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.

Gender diversity begets gender diversity for invited conference speakers

Arturo Casadevall, Jo Handelsman. 2014. The Presence of Female Conveners Correlates with a Higher Proportion of Female Speakers at Scientific Symposia. mBio: 5(1). 

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.

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.

The generosity of academics

A cool tumblr gives credit to the often under-acknowledged kindness of academics http://academickindness.tumblr.com/. 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 academickindness.tumblr.com are a reminder of that altruism is still alive and well in academia.

More links for 2013: the 'new' conservation, the IPCC report in haiku, and more.

Conservation science has been at the receiving end of some harsh criticisms in the last couple of years, particularly from the current chief scientist of the Nature Conservancy, Peter Kareiva (e.g. 1).  They have suggested that conservation science needs to be redefined and refocused on human-centred benefits and values if it is to be successful. Some pushback in the form of TREE article from Dan Doak et al. suggests that reframing conservation in terms of its human benefits is not the best or only solution.

In a similar vein, another new paper in TREE asks what issues should the conservation community be addressing. A short-list of 15 issues suggests highly specific problems that should be addressed soon, including the exploitation of Antarctica, rapid geographic expansion of macroalgal cultivation for biofuels, and the loss of rhinos and elephants.

Even if the official IPCC report proves too long or dry for the average person to read before the end of the year, there is also a haiku version. The pretty watercolour illustrations don't make the report any more cheerful, unfortunately.

Finally, a new journal, "Elementa: Science of the Anthropocene" seems positioned to focus precisely on these kind of issues. According to their website: 

"Elementa is a new, open-access, scientific journal founded by BioOne, Dartmouth, Georgia Tech, the University of Colorado Boulder, the University of Michigan, and the University of Washington.
Elementa represents a comprehensive approach to the challenges presented by this era of accelerated human impact, embracing the concept that basic knowledge can foster sustainable solutions for society....Elementa publishes original research reporting on new knowledge of the Earth’s physical, chemical, and biological systems; interactions between human and natural systems; and steps that can be taken to mitigate and adapt to global change. "

It will be interesting to see how it develops.

(from: http://www.phdcomics.com/comics.php?f=1459)

Holiday caRd 2013

A holiday pResent made of competition from the EEB & Flow :-)

(Easy to copy and run if you choose "view raw" in the lower right hand corner. Just copy and paste into R, it will do all the work. You will need to download and install R if you don't already have it.)

Ecological processes may diffuse through evolutionary time: an example from Equidae

Body mass evolution and diversification within horses (family Equidae). Lauren Shoemaker, Aaron Clauset. 2013. Article first published online: 5 DEC 2013. Ecology Letters. DOI: 10.1111/ele.12221

One of the things that community phylogenetic approaches have tended to overlook is that how we interpret phylogenetic relationships depends on a model of evolution. For example, the assumption that closely related species also are similar in their traits is implicitly relying on a particular model of trait evolution. One downside to this approach is that different models of evolution may provide different conclusions about macroecological patterns and processes (competition, environmental filtering, facilitation). 

For example, a new paper in Ecology Letters provides an example of how patterns of trait divergence and adaptive radiation can evolve as a result of diffusion evolution, rather than from a single strong ecological pressure. The paper by Shoemaker and Clauset focuses on the Equidae (horse) family, which underwent an adaptive radiation 56 million years ago, resulting in massive increases in diversity and in trait variation, particularly in body size, habitat type and range size, diet, life span and reproductive traits. Several explanations have been proposed for this radiation and in particular the great increase in body size variation (species are estimated to have ranged between 10-1200 kg). A diversity-focused model explains body size divergence as the result of macroecological competition for niches. A limited number of niches at a given size are assumed to be available, and these niches vary in quality or attractiveness. Increasingly extreme body sizes (and presumably less desirable niches) evolve as niches are filled at more desirable sizes. The result is a correlation between diversity and body size variation, much like the one seen in Equidae. The alternative model considered suggests that trait space is filled via diffusion or a random walk, with the only assumption being that there are some physiological constraints – here a hard limit on minimum size, and an assumption of increasing extinction risk as maximum size increases.

From Shoemaker + Clauset, 2013.

