87 years ago, in ecology

Louis Emberger was an important French plant ecologist in the first half of the last century, known for his work on the assemblages of plants in the mediterranean.

For example, the plot below is his published diagram showing minimum temperature of the coolest month versus a 'pluviometric quotient' capturing several aspects of temperature and precipitation from:

Emberger; La végétation de la région méditerranienne. Rev. Gén. Bot., 42 (1930)

Note this wasn't an unappreciated or ignored paper - it received a couple hundred citations, up until present day. Further, updated versions have appeared in more recent years (see bottom).

So it's fascinating to see the eraser marks and crossed out lines, this visualisation of scientific uncertainty. The final message from this probably depends on your perspective and personality:

• Does it show that plant-environment modelling has changed a lot or that plant environmental modelling is still asking about the same underlying processes in similar ways?
• Does this highlight the value of expert knowledge (still cited) or the limitations of expert knowledge (eraser marks)? 

It's certainly a reminder of how lucky we are to have modern graphical software :)

E.g. updated in Hobbs, Richard J., D. M. Richardson, and G. W. Davis. "Mediterranean-type ecosystems: opportunities and constraints for studying the function of biodiversity." Mediterranean-Type Ecosystems. Springer Berlin Heidelberg, 1995. 1-42.











Thanks to Eric Garnier, for finding and sharing the original Emberger diagram and the more recent versions.

Can coexistence theories coexist?

These days, the term ‘niche’ manages to cover both incredibly vague and incredibly specific ideas. All the many ways of thinking about an organism’s niche fill the literature, with various degrees of inter-connection and non-independence. The two dominant descriptions in modern ecology (last 30 years or so) are from ‘contemporary niche theory’ and ‘modern coexistence theory’. Contemporary niche theory is developed from consumer-resource theory, where organisms' interactions occur via usage of shared resources. (Though it has expanded to incorporate predators, mutualists, etc), Analytical tools such as ZNGIs and R* values can be used to predict the likelihood of coexistence (e.g. Tilman 1981, Chase & Leibold 2003). Modern coexistence theory is rooted in Peter Chesson’s 2000 ARES review (and earlier work), and describes coexistence in terms of fitness and niche components that allow positive population growth.

On the surface these two theories share many conceptual similarities, particularly the focus on measuring niche overlap for coexistence. [Chesson’s original work explicitly connects the R* values from Tilman’s work to species’ fitnesses in his framework as well]. But as a new article in Ecological Monographs points out, the two theories are separated in the literature and in practice. The divergence started with their theoretical foundations: niche theory relied on consumer-resource models and explicit, mechanistic understanding of organisms’ resource usage, while coexistence theory was presented in terms of Lotka-Volterra competition models and so phenomenological (e.g. the mechanisms determining competition coefficients values are not directly measured). The authors note, “This trade-off between mechanistic precision (e.g. which resources are regulating coexistence?) and phenomenological accuracy (e.g. can they coexist?) has been inherited by the two frameworks….”

There are strengths and weaknesses to both approaches, and both have been used in important ecological studies. So it's surprising that they are rarely mentioned in the same breathe. Letten et al. answer an important question: when directly compared, can we translate the concepts and terms from contemporary niche theory into modern coexistence theory and vice versa?

Background - when is coexistence expected? 
Contemporary niche theory (CNT) (for the simplest case of two limiting resources): for each species, you must know the consumption or impact they have on each resource; the ratio at which the two resources are supplied, and the ZNGIs (zero net growth isoclines, which delimit the resource conditions a species can grow in). Coexistence occurs when the species are better competitors for different resources, when each species has a greater impact on their more limiting resource, and when the supply ratio of the two resources doesn’t favour one species over the other. (simple!)

For modern coexistence theory (MCT), stable coexistence occurs when the combination of fitness differences and niche differences between species allow both species to maintain positive per capita growth rates. As niche overlap decreases, increasingly small fitness differences are necessary for coexistence.

Fig 1, from Letten et al. The criteria for coexistence under modern coexistence theory (a) and contemporary niche theory (b).  In (a), f1 and f2 reflect species' fitnesses. In (b) "coexistence of two species competing for two substitutable resources depends on three criteria: intersecting ZNGIs (solid red and blue lines connecting the x- and y-axes); each species having a greater impact on the resource from which it most benefits (impact vectors denoted by the red and blue arrows); and a resource supply ratio that is intermediate to the inverse of the impact vectors (dashed red and blue lines)."

So how do these two descriptions of coexistence relate to each other? Letten et al. demonstrate that:
1) Changing the supply rates of resources (for CNT) impacts the fitness ratio (equalizing term in MCT). This is a nice illustration of how the environment affects the fitness ratios of species in MCT.

2) Increasing overlap of the impact niche between two species under CNT is consistent with increasing overlap of modern coexistence theory's niche too. When two species have similar impacts on their resources, there should be very high niche overlap (weak stabilizing term) under MCT too.

3) When two species' ZNGI area converge (i.e. the conditions necessary for positive growth rates), it affects both the stabilizing and equalizing terms in MCT. However, this has little meaningful effect on coexistence (since niche overlap increases, but fitness differences decrease as well).

This is a helpful advance because Letten et al. make these two frameworks speak the same (mathematical) language. Further, this connects a phenomological framework with a (more) mechanistic one. The stabilizing-equalizing concept framework (MCT) has been incredibly useful as a way of understanding why we see coexistence, but it is not meant to predict coexistence in new environments/with new combinations of species. On the other hand, contemporary niche theory can be predictive, but is unwieldy and information intensive. One way forward may be this: reconciling the similarities in how both frameworks think about coexistence.

Letten, Andrew D., Ke, Po-Ju, Fukami, Tadashi. 2016. Linking modern coexistence theory and contemporary niche theory. Ecological Monographs: 557-7015. http://dx.doi.org/10.1002/ecm.1242
(This is a monograph for a reason, so I am just covering the major points Letten et al provide in the paper. It's definitely worth a careful read as well!).

