#ESA2014: Day two, what are we measuring and how?

It's probably in part because I attended sessions that are along similar lines today, but I noticed a common theme played across a number of talks. Ecological data is in some ways becoming very complex - a single analysis may include traits, phylogenetic distances, and taxonomic information, and climate and soil variables, possibly at multiple spatial scales. How to combine disparate data appropriately and how to determine the comparable "scales" across which to measure each variable is more important than ever. But it is still difficult to determine what an appropriate comparison actually is.

Studies of intraspecific variation frequently have to determine how to measure and compare variables. (i.e. Do you measure intraspecific trait variation at the genotype level, the individual level, etc?) For example, in a nice talk by Jessica Abbott, the effects of intraspecific variation in genetic relatedness and trait similarity on intraspecific competition among eelgrass hit upon exactly this point. There was no relationship between trait similarity between genotypes and their degree of genetic relatedness. Traits, not relatedness, were the clearest predictor of competitive success. A number of the talks I saw today incorporated intraspecific variation, including a couple of excellent talks on Daphnia by Sarah Duple and Chris Holmes. Both of the Daphnia talks found evidence of great intraspecific trait variation in the Daphnia but weak relationships between that variation and competitive interactions or diversity. These talks were all nice examples of how empirical work can relate to larger ecological theory, and found fairly mixed evidence for the importance of intraspecific variation. There are many reasons why intraspecific variation is not always strongly tied to ecological processes - intraspecific variation may simply have low explanatory power, for example. But it is also interesting to consider the issues that arise as we ask questions at ever smaller and more precise scales. How do we distinguish a low importance of intraspecific variation, or trait variation, or phylogenetic variation from incorrect scale of measurement? Asking questions with multiple measures opens up new and important issues - how should we measure genetic relatedness to be truly comparable to trait variation at intraspecific or interspecific scales? How does combining mismatched variables (intraspecific trait values with interpolated large scale environmental values, for example) affect the explanatory power of those variables? Given the increasingly multi-faceted nature of ecological analyses it seems important that we consider these questions.


#Lauren Shoemaker
I started Day 2 of ESA attending talks focusing on quantifying coexistence mechanisms and the role of intraspecific competition in coexistence. Yue Li and Peter Chesson started the day presenting work quantifying the storage effect in three desert winter annuals in Arizona’s Goldwater Range. This work highlighted the methodology for quantifying the storage effect in empirical systems—which was refreshing for me since I spend so much time thinking about spatial storage mechanisms in simplified, theoretical systems.

In the same session, Peter Adler presented his work with Chengjin Chu examining the strength of stabilizing niche differences and fitness differences. When stabilizing niche differences are too low relative to fitness differences, competitive exclusion occurs, while high stabilizing niche forces create coexistence. Using long-term demographic data of perennial grasses from five communities, they found that all species exhibited high niche differences and low fitness differences, creating high coexistence strength. For all communities, stabilizing niche differences likely resulted from recruitment. The high niche differentiation highlights the need for a stronger focus on intraspecific density dependence and for more models of coexistence with explicit intraspecific competition.

In the afternoon, Louie Yang argued that ecologists as a whole need to more explicitly consider changes in species interaction through time, especially with increasing effects of climate change. Using an example of 17-year cicada cycles, he showed that questions of “bottom up or top-down” are often really bottom up and then top-down when viewed in a temporally explicit framework. He even ended his talk with an excellent analogy comparing historic artwork and ecology—a hard analogy to pull off!

As an added bonus, I finished the day with a long list of paper citations to look up and read after the conference.

#ESA2014: Day 1, just getting started

First off, apparently I wrote that I would be 'live blogging ESA'. Actually, all that means is that, I'm alive, I'm blogging, and I'm at ESA. :-)

Secondly, several other people will be giving snippets from their days this week, including Lauren Shoemaker, and Geoff Legault (below).

The first day is always more about the experience than the content: you are often lost, have no firm idea of where you need to be, and are constantly running into friends and acquaintances. It's great, but not conducive to settling into talks.

For that reason, I'll just mention the experiences that I found most exciting today. First, I saw a number of Ignite talks. These are a recent addition to ESA and are basically 5 minute talks using slides that advance every 15s. This requires a certain ability on the part of the speaker to be brief and yet informative, minimalist but not inaccurate, practiced, but not robotic. I thought that many of the speakers in the Ecosystems in the Third National Climate Assessment achieved this. One speaker, Linda Joyce said -  "if you want to feel like a graduate student again, sign up for an Ignite talk." Presumably because it makes you feel nerves like you haven't felt in years!

Joyce gave a great talk, as did others. Some of the conversation around the ecosystem assessment fell into the discourse that ecosystems provide services, and services imply people. Are ecosystem assessments only about people? Obviously this is too challenging a topic for a 5 minute talk, but it certainly sparks to further discussion on the topic, as it was meant to.

