Tuesday, July 15, 2014

What papers influenced your journey as an ecologist?

For ESA’s centennial year, they are running a pretty cool series called “The Paper Trail”. A variety of ecologists write about the particular paper or papers that catalyzed their research path. Sometimes the papers are valuable for bringing up particular questions, sometimes they facilitated the connection of particular ideas.

William Reiner provides some insight into the value of this exercise: “What are some of the generalizations one can deduce from this paper trail? For me there are five. First, in ecology one cannot take too large a view of the problem one is addressing. Second, it is useful to step out of one's science into others to gain useful new ways of addressing questions. Collaboration with others outside ones field facilitates this complementarity. Third, teaching provides a useful forum for developing one's ideas. Fourth, there is no literature that is too old to have no value for current issues. And fifth, one must take time to read to be a thoughtful, creative scholar.”

In general, people are writing about papers that either specifically related to their own research at the time and opened their eyes to something new, or else broadly inspired or fascinated them at a critical time. (For Lee Frelich, this was reading The Vegetation of Wisconsin, an Ordination of Plant Communities at 12 years old.) I probably fall into the second group. My undergrad degree was in general biology and math, so although I had taken a couple of ecology courses, I knew essentially nothing about the fundamentals of ecological literature. So I was an impressionable PhD student, and I read a lot of papers. When I started, my plan was to do something related to macroecology, and the first paper I remember being excited about was James H. Brown’s 1984 “On the Relationship between Abundance and Distribution of Species”. It is everything a big idea paper should be – confident, persuasive, suggesting that simple tradeoffs may allow us to predict broad ecological patterns. And while with time I feel that some of the logic in the paper is flawed or at least unsupported, it definitely is a reminder of how exciting thinking big can be (and 1870 citations suggests others agree).

The next paper was R.H. Whittaker’s “Gradient analysis of vegetation” (1967). There is a lot of recommend in Whittaker’s work, in particular the fact that it straddles so well modern ecology and traditional ecology. He introduces early multidimensional analyses of plant ecology and asks what an ecological community is, while also having such a clear passion for natural history.

Finally, and perhaps not surprisingly, the biggest influence was probably Chesson (2000) “Mechanisms of the maintenance of species diversity”. The value of the ideas in this paper is that they can (and have) be applied to many modern ecological questions. In many ways, this felt like the most important advance in ecological theory in some time. It is also the sort of paper that you can read many times (and probably have to) and still something new every time.

Of course, there are many other papers that could be on this list, and I’ve probably overlooked something. Also, makes me miss having free time to read lots of papers :)

Monday, July 7, 2014

Phylogeny, competition and Darwin: a better answer?

*Sorry for the low frequency of posts these days – I seem to be insanely busy this summer 

Oscar Godoy, Nathan Kraft, Jonathan Levine. 2014. Phylogenetic relatedness and the determinants of competitive outcomes. Ecology Letters.

Ecology is hard in part because of the things we can’t (at least easily) measure: fitness, interaction strengths, and the niche, all fundamental ecological concepts. Since we are unable to measure these concepts directly, ecologists have come up with proxies and correlates. Take Darwin’s hypothesis that competition should be greater between closely related species. It relies a chain of assumptions about proxy relationships – first that relatedness should correlate with greater similarity of traits, secondly that similar traits should correlate with greater niche overlap. The true concept of interest, the niche, is un-measurable (if it is an n-dimensional hypervolume) so instead shared evolutionary history provides possible insight into species coexistence.

Ecophylogenetic studies have adopted Darwin's hypothesis as an example of how  molecular phylogenies may provide information about evolutionary history which in turn informs current ecological interactions. Phylogenies ideally capture feature diversity, and so (all things being equal) should provide information about similarity between species based on their relationship.  Despite this, studies have been mixed in terms of finding the relationship predicted by Darwin between phylogenetic relatedness and competition. It is not clear whether this mixed result suggests problems with the phylogenetic approaches being used, or non-generality of Darwin’s hypothesis.

