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.

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!

P-hacking and Paul Feyerabend

P-hacking, or researcher degrees of freedom, it’s a worrying issue in science. Specially, because p-hacking is not a black and white issue. On the blackest side there is deliberate p-hacking with the only purpose to advance your career. This is bad, but I hope it’s rare*. The grey area is more intriguing, because it concerns researchers not doing it consciously. I used to think that this include researchers that never had a proper statistical training, with too much pressure to publish too small datasets or that fool themselves thinking that this new analysis/subset of the data is what he/she should be testing in first place, so it doesn’t matter really the 200 previous analysis/subsets (which is false, they matter!). This is equally bad for science (even if the motivation is not as bad).

But then I read “against method” of Paul Feyerabend**. Despite some passages are really slow and repetitive, I liked it. A big part of the book explains Galileo Gallilei story. Galileo changed the paradigm based in incomplete theory, iffy data and measurement tools, and lots of propaganda. He used more its intuition than a proper scientific method. He was still right and most of his ideas were confirmed years later.

And that rang a bell. I’ve heard before scientists saying things like “well, we can’t measure it accurately, but trust me I know the system and this is what is happening”. From here to do a bit of conscious or unconscious p-hacking to support your hypothesis there is a small step. This researchers are using intuition, hours of thought and lots of knowledge. This scientists are putting forward their ideas. Ideas in which they believe, but they can’t just prove unequivocally with the data at hand because of the complexity of the problem.

Paul Feyerabend said that “everything goes” if it advances science. I am not justifying p-hacking to support something that it’s hard to  prove but you think is true, but after reading Feyerabend I am also less worried about adding some subjectivity to the scientific method, because being completely objective and following the method strictly may also slow down science. Maybe the middle ground is being able to recognizing when something is an opinion, and not facts, and avoid sticking a p-value to this opinion, but defend it anyway in the light of the data available and try to push forward the agenda to get better data, better methods, or whatever you need to support it. It’s complicated.

*people that only want to advance their careers choosed politics in first place, not science, right?  I know this is probably a wrong assumption.

**In a nutshell he praises that an objective scientific method is unattainable and rarely applied, and that we should free ourselves from using it as the single tool to do science. I liked for example the idea of aiming to create a plethora of theories (with no historical constraints or resistance from the status quo to accept compatible alternative explanations) that can cohabit and let time to do the thinning a posteriori. More on wikipedia.

Why collecting Long Term Ecological Data is not cool enough for funding agencies?

Most of my papers include in one way or another a sentence apologising for not having long term data, and excusing myself for using either a snapshot of whatever happens in a given year, or using long term data that is limited in regards of its completeness or has sampling limitations. This is because most pressing questions in ecology require to consider time to fully understand how things work, but temporally replicated data is rare.

The solution? Let’s collect the appropriate data, then! Not that simple. Funding for long term ecological data is almost non existing in EU*. I guess they take too much time to build up, and do not produce high impact factor papers in the first year. However, most long term research is not expensive, and can be maintained with a small budget, but surprisingly nobody wants to fund it. And yes, I tried, and I got comments like “not novel enough”.

Why I am writing this now? The Doñana Biological Station has been doing some monitoring programs for the last ~10 years. I am not going to explain the details, because I’ll probably do it wrong, but the fact is that the long term monitoring funds externally granted for 2014 didn’t arrived and the monitoring of e.g. butterflies in the park where about to be suspended in 2015. It was only thanks to the direction of EBD, and individual researchers that we can finally maintain this going, and avoid losing the temporal series, at least,  in 2015. For example, the cost of maintaining the butterfly monitoring is under 1000 EUR, which I will cover this year from my personal grants**. I am not using this data right now and the data is publicly released, but I see the value of having it. With the several threats the park has right now, including climate and land use change, having a baseline data on how communities fluctuate is critical to understand how the ecosystem will respond.

