Showing posts with label intraspecific. Show all posts
Showing posts with label intraspecific. Show all posts

Thursday, October 6, 2016

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.

Friday, April 22, 2016

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.

Monday, September 23, 2013

Can intraspecific differences lead to ecosystem differences?

Sara Lindsay Jackrel and J. Timothy Wootton. 2013. Local adaptation of stream communities to intraspecific variation in a terrestrial ecosystem subsidy. Ecology. Online early.

It’s funny how complex outcomes can arise from simple realizations. For example, it is plausible that when there are differences among individuals of a species (like when local populations are adapted to the local environment), these could implications for function on the ecosystem scale. But while there is increasing evidence for the importance of intraspecific variation for ecological interactions within communities, the question of how intraspecific diversity scales up to ecosystem functioning is still ambiguous.

Sara Jackrel and Timothy Wootton explore this question in “Local adaptation of stream communities to intraspecific variation in a terrestrial ecosystem subsidy”. The basis for their study was simple: local adaptation is common, and populations/genotypes/ecotypes tend to be best adapted to the particular conditions of their locale. For example, “spatial variation in prey and predators can lead to a geographic mosaic of co-evolutionary interactions”. Further, these localized interactions can affect the greater ecosystem, if individuals or materials move between ecosystem boundaries.

In particular, the authors note that there is evidence that tree species composition riverside can alter the composition of the local aquatic community. This occurs via leaf litter fluxes into the river: the type and amount of leaf litter that falls into streams varies, and so the type of macroinvertebrates in the recipient stream also varies in response. These macroinvertebrates break down the leaf litter via shredding, collecting, and filtering, playing an important role in nutrient cycles. Leaf litter is carried from a given tree by wind or water and may decompose near or far away, creating a connection between ecosystems. The question then is whether macroinvertebrate compositional shifts will occur in response to intraspecific differences in leaf (i.e. trees), and what the implications might be for ecosystem functions such as leaf decomposition. To explore this question, Jackrel and Wootton performed reciprocal transplants of leaf litter material between eight sites along rivers in the Olympic Peninsula of Washington.

All eight of these sites were early successional forests dominated by red alder. The authors collected fresh leaves from alder trees, bagging leaves from each tree separately. These bags of leaves were either placed in the river adjacent to the trees they were taken from, or in a more distant site. Non-adjacent sites were either in the same river as the home site, or in different river all together. Leaf packs were weighed before and after spending 17-18 days in the river. This would allow comparison of how decomposition rates vary between home and away sites, and between home and away rivers.

Their results suggested a few interesting things. First, the identity of a tree affects the rate of decomposition of its leaves: individual alder trees’ leaves were highly variable in the rate of decomposition. Second, the combined identities of trees at a site seem to have affected the composition of the decomposer community at the home river site: put leaves from that site in another river with a new community of decomposers, and the decomposition rate drops significantly. In general, leaves decomposed significantly more rapidly when in their home river, regardless of whether at the home site or elsewhere along the river. But if they put leaves upstream from the home site, but in the same river, the rate of decomposition also dropped. Upstream decomposer communities were apparently much worse at breaking down leaves from novel communities of alders. However, if you put the leaves in sites downstream from home, the decomposition rates are not significantly different than in the home site. This is likely because of the directional movement of a river, such that downstream locations receive leaf litter from all upstream sites, and so downstream decomposer communities experience a greater variety of leaf litter than upstream sites. This might lead to upstream sites being more closely adapted to the individual trees in their neighbourhood than downstream sites, which receive inputs from a wide variety of trees. These results suggest that individual differences in trees at different spatial locations can matter, both locally, across trophic levels, and even across ecosystems.

Admittedly there is not a lot you can infer about the mechanisms at play from this preliminary experiment. One interesting follow up would be to measure compositional differences in aquatic macroinvertebrates at very fine scales in correspondence with differences in trees. Another important question is whether these communities differ via phenotypic plasticity, adaptation to local sites, or species sorting. But this paper does hint at one way in differences among individuals can shape local ecosystems and even structure distant ecosystems (e.g. downstream decomposer communities) through fluxes across boundaries. And that is a rather complicated implication from a logical and simple starting point.

