Teaching complex science to 8-year-old kids

Science is often taught as magic. You pour sodium bicarbonate and Booom, you rocket launches. Which for kids, it is not different from saying “Alohomora” and boom, the magician disappears. Both things are fundamentally different, but if you only see the result, they are hard to tell apart, especially for kids. This is why I became interested in teaching the process and not the result. In fact, the next two ideas I successfully ran at my daughter class focus on playing and experimenting, and not on learning concepts.

Evolution:

Objective: See at play the heritability and natural selection concepts.
Material: Paper and pencil
Time: 45 min

Don’t tell them this activity is about evolution. Start by drawing 4-5 animal shaped sketches. Ask the kids to draw their offspring. Look at the children’s drawings and reflect about inheritance. Are they equal? No. Are they very different? No. Lesson one: There is heritance, but with variability.

Now “kill” a few drawings. Only the ones with long-ish neck survived, or only the ones with thick fur. You are acting as the natural selection. Then ask again the kids to draw the next generation of the surviving animals. I bet some will try to emphasize the surviving characteristics. Repeat the process and kill again the unfitted drawings. If any kid draws a super long neck or a super furry animal (or an animal with newly grown wings), kill it too, as it’s impossible from such parents to create such offspring.

After 3-5 generations wrap up the best you can and compare the first generation with the last one. Are they from the same species? has the species evolved?

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Alternative: If the group is big, split it in two after the first generation. The second group will migrate to an island, where selective pressures are different. Now you can compare the original parents with the two evolving lines and see speciation.

Network Theory:

Objective: Understand that networks are everywhere and that network structure matters.
Material: cork, thumbtacks, and rubber bands
Time: 45 min

I started the activity by asking which type of networks they know (P2P, Facebook, trophic networks…). Then I took a well-connected web made out of yarn and knots and ask a volunteer to cut a link. The web was unaffected. Then I took a second web with few connections and ask again to cut a link. The web was easily broken.

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However, the main activity consists of running a “bingo” game on a network like the one in the photo above, which mimics a plant-pollinator network with thumbtacks (species) linked by rubber bands (interactions). You give one network to each group. When a plant is randomly selected, you remove the thumbtacks and all rubber bands attached to it. If a bee runs out of rubber bands, it dies. The game ends when a group loses 6 bees. The interesting thing is to see how some groups lose bees way faster than others? Why? Kids tend to say because some networks have more rubber bands, but no. All should have the same number. It only depends on the structure. Hence, you should give them contrasting structures.

Now you can make a nested network and explain this is the shape they have in nature. Next, you can ask half of the groups to start “killing” the smaller, less abundant plants, and the other the larger, more abundant plants. Start by asking the second group if this structure is robust. They will say no! Removing 2-3 plants kills rapidly 6-7 bees. Now ask the second group. They will say it is robust, as removing almost all plants didn’t kill a single bee (see attached presentation). Wrap up explaining that in nature rare plants are gone first, and abundant plants are unlikely to get locally extinct first.

As a final wrap up, I made a fake social network of themselves (again, see presentation). I started by adding nodes strongly connected (best friends), then add modules (gangs or groups of friends) and then connectors (kids that like to play with different groups) and stress that these roles are dynamic, and ALL are important to make a robust network.

Find here the presentation I used and the PDF of the bee drawings made by my friend Paula Pereletegui (Thanks!). Corks can be bought at IKEA.

Disclaimer: These are quick notes for scientists that already know about evolution or plant-pollinator networks and want inspiration to reach out. If you want to do this at your school, but you are not familiar with the basic concepts I am happy to help. Just email me.

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Happy women and girls in science day!

Today we celebrate an important day. We celebrate equality in science! Hence, I want to make a post highlighting a few great researchers I have the privilege to work with. I was lucky enough to interact with lots of great female scientists and my stereotype of a scientist’s is not an old white man. I know this is not common, and this is why it’s important to show that there are plenty of awesome female researchers like the ones I met, specially to ensure young girls have a diversity of role models.

So here there are four great researchers in different career stages. First, my PhD Advisor, Montse Vilà. She investigates the impacts of invasive plants and was my first contact with a real scientist. Second, my PostDoc advisor, Rachael Winfree, from whom I learnt a million things. She investigates how bee diversity determines ecosystem functioning. Third, Romina Rader, who studies non bee pollinators. We met as postdocs and has become one of my usual collaborators. Finally, Ainhoa Magrach, which I hired as Postdoc last year and it was super-stimulating to work with. She is now studying the impact of global change on biodiversity. I could name many more because almost half of my coauthors are great female scientists, but I’ll stop here. Today several initiatives are highlighting the awesome work that women do in ecology, for example here (spanish) or here, so check it out and spread the word, specially among young girls and schools!

