Holiday caRd 2018!

I had a busy year, and it completely slipped my mind that I usually do a caRd for the blog! So it's a little late, but hopefully provides a little end of year cheeR ;-)

A short warning: I've stopped trying to make these compatible with RStudio. I know that RStudio is very popular, but I struggle to get its internal plotting device to update iteratively to make an animation (despite trying various things, like while() or if() statements, or Sys.sleep()). If someone has a solution, please share.

To enjoy these plots you should see the animation in real time: e.g. Base R (any operating system) or R run via emacs or vim.

To run, you can access the source file on github

or run this code:

source("https://tinyurl.com/2018rcard")

If you prefer not to run the code, the gif version can be found here!

Holiday caRd 2017!

Here is this year's card, with best wishes from both of us at the EEB & Flow!

It gets a little harder every year to figure these out. R's plotting capabilities improve every year, but usually via specialized packages. I've tried more and more to use as few additional packages beyond base, and to produce a script that is hopefully compatible across platforms.

• For best performance, users must install the 'deldir' package and the 'RCurl' package. This lets you download the necessary data file with as little effort as possible. 
• If you have trouble accessing the file via the URL, you can just download the data file from Github directly, making sure to load the file into R using the hashed out code in Lines6-7.

Then to run, copy the full code (below), OR download the source file from github ,
OR, the easiest way, run this quick code directly:

Full script here.
(Bonus points for those who can guess which species of McArthur's warblers these are meant to be ;) )

The most "famous" ecologists (and some time wasting links) (Updated)

(Update: This has gotten lots more attention than I expected. Since first posted, the top 10 list has been updated 2 times based on commenters suggestions. You can also see everyone we looked up here. Probably I won't update this again, because there is a little time wasting, and there is a lot of time wasting :) )

At some point my officemates Matthias and Pierre and I started playing the 'who is the most famous ecologist' game (instead of, say, doing useful work), particular looking for ecologists with an h-index greater than 100. An h-index of 100 would mean that the scientist had 100 publications with at least 100 citations  and their other papers had less than 100 citations. Although the h-index is controversial, it is readily available and reasonably capture scientists that have above average citations per paper and high productivity. We restricted ourselves to only living researchers. We used Publish or Perish to query Google Scholar (which now believes everyone using the internet in our office may be a bot).

We identified only 12 ecologists at level 100 or greater. For many researchers in specialized subfields, an h-index this high is probably not achievable. The one commonality in these names seems to be that they either work on problems of broad importance and interest (particularly, climate change and human impacts on the landscape) or else were fundamental to one or more areas of work. They were also all men, and so we tried to identify the top 12 women ecologists. (We tried as best as we could, using lists here and here to compile our search). The top women ecologists tended to have been publishing for an average of 12 years less than the male ecologists (44 vs. 56 years) which may explain some of the rather jarring difference. The m-index is the h-index/years publishing and so standardizes for differences in career age.

(It's difficult to get these kind of analyses perfect due to common names, misspellings in citations, different databases used, etc. It's clear that for people with long publication lists, there is a good amount of variance depending on how that value is estimated).

Other links: 

(I've been meaning to publish some of these, but haven't otherwise had a time or space for it.. )

Helping graduate students deal with imposter syndrome (Link). Honestly, not only graduate students suffer from imposter syndrome, and it is always helpful to get more advice on how to escape the feeling that you've lucked into something you aren't really qualified for. 

A better way to teach the Tree of Life (Link). This paper has some great ideas that go beyond identifying common ancestors or memorizing taxonomy.

Analyzing scientists are on Twitter (Link). 

Recommendation inflation (Link). Are there any solutions to an arms race of positivity?  

87 years ago, in ecology

Louis Emberger was an important French plant ecologist in the first half of the last century, known for his work on the assemblages of plants in the mediterranean.

For example, the plot below is his published diagram showing minimum temperature of the coolest month versus a 'pluviometric quotient' capturing several aspects of temperature and precipitation from:

Emberger; La végétation de la région méditerranienne. Rev. Gén. Bot., 42 (1930)

Note this wasn't an unappreciated or ignored paper - it received a couple hundred citations, up until present day. Further, updated versions have appeared in more recent years (see bottom).

