ESA Portland: Day 1

Today proved a typical first day of ESA, with delayed flights, hotel difficulties and luggage to carry around. Then you head to the convention centre and are reminded all over again just how big ESA actually is. The benefit of the crowds is that the sessions take on a specificity and quantity that you can't find anywhere else. The bad news is that you will have to make choices.

Today's choices weren’t too difficult - I moved between the Community Assembly and Neutral I and Community Pattern and Dynamics I sessions to start. Some common themes emerged, especially that people are quite interested in the relationship between diversity and phylogeny, and then phylogeny and traits, and also in patterns of beta and alpha-diversity along environmental gradients. 

A few talks stood out: Emma Moran spoke about identifying the processes of deterministic assembly and stochasticity that drive community diversity. In agreement with previous work from Jonathan Chase, her co-author, she spoke about how using null models allows scientists to differentiate between these two processes by observing temporal and spatial patterns of species diversity and comparing them to those patterns expected by chance alone. By looking at both temporal and spatial patterns of diversity, it is possible to differentiate between stochastic arrival of species at a site, followed by deterministic interactions (spatial stochasticity + temporal determinism) and purely stochastic assembly (both temporal and spatial patterns of diversity random), for example. Using both simulations and empirical data, she demonstrated the patterns of diversity that might be expected and tested for under these scenarios. When she used a null model that controlled for random expectations, her conclusions about which processes were important were dramatically different from those arrived at without a null model.

In the Population Dynamics session, Emanuel Fronhofer asked 'Why are metapopulations so rare' and came to the possibly controversial conclusion that they are rare because there aren't many conditions that should result in metapopulations. Metapopulations are a common concept in ecology, based on the idea that population dynamics in different patches are linked via dispersal between those patches. However, it's unclear how common metapopulations really are in nature. Fronhofer used individual based models (IBMs) to explore the range of dispersal values, environmental stochasticity, or reproductive system type, for example, that would result in a metapopulation. In particular, he looked at the most strict definition of a metapopulation: occupancy of patches less than 1 and more than 0, turnover through time, and FST values such that populations are genetically differentiated. What he found agreed with the nay-sayers: only quite narrow values of parameters like dispersal resulted in true metapopulations. Does this mean metapopulation ecology is a highly specialized field? Difficult to say, although it maybe that a particularly stringent definition of a metapopulation (with occupancy between 0 and 1, for example) is not necessary to describe the movement of individuals and alleles between patches in a way that is consistent with metapopulation dynamics. 

Finally, Geoff Legault discussed spatial synchrony among populations, in which population cycles in different spatial patches become synchronized. In particular, using a protist predator-prey microcosm, he showed that in agreement with some theory, there is a dispersal threshold after which synchrony is achieved between the two populations. This leads to interesting questions about what determines the level of dispersal required to produce synchrony, and how factors such as population growth rates alter this threshold, something which a microcosm is particularly useful to address. 

EEB & Flow Portland bound

Just a heads up that Marc Cadotte and I will be live blogging the Ecological Society of America's Annual Meeting in Portland, from Aug. 6-10th. As always, this is a great chance for ecologists to hear about great science and run into old and new friends. If you see Marc Cadotte there, be sure to harass him to post on time, as he claims to be 'busy' ;)

Why non-theoreticians don’t cite your paper

T.W. Fawcett and A. D. Higginson. 2012. Heavy use of equations impedes communication among biologists. PNAS.

The more equations a paper has, the less it will be cited by other biologists. This should come as a surprise to few people, but if it does, Fig.1 from Fawcett and Higginson (2012) makes this pretty clear. Papers with many equations per page are cited less often by non-theoretical papers (A). In fact, citations by non-theoretical papers decrease by 35% for each additional equation per page. This is not true of theoretical papers, which happily cite other equation-filled theoretical works (B). It’s an interesting conundrum: theory unifies empirical observations and generates predictions, but theory uses equations. And papers with equations have less impact.

The authors make suggestions for both sides of this divide. All biologists should have adequate mathematical training so that equations are not necessarily considered daunting or confusing. Theoreticians should strive to communicate their works in accessible ways (something Steve Ellner covered nicely in the aptly named “How to write a theoretical ecology paper that people will cite”). The authors also suggest increased placement of equations in appendices, where they do not decrease citation rates. (However, if equations don’t decrease citation rates when in the appendix, you wonder if this is because equations are easier to ignore there). The surprising thing about this  bias is that I don’t think it exists as much in the other direction. Theoretical papers generally do cite empirical works. Reviewers frequently require that model assumptions be justified based on empirical knowledge. A balance between theory and empiricism seems important for ecology, and while this paper doesn't tell us anything surprising, it makes it quite clear that there is a problem.

From Fawcett and Higginson 2012.