Monday, May 27, 2013

Evidence for the evolutionary diversity-productivity relationship at several scales

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

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

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

The experiment involved old field plots, planted with between 1 and 4 species chosen from a pool of 17 possible species; evolutionary diversity (high, medium, or low) and species richness are manipulated to include all possible combinations. The study found found a much stronger relationship between phylogenetic diversity (PD) and biomass production then between species richness and biomass production, but this isn't especially novel. What is interesting is that it could also identify how selection effects and complementarity were driving this response. High levels of complementarity were associated with high levels of PD: polyculture plots with high complementarity values were much more likely to show transgressive overyielding. Plots with close relatives had a negative or negligible complementarity effect (negative suggesting competitive or other inhibitory interactions). There was also evidence for a selection effect, which was best captured by an abundance-weighted measure of evolutionary diversity (IAC), which measured the abundance of closely related species in a plot. Together, PD and IAC explain 60% of the variation in biomass production.
From Cadotte (2013).

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

Similar to most community-level experiments, Stachowicz et al. found that looking at past experimental data suggested there was a strong relationship between genetic relatedness and biomass/density in eelgrass beds. Taxa (i.e. the number of genotypes) tended to be a poorer predictor of productivity. However, the relationship was in the opposite direction usually seen – increasing relatedness predicted higher biomass. This is difficult to explain, since it goes against the expected direction of complementarity or selection effects. Possibly cooperative/facilitative relationships are important in eelgrass monocultures. Data obtained from field surveys (rather than experimental data) suggested an alternative: possibly these studies didn’t cover a large enough range of relatedness. This field data covered a much larger range of relatedness values, and showed a unimodal relationship (below), indicating that the productivity-relatedness relationship had an optimum, where highly related or highly diverse assemblages were less productive. Although further work needs to be done, this is an intriguing possibility.
From Stachowicz et al. (2013). Grey dots represent range of relatedness values from experimental data only, compared to range covered by field survey.

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

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

Tuesday, May 7, 2013

Testing the utility of trait databases

Cordlandwehr, Verena, Meredith, Rebecca L., Ozinga, Wim A., Bekker, Renée M., van Groenendael, Jan M., Bakker, Jan P. 2013. Do plant traits retrieved from a database accurately predict on-site measurements? Journal of Ecology. 101:1365-2745.

We are increasingly moving towards data-sharing and the development of online databases in ecology. Any scientist today can access trait data for thousands of species, global range maps, gene sequences, population time series, or fossil measurements. Regardless of arguments for or against, the fact that massive amounts of ecological data are widely available is changing how research is done.

For example, global trait databases (TRY is probably best known) allow researchers to explore trait-based measures in communities, habitats, or ecosystems without requiring that the researchers have actually measured the traits of interest in the field. And while few researchers would suggest that this is superior to making the measurements in situ, the reality is that there are many situations where trait data might be required without the researcher being able to make them. In these cases, online databases are like a one-stop shop for data. But despite the increasing frequency of citations for trait databases, until now there has been little attempt to quantify how well database values act as proxies for observed trait values. How much should we be relying on these databases?

There are many well-recorded reasons why an average trait value might differ from an individual value: intraspecific differences result from plasticity, genotype differences, and age or stage differences, all of which may vary meaningfully between habitats. How much this variation actually matters to trait-based questions is still up for debate, but clearly affects the value of such databases.  To look at this question, Cordlandwehr et al. (2013) examined how average trait values calculated with values from a North-west European trait database (LEDA) corresponded with average trait values calculated using in situ measurements. Average trait values were calculated across several spatial scales and habitat types. The authors looked plant communities growing in 70 2m x 2m plots in the Netherlands, divided between wet meadow and salt marsh habitats. In each community, they measure three very common plant traits: canopy height (CH), leaf dry matter content (LDMC), and specific leaf area (SLA).

In situ measurements were made such that the trait value for a given plot was the median value of all individuals measured; for each habitat it was the the median value of all individuals measured in the habitat. The authors calculated the average trait values (weighted by species abundance) across all species for each community (2m x 2m plot) and each habitat (wet meadow vs. salt marsh). They then compared the community or habitat average as calculated using the in situ values and the regional database values. 
From Cordlandwehr et al. 2013. Habitat-level traits at site scale plotted against habitat-level traits calculated using trait values retrieved from a database. 

