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.

About motivation

My wife and I are getting really concerned regarding the education system our kids are in. Ken Robinson summarises the main points in his TED talks, like this one. I agree with him that current schools kill creativity and do not set the conditions to let talent flourish. However is hard to change the system from scratch. Then I realised this scales up to higher degree education (e.g. PhD) where I do have the power to change things (at least for my three PhD students).

During the PhD, the conditions are way better than in primary school. You learn by doing. Your effort has a clear purpose of advancing Science. You have flexibility in schedules and to some degree on topics. However, is not rare that PhD students are not highly motivated. How can we set the right conditions (i.e. motivate them) to maximise the learning experience during a PhD? This is a discussion I have no answer, but we should have. A good place to start are Uri Alon talks and papers (here and here).

One thing I already talk about is the idea of self-organizing teams and adopting “agile /scrum” ideas. Frequent stand-up meetings, or encourage side-projects that reflect what you are really passionate about, are things help keeping motivation high.

Another powerful idea is the concept of “flow“. The term “flow” was created by psychologist and father of Positive Psychology, Mihaly Csikszentmihalyi and I learnt about it when I used to climb big walls. You flow when doing difficult things that you master, and its great. The following graph shows the idea.

Vertical Rock Indoor Climbing Center – A Discussion Of “The Flow State” In Rock Climbing

The problem is that its tricky to suggest increasing challenges to PhD students that don’t become “boring”, but do not create anxiety. However, it’s worth trying to think with them along this lines. We are practicing to ring the alarm bell early on if they get anxious, so we can work on getting the skills first.

Any other things that work for you?

Can functional diversity indices predict ecosystem function?

That is exactly the question Vesna and I had after reading a few papers using FD indices as proxies of function. Other than a few papers on plants (mostly experimental and with biomass production as the function studied) we couldn’t find the answer. Then is when we started a side project to gather the data necessary to answer our question. The problem is that there are many FD indexes (see previous post here) and many functions to test. So after realising that this was not a side project anymore we gathered data on as many functions as we can find (a.k.a. calling past collaborators and assaulting ecologists for data in the darker corridors of the conferences we attended) and developed a serious analysis.

The answer? Well, I wouldn’t use FD metrics to predict a single function (i.e. pollination to a single plant species or predation of a single aphid species) because their predictive power are usually below 0.5. However, it worth nothing that non-experimental single functions are usually better described by a few traits than by classic taxonomic diversity indices. It seems it depends on the function studied, but functional identity and to some degree weighted functional diversity metrics can improve our mechanistic understanding of the BEF effects, and this is pretty cool.

Plus, this is the first paper with two lead authors and lots of GitHub commits I did, and it worked really well!

Enjoy the paper here.

J.A Valverde: Memories of an heterodox biologist

A friend of mine recommended me to read Valverde’s autobiography, both because I am moving to the Doñana Biological Station, of which he was the main advocate, and because the book is really awesome. In fact, he writes really well and the book is full of fun stories and subtle puns (not sure if it’s translated to english, though).

Valverde, as I see him after reading the books, is the perfect blend between the traditional English naturalist and the Spanish picaresque. He had a pretty rough childhood including a pretty bad tuberculosis in the midst of the spanish civil war. With no official studies he learnt ecology the hard way by reading whatever book he can find and collecting observations of the fauna around his home town. While reading it I really regretted not being a better naturalist myself and now I really want to get out of my computer and just look at nature for a while. He also had a great ego (which is see as a positive thing). As an example, I love when after years of collecting observations on bird communities around his home town, he reads Elton influential book and says something like he didn’t learnt anything new in Elton’s that he had not discovered himself before, but that the book was very good to put names to familiar concepts. And I believe that is true and not arrogance. How Doñana Biological Station was created is also quite epic, and i didn’t know that they (WWF, among others) actually bought the lands to create the reserve. Really if there is a will, there is a way. However, is quite astonishing also to see its actual destruction despite the conservation efforts.

But read it, is long but easy to read (start for volumes I and IV), and you will get a glimpse of how ecology developed in Spain* since the 1930’s.

* in Valverde’s words, the two main revolutions in the field of ecology in Spain were the Peterson bird guide and the SEAT 600 car; the first allow anyone to identify birds effortless, and the second to travel around.

F1000 Research waves fees for ecologists

Quick post to say that if you are en ecologist you can try now F1000Research for free until the end of the year (just enter the code ECOL16 during submission). Other than the beauty of open access, open peer-review (yes, where reviewers get credit too for doing a good job), and fast publication, I like the freedom of formats offered. They are introducing a new format, “observation papers”, for sharing observations that will not be enough for a full paper, but you don’t want them to be lost forever. Tons of small datasets are wasted because there is no enough data to make a full story. I am thinking specially in master thesis, or studies with low replication. Achieving those in a common place can be a good practice. I see two reasons for doing that at F1000Research. It has a good search tool and they only publish papers if data is released, hence, this data can be used on meta-analysis, for example. The drawback is that it can be expensive for master students, but you can try for free now.

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.