Re-imagining the purpose of conferences in a time of isolation

It is now trite to say that the COVID-19 pandemic has impacted many aspects of routine life, from our personal to our professional realities. Every part of academic life has been touched by the pandemic, reducing all aspects of our research endeavours to virtual platforms, from coursework and student mentoring to faculty meetings and conferences.  Zooming in and out of meetings has become the norm for all of us. While there are obvious restrictions to life on an e-platform, I see an opportunity for us to use it to our advantage, to increase the impact and extent of how we communicate our science.  
 

The obligatory Zoom lab meeting screen capture.

I’ve been involved with a number of e-activities including giving departmental seminars, giving a conference talk and helping to organize a weekly on-line seminar series (Ecology Live). These experiences have led me to think quite a bit about new opportunities for giving talks and sharing ideas and findings.

One obvious casualty of COVID-19 restrictions has been conferences -large gatherings are simply untenable even if some regions are starting to reopen some activities. Some conferences were simply canceled outright early on while others have switched to online formats. These e-conferences seem like the best-case scenario allowing for scientists to share their findings while observing gathering and travel restrictions. I gave a talk in an organized oral session in an e-conference and have been contemplating signing up for another.  But I have mixed feelings. Let me be clear, the decision to move to e-format is the best decision for these conferences that have had to respond to these unprecedented changes, but moving forward are there other ways to facilitate interaction and communication? To me, the answer is yes.

The cons of the e-conference:

1. Spontaneous conversations

I don’t think fitting a traditional conference into an e-format works all that well. The point of a conference, to me, is more about the random meetings and discussions with friends and collaborators, rather than the extensive back-to-back talks, for which I have a rather low limit that I can actively listen to.  These sporadic encounters, which often amount to valuable research outputs and collaborations, are lost in the e-conference.

A mock debate at the last conference I attended before the pandemic

2. Child-care

Physical conferences have become better about providing childcare options for attendees. But, with e-conferences, the parents stuck at home with children might not have childcare options, making it difficult to attend whole sessions, and remain fully focused on the science. Added to this, is that the e-conference format with multiple concurrent sessions over the whole day is not that convenient for people at home, even without children.

3. Fees and funding

In my experience thus far, e-conferences appear to still be limiting attendance with still rather steep paywalls. The one I spoke in still had substantial fees to attend even though they were using what appeared to be university Zoom accounts. I totally understand that there are expenses, but the hefty fees limit an amazing opportunity to reach a broader audience.

Related to this, conferences traditionally are quite exclusive. Fees, travel, housing, visas and immigration all exclude people from different parts of the world, especially those who don’t have access to the same level of funding as researchers in North America and Europe. E-conferences can change this, but they have yet to. More than this, many of us are accustomed to being at institutions that bring in weekly seminar speakers, and again, people in other parts of the world have no opportunity to access these.

A route forward?

1. Seminars open for all

Working with the British Ecological Society to bring the weekly Ecology Live seminar series has been an incredible experience. Firstly, the BES has been amazingly supportive of this idea and helping to make it work. More than 3000 people have registered for this series, and from all over the world. The response from people has been phenomenal.

The lesson I take from this experience is that there is a demand for high-quality talks and that there are numerous colleagues from the global south who jump at the opportunity to hear from cutting-edge researchers. Many of these people are excluded from traditional, and likely online, conferences. If we are moving to an online format, accessibility and inclusion should be a motivating factor.

Obviously, there are expenses with delivering online content, but costs can be covered in other ways. Traditional conferences have sponsors and companies advertising their products in the main halls. These groups can still be engaged and in fact access to online audiences around the world and in permanent on-demand formats could be quite attractive to sponsors. We’ve now started including advertisements on the opening and closing slides of Ecology Live to keep the webinars free to watch.

2. Freed from time restrictions to conference length

Traditionally conferences are restricted to 3-5 days but switching to an online format means that societies are no longer subject to a conference structure. Without time limitations, e-conferences do not have to conform to sessions occurring simultaneously. By spreading talks over time, perhaps grouping by thematic topic areas, researchers would be able to attend far more talks than they would normally be able to in a traditional conference setting. I’d watch four 15-min talks on a specific area every couple of weeks.

