Why I don't plan to take PhD students in the future

In a recent Walkabout I let slip that I don't plan to take PhD students in the future. That triggered some comments on FB wanting more details, so here goes.
Before I go any further, I should make one thing very clear. This decision was not a result of my experiences advising PhD students. Max, Adam, and Trevor were great students, and those experiences are not why I am choosing to stop training PhDs.

I'm also currently advising 2 PhD students who fall into a different category altogether. Both of them are already full time employed and are looking at getting a PhD as part of their ongoing professional development. Both are "self-funded" as well. They're also not who I'm talking about; I wouldn't rule out having such students in the future if they can convince me that my help in getting a PhD will benefit their careers.

The question that led to this decision was a simple one: "Why am I training PhD students?". From a decision analysis perspective, what are my objectives? One reason is that I kept hearing that people like me are in high demand as researchers and analysts. So, producing clones of myself* seemed like a good service to the field of conservation and wildlife ecology, and it appeared to be a good career path for the students as well. I was also keen to "pass forward" the great experiences I had as a graduate student in Bernie Roitberg's and Hugh Possingham's lab groups. In both places I benefited from being a part of a bigger group of people interested in hard problems. I wanted to create that at UNL. I failed to achieve that largely because of how students are funded in my academic unit. More recently, I've decided to make a virtue out of a necessity for reasons I'll outline below.

Structural problems with funding

One thing I failed to appreciate before coming to UNL was how much Canada and Australia do to support work like mine by providing direct funding to graduate students. At present, NSF provides 3 years of funding to 2000 students a year with the graduate fellowships program. In 2014 NSERC offered 710 postgraduate scholarships - Doctoral, each of which would provide up to 3 years of funding. In addition, NSERC offers 2000 postgraduate scholarships - Masters, each providing 12 months of funding. Remember that there are approximately 10 x fewer Canadians than Americans on the planet, so NSERC is funding graduate students at double the rate of NSF, at a minimum. Sure there are other places to get postgraduate scholarships in the USA, but there are in Canada too. I think those funding levels from NSERC are likely down from the past as well, in addition to being distributed differently from when I received similar awards**. Australia has a similar system of funding postgraduate students directly.

That postgraduate funding allowed me to spend a significant amount of time learning how to do modelling and statistics without having to produce a product. Even after the NSERC funds were exhausted I was able to get funding from SFU as a teaching assistant and graduate research assistant, neither of which were tied to a grant with specific milestones. In contrast, the School of Natural Resources (SNR) at UNL largely relies on faculty obtaining grant funds to support graduate students. There are a handful of graduate research assistant-ships available (and one of my MSc students received one) each year, but competition for those is steep. So, to get a student you first have to get a grant and then recruit a student to fill the position. The trouble is, doing what I can get money for means that a student has to come up to speed on complex modelling and analysis very fast. Unfairly fast. And, because there is no "backup system" in SNR for supporting students, if I can get money for 1 year it isn't enough to recruit a student on. 

This uncertainty in funding issue makes recruiting quality students harder, and it doesn't just affect me. Other colleagues with experience at other institutions complain about the same thing in SNR. When the competition can offer a guarantee of 4 or 5 years of funding (mostly from Teaching Assistant-ships, it is true), we have to be that much more attractive as a place to be. I had to tell Adam, "I've got two years of funding and I promise I'll try really hard to get you some more funding".  He came anyway, for which I am extremely grateful! 

The best strategy I've found is to sell my time, and then using the resulting salary savings to support a student like Adam. Unfortunately that caps the amount I can spend on students because I only have so much time to sell. 

So, as a result of being in the USA, and more specifically, in SNR, I'm never going to be able to build the kind of large, dynamic lab group that I benefited from. 

Making a virtue from a necessity

So I have trouble funding graduate students. So what? The bottom line is there are too many PhDs already. Here is one list of studies documenting that fact. Here's another chart (admittedly about humanities, but broader point is true too). An excess of PhDs means that getting a job that actually needs a PhD is increasingly difficult according to NPR*** and the Washington Post. So, I begin to think I shouldn't be contributing to this problem. 