The authors found the correspondence between average trait values measured using in situ or database values varied with the scale of aggregation, the type of trait and the particular habitat. For example, leaf dry matter content varied very little but SLA was variable. The mesic habitat (wet meadow) was easier to predict from database values than the salt marsh habitat, probably because salt marshes are stressful environments likely to impose a strong environmental filter on individuals, so that trait values are biased. While true that rank differences in species trait values tended to be maintained regardless of the source of data, intraspecific variation was high enough to lead to over- or under-prediction when database values were relied on. Most importantly, spatial scale mattered a lot. In general, database values at the habitat-scale were reasonable predictors of observed traits. However, the authors strongly cautioned against scaling such database values to the community level or indeed using averaged values of any type at that scale: “From the poor correspondence of community-level traits with respect to within-community trait variability, we conclude that neither average trait values of species measured at the site scale nor those retrieved from a database can be used to study processes operating at the plot scale, such as niche partitioning and competitive exclusion. For these questions, it is strongly recommended to rigorously sample individual plants at the plot scale to calculate functional traits per species and community.” 

There are two conclusions I take from this. First, that the correlation between sampling effort and payoff is still (as usual) high. It may be easier to get traits from a database, but it is not usually better. The second is that studies like this allow us to find a middle ground between unquestioning acceptance or automatic criticism of trait databases: they help scientists develop a nuanced view that acknowledges both strengths and weaknesses. And that's a valuable contribution for a study to make.

Thursday, May 31, 2012

Putting ecological niche models to good use

I won’t be the first or the last person to state that I find ecological niche models (ENMs) a bit problematic. In their simplest form, ENMs are statistical models correlating species presences or presences and absences with climatic factors. These models can then be used to predict the location of suitable habitat either elsewhere in space or later in time. They can be used to examine how species’ ranges may shift with climate change, to predict where invasive species’ ranges will expand, or to suggest appropriate locations for new reserves. Over the last while, they’ve faced a fair amount of criticism. For example, most fail to incorporate biotic interactions and so they capture a species’ realized niche: this means that it might not be accurate to extrapolate the model to areas where the biotic environment is different. There are also questions of what is the appropriate spatial scale for environmental data; the problem that many populations’ dynamics (especially invasive species) are not at equilibrium with the environment, so their observed relationship with climatic factors may not represent their niche; statistical and data-quality issues; and the difficulties of validating predictions that may be made for changes in habitat 50+ years in the future. Like many new techniques, ENMs became popular quickly, before they developed an appropriate foundation, and so they were subject to misuse and inappropriate conclusions. But this is a typical pattern – the development of ecophylogenetic tools has followed a similar path.

While this period of early growth has tarnished some people’s view of ENMs, it would be a shame to disregard them altogether when there are people still using them in interesting and inventive ways. A great example is Banta et al. (2012), which combines a model organism, intraspecific phenotypic variation, and spatial structure of genetic variation with ecological niche modelling. Banta et al. focus on the problematic assumption of such models that intraspecific variation in climatic tolerances is minimal or unimportant. One approach to exploring this issue more is to develop intraspecific ENMs using genotypes, rather than species, as the unit of analysis.

Banta et al. take advantage of the fact that the model organism Arabidopsis thaliana is genetically well understood, allowing them to identify ecologically different genotypes, and is widely distributed across highly varied habitats. The authors looked at genotypes of Arabidopsis that varied in flowering time and asked whether these ecologically differentiated genotypes had different niche breadths and potential range sizes. They also looked at the classic macroecological question of whether niche breadth and range size are correlated (in this case, intraspecifically). To answer these questions, they identified 15 single locus genotypes for flowering time (henceforth “genotypes”), and developed ENMs for each, looking at the climatic conditions associated with each genotype. Using the output from these models, Banta et al. calculated the niche breadth (measured based on how much suitability varies among habitat types) and the size of potential habitat (the sum of units of suitable habitat) for each genotype.

The authors could then look at how intraspecific variation in flowering time related to differences in niche breadth and range size among the different Arabidopsis genotypes. They found that genotypes tended to differ from each other in both niche breadth and range size. This is important because ENMs assume that small amounts of genetic variation among populations shouldn’t affect the accuracy of their results. In fact, even differences in a single gene between genotypes could be associated with differences in niche breadth and potential range. In general, late flowering genotypes tended to have smaller potential ranges. The authors suggest a few explanations for this, including that late flowering genotypes may be adapted to harsher conditions, where flowering late is beneficial, but unable to compete in less stressful habitat. Regardless of the particular explanation, it shows that single locus differences can drive phenotypic differences among individuals, which in turn have notable macroecological effects.
From Banta et al. 2012. Relationship between potential range size and flowering time/niche breadth

