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Eco-evolutionary Island Biogeography

(This post is by Tim Farkas. I am just putting it up. Andrew)

If pressed to list the most influential paradigms of the last century, few ecologists would forget MacArthur and Wilson’s (1967) theory of island biogeography, and even fewer would intentionally exclude it. Since its inception, island biogeography has served as a powerful neutral model to explain patterns of biodiversity across space, and has seen its share of modifications, amendments, and criticisms, as befits any theory so prominent and enduring. Even when challenged to compete with metacommunity theory – its freshly minted and highly comprehensive contemporary – island biogeography performs surprisingly well, boasting a highly intuitive framework within which to develop new theory.

Perhaps it was only a matter of time, then, as the eco-evolutionary synthesis came into focus, that island biogeography was reconsidered in the light of rapid and ecologically powerful evolutionary dynamics. In the March issue of Trends in Ecology and Evolution, my colleagues and I (Farkas et al. 2015) point out that habitat area and isolation, central variables that influence equilibrium species richness in island biogeography theory (MacArthur and Wilson 1967), can also influence the fundamental processes of ecological genetics: gene flow, mutation, genetic drift, and natural selection. Hence, while area and isolation determine colonization and extinction rates through neutral processes, they can also cause rapid evolution.

We go on to lay down a general framework for how rapid evolution itself can influence equilibrium species richness, though effects on colonization and extinction. The key is that different evolutionary processes either promote or break down local adaptation, so habitat isolation and area can determine the location of a species on the “(mal)adaptation” continuum (Hendry and Gonzalez 2008). Building off the trophic theory of island biogeography (Gravel et al. 2011), (mal)adaptation in a single species can ripple through food webs and impact community-level patterns of colonization and extinction, ultimately influencing species richness at equilibrium.

The take-home: habitat area and isolation can have effects on equilibrium species richness mediated by both ecology and evolution, and those effects might reinforce or oppose one another.

Gene flow is perhaps the best process with which to illustrate eco-evolutionary island biogeography, because it is dependent on dispersal, which is heavily influenced by habitat isolation. A highly isolated habitat is expected to have low species richness at equilibrium, because colonization events will be rare, relative to a well-connected habitat (MacArthur and Wilson 1967). However, isolation will also reduce gene flow. Gene flow can have a diversity of consequences, but if gene flow is strong, and comes from populations locally adapted to divergent habitats, it breaks down local adaptation. Supposing gene flow causes local maladaptation, what may be predicted for colonization and extinction throughout the community? It depends on the role of the maladapted species in the food web. If maladaptation in a generalist pollinator reduces its abundance, the likelihood of extinction for plant mutualists might increase, reducing their species richness. On the other hand, if maladaptation reduces the abundance of a dominant consumer (e.g., a granivorous rodent), it could increase the species richness of competitors (other rodents).

In one of the examples above, the effects of isolation on species richness mediated by ecology and evolution oppose one another. In an extreme, where the evolutionary effect outweighs the ecological effect, isolated habitats could in theory have higher species richness than well-connected habitats, at least for particular guilds. This outcome can be illustrated using MacArthur and Wilson’s (1967) equilibrium figure of crossing extinction and colonization curves.

We draw three primary conclusions in this article. First, eco-evolutionary dynamics research would benefit from the explicit inclusion of gene flow, mutation, and genetic drift alongside natural selection. Second, a study of (mal)adaptation is likely a profitable means by which to accomplish that goal.  Third, rapid evolution can have a strong influence on species richness, and in particular can modify the core predictions of island biogeography. We hope this perspective encourages evolutionary ecologists to focus on (mal)adaptation and to incorporate evolutionary dynamics into their studies of biogeographic patterns.


Farkas, T. E., A. P. Hendry, P. Nosil, and A. P. Beckerman. 2015. How maladaptation can influence biodiversity: Eco-evolutionary island biogeography. Trends in Ecology & Evolution 30.

Gravel, D., F. Massol, E. Canard, D. Mouillot, and N. Mouquet. 2011. Trophic theory of island biogeography. Ecology Letters 14:1010–1016.

Hendry, A. P., and A. Gonzalez. 2008. Whither adaptation? Biology & Philosophy 23:673–699.