So it's fascinating to see the eraser marks and crossed out lines, this visualisation of scientific uncertainty. The final message from this probably depends on your perspective and personality:

• Does it show that plant-environment modelling has changed a lot or that plant environmental modelling is still asking about the same underlying processes in similar ways?
• Does this highlight the value of expert knowledge (still cited) or the limitations of expert knowledge (eraser marks)? 

It's certainly a reminder of how lucky we are to have modern graphical software :)

E.g. updated in Hobbs, Richard J., D. M. Richardson, and G. W. Davis. "Mediterranean-type ecosystems: opportunities and constraints for studying the function of biodiversity." Mediterranean-Type Ecosystems. Springer Berlin Heidelberg, 1995. 1-42.











Thanks to Eric Garnier, for finding and sharing the original Emberger diagram and the more recent versions.

2016 holiday caRd

Once more, tis the season! Hope you had an excellent year of science and R coding. This card requires the igraph library - it (loosely) relies on an infection (S-I model) moving through a network :-)

To view season's greetings from 2016:
Go to the gist and download the file directly ("download gist") or hit "raw" and copy/paste. Or, copy and paste the code below.

Users of Rstudio will not be able to see the animation, so base R is highly recommended.

For those not able or willing to run the card, you can view it and the past years' cards here!

2015 caRd - A diveRsity of Santas

A keen observer will note that there are a number of similar taxa that are active this time of year.

Although well described in the literature, surprisingly little attention has been given to the ecology of these creatures. Observational data allows some traits to be compiled, however, and some simple exploratory analyses may allow us to better understand the 'Santa' assemblage.

print(santatraits)
                       heft transport first.appearance
Coca.cola.Santa         fat  reindeer             1900
Department.Store.Santa  fat  reindeer             1900
Salvation.Army.Santa    fat  reindeer             1890
Kriss.Kringle           fat  reindeer             1800
Santa.Claus             fat  reindeer             1700
Pere.Noel              thin    donkey             1400
Father.Christmas        fat      foot             1400
Sinterklaas            thin     horse              400
Saint.Nicholas         thin      foot              300
Ded.Moroz              thin      foot             1937

We are fortunate to also have sequence data (from DNA on milk glasses and lost beard hairs), so we can add additional information about relatedness amongst these species.

plot(xmas.tree, type = "c", FALSE, edge.color="darkgreen",  edge.lty=1, edge.width=18, label.offset = 1, direction="downward", font=3, tip.color="darkred")

The phylogeny shows that there seems to be an early divergence between European and North American santas. Indeed, there is a group of North American santas (Mall Santa, Coca-cola Santa, Salvation Army Santa) which are closely related (and also appear to share very similar traits, based on the table above). (Note that branch lengths in this phylogeny show nucleotide substitutions, and it is not time-calibrated, due to the absence of santa fossils).

One approach is to identify a few traits and plot them on the phylogeny to compare how traits vary among santas. Let's start with anatomical characteristics:

#Plot traits (fatness) against Santa
co1 = c("blue", "purple")
tiplabels(pch = 12, col = co1[as.factor(heft)], cex = 3.5, adj=c(0.5, 0), lwd=2)

#Let's see the transportation mode trait too:
co2 <- c("yellow", "gold", "darkorange", "red")
tiplabels(pch = 8, col = co2[as.factor(transport)], cex = 2, adj=c(0.5, 0), lwd=2)

#Legends
legend("topleft", legend=c("reindeer", "donkey", "foot", "horse"), fill=rev(c("yellow", "gold", "darkorange", "red")))
legend("topright", legend=c("fat", "thin"), fill=c("blue", "purple"))

For a future study, we could ask whether the apparent correlation between fatness and reindeer usage is significant, once the underlying phylogenetic relationships were controlled for. 

We can also reconstruct santa traits (here, we look at the form of transportation) to explore what form of transportation ancestral santas likely used:

#reconstruct ancestral state
cc = ace(transport, xmas.tree, type="discrete")
co2 = c("yellow", "gold", "darkorange", "red")
nodelabels(pie = cc$lik.anc, piecol = co2, cex = c(1.5, rep(1, 8)))

The markers at each node show the probability that this ancestral taxa used each of the four possible types of transportation. It seems that the North American santas and their ancestors have long relied on a trusty reindeer mutualism.