Oscar Godoy, Nathan Kraft, and Jonathan Levine attempt to explore this question once again, but through the lens of Chesson’s coexistence framework (2000). Chesson’s framework describes competitive differences between species not as a single quantity, but instead the outcome of both stabilizing niche differences and equalizing fitness differences between species. This framework predicts that competitive differences should be greatest when species have similar niches (low stabilizing niche differences) and/or when they have large differences in fitness. This divisions alters the predictions from Darwin's hypothesis: if closely related species have similar niches, they should compete more strongly, but on the other hand, if closely related species have similar fitnesses, they should compete less strongly. Darwin’s hypothesis as it has been tested may be too simplistic.

The authors used an experiment involving 18 California grassland species to look at first, whether competitive ability is conserved, and more generally to explore whether phylogenetic distance predicts “the niche differences that stabilize coexistence and the fitness differences that drive competitive exclusion?” Further, can this information be used to predict the relationship between phylogeny and competitive outcomes? To determine this, they quantified germination, fecundity, seed survival, and interaction coefficients for the 18 species based on competition with different competitors (both by identity and density), and quantified the strength of stabilizing and equalizing forces (as in previous works). With this information, they calculated for each species the average fitness and ranked species in a competitive hierarchy using a fully parameterized annual plant population model. Species’ competitive rank did in fact show a phylogenetic signal (Figure 1), and the strongest competitors were clustered in the Asteraceae and its sister node.
Fig 1. Relationship between competitive rank among the 18 CA grassland species.
Competitive rank was then decomposed into fitness differences and niche differences. Fitness differences showed the clearest relationship with phylogeny - distantly related competitors had significantly greater asymmetries in fitness, closely related species had similar fitnesses (Figure 2). However stabilizing niche differences showed no phylogenetic signal at all (Figure 3, solid line).
Fig. 2. Relationships between fitness differences and phylogenetic distance.
Fig 3. Solid line - observed niche distances as a function of phylogenetic distance. Dashed line, size of distances actually needed to assure coexistence.
The authors could then calculate, for a given pair of species with a given phylogenetic distance, the expected fitness difference (based on the fitness difference-phylogeny relationship), and given this, the amount of stabilizing niche differences that would be necessary to prevent competitive exclusion between pairs of species. When they did this, they found that the required stabilizing niche differences were much larger than those that actually existed between the plants. This was especially true between distant related species(dashed line, Figure 3). Darwin’s hypothesis, that closely related species should be more likely to coexist, seemed to be reversed for these species.

How should we interpret these results more broadly? Is this reinforcement of the use of phylogenetic information to answer ecological questions, provided the questions are asked correctly? One of the most interesting contributions of this paper is their discussion of the oft-seen, but poorly incorporated, increase in variation in a trait (here fitness differences) as phylogenetic distances increase. This uneven variance often leads to phylogenetic-trait correlations being labelled non-significant, since it violates the assumptions of linear models. In contrast, here the authors suggest that this uneven variance is important. “For example, even if on average, both niche and fitness differences increase with phylogenetic distance, the increasing variance in these relationships means that only distant relatives are likely combine large competitive asymmetries with small niche differences (rapid competitive exclusion), or large niche differences with small competitive asymmetries (highly stable coexistence). Overall, our results suggest that increasing variance in niche or fitness differences with phylogenetic distance may play a central role in determining the phylogenetic relatedness of coexisting species.”

This discussion is important for questions about phylogenetic relatedness and coexistence – variability is part of the answer, not evidence against the existence of such relationships. However, a few caveats seem important: Because fitness differences and niche differences as defined in the Chesson framework may not be easily associated with traits (since a single trait might contribute to both components), it seems that it will be a little difficult to expand these analyses to less rigourous experimental settings. This might also be important to hypothesize how fitness or niche differences per se become associated with phylogenetic differences, since traits/genes are actually under selection. But the paper definitely provides an interesting direction forward.