Insight and advocacy: transitioning from scientist to advocate (Guest Post)

In Malcolm Gladwell’s “The Tipping Point” he describes how information is disseminated. It takes three types of people: a collector, a connector and a persuader. As a research scientist, I am familiar with being a collector. I have spent years reading papers, testing hypotheses and validating assumptions to develop a personal understanding of fisheries and ecology. Until recently, I was content to let my perspectives circulate among a small group of colleagues. Until recently, I did not see a need to address the connector or persuader in my academic life. But I do now. I am not an advocate. I have on occasion written a letter to my MP, signed a petition or joined a protest but always as a follower of those who, I felt, were much better suited for it. And this is because on most political issues I am as informed as the news/internet media will allow me to be. So when somebody with some good insight steps forward, I’m more likely to egg them on then run with their thunder.

But recently I have found myself to be one with insight. It was a startling moment. Natural Resource Minister Joe Oliver was on the news plugging the dismantling of Canada’s environmental legislation. He’d said that our environmental safeguards held up badly needed economic development and as an example he used Enbridge’s Gateway Pipeline. I had worked on the environmental permitting for that pipeline, and I didn’t agree with him. Working as an environmental consultant in Alberta was a wonderful life spent on deserted oil roads assessing fish habitat and negotiating permits for industrial development. Over that time I observed first hand that Canada’s environmental laws did not hold up pipelines, mines or bridge crossings any longer than the lengthy processes of engineering, surveying, contracting or First Nation consultation. Environmental permits typically cost a small fraction of the total development, were often acquired concurrently with the general planning process, and were unquestionably necessary to protect the health of the natural resources that belong to all Albertans and Canadians. Beyond my first hand experiences, I found no independent studies that could back up Minister Oliver’s statement. In fact, in a series of papers examining Canadian and American environmental legislation, their overall effect on the economy was determined to be either “overstated” or even “a net benefit”.

Politicians embellish, and perhaps I would have left it there, but over the next few weeks news emerged that the federal government were scrapping the National Round Table on the Environment and the Economy, the Experimental Lakes Area, the Marine Pollution Program, the Kluane Arctic Research Station, Ozone Monitoring Stations, the Species at Risk scientists at Fisheries and Oceans Canada, fish habitat protection under the Fisheries Act. This after years of muzzling government scientists, laying off climate change researchers, and cutting funding to non-business partnered science in Canada. And last, and cruellest, most of these recent changes were being done wrapped up in a budget, Bill C-38, thus circumventing a proper discussion in Parliament. I was shaken by this policy direction.

It is difficult not to be emotionally invested when ideals and institutes you believe in get torn down. I found that many Canadians including environmentalists, economists, politicians and advocates were appearing on the news, writing op-eds and tweeting their concerns. Their seat in this public debate was one earned from decades of being public figures, which connected them to a wide network and taught them how to engage those around them. I realized my opportunity was to share my insights with them, and provide more substance to their thunder. I researched further the economic role of environmental legislation in Canada and canvased old colleagues from consulting firms on permit wait times. Next, I began to share. I put out these insights to my own social and professional network. I was amazed by how quickly people responded. With one LinkedIn post and an email to 75 contacts I received responses from most of my immediate contacts, but also from people across the country that I had never met. I heard from collectors who shared their insights with me, connectors who forwarded mine on, and persuaders who were still appearing in the news. I was amazed and heartened by how quickly an insight could spread.

Insight is a powerful and rare commodity, because it can comment on current issues yet is not necessarily advocacy. For example, eminent researcher David Schindler’s paper on oil sands contamination was not advocacy; it was insight into contaminant levels in the Athabasca River. Yet the paper sent shockwaves through a political system that had been repeating for over a decade that the oil sands had a clean record and was picked up by advocates who further publicized it. It can gain such traction because there is a vacuum of objective facts and concrete statements in today’s political theater. Over the last few decades our political leaders have increasingly changed their dialogue to reflect emotional, persuasive and ideology driven statements. For example, in Canada Ministers Kent, Ashfield and Oliver discuss “protecting” our “valuable” species, and “modernizing” our legislation. Other ministers present economic or foreign affairs in similar vague terms. This type of dialogue puts a new onus on economists and scientists to share their perspectives beyond the academic walls. It seems like an insurmountable hurdle as many of us are not connectors or persuaders, but the traction for a pure nugget of insight may surprise you. So I encourage you all to keep collecting but to also start sharing beyond our academic circles, where your contribution may be more meaningful that you realize.

   

By Derrick de Kerckhove

Putting ecological niche models to good use

Banta et al. 2012. Climate envelope modelling reveals intraspecific relationships among flowering phenology, niche breadth and potential range size in Arabidopsis thaliana. Ecology Letters. doi:10.1111/j.1461-0248.2012.01796.x.

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

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

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

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

From Banta et al. 2012. Relationship between potential range size and flowering time/niche breadth

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

Writing about writing about research.

Hadas Shema, Judit Bar-Ilan, Mike Thelwall. 2012. Research Blogs and the Discussion of Scholarly Information. PLoS ONE.

