We wrapped up the last day with age-depth models and independent project presentations.

3 classical age models include linear interpolation (connect the dots), regression (linear or polynomial), and smooth spline. If you use classical age models, CLAM (classical age models) is an R package that is good step forward.

The field is moving away from classical models and starting to use Bayesian Age Models (started in the mid-1990s). The age model we discussed and applied to actual data is BACON.

Here is an example of the age model example the class walked through for sediment collected from Glimmerglass Lake. It is a nice example of a model that is well mixed, fits well, and goes through all radiocarbon dates.

The afternoon was dedicated to student presentations on their independent projects. There were a wide range of independent project topics including:

1. State space model for plant macrofossils
2. Quantify variance between lake pollen loadings using observed pollen proportions
3. 2000 years of inferred forest structure in the southern Sierra Nevada from a wet meadow
4. Empirical succession model application to the fading record problem and uncertainty in forest density trajectories
5. Using pollen and squirrel fossils to explore two datasets with irregular spacing of time
6. Exploring Colombian vegetation using 4 proxies: 13C, volcanic minerals, archaeological artifacts and charcoal
7. Fire signals in southern CA
8. Pollen in Australian lakes
9. MCMC of Alaska Picea glauca (white spruce)
10. Detecting dust storms in lake sediment cores – are the dust storms increasing?
11. Using eddy flux data and terrestrial models to estimate NPP and biomass from tree rings

For only having 6 hours work, the projects turned out really well, with lots of converged iterations. It was great to see how much the students were able to pick up over the past 6 days.

We wrapped up big week with lots of hard work by celebrating with a classic Friday night fish fry dinner at Bent’s Camp!

These two days involved focusing on learning about and applying data assimilation in the lab.

Image source: http://bit.ly/VI9YI7

Students learned about PDA – parameter data-assimilation NOT public displays of affection (although successful parameters data-assimilation could make you want to celebrate with public displays of affection for your computer).

During these two days students learned:

1. The need to test-run your model.  If you know the model is wrong – tuning the parameters is not going to help.
2. Adjusting your priors after your model run is a cardinal sin.  The solution is not to move your prior, but rather to go back and work on your model!
3. An interesting problem in the iterative process of modeling is knowing when to stop.  When are your models good enough?  How much uncertainty is acceptable? This is especially important when thinking about the interface between science and policy.  What level of uncertainty is needed in order to make policy? While the course didn’t provide the answers to these questions – it did give students food for thought.

The students were able to assimilate the tree ring data measured from their tree cores into the PEcAn ecosystem model they had learned about on Day 3.

The following are some of the results from the tree ring data assimilation.

Figure 1. The ring widths from Eastern Hemlocks and Yellow Birches. Hemlocks went back to mid/early 1700s, birches to probably at least the early 1800s.

Figure 2. Tree Ring Year effects: For tree-ring folks, this is essentially your master chronology with uncertainty around it.

Figure 3. The above year effects can get combined with growth to look at annual aboveground biomass change (top) or cumulative aboveground biomass change (below).

Figure 4. The tree rings can also be used to get a (rough) disturbance histories of the two plots (Red plot on top and Yellow plot on bottom).

Chris Paciorek, Research Statistician at the University of California, Berkeley

The lake sediment and tree cores have gotten us out to the field and demonstrated the types of methods used to collect paleodata. They have also provided us with new paleodata for the course. However, although going to the field to collect data is fun and important, it is the Bayesian statistics and ecosystem modeling that are the focus for camp. Without the statistical and ecosystem modeling tools, our analysis of the data would be very limited.

Our course is set up to introduce topics and then build on them throughout the week. For example, over Day 1 and 2 we have covered introductions to R, probability and Bayesian statistics, as well as dendrochronology, and ecosystem modeling. Day 3 was a big day for starting to put the pieces together with a statistical discussion in the morning by Chris Paciorek on running MCMC using JAGS and followed by an ecosystem model discussion by Mike Dietze about using PEcAn.