Chesson, P. 2000. Mechanisms of maintenance of species diversity. Annual Review of Ecology and Systematics 31:343-366.

Friday, June 20, 2014

Gordon Conference: Unifying Ecology Across Scales

The Gordon Conference on Unifying Ecology Across Scales is open for registration. It runs July 20-24, and should be an interesting meeting and well worth going.

Tuesday, June 10, 2014

Valuing Toronto's urban forest: seeing the forest for the trees

Many news outlets in Toronto reported on a study released by the chief economist at TD bank about the value of urban trees in Toronto. Toronto has been called ‘the city in a park’ because of the heavily forested urban landscape we have here (though when you Google ‘city in a park’ a plethora of cities have the same view of themselves). The value of Toronto’s urban forest estimated by the economists was 7 billion dollars. This seems like an astronomical amount, and that a large bank is the one forwarding this view of the value of an urban forest is in itself an amazing development (note: I do have an obvious conflict of interest as my professorship is endowed by TD).

TD's valuation of the urban forest relied on per-species estimates of net benefits, including carbon sequestration, air quality improvement, storm water flow, and energy savings though shading of buildings. These economic returns more than justify municipal expenses for maintaining parks and urban trees. This approach to quantifying the value of trees has been forwarded by new initiatives such as iTree that provide information on the benefits of tree species. The TD report does go on to say that there are other unquantified benefits of the urban forest such as aesthetic values and importance to communities. But the question is, is cumulative economic benefit a sum of individual trees or is there something more to a forest?

While individual trees have clear economic benefits, captured nicely in the report, and which often increase with the age or size of the tree, there may be direct economic benefit from forested lands that is greater than the sum of the individual trees. In essence, we need to see the additional value of the forest for the trees. Individual trees do not make a forest, and there is something special about a forest.

The simplest way in which a forest supplies additional value is through diversity effects. Different tree species may utilize differing resources or niches and by occurring together are able to turn more of the total local resources into growth, thus sequestering more carbon dioxide then if they were grown alone or only with other trees of the same species. As an example, if you grow a tall canopy tree and a medium shade tolerant species underneath it, the cumulative energy savings through shading are much greater than growing two tall canopy trees or two medium shade tolerant trees. This is often referred to as ‘complementarity’
Photo I took while on a hike in Toronto's Rouge National Urban Park

More than species complementing one another, in forests we often see species facilitating one another, meaning that individual trees perform better with other tree species around it, then when grown alone. Again, using carbon sequestration as the example, facilitation means that more carbon is taken up then when trees are isolated from one another.

Forests also provide habit for other plants and animals that individual trees do not provide. A forest can also better support pollinators by including different tree species that flower at different times of the season. Further, forests provide recreational activities (biking, hiking, camping, etc.) that are economically measurable (gas costs to travel there, user fees, tax support, etc.).

Taken all together, intact forests supply even greater economic, health, and environmental benefits than individual trees. If the trees of Toronto are valued at 7 billion dollars, then the forests of Toronto must be worth much more.

Monday, May 19, 2014

Guest Post: You teach science, but is your teaching scientific? Part 2: Flipping your class.

The second in a series of guest posts about using scientific teaching, active learning, and flipping the classroom by Sarah Seiter, a teaching fellow at the University of Colorado, Boulder. 

When universities first opened in the middle ages, lecturing was the most cutting edge information technology available to a professor - books were copied by hand so the fastest way to transfer information was to talk at your students (see the awesome TED talk below for a breakdown of how universities can and should change). Lecturing is still the default at most universities, and faculty spend hours developing their lecture skills. But studies have shown over and over again that lecturing is one of the worst possible ways to get students to learn. This means that our most accomplished scientists are working like crazy to master a method of teaching that is straight up medieval.
Lecturing isn’t going away any time soon, but you do a lot for your classes by incorporating active learning techniques, sometimes called “flipping” a class. The main feature of a flipped class is that students do the knowledge acquisition (the lecture-like) part of the course at home, and then do “homework” in the classroom with the instructor and peers to help them apply knowledge.