3. A permanent record accessible by all, always

Many ecologists are quite overcome by a deluge of webinars, zoom meetings, etc. Taking the time to spend days in an e-conference is a daunting choice. Even if they are unable to watch talks live, conference organizers could make them permanent, searchable, and linkable. We post the Ecology Live talks to YouTube afterward and some of our earlier seminars have been viewed thousands of times. There is a general move towards open and transparent science, and free online talks that are permanently available is another step towards this. 

We live in a world where access to new ideas and hearing about cutting edge research is divided between the have and have-nots. Despite the limitations of COVID-19, given some planning, e-conferences can provide a powerful means to connecting the ecologists across the world, but there might be better ways forward to use these recent moves to on-line formats to better engage diverse audiences in a much more inclusive way.

 

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Have you attended an e-conference recently, or plan on attending one soon? Let me know your thoughts and opinions down below.

Publication Partners: a COVID-19 publication assistance program in conservation science

Researchers around the world are trying to keep up on work duties and responsibilities while being required to stay at home. For some people this means caring for young children or other family members, devising homeschooling, switching courses to online delivery, scheduling meetings with team members, receiving new duties from superiors, and perhaps worrying about job security. It is natural that these people may feel overwhelmed and that routine tasks, like checking references or proofreading manuscripts, might seem insurmountable.

However, for others, COVID-19 lockdowns have resulted in more time to push projects to completion and clear out backlogs. There is then inequality in the impact of COVID-19 restrictions on individuals.

These COVID-19 impacts on individuals not only have these unequal impacts on mental wellbeing and career trajectories but are on top of the desperate necessity of conservation science to continue. We win by having a greater diversity of experts communicating with one another.

Publication Partners is an attempt to address some of this COVID-19 impact inequality and to ensure that conservation science is still being published by assisting people with their manuscript preparation. This is a match-making service of the conservation community to bring researchers struggling with their current working conditions together with those that feel that have extra capacity and are willing to help others in this difficult time. The partner might be asked for publication advice, to assist with manuscript editing, help sorting and checking references, organizing tasks for revisions or preparing figures.

The idea is that the Publication Partners would normally be contributing less than would be expected for authorship and thus will be listed in the acknowledgments of the resulting paper. Publication Partners will match volunteers with those requesting support.

To volunteer or request a partner, please see this document with contact instrucitons.

As a journal editor, I see this a valuable and much needed assistance strategy. And I’m not alone. Many of the most important conservation journals have signaled their support and welcome submissions using this service. The journals support Publication Partners includes (please note that the list of journals is being updated and so will change over time):

Avocetta - Journal of Ornithology 

Biological Conservation

Conservation Biology

Conservation Evidence

Conservation Letters 

Conservation Physiology

Conservation Science and Practice

Ecological Solutions and Evidence

Endangered Species Research

Functional Ecology 

Global Ecology and Conservation

IBIS– International Journal of Avian Science

Journal of Animal Ecology

Journal of Applied Ecology

Journal of Ecology

Journal of Insect Conservation

Methods in Ecology and Evolution

Mires and Peat

Natural History Sciences 

Oryx

People and Nature 

Restoration Ecology

RIO - Rivista italiana di ornitologia - Research in Ornithology

Tropical Conservation Science

Wildfowl

 *Thanks to Bill Sutherland for sharing his thoughts on this post.

Can skipping the peer-review process be a legitimate way to communicate science?

Science is an approach to inquiry and knowledge production that provides an unsubstitutable approach to evaluating empirical claims. And it is a specific and particular thing. Beyond the experiments and data collection, science must be communicated in order to impact knowledge and inform humanity’s understanding of the world around us and potential solutions to problems of our own making. The gold standard for communicating scientific findings is through peer-review. Peer review is the process by which research articles are assessed by other experts and these reviewers are the gatekeepers that determine if papers should be published and how much revision they require. These reviewers look for flaws in logic, methodology and inference, and ensure that findings are set in their proper context.

