The future of community phylogenetics

Community phylogenetics has received plenty of criticism over the last ten years (e.g. Mayfield and Levine, 2010; Gerhold et al. 2015). Much of the criticism is tied to concerns about pattern-based inference, the use of proxy variables, and untested assumptions. These issues are hardly unique to community phylogenetics, and I think that few ideas are solely ''good or solely 'bad'. They are useful in moulding our thinking as ecologists and inspiring new directions of thought. Many influential ideas in ecology have bobbled in confidence through time, but remain valuable nonetheless [e.g. interspecific competition, character displacement (Schoener 1982; Strong 1979)]. But still, it can be hard to see exactly how to use phylogenetic distances to inform community-level analyses in a rigorous way. Fortunately, there is research showing exactly this. The key, to me at least, to avoid treating a phylogeny as just another matrix to analyze, but to consider and test the mechanisms that might link the outcome of millions of years of evolution to community-level interactions.

A couple of potential approaches to move forward questions about community phylogenetics are discussed below. The first is to consider the mechanisms behind the pattern-inference analyses and ask whether assumptions hold.

1) Phylogenies and traits - testing assumptions about proxy value
As you know, if you have read the introductory paragraph of many community phylogenetic papers, Charles Darwin was the first to highlight that two closely related species might have different interactions than two distantly related species. People have tested this hypothesis in many ways in various systems, with mixed results. The most important directions forward is to make explicit the assumptions behind such ideas and experimentally test them. I.e. Do phylogenetic distances/divergence between species capture trait and ultimately ecological divergence between species?

From Kelly et al. 2015 Fig 1b.

Because evolutionary divergence should relate to feature divergence (sensu Faith), the most direct question to ask is how functionally important trait differences increase with increasing phylogenetic distances. For example, Kelly et al. (2014) found that “close relatives share more features than distant relatives but beyond a certain threshold increasingly more distant relatives are not more divergent in phenotype”, although in a limited test based only on patristic distances. This suggests that at short distances, phylogenetic distances may be a reasonable proxy for feature divergence, but that the relationship is not useful for making predictions about distant relatives.

Phylogenies and coexistence/competition. Ecological questions about communities may not be interested in traits alone. The key assumption behind many early analyses was that closely related species shared more similar *niches*, and so competed more strongly than distantly related species. Thus the question is one step removed from trait evolution, asking instead how phylogenetic divergence correlates into fitness differences or interaction strength. Not surprisingly, current papers suggest there is a fairly mixed, less predictable relationship between phylogenetic relatedness and competitive outcomes.

Recent findings have varied from “Stabilising niche differences were unrelated to phylogenetic distance, while species’ average fitness showed phylogenetic structure” (California grassland plants, Godoy et al. 2014); to, there is no signal in fitness or niche differences (algae species, Narwani et al. 2013); to, when species are sympatric, both stabilizing and fitness differences increase with phylogenetic distance (mediterranean annual plants; Germain et al. 2016). Given constraints, tradeoffs and convergence of strategies, it is really not surprising that the idea of simply inferring the importance of competition from patterns along a phylogenetic tree is not generally possible (Kraft et al. 2015; blogpost).

2) Phylogenies and the regional species pool
Really more interesting than testing for proxy value is to think about the mechanisms that tie evolution and community dynamics together. A key role for evolution in questions about community ecology is to ask what we can learn about the regional species pool—from which local communities are assembled. What information about the history of the lineages in a regional species pool informs the composition of local composition?

The character of the regional species pool is determined in part by the evolutionary history of the region, and this can in turn greatly constrain the evolutionary history of the community (Bartish et al. 2010). The abundance of past habitat types may alter the species pool, while certain communities may act as 'museums' harbouring particular clades. For example, Bartish et al. 2016 found that the lineages represented in different habitat types in a region differ in the evolutionary history they represent, with communities in dry habitats disproportionately including lineages from dry epochs and similar for wet habitats. Here, considering the phylogeny provides insight into the evolutionary component of an ecological idea like 'environmental filtering'.

Similarly, species pools are formed by both ecological processes (dispersal and constraints on dispersal) and evolutionary ones (extinctions, speciation in situ), and one suggestion is that appropriate null models for communities may need to consider both ecological and evolutionary processes (Pigot and Etienne, 2015).
Invasive species also should be considered in the context of evolution and ecology. Gallien et al. 2016 found that “currently invasive species belong to lineages that were particularly successful at colonizing new regions in the past.”

I think using phylogenies in this way is philosophically in line with ideas like Robert Ricklef's 'regional community' concept. The recognition is that a single time scale may be limiting in terms of understanding ecological communities.

