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.

The birds and the bees and the microbes

Vannette et al. 2013. “Nectar bacteria, but not yeast, weaken a plant-pollinator mutualism”. Proceedings of the Royal Society B-Biological Sciences.

"When we try to pick out anything by itself, we find it hitched to everything else in the Universe."- John Muir

You can’t help but marvel at the complexity of ecology, at the intricacy and multiplicity of species interactions. But this complexity is also problematic. For many ecologists, it becomes necessary to focus on a single type of interaction, or on interactions limited to only a few species. But real ecological systems are hardly ever limited to a single important process. They might include competition, mutualism, facilitation, predation, environmental constraints and fluctuations, additive and interactive effects, nonlinearities, thresholds and emergent properties. Can knowledge of  omplexity emerge from simplicity? Can simplicity emerge from complexity? These are important and longstanding questions in ecology, the focus of some of our smartest minds. We may not have the answer yet, but if nothing else, it is helpful when experimental work in community ecology attempts to explore multiple interactions.

For example, some of the work from Tadashi Fukami’s lab is focused on how communities of microorganisms (yeast and bacteria species) assemble in Mimulus aurantiacus nectar. In the past, this work has focused particularly on priority effects and resource competition, which plays an important role in structuring these communities. While past work has suggested the importance of pollinators as a dispersal vector for microorganisms, fascinating new work suggests that microbial communities have important effects on pollinators and their mutualistic interactions with the host plant as well.

Vannette et al. (2013) focused on the effects of the two most abundant species in Mimulus nectar, Gluconobacter sp., an acid-producing bacteria, and Metschnikowia reukaufii, a yeast. They then looked at three related questions – how do nectar microbes affect nectar chemistry, how do they affect nectar removal by hummingbird pollinators, and how to they affect pollination success and seed set. Basically, do nectar microbes disrupt important mutualistic interactions between the plant and their pollinators, or are their effects neutral?

This is where the story becomes interesting. Both types of microbes altered nectar chemistry, but in different ways. The bacteria acidified the nectar (to ~ pH 2.0) far more than the yeast species, and tended to also reduce the sugar content of the nectar far more than the yeast. Hypothesizing that these changes could ultimately affect pollinator preference, the authors then filled artificial flowers with nectar containing either the bacteria, yeast, or no microbes. Half of these flowers were bagged to prevent hummingbird access. Compared to the bagged controls, flowers with bacteria-inoculated nectar had less nectar removed than either yeast-inoculated or sterile nectar. It appeared that nectar removal was related to the changes in chemistry driven by bacterial growth in the nectar. Finally,  the authors looked pollination success in relation to microbial inoculation. Flowers inoculated with bacteria did indeed have less pollination success (measured as the number of stigmas closed) and had decreased seed numbers. Microbial communities were not isolated from the ecology of the plant.

Perhaps none of this is that surprising – hummingbirds are intelligent and will preferentially feed, and pollinator choice is important for plant fitness. However, these bacteria and yeast species are specialized for growth in the hypertonic nectar environment and their continued presence in the ecosystem depends on dispersal from one flower to the next before their host flower dies. The transient nature of this nectar habitat suggests that obtaining a dispersal vector should be important. The fact that Gluconobacter alters nectar chemistry in a way which negatively affects their likelihood of movement to other patches suggests an interesting paradox and a complicated relationship between plants, their nectar microbes, and pollinators. Gluconobacter species growing in Mimulus flowers produce acidifying H+ ions and reduce sugar concentrations in nectar – this increases their likelihood of winning competitive interactions with other microbes in the nectar, which should select for the maintenance of acidifying, sugar-reducing characteristics. But on the other hand, these characteristics reduce the likelihood of being transported to new flowers and persisting in the metacommunity. Further, these pollinator-decreasing characteristics may result in selection by the Mimulus plants for nectar compounds that reduce microbial contamination. So understanding competitive interactions in microbial communities, or understanding pollinator-plant interactions, or understanding pollinator-microbial interactions on their own might be inadequate to understand the important ecological and evolutionary processes structuring the entire system.

Given that the question of simplicity vs. complexity is still so difficult and at least for me, uncertain, I would hesitate to draw a general conclusion about whether this is the kind of work all community ecologists should strive for. But it seems that recognizing ecological and evolutionary context is key, whether you work with Arabidopsis, microcosms, or tropical forests.

Reinventing the ecological wheel – why do we do it?

Are those who do not learn from (ecological) history are doomed to repeat it?

A pervasive view within ecology is that discovery tends to be inefficient and that ideas reappear as vogue pursuits again and again. For example, the ecological implications of niche partitioning re-emerges as an important topic in ecology every decade or so. Niche partitioning was well represented in ecological literature of the 1960s and 1970s, which focused theoretical and experimental attention on how communities were structured through resource partitioning. It would be fair to say that the evolutionary causes and the ecological consequences of communities structured by niche differences were one of the most important concepts in community ecology during that time. Fast-forward 30 years, and biodiversity and ecosystem functioning (BEF) research slowly  has come to the conclusion that niche partitioning to explains the apparent relationship between species diversity and ecosystem functioning. Some of the findings in the BEF literature could be criticized as simply being rediscoveries of classical theory and experimental evidence already in existence. How does one interpret these cycles? Are they a failure of ecological progress or evidence of the constancy of ecological mechanisms?

Ecology is such a young science that this process of rediscovery seems particularly surprising. Most of the fundamental theory in ecology arose during this early period: from the 1920s (Lotka, Volterra), 1930s (Gause) to 1960s (Wilson, MacArthur, May, Lawton, etc). There are several reasons why this was the foundational period for ecological theory – the science was undeveloped, so there was a void that needed filling. Ecologists in those years were often been trained in other disciplines that emphasized mathematical and scientific rigor, so the theory that developed was in the best scientific tradition, with analytically resolved equations meant to describe the behaviour of populations and communities. Most of the paradigms we operate in today owe much to this period, including an inordinate focus on predator-prey, competitive interactions, and plant communities, and the use of Lotka-Volterra and consumer-resource models. So when ecologists reinvent the wheel, is this foundation of knowledge to blame, is it flawed or incomplete? Or does ecology fail in education and practice in maintaining contact with the knowledge base that already exists? (Spoiler alert – the answer is going to be both).

