Guest post: One way to quantify ecological communities

This is a guest post by Aspen Reese, a graduate student at Duke University, who in addition to studying the role of trophic interactions in driving secondary succession, is interested in how ecological communities are defined. Below she explains one possible way to explicitly define communities, although it's important to note that communities must explicitly be networks for the below calculations.

Because there are so many different ways of defining “community”, it can be hard to know what, exactly, we’re talking about when we use the term. It’s clear, though, that we need to take a close look at our terminology. In her recent post, Caroline Tucker offers a great overview of why this is such an important conversation to have. As she points out, we aren’t always completely forthright in laying out the assumptions underlying the definition used in any given study or subdiscipline. The question remains then: how to function—how to do and how to communicate good research—in the midst of such a terminological muddle?

We don’t need a single, objective definition of community (could we ever agree? And why should we?). What we do need, though, are ways to offer transparent, rigorous definitions of the communities we study. Moreover, we need a transferable system for quantifying these definitions.

One way we might address this need is to borrow a concept from the philosophy of biology, called entification. Entification is a way of quantifying thingness. It allows us to answer the question: how much does my study subject resemble an independent entity? And, more generally, what makes something an entity at all?

Stanley Salthe (1985) gives us a helpful definition: Entities can be defined by their boundaries, degree of integration, and continuity (Salthe also includes scale, but in a very abstract way, so I’ll leave that out for now). What we need, then, is some way to quantify the boundedness, integration, and continuity of any given community. By conceptualizing the community as an ecological network*—with a population of organisms (nodes) and their interactions (edges)—that kind of quantification becomes possible.

Consider the following framework: 

Boundedness
Communities are discontinuous from the environment around them, but how discrete that boundary is varies widely. We can quantify this discreteness by measuring the number of nodes that don’t have interactions outside the system relative to the total number of nodes in the system (Fig. 1a). 

Boundedness = (Total nodes without external edges)/(Total nodes)

Integration
Communities exhibit the interdependence and connections of their parts—i.e. integration. For any given level of complexity (which we can define as the number of constitutive part types, i.e. nodes (McShea 1996)), a system becomes more integrated as the networks and feedback loops between the constitutive part types become denser and the average path length decreases. Therefore, degree of integration can be measured as one minus the average path length (or average distance) between two parts relative to the total number of parts (Fig. 1b).

Integration = 1-((Average path length)/(Total nodes))

Continuity
All entities endure, if only for a time. And all entities change, if only due to entropy. The more similar a community is to its historical self, the more continuous it is. Using networks from two time points, a degree of continuity is calculated with a Jaccard index as the total number of interactions unchanged between both times relative to the total number of interactions at both times (Fig. 1c).

Continuity = (Total edges-changed edges)/(Total edges)

Fig 1. The three proposed metrics for describing entities—(A) boundedness, (B) integration, and (C) continuity—and how to calculate them. 

Let’s try this method out on an arctic stream food web (Parker and Huryn 2006). The stream was measured for trophic interactions in June and August of 2002 (Fig. 2). If we exclude detritus and consider the waterfowl as outside the community, we calculate that the stream has a degree of boundeness of 0.79 (i.e. ~80% of its interactions are between species included in the community), a degree of integration of 0.98 (i.e. the average path length is very close to 1), and a degree of continuity of 0.73 (i.e. almost 3/4 of the interactions are constant over the course of the two months). It’s as easy as counting nodes and edges—not too bad! But what does it mean?

Fig. 2: The food web community in an arctic stream over summer 2002. Derived from Parker and Huryn (2006). 

Well, compare the arctic stream to a molecular example. Using a simplified network (Burnell et al. 2005), we can calculate the entification of the cellular respiration pathway (Fig. 3). We find that for the total respiration system, including both the aerobic and anaerobic pathways, boundedness is 0.52 and integration is 0.84. The continuity of the system is likely equal to 1 at most times because both pathways are active, and their makeup is highly conserved. However, if one were to test for the continuity of the system when it switches between the aerobic and the anaerobic pathway, the degree of continuity drops to 0.6.

Fig. 3: The anaerobic and aerobic elements of cellular respiration, one part of a cell’s metabolic pathway. Derived from Burnell et al. (2005)

Contrary to what you might expect, the ecological entity showed greater integration than the molecular pathway. This makes sense, however, since molecular pathways are more linear, which increases the average shortest distance between parts, thereby decreasing continuity. In contrast, the continuity of molecular pathways can be much higher when considered in aggregate. In general, we would expect the boundedness score for ecological entities to be fairly low, but with large variation between systems. The low boundedness score of the molecular pathway is indicative of the fact that we are only exploring a small part of the metabolic pathway and including ubiquitous molecules (e.g. NADH and ATP).

