Speaking the language: is jargon always bad?

You hear mostly about the evils of jargon in science. Undeniably jargon is a huge barrier between scientific ideas and discoveries and non-scientists. Translating a complex, nuanced result into a sound bite or recommendation suitable for consumption by policymakers or the public can be the most difficult aspect of a project (something Alan Alda, as part of his Center for Communicating Science, is attempting to assist scientists with). But sometimes the implication in general seems to be that scientific jargon is always undesirable. Is jargon really always a bad thing?

Even between scientists, you hear criticism about the amount of jargon in talks and papers. I have heard several times that community ecology is a frequent offender when it comes to over-reliance on jargon (defn: “words or expressions that are used by a particular profession or group and are difficult for others to understand”). It is fun to come up with a list of jargon frequently seen in  community ecology, because examples are endless: microcosm, mesocosm, niche, extinction debt, stochastic, trophic cascades, paradigm shift, priority effects, alternate stable states, or any phrase ending in ‘dynamics’ (i.e. eco-evolutionary, neutral, deterministic). Special annoyance from me at the usage of multidisciplinary, trans-disciplinary, and inter-disciplinary to all express the exact same thing. I don’t think, despite this list, that jargon is necessarily problematic.

If the meaning implied by the word or phrase is more than the sum of its parts it is probably jargon. Ideally, jargon is a shared, accurate shorthand for communicating with colleagues. A paper published without any jargon at all would be much longer and not necessarily clearer. Instead of saying, “we used protist microcosms”, it would have to say, “we used a community of protist species meant to encapsulate in miniature the characteristic features of a larger community”. (And arguably ecology is still relatively understandable for a newcomer, compared to disciplines like cell and systems biology, where an abstract might seem impenetrable: “Here, we report that, during mouse somatic cell reprogramming, pluripotency can be induced with lineage specifiers that are pluripotency rivals to suppress ESC identity, most of which are not enriched in ESCs.”)

Jargon is useful as a unifying tool: if everyone is using the same nicely defined label for a phenomenon, it is easier to generalize, contrast and compare across research. Jargon is many pieces of information captured in a single phrase: for example, using the term 'ecophylogenetics' may imply not only the application of phylogenetic methods and evolutionary biology to community ecology, but also the accompanying subtext about methodology, criticism, and research history. At its best, jargon can actually stimulate and unify research activities – you could argue that introducing a new term (‘neutral dynamics’) for an old idea stimulated research into the effects of stochasticity and dispersal limitation on community structure.

That’s the best case scenario for jargon. There are also consequences to developing a meaning-laden dialect unique to a subdiscipline. It is very difficult to enter a subdiscipline or move between subdisciplines if you don’t speak the language. New students often find papers difficult to penetrate because of the heavy reliance on jargon-y descriptions: obtaining new knowledge requires you already have a foundation of knowledge. Moving between subdisciplines is hard too – a word in one area may have completely different meaning in another. In a paper on conservation and reserve selection, complementarity might refer to the selection of regions with dissimilar species or habitats. In a biodiversity and ecosystem functioning paper, a not-very distant discipline, complementarity might refer to functional or niche differences among co-occurring species. Giving a talk to anyone but the most specialist audience is hampered by concerns about how much jargon is acceptable or understandable.

Jargon also leads to confusion. When using jargon, you can rely on understood meaning to delimit the boundaries of your meaning, but you may never specify anything beyond those boundaries. Everyone has heard a 30-second spiel so entirely made of jargon that you never develop a clear idea of what the person does. The other issue is that jargon can quickly become inaccurate, so laden with various meanings as to be not useful. The phrase ‘priority effect’, for example, has had so many particular mechanisms associated with it that it can be uninformative on its own. And I think most ecologists are well aware that jargon can be inaccurate, but it’s a difficult trap to get out of. The word “community”, essential to studying community ecology, is so broadly and inconsistently defined as to be meaningless. Multiple people have pointed this out (1, 2, 3) and even suggested solutions or precise definitions, but without lasting impact. One of the questions in my PhD defense was “how did I define an ecological community and why?”, because there is still no universal answer. How do we rescue words from becoming meaningless?