When individual differences matter - intraspecific variation in 2016

Maybe it is just confirmation bias, but there seems to have been an upswing in the number of cool papers on the role of intraspecific variation in ecology. For example, three new papers highlight the importance of variation among individuals for topics ranging from conservation, coexistence, and community responses to changing environments. All are worth a deeper read.

‘An Anthropocene map of genetic diversity’ asks how intraspecific variation is distributed globally, a simple but important question. Genetic diversity in a species is an important predictor of their ability to adapt to changing environments. For many species, however, as their populations decline in size, become fragmented, or experience strong selection related to human activities, genetic diversity may be in decline. Quantifying a baseline for global genetic diversity is an important goal. Further, with the rise of ‘big data’ (as people love to brand it) it is now an accessible one: there are now millions of genetic sequences in GenBank and associated GPS coordinates. 

Many of the global patterns in genetic diversity agree with those seen for other forms of diversity: for example, some of the highest levels are observed in the tropical Andes and Amazonia, and there is a peak in the mid-latitudes and human presence seems to decrease genetic diversity.

From Miraldo et al. (2016): Map of uncertainty. Areas in green represent high sequence availability and taxonomic coverage (of all species known to be present in a cell). All other colors represent areas lacking important data.

The resulting data set represents ~ 5000 species, so naturally the rarest species and the least charismatic are underrepresented. The authors identify this global distribution of ignorance, highlighting just how small our big data still is.

Miraldo, Andreia, et al. "An Anthropocene map of genetic diversity." Science353.6307 (2016): 1532-1535.


In ‘How variation between individuals affects species coexistence’, Simon Hart et al. do the much needed work to answer the question of how intraspecific variation fits into coexistence theory. Their results reinforce the suggestion that in general, intraspecific variation should making coexistence more difficult, since it increases the dominance of superior competitors, and reduces species' niche differentiation. (Note this is a contrast to the argument Jim Clark has made with individual trees, eg. Clark 2010)

Hart, Simon P., Sebastian J. Schreiber, and Jonathan M. Levine. "How variation between individuals affects species coexistence." Ecology letters (2016).


The topic of evolutionary rescue is an interesting, highlighting (see work from Andy Gonzalez and Graham Bell for more details) the ability of populations to adapt to stressors and changing environments, provided enough underlying additive genetic variation and time is available. It has been suggested that phenotypic plasticity can reduce the chance of evolutionary rescue, since it reduces selection on genetic traits. Alternatively, by increasing survival time following environmental change, it may aid evolutionary rescue. Ashander et al. use a theoretical approach to explore how plasticity interacts with a change in environmental conditions (mean and predictability/autocorrelation) to affect extinction risk (and so the chance of evolutionary rescue). Their results provide insight into how the predictability of new environments, through an affect on stochasticity, in turn changes extinction risk and rescue.

Ashander, Jaime, Luis-Miguel Chevin, and Marissa L. Baskett. "Predicting evolutionary rescue via evolving plasticity in stochastic environments." Proc. R. Soc. B. Vol. 283. No. 1839. The Royal Society, 2016.

The problematic effect of small effects

Why do ecologists often get different answers to the same question? Depending on the study, for example, the relationship between biodiversity and ecosystem function could be positive, negative, or absent (e.g. Cardinale et al. 2012). Ecologists explain this in many ways - experimental issues and differences, context dependence. However, it may also be due to an even simpler issue, that of the statistical implications of small effect sizes.

This is the point that Lemoine et al. make in an interesting new report in Ecology. Experimental data from natural systems (e.g. for warming experiments, BEF experiments) is often highly variable, has low replication, and effect sizes are frequently small. Perhaps it is not surprising we see contradictory outcomes, because data with small true effect sizes are prone to high Type S (reflect the chance of obtaining the wrong sign for an effect) and Type M (the amount by with an effect size must be overestimated in order to be significant). Contradictory results arise from these statistical issues, combined with the idea that papers that do get published early on may simply have found significant effects by chance (the Winner's Curse). 

Power reflects the chance of failing to correctly reject the null hypothesis (Ho). The power of ecological experiments increases with sample size (N), since uncertainty in data decreases with increasing N. However, if your true effect size is small, studies with low power have to significantly overestimate the effect size to have a significant p-value. This is the result of the fact that if the variation in your data is large and your effect size is small, the critical value for a significant z-score is quite large. Thus for your results to be significant, you need to observe an effect larger than this critical value, which will be much larger than the true effect size. It's a catch-22 for small effect sizes: if your result is correct, it very well may not be significant; if you have a significant result, you may be overestimating the effect size. 

From Lemoine et al. 2016. 

The solution to this issue is clearly a difficult one, but the authors make some useful suggestions. First, it's really the variability of your data, more than the sample size, that raises the Type M error. So if your data is small but beautifully behaved, this may not be a huge issue for you (but you must be working in a highly atypical system). If you can increase your replication, this is the obvious solution. But the other solutions they see are cultural shifts when we publish statistical results. As with many other, the authors suggest we move away from reliance on p-values as a pass/fail tool for results. In addition to reporting p-values, they suggest we report effect sizes and their error rates. Further, that this be done for all variables regardless of whether the results are significant. Type M error and power analyses can be reported in a fashion meant to inform interpretation of results: “However, low power (0.10) and high Type M error (2.0) suggest that this effect size is likely an overestimate. Attempts to replicate these findings will likely fail.” 

Lemoine, N. P., Hoffman, A., Felton, A. J., Baur, L., Chaves, F., Gray, J., Yu, Q. and Smith, M. D. (2016), Underappreciated problems of low replication in ecological field studies. Ecology. doi: 10.1002/ecy.1506

The Forest, the Trees, and the Phylo-diversity Jungle

with Florent Mazel

As has been a recurrent topic on the blog recently (here, and here and elsewhere), it is difficult to know when it is appropriate and worthwhile to write responses to published papers. Further, a number of journals don't provide clear opportunities for responses even when they are warranted. And maybe, even when published, most responses won't make a difference anyways. 