References:

1. Mayfield, Margaret M., and Jonathan M. Levine. "Opposing effects of competitive exclusion on the phylogenetic structure of communities." Ecology letters 13.9 (2010): 1085-1093.
2. Gerhold, Pille, et al. "Phylogenetic patterns are not proxies of community assembly mechanisms (they are far better)." Functional Ecology 29.5 (2015): 600-614.
3. Schoener, Thomas W. "The controversy over interspecific competition: despite spirited criticism, competition continues to occupy a major domain in ecological thought." American Scientist 70.6 (1982): 586-595. 
4. Strong Jr, Donald R., Lee Ann Szyska, and Daniel S. Simberloff. "Test of community-wide character displacement against null hypotheses." Evolution(1979): 897-913. 
5. Kelly, Steven, Richard Grenyer, and Robert W. Scotland. "Phylogenetic trees do not reliably predict feature diversity." Diversity and distributions 20.5 (2014): 600-612.
6. Godoy, Oscar, Nathan JB Kraft, and Jonathan M. Levine. "Phylogenetic relatedness and the determinants of competitive outcomes." Ecology Letters17.7 (2014): 836-844.
7. Narwani, Anita, et al. "Experimental evidence that evolutionary relatedness does not affect the ecological mechanisms of coexistence in freshwater green algae." Ecology Letters 16.11 (2013): 1373-1381.
8. Rachel M. Germain, Jason T. Weir, Benjamin Gilbert. Species coexistence: macroevolutionary relationships and the contingency of historical interactions. Proc. R. Soc. B 2016 283 20160047
9. Nathan J. B. Kraft, Oscar Godoy, and Jonathan M. Levine. Plant functional traits and the multidimensional nature of species coexistence. 2015. PNAS.
10. Bartish, Igor V., et al. "Species pools along contemporary environmental gradients represent different levels of diversification." Journal of Biogeography 37.12 (2010): 2317-2331.
11. IV Bartish, WA Ozinga, MI Bartish, GW Wamelink, SM Hennekens. 2016. Different habitats within a region contain evolutionary heritage from different epochs depending on the abiotic environment. Global Ecology and Biogeography
12. Pigot, Alex L., and Rampal S. Etienne. "A new dynamic null model for phylogenetic community structure." Ecology letters 18.2 (2015): 153-163.
13. Gallien, L., Saladin, B., Boucher, F. C., Richardson, D. M. and Zimmermann, N. E. (2016), Does the legacy of historical biogeography shape current invasiveness in pines?. New Phytol, 209: 1096–1105.

More ways to understand traits in ecology

It seems that increasingly, ecology is moving away from relying primarily on summary statistics and approximations, to considering measures that recognize the often meaningful variation in ecological data. Using only the mean of a variable, for example, may be informative in some ways, but insufficient in others. Indices of diversity increasingly reflect that ecologically relevant information is not restricted to a single moment (as seen in the framework for measuring trait diversity (Villeger et al. (2008)) and the analogous framework for phylogenetic diversity (e.g. detailed in Pavoine and Bonsall (2011); also Tucker et al. (2016)).

Particularly, the functional ecology literature has developed increasingly complex and integrative methods for measuring and comparing trait diversity. The literature has gone from descriptions of general types or traits (e.g. Whittaker 1956), to measuring measuring individual traits and relating them to particular ecologically relevant variables (e.g. Gaudet and Keddy (1988)); to calculating community-weighted values for individual traits (e.g. D Schluter, (1986)); to incorporating multiple variables into single measures (e.g. FD package); to a framework reflecting mathematical moments in data (Villeger et al. (2008); and to the use of multivariate hypervolumes to describe the multi-dimensional shape and volume of trait space to be measured (e.g. Blonder et al. 2014).

A new paper in TREE does a nice job of summarizing and integrating these developments with yet another addition: a ‘trait probability density’ approach. In  “Traits Without Borders: Integrating Functional Diversity Across Scales", Carlos P. Carmona, Francesco de Bello, Norman W.H. Mason, and Jan Lepš nicely illustrate a way to capture the complexity inherent to a concept such as the ‘functional niche’. [The "region of the functional space containing all the trait combinations displayed by the individuals of a species"].

The truth about traits is that there is meaningful variation at every scale at which we measure them (including variation between individuals, variation between populations, variation between species, and variation between communities). Often decisions are made to ignore or collapse unwanted levels of variation (such as using a mean value across several individuals to calculate a single species-level value). The authors suggest that we can instead incorporate this variation appropriately. A probability density function can be defined for the multi-trait space, with probabilities representing the relative abundances of each combination of trait values. Thus, for a species, the curve (Figure IA) would show the multivariate trait space seen across all measured individuals, with uncommon combinations of traits seen in few individuals shown at the tails of the distribution. Outliers and extreme values are incorporated but not overemphasized as they can be in convex hull approaches.

