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. 

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.

Progress on the problem of pattern, process and scale

Jérôme Chave. 2013. The problem of pattern and scale in ecology: what have we learned in 20 years? Ecology Letters. DOI: 10.1111/ele.12048.

Why do patterns get so much attention from ecologists? MacArthur (1972) suggested it was because patterns imply repetition, and repetition implies predictability. And prediction is the Holy Grail of ecology. Of course, patterns are meaningless without consideration of spatial or temporal scale. As Levin put it in his MacArthur lecture (1992) "the description of pattern is the description of variation, and the quantification of variation requires the determination of scales". Observing, modelling, and predicting ecological patterns at differing spatial scales has dominated much of ecological thought since Levin’s paper – today, entire subfields heavily focus on patterns through space or time (species-area relationships, macroecology, biogeography, etc).

When ecological research focuses on pattern, but lacks attention to process and scale, it has received much (deserved) criticism. Even when patterns are considered at the appropriate scale and with regard to process, the ability to understand how these processes and patterns translate from one scale to the next (i.e. how do we explain the differing relationship between invasion success and community diversity at local compared to regional scales?) is still limited. And yet clearly connecting processes across scales is a central goal. In the upcoming issue of Ecology Letters, a review article by Jérôme Chave looks at how ecology has progressed in dealing with patterns and scale in the last 20 years.

Chave does a great job of placing current ecological thought into historical context. Sometimes we forget that one of the benefits of ecology’s youth is that ecology has developed concurrently with necessary technological advancements and demand for ecological knowledge. As a result, the need for ecological knowledge and the ability to provide it are tightly linked in time. As a result, Chave suggests that ecology is making noticeable progress, particularly in four focal areas: 1) coupling ecology and evolution, 2) global change, 3) modularity in interaction networks, and 4) spatial patterns of diversity.

The first two topics reflect ongoing issues in ecology. The incorporation of evolutionary dynamics into ecology is an increasingly popular topic (for example), and it is not uncommon for ecological and evolutionary dynamics to have similar temporal scales. Explaining temporal patterns then may require coupling models of ecology and evolution: for example a study of Darwin’s finches found that for one period evolutionary dynamics were occurring on a more rapid temporal scales than ecological dynamics. Global change has dominated ecological research and the problem of scaling processes up from local to global or from global to local effects (of temperature on productivity, etc) is another clear area of growth. This may be the most successful attempts to scale, since models of global carbon cycles have progressed from empirical data and models to predictive models. An apparent example of what can be achieved when demand and appropriate technology are both present.

The remaining two foci relate to networks, and spatial patterns of diversity. The first, modularity in interaction networks, allows groups of interactions to be incorporated into larger scale networks; for individual variation could be incorporated into interactions between species. More generally, Chave suggests that the “abstracted multidimensional space of an interaction network” might be one way to simplify temporal and spatial scales. He suggests that this is where ecology could learn from other studies of complex biological systems such as cellular networks and networks of human governance and management. Finally, spatial patterns of diversity – a striking and oft-considered issue in ecology – are suggested as an area in ecology that has seen advances. Biological diversity is patchy through space, and the amount of patchiness is dependent on the scale of observation. Planktonic blooms might be patchy on a global scale while tropical trees might be patchy over meters. Scaling from local patterns to global has been difficult – for example, models of local dispersal don’t necessarily predict regional dispersal patterns. Chave suggests that one problem in the past was the ignorance of processes at larger scales (i.e. systematics, biogeography) and a predominant focus is on local processes. He provides a few examples that have bridged this issue, for example neutral theory includes both regional and local processes, while ecophylogenetics incorporates evolutionary history.

The review focuses attention on several relevant or insightful approaches to the problem of pattern and scale, and suggests possible connections between ecology and other areas of work (for example, interaction networks and metabolic networks). Although it provides interesting examples, it offers little synthesis or ideas for reconciling issues of pattern and scale, and while the four foci are valid and appropriate, they feel like a rather patchy way of covering a larger and more general issue. This may simply be too complicated and large a topic to cover in a single short review. Chave seems a little generous is giving props to approaches which at their best do incorporate multiple scales (e.g. neutral theory and ecophylogenetics), but which arguably have relied heavily on pattern analyses without a strong focus on process, something that seems to go against the spirit of the review. In addition, some of the explicitly general attempts to reconcile scale and pattern in community ecology are missing. For example, a series of papers from Brett Melbourne and Peter Chesson used 'scale transition theory' to model dynamics across multiple scales. This framework has been applied at least to a few fisheries-related papers. In addition, research on predator-prey dynamics has long considered the question of how functional responses scale up (one review). That said, it's clear that ecology has made progress in some areas and that there are options for moving forward.

Ultimately, Chave seems to suggest that the question of how well ecology can deal with patterns and scale depends on whether complexity is reducible or intrinsic to understanding natural systems. He goes so far as to state “This suggests that in approaching novel frontiers of the study of complex ecological systems we need to pause about the challenge ahead of us...Once we enter the realm of complex systems, neither physics nor biology are well equipped to progress.” This is obviously a pessimistic take on the future for ecology. Is it true?