Saturday 28 April 2012

PhD week 8: Structuring taxonomic descriptions

Taxonomic data models
Biological taxonomy is the science of categorising the plants and animals that share this world with us. A critical part of this characterisation is the writing of a description detailing the features which (done well) allows others to identify members of the species in question. Traditionally, this is a piece of prose that is very technical and very dry to read. There is, however, a move towards standardising and atomizing descriptive information, to enable it to be more readily re-used for a variety of applications.

The first attempt at this was the DELTA format, which was drafted as early as 1975. This format is becoming somewhat dated, and efforts are being made to produce an XML-based standard, known as Structured Descriptive Data (SDD).

Computer programs that can be used to produce these structured datasets include LUCID, xper2 and Open DELTA. Somewhat more complex is the taxonomy editor produced by the European Distributed Insitute of Taxonomy, that appears to be the tool for populating their scratchpads.

For some fairly detailed commentary regarding the promises and challenges offered by this revolution in taxonomic data management, ZooKeys published a special issue on e-Infrastructures for data publishing in biodiversity science.


Read:
   Wallis GP, Trewick SA. 2009. New Zealand phylogeography: evolution on a small continent. Molecular Ecology 18: 3548–3580
    Grant PR, Grant BR. 2008. How and Why Species Multiply. The Radiation of Darwin's Finches. Princeton: Princeton University Press
   McCulloch D. 2010. A History of Christianity: The First Three Thousand Years London: Penguin
   Psalms 44–47

Websites:
New Zealand's Geological Timescale
Importing DICOM images into Blender
3D slicer
ImageJ wiki

Watched:
Game of Thrones Season One

Friday 20 April 2012

PhD week 7: Plotting and NIR spectroscopy

NIR spectra-structure chart
Near-infrared (NIR) spectroscopy is a technique that measures the amount of heat absorbed or emitted by certain materials. It is used in a variety of applications, but in the agricultural world, it is often used to determine the quality and composition of mixed materials such as stock forage. It uses electromagnetic radiation in the 800 to 2500 nm range, which is just beyond the limits of our vision.

Friday 13 April 2012

PhD week 6: Laser Scanning Confocal Microscopy

Bone cells imaged using confocal microscopy
Bone cells viewed by laser confocal microscopy. Courtesy of the Wellcome Images photostream. License: CC: BY-NC-ND


This week, I had the opportunity to learn more about laser scanning confocal microscopy (LSCM). This type of microscopy uses lasers to excite fluorescent compounds in a very thin layer of the sample, allowing very clear images to be produced. Images can be made at a ranges of depths in the sample, and can then be merged together to give an idea of the 3D structure of the subject being imaged, such as the proturan leg shown in this video.

While it is frequently held that chitin can fluoresce to a degree, it appears that it is rather the presence of proteins and other sclerotizing agents in the cuticle that is primarily responsible for any autofluorescence seen. In the celebrated case of scorpions, it is believed that an alkaloid and a coumarin compound causes the fluorescence, while in froghoppers, it is resilin.

If the chitin is not fluorescing adequately, there are a number of stains that can be used. Eosin Y was judged to be most useful of a number of stains in one study, however in the test I did this week, I judged that it primarily stained the membranes, and not so much the sclerotised structures. A few other stains that may or may not prove useful include calcofluor, primulin and Congo red.


Read:
Koerner L, Gorb SN, Betz O. 2012. Functional morphology and adhesive performance of the stick-capture apparatus of the rove beetles Stenus spp. (Coleoptera, Staphylinidae). Zoology 115: 117-127

Grant PR, Grant BR. 2008. How and Why Species Multiply. The Radiation of Darwin's Finches. Princeton: Princeton University Press

McCulloch D. 2010. A History of Christianity: The First Three Thousand Years London: Penguin

Psalms 37–38

Websites:
New Zealand Tango Festival, 19–26 June 2012

OsiriX an open-source volume rendering program.

Suraj Gupta—How R Searches and Finds Stuff

Steveko's Blog—10 things I hate about Git

BeetleBase and FlyBase genomic databases.

Installing Arial fonts in Wine

WineHQ information about Leica LAS AF Lite

Volume 3 of the NZ Inventory of Biodiversity published

Listened:
Demon Hunter—45 Days

Zao—Awake?

Foo Fighters—Wasting Light

Watched:
Star Trek: Deep Space Nine Season 3

Further Seems Forever—Light Up Ahead music video

Friday 6 April 2012

Dynamite plots in R

For some time I've contemplated creating a function for creating the dynamite plots beloved by many of the applied sciences. There's a lot of criticism regarding their utility, and there are several ways that present data in a more intelligible way. A search on the subject brings up pages with such emotive titles as "Dynamite plots: unmitigated evil?" and "Why dynamite plots are BAD". The "Beware of Dynamite" poster sums up the main problem with dynamite plots by concluding "Intentionally or not, a dynamite plot hides more than it reveals".

All that said, I'm an R advocate. If being able to to create dynamite plots is going to encourage people to use R, I can cope with their inadequacies. Here's hoping that the paucity of information required to create them makes people reconsider.
dynamitePlot <- function(height, error, names = NA,
        significance = NA, ylim = c(0,maxLim), ...){
    maxLim <- 1.1* max(mapply(sum, height, error))
    bp <- barplot(height, names.arg = names, ylim = ylim, ...)
    arrows(x0 = bp, y0 = height, y1 = height + error, angle = 90)
    text(x = bp, y = 0.2 + height + error, labels = significance)
}

Values <- c(1,2,5,4)
Errors <- c(0.25, 0.5, 0.33, 0.12)
Names <- paste("Trial", 1:4)
Sig <- c("a", "a", "b", "b")

dynamitePlot(Values, Errors, names = Names, significance = Sig)
The result looks like this:Dynamite plot The code is also available on gitHub.