Here are three ways such a system could improve community ecology: First, the process can highlight interesting ecological aspects of the system that aren’t immediately obvious. For example, food webs display much higher integration when parasites are included, and a recent call (Lafferty et al. 2008) to include these organisms highlights how a closer attention to under-recognized parts of a network can drastically change our understanding of a community. Or consider how the recognition that islands, which have clear physical boundaries, may have low boundedness due to their reliance on marine nutrient subsidies (Polis and Hurd 1996) revolutionized how we study them. Second, this methodology can help a researcher find a research-appropriate, cost-effective definition of the study community that also maximizes its degree of entification. A researcher could use sensitivity analyses to determine what effect changing the definition of her community would have on its characterization. Then, when confronted with the criticism that a certain player or interaction was left out of her study design, she could respond with an informed assessment of whether the inclusion of further parts or processes would actually change the character of the system in a quantifiable way. Finally, the formalized process of defining a study system will facilitate useful conversation between researchers, especially those who have used different definitions of communities. It will allow for more informed comparisons between systems that are similar in these parameters or help indicate a priori when systems are expected to differ strongly in their behavior and controls.

Communities, or ecosystems for that matter, aren’t homogeneous; they don’t have clear boundaries; they change drastically over time; we don’t know when they begin or end; and no two are exactly the same (see Gleason 1926). Not only are communities unlike organisms, but it is often unclear whether or not communities or ecosystems are units of existence at all (van Valen 1991). We may never find a single objective definition for what they are. Nevertheless, we work with them every day, and it would certainly be helpful if we could come to terms with their continuous nature. Whatever definition you choose to use in your own research—make it explicit and make it quantifiable. And be willing to discuss it with your peers. It will make your, their, and my research that much better.

By Aspen Reese.

A multiplicity of communities for community ecology

Community ecologists have struggled with some fundamental issues for their discipline. A longstanding example is that we have failed to formally and consistently define our study unit – the ecological community. Textbook definitions are often broad and imprecise: for example, according to Wikipedia "a community...is an assemblage or associations of populations of two or more different species occupying the same geographical area". The topic of how to define the ecological community is periodically revived in the literature (for example, Lawton 1999; Ricklefs 2008), but in practice, papers rely on implicit but rarely stated assumptions about "the community". And even if every paper spent page space attempting to elucidate what it is we mean by “community”, little consistency would be achieved: every subdiscipline relies on its own communally understood working definition.

In their 1994 piece on ecological communities, Palmer and White suggested “that community ecologists define community operationally, with as little conceptual baggage as possible…”. It seems that ecological subdisciplines have operationalized some definition of "the community", but one of the weaknesses of doing so is that the conceptual basis for these communities is often obscured. Even if a community is simply where you lay your quadrat, you are making particular assumptions about what a community is. And making assumptions to delimit a community is not problematic: the problem is when results are interpreted without keeping your conceptual assumptions in mind. And certainly understanding what assumptions each subfield is making is far more important than simply fighting, unrealistically, for consistent definitions across every study and field.

 

Defining ecological communities.

Most definitions of the ecological community vary in terms of only a few basic characteristics (figure above) that are required to delimit *their* community. Communities can be defined to require that a group of species co-occur together in space and/or time, and this group of species may or may not be required to interact. For example, a particular subfield might define communities simply in terms of co-occurrence in space and time, and not require that interactions be explicitly considered or measured. This is not to say they don't believe that such interactions occur, just that they are not important for the research. Microbial "communities" tend to be defined as groups of co-occurring microbes, but interspecific interactions are rarely measured explicitly (for practical reasons). Similarly, a community defined as "neutral" might be studied in terms of characteristics other than species interactions. Studies of succession or restoration might require that species interact in a given space, but since species composition has or is changing through time, temporal co-occurrence is less important as an assumption. Subdisciplines that include all three characteristics include theoretical approaches, which tend to be very explicit in defining communities, and studies of food webs similarly require that species are co-existing and interacting in space and time. On the other hand, a definition such as “[i]t is easy to define local communities where in species interact by affecting each other’s demographic rates” (Leibold et al. 2004) does not include any explicit relationship of those species with space – making it possible to consider regionally coexisting species.

