Thursday, October 27, 2011

halloween caRd

To view the card, run the code below using R


###required libraries
##if you do not have the "fields" and "MBA" libraries, you need this step to install them
## load libraries


par(bg = "black")
mtext("Happy Halloween",side=3,col="green3",cex=3.5,font=4)
mtext("from the EEB and Flow",side=1,col="green3",cex=2.5,font=4)

Wednesday, October 12, 2011

Seed dispersal: plant height seems to be more important than seed size!

I really like papers that teach me something that I didn’t know. But, I love papers that show me that what I learned is wrong. This is the case of a new paper by Fiona Thomson, Angela Moles, Tony Auld, and Richard Kingsford on seed dispersal that appears in the last issue of the Journal of Ecology. This group from Australia analyzed the effects of seed size and plant height on their dispersal abilities. They reviewed intensively the literature gathering data on 200 species from 148 studies around the world. Surprisingly to me, they found plant height was much better at predicting seed dispersal than seed size. This might not sound so surprising for many people (and after seeing the paper, kind of intuitive), but there was a lot of evidence that seed size was the best predictor of dispersal, with species with smaller seeds dispersing further than species with bigger seeds. For wind dispersed species, their results are more intuitive, but they found this pattern in a number dispersal syndromes analyzed (i.e. unassisted, wind, ballistic, ingestion, and ant dispersal). So, in your next study on seed dispersal consider adding plant height as an explanatory variable.

Thomson, F. J., A. T. Moles, T. D. Auld, and R. T. Kingsford. 2011. Seed dispersal distance is more strongly correlated with plant height than with seed mass. Journal of Ecology 99:1299-1307. DOI 10.1111/j.1365-2745.2011.01867.x

Tuesday, October 4, 2011

The four types of failure, or how to fail in science

As scientists, we’re all wrong, at least sometimes. The question is, how are we wrong?

The arsenic bacteria saga, which we’ve discussed on this blog before, is turning out to be a very public example of failure in science. First announced by NASA press conference in December 2010, authors lead by Felisa Wolfe-Simon shared their discovery of a bacterium capable of replacing phosphorus in its DNA with arsenic, suggesting the possibility of life in phosphorus-limited conditions. This apparently momentous discovery was published in Science, and met with disbelief and severe criticism. Critics throughout the blogosphere and academic departments began to compile a comprehensive list of failings on the part of the paper—8 technical criticisms were published in Science—and as the result of the intense focus on the paper’s lead author is no longer associated with the lab group where this research was carried out. This is failure at its worst—the science was flawed and it drew immediate and intense censure. This is the kind of failure that most young scientists fear: judgment, intense criticism, career-long repercussions. But it’s also probably the least common type of failure in science.

However, it’s arguable that the saddest form of failure is the opposite of this: when a paper is right—innovative, ahead of its time—but somehow never receives the attention it deserves. There are lots of famous examples of scientific obscurity, with Gregor Mendel being the poster child for scientists who toil for years in anonymity. In ecology, for example, papers that considered species as equivalent (a la neutral theory) to explain coexistence were around in the 1950’s-1960s, but received little attention. Other papers suggesting variation in environment as a possible mechanism for plant coexistence were published prior to Chesson and Huntly's influential paper, yet essentially uncited. Most researchers can name at least one paper that foreshadows the direction the field will take many years later, yet is unacknowledged and poorly cited. There are many reasons that papers could be under recognized—they are written by scientists outside of the dominant geographical areas or social networks, or who lack the ability to champion their ideas, either in writing or in person. In some instances the intellectual climate may not be conducive to an idea that, at a later time, will take off.

If that is the saddest type of failure, then the best type of failure is when being wrong inspires an explosion of new research and new ideas. Rather than causing an implosion, as the arsenic-bacteria paper did, these wrong ideas reinvigorate their field. Great examples in ecology include Steve Hubbell’s Unified Neutral Theory of Biodiversity, which although criticized rightly for its flaws, produced a high-quality body of literature debating its merits and flaws. When Jared Diamond (1975) proposed drawing conclusions about community assembly processes based on patterns of species co-occurrence, the disagreement, led by Dan Simberloff ultimately led to the current focus on null models. Cam Webb’s hypothesis that there should be a relationship between phylogenetic patterns in communities and the importance of different processes in structuring those communities sparked a decade-long investigation into the link between phylogenetic information and community assembly. Although Webb’s hypothesis proved too simplistic, it still informs current research. This is the kind of failure on which you can build a career, particularly if you are willing to continually revisit and develop your theory as the body of evidence against it grows.

However, the most common form of failure occurs when a paper is published that is wrong, yet no one notices or worse, cares. For every paper that blows up to the proportion of the arsenic bacteria paper, or inspires years of new research, there are hundreds of papers that just fade away, poorly cited and poorly read. Is it better to fail quietly, or to take the chance at public failure, with all its risks and rewards?

