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Goats gnaw on geographic given

This post was chosen as an Editor's Selection for ResearchBlogging.orgWe’re used to thinking — or at least assuming — in agrobiodiversity conservation that genetic distance is a monotonically increasing function of geographic distance. It is, after all, a reflection of the great Waldo Tobler’s First Law of Geography: “Everything is related to everything else, but near things are more related than distant things.” And yet. Why should that necessarily be so for crops and livestock, so willfully and incessantly moved to and fro by people in all kinds of unpredictable ways?

A paper just out in Molecular Ecology in effect tests the First Law of Geography with goat genetic diversity data, microsatellites in fact. 1. Goat populations were sampled in 3-8 villages in each of 2-5 communes in each of 10 districts in the remote, mountainous, ethnically mixed Vietnamese province of Hang Giang, for a total of 492 animals. The genetic relationships among the animals were then analyzed.

To the surprise of the authors, the spatial structure of the overall population was poorly explained by simple geographic distance. The ethnicity of their keepers and the husbandry practices to which they were subjected did a much better job of predicting the genetic distance between goats. The most dissimilar goats were not necessarily the ones which lived furthest apart, but rather the ones which were kept in different ways by people of different ethnic groups.

So, if you wanted to maximise the diversity in a Vietnamese goat conservation programme, or your chances of hybrid vigour, you’d pick animals from different ethnic groups or production systems, and not necessarily from different ends of the country. Which is something that I remember sort of almost subconsciously doing when I was collecting crops, but it is nice to see it validated like this. I can’t remember offhand similar work on crops, but no doubt Jacob will set me straight soon enough. In the meantime, I revel in a rule proven.

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How would you PageRank genebank accessions?

Various friends have sent me, over the past few days, different takes on a recent paper which used the Google PageRank algorithm to identify the most “important” species in food webs, perhaps because they know I’m a sucker for examples of cross-pollination between disciplines. The BBC had its say, and also ScienceDaily, among others. I posted the ScienceDaily article on Facebook, as I am wont to do when I think something is interesting — maybe even have a gut feeling it might be relevant to agrobiodiversity conservation — but don’t know quite what to make of it. Sure enough, someone left a comment that he thought the algorithm was a secret, which was also my understanding: Google don’t want people to manipulate the rank of their web pages. But then someone else came in and said that the basics of how the thing works are in the public domain.

To prove it, he provided a link to an American Mathematical Society article entitled How Google Finds Your Needle in the Web’s Haystack. Which is why I love social networking, but that’s another story. Now, that article is definitely NSFW, unless you work at the American Mathematical Society, so think twice before clicking, but here’s the lede:

Imagine a library containing 25 billion documents but with no centralized organization and no librarians. In addition, anyone may add a document at any time without telling anyone. You may feel sure that one of the documents contained in the collection has a piece of information that is vitally important to you, and, being impatient like most of us, you’d like to find it in a matter of seconds. How would you go about doing it?

And I thought to myself: just change that 25 billion, which of course refers to the number of pages on the internet, to 6.5 million or 7.2 million or whatever, and the guy could just as easily be talking about accessions in the world’s genebanks.

Now, basically we search for the germplasm we need by starting with a big dataset and applying filters: wheat, awnless wheat, awnless wheat with such and such resistance, awnless wheat with such and such resistance from areas with less than x mm of rainfall per annum, and so on. Would it make any sense to rank the accessions in that initial big dataset? On what basis would one do that anyway? That is, what is the equivalent of hyperlinks for accessions? Because the essence of PageRank is that important pages receive lots of hyperlinks from important pages. So, numbers of requests? Amount of data available on the accession? But wouldn’t that just mean that only the usual suspects would get picked all the time? Genetic uniqueness, perhaps, then? That would be turning the algorithm on its head. Looking for lack of connections rather than connections to other accessions. You could in fact have different ranking criteria for different purposes, I suppose.

Ok, now my brain hurts. This cross-pollination stuff can be fun, but it is hard work.

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Featured: Tipping point

Jacob tackles tipping points:

I think that an explosion of local varieties, for instance, could count as variance amplification. An increase in variety names could be due to a fragmented knowledge system with lots of redundancies (the same variety having different names in different places) as farmers fail to trace each variety. At a given moment, the names become meaningless. This can then lead to genetic erosion, as farmers fail to find certain varieties due to the name confusion. Less common varieties that survive by going from hand to hand will be the first to go extinct. Has anyone observed this?

Well, has anyone observed this? Read the full comment, there’s also stuff in there about why a locally-based “genetic erosion monitoring portal” wouldn’t be much use.

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Ghanaian buffet

Ghana has forty-seven different kinds of edible green leaves, each with a distinctive flavor.

I bet. And the diversity doesn’t stop there.

I think of Ghanaian cuisine as a kind of culinary jazz. The pepper, tomatoes, and onions, and possibly the oil, form the rhythm section. The stew is one musical form, like blues, the soup and one-pot dishes are others. Like a successful improvisation, the additional ingredients—vegetables, seeds and nuts, meat and fish—harmonize and combine into vibrant, mellow creations.

Dip into the sampler CD at Global Voices Online.