Marc Cadotte and I and our coauthors experienced this first hand when we felt a paper of ours had been misconstrued. We wanted to provide a useful, positive response, but whether the time investment was worthwhile was unclear. The journal then informed us they didn't publish responses. We tried instead to write a 'News and Views' piece for the journal, which it ultimately declined to publish. And really, a response piece is at cross-purposes from the usual role of N&V (positive editorials). In the end, rather than spend more time on this, we made the manuscript available as a preprint, found here. 

The initial response was to a publication in Ecography from Miller et al. (2016) [citations below]. Their paper that does a nice job of asking how well 32 phylo-diversity metrics and nine null models discriminate between community assembly mechanisms. The authors first simulated communities under three main assembly rules, competitive exclusion, habitat filtering, and neutral assembly. They then tested which combination of metrics and null models yielded the best statistical performance. Surprisingly, only a fraction of phylo-diversity metrics and null models exhibited both high statistical power coupled with low Type I error rate. Miller et al. conclude that, for this reason, some metrics and null models proposed in the literature should be avoided when asking if filtering and competition play an important role in structuring communities. This is a useful extension for the eco-phylogenetic literature. However, the authors also argue that their results show that a framework for phylodiversity metrics introduced in a paper by myself and coauthors (Tucker et al. 2016) was subjective and should not be used. 

What was disappointing is that there is a general issue (how can we best understand phylogenetic metrics for ecology?) that could benefit from further discussion in the literature.

Metrics can be analysed and understood in two ways: (1) by grouping them based on their underlying properties (e.g. by comparing mathematical formulations); and (2) by assessing context-dependent behaviour (e.g. by comparing metric performance in relation to particular questions). The first approach requires theoretical and cross-disciplinary studies to summarize the main dimensions along which phylo-diversity metrics vary, while the second provides a field-specific perspective to quantify the ability of a particular metric to test a particular hypothesis. These two approaches have different aims, and their results are not necessarily expected to be identical.

One reason there are so many metrics is that they have been pooled across community ecology, macroecology and conservation biology. The questions typically asked by conservationists and macroecologists, for example, differ from those of community ecologists. Different metrics frequently perform better or worse for different types of problems. The second approach to metrics provides a solution to this problem through explicitly simulating the processes of interest for a given research question (e.g. vicariance or diversification processes in macroecological research), and selecting the most appropriate metric for the task. The R package presented by Miller et al., as well as others (e.g. Pearse et al. 2015) all help facilitate this approach. And it can be very useful to a field when this is done thoroughly.

But this approach has some limitations as well - it is inefficient and sensitive to choices made in the simulation process. It also doesn't provide a framework or context in which to understand results. The general approach fills this need: the Tucker et al. paper took this approach and classified 70 phylo-diversity metrics along three broad mathematical dimensions: richness, divergence and regularity--the sum, mean and variance of phylogenetic distances among species of assemblages, respectively. This framework is analogous to a system for classifying functional diversity metrics (e.g. Villéger et al. 2008), allowing theoretical linkages between phylogenetic and functional approaches in ecology. We also carried out extensive simulations to corroborate the metric behaviour classification system across different assembly scenarios.

The minor point to me is that, although Miller et al. concluded this tripartite framework performed poorly, their results appear to provide independent support for the tripartite classification system. (And this is despite some methodological differences, including using a clustering algorithm instead of an ordination approach for metric grouping). The vast majority of metrics used by Miller et al. on their simulated communities group according to this richness-divergence-regularity classification system (see our Fig 2 vs. Miller et al.'s Fig 1B). And metrics like HAED and EED, which stem from a mathematical combination of richness and regularity dimensions, are expected to sometimes cluster with richness (as observed by Miller et al. but noted as evidence against our framework), and sometimes with regularity. There is specific discussion on this type of behaviour in Tucker et al., 2016.

Tucker et al. Fig. 2. "Principal components analysis for Spearman’s correlations between the a-diversity metrics shown in Table 1. Results represent measures taken from 800 simulated landscapes, based on 100 simulated phylogenetic trees and eight landscape types defined in Table 2 (see Appendix S2) for detailed methods. (A) All metrics excluding abundance-weighted metrics and those classified as parametric indices. (B) As in A, but with abundance-weighted metrics included (underlined). (C) As in B, but with parametric indices (black), and indices that incorporate multiple dimensions (underlined) included (e.g. all a-diversity metrics). X and Y axes are scaled to reflect explained variance (PC1 = 41.8%; PC2 = 20.5% for the PCA performed with all metrics, shown in (C))." 

Miller et al. Fig 1B.  "Dendrogram of intercorrelations among the phylogenetic community structure metrics, including species richness itself (labeled richness). Group 1 metrics focus on mean relatedness; Group 2 on nearest-relative measures of community relatedness; and Group 3 on total community diversity and are particularly closely correlated with species richness. Four metrics, PAE, EED , IAC, and EAED show variable behavior. They do not consistently cluster together or with each other, and we refer to their placement as unresolved. The branches of the dendrogram are colored according to the metric classifications proposed by Tucker et al. (2016): green are “regularity” metrics, pink are “richness” metrics, and yellow are “divergence” metrics."

The major point is that dismissing general approaches can lead to more confusion about phylogenetic metrics, leading users to create even more metrics (please don't!), to conclude that particular metrics should be discarded, or to adopt hard-to-interpret metrics because some study found they were highly correlated with a response. Context is necessary.

I think both approaches have utility, and importantly, both approaches benefit each other. On one hand, detailed analyses of metric performance offer a valuable test of the broader classification system, using alternative simulations and codes. On the other hand, broad syntheses offer a conceptual framework within which results of more focussed analyses may be interpreted.

For example, comparing Miller et al.'s results with the tripartite framework provides some additional interesting insight. They found that metrics closely aligned with only a single dimension are not the best indicators of community assembly. In their results, sometimes the metrics with the best statistical performances are Rao’s quadratic entropy and IntraMPD. Because of the general framework, we know that these classified as are 'hybrid' metrics that include both richness and divergence in phylogenetic diversity. Taking it one step further, because the general framework connects with functional ecology metrics, we can compare their findings about Rao's QE/IntraMPD to results using corresponding dimensions in the functional trait literature. Interestingly, functional ecologists have found that community assembly processes can alter multiple dimensions of diversity (e.g. both richness and divergence)(Botta-Dukát and Czúcz 2016), which may provide insight to why a hybrid metric is useful for understanding community assembly.

