Don't forget the details! Trait ecology and generality

The search for generality is perhaps the greatest driver of modern ecology and probably also the greatest source of ecological angst. Though ecological trends frequently reflect the newest, brightest hope for generality, the search for generality (perhaps by definition) encourages us to ignored details and complexities. Maybe this means that some areas of study won't develop fully until they've fallen out of fashion. And maybe this means that the most interesting science happens when the pressure to 'save community ecology' is gone. A great example of the kind of post-hype, thoughtful approach for trait-based ecology comes from Reynolds et al. (2017) in Tree Physiology. They do a really nice job of highlighting some of the details that must inform trait-based ecology. Here, Reynolds et al. take a broad comparative approach across species, but incorporate important details that have at times been overlooked - especially the role of the environment, recognizing and measuring both constitutive and plastic traits, captures that there are multiple paths (or trait combinations) that can result in similar functioning.

The authors look at four conspecific tree species (Brachychiton spp.) with different average positions along an observed moisture gradient (CMD or climate moisture deficit). Two species occupied drier areas of Australia ('xeric species'), while the other two were found in more moderate areas ('mesic species'). The authors assumed that the different distributions of these species reflect different hydraulic niches. Were species' hydraulic niches associate meaningfully with their traits, specifically those trait associated with drought stress responses. Though these species are closely related--and so huge divergences in form and function might not be expected--the costs and benefits of drought resistance should differ among the species. In dry environments, drought resistance strategies should be more important, and may select for particular traits or sets of traits. Trait states associated with drought conditions include "reduced leaf area, enhanced stomatal control, safer or more efficient xylem, increased tissue water capacitance...and/or deeper root systems " may all be selected for. On the other hand, investment in these traits when water is not limited is often costly, reducing growth and competition. This suggests a meaningful selective regime associated with the CMD gradient and trait values might exist.

One important, but oft-overlooked aspect of trait ecology is that trait values depend on both genes and the environment. Reynold et al. incorporate this fact this by manipulating water availability between drought and control treatments. They measured both constitutive components of trait values – those driven by genetics and expressed regardless of environments – and the plastic or environment-dependent components. For instance, in the presence of prolonged drought, trees might increase root production or change leaf characteristics. In addition to manipulating water availability between treatments, the authors measured nine traits related to morphology and allocation.

From Reynolds et al. 

Given the expectation that trait values reflect the complex interaction of genetics and the environment in different species, is it possible to even make simple predictions about trait-environment relationships? The authors find that "These complex relationships illustrate that assuming that individual traits (often measured on individuals under a single set of environmental conditions) reflect drought resistance is likely to be overly simplistic and may be erroneous for many species. However, our results do suggest that generalization may be possible, provided multiple traits are measured to explore specific integrated drought strategies."

Indeed, some results are relatively predictable relationships: under well-watered control conditions, the allocation of biomass matched the expectation: xeric species had higher investment in below-ground biomass and in transport tissues than the mesic species (both characteristic of a water-conserving species).

On the other hand, leaf traits such as SLA did not show any trend related to species' assumed drought tolerance, either for constitutive or plastic trait components. Sometimes traits associated with the leaf economic spectrum such as SLA are assumed to indicate stress tolerance, but this was not the case.

By far the most interesting result was the observation that the xeric species had the highest assimilation and stomatal conductance rate and the lowest water use efficiency under well-watered conditions. Only by also examining these species under drought conditions was it possible to observe that they are highly plastic with regards to water use efficiency. In fact, they show a feast or famine approach to water usage - "where high photosynthetic rates per unit leaf area and high investment in root and stem tissue even in well-watered conditions are achieved through profligate water use during rare periods of water availability, in order to establish a root system and stem storage tissues necessary to survive long periods of water stress." Under drought conditions, these species show reduced root tissue investment; in contrast, mesic species follow expected patterns and plastically increase root tissue investment.