MacArthur, R. H., and E. O. Wilson. 1967. The Theory of Island Biogeography. Princeton University Press, Princeton, NJ.
Posted by Andrew Hendry at 9:57 AM No comments: 
How to Succeed in Graduate School - Part 2

Almost immediately after posting “How to Succeed in Graduate School” 10 days ago, I started receiving comments reminding me of other tips that I should have included. Now 1300+ views later, the original post clearly has to be Part 1 and I here provide Part 2. (Don't worry, I won't test your patience further with a Part 3.) As in Part 1, this post applies most directly to students who wish to make a career of research in academia or, to some extent, in government/industry/NGO. In addition, all of the suggestions apply to PhD students, whereas only some apply to MSc students. Finally, my experience - and therefore advice - relates most directly to students in ecology and evolution, although I am sure much of it applies more broadly.


Driven in part by the requirements of committee meetings and qualifying exams, students try to map out their thesis in precise detail: chapters, papers, time lines, sampling/experimental design, stats, etc. Doing so is all well and good, but 12 years of experience on student committees has made clear that such plans are NEVER realized. I would suggest a crude estimate that  only 1-2 proposed chapters actually make it into a thesis, and each of those that do ultimately look quite different from what was proposed. In short, planning is great but flexibility and opportunity are just as critical. Keep your eye out for exciting new ideas even if they weren’t in your original thesis plan; these inspirations often pan out as well or better than the original plan. And don’t stress out too much when your careful plan implodes – just accept from the beginning such an outcome is likely. As a specific implementation of this general suggestion, never apologize for failing to realize your original plan when discussing your work in talks or presentations (and try to avoid it in committee meetings). In reality, no one cares what you didn’t do, they only care what you did do. Talking about what you didn’t do just distracts and (sometimes) annoys the listener. In short, you should focus on what you have actually achieved and what you plan to do next.


Students are often encouraged to provide a strong a priori prediction for a given study, which can lead to several problems. First, it is usually easy to make a reasonable prediction that is directly contrary to the prediction advanced by the student: increasing A could just as easily lead to decreasing B as increasing B. Second, in correlative studies, one can nearly always conceptually invert the x-axis (presumed to be the cause) and the y-axis (assumed to be the effect) and yet still have a perfectly reasonable interpretation: see my post on “Faith’s Conjecture.” Third, when the study is actually conducted, a priori predictions are often NOT confirmed. Instead, negative (non-significant) or contrary results frequently emerge. Fourth, a strong a priori prediction can lead a student to assert support for the hypothesis when, in reality, the data more strongly support an alternative. This disconnect is very common in papers that I edit/review: in essence, the inferences aren't supported by the data. (Many journals even have a check-box in their reviewer forms for just this outcome.) For all of these reasons, a single prediction is usually not optimal. Instead, it is much more useful generate plausible alternative predictions that correspond to alternative mechanisms/processes/effects. That way, no matter what the outcome, you already anticipated it and you have an explanation for it. Moreover, you are less likely to be disappointed when your data don’t support a prediction, and disappointment in such cases has a huge influence on how enthusiastic and confident you are in presenting your results. In closing this point, I need to note that your supervisor might have a particularly strong opinion about predictions and, if so, you obviously need to take that into consideration.


Following from the above point, a given project always looks best before the actual work starts. After that, entropy happens and things start to fall apart. Targeted populations can’t be collected. Permits can’t be obtained. PCRs don’t work. DNA degrades. Funds run out. Hurricanes or floods destroy replicates. Hard drives crash (whatever else you do, make frequent backups of everything). And so on. Yet the project often turns out OK anyway. The problem is that most students lose excitement and interest as their ideal visualization transforms into messy reality – and this disillusionment increases with time, which decreases the motivation and drive to publish the work. Then they move on to something new – like a postdoc – without having published their previous work and the “grass is greener” syndrome kicks in. Now you are planning a new project, which as noted above looks ideal in concept, whereas the older reality is tarnished. As a result, efforts are often directed toward the newer work and the older stuff languishes. Yet it remains valuable to publish the older work as soon as possible. Stated in a more concrete fashion, you shouldn’t neglect your unpublished PhD work after you graduate. One reason is that your new project will also pick up considerable tarnish before you get around to publishing it. Another reason is that most people get their 2nd post-PhD position on the merits of their PhD work, not on work conducted during their 1st post-PhD position. Timeframes for transitions between most positions (e.g., postdocs) are simply too short to get much on your CV from your current position before moving on to the next one. A third (related) reason is that following through on your PhD work is usually by far the quickest route to a new publication, and lots of publications produced quickly will help your career. (Great advice on manuscript necromancy – breathing life into a “dead” manuscript – can be found here.) Despite external evidence of their competence, those with the [imposter syndrome] remain convinced that they are frauds and do not deserve the success they have achieved. Proof of success is dismissed as luck, timing, or as a result of deceiving others into thinking they are more intelligent and competent than they believe themselves to be. (via WIKIPEDIA) A number of students fall into this trap and yet I can assert with confidence that success in graduate school comes FROM YOU. Certainly it helps to have a supportive university, department, supervisor, and lab, but research success requires you. If you have a cool research result, you obtained it. If you publish a paper, you did it. If you get an award, it is because you deserved it. None of this would have happened without you. Note that acknowledging your own abilities  doesn’t mean you shouldn’t get help from others – that is often essential too. No one can be an expert in everything, and it makes the utmost sense to get help from experts. I am reminded of the pithy qualifier offered at the start of his defense presentation by a fellow graduate student, Andy Dittman: During my talk, when I say “I”, I really mean “we”; and when I say “we”, I really mean “they.”


