ESA Portland day 2: march of the phylogeny

The day phylogenies took over. This is how I would describe the talks I attended on day two. There was a palpable collective enthusiasm for what phylogenies can bring to understanding ecological patterns. It seemed like every session I went to there were several talks that test for phylogenetic patterns and it will be interesting to see where this all goes in the future. For me, this phylogenetic onslaught was heralded by the very first talk I went to by Jeannine Cavender-Bares. She spoke about how phylogenetic relatedness and species traits can provide important insights into community patterns and ecosystem function. She ask some of the most pertinent questions such as: how do evolutionary processes affect ecological processes; and how deep in the phylogeny is the evolutionary signal in community assembly. This last question is interesting because it can potentially tell us about past environments when certain lineages evolved. Her talk was divided into three parts. In the first part, she discussed how certain plant traits, like specific leaf area (SLA), were correlated with fire frequency. At extremely low and high fire frequencies, there is a strong trait pattern associated with communtiy membership, and with a strong phylogenetic pattern as well. But this wasn't the case with intermediate fire frequencies. In the second part, she discussed plant community patterns across an urban to natural gradient. There were important trait differences, with species having smaller seeds and higher specific leaf area in urban areas. There were more species in urban areas, but they represented less phylogenetic diversity than in natural areas -meaning that there is an environmental filter selecting for similar species. In the third part, she investigated oak adaptive radiations in North America and the resulting biogeographical patterns. There we differences in diversity across latitude, with high diversity regions also have more close relatives.

The were a number of other very interesting talks, and I spent the day fluttering from room to room, like a confused butterfly in search of sweet rewards. And rewarded I was. There were handful of very memorable talks. By both young graduate students and established researchers. Christina Lamanna gave a nice talk about phylogenetic and functional diversity (PD and FD, respectively) across an elevation gradient, which in part she used to highlight a new measure of species functional overlap. Richness and FD peak at intermediate elevation. She also examined the turnover in FD and PD and that both of these show decreasing turnover at higher elevations. At high elevations, PD was found to be overdispersed as were some of the traits, but other traits appeared underdispersed, indicating the combination of traits under very different selective regimes.

In a session on ecosystem function Jane Cowles told us how diversity and warming interact to shape patterns of ecosystem function. The experiment was great, and they overlaid warming arrays on some of the plots at the classic biodiversity experiment at Cedar Creek, Minnesota. The arrays warmed 1.5 and 3 degrees on 1, 4 and 16 spp plots and they measured aboveground and belowground biomass. More aboveground biomass was observed with warming, but not for belowground, except for deeper roots. Dominant species increased the most in aboveground biomass, seeming to respond to large pools of nitrogen available in early spring. 

One of the two best talks I saw today was given by Amelia Wolf. She constructed a biodiversity-ecosystem function experiment based on realistic scenarios of species loss. Whereas most experiments randomly assemble species together, realistic species loss selects species with certain traits, and once they are lost, those species are not part of the system at lower diversity. She used 20 years of observational data to select those species most susceptible to extinction and then created a series of plots where diversity was based on removing susceptible species. These plots were nested in that when a species was excluded from say the highest to next highest treatment, it could not be included in a lower diversity treatment. She compared this to random diversity treatments and found that the realistic species loss had a stronger effect on ecosystem function. But she suggested that this could be due to the nested structure and not the realistic scenario. So, to cover all her bases, she created 32 different nested loss regimes that were not the realistic one, and found that they were no different than random. Thus species identity and susceptibility really matter for ecosystem function decline with species extinction, as most susceptible species are often from the same functional group.

The other superb talk was from Jay Stachowicz on the influence of eelgrass genotypic richness, relatedness and trait diversity on productivity. Genotypes interact though a number of mechanisms including competition, cooperation, interbreeding, and so there are complex possibilities for the influence of genotype on productivity. From experimental combinations, he found that, counter to his expectations, plots with closer relatives had higher productivity. Further these plots with close relatives also had greater trait diversity, highlighting the complex nature of species interactions and differentiation.

Andrew Siefert gave a talk on disentangling multiple drivers on species turnover in space. Betadiversity is driven by both niche based decay of environmental similarity and stochastic due to dispersal limitation. Both generate similar patterns. But if one uses functional traits, then you can see higher or lower functional turnover than expected from chance, which indicates niche based turnover. He reported the results from 1500 forest plots across eastern USA,  with climate data and data on four functional traits. He found high turnover in soils and species, lower for climate and functional diversity. Both taxonomic and functional betadiversity best explained by climate. Close sites have high taxonomic turnover, but low functional turnover, thus climate filtering.

Finally, Elizabeth Boyle exmined arthropod phylogenetic community patterns in near arctic aquatic systems (ponds, streams, rivers, etc.). These habitats harbor an amazing diversity of insects and Elizabeth collected data from dozens of habitats over a large area, for hundreds of species and constructed a molecular phylogeny based on her own genetic work. An amazing effort for a masters project! She resampled the habitats through the summer and found that many of the habitats started off as phylogenetically clustered but became overdispersed through time. But not all habitats showed the same response, and she found that some environmental variables seemed to be strongly correlated with relatedness patterns. She also questioned whether the emergence of adults caused some of these patterns as the timing of emergence is phylogenetically nonrandom, which to me is a new explanation of potential phylogenetic patterns.

- Posted using BlogPress from my iPad

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.