Image in academia

Not many seminar speakers are introduced with a discussion of their pipetting skills. When we talk about other scientists we discuss their intelligence, their rigour, their personality, above and beyond their learned skills. Most people have an image of what a scientist should be, and judge themselves against this idealized vision. There are a lot of unspoken messages that are exchanged in science and academia. It’s easy to think that the successful scientists around one interacts with are just innately intelligent, confident, passionate, and hard-working. No doubt imposter syndrome owes a lot to this one-sided internalization of the world. After all, you don’t feel like you fulfill these characteristics because you have evidence of your own personal struggles but not those of everyone else. 

"Maybe no one will notice".

The most enlightening conversation I had this year (really! Or at least a close tie with discovering that PD originally was discussed as a measure of homologous characters…) was with a couple of smart, accomplished female scientists, in which we all acknowledged that we—not infrequently—suffered from feeling totally out of our depths. It is hard to admit our failings or perceived inadequacies, for fear we’ll be branded with them. But it’s really helpful for others to see that reality is different than the image we’ve projected. If everyone is an imposter, no one is. There is something to be said for confidence when scientists are presenting consensus positions to the public, but on the other hand, I think that being open about the human side of science is actually really important. 

For those who already feel like outsiders in academia, perhaps because they (from the perspective of race, gender, orientation, social and economic background, etc) differ from the dominant stereotype of a ‘scientist’, it probably doesn’t take much to feel alienated and ultimately leave. Students have said things to me along the lines of “I love ecology but I don’t think I will try to continue in academic because academia is too negative/aggressive/competitive”. Those are legitimate reasons to avoid the field, but I always try to acknowledge that I feel the same way too sometimes. It’s helpful to acknowledge that others feel the same way, and that having this kind of feeling (e.g. that you aren’t smart enough, or you don’t have a thick enough skin) isn’t a sign that you don’t actually belong. Similarly, it’s easy to see finished academic papers and believe that they are produced in a single perfect draft and that writing a paper should be easy. But for 99% of people, that is not true, and a paper is the outcome of maybe 10 extreme edits, several rounds of peer review, and perhaps even a copy-editor. Science is inherently a work-in-progress and that’s true of scientists as well.

The importance of personal relationships and mentorship to help provide realistic images of science should be emphasized. Mentorship by people who are particularly sympathetic (by personal experience or otherwise) to the difficulties individuals face is successful precisely for this reason. This might be why blog posts on the human side of academia are so comparatively popular – we’re all looking for evidence that we are not alone in our experiences. (Meg Duffy writes nice posts along these lines, e.g. 1, 2). And though the height of the blogosphere might be over, the ability of blog posts to provide insight into humanity of academia might be its most important value.

Experimental macroevolution at microscales

Sometimes I find myself defending the value of microcosms and model organisms for ecological research. Research systems do not always have to involve a perfect mimicry of nature to provide useful information. A new paper in Evolution is a great example of how microcosms provide information that may not be accessible in any other system, making them a valuable tool in ecological research.

For example, macroevolutionary hypotheses are generally only testable using observational data. They suffer from the obvious problem that they generally relate to processes of speciation and extinction that occurred millions of years ago. The exception is the case of short generation, fast evolving microcosms, in which experimental macroevolution is actually possible. Which makes them really cool :-) In a new paper, Jiaqui Tan, Xian Yang and Lin Jiang showing that “Species ecological similarity modulates the importance of colonization history for adaptive radiation”. The question of how ecological factors such as competition and predation impact evolutionary processes such as the rapid diversification of a lineage (adaptive radiation) is an important one, but generally difficult to address (Nuismer & Harmon, 2015; Gillespie, 2004). Species that arrive to a new site will experience particular abiotic and biotic conditions that in turn may alter the likelihood that adaptive radiation will occur. Potentially, arriving early—before competitors are present—could maximize opportunities for usage of niche space and so allow adaptive radiation. Arriving later, once competitors are established, might suppress adaptive radiation.

More realistically, arrival order will interact with resident composition, and so the effects of arriving earlier or later are modified by the identities of the other species present in a site. After all, competitors may use similar resources, and compete less, or have greater resource usage and so compete more. Although hypotheses regarding adaptive radiation are often phrased in terms of a vague ‘niche space’, they might better be phrased in terms of niche differences and fitness differences. Under such a framework, simply having species present or not present at a site does not provide information about the amount of niche overlap. Using coexistence theory, Tan et al. produced a set of hypotheses predicting when adaptive radiation should be expected, given the biotic composition of the site (Figure below). In particular, they predicted that colonization history (order of arrival) would be less important in cases where species present interacted very little. Equally, when species had large fitness differences, they predicted that one species would suppress the other, and the order in which they arrived would be immaterial. ­

From Tan et al. 2017

The authors tested this using a bacterial microcosm with 6 bacterial competitors and a focal species – Pseudomonas fluorescens SBW25. SBW25 is known for its rapid evolution, which can produce genetically distinct phenotypes. Microcosm patches contained 2 species, SBW25 and one competitor species, and their order of arrival was varied. After 12 days, the phenotypic richness of SBW25 was measured in all replicates.

From Tan et al. 2017. Competitor order of arrival in general altered the final phenotypic richness of SBW25.

Both order of arrival and the identity of the competitor did indeed matter as predictors of final phenotypic richness (i.e. adaptive radiation) of SBW25. Further, these two variables interacted to significantly. Arrival order was most important when the 2 species were strong competitors (similar niche and fitness differences), in which case late arrival of SBW25 suppressed its radiation. On the other hand, when species interact weakly, arrival order had little affect on radiation. The effect of different interactions were not entirely simple, but particularly interesting to me was that fitness differences, rather than niche differences, often had important effects (see Figure below). The move away from considering the adaptive radiation hypothesis in terms of niche space, and restating it more precisely, here allowed important insights into the underlying mechanisms. Especially as researchers are developing more complex models of macroevolution, which incorporate factors such as evolution, having this kind of data available to inform them is really important.

