This is a Research Summary of: White, E.P., K.M. Thibault, and X. Xiao. 2012. Characterizing species-abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology. http://dx.doi.org/10.1890/11-2177.1*

The species-abundance distribution describes the number of species with different numbers of individuals. It is well known that within an ecological community most species are relatively rare and only a few species are common, and understanding the detailed form of this distribution of individuals among species has been of interest in ecology for decades. This distribution is considered interesting both because it is a complete characterization of the commonness and rarity of species and because the distribution can be used to test and parameterize ecological models.

Numerous mathematical descriptions of this distribution have been proposed and much of the research into this pattern has focused on trying to figure out which of these descriptions is “the best” for a particular group of species at a small number of sites. We took an alternative approach to this pattern and asked: Can we explain broad scale, cross-taxonomic patterns in the general shape of the abundance distribution using a simple model that requires only knowledge of the species richness and total abundance (summed across all species) at a site?

To do this we used a model that basically describes the most likely form of the distribution if the average number of individuals in a species is fixed (which turns out to be a slightly modified version of the classic log-series distribution; see the paper or John Harte’s new book for details). As a result this model involves no detailed biological processes and if we know richness and total abundance we can predicted the abundance of each species in the community (i.e., the abundance of the most common species, second most common species… rarest species).

Since we wanted to know how well this works in general (not how well it works for birds in Utah or trees in Panama) we put together a a dataset of more than 15,000 communities. We did this by combining 6 major datasets that are either citizen science, big government efforts, or compilations from the literature. This compilation includes data on birds, trees, mammals, and butterflies. So, while we’re missing the microbes and aquatic species, I think that we can be pretty confident that we have an idea of the general pattern.

In general, we can do an excellent job of predicting the abundance of each rank of species (most abundant, second most abundant…) at each site using only information on the species richness and total abundance at the site. Here is a plot of the observed number of individuals in a given rank at a given site against the number predicted. The plot is for Breeding Bird Survey data, but the rest of the datasets produce similar results.

Observed-predicted plot for nearly 3000 Breeding Bird Survey communities. Since there are over 100,000 points on this plot we’ve color coded them by the number of points in the vicinity of the focal point, so red areas have lots of points nearby and blue areas have very few points. The black line is the 1:1 line.

The model isn’t perfect of course (they never are and we highlight some of its failures in the paper), but it means that if we know the richness and total abundance of a site then we can capture over 90% of the variation in the form of the species-abundance distribution across ecosystems and taxonomic groups.

This result is interesting for two reasons:

First, it suggests that the species-abundance distribution, on its own, doesn’t tell us much about the detailed biological processes structuring a community. Ecologists have know that it wasn’t fully sufficient for distinguishing between different models for a while (though we didn’t always act like it), but our results suggest that in fact there is very little additional information in the distribution beyond knowing the species richness and total abundance. As such, any model that yields reasonable richness and total abundance values will probably produce a reasonable species-abundance distribution.

Second, this means that we can potentially predict the full distribution of commonness and rarity even at locations we have never visited. This is possible because richness and total abundance can, at least sometimes, be well predicted using remotely sensed data. These predictions could then be combined with this model of the species-abundance distribution to make predictions for things like the number of rare species at a site. In general, we’re interested in figuring out how much ecological pattern and process can be effectively characterized and predicted at large spatial scales, and this research helps expand that ability.

So, that’s the end of our first Research Summary. I hope it’s a useful thing that folks get something out of. In addition to the science in this paper, I’m also really excited about the process that we used to accomplish this research and to make it as reproducible as possible. So, stay tuned for some follow up posts on big data in ecology, collaborative code development, and making ecological research more reproducible.

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*The paper will be Open Access once it is officially published but ,for reasons that don’t make a lot of sense to me, it is behind a paywall until it comes out in print.

• Fundamental research is where a lot of our methodological advances come from.
• Fundamental research provides generally-applicable insights.
• Current applied research often relies on past fundamental research.
• Fundamental research often is relevant to the solution of many different problems, but in diffuse and indirect ways.
• Fundamental research lets us address newly-relevant issues.
• Fundamental research alerts us to relevant questions and possibilities we didn’t recognize as relevant.
• Fundamental research suggests novel solutions to practical problems.
• The only way to train fundamental researchers is to fund fundamental research.

