Quick link dump for a Friday: A whirlwind guide to mixed modelling

If you want to get a brief practical intro into mixed modelling in R I can highly recommend this link: http://ase.tufts.edu/gsc/gradresources/guidetomixedmodelsinr/mixed%20model%20guide.html.

This link combined with the excellent book Mixed Effects Models and Extensions in Ecology With R by Zuur and colleagues can greatly help navigate the world of mixed effects models using R.

Possibly the greatest quantitative methods lecturer ever

I’ve been a fan of Professor Andy Field for a long time – his books are great but his lectures on statistics are the best! He provides his lectures for free on youtube which is very nice of him (and presumably a good way to sell books).  This particular lecture is particularly brilliant: https://www.youtube.com/watch?v=u5JnILqlX9w&index=12&list=PL343F1B5F55734D55 – A lecture on mixed design ANOVA unlike any other.

Mathematical detective work: Using community ecology and agent/systems dynamics models to help crack the case of bird flu outbreaks.

It was a dark freezing day in Minnesota in March when a catastrophic event happened. A pathogen infiltrated one farm and set off a chain reaction that led to the deaths of millions of turkeys and chickens, and cost the poultry industry in the Mid-West US hundreds of millions of dollars. Furthermore, the scale of the disposal effort for the bodies was also a huge logistical and environmental problem. The culprit? Highly pathogenic avian influenza (HPAI) sub-type H5N2. How it broke into the farm, how it spread from farm to farm and what conditions are likely to trigger another outbreak are still unknown. The problem being that the perpetrator is a particularly difficult one to catch outside the farm. It’s like looking for a needle in a haystack but the needle only exists for a brief period of time. Also we know lots about the behaviour of other sub-types of bird flu – but this one breaks all the rules….. so how then can we possibly get a handle on this situation? The answer is a mixture of community ecology tools and modelling.

Currently I’m in a team leading the modelling effort to do the detective work to understand which potential pathways (from wildlife and farm practices there could be thousands) are could explain the pattern of the outbreak and focus on these in detail to work of ways to shut these pathways down and reduce the risk of another outbreak. We may not be able to catch this particular ‘criminal’ but we can find ways to limit the likelihood of them striking again.

I’ll let you know how we go…

  • Of course these opinions expressed are solely my own and do not express the views or opinions of my employer.

Virophages (things that ‘eat’ viruses) and the ecological succession of viruses in the Antarctic

My knowledge of the microbiome is clearly inadequate –  I had no idea that there was a class of organisms that actually are parasites of viruses themselves. Virophages, as there known, use a viruses reproductive machinery against them to restrict viral replication. I wonder how common the following scenario is: mammal gets infected by a parasitic organism that is already infected by a virus, yet this virus is parasitized by a virophage and so on….. it hurts my head! How (or if) the trophic levels of parasitism interact to shape the health of the final mammal (for example) host seems like a question well worth answering if possible).

What got me thinking about this was an excellent series of papers about RNA viral communities in Antarctic lakes by Alberto Lopez-Bueno and colleagues. Not only were they the first to discover RNA viruses in the Antarctic (10 000 gentotypes), they have described a temporal succession of viruses from spring to summer, and that viruses may impact the community composition of other microorganisms.


Figure from Cavicchioli&Erdman (2015) Molecular Ecology 

In their latest paper (link below), they show for the first time that ecological connectivity (how well viral habitats are connected) shapes the complexity of RNA virus communities in the Antarctic. Again this work shows the potential power of community based approaches coupled with molecular methods to provide fundamental insights into the complex world that we can’t see.

Here is the paper: http://onlinelibrary.wiley.com/enhanced/doi/10.1111/mec.13321.

Here is a open access summary of their work: http://onlinelibrary.wiley.com/enhanced/doi/10.1111/mec.13387