While peer-review is a generally robust process, it can be corrupted. There are plenty of examples, including: fraudulent data appearing research articles, corrupted analyses to support personal agendas, journals realizing economic benefits by not adhering to peer-review best practice, or non-scientific agendas creeping into peer-reviewed journals.   

So, peer-review is not perfect, but it is necessary and can always be improved. However, there is another question: is it always needed? Are there legitimate reasons for a scientist to skip the peer-review process?

To me, there could be reasons to skip the peer-review process, but the goals should be clear and we need to acknowledge that conclusions and inferences will always be in doubt. Yet, impacting the scientific understanding of some phenomenon and communicating it to other experts might not be the goal. Like this blog post, for example. There can be other communication objectives that do not necessarily require peer-review.

Here are three non-peer reviewed communication pathways that I’ve personally pursued, and I’m not including blogs and other social media here, because I think they differ in their goals and objectives, but these are communication approaches you might want to consider:

1-    You might want to capture a broader public readership, to tell a story in a way that captures a non-specialist audience. For example, you might want to extend your science to a call for policy or societal change or to draw attention of the public and policymakers to a critical issue. I was recently a co-author on several papers that attempted to do this, for example, one on the need to protect the Tibetan Plateau, and another on the globally uneven distribution of the readership and submissions of applied ecology papers.

2-    You might want to target a specific audience that does not need to access peer-reviewed literature. Especially for agencies and NGOs that need specific guidance and summary of best practice. The grey literature is a rich and diverse set of communication pathways, which is not well captured in journals nor permanently available (something with the British Ecological Society that we’ve been trying to overcome!).

3-    You may desire to publish information or findings that are desperately needed and extremely time-sensitive. I recently decided to skip the typical peer review pipeline to get out analyses showing that governmental responses to COVID19 quickly resulted in significant drops in air pollution, across six different air pollutants for those cities impacted in February. I published the findings in this blog and posted the manuscript to EarthRQiv.

Why would I do this, especially when I am reporting the outcomes of hypothesis tests and data analysis? I did submit the manuscript to Science and it was quickly rejected, and I’m sure legitimate biogeochemists and atmospheric chemists are already submitting better analyses. However, I told myself before submission that if it was rejected, I would immediately go to plan B, which I did. I felt that the need to engage in this conversation and to shine the light on policy decisions that would lead to reduced pollution were too important for me to pursue the lengthy peer-review process, especially one that is not in my area of research. So, my plan B was to post to a preprint server and blog it. My hope is that it will spur more discussion and further analyses.

In some ways, these alternative vehicles for communicating science have been an experiment for me, but I have the luxury to do this given that I now have a mature research program and rather large group. Its is important to evaluate how we value non-peer reviewed material, or more importantly, how you use these to tell the story about your contributions to society and your impact. While we clearly need to distinguish peer-reviewed and non-reviewed material, and that there is no replacing the impact of peer-review, we should view non-peer-reviewed material more positively and as a way for knowledge mobilization and engage other communities in discussion. As scientists, we need to think carefully about when and how to communicate and the value of this communication to both society and to our careers. But certainly, these alternative forms of scientific communication can help make the broader impact statements on grant and tenure applications more compelling.

We are ultimately evaluated primarily on our peer-reviewed science, as it should be, we can better tell our story about our contribution with a complementing minority of other communication types. I would go so far as to say that a scientist who only publishes peer-reviewed articles might be missing important opportunities to share their knowledge and have an impact on societally important issues.

Excluding blog posts and tweets, about 30% of my contributions are not peer-reviewed. If I include blog posts, then I’d guess I’m at about a 1:1 ratio, peer-reviewed to not. But I am at the stage in my career where this is less risky to do. Pursuing alternative communication forms needs to be non-linear, you need more peer-reviewed articles upfront to establish your credibility which then frees you to pursue other intellectual endeavours and modes of communication. But perhaps more importantly, you’ve established that you are knowledgeable and a trusted authority, meaning that your non-peer-reviewed writings have greater impact.

