Author Archives: Ethan White
I am incredibly excited to announce that I am the recipient of one of the Moore Foundation’s Investigators in Data-Driven Discovery awards.
To quote Chris Mentzel, the Program Director of the Data-Driven Discovery Initiative:
Science is generating data at unprecedented volume, variety and velocity, but many areas of science don’t reward the kind of expertise needed to capitalize on this explosion of information. We are proud to recognize these outstanding scientists, and we hope these awards will help cultivate a new type of researcher and accelerate the use of interdisciplinary, data-driven science in academia.
I feel truly honored to have been selected. All the finalists that I met at the Moore Foundation in July were amazing as were all of the semi-finalists that I knew. I did not envy the folks making the final decisions.
So what will we be doing with this generous support from the Moore Foundation?
- Doing data-intensive prediction and forecasting in ecological systems: We’ll be focusing on population and community level forecasting as well as ecosystem level work where it interfaces with community level approaches. We’ll be using both process based ecological approaches with machine learning, with an emphasis on developing testable predictions and evaluating them with independent (out-of-sample) data. As part of this effort we’ll be making publicly available forecasts for large ecological datasets prior to the collection of the next round of data, following Brian McGill’s 6th P of Good Prediction (in fact we’ll be trying to follow all of his P’s as much as possible). There’s a lot of good work in this area and we’ll be building on it rather than reinventing any wheels.
- Increasing the emphasis on testable prediction and forecasting in ecology more broadly: Industry and other areas of science have improved their prediction/forecasting through competitions that provide data with held out values and challenge folks to see who can do the best job of predicting those values (most notable in Kaggle competitions). We’ll be helping put together something like this for ecology and hopefully integrating that with our advanced predictions to allow other folks to easily make predictions from their models public and have them evaluated automatically as new data is released.
- Tools for making data-intensive approaches to ecology easier: We’ll be continuing our efforts to make acquiring and working with ecological data easier. Our next big step is to make combining numerous ecological and environmental datasets easy so that researchers can focus on doing science rather than assembling data.
- Training: We’ll be helping build and grow Data Carpentry, a new training effort that is a sister project to Software Carpentry with a focus on data management, manipulation and analysis.
I’m very excited to be joined in this honor by my open science/computational training/data-intensive partner in crime C. Titus Brown(@ctitusbrown). I was also particularly thrilled to find out that I wasn’t the only investigator studying ecological systems. Laurel Larsen is in the Geography department at Berkeley and I can’t wait to interact with her more as we both leverage large amounts of ecological data to improve our understanding of ecological systems and our ability to forecast their states in the future. We are joined by astronomers, statisticians, computer scientists, and more. Check out the entire amazing group at the official Moore Foundation Investigators site and see the full press release for additional details about the program.
The award is being run through the University of Florida since we are in the process of relocating there, but I owe a huge dept of gratitude to the Biology Department and the Ecology Center at Utah State University for always supporting me while I spent time developing software, working on computational training initiatives, and generally building a data-intensive ecology program. Without their support I have no doubt that I wouldn’t be writing this blog post today.
We are excited to announce that Weecology will be moving to the University of Florida next summer. We were recruited as part of the UF Rising Preeminence Plan, a major hiring campaign to bring together researchers in a number of focal areas including Big Data and Biodiversity. We will both be joining the Wildlife Ecology and Conservation department, Ethan will be part of UF’s new Informatics Institute, and Morgan will be part of UF’s new Biodiversity Initiative.
As excited as we are about the opportunities at Florida, we are also incredibly sad to be saying goodbye to Utah State University. Leaving was not an easy decision. We have amazing colleagues and friends here in Utah that we will greatly miss. We have also felt extremely well treated by Utah State. They were very supportive while we were getting our programs up and running, including helping us solve the two-body problem. They allowed us to take risks in both research and the classroom. They have been incredibly supportive of our desires for work-life balance, and were very accommodating following the birth of our daughter. It was a fantastic place to spend nearly a decade and we will miss it and the amazing people who made it home.