Flipped Classroom Fears:

Instructors often imagine a Lord of The Flies style scenario when they start flipping their classrooms, but this isn’t usually the case. In fact, most students are actually so conditioned to sit quietly in class that it can be difficult to get them to talk about the material. However, there are a few things you can do to get students in the frame of mind for productive discussion.
Flipping your classroom will probably not result­ in chaos. Nobody is going to smash the conch shell and kill Piggy, but they might learn something.
  • Start small: If you’re just getting into transforming your class, it can be helpful to start with something small, like flipping once a week.
  • Get extra staff: Since group work key to flipped classroom, it helps to have extra staff to facilitate peer discussions. If you have graduate TAs, consider deputizing them to lead group exercises. If your university has an undergrad TA program, get as many as you can and spend a day training them on how to ask good questions and facilitate conversations.
  • Explain to students why flipping works: Students will sometimes complain if they’re used to sitting passively in lecture, and they’re suddenly forced to do homework in class. But flipping builds skills that they’ll need in the workplace or graduate school, so reemphasizing what they’re gaining can help get them to buy in. 

Tools For Flipping: Case Studies

Case studies usually involve taking scientific data or ideas and then applying them to a real world situation (medical, law and business schools have been using them for years). Case studies are all over the internet, although the largest clearing house is the National Case Study Library (the American Museum of Natural History, the National Geographic Society, the Smithsonian, and the Understanding Evolution project at Berkeley also have great resources). The National Case Study Library is the largest and is searchable by topic and age, and includes teaching notes for each case, and can be a great place to get started.

Picking Case Studies: Some case studies are purely hypothetical, but I tend to gravitate to those that use real data from published studies like this one on the evolution of skin color that uses studies from a lot of disciplines to build to a conclusion, or this one on conservation corridors and meta-populations. A lot of case studies open with a fictional story, but this approach is a little corny for me, and I’d rather focus on the real scientists and their questions (the narrative case studies can also get weird (like this paternity case study that could also be a great Maury episode). In general, just pick things that work for you and your students. 

DIY Case Studies: If you have papers that you already like to teach, then consider turning them into a case study. To do this, I usually write an intro briefly framing the problem or question. Then I give students actual graphs from the paper with follow up questions to help them process the information. It is OK if the study has a few confusing elements; while we often want a clear story to present to our classes, there’s great evidence that using “messy” data builds scientific skills. You may have to modify graphs, or remake them for extra readability. This could mean re-labeling axes to remove jargon (e.g. in a paper on insects, “instar” becomes “developmental stage”). It might mean dropping some treatments (you don’t need 10 nitrogen treatments to understand eutrophication). Usually I follow every graph with 2-3 questions that follow a basic format:
  • Question 1: Ask students to detect any trends or differences in the graph. 
  • Question 2: Have students think of an explanation for the results
  • Question 3: Ask students to apply their “findings” to the question or problem posed in the case study
The above formula is just a starting place so add or alter questions to suit your needs. Sometimes I’ll use two or three graphs, and use the formula above. I usually end with a question that ties all the graphs together, like asking them to recommend a policy solution, or contrast the findings of different researchers.

Using Case Studies In Class: You can prepare students for a case either through a short lecture or through a homework assignment or reading quiz (this can be done using classroom management software like Blackboard, or Sakai). Once students have the background, have them break into groups of two to three, and work through the questions. It can be helpful to stop every few minutes to go through the answers (some case studies build on earlier questions, so early feedback is key). A great feature of case studies is that they can take nearly an entire class period, so you can go an entire day without having to lecture.

Clicker Questions 

The other main tool for flipping your classroom is clicker questions. Clickers are basically a real-time poll of your students so you can check how they are learning. Most instructors use them for participation points, rather than grading them for correctness (this encourages students to jump in and grapple with material, and not worry about making mistakes). Your university might have a set of clickers that you can borrow, or you have students use laptops, tablets and smartphones in place of clicker with apps like Poll Everywhere, GoSoapbox, Pinnion, or Socrative (these have different features and price points, so see what works for you). For a more comprehensive list of clicker tools , see this article from a team at Princeton.