The second session of interest to me was an organized symposium in which early career scientists gave talks about their work. The central thread was simply that all of the speakers were pre-tenure academics. This really worked as a theme to tie the session together. At the end, the speakers answered questions briefly about their careers, advice, and research. Their best advice was really very good, if in line with what you here on attempting a job in academia. Find mentors. Set boundaries between your personal and private life. Say no sometimes, if it means maintaining some sort of sanity (e.g travel less, have more time with your family). A point that came up multiple times was simply, you have to have passion for science, have to love talking about your work. Having something you're passionate about is better than having ten things you are lukewarm on. And always find people to collaborate with, to talk with, to support.

Finally, there are many paths to success. And failure is universal, but not final.

(My favourite quote - someone who mentioned measuring effort in 'undergraduate work hours')

#Lauren Shoemaker

ESA had some excellent talks to start the 99th conference in Sacramento, California. I stayed in Community Assembly and Neutral Theory for several talks before running back and forth between the Hyatt, Sheraton, and conference center (missing the first few minutes of several talks).

In Community Assembly, Maria Stockenreiter gave a fantastic talk on community assembly in phytoplankton communities while building on the theory of Miller et al. (2009) examining the role of unsuccessful invaders in shaping communities. Even unsuccessful invaders within a community can alter environmental conditions or species distributions such that an unsuccessful invasion can exclude a current or future potentially successful invader. Maria tested this theory using two phytoplankton communities—a lab strain with no shared ecological history and a Gull lake community with shared history. While all invaders were unsuccessful in the experiments, they had large effects on community diversity. Unsuccessful invasion decreased diversity in the lab strains but increased diversity in the Gull Lake community, showing both the “ghost effect” of competition and the role of shared ecological histories.

In Paleoecology, Matthew Knope examined the functional diversity-taxonomic diversity relationships for marine animals during the past 500 million years. It was fun to think of a relationship I only consider in current-times over such a long timescale. Matthew categorized marine mammals according to their location in a discrete 3-dimensional niche space (tiering on sea floor, feeding mode, and motility). The data show that the amount of functional diversity was far lower than expected based on taxonomic diversity until only recently. Additionally, I was amazed to see a consistent trend (from 3 different mass extinctions in the dataset) that mass extinctions promote functional diversity 10-20 million years post extinction leading to even higher functional diversity than pre-extinction.

Back at the convention center in the Biodiversity I session, Pascal Niklaus examined if interspecific vertical canopy space partitioning promoted productivity in subtropical forests. While light is a directional resource, creating a large advantage for being tall, Pascal found that vertical niche partitioning still occurred when comparing monocultures to multiple species assemblages. Species in higher diversity communities also had narrower niches, and similar species shifted their vertical leaf biomass niche, but only in shaded treatments. Vertical niche partitioning did, indeed, promote higher ecosystem function.

#Geoff Legault

I arrived in Sacramento this afternoon so I did not get a chance to see many talks (though I did enjoy Meghan Duffy’s talk about possible hydra effects in Daphnia). I did, however, see a number of excellent posters, particularly one by Nick Rasmussen on the interactive effects of density and phenology on the recruitment of toads. I was impressed by his use of mesocosms to directly manipulate these factors and found that he made a compelling case for the idea that the degree of synchrony in hatching can determine which form of intraspecific competition dominates recruitment.

#ESA2014 : Getting ready for (and surviving) ESA

There is less than one week until ecology's largest meeting. ESA's annual meeting starts August 10th in Sacramento, California, and it can be both exciting and also be overwhelming in its size and scope. Here are a few suggestions for making it a success.

Getting ready for ESA.
Sure, things start in a week and you're scheduled for a talk/poster/meeting with a famous prof, but you haven't started preparing yet.

First off, no point beating yourself up for procrastinating: if you've been thinking about your presentation but doing other projects, you might be in the company of other successful people.

If you're giving a talk, and given it before or are an old hand at this sort of thing, go ahead and put it together the night before your talk. One benefit for the truly experienced or gifted speaker is that this talk will never sound over-rehearsed.

Regardless, all speakers should try for a talk that is focused, with a clear narrative and argument, and within the allotted time. (Nothing is more awkward for everyone involved than watching the moderate have to interrupt a speaker). The good news is that ESA audiences will probably be a) educated to at least a basic level on your topic, and b) are usually generous with their attention and polite with their questions. This blog has some really practical advice on putting together an academic talk.
If at all possible, practice in front of a friendly audience ahead of time.

The questions after your talk will vary, and if you're lucky they will relate to future directions, experimental design, quantitative double-checks, and the truly insightful thoughts. However, there other common questions that you should recognize: the courtesy question (good moderators have a few in hand), the "tell-me-how-it-relates-to-my-work" question, and the wandering unquestion.