I would like to do more long term ecological research in my lab. I calculated this research will cost less than ~4 000 EUR/year. Why the Spanish ministry is willing to give me a ~50 000 budget for a 3 years project, but not a 12 year project with the same budget? I know it’s a political constrain, but Science should be beyond politics.

*LTER sites in the US is not optimal, but works better than here.

** And I know other researchers are assuming costs of monitoring other organisms.

Postdoc position available with me in plant-pollintor networks

PDF version here.

We are currently seeking applicants for a 18 month Postdoc position at Estación Biológica de Doñana (EBD-CSIC) in Sevilla to conduct synthesis work on the effect of landscape structure and mass-flowering crops on pollinator function to native plants, and plant-pollinator networks across Europe.

The postdoc will contribute to the funded BIODIVERSA project (Enhancing biodiversity-based ecosystem services to crops through optimized densities of green infrastructure in agricultural landscapes (ECODEAL, http://www.cec.lu.se/research/ecodeal). 

Candidates should have an interest in pollination ecology, know how to handle large complex databases, and have strong writing and statistical skills (preferably in R).

If you are interested in this position, please, send your CV with a complete list of your publications and the contact details of two reference persons to nacho.bartomeus@gmail.com. Please, merge all documents into a single PDF file and include your name in the file name.

Salary: 35.040 € per yr. before taxes

Deadline for interested applicants: March 30th, 2015

Montserrat VilàNacho Bartomeus 

Ramon Y Cajal advice

This post has two purposes, first, celebrate that I was awarded a RyC fellowship to go back to Spain, which is very exciting. Second to recommend to everyone the reading of Ramón y Cajal advice for a young researcher [PDF here].

It was written in 1920’s and is surprisingly modern. He makes a strong argument to let the data talk for your science and he make some very relevant points against the inclusion of honorary authors. I also love his steps to write a paper:

(1) Have something to say, (2) say it, (3) stop once it is said, and (4) give the article a suitable title and order of presentation.

He is a little bit too harsh on substituting talent by working hard, but I agree that working hard (i.e. don’t expect discoveries to come easy) is a good advice. Putting that together with his advice on how to criticise others work without hurting any feeling (i.e,  always acknowledging the good points first), I can summarise it with a quote borrowed from my father: “work hard and be nice to people”. On my own experience, I recommend anyone to maximize the feeling that Science is a big community of helpful people with a common purpose rather than a competition among researchers.

The advice for the Spaniards (how to do science from a country on the cue of scientific production and with very limited funding in 1920) is not as up-to-date nowadays, but I am affraid we will have to apply some of his advide on that soon, if things keep that way.

I don’t agree with everything. For example, I think working in group and establish collaborations is basic to get the most of our imagination and talent, instead of working alone for long hours. I also think is funny the advise he gives in order to find an appropriate wife, and it may look even a bit offensive nowadays, although the bottom line is quite true: find someone who understands you!

The last thing I want to highlight is that i love how he transmit the ideal of a scientist as a nobel pursuser of the truth, unbiased, humble, honorable, almost kind of a knight extracted from a tale. But I’ll let you read the rest. Enjoy.

Biodiversity shared-blog

I am rather a chaotic blog reader. I scan some RSS feeds, check blog aggregators from time to time (R-bloggers and Ecobloggers) and rely on tweeter as a curating tool that brings me the best posts directly to my timeline. Today I discovered http://www.biodiverseperspectives.com/ and it’s clearly a must read.

Cool posts like this one a side, I think is a wonderful idea. Grad students (and faculty) around the globe can register and post about a common topic: Biodiversity (check out their diagram on the upper-right part of the homepage, which covers a lot of ground). Having a personal blog may be not for everyone (I’m still exploring it), so it’s great to have a platform to share something from time to time, and with a nice readership already built.

How to decide where to submit your paper (my two cents)

Following Jeremy Fox interesting blog post, and at least three other people follow-up (herehere and here).  Here are my thoughts on where to submit your paper. In a nutshell, I think times are changing. If you are in a strong position, you can bet for the model you think is best. But if you are not settled yet, I think is wise to have a compromise between publishing some old school papers based on journals prestige, but also make your bet by submitting other manuscripts to faster and open access Journals. That way you can defend your position in a variety of situations.