But even if there are too many PhDs in general, that doesn't mean that people specifically like me won't be highly successful. However, when I look back at my three successful PhDs I couldn't help but notice a difference between Max on the one hand and Adam or Trevor on the other. Max did not acquire a degree in statistics alongside his Natural Resources PhD, while Adam took an MSc in Statistics and Trevor has a joint PhD in Statistics and Natural Resources. Even though all three are successful, I would say that Max had the hardest road to a permanent position; both Adam and Trevor had permanent or next-to-permanent employment straight out of their degrees. And it was the statistics degrees that made that difference. And there's the rub: I can't advise statistics students, because I myself do not have a degree in statistics.

When I look around at the people publishing papers that I wish I'd written, I can't help but notice that they usually have a PhD in statistics (Andy Royle, Jim Nichols, Ken Burnham, Mevin Hooten ... the list goes on). I've had papers rejected by reviewers saying they wanted to see the result produced 'by a real statistician'. So the main conclusion I take away is that I've ventured much, much too far away from my expertise and credibility as an ecologist. If someone really wants to be successful doing what I do (exclusively data analysis and modelling), they are much better served by pursuing a degree in Statistics.

I should say that plenty of my colleagues excel by doing a lot of data analysis coupled with good field ecology (Jim Peterson, Mike Conroy, Larkin Powell, just to name a few). But they largely have students who do field work, and then do a great job of analyzing the resulting data and building simulation models to make predictions. That is still a very viable path for a student to follow, although picking up an MSc in statistics makes it even more credible. In contrast, my students generally have not collected data. I don't collect data. What I do is pretend to be a statistician, and that's the problem. Truth will out.

What's the fix?

I imagine there are a few ways I could try to continue advising PhD students. For example, I could try replicating the experience I had with Trevor, co-advising statistics PhD students. This works great for my spouse, Brigitte Tenhumberg, who typically has 1 or 2 students co-advised with colleagues in the Math department. For that to work, I have to bring something to the partnership. In Trevor's case, co-advising was key to accessing the NSF IGERT funds. Normally however I don't have funding to bring to the table (see above); in fact, Statistics has much more support for graduate students as Teaching Assistants. I also don't really have data or problems to provide -- pretending to be a statistician, remember?

I could try the Adam model again, selling my time as an SDM coach to build up the funds necessary to support a student. Having worked as a consultant a few times, I can say that the market is pretty thin. There are plenty of people coaching for free (like Max!) within the USGS and USFWS. Also, keeping the money within the University is problematic. In the first place, I have to sell a large portion of my time at my salaried rate to make enough to pay a PhD student. In the second place, the University doesn't like it if you hang on to those salary savings too long. I've had such funds disappear from my accounts altogether after fiscal year end, and had to go find them again!

I could also go back to being an ecologist, getting funding to collect data and do a good job of analyzing it. Trouble is, I've never developed the credibility of a good field ecologist either. Funding agencies don't hand out money to just anyone, especially these days. I am trying, but I expect this path was foreclosed a long time ago.

Or, I could simply stop advising PhD students altogether. This frees me up to focus on my special niche, which is helping graduate students develop the basic data manipulation and analysis skills they need to be successful in the 21st century. I seem to be good at that, possibly because I had to learn it all the hard way without the benefit of a solid background in statistics. My colleagues around me have plenty of students that need this training.

So there it is. Funding PhD students is hard, and training students to be like me is not an optimal career path for the student, so I'm not going to do it anymore.

*  Note that I use 'clone' here to be a bit funny; none of my students is exactly like me. In fact, I'd argue that all of them have wildly exceeded me.
**For example, the PhD scholarships come in 2 different levels of support, whereas back in the day there was only the lower level of support. If they discontinued the higher level of support they could fund nearly 50% more people for the same total cost.
***NPR link has PhD comics, if that helps you decide which one to visit first.