I suppose it was inevitable that someone would publish a scientific paper about blogs that write about scientific publications. That’s either very meta, or a little myopic, or both. Appropriately then, the paper “Research Blogs and the Discussion of Scholarly Information” is published in PLoS ONE, the most prominent open access journal. The internet has expanded scientific discourse beyond the traditional forms of published media, and blogs tend to provide a less formal, more accessible form of communication. The authors were particularly interested in how discussion of published works on research blogs related to the citation of published works in the traditional published literature. When we discuss and cite papers in blogs, those citations are meaningless in the traditional sense, in that they aren’t incorporated into citation analyses.

The authors used the blog aggregator ResearchBlogging.org to identify well-established science blogs. They surveyed 126 blogs, recording the names and fields of journals of the 10 most recently reviewed articles on each blog. They also recorded general information about the blog author(s). Life sciences were by far the most common area blogged about (39% of blogs), although life sciences account for only 21% of all publications. Given the fact that women now receive similar numbers of life science degrees, it is perhaps surprising that the vast majority of blogs have male authors (~67% have a single male author, and ~9% have multiple authors, at least one of which is male).

Regardless of who authors the blogs, the papers that are cited in blogs are predominantly from the highest profile journals – Science, Nature, and PNAS. These journals all have expensive paywalls for non-subscribers. The fourth most cited journal, by contrast, is PLoS ONE. It’s hard to say what this means. It may just be that Science, Nature, and PNAS are well represented in their sample because they are interdisciplinary, and so many blogs will cite them. Or, it may be that bloggers are attracted to the same types of papers that Science and Nature are – high profile, “important”, maybe controversial. Further, bloggers may write about high profile papers, but they do so with greater depth and knowledge than most mainstream media.

There’s only so much that you can draw from a relatively small, simple survey, but some of the trends seem contrary to the supposed openness and accessibility of web-based science communication. Research blogs are written primarily by men, and focus on high-profile, non-open access papers. Does the open-access nature of a blog overcome the non-open access nature of the papers they write about? Does writing about a Science paper make the information within it accessible to more people, or does it decrease the number of people who can fully appreciate your post? Ultimately research blogging is complex, like any form of online media; it can improve on traditional communication while still showing some of the same limitations. It does bode well though that, given the number of blogs commenting on this paper, research bloggers tend to be informed and pretty self-aware.

Robert Sokal: Statistical giant in ecologists' boots

Robert Sokal (1960):
from Wikipedia

No student of my generation, trained in ecology and evolutionary biology, will not have heard of Sokal and Rholf’s Biometry textbook. Most would have used it in a class or to inform their analyses. Sadly, Robert Sokal passed away last month at the age of 86. He had a tremendous career, mostly at Stony Brook University in New York, and contributing to statistics and science for over half a century. As a testament to his impact, the third edition of Biometry has been cited over 14000 times! It is the canon for experimental design and analysis in the biological sciences.

He had extraordinary and tumultuous experiences as a youth -fleeing Nazi Germany and being raised in China. Whether, such experiences give rise to greatness, or whether his innate intellectual abilities sealed his destiny is an interesting question. Regardless, his impact and legacy will be deservedly long lasting.

47th Carnival of Evolution: catch the news.

The latest edition of the Carnival of Evolution is up at Evolving Thoughts. Read it, enjoy it, pass it along.

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

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

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

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

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

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

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

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

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

Disagreeing about ecology: how debate advances science

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

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

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

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

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

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

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

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

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

NCEAS is dead; long live NCEAS. A view towards NCEAS 2.0.

"is this a wake or revival?" Jim Brown

March 21-22, 2012, Santa Barbara, CA. National Center for Ecological Analysis and Synthesis (NCEAS) symposium.

A special invitation- only symposium marking the end of NCEAS as we know it, saw a number of interesting talks and retrospectives about where NCEAS has been and where it is going. 170 people attended, including some former postdocs, working group participants and leaders in ecology. The reason for this introspective meeting is that NCEAS's core NSF funding is about to end, without renewel. Jim Brown's quote from his talk, whether we were here for a wake or a revival really captured the spirit of the meeting.

The goals were twofold. First was to look back and celebrate the accomplishments of NCEAS. University of California at Santa Barbara is globally one the top influential research institutions in the world, and this success has been driven in large part by the success of NCEAS. More than 5000 people have come to NCEAS and their efforts have resulted in thousands of publications, and many citation classics. The early visions of NCEAS were broad and fuzzy and by all accounts NCEAS has exceeded all expectations.

The second motivation for ts meeting was to think about the future. What can NCEAS be under different funding regimes, and how should it move forward? The is no doubt that it will be fundamentally different, but can there be a successful continuation of the NCEAS model, will it die, or will it give birth to a new enerprise, NCEAS 2.0?

The symposium saw great talks, from people like Jim Brown and Jane Lubchenco, and interesting panel discussions on numerous topics (see #treas2012 in twitter for synopsis of the meeting). There were a lot of past tense statements.

