[Note: My first post in which I had written commentary mysteriously lost all of its content, posting nothing but white space. This is some sort of internal wordpress error, but has never happened before. I have some elements restored below, but my original commentary is lost. -Anthony]

I’ve written before about the difficulties associated with extracting a valid temperature signal due to all of the confounding variable in Liebigs law of the minimum, which I describe in detail here: A look at treemometers and tree ring growth

Now a new confounding variable has been introduced that does not bode well for tree ring studies such as Mann et al.

Bishop Hill writes:

A new paper by Brienen et al in the journal Global Biogeochemical Cycles suggests that there may be a whole new set of biases in tree ring studies. Tree ring analysis allows reconstructing historical growth rates over long periods. Several studies have reported an increasing trend in ring widths, often attributed to growth stimulation by increasing atmospheric CO 2 concentration. However, these trends may also have been caused by sampling biases. Here we describe two biases and evaluate their magnitude. (1) The slow–grower survivorship bias is caused by differences in tree longevity of fast- and slow-growing trees within a population. If fast-growing trees live shorter, they are underrepresented in the ancient portion of the tree ring data set. As a result, reconstructed growth rates in the distant past are biased toward slower growth. (2) The big–tree selection bias is caused by sampling only the biggest trees in a population. As a result, slow-growing small trees are underrepresented in recent times as they did not reach the minimum sample diameter. We constructed stochastic models to simulate growth trajectories based on a hypothetical species with lifetime constant growth rates and on observed tree ring data from the tropical tree Cedrela odorata. Tree growth rates used as input in our models were kept constant over time. By mimicking a standard tree ring sampling approach and selecting only big living trees, we show that both biases lead to apparent increases in historical growth rates. Increases for the slow-grower survivorship bias were relatively small and depended strongly on assumptions about tree mortality. The big-tree selection bias resulted in strong historical increases, with a doubling in growth rates over recent decades. A literature review suggests that historical growth increases reported in many tree ring studies may have been partially due to the big-tree sampling bias. We call for great caution in the interpretation of historical growth trends from tree ring analyses and recommend that such studies include individuals of all sizes. Presumably, this new source of bias applies just as much to tree ring studies where the increase in growth is ascribed to temperature. ================================================================== Here is the abstract from GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 26, GB1025, 13 PP., 2012 doi:10.1029/2011GB004143

Detecting evidence for CO 2 fertilization from tree ring studies: The potential role of sampling biases

Key Points

Observed increases in tree ring widths may be caused by sampling biases

Standard sampling methods lead to spurious trends in historical growth rates

Reported increases in ring width may often not be due to CO2 fertilization

Roel J. W. Brienen

School of Geography, University of Leeds, Leeds, UK

Programa de Manejo de Bosques de la Amazonía Boliviana, Riberalta, Bolivia

Emanuel Gloor

School of Geography, University of Leeds, Leeds, UK

Pieter A. Zuidema

Programa de Manejo de Bosques de la Amazonía Boliviana, Riberalta, Bolivia

Ecology and Biodiversity, Institute of Environmental Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands

Forest Ecology and Forest Management, Centre for Ecosystem Studies, Wageningen, Netherlands

Tree ring analysis allows reconstructing historical growth rates over long periods. Several studies have reported an increasing trend in ring widths, often attributed to growth stimulation by increasing atmospheric CO 2 concentration. However, these trends may also have been caused by sampling biases. Here we describe two biases and evaluate their magnitude. (1) The slow–grower survivorship bias is caused by differences in tree longevity of fast- and slow-growing trees within a population. If fast-growing trees live shorter, they are underrepresented in the ancient portion of the tree ring data set. As a result, reconstructed growth rates in the distant past are biased toward slower growth. (2) The big–tree selection bias is caused by sampling only the biggest trees in a population. As a result, slow-growing small trees are underrepresented in recent times as they did not reach the minimum sample diameter. We constructed stochastic models to simulate growth trajectories based on a hypothetical species with lifetime constant growth rates and on observed tree ring data from the tropical tree Cedrela odorata. Tree growth rates used as input in our models were kept constant over time. By mimicking a standard tree ring sampling approach and selecting only big living trees, we show that both biases lead to apparent increases in historical growth rates. Increases for the slow-grower survivorship bias were relatively small and depended strongly on assumptions about tree mortality. The big-tree selection bias resulted in strong historical increases, with a doubling in growth rates over recent decades. A literature review suggests that historical growth increases reported in many tree ring studies may have been partially due to the big-tree sampling bias. We call for great caution in the interpretation of historical growth trends from tree ring analyses and recommend that such studies include individuals of all sizes.

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