This paper is a reminder that the details are also fascinating and informative. As humans, we may have a simplistic understanding of the realized environment sometimes. To us perhaps all water stress is similar, but for each species in this study, the long term selective pressures may be meaningfully different - in timing, duration, and life stage. This creates the potential for complex differences between species which may best be reflected via life history strategies involving multiple traits. That may still imply some degree of generality is possible, but it is multi-dimensional.

Works cited:
Victoria A Reynolds, Leander D L Anderegg, Xingwen Loy, Janneke HilleRisLambers, Margaret M Mayfield; Unexpected drought resistance strategies in seedlings of four Brachychiton species, Tree Physiology, https://doi.org/10.1093/treephys/tpx143

Decomposing diversity effects within species

The relationship between biodiversity and ecosystem functioning is so frequently discussed in the ecological literature that it has its own ubiquitous acronym (BEF). The literature has moved from early discussions and disagreements about mechanism, experimental design, and species richness to ask how different components of biodiversity might contribute differentially to functioning. The search is for mechanisms which hopefully will lend predictability to biodiversity-function relationships. One approach is to independently manipulate different facets of biodiversity – whether species, phylogenetic, trait-based, or genetic diversity – to help disentangle the relative contribution of each.

A new paper extends this question by considering how within-species diversity – including genotypic richness, genetic differences, and trait differences – contribute to functioning. Abbott et al. (2017, Ecology) use a field-based eelgrass system to explore how independent manipulations of genotypic richness and genetic relatedness affected biomass production and invertebrate community richness. They collected 41 unique genotypes of eelgrass (Zostera marina), and used 11 species-relevant loci to determine the relatedness of each genotype pair. The authors also measured 17 traits relevant to performance including "growth rate, nutrient uptake, photosynthetic efficiency, phenolic content, susceptibility to herbivores, and detrital production ".

Eelgrass meadow.
From
http://www.centralcoastbiodiversity.org/
eelgrass-bull-zostera-marina.html

Each of these of these measures are inter-related, but not necessarily in clear, predictable fashions. Genotypes likely differ functionally, but some traits and some genotypes will vary more than others. Genetic distances or relatedness between species similarly may be proxies for trait differences, but this depends on the underlying evolutionary processes. The relationship between any of these measures and functions such as biomass production are no doubt varied and dependent on the mechanism.

The authors established plots with two levels of genotypic richness, either 2 genotypes or 6 genotypes, where genotypes varied among the 41 available. Fully crossed with the genotypic richness treatment was a genetic relatedness treatment: genotypes were either more closely related than a random selection, less closely related, or as closely related as random. At the end of the experiment, above and belowground biomass were collected, and epifaunal invertebrates were collected, and modelled as a component of the biodiversity components.

Because of early die-offs in many plots, planted genotype richness differed from final richness greatly (very few plots had 6 genotypes remaining, for example). For that reason, final diversity measures were used in the models. The relationship between aboveground biomass or belowground biomass and biodiversity were similar: both genotypic richness and genotypic evenness were positively related to total final biomass, but genetic relatedness was negatively correlated. That is, plots with more related genotypes were less productive. Other variables such as trait diversity was not as important, and in fact they did not find any relationship between trait differences and degree of genetic relatedness between genotypes. Since relatedness seemed unrelated to functional similarities, between genotypes, the authors suggested that possibly that reduced biomass among related genotypes is due to self-recognition mechanisms. Most interestingly, the best predictors of invertebrate grazer diversity were opposite -  – the best predictor was trait diversity, not genotypic richness or genetic relatedness.

Even in this case, where Abbott et al. were able to separate different diversity components experimentally, it's clear that simplistic predictions as to how they contribute to functioning are insufficient. The contributions of genotypic versus trait diversity were not strongly related. Further, trait diversity performed best on the function for which genotypic diversity performed worst. Understanding what this means is difficult - are the traits relevant for understanding intraspecific interactions (resource usage, etc) so incredibly different from those relevant for interspecific interactions with herbivores? Are the 17 traits too few to capture all differences, or too many irrelevant traits? Do we expect different biodiversity facets have unique independent effects on ecosystem functions, or does the need to consider multiple facets simply mean we have an imperfect understanding of how different facets are related? 