However, it was clear that there was much to celebrate. NCEAS clearly impacted ecology. Did its success simply coincide with cultural changes in the field or did it drive changes? The consensus was that it drove changes. It fostered large collaborations. Dave Tilman said that before NCEAS, ecology was largely local and lab-driven, but NCEAS offered a way to get people together to ask bigger questions. The postdoctoral fellows have been extremely successful, with the vast majority ending up in faculty positions in top institutions. It was acknowledged that many sub fields were created or coalesced at NCEAS, including disease ecology and metacommunity dynamics.

Why has it been so successful? NCEAS is a special inclusive place where people want to come, away from their responsibilities. The technical help here and expertise that made anything possible, any data challenges were overcome and analytical difficulties solved. Postdocs were given complete independence and were allowed to pursue collaboration and networking. Jim Brown remarked that NCEAS is the single greatest event in the history of ecology. Subfields now talk, lab projects are now geared towards collaboration and linkages with other work in ways that did not exist before.

So then, what will the future hold for NCEAS? The answer to this was left vague and uncertain. People argued for what NCEAS 2.0 should look like. For example, it was argued that NCEAS 2.0 should resurface something like science 2.0, making the focus data and data sharing, changing methods and philosophy of how science is done. Massive anonymous collaboration requires assumed standards and altruism. Other arguments focused on the need for NCEAS to reach out to new partners and to go global.

Peter Karieva said it well. NCEAS 2.0 should be interacting with major corporations, since they represent the drastic impacts on ecological systems around the world. 1.0 was about data accessability, 2.0 should about applicability and tools to affect change.

Whatever NCEAS 2.0 looks like, it will be different. There seems to be two ways forward. One is that it struggles to maintain its past activities or one that like the Phoenix rises from the ashes and boldly goes forward to again push the ecology in new directions.

On rejection: or, life in academia

I guess it’s not surprising, given that I’ve written about failure in science, that I would write a post about rejection as well. Actually, I’m not so interested in writing about rejection as I am in hearing how people have learned to deal with it.  

Academia is a strange workplace. It’s stocked with bright people who’ve been successful throughout their previous academic endeavours (with some exceptions*). For the most part, they haven’t faced too much criticism of their intellectual abilities. But in academia you will spend your career being questioned and criticized, in large part by your peers. You will constantly be judged (with every submitted manuscript, grant application, or tenure review). And this is the universal truth about academia: you will be rejected. And for some (many?) people, that's a difficult thing to accept.

Rejection may be so painful in part because it can be hard to interpret. After all, it’s an old trope that rejection is a normal part of academia. But how much rejection is normal, when is it just a numbers game and when is it a sign of professional failing? Let alone the fact that rejection depends on a shifting academic landscape where available funding, journal quotas, and research caliber are always changing. So I’m curious: does the ability to deal with rejection factor into academic success? Are some people, based on personality, more likely to weather rejections successfully, and does this translate into academic success? Or is the development of a thick skin just the inevitable outcome of an academic life?

*A couple of the people I know who are generally unfazed by rejections would say that they deal well with rejection because they weren’t particularly great students and so academic failure isn’t new or frightening to them. 

The niche as a changeable entity: phenotypic plasticity in community ecology

Schiffers, K. et al. 2011. Root plasticity buffers competition among plants: theory meets experimental data. Ecology. 92:3.

Ashton et al. 2010. Niche complementarity due to plasticity in resource use: plant partitioning of chemical N forms. Ecology. 91:11.

Nearly all explanations for coexistence in communities focus on differences between species. The scale of these differences may occur over large temporal (e.g. evolutionary history, phylogenetic relationships) or spatial scales (e.g. environmental tolerances), or at the scale of the individual. In plants, interactions at the local scale are given particular attention, including interactions mediated by trait differences between species. At finer scales still, there has been recent focus on differences between individuals of the same species, whether they are driven by genotypic differences (link) or plastic changes in individual phenotypes.

From Ashton et al. 2010

Phenotypic plasticity can be defined as phenotypic differences among individuals of the same genotype that occur in response to an environmental cue. The ability of plant species to alter their usage of resources, for example, has clear relevance to resource partitioning among species, since a given individual could adaptively take advantage of alternate resources in response to their particular competitive environment. In such a case, an individual’s realized niche is a function of phenotypic changes in response to the biotic and abiotic environment and thus physiologically-determined. This is in contrast to the usual approach to species’ niches, where physiological constraints are considered to determine a species’ fundamental niche. Although the plant literature shows clear examples of phenotypic plasticity among plants, including in response to competition (for example, perception of light quality leading to changes in growth form), the topic usually receives only passing mention in the community ecology literature.

The number of papers addressing questions of coexistence and competition through the lens of phenotypic plasticity is slowly rising.