Modern ecologists face the unenviable task of prioritizing and decoding an exponentially growing body of literature. Ecologists in the 1960s could realistically read all the literature pertaining to community ecology during their PhD studies –something that is impossible today with an exponentially growing literature. Classic papers can be harder to access than new ones: old papers are less likely to be accessible online, and when they are, the quality of the documents is often poor. The style and accessibility of some of these papers is also difficult for readers used to the succinct and direct writing more common today. The cumulative effect of all of this is that we read very little older literature and instead find papers that are cited by our peers.

True, some fields may have grown or started apart from a base of theory that would have been useful during their development. But it would also be unfair to ignore the fact that ecology’s foundation is full of cracks. Certain interactions are much better explored than others. Models of two species interactions fill in for complex ecosystems. Lotka-Volterra and related consumer-resource models make a number of potentially unrealistic assumptions, and parameter space has often been incompletely explored. We seem to lack a hierarchical framework or synthesis of what we do know (although a few people have tried (Vellend 2010)). When models are explored in-depth, as Peter Abrams has done in many papers, we discover the complexity and possible futility of ecological research: anything can result from complex dynamics. The cynic then, would argue that models can predict anything (or worse, nothing). This is unfair, since most modelling papers test hypotheses by manipulating a single parameter associated with a likely mechanism, but it hints at the limits that current theory exhibits.

So the bleakest view of would be this: the body of knowledge that makes up ecology is inadequate and poorly structured. There is little in the way of synthesis, and though we know many, many mechanisms that can occur, we have less understanding of those that are likely to occur. Developing areas of ecology often have a tenuous connection to the existing body of knowledge, and if they eventually connect with and contribute to the central body, it is through an inefficient, repetitive process. For example a number of papers have remarked that invasion biology has dissociated itself from mainstream ecology, reinventing basic mechanisms. The most optimistic view, is that when we discover similar mechanisms multiple times, we gain increasing evidence for their importance. Further, each cycle of rediscovery reinforces that there are a finite number of mechanisms that structure ecological communities (maybe just a handful). When we use the same sets of mechanisms to explain new patterns or processes, in some ways it is a relief to realize that new findings fit logically with existing knowledge. For example niche partitioning has long been used to explain co-occurrence, but with a new focus on ecosystem functioning, it has leant itself as an efficacious explanation. But the question remains, how much of what we do is inefficient and repetitive, and how much is advancing our basic understanding of the world?

By Caroline Tucker & Marc Cadotte

Justifying assumptions: tests of seed size/mass tradeoffs

Ben-Hur, E. et al. 2012. Functional trade-offs increase species diversity in experimental plant communities. Ecology Letters. DOI: 10.1111/j.1461-0248.2012.01850.x

When ecologists develop theory and models, we generally need to make assumptions. The nicest definition of an assumption is that they are the framework we use to capture our beliefs about a system. All future analyses will treat these assumptions as true, and so ultimately the validity of a model is tied to the validity of its assumptions. As Joseph Connell said: “Ecological theory does not establish or show anything about nature. It simply lays out the consequences of certain assumptions. Only a study of nature itself can tell us whether these assumptions and consequences are true.” Often times the most interesting advances in ecology come when we questions popular assumptions, such as that species are ecologically different, that interspecific differences are more important than intraspecific differences, or that ecological interactions occur much more rapidly then evolutionary changes. 

Assumptions in models and theory can often serve as a sort of shorthand for ideas that there is some general evidence for, but for which comprehensive data may be lacking. Community ecology is full of assumptions about functional tradeoffs that mediate coexistence between species. Various assumptions about plant species coexistence include that species experience tradeoffs between competition and colonization, growth versus reproduction, or seed size versus seed number. A simplistic explanation for such tradeoffs is that you can’t do everything well: a strong competitor can’t be a good colonizer too, which creates opportunities for strong colonizers but poor competitors, etc.

Tests of these functional tradeoffs are lacking, or lag behind the theory that relies on them. For example, the idea that there should be a tradeoff between seed size and seed number has long been proposed to explain why plants have highly variable seed sizes. Plants with small seeds should produce more offspring, and these seeds should be more successful at reaching empty sites. Large seeded species should be more competitive in the seedling stage or more tolerant of difficult conditions, and so have higher survival. Theoretical models that rely on such a tradeoff suggest that many species could co-exist and that the resulting community would exhibit a wide variety of seed sizes. 

But though many studies and theories depend on this assumed tradeoff, a comprehensive experimental test was lacking. Ben-Hur et al. have finally provided such an experiment, testing the basic prediction that a negative correlation between seed size and seed number should increase species richness. They also tested whether small-seeded species were more likely to remain in the community when this tradeoff existed, increasing the amount of among-species variation in seed size. To do so, the authors created 3 ‘community treatments’ of 15 plant species. The abundance of each species in the starting seed mix was manipulated to create either (1) positive correlation between seed mass and seed number; (2) negative correlation between seed mass and seed number or (3) random allocation of the 15 species regardless of seed size.

From Ben-Hur et al. 2012. Ecology Letters. a) Final number of species in the community, when the correlation between seed size and seed number is negative, random, or positive. b) Seed mass distribution in community under positive correlation between seed size and seed number (left), and negative correlation (right). 