Problems with over-generalizing the dynamics of communities

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

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

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

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

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

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

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

When is the same trait not the same?

Different clades and traits yield similar grassland functional responses. 2016. Elisabeth J. Forrestel, Michael J. Donoghue,  Erika J. Edwards,  Walter Jetz,  Justin C. O. du Toite, and Melinda D. Smith. vol. 114 no. 4, 705–710, doi: 10.1073/pnas.1612909114

A potential benefit of trait-centric approaches is that they may provide a path to generality in community ecology. Functional traits affect growth, reproduction, and survival, and so--indirectly--should determine an organism's fitness; differences in functional traits may delineate niche differences. Since fitness is dependent on the environment, it is generally predicted that there should be strong and consistent trait–environment relationships. Species with drought-tolerant traits will be most dominant in low precipitation regions, etc, etc. Since productivity should also relate to fitness, there should be strong and consistent trait–ecosystem functioning relationships.

There are also quite general descriptions of species traits, and the life histories they imbue (e.g. the leaf economic spectrum), implying again that traits can yield general predictions about an organism's ecology. Still, as McIntyre et al. (1999) pointed out, "A significant advance in functional trait analysis could be achieved if individual studies provide explicit descriptions of their evolutionary and ecological context from a global perspective."

A new(ish) paper does a good job of illustrating this need. In Forrestel et al. the authors compare functional trait values across two different grassland systems, which share very similar environmental gradients and grass families present but entirely different geological and evolutionary histories. The North American and South African grasslands share similar growing season temperatures and the same precipitation gradient, hopefully allowing comparison between regions. They differ in grass species richness (62 grass species in SA and 35 in NA) and species identity (no overlapping species), but contain the same major lineages (Figure below).

From Forrestel et a. Phylogenetic turnover for major lineages along a
precipitation gradient differed between the 2 regions.

Mean annual precipitation (MAP) is well-established as an important selective factor and many studies show relationships between community trait values and MAP. The authors measured a long list of relevant traits, and also determined the above ground net primary productivity (ANPP) for sites in each grassland. When they calculated the community weighted mean value (CWM) of traits along the precipitation gradient, for 6 of the 11 traits measured region was a significant covariate (figure below). The context (region) determined the response of those traits to precipitation.

From Forrestel et al.

Further, different sets of traits were the best predictors of ANPP in NA versus SA. In SA, specific leaf area and stomatal pore index were the best predictors of ANPP, while in NA height and leaf area were. The upside was that for both regions, models of ANPP explained reasonable amounts of variation (48% for SA, 60% for NA).

It's an important message: plant traits matter, but how they matter is not necessarily straightforward or general without further context. The authors note, "Instead, even within a single grass clade, there are multiple evolutionary trajectories that can lead to alternative functional syndromes under a given precipitation regime" 

#EcoSummit2016 Day 1 - Reconciling the warp and weft of ecology

For the first time since 2008 I didn’t make it to ESA, and instead I get to attend my first EcoSummit, here in Montpellier. Participants represent a more European contingent than the typical ESA, which is a great opportunity to see a slightly different group of people and topics.

Two plenary talks were particularly memorable for me. First, Sandra Diaz gave a really elegant talk that spanned from patterns of functional diversity to the philosophy of ecology. A woven carpet provided the central analogy. A carpet includes the warp – the underlying structure of the carpet – and the weft – the supplementary threads that produce the designs. Much like species, a great diversity of colors and patterns arise from the weft, but the warp provides the underlying structure. The search for a small number of general functional relationships one way ecologists can look for the structural fabric of life. Much like Phil Grimes, an earlier speaker, Diaz has attempted to identify generalities in ecology. It’s worth reading the paper she discussed for much of her talk, which attempts to describe a global spectrum of plant function (Diaz et al. 2016). Diaz noted, however, that your focus should be determined by your questions. And you need both details and generalities if you want to provide predictions at a global scale but with a local resolution.