From Schiffer et al. 2011, Lithium uptake is
significantly higher on the non-competitor side

A couple of papers from the last few years provide tantalizing glimpses into the possible contribution of plasticity to coexistence. In Schiffers et al. (2011), the authors use experimental and modeling approaches to test whether root uptake can change in response to the proximity of competitors. In the experimental study, the authors looked at the uptake of lithium (a stable nutrient that will be taken up in the place of potassium) by Bromus hordeaceus. They planted pairs of B. hordeaceus  at varying distances apart and then injected lithium into the soil at different differences from the focal plant. They found that lithium uptake was significantly higher on the non-competitor side of the focal plant than on the competitor side, suggesting that plastic changes in resource uptake occurred in response to competitor proximity. Modelling results from the same study suggest that plasticity may allow individuals minimize competitive pressure by making changes in belowground architecture, thereby using available space more efficiently.

Ashton et al. (2010) take a similar approach, looking at how the uptake of nutrients (in this case three forms of nitrogen (N)) varies among species depending on their competitive environment. They explored the ways in which plasticity could lead to changes in the realized niche. In particular, they explored two hypotheses: that plants would exhibit niche preemption, where the inferior competitor switched to a different form of nitrogen in the presence of the superior competitor; or dominant plasticity, where plasticity actually enhances competitive ability.  The authors looked at 4 species, 3 common and 1 rare(r), in an alpine tundra community, isolating naturally occurring pairs of each combination of species. These ‘competitive arenas’ were isolated, and other species within the arena were removed. After a year, the authors added N¹⁵ tracers to each arena, in three forms (NH4+, NO3-, and glycine): these tracers would allow them to track the N once it was incorporated into the plant tissue. The plants were then harvested and the amount of each type of nitrogen in each was measured. Plant biomass was also recorded, and used to estimate the ‘competitive response’ (basically the ratio of biomass when grown with a competitor compared biomass to when grown solo). Their findings were rather neat: the 3 common plants experienced no negative effect on biomass from growing in competition with the rare plant, but the rare plant had much lower biomass when grown in the presence of any of the common plants. Further, while the common plants showed changes in the form of N they used when growing with the rare plant, the rare plant did not switch its N preference. The rare plant’s lack of plasticity in response to competition may relate to its lower biomass when grown with superior competitors, and ultimately its lower abundance.

Although limited, these studies hint at the role that phenotypic plasticity could play in interspecific interactions. Unfortunately plasticity may be difficult to measure in many contexts, particularly since variation within a species can be attributed to genetic differences or phenotypic plasticity, and these factors must be teased apart. Further, there is an issue of differentiating the effects of resource limitations from ‘adaptive’ plastic changes in growth. While plants are relatively tractable for these types of studies (they’re sessile, they use limited abiotic resources), other organisms are less explored for a reason.

What these studies can’t address is the question of ‘how important is phenotypic plasticity, really’? Reviews of coexistence mechanisms list numerous possible ways by which coexistence is facilitated among species. For plants especially, the limited number of resources required for survival has lead to great consideration of the possible niche axes over which species can differentiate themselves. Phenotypic plasticity's contribution to coexistence may be that it provides another way by which plants can partition resources at very fine scales. And if nothing else, such results provide further evidence that variation within species may be an important component of coexistence.

Thanks to Kelly Carscadden for discussions on the topic.

Island biogeography explains how you feel about giving talks

A good null model is hard to find

de Bello, F. The quest for trait convergence and divergence in community assembly:are null-models the magic wand? Global Ecology and Biogeography, (Global Ecol.Biogeogr.) (2012) 21, 312–317

Ecologists have always found the question of how communities assemble to be of great interest. However, studies of community assembly are often thwarted by the large temporal and spatial scales over which processes occur, making experimental tests of assembly theory difficult. As a result, researchers are often forced to rely on observational data and make inferences about the mechanisms at play from patterns alone. While historical assembly research focused on inferring evidence of competition or environmental filtering from patterns of species co-occurrence, more recent research often looks at patterns of phylogenetic or trait similarity in a community to answer these questions. 

Not surprisingly, when methods rely heavily on observational data they are open to criticism: one of the most important outcomes of early community assembly literature was the recognition that patterns that appeared to support a hypothesis about competition or environmental filtering could in fact result by random chance. This ultimately lead to the widespread incorporation of null models, which are meant to simulate patterns that might be observed by random chance (or other processes not under study), against which the observed data can be compared. Patterns of functional and phylogenetic information in communities can also be compared against null expectations to ensure that patterns of phylogenetic or functional over- or under-dispersion can't arise due to chance alone. However, while null models are an important tool in assembly research, they are sometimes as the foolproof solution to all of its problems.

In a new paper by Francesco de Bello, the author states frankly “whilst reading null-model methods applied in the literature (indeed including my work), one may have the impression of reading a book of magic spells”. While null models are increasingly sophisticated, allowing researchers to determine which processes to control for and which to leave out, de Bello suggests that the decision to include or omit particular factors from a null model can be unclear, making it difficult to interpret results or compare results across studies. Further, results from null models may not mean what researchers expect them to mean.

