A simple observation of single plant flower production

Violeta and Ignasi Bartomeus

As a simple game with the kids, we started counting daily flower production on a single plant of Cistus albidus that we transplanted last year (so it’s 3-4 year old, and 1.5 meters tall). It turned out quite surprising! I was expecting a clear peak of flowering around a normal distribution, but the flowering went on for > 100 days (very rainy season), with two distinctive flowering peaks. When C. crispus started flowering we did the same with another single individual (1 m tall). In this case, the peak is more clear.

Flower production per day of two individuals of two Cistus species. Days start counting from 1 January (day 55 = 23 February). The date of nest completion by three Osmia bicornis species is marked (O.b1, 2 and 3).

We also recorded nest completion by three Osmia bicornis female bees that regularly visited C. albidus. Bee “O.b1” completed two nests, but the other two only one. Note it took a long time for O.b1 to build the second nest!

Conclusions: Now I have way more questions than when I started. I wonder about individual lifetime flower production over years, variability across individuals, relationship with fitness, how this compare to community level phenology patterns, …

Bees of my garden

This is basically a quick note for me to remember which bees visited us these last years. As I am keen on promoting pollinator-friendly gardens, this may be interesting for someone else. Maybe.

Context: We moved to this house two springs ago. It has a ~4*8 m garden and is located in a residential area with allotments (great for bees) nearby.

2018: The garden was a perfect lawn without a single flower + a patch of bamboo and a patch of bird-of-paradise flowers (useless for native bees). I don’t remember seeing any bee that year. I stopped watering the lawn (makes no sense in Seville to have a water-consuming lawn).

2019: We dig out the bamboo and the bird-of-paradise flowers (harder than I would ever though!) and planted Rosemary, Lavanda, Rockroses, and Teucrium. I also removed a 1*4m patch of lawn to plant some vegetables. I added vertical bamboo reeds (I had plenty!) in the soil and add a few old logs in a corner.


  1. Eucera sp. Early season in lavanda.
  2. Chalicodoma sicula in Teucrium.
  3. Megachile sp nesting in the ground (in a pot!) Entering leaves, Honeybee size.
  4. Ceratina curcubitina (?) nesting in the vertical bamboo reeds.
  5. Holpitis sp. hovering around the logs.
  6. Xylocopa violacea Loves teucrium. Males using some horizontal bamboo reeds to sleep.
  7. Apis mellifera Rare, in teucrium.

2020: I installed a couple of bee hotels for megachilids, let the lawn go wild. The grama is still languishing, now intermixed with clover (Trifolium repens), melilotus (Melilotus sp.), and other annuals well-adapted to our climatic conditions.

Bees: (Eucera, Chalicodoma and the big Megachile not seen again)

  1. Xylocopa violacea lots in teucrium (March-April), and females using the new bee hotels and expanding the canes.
  2. Anthophora sp. Rare in teucrium.
  3. Osmia bicornis mainly in Cistus albidus. 3 females, 4 nests and 2 not finished.
  4. Hylaeus sp. visiting broccoli. I think they nest in the vertical reeds also.
  5. Ceratina curcubitina (?). A real explosion this year. All vertical canes full. Cistus crispus, teucrium, broccoli, strawberries, Melilotus, clover.
  6. Anthidium manicatum (?) in Teucrium. Males patroling and females gathering leave hairs. Earl morning to late evening (May).
  7. Anthidiellum breviusculum (?) in clover.
  8. Hoplitis sp1. Again in the logs. Also in Lavanda from time to time. No idea where are they nesting.
  9. Andrena sp. Very rare (seen three times maybe?). In C. crispus and once I saw it digging in the vegetable plot, but never again.
  10. Apis mellifera in Teucrium and clover. More common this year, to my disgrace.
  11. Lasioglossum sp (the golden one, similar to gemmeus) in Brocoli.
  12. Megachile sp. in Melilotus A very small one close to M. apicalis. It nests in the vertical reeds and uses petals to close them.
  13. Hoplitis sp2. Late season. Looks like adunca, but I have no echium nearby. Active at dusk, but no nests completed yet (still active, but is already 30ºC). UPDATE: two nests colsed + two not finished by 7 June

Maybe I am missing some super tiny bees, as the ceratinas + hylaeus + lasioglossums sometimes fly fast and who knows what else is mixed there. All ID’s are mine, so maybe some are incorrect.