Using mathematical models of Equidae body size evoluation, the authors’ results were very clear (figure below): “Using family Equidae as a model system, we found that macroevolutionary ‘diffusion’, in which selective effects on species body size vary independently of the occupation status of nearby niches, explains substantially more of the observed changes in the Equidae body mass distribution over 56 Myr (Fig. 5) than does a diversity-driven mechanism...”. The results are interesting because they are a reminder that the relationship between macroecological patterns (for example, of traits like body size) may be related to evolutionary history in a much more nuanced way than ecophylogenetic studies sometimes assume. Rather, Shoemaker and Clauset suggest that the better performance of the diffusion model--rather than indicating that competition is *not* important--may be effective at capturing many independent ecological interactions and selective effects all driving body size evolution. A macroevolutionary model of competitive effects on trait divergence is may simply be unrealistic, since competition and ecological interactions may be more localized and less generalized in their effects across the entire Equidae family.

From Shoemaker and Clauset, 2013. Left - competition model, Right - diffusion model

“A large role for diffusion does not undermine the general ecological importance of competition, but rather clarifies its role in generating broad-scale patterns for horses in particular, and for evolving systems in general. Macroevolutionary diffusion is an effective large-scale description of many roughly independent ecological interactions and evolutionary constraints on species size variation. Short-term selective effects on size for a particular species can stem from any number of specific mechanisms, including but not limited to competition over ecological niches. So long as the magnitude and direction of these effects, as defined at the species-level, are roughly independent across the taxonomic group, the large-scale pattern will be well described by diffusion. Ecological competition may thus be crucial for individual species, but its effects are more diffuse at the large scale because competition is typically a local process.”

This is a reminder that many phylogenetic hypotheses (trait divergence or convergence in communities, etc) are too simplistic in their assumptions that broad macroecological processes dominating, and instead need to recognize that ecological processes are often numerous, independent, and local, making outcomes more nuanced than usually assumed. 

What can the future of ecology learn from the past?

Ecology has been under pressure to mature and progress as a discipline several times in its short life, always in response to looming environmental threats and the perception that ecological knowledge could be of great value. This happened notably in the 1960s, when the call for ecology to be better applicable occurred in relation to the publication of Silent Spring and fears about nuclear power and the Manhattan Project. Voices in academia, government, and the public called for ecology to become a “Big Science”, and focus on bigger scales (the ecosystem) and questions. And yet, “[Silent Spring] brought ecology as a word and concept to the public…A study committee, prodded by the publication of the book, reported to the ESA that their science was not ready to take on the responsibility being given to it.”

Arguably ecology has grown a lot since then: there have been advances in statistical approaches, spatial and temporal considerations, mechanistic understanding of multiple processes, in the number and type of systems and species studied, and the applications being considered. But it is once again facing a call (one that frankly has been ongoing for a number of years) to quickly progress as a science. The Anthropocene has proven an age of extinctions, human-mediated environmental changes, and threats to species and ecosystems from warming, habitat loss and fragmentation, extinctions, and invasions abound. Never has (applied) ecology appeared more relevant as a discipline to the general public and government. This is reflected in the increasing inclusion of buzzwords like “climate change”, “restoration”, “ecosystem services”, “biodiversity hotspot”, or “invasion” as keys to successful self-justification. Also similar to the 1960s is the feeling that ecology is not ready or able to meet the demand. Worse, that the time ecology has to respond is more limited than ever.

This first point--that ecology isn’t ready--is repeated in Georgina Mace’s (the outgoing president of the British Ecological Society) must-read editorial in Nature. The globe is in trouble, from climate change, disease, overpopulation, loss of habitat and biodiversity and Mace argues that ecology is incapable in its current form of responding to the need. She suggests that unless ecology evolves, it will fail as a discipline. Despite the growth of ecology that followed the 1960s, it is still a 'small' discipline: collaborations are mostly intra-disciplinary, data has been privately controlled, and the tendency remains to specialize on a particular system or organism of interest. However, this 'small' approach provides very little insight into the big problems of today - particularly understanding and predicting how the effects of global change on ecosystems and multispecies assemblages. To Mace, the solution, the undeniable necessity, is for ecology to get bigger. In particular, collaborations need to be broader and larger, with data sharing and availability (“big data”) the default. Ecological models and experiments/observations should be scaled up so that we can understand ecosystem effects and identify general trends across species or systems. In this new 'big' ecology, “[g]oals would be shaped by scientists, policy-makers and users of the resulting science, rather than by recent publishing trends”. Making research more interdisciplinary and including end-product users would allow the most important questions to receive the attention they deserve.