So why are we leaving? It was a many faceted decisions, but at its core was the realization that the scale of the investment and recruiting of talented folks in both of our areas of interest was something we were unlikely to see again in our careers. The University of Florida has always had a strong ecology group, but between the new folks who have already accepted positions and those we know who are being considered, it is going to be such a talented and exciting group that we just had to be part of it!
As part of the move we’ll be hiring for a number of different positions, so stay tuned!
As announced by Noam Ross on Twitter (and confirmed by the Editor in Chief of Ecology Letters), Ecology Letters will now allow the submission of manuscripts that have been posted as preprints. Details will be published in an editorial in Ecology Letters. I want to say a heartfelt thanks to Marcel Holyoak and the entire Ecology Letters editorial board for listening to the ecological community and modifying their policies. Science is working a little better today than it was yesterday thanks to their efforts.
For those of you who are new to the concept of preprints, they are manuscripts, that have not yet been published in peer reviewed journals, which are posted to websites like arXiv, PeerJ, and bioRxiv. This process allows for more rapid communication of scientific results and improved quality of published papers though more expansive pre-publication peer-review. If you’d like to read more check out our paper on The Case for Open Preprints in Biology.
The fact that Ecology Letters now allows preprints is a big deal for ecology because they were the last of the major ecology journals to make the transition. The ESA journals began allowing preprints just over two years ago and the BES journals made the switch about 9 months ago. In addition, Science, Nature, PNAS, PLOS Biology, and a number of other ecology journals (e.g., Biotropica) all support preprints. This means that all of the top ecology journals, and all of the top general science journals that most ecologists publish in, allow the posting of preprints. As such, there is not longer a reason to not post preprints based on the possibility of not being able to publish in a preferred journal. This can potentially shave months to years off of the time between discovery and initial communication of results in ecology.
It also means that other ecology journals that still do not allow the posting of preprints are under significant pressure to change their policies. With all of the big journals allowing preprints they have no reasonable excuse for not modernizing their policies, and they risk loosing out on papers that are initially submitted to higher profile journals and are posted as preprints.
It’s a good day for science. Celebrate by posting your next manuscript as a preprint.
Preprints are rapidly becoming popular in biology as a way to speed up the process of science, get feedback on manuscripts prior to publication, and establish precedence (Desjardins-Proulx et al. 2013). Since biologists are still learning about preprints I regularly get asked which of the available preprint servers to use. Here’s the long-form version of my response.
The good news is that you can’t go wrong right now. The posting of a preprint and telling people about it is far more important than the particular preprint server you choose. All of the major preprint servers are good choices.Of course you still need to pick one and the best way to do that is to think about the differences between available options. Here’s my take on four of the major preprint servers: arXiv, bioRxiv, PeerJ, and figshare.
arXiv is the oldest of the science preprint servers. As a result it is the most well established, it is well respected, more people have heard of it than any of the other preprint servers, and there is no risk of it disappearing any time soon. The downside to having been around for a long time is that arXiv is currently missing some features that are increasingly valued on the modern web. In particular there is currently no ability to comment on preprints (though they are working on this) and there are no altmetrics (things like download counts that can indicate how popular a preprint is). The other thing to consider is that arXiv’s focus is on the quantitative sciences, which can be both a pro and a con. If you do math, physics, computer science, etc., this is the preprint server for you. If you do biology it depends on the kind of research you do. If your work is quantitative then your research may be seen by folks outside of your discipline working on related quantitative problems. If your work isn’t particularly quantitative it won’t fit in as well. arXiv allows an array of licenses that can either allow or restrict reuse. In my experience it can take about a week for a preprint to go up on arXiv and the submission process is probably the most difficult of the available options (but it’s still far easier than submitting a paper to a journal).
bioRxiv is the new kid on the block having launched less than a year ago. It has both commenting and altmetrics, but whether it will become as established as arXiv and stick around for a long time remains to be seen. It is explicitly biology focused and accepts research of any kind in the biological sciences. If you’re a biologist, this means that you’re less likely to reach people outside of biology, but it may be more likely that biology folks come across your work. bioRxiv allows an array of licenses that can either allow or restrict reuse. However, they explicitly override the less open licenses for text mining purposes, so all preprints there can be text-mined. In my experience it can take about a week for a preprint to go up on bioRxiv.