In summary, there is both a forest and individual trees, and both of these are valid approaches. I hope that we can continue complement broad-scale syntheses with question- and hypothesis-specific studies, and that as a result the field can be clarified.

References:
Botta-Dukát, Z. and Czúcz, B. 2016. Testing the ability of functional diversity indices to detect trait convergence and divergence using individual-based simulation. - Methods Ecol. Evol. 7: 114–126. 

Bryant, J. A. et al. 2008. Microbes on mountainsides: contrasting elevational patterns of bacterial and plant diversity. - Proc. Natl. Acad. Sci. U. S. A. 105: 11505–11. 

Graham, C. H. and Fine, P. V. A. 2008. Phylogenetic beta diversity: linking ecological and evolutionary processes across space in time. - Ecol. Lett. 11: 1265–1277. 

Hardy, O. 2008. Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community. - J. Ecol. 96: 914–926. 

Isaac, N. J. B. et al. 2007. Mammals on the EDGE: conservation priorities based on threat and phylogeny. - PLoS One 2: e296. 

Kraft, N. J. B. et al. 2007. Trait evolution, community assembly, and the phylogenetic structure of ecological communities. - Am. Nat. 170: 271–283. 

Miller, E. T. et al. 2016. Phylogenetic community structure metrics and null models: a review with new methods and software. - Ecography. DOI: 10.1111/ecog.02070

Pavoine, S. and Bonsall, M. B. 2011. Measuring biodiversity to explain community assembly: a unified approach. - Biol. Rev. 86: 792–812. 

Pearse, W. D. et al. 2014. Metrics and Models of Community Phylogenetics. - In: Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology. Springer Berlin Heidelberg, pp. 451–464. 

Pearse, W. D. et al. 2015. pez : phylogenetics for the environmental sciences. - Bioinformatics 31: 2888–2890. 

Tucker, C. M. et al. 2016. A guide to phylogenetic metrics for conservation, community ecology and macroecology. - Biol. Rev. Camb. Philos. Soc. doi: 10.1111/brv.12252.

Vellend, M. et al. 2010. Measuring phylogenetic biodiversity. - In: McGill, A. E. M. B. J. (ed), Biological diversity: frontiers in measurement and assessment. Oxford University Press, pp. 193–206. 

Villéger, S. et al. 2008. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. - Ecology 89: 2290–2301. 

Webb, C. O. et al. 2002. Phylogenies and Community Ecology. - Annu. Rev. Ecol. Evol. Syst. 33: 475–505. 

Winter, M. et al. 2013. Phylogenetic diversity and nature conservation: where are we? - Trends Ecol. Evol. 28: 199–204.

The pessimistic and optimistic view of BEF experiments?

The question of the value of biodiversity-ecosystem function (BEF) experiments—their results, their relevancy—has become a heated one in the literature. An extended argument over the last few years has debated the assumption that local biodiversity is in fact in decline (e.g. Vellend et al. 2013; Dornelas et al. 2014; Gonazalez et al. 2016). If biodiversity isn't disappearing from local communities, the logical conclusion would be that experiments focussed on the local impacts of biodiversity loss are less relevant.

Two papers in the Journal of Vegetation Science (Wardle 2016 and Eisenhauer et al. 2016) continue this discussion regarding the value of BEF experiments for understanding biodiversity loss in natural ecosystems. From reading both papers, it seems as though broadly speaking, the authors agree on several key points: that results from biodiversity-ecosystem functioning experiments don’t always match observations about species loss and functioning in nature, and that nature is much more complex, context-dependent, and multidimensional than typical BEF experimental systems. (The question of whether local biodiversity is declining may be more contested between them). 

Biodiversity and ecosystem experiments typically involve randomly assembled plant communities containing either the full complement of species, or subsets containing different numbers of species. Communities containing lower numbers are meant to provide information about the loss of species diversity a system. Functions (often including, but not limited to, primary productivity or biomass) are eventually measured and analysed in relation to treatment diversity. Although some striking results have come out of these types of studies (e.g. Tilman and Downing 1996), they can vary a fair amount in their findings (Cardinale et al. 2012).

David Wardle’s argument is that BEF experiments differ a good deal from natural systems: in natural systems, BEF relationships can take different forms and explain relatively little variation, and so extrapolating from existing experiments seems uninformative. In nature, changes in diversity are driven by ecological processes (invasion, extinction) and experiments involving randomly assembled communities and randomly lost species do nothing to simulate these processes. Wardle seems to feel that the popularity of typical BEF experiments has come at the cost of more realistic experimental designs. This is something of a zero-sum argument, (although in some funding climates that may be true...). But it is true that big BEF experiments tend to be costly and take time and labour, meaning that there is an impetus to publish as much as possible from each one. Given BEF experiments have changed drastically in design once already, in response to criticisms about their inability to disentangle complementarity vs. portfolio effects, it seems they are not inflexible about design though.

Eisenhauer et al. agree in principle that current experiments frequently lack a realistic design, but suggest that there are plenty of other types of studies (looking at functional diversity or phylogenetic diversity, for example, or using random loss of species) being published as well. For them too, there is value in having multiple similar experiments: this allows metaanalysis and comparison aggregation, and will help to tease apart the important mechanisms eventually. Further, realism is difficult to obtain in the absence of a baseline for a “natural, untouched, complete system” from which to remove species.

The point that Eisenhauer et al. and Wardle appear to agree on most strongly is that real systems are complex, multi-dimensional and context-dependent. Making the leap from a BEF experiment with 20 plant species to the real world is inevitably difficult. Wardle sees this is a massive limitation, Eisenhauer et al. sees it as a strength. Inconsistencies between experiments and nature are information that highlight when context matters. By having controlled experiments in which you vary context (such as by manipulating both nutrient level and species richness), you can begin to identify mechanisms.