The difficulty with the looming environmental crises and the pressure on ecology to grow, is that the important decisions to be made have to be made rapidly and perhaps without complete information. Often scientific progress is afforded the time for slow progression and self-correction. After all, change is costly and risky, it requires reinvesting effort and funding, and may or may not pay off, and so science (including ecology) is often conservative. For example, a conservative mind would note that Mace’s suggestions are not without uncertainty and risk. Big data, for example, is acknowledged to have its strengths and its weaknesses, it may or may not be the cure-all it is touted as. Regardless of the amounts of data, good questions need to be asked and data, no matter how high quality, may not be appropriate for some questions. Context is often so important in ecology that attempts to combine data for meta-analysis may be questionable. Long running arguments within ecology reflect the fear that making ecological research more useful for applications and interdisciplinary questions may come at the expense of basic research and theory. It seems then that ecology is in an even worse scenario than Mace suggests, since not only must ecology change in order to respond to need, but it also must predict with incomplete information which future path will be most effective.

So ecological science is at an important junction with choices to make about future directions, limits on the information with which to make those choices, little time to make them, and much pressure to make them correctly. Perhaps we can take some comfort from the fact that ecology has been here before, though. There are some lessons we can draw from ecology’s last identity crisis, both the successes and failures. The last round resulted in ecology gaining legitimacy as a science and being integrated into policy and governance (the EPA, environmental assessments, etc). It appears, particularly in some countries, that ecology is more difficult to sell to policy and government today, but at the very least ecology has established a toehold it can take advantage of. Ecology also tried to focus on bigger scales in the 1960s--the concept of the 'ecosystem' resulted from that time--but the criticism was that the new ideas about ecosystems and evolutionary ecology weren't well integrated into ecological applications, and so their effect wasn't as broad as it could have been. Concepts like ecosystem services and function today integrate ecosystem science into applied outputs, and the cautionary tale is the value of balancing theoretical and applied development. It also seems that ecology must first consider what its duty as a science is to society (Mace’s assumption being that we have a great duty to be of value), since that is the key determinate of what path we decide to take. Then, we can hopefully consider what have we done right in recent years, what have we done wrong, and then decide where to go from here.

Page from "Silent Spring", Rachel Carson.

Biodiversity hotspots: are we missing other priorities?

Biodiversity hotspots are regions that harbour disproportionate biodiversity, especially of species with small ranges, and regarded as major conservation priorities (Zachos and Habel 2011). Biodiversity hotspots occur in some of the most exotic and romanticized regions around the world, such as Madagascar, the Caribbean Islands, the Western Ghats of India, and the Succulent Karoo of South Africa. By preserving these regions, we disproportionately preserve the diversity of life on Earth, and thus these conservation efforts are seen as critically important.

However, some argue that the emphasis on global biodiversity hotspots leaves other unique or less diverse regions open to human impacts since they have a perceived low natural value, and certainly not valuable enough to stem other economically motivated activities. This mind set may put large habitats under increased risk. This conflict is front and center in a recent paper by Durant and colleagues in Diversity and Distributions (Durant et al. 2013). In this paper, Durant et al. argue that large, globally relevant systems like hot deserts are under-protected, leading to potentially major collapses in these systems.

Ahaggar Mountains Oasis, from Wikipedia

They use the Sahara desert as the case study and show that while conservation efforts have been focused on hotspots, the majority of large vertebrates in the Sahara desert are now extinct or critically endangered.  System like hot deserts are important for human economic well-being, but our activities there have greatly reduced the amount of intact, undisturbed habitat.

Durant et al. argue, that had there been greater conservation effort and scientific interest in the Sahara, the catastrophic declines in large vertebrates may have been averted. This paper highlights the reality that we often undervalue certain ecosystems, regardless of the important ecosystem services and functions that they deliver.

S. M. Durant, T. Wacher, S. Bashir, R. Woodroffe, P. De Ornellas, C. Ransom, J. Newby, T. Abáigar, M. Abdelgadir, H. El Alqamy, J. Baillie, M. Beddiaf, F. Belbachir, A. Belbachir-Bazi, A. A. Berbash, N. E. Bemadjim, R. Beudels-Jamar, L. Boitani, C. Breit (2013). Fiddling in biodiversity hotspots while deserts burn? Collapse of the Sahara's megafauna

 Diversity and Distributions DOI: 10.1111/ddi.12157

Zachos, F. E. and J. C. Habel, editors. 2011. Biodiversity Hotspots: Distribution and Protection of Conservation Priority Areas. Springer-Verlag+.