I don’t have a lot to add to what Jeremy has already said, except that I strongly agree with the points that he has made and think that in an era where much of ecology has direct applications to things like global change we need to guard against the temptation to justify all of our research based on its applications.

When I think about the value of fundamental research I always recall a scene from an early season of The West Wing where a politician (SAM) and a scientist (MILLGATE) are discussing how to explain the importance of something akin to the Large Hadron Collider. It loses a little something as a script (complements of Unofficial West Wing Transcript Archive), but nonetheless:

SAM
What is it?

MILLGATE
It’s a machine that reveals the origin of matter… By smashing protons together at very high speeds and at very high temperatures, we can recreate the Big Bang in a laboratory setting, creating the kinds of particles that only existed in the first trillionth of a second after the universe was created.

SAM
Okay, terrific. I understand that. What kind of practical applications does it have?

MILLGATE
None at all.

SAM
You’re not in any way a helpful person.

MILLGATE
Don’t have to be. I have tenure.

SAM
Doctor.

MILLGATE
There are no practical applications, Sam. Anybody who says different is lying.

…

ENLOW
If only we could only say what benefit this thing has, but no one’s been able to do that.

MILLGATE
That’s because great achievement has no road map. The X-ray’s pretty good. So is penicillin. Neither were discovered with a practical objective in mind. I mean, when the electron was discovered in 1897, it was useless. And now, we have an entire world run by electronics. Haydn and Mozart never studied the classics. They couldn’t. They invented them.

SAM
Discovery.

MILLGATE
What?

SAM
That’s the thing that you were… Discovery is what. That’s what this is used for. It’s for discovery.

The episode is “Dead Irish Writers” and I’d highly recommend watching the whole thing if you want to feel inspired about doing fundamental research.

In this talk, Jay said that MacArthur claimed the best ecologists had blurry vision so they could see the big patterns without being overly distracted by the contradictory details. This immediately made a huge amount of sense to me. Biology is so full of special cases, of details that don’t fit theories, that it is easy to despair of advancing with broad, general theories. But we need those theories, for they tell us where to look next, what data to collect, and even what theory to challenge. I am a details person, but love the big theories.

The whole post is definitely worth a read.

Despite my concerns about the impacts of increasingly closed journals, with increasingly high costs, on the dissemination of research and the ability of universities to support their core missions of teaching and research, I have continued to volunteer my time and effort as a reviewer to Elsevier and Wiley-Blackwell. I did this because I have continued to see valuable contributions made by these journals and I felt that this combined with the contribution that I was making to science by helping improve the science published in high profile places made supporting these journals worthwhile. I no longer believe this to be the case and from now on I will no longer be reviewing for any journal that is published by Elsevier, Springer, or Wiley-Blackwell (including society journals that publish through them).

Why have I changed my mind? Because of the pursuit/support by these companies of the Research Works Act. This act seeks to prevent funding agencies from requiring that the results of research that they funded be made publicly available. In other words it seeks to prevent the government (and the taxpayers that fund it), which pays for a very large fraction of the cost of any given paper through both funding the research and paying the salaries of reviewers and editors, from having any say in how that research is disseminated. I think that Mike Taylor in the Guardian said most clearly how I feel about this attempt to exert legislative control requiring us to support corporate profits over the dissemination of scientific research:

Academic publishers have become the enemies of science

This is the moment academic publishers gave up all pretence of being on the side of scientists. Their rhetoric has traditionally been of partnering with scientists, but the truth is that for some time now scientific publishers have been anti-science and anti-publication. The Research Works Act, introduced in the US Congress on 16 December, amounts to a declaration of war by the publishers.

You should read the entire article. It’s powerful. There are lots of other great articles about the RWA including Michael Eisen in the New York Times, a nice post by INNGE, and a interesting piece by Paul Krugman (via oikosjeremy). I’m also late to the party in declaring my peer review strike and less eloquent than many of my peers in explaining why (see great posts by Michael Taylor, Gavin Simpson, and Timothy Gowers). But I’m here now and I’m letting you know so that you can consider whether or not you also want to stop volunteering for companies that don’t have science’s best interests in mind.

If you’d like to read up on the publisher’s side of this argument (they have costs, they have a right to recoup them) you can see Springer’s official position or an Elsevier Exec’s exchange with Michael Eisen. My problem with all of these arguments is that there is nothing in any funding agency’s policy that requires publishers to publish work funded by that agency. This is not (as Springer has argued) an “unfunded mandate”, this is a stake holder that has certain requirements related to the publication of research in which they have an interest. This is just like an author (in any non-academic publishing situation) negotiating with a publisher. If the publisher doesn’t like the terms that the author demands, then they don’t have to publish the book. Likewise, if a publisher doesn’t like the NIH policy then they should simply not agree to publish NIH funded research.