Perhaps this is the greatest problem with past BEF work, is that there is a tendency to oversimplify the interpretation of results – to conclude that ‘loss of diversity is bad’ but with less attention to ‘why’, 'where', or 'when’. The best way to do this depends on your view of how science should progress. 

Wardle, D. A. (2016), Do experiments exploring plant diversity–ecosystem functioning relationships inform how biodiversity loss impacts natural ecosystems?. Journal of Vegetation Science, 27: 646–653. doi: 10.1111/jvs.12399

Eisenhauer, N., Barnes, A. D., Cesarz, S., Craven, D., Ferlian, O., Gottschall, F., Hines, J., Sendek, A., Siebert, J., Thakur, M. P., Türke, M. (2016), Biodiversity–ecosystem function experiments reveal the mechanisms underlying the consequences of biodiversity change in real world ecosystems. Journal of Vegetation Science. doi: 10.1111/jvs.12435

Additional References:
Vellend, Mark, et al. "Global meta-analysis reveals no net change in local-scale plant biodiversity over time." Proceedings of the National Academy of Sciences 110.48 (2013): 19456-19459.

Dornelas, Maria, et al. "Assemblage time series reveal biodiversity change but not systematic loss." Science 344.6181 (2014): 296-299.

Gonzalez, Andrew, et al. "Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity." Ecology (2016).

Tilman, David, and John A. Downing. "Biodiversity and stability in grasslands." Ecosystem Management. Springer New York, 1996. 3-7.

Cardinale, Bradley J., et al. "Biodiversity loss and its impact on humanity."Nature 486.7401 (2012): 59-67.

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.

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.

New ways to address an old idea: rethinking the regional species pool

Like many concepts in ecology (metacommunity, community), the idea of a regional species pool is useful, makes conceptual sense, and is incredibly difficult to apply to real data. Originally, the idea of a species pool came from the theory of island biogeography (MacArthur and Wilson, 1967), where it referred to all the species that could disperse to an island. Today, the regional species pool appears frequently, across null models, studies of community assembly both empirical and theoretical, and metacommunity theory. 

Understanding how particular processes shape community membership—whether the environmental, competition, or dispersal limitation—depends on knowing the identity of all the species that could have potentially assembled there. The species pool as defined by the research provides the frame of reference against which to consider a community's composition. Most null models of community assembly rely on correctly identifying this set of species, and worse, tend to be very sensitive to bias in how the regional pool is defined. If you include all species physically present in a region, in your species pool, environmental filtering may appear to be particularly important simply because many of those species can’t actually survive in your community (the narcissus effect). Given the importance of null models to community ecology, defining the species pool appropriately is an ongoing concern.

There are many decisions that can be made when asking 'which species could potentially be members of a community'? You could include all species that can physically arrive at a site (so only dispersal or geographic distance limits membership), or only include those species that can both arrive and establish (both dispersal and environmental conditions limit membership). Further, the availability of data is key: if you use observational data used to determine the environmental limitations, you may also incorporate the outcome of biotic interactions indirectly. If some species are rare and have low observation likelihoods, they will be under-represented. Abundances may be useful but inaccurate depending on how they are measured. Finally, it is common to define species as either present or not present for a species pool; this binary approach may conceal ecologically important information.

The 'filtering' heuristic for understanding community membership. Species groups 1-3 could each be defined as a regional species pool, depending on the definition applied.

A number of recent papers provide alternative approaches to constructing species pools, meant to avoid these pitfalls. Researchers can define multiple contrasting species pools, each pool representing an ecological process (or perhaps multiple processes) of interest. Each species pool can be modified further to reflect the strength of a particular process in constraining membership. The regional pool is not seen as a single entity but as a number of possible configurations whose utility is in comparison.

Lessard et al. (2016) illustrates how to produce this kind of process-based species pool with various constraints (figure below). Their three-step approach is to:

1. Define absolutely all possible members of regional pool. This is determined by identifying all assemblages in the region containing at least one species also found in the focal community (creating a 'dispersion field') (figure below, section A). This delineates a large region and identifies all species within it.
2. Calculate the probability of resampling a species from the focal community elsewhere in the dispersion field. This is done in the context of the process of interest. For example, the probability of observing a species in the focal community and another community might be determined based on the geographical or environmental distance between those sites. Every site in the dispersion field would now have a probability (or distance really) associated with it, representing some similarity with the focal site.
3. Finally, apply constraints to the calculated probabilities. You might choose to consider only the species within communities that are at least 50% similar to the focal community, for example. Such constraints would reflect the strength or importance of filtering by the process of interest.

Another recent paper (Karger et al., 2016) takes an approach with a number of commonalities to the Lessard et al. method. However, rather than resampling to produce potential pools of species (with species being defined as present or absent), they advocate a probabilistic approach to species pools. They suggest that species pools should be thought of as a set of probabilities of membership, which may be more reflective of ecological reality. In some ways, this is a simply a formalization of probabilistic sampling from Lessard, but instead of applying constraints, the researcher acknowledges that probabilities vary for different species. “Hence, a species pool can simply be defined as a function of probabilities of a species’ occurrence in the focal unit given the unit’s environmental and biotic conditions, geographical location and the time frame of interest”.

Both comparative and probabilistic approaches to defining species are logical advances, and one way of dealing with the untidy concept of the species pool. If this topic is of interest, a few other papers, albeit slightly less recent, are definitely worth reading: Pigot and Etienne 2015; Lessard et al. 2012, Carsten et al., 2013.

From Lessard et al., 2016. The three steps to build a species pool.

Have humans altered community interactions?

