Ask us anything: recent community ecology research that belongs in the textbooks

The next questions in our annual ask us anything series come from Tadhg Carroll:

Brian, if you were still working on the 2nd edition of “Community Ecology” with Gary Mittelbach, are there any recent lines of research you would like to include that have emerged over the last year or so? These might include topics you’d like to place more (or less) emphasis on, particularly impressive papers (or clusters of papers?!), or enlightening reviews, meta-analyses or books. It would be particularly interesting to hear if there’s been any recent work that has changed or crystallized your thinking on a topic, or progress that has impressed you on any areas from the “Looking Ahead” section of the final chapter.

Jeremy, I’ll give you a multiple choice question related to this (that is, choose a question, not the traditional choose an answer…). Are there any topics or approaches that you would place more emphasis on if you were writing a textbook on Community Ecology?; AND/OR, what recent line of research have you found particularly impressive, interesting or enlightening over the same period of the last year or two.

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Ask us anything: what will the short- and long-term academic job market in EEB look like?

A while back we invited our readers to ask us anything. Here’s the next question, from an anonymous postdoc, and our answers:

How do you think the Ecology and Evolution job market will fare in the near and long term? In what ways might it resemble and differ from the 2008 recession?
I am particularly concerned about the disproportionate toll of the pandemic/protests on those of us who are parents, POC, etc, who may not have the same productivity during this time to remain competitive after the jobs come back (to a saturated field of PhDs).

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Ask us anything: the future of machine learning in ecology

Every year we invite you to ask us anything. Here’s our next question, from Mike Mahoney, and our answers:

What (if any) role do you see for less-interpretable machine learning (& similar techniques) in ecology? In particular I’m thinking of Brian’s posts on statistical machismo & also the need for more prediction in ecological studies, and Peter Adler’s post on ecological forecasting — I’m wondering if you see any areas in ecology where the increase in predictive accuracy offsets not being able to understand how the model got there.

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Ask us anything: when did you realize you’re a “mid-career” researcher?

Every year we invite you to ask us anything. Here’s our next question, from Falko Bruschke, and our answers:

Do you think there are any general milestones to signal that someone has moved from early-career to mid-career as an ecological researcher? Would it be based on time post-PhD, age, number of papers, number of students supervised, the number of years in a job, a combination of all of these? Is it even something generalisable across individual careers?

In this context, were there specific moments in your own careers where you felt that your standing as ecology researchers was based less on your future potential, and more on your past track-records?


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Saturday blast from the past: what is, or will be, your old school science cred?

A conversation with a friend who can code in FORTRAN reminded me to re-up this old post of Meghan’s. What’s the scientific or academic thing you’ve done or experienced that someday will make you seem really, really old? Both the post and the comment thread are fun, you should read them. We all deserve more fun than we’re having right now.