Giving a poster is much different than giving a talk, and it has pros and cons. First, you have to have it finished in time to have it printed, so procrastination is less possible. Posters are great if you want one-on-one interactions with a wide range of people. You have to make your poster attractive and interesting: this always means don't put too much text on your poster. The start of this pdf gives some nice advice on getting the most out of your poster presentation.

For both posters and presentations, graphics and visual appeal make a big difference. Check out the blog, DeScience, which has some great suggestions for science communication.

Academic meetings. These run the gamut from collaborators that you're just catching up with, to strangers that you have contacted to meet to discuss common scientific interests. If scientists that you share common research activities and interests with are attending ESA, it never hurts to try to meet with them. Many academics are generous with their time, especially for young researchers. If they say yes, come prepared for the conversation. If necessary, review their work that relates to your own. Come prepared to describe your interests and the project/question/experiment you were looking for advice on. It can be very helpful to have some specific questions in mind, in order facilitate the conversation.

What to wear. Impossible to say. Depending on who you are and wear you work normally, you can wear anything from torn field gear and binos to a nice dress or suit (although not too many people will be in suits).

Surviving ESA.
ESA can be very large and fairly exhausting. The key is to pace yourself and take breaks: you don't need to see talks all day long to get your money's worth from ESA. Prioritize the talks that you want to see based on things like speaker or topic. Sitting in on topics totally different from those you study can be quite energizing as well. In this age of smartphones, the e-program is invaluable.

Social media can help you find popular or interesting sounding talks, or fill you in on highlights you missed. This year the official hashtag on twitter is #ESA2014.

One of the most important things you can do is be open to meeting new people, whether through dinner and lunch invites, mixers, or other organized activities. Introverts might cringe a little, but the longest lasting outcome from big conferences is the connections you make there.

Eat and try to get some sleep.

**The EEB & Flow will be live-blogging during ESA 2014 in Sacramento, as we have for the last few years. See everyone in Sacramento!**

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 :)

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.

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.

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.

Guide for writing the ultimate ecology introduction**

**Or find better advice here, here, here.

If only it were so easy...

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.

Example Clicker Questions from sarahseiter

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

Resources

Videos on Flipped Classrooms:
https://www.youtube.com/watch?v=EMhJcwvmamY

Resources for Clicker Qs:
Clicker Question Guides from University of Colorado Boulder
http://www.colorado.edu/sei/documents/clickeruse_guide0108.pdf
http://www.slideshare.net/stephaniechasteen/writing-great-clicker-questions
Vanderbilt:
http://cft.vanderbilt.edu/guides-sub-pages/clickers/

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.

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.

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

There 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.

References

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

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.

Guest Post: You teach science, but is your teaching scientific? (Part I)

The first 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. 

As a faculty member teaching can sometimes seem like a chore – your lectures compete with smartphones and laptops. Some students see themselves as education “consumers” and haggle over grades. STEM (science, technology, engineering, and math) faculty have a particularly tough gig – students need substantial background to succeed in these courses, and often arrive in the classroom unprepared. Yet, the current classroom climate doesn’t seem to be working for students either. About half of STEM college majors ultimately switch to a non-scientific field. It would be easy to frame the problem as one of culture – and we do live in a society that doesn’t always value science or education. However, the problem of reforming STEM education might not take social change, but rather could be solved using our own scientific training. In the past few years a movement called “scientific teaching” has emerged, which uses quantitative research skills to make the classroom experience better for instructors as well as students.

So how can you use your research skills to boost your teaching? First, you can use teaching techniques that have been empirically tested and rigorously studied, especially a set of techniques called “active learning”. Second, you can collect data on yourself and your students to gauge your progress and adjust your teaching as needed, a process called “formative assessment”. While this can seem daunting, it helps to remember that as a researcher you’re uniquely equipped to overhaul your teaching, using the skills you already rely on in the lab and the field. Like a lot of paradigm shifts in science, using data to guide your teaching seems pretty obvious after the fact, but it can be revolutionary for you and your students.

What is Active Learning:

There are a lot of definitions of active learning floating around, but in short active learning techniques force students to engage with the material, while it is being taught. More importantly, students practice the material and make mistakes while they are surrounded by a community of peers and instructors who can help. There are a lot of ways to bring active learning strategies to your classroom, such as clicker response systems (handheld devices that allow them to take short quizzes throughout the lecture). Case studies are another tool: students read about scientific problems and then apply the information to real world problems (medical and law schools have been them for years). I’ll get into some more examples of these techniques in post II; there are lots of free and awesome resources that will allow you to try active learning techniques in your class with minimal investment.