What examiners want to see at your defense is a colleague rather than a student. The whole point of being awarded a PhD is that you are being recognized as intellectually equivalent to the people sitting in judgement on whether or not you should enter that club. Thus, your defense will always go most smoothly if you imagine yourself as a professor giving a seminar in another department, rather than a graduate student humbly seeking approval. Importantly, this advice is not an encouragement for you to be arrogant or dismissive, as I suppose sometimes happens with seminar speakers. With this in mind, emulate seminar speakers that have come to your department and whose talks (and answers to questions) you, and others in the department, reacted to best. Be confident but not arrogant. Be assertive but not abrasive. Admit when you don’t know something but don’t apologize for it. Listen respectfully to questions but don’t be cowed by them. Those people out there want to see you as their equal and you should proceed accordingly. (Here is some related advice from The Professor Is In)


The maximum length of your degree is often set by your university, department, or supervisor – and you need to meet those deadlines. So you can’t take too long. However, you also want to be to quick about it. Trying to finish too quickly will generally decrease your publication quality, quantity, or both. These outcomes are problematic because getting your next position depends largely on your PhD publications. Thus, you shouldn’t rush yourself out the door unless it is required. Note that following this advice will also help to fix problems that can arise from the “grass is greener” syndrome noted above. However, you certainly shouldn’t PLAN to take a long time, nor PLAN to exceed the time that you initially schedule. In such cases, you will find yourself running afoul of Hofstadter’s Law:  It always takes longer than you expect, even when you take into account Hofstadter's Law. Thus, make a realistic time plan and try to meet it but don’t rush yourself at the end if opportunities permit. Moreover, it is critical that you use such extra time productively – you need to get papers out!!!!!!


In direct contrast to the above encouragement, some students drag on way too long. They insist on getting everything perfect. They want to publish every paper before submitting their thesis. Or they simply procrastinate endlessly. This strategy is also a very bad idea. You need to work productively and efficiently and submit your thesis and move to the next position without worrying about publishing everything.  I suggest you start planning your next position, such as a postdoc, at least a year in advance. Networking is critical: start talking to profs at meetings. Grants are great: apply for postdoctoral fellowships as they will give you the most flexibility for the future. But, critically, don’t spend so much time networking and writing grants that you diminish your publications, because these are by far the most important predictor of success in obtaining your next position. Thus, you need to find the sweet spot between not rushing and not dragging. My former student Ben suggests a nice analogy: “I find myself with the mental image of a pendulum swinging; when you graduate, you want to be on the upswing, but not yet to that pause at the top of the swing where you no longer have any momentum.”


I am have encouraged to also talk about personal interactions, such as when you and your supervisor don’t get along. Or you hate your lab/office/field mates. Much has been written on these points by others and I am not going to get into them here. They are simply too context-specific to fit into this rapid point-by-point style of advice. If you really want to work through these issues, I suggest you first talk to the people with whom you are having trouble and then talk confidentially to a neutral third party (another prof, for example). And always be respectful, regardless of how you feel about someone.

Previous posts in this "How to" series

From the Bruna Lab
From Dynamic Ecology
From the Hall Lab
From Aerin Jacob
From the Keogh Lab
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