Ben-Hur et al.’s results strongly suggest that a seed size/seed mass functional tradeoff can increase species richness (figure, a). Further, when there is such a tradeoff, the variation in seed size represented in a community increases, again in agreement with predictions (figure, b). The results are particularly convincing because the authors used experimental manipulation of the strength of the correlation (i.e. from negative to positive) to test its importance. The authors suggest that the tradeoff they simulated did not involve competitive differences (i.e. was not a competition-colonisation tradeoff), and more likely reflects a trade-off in establishment probability and colonisation (Dalling and Hubbell 2002; Muller-Landau 2010).

Of course, these results represent relatively short-term coexistence, and community richness may have changed had the experiment been allowed to continue for longer. But as a starting point, this suggests that theories that rely on functional tradeoffs in seed characteristics to explain coexistence are capturing a mechanism that has some experimental support. 

Community ecology is complicated: revisiting Robert May’s weak interactions

Stefano Allesina & Si Tang. 2012. Stability criteria for complex ecosystems. Nature 483, 205–208 (08 March 2012). doi:10.1038/nature10832.

When it comes to explaining species diversity, Stefano Allesina differs from the traditional approach. Community ecology has long focused on the role of two species interactions in determining coexistence (Lotka-Volterra models, etc), particularly in theory. The question then is whether two species interactions are representative of the interactions that are maintaining the millions of species in the world, and Allesina strongly feels that they are not.

In the paper “Stability criteria for complex ecosystems”, Stephano Allesina and Si Tang revisit and expand on an idea proposed by Robert May in 1972. In his paper “Will a large complex system be stable?” Robert May showed analytically that the probability a large system of interacting species is stable – i.e. will return to equilibrium following perturbation – is a function of the number of species and their average interactions strength. Systems with many species are more likely to be stable when the interactions among species are weak.

May’s paper was necessarily limited by the available mathematics of the time. His approach examined a large community matrix, with a large number of interacting species. The sign and strength of the interactions among species were chosen at random. Stability then could be assessed based on the sign of the eigenvalues of the matrix – if the eigenvalues of the matrix are all negative the system is likely to be stable. Solving for the distribution of the eigenvalues of such a large system relied on the semi-circle law for random matrices, and looking at more realistic matrices, such as those representing predator-prey, mutualistic, or competitive interactions, was not possible in 1972. However, more modern theorems for the distribution of eigenvalues from large matrices allowed Allesina and Tang to reevaluate May’s conclusions and expand them to examine how specific types of interactions affect the stability of complex systems.

Allesina and Tang examined matrices where the interactions among species (sign and strength) were randomly selected, similar to those May analyzed. They also looked at more realistic community matrices, for example matrices in which pairs of species have opposite-signed interactions (+ & -) representing predator prey systems (since the effect of a prey species is positive on its predator, but that predator has a negative effect on its prey). A matrix could also contain pairs of species with interactions of the same sign, creating a system with both competition (- & -) and mutualism (+ & +). When these different types of matrices were analyzed for stability, Allesina and Tang found that there was a hierarchy in which mixed competition/mutualism matrices were the least likely to be stable, random matrices (similar to those May used) are intermediate, and predator–prey matrices were the most likely to be stable (figure below).

When the authors looked at more realistic situations where the mean interaction strength for the matrix wasn’t zero (e.g. so a system could have all competitive or all mutualistic interactions), they found such systems were much less likely to be stable. Similarly, realistic structures based on accepted food web models (cascade or niche type) also resulted in less stable systems.

The authors reexamined May’s results that showed that weak interactions made large systems more likely to be stable. In particular they examined how the distribution of interactions strengths, rather than the mean value alone, affected system stability. In contrast to accepted ideas, they found that when there were many weak interactions, predator-prey systems tended to become less stable, suggesting that weak interactions destabilize predator-prey systems. In contrast, weak interactions tended to stabilize competitive and mutualistic systems. The authors concluded, “Our analysis shows that, all other things being equal, weak interactions can be either stabilizing or destabilizing depending on the type of interactions between species.” 

Approaching diversity and coexistence from the idea of large systems and many weak interactions  flies in the face of how much community ecology is practiced today. For that reason, it wouldn't be surprising if this paper has little influence. Allesina suggests that focusing on two species interactions is ultimately misleading, since if species experience a wide range of interactions that vary in strength and direction, sampling only a single interaction will likely misrepresent the overall distribution of interactions. Even when researchers do carry out experiments with multiple species, finding a result of very weak interactions between species is often interpreted as a failure to elucidate the processes maintaining diversity in the system. That said, Allesina’s work (which is worth reading, few people explain complex ideas so clearly) doesn’t necessarily make itself amenable to being tested or applied to concrete questions. Still, there’s unexplored space between traditional, two-species interactions and systems of weak interactions among many species, and exploring this space could be very fruitful. 

Disagreeing about ecology: how debate advances science

• Kraft N.J.B. et al. 2011. “Disentangling the Drivers of β-Diversity Along Latitudinal and Elevational Gradients” Science. Vol. 333:6050 pp.1755-1758
• Qian. H. et al. 2012. Comment on “Disentangling the Drivers of β Diversity Along Latitudinal and Elevational Gradients”. Science 30:1573.
• Tuomisto, H. and Ruokolainen, K. 2012. Comment on“Disentangling the Drivers of β Diversity Along Latitudinal and Elevational Gradients”. Science 30:1573.
• Kraft N.J.B. et al. 2012. Response to Comments on“Disentangling the Drivers of β Diversity Along Latitudinal and Elevational Gradients”. Science 30: 1573.

A good scientific debate makes for excellent spectator sport (although it’s probably less fun for the participants). Many of the best ecological debates are now classics of the literature—Diamond vs. Simerberloff, Lawton vs. Simberloff, Hubbell vs. many—and these historical debates influence present day ecology. Interestingly, debates in ecology seem to revolve around two particular issues: whether the data is appropriate and whether the methods are adequate to draw conclusions about a particular process.