The other plenary of note was from Stephen Hubbell (it actually preceded Diaz’s talk), and it provided a contrasting approach. Hubbell discussed a number of detailed analyses to derive a general conclusion about processes maintaining tropical tree diversity.  Data from the Barro Colorado island provides information about changes in growth rate, abundances, presence/absence, distances between species. It shows seemingly large shifts in abundance and composition through time. And Hubbell (in a fairly provocative mood) suggests that it shows that ‘community ecology is a failure’. I would argue against that statement, and what Hubbell really seemed to be saying is that expectations of equilibrium and equilibrium models (L-V, etc) are not useful. Instead, factors such as weak stabilizing mechanisms and demographic stochasticity may be enough to understand high diversity regions.

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 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.

#ESA100: The next dimension in functional ecology

The third day of ESA talks saw an interesting session on functional ecology (Functional Traits in Ecological Research: What Have We Learned and Where Are We Going?), organized by Matt Aiello-Lammens and John Silander Jr.

As outlined by McGill and colleagues (2006), a functional trait-based approach can help us move past idiosyncrasies of species to understand more general patterns of species interactions and environmental tolerances. Despite our common conceptual framework that traits influence fitness in a given environment, many functional ecology studies have been challenged to explain much variation in measured functional traits using underlying environmental gradients. We might attribute this to a) measuring the ‘wrong’ traits or gradients, b) several trait values or syndromes being equally advantageous in a given environment, or c) limitations in our statistical approaches. Several talks in this organized session built up a nuanced story of functional trait diversity in the Cape Floristic Region (CFR) of South Africa. Communities are characterized by high species but low functional turnover (Matt Aiello-Lammens; Jasper Slingsby), and only in some genera do we see strong relationships between trait values and environments (Matt Aiello-Lammens; Nora Mitchell). Nora Mitchell presented a novel Bayesian approach combining trait and environmental information that allowed her to detect trait-environment relationships in about half of the lineages she investigated. These types of approaches that allow us to incorporate phylogenetic relationships and uncertainty may be a useful next step in our quest to understand how environmental conditions may drive trait patterns.

Another ongoing challenge in functional ecology is the mapping of function to traits. This is complicated by the fact that a trait may influence fitness in one environment but not others, and by our common use of ‘soft’ traits, which are more easily measurable correlates of the trait we really think is important. Focusing on a single important drought response trait axis in the same CFR system described above, Kerri Mocko demonstrated that clades of Pelargonium exhibited two contrasting stomatal behaviours under dry conditions: the tendency to favor water balance over carbon dioxide intake (isohydry) and the reverse (anisohydry). More to my point, she was able to link a more commonly measured functional trait (stomatal density) to this drought response behavior.

Turning from the macroevolutionary to the community scale, Ben Weinstein evaluated the classic assumption of trait-matching between consumer (hummingbird beak length) and resource (floral corolla length), exploring how resource availability might shape this relationship. Robert Muscarella then took a community approach to understanding species distributions, testing the idea that we are most likely to find species where their traits match the community average (community weighted mean). He used three traits of woody species to do so, and perhaps what I found most interesting about this approach was his comparison of these traits – if a species is unlike the community average along one trait dimension, are they also dissimilar along the other trait dimensions?

Thinking of trait dimensions, it was fascinating to see several researchers independently touch on this topic. For my talk, I subsampled different numbers and types of traits from a monkeyflower trait dataset to suggest that considering more traits may be our best sampling approach, if we want to understand community processes in complex, multi-faceted environments. Taking trait dimensionality to the extreme, perhaps gene expression patterns can be used to shed light on several important pathways, potentially helping us understand how plants interact with their environments across space and time (Andrew Latimer).

To me, this session highlighted several interesting advances in functional ecology research, and ended with an important ‘big picture’. In the face of another mass extinction, how is biodiversity loss impacting functional diversity (Matthew Davis)?

McGill, B. J., Enquist, B. J., Weiher, E., & Westoby, M. (2006). Rebuilding community ecology from functional traits. Trends in ecology & evolution, 21(4), 178-185.

Can there be a periodic table of niches?