A recent Nature paper argues that there is evidence for human impacts on communities starting at least six thousand years ago, which altered the interactions that structure communities. “Holocene shifts in the assembly of plant and animal communities implicate human impacts” from Lyons et al. (2016, Nature) analyses data spanning modern communities through to 300 million year old fossils, to measure how the co-occurrence structure of communities has changed. The analyses look at the co-occurrence of pairs of species, and identifies those that are are significantly more likely ('aggregation') or less likely ('segregation') than a null expectation. Once the authors identified the species pairs with non-random co-occurrences, they calculated the proportion of these that were aggregated (i.e. y-axis on Figure 1). Compared to the ancient past, the authors suggest that modern species had fewer aggregated species pairs than in the past, perhaps reflecting an increase in negative interactions or distinct habitat preferences. 

Main figure from Lyons et al. 2016.

The interpretation offered by the paper is “[o]ur results suggest that assemblage co-occurrence patterns remained relatively consistent for 300 Myr but have changed over the Holocene as the impact of humans has dramatically increased.” and "...that the rules governing the assembly of communities have recently been changed by human activity". 

There are many important and timely issues related to this – changing processes in natural systems, lasting human effects, the need to use all available data from across scales, the value of cross-disciplinary collaboration. But, in my view, the paper ignores a number of the assumptions and considerations that are essential to community ecology. There are a number of statistical issues that others have pointed out (e.g. temporal autocorrelation, use of loess regression, null model questions), but a few in particular are things I was warned about in graduate courses. Such as the peril of proportions as response data (Jackson 1997), and the collapsing of huge amounts of data into an analysis of a summary of the data ("the proportion of significant pairwise associations that are aggregated"). Beyond the potential issues with calculating correct error terms, interpretation is made much more difficult for the reader. 

Most importantly, in my view, the Nature paper commits the sin of ignoring the essential role of scale in community ecology. A good amount of time and writing has been spent reconciling issues of spatial and temporal scale in ecology. These concepts are essential even to the definition of a 'community'. And yet, scale is barely an afterthought for these analyses.  (Sorry, perhaps that's a bit over-dramatic....) Fossils—undeniably an incomplete and biased sample of the an assemblage—can't be described to more than a very broad spatial and temporal scale. E.g. a 2 million year old fossil and a 2.1 million year old fossil may or may not have interacted, habitats may have varied between those times, and populations of S1 and S2 may well have differed greatly over a few thousand years. Compare this to modern data, which represents species occurring at the exact same time and in relatively small areas. The differences in scale is huge, and so these data are not directly comparable.

Furthermore, because we know that scale matters, we might predict that co-occurrences should increase at larger spatial grains (you include more habitat, so more species with the same broad requirements will be routinely found in a large area). But the authors reported that they found no significant relationship between dataset scale and the degree of aggregation observed (their Figure 2, not replicated here): this might suggest the methodology or analyses needs further consideration. Co-occurrence data is also, unfortunately, a fairly weak source of inference for questions about community assembly, without other data. So while the questions remain fascinating to me - is community assembly changing fundamentally over time? is that a frequent occurrence or driven by humans? what did paleo-communities look like? - I think that the appropriate data and analyses to answer these questions are not so easy to find and apply.


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Response from Brian McGill:

My comment I was trying to post was:

Interesting perspective Caroline! As a coauthor, I of course am bound to disagree. I'll keep it short, but 3 thoughts:

1) The authors definitely agonized over potential confounding effects. Indeed spent over a year on it. In my experience paleoecologists default to assuming everything is an artefact in their data until they can convince themselves otherwise, much more than neo-ecologists do.

2) They did analyze the effects of scale (both space and time) and found it didn't seem to have much effect at all on the variable of interest (% aggregations). You interpret this as "this might suggest the methodology or analyses needs further consideration". But to me, I hardly think we know enough about scaling of species interactions to reject empirical data when it contradicts our very limited theoretical knowledge (speculation might be a better word) of how species interactions scale.

3) To me (and I think most of the coauthors) by far the most convincing point is that the pattern (a transition around 8000 years ago plus or minus after 300,000,000 years of constancy) occurs WITHIN the two datasets that span it (pollen of North America and mammal bones of North America both span about 20,000 years ago to modern times) and they have consistent taphonomies, sampling methods, etc and yet both show the transition.

I agree that better data without these issues is difficult (impossible?) to find. The question is what you do with that. Do you not anwwer certain questions. Or do you do the best you can and put it it out for public assessment. Obviously I side with the latter.

Thanks for the provoking commentary.

Cheers

Brian

Paper of the lustrum*

(*lustrum = five years)

I’m co-teaching (with Kendi Davies and Julian Resasco) a graduate seminar focused on current trends and advances in community ecology. It’s been great, and having a small group with varied backgrounds (disease ecology, microbial ecology, restoration, community ecology theory, etc) allows for flexible and interesting discussions. Somehow the topic last week drifted to favourite papers, and we ended up with a plan to choose and defend the paper that was—in our opinion—the best one published in ecology in the last 5 years.

Today we described and defended our choices and tried to decide what the ‘best’ actually means, anyways. I don’t think anyone quite realised just how difficult this exercise would be. First, 5 years isn’t actually a very long time when measured in academic publishing years. That’s only the time of the average PhD, or less than the entire tenure-track period. I immediately thought of several papers I love, only to realize that sadly, they were from before 2010 (e.g. papers like these). 

Nearly everyone started their search the same way: with a Google Scholar search, looking at the most cited papers between 2010-2015. Some people looked at the most popular papers from high impact journals (Ecology Letters, Science, Nature, PNAS, etc); others looked at the output of eminent ecologists during that time period. At least one used his committee members for advice, and for the new grad students this was a nice crash course in the recent literature. Citations, quality journals, or eminent names might have been starting points for finding these papers, but it was interesting how little these actually seemed to matter. When defending their choice of paper, absolutely no one mentioned citations or journal as deciding factors. 

The papers we chose, and why: 

Conceptual synthesis in community ecology. (The Quarterly Review of Biology) Vellend 2010  

This was my choice, although I went back and forth between a short list of papers. For me, the ‘best’ paper had to either change how we do ecology, or how we think about ecology. I think Vellend 2010 has a lot of value as a pedagogical tool, and a device for organizing ecological knowledge. It has the potential to aggregate the varied, context dependent data that ecologists have been collecting for generations. Further, rather than the disjointed approach my undergraduate texts took for community ecology (productivity here, lynx-hare plot there), a single framework should help students understand community ecology as a cohesive set of ideas. And I admire papers that have big ideas.

