Your curiosity is understandable and legitimate. The reason the statistical approach used in this paper is not used more broadly is because of the complexity and data-hunger of this model. Why parameterize a full model using masses of data if you can simplify the problem down to essentials and reduce the cost of sampling by orders of magnitude? You will note that these authors STILL have not dealt with the synergies, nonlinearities, disturbances, non-uniformities that other dendros have ignored – so what is gained through the use of this model? Relatively little compared to what could be gained by addressing those other shortcomings.

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Lastly, These are not “students”. One is a professor.

**RomanM: Bender, why do you persist in making this incorrect statement? Eric Cormier was an undergraduate student who graduated this spring and received the UVic Jubilee Medal for Science (for having the top undergraduate GPA). Zheng Sun is listed as a graduate student on the Math and Stat website.**

**Did you really expect these students to make it their life’s work after the competition was presented in May? Give the undeserved criticism a rest please.**

The appearance to a layperson, like me, is that the scientists working in this area write about the specific explanatory variables (months of the year, MXD and TR mix, max, min average temperatures, and even after the fact tree selection) for temperature that they use in their proxies as though these selection criteria are not determined a priori, but merely fitting the selections to provide a model with the highest explanatory power.

How much effort do these scientists put into to determining and understanding the growth factors before undertaking the construction of models relating those factors to temperature (or other climate variables)? How much of this preparatory work can be generalized such that the proxy model does not, or at least not appear to, require “fitting” as though each case and location has very unique properties?

Roman’s thread about these student’s work got me to thinking again about my understanding (or lack thereof) of this situation.

]]>Ken, I’ve tried to asnwer your questions already. I’ll try again.

This work has potential importance at three levels: (1) stated objective regarding effects of biomass and climate on young lodgepole pine in fire-origin stands in BC; (2) relevance of results to dendroclimatology; (3) relevance of statistical methods for dendroecology.

(1) IMO we can not assess the validity or significance of the results until we understand how plant biomass was estimated over time.

(2) The results in this paper have no relevance for dendroclimatology because of the types of trees and stands chosen for study. The climatic responses exhibited are not