Are there a limited number of categories or groupings into which all niches can be classified?  I’ll 

admit that my first reaction is skepticism. For those ecologists who think of the similarities and generalities across systems, this may be an easier sell, compared to those who get caught up in the complexities of ecological systems. Classifying niches in this way is apparently a vision that distinguished ecologists have voiced: MacArthur: “I predict there will be erected a two- or three-way classification of organisms and their geometrical and temporal environments, this classification consuming most of the creative energy of ecologists.” 

From Winemiller et al. 2015.

Kirk O. Winemiller, Daniel B. Fitzgerald, Luke M. Bower, and Eric R. Pianka, takes on this rather ambitious goal in a new paper: “Functional traits, convergent evolution, and periodic tables of niches”. The periodic table, of course, is the foundation of chemistry – the predictive, descriptive arrangement of chemical elements based on their atomic number. Ecology may never achieve a similarly simple foundation, but the authors suggest that such a general classification of possible niches (and the species that are within them) is possible. A niche within a table would extend across taxa, habitats, and biomes, and would be seen repeatedly (i.e. periodically) across these.

Perhaps because they (and their reviewers) recognized the ambitious nature of this task, the paper helpfully acknowledges the reasons that a periodic table of niches might be a terrible idea right away. Unlike chemistry, ecology is strongly dependent on context, and stochasticity limits generality. The multi-dimensionality of the modern niche concept limits how few axes such a table could be reduced to. Evolution means that classifying a species’ niche is like trying to hit a moving target.

Examples of convergent evolution are common.

Still, even the chemical periodic table has some fuzzy matching going on – isotopes still group together under a given element, despite variation. “In the same way, elements can have different isotopes,…a niche category could have phenotypic variants but still have ecological properties or functions that are essentially the same.” In particular, the authors argue that convergent evolution has recreated particular suites of traits (niches) in different habitats and distantly related taxa. This has some connection to the idea that, perhaps, much like complex systems, complex arrays of traits may reoccur because they provide stability (e.g. are selected for).

How then to approach this task? Here the periodic table is rooted in a functional trait approach, where observable phenotypes capture niche information. The dimensions of the table are determined based on what must have been the result of long discussions and much difficulty, but the authors restricted themselves to five essential components: abiotic habitat, life history strategy, trophic position, defense mechanisms, and metabolic allocation strategies.

 

From Winemiller et al 2015.

From here, the use of various ordination approaches allow researchers to begin to identify species sharing trait combinations, allowing them to be classified within the table (see paper text for more detail). The combinations of these dimensions observed or unobserved in nature should inform us about the stability of certain niches, and perhaps provide predictions about which species to use for restoration approaches, which species may be invasive in a given system, or to predict shifting distributions.

If you had many different ecologists each develop a ‘periodic table of niches’, each table would be unique, evidence for how difficult drawing general principles and identifying the fundamental ecological dimensions is. Another person might consider dispersal its own dimension, for example, or dismiss defenses. This is especially true because the periodic table presented in this paper is phenomenological, lacking a clear connection with theoretical work, for example. The proof will be in its application and utility – do others adopt it, is it predictive, does it extend our understanding of the niche or improve applications? And I think there is a direction for functional ecology implicit in this work.

Their hearkening to MacArthur makes me wonder what MacArthur would think if he saw ecology today. His prediction that “there will be erected a two- or three-way classification of organisms and their geometrical and temporal environments, this classification consuming most of the creative energy of ecologists” falls short, but not in the ways he might have expected. Here then, is a classification system (and there have been other ideas and versions since his time), but even the 2 or 3 dimensions he generously offers aren't deemed nearly enough to capture ecological diversity. Is the simplicity that MacArthur mentions still considered possible? And I don't think the creative energy of ecologists has been focused on classifying niches in the way he mentions: it is more dispersed amongst topics, and human effects (climate change, fragmentation, habitat loss) have had a dominant role.