Can you teach an old bird new (migratory) tricks?

Jennifer A. Gill, José A. Alves, William J. Sutherland, Graham F. Appleton, Peter M. Potts and Tómas G. Gunnarsson. 2013. “Why is timing of bird migration advancing when individuals are not?” Proc. B. Vol. 281, no. 1774.

Phenological responses have been used as one of the major indicators of climate change. The timing of flowering and fruiting, the return of migrant birds and insects from winter habitats are easily and often measured, and records going back decades or centuries sometimes exist. Most importantly, shifts in phenological indicators are some of the strongest connections between rising temperatures and biological and ecological responses (for example). There is plenty of evidence, for example, that some migrant bird species are returning to their breeding grounds earlier than ever. These migratory birds may be responding (via migration timing) to warming temperatures in several ways: there may be plasticity or flexibility in individual timing of migration which allows them to respond to changing temperature cues; or species may also show adaptation via changes in the frequency of individuals with different migratory timings (microevolution). In cases where migratory species are responding to climate change, distinguishing the mechanisms allowing them to do so is surprisingly hard. Early arrival of migratory bird species is often explained as being due to individual plasticity or flexibility in “choosing” the date of migration, but the majority of studies of this phenomenon include little or no information about individual behaviour, only changes in the mean date of arrival for the entire population.

For this reason, Gill et al. looked at individual, rather than average population, arrival dates for Icelandic black-tailed godwits in south Iceland. Icelandic black-tailed godwits (“godwits” for the sake of brevity) have shown significant advances in the last 20 years in the timing of their spring arrival to the shores of Iceland, and these advances appear to relate to increasing temperatures. The population has also been banded such that 1-2% can be individually identified and tracked throughout their migratory range. Although only adults (of unknown age) were banded at the start of the experiment in 1999, recently chicks have also been banded and released and so a wide range of demographic classes are included with the banded birds.

From Gill et al. 2013.

When Gill et al. looked at date of arrival across 14 years for each individual, their results were surprisingly clear and cohesive. As previously reported, the population mean date of arrival in South Iceland had advanced as much as 2 weeks. But, this advance is not reflected in individual timing of arrivals over that same period – if a bird tends to arrive on a given day, they will continue to arrive on approximately that day every year, independent of temperature conditions. Instead, the population trend appears to be driven entirely by birds born in recent years – young individuals (recently hatched) tend to have arrival dates much earlier than older individuals. At least for the godwits, population wide trends in migration dates are actually driven by only a subset of the individuals.

From Gill et al. (2013)

Often it is assumed that migratory birds are responding to warming temperatures on an individual level: individuals respond to changing cues, resulting in shifts in arrival date. This study suggests otherwise, and finds that the important mechanism is not individual plasticity or microevolution but rather related to demographic shifts in arrival time. As to why younger birds arrive earlier, it is not clear, but may relate to the observation that nest building and hatching dates are also advancing. It may be that natal conditions are important – the authors observed a variety of possibly inter-related changes such that hatching dates are advancing and chick sizes are increasing, and the suggestion that mortality rates of later arriving individuals may also be higher. "Environmentally induced advances in arrival dates of recruits could operate through: (i) carry-over effects of changing natal conditions, (ii) changing patterns of mortality of individuals with differing arrival times, or (iii) arrival times being initially determined by conditions in the year of recruitment and individuals repeating those timings thereafter."

These results make some predictions about which populations of migratory birds might have the most ability to respond to warming climate - most likely those with shorter migratory distances, shorter times to reproduction and shorter-lifespans (hence decreasing the lag-time required for the population to catch up to temperature). It may also have relevance for other non-bird species that also rely on careful timing between phenology and temperature. Correspondingly, it suggests limitations - if individual behaviour is so inflexible and constrained, our hopes that some species may respond to climate change with behavioural changes seem far to simplistic.

How many traits make a plant? How dimensionality simplifies plant community ecology.

