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.
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Where are the kids born in December?

This is the question Xavier Sala i Martín made in a catalan TV show about economic sciences (yes, pretty cool you can talk about that in the TV!). In a nutshell, he described the relative age effect. A pattern for which most elite football and hockey players are born in the first 6 months of the year because young kids from a given age are put to compete together and the older ones are bigger and stronger. Then coaches dedicate more time to them, and by the time the physical capabilities are even among all kids born the same year, kids born in January have trained more, get more positive reinforcement, etc…

But he did not answer where are the kids born in december. I speculated that those “bad” at sports would have more time to do arts, like play music. Lets test the hypothesis! I found a list of Musicians by birthday in wikipedia and @vgaltes scrap it for me*. Amazing! 57% of musicians in wikipedia are born in the latests 6 months of the year (yes, a chi square is highly significant with this sample size), and january is the only month that goes against our prediction.

Rplot03

Each bar is the number of musics per month starting in January. Black line is the expected number. Sorry for the terrible graph with no axes.

We should have stopped here. Publish it and be famous. Unfortunatelly we got excited. @vgaltes found this web page with lots of birthday summaries by profession and by eyeballing the numbers there is no clear pattern for musicians. Then @dukjb started pointing out that we should correct for number of days that each months has, and more importantly, for the natural birth rate per month, which is likely not uniform. Then we lost momentum, we got distracted by other things and the conversation fade out. But at least we had some fun, no excuse for being bad at sports** and this post!


*I am ashamed, but It would be too time consuming to do in R for me for a side, side, side, side project.

**I was born in early April.

Book: Nature’s economy, A History of Ecological Ideas

I had an uneasy feeling about not knowing enough about the history of ecology and after some googling I tried reading Nature’s economy (http://www.amazon.com/Natures-Economy-History-Ecological-Environment/dp/0521468345). I am glad I did. Despite the first 300 pages are a bit slow and deal with the historical process from White and Linneaus, to Thoreau passing through the key figure of Darwin, it’s well written and helps you understanding the different views of nature along our history, which range from “an enemy to tame” to “an entity to conserve”. I didn’t learn a ton from this part, but I enjoyed going through the well connected dots. However, the last ~100 pages were eyeopener and something I highly recommend its read to anyone in ecology. I think this part would do a great lecture to discuss in lab meetings and the like.

Before reading chapter IV, I had a set of snapshots in my head with niche theory, food-webs, Lotcka-Volterra models, the island biogeography theory, Gaia hypothesis, emergence properties of ecosystems, deterministic population dynamics, and so forth… But connecting all this dots through history helped me a lot to understand where we are coming from. It feels that knowing the historical development of the subject helps seeing some historical constrains and even helps re-evaluate the kind of ecology we are doing (e.g. why I am closer to community ecology than to population ecology?). I am not going to try to summarize this last chapter IV here because I would do a poor job, but I think it can be read as a stand alone text, and I encourage you to have a look. If you know any other short-ish summary of the main development of ideas in ecology let me know in the comments. I feel is good to see different viewpoints on this kind of historical perspectives and also is always good to go through it a few times in order to interiorize the story.

Peer-Review, making the numbers

We know it, the system is saturated, but what are we doing? Here are some numbers from 4 recent Journals I reviewed and published (or tried to publish) recently.

Time given to me to complete the review Time to take a 1st decision in my ms
PNAS 10 days > 3 months
PLoSOne 10 days 2 months
EcolLett 20 days 2 months
GCB 30 days 2 months

I think most reviewers do handle the ms on time (or almost on time), and that editors handle ms’s as fast as possible, so where are we losing the time? On finding the reviewers! In my limited experience in J Ecol I have to invite 6-10 reviewers to get two to accept, and that imply at least 15 days delay at best. And note that all the above are leading journals, so I don’t want to know how much it take for a low-tier Journal.

However, the positive line is: There are people willing to review all this papers. Seriously, there is a lot of potential reviewers that like to read an interesting paper on their topic, specially if they get some reward other than being the first on knowing about that paper. So I see two problems, which rewards can we offer and how to find the people who is interested in reviewing that paper efficiently.

1) Rewards: Yes, I love reviewing, I learn and I feel engaged with the community, but it also takes a lot of time. However, a spoon full of sugar helps the medicine go down. I don’t want money, I want to feel appreciated. For example, Ecol Lett offers you a free subscription for 3 months or GCB a free color figure in your next accepted ms (given that you manage to get one accepted). I am sure other options are out there, including some fun rewards, like for example “the person with more reviews in a year wins a dinner at next ESA/Intecol meeting with the chief editor” to put a silly example. Recognition is another powerful reward, but more on that line in the next item.