As an example, there’s a typical ecological debate occurring in Science over Kraft et al.’s “Disentangling the drivers of β-diversity along latitudinal and elevational gradients”. In this paper, the authors reevaluate the mechanisms that drive changes in species identity along latitudinal and elevation gradients using a null model. Although β-diversity may vary along biogeographical gradients as a result of processes such as dispersal limitation, range size, and habitat filtering, total (γ) diversity also varies along these gradients (we know that richness is generally higher in the tropics and the lowlands). Since this suggests that γ- and β-diversity aren’t independent, it may be that changes in γ-diversity need to be accounted for as an explanation for changes in β-diversity (Chase 2011). When Kraft et al. controlled for γ-diversity using a null model, they found that the magnitude of β-diversity did not vary along latitudinal or elevational gradients. They stated that this means: “there may be no need to invoke differences in the mechanisms of community assembly in temperate versus tropical systems to explain these global-scale patterns of β-diversity.”

This conclusion is in contrast to multiple papers that have suggested that tropical communities are somehow structured differently from temperate communities. Such work has been far from conclusive, however, finding evidence for everything from stochastic assembly to microhabitat-driven assembly in tropical regions. However, given the strong conclusion from the Kraft et al. paper, it’s not surprising that there were several responses from other researchers of β-diversity (Tuomisto and Ruokolainen and Qian et al.). It’s also not surprising that the points raised in these responses are fairly typical for debates in community ecology, calling into question the suitability of the data, the appropriateness of the spatial scale for capturing the processes of interest, and the question of whether the methods are correct. The debate is as much about the fundamental questions of how we define and measure β-diversity as it is about the particulars of the Kraft et al. article.

For example, both Tuomisto and Ruokolainen and Qian et al. questioned the sampling design of the data, as to whether there was too much within-plot variation (Tuomisto and Ruokolainen) or, alternately, too little between-plot variation (Qian et al.) to correctly capture the amount of β-diversity. Tuomisto and Ruokolainen further suggested that the plots used in the original study undersample local (α) diversity and therefore overestimate the differences between plots. Both sets of authors suggest that inappropriate sampling would make it difficult to generalize Kraft et al.’s results to other studies of β-diversity. Kraft et al.’s response was that although plots are placed to minimize among-plot environmental variation, this does not make them inappropriate to test for finer scale evidence of environmental processes, and that β-diversity still varies markedly between plots. However, given that this debate - about whether there is a “best” spatial scale at which to examine the ecological causes of β-diversity and a “best” way to sample to capture variation among communities – is occurring among experienced β-diversity researchers suggests that these are still fuzzy areas.

Another aspect of this debate relates to the ongoing discussion about the appropriate definition and calculation of β-diversity (Tuomisto 2010). The most traditional methods define β-diversity as a multiplicative or additive function of α- and γ-diversity, and Kraft et al. argue that as a result β-diversity is not independent of those variables. To account for this fact, Kraft et al. use a null model that incorporates γ-diversity, to predict β-diversity under random or stochastic assembly. However, Tuomisto and Ruokolainen argue that the measure of β-diversity used (β_P = 1 – α/γ) is such that γ-diversity can vary without affecting β-diversity, provided alpha-diversity is also free to vary. However, Kraft et al. dispute this, suggesting that perfectly scaled changes in both γ- and α-diversity, such that β-diversity remains unchanged, represent a special case that does not appear in their data set.

Of course, other points were discussed among the authors. Qian et al. disagreed with the use of latitudinal gradients, noting that the ecological “meaning” of a given latitude is rather vague. However, given that the authors admit their site data is likely to capture small-scale variation in β-diversity, it seems that trying to relate their results to large-scale latitudinal or elevational gradients is a greater issue.

Kraft et al. suggested in their response that many of the criticisms were misunderstandings of the methods and findings of the original paper. You might more correctly say that disagreements like this capture important weaknesses or ambiguities in current understanding and theory. It’s true that at their worst, debates create conflict and that since responses are rarely peer-reviewed to the same extent the original publication is, too much weight may be given to meritless counter-arguments. However, good debate should drive progress, force researchers to reevaluate their assumptions, and ultimately hold science accountable. And for that reason it should be encouraged.

**I should note that this post is specifically meant in relation to debate among researchers, not to situations where scientists are in agreement and the debate is occurring in the public sphere.

Rumors of community ecology’s death were greatly exaggerated: reflections on Lawton 1999

In 1999, John Lawton, eminent British ecologist, published a lament for the state of community ecology entitled “Are there general laws in ecology?” Cited more than 600 times, Lawton’s paper forced a re-evaluation of community ecology’s value, success, and even future existence. Other scientists at the time seemed to agree, with papers starting with phrases like “Although community ecology is a struggling science…” and “Given the lack of general laws in ecology…”. Lawton appeared to be suggesting that community ecology be abandoned for the generality of macroecology or the structure of population ecology.

An important point to be made is that Lawton was simply making a particularly public expression of ecology’s growing pains. In 1999, ecology was at a crossroads between the traditional approach of in-depth system-based study, and a fairly single-minded focus on competition as an explanation for patterns (e.g., Cooper 1993 ‘The Competition Controversy in Community Ecology’ Biology and Philosophy 8: 359-384), while at the same time there were emergent approaches and explanations like neutrality, macroecology, spatial ecology, ecophylogenetics, and improved computer and molecular methods. There was also growing dissent about ecology’s philosophical approach to ecology (e.g., Peters 1991 ‘A Critique for Ecology’; Haila and Heininen 1995 ‘Ecology: A New Discipline for Disciplining’ Social Text 42: 153-171): ecologists tended to ignore the Popperian approach, which required falsification of existing hypothesis, instead tending to look for support for an existing hypothesis, or at least advocated looking for patterns without considering alternative mechanisms. Not only this, but the applications for ecology were more clear than ever – the Intergovernmental Panel for Climate Change was meeting , and the ecological consequences of human actions were perhaps more obvious they had ever been. But ecologists were failing at providing solutions –Lawton argued-correctly-that in 1999 ecologists could provide little insight into how a community might change in structure and function in response to changing climate.