Interaction between final phenotype richness and arrival order for B) niche differences and D) fitness differences. S-C refers to arrival of SWB25 first, C-S refers to its later arrival. 

Problems with over-generalizing the dynamics of communities

Community ecologists talk about communities as experiencing particular processes in a rather general way. We fall into rather Clementsian language, asking whether environmental filtering dominates a community or if biotic interactions are disproportionately strong. This is in contrast to the typical theoretical focus on pairwise interactions, as it acts as though all species in a community are responding similarly to similar processes.

Some approaches to community ecology have eschewed this generality, particularly those that focus on ecological ‘strategies’ differentiating between species. For example, Grimes argued that species in a community represented a tradeoff between three potential strategies - competitive, stress-tolerant, and ruderal (CRS). Other related work describes rarity as the outcome of very strong density-dependence. The core-transient approach to understanding communities differentiates between core species, which have deterministic dynamics tied to the mean local environment, in contrast to transient species which are decoupled from local environmental conditions and have dynamics are driven by stochastic events (immigration, environmental fluctuations, source-sink dynamics). Assuming environmental stationarity, core species will have predictable and consistent abundances through time, in comparison to transient species.

If species do respond differently to different processes, then attempting to analyse all members of a community in the same way and in relation to the same processes will be less informative. Tests for environment-trait relationships to understand community composition will be weaker, since the species present in a community do not equally reflect the environmental conditions. In “A core-transient framework for trait-based community ecology: an example from a tropical tree seedling community”, Umana et al (2017) ask whether differentiating between core and transient species can improve trait-based analyses. They analyse tropical forest communities in Yunnan, China, predicting that core species "will have strong trait–environment relationships that increase the growth rates and probability of survival that will lead to greater reproductive success, population persistence and abundance".

The data for this test came from 218 1 m2 seedling plots, which differed in soil and light availability. The authors estimated the performance of individual seedlings in terms of relative growth rate (RGR). They also gathered eight traits related to biomass accumulation, and stem, root and leaf organ characteristics. They were particularly interested in how the RGR of any individual seedling differed from the mean expectation for their species. Did this RGR deviation relate to environmental differences between sites?  If a species’ presence is strongly influenced by the environment, then RGR deviation should vary predictably based on environmental conditions.

They then modelled RGR deviation as a function of the traits or environmental conditions (PCA axes). They considered various approaches for binning species based on commonness vs. rarity, but the general result was that bins containing rarer species had fewer PCA axes significantly associated with their RGR deviation and/or those relationships were weaker (e.g. see Figure below).

They conclude  that “the main results of our study show that the strength of demography-environment/trait and trait-environment relationships is not consistent across species in a community and the strength of these effects is related to abundance”. Note that other studies similarly find variation in the apparent mechanism of coexistence in communities. For example, Kraft et al. 2015  found that local fitness and niche differences only predict coexistence for a fraction of species co-occurring in their sites.

Umana et al.'s result is a reminder that work looking for general processes at the community level may be misleading. It isn't clear that there is a good reason to divide species into only two categories (e.g. core versus transients): like unhappy families, transient species may each be transient in their own way.

The most "famous" ecologists (and some time wasting links) (Updated)

(Update: This has gotten lots more attention than I expected. Since first posted, the top 10 list has been updated 2 times based on commenters suggestions. You can also see everyone we looked up here. Probably I won't update this again, because there is a little time wasting, and there is a lot of time wasting :) )

At some point my officemates Matthias and Pierre and I started playing the 'who is the most famous ecologist' game (instead of, say, doing useful work), particular looking for ecologists with an h-index greater than 100. An h-index of 100 would mean that the scientist had 100 publications with at least 100 citations  and their other papers had less than 100 citations. Although the h-index is controversial, it is readily available and reasonably capture scientists that have above average citations per paper and high productivity. We restricted ourselves to only living researchers. We used Publish or Perish to query Google Scholar (which now believes everyone using the internet in our office may be a bot).

We identified only 12 ecologists at level 100 or greater. For many researchers in specialized subfields, an h-index this high is probably not achievable. The one commonality in these names seems to be that they either work on problems of broad importance and interest (particularly, climate change and human impacts on the landscape) or else were fundamental to one or more areas of work. They were also all men, and so we tried to identify the top 12 women ecologists. (We tried as best as we could, using lists here and here to compile our search). The top women ecologists tended to have been publishing for an average of 12 years less than the male ecologists (44 vs. 56 years) which may explain some of the rather jarring difference. The m-index is the h-index/years publishing and so standardizes for differences in career age.

(It's difficult to get these kind of analyses perfect due to common names, misspellings in citations, different databases used, etc. It's clear that for people with long publication lists, there is a good amount of variance depending on how that value is estimated).

Other links: 

(I've been meaning to publish some of these, but haven't otherwise had a time or space for it.. )

Helping graduate students deal with imposter syndrome (Link). Honestly, not only graduate students suffer from imposter syndrome, and it is always helpful to get more advice on how to escape the feeling that you've lucked into something you aren't really qualified for. 

A better way to teach the Tree of Life (Link). This paper has some great ideas that go beyond identifying common ancestors or memorizing taxonomy.

Analyzing scientists are on Twitter (Link). 

Recommendation inflation (Link). Are there any solutions to an arms race of positivity?