PeerJ Preprints is another new preprint server that is focused on biology and accepts research from across the biological sciences. Like bioRxiv it has commenting and altmetrics. It is the fastest of the preprint servers, with less than 24 hours from submission to posting in my experience. PeerJ has a strong commitment to open access, so all of it’s preprints are licensed with the Creative Commons Attribution License. PeerJ also publishes an open access journal, but you can post preprints to PeerJ Preprints with out submitting them to the journal (and this is very common). If you do decide to submit your manuscript to the PeerJ journal after posting it as a preprint you can do this with a single click and, should it be published, the preprint will be linked to the paper. PeerJ has the most modern infrastructure of any of the preprint servers, which makes for really pleasant submission, reading, and commenting experiences. You can also earn PeerJ reputation points for posting preprints and engaging in discussions about them. PeerJ is the only major preprint server run by a for-profit company. This is only an issue if you plan to submit your paper to a journal that only allows the posting of non-commercial preprints. I only know of only one journal with this restriction, but it is American Naturalist which can be an important journal in some areas of biology.
figshare is a place to put any kind of research output including data, figures, slides, and preprints. The benefit of this general approach to archiving research outputs is that you can use figshare to store all kinds of research outputs in the same place. The downside is that because it doesn’t focus on preprints people may be less likely to find your manuscript among all of the other research objects. One of the things I like about this broad approach to archiving anything is that I feel comfortable posting that isn’t really manuscripts. For example, I post grant proposals there. figshare accepts research from any branch of science and has commenting and altmetrics. There is no delay from submission to posting. Like PeerJ, figshare is a for-profit company and any document posted there will be licensed with the Creative Commons Attribution License.
Those are my thoughts. I have preprints on all three preprint servers + figshare and I’ve been happy with all three experiences. As I said at the beginning, the most important thing is to help speed up the scientific process by posting your work as preprints. Everything else is just details.
UPDATE: It looks like due to a hiccup with scheduling this post than an early version went out to some folks without the figshare section.
UPDATE: In the comments Richard Sever notes that bioRxiv’s preprints are typically posted within 48 hours of submission and that their interpretation of the text mining clause is that this is covered by fair use. See our discussion in the comments for more details.
As I’ve argued here, and in PLOS Biology, preprints are important. They accelerate the scientific dialog, improve the quality of published research, and provide both a fair mechanism for establishing precedence and an opportunity for early-career researchers to quickly demonstrate the importance of their research. And I’m certainly not the only one who thinks this:
- Population biologists turn to pre-publication server to gain wider readership and rapid review
- All the cool kids are on arXiv and Haldane’s Sieve… why you should be too
- Open science and the econoblogosphere
- A good way to publish – arXiv FTW
- Nature respects preprint servers
- ESA changes Arxiv policy following community comments
One of the things slowing the use of preprints in ecology is the fact that some journals still have policies against considering manuscripts that have been posted as preprints. The argument is typically based on the Ingelfinger rule, which prohibits publishing the same original research in multiple journals. However, almost no one actually believes that this rule applies to preprints anymore. Science, Nature, PNAS, the Ecological Society of America, the British Ecological Society, the Royal Society, Springer, Wiley, and Elsevier all generally allow the posting of preprints. In fact, there is only one major journal in ecology that does not consider manuscripts that are posted as preprints: Ecology Letters.
I’ve been corresponding with the Editor in Chief of Ecology Letters for some time now attempting to convince the journal to address their outdated approach to preprints. He kindly asked the editorial board to vote on this last fall and has been nice enough to both share the results and allow me to blog about them.
Sadly, the editorial board voted 2:1 to not allow consideration of manuscripts posted as preprints based primarily on the following reasons:
- Authors might release results before they have been adequately reviewed and considered. In particular the editors were concerned that “early career authors might do this”.
- Because Ecology Letters is considered to be a quick turnaround journal the need for preprints is lessened
I’d like to take this opportunity to explain to the members of the editorial board why these arguments are not valid and why it should reconsider its vote.
