Friday, January 29, 2016

Commenting Issues, solved(?)

A few people mentioned that they have had problems commenting on the EEB & Flow for a while. I think the problem a blogger issue with the embedded comment format (if your computer doesn't accept 3rd party cookies, it looks like your comments may not post). I've changed the comment format to hopefully fix this issue.

My apologies, and if you still run into problems, please let me know!

Tuesday, January 26, 2016

Things to keep in mind when finding a PhD

A wonderful student who worked with me when I was a graduate student is in the midst of applying for graduate school, and has been going through the process of finding a suitable program and advisor. It's been nearly 7 years (!?) since I was first in graduate school and, in my case, I mostly lucked my way from undergraduate to a great lab without nearly enough due diligence (and no one I knew or in my family had been to grad school to provide advice).

If asked during grad school, I had a list of advice I would have liked to have received (admin questions, funding issues, how to get to campus on public transport). But the advice I think is important has actually changed a lot, from just “make sure you love research” (although you should, at least most of the time), to more strategic and practical considerations.

I now think the most important thing is to ask yourself while you consider graduate school is, "Why do I want to get a PhD?" Note that there is absolutely no right answer to this question, but there are some wrongs ones, e.g. "I don’t know what else to do next" or "I have good grades". The problem is that these answers aren’t enough to motivate you through a PhD program. And some people find themselves 5 years later, still not knowing what they’re going to do next or why they got a PhD. It’s okay to answer "I like the research I did as an undergrad" or "I want to develop strong quantitative skills", or "I love working with ideas", because these kind of answers mean you want something from your experience and you've thought about what that is.

Educate yourself about the opportunities that a PhD will bring, both academic and non-academic. Continue this education while you are in graduate school. [Departments, offer more opportunities for students to learn about non-academic jobs.] The reality is that getting the oft-desired research professorship is very difficult (e.g. 200+ applicants for a general ecology position is not unusual). But PhDs produce desirable skill sets and there are other opportunities, so long as you are aware of them. There are many LACs (liberal arts schools) in the US, and thus more teaching oriented professorships advertised every year than there are R1 professorships. There are NGO and government research jobs. And as many of my grad school friends leave academia, it’s a relief to see that their skills – strong quantitative abilities, good data management, a clarity of vision on how to ask questions and answer them with appropriate data – make them employable across a range of professions.

Ask questions ask questions ask questions. Don’t go into a program without knowing what it will entail. Ask the same questions of both faculty and students and see how their answers compare. 

To understand a department, you want to know what the teaching load is on average, how funding works (and for how long!). You should find out the average time to completion of a PhD program, what classwork looks like, whether there are student-lead reading or discussion groups? Is there funding for student travel to conferences or meetings?

If you have a lab in mind, you need to similarly learn about that lab. Find out, from both the PI and their students, how the lab works. What is the supervisory style? Does the PI tend to be hands on, or expect more independent research? How does your personal approach to working mesh with their style? Don't assume that if you like to have structure and feedback and the PI only is around once a month, it will just work out. How often are they physically on campus? How often would you meet? What are other students in the lab working on? Is the lab collaborative? Do students publish together? What skills are emphasized in the group? Has the PI published recently (last 2-3 years, depending on context) and, perhaps most importantly, have they graduated any students? If not, try to figure out why.

Once you’ve found a place, remember that how you feel about your PhD will rise and fall all the time. That’s normal. Avoid the worst of these dips by taking care of your mental health. The sort of unstructured, isolating, often un-rewarded work that goes into a PhD can be draining. But it is also 100% okay to change your mind, to decide a Master’s is sufficient, to hate everything you are doing and quit. Seriously. The sunk-cost fallacy will make you (and people around you) miserable.

Of course, grad school—like life—is stochastic and full of uncertainty. But its possible, with care to increase the probability that you find a supportive, nurturing lab and have a wonderful time as a graduate student. 

Monday, January 18, 2016

Have humans altered community interactions?

A recent Nature paper argues that there is evidence for human impacts on communities starting at least six thousand years ago, which altered the interactions that structure communities. “Holocene shifts in the assembly of plant and animal communities implicate human impacts” from Lyons et al. (2016, Nature) analyses data spanning modern communities through to 300 million year old fossils, to measure how the co-occurrence structure of communities has changed. The analyses look at the co-occurrence of pairs of species, and identifies those that are are significantly more likely ('aggregation') or less likely ('segregation') than a null expectation. Once the authors identified the species pairs with non-random co-occurrences, they calculated the proportion of these that were aggregated (i.e. y-axis on Figure 1). Compared to the ancient past, the authors suggest that modern species had fewer aggregated species pairs than in the past, perhaps reflecting an increase in negative interactions or distinct habitat preferences. 
Main figure from Lyons et al. 2016.
The interpretation offered by the paper is “[o]ur results suggest that assemblage co-occurrence patterns remained relatively consistent for 300 Myr but have changed over the Holocene as the impact of humans has dramatically increased.” and "...that the rules governing the assembly of communities have recently been changed by human activity". 

There are many important and timely issues related to this – changing processes in natural systems, lasting human effects, the need to use all available data from across scales, the value of cross-disciplinary collaboration. But, in my view, the paper ignores a number of the assumptions and considerations that are essential to community ecology. There are a number of statistical issues that others have pointed out (e.g. temporal autocorrelation, use of loess regression, null model questions), but a few in particular are things I was warned about in graduate courses. Such as the peril of proportions as response data (Jackson 1997), and the collapsing of huge amounts of data into an analysis of a summary of the data ("the proportion of significant pairwise associations that are aggregated"). Beyond the potential issues with calculating correct error terms, interpretation is made much more difficult for the reader. 

