Love the lab you’re with or find the lab you love? Being happy in grad school.

Every grad student is unhappy at some point; existential angst is basically required, hence the success of PhD comics. A surprisingly common reason for grad school unhappiness results when students feel they have diverged from the path they want to be on - that they are somehow in the wrong lab, learning the wrong thing, or working with the wrong person. That they dislike their research. Some people might argue that few people start in their dream job, but grad school is more like an apprenticeship than a 9-5 job: a place to obtain skills and experiences rather than a source of income.

Every unhappy student is different in their own way, but there are a few predictable causes. The path between undergrad and grad student is highly stochastic. Most undergraduates make choices about grad school while under-informed about their options and unclear on where their interests truly lay. Choosing a lab for a PhD or Masters is a huge commitment for an undergrad who has had comparatively limited interactions with ecological research. Academic labs tend to be so specialized that even if an undergrad has had the opportunity and motivation to interact with a number of labs as a student, they have experienced only a tiny fraction of the areas available for study. Students in schools with general biology programs, rather than specialized EEB departments, may be more limited again in the ecological experiences they can have. I can’t help but think that few undergrads are really equipped to make a definite, informed decision about what they want to spend the next 5 years (or more) of their lives doing. Even if they are, a successful graduate student should grow as a researcher and their interests will naturally expand or shift. Expanding interests and changing foci are part of a successful graduate experience, but what initially felt like a good fit may suddenly feel less comfortable. Students may also end up in uncomfortable fits simply because their choices for grad school were limited by geographical constraints or the availability of funded positions, causing them to compromise on their interests.

My own experience moving from undergrad to PhD student was pretty much in line with this. I knew I wanted to go to grad school and I felt reasonably prepared – I had good grades, three years of research experience in a lab, I researched and contacted a few potential supervisors – but I hadn’t specialized in ecology and didn’t exactly know what my interests were. It took more than a year as a PhD student, reading deep into the literature and taking classes, to realize what I really was interested in was completely different than what I was supposed to be doing. This was accompanied by a period of unhappiness and confusion – I had apparently gotten what I wanted (grad school, funding, etc), but it wasn’t what I wanted after all. No one prepared me for this possibility. Eventually, but with some hassle, I changed labs and was lucky to have the opportunity to get the skills I was really wanted.

I don’t think this outcome is anyone’s “fault”. I think most departments and many supervisors are sympathetic to these sort of graduate student issues. Formal advising of undergraduates at the department level, in addition to the usual informal advising that grad students and advisors provide, should be focused on guiding students in determining what they want from grad school (or if they really want grad school at all!), how to identify areas of interest and programs/supervisors that would suit their interests. In particular, they need to be empowered in how to contact potential supervisors and how to discuss the supervisor’s expectations and approach, what changing interests mean on a laboratory and department level, and what resources are available for student who may wish to obtain particular skills not available in the lab. Supervisors benefit too when their students are informed and more likely to be happy and engaged.

The lab rotation system, which some departments have, also seems like a good way to expose students to their options (although I have no personal experience with it). In addition, when grad student funding comes through the department, rather than from individual supervisors, students can change labs with less difficulty. Some supervisors have very relaxed approaches to grad student projects, allowing students to explore their interests well outside of the lab’s particular approach. But other supervisors (or funding sources) are very much organized around a particular project, making it difficult for students to do anything but the project they were hired to work on.