First, the idea that authors might release results before they have been sufficiently reviewed is not a legitimate reason for a journal to not consider preprinted manuscripts for the following reasons:
- This simply isn’t a journal’s call to make. Journals can make policy based on things like scientific ethics, but preventing researchers from making poor decisions is not their job.
- Preprints are understood to not have been peer reviewed. We have a long history in science of getting feedback from other scientists on papers prior to submitting them to journals and I’ve personally heard the previous Editor in Chief of Ecology Letters argue passionately for scientists to get external feedback before submitting to the journal. This is one of the primary reasons for posting preprints; to get review from a much broader audience than the 2-3 reviewers that will look at a paper for a journal.
- All of the other major ecology and general science journals already allow preprints. This means that any justification for not allowing them would need to explain why Ecology Letters is different from Science, Nature, PNAS, the ESA journals, the BES journals, the Royal Society journals, and several of the major corporate publishers. In addition, since every other major ecology journal allows preprints, this policy would only influence papers that were intended to be submitted to Ecology Letters. This is such a small fraction of the ecology literature that it will have no influence on the stated goal.
- We already present results prior to publication in all kinds of forms, the most common of which is at conferences, so unless we are going to disallow presenting results in talks that aren’t already published this won’t accomplish its stated goal.
Second, the idea that because Ecology Letters is so fast that preprints are unnecessary doesn’t actually hold for most papers. Most importantly, this argument ignores the importance of preprints for providing prepublication review. In addition, in the best case scenario this reasoning only holds for articles that are first submitted to Ecology Letters and are accepted. Ecology Letters has roughly a 90% rejection rate (the last time I heard a number). Since a lot of the papers that are accepted there are submitted elsewhere first I suspect that the proportion of the papers they handle that this argument works for is <5%. For all other papers the delay will be much longer. For example, let’s say I do some super exciting research (well, at least I think it’s super exciting) that I think has a chance at Science/Nature. Science and Nature are fine with me posting a preprint, but since there’s a chance that it won’t get in there, I still can’t post a preprint because I might end up submitting to Ecology Letters. My paper goes out for review at Science but gets rejected, I send it to Nature where it doesn’t go out for review, and then to PNAS where it goes out again and is rejected. I then send it to Letters where it goes out for 2 rounds of review and is eventually accepted. Give or take this process will take about a year, and that’s not a short period of time in science at all.
So, I am writing this in the hopes that the editorial board will reconsider their decision and take Ecology Letters from a journal that is actively slowing down the scientific process back to its proud history of increasing the speed with which scientific communication happens. If you know members of the Ecology Letters editorial board personally I encourage you to email them a link to this article. If any members of the editorial board disagree with the ideas presented here and in our PLOS Biology paper, I encourage them to join me in the comments section to discuss their concerns.
UPDATE: Added Wiley to the list of major publishers that allow preprints. As Emilio Bruna points out in the comments they are happy to have journals that allow posting of preprints and Biotropica is a great example of one of their journals making this shift.
UPDATE: Fixed link to Paul Krugman’s post.
A couple of weeks ago Eli Kintisch (@elikint) interviewed me for what turned out to be a great article on “Sharing in Science” for Science Careers. He also interviewed Titus Brown (@ctitusbrown) who has since posted the full text of his reply, so I thought I’d do the same thing.
How has sharing code, data, R methods helped you with your scientific research?
Definitely. Sharing code and data helps the scientific community make more rapid progress by avoiding duplicated effort and by facilitating more reproducible research. Working together in this way helps us tackle the big scientific questions and that’s why I got into science in the first place. More directly, sharing benefits my group’s research in a number of ways:
- Sharing code and data results in the community being more aware of the research you are doing and more appreciative of the contributions you are making to the field as a whole. This results in new collaborations, invitations to give seminars and write papers, and access to excellent students and postdocs who might not have heard about my lab otherwise.
- Developing code and data so that it can be shared saves us a lot of time. We reuse each others code and data within the lab for different projects, and when a reviewer requests a small change in an analysis we can make a small change in our code and then regenerate the results and figures for the project by running a single program. This also makes our research more reproducible and allows me to quickly answer questions about analyses years after they’ve been conducted when the student or postdoc leading the project is no longer in the lab. We invest a little more time up front, but it saves us a lot of time in the long run. Getting folks to work this way is difficult unless they know they are going to be sharing things publicly.