To be clear, I am not as extreme in my position as some. I still support and will review for independent society journals like Ecology and American Naturalist even though they aren’t Open Access and even though ESA has made some absurd comments in support of the same ideas that are in RWA. The important thing for me is that these journals have the best interests of science in mind, even if they are often frustratingly behind the times in how they think and operate.

And don’t worry, I’ve still got plenty of journal related work to keep me busy, thanks to my new position on the editorial board at PLoS ONE.

UPDATE: The links to the INNGE and Timothy Gowers post have now been fixed, and here are links to a couple of great posts by Casey Bergman that I somehow left out: one on how to turn down reviews while making a point and one on the not so positive response he received to one of these emails.

UPDATE 2: A great collection of posts on RWA. There are a lot of really unhappy scientists out there.

UPDATE 3: A formal Boycott of Elsevier. Almost 1000 scientists have signed on so far.

UPDATE 4: Wiley-Blackwell has now distanced itself from RWA and said that “We do not believe that legislative initiatives are the best way forward at this time and so have no plans to endorse RWA. Instead we believe that research funder-publisher partnerships will be more productive.” In addition, it was announced that a bill that would do the opposite of RWA has now been introduced. Hooray for collective action!

One of the things we’ve been reading is a great report by the US Department of Education’s Institute of Education Sciences on Organizing Instruction and Study to Improve Student Learning. The report synthesizes existing research on what to do in the classroom to facilitate meaningful long-term learning, and distills this information into seven recommendations and information on how strongly each recommendation is supported by available research.

Recommendations

1. Space learning over time. Arrange to review key elements of course content after a delay of several weeks to several months after initial presentation. (moderate)
2. Interleave worked example solutions with problem-solving exercises. Have students alternate between reading already worked solutions and trying to solve problems on their own. (moderate)
3. Combine graphics with verbal descriptions. Combine graphical presentations (e.g., graphs, figures) that illustrate key processes and procedures with verbal descriptions. (moderate)
4. Connect and integrate abstract and concrete representations of concepts. Connect and integrate abstract representations of a concept with concrete representations of the same concept. (moderate)
5. Use quizzing to promote learning.
   1. Use pre-questions to introduce a new topic. (minimal)
   2. Use quizzes to re-expose students to key content (strong)
6. Help students allocate study time efficiently.
   1. Teach students how to use delayed judgments of learning to identify content that needs further study. (minimal)
   2. Use tests and quizzes to identify content that needs to be learned (minimal)
7. Ask deep explanatory questions. Use instructional prompts that encourage students to pose and answer “deep-level” questions on course material. These questions enable students to respond with explanations and supports deep understanding of taught material. (strong)

(Quoted directly from the original report via a Software Carpentry blog post)

This is a nice summary, but it’s definitely worth reading the whole report to explore the depth of the thought process and learn more about specific ideas for how to implement these recommendations.

How am I doing?

Recently I’ve been teaching two courses on programming and database management for biologists. Because I’m not a big believer in classroom lecture, for this type of material, a typical day in one of these courses involves: 1) either reading up on the material in a text book or viewing a Software Carpentry lecture before coming to class; 2) a brief 5-10 minute period of either re-presenting complex material or answering questions about the reading/viewing; and 3) 45 minutes of working on exercises (during which time I’m typically bouncing from student to student helping them figure out things that they don’t understand). So, how am I doing with respect the the above recommendations?

1. Space learning over time. I’m doing OK here, but not as well as I’d like. The nice thing about teaching introductory programming concepts is that they naturally build on one another. If we learned about if-then statements two weeks ago then I’m going to use them in the exercises about loops that we’re learning about this week. I also have my advanced class use version control throughout the semester for retrieving data and turning in exercises to force them to become very comfortable with the work-flow. However, I haven’t done a very good job of bringing concepts back, on their own, later in the semester. The exercise based approach to the course is perfect for this, I just need to write more problems and insert them into the problem-sets a few weeks after we cover the original material.