Winemiller, Kirk O., Fitzgerald, Daniel B., Bower, Luke M., Pianka, Eric R. 2015.  Functional traits, convergent evolution, and periodic tables of niches. Ecology Letters. DOI: 10.1111/ele.12462

Equalizing and stabilizing traits?

Plant functional traits and the multidimensional nature of species coexistence. 2015. Nathan J. B. Kraft, Oscar Godoy, and Jonathan M. Levine. PNAS.

(This isn’t a brand new paper, but somehow I’m already behind on reading papers in the new year...) 

A recent paper from Kraft et al. in PNAS does a really nice job in filling a gap that has been in literature for a while, which is to extend the influential theoretical work on coexistence from Chesson (and extended more recently by Jonathan Levine et al.) to explicitly incorporate functional traits and trait-based approaches to ecology. Chesson’s work (particularly ARES 2000) lays out a framework for understanding coexistence and competitive interactions, which focuses on the importance of stabilizing effects (niche differences) and equalizing effects (fitness differences) between competing species (e.g.). This theory makes strong predictions of when and how coexistence is expected (for example, when species have strong enough niche differences). However, accurate application of the theory is somewhat difficult, perhaps because identifying and calculating niche and fitness differences requires heavy use of mathematical models and careful experimental design.

In contrast, the value of the focus on functional traits in ecology is that they are readily measured, easily conceptualized, and databases of values already exist. In common with equalizing/stabilizing effects, traits are meant to inform our understanding of species' niches, but in contrast, traits are empirically friendly. One of the more common critiques of the Chesson framework was that empirical measures, particularly traits, couldn't be shoehorned into it. After all, traits likely contribute to both equalizing and stabilizing forces in complicated ways that may well shift during a species' life.

What Nathan Kraft and coauthors have done is show that this is not a limitation - traits can contribute to both equalizing and stabilizing forces, and mathematical models can tease these effects apart. They relate detailed measurements of leaf, root, seed and whole plant traits for 18 California annual plants with the results of mathematical models of competition and coexistence between these species. The authors found strong and exciting relationships between the theoretically motivated measures of competitive processes and species' traits. Average fitness differences had significant correlations with functional traits, particularly maximum height, leaf [N], leaf area, rooting depth, and phenology.

From Kraft et al. 2015: Correlations between species traits and A) Stabilizing niche differences, and B) Average fitness differences.
The key to interpreting these plots is to understand that where the coloured line overlaps with the grey shading, the correlation is not different than the null model. When the line is between the null and the center of the figure, the correlation is significant and negative; where it is between the null and the external edge, the correlation is significant and positive.

No individual traits correlated with niche differences, but models including multiple traits considered together do correlate with niche differences. A rather nice bit of support for the multidimensional view of the niche.

This paper does a nice job of expanding Chesson's framework a little bit farther towards empirical applications. Further, it reinforces the value of trait approaches. There are still some important limitations - the first is that this particular system of annual plants has been studied in great detail. It seems unlikely that the traits identified in this paper can necessarily be generalized as "equalizing" traits. A trait with an equalizing effect in a California grassland may well contribute less to fitness in a desert system, for example. Perennial species are altogether less integrated into experimental applications of Chesson's framework (life time fitness, among other things, being much easier to capture in annual plants). But this paper is a suggestion of a useful way forward, albeit a way that requires much more data and careful experimentation. The authors acknowledge that more study is due, but also the potential: “These complex relationships argue against the simple use of single traits to infer community assembly processes but lay the foundation for a theoretically robust trait-based community ecology.”

Identifying the correct spatial scale, a work in progress

Are ecologists conducting research at the optimal scale? Jackson, H.B. and Fahrig, L. 2014. Global Ecology and Biogeography. 24, 52–63.

It’s a truism in ecology that there is a spatial scale at which to each ecological process and interaction occurs. Competition often occurs at local scales, speciation generally occurs at biogeographic scales. Empirical evidence seems to support this - the relationship between, for example, forest cover and beetle abundance changes from strong to nonexistent as the spatial scale of analysis increases, suggesting small scales are most meaningful for the relationship (Holland et al 2004).

