise as above. Use clicker to poll for phenotypes & genotypes. Discuss why or why not these frequencies have changed, and if they are getting closer to H-W equilibrium (hopefully they are).

Get class to repeat random mating exercise one additional time above. Use clicker to poll for phenotypes & genotypes. Discuss why or why not these frequencies have changed, and if they are getting closer to H-W equilibrium (hopefully they have). Use this data to indicate that as long as the assumptions are not violated, and that p and q remain constant, the genotype frequencies will hold constant at the Hardy-Weinberg equilibrium values, generation after generation.

Now, we want to see what happens if we start to violate the assumptions, starting with random mating.
Ask students to “mate” with others of the same phenotype (RED with RED; BLACK with BLACK). This is assortative mating.

Ask students what they think will happen to phenotypes/genotypes (same as before;
Increase in BLACK (more BB) decrease in RED; Increase in BLACK (more Br) decrease in RED, Decrease in BLACK (but more Br) increase in RED, Decrease in BLACK (but more BB) increase in RED.

Poll students using iClicker to determine the distribution of RED & BLACK phenotypes in the population. Were the results (hopefully Decrease in BLACK (but more BB) increase in RED) what people predicted?

Discuss how while mating in the whole population was non-random (assortative mating), that within the subset of BLACK phenotypes mating was random (because mating was based on phenotypes, not genotypes.
Assuming that there were (before the assortative mating) 135 Br individuals in the class & 67 BB individuals (H-W predicts 67.5), then for the 202 individuals (and 404 alleles in the population), there should be 269 B alleles (p=0.665) and 135r alleles (q=0.335). From these p & q values, we can predict how many BB, Br & rr individuals would be produced. BB: p2=0.44 (~89 individuals), Br: 2pq=0.44 (~89 individuals), rr: q2=0.11 (~23 individuals).

Ask students what would happen if we continued to have assortative mating? Changes in phenotypes/genotypes?

Now, get students to undergo dissortative mating (BLACK with RED, whenever possible).

Ask students what will happen to phenotypes/genotypes (same as before;
Increase in BLACK (more BB) decrease in RED; Increase in BLACK (more Br) decrease in RED, Decrease in BLACK (but more Br) increase in RED, Decrease in BLACK (but more BB) increase in RED. This should show how we get an increase in the amount of heterozygotes.

To show how random mating will restore population to H-W equilibrium, get students to undergo one round of random mating. Use iClicker to examine genotypes.

Next, we shall consider why a violation of the assumption of large population might affect our estimate of H-W equilibrium. Pick a row of ~10 students at random from the class, and get them to input (via iClicker, their genotypes). How close are their values to the p & q of the whole population and the predicted H-W values? Repeat with another row (this is to increase your odds of getting some atypical p & q values). This will show how small samples may not provide accurate representations.

Using a shuffled spare deck of cards, get 5-10 students to select 2 cards each at random from the deck. While p & q are=0.5, the observed frequencies of BB, Br & rr should (hopefully) not be in H-W equilibrium.

Next we shall consider effect of selection. Start by imagining that people don’t like RED Cocker Spaniels, and start only breeding BLACK dogs with BLACK dogs. This means that RED dogs don’t contribute to the next generation. Ask all students with 2 red cards to sit out the next mating round. Quickly survey the students on their genotypes before and after 2 rounds of mating (with any RED phenotypes) getting dropped from the population. Ask the students if they think that the r allele will be lost from the population selection against RED continues?

Demonstrate (using the rare (q=0.038) green-backed cards that have been mixed in with the regular black-backed cards q=0.962) how that even there is strong selection, that rate recessive deleterious genes will be retained in low frequencies (mostly as heterozygotic state). In a class of 270, there should be ~20 green cards in total, H-W predicts that there will be 0.38 individuals that are greenback/greenback (i.e.~0), 19.7 individuals that greenback/blackback are and 250 that are blackback/blackback. This means that >98% of green cards will be in heterozygous state (and hidden from selection).

Ask how the efficiency of selection would be affected if r or greenback alleles were dominant or co-dominant?

Next, let us consider selection acting on a quantitative trait (ask students to define quantitative vs. quantitative: perhaps asking them to write a list of 4 quantitative and 4 qualitative traits). Using the values on the face of the cards as allelic value (A=1, 2,3,4,5,6,7,8,9,10,J=11,Q=12, &K=13), get students to indicate phenotypic values (in clicker group into sets of 5: (2-6, 7-11, 12-16, 17-21, 22-26). Discuss the bell-shaped nature of the data: all value cards are in same proportions in the gene pool, but few outliers.

Perform directional selection (all those with values less than 7 must sit out) in two successive rounds of mating. Note how mean and distribution of population phenotypes changes.

Next, start stabilizing selection (all those with values less than 7 or more than 18 must sit out). Note how mean and distribution of population phenotypes changes.

If time permits, do several rounds of assortative mating and divergent selection: Selection for high values of BLACK, and low values of RED. Examine phenotypes over time.