How you define the scale of interest is perhaps more important in distinguishing communities than the particulars of space, time, and interactions. Even if two communities are defined as having the same components, a community studied at the spatial or temporal scale of zooplankton is far different than one studied in the same locale and under the same particulars, but with interest in freshwater fish communities. The scale of interactions considered by a researcher interested in a plant community might include a single trophic level, while a food web ecologist would expand that scale of interactions to consider all the trophic levels. 

The final consideration relates to the historical debate over whether communities are closed and discrete entities, as they are often modelled in theoretical exercises, or porous and overlapping entities. The assumption in many studies tends to be that communities are discrete and closed, as it is difficult to model communities or food webs without such simplifying assumptions about what enters and leaves the system. On the other hand, some subdisciplines must explicitly assume that their communities are open to invasion and inputs from external communities. Robert Ricklef, in his 2008 Sewall Wright Address, made one of the more recent calls for a move from unrealistic closed communities to the acceptance that communities are really composed of the overlapping regional distributions of multiple organisms, and not local or closed in any meaningful way.

These differences matter most when comparing or integrating results which used different working definitions of "the community". It seems more important to note possible incompatibilities in working definitions than to force some one-size-fits-all definition on everything. In contrast to Palmer and White, the focus should not be on ignoring the conceptual, but rather on recognizing the relationship between practice and concept. For example, microbial communities are generally defined as species co-occurring in space and time, but explicit interactions don't have to be shown. While this is sensible from a practical perspective, the problem comes when theory and literature from other areas that assume interactions are occurring is directly applied to microbial communities. Only by embracing this multiplicity of definitions can we piece together existing data and evidence across subdisciplines to more fully understand “community ecology” in general.

Community structure - what are we missing?

Some of the most frequently used ecological concepts can be difficult to define. Sometimes this lack of clarity leads to a poor understanding and a weak base for further research. A great example is “community structure”, a concept frequently mentioned and rarely defined that probably changes a lot from use to use. The phrase “we’re interested in how communities are structured” is tossed around a lot, and I suppose an understood definition is that community structure encompasses the species that are present in a community and their abundances. Community structure may refer to  both a very simple concept (the abundances of species present in a community) and a very complicated one, connecting as it does mechanisms and models, observational data, and statistical measures. As a result, the precise way that ecologists delineate community structure and quantify it is both varied and vague.

The connection between models, community
structure and metrics.

In the literature, it seems that there are two ways of approaching “community structure”: bottom-up, in which community structure is a predicted outcome of theoretical models of different mechanisms, and top-down, in which community structure is measured in a relatively statistical or descriptive fashion. Both are valuable approaches: while statistical metrics often are interpreted as providing evidence for particular models or mechanisms, the reverse logic – that a model predicts particular results for a given metric – is rarely explicitly considered. Making connections between the model results and the descriptive metrics might actually be fairly difficult. Model predictions are often complex and multidimensional, predicting changes through time, growth rates, the combinations of species that can or cannot coexist (but only if assumptions hold), or particular relationships between measures like diversity, abundances, and range sizes. Metrics are necessarily confined to a few dimensions (or perhaps are ordination approaches), focus on straightforward observational measures like abundance and presence, and further include observational error (sampling, etc). Because community structure means something different to these two approaches, the connections between metrics and models are poorly explored. A theoretician might find it difficult to relate ordinations of communities with the typical predictions from a mathematical model (which might be something like growth rates in relation to changes in abundance), while someone collecting field data might feel that the data they can collect is difficult to relate to the predictions of models.

Part of the problem is that for a long time, the default focus was on what types of interactions structured communities (environment, competition, predation, mutualisms), and niches were assumed to be necessarily driving community structure. The type of measurements and metrics used reflected this search for niches (e.g. comparing environmental gradients with community structure). Many quantitative metrics may tell you something about how community structure relates to different variables (spatial, environment, biotic) and how much variation is still unexplained. The consideration that niches might not always be important eventually led ecologists to compare patterns in community structure to random, null, or neutral expectations. As a result, in the simplest cases the answers to questions about community structure and niches are binary – different from random (niches matter), or not. Looking for complex patterns predicted by models-for example, the relative contribution of niche based and neutral processes to community structure-is difficult using common metrics of community structure (although there are some papers that do a good job of this).