Similar to the pattern found in a number of interspecific studies, the authors found a strong correlation between potential range size and niche breadth. This matches the oft-quoted statement by Brown (1984) that generalist species should have large potential ranges compared to specialist species, which should have small potential ranges since they only tolerate a narrow range of environments. It should be noted that this explanation is based on the assumption that habitat types are equally common: should a specialist species be adapted (only) to a widespread habitat type, the correlation between niche breadth and potential habitat size would be weakened. Because this study didn’t incorporate competition or other biotic interactions, it is not possible to conclude that there are differences in climatic tolerances among genotypes rather than differences in competitive abilities, for example. Inferior competitors may be exclude from ideal habitats and so appear to be specialized to harsh conditions (and the authors note this). This is always the difficulty with interpreting observational patterns, and further, the ongoing difficulty with defining a species’ niche based on observational data. In any case, this study does a nice job of exploring the underpinnings of macroecological variation and uses EMNs in an informative way, and suggests many interesting extensions.

Thursday, December 1, 2011

What should be the basic unit of community ecology, 2011.

Why intraspecific variation matters in community ecology Bolnick et al. 2011, Trends in Ecology and Evolution.

Intraspecific variation in gastropod
shell morphology (Goodrich 1934).
 There has been a long debate in community ecology on the fundamental unit, a debate on what Tansley described as the “necessity of first determining empirically our natural units”. In early years, it involved tension between Clements' and Gleason’s view of the plant community, either as a “superorganism” or simply as a conglomeration of co-occurring species. This latter, Gleasonian view won out, signaling a move towards the species-oriented approach that dominates community ecology today. In later years, there was a push to view the individual—not the species—as the fundamental unit, championed by people like Dan Simberloff. However, though this view has had some influence, it has never been mainstream.

 The basis of these debates about the basic unit is simple: do similarities matter more than differences? Recently, the argument that intraspecific differences are important and that community ecology should consider individuals has become much stronger. In “Why intraspecific variation matters in community ecology”, Bolnick et al. suggest that a species-level view of community ecology is an incomplete one, and that we should be aware of making simplifying assumptions about intraspecific variation (e.g. that it is minimal and species-level means are appropriate). Bolnick et al. state their hypothesis clearly:
  “… many models of species’ interactions implicitly assume that all conspecific individuals are effectively interchangeable. In this paper we argue that this assumption is misleading and that intraspecific trait variation can substantially alter ecological dynamics.” 
 To that end, the paper does an excellent job of identifying the key mechanisms by which intraspecific variation might be expected to alter ecological dynamics (especially as summarized in the paper's Table 1). Some of these mechanisms might be fairly ubiquitous. For example, when there are nonlinear relationships between trait values and interaction strengths, Jansen’s Inequality means that the amount of intraspecific variation around the species mean will alter the strength of that interaction. The mechanisms discussed make a convincing argument that intraspecific variation can alter ecological interactions and evolutionary dynamics.

However, a move to individual level ecology has many practical implications*: for example, it would require that we move beyond using average species-level demographic rates, dispersal abilities, and interaction strengths, since these miss important intraspecific variation; that phylogenetic trees be built to the level of the individual, requiring additional genetic information; and that perhaps fundamental changes be made to current coexistence theory. Possibly this would mean many more hours of fieldwork, more complex theory, and much more explanatory power is required. On the other hand, it could mean breakthroughs in how we understand longstanding ecological problems like ecosystem functioning, species diversity and coexistence, or trophic web structure.

For that reason, the fact that Bolnick et al. doesn’t demonstrate very clearly the gains or breakthroughs that could result from including intraspecific differences is a bit of a disappointment. Will we find that increasingly smaller amounts of variation are explained as we divide our units increasingly smaller? Or is the key to explaining community-level interactions found at the individual scale? Most of the examples in this paper are too simplistic to be useful, and for understandable reasons of space, there is little review of the literature (though they cite a number of important papers). That’s really too bad, since there are some subfields that have focused on intraspecific differences (for example, the ecosystem functioning literature), and their findings would contribute to the question of what makes intraspecific differences so promising for community ecologists. Despite that, when the mechanisms presented in Bolnick et al. are considered in combination with papers such as Crutsinger et al. 2006, Clark et al 2010, Albert et al. 2011, and Schindler et al. 2010 (just as a few examples), there is some tantalizing evidence suggesting that intraspecific variation can and does matter.

 *Although no doubt similar concerns about workload have accompanied any shift in approach throughout ecology's history. And certainly most shifts in ecological approach (spatial, phylogenetic, etc) only occur once the necessary methodological infrastructure was in place.