Drawdown

WhatsApp Image 2020-01-06 at 12.16.21Drawdown is the point when the current CO2 worldwide concentration starts to decrease. It’s a book highlighting 100 solutions already available to reverse climate change and a few more solutions to come. Its a positive and optimistic book and it’s what I needed to read after a few months feeling quite pessimistic about the actual situation.

I bought the book for my kids’ school to show we have solutions and not only problems and spark the conversation. I expected a quite dense book (with 100 examples listed one by one) and nothing you can read in one sitting. I was wrong. I read it all in three weeks and I am fascinated. The book is very well written, not too technical (but detailed!) and with lots of curiosities and personal stories that introduce the topics. I learned a lot.

There are no especially surprising solutions if you are already into climate change and sustainability, but it made a great job for me to recover the faith in humanity by showing what its already being done, and the potential to upscale it. Most solutions are not only good for reversing climate change but are economically viable in the long run (but unfortunately not in the short term, which is what is slowing down uptake) and are good for biodiversity and human wellbeing.

Maybe Drawdown is too optimistic, but I think is the kind of information we need to spread out and quickly if we want to change for good.

A climate change scarf

I have a new scarf depicting the temperatures of the last 100 years in Barcelona. The warmer the colors, the hotter the temperatures. This was a very cool project we did with my wife (she is the artist, I just crunched the numbers). It’s not only super pretty but a great conversation opener. Especially for young people, who suddenly realize all their life has been warmer than it used to be. I love it. But it’s ironic that winters are getting shorter and milder and I can’t use my scarf as much as I would like.

WhatsApp_Image_2019-12-21_at_21_18_43

Showing off my new scarf to my beloved friends.

It has been inspired by the knit linen stitch version of the Tempestry Project. Every year is represented by four rows in linen stitch (right side, wrong side, rs, ws), in a color that represents the mean temperature of that year. Data comes from the Barcelona meteorological observatory ;  (which has more than 200 years of monthly mean temperature data!)

We categorized the temperatures in 7 categories and selected 7 colors within a blue-red gradient. Here is the lines pattern we used starting on 1918 and ending in 2018. “1” represents the colder years, “7” the warmer years:

1 3 4 2 2 3 1 4 4 4 3 4 3 1 2 4 2 2 5 3 2 2 1 3 5 3 5 2 6 5 6 5 2 4 4 2 4 1 2 3 3 2 5 2 2 4 2 3 4 3 1 3 2 1 3 2 2 3 2 2 3 1 4 4 5 2 3 4 4 5 6 6 3 4 3 6 6 3 7 5 5 6 6 5 7 5 4 7 6 5 7 4 7 6 6 7 7 7 7 7

The code I used to convert temperature to colors is as follows:

#read the data:
bcn <- read.table(url("http://static-m.meteo.cat/wordpressweb/wp-content/uploads/2019/01/01160205/Barcelona_TM_m_1780_2018.txt"))
head(bcn)
#Create a dataframe with mean anual temperatures per year
d <- data.frame(year = bcn$V1, mean_t = rowMeans(bcn[,2:13]))
#select the last 100 years (use more if you want a longer scarf)
d <- tail(d,100)
#create a nice color gradient from blue to red (7 colors)
mypal <- colorRampPalette(c( "blue", "red" ))(7)
mypal

[1] “#0000FF” “#2A00D4” “#5500AA” “#7F007F” “#AA0055” “#D4002A” “#FF0000”

#Create a function to convert numbers to colors
map2color<-function(x,pal,limits=NULL){
if(is.null(limits)) limits=range(x)
pal[findInterval(x,seq(limits[1],limits[2],length.out=length(pal)+1), all.inside=TRUE)]
}
colors <- map2color(d$mean_t,mypal)
#visualize it
barplot(height = rep(1,length(d$mean_t)), col = colors)

bcn

#and extract the row numbers, 
#as.numeric(as.factor(colors))

1 3 4 2 2 3 1 4 4 4 3 4 3 1 2 4 2 2 5 3 2 2 1 3 5 3 5 2 6 5 6 5 2 4 4 2 4 1 2 3 3 2 5 2 2 4 2 3 4 3 1 3 2 1 3 2 2 3 2 2 3 1 4 4 5 2 3 4 4 5 6 6 3 4 3 6 6 3 7 5 5 6 6 5 7 5 4 7 6 5 7 4 7 6 6 7 7 7 7 7

Thise are all available years from 1780 to 2018, FYI:

3 4 2 4 2 3 3 3 4 3 4 4 4 4 4 3 3 3 5 4 3 3 3 3 4 2 3 3 2 2 2 4 2 1 1 2 1 2 3 4 2 4 5 3 3 3 3 2 4 2 3 5 3 3 4 2 2 2 3 3 3 3 3 2 3 3 6 5 3 5 3 2 4 2 2 3 3 3 4 4 3 4 4 4 4 5 5 4 4 4 4 3 4 4 4 4 4 4 3 2 3 3 5 2 5 4 2 1 2 4 3 3 3 4 4 5 4 6 4 5 5 3 3 3 4 3 4 3 3 2 3 5 4 3 3 3 4 2 3 3 5 5 4 4 4 3 5 5 5 4 5 4 3 4 5 4 4 6 5 4 4 3 4 6 4 6 4 6 6 6 6 4 5 5 4 5 3 4 5 5 4 6 4 3 5 4 5 5 5 3 5 4 3 4 4 4 4 4 4 5 3 5 5 6 4 5 5 5 6 7 6 5 5 5 7 6 5 7 6 6 6 7 6 7 6 5 7 6 6 7 5 7 7 6 7 7 7 7 7