Finally, we can look at the geography of all the various santas:

phylo.to.map(xmas.tree, locales)

To run this caRd yourself, follow the link to the R code: https://gist.github.com/cmtucker/8e5677bdd5c409d70738

I'll take 'things that have nothing to do with my research' for $400

I guess I do have a couple papers with the word fire in their titles?
And to Burns and Trauma's credit, this is a nicely formatted email and the reasons to publish with them are pretty convincing :-)

Holiday caRd 2014: Snowflakes

Apparently it's that time of year again! The R circlize package plays a prominent role in this caRd.
Like snowflakes, no two cards are likely to be identical, so try it a few times :)

Lots of options for viewing the R code:

1) Run it automatically by just using the following few lines of R code. Probably the easiest way, provided you've installed RCurl: it allows you to directly run the github code from its url.

install.packages("RCurl")

library(RCurl)
options(RCurlOptions = list(verbose = FALSE, capath = system.file("CurlSSL", "cacert.pem", package = "RCurl"), ssl.verifypeer = FALSE))
#this seems necessary for the Windows people only?
#
eval(expr = parse(text = getURL("https://gist.githubusercontent.com/cmtucker/c591e868c76de1ac81e6/raw/ea3581a2d7f10810023529c7046edb40f099cbb3/snowflakeCode")))

2) Go to https://gist.github.com/cmtucker/c591e868c76de1ac81e6 and access directly. You can download the file directly ("download gist") or hit "raw" and copy/paste.

3) Copy and paste the code below.

Links: Reanalyzing R-squares, NSF pre-proposals, and the difficulties of academia for parents

First, Will Pearse has done a great job of looking at the data behind the recent paper looking at declining R and p-values in ecology, and his reanalysis suggests that there is a much weaker relationship between r2 values and time (only 4% rather than 62% as reported). Because the variance is both very large within-years and also not equal through time, a linear model may not be ideal for capturing this relationship.
Thanks @prairiestopatchreefs for linking this.

From the Sociobiology blog, something that most US ecologists would probably agree on: the NSF pre-proposal program has been around long enough (~3 years) to judge on its merits, and it has not been an improvement. In short, pre-proposals are supposed to use a 5 page proposal to allow NSF to identify the best ideas and then invite those researchers to submit a full proposal similar to the traditional application. Joan Strassman argues that not only is this program more work for applicants (you must write two very different proposals in short order if you are lucky to advance), it offers very few benefits for them.

The reasons for the gender gap in STEM academic careers gets a lot of attention, and rightly so given the continuing underrepresentation of women. The demands of parenthood often receive some of the blame. The Washington Post is reporting on a study that considers parenthood from the perspective of male academics. The study took an interview-based, sociological approach, and found that the "majority of tenured full professors [interviewed] ... have either a full-time spouse at home who handles all caregiving and home duties, or a spouse with a part-time or secondary career who takes primary responsibility for the home." But the majority of these men also said they wanted to be more involved at home. As one author said, “Academic science doesn’t just have a gender problem, but a family problem...men or women, if they want to have families, are likely to face significant challenges.”

On a lighter note, if you've ever joked about PNAS' name, a "satirical journal" has taken that joke and run with it. PNIS (Proceedings of the Natural Institute of Science) looks like the work of bored post-docs, which isn't necessarily a bad thing. The journal has immediately split into two subjournals: PNIS-HARD (Honest and Real Data) and PNIS-SOFD (Satirical or Fake Data), which have rather interesting readership projections:

Guide for writing the ultimate ecology introduction**

**Or find better advice here, here, here.

If only it were so easy...

EEB & Flow inclusion in Library of Congress Web Archives

I just received this email the other day, and nearly deleted it as another spam email (along with fake conference invites, obscure journal submission invites, and offers to make millions). But apparently it's legit, and the US Library of Congress has been archiving web sites for some time. They are now building a collection of science blogs (link, link), which is a pretty cool idea, and we're excited to be part of it :-)

Holiday caRd 2013

A holiday pResent made of competition from the EEB & Flow :-)

(Easy to copy and run if you choose "view raw" in the lower right hand corner. Just copy and paste into R, it will do all the work. You will need to download and install R if you don't already have it.)

What scares ecologists?