So what is a student who realizes they want to be working with a different system, approach, sub-discipline, or supervisor, supposed to do? How much does unhappiness really matter in the long run? This depends a lot on what a student wants to get out of grad school and what they need to achieve it. One thing students need to do is elucidate what they hope to achieve as a grad student. Though a student may ultimately be unsatisfied with some aspects of their position, they may be able to gain the experiences they want from grad school regardless. There are many tangible and intangible skills students learn in grad school. Students may decide that they want to obtain particular quantitative skills (statistics, ArcGIS, coding and modeling experience, etc, etc) that they want for the job market; if these aren’t available, change may be necessary. On the other hand, even if a student is less interested in the particular system they are working in, it may be possible to obtain experimental and technical skills that are transferrable elsewhere. If students wish to remain in academia, but realize they are interested in a different subdiscipline than where they work, one consideration is whether it will be easier to make the shift now compared when finding a postdoc and attempting to convince a potential employer that their knowledge is transferrable. This is a difficult question – having read a large number of Ecolog post-doctoral position ads, it seems that the request for system-specific experience occurs in about 50% of ads. The need to have a particular skill set (say, Python and R, or experimental design) tends to be mentioned in every ad. So if you want to go from a protist-microcosm PhD to a postdoc in kangaroo ecology, it seems difficult to predict how well your experimental design skills will trump your lack of understanding of Australian ecosystems.

Of course, there is no one-size-fits-all answer about what to do. Sometimes, unhappiness will pass, sometimes it won’t. Students need to be proactive above all. The truth is that sometimes it is better to be willing to drop out, to change labs, or take other drastic action. Students commonly fall victim to the sunk-cost fallacy, the idea that they’ve spent 2 years on this degree, so they might as well not “waste” it. Sometimes it is worth sticking it out, but there should be no stigma in deciding not to.

Testing the utility of trait databases

Cordlandwehr, Verena, Meredith, Rebecca L., Ozinga, Wim A., Bekker, Renée M., van Groenendael, Jan M., Bakker, Jan P. 2013. Do plant traits retrieved from a database accurately predict on-site measurements? Journal of Ecology. 101:1365-2745.

We are increasingly moving towards data-sharing and the development of online databases in ecology. Any scientist today can access trait data for thousands of species, global range maps, gene sequences, population time series, or fossil measurements. Regardless of arguments for or against, the fact that massive amounts of ecological data are widely available is changing how research is done.

For example, global trait databases (TRY is probably best known) allow researchers to explore trait-based measures in communities, habitats, or ecosystems without requiring that the researchers have actually measured the traits of interest in the field. And while few researchers would suggest that this is superior to making the measurements in situ, the reality is that there are many situations where trait data might be required without the researcher being able to make them. In these cases, online databases are like a one-stop shop for data. But despite the increasing frequency of citations for trait databases, until now there has been little attempt to quantify how well database values act as proxies for observed trait values. How much should we be relying on these databases?

There are many well-recorded reasons why an average trait value might differ from an individual value: intraspecific differences result from plasticity, genotype differences, and age or stage differences, all of which may vary meaningfully between habitats. How much this variation actually matters to trait-based questions is still up for debate, but clearly affects the value of such databases.  To look at this question, Cordlandwehr et al. (2013) examined how average trait values calculated with values from a North-west European trait database (LEDA) corresponded with average trait values calculated using in situ measurements. Average trait values were calculated across several spatial scales and habitat types. The authors looked plant communities growing in 70 2m x 2m plots in the Netherlands, divided between wet meadow and salt marsh habitats. In each community, they measure three very common plant traits: canopy height (CH), leaf dry matter content (LDMC), and specific leaf area (SLA).

In situ measurements were made such that the trait value for a given plot was the median value of all individuals measured; for each habitat it was the the median value of all individuals measured in the habitat. The authors calculated the average trait values (weighted by species abundance) across all species for each community (2m x 2m plot) and each habitat (wet meadow vs. salt marsh). They then compared the community or habitat average as calculated using the in situ values and the regional database values. 

From Cordlandwehr et al. 2013. Habitat-level traits at site scale plotted against habitat-level traits calculated using trait values retrieved from a database. 