A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. (Science) Jinek et al. 2012 

 This was a cool choice, because it turns out to be a massively important development that many of the less molecularly-inclined knew little about. This paper introduced the use of CRISPR/Cas for gene editing. The CRISPR system is been found in archaea and bacteria, and provides a form of adaptive immunity against viruses. Importantly, it has been developed for use in incredibly precise genome editing that is heritable. It has massive implications for the study of evolution, microbial ecology, disease, population genetics, and everything in between. It is also the source of ethical concerns because it can (and has) be used to modify human embryos. 

Biodiversity loss and its impact on humanity. (Nature) Cardinale et al. 2012 

This was the choice of two students, so it may have been the de facto winner. It is a massively cited paper (>1000), and both students chose it in part because it makes a clear contribution to human welfare and society. It represents a massive undertaking (they analysed more than 1000 papers) reviewing research on how biodiversity relates to a large number of relevant ecosystem services. In particular, Table 1 (below) can be used for applied and basic research, and shows where research and data agree, disagree, or are lacking. This is certainly a must read for ecologists.

Why intraspecific trait variation matters in community ecology. (TREE) Bolnick et al. 2011. 

This paper helped to concentrate and inspire research on intraspecific variation and to highlight the areas of research that are still poorly studied (and it actually made my short list too). There is obvious variation within species (long acknowledged as important to evolution, starting with Darwin) but this is often ignored in community ecology. Bolnick et al. point out the many possible and important implications that arise from such variation. The writing is clear and highlights extremely well the general mechanisms that might interact with intraspecific variation. For the student who chose it, it was inspiring enough when it first came out, that they changed their research direction. 

Table 1: Bolnick et al. 

Bottom-up regulation of malaria population dynamics in mice co-infected with lung-migratory nematodes. (Ecology Letters) Griffith et al. 2015  

This paper was chosen in an opposite fashion: it is brand new, and rather than having inspired current research, the student thought it would inspire future approaches. The paper integrates community ecology and disease ecology in a novel and sophisticated way, advancing an area of research currently receiving a lot of attention. In this paper, mice are ‘mesocosms’ in which the importance of bottom-up versus top-down control of infection (by malaria and a nematode) could be tested. (Quote: "It's a real page-turner"). 

Global marine bacterial diversity peaks at high latitudes in winter. (ISME) Ladau et al. 2013. 

This was another paper chosen because it inspired the student's current studies. Ladau et al. brought together a massive data set for marine bacterial biodiversity, allowing them to map it on a global scale and develop predictive distribution models. Interestingly, they found that diversity patterns were lower at the equator, contrary to typical findings in other organisms. The student cited the careful methodology, extensive data, and comparison of results to those in macro-scale systems as the paper’s strengths. 

From Ladau et al. "Maps of predicted global marine bacterial diversity. Color scale shows relative richness of marine surface waters as predicted by SDM. Samples were rarefied to 4266 rDNA sequences to enable accurate estimation of relative richness patterns on a global scale from data sets with different sequencing depths. True richness is expected to exceed estimated values. (a) In December, OTU richness peaks in temperate and higher latitudes in the Northern Hemisphere. (b) In June, OTU richness peaks in temperate latitudes in the Southern Hemisphere..."

Plant functional traits and the multidimensional nature of species coexistence. (PNAS). Kraft, Godoy, Levine. 2015.  

The final paper was Kendi’s choice. Community ecology has struggled with weak connections between pattern and process. The experimental and quantitative work coming from this research group has provided multiple examples for how to connect theory, statistics, and experimental results in a very rigourous fashion. In this paper, the focus is particularly on functional/trait approaches to community assembly and coexistence, and the authors manage to connect careful experimental data with Chessonian coexistence theory, using trait data to estimate species’ fitness and niche differences, and then using these to predict species coexistence.

After the fact, of course, lots of other great papers came to mind. It isn't really possible to choose one best paper, either. But the characteristics people looked for in a great paper were pretty similar - inspiring, providing novel approaches to particular questions, focused on big questions or ideas, and making contributions that go beyond academic ecology.

Challenges for microbial ecology

It is common in ecology for promising new areas of research to grow rapidly in terms of funding, students, and papers. Sometimes, such growth outpaces supporting development. This can lead to criticisms, which, when properly dealt with, can help such burgeoning subfields to mature. These are challenges currently facing microbial ecology as well. [Note I use the term microbial ecology here to refer to the ecology of microbes, not simply ecology that happens to use microbes as a study organism (e.g. Graham Bell or Lin Jiang’s experimental work).]

Microbes are fascinating. They are a very large and important group that has been under appreciated in ecological research until recently. Now, thanks to ever-improving molecular methods, the ecology of microbes is increasingly accessible. It has formed the basis of some great citizen science and public outreach (microbes in space, your home, your cat). And scientifically, work from this emerging subfield is often excellent, with broad implications to other areas of ecology (just as a couple of cool examples). Microbes are different from other taxa for all sorts of cool reasons - horizontal transfer of genes, tiny genomes, and immense functional plasticity – and this makes for fascinating discoveries.

However, the newness of this subfield is apparent as it attempts to mesh microbiology with the existing body of ecological knowledge and approaches. The result, at times, is that existing ecological theory and methods are applied unquestioningly to microbial datasets, but may not be appropriate. Unfortunately, the assumptions behind such analyses and their limitations with respect to microbial datasets aren’t always recognized, leading to questionable interpretations. There is sometimes also an over-reliance on “pipeline” approaches to microbial research; for example: collect samples, extract DNA, sequence, run through the QIIME pipeline, and present descriptive analyses, particularly beta-diversity metrics (Unifrac), PCoA or NMDS plots, and permutation-based statistical tests (e.g. ANOSIM) to determine whether assemblages of interest differ in composition. These pipelines originally arose because of the difficulties in handling such data sets and the need for specific software for analyses.