The probabilistic approach reflects that a niche *is* probabilistic for a species - after all, it is unlikely that the niche is simply a fixed set of traits that is identical for all individuals or populations. However, not all combinations of trait values (niche dimensions) are equally likely for members of a species, and these curves reflect that. And when probabilities are incorporated into trait measurements, greatly different conclusions may be made about how similar or dissimilar assemblages may be (e.g. Fig IC).

Reproduced from Fig I., Carmona et al. 2016 TREE.

One concern--one that is pretty much universal to all analyses in functional ecology--is about how the biases and limitations of available data will affect this type of measure. Some species are better described, some traits are not available for most species, some studies lack interspecific measures, some lack local measures (relying instead on general databases of trait values). In addition, some intraspecific variation arises from other sources of noise like stochasticity and measurement error. This is all part of a bigger question about sufficient data: not only do we need to know how many traits are needed to define a species, but we need to decide how much and what kind of data is necessary to understand a trait…

Fig. 2 from Carmona et al. TREE 2016. It is possible to incorporate existing measures of functional diversity (richness, evenness, divergence) into the probabilistic definition.

Who should communicate the policy implications of ecological research?

Ecology is a science that tries to understand the world. How is the diversity of organisms distributed around the world? How do extreme climate events influence populations of animals and plants? How does the diversity of organisms in a landscape influence its function and the delivery of services to humanity? These are all questions routinely asked by ecologists and, importantly, are topics that most academic ecologists would believe are necessary for providing evidence for policy and management of habitats and natural resources. Yet policy makers, managers and practitioners seldom access ecology research. There is a research-policy divide that needs to be overcome.

Spanning the chasm between academic research and policy (from http://www.adventureherald.com/8-scary-suspension-bridges-you-do-want-to-cross/)

 

Many ecologists are reluctant to promote the policy implications of their research because they do not feel comfortable or connected enough to talk to non-academics. But if not them, then who is responsible to communicate the policy repercussions of their research?

The romanticized view of an untouched, pristine ecosystem no longer exists. We now live in a world where every major ecosystem has been impacted by human activities. From pollution and deforestation, to the introduction of non-native species, our activity has influenced every type of habitat. But this is where management and applied ecology have relevance. The study of human physiology has direct relevance for health science –that is, the value of this basic biological science is measured in its ability to help sick people, and not necessarily in its ability to better understand how healthy people function. So to does ecology need to be relevant for our ‘sick people’, that is, human-impacted landscapes. We have spent much of our collective effort studying intact, semi-natural systems, and this is necessary to understand the basic operations of nature. But now we are required to apply this understanding to improve ecological integrity and human wellbeing. We are surround by sick ecosystems and ecology is desperately needed to influence policy and management.

I just attended the joint symposium “Making a Difference in Conservation: Improvingthe Links Between Ecological Research, Policy and Practice”, put on by the British Ecological Society and the Cambridge Conservation Initiative. This meeting was attended by a nice mix of academic researchers and practitioners, and covered a broad range of ideas, issues and solutions to overcoming barriers to implementing evidence-based policy. Overcoming these barriers requires communication, and scientists need to be at the table. In arguing the case that scientists need to communicate the policy implications of their research below, I take ideas and information passed on in a number of excellent talks, including from: John Altringham, Malcolm Ausden, John Beddington, Ian Boyd, Fiona Fox, Georgina Mace, Andrew Miller, E. J. Milner-Gulland and Des Thompson, and my own workshop on communicating research to maximise policy impact.

A guy who probably doesn't know what he is talking about, talking about policy. Perhaps a bit outside my comfort zone. (photo by Martin Nunez)

The Hurdles

The hurdles to the uptake of research and evidence into policy decisions are complex and multifaceted. On the scientists’ side, the hurdles are mainly a lack of training, experience and comfort promoting the policy implications of their work. In graduate school, very few scientists-in-training take journalism and media courses, and so are not well versed in the ways to communicate in a broadly approachable way. Instead, we are taught to communicate in technically precise ways that can only be understood by similarly trained experts.

On the practitioner side, there are a number of pragmatic and systemic limitations to the uptake of evidence into policy and management decisions:

1.       Structural: There is a lack of resources and time to read and synthesize scientific research. A lack of access because of expensive subscription fees, is a pervasive problem for individuals and small organizations.

2.       Systemic: Big organizations and agencies are complex and communication of best practices or idea sharing might be lacking. Frequent staff turnover means that research understanding and institutional memory is lost.