2. Interleave worked example solutions with problem-solving exercises. I think I’m doing a pretty good job here. Student’s see worked examples for each concept in either a text book or video lecture (viewed outside of class) and if I think they need more for a particular concept we’ll walk through a problem at the beginning of class. I often use the Online Python Tutor for this purpose which provides a really nice presentation of what is going on in the program. We then spend most of the class period working on problem-solving exercises. Since my classes meets three days a week I think this leads to a pretty decent interleaving.

3. Combine graphics with verbal descriptions. I do some graphical presentation and the Online Python Tutor gives some nice graphical representations of running programs, but I need to learn more about how to communicate programming concepts graphically. I suspect that some of the students that struggle the most in my Intro class would benefit from a clearly graphical presentation of what is going happening in the program.

4. Connect and integrate abstract and concrete representations of concepts. I think I do this fairly well. The overall motivation for the course is to ground the programming material in the specific discipline that the students are interested in. So, we learn about the general concept and then apply it to concrete biological problems in the exercises.

5. Use quizzing to promote learning. I’m not convinced that pre-questions make a lot of sense for material like this. In more fact based classes they are helping to focus students’ attention on what is important, but I think the immediate engagement in problem-sets that focus on the important aspects works at least as well in my classroom. I do have one test in the course that occurs about half way through the Intro course after we’ve covered the core material.  It is intended to provide the “delayed re-exposure” that has been shown to improve learning, but after reading this recommendation I’m starting to think that this would be better accomplished with a series of smaller quizzes.

6. Help students allocate study time efficiently. I spend a fair bit of time doing this when I help students who ask questions during the assignments. By looking at their code and talking to them it typically becomes clear where the “illusion of knowing” is creeping in and causing them problems and I think I do a fairly good job of breaking that cycle and helping them focus on what they still need to learn. I haven’t used quizzes for this yet, but I think they could be a valuable addition.

7. Ask deep explanatory questions. One of the main focuses in both of my courses is an individual project where the students work on a larger program to do something that is of interest to them. I do this with the hope that it can provide the kind of deep exposure that this recommendation envisions.

So, I guess I’m doing OK, but I need to work more on representation of material both through bringing back old material in the exercises and potentially through the use of short quizzes throughout the semester. I also need to work on alternative ways to present material to help reach folks whose brains work differently.

If you are a current or future teacher I really recommend reading the full report. It’s a quick read and provides lots of good information and food for thought when figuring out how to help your students learn.

Thanks for listening in on my self-reflection. If you have thoughts about this stuff I’d love to hear about it in the comments.

Announcing the Mammal Community Database (MCDB)

Over the past couple of years we’ve been working to fill that gap as best we could. Since coordinated continental scale surveys of mammals don’t yet exist [1] we dug into the extensive mammalogy literature and compiled a database of 1000 globally distributed communities. Thanks to Kate Thibault‘s leadership and the hard work of Sarah Supp and Mikaelle Giffen, we are happy to announce that this data is now freely available as a data paper on Ecological Archives.

In addition to containing species lists for 1000 locales, there is abundance data for 940 of the locations, some site level body size data (~50 sites) and a handful of reasonably long (> 10 yr) time-series as well. Most of the data is restricted to the particular mode of sampling that an individual mammalogist uses and as a result much of the data is for small mammals captured in Sherman traps.

Working with data compilations like this is always difficult because the differences in sampling intensity and approaches between studies can make it very difficult to compare data across sites. We’ve put together a detailed table of information on how sampling was conducted to help folks break the data into comparable subsets and/or attempt to control for the influence of sampling differences in their statistical models.

The joys of Open Science

We’ve been gradually working on making the science that we do at Weecology more and more open, and the MCDB is an example of that. We submitted the database to Ecological Archives before we had actually done much of anything with it ourselves [2], because the main point of collecting the data was to provide a broadly useful resource to the ecological community, not to answer a specific question. We were really excited to see that as soon as we announced it on Twitter

We just published a new data set of 1000 mammal communities esajournals.org/doi/abs/10.189… Check it out and do something cool with it.

—
  (@weecology) December 28, 2011

folks started picking it up and doing cool things with it [3]. We hope that folks will find all sorts of uses for it going forward.

Going forward

We know that there is tons more data out there on mammal communities. Some of it is unpublished, or not published in enough detail for us to include. Some of it has licenses that mean that we can’t add it to the MCDB without special permission (e.g., there is a lot of great LTER mammal data out there). Lots of it we just didn’t find while searching through the literature.

