Benjamin

As palaeoecologist, I work on data retrieved from natural archives, going back in time. It can be a lake sediment core or peat. The time scale of the data, actually, is built by interpolation of a few radiocarbon dates, measured at particular places along the core. This creates an age-depth model. I routinely use two R packages from Maarten Blaauw to do this:

• clam, for classical age-modelling,
• and rbacon, for Bayesian accumulation (and it’s an R package, obviously).

Each package defines its own function (clam() and Bacon(), respectively) which only needs the name of the core, to find a CSV file on your computer with the same name. This is in this CSV file that one saves the results of the radiocarbon datings to use them with either clam or rbacon. However, each package expects the data to be presented in a slightly different fashion. But in a file having the same name. Computers don’t allow that. And sometimes, I want to be able to use either clam or rbacon.

I am currently augmenting the pollen data from two sites I studied during my PhD research, thanks to a grant from the Swiss Foundation for Alpine Studies. The one I’m dealing with at the moment is a small peat bog at a locality called Saglias, near the village of Ardez in the Grisons, Switzerland.

I usually seek for high quality and reliability of palynological data. Depending on the context, I try to identify about 1000 pollen grains per sample, or 500 tree pollen grains, and look at the taxa accumulation curve. These two indices are easily accessible in real-time counting thanks to PolyCounter, the software I’m using.

Now, some samples clearly want to drive analysts crazy. Most often they contain very few pollen grains. A typical reason for this is a poor pollen preservation. It can be useful to have a closer look at them and see if there is something one can do to improve the situation. I’m doing this by looking at a few other parameters. Again, PolyCounter is your best friend. That’s easy to import the count data and metadata (such as the number of marker spiked added) into R and compute variables to address key questions:

Since introduced by the middle of 20th century, radiocarbon dating has been used in a large variety of disciplines. In palaeoecology, it has allowed for a major improvement in dating past phenomena. The ground principle is fairly simple: the isotope 14 of carbon (14C) is radioactive and decays following one criterion, namely the half-life. The half-life means the time necessary for the original quantity to reduce to half. For 14C, it is about 5730 years!

Living organisms, just like you, accumulate carbon in their tissues all life long. This includes a certain proportion of radioactive carbon as well, but don’t be afraid, you don’t bare any risk 🙂 This proportion is kept similar to the atmospheric level of radioactive carbon, but by the death of an organism, it is not renewed anymore and starts to decay. Half of it, every 5730 years. Therefore, if you can measure how much of radiocarbon is left in a sample today and how much of non-radioactive carbon it contains, you can use its half-life, do the reverse maths and deduce the date of this (sad) event.