3.       Relevance: Practitioners need research relevant to their problem and trolling the impossibly large literature is not an efficient way to find the necessary information.

4.       Timescale: Practitioners and policy makers work at a variety of speeds, dictated by priorities, contracts, etc., and looking for resources may not work within these timeframes.

These limitations and the lack of relevant research uptake result in policies and management strategies that are not adequately informed by research, which can waste money and may not produce in the desired results. We heard about the requirement to build bat crossings across new highways (to avoid car collisions), costing millions of dollars, but research has not supported their efficacy. 

Random bat picture to break up the flow (from http://www.bugsbirdsandbeasts.co.uk/go-batty)

Should scientists engage policy makers? 

I do think that scientists have a responsibility to communicate, and perhaps advocate, for evidence to be used in policy decision-making. There is a line between being seen as objective versus as an advocate, and scientists need to do what they are comfortable with, but remember:

1. You are an expert on your research; you are uniquely position to comment on it.
2. Related to the previous point, you may not want other, untrained, people to represent and communicate your work.
3. You have an obligation to the public. You are likely paid by tax dollars and your research is funded by public grants. A part of the responsibility then is to not only do research but to ensure that it is communicated and if the people who ultimately pay you would benefit from learning about your findings, you owe it to them to communicate it.
4. There are positive feedbacks for your career. Being seen as a scientist who engages and does relevant work will mean that you achieve a higher profile.

Citizens and policy-makers get the most out of their new information (which forms the basis for their opinions) from media news. If the only voices being heard are advocates and interest groups, then evidence will be lacking or misrepresented. Scientists’ voices are needed in the media, and here you can educate many concerned people. The former British Education minister, Estelle Morris, when speaking about the Fukushima reactor meltdown, said that she learned more about radiation from scientific experts in the media than she had during her education.

Of course it is important to remember that science is only a part of the solution, human needs, economics and social values are also important. But without scientists’ involvement, evidence will not be an important part of solutions to crises. 

How to communicate

Scientists are often driven by immediate career concerns and they need to publish high profile, impactful papers in peer-reviewed scientific journals. And this won’t change. But as Georgina Mace said in her presentation, overselling the implications of research in papers diminishes their value and confuses practitioners and policy makers. Policy implications contained within publications is one avenue to influence policy makers, but rather than tacking on broad policy recommendations, consider consulting them before writing the paper, or even better, include them in the planning stage of the study. One speaker commented that instead of asking for a letter of support for a grant proposal from a non-academic partner at the 11^th hour, discuss the ideas with them at the outset.

How should scientists communicate their research?

1. Discuss finings with local interest groups (e.g., park managers).
2. Give a public lecture to community organizations (e.g., naturalist club).
3. Talk to local politicians.
4. Use social media –create a persona that acts as an information broker.
5. Write opinion articles for magazines or newspaper editorials.
6. Be accessible to journalists (e.g., get yourself listed in your university expert database).

The UK as a model

The UK provides one of the best examples of meaningful interactions between scientists and policy makers. Perhaps a better way to state it, is that there is a gradient of engaged individuals from pure scientist to local practitioner. There are robust organizations that span government agencies, NGOs, and universities that bring scientists and practitioners into contact with one another. They have Chief Scientific Officers and advisory groups at multiple levels of government. These groups develop the risk registry that estimates the likelihood and the potential repercussions of environmental and biological disasters or emergencies (e.g., influenza pandemic, severe drought, etc.). There is a well respected and effective Science Media Centre that organizes briefing sessions that bring scientists together with journalists on timely and important topics. These briefings result in influential news stories that sometimes challenge government policy or public sentiment (e.g., badger culls, links between vaccines and autism, etc.). This is a system to be emulated.

So, should scientists communicate their findings and engage policy makers, managers and the public. Absolutely. It may seem like you are entering uncharted territory, but believe me, your voice is desperately needed.

If you want advice, encouragement or more information, feel free to contact me.

What are important directions for ecology?

I was recently asked “what is the most important problem in ecology?”. I was dissatisfied with whatever I ended up answering, so it has been on my mind. I think there is an analogy with medicine here – it’s a little like asking a medical scientist “what is the most important disease to cure?” Similarly, there are multiple possible answers, and the one you give will depend on your area of interest/what type of doctor you are. (I also assume this is a question about basic research, and the answer is not as simple as saying, stop extinctions or prevent habitat loss).

Levels of biological organisation.