Daniel C. Laughlin. 2013. The intrinsic dimensionality of plant traits and its relevance to community assembly. Journal of Ecology. Accepted manuscript online: 4 NOV. DOI: 10.1111/1365-2745.12187

Community ecology is difficult in part because it is so multi-dimensional: communities include possibly hundreds of species present, and in addition the niches of each of those species are multi-dimensional. Functional or trait-based approaches to ecology in particular have been presented as a solution to this problem, since fewer traits (compared to the number of species) may be needed to capture or predict a community’s dynamics. But even functional ecology is multi-dimensional, and many traits are necessary to truly understand a given species or community. The question, when measuring traits to delineate a community is: how many traits are necessary to capture species’ responses to their biotic and/or abiotic environment? Too few and you limit your understanding, too many and your workload becomes unfeasible.

Plant communities in particular have been approached using a functional framework (they don't move, so trait measurements aren't so difficult), but the number and types of traits that are usually measured vary from study to study. Plant ecologists have defined functional groups for plants which are ecologically similar, identified particular (“functional”) traits as being important, including SLA, seed mass, or height, or taken a "more is more" approach to measurements. There are even approaches that capture several dimensions by identifying important axes (leaf-height-seed strategy, etc.). Which of these approaches is best is not clear. In a new review, Daniel Laughlin rather ambitiously attempts to answer how many (and which) traits plant ecologists should consider. He asks whether the multi-dimensional nature of ecological systems is a curse (there is too much complexity for us to ever capture), or a blessing (is there a limit on how much complexity actually matters for understanding these systems)? Can dimensionality help plant ecologists determine the number of traits they need to measure? 

From Laughlin 2013. The various trait axes (related to plant organs) important for plant function.

Laughlin suggests that an optimal approach to dimensionality should consider each plant organ (root, leaves, height, figure above). Many of the traits regularly measured are correlated (for example, specific leaf area, leaf dry matter content, lifespan, mass-based maximum rate of photosynthesis, dark respiration rates, leaf nitrogen concentration, leaf phosphorus concentration are all interrelated), and so potentially redundant sources of information. However, there are measurements in the same organ that may provide additional information – leaf surface area provides different information than measures of the leaf economic spectrum – and so the solution is not simply measuring fewer traits per organ. Despite redundancy in the traits plant ecologists measure, it is important to recognize that dimensionality is very high in plant communities. Statistical methods are useful for reducing dimensionality (for example, principle coordinate analysis), but even when applied, Laughlin implies that authors often over-reduce trait data by retaining to only a few axes of information.

Using 3 very large plant species-trait datasets (with 16-67(!) trait measures), Laughlin applies a variety of statistical methods to explore effective dimensionality reduction. He then estimates the intrinsic dimensionality (i.e. the number of dimensions necessary to capture the majority of the information in community structure) for the three datasets (figure below). The results were surprisingly consistent for each data set – even when 67 possible plant traits were available, the median intrinsic number of dimensions was only 4-6. While this is a reasonably low number, it's worth noting that the number of dimensions analyzed in the original papers using those datasets were too low (2-3 only).

From Laughlin 2013. The intrinsic number of traits/dimensions
necessary to capture variation in community structure.

For Laughlin, this result shows that dimensionality is a blessing, not a curse. After all, it should allow ecologists to limit the number of trait measures they need to make, provided they choose those traits wisely. Once the number of traits measured exceeds 8, there appears to be diminishing returns. The caveat is that the traits that are important to measure might differ between ecosystems – what matters in a desert is different than what matters in a rainforest. As always, knowing your system is incredibly important. Regardless, the review ends on a highly optimistic note – that complexity and multi-dimensionality of plant communities might not limit us as much as we fear. And perhaps less work is necessary for your next experiment.

Exploring the intersection of conservation, ecology and human well-being

I've seen a number of articles recently that explore in different way the intersection of environment and ecology, conservation and human societies. In particular, Frontiers in Ecology and Evolution (the free ESA journal you are gifted as a member) has dedicated an entire issue to the question of climate impacts on humans and ecosystems, and the papers cover important topics relating to changing climate and its effects on biodiversity, ecosystem integrity and human societies. Economic predictions suggest costs from fires, drought, and rising sea levels: whether protecting ecosystems will preserve their function and so mediate these costs to humans and other organisms is explored in depth. Of course, scholarly papers can be impersonal, but another article about the struggles of Inuit in the north to adapt (or not) to changing ecosystems provides a smaller, more human look at climate, development, and cultural change. Another study predicts that for some cultures, climate change (and the resulting difficulties growing food, maintaining livelihoods, obtaining water and human health risks) may be too much for them to withstand.