In the spirit of Halloween, a non-comprehensive list of a few of the things that have frightened ecologists. The things we are scared of vary, with career trajectory and stage (graduate student, post-docs, faculty, non-academic), educational background, and goals. And even if the business of ecology doesn't scare you, some of the things we study should...


A few scary things

-That ghostly thesis - will it ever solidify?

-The undead paper

-Co-authors and supervisors

-Vampire reviewers: they've been around forever, they've seen everything, and they're looking for blood. (uncommon)

The whole process is pretty frightening.

-The Bayesian conspiracy:

• "Q. What is the Bayesian Conspiracy? A. The Bayesian Conspiracy is a multinational, interdisciplinary, and shadowy group of scientists that controls publication, grants, tenure, and the illicit traffic in grad students. The best way to be accepted into the Bayesian Conspiracy is to join the Campus Crusade for Bayes in high school or college, and gradually work your way up to the inner circles. It is rumored that at the upper levels of the Bayesian Conspiracy exist nine silent figures known only as the Bayes Council."

-The job market (or lack thereof?)

-The two-body monster

Fun but troublesome

-That our work is invisible

(0?)

-That our ideas come to life and go terribly wrong

-The science news makeover

And a few (of many) ecological topics that are truly scary

Kudzu

Zombie ants

Toxoplasmosis and human mind control

And the incredibly deadly mosquitoes?

(and all the pathogens it carries)

Can common tradeoffs predict your supervisor’s functional type?

With Lanna Jin

If you’re a graduate student, the most important question (even more important than who you’re writing your paper with) is who is running your lab. New graduate students everywhere are settling in and getting to know their supervisors are little better. Supervisors come in all types, and hypothesizing a priori what their style is can be difficult. Fortunately, a couple of common tradeoffs underlie most functional styles...

---

1)

The invisible man/woman: The ultimate laissez-faire approach. You have the freedom to choose the ideas and projects that interest you, and the responsibility to make them work. Freedom can be replaced by frustrations when you need a signature, some support, or a manuscript commented on.

El generalissimo: "Here's my idea, now go do it." These labs are usually run in a top-down manner. Day to day operations are fairly hands off though, giving you room to work through those problems on your own. El generalissimo will reward their supporters well for good work.

The coach: The coach provides you the best of both worlds: enough rope to explore your ideas, but not so much to hang yourself. They are available for troubleshooting and brainstorming, but you ultimately have responsibility for your project. But if you rely on them to much, it's going to be hard to function without them.

The micromanager: These supervisors expect regular presence, frequent meetings, records of progress, and milestones to be met promptly. If you like working from home, leaving early or starting late, or need lots of freedom to be most productive it could be a poor fit. For students who thrive on structure and prefer set goals though, this might be an ideal environment.

---

2)

The skeleton: This supervisor has established themselves in their career and been active for some time, but now other interests consume them. The scientific meat that made their name seems to be gone, and all that's left is the skeleton of their earlier career. They are happy to chat with you about the many things they are interested in, but supervising your science doesn't seem to be a priority. They often see you as a person who has with non-academic interests and responsibilities, which can be a nice feeling.

The superwoman/superman: This career superstar has made their name, possibly quite early, and they are passionate about their science. This can make for an exciting and successful lab experience, as new ideas and opportunities are always on the horizon. But since they have so many demands on their time, sometimes their capes (and they) are feeling a bit ragged.

The silverback: Labs of influential individuals can be an amazing opportunity for a graduate student. Silverback experiences might be quite variable, depending on how involved they are in day to day lab activities, their travel schedules, and the size of the lab. When they are available, they have a lot to teach a student about making a successful academic career.

Bad idea: This corner of the tradeoff (low interest in science, poor establishment in the career) probably doesn't exist in tenure-track faculty. If you do manage to find such a person, run away.

The unknown: A motivated but still unestablished supervisor is a blank slate. Their early career state means that they might have time and energy to devote to you and be especially motivated to see you and the lab succeed. On the other hand, they may not yet know how to manage people and their supervisor style could morph into anything - the coach, the micromanager, el generalissimo. A bit of a gamble.

The evolution of evolution, LEGO in the lab and other Science-y links

My week is coming to an early end as I head off to some friends' wedding tomorrow, so in lieu of another post, here are some interesting science links from around the internet this week :)

This infographic explores how thinking about evolution has changed since Darwin. It shows pretty clearly the circuitous path that science takes, the way ideas converge and diverge, and ultimately become more nuanced and complicated.