The medical analogy breaks down a little because ecology is *far* more complicated than medical science. Medicine has a foundation in anatomy and physiology, which in turn rely on basic sciences like cell biology and genetics. This creates a reasonably constrained framework within which further learning/investigation can be organized. Medicine typically stops at the level of the individual, but ecology inherently involves many additional levels of organization (from individuals, to populations, to species and communities, to ecosystems, and beyond). Within any one of these higher levels of organization (population, community, ecosystem), there can be such an immense amount of variation in outcomes and dynamics that ecologists can lose sight of connections with lower and higher levels. For example, community ecology encompasses so much complexity on its own, that also considering the impacts of population level processes and on ecosystem level processes is a tall order. But, we should also appreciate, given these barriers to understanding, just how far ecology has actually advanced in the last 100 years. The combination of reductionist experiments and descriptive work at all scales has been immensely successful (e.g. see this blog post for a partial list). Many general tools have been developed that we can then use to answer specific ecological questions (the integration with statistics with ecology has been highly successful; the use of specific mathematical models). Still, the ability to reconcile multiple levels of organization and scales still limits ecology.

This is a problem that cell biology has also experienced, and is now approaching via systems biology: "The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models"(1): to me, this quote rings so true for ecology as well. Systems biology uses mechanistic, mathematical and computational models to attempt to represent multi-scale complexity.

Of course, the optimism about systems biology might be premature in that it hasn’t produced many useful models yet, such that it may be “more of an agenda than a body of results.”. Some of the best “systems ecology” (e.g. meta-ecosystem models) are very system specific and data-heavy (e.g. 2). Can they inform us about generality in ecology?

All of which is to say, I think the most important problems in ecology relate to this need to make the connections between studies and systems and levels of organization. But, doing so may be difficult.

More specific problems

1. The scaling of ecological processes. Many ecologists include a line about being ‘interested in questions of scale’ on their website blurbs. Despite this, our understanding of the aggregate outcome of multiple processes that are occurring at different spatial or temporal scales remains limited, and poorly predictive. There have been a few useful starts (particularly in Peter Chesson’s scale transition papers (3, 4)), but recent theoretical interest seems to be low. We have data at the community scale, and data at the macro-scale. How do we connect these (and can we)? Models describing how processes occurring at smaller scales produce larger scale dynamics can be complex: they may include non-linearities, autocorrelation between regions, the combination of discrete and continuous events, and multiple attractors.

2. Mechanisms maintaining multi-species coexistence in the real world. Hutchinson’s paradox of the plankton remains unsolved*. Community ecologists have invested a lot of time and energy into understanding species interactions as seen in natural communities. To explore the mechanisms behind coexistence, usually (but not always) ecologists have focused on two-species interactions (or maybe 3): understanding coexistence in larger groups tends to be mostly restricted to theory. But fitting the individual pieces into the larger puzzle is exponentially more difficult: in observed large groups of interacting species, what is the relative contribution of the many coexistence mechanisms identified? Which mechanisms are most important, and how do they change through space and time?

*Perhaps not surprisingly, given it is a paradox...

3. Moving farther away from species. In so many ways, focusing on ‘species’ as the unit of measurement is limiting, because ‘species’ is a discrete term and ecology is interested in quantitative measures. Important advances have been made by redefining ecology as the outcome of species traits and species interactions (5). But I think our ability to connect these ideas more closely to species’ multidimensional niches can still improve. In particular, understanding that traits and interactions can change in context-dependent ways (plasticity, ontogeny, environment) will be important (6, 7).

4. Reproducibility of ecological research. This is more of a philosophical question - how do we achieve reproducibility in a science where context-dependence, alternative stable states, chaos and stochasticity all affect results? How do we differentiate between reproducibility (same results under identical conditions) and generality (same results under similar conditions) in results?

References:
1) Sauer, Uwe; Heinemann, Matthias; Zamboni, Nicola. Genetics: Getting Closer to the Whole Picture. Science 316 (5824): 550–551. doi:10.1126/science.1142502. PMID 17463274.

2) Dominique Gravel, Frédéric Guichard, Michel Loreau and Nicolas Mouquet. Source and sink dynamics in meta-ecosystems. Ecology 91(7): 2172-2184.

3) Chesson, Peter. Scale transition theory with special reference to species coexistence in a variable environment. Journal of biological dynamics 3.2-3 (2009): 149-163.

4) Melbourne, Brett A., and Peter Chesson. The scale transition: scaling up population dynamics with field data. Ecology 87.6 (2006): 1478-1488.

5) McGill, Brian J., et al. Rebuilding community ecology from functional traits. Trends in ecology & evolution 21.4 (2006): 178-185.

6) Poisot, T., Canard, E., Mouillot, D., Mouquet, N., Gravel, D. & Jordan, F. (2012) The dissimilarity of species interaction networks. Ecology letters, 15, 1353–61.