Sunday, October 2, 2011

The European Ecology Federation Congress, day 3

*sorry for the delay in getting the last day up, I've been catching up. The first talk of the morning was by Georgina Mace -great talk, and I will have an extended post on it later. Here are the other talks. This meeting was great!

Elisa Thebault. This was a great talk. She talked about the structure and stability of mutualistc and antagonistic networks. Nested interactions means that several generalists and specialists, but specialists use the same resource as generalists and do list overlap with other specialists. She addressed two main questions. First are there differences between mutualistic and antagonistic networks? Second, do these differences have consequences for coexistence and stability? First question, herbivores seem to have less nestedness and interact with closely related plants, while pollinators are more nested but less phylogenetically structured. For the second question, with is examined using modeling, using coupled predator prey equations (with a positive effect in the mutualism model) and simulated communities. She looked at two types of stability, persistence of species and resilience. She showed some very interesting results, for mutualistic networks, connectence and diversity increase stability, while for antagonistic, the opposite. Because of diversity change in the simulation, the mutualistic networks become more nested and more connected, again the opposite for antagonistic network, which becaome less connected and nested. What happens when you put these interactions together with both mutualistc and antagonist models? The same patterns emerge with muralists being more nested and connected.

Pedro Jordano. He talked about the functional role of complex networks including different types of seed dispersers and pollinators. Can phylogenetic relationships explain patterns of interactions between the seed dispersers and plants. In degraded habitats, through hunting, only a restribected subset of species are interacting with plants. What is the minimum complexity required to maintain ecosystem function.

Jason Tylianakis. He talked about global change and ecosystem function. In an example dataset, soil resource availability and grazing intensity affected trait compositiona dn diverisyt and changed plant productivity. When resources are heterogeneous then diversity affects function, but not when resources were homogeneous. Across a gradient of land use intensitfication, networks become simpler with functional links being dominated by few species. He looked at 133 host-parasitoid interaction webs. These webs deviate from null expectation and some habitats were significantly less complex than predicted.

Daniel Stouffer. He talk about understanding species roles and importance in food webs. Different types of interactions (sub webs) have differential probabilities of being present. Certain motifs appear to differentially contribute to stability. This approach can inform species conservation if a particular species appears in different motifs that contribute to network function or stability. Certain species may be common in motifs that reduce stability. Using New Zealand river food webs, he asked three questions: is the benefit of species phylogenetically conserved -yes, certain clades add benefit. Are these benefits community specific? No, beneficial species are so in all communities (bit similar communities). How general are these results? He compared the results to webs elsewhere in the world. Similar species are similarly beneficial elsewhere.

-Here I lost my notes from Jane Memmott’s plenary talk (sorry Jane!). It was a great overview of her research in restoration. At the heart of her talk was about making restoration scientifically rigorous.

Henrique Pereira. His talk was on modeling the response of biodiversity to global change. Biodiversity indicators for global change are biassed towards North America and Europe and certain taxa. Major uncertainty in extinction rates and what are the sources of uncertainty? A big source is the differences in scenarios for land change and human population growth. Also lack of ecological knowledge. Finally there are differences between models. He proposes a countryside species area relationship (cSAR) instead of regular SAR, which assumes an uninhabited matrix. Multiplies area by the affinity of species to live in that area, and so as long as a species has an affinity greater than zero for marginal habitat, it can persist in those areas –changing our predictions about habitat loss on species persistence. The cSAR predicts much lower extinction rates compared to classical SARs. Need data to classify affinities, such as uses surveys to cluster species by where they are found. The cSAR fits real data better than SAR.

Christophe Randin. His presentation was on whether elevational limits of deciduous trees match their thermal latitudinal limits. Species often not at equilibrium with their predicted fundamental niche, may reflect dispersal limitation. Species should reach their equilibrium since climate change so much quicker. Based olots of data, he presented where the distributional limitation should be and examined the distance from that edge. Surprisingly, the latitudinal limit was less likely to be reached by. Species, thus they are lagging on mountains.

Rita Bastos. She used a Dynamic model for understanding the recovery of the Azorean bullfinch in a changing environment, a lot a land use change and invasive species. Specifically, the model is a stochastic, spatially explicit model that incorates environmental variables and projected habitat change. She was able to test different management scenarios. Certain management actions on habitats can significantly increase population sizes but not spread.

Diogo Alagador. He spoke on adjusting protected areas to account for climate range adjustments. Species will move with climate change, but reserves do not move. Planning must involve multiple potential reserves and likely assisted migration. It is difficult to extrapolate for multiple species. Persistence then is the product of suitablility and dispersal ability for a species for each time period in future projections. This can be summed across species. This was tested for seveal species across all major taxa. There is variability in persistence across species and are very sensitive to disperal pathways.