Of course, it is important to keep in mind that microbial ecology is in an early phase, where accumulating data and cataloging diversity is a priority. Mostly, issues arise when major questions in ecology are posed but perhaps without quite having appropriate methods or data to answer them. To provide an example, I sometimes see microbial ecology papers attempting to differentiate between niche and neutral processes as the drivers of microbial community assembly. Microbes are often thought of as lacking meaningful dispersal limitation (‘everything is everywhere; the environment decides’ is a common heuristic). As a result, it may be that communities assemble in a highly stochastic fashion (random arrival) or perhaps environmental filters and interactions do matter. But the issue of “niche” versus “neutrality” is a difficult question to answer using observational data in any system. It requires considering the many assumptions that underlie “niche” and “neutral”, making predictions about the patterns that would arise from these mechanisms, and then being able to differentiate these patterns from others that you might observe. This is a tall order for any observational data set, and I think that is especially true for microbial data sets.

Below I have listed in more detail the challenges arising when attempting to integrate ecology and microbiology. These relate to all sorts of ecological questions and analyses, including but not limited to “niche versus neutral”.

a) True measures of abundance are not typically available, and 16S copy number is incorrectly used as a measure of abundance. 16S ribosomal RNA is the typical target of studies of bacterial ecology. However, counts of 16S copies per taxa are not equivalent to abundances (as say, counts of individuals in macro-systems are): instead, different taxa can have different copy numbers. Where one taxa might have 2 copies, another might have 10. 

Despite this, it is common to see it used as a proxy for abundances; for example, to calculate beta-diversity measures such as Bray-Curtis. Since neutrality predicts patterns related to species' abundance distributions, and changes in diversity through time, when conclusions rely on 16S-based ‘abundance’ metrics, they are suspect. Some attempts are being made to address this – for example, this paper from Steve Kembel et al. (2012) recognizes that copy number is a conserved trait and so could be controlled for in a phylogenetically-informed way. qPCR can also be used to measure true abundances in samples. (See comments).

b) What spatial scale is relevant to microbes? Bacteria are very small (of course). However, sampling methods often involve fairly large samples in relation to bacterial body size. 1 g of soil, although tiny compared to many ecological samples, is a massive amount of material in the context of bacteria. There can be 10^8 cells/g of soil, and by one estimate the interaction distance between individuals is ~20um, and so it is not likely that a 1g sample is equivalent in scale to a “community”.

If a typical observational sample is not representative of a community, community ecology theory, which is dependent on assumptions about local interactions and environmental filters at particular spatial scales may not be relevant. Scale issues are an ongoing problem in ecology, and defining the ‘community’ is a thorn in our sides. It is understandable that this is a problem for a new field. Thinking about the kind of data collected as relating to macroecology may be a fruitful approach (see this paper for similar ideas on the topic). 

c) Temporal scale has similar issues. Unlike in macro-scale systems, microbial time scales are very rapid, with approximately 100-300 generations per year (with some variation between taxa). The scale of environmental variation that affects these communities should be finer as well. This is a benefit and a difficulty of the system. For examples, one can potentially observe a community assemble to equilibrium in a bacterial system. But describing changes in bacterial composition observed over 1 year as succession and placing them in the context of ecological literature on plant succession seems imprecise. The scale of observation is of particular importance.

d) There can be issues in differentiating between active and inactive taxa, since microbes may be present in a sample but dormant. Methods exist to differentiate between these taxa, but when not applied, an apparently rare taxa in an assemblage may actually be an inactive taxa.

e) Sampling artifacts and other biases can arise between labs and runs, including biases related to PCR, primers, DNA extraction, storage, rarefaction, and more. This is an issue equivalent to limitations in methodological approaches in every field, and one that is actively being worked on (for example, developing standardized approaches). Further, the existing technology is pretty amazing.

f) Limitations of the current null models and statistical methods being applied. Null models are still a work in progress for ecology, and need to continue to be developed and perfected. But I think that there are specific issues that need to be considered in applying some of these methods to microbial data in particular, and there is a need for concerted research on developing statistical methods for such massive datasets.

In particular, I suspect there is an issue regarding heightened Type 1 error rates and issues with inadequately randomizing very large data sets. Ulrich and Gotelli (2012) hint at some of these possible issues:

“null model analysis may not be well-suited to such large data sets. The general statistical problem is that with very large data sets, the null hypothesis will always be rejected unless the data were actually generated by the null model process itself. So, large data sets may often deviate significantly from null models in which row and column sums are fixed, regardless of whether species occurrences are random or not (Fayle and Manica 2010). This was not a problem in the early history of null model analysis, when ecologists worried that apparent patterns in relatively small data sets might reflect random processes”

There is not enough time here to delve into most of these issues in detail, but permutation tests/Mantel test type analyses have a number of important limitations and assumptions must be tested for appropriate usage (from Pierre Legendre). From the ANOSIM website: 

“Recent work…has shown distance-based methods (e.g., ANOSIM, Mantel Test, BIOENV, BEST) are inappropriate for analyzing Beta diversity because they do not correctly partition the variation in the data and do not provide the correct Type-I error rates.” 

If Type I error rates are frequently high in past analyses, or inappropriate statistical models were used, data can be re-analysed as better procedures arise. But we should also recognize that there is uncertainty in past results (particularly weak or barely significant patterns). It should also suggest that we have yet to gain a true understanding of what patterns and relationships in microbial ecology are truly significant.

Microbial research produces some of the most complex and large datasets that ecology has ever had to deal with. As a result, developing specific theory and appropriate methods for this data should be a priority alongside discovery-focused research. Fortunately, this creates opportunities for ecologists to develop methods for complex systems, which should be beneficial for the entire ecological discipline. And many people are already attempting to fill these knowledge gaps, so this is not to underplay their accomplishments. Hopefully there will continue to be developments in microbe-specific theory, with appropriate assumptions regarding temporal and spatial scale. Microbial ecologists can do better than co-opt standard ecological approaches, they can improve on them (e.g. Coyte et al. (2015)).