Using the example of functional diversity (FD; variation in trait values among species in a community), de Bellow illustrates how null models may have different meanings than expected. Ideally, a null model for FD should produce random values of FD, against which the observed values of FD can be compared. Interpreting the difference between the observed and random results can be done using the standardized effect size (SES, the standardized difference between the observed and randomly generated FD values); SES values >0 show that traits are more divergent than expected by chance, suggesting competition structures communities. If SES<0, traits are more convergent than expected by chance, suggesting environmental conditions structure communities. Finally, if SES ~0, then trait values aren’t different from random. However, de Bello shows that the SES is driven by the observed FD values, because the ‘random’ FD values are dependent on the pool of observations sampled. This means that the values the null model produces are ultimately dependent on those observed values, despite the fact you plan to make inferences by comparing the null and observed values as though they are independent. For example, consider the situation where you are building a null model of community structure for plant communities found along two vegetation belts. If the null model is constructed using all the plant communities, regardless of the habitat they are found in, the resulting null FD value will be higher, since species that are dissimilar and found in different vegetation belts are being randomly selected as occurring in a community. If null models are constructed separately for both vegetation belts, the null FD value is lower, since species are more similar. The magnitude of the difference between the null model and the observed values, and further, the biological conclusions one would take from this study, would therefore depend on which null model was constructed.

from de Bello 2012, illustrating how combining species pools (right) can lead to entirely different decisions about whether communities are convergent or divergent in terms of traits than when they are considered separately (left, centre).

De Bello’s findings make important points about the limitations of null models, particularly for functional diversity, but likely for other types of response variable. The type of null model he explores is relatively simplistic (reshuffling of species among sites), and the suggestion that the species pool affects the null model is not unique (Shipley & Weiher, 1995). However, even sophisticated and complex null models need to be biologically relevant and interpretable, and null models are still frequently used incorrectly. Although only mentioned briefly, De Bello also notes another problem with studies of community assembly, which is that popular indices like FD, PD, and others may not always be able to distinguish correctly between different assembly mechanisms (Mouchet et al. 2010, Mayfield & Levine, 2010), something that null model do not control for. 

Should we still be testing neutral theory? If so, how?

Siepielski, A.M. , Hung, K-L., Bein, E.E.B, McPeek, M.A. 2010. Experimental evidence for neutral community dynamics governing an insect assemblage. Ecology. 91. 847-857

For many ecologists, neutral theory was a (good/bad, you choose) idea that dominated ecology for the last decade but failed to provide the burden of empirical proof necessary for its acceptance. Even its creator Stephen Hubbell  recently suggested that the controversial hypothesis is no longer a plausible description of community structure, going as far to say that it is “good starting point”, a “valuable null model”, and a “useful baseline” (in Etienne et al 2011). 

But ideas, when they’re shared, are no longer the sole property of their creators. Other researchers continue to study neutral theory, and despite the apparent consensus that neutral theory is not an important explanation of community structure and dynamics, papers testing neutral theory continue to be published. This leads to an important question: do we still want to test for neutral dynamics? And if we do, how should we approach it, given what we have learned from the past decade of strawman arguments and using pattern-based evidence for processes (e.g. looking at species-area relationships and species abundance distributions)? What empirical evidence would provide strong support for the predictions of neutral theory?

Damselfly larvae
(http://www.uta.edu/biology/robinson/odonate_research.htm)

In “Experimental evidence for neutral community dynamics governing an insect assemblage”, Siepielski et al. (2010) attempt to provide a more rigorous test of neutral theory using two Enallagma (damselfly) larvae. Siepielski et al. focus on changes in demographic rates (growth, mortality) in response to changes in species relative and total abundances. In particular, they predicted that if niche differences drive coexistence, increasing a species’ relative abundance should drive lower growth rates and higher mortality, since that species is above its equilibrium; lowered relative abundances should result in higher growth rates and lowered mortality since the species is below its equilibrium density. As a result, species should return to their equilibrial abundances. Raising the total abundances but leaving the relative abundances untouched should have similar demographic responses across species and have no effect on the relative abundances. In contrast, neutral theory predicts that if all species are equal, their demographic rates depend on the density of the entire group (total abundance) and not on each individual species’ relative abundance. Therefore the response of demographic rates to changes in species relative abundances, while the total abundance is held constant, should provide support to either neutral or niche theory.

For two Enallagma sp. larvae Siepielski et al. used cages in the littoral zone of lakes, with cages receiving different treatments of relative abundance and/or total abundance manipulation. The result of these manipulations were that replicates with increased total abundances and constant relative abundances had lowered per-capita growth rates, while replicates with manipulated relative abundances and constant total abundances showed no change in demographic rates. Both species had similar mortality rates across the experimental treatments, although their growth rates differed slightly. From these results, Siepielski et al. concluded that these species are ecologically equivalent.