Wednesday, June 14, 2017

My commencement address

 Today (June 14th) I received the Laurier Teaching Award for Sustained Excellence at the Spring 2017 convocation. As the recipient of this award I was asked to address the graduating class of Biology students (and their guests). I decided that it would be a good opportunity to talk about how success does not necessarily come easily. Specifically I wanted to focus on my initial difficulties with teaching
(starts ~38min mark)

It is a great honour to be the recipient of this award and I would like to thank all the individuals who nominated, supported and selected my nomination. As the recipient of this award, it is my privilege to be asked to give the commencement address for the class of 2017. Convocation is an excellent opportunity not only to celebrate your accomplishments, and to imagine the next steps of your journey, but also a chance reflect back on the challenges you have faced to get where you are today, and how you were able to overcome them. For me, receiving this award is –as I said- a great honour, but it also somewhat ironic, because it was not too long ago that I had serious doubts about my future as an educator.

When I began my position at Laurier it was with a great deal excitement and (and an equal amount of nervousness). Landing a tenure-track position was an amazing opportunity, and one that I (initially) thought I was well prepared for.  Throughout my graduate studies I had had the opportunity to work as a teaching assistant, and I had taken numerous elective courses and workshops on effective teaching practices. So I thought I would be able to at least hold my own when it came to teaching my first class. How wrong I was.

The first time I taught BI111 – Biological Diversity and Evolution – it felt like everything went wrong. I had taken over the course from a recently departed and much beloved instructor who I looked to as a model for how to run the course. But in that first class nothing clicked, nothing worked. As I stood in front of my sometimes confused, often bored, and occasionally frustrated, students I felt like a failure both professionally and personally. At the end of the semester my departmental chair came into my office – closed the door- and told me how worried she was for my future prospects at Laurier based on my teaching evaluations.

Now I don’t know if you – the class of 2017 – can empathize: Your first year at Laurier and getting much worse grades than you had been expecting based on you previous experiences – but I hope you can use your imagination.

Talking about failure is tough. Which is strange because we all encounter it. Far too frequently reality doesn’t match our expectations. For me it took some time to figure out how to identify why my teaching wasn’t working, and to start upon a better path. But I did not find my way along that path alone.

I am extremely fortunate to be surrounded by many excellent instructors both in my department and across the Laurier campuses who I have looked to for mentorship, for guidance and for conversation. I am here today because of Faculty, friends, and family members who shared with me their experiences and advice. I am also here because of some of the most important feedback I got was from my students on what they found effective, and what they found challenging. They inspired and encouraged me to take greater risks in my teaching. To imagine new approaches to learning about the amazing world in which we live, such as play-acting the process of secondary growth in eudicots, taking a busload of biostatistics students on a field trip to a literal field to collect their data, or learning the principles of Hardy-Weinberg equlibrium with thousands of playing cards (and the occasional pictures of cats). They helped me avoid getting discouraged if these experiments in teaching didn’t go as planned (which they sometimes did not).

And so here we are 6 years and roughly 4000 students later. I am on this stage because of the support of Laurier community and it is to them that I am eternally grateful. Class of 2017, today marks an important milestone in your lives.

For many of us in this room there will be challenges ahead, dark days in which you question your abilities and the path you have taken. Please remember you do not have to travel alone.  

Thank you

Monday, March 27, 2017

Thoughts on an predator-prey active learning exercise

Today was a very exciting day for me in my BI111 class, as I got to try out a role-playing game that I have been thinking / planning for a long while. At this point in the semester, my course (Biological Diversity & Evolution) has reached the topic of community ecology. One of the phenomenon I talk about this week is how predators and prey can become linked into cyclical patterns. A well-known example of this involves the  Candian Lynx, Lynx canadensis and its prey, the snowshoe hare, Lepus americanus. I've been wanting to try and let my students seem how this pattern, arises, so I devised an exercise that could be done in my two sections for BI111.
For context, this class has ~400 students per section, and I run i out of a lecture hall that holds 450 students (see pictures below)

Here is the set of rules I came up with, and shared with the students before today's lecture.

Now before you get too worried, I chose soft foam practice golf balls (I bought 4 sets from amazon) for use by the "lynx".

OK. So, flash forward to the exercise in question. Overall. I felt it went well, but there are a few tweaks that I'm going to consider for next year's implementation.
1. Lynx - it was perhaps too easy for the lynx to survive and reproduce. In my 10:30 class, I followed the original plan, and it it didn't take long for the lynx population so rapidly rise. Things were a bit better in the second round, where I changed the rule to 4 hits =survival, 5=1 offspring and 6 hits=2 offspring. Lynx numbers still did increase pretty high, but it took a bit longer (I'll upload some scans of the data sheets later).
2. Hares. It became clear early on that the hares that survived needed bigger litters. Perhaps it was the orientation of the room, or the skill of the lynx at throwing, but predation success was greater than I anticipated. I tired out 3 offspring per litter. That seemed to work.
3.Time. I had expected that 5-6 rounds of predation + instructions preamble would take ~20 minutes (to give time for cycles to become apparent). It ended up taking about 30. Perhaps reducing the number of balls/lynx/round might speed things up.
4. Loundness. This was loud exercise. I knew it would, but - wow! Lots of excitement from the students (good), but hard to keep focus on the exercise. I'll need to think about what can be done.