Finally, a long-form story by Paul Voosen in The Chronicle of Higher Education asks "Who is conservation for?". While not a novel question, through interviews with Gretchen Daily and Michael Soule, Voosen does a thorough job of illuminating conservation biology in the context of real-world limitations and realities, historical precedents, ongoing tensions between new and old approaches to conservation, and economic development. In the end it asks what motivates conservation: do we conserve purely for the sake of biodiversity alone, for economic and functional benefits, for aesthetic reasons, for charismatic and at-risk species? As Voosen subtly hints in the article, if leading conservation biologists can't agree on the answer, will it ever be possible to be effective?

Slightly unrelated, but there is a great short film online about the life of Alfred Russel Wallace, the less celebrated co-discoverer of natural selection.

Managing uncertain restoration outcomes*

Human activity has impacted ecosystems around the globe, and the value of intact, functioning habitats is increasingly appreciated. One of the most important management options to maintain or increase the amount of functioning habitat is to restore destroyed, disturbed and degraded habitats. However, there is much concern about how predictable restoration efforts are and the management strategies that will maximize success. The reality that systems may reach very different, alternative ecosystem states is a problem for managers when they desire well defined outcomes. Thus the ability to understand and predict how different factors affect restoration outcomes would be an important development.

In the current issue of the Journal of Applied Ecology, Grman and colleagues examine how different factors influence prairie restoration outcomes –specifically the diversity and composition of the restored habitat. They considered several management, historical and environmental factors. For management, they compiled information on the type of planting, the diversity and density of sown seeds and fire manipulation. For local environmental variables, they considered different soil characteristics, shade levels, and site area. The historical influences included land-use history, rainfall during seed sowing and site age. Finally, they also considered the landscape context; specifically what habitats surrounded the restoration site.

Grman and colleagues show that restoration outcomes are most influenced by management decisions and site history. The density, composition and diversity of sown seeds had the greatest impact on restoration outcomes. Species richness was highest in sites sown with high diversity. High sowing density resulted in high beta diversity among sites. Site history had significant effects on non-sown diversity, but did not influence the diversity of sown species. Site characteristics failed to predict local diversity, but they were important for among site beta-diversity.

If success is measured in terms of species diversity, then this work clearly shows that management decisions directly influence success. Surprisingly, site characteristics had a minor influence on success, despite conceptual and theoretical models that predict system sensitivity to abiotic influences. This work reinforces the need to develop the best management options for prairie restoration and that the influences of site history and local conditions can be overcome by sowing decisions and site management.

Grman E., Bassett T. & Brudvig L.A. (2013). Confronting contingency in restoration: management and site history determine outcomes of assembling prairies, but site characteristics and landscape context have little effect. Journal of Applied Ecology, 50, 1234-1243.

*Note: this is from an Editor's Choice piece I wrote for Journal of Applied ecology.

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.

What scares ecologists?

In the spirit of Halloween, a non-comprehensive list of a few of the things that have frightened ecologists. The things we are scared of vary, with career trajectory and stage (graduate student, post-docs, faculty, non-academic), educational background, and goals. And even if the business of ecology doesn't scare you, some of the things we study should...


A few scary things

-That ghostly thesis - will it ever solidify?

-The undead paper

-Co-authors and supervisors

-Vampire reviewers: they've been around forever, they've seen everything, and they're looking for blood. (uncommon)

The whole process is pretty frightening.

-The Bayesian conspiracy:

• "Q. What is the Bayesian Conspiracy? A. The Bayesian Conspiracy is a multinational, interdisciplinary, and shadowy group of scientists that controls publication, grants, tenure, and the illicit traffic in grad students. The best way to be accepted into the Bayesian Conspiracy is to join the Campus Crusade for Bayes in high school or college, and gradually work your way up to the inner circles. It is rumored that at the upper levels of the Bayesian Conspiracy exist nine silent figures known only as the Bayes Council."

-The job market (or lack thereof?)

-The two-body monster

Fun but troublesome

-That our work is invisible

(0?)

-That our ideas come to life and go terribly wrong

-The science news makeover

And a few (of many) ecological topics that are truly scary

Kudzu

Zombie ants

Toxoplasmosis and human mind control

And the incredibly deadly mosquitoes?

(and all the pathogens it carries)