- One of the biggest benefits of sharing code and data is in competing for grants. Funding agencies want to know how the money they spend will benefit science as a whole, and being able to make a compelling case that you share your code and data, and that it is used by others in the community, is important for satisfying this goal of the funders. Most major funding agencies have now codified this requirement in the form of data management plans that describe how the data and code will be managed and when and how it will be shared. Having a well established track record in sharing makes a compelling argument that you will benefit science beyond your own publications, and I have definitely benefited from that in the grant review process.
What barriers exist in your mind to more people doing so?
There is a lot of fear about openly sharing data and code. People believe that making their work public will result in being scooped or that their efforts will be criticized because they are too messy. There is a strong perception that sharing code and data takes a lot of extra time and effort. So the biggest barriers are sociological at the moment.
To address these barriers we need to be a better job of providing credit to scientists for sharing good data and code. We also need to do a better job of educating folks about the benefits of doing so. For example, in my experience, the time and effort dedicated to developing and documenting code and data as if you plan to share it actually ends up saving the individual research time in the long run. This happens because when you return to a project a few months or years after the original data collection or code development, it is much easier if the code and data are in a form that makes it easy to work with.
How has twitter helped your research efforts?
Twitter has been great for finding out about exciting new research, spreading the word about our research, getting feedback from a broad array of folks in the science and tech community, and developing new collaborations. A recent paper that I co-authored in PLOS Biology actually started as a conversation on twitter.
How has R Open Science helped you with your work, or why is it important or not?
rOpenSci is making it easier for scientists to acquire and analyze the large amounts of scientific data that are available on the web. They have been wrapping many of the major science related APIs in R, which makes these rich data sources available to large numbers of scientists who don’t even know what an API is. It also makes it easier for scientists with more developed computational skills to get research done. Instead of spending time figuring out the APIs for potentially dozens of different data sources, they can simply access rOpenSci’s suite of packages to quickly and easily download the data they need and get back to doing science. My research group has used some of their packages to access data in this way and we are in the process of developing a package with them that makes one of our Python tools for acquiring ecological data (the EcoData Retriever) easy to use in R.
Any practical tips you’d share on making sharing easier?
One of the things I think is most important when sharing both code and data is to use standard licences. Scientists have a habit of thinking they are lawyers and writing their own licenses and data use agreements that govern how the data and code and can used. This leads to a lot of ambiguity and difficulty in using data and code from multiple sources. Using standard open source and open data licences vastly simplifies the the process of making your work available and will allow science to benefit the most from your efforts.
And do you think sharing data/methods will help you get tenure? Evidence it has helped others?
I have tenure and I certainly emphasized my open science efforts in my packet. One of the big emphases in tenure packets is demonstrating the impact of your research, and showing that other people are using your data and code is a strong way to do this. Whether or not this directly impacted the decision to give me tenure I don’t know. Sharing data and code is definitely beneficial to competing for grants (as I described above) and increasingly to publishing papers as many journals now require the inclusion of data and code for replication. It also benefits your reputation (as I described above). Since tenure at most research universities is largely a combination of papers, grants, and reputation, and I think that sharing at least increases one’s chances of getting tenure indirectly.
UPDATE: Added missing link to Titus Brown’s post: http://ivory.idyll.org/blog/2014-eli-conversation.html
EcoData Retriever: quickly download and cleanup ecological data so you can get back to doing science
If you’ve every worked with scientific data, your own or someone elses, you know that you can end up spending a lot of time just cleaning up the data and getting it in a state that makes it ready for analysis. This involves everything from cleaning up non-standard nulls values to completely restructuring the data so that tools like R, Python, and database management systems (e.g., MS Access, PostgreSQL) know how to work with them. Doing this for one dataset can be a lot of work and if you work with a number of different databases like I do the time and energy can really take away from the time you have to actually do science.
Over the last few years Ben Morris and I been working on a project called the EcoData Retriever to make this process easier and more repeatable for ecologists. With a click of a button, or a single call from the command line, the Retriever will download an ecological dataset, clean it up, restructure and assemble it (if necessary) and install it into your database management system of choice (including MS Access, PostgreSQL, MySQL, or SQLite) or provide you with CSV files to load into R, Python, or Excel.