Writing Good Clicker Questions: Good clicker questions should encourage discussion, and force students to apply their knowledge, not just test what they remember. This can mean using information to make recommendations, doing a calculation, or making predictions about the outcome of experiment. Standard clickers only allow for multiple choice questions, but other web-based tools will allow your students to do free responses, draw graphs, or give other types of answers. There are lots of great web resources on how to design clicker questions (in appendix). The slide show below shows some clicker questions we used in our flipped evolution class at CU Boulder.

Using Clickers In Class: Once you have your clicker questions written, then you’ll need to deploy them in class. Below is a basic blueprint for how to run a clicker question

1. Tell students to break into groups and get ready to discuss a clicker question
2. Give students about a minute to discuss the question, and open whatever clicker software you’re using. You’ll usually hear a 30 second surge in talking that dies down after about a minute. After about a minute give students a warning and tell then close out the clicker question.
3. At this point you can show the results of the clicker poll and start to unpack the question. If your questions are challenging, you should be getting significant amounts of wrong answers, so seeing a wide range of answers means you’re doing it right. Usually if 10% of your students are getting the question wrong, it is worth discussing the question in depth
4. Make students be able to articulate why right answers are right and why wrong answers are wrong. You can call on groups to get them to explain their answers (this is nicer than cold-calling individual students). If nobody wants to talk about wrong answers, say something like “why might someone think that B is a tempting answer?” so that nobody has to admit to being wrong in front of their peers.
5. It can be helpful to follow up with another question asking them to apply the material in a different way.

In conclusion, flipping your classroom can be done pretty cheaply and without that much more work than lecturing. This post is really just a starting place, and there are ton of great resources on the web to take you further. I’ve compiled just a few of them below. Good luck and happy flipping!

By Sarah Seiter


Videos on Flipped Classrooms:

Resources for Clicker Qs:
Clicker Question Guides from University of Colorado Boulder

Wednesday, May 14, 2014

Addressing the mental health problem in academia

The Guardian UK is publishing an insightful series this May called “Mental health: a university crisis”, as part of Mental Health Month. Although mental health issues for undergraduates are the focus of a variety of different services and programs at most universities, the Guardian includes a unique focus on the issues of academics—graduate students, postdocs, professors and other researchers—for whom it seems that mental health issues are disproportionately common.

The whole series is an important read, and comes at the issue from many different perspectives. A recent survey of university employees not surprisingly found that academics have higher stress levels than other university employees, which they attribute to heavy workloads (!), lack of support (from the department or otherwise), and particularly for early career researchers, feelings of isolation. One particularly insightful piece (with the tagline "I drink too much and haven't had a good night's sleep since last year. Why? Research") argues that academics have particularly unique problems leading to mental health issues. There are typical issues that many high stress jobs include—the ever-regenerating todo list, and the many teaching, research, and service tasks that academics need to accomplish. But academia also seems to attract a high proportion of intense, perfectionistic, passionate people willing to go the extra mile (and encouraged to, given the difficult job market). Worse, research is a creative, even emotional activity – there are highs and lows and periods of intense work that come at the expense of everything else. Ideas are personal, and so the separation between person and research is very slim. The result is often a lack of work-life balance that might produce academic success, but strains mental health. Mental health issues further have dire implications for most research activities, since the symptoms – loss of motivation, concentration and clarity of thought – affect crucial academic skills.