One of the reasons work (such as this) from Mark McPeek’s lab is interesting is because he is an outlier: someone whose work is deeply rooted in a natural system, and yet who also argues that ecological equivalency seems plausible, and attempts to support that argument. Regardless of whether the Enallagma species are in fact ecologically equivalent, this paper provides an example of how coexistence theory can be more rigorously tested than simply observing species co-ocurrences and concluding species coexistence. Further, it provides some interesting discussion about whether ecological equivalency is possible within functional groups, with niche differences occurring between functional groups (see Leibold and McPeek 2006, and from MacNaughton and Wolf 1970 for similar suggestions). Future work might focus on questions such as how to capture the effects of small niche differences, which, if balanced against very similar fitnesses could explain stable coexistence. In addition, it might be valuable to look at how resources fluctuate and how much overlap there is in resource requirements among species, when looking at how growth and mortality change with species densities.

With Adam Siepielski, Mark McPeek also published the paper “On the evidence for species coexistence: a critique of the coexistence program” about the apparently lowered standards for tests of niche-based species coexistence compared to those of neutral theory. What is certainly true is that experimental tests of coexistence theory are often less rigorous than necessary to support any coexistence theory, and should strive to take a more rigorous approach. If nothing else, this will allow criticism of particular theories to focus on the ideas themselves, rather than on how those ideas were tested.

Trends in ecology, 2011

What were the topics of research that dominated ecology in 2011, and where is ecology likely to head in 2012?

A brief answer can be found by looking at the most common keywords found in ecology papers published during 2011*. "Abundance" proved the most common keyword. Interestingly, "climate change" and "global warming" appeared less common as keywords compared to last year. In contrast, words tying research to places ("Great Barrier Reef") and systems ("rainforest") seemed more common. Although it's hard to draw any specific conclusions from this kind of thing, it's notable that many of the most common words are related to community ecology, lending credence to Marc Cadotte's assertion that community ecology is flourishing as a discipline.

*Although hardly rigorous, I analyzed the keywords from 4000 randomly selected ecology papers published in 2011 found using a Web of Science search. The most common 150 terms are represented in the word cloud, where text size represents the frequency with which a word appears on the list.

Carnival of Evolution 43!

The history of human thought is an epic adventure of exploration and discovery. Since the beginning of time, humans have been curious about order and chaos in nature and our place in the world. By understanding the natural world around us, we understand ourselves better. But how we attempt to answer these fundamental questions has evolved over time. This evolving history, looks something like this:

146,000 BCE
Targ: "Hey Lerb, why big cat have long teeth?"
Lerb: "I dunno Targ, but cousin Seb went for look. He gone"
Targ: " Cat lucky, seem good for people eating. I go for closer look."

523 BCE
Anaximander: "Thales, my teacher, how is it that animals take their form?"
Thales: "Anaximander, all matter is an aggregation formed from a single substance, water, and qualities are obtained through need"
Anaximander: "Ah yes, water, I will now think about how air can be the primordial substance."

1849 CE
Thomas Thomson: "I do say, the flora of northern India is peculiar in the sheer number of forms of life that populate this region. I do wonder though, what the cause is for such brilliant numbers of species?"
Joseph Hooker: "My dear Thomas, the flora of northern India is brilliant indeed! These forms find their origins in those very places where they live. Of course Lamarck believes that the crises endured by the tissues of organisms, themselves pass on the incentive to produce offspring better equipped to endure such crises. However, in my correspondence with Charles Darwin, he confirms that variation is an inherent aspect of life and gives rise to the diversity we see."

2012 CE
You: "Man, I wish I new more about evolution. Hey, what is this Carnival of Evolution? OMG, this is totally sick."
You no longer need to ponder the mysteries of life, travel the globe making observations, or running complex experiments to test hypotheses; everything you want to know about evolution today can be found by reading the monthly installments of the Carnival of Evolution!

The first installment of 2012 (or is this the last of 2011?) offers a great smattering of many different aspects of current evolutionary understanding. These 26 posts cover many of the major areas of research that define current evolutionary biology.

Most evolutionary research aims to understand how the amazing diversity of life came to be. Core to this is studying both paleontological record and patterns among modern organisms. Early explosions of diversity have always captured scientists imaginations, and Larry Moran at Sandwalk (and fellow Torontonian) explains that recent evidence is casting doubt that the Cambrian explosion was actually an explosion, at least according to genetic evidence. Much later on, ray-finned fish became extremely successful and are now the dominate form of fish on Earth. Their success and resulting diversification is likely better explained by rapid morphological changes to head shape and not fin evolution, according to Lucas Brouwers at Thoughtomics. Nothing in the paleontological record excites the imagination more than dinosaurs. Recent work has developed a detailed understanding of the ecology and evolution of these amazing creatures. Marc Vincent at Love in the Time of Chasmosaurs describes research that indicates that head crests and feathers on many dinosaurs were likely to product of sexual selection. While, according to David Orr, also at Love in the Time of Chasmosaurs, the big toe claw on both hind feet of Deinonychus evolved to pin down small prey, and not to slice open large prey (thank you Jurassic Park). In one of the best, 'huh, I didn't know that' posts, Fins to Feet shows that Mosasaurs -giant predatory marine reptiles found during the time of the dinosaurs, are likely closely related to monitor lizards and not part of the ancestral lineage that includes dinosaurs.