Formative Assessment:

The other way data can help you overhaul your class is through formative assessment, a series of small, frequent, low stakes assessment of student learning. A lot of college courses use what’s called summative assessment – one or two major exams that test a semester’s worth of material, with a few labs or a term paper for balance. If your goal is to see if your students learned anything over a semester this is probably sufficient. This is also fine if you’re trying to weed out underperforming students from your major (but seriously, don’t do that). But if you’re interested in coaching students towards mastery of the subject matter, it probably isn’t enough to just tell them how much they learned after half the class is over. If you think about learning goals like we think of fitness goals, this is like asking students to qualify for the Boston marathon, without giving them any times for their training runs.

Formative assessment can be done in many ways: weekly quizzes or taking data with classroom clicker systems. While a lot of formative assessment research focuses on measuring student progress, instructors have lots to gain by measuring their own pedagogical skills. There are a lot of tools out there to measure improvement in teaching skills (K-12 teachers have been getting formatively assessed for years), but even setting simple goals for yourself (“make at least 5 minutes for student questions”) and monitoring your progress can be really helpful. Post III will talk about how to do (relatively) painless formative assessment in your class.

How does this work and who does it work for:

Scientific teaching is revolutionary because it works for everyone, faculty and students alike. However, it has particularly useful benefits for some types of instructors and students.

New Faculty: inexperienced faculty can achieve results as good or better than experienced faculty by using evidence based teaching techniques. In a study at the University of Colorado, physics students taught by a graduate TA using scientific teaching outperformed those taught by an experienced (and well loved) professor using a standard lecture style (you can read the study here). Faculty who are not native English speakers, or who are simply shy can get a lot of leverage using scientific teaching techniques, because doing in-class activities relieves the pressure to deliver perfect lectures.

Test scores between a lecture-taught physics section
and a section taught using active learning techniques.

Seasoned Faculty: For faculty who already have their teaching style established, scientific teaching can spice up lectures that have become rote or help you address concepts that you see students struggle with year after year. Even if you feel like you have your lectures completely dialed in, consider whether you’re using the most cutting edge techniques in your lab, and if you your classroom deserves the same treatment.

Students also stand to gain from scientific teaching, and some groups of students are particularly poised to benefit from it:
Students who don’t plan to go into science: Even in majors classes, most of the students we teach won’t go on to become scientists. But skills like analyzing data, and writing convincing evidence based arguments are useful in almost any field. Active learning trains students to be smart consumers of information, and formative assessment teaches students to monitor their own learning – two skills we could stand to see more of in any career.

Students Who Love Science: Active learning can give star students a leg up on the skills they’ll need to succeed as academics, for all the reasons listed above. Occasionally really bright students will balk at active learning, because having to wrestle with complicated data makes them feel stupid. While it can feel awful to watch your smartest students struggle, it is important to remember that real scientists have to confront confusing data every day. For students who want research careers, learning to persevere through messy and inconclusive results is critical.

Students who struggle with science: Active learning can be a great leveler for students who come from disadvantaged backgrounds. A University of Washington study showed that active learning and student peer tutoring could eliminate achievement gaps for minority students. If you partially got into academia because you wanted to make a difference in educating young people, here is one empirically proven way to do that.

Are there downsides?

Like anything, active learning involves tradeoffs. While the overwhelming evidence suggests that active learning is the best way to train new faculty (the white house even published a report calling for more of it!), there are sometimes roadblocks to scientific teaching.

Content Isn’t King Anymore: Taking time to work with data, or apply scientific research to policy problems takes more time, so instructors can cover fewer examples in class. In active learning, students are developing scientific skills like experimental design or technical writing, but after spending an hour hammering out an experiment to test the evolution of virulence, they often feel like they’ve only learned about “one stupid disease”. However, there is lots of evidence that covering topics in depth is more beneficial than doing a survey of many topics. For example, high schoolers that studied a single subject in depth for more than a month were more likely to declare a science major in college than students who covered more topics.

Demands on Instructor Time: I actually haven’t found that active learning takes more time to prepare –case studies and clickers actually take a up a decent amount of class time, so I spend less time prepping and rehearsing lectures. However, if you already have a slide deck you’ve been using for years, developing clicker questions and class exercises requires an upfront investment of time. Formative assessment can also take more time, although online quiz tools and peer grading can help take some of the pressure off instructors.

If you want to learn more about the theory behind scientific teaching there are a lot of great resources on the subject:

These podcasts are a great place to start:
http://americanradioworks.publicradio.org/features/tomorrows-college/lectures/

http://www.slate.com/articles/podcasts/education/2013/12/schooled_podcast_the_flipped_classroom.html

This book is a classic in the field:
http://www.amazon.com/Scientific-Teaching-Jo-Handelsman/dp/1429201886

By Sarah Seiter