Thursday, June 6, 2013

Heterogeneity in N availability

We have species area curves to understand the spatial diversity of species. Alpha-diversity tells us how many species are in a spot. Beta-diversity the accumulation of species across habitats.

Part of understanding alpha and beta diversity is the underlying differences in resource availability.

Beta diversity on N availability is difficult to assess.

Landscape ecology and geospatial statistics start to get at this, but they haven't been applied too broadly.

I was playing with the global foliar 15N data to try to see what the range in foliar 15N was for a given site.

It's not a perfect metric but informative. Again, there are a number of things that foliar del15N reflects, but essentially it's N availability, the form of N taken up by plants (under some circumstances), the depth of N, and the dependence on mycorrhizal fungi.

We had data on ~500 sites. Some had just one sample, some two, others 100's.

What does the relationship between number of samples from a site and the range in foliar 15N look like?

It's a logarithmic relationship, just like a species-area curve.

r2 = 0.41. Not too bad.

This isn't spatial per se, since this is generally number of plants. The spatial pattern is unknown. 

But it's interesting to think about what this pattern reflects. For a given amount of sampling effort, you expect a certain range in foliar 15N. Measure 100 plants, and you expect a range of 10‰.

But what about the residuals? Why would some sites have a greater range? Why less? 

What is it about the N cycle in those sites with a greater than expected range? Greater variation in disturbance? Partial nitrification allowing some plants to acquire NH4+ and others depleted NO3-?

The one point at the top of the graph? Konza. We sampled a lot of plants, but again there was a lot greater variation in 15N there than expected. And Konza has a lot of disturbance paired with undisturbed areas.

It'd be pretty fascinating to try to replicate this approach experimentally across ecosystems to better understand variation in N cycling characteristics across sites.

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