13 species in 2019, 16 overall! Looks like megachilids are over-represented. I miss more Andrenas, and I did not see a single parasite yet. No bumblebees, but this is expected as they are rare in Seville. Next year, more!

Managing people: Radical candor in academia.

As researchers, we are supposed to be good at a plethora of things. Managing people is one of those things that nobody teaches us, but that ends up being pivotal for the lab functioning. In fact, I would say that researchers, in general, don’t feel comfortable being a boss and see the time invested in managing people as a burden that prevents them to do more important things such as actual research. I’ve been there, and I think that it is pivotal that (we like it or not, we are good at it or not) we assume part of our job as IP is to be a boss, and we try to be a good one.

With this in mind, I have read several pieces on scrum and agile culture and tried to understand how to create an efficient and happy team. The last book I read was Radical Candor, by Kim Scott. It is focused on managing people at big companies, but I think a lot of stuff can be applied to academics.  The main message of the book is that you need to create a culture of caring for people (and for the science you are doing) and of giving clear and honest feedback.


I think that in academia (in general) we are good at caring personally (small labs, with people passionate for science and mind alike, makes it easier), but we don’t always challenge directly. In my own experience, when things go well, it’s super easy for me to give honest feedback and improve the project even more, but when someone is under-performing I have a hard time making that clear, and this is bad for you, for him/her and for the team.

The following are basically notes for me and the lab, with no ambition to be comprehensive or detailed:

The book makes crystal clear that spending a morning listening to your team complain about personal stuff, or about an internal fight or celebrating their success is an integral part of your job. Not a distraction. Your job. This makes it easier to allocate time to that in your daily schedule.

It also encourages focusing not only on people writing the important papers, but on hard workers that make this possible. Having a “stable” lab technician who solves the day to day field and lab work is the best decision I toke as PI so far. Invest in core people. This is really hard in academia where positions depend on short term projects.

Do not personalize. People are not sloppy, they may have done a particular job in a sloppy way. Then, Its easier to fix specific actions. The “Situation-behaviour-impact” chain is the best to describe a problem. The best feedback is given often and in impromptu situations, is specific about the problem and offers solutions.

If you want feedback (or criticism as stated in the book) to be part of your team, cultivate it at all levels. Encourage criticism also towards yourself (or your ideas) and among your peers.  I like the following process to encourage criticisms, starting by listening (not by replying to criticism or cutting it or offering excuses). When you got criticism, ask to give details and strive to understand it. Do not react. (I’ll apply this also to my normal life).


I was reinforced on running retrospectives after papers are published in the lab. The book also recommends blocking time for thinking and for working on personal projects, something I am also doing (albeit I should do more) so I don’t spend the day in meetings.

Finally, It made me think about the lab culture. It’s hard to judge internally, but I would like to think the lab culture is to be open and replicable (even if we often do not achieve it!), to put people first and to be risky in our ideas and approaches (even if this means we often go for the big picture, and miss some of the important details).

Everybody is music

Those are difficult times, and I am not going to talk about #covid19 and its interactions with science, academia, ecology, society, well-being or climate as enough has been already said.  But I thought it’s a good moment to share a collaborative playlist the lab has created just for fun. Because “quien canta su mal espanta“*. Thanks to all the lab members and close collaborators for sharing your good music taste. Enjoy, stay safe, take care of yourself and your loved ones and sing aloud!

  • “who sings his evil scares”


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.


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"))
#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)

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

#Create a function to convert numbers to colors
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)


#and extract the row numbers, 

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.


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?


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.


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!