Just click on the box to get the data:
Or run a command like this from the command line:
retriever install msaccess BBS --file myaccessdb.accdb
This means that instead of spending a couple of days wrangling a large dataset like the North American Breeding Bird Survey into a state where you can do some science, you just ask the Retriever to take care of it for you. If you work actively with Breeding Bird Survey data and you always like to use the most up to date version with the newest data and the latest error corrections, this can save you a couple of days a year. If you also work with some of the other complicated ecological datasets like Forest Inventory and Analysis and Alwyn Gentry’s Forest Transect data, the time savings can easily be a week.
The Retriever handles things like:
- Creating the underlying database structures
- Automatically determining delimiters and data types
- Downloading the data (and if there are over 100 data files that can be a lot of clicks)
- Transforming data into standard structures so that common tools in R and Python and relational database management systems know how to work with it (e.g., converting cross-tabulated data)
- Converting non-standard null values (e.g., 999.0, -999, NoData) into standard ones
- Combining multiple data files into single tables
- Placing all related tables in a single database or schema
The EcoData Retriever currently includes a number of large, openly available, ecological datasets (see a full list here). It’s also easy to add new datasets to the EcoData Retriever if you want to. For simple data tables a Retriever script can be as simple as:
name: Name of the dataset description: A brief description of the dataset of ~25 words. shortname: A one word name for the dataset table: MyTableName, http://awesomedatasource.com/dataset
We also have some exciting new features on the To Do list including:
- Automatically cleaning up the taxonomy using existing services
- Providing detailed tracking of the provenance of your data by recording the date it was downloaded, the version of the software used, and information about what cleanup steps the Retriever performed
- Integration into R and Python
Let us know what you think we should work on next in the comments.
This is a guest post by Elita Baldridge (@elitabaldridge). She is a graduate student in our group who has been navigating the development of a chronic illness during graduate school. She is sharing her story to help spread awareness of the challenges faced by graduate students with chronic illnesses. She wrote an excellent post on the PhDisabled blog about the initial development of her illness that I encourage you to read first.
During my time as a Ph.D. student, I developed a host of bizarre, productivity eating symptoms, and have been trying to make progress on my dissertation while also spending a lot of time at doctors’ offices trying to figure out what is wrong with me. I wrote an earlier blog post about dealing with the development of a chronic illness as a graduate student at the PhDisabled Blog.
When the rheumatologist handed me a yellow pamphlet labeled “Fibromyalgia”, I felt a great sense of relief. My mystery illness had a diagnosis, so I had a better idea of what to expect. While chronic, at least fibromyalgia isn’t doing any permanent damage to joints or brain. However, there isn’t a lot known about it, the treatment options are limited, and the primary literature is full of appallingly small sample sizes.
There are many symptoms which basically consisting of feeling like you have the flu all the time, with all the associated aches and pains. The worst one for me, because it interferes with my highly prized ability to think, is the cognitive dysfunction, or, in common parlance, “fibro fog”. This is a problem when you are actively trying to get research done, as sometimes you remember what you need to do, but can’t quite figure out how navigating to your files in your computer works, what to do with the mouse, or how to get the computer on. I frequently finish sentences with a wave of my hand and the word “thingy”. Sometimes I cannot do simple math, as I do not know what the numbers mean, or what to do next. Depending on the severity, the cognitive dysfunction can render me unable to work on my dissertation as I simply cannot understand what I am supposed to do. I’m not able to drive anymore, due to the general fogginess, but I never liked driving that much anyway. Sometimes I need a cane, because my balance is off or I cannot walk in a straight line, and I need the extra help. Sometimes I can’t be in a vertical position, because verticality renders me so dizzy that I vomit.