Something interesting, that you rarely see expressed about jargon is that linguists tells us that language is knowledge: how we understand something is not independent of the language we use to describe it. The particular language we think in shapes and limits what we think about: perhaps if you have many ways of finely delineating a concept you will think about it as a complex and subtle idea (the 100-words-for-snow idea). On the other hand, what if you have to rely on vague catch-alls to describe an idea? For example, a phrase like ‘temporal heterogeneity’ incorporates many types of differences that occur through time: is that why most researchers continue to think about differences through time in a vague, imprecise manner? Hard to say. It is hard to imagine where community ecology would be without jargon, and even harder to figure out how to fix all the issues jargon creates. 

Some ways you might not expect your research to be used

Most ecologists recognize that ecological knowledge is a tool, with useful applications to conservation and management, recreation, and ecosystem services and goods. Many of us have even written or said something suggesting uses for our work, no matter how likely. But few ecologists expect their research to be cited for military applications or support for the superiority of capitalism.

For example, a recent editorial in the New York Times detailed how conservation of biodiversity became part of American Cold War strategies. In those days, the American military was considering the role for ‘environmental warfare’, and the research of Charles Elton, who wrote of the dangers of simplifying landscapes by reducing biodiversity resonated. Strategists advocated maintaining biodiversity in food supplies and stockpiles (wisdom which transcends the military motivation). Ecological research into invasive species has also informed the US military in modern times. For example, the report "Invasive Threats to the American Homeland" considers the possibility of introduced species being used as terrorist weapons. Such introduced species might be crop parasites or vectors for human diseases, theoretically wreaking economic, structural, and human costs. 

Sometimes attempts to adapt research to other uses fall rather short of the mark. Evolutionary biology is not unfamiliar with this: for example, the misapplication of evolution to social Darwinism and some of the ideas touted in evolutionary psychology misrepresent evolutionary theory. This can happen in ecology too. A recent PNAS paper presented the result that evolutionary diversity increases ecosystem productivity. One writer in the Washington Post blogging community presented this finding as evidence that capitalist concepts like division of labour are found even in nature. It seems difficult to accept the link the writer attempts to make (the title is rather over the top as well: “Darwin’s free market wisdom: division of labor starts in the genes”). The writer states that nature wouldn’t exhibit a relationship between diversity and higher productivity if it wasn’t optimal, so “[t]he same findings would also appear to suggest that species, like humans, are not all created equal and some are more adept at certain tasks than others.” Therefore, apparently, capitalism is superior to communism. 

This kind of thing makes me think that Darwin was lucky that he did not live to see his words and ideas so frequently misquoted and misapplied (although he certainly suffered this during his own lifetime). This is the danger of sending an idea or result into the world: you no longer fully control how it is used and understood. A successful idea is one that, for better or worse, has an independent life. 

(There are probably many misapplications or unusual uses of ecology and evolution that I haven't thought of. If you think of other examples, feel free to mention them in the comments.)

Has academic advancement changed your point of view?

From http://researchinprogress.tumblr.com/

We regret to inform you that you paper has not been accepted

as a graduate student:

as a postdoc:

 as a professor:

We are pleased to inform you that your paper has been accepted

as a graduate student:

as a postdoc:

as a professor:

Evidence for the evolutionary diversity-productivity relationship at several scales

Cadotte, M. W. 2013. Experimental evidence that evolutionarily diverse assemblages result in higher productivity. Proceedings of the National Academy of Sciences.

John J. Stachowicz, Stephanie J. Kamel, A. Randall Hughes, and Richard K. Grosberg. Genetic Relatedness Influences Plant Biomass Accumulation in Eelgrass (Zostera marina). The American Naturalist, Vol. 181, No. 5 (May 2013), pp. 715-724

Ecology is increasingly recognizing the value of non-species based measures of diversity in relation to ecosystem services, community diversity and invasibility, and conservation activities. One result is that we are seeing increasingly strong and interesting experimental evidence for the importance of genetic diversity in understanding populations, species, and communities are structured. Two recent papers are good examples of how our understanding is progressing.