The authors found the correspondence between average trait values measured using in situ or database values varied with the scale of aggregation, the type of trait and the particular habitat. For example, leaf dry matter content varied very little but SLA was variable. The mesic habitat (wet meadow) was easier to predict from database values than the salt marsh habitat, probably because salt marshes are stressful environments likely to impose a strong environmental filter on individuals, so that trait values are biased. While true that rank differences in species trait values tended to be maintained regardless of the source of data, intraspecific variation was high enough to lead to over- or under-prediction when database values were relied on. Most importantly, spatial scale mattered a lot. In general, database values at the habitat-scale were reasonable predictors of observed traits. However, the authors strongly cautioned against scaling such database values to the community level or indeed using averaged values of any type at that scale: “From the poor correspondence of community-level traits with respect to within-community trait variability, we conclude that neither average trait values of species measured at the site scale nor those retrieved from a database can be used to study processes operating at the plot scale, such as niche partitioning and competitive exclusion. For these questions, it is strongly recommended to rigorously sample individual plants at the plot scale to calculate functional traits per species and community.” 

There are two conclusions I take from this. First, that the correlation between sampling effort and payoff is still (as usual) high. It may be easier to get traits from a database, but it is not usually better. The second is that studies like this allow us to find a middle ground between unquestioning acceptance or automatic criticism of trait databases: they help scientists develop a nuanced view that acknowledges both strengths and weaknesses. And that's a valuable contribution for a study to make.

Navigating the complexities of authorship: Part 2 -author order

Authorship can be tricky business. It is easy to establish agreed upon rules within, say, your lab or among frequent collaborators, but with large collaborations, multiple authorship traditions can cause tension. Different groups may not even agree on who should be included as an author (see Part 1), much less what order they should appear. The number of authors per paper has steadily increased over time reflecting broad cultural shifts in science. Research is now more collaborative, relying on different skill sets and expertise.

 Average number of authors per publication in computer science, compiled by Sven Bittner

Within large collaborations are researchers who have contributed to differing degrees and author order needs to reflect these contribution levels. But this is where things get complicated. In different fields of study, or even among sub-disciplines, there are substantial differences in cultural norms for authorship. According to Tscharntke andcolleagues (2007), there are four main author order strategies:

1.        Sequence determines credit (SDC), where authors are ordered according to contribution.
2.        Equal contribution (ED), where authors are ordered alphabetically to give equal credit.
3.        First-last-author emphasis (FLAE), where last author is viewed as being very important to the work (e.g., lab head).
4.        Percent contribution indicated (PCI), where contributions are explicitly stated.

The main approaches in ecology and evolutionary biology are SDC and FLAE, though journals are increasingly requiring PCI, regardless of order scheme. This seems like a good compromise allowing the two main approaches (SDC & FLAE) to persist without confusing things. However, PCI only works if people read these statements. Grant applications and CVs seldom contain this information, and the perspective from these two cultures can bias career-defining decisions.

I work in a general biology department with cellular and molecular biologists who wholeheartedly follow FLAE. They may say things like “I need X papers with me as last author to get tenure”. As much as I probe them about how they determine author order in multi-lab collaborations, it is not clear to me how exactly they do this. I know that all the graduate students appear towards the front in order of contribution, but the supervisor professors appear in reverse order starting from the back. Obviously an outsider cannot disentangle the meaning of such ordering schemes without knowing who the supervisors were.

The problem is especially acute when we need to consider how much people have contributed in order to assign credit (see Part 3 on assigning credit). With SDC, you know that author #2 contributed more than the last author. With FLAE, you have no way of knowing this. Did the supervisor fully participate in carrying out the research and writing the paper? Or did they offer a few suggestions and funding? The are cases where the head of ridiculously large labs appears as last author on dozens of publications a year, and grumbling from those labs insinuate that the professor hasn’t even read half the papers.

Under SDC, this person should appear as the last author, reflecting this minimal contribution, but this shouldn’t give the person some sort of additional credit.