I am actually doing really well for a fibromyalgia patient. I know this, because the rheumatologist who diagnosed me told me that I was doing remarkably well. I am both smug that I am doing better than average, because I’m competitive that way, and also slightly disappointed that this level of functioning is the new good. I would have been more disappointed, only I had a decent amount of time to get used to the idea that whatever was going on was chronic and “good” was going to need to be redefined. My primary care doctor had already found a medication that relieved the aches and pains before I got an official diagnosis. Thus, before receiving an official diagnosis, I was already doing pretty much everything that can be done medication wise, and I had already figured out coping mechanisms for the rest of it. I keep to a strict sleep schedule, which I’ve always done anyway, and I’ve continued exercising, which is really important in reducing the impact of fibromyalgia. I should be able to work up my exercise slowly so that I can start riding my bicycle short distances again, but the long 50+ mile rides I used to do are probably out.
Fortunately, my research interests have always been well suited to a macroecological approach, which leaves me well able to do science when my brain is functioning well enough. I can test my questions without having to collect data from the field or lab, and it’s easy to do all the work I need to from home. My work station is set up right by the couch, so I can lay down and rest when I need to. I have to be careful to take frequent breaks, lest working too long in one position cause a flare up. This is much easier than going up to campus, which involves putting on my healthy person mask to avoid sympathy, pity, and questions, and either a long bus ride or getting a ride from my husband. And sometimes, real people clothes and shoes hurt, which means I’m more comfortable and spending less energy if I can just wear pajamas and socks, instead of jeans and shoes.
Understand that I am not sharing all of this because I want sympathy or pity. I am sharing my experience as a Ph.D. student developing and being diagnosed with a chronic illness because I, unlike many students with any number of other short term or long term disabling conditions, have a lot of support. Because I have a great deal of family support, departmental support, and support from the other Weecologists and our fearless leaders, I should be able to limp through the rest of my Ph.D. If I did not have this support, it is very likely that I would not be able to continue with my dissertation. If I did not have support from ALL of these sources, it is also very likely that I would not be able to continue. While I hope that I will be able contribute to science with my dissertation, I also think that I can contribute to science by facilitating discussion about some of the problems that chronically ill students face, and hopefully finding solutions to some of those problems. To that end, I have started an open GitHub repository to provide a database of resources that can help students continue their training and would welcome additional contributions. Unfortunately, there doesn’t seem to be a lot. Many medical Leave of Absence programs prevent students from accessing university resources- which also frequently includes access to subsidized health insurance and potentially the student’s doctor, as well as removing the student from deferred student loans.
I have fibromyalgia. I also have contributions to make to science. While I am, of course, biased, I think that some contribution is better than no contribution. I’d rather be defined by my contributions, rather than my limitations, and I’m glad that my university and my lab aren’t defining me by my limitations, but are rather helping me to make contributions to science to the best of my ability.
The British Ecological Society has announced that will now allow the submission of papers with preprints (formal language here). This means that you can now submit preprinted papers to Journal of Ecology, Journal of Animal Ecology, Methods in Ecology and Evolution, Journal of Applied Ecology, and Functional Ecology. By allowing preprints BES joins the Ecological Society of America which instituted a pro-preprint policy last year. While BES’s formal policy is still a little more vague than I would like*, they have confirmed via Twitter that even preprints with open licenses are OK as long as they are not updated following peer review.
Preprints are important because they:
- Speed up the progress of science by allowing research to be discussed and built on as soon as it is finished
- Allow early career scientists to establish themselves more rapidly
- Improve the quality of published research by allowing a potentially large pool reviewers to comment on and improve the manuscript (see our excellent experience with this)
BES getting on board with preprints is particularly great news because the number of ecology journals that do not allow preprints is rapidly shrinking to the point that ecologists will no longer need to consider where they might want to submit their papers when deciding whether or not to post preprints. The only major blocker at this point to my mind is Ecology Letters. So, my thanks to BES for helping move science forward!
*Which is why I waited 3 weeks for clarification before posting.
This is a guest post by Dan McGlinn, a weecology postdoc (@DanMcGlinn on Twitter). It is a Research Summary of: McGlinn, D.J., X. Xiao, and E.P. White. 2013. An empirical evaluation of four variants of a universal species–area relationship. PeerJ 1:e212 http://dx.doi.org/10.7717/peerj.212. These posts are intended to help communicate our research to folks who might not have the time, energy, expertise, or inclination to read the full paper, but who are interested in a <1000 general language summary.