Making a sustainable lab

At this point, there is no need for an introduction on why we need to make our life more sustainable. We just need to do it and do it now. This includes being more sustainable at work.
Last week, all lab members gathered together and brainstorm a bit on which actions are we already doing or can start doing. By no means, this “light” measures will fix a global problem, but we think all contributions are welcomed.
  1. Traveling: Select carefully which trips you need to do and arrange Skype meetings when possible. Go by train for trips < 1000 km. Limit international conferences to max. 1 EU conference a year and 1 intercontinental conference every 3-5 years. When flying, on a personal decision, we encourage lab members to compensate for CO2 emissions*. Most projects do not allow paying CO2 compensations directly from the project budget, but we can use the leftovers of the stipulated diet reimbursement to that end.
  2. Daily live: Most of us bike to work. We bring our own food (but we can do better). We use natural air conditioning/heater when possible (open windows in the morning in summer, use a pullover in winter, etc…)
  3. Lab work: We do kill pollinators, but we do not kill any pollinator without a clear purpose and which is not going to be properly curated and databased. We don’t use much single-use plastic, but we plan to change our marking methods for plants (from single-use plastic to re-usable wire). Killing jars are re-used as much as possible. We try to optimize fieldwork by carpooling or visiting sequential sites on the same day. We re-use (and re-assemble) electronic material and computers (Thanks David!).

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?

IMG_0003.jpg

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.

ntw.jpg

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.

Comment on “Maintaining Scientific Integrity in a Climate of Perverse Incentives and Hypercompetition”

I just read this worrying paper summarizing a big problem we should all be aware: “Maintaining Scientific Integrity in a Climate of Perverse Incentives and Hypercompetition“.

I don’t have a perfect solution to change the system for good, but I have an easy patch to help your integrity and the integrity of your group. And I say this because I am very conscious that I am (we all are) weak and when under pressure, the easier person to fool is yourself. This means, that even if you don’t want to cheat consciously, behaviors like p-hacking, ad hoc interpretations and not double checking results that fit your expectations are hard to avoid if you are on your own. So this is the patch: Don’t do things alone. You can fool yourself, but it’s harder to fool your team-mates. And as a corollary, don’t let your students do field work, data cleaning, analysis, etc… alone. Somedays I may be tired and tempted to be more sloppy during fieldwork, for example, but if I have a team-mate with me, it’s easier overcome the situation as a team. In our lab, one way to do this is using git collaboratively. Git tracks all steps of your research since data entry. The first thing we do when we have raw data is to upload it to git and check it ideally at least among 2 of us. This creates a permanent record and avoids the temptation of editing anything there if results are not what you expected later on. Same with data cleaning, and analysis. When those steps are shared and your actions are tracked, it’s easier to be honest. Just to be clear, this mechanism doesn’t work as the threat of a “big brother” that is watching you, is more a feeling of teamwork, where you want to live for the team expectations.

Marie Curie, concessions, and pressure to publish.

I have to admit I didn’t know much about Marie Curie a few days ago (other than the “trivia” facts such as that she discovered the radioactivity and was the first women winner of a Nobel prize). But I just read a book* about her and I really loved it. Oh my god, she was unique in a thousand ways. The book is written by Rosa Montero, and uses Marie Curie’s diary written after Pierre Curie death to talk about very personal things including death, gender balance, society pressures, self-esteem, and many other main topics in life. So it’s not a typical biography, but an excuse to reflect on important things. I won’t go into details, but I highly recommend it.

And while reading the book I found a quote by Pierre Curie that reflects at perfection my actual feeling in science.

“Besides, we must make a living, and this forces us to become a wheel in the machine. The most painful are the concessions we are forced to make to the prejudices of the society in which we live. We must make more or fewer compromises according as we feel ourselves feebler or stronger. If one does not make enough concessions he is crushed; if he makes too many he is ignoble and despises himself”

I do think finding this balance is what kept you (and your science) alive in this world.

Which brings us to the last point. I just discussed a result with my PhD student. It is not significant (p = 0.08), but the effect size is quite big (probability something happening goes from 0.6 to 0.2), but the sample size is small (n < 20). The unavoidable question raised. “It’s 0.08 marginally significant?”, “can we say there is an effect?” My reply was that in a perfect world we would use this data to frame a hypothesis. Then, we would collect 30 more independent data points and test it for real. But the project is almost over, he needs to defend the PhD soon and we are not in a perfect world. So we make concessions. And we will try to publish what we have and cross our fingers hoping that someone else will validate our finding. But we don’t concede too much either, and we should make sure to discuss the result appropriately. A potential large effect size, but very variable and based on a limited sampling size. Or in other words, we will try to avoid the p-value dichotomy once more.

*The book is edited only in Spanish, French, Dutch, and Portuguese… for once, sorry English speakers!

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!