That's it for now,  but I'll update this post when I get the student feedback on the exercise.

Thursday, May 21, 2015

You are never too young (or to old) to be thinking about data visualization!

Over the last couple of weeks, I have been working with my son on his first science fair project. It has been lots of fun, working with him to develop a question, a prediction, and design a meaningful experiment. Collecting the data was also great - as you can (hopefully) see in the pictures below, we were investigating how rubber ball bounced at different temperatures. When it came time to "writing" up his results, we decided that the best approach would be to plot all his data (using stickers to represent the heights of the first bounce, of balls dropped from a height of 2m). While that may seem obvious, for many scientists, there is a great adversion to plotting raw data. It would be far more likely to see a "professional" version of this experiment present results using bar plots of mean values (and either SE, SD or 95%CI error bars). This is very unfortunate, as bar plots forsake a great deal of useful visual information about the distribution of the data. By coincidence, this was also (one of) the take-home message(s) of a recent paper:

Weissgerber, T. L., Milic, N. M., Winham, S. J., & Garovic, V. D. (2015). Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biology: e1002128.

that we has chosen to read in this week's Long Lab journal club. Overall, I thought the authors did a commendable job, and it is evident from the paper's metadata that their message is reaching a large audience. While I am in favour of anything that turns the tide against bar plots, I do wish they would have given boxplots as much publicity as the univariate scatterplots that were heavily featured in the manuscript. I suspect that as the sample sizes in the literature they were surveying (physiology) tended to have small sample sizes  According to the authors "the minimum and maximum sample sizes for any group show in a figure ... were 4... and 10 respectively". These results are presented in panel C of supplemental figure S2*

I have nothing against univariate scatterplots. In fact, for small sample sizes (say <30 elements/group), directly plotting data reveals a great deal about the distribution of the data. However, after a certain point the usefulness of this approach starts to wain, as there will be more overlap in points. In such cases, a box-plot is a more desirable solution. Not only is as aesthetic, but is also clearly indicates meaningful visual information to the reader about the centrality, the skew and the distribution of the data. *I suspect that is why, when Weissgerber et al. presented their data of their hundreds of figures, they did so using a box-plot.

"let me tell you about the wonders of data visualization"

Tuesday, April 14, 2015

Statistically, it's mayhem*

Below is a letter to the editor I recently wrote in response to the potential flaws in the analysis that forms the basis of the Waterloo Region Record's recent article "Police call records reveal region's trouble hot spots" which can be read here ->

One of the first things that I emphasize to the students in my biostatistics class at Wilfrid Laurier University is that statistics are a powerful tool. Used carefully and properly, statistics can provide valuable insight into the factors that shape the world around us - but used or interpreted incorrectly, statistics can potentially lead to conclusions that are unjustified or altogether incorrect​. Your recent "analysis" of police call data seems to fall into the latter category due to problems with your data set, and in the conclusions drawn from them.

First, let's consider your data set. Of the ~903,000 calls in your initial data set almost half were excluded from the analysis for a variety of reasons. Whenever data is dropped, there is the strong possibility that what remains is a non-random (and thus biased) set of data. Furthermore, the remaining data points "do not measure crime" (as belatedly stated in the 30th inch of the story) -but instead capture a wide variety of incidents (including "enforcement of traffic laws" and "attend at collisions" that are not necessarily linked to the residents of that region). It should go without saying that if your data does not contain variables are relevant to the question, then the conclusions drawn from them will be suspect. 

Using this questionable data set, the conclusion "the poorer the zone, the more often people call police and the more time police spend there, responding to distress" is drawn, without any thought of potentially confounding effects. There are potentially dozens of other factors besides average household income that differ between the patrol zones that may be ultimately responsible for the observed patterns. For instance, a cursory search on Google Maps seems to indicate that the regions with the highest frequencies of calls to the police also have a greater density of Tim Hortons locations - but you would not (hopefully) conclude that their presence is responsible for "where trouble lives". 

Generations of statisticians have warned that "correlation does not imply causation", but that message seems to have been ignored in the construction of this article, to the detriment of your readership. 


Tristan A.F. Long

*The title for this post is taken from one of the hyperbolic statements made in the article. I think that, ironically, this statement is an apt description of the statistics used in the analysis.

Sunday, September 28, 2014

Long Lab (summer fun edition)

L->R: Arnold Mak, David Filice, Katie Plagens, Tristan Long, Thiropa Balasubramaniam, Mireille Golemiec, Emily Martin