It is well established in ecology that if the area of a sample is increased you will in general see an increase in the number species observed. There are a lot of different reasons why larger areas harbor more species: larger areas contain more individuals, habitats, and environmental variation, and they are likely to cross more barriers to dispersal – all things that promote more species to be able to exist together in an area. We typically observe relatively smooth and simple looking increases in species number with area. This observation has mystified ecologists: How can a pattern that should be influenced by many different and biologically idiosyncratic processes appear so similar across scales, taxonomic groups, and ecological systems?
Recently a theory was proposed (Harte et al. 2008, Harte et al. 2009) which suggests that detailed knowledge of the complex processes that influence the increase in species number may not be necessary to accurately predict the pattern. The theory proposes that ecological systems tend to simply be in their most likely configuration. Specifically, the theory suggests that if we have information on the total number of species and individuals in an area then we can predict the number of species in smaller portions of that area.
Published work on this new theory suggests that it has potential for accurately predicting how species number changes with area; however, it has not been appreciated that there are actually four different ways that the theory can be operationalized to make a prediction. We were interested to learn
- Can the theory accurately predict how species number changes with area across many different ecological systems, and
- Do the different versions of the theory consistently perform better than others
To answer these questions we needed data. We searched online and made requests to our colleagues for datasets that documented the spatial configuration of ecological communities. We were able to pull together a collection of 16 plant community datasets. The communities spanned a wide range of systems including hyper-diverse, old-growth tropical forests, a disturbance prone tropical forest, temperate oak-hickory and pine forests, a Mediterranean mixed-evergreen forest, a low diversity oak woodland, and a serpentine grassland.
Fig 1. A) Results from one of the datasets, the open circles display the observed data and the lines are the four different versions of the theory we examined. B) A comparison of the observed and predicted number of species across all areas and communities we examined for one of the versions of the theory.
Across the different communities we found that the theory was generally quite accurate at predicting the number of species (Fig 1 above), and that one of the versions of the theory was typically better than the others in terms of the accuracy of its predictions and the quantity of information it required to make predictions. There were a couple of noteworthy exceptions in our results. The low diversity oak woodland and the serpentine grassland both displayed unusual patterns of change in richness. The species in the serpentine grassland were more spatially clustered than was typically observed in the other communities and thus better described by the versions of the theory that predicted stronger clustering. Abundance in the oak woodland was primarily distributed across two species whereas the other 5 species where only observed once or twice. This unusual pattern of abundance resulted in a rather unique S-shaped relationship between the number of species and area and required inputting the observed species abundances to accurately model the pattern.
The two key findings from our study were
- The theory provides a practical tool for accurately predicting the number of species in sub-samples of a given site using only information on the total number of species and individuals in that entire area.
- The different versions of the theory do make different predictions and one appears to be superior
Of course there are still a lot of interesting questions to address. One question we are interested in is whether or not we can predict the inputs of the theory (total number of species and individuals for a community) using a statistical model and then plug those predictions into the theory to generate accurate fine-scaled predictions. This kind of application would be important for conservation applications because it would allow scientists to estimate the spatial pattern of rarity and diversity in the community without having to sample it directly. We are also interested in future development of the theory that provides predictions for the number of species at areas that are larger (rather than smaller) than the reference point which may have greater applicability to conservation work.
The accuracy of the theory also has the potential to help us understand the role of specific biological processes in shaping the relationship between species number and area. Because the theory didn’t include any explicit biological processes, our findings suggest that specific processes may only influence the observed relationship indirectly through the total number of species and individuals. Our results do not suggest that biological processes are not shaping the relationship but only that their influence may be rather indirect. This may be welcome news to practitioners who rely on the relationship between species number and area to devise reserve designs and predict the effects of habitat loss on diversity.
Harte, J., A. B. Smith, and D. Storch. 2009. Biodiversity scales from plots to biomes with a universal species-area curve. Ecology Letters 12:789–797.
Harte, J., T. Zillio, E. Conlisk, and A. B. Smith. 2008. Maximum entropy and the state-variable approach to macroecology. Ecology 89:2700–2711.