Although everyone should read Lawton’s paper, a simple synthesis of his concerns would be this – that community ecology is too contingent, communities are too complex, and therefore community ecology cannot formulate any laws, cannot make predictions, cannot be generalized from one system to another. This makes community ecology suspect as a science (physics being the most common example of an “ideal” science), and certainly not very useful. Lawton suggests that population ecology, where only a few models of growth could explain the majority of species’ dynamics, or macroecology, which focuses on the most general, large-scale patterns, were a better example of how ecology should be practiced.

Community ecology, rather than dying, has experienced an incredible surge in popularity, with a large contingent represented at meetings and in journal publications. Ecology itself is also thriving, as one of the fastest growing departments in universities. So what, if anything, has changed? Has ecology addressed Lawton’s criticisms?

Two major things happened in the late 1990’s and early 2000’s, which helped ecologists see beyond this general malaise. The first was that a number of well-thought out alternative ecological mechanisms explaining community membership were published. Before the late 90’s community ecologists looked for evidence of competition in patterns of community composition, either among locales or through time following disturbance. When local competition was insufficient to explain patterns, researchers likely cited, but did not test other mechanisms. Or if they did test other mechanisms, say predation, it was as an alternative, mutually exclusive mechanism. The new publications, drawing on previous ideas and concepts formalized assembly mechanisms like neutral processes or metacommunity dynamics where uneven fitnesses in a heterogeneous landscape can affect local coexistence. More than these as solely alternative mechanisms, these allowed for a synthesis where multiple mechanisms operate simultaneously to affect coexistence. Probably the most emblematic paper of this renewed excitement is Peter Chesson’s 2000 ‘Mechanisms of maintenance of species diversity’ published in Annual Reviews of Ecology and Systematics. This paper, cited over a thousand times, offers a way forward with a framework that includes competitive and niche differences but can also account for neutral dynamics.

A second major development that rejuvenated ecology was the formation of technological and statistical tools engendering broad-scale synthetic research. Suddenly the search for general explanations – Lawton’s most piercing criticism - became more common and more successful. With the advent of on-line databases, meta-analytic procedures and centers (e.g., the National Center for Ecological Analysis and Synthesis) that foster synthetic research, ecologists routinely test hypotheses that transcend local idiosyncrasies. Often, the capstone publication on a particular hypothesis is no longer a seminal experiment, but rather a meta-analysis that is combines all the available information to assess how strongly and how often a particular mechanism affects patterns.

While these theoretical and technological developments have been essential ingredients in this ecological rejuvenation, there has also been a subtle shift the philosophical approach to what it is ecological theory can and should do. Criticism in the 1990’s (e.g., Peters 1991 ‘A Critique for Ecology’) centered on the inability of ecological theory to make accurate predictions. The concept of science common in ecology in the 1990’s was that a rigorous, precise science (i.e., with laws) results in the ability to accurately predict species composition and species abundances given a set of mechanisms. This view of ecological science has been criticized as simplistic ‘physics-envy’ (e.g., see Massimo Pigliucci’s PhD dissertation ‘Dangerous habits: examining the philosophical baggage of biological research’published by the University of Tennessee in 2003). The subtle philosophical change has been a move from law=prediction to law=understanding. This is as true for physics as it is for ecology. We don’t expect a physicist to predict precisely where a falling feather will land, but we do expect to totally understand why it landed where it did based on fundamental processes. (for more on the contrast of prediction and understanding, see Wilhelm Windelband’s nomothetic and idiographic knowledge)


While the feather example above is simplistic, it is telling. In reality a physicist can produce probability contours of where the feather is likely to land, which could be very focused on a calm day or broad on a windy one. This is exactly what ecologists do. Once they understand how differing mechanisms come together to shape diversity, they make probabilistic predictions about the outcome of a set of known mechanisms.

Ecology today is as vibrant as ever. This is not a result of finding new laws that proved Lawton incorrect. Rather, ecologists now have a more sophisticated understanding of how various mechanisms operate in concert to shape diversity. Moreover, conceptual, technological and philosophical revolutions have fundamentally changed what ecologists do and what they are trying to explain. It is a great time to be an ecologist.

Lawton, J. H. (1999). Are there general laws in ecology? Oikos, 84(2), 177-192.


By Marc Cadotte and Caroline Tucker

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.

Ecology needs more evolution (and vice versa)

One historical weakness in community ecology is its singular focus on ecology in the absence of any consideration of the role of evolution. Ecological theory may attempt to explain and expand on mechanisms of coexistence, but this is done in ignorance of whether such a mechanism could have reasonably evolved in the first place. Evolutionary biology has equally ignored the role of ecology (for example, just-so stories invented in the absence of ecological support). Fortunately, it is becoming more common to see papers that incorporate, empirically or theoretically, evolution and community ecology.

A recent paper by Robin Snyder and Peter Adler attempts to incorporate both ecology and evolution in reference to the storage effect, a mechanism in which species coexist as a result of environmental variability and corresponding differential variation in species fecundity in response to the environment. As a simplistic example, consider a system of two annual plants for which each species has their highest recruitment at different temperatures, and temperature varies randomly between years. Each species is expected to have high recruitment in different years/environmental conditions, and this high recruitment in good years can then buffer that species’ fitnesses in years of poor conditions, provided the species have some way of “storing” fitness (such as long-lived seedbanks). The storage effect therefore predicts that environmental variability can mediate the coexistence of otherwise unequal competitors. Because the requirements of the storage effect appear so ubiquitous (environmental variation, differential species responses to the environment, some sort of buffer), it seems that the storage effect could be very common. However, there is also theory suggesting that variation in demographic rates should come at a fitness cost, since the long term mean growth rate will be lower if demographic rates vary than if they are fixed (as the result of geometric averaging). This predicts that there should be selection against flexible—rather than fixed—demographic rates, including rates that vary in response to environmental or other cues. Is it possible then for variable demographic rates, which are necessary for the storage effect, to evolve?