Regardless, many of us got into this business to expand our collective understanding of the world around us or to make the world a better place. Neither of these goals is achievable if we are not communicating to non-scientists.

Some of the best advice on the internet: several years of links

I started off the New Year with a much-needed bookmark reorganization and deletion, which also gave me a chance to re-read some of the links I've held onto (sometimes for years). There's an ever-increasing amount of useful content on the internet, but these have proven some of the most helpful, concrete, and lasting guides for navigating a scientific life.

I thought I'd collate the list here with the hope others might find some of these useful.

How to make it as early career researcher and new faculty: 

• There's no one way to be successful (in fact there are many ways), but hearing what not to do can be as important as all the other advice you will get: Mistakes I've made as an early career researcher (Times Higher Education)
• Lots of what-to-do and what-not-to-do advice here! How to be a new professor (Eco-Evo Evo-Eco)
• It's hard enough that Berkley's career centre has written pages on the topic: Transitioning from PhD to Prof (Careers at Berkley)

Identity and academia:

• I think most of us took different and often interesting routes to science (for example, I grew up in an evangelical Christian family, took a number of years to finally start my undergrad, and had no particular knowledge of ecology when I started my BSc. I wanted to be a vet, but now I'm an ecologist. Close enough :) ) and so I like to hear the many different routes by which scientists found science (SEAS).
• Overcoming imposter syndrome - there are many websites devoted to the topic, but this one provides particularly concrete steps to overcoming this common problem. 
• No one is perfect, and feedback can hurt - why feedback hurts and how to over come that. And no, it isn't enough to say, 'grow a thicker skin' (The Thesis Whisperer).
• Diversify EEB - a useful list of women and minorities working in EEB, worth keeping in mind when making nominations, selecting reviewers, and making various invitations. 
• And it's worth remembering that there is a dark side (one slightly bitter take on it). (Fear and Loathing in Academia)

Mentoring and leadership:

• Mentoring plans - this is super-useful whether you are faculty or a student. In a lot of places there may not be a tradition of having written plans and this provides examples of how to put them into practice. (Meg Duffy, Dynamic Ecology)
• Running successful lab meetings from all perspectives. I like the idea of explicitly deciding what you value and then modelling that when leading lab meetings. (Neuromechanics Lab at Emory)
• Discussing and leading successful journal clubs especially for graduate students.  (Tulane University, Dept of EEB) 
• Running successful scientific working groups and workshops (Conservation Bytes) 
• Preparing PhD students for non-academic careers (The Versatile PhD)

Computing/Data management:

• How to organize your computational projects (PlosBiol)
• Ten commandments for good data management (Brian McGill, Dynamic Ecology)
• Proselytizing about version control (Jennifer Bryan, Peer J)
• A list of lists of links for R statistical resources on Google Documents  
• Data sources worth checking out at The ecological data wiki

Data visualization:

• There are some really beautiful infographics about science from Eleanor Lutz here (Tabletop Whale).
• Information is Beautiful - infographics for inspiration
• Show me Shiny - some great examples of how R Shiny has transformed data visualization and interaction.
• If you are familiar with Edward Tufte's influential work on data visualization, you can use R to produce similar plots here. (Lukasz Piwek)

Teaching:

• The Eco-Evo Evo-Eco blog has a great how-to series, but this one especially has really concrete examples and suggestions for EEB lecturers: How to Teach (Eco-Evo Evo-Eco). 
• An article on how to use new learning methods for quantitative biology and models at Active learning and quantitative biology (Integrative and Comparative Biology)  
• The National Centre for Case Study Teaching in Science for already developed case studies and activities. 
• Common mistakes using active learning - nice to see legit critiques and ways to improve.
• Smart advice on avoiding bad teaching reviews (Terry McGlynn, Small Pond Science)

Miscellaneous links:

• Now part of the PhD canon: The infamous Snake Fight for your thesis to distribute to your PhD students (McSweeney's Internet Tendency).
• Wall of the Dead - poignant memorial list of ecologists lost while working  (Richard Conniff).
• The original text for Gause's The Struggle for Existence and plots. Ecology has changed so much (and so little).
• Visualize the tree of life beautifully and interactively (and get your name on it (sort of)!) (One Zoom)

More authors, more joy?