In my lab, I try to enforce a strict SDC policy, which is why I appear as second author on a number of multi-authored papers coming out of my lab. I do need to be clear about this when my record is being reviewed in my department, or else they will think some undergrad has a lab somewhere. Even with this policy, there are complexities, such as collaborations with other labs we follow FLAE, such as with many European colleagues. I have two views on this, which may be mutually exclusive. 1) There is a pragmatic win-win, where I get to be second author and some other lab head gets the last position and there is no debate about who deserves this last position. But 2) this enters morally ambiguous territory where we each may receive elevated credit depending on whether people look at the order through SDC or FLAE.

I guess the win-win isn’t so bad, but it would nice if there was an unambiguous criterion directing author order. And the only one that is truly unambiguous is SDC –with ED (alphabetical) for all the authors after the first couple in large collaborations. The recent paper by Adler and colleagues(2011) is a perfect example of how this should work.

References:

Adler, P. B., E. W. Seabloom, E. T. Borer, H. Hillebrand, Y. Hautier, A. Hector, W. S. Harpole, L. R. O’Halloran, J. B. Grace, T. M. Anderson, J. D. Bakker, L. A. Biederman, C. S. Brown, Y. M. Buckley, L. B. Calabrese, C.-J. Chu, E. E. Cleland, S. L. Collins, K. L. Cottingham, M. J. Crawley, E. I. Damschen, K. F. Davies, N. M. DeCrappeo, P. A. Fay, J. Firn, P. Frater, E. I. Gasarch, D. S. Gruner, N. Hagenah, J. Hille Ris Lambers, H. Humphries, V. L. Jin, A. D. Kay, K. P. Kirkman, J. A. Klein, J. M. H. Knops, K. J. La Pierre, J. G. Lambrinos, W. Li, A. S. MacDougall, R. L. McCulley, B. A. Melbourne, C. E. Mitchell, J. L. Moore, J. W. Morgan, B. Mortensen, J. L. Orrock, S. M. Prober, D. A. Pyke, A. C. Risch, M. Schuetz, M. D. Smith, C. J. Stevens, L. L. Sullivan, G. Wang, P. D. Wragg, J. P. Wright, and L. H. Yang. 2011. Productivity Is a Poor Predictor of Plant Species Richness. Science 333:1750-1753.

Tscharntke T, Hochberg ME, Rand TA, Resh VH, Krauss J (2007) Author Sequence and Credit for Contributions in Multiauthored Publications. PLoS Biol 5(1): e18. doi:10.1371/journal.pbio.0050018

Why pattern-based hypotheses fail ecology: the rise and fall of ecological character displacement

Yoel E. Stuart, Jonathan B. Losos, Ecological character displacement: glass half full or half empty?, Trends in Ecology & Evolution, Available online 26 March 2013

Just as ecology is beginning to refocus on integrating evolutionary dynamics and community ecology, a paper from Yoel Stuart and Jonathan Losos (2013) suggests that perhaps the best-known eco-evolutionary hypothesis - Ecological Character Displacement (ECD) – needs to be demoted in popularity. They review the existing evidence for ECD and in the process illustrate the rather typical path that research into pattern-based hypotheses seems to be taking.

ECD developed during that period of ecology when competition was at the forefront of ecological thought. During the 1950s-1960s, Connell, Hutchinson and McArthur produced their influential ideas about competitive coexistence. At the same time, Brown and Wilson (1956) first described ecological character displacement. ECD is defined as involving first, competition for limited resources; second, in response, selection for resource partitioning which drives populations to diverge in resource use. Ecological competition drives adaptive evolution in resource usage – either resulting in exaggerated divergence in sympatry or trait overdispersion. ECD fell in line with a competition-biased worldview, integrated ecology and evolution, and so quickly became entrenched: the ubiquity of trait differences between sympatric species seemed to support its predictions. Pfennig and Pfennig (2012) go so far as to say ‘Character displacement...plays a key, and often decisive, role in generating and maintaining biodiversity.’