Snyder and Adler discuss this disconnect between community ecology and evolution, questioning whether the storage effect can be supported by both evolutionary and ecological theory. To this end, the authors explore whether, and under what conditions, the storage effect could evolve. Snyder and Adler use a simple model of competition between two annual plants, in which fecundity fluctuates due to environmental variation, and germination rate can be temporally fixed, or variable. Germination rates should be constant, despite environmental variation, due to the cost of variability. Germination rates would be expected to vary year to year only if this conferred a fitness benefit to the species. Hypothesized benefits of variable germination rates include if germination rates are positively correlated with fecundity (that is, in good years germination is higher as well), or if it allows a species to avoid competition (by having high germination when their competitor has low germination). To test this hypothesis, the authors varied the correlation between fecundity and germination, and the correlation between the two species’ germination rates. They then examined the conditions under which variable germination rates were an evolutionarily stable strategy (ESS).

Snyder and Adler’s results suggest that the storage effect is expected to evolve only under anarrow set of conditions. A variable germination rate was most likely to evolve if there was a strong correlation between fecundity and germination rate. They note that such a correlation might occur if seed production and germination depended on similar environmental cues or similar resource requirements. A variable germination rate was also a stable strategy if one species was limited in its ability to evolve, in which case the other species evolved variable germination rates. If these specific conditions didn’t hold, the storage effect was not evolutionarily stable.

These results are meaningful because they highlight how different the conclusions of community ecology, which has proposed that the storage effect could be a widespread contributor to coexistence, and evolutionary theory, which suggests that the storage effect may only occur under particular conditions, can be. This kind of reconciliation of community ecology and evolution tells us more about natural systems than either approach can on its own. It also hints that theory and conclusions we’ve drawn in community ecology in the absence of evolution may be limited and incomplete.

How is a species like a baseball player?

Biomass is to runs as species is to player, and as ecologist is to Brad Pitt.

Community ecology and major league baseball have a lot to learn from each other.

Let's back up. As a community ecologist, I think about how species assemble into communities, and the consequences for ecosystems when species disappear. I'm especially interested using traits of species to address these issues. For the grassland plants that I often work with, the traits are morphological (for example, plant height and leaf thickness), physiological (leaf nitrogen concentration, photosynthetic rate), and life history (timing and mode of reproduction).

As a baseball fan, I spend a lot of time watching baseball. Actually, I'm watching my Red Sox now (multitasking as usual; I freely admit there's a lot of down time in between pitches). I care about how the team does, mostly in terms of beating the Yankees. I'm especially interested in how individual players are doing at any time; for fielders I care about their batting average and defensive skills, and for the pitchers I care about how few runs they allow and how many strikeouts they get.

So my vocation and avocation have some similarities. Both ecology and baseball have changed in the last decade or so to become more focused on 'granular' data at the individual level. In ecology this has been touted as a revolutionary shift in perspective, but is really a return to the important aspects of what roles organisms play in ecosystems, and how ecosystems are shaped by the organisms in them. This trait-based approach has shifted the collection and sharing of data on organism morphology, physiology, and life history into warp speed, to the great benefit of quantitatively-minded ecologists everywhere.

In baseball, the ability to collate and analyze data on every pitch and every play has lead to an explosion of new metrics to evaluate players. One of the simplest of these new metrics, which even the traditionalists in baseball now value, is "on base plus slugging" (OPS, see all the details here). This data-intensive approach to analyzing player performance was most famously championed by the manager of the Oakland Athletics in the late 1990's, now being played by Brad Pitt in the upcoming movie Moneyball.

There is no one ecologist in particular who can claim credit for popularizing trait-based approaches in community ecology, but for the sake of laughs let's make Owen Petchey the Brad Pitt analogue.

What can we do with this analogy? For pure nerd fun, we can think about what these two worlds can learn from each other.

What can baseball learn from community ecology?

One of the most notable trait-centric innovations in community ecology has been the use of functional diversity (FD), which represents how varied the species in a community are in terms of their functional traits. Many flavors of FD exist (one of which was authored by Owen Petchey, above), but the goal is to use one value to summarize the variation in functional traits of species in a community. A high value for a set of communities indicates greater distinctiveness among the community members, and is taken to represent greater niche complementarity.

For fun, I've taken stats from a fantastic baseball database[i] and calculated the FD of all baseball teams from 1871 to 2010. I used a select set of batting, fielding, and pitching statistics[ii], and you can see the data here. For the two teams that I pay the most attention to, I plotted their FD against wins, with World Series victories highlighted:

Given that these FD values represent how different the members of a team are, it's surprising that there is much of a pattern at all. But the negative relationship between wins and FD is strong and significant by several measures[iii]. So: the more similar a team is in terms of player statistics, the better the team does!

This pattern of less dissimilarity among players correlating with better performance at the team level has apparently been noticed before, by Stephen Jay Gould, who extrapolated this pattern also across teams to explain the gradual shrinking of differences among players over time:

"if general play has improved, with less variation among a group of consistently better payers, then disparity among teams should also decrease"

and so:

"As play improves and bell curves march towards right walls, variation must shrink at the right tail." (from "Full House", thanks to Marc for this quote!).

Interesting, but is it useful? One obvious drawback in this approach of examining variation in individual performance is that it ignores the fact that in baseball, we know that a high number of earned runs allowed is bad for a pitcher, and a low number for hits is bad for a hitter. In contrast, a high value for specific leaf area is neither good nor bad for a plant, just an indication of its nutrient acquisition strategy.

There are many exponentially more nerdy avenues to go with applying community ecology tools to baseball data, but I'll spare you from that for now!

What can community ecology learn from baseball?