Studying and explaining patterns among modern day critters is the evolutionary biologists' bread and butter, and studies of organisms seem to constantly shed light on new ways in which evolution has shaped life. The interesting story of the oil beetle and how it has evolved to hitch rides on other insects is presented by Anne Buchanan The Mermaid's Tale. As relayed by Jeremy Yoder at Denim and Tweed, birds that lay eggs in the nests of other species (nest parasites) have been associated with the same hosts for millions of years. Flower colors are commonly thought to be shaped by pollinator preference, but Zen Faulkes at NeuroDojo shows evidence that white variants of bluebells (are they still bluebells?) do not see different pollinator visitation rates. Species differences can be difficult to identify using our human senses, but Lucas Brouwers at Thoughtomics explains how echolocation has diverged between indistinguishable bat species. Lungfish are oft-cited exemplars of evolution, mainly because they are so fascinating -not only do they have lings, but they walk too. Which is why they are the subject of two posts this month (one by Matthew Cobb at Why Evolution is True and one by Carl Zimmer at the Loom), both about how they move and how they may have transitioned to walking.

Evolutionary change for many animals is often not a linear move from genes to fitness, but rather behavior has the potential to affect evolution in complex ways. In one example, Jeremy Yoder at Denim and Tweed, tells the tale of how fear of being eaten can lower fitness. In another example, Simon's Science explains research that shows female stickleback fish, which are raised by their fathers, will prefer mates from their father's species, even when experimentalists switch the species providing parental care.

To unveil the wizard, a number of posts show how evolutionary research is done and how our understanding evolves. In two posts at BEACON, researchers Tasneem Pierce and Michael DeNieu give fascinating firsthand accounts of doing research. You can sense the wonder and excitement of doing scientific research from their posts. Stan Rice at Honest Ab has a wonderful sequence of five posts relaying his dinosaur adventure -at least playing with paleontology and avoiding creationists. John Wilkins at Evolving Thoughts examines the definition of evolutionary novelty in an ongoing series (maybe his next book?), and looks at comparative versus functional definitions.

For most people, evolution is central to the ultimate questions about who we are and where we come from. True to this anthropocentric* view of evolution, there are a large number of excellent posts about human evolution and why we are the way we are. For those people who feel that the Carnival of Evolution does not provide all the answers to their questions about human evolution, Greg Laden reviews two new books on understanding human evolution. Suzanne Elvidge at Genome Engineering reports that scientists have sequenced the genome of a descendent of Genghis Khan. Why is this interesting? Well it turns out that millions of people -half a percent of the current global population, are related to Genghis Khan! The obvious question to me was how is this possible? It turns out that, according to Wikipedia, Genghis Khan had a harem of between 2000-3000 women and many of his many, many sons also had obscenely** large harems. Thus, by the time Genghis was a dirty old man, he could have had 10,000 descendents.

Often the need for evolutionary explanations comes from the question: "Why the heck do we do that?". True to this question, there are four posts that look at human behavior. In a controversial but intriguing post, Khudadad Azara at Khudadad's Knols suggests that terrorism is a macho impulse for glory and honor shaped by sexual selection. The most convincing parts are that males often do stupid things for sexual advantage, and terrorism is a stupid thing. Why the hell are yawns contagious? Well, according to Suzanne Elvidge at Genome Engineering, yawns may be evolutionary as they are most contagious among close relatives. Holly Dunsworth at The Mermaid's Tale makes the case that the uniquely human ability to throw (chimps actually aren't very good at it) is not so much an anatomical thing, but a brain thing, interesting. PZ Myers at Pharyngula asks why women menstruate and suggests that it is the evolutionary result of mother-fetus conflict.

A big part of human history, culture and belief, is our conflict with disease. This month there are several very interesting posts on evolution and human disease. Swenson at Nothing in Biology Makes Sense! discusses how reconstructing the evolutionary relationships among HIV samples dating as far back as 1959 reveals that there are deep divergences indicating that HIV has likely been in humans since the late 1800's! Carl Zimmer at the Loom relays the latest research showing that Syphilis evolved in the New World and was likely brought to Europe (Italy) from early European explorers. Ford Denison writes at This Week in Evolution that a genetic mutation increasing the risk of breast cancer in women is also associated with increased fertility. This invites the conclusion that there may be a tradeoff between longevity and fertility.

That is all for this month from the Carnival of Evolution. Everything you ever wanted to know about evolution but were afraid to ask. When you start to have new questions, luckily there will be a new edition of the Carnival in a months time.


*I realized after I wrote this sentence that it sounds negative. I do not mean the increasing pejorative 'unnatural', but rather legitimately human-focused.

**Having a harem of any size is obscene, but what adjective can you use for harems with thousands of women?