For example, we are now at the point where research has clearly demonstrated the relationship between ecosystem functioning and evolutionary history, and now well-designed experiments can begin to explore the mechanisms that underlie the ecosystem functioning-evolutionary diversity link. The oft-demonstrated correlation between evolutionary diversity and productivity is explained based on the assumption that ecological similarity and evolutionary relatedness are connected. Diverse communities are often thought to have lower niche overlap (i.e. higher complemenarity), but these experiments often rely on highly distinct species (such as a grass and a N-fixer), which could over-emphasize the importance of this relationship. In Cadotte (2013), independent manipulations of phylogenetic diversity and species richness allow the author to explore separately the role of complementarity and selection effects (the increased likelihood that a highly productive species will be present as species richness increases).

The experiment involved old field plots, planted with between 1 and 4 species chosen from a pool of 17 possible species; evolutionary diversity (high, medium, or low) and species richness are manipulated to include all possible combinations. The study found found a much stronger relationship between phylogenetic diversity (PD) and biomass production then between species richness and biomass production, but this isn't especially novel. What is interesting is that it could also identify how selection effects and complementarity were driving this response. High levels of complementarity were associated with high levels of PD: polyculture plots with high complementarity values were much more likely to show transgressive overyielding. Plots with close relatives had a negative or negligible complementarity effect (negative suggesting competitive or other inhibitory interactions). There was also evidence for a selection effect, which was best captured by an abundance-weighted measure of evolutionary diversity (IAC), which measured the abundance of closely related species in a plot. Together, PD and IAC explain 60% of the variation in biomass production.

From Cadotte (2013).

The second study asks the exact same question – what is the relationship between biomass production and genetic diversity - but within populations. Stachowicz et al. (2013) looked at genetic relatedness among individuals in monocultures of the eelgrass Zostera marina and its relationship to productivity. Variation within a species has many of the same implications as variation within a community – high intraspecific variation might increase complementarity and diverse assemblages might also contain more productive genotypes leading to a selection effect. On the other hand, it is possible that closely related, locally adapted genotypes might be most productive despite their low genotypic variation. 

Similar to most community-level experiments, Stachowicz et al. found that looking at past experimental data suggested there was a strong relationship between genetic relatedness and biomass/density in eelgrass beds. Taxa (i.e. the number of genotypes) tended to be a poorer predictor of productivity. However, the relationship was in the opposite direction usually seen – increasing relatedness predicted higher biomass. This is difficult to explain, since it goes against the expected direction of complementarity or selection effects. Possibly cooperative/facilitative relationships are important in eelgrass monocultures. Data obtained from field surveys (rather than experimental data) suggested an alternative: possibly these studies didn’t cover a large enough range of relatedness. This field data covered a much larger range of relatedness values, and showed a unimodal relationship (below), indicating that the productivity-relatedness relationship had an optimum, where highly related or highly diverse assemblages were less productive. Although further work needs to be done, this is an intriguing possibility.

From Stachowicz et al. (2013). Grey dots represent range of relatedness values from experimental data only, compared to range covered by field survey.

At some scales, ecologists are now refining what we know about popular research questions, while at others we are just scratching the surface. Stachowicz et al. suggest that as we scale up or down our expectations should differ -  “the slope and direction of the relationship between genetic differentiation and ecological functioning might depend on the genetic scale under consideration”.

(Disclaimer - obviously Marc Cadotte was my PhD supervisor until very recently. But I think it's a nice paper, regardless, and worth a post :) )

The end of the impact factor

Recently, both the American Society for Cell Biology (ASCB) and the journal Science both publicly proclaimed that the journal impact factor (IF) was bad for science. The ASCB statement argues that IFs limit meaningful assessment of scientific impact for both published articles and especially other scientific products. The Science statement goes further, and claims that assessments based on IFs lead researchers to alter research trajectories and try to game the system rather than focussing on the important questions that need answering.


Impact factors: tale of the tail
The impact factor was created by Thomson Reuters and is simply the number of citations a journal has received in the the previous two years, divided by the number of articles published over that time span. Thus it is a snapshot of a particular type of 'impact'. There are technical problems with this metric -for example, that citations accumulate at different rates across different subdisciplines. More importantly, and what all publishers and editors know, is that IFs generally rise and fall with the extreme tail of the distribution of the number of citations. For a smaller journal, it just takes one heavily cited paper to make the IF jump up. For example if a journal publishes one paper that accumulates 300 citations and it published just 300 articles over the two years, then its IF can jump up by 1, which can alter the optics. In ecology and evolution, IFs greater than 5 are usually are viewed as top journals.