If such issues are so common in academia (and there’s a form of anxiety ubiquitous among graduate students, the imposter syndrome; other common illnesses include anxiety, depression, and panic attacks), why are most of the lecturers and postdocs writing about their mental health experiences for the Guardian choosing to be anonymous? It still seems common to simply downplay or hide problems with stress and mental illness (in the linked study, 61% of academics with mental health problems say their colleagues are unaware of their problems). This may be a reflection of the fact that academia is focused individual performance and individual reputation. Colleagues choose to work with you, to invite you to their department, to hire you, based in no small part on your reputation. Admitting to having suffered from mental illness can feel like adding an obstacle to the already difficult academic landscape. For many, admitting to struggling can feel like failure, particularly since everyone around them seems to be managing the harsh conditions just fine (whether or not that is really true). Academic workdays have less structure than most, which can be isolating. Academics can keep unpredictable hours, disappear for days, send emails at 2 am, sleep at work, and be unkempt and exhausted without much comment; as a result, it can be difficult to identify those colleagues who are at risk (compared to those who are simply unconventional :-) ).

It will be interesting to see where the Guardian series goes. Mental health issues in academia are in many ways the same as those that have affected women and minorities looking for inclusion in academia – subtle comments or stigma, lack of practical support. I remember once hearing a department chair disgusted a co-author who had failed to respond to emails because they were “certifiably crazy; in a mental hospital”. No doubt that was exactly the response the co-author was hoping to avoid. More subtle but more common is lip-service to work-life balance that is counterbalanced by proud references to how hard one or one’s lab works. There is nothing wrong with working hard, but maybe we should temper our praise of sleeping in the lab, coming in every holiday and weekend. It happens and it may be necessary, but is that the badge of honour we really want to claim? It would be sad if the nature of academia, its competitiveness and atmosphere of masochism (“my students are in the lab on Christmas”) limits progress.

Friday, May 9, 2014

Scaling the publication obstacle: the graduate student’s Achilles’ heel

There is no doubt that graduate school can be extremely stressful and overwhelming. Increasingly, evidence points to these grad school stressors contributing to mental health problems (articles here and here). Many aspects of grad school contribute to self-doubt and unrelenting stress: is there a job for me after? am I as smart as everyone else? is what I’m doing even interesting?

But what seems to really exacerbate grad school stress is the prospect of trying to publish*. The importance of publishing can’t be dismissed. To be a scientist, you need to publish. There are differing opinions about what makes a scientist (e.g., is it knowledge, job title, etc.), but it is clear that if you are not publishing, then you are not contributing to science. This is what grad students hear, and it is easy to see how statements like this do not help with the pressure of grad school.

There are other aspects of the grad school experience that are important, like teaching, taking courses, outreach activities, and serving on University committees or in leadership positions. These other aspects can be rewarding because they expand the grad school experience. There is also the sense that they are under your control and the rewards are more directly influenced by your efforts. Here then, publishing is different. The publication process does not feel like it is under your control and that the rewards are not necessarily commensurate with your efforts.

Cartoon by Nick Kim, Massey University, Wellington, accessed here

Given the publishing necessity, how then can grad students approach it with as little trauma as possible? The publication process will be experienced differently by different people, some seem like they can shrug off negative experiences while others internalize them, with negative experiences gnawing away at their confidence. There is no magic solution to making the publishing experience better, but here are some suggestions and reassurances.

1) It will never be perfect! I find myself often telling students to just submit already. There is a tendency to hold on to a manuscript and read and re-read it. Part of this is the anxiety of actually submitting it, and procrastination is a result of anxiety. But often students say that it doesn’t feel ready, or that they are unhappy with part of the discussion, or that it is not yet perfect. Don’t ever convince yourself that you will make it perfect –you are setting yourself up for a major disappointment. Referees ALWAYS criticize, even when they say a paper is good. There is always room for improvement and you should view the review process as part of the process that improves papers. If you think of it this way, then criticisms are less personal (i.e., why didn’t they think it was perfect too?) and feel more constructive, and you are at peace with submitting something that is less than perfect.

2) Let's dwell on part of the first point: reviewers ALWAYS criticize. It is part of their job. It is not personal. Remember, the reviewers are putting time and effort into your paper, and their comments should be used to make the product better. Reviewers are very honest and will tell you exactly what could be done to improve a manuscript. They are not attacking you personally, but rather assessing the manuscript. 