It is interesting that this problem of disconnection between theoretical models of community structure and community structure metrics received the most attention through criticisms of phylogenetic metrics of diversity. There, patterns of over- and under-dispersion were criticized for not being the necessary outcome from models of competition or environmental filtering (i.e. Mayfield and Levine 2010). While those criticisms were mostly fair, they are equally deserved in most studies of species diversity, where patterns in ordinations or beta-diversity are frequently used to infer mechanisms. In contrast, one of the best approaches thus far to integrating model predictions for community structure with metrics of community structure are null models. Though they differ greatly in ecological realism and complexity, null models suggest expected community structure or metric values if none of the expected processes are structuring a community.

One of the greatest failings of the top-down approach is that recognizing patterns outside of the expected, such as those that include stochasticity or a combination of different processes, or the effects of history, is nearly impossible. Models that can incorporate these complexities provide little suggestion of how the patterns we can easily record in communities might reflect complex structuring processes. Ecological research is limited by the poor connection between both top-down and bottom-up approaches and its vague definition of community structure. Patterns more complicated than those that the top-down approach searches for are likely being missed, while relations between models and metrics (or development of new metrics) aren’t considered often enough. One solution might be to more meaningfully define community structure, perhaps as the association (or lack thereof) between the combination of species present in a community and the combination of abiotic and/or biotic processes present. This association is generally compared to an association between species and processes that might arise from random effects alone. The difference is that structure shouldn’t be considered separately from the processes that produce it, and the connections should be explicitly rather than implicitly made.

Reinterpreting phylogenetic patterns in communities

Examining the phylogenetic structure of a community in order to understand patterns of community assembly has become an increasingly popular approach. A quick web search of “community”, “phylogenetics”, and “ecology” finds several hundred papers, most written in the last ten years.

Eco-phylogeneticists examine how patterns of evolutionary relatedness within communities may reflect the processes structuring those communities. In particular, a commonly tested hypothesis is the competition-relatedness hypothesis, which suggests that more closely-related species having more similar niches and therefore stronger competitive interactions, making coexistence between them less likely. As a result, if competition is important, communities may exhibit phylogenetic overdispersion, with species being less related on average than if drawn randomly from the regional species pool. The contrasting pattern, phylogenetic clustering, where species tend to be more closely related than expected, is often interpreted as being the result of strong environmental filtering, such that only a closely related group of species, best adapted to that environment, surviving in the community.

Evidence for the competition-relatedness hypothesis has been mixed, and since most tests of this hypothesis focus on patterns in observed data, conclusions about the underlying mechanism driving community phylogenetic patterns are rarely testable, and yet widely made.

In Mayfield and Levine (2010, Ecology Letters), the authors critique the current ecological justification for the competition-relatedness hypothesis, noting that it does not agree with a more current view of the processes driving species coexistence. As established by Chesson (2000, Annual Review of Ecology and Systematics), coexistence can involve both stabilizing forces (niche differences between species), and equalizing forces (fitness differences between species). In a simplistic example, plants using different soil types (niche differences) may coexist, while plants with similar high growth rates may exclude those species with lower growth rates (fitness differences). The final community should reflect the interplay of both these processes.

The implication of this view of species coexistence is that there is no preconceived phylogenetic pattern which should reflect competition: if species with the highest heights are competitively superior and exclude other species (coexistence driven by fitness differences), and height is a phylogenetically conserved trait, the community will appear to be phylogenetically clustered. Traditionally, a clustered pattern would not be considered to indicate the effects of competition. In fact, Mayfield and Levine show that the expected phylogenetic pattern depends entirely on whether niche and/or fitness differences are important and/or related to phylogenetic distance.

This suggest that conclusions in past studies may need to be reinterpreted. It also adds to the list of assumptions about evolutionary relatedness and ecological function which need to be tested: for example, how do niche and fitness differences tend to change through time? Do they tend to be conserved among closely related species? Does one or the other tend to dominate as a driver of coexistence in different systems? If nothing else, we need to be careful about making generalizations which don’t account for the differing evolutionary history, geographical location, and ecological setting that communities experience, when interpreting observed patterns in those communities.

The sushi of tomorrow… Jellyfish rolls?

With the world’s fisheries teetering on the edge of collapse, familiar items at your local sushi bar might disappear in the near future. One candidate for replacing the Hamachi, Ikura, Maguru, Tai, and Toro on the menu is the jellyfish, which seems to be doing well – too well, actually – in today’s environment.

