Wednesday, January 29, 2014

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: 

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)

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))

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.

Monday, January 27, 2014

Gender diversity begets gender diversity for invited conference speakers

There are numerous arguments for why the academic pipeline leaks - i.e. why women are increasingly less represented in higher academic ranks. Among others, the suggestion has been made there can be simple subconscious biases regarding the image that accompanies the idea of "a full professor" or "seminar speaker". A useful new paper by Arturo Casadevall and Jo Handelsman provides some support for this idea. The authors identified invited talks at academic conferences as an example of important academic career events, which provide multiple benefits and external recognition of a researcher’s work. However, a number of studies have shown that women are less represented as invited speakers, but proportionally and in absolute numbers. To explore this further, the authors asked whether the presence or absence of women as conveners for the American Microbial Society (ASM) meetings affects the number of female invited speakers. Conveners for ASM meetings are involved of selection of speakers, either directly or in consultation with program committee members. The two annual meetings run by the ASM involve 4000-6000 attendees, of which female members constitute approximately 40% (37% when only full members were considered). Despite this nearly 40% female membership, for session where all conveners were male, the percentage of invited speakers who were female was consistently near 25%. While explanations for these sorts of poor representation of females in academia are often structural, the authors show that in this case, simple changes might change this statistic. If one or more women were conveners for a session, the proportion of female invited speakers in that session rises to around 40%, or in line with women’s general representation in the ASM. The authors don’t offer precise explanations for these striking results, but note that women conveners may be more likely to be aware of gender and may make a conscious effort to invite female speakers. Implicit biases, our “search images”, may unconsciously favour males, but these results are positive in suggesting that even small changes and greater awareness can make a big difference.

The proportion of invited speakers in a session who are female from 2011-2013, for the two annual meetings (GM & ICAAC) organized by the ASM. Compare black bars - no female conveners - and grey bars - at least one female convener.

Tuesday, January 21, 2014

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 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.

Monday, January 13, 2014

The generosity of academics

A cool tumblr gives credit to the often under-acknowledged kindness of academics It’s a topic I sometimes think about, because the culture of academics (at least for ecology) has always seemed to me to be driven by generous interactions.

Most of us have a growing lifetime acknowledgement list starting at the earliest point in our careers. After four years in my PhD, my thesis’ acknowledgements included other graduate students and lab mates, post-docs, undergrads, faculty at several institutions, and my supervisor. Almost everyone on this list expected nothing in exchange for their time and knowledge. Of course there are going to be exceptions, people who refuse to share their data, rarely interact with strangers, have little time for grad students, or are difficult to interact with. But that's pretty exceptional. Instead, one-sided  interactions regularly occur. Where else could you email a stranger, hoping they will meet with you at a conference to talk about your research? Or have a distant lab mail you cultures to replace ones that died? Or email the creator of an R package, because you can’t figure out where your data is going wrong, and get a detailed reply? And these aren’t untypical interactions in academia.

The lower you are down the academic ladder, the more you benefit from (maybe rely on) the kindness of busy people – committee members, collaborators, lab managers. Busy, successful faculty members, for example, took time to meet with me many times, kindly and patiently answering my questions. I can think of two reasons for this atmosphere, first that most ecologists simply are passionate about their science. They like to think about it, talk about, and exchange ideas with other people who are similarly inclined. The typical visit of an invited speaker includes hours and hours of meetings and meals with students, and most seem to relish this. Like most believers, they have a little of the zeal of the converted. Secondly, many of the structures of academic science rely heavily on goodwill and generosity. For example, reviews of journal submissions rely entirely on a system of volunteerism. That would be untenable for most businesses, but has survived this far in academic publishing. Grad student committees, although they have some value for tenure applications, are mostly dependent on the golden rule (I’ll be on your student’s committee, if you’ll be on mine). And then there are supervisor/supervisee relationships. These obviously vary between personalities, and universities, and countries, but good supervisors invest far more time and energy than the bare minimum necessary to get publications and a highly qualified personal out of it. That we rely on these interactions so heavily becomes most apparent when they fail—when you wait months on a paper because there are no reviewers, when your supervisor disappears—progress stops.

Of course, this sort of system only lasts if everyone feels like they gain some benefit, and everyone feels like the weight on them is fair. The ongoing problems with the review system suggest that this isn’t always true. Still, the posts on are a reminder of that altruism is still alive and well in academia.