7) Siefert, A., Violle, C., Chalmandrier, L., Albert, C.H., Taudiere, A., Fajardo, A., Aarssen, L.W., Baraloto, C., Carlucci, M.B., Cianciaruso, M.V. and L Dantas, V. A global meta‐analysis of the relative extent of intraspecific trait variation in plant communities. Ecology letters 18.12 (2015): 1406-1419.

The evolutionary canary in the coal mine*

*note -this post originally appeared on the Applied Ecologist's blog

Like canaries in coal mines, species can provide important information about deteriorating environmental conditions. A whole sub-discipline of environmental biomonitoring has emerged to provide the necessary tools to evaluate biological responses to changes in environmental conditions. While historically biomonitoring focused on contaminant concentrations in sentinel species –such as heavy metals in clams; modern biomonitoring uses information across multiple biological levels of organisation, from tissues, to organism behaviour, to the abundances and distributions of species. Since it is impossible to assess every aspect of an ecosystem’s response to pollution, scientists and practitioners still need to make decisions about which elements of an ecosystem should be monitored.

A coal miner with a canary –the classic sentinel species (url for photo: http://www.academia.dk/Blog/wp-content/uploads/CanaryInACoalMine_2.jpg)

In freshwater systems, diatoms are often the preferred organisms for monitoring since they have high diversity and diatom communities are structured strongly by local environmental conditions. Because of their long use in biomonitoring, freshwater biologists have sensitivity and indicator values for thousands of diatom species. Thus, in principle, you should be able to sample diatom communities in lakes and rivers of interest, and then assess the water quality based on the presence and abundance of different diatom species. While such proxies should always be validated and interpreted carefully (Stephens et al. 2015), there is a long and successful history of using diatoms for environmental monitoring.

Image of diatoms from a scanning electron microscope. (By Kostas Tsobanoglou - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45315566)

The difficulty in practice is to identify diatom species, which requires expert training and can be time consuming. A number of researchers have pursued proxies and surrogates, for example using life form (e.g., diatom shape) or higher taxonomic groupings, instead of identifying species (Wunsam, Cattaneo & Bourassa 2002). In a recent article in the Journal of Applied Ecology, Francois Keck and colleagues (Keck et al. 2016) take this one step further, by using diatom evolutionary relationships as the biomonitoring tool.

Keck et al. employ novel statistical methods to create clusters of species based on their evolutionary relatedness from a phylogenetic tree and species’ sensitivity to pollution and show that these clusters, when delineated by short to moderate phylogenetic distances, do a good job of replicating species-level community pollution sensitivity indices.

This may seem like a onerous task, to assign diatoms to a correct position on a phylogenetic tree, but with the availability and now widespread use of DNA barcoding techniques, it is becoming easier to get genetic data for microscopic assemblages than to identify cells to species. This means that samples can be fit to the phylogenetic clusters without needing to shift through samples. Further, if species are observed, which have not been properly assessed for their sensitivity, they can be assigned an expected sensitivity value based on their relatedness to assessed species.

The phylogenetic tree and species’ sensitivities (Fig. 2 in Keck et al.).

While diatom evolutionary history may not have been strongly influenced by environmental pollutants in the past –because they are relatively recent stressors; it is clear from Keck et al.’s results that closely related species are similarly sensitive to pollution. Other fields of applied management have also begun to incorporate evolutionary history in the design and assessment of applied actions –for example, restoration (Hipp et al. 2015). Evolutionary history can provide important insights and management tools for dealing with the consequences of environmental change.

References

Hipp, A.L., Larkin, D.J., Barak, R.S., Bowles, M.L., Cadotte, M.W., Jacobi, S.K., Lonsdorf, E., Scharenbroch, B.C., Williams, E. & Weiher, E. (2015) Phylogeny in the Service of Ecological Restoration. American Journal of Botany, 102, 647-648.

Keck, F., Bouchez, A., Franc, A. & Rimet, F. (2016) Linking phylogenetic similarity and pollution sensitivity to develop ecological assessment methods: a test with river diatoms (microalgae). Journal of Applied Ecology.

Stephens, P.A., Pettorelli, N., Barlow, J., Whittingham, M.J. & Cadotte, M.W. (2015) Management by proxy? The use of indices in applied ecology. Journal of Applied Ecology, 52, 1-6.

Wunsam, S., Cattaneo, A. & Bourassa, N. (2002) Comparing diatom species, genera and size in biomonitoring: a case study from streams in the Laurentians (Quebec, Canada). Freshwater Biology, 47, 325-340.