It seems that ecologists have been complaining that no one writes single author manuscripts anymore since at least the 1960s. de Solla Price predicted in 1963:

"…the proportion of multi-author papers has accelerated steadily and powerfully, and it is now so large that if it continues at the present rate, by 1980 the single-author paper will be extinct”

Fortunately, an interesting new editorial in the Journal of Applied Ecology has the data (from their archives of published and submitted papers) to evaluate to ask whether this disastrous outcome has actually occurred.

It turns out that Price was wrong about single-author extinction, although he hadn't misread the trends. Since the 1970s, the proportion of single-authored papers at the journal have declined to less than 4% and the mean number of authors has risen to more than 5 (Figure 1).

Fig 1. 

It's also notable that single-authored papers are cited significantly less often and are 2.5x less likely to be accepted (!). (If that statistic doesn't make you want to gather some coauthors, nothing will). These trends agree with others reported in the literature.

The authors hypothesize that a number of factors drive this result. Ecology has gotten 'bigger' in many ways - analyses are less likely to focus on single populations or species and more likely to be replicated through space and or time. This increased breadth requires more students or assistants to aid with experimental or field work, or collaborations with other labs to bring such data together. Similarly, ecological data collection and analyses often require multiple types of specialized knowledge, whether statistical, mathematical, technological, or systems-based. And by relying on multiple researchers to play specialized roles, the overall quality of a manuscript might be higher (as compared to a jack-of-all trades). The authors also suggest that factors including the growing number of ecologists, the more international scope of many research activities, and more democratic approaches to authorship have increased the mean number of co-authors.

What makes these results particularly interesting is that I think there is still something of a cachet for the sole-authored paper. The conceit is that writing a sole authored paper means that you have a fully realized research plan, and you're accomplished enough to bring it to fruition by yourself. But these stats at least seem to suggest that you're better off with a few friends :)


Barlow, Jos, Stephens, Philip A., Bode, Michael, Cadotte, Marc W., Lucas, Kirsty, Newton, Erika, Nuñez, Martin A., Pettorelli, Nathalie. On the extinction of the single-authored paper: The causes and consequences of increasingly collaborative applied ecological research. J Appl Ecol. 55(1): 1365-2664. doi.org/10.1111/1365-2664.13040

Blogging about science for yourself

In case you missed it, a new paper in Royal Society Open Science from seven popular ecology blogs discusses the highlights and values of science community blogging. It provides some insights into the motivations behind posting and the reach and impacts that result. It's a must-read if you've considered or already have a blog about science.

It was nice to see how universal the 'pros' of blogging seem to be – the things I most appreciate about contributing to a blog are pretty similar to the things the authors here reported on too. According to the archives, I've been posting here since 2010, when I was a pretty naïve PhD student interacting with the ecological literature for the first time. I had a degree of enthusiasm and wonder upon interacting with ideas for the first time that I miss, actually. I just started a faculty job this fall, and I think that the blog allowed me to explore and experiment with ideas as I figured out where I was going as a scientist (which is still an ongoing process).

As Saunders et al. note, one of the other major upsides to blogging is the extent to which it produces networking and connections with colleagues. In a pretty crowded job market, I think it probably helped me, although only as a complement to the usual suspects (publications, 'fit', research plans, interviewing skills). Saunders et al. also mentioned blogging as relevant to NSF's Broader Impacts section, which I actually hadn't considered. Beyond that, the greatest benefit by far for me is that forcing oneself to post regularly and publicly is amazing practice for writing about science.

Despite these positives, I don't necessarily think a science blog is for everyone and there are definitely things to consider before jumping in to it. It can be hard to justify posting on a blog when your to-do list overflows, and not everyone will –understandably- think that's a good use of their time. There is a time commitment and degree of prioritisation required that is difficult. This is one reason that having co-bloggers can be a lifesaver. It is also true that while writing a blog is great practice, it probably selects for people able to write quickly (and perhaps without perfectionistic tendencies).