2) Interests matching: Rather than a blind guess from the editor of who will be interested in reviewing a paper, we should be able maximize interests. Can we adapt an internet dating system for finding a suitable partner to find a suitable reviewer? As an editor, I would love to see which reviewers with “my interests” are “single” (i.e. available) at this moment. Why sing in as a reviewer? May be because you want the free subscription to Ecol Lett or you die for this dinner with Dr. X. Also, by making your profile and activity public is easy to track your reputation as a reviewer (and of course you can put your reputation-score in your CV). Identify cheaters in the system (which submit papers but don’t review) will be also easy, and new PhD students can enter the game faster. Any entrepreneur wants to develop it?

While  there is still also a lot of bad advice out there which contribute to saturate the system, other models to de-saturate the system are possible (PubCreds are an other awesome idea). I am looking forward to see how all it evolves.

TraitBank

In brief: Who is in to create an Open Trait Data repository?

In this same moment at least 10 researchers (but mostly undergrads) are compiling trait data for some exciting analysis. That includes myself. In fact, most trait analysis are hampered by the quality of the traits, which are often lumped to the species level, and hence do not capture the natural variation, or info for some species is based on just one population with the hope that it is representative. Paradoxically, I think this trait data is very abundant, but not available. Thousands of researchers have measures, for example, of body size for a bunch of specimens of his/her preferred taxa. This data is just not accessible or is scattered on the net.

There are some databases (some open, some not) with traits for some groups (plants, birds and mammals) but not a joint effort to capture all this knowledge like the GenBank initiative. So I propose to create a TraitBank. The technology is easy to implement (from a SQL server liked to a web, to a simple Google spreadsheet), but the key aspect would be to enroll the community to make trait data deposition encouraged upon manuscript acceptance. Do you think that the leading journals will ask authors to deposit any morphological or life history measurement reported in the paper? It will also be important that a well-known independent organisation host the data. Any idea on who to contact? would Figshare be an option?

The fields should be very delimited to allow an easy search and compilation of information; as a first pass I would propose:

– Publication associated with the data and/or author
– Species taxonomy (full taxonomy can be retrieved from ITIS)
– 
Measurement is in wild or captive populations
– Region and Lat/Long of the measurement
– Category (morphological;life history; or ecological trait)
– Subcategory (e.g. body mass; clutch size; survival; phenology…)
– Mean value, SE and n: Units should be fixed by the subcategory.

A form and an option to upload a large csv should be enough. An API that allow connecting to R would be a blast. So how can we move that idea forward?

Can niche and fitness differences explain biological invasions

Following up with my “Theory vs Data” post, I want to share an example of a beautiful theoretical framework to understand the invasion processes and an idea on which can be the perfect study system to validate it.

Mc Dougall et al (2009) have one of the more compelling figures I saw summarising the hypothesis that niche and competitive differences between exotic and native species can explain the outcome of the invasion process. The figure speaks for itself:

While I was in the US I planned to use this framework to understand the effects of Osmia cornifrons (a mason bee) invasion on the native Osmia lignaria, but I had no time to follow-up on this. Anyway, for that end you would need to prove the following:

1) Are their niches overlapping? Both bees are on the same genus, have similar size, phenology, nesting habits, and probably visit similar flowers, you just need to put numbers on those things (e.g. which hole diameter they prefer to nest on). For example, this data is from a preliminary experiments I did on its phenology. Interestingly, the result suggest the invader emerge slightly (but significantly) earlier than the native. So, quantifying all this can be important.

2) Is their fitness different when raised alone? Buying this bees and monitoring its nests is easy. Moreover, measuring offspring (a fairly good proxy of fitness) is a piece of cake compared with other species. Well, at least in theory, because I tried it in 2011 and an April snowstorm killed 80% of both populations. Hence, I have no data here.

3) It’s the native fitness lowered when they are raised together? That’s an important part (especially the effect size), because they may coexists just fine (even if sharing niches).

I am not in the US anymore, so, is impossible for me to do the experiments. If anyone wants to explore this idea further (undergraduates seeking for a project, jump in!), the idea is here, and it’s for free!

———————————————————————————————-
MacDougall A.S., Gilbert B. & Levine J.M. (2009). Plant invasions and the niche, Journal of Ecology, 97 (4) 609-615. DOI: ———————————————————————————————-

And now, what can occur with this post? 

– Worst thing it can happen is that nothing happens.

– Will be pretty cool if someone does this or similar experiments, even if I never know of its existence (well, I hope at least not to miss the article when it gets published!). I would be happy with that because despite I did some thinking on this I assume this ideas are “on the air”, and that’s precisely why I post them here.

– Will be awesome if that someone also contact me and we end up collaborating. (incentive: I have also some ideas on how to analyse it)

– Will be terrific (I am running out of superlatives) if people start reporting they have data on niche and fitness differences for other systems and we end up with a meta-analysis proving (or disproving) that this theory can correctly predict invasion outcome with some generality. For example, where is propagule pressure fitting in this framework? Niche and population growth/species traits hypothesis clearly are captured here, you can even account for lowered native fitness due to disturbance (wow!), but the number of invaders arriving may be a missing piece. Also scaling up to the community level seems a daunting task.