3) Building on point 2, the reviewers may not always be correct or provide the best advice. It is OK to state why you disagree with them. You should always appreciate their efforts (unless they are unprofessional), but you don’t have to always agree with them.

4) Not every paper is a literature masterpiece. Effective scientific communication is sometimes best served by very concise and precise papers. If you have an uncomplicated, relatively simple experiment, don’t make more complex by writing 20 pages. Notes, Brevia, Forum papers are all legitimate contributions.

5) Not every paper should be a Science or Nature paper (or whatever the top journals are in a given subdiscipline). Confirmatory or localized studies are helpful and necessary. Large meta-analyses and reviews are not possible without published evidence. Students should try to think how their work is novel or broadly general (this is important for selling yourself later on), but it is ok to acknowledge that your paper is limited in scope or context, and to just send it to the appropriate journal. It takes practice to fit papers to the best journals, so ask colleagues where they would send it. This journal matching can save time and trauma.

6) And here is the important one: rejection is ok, natural, and normal. We all get rejections. What I mean by this is that we all get rejections. Your rejection is not abnormal, you don’t suck more than others, and your experience has been experienced by all the best scientists. When your paper is reviewed, and then rejected, there is usually helpful information that should be useful in revising your work to submit elsewhere. Many journals are inundated with papers and are looking for reasons to reject. In the journal I edit, we accept only about 18% of submissions, and so it doesn’t take much to reject a paper. This is unfortunate, but currently unavoidable (though with the changing publishing landscape, this norm may change). Rejection is hard, but don’t take it personally, and feel free to express your rage to your friends.

Publishing is a tricky, but necessary, business for scientists. When you are having problems with publishing, don’t internalize it. Instead complain about it to your friends and colleagues. They will undoubtedly have very similar experiences. Students can be hesitant to share rejections with other students because they feel inferior, but sharing can be therapeutic. When I was a postdoc at NCEAS, the postdocs would share quotes from their worst rejection letters. What would have normally been a difficult, confidence-bashing experience, became a supportive, reassuring experience.

Publishing is necessary, but also very stressful and potentially adding to low-confidence and a feeling that grad school is overwhelming. I hope that the pointers above can help make the experience less onerous. But when you do get that acceptance letter telling you that your paper will be published, hang on to that. Celebrate and know that you have been rewarded for your hard work, but move on from the rejections.

*I should state that my perspective is from science, and my views on publishing are very much informed by the publishing culture in science. I have no way of knowing if the pressures in the humanities or economics are the same for science students.

Tuesday, April 29, 2014

Unexpected effects of global warming in novel environments: butterflies emerge later in warming urban areas.

ResearchBlogging.orgThere is now ample evidence that warming temperatures cause advances in the timing of organismal activity (i.e., phenology). Studies have shown that rising temperatures are responsible for earlier plant leafing and flowering (Miller-Rushing & Primack 2008, Wolkovich et al. 2012), pest insect emergence and abundance (Willis et al. 2008), and even local species loss and reduced diversity (Willis et al. 2008). One emerging expectation from global warming studies is that insects should emerge earlier since winters are milder and spring temperatures are warmer. This expectation should hold so long as high temperatures or other environmental stressors don’t adversely affect the insects. And the concern about shifts in emergence and insect activity is the potential for mismatches between plant flowering and the availability of pollinators (Willmer 2012) –if insects emerge too soon, they may miss the flowers.

Photo by Marc Cadotte

In a forthcoming paper in Ecology by Sarah Diamond and colleagues study 20 common butterfly species across more than 80 sites in Ohio. These sites were located in a range of places across a rural to urban gradient. Instead of finding earlier emergence in warmer places, which were typically urban areas, they found that a number of species were delayed in warmer urban areas. Even though the butterflies might emerge earlier in warmer rural habitats, they were adversely affected in urbanized areas. 

These results highlight the need to consider multiple sources of stress from different types of environmental change. Observations from a few locales or from controlled experiments may not lead to conclusions about interactive influences or warming and urbanization, and that's why this study is so important. It observes a counter-intuitive result because of the influence of multiple stressors. 