When students ask me about blogging, they often hint at concerns in sharing their ideas and writing. It can be really difficult to put your ideas and writing out there (why invite more judgement and criticism?) and this is can feedback with imposter syndrome (speaking from my own experience). For a long time, minorities, women, students have been under-represented in ecology blogs, and I think this may be a contributor to that. It's nice to see more women blogging about these days, and hopefully there is a positive feedback from increasing the visibility of under-represented groups.

In any case, this paper was especially timely for me, because I've been re-evaluating over the past few months about whether to keep blogging or not, and this provided a reminder of the positive impacts that are easy to overlook.

Novelty or prediction or something else?

There is an interesting editorial at elife from Barak Cohen on "How should novelty be valued in science?" It connects to some of the discussions on this blog and in other places concerned about the most efficient and effective path for science (Cohen suggests a focus on predictive ability).

One relevant question is how 'understanding' differs from 'predicting' and whether a focus on 'prediction' can produce perverse incentives too, as the focus on novelty has.

[This pessimistic image about perverse incentives from Edwards and Roy (2017) and the discussion from Mike Taylor seemed an appropriate addition.

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Image in academia

Not many seminar speakers are introduced with a discussion of their pipetting skills. When we talk about other scientists we discuss their intelligence, their rigour, their personality, above and beyond their learned skills. Most people have an image of what a scientist should be, and judge themselves against this idealized vision. There are a lot of unspoken messages that are exchanged in science and academia. It’s easy to think that the successful scientists around one interacts with are just innately intelligent, confident, passionate, and hard-working. No doubt imposter syndrome owes a lot to this one-sided internalization of the world. After all, you don’t feel like you fulfill these characteristics because you have evidence of your own personal struggles but not those of everyone else. 

"Maybe no one will notice".

The most enlightening conversation I had this year (really! Or at least a close tie with discovering that PD originally was discussed as a measure of homologous characters…) was with a couple of smart, accomplished female scientists, in which we all acknowledged that we—not infrequently—suffered from feeling totally out of our depths. It is hard to admit our failings or perceived inadequacies, for fear we’ll be branded with them. But it’s really helpful for others to see that reality is different than the image we’ve projected. If everyone is an imposter, no one is. There is something to be said for confidence when scientists are presenting consensus positions to the public, but on the other hand, I think that being open about the human side of science is actually really important. 

For those who already feel like outsiders in academia, perhaps because they (from the perspective of race, gender, orientation, social and economic background, etc) differ from the dominant stereotype of a ‘scientist’, it probably doesn’t take much to feel alienated and ultimately leave. Students have said things to me along the lines of “I love ecology but I don’t think I will try to continue in academic because academia is too negative/aggressive/competitive”. Those are legitimate reasons to avoid the field, but I always try to acknowledge that I feel the same way too sometimes. It’s helpful to acknowledge that others feel the same way, and that having this kind of feeling (e.g. that you aren’t smart enough, or you don’t have a thick enough skin) isn’t a sign that you don’t actually belong. Similarly, it’s easy to see finished academic papers and believe that they are produced in a single perfect draft and that writing a paper should be easy. But for 99% of people, that is not true, and a paper is the outcome of maybe 10 extreme edits, several rounds of peer review, and perhaps even a copy-editor. Science is inherently a work-in-progress and that’s true of scientists as well.

The importance of personal relationships and mentorship to help provide realistic images of science should be emphasized. Mentorship by people who are particularly sympathetic (by personal experience or otherwise) to the difficulties individuals face is successful precisely for this reason. This might be why blog posts on the human side of academia are so comparatively popular – we’re all looking for evidence that we are not alone in our experiences. (Meg Duffy writes nice posts along these lines, e.g. 1, 2). And though the height of the blogosphere might be over, the ability of blog posts to provide insight into humanity of academia might be its most important value.

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?  

Data management for complete beginners

Bill Michener is a longtime advocate of data management and archiving practices for ecologists, and I was lucky to catch him giving talk on the topic this week. It clarified for me the value of formalizing data management plans for institutions and lab groups, but also the gap between recommendations for best practices in data management and the reality in many labs.

