CLAM age-depth modeling using monotonic splines.
The CLAM age-depth modeling R program fits age-depth relationships through a series of stratigraphically ordered radiocarbon dates. The original program includes several curve-fitting
options, but no option in which a spline curve is constrained such that age increases monotonically with depth rather than "overshooting" at locations where there is a change in the slope.
The monotonic spline option to CLAM was recommended by Trachsel and Telford 2017.
The code was used in a paper by Schworer et al. 2016. Each of the three methods produces slightly different results. I have not (yet) explored the mathematical differences in these methods, and no doubt other methods exist as well.
In my experience, the Bacon age-depth approach is difficult to apply to cases of sharp inflections in the sedimentation rate, and therefore prompting the need for a monotonic spline curve-fitting option.
- Install the R package
stinepack. Install Clam version 2.2.
- Copy the following file into the 'clam' folder on your computer. clam_mod_v1.txt.
- Change the .txt extension on the filename to .R. Load the CLAM program using the command
source("clam_mod_v1.R"). This file substitutes for clam.R.
The program is unchanged from version 2.2, but with the addition of three new curve-fitting options:
type=6. This uses a monotonic (or "constrained") spline. It is slower to run than the other options because the spline curve is not a compiled function; it is embedded in the Clam code. It uses an unpublished algorithm by CJC Kruger. The algorithm allows extrapolation to depths beyond the data.
type=7. A stineman spline. Stineman, R. W. 1980. A consistently well-behaved method of interpolation. Creative Computing 6:54-57.
type=8. The scaled stineman spline. Neither stineman spline can extrapolate beyond the upper-most or lower-most age control point.
K1D Version 1.2
Last update: July 2010
program to analyze the dependence of two or more time event records
using the Ripley K-function on one dimension. Examines multiple
hypotheses regarding the form of relationship between events.
User's Guide (.pdf)
Mac OS X (.zip)
[Example data file] [Example intensity file]
that have implemented this software:
- Gavin, D.G., F.S.
Hu, K.P. Lertzman and P. Corbett. 2006. Weak climatic control of forest
fire history during the late Holocene. Ecology 87:1722-1732.
F.S., L.B. Brubaker, D.G. Gavin, P.E. Higuera, J.A. Lynch, T.S. Rupp
and W. Tinner. 2006. How climate and vegetation influence the fire
regime of the Alaskan Boreal biome: The Holocene perspective.
Mitigation and Adaptation Strategies for Global Change 11:829-846.
- Bigler, C., D.G. Gavin, C. Gunning and T.T. Veblen.
2007. Drought induces lagged tree
mortality in a subalpine forest in the Rocky Mountains. Oikos
- Schoennagel, T.,
T.T. Veblen, D. Kulakowski and A. Holz. 2007. Multidecadal climate
variability and interactions among Pacific and Atlantic sea surface
temperature anomalies affect subalpine fire occurrence, western
Colorado (USA). Ecology.
- Long, C.J.,
C. Whitlock and P.J. Bartlein 2007. Holocene vegetation and fire
history of the Coast Range, western Oregon, USA. The Holocene
- Ali, A.A., C. Carcaillet and Y. Bergeron. 2009.
Long-term fire frequency
variability in the eastern Canadian boreal forest: the influences of
climate vs. local factors. Global Change Biology 15:1230-1241.
C., A.A. Ali, O. Blarquez, A. Genries, B. Mourier, and L. Bremond.
2009. Spatial variability of fire history in subalpine forests: From
natural to cultural regimes. Ecoscience 16:1-12.
- Hallett, D.J. and R.S. Anderson. 2010. Paleofire
reconstruction for high-elevation forests in the Sierra Nevada,
California, with implications for wildfire synchrony and climate
variability in the late Holocene
Quaternary Research 73: 180-190.
Last update: March 2007
program for computing the annual climatic water balance using the
modified Thornthwaite method.
Mac OS X
AET Calculator; R Code
This is the same code as used in the stand-alone program used above,
however, it uses DAILY data and computes variables based on a daily
time step. Thus, it is more realistic, though it applies
to daily data relationships that were developed for monthly data.
effect of this has not been explored. Knowledge of
language is needed.
Program file (last update: March 2009):
Example data file: BTV_HCN_DAILY.csv
GDD Calculator; R Code
Computes accumulated growing degree-days using daily
climate data and the Baskerville-Emin sine-curve method.
input data same as above.
Program file (last update: March 2009): GDD_calculator.txt
Charster; version 0.8.3
Last update: June 2006
program for the exploratory analysis and archiving of lake sediment
More sophisticated analyses are implemented in the
next-generation software by Phil Higuera: http://CharAnalysis.googlepages.com.
See the User's Guide for a comparison of these programs.
Max OS X (.zip)
Example raw data
for import (.txt)
Example Charster file
notes (esp. for Windows users).