A next step should be to determine if pollinator-plant interactions are being disrupted in these urban areas. The reason why we should care so much about pollinator emergence is that they provide a key ecological service by pollinator wild, garden, and agricultural plants, as well has being an important food source to other species. A mismatch in timing and disrupt these important interactions.


Diamond S.E., Cayton H., Wepprich T., Jenkins C.N., Dunn R.R., Haddad N.M. & Ries L. (2014). Unexpected phenological responses of butterflies to the interaction of urbanization and geographic temperature. Ecology.

Miller-Rushing A.J. & Primack R.B. (2008). Global warming and flowering times in Thoreau's Concord: a community perspective Ecology, 89, 332-341.

Roos J., Hopkins R., Kvarnheden A. & Dixelius C. (2011). The impact of global warming on plant diseases and insect vectors in Sweden. Eur J Plant Pathol, 129, 9-19.

Willis C.G., Ruhfel B., Primack R.B., Miller-Rushing A.J. & Davis C.C. (2008). Phylogenetic patterns of species loss in Thoreau's woods are driven by climate change. Proceedings of the National Academy of Sciences, 105, 17029-17033.

Willmer P. (2012). Ecology: pollinator-plant synchrony tested by climate change. Curr. Biol., 22, R131-R132.

Wolkovich E.M., Cook B.I., Allen J.M., Crimmins T.M., Betancourt J.L., Travers S.E., Pau S., Regetz J., Davies T.J., Kraft N.J.B., Ault T.R., Bolmgren K., Mazer S.J., McCabe G.J., McGill B.J., Parmesan C., Salamin N., Schwartz M.D. & Cleland E.E. (2012). Warming experiments underpredict plant phenological responses to climate change. Nature, 485, 494-497.

Diamond, S., Cayton, H., Wepprich, T., Jenkins, C., Dunn, R., Haddad, N., & Ries, L. (2014). Unexpected phenological responses of butterflies to the interaction of urbanization and geographic temperature Ecology DOI: 10.1890/13-1848.1

Thursday, April 24, 2014

Data merging: are we moving forward or dealing with Frankenstein's monster

I’m sitting in the Sydney airport waiting for my delayed flight –which gives me some time to ruminate about the mini-conference I am leaving. The conference, hosted by the Centre for Biodiversity Analysis (CBA) and CSIRO in Australia, on "Understanding biodiversity dynamics using diverse data sources", brought together several fascinating thinkers working on disparate areas including ecology, macroecology, evolution, genomics, and computer science. The goal of the conference was to see if merging different forms of data could lead to greater insights into biodiversity patterns and processes. 

Happy integration

On the surface, it seems uncontroversial to say that bringing together different forms of data really does promote new insights into nature. However, this only really works if the data we combine meaningfully complement one another. When researchers bring together data, there are under-appreciated risks, and the resulting effort could result in trying to combine data that make weird bedfellows.
Weird bedfellows

The risks include data that mismatch in the scale of observation, resulting in meaningful variation being missed. Data are often generated according to certain models with specific assumptions, and these data-generation steps can be misunderstood by end-users, resulting in inappropriate uses of data. Further, different data may be combined in standard statistical models, but the linkages between data types is much more subtle and nuanced, requiring alternative models.

Why these are issues stems from the fact that researchers now have an unprecedented access to numerous large data sets. Whether these are large trait data sets, spatial locations, spatial environmental data, genomes, or historical data, they are all built with specific underlying uses, limitations and assumptions.  

Regardless of these issues of concern, the opportunity and power to address new questions is greatly enhanced by multiple types of data. One thing I gained from this meeting is that there is a new world of biodiversity analysis and understanding emerging by smart people doing smart things with multiple data. We will soon live in a world where the data and analytical tools allow research to truly combine multiple processes to predict species' distributions, or to move from evolutionary events in deep history to modern day ecological patterns.