Bad Science

I just finished reading “Bad Science” by Ben Goldacre. It’s an entertaining read discussing the various tricks employed by the “alternative” medicine circles and pharmaceutical industry to peddle  drugs / miracle cures and therapies (you will never buy vitamins again).

In particular, the chapter “How the Media Promote the Public Misunderstanding of Science” stayed with me. This chapter discusses the role of media in actively promoting false ideas, mostly driven by profit or a lack of understanding. Most of the issues presented in this chapter apply to science in general and not only to medicine related topics. It also stresses the need for scientists to communicate science better and take a more active role in how their results are presented. It’s often all too clear that climate deniers exploit the weaknesses of the media to push their agenda. Media want easy (positive) headlines with an easy to understand storyline - e.g. miracle cures. Sadly, the world and science is often more complicated. But this does not mean we should not try to word things differently as to reach more people with research.

I can only recommend reading this book and I wholeheartedly support the author’s crusade against bad science in general.

DaymetR, a Daymet single pixel subset tool for R

Daymet is a collection of algorithms and computer software designed to interpolate and extrapolate from daily meteorological observations to produce gridded estimates of daily weather parameters - as concisely described on the Daymet website.

As I’m extensively using daily meteorological data to drive my grassland model, quick and easy access to this data is key. However accessing single pixel values through the java tool provided was a bit cumbersome and did not fit my workflow. As such I wrote my own tool which queries the website and allows you to subset time series of a single pixel location (given a latitude, longitude position) all within R. Data is either just dowloaded to the current working directory or imported as a structured array into your R workspace for further analysis or formatting.

You can find a link to the DaymetR code on my software page or follow this link to my github.

ps. since this post I have added a python version of the same code. A link can be found on the software section of my website.

Best of PhenoCam

As I wander through the PhenoCam archives, I encounter truly stunning or interesting images. From time to time I’ll post these “best of PhenoCam” images on my blog together with some location information.

This first image is an old one, posted to my Flickr site a long time ago. It shows an early sunrise at a boreal Jack pine (Pinus banksiana) forest roughly 30 km south of Chibougamau, Quebec, Canada. Jack pines form fire adapted stands of 9-22 m tall and lanky trees. The image was taken from a 30 m high scaffolding tower harbouring an eddy covariance flux system.

Battery run

Past January, after 10 years of service, the battery bank of the Bartlett flux tower gave in to too many cycles and harsh working conditions. Since it is a key component of the flux tower, replacements were bought and a large part of the lab made a trip to Bartlett to carry 6 large deep cycle batteries into the New Hampshire woods. Below the rather funny video of me sledding a battery into the woods. After a day of work, things are back up and running and have been ever since, hopefully for another 10 years.


Dating the forest

Determining stand ages in a tropical humid forests remains a challenge, mainly due to the lack of (annual) growth rings in the wood of many tropical trees. A few months ago I was offered the opportunity to date wood core samples using radio carbon dating at the UC Irvine Keck Carbon AMS facilities, resolving the issue of a lack of visible characteristics to determine the age of a tree. I’m indebted to Prof. dr. Trumbore for providing this generous offer as these tests are expensive.

Three samples along a wood core for two tropical humid forest species were selected. One species consisted of a dominant canopy species Scorodophleus zenkeri and a common mid-sized / understory species Panda oleosa.

Only the  youngest of the three samples for each species was valuable in this exercise. Other samples fell within a region which is hard to date using radio carbon dating as the calibration curve for this region is relatively flat (~1700 - 1950) resulting in large uncertainties when relating 14C-age to calendar years (see examples Figure 1).


Although the data is limited to two data points per species, the first being the time at which the wood core sample was extracted and a second the dated sample as determined by radio carbon dating, we could get a rough estimate of the growth rates for both species during the last few decades.

Ages of the most recent samples were determined to be 1962 / 1985 and 1966 translating into growth speeds of~0.8 / 1.3 mm/yr and 0.7 mm/yr for Scorodophleus zenkeri and Panda oleosa respectively. These values fall well within the ranges as defined by literature but on the lower end of the range.

The slow growth rate of Panda oleosa is not surprise as it is often light limited, being a mid-sized species. The stature of these understory trees pales in comparison to emergent trees such as Petersianthus macrocarpus (Figure 2), their age however might not. Take home message: don’t judge a tree’s age by it’s stature.


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