One new baseball stat that gets a lot of attention during trades is 'wins above replacement'. This is such a complicated statistic to calculate that the "simple" definition is that for fielders, you add together wRAA and UZR, while for pitchers it is based off of FIP. I hope that cleared things up.

The point in the end is to say how many wins a player is worth, when compared to the average player. In ecology, the concept of 'wins above replacement' has at least two analogies.

First, community ecologists have been doing competition experiments since the dawn of time. The goal is to figure out what the effect of a species is at the community level, although fully factorial competition experiments at the community level are challenging to carry out. For example, Weigelt and colleagues showed that there can be non-additive effects of competitor plant species on a target species, but could rank the effect of competitors. This result allowed them to predict the effect of adding or removing a competitor species from a mixture, in a roughly similar way to how a general manager would want to know how a trade would change his or her team's performance.

Second, ecologists have shown that both niche complementarity and a 'sampling effect' are responsible for driving the positive relationship between biodiversity and ecosystem functioning. The sampling effect refers to the increasing chance of including a particularly influential species when the number of species increases. Large-scale experiments in grasslands have been carried out where plants are grown in monoculture and then many combinations, up to 60 species. The use of the monocultures allows an analysis similar in spirit to 'wins above replacement', by testing how much the presence of a particular species, versus the number of species, alters the community performance.

We could take this analogy further, and think of communities more like teams. A restoration ecologist might calculate 'wins above replacement' for all the species in a set of communities, and then create All Star communities from the top performers.

Lessons learned

A. Shockingly, there are baseball nerds, and there are ecology nerds, and there are even double-whammy basebology nerds.

B. There are quantitative approaches to analyzing individual performance in these crazily disparate realms which might be useful to each other.

C. I might need to spend more time writing papers and less time geeking out about baseball!

More analogies to consider:

Reciprocal transplants: trades?

Trophic levels: minor league system?

Nitrogen fertilization: steroids?

---

[i] One of the most astonishing databases around: complete downloadable stats for every player since 1871. This database is what NEON should aspire to be, except that this one was compiled completely privately by some single-minded and visionary baseball geeks!

[ii] Batting: Hits, at bats, runs batted in, stolen bases, walks, home runs

Fielding: Put outs, assists, errors, zone rating

Pitching: Earned run average, home runs allowed, walks, strike outs.

[iii] E.g. even after taking into account other more typical measures of success in offense (runs, R) and defense (runs allowed, RA), within years, there is still a negative slope for FD on wins:

lme(win ~ R + RA + FD, random = ~1|yearID, data = team)

Value Std.Err DF t-value p-value

(Intercept) 80.289 0.7411 2159 108.3 <0.001

R 0.107 0.0009 2159 116.8 <0.001

RA -0.105 0.0009 2159 -115.6 <0.001

FD -1.729 0.8083 2159 -2.1 0.0325

The regional community, maximum entropy, and other ideas in ecology

Looking through my feed of community ecology papers this month, I couldn’t help but notice that while most tested well-established concepts–density-dependence, niche partitioning, metacommunities, competition, dispersal limitation–there was also–as I suppose is usually true–a subset of papers championing newer, less established ideas.

For example, the article “Applying a regional community concept to forest birds of eastern North America” by Robert Ricklefs, furthers the regional community concept he introduced in 2008. Ricklefs is uncomfortable with how ecologists typically define local communities – i.e as spatially and ecologically discreet entities – and the predominant focus in community ecology on local coexistence. He argues that communities make sense as entities only at a larger scale, taking into account that local communities are not isolated, but instead interact as a function of overlapping ranges and species dispersal. In this paper he applies this concept to Breeding Bird Survey data to examine the distribution and abundance of birds in eastern NA.

Partel, Szava-Kovats, and Zobel are also critical of the predominant focus on local diversity. In their paper “Dark diversity: shedding light on absent species”, they pitch the idea of “dark diversity” as a valid diversity metric. Dark diversity accounts for the number of species which belong to the species pool for a particular habitat in a region but are not actually present in a local community of that habitat type. The resulting value can be used to calculate a dimensionless ratio of local to dark diversity, suitable for comparison of diversity components in dissimilar regions.

Lastly, in “A strong test of a maximum entropy model of trait-based community assembly”, Shipley et al. further test Shipley’s model of Entropy Maximization, using it to predict the composition of communities in the South African fynbos. The model predicts community composition (species identity and relative abundances) through an assumption of random assembly (or entropy maximization) within environmental constraints on species traits.

New ideas are a constant in ecology, but they face stiff competition in an already crowded field. The possible mechanisms of local coexistence, for example, are already a long list. What determines which of these–or any–ideas become entrenched in ecology? The likelihood of a concept becoming established must be a complex function relying on a cost-benefit analysis–what does applying this idea cost compared to the gain in understanding it produces?–further adjusted by intangible variables like timing and the skill and prestige of an idea’s advocate. After all, some ideas require decades to establish properly, requiring changes in the theoretical climate or technical capabilities, for example, neutral theory or spatial ecology. Others seem to catch on immediately. Philosophers have written more cogently on how scientific ideas change and paradigms shift, but as participants in the process, we have a rather unique perspective. After all, as scientists we play an active role in driving these shifts in thought and action. You might argue that the merit of the ecological ideas that become established are as much a reflection on those who accept and institute them, as on those who propose them.

Competitive coexistence, it's all about individuals.

Understanding how species coexist has been the raison d'etre for many ecologists over the past 100 years. The quest to understand and explain why so many species coexist together has really been a journey of shifting narratives. The major road stops on this journey have included searching for niche differences among species -from single resources to multidimensional niches, elevating the role for non-equilibrial dynamics -namely disturbances, and assessing the possibility that species actually differ little and diversity patterns follow neutral process. Along this entire journey, researchers (especially theoreticians) have reminded the larger community that that coexistence is a product of the balance between interactions among species (interspecific) and interactions among individuals within species (intraspecific). Despite this occasional reminder, ecologists have largely searched for mechanisms dictating the strength of interspecific interactions.