But do most ecological studies choose the appropriate scale for data collection and analysis? A new meta-analysis from Heather Bird Johnson and Lenore Fahrig suggests that ecological studies, even multi-scale studies, rarely do. Multi-scale studies can show how a relationship changes in strength with spatial scale, and so should be ideal for identifying the “intrinsic scale” or the “scale of effect” – the spatial extent that best predicts a particular ecological process. (Figure below)

From Jackson and Fahrig 2014. The scale of effect illustrated using a multi-scale study: the strength of the relationship between abundance and spatial scale is strongest at 4 km (radius).

Identifying the appropriate spatial scale for a question and system is of course ideal for a researcher. Researchers can then collect appropriate data, choose to focus on interactions with processes occurring at the same scale, and to correctly analyze data. However, the appropriate spatial scale may not be easy to identify: appropriate spatial scales must be included a multi-scale study. If a study includes spatial scales that cover too small or too large an extent, or has divides the extent into too few sub-scales, simply having a study with multiple scales may still be insufficient.

Theory suggests that species' traits--e.g. dispersal distances and reproductive rates--should be strongly related to the scale of effect, but empirical evidence is surprisingly inconclusive. If studies are already successfully identifying the scale of effect, the authors hypothesized that the observed scale of effect (the scale of prediction at which results are strongest) should vary with taxonomic group, body size, species’ mobility, reproductive rates, and species function. On the other hand, if the scale of effect is being inappropriately determined, perhaps due to decisions about the number of scales to include, and the minimum and maximum spatial scale considered, then these may be the primary correlate of the scale of effect.

To determine whether multi-scale studies were successfully identifying the scale of effect, the authors performed a literature review and meta-analysis. They identified studies that featured abundance or occurrence, which was measured at multiple nested spatial scales, for multiple taxonomic groups. The scales considered in these studies ranged from 10m to 100km.

The results were rather disappointing. By far the strongest predictors of the scale of effect in a study were the largest or smallest scales they considered. This suggests that the true scale of effect might be outside the scales considered by such studies. Worse, differences between taxonomic groups disappeared when you controlled for the minimum and maximum spatial scales used in a study. Where the same species appeared in several different studies, their scales of effect from each study were no more similar than if you had chosen any random species in the same taxonomic group.

From Jackson and Fahrig 2014. There were no significant differences between the observed scale of effect and taxonomic groups. Instead, the largest and smallest spatial scales evaluated in the study drove the conclusion about the scale of effect.

The good news is that the more scales that were considered in a study, the less likely it was that the minimum or maximum scales considered appeared to be driving the results.

From Jackson and Fahrig 2014. The more scales evaluated, the less likely that choice of minimum or maximum scale was driving the result.

In addition, the authors found that the relationship between observed scale of effect and species traits was weak to non-existent in most studies. This is particularly unfortunate given the recent focus on species traits as useful predictors of ecological relationships. The inability to correctly identify the scale of effect certainly may limit our ability associate spatial scale and traits. It is also likely that context modifies the effect of traits (for example, body size may have different effects on dispersal depending on the type of matrix and the environment), further weakening the observed relationship.

One of the largest issues Jackson and Fahrig identified is that in many of the papers, choice of scales was driving by methodological (data availability, precedent, etc.) issues rather than biological justifications. Questions about trait-scale relationships may well be unanswerable until studies have the data for a necessary range of spatial scales. Until then, Jackson and Fahrig recommend that studies be more forthright about their limitations, something this paper will hopefully draw attention to.

Putting invasions into context

How can we better predict invasions?

Ernesto Azzurro, Victor M. Tuset,Antoni Lombarte, Francesc Maynou, Daniel Simberloff,  Ana Rodríguez-Pérez and Ricard V. Solé. External morphology explains the success of biological invasions. Ecology Letters (2014) 17: 1455–1463.