A theoretical physicist blogger answers the question "should you write a science blog?". She mentions the basic, but undeniably key points - do you have time? do you really have time? do you like writing? I also like her advice: "don't be afraid of your readers".

Mental illness can be exacerbated or first show up during grad school. Even in liberal academia, talking about mental health issues can be a bit taboo, something that doesn't help anyone. A blog post from Nash Turley considers the issues and implications.

Another serious issue, related to issues of gender in science: an article in the Economist presents evidence that women authors tend to be less cited than male authors, and this was in part due to less self-citation by women.

Also, LEGO now has its first female scientist character, and with her short hair, goggles, lab coat and gloves, she's a great lab safety role model too ;)

Of course, LEGO has many other science-related applications :)

Edit - the link about blogging was initially incorrect, should be correct now.

Has academic advancement changed your point of view?

From http://researchinprogress.tumblr.com/

We regret to inform you that you paper has not been accepted

as a graduate student:

as a postdoc:

 as a professor:

We are pleased to inform you that your paper has been accepted

as a graduate student:

as a postdoc:

as a professor:

Who are you writing your paper with?

Choosing who you work with plays an important role in who you become as a scientist. Every grad student knows this is true about choosing a supervisor, and we’ve all heard the good, the bad, and the ugly when it comes to student-advisor stories. But writing a paper with collaborators is like dealing with the supervisor-supervisee relationship writ small. Working with coauthors can be the most rewarding or the most frustrating process, or both. Ultimately, the combination of personalities involved merge in such a way as to produce a document that is usually more (but sometimes less) than the sum of its parts. The writing process and collaborative interactions are fascinating to consider all on their own.

Field Guide to Coauthors

The Little General

The Little General is willing to battle till the death for the paper to follow his particular pet idea. Regardless of the aim or outcome of an experiment, a Little General will want to connect it to his particular take on things. Two Little Generals on a paper can spell disaster.

The Silent Partner

These are the middle authors, the suppliers of data and computer code, people who were involved in the foundations of the work, but not actively a part of the writing process.

The Nay-sayer

These are the coauthors who disagree, seemingly on principle, with any attempt to generalize the paper. Given free rein, such authors can prevent a work from having any generality beyond the particular system and question in the paper. These authors do help a paper become reviewer-proof, since every statement left in the paper is well-supported.

The Grammar Nazi

The Grammar Nazi returns your draft of the paper covered in edits, but he has mostly corrected for grammar and style rather than content. This is not the worst coauthor type, although it can be annoying, especially if these edits are mostly about personal taste.

The Snail

This is the coauthor that you just don’t hear from. You can send them reminder emails, give them a phone call, pray to the gods, but they will take their own sweet time getting anything back to you. (And yes, they are probably really busy).

 The Cheerleader

The Cheerleader can encourage you through a difficult writing process or fuel an easy one. These are the coauthors who believe in the value of the work and will help motivate you through multiple edits, rejections, or revisions, as needed.

The Good Samaritan

The Good Samaritan is a special type of person. They aren’t authors of your manuscript, but they read it for you out of pure generosity  They might provide better feedback and more useful advice than any of your actual coauthors. They always end up in the acknowledgements, but you often feel like you owe them more.

The Sage

The Sage is probably your supervisor or some scientific silverback. They read your manuscript and immediately know what’s wrong with it, what it needs, and distill this to you in a short comment that will change everything. The Sage will improve your work infinitely, and make you realize how far you still have to go.

There are probably lots of other types that I haven't thought of, so feel free to describe them in the comments. And, it goes without saying that if you coauthored a paper with me, you were an excellent coauthor with whom I have no complaints. Especially Marc Cadotte, who is often both Cheerleader and Sage :)

Thanks to Lanna Jin for the amazing illustrations!

Rumors of our death were greatly exaggerated

We're not dead yet!

holiday caRd from the EEB & Flow 2012

To celebrate the start of the holiday season for many of us, the end of exams and marking for others, and for fellow Canadians, snow, enjoy this caRd from the EEB & flow! We will see you around the New Year with our traditional year-end post about the current state of ecology.

You should be able to download the R code directly, here. 

Or, copy and paste the code here into your R console. 

Island biogeography explains how you feel about giving talks