Image used under Flickr creative commons license, taken by Tinken

In order for two species to coexist, intraspecific competition must be stronger than interspecific -so sayeth classic models of competition. While people have consistently looked for niche differences that reduce interspecific competition, no one has really assessed the strength of intraspecific competition. Until now that is. In a recent paper in Science, Jim Clark examines intra- vs interspecific interactions from data following individual tree performances, across multiple species, for up to 18 years. This data set included annual growth and reproduction, resulting in 226,000 observations across 22,000 trees in 33 species!

His question was actually quite simple -what is the strength of intraspecific interactions relative to interspecific ones? There are two alternatives. First, that intraspecific competition is higher, meaning that among species differences only need to be small for coexistence to occur; or secondly, that intraspecific competition is lower, requiring greater species niche differences for coexistence. To answer this he looked at correlations in growth and fecundity between individuals either belonging to the same or different species, living in proximity to one another. He took a strong positive correlation as evidence for strong competition and a negative or weak correlation as evidence for resource or temporal niche partitioning. What he found was that individuals within species were much more likely to show correlated responses to fluctuating environments, than individuals among species.

This paper represents persuasive evidence that within-species competition is generally extremely high, meaning that to satisfy the inequality leading to coexistence: intra > inter, subtle niche differences can be sufficient. These findings should spur a new era of theoretical predictions and empirical tests as our collective journey to understanding coexistence continues.

Clark, J. (2010). Individuals and the Variation Needed for High Species Diversity in Forest Trees Science, 327 (5969), 1129-1132 DOI: 10.1126/science.1183506

Plant genotypic diversity supports pollinator diversity

Research over the past 20 years has shown that plant communities with greater diversity maintain higher productivity, greater stability and support more diverse arthropod assemblages. More recently, several experiments have shown that interspecific diversity (namely genotypic differences) also affects community functioning. Pollination is often considered an essential function, and does plant genotypic diversity affect pollinator diversity and frequency?

In a recent paper in PLoS ONE, Genung and colleagues test whether plant genotypic diversity affects pollinator visits. They use an experimental system set-up by Greg Crutsinger that combines multiple genotypes of the goldenrod, Solidago altissima, and record pollinator visits over two years. Experimental plots contained 1, 3, 6, or 12 genotypes of S. altissima. After accounting for differences in abundance, Genung et al. show that as genotypic diversity increases, both pollinator richness and number of visits to the plot significantly increase. This increase is greater than expectations of randomly simulated assemblages combining proportional pollinator visits from monocultures.

The previous research at the species-level has made a persuasive rationale to protect species diversity in order to maintain ecosystem functioning. Now, research like this is making a case that there are consequences for not explicitly considering genetic diversity in conservation planning and habitat restoration.

Genung, M., Lessard, J., Brown, C., Bunn, W., Cregger, M., Reynolds, W., Felker-Quinn, E., Stevenson, M., Hartley, A., Crutsinger, G., Schweitzer, J., & Bailey, J. (2010). Non-Additive Effects of Genotypic Diversity Increase Floral Abundance and Abundance of Floral Visitors PLoS ONE, 5 (1) DOI: 10.1371/journal.pone.0008711

Taking below-ground processes seriously: plant coexistence and soil depth

Some of the earliest ecologists, like Eugen Warming and Christen Raunkiaer, were enthralled with the minutia of the differences in plant life forms and how these differences determined where plants lived. They realized that differences in plant growth forms corresponded to how different plants made their way in the world. Since this early era, understanding the mechanisms of plant competition is one of the most widely-studied aspects of ecology. This is such an important aspect of ecology because understanding plant coexistence allows us to understand what controls productivity in the basal trophic level for most terrestrial food webs. There are a plethora of plausible mechanisms for how plants are able to coexist, and most involve above-ground partitioning strategies (such as different leaf shapes) or phenological differences (such as germination or bolting timing). Yet, below-ground interactions among plants as a way to understand competition and coexistence have been making a strong resurgence in the literature lately. This resurgence has been driven by new hypotheses and technologies.In what is probably the best hypothesis test of the role for below-ground niche partitioning, Mathew Dornbush and Brian Wilsey reveal how soil depth can affect coexistence. They seeded 36 tallgrass prairie species into plot that were either shallow, medium or deep soiled, and asked if species richness and diversity were affected after 3 years. They found that species richness significantly increased with increased soil depth, revealing that deeper soils likely had greater niche opportunities for species. Not only did deeper soils harbor greater richness, but compositions were non-random subsets. The species inhabiting shallow soils were a subset of medium soils, and medium a subset of deep. This means that increasing depth opened new niche opportunities, unique from the ones for shallow soils.

This study is the first field-based experiment of soil depth and coexistence, that I know of and the results are compelling. Plant species are segregating below-ground niches, and perhaps we look for other partitioning strategies for species that inhabit the same soil depth.

Dornbush, M., & Wilsey, B. (2009). Experimental manipulation of soil depth alters species richness and co-occurrence in restored tallgrass prairie Journal of Ecology DOI: 10.1111/j.1365-2745.2009.01605.x

Other notable recent papers on below-ground processes:

Bartelheimer, M., Gowing, D., & Silvertown, J. (2009). Explaining hydrological niches: the decisive role of below-ground competition in two closely related species Journal of Ecology DOI: 10.1111/j.1365-2745.2009.01598.x

Cramer, M., van Cauter, A., & Bond, W. (2009). Growth of N-fixing African savanna species is constrained by below-ground competition with grass Journal of Ecology DOI: 10.1111/j.1365-2745.2009.01594.x

Meier, C., Keyserling, K., & Bowman, W. (2009). Fine root inputs to soil reduce growth of a neighbouring plant via distinct mechanisms dependent on root carbon chemistry Journal of Ecology, 97 (5), 941-949 DOI: 10.1111/j.1365-2745.2009.01537.x