One problem was that tests of ECD tended to make it a self-fulfilling prophecy. Differences in resource usage are expected when coexisting species compete; therefore if differences in resource usage are observed, competition is assumed to be the cause. In the ideal test, divergent sympatric species would be found experimentally to compete, and ECD could be used to explain the proximal cause of divergence. But the argument was also made that when divergent sympatric species were not found to compete, this was also evidence of ECD, since “ghosts of competition past” could have lead to complete divergence such that competition no longer occurred. This made it rather difficult to disprove ECD.

There was pushback in the 1970s against these problems, but interestingly, ECD didn’t fall out of favour. A familiar pattern took form: initial ecstatic support, followed by critical papers, which were in turn rebutted by new experimental studies. Theoretical models both supported or rebutted the hypothesis depending on the assumptions involved. In response the large literature, several influential reviews were written (Schluter (2000), Dayan and Simberloff (2005)) that appeared to suggest at least partial support for the ECD from existing data. Rather than dimming interest in ECD, debate kept it relevant for 40+ years. And continued relevance translated to the image of ECD as a longstanding (hence important) idea. Stuart and Losos carry out a new evaluation of the existing evidence for ECD using Schluter and McPhail’s (1992) ‘6 criteria’, using both the papers from the two previous reviews and more recent studies. Their results suggest that strong evidence for ECD is nearly non-existent, with only 5% of all 144 studies meeting all 6 criteria. (Note: this isn't equivalent to suggesting that ECD is nearly non-existent, just that currently support is limited. There's a good discussion as to some of the possible reasons that ECD has been rarely observed, in the paper).

From Stuart and Losos (2013). Fraction of cases from Schluter 2000, Dayan and Simberloff 2005, and this study that meet either 4 or all 6 of the criteria for ECD.

The authors note that there are many explanations for this finding of weak support: the study of evolution in nature is difficult, particularly given the dearth of long term studies. The 6 criteria are very difficult to fulfill. But they also make an important, much more general point: character displacement patterns can result from multiple processes that are not competition, so patterns on their own are not indicative. Patterns that result from legitimate ecological character displacement may not show the predicted trait overdispersion. The story of the rise and fall of ECD is a story with applications to many pattern-driven ecological hypotheses. There are many axiomatic relationships you learn about in introductory courses: productivity-diversity hump shaped relationships, the intermediate disturbance hypothesis, ECD, etc, etc. These have guided hypothesis formation and testing for 40 years and have become entrenched in the literature despite criticism. And similarly, there are recent papers suggesting that long-standing pattern-based hypotheses are actually wrong or at least misguided (e.g. 1, 2, 3, etc). Why? Because pattern-driven hypotheses lack mechanism, usually relying on some sort of common-sense description of a relationship. The truth is that the same pattern may result from multiple processes. Further, a single process can produce multiple patterns. So a pattern means very little without the appropriate context.

So have we wasted 40 years of time, energy and resources jousting at windmills? Probably not, data and knowledge are arrived at in many ways. And observing patterns is important - it is the source of information from natural systems we use to develop hypotheses. But it is hopeful that this is a period where ecology is recognizing that pattern-based hypotheses (and particularly the focus on patterns as proof for these hypotheses) ask the right questions but focus on the wrong answers.

Long-term studies of Darwin's finches have provided strong evidence for ECD.

Wine-ing about climate change

Lee Hannah, Patrick R. Roehrdanz, Makihiko Ikegami, Anderson V. Shepard, M. Rebecca Shaw,Gary Tabor, Lu Zhi, Pablo A. Marquet, and Robert J. Hijmans. 2013. Climate change, wine, and conservation. PNAS. 110 (17) 6907-6912.