If folks know of more data we’d love to hear about it. If you can give us permission to add data that has more restrictive licensing then we’d love to do so [4]. If you’re interested in collaborating on growing the database let us know. If there’s enough interest we can invest some time in developing a public portal.

The footnotes [5]

[1] We are anxiously awaiting NEON’s upcoming surveys, headed up by former Weecology postdoc Kate Thibault.

[2] We have a single paper that is currently in review that uses the data.

[3] Thanks to Scott Chamberlain and Markus Gesmann. You guys are awesome!

[4] To be clear, we haven’t been asking for permission yet, so no one has turned us down. We wanted to get the first round of data collection done first to show that this was a serious effort.

[5] Because anything that David Foster Wallace loved has to be a good thing.

http://www.nsf.gov/pubs/2011/nsf11573/nsf11573.htm#prep

Happy post-holiday grant writing to all.

UPDATE 1: Also note that the Biosketches are different for the pre-proposals (changes noted in bold-italics)

Biographical Sketches (2-page limit for each) should be included for each person listed on the Personnel page. It should include the individual’s expertise as related to the proposed research, professional preparation, professional appointments, five relevant publications, five additional publications, and up to five synergistic activities. Advisors, advisees, and collaborators should not be listed on this document, but in a separate table (see below).

UPDATE 2: Though it is not explicitly clear from the link above, Current & Pending Support should NOT be included in pre-proposals (thanks to Alan Tessier for clearing this up).

CONSENT
We appreciate your involvement with this publication, which is published by a John Wiley & Sons company. The publisher would like to contact you by email/post with details of publications and services that may be of interest to you, specific to your subject area, from companies in the John Wiley & Sons group (only) worldwide. Your information will never be passed to any third party companies and as part of any communications you will be given the opportunity to unsubscribe from receiving further contact. Please indicate whether you wish to receive this information by answering the CONSENT question below.

Asking someone who is already working for you for free if it’s OK to also try to sell them stuff while they’re doing it seems like a pretty good definition of classless to me.

• do you study some aspect of biology (defined as anything from the molecular to ecosystem levels)?,
• do you intend to submit a proposal to NSF someday?
•  If you answered yes to these questions, then the probability is high that this pertains to you (though the details of what I say below may differ depending upon the Division you tend to apply to).

Anyway, we’ve covered the basics of this shift here before, but this week the DEB (Division of Environmental Biology) at NSF conducted a webinar on the changes and a few additional pieces of info were added.

Some important additional pieces of info

1)The following solicitations are NOT impacted by the preproposal rules:

Assembling the Tree of Life, CAREER, Dynamics of Coupled Nautral and Human Systems, Dimensions of Biodiversity, Ecology of Infectious Disease, OPUS, RCN, and the DDIGs.

What does that mean? 1) Those solicitations are operating under their own rules, so read their solicitations for details of how to submit and 2) submitting to them won’t count against your 2 preproposals per year limit.

2) Timeline (for DEB, supposedly IOS – Integrative Organismal Systems – will be similar):

Preproposals: due Jan 9, Preproposal review panel meetings March-April, Invite/Not invite decision by May 1(ish).

Full proposals (for those invited): Due Aug 2, Panel Review Oct/Nov, Award/Decline decision by December. In theory this will give you close to a month to revise your preproposal.

3) The webinar provides info on what should be in a preproposal, what the panel will be asked to assess, and what the basis for invite/not invite will be. I recommend perusing through the last few slides (see links below). There is a lot of emphasis (in my opinion) on how bold, compelling, general the research will be. If this is how the panels will be instructed, I think this is a good thing – but again that’s just my opinion.

4) There is also info on trends in funding rates, proposal submissions, and numbers of reviewers that were being required for all the proposals. If you’ve been submitting to NSF you know things have been grim, but there’s something about seeing the numbers that make you realize that regardless of whether you think this is the best change, things really had to change.

If you’d like to see the webinar here are some links for you (I tried the ‘streaming’ one and it worked fine, there is an executable that will be downloaded to your computer to run it)

Streaming recording link: https://mmancusa.webex.com/mmancusa/ldr.php?AT=pb&SP=MC&rID=43949312&rKey=2ad726bf2f77bd13

Download recording link: https://mmancusa.webex.com/mmancusa/lsr.php?AT=dw&SP=MC&rID=43949312&rKey=1fe4937906efe109