Debating limits on diversity in class

I wrote a while ago about the debate on whether global diversity has ecological limits, based on two papers from Harmon and Harrison, and Rabosky and Hurlbert. This was in turn based on a debate from the ASN meeting (aside: there should be more formal debates at conferences). I decided to try replicating this debate in the Advanced Ecology class I'm teaching with Kendi Davies, and I was pleasantly impressed with the outcome. The class is mostly upper year students and small (~25 people), and the focus is on reading the primary literature and exploring key topics in ecology using active learning techniques (e.g. 1, 2). Since we're reading about patterns and processes of diversity through space and time, the debate topic was fitting.

The debate was split over two classes - in the first, students were split into two groups and they prepared their opening and closing statements and their supporting arguments. I've tried having students use Google documents and slides for these kind of group collaborative activities, and it seems to work well. (This is in part because there are 'lender laptops' available from the department's IT, which means that all students can participate, even without owning a personal laptop). What is great about Google docs is that when anyone adds or removes or edits text, the other members of the group can see it in real time, which seems to encourage more students to be actively involved than if, say, a single student is taking notes. Each group decided who would present the opening statement, each supporting argument, the rebuttal statement, and the closing statement, and who would take notes and prep the rebuttal.

To raise the stakes a bit, the winning team would get a pass on one homework assignment (the other motivator presumably being fear of letting their group down). What impressed me was how engaged students were during prep and during the actual debate. (For example, during prep, students were watching videos on how to debate, and expressed some concerns about espionage by the other teams ;-) ) More seriously, they took the time to understand the arguments presented in the source literature, and went beyond that to integrate support from other primary literature. I think at times students (okay, most of us) can get away with skimming papers for the key points: this rewarded them for reading carefully and thoughtfully.

Current US political debates provided instruction
on what not to do (from cnn.com).

The judges were a few generous postdocs (motivated by the promise of free food), who not only scored the debates, but gave some feedback to the teams. Ironically, the winning team had argued that “Species Diversity Is Dynamic and Unbounded at Local and Continental Scales” (after Harmon and Susan Harrison), but the class was nearly unanimous that they personally felt that there likely were ecological limits on diversity.

What I would do differently next time:

• Plan some redundancy - a couple of people were sick, etc, who had roles in the debate. This left team members scrambling a bit. 
• Group sizes: 12 people is a bit big for a group and makes coordination difficult. It might be possible to have smaller groups and do 2 sets of debates. Or, alternatively, to assign half the class as judges (or press - another prof here uses students as press who have to prepare questions for the debaters).
• Consider not randomly assigning people to groups - it might be better to try to balance teams.
• Public speaking and argument logic - interestingly, most of the students have little experience in constructing convincing and well supported arguments. We talk a lot about hypothesis construction with STEM students, but persuasive speech and writing receive less attention. Things like 'signposting' important points could use more practice.

Pulling a fast one: getting unscientific nonsense into scientific journals. (or, how PLOS ONE f*#ked up)

The basis of all of science is that we can explain the natural world through observation and experiments. Unanswered questions and unsolved riddles are what drive scientists, and with every observation and hypothesis test, we are that much closer to understanding the universe. However, looking to supernatural causes for Earthly patterns is not science and has no place in scientific inquiry. If we relegate knowledge to divine intervention, then we fundamentally lose the ability to explain phenomena and provide solutions to real world problems.

Publishing in science is about leaping over numerous hurdles. You must satisfy the demands of reviewers and Editors, who usually require that methodologies and inferences satisfy strict and ever evolving criteria -science should be advancing. But sometimes people are able to 'game the system' and get junk science into scientific journals. Usually, this happens by improper use of the peer review systems or inventing data, but papers do not normally get into journals while concluding that simple patterns conform to divine intervention.

Such is the case in a recent paper published in the journal PLOS ONE. This is a fairly pedestrian paper about human hand anatomy and they conclude that anatomical structures provide evidence of a Creator. They conclude that since other primates show a slight difference in tendon connections, a Creator must be responsible for the human hand (well at least the slight, minor modification from earlier shared ancestors). Obviously this lazy science and an embarrassment to anyone that works as an honest scientist. But more importantly, it calls into question the Editor who handled this paper (Renzhi Han, Ohio State University Medical Center), but also PLOS ONE's publishing model. PLOS ONE handles thousands of papers and requires authors to pay for the costs of publishing. This may just be an aberration, a freak one-off, but the implications of this seismic f$@k up, should cause the Editors of PLOS to re-evaluate their publishing model.  

What explains persistent species' rarity in communities?

Someone asked me what is the most important or lingering issue in community ecology recently. (There’s probably a whole post to answer that question (to come...)). One answer is the mystery of species coexistence: for more than 50 years (from Hutchinson’s paradox of the plankton through today) we have tried to explain the immense and variable diversity on earth by understanding what allows two or more species to coexist. There are many ways to explain coexistence, and yet the details and the specifics for any given system are also still usually incompletely understood.