 

Bugs, kids and swimming pools

I accidentally found a great way to make kids into natural history/entomology during summer holidays.
All you need is a catch-net, Chinery’s Insect of European insects book (or any other general insect book for your region) and a swimming pool.
Kids catch drowned insects in the pool with the net (quite frequent drownings in our case*, I have to say, and I wonder how high are the death rates caused by swimming pools globally!). Collecting that way is already quite fun. Most bugs are still alive and we let them dry gently. It’s quite rewarding to see them fly away after a couple of minutes, which is wonderful because kids are now bug-rescuers, not bug-killers! Additionally, wet bugs are slow and let you see them and try to id them (i.e. at least to family or genus). So this is a kill free, easy way to see bugs
If you want the scientific bonus, we also recorded the frequency of each species, time of the day and if it was dead of alive. You can then explore the data. We found 52 specimens of 22 morphospecies. Of the several insect groups recorded (wasps, bees, ants, flies, beetles, moths, harvestmen (opiliones), heteropterans, milipids, …), the most numerous were wasps. Interestingly, among the two most common species, Polistes has higher mortality rates (4 out of 8 ;50%) than Vespula (3 out of 9; 33%). The coolest insect was probably the stick bug!
*If not much insects fall in the pool, open the filter and you should find dozens of mostly dead bodies.

Preferring a preference index II: null models

This is a guest post by my PhD student Miguel Ángel Collado. My last post on preferring a preference indexes was not satisfactory to us, so we have better options now. Read Miguel Ángel solution below.


We are working on the ecological value of various habitats or sites. In addition to different classical biodiversity indexes, we want to know if we have some sites that are not specially diverse, but they have some ecologically important species attached to them, we could measure this through preference analyses, using null models to compare with our data.

We can define “preference” for an species if the presence of this species on a given site is bigger than expected by random. A way to know this is comparing to null models and establishing an upper threshold for preference, and a lower one for avoidance, this way we would know whether some species of interest have affinity for some sites or just use them as expected.

To see an example of this

Ecoflor 2016

Ecoflor is an annual Spanish meeting on everything related to flowers (from evolution to pollinators). The level is amazingly high for being a small “unorganized” local meeting and the most important part is that is a fun forum to discuss crazy ideas, and not just finished work. Here there are some of the things I learnt this year in no particular order:

  • You can do biogeography using Arabidobsis taliana. Moreover, flowering time can be regulated by photoperiod or vernalization and you can map responsible gens across regions (by X. Picò).
  • Plants can cooperate or be selfish depending on its genotype (by R. Torices).
  • The coolest talk was on epigenetics, which can redirect the course of evolution. With experimental data on radish exposed to herbivory. (by M. Sobral).
  • Invasive Oxalis pes-caprae was thought to have only one morph in its invasive rage and hance reproduce vegetatively only, but the second morph has arrived (and its here to stay) (by S. Castro)
  • Plant-pollinator networks can be better plotted than with bipartite (by J. Galeano)
  • And it was the first time one of my students talked in public. Definitively a great talk by Miguel Angel Collado on pollinator habitat preferences.

Next year will be in Seville, join us*!

*You need probably to know some spanish, but some talks are always in english an all slides are english.

Lab decalogue

A while ago I wrote a lab decalog and I was not brave enough to post it. I was afraid of being judged as silly, or idealist, or naive, but here it is. I may not always accomplish to follow the following decalog, but trying is the first step.

Lab members should aim to (in no particular order):
  1. Be passionate and curious, enjoy science. We dedicate a lot of time to science, regardless of the low job stability and relatively low salaries, so we should love what we do.
  2. Be nice. You may think science is about ideas and data, but at the end its about the people who is behind. A good rule of thumb in case of conflict is to always assume good faith. The best way to solve problems is to talk about it. Even among labs (but specially within), we are not competitors, but team mates.
  3. Support open science in the degree it is possible for you. Assure reproducibility of your results, deposit you data and code (use Git!), engage with the scientific community, participate of the peer review process and sign your reviews.
  4. Think big. Which is the relevant question that science and/or society needs an answer. Then think how you can contribute. Resources in science are scarce, so we should focus on answering relevant questions (from small applied problems to big unifying theories, but relevant)
  5. Talk a lot. Gain confidence to say what you think. Ask for help when needed, offer collaboration when you can. Know new people and see new points of view. Best ideas can come from anyone.
  6. Go for quality, not quantity. Good experimental designs, solid datasets, well developed methods take time and I understand there is pressure for publishing, but I believe it pays off in the long run.
  7. Never stop learning. And take your time to think about what you learned in each project and make it count.
  8. Prioritize. Be engaged, but say “No” when you don’t have the time to dedicate. Prioritize your goals and do not compromise if you can’t. There is also lots of good things to do in life beside work, and those needs time too.
  9. Read broadly and read a lot. Part of our job is reading papers. Having a holistic view require time to read. Learn how to read, while in some papers you would need to focus in the introduction, others you would like to dissect the methods, do not treat all papers equal.
  10. Start side projects. Even better if you finish them. I explicitly encourage you to make use of the 20% rule. Use up to one day a week for other activities not directly related with your PhD/main project. You can involve me in it or not, you can learn something new, draw bees, have a blog, do an outreach project, develop and test a new method that may not work, collaborate in a crazy idea with someone else, read about a topic out of your area. In the long run, it pays off.
  11. Above all, be flexible. As scientists we require the flexibility to have eleven items in a  decalog. To change our mind on how to best be productive. To adapt to the new challenges.