Michener started his talk with two contrasting points. First, we are currently deluged by data. There is more data available to scientists now than ever, perhaps 45000 exabytes by 2020. On the other hand, scientific data is constantly lost. The longer since a paper is published, the less likely its data can be recovered (one study he cited showed that data had a half life of 20 years). There are many causes of data loss, some technological, some due to changes in sharing and publishing norms. The rate at which data is lost may be declining though. We're in the middle of a paradigm shift in terms of how scientists see our data. Our vocabulary now includes concepts like 'open access', 'metadata', and 'data sharing'. Many related initiatives (e.g.  GenBank, Dryad, Github, GBIF) are fairly familiar to most ecologists. Journal policies increasingly ask for data to be deposited into publicly available repositories, computer code is increasingly submitted during the review process, and many funding agencies now require statements about data management practices.

This has produced huge changes in typical research workflows over the past 25 years. But data management practices have advanced so quickly there’s a danger that some researchers will begin to feel that it is unobtainable, due to the level of time, expertise, or effort involved. I feel like sometimes data management is presented as a series of unfamiliar tools and platforms (often changing) and this can make it seem hard to opt in. It’s important to emphasize good data management is possible without particular expertise, and in the absence of cutting edge practices and tools. What I liked about Michener's talk is that it presented practices as modular ('if you do nothing else, do this') and as incremental. Further, I think the message was that this paradigm shift is really about moving from a mindset in which data management is done posthoc ('I have a bunch of data, what should I do with it?') to considering how to treat data from the beginning of the research process.

Hierarchy of data management needs.

One you make it to 'Share and archive data', you can follow some of these great references.

Hart EM, Barmby P, LeBauer D, Michonneau F, Mount S, Mulrooney P, et al. (2016) Ten Simple Rules for Digital Data Storage. PLoS Comput Biol 12(10): e1005097. doi:10.1371/journal.pcbi.1005097

James A. Mills, et al. Archiving Primary Data: Solutions for Long-Term Studies, Trends in Ecology & Evolution, Volume 30, Issue 10, October 2015, Pages 581-589, ISSN 0169-5347.

https://software-carpentry.org//blog/2016/11/reproducibility-reading-list.html (lots of references on reproducibility)

K.A.S. Mislan, Jeffrey M. Heer, Ethan P. White, Elevating The Status of Code in Ecology, Trends in Ecology & Evolution, Volume 31, Issue 1, January 2016, Pages 4-7, ISSN 0169-5347.

Thanks to Matthias Grenié for discussion on this topic.

The removal of the predatory journal list means the loss of necessary information for scholars.

We at EEB & Flow periodically post about trends and issues in scholarly publishing, and one issue that we keep coming back to is the existence of predatory Open Access journals. These are journals that abuse a valid publishing model to make a quick buck and use standards that are clearly substandard and are meant to subvert the normal scholarly publishing pipeline (for example, see: here, here and here). In identifying those journals that, though their publishing model and activities, are predatory, we have relied heavily on Beall's list of predatory journals. This list was created by Jeffrey Beall, with the goal of providing scholars with the necessary information needed to make informed decisions about which journals to publish in and to avoid those that likely take advantage of authors.

As of a few days ago, the predatory journal list has been taken down and is no longer available online. Rumour has it that Jeffrey Beall removed the list in response to threats of lawsuits. This is really unfortunate, and I hope that someone who is dedicated to scholarly publishing will assume the mantle.

However, for those who still wish to consult the list, an archive of the list still exists online -found here.

True, False, or Neither? Hypothesis testing in ecology.

How science is done is the outcome of many things, from training (both institutional and lab specific), reviewers’ critiques and requests, historical practices, subdiscipline culture and paradigms, to practicalities such as time, money, and trends in grant awards. ‘Ecology’ is the emergent property of thousands of people pursuing paths driven by their own combination of these and other motivators. Not surprisingly, the path of ecology sways and stalls, and in response papers pop up continuing the decades old discussion about philosophy and best practices for ecological research.