Regardless of these issues, the main concern expressed by ACSB and Science is that a journal-level metric should not be used to assess an individual researcher's impact. Should a researcher publishing in a high IF journal be rewarded (promotion, raise, grant funded, etc.) if their paper is never cited? What about their colleague who publishes in the lower IF journal, but accrues a high number of citations?

Given that rewards are, in part, based on the journals we publish in, researchers try to game the system by writing articles for certain journals and journals try to attract papers that will accrue citations quickly. Journals with increasing IFs usually see large increases in the number of submissions, as researchers are desperate to have high IF papers on their CVs. Some researchers send papers to journals in the order of their IFs without regard for the actual fit of the paper to the journal. This results in an overloaded peer-review system.

Rise of the altmetric
Alternative metrics (altmetrics) movement means to replace journal and article assessment from one based on journal citation metrics to a composite of measures that include page views, downloads, citations, discussions on social media and blogs, and mainstream media stories. Altmetrics attempts to capture a more holistic picture of the impact of an article. Below is a screenshot from a PLoS ONE paper, showing an example of altmetrics:

By making such information available, the impact of an individual article is not the journal IF anymore, but rather how the article actually performs. Altmetrics are particularly important for subdisciplines where maximal impact is beyond the ivory towers of academia. For example, the journal I am an Editor for, the Journal of Applied Ecology, tries to reach out to practitioners, managers and policy makers. If an article is taken up by these groups, they do not return citations, but they do share and discuss these papers. Accounting for this type of impact has been an important issue for us. In fact, even though our IF may be equivalent to other, non-applied journals, our articles are viewed and downloaded at a much higher rate.

The future
Soon, how articles and journals are assessed for impact will be very different. Organizations such as Altmetric have developed new scoring systems that take into account all the different types of impact. Further, publishers have been experimenting with altmetrics and future online articles will be intimately linked to how they are being used (e.g., seeing tweets when viewing the article).

Once the culture shifts to one that bases assessment on individual article performance, where you publish should become less important, and journals can feel free to focus on an identity that is based on content and not citations. National systems that currently hire, fund and promote faculty based on the journals they publish in, need to carefully rethink their assessment schemes.

May 21st, 2013 Addendum:

You can sign the declaration against Impact Factors by clicking on the logo below:

Holding fast to a good(?) idea

One of my favourite lists on the internet is tucked away in the credits for the PHYLIP software. PHYLIP was authored by Joe Felsenstein, a professor at the University of Washington and expert on methods for phylogenetic inference. PHYLIP is a free package of programs for inferring phylogenies, and probably the first and oldest widely-distributed phylogenetic program. Programs like PHYLIP made phylogenetic approaches easily accessible to ecologists and evolutionary biologists. Apparently it took years to get from the idea for PHYLIP to funding, and Felsenstein memorializes this with his “No thanks to” list (below). The list includes reviewers and panels from the US Dept of Energy, NSF, and NIH that turned down his proposals and made comments like "The work has the potential to define the field for many years to come.... All agreed that the proposal is somewhat vague. There was also some concern that the proposed work is too ambitious.”

(Click to enlarge)

There are obvious responses to this list, mostly relating to the short-sightedness of funding agencies, meaningless requirements for ‘broader impacts’, the fact that proposals might be improved through the process of multiple failed applications, and of course the benefit of being long-established and respected when posting such lists on your website. But what I always wonder about is how long do you hold on to an idea, a proposal, or a manuscript that it is repeatedly rejected, before you give up on it? 

This question is interesting to me for a couple of reasons. Firstly, because personality is so intertwined with confidence about an idea’s success. We all know people who would argue that all their ideas are Nature-worthy, criticism be damned. Other people need to be convinced of the merit of their own ideas. Obviously past success probably helps with judgment – having experience in identifying good ideas builds confidence in your ability to do so again. But what is the line between self-confidence and self-delusion? Secondly, it is a reminder that lots of good ideas and good papers were rejected many times. In any case, I am curious whether people tend give up on an idea simply because they became discouraged at the prospects of getting it published, or because they lost faith in the idea, or a combination of both.