In recent years, jellyfish outbreaks have become more frequent and more severe. These outbreaks can have lasting ecological and economic consequences. They can wreak havoc on the tourist industry by closing beaches and harming swimmers, cause power outages by blocking cooling intakes at coastal power plants, reduce commercial fish abundance via competition and predation, spread fish parasites, burst fishing nets, and contaminate catches.

A review by Anthony Richardson and his collaborators suggests that human activities such as overfishing, eutrophication, climate change, translocation, and habitat modification have dramatically increased jellyfish numbers. Their research, which was published this week in Trends in Ecology and Evolution, highlights that the structure of pelagic ecosystems can abruptly transition from one that is dominated by fish to one that is dominated by jellyfish.

Richardson and his collaborators present a potential mechanism to explain how local jellyfish aggregations can spread, displace fish, and form an alternative stable state to fish-dominated ecosystems. Jellyfish are like the opportunistic weed of the sea, giving them an edge in environments stressed by climate change, eutrophication, and overfishing. In these disturbed environments, the abundance of jellyfish relative to filter-feeding fish increases until a tipping point is reached. Under normal conditions, filter-feeding fish keep jellyfish populations in check via competition for planktonic food and (perhaps) predation on an early life-stage of the jellyfish. At the tipping point, jellyfish numbers are such that they begin to overwhelm any control of their vulnerable life-cycle stages by fish predators. At the same time, jellyfish progressively eliminate competitors and predators via their predation on fish eggs and larvae. As jellyfish abundance increases, sexual reproduction becomes more efficient, allowing them to infest new habitats where fish might have formally controlled jellyfish numbers.

Richardson and his collaborators suggest that one way to hit the brakes on what they call the “the never-ending jellyfish joyride” is to harvest more jellyfish for human consumption. Jellyfish have been eaten for more than 1000 years in China, where they are often added to salads. In Japan they are served as sushi and in Thailand they are turned into a crunchy noodle concoction. Although the taste and texture of jellyfish might not be appealing to some westerners, I for one have yet to meet a sushi that I didn’t like. Of course, jellyfish harvesting is unlikely to return systems to their fish-dominated state if the stresses that caused the ecosystem shift remain.

Richardson, A. J., A. Bakun, G. C. Hays, and M. J. Gibbons. 2009. The jellyfish joyride: Causes, consequences and management responses to a more gelatinous future. Trends in Ecology and Evolution, 24 (6), 312-322 DOI: 10.1016/j.tree.2009.01.010

Functional traits and trade-offs explain phytoplankton community structure

After attending the presentation by Elena Litchman at the ASLO Aquatic Science Meeting in Nice three weeks ago I came across this paper. Although it was published already two years ago, this works need to be highlighted! Marine phytoplankton is important. It contributes approximately 50% to world primary productivity. Among other factors phytoplankton communities are structured by competition for limiting nutrients (mainly for nitrate and ammonia) in the ocean. Litchman et al. base their paper on the presumption that phytoplankton organisms can achieve higher competitive ability (Tilman’s R*) by different strategies. That is, the organisms can either increase their maximum nutrient uptake and/or growth rate or they decrease the minimum cell quota, the half saturation constant for nutrient uptake and/or their mortality. Litchman et al. tested if they can find constraints and trade-offs on the evolution of better competitive abilities (lower R*) in major phytoplankton groups. Specifically they asked if there is a positive relationship between maximum growth rate and R* which would show a gleaner-opportunist trade-off.
The authors show positive relationships between measurements for growth and nitrate uptake which can constrain the evolution on competitive ability. Indeed major groups of phytoplankton group along these trade-off curves. Whereas coccolithophores e.g. show low nitrate uptake rates and low half-saturation constants, diatoms and dinoflagelates show the opposite nitrate uptake strategy with high uptake rates and high half-saturation constants. A gleaner-opportunist trade-off, i.e. a positive correlation between maximum growth rates and R*which would result in a super species, could not be found across major groups but within the diatoms. The paper gives more results about trait differences among taxonomic groups and allometric scaling relationships. Trade-offs and different strategies in nutrient uptake are discussed in a very concise way either from a mechanistic physiological view as well as from the evolutionary history perspective.


Elena Litchman, Christopher A. Klausmeier, Oscar M. Schofield and Paul G. Falkowski (2009) The role of functional traits and trade-offs in structuring phytoplankton communities: scaling from cellular to ecosystem level. Ecology Letters. DOI: 10.1111/j.1461-0248.2007.01117.x