Thursday 5 April 2012

PhD week 5: Adaptive radiation

Hawaiian honeycreepers
Hawaiian honeycreepers. Courtesy of the Smithsonian's National Zoo photostream. License: CC: BY-NC-ND

This week has been spent mainly reading about adaptive radiation. Adaptive radiation, according to the definition I find describes it the clearest is:
A proliferation of species within a single clade accompanied by significant interspecific divergence in the kinds of resources exploited and in the morphological and physiological traits used to exploit these resources (Schluter, 1996 in Givnish and Sytsma 2000)
One of the textbook examples of adaptive radiation are the Hawaiian honeycreepers (pictured above). These birds form a natural group, but their feeding habits differ between species, and are wildly different from the finches which are believed to be their nearest relatives. These differences are reflected in the variation which is most evident in the bill shapes of each of the species. A blog post discussing the evolution of the Hawaiian honeycreepers in greater detail has been written by GrrlScientist.

For me, the concept of adaptive radiations is important in providing a framework for my research. This allows questions to be asked, and predictions made, that will give my studies direction and purpose. The study of adaptive radiations also opens our eyes to our dynamic biological systems and organisms can be, and give us an appreciation of the richness of life on this planet.

Among the other things I did this week, was give a talk to some Year 9 students at Lincoln High School about my work as an entomologist, and what sort of things it involves. It was great to present to a class of interested kids, and it was followed by a question time with such queries as the most humane ways to kill insects, the taste of insects and whether they're suitable for vegetarians, and whether or not insects sleep.

References:
Schluter D. 1996. Ecological causes of adaptive radiation. American Naturalist 148:S40–S64.

Givnish TJ, Sytsma KJ (Eds). 2000. Molecular Evolution and Adaptive Radiation. Cambridge: Cambridge University Press



Read:
Gillespie RG. 2008. Adaptive radiation. In: Gillespie RG, Clague DA (eds). Encyclopedia of islands Berkeley: University of California press, pp. 1–7

Grant PR, Grant BR. 2008. How and Why Species Multiply. The Radiation of Darwin's Finches. Princeton: Princeton University Press

McCulloch D. 2010. A History of Christianity: The First Three Thousand Years London: Penguin

Kirkpatrick R. 2009. Beyond the Wall of Time. Sydney: Voyager

Psalms 33–36; 104

Websites:
Evolving Thoughts—Bayes, evolutionary clocks, and biogeography

Listened:
Gotan Project—La Revancha del Tango

Radio NZ Classics podcast—Brahms Piano Quartet No 1 in G minor Op 25

Facedown Records–Facedown Festival 2011 Sampler

Watched:
The Hobbit: An Unexpected Journey movie trailer

Norma Jean—Absentimenal:Street Clam music video

Wednesday 4 April 2012

Panbiogeography of New Caledonia

Map of New Caledonia and the Loyalty Islands. Courtesy of Eric Gaba.
Two weeks ago, I reviewed Nattier et al's paper concerning the dispersal of the eneopterine crickets to New Caledonia. This week I discuss the opposing view of Michael Heads, who vociferously promotes the idea that the biota of New Caledonia has its origins in a vicariance framework; i.e. plate tectonic processes have had more of an influence on organism distribution than chance dispersal processes. In particular, Heads is a practicioner of the method known as panbiogeography—a method that emphasises the importance of recurring patterns in the distribution of organisms.

After giving an overview of New Caledonian geology, Heads discusses the various distributional patterns displayed by a variety of taxa in New Caledonian mainland, including the Loyalty Islands. He identifies 10 primary patterns, which can be broadly summarised as restricted to the Loyalty Islands; shared between the Loyalty Islands and Grande Terre; and distributions corresponding to the geology of Grande Terre. Of particular note is his observation that the strange shrub Amborella is restricted to central Grande Terre, on what Heads calls the basement terranes.

I enjoy reading Head's papers. His perspective is an interesting one, his promotion of mapping distributions and having an understanding of geological processes is important and his papers are full of fascinating examples. However, I do see a something of a contradiction in some of his views. He's a proponent of the metapopulation theory, whereby organisms jump between islands that are emerging and disappearing as part of island building processes, resulting in organisms having a longer evolutionary history than the islands that they currently inhabit. I don't have problems with that. However, once land gets accreted, his explanations rely on organisms remaining on those terranes, and not moving far from them at all. The combination of these two views sits somewhat uneasily with me.

Biogeography is a fascinating subject. What I also find amazing is that debates regarding biogeographic processes and methods become incredibly passionate. Panbiogeography is one of those sub-disciplines that is fiercely defended by its proponents and viciously denigrated by its critics. I don't count myself in either camp, preferring to take the useful bits out of any research and always bearing in mind that our perception of the past will always be incomplete, and that discussions regarding the past should be conducting in the light of that fact.

References:
Heads M. 2008. Panbiogeography of New Caledonia, south-west Pacific: basal angiosperms on basement terranes, ultramafic endemics inherited from volcanic island arcs and old taxa endemic to young islands. Journal of Biogeography 35: 2153–2175