Fridley, J. D. and Sax, D. F. (2014), The imbalance of nature: revisiting a Darwinian framework for invasion biology. Global Ecology and Biogeography, 23: 1157–1166. doi: 10.1111/geb.12221

Active research programs into invasion biology have been ongoing since the 1990s, but their results make clear that while it is sometimes possible to explain invasions post hoc, it is very difficult to predict them. Darwin’s naturalization hypothesis gets so much press in part because it is the first to state the common acknowledgement that the struggle for existence should be strongest amongst closely related species, implying that ‘invasive species must somehow be different than native species to be so successful’. Defining more generally what this means for invasive species in terms of niche space, trait space, or evolutionary history has had at best mixed results. 

A couple of recent papers come to the similar-but rather different-conclusion that predicting invasion success is really about recognizing context. For example, Azurro et al. point out that despite the usual assumption that species’ traits reflect their niches, trait approaches to invasion that focus on the identifying traits associated with invasiveness have not be successful. Certainly invasive species may be more likely to show certain traits, but these are often very weak from a predictive standpoint, since many non-invasive species also have these traits. Morphological approaches may still be useful, but the authors argue that the key is to consider the morphological (trait) space of the invaders in the context of the morphological space used by the resident communities.

Figure 1. From Azurro et al. 2014. A resident community uses morphospace as delimited by the polygon in (b). Invasive species may fill morphospace within the same area occupied by the community (c) or (d)) or may use novel morphospace (e). Invasiveness should be greatest in situation (e). 

The authors use as an illustration, the largest known invasion by fish - the invasion of the Mediterranean Sea after the construction of the Panama Canal, an event known as the ‘Lessepsian migration’. They hypothesize that when a new species entering a community that fills some defined morphospace will face one of 3 scenarios (Figure 1): 1) they will be within the existing morphospace and occupy less morphospace than their closest neighbour; 2) they will be within the existing morphospace but occupy more morphospace than their closest neighbour; or 3) they will occupy novel morphospace compared to the existing community. The prediction being that invasion success should be highest for this third group, for whom competition should be weakest. Their early results are encouraging, if not perfect – 73% of species located outside of the resident morphospace became abundant or dominant in the invaded range. (Figure 2)

Figure 2. From Azurro et al. 2014. Invasion success of fish to the Mediterranean Sea in relation to morphospace, over multiple historical periods. Invasive (red) species tended to exist in novel morphospace compared to the resident community. 

A slightly different approach to invasion context comes from Jason Fridley and Dov Sax, who revision invasion in terms of evolution - the Evolutionary Imbalance Hypothesis (EIH). In the EIH, the context for invasion success is the characteristics of the invaders' home range. If, as Darwin postulated, invasion success is simply the natural expectation of natural selection, then considering the context for natural selection may be informative. 

In particular, the postulates of the EIH are that 1) Evolution is contingent and imperfect, thus species are subject to the constraints of their histories; 2) The degree to which species are ecologically optimized increases as the number of ‘evolutionary experiments’ increases, and with the intensity of competition (“Richer biotas of more potential competitors and those that have experienced a similar set of environmental conditions for a longer period should be more likely to have produced better environmental solutions (adaptations) to any given environmental challenge”); and 3) Similar sets of ecological conditions exist around the world. When these groups are mixed, some species will have higher fitness and possibly be invasive. 

Figure 3. From Fridley and Sax, 2014.

How to apply this rather conversational set of tenets to actual invasion research? A few factors can be considered when quantifying the likelihood of invasion success: “the amount of genetic variation within populations; the amount of time a population or genetic lineage has experienced a given set of environmental conditions; and the intensity of the competitive environment experienced by the population.” In particular, the authors suggest using phylogenetic diversity (PD) as a measure of the evolutionary imbalance between regions. They show for several regions that the maximum PD in a home region is a significant predictor of the likelihood of species from that region becoming invasive. The obvious issue with max PD being used as a predictor is that it is a somewhat imprecise proxy for “evolutionary imbalance” and one that correlates with many other things (including often species richness). Still, the application of evolutionary biology to a problem often considered to be primarily ecological may make for important advances. 

Figure 4. From Fridley and Sax 2014. Likelihood of becoming invasive vs. max PD in the species' native region.