Most importantly, in my view, the Nature paper commits the sin of ignoring the essential role of scale in community ecology. A good amount of time and writing has been spent reconciling issues of spatial and temporal scale in ecology. These concepts are essential even to the definition of a 'community'. And yet, scale is barely an afterthought for these analyses.  (Sorry, perhaps that's a bit over-dramatic....) Fossils—undeniably an incomplete and biased sample of the an assemblage—can't be described to more than a very broad spatial and temporal scale. E.g. a 2 million year old fossil and a 2.1 million year old fossil may or may not have interacted, habitats may have varied between those times, and populations of S1 and S2 may well have differed greatly over a few thousand years. Compare this to modern data, which represents species occurring at the exact same time and in relatively small areas. The differences in scale is huge, and so these data are not directly comparable.

Furthermore, because we know that scale matters, we might predict that co-occurrences should increase at larger spatial grains (you include more habitat, so more species with the same broad requirements will be routinely found in a large area). But the authors reported that they found no significant relationship between dataset scale and the degree of aggregation observed (their Figure 2, not replicated here): this might suggest the methodology or analyses needs further consideration. Co-occurrence data is also, unfortunately, a fairly weak source of inference for questions about community assembly, without other data. So while the questions remain fascinating to me - is community assembly changing fundamentally over time? is that a frequent occurrence or driven by humans? what did paleo-communities look like? - I think that the appropriate data and analyses to answer these questions are not so easy to find and apply.

Response from Brian McGill:
My comment I was trying to post was:

Interesting perspective Caroline! As a coauthor, I of course am bound to disagree. I'll keep it short, but 3 thoughts:

1) The authors definitely agonized over potential confounding effects. Indeed spent over a year on it. In my experience paleoecologists default to assuming everything is an artefact in their data until they can convince themselves otherwise, much more than neo-ecologists do.
2) They did analyze the effects of scale (both space and time) and found it didn't seem to have much effect at all on the variable of interest (% aggregations). You interpret this as "this might suggest the methodology or analyses needs further consideration". But to me, I hardly think we know enough about scaling of species interactions to reject empirical data when it contradicts our very limited theoretical knowledge (speculation might be a better word) of how species interactions scale.
3) To me (and I think most of the coauthors) by far the most convincing point is that the pattern (a transition around 8000 years ago plus or minus after 300,000,000 years of constancy) occurs WITHIN the two datasets that span it (pollen of North America and mammal bones of North America both span about 20,000 years ago to modern times) and they have consistent taphonomies, sampling methods, etc and yet both show the transition.

I agree that better data without these issues is difficult (impossible?) to find. The question is what you do with that. Do you not anwwer certain questions. Or do you do the best you can and put it it out for public assessment. Obviously I side with the latter.

Thanks for the provoking commentary.



Tuesday, January 5, 2016

Resolutions for 2016

Having now been a postdoc for a couple of years, I think I’ve slowly developed more perspective about the day-to-day aspects of working as a researcher and the costs and benefits of various approaches. So this year, I am resolving to be proactive about the various challenges of academic life, and try some things meant to make my work life more productive and better: 

1) Carve out more time to read the literature. The busier I get, the more difficult it is to keep up with new papers that aren’t directly connected to my current projects. One of the best parts of being a grad student (without a heavy teaching load) was how much time I had to keep up with the literature. As my time is more scheduled and there are more concrete deadlines, it is harder to make time for activities like reading that don’t have an immediate pay off. I have a feed reader, but I find that I only check it monthly; I also come across interesting papers while doing lit reviews/etc and leave those open in my browser, planning to get to them eventually...

I know that braver souls than I are tackling this problem #365papers style, but I don’t think that’s what I want. Instead, I am scheduling three 1-hour slots per week, and I think that’s a manageable goal.

2) Continue to work on good project management practices (such as those described here). I use the suggestions for predictable directory structures, separation of code into different types of scripts and of course, version control, and find them very helpful. I wish that I had learnt best practices for coding and project management as a grad student, but it’s never too late.

3) Take vacations (and mean it!). Every academic I know who has good work-life balance takes vacations. That means not working—at all, including no responding to emails. This is one of the things I admire most about my European colleagues, and I look forward to enjoying the French holidays when I start a fellowship in Montpellier this spring :-)

4) Maintain relationships across distances. It can be difficult to connect with people during short postings here and there, and even harder to maintain those relationships after you move on to the next place. The tools are there (Skype, Facebook, Twitter, email, etc), so I shouldn’t forget to take advantage of them.

5) Learn a new skill. Still deciding what, given the many things I want to learn!

6) Emphasize the positive more often. In general, I think people (or at least me) can be overwhelmed by the negatives in academia – e.g. the rejections of manuscripts and applications, the difficulties in securing the next job, etc. Unlike in undergrad, we don’t get grades or quantitative measures of our success too often (and I haven’t gotten a sticker on my work in years). And when we do get praise, it is often informal (e.g. “good talk”, “I liked your paper”), or balanced with criticism (e.g. “accept but with major revisions”). This is all in pursuit of improvement, but it can be difficult to keep even constructive criticism in perspective because the brain is biased towards remembering the negative. So I’m considering keeping an explicit list of successes to help highlight the positive.