Mike Dietze, Professor of Ecosystem Modeling at Boston University

The following are a few nuggets that were covered on Day 3:
1. Bayes theorem, priors and posteriors.
2. Bayesian credible intervals vs. 95% confidence intervals – the Bayesian credible interval is what many ecologists typically think their 95% confident interval is!
3. Bayesian approaches are really nice for common issues in paleoecology – sampling that is not orthogonal, data that is not evenly spaced through time.
4. Acknowledging uncertainty and including it in statistical models is important.
5. When creating ecosystem modeling, it is important to quantify BOTH uncertainty in the data and the uncertainty in the models.
6. Think of models as scaffolds for data synthesis. Models are working theory on how things work. Models represent different spatial and temporal scales. Model acts like one giant covariance matrix.
7. Observations inform models but models can also inform what is measured in the field.

Lastly, it’s important to have fun and collaborate with your peers!

Post by Jody Peters at the University of Notre Dame

Hemlock on Guido Rahr Sr. Tenderfoot Nature Reserve

In addition to the lake sediment coring that took place on Day 1, there was also a lot of tree coring that happened. We set up 2, 0.5 ha plots in The Nature Conservancy’s Guido Rahr Sr. Tenderfoot Forest Reserve where we recorded dbh (diameter at breast height) of every tree, tagged those that were large enough and took cores from trees greater than 10 cm dbh. The two stands were mainly comprised of beautiful, large, old hemlocks, as well as some yellow birch, sugar maples, and northern white cedars.

Measuring dbh	Tree 1156
Coring a hemlock with dbh 71.8 cm	Extracting a core

After the Day 1 coring, the job on Day 2 was to mount the cores and prepare them for drying.

On Day 3 we will move on to sanding and measuring the tree rings so that we can use them as part of a data assimilation exercise led by Michael Dietze to estimate Net Primary Production using the software package PEcAn on Day 5.

Post by Simon Goring, Postdoc at the University of Wisconsin-Madison.
This post originally appeared at downwithtime.

We’re at Camp PalEON this week. It’s lots of fun and I think that the attendees get a lot out of it. Effectively we’re trying to distill process associated with the entire seven year project into one week of intensive learning. We teach probability theory, Bayesian methods, ecosystem modelling, dendrochronology, paleoecology and pollen analysis, age modelling and vegetation reconstruction to seventeen lucky early-career researchers in six intensive days (people were still plunking away at 11pm last night, our first day!).

We spend a lot of our time at the University of Notre Dame’s Environmental Research Center indoors looking at computers, but we had a very nice time yesterday afternoon. I hung out on a raft with Jack Williams and Jason McLachlan, coring with a frozen finger. The frozen finger is a special kind of corer, used to recover lake sediment that preserves the sediment stratigraphy in a much cleaner way than many other coring techniques.

Jack Williams and Jason McLachlan filling the core casing with ethanol so that the cold slurry conducts to the outer wall. (photo credit: Jody Peters)

When using the frozen finger we fill the base of the corer with dry ice, and suspend the dry ice in ethanol to create an incredibly cold surface. We then drop the casing into the lake sediment. The sediment freezes to the surface of the core casing over the course of ten or fifteen minutes before we pull the corer back to the surface of the lake. The freeze-corer (or frozen finger) is often used for ancient DNA studies (e.g., Anderson-Carpenter et al., 2011) since the freezing process helps stabilize DNA in the lake sediment until the core can be brought back to the lab and analyzed.

The sediment on the outside of the core casing is peeled off carefully and wrapped before it is stored in a cooler of dry ice. (photo credit: Jody Peters)

Jason McLachlan and I are going to go sieve the sediment ourselves later this afternoon to give the workshop participants a chance to take a look at lake sediment, pick charcoal and find macrofossils later tonight. Meanwhile everyone is hard-coding an MCMC model in R and, later today, learning about Midwestern Paleoecology. All in all, it’s a great course and I’m happy to be involved with it for a second time. Hopefully we’ll have some more posts, but in the meantime we’ve made the preliminary readings open to the public on our project wiki, and most of our R work is up and available on GitHub so that you can take a look and work along.