A new paper from Betini et al. in the Royal Society Open Science contributes to this discussion by asking why ecologists don’t test multiple competing hypotheses (allowing efficient falsification or “strong inference” a la Popper). Ecologists rarely test multiple competing hypothesis test: Betini et al. found that only 21 of 100 randomly selected papers tested 2 hypotheses, and only 8 tested greater than 2. Multiple hypothesis testing is a key component of strong inference, and the authors hearken to Platt’s 1964 paper “Strong Inference” as to why ecologists should be adopting adopt strong inference. 

Platt

From Platt: “Science is now an everyday business. Equipment, calculations, lectures become ends in themselves. How many of us write down our alternatives and crucial experiments every day, focusing on the exclusion of a hypothesis? We may write our scientific papers so that it looks as if we had steps 1, 2, and 3 in mind all along. But in between, we do busywork. We become "method-oriented" rather than "problem-oriented." We say we prefer to "feel our way" toward generalizations.”

[An aside to say that Platt was a brutally honest critic of the state of science and his grumpy complaints would not be out of place today. This makes reading his 1964 paper especially fun. E.g. “We can see from the external symptoms that there is something scientifically wrong. The Frozen Method. The Eternal Surveyor. The Never Finished. The Great Man With a Single Hypothesis. The Little Club of Dependents. The Vendetta. The All-Encompassing Theory Which Can Never Be Falsified.”]

Betini et al. list a number of common practical intellectual and practical biases that likely prevent researchers from using multiple hypothesis testing and strong inference. These range from confirmation bias and pattern-seeking to the fallacy of factorial design (which leads to unreasonably high replication requirements including of uninformative combinations). But the authors are surprisingly unquestioning about the utility of strong inference and multiple hypothesis testing for ecology. For example, Brian McGill has a great post highlighting the importance and difficulties of multi-causality in ecology - many non-trivial processes drive ecological systems (see also). 

Another salient point is that falsification of hypotheses, which is central to strong inference, is especially unserviceable in ecology. There are many reasons that an experimental result could be negative and yet not result in falsification of a hypothesis. Data may be faulty in many ways outside of our control, due to inappropriate scales of analyses, or because of limitations of human perception and technology. The data may be incomplete (for example, from a community that has not reached equilibrium); it may rely inappropriately on proxies, or there could be key variables that are difficult to control (see John A. Wiens' chapter for details). Even in highly controlled microcosms, variation arises and failures occur that are 'inexplicable' given our current ability to perceive and control the system.

Or the data might be accurate but there are statistical issues to be concerned about, given many effect sizes are small and replication can be difficult or limited. Other statistical issues can also make falsification questionable – for example, the use of p-values as the ‘falsify/don’t falsify’ determinant, or the confounding of AIC model selection with true multiple hypothesis testing.

Instead, I think it can be argued that ecologists have relied more on verification – accumulating multiple results supporting a hypothesis. This is slower, logically weaker, and undoubtedly results in mistakes too. Verification is most convincing when effect sizes are large – e.g. David Schindler’s lake 226, which provided a single and principal example of phosphorus supplementation causing eutrophication. Unfortunately small effect sizes are common in ecology. There also isn’t a clear process for dealing with negative results when a field has relied on verification - how much negative evidence is required to remove a hypothesis from use, versus just lead to caveats or modifications?

Perhaps one reason Bayesian methods are so attractive to many ecologists is that they reflect the modified approach we already use - developing priors based on our assessment of evidence in the literature, particularly verifications but also evidence that falsifies (for a better discussion of this mixed approach, see Andrew Gelman's writing). This is exactly where Betini et al.'s paper is especially relevant – intellectual biases and practical limitations are even more important outside of the strict rules of strong inference. It seems important as ecologists to address these biases as much as possible. In particular, better training in philosophical, ethical and methodological practices; priors, which may frequently be amorphous and internal, should be externalized using meta-analyses and reviews that express the state of knowledge in unbiased fashion; and we should strive to formulate hypotheses that are specific and to identify the implicit assumptions.