If you like wine, particularly Old World wines, a recent paper by Lee Hannah et al (PNAS 2013), suggests that climate change is going to put a dent in your drinking habits. One way of communicating the ecosystem and economic effects of global warming has been to relate them to products or factors that affect the general population directly (an approach which has had mixed success). Wine (from Vitis vinifera grapes) is a great focal product - the success and quality of winemaking depends on terroir, which results from local temperatures and soil moisture. Changes in climate suitability for grapes reflects changes in suitability for many other agricultural and native species. Also, the motivations behind examining the effects of climate change on vineyards is more than economic – viticulture particularly thrives in Mediterranean-type ecosystems (France, Spain, Italy, California, Chile, South Africa, and Australia), which are areas with particularly high biodiversity and endemism. Vineyards use large amounts of fresh water and house low numbers of native species – so changes in their location and size may have contrasting effects on native biodiversity, local economies, and water supplies.

Given these relationships, the authors suggest that modeling regional changes in viticulture suitability provides insight into changes in ecosystem services and diversity. They examined 17 possible climate  models (GCMs) to look at how appropriate conditions for viticulture might shift by 2050. More than 50% of the models predicted that traditional wine producing regions (Bordeaux and Rhône valley regions in France and Tuscany in Italy) will decline greatly. However, regions farther north in Europe may become increasingly suitable. 

From Hannah et al. 2013. PNAS. The percentage of GCMs supporting a prediction reflects the degree of certainty behind it. Click for larger image.

New World vineyards receive a less dire forecast – some areas in Australia, Chile, California, and South Africa will remain suitable for viticulture in the future and new areas to the north are likely to become available. According to model predictions, New Zealand may one day produce many times more wine than it does currently. Such predicted increases in wine production in novel regions may be accompanied by viticulture’s increased ecological footprint. Some shifts take advantage of high elevations with cooler temperatures, leading to the development of areas that are currently relatively preserved. Water usage demands are likely to be problematic in the future: for example, vineyards in Chile’s Maipo Valley rely on runoff mountain basins that are vulnerable to warming conditions.

From Hannah et al. 2013. PNAS. (CA, California floristic province; CFR, Cape floristic region (South Africa); CHL, Chile; MedAus, Mediterranean-climate Australia; MedEur, Mediterranean-climate Europe; NEur, Northern Europe; NMAus, non–Mediterranean-climate Australia; NZL, New Zealand; WNAm, western North America).

Wine is a useful focal point for another reason - it exemplifies the complicated nature of most predictions related to climate change: positive outcomes (increased wine production in NZ) may be linked to negative changes (threatened water supply and native diversity in these new areas). Wine producers in a number of regions have recognized the possible impacts of vineyards, and groups such as the Biodiversity and Wine Initiative in the Cape Floristic Region of South Africa, and the Wine, Climate Change and Biodiversity Program in Chile exist to reconcile conflicting interests. There may be ways to mediate the effects of changing climate on viticulture, including developing tolerant varieties, changing methodologies, or the separation of varieties from their traditional regions. 

Making predictions about how ecosystems will change in the future is still difficult. However, the climate envelope model approach is actually well suited for situations like human agriculture, where dispersal limitation, competition, and non-equilibrium conditions are unlikely to be an issue. Cultivated crops are limited mostly by human/economic motivation. The results across most models strongly support the idea that Mediterranean climate growing regions will experience decreased viticultural suitability. It is likely more difficult on a fine scale to determine which regions will become more suitable in the future (i.e. probably don’t invest in land in New Zealand, assuming you can start a vineyard there in 50 years) but the strong agreement between models suggests that you should enjoy some French or Italian wine sooner rather than later.

Be vigilant against predatory journals

I'm sure most of the academic readers of this blog are frequently inundated by numerous requests to serve on the editorial boards of journals you've never heard of. Many of these claim to be 'open access' even though they do not adhere to the open access code of conduct. Rather, they are following a business model where the researcher pays to publish, while the predatory journal fails to provide even base services or indexing for your paper. The problem is that we often receive e-mails from legitimate start-up open access journals, and people need to separate the two. Jeffrey Beall has developed a set of guidelines to help you determine the legitimacy of the journal, as well as providing a list of known predatory publishers. These are great resources to ensure that you do not get duped.