Following Jeremy’s points:

  • Aim as high as you reasonably can. Agreed, but “high” is a vague term. Impact factor is not a reliable measure and “prestige” is difficult to asses. I think like Jan, that the difference is between the 3 top interdisciplinary journals, the top journals of your field, and then everything else. Within this categories, I don’t worry anymore about the journal in terms of “high impact”. (OA discussed below)
  • Don’t just go by journal prestige; consider “fit”. I do think fit is important, but not in terms of people finding your paper (despite lots of researchers keep using TOC’s of a few well-known journals), but because having a type of journal (or reader) in mind helps you frame your article. For example, I’d expect different things from the same title in Am Nat, than in Ecology.
  • How much will it cost? Important only if you don’t have the money.
  • How likely is the journal to send your paper out for external review? I liked Ethan’s advice on the importance of the speed of the process. By maximizing your chances of being sent to review, not only you can accumulate citations faster but also it reduce the amount of frustration.
  • Is the journal open access? Ideally, Yes, is very important for me. In reality, well, my projects rarely have the money to pay for it, so I end up not making them open.
  • Does the journal evaluate papers only on technical soundness? I think this is a model that will substitute all low tier journals. I’m writing mainly three types of papers. Papers that I hope can make a great advance on Ecology and that I would like to see in a top journal. Papers that has an specific niche, and where I want to target people working on this niche. And good papers that I think can make its moderate contribution, and I want them out there fast for people to read. This papers are ideal for open access and evaluated on technical soundness.
  • Is the journal part of a review cascade? Again, completely agree with Ethan. In fact I would love a model where papers are valued on technical soundness and then there is an “editors choice” or something like that.
  • Is it a society journal? I value supporting Societies. But most important: Is the publisher making profit? Is Copyright retained to the author? Society journals or other organisational journals (i.e. PLOS) has the great advantage from my point of view that revert the benefits to the community, and usually they require a licence to publish, but not a copyright transfer. It’s important for me to avoid as much as possible making a business of science.
  • Have you had good experiences with the journal in the past? I don’t think that’s relevant.
  • Is there anyone on the editorial board who’d be a good person to handle your paper? I’ve never thought on that.

Extra stuff:

  • Publish in a diversity of journals: If you want to increase your readership, increase the spectrum of journals you publish. Publish in general ecology Journals, in more specialised journals, Plos ONE stile. That would help you gain experience with the system too.
  • Listen to your feelings: Is there any journal you like (rationally or irrationally) specially? Forget the pros and cons. Publishing is hard, and its also important to fulfil your whims.

Context dependent

In the last meetings I attended, I observed an interesting behaviour among ecologists (including myself), and is that despite sometimes they present contradictory results, they get along pretty well. I understand that this is not the case among taxonomists, or physicists, where there is a single right answer (comic is not based on a true story). Ecologists can discuss several options regarding why the results presented are not general, but at the end, nobody claims to have the truth. Is there a “right answer” when understanding ecological processes? Is just that is hard to measure all the relevant variables, or even in the case where we can measure everything accurately, the stochasticity is too high?

I like to think that ecological theory is not only intellectually exciting, but that it allows for a general understanding of ecosystems. However, I keep finding context specific ecological responses everywhere, and few theories in ecology has a good predictive power. Maybe ecosystems are too complex to be predictable at fine scales. Maybe, like climatic models, we can predict next year general functioning of an ecosystem, but we can’t tell if a species will interact with another one next week. But maybe we just need to bring together better theory and better data and see if we can make sense of it. I am trying to put together data that usually is analyzed independently, but that is potentially affecting the same process. I hope that the combination of datasets fits better the theory than it does when data is analyzed independently. Despite stochasticity is everywhere, I want to think there is still room to improve the mechanistic understanding of complex ecosystems.