A good and fascinating example is that of persistent rarity. Why are so many species in communities rare? What allows species to remain rare for long periods of time, given that small populations should be at greater risk for stochastic extinction? A new preprint from Yenni et al. (1) considers the empirical evidence for one potential explanation for persistent rarity: asymmetric negative frequency dependence (see also Yenni et al. 2012 (2)).

Coexistence theory (Chesson 2000) considers stabilizing mechanisms to be those that allow intraspecific competition to be greater than interspecific competition (often defined as ‘niche’ mechanisms). The strength of such stabilizing mechanisms can be estimated by looking at how a species’ population growth rate is limited by the frequency of conspecifics compared to the frequency of heterospecifics in the community. Negative frequency dependence is expected when stabilizing mechanisms are strong. This allows species to increase when rare, since limitation by conspecifics is low, followed by a decline in growth rates as conspecific frequency increases.

Asymmetric negative frequency dependence may explain persistent rarity, since it suggests especially strong conspecific limitation. As a species’ frequency increases, their growth rate greatly declines and intraspecific interactions, rather than interspecific competition, determine abundances. Species are rare, but also less likely to experience extinctions through competition with other species. The authors suggest that as a result of this, we should expect rare species to have stronger negative frequency dependence, in comparison to more common species. They look for evidence for asymmetric frequency dependence using data from 148 communities collected across multiple taxonomic groups (birds, fish, herpetofauna, invertebrates, mammals, and plants), 5 continents, and 3 trophic levels. The data represented time series of species abundances, which the authors used to estimate negative frequency dependence as the relationship between a species’ frequency in the community and their annual per capita population growth rate.

Several aspects of the results are particularly interesting. First, the authors had to omit rare species that are not persistent, since other processes likely explain the presence of such ephemeral members of communities. The frequency of ephemeral species (not stably coexisting at a local scale), for example, was quite high, particularly in plant communities (average of 82 species per community, of which only 22.6 species were on average identified as ‘persistent’). This may suggest the importance of spatial mechanisms for coexistence or co-occurrence. Their overall prediction of stronger negative frequency dependence in rare species appeared to holds in 46% of the communities they examined, consistently for all of the taxonomic groups but one (herps!). Additionally, the opposite pattern (common species having stronger negative frequency dependence) was never observed.

Rarity in nature is common :-) but not well predicted using most coexistence theory. Many interesting and important questions arise from it, and from results like those shown in Yanni et al. For example, do rare species have rare traits or rare niches? Is the frequency dependent growth rate context dependent (i.e. can a species be strongly limited by conspecifics in one environment but not another)?

*Note I haven’t reproduced any figures here, since this is a preprint. However, it is openly available, so do have a look (link 1 below). I’m not certain if there is a rule of thumb on blogging about preprints, but I imagine it is much like blogging about conference talks. The work may not have been peer reviewed/published yet, but the broad results and ideas remain interesting to discuss.

References:

1. Glenda Yenni, Peter Adler, Morgan Ernest. Do persistent rare species experience stronger negative frequency dependence than common species? doi: http://dx.doi.org/10.1101/040360. Preprint.

2. Yenni, Glenda, Peter B. Adler, and S. K. Ernest. "Strong self‐limitation promotes the persistence of rare species." Ecology 93.3 (2012): 456-461.

Grad school is like...

This post exists for no reason other than that I heard some fantastic analogies for graduate school/academic endeavours too good not to share... :-)

Starting grad school is like being dropped into a jungle with a machete and being told "find something new". Maybe (video game-like) you have a supportive supervisor, and so you are given a crude map. If you have labmates or fellow students, you can fend off the predators together. A good funding source allows you to travel faster. Best of all, maybe you come across some Tilley-hatted explorer who is so excited about the jungle that they give you detailed directions. There are infinite paths through the jungle, but some are harder than others.

The other analogy was for how to be a good supervisor, which is like a parent teaching a child to ride a bike. The parent can push a child off, and say "peddle!". This will be followed by lots of crashes and scraped knees and maybe the odd close call with traffic. Maybe the child figures it out, and is a fearless cyclist. But they might give up on biking all together, too. Or, the parent can hold the handles the whole time and say "great work! you're riding a bike all by yourself!" The result is a confident little cyclist who will probably crash when they finally get the opportunity to ride without help. A good supervisor probably holds on at first, then graduates you to training wheels and then takes them off. There will still be a few crashes, but the result is a cyclist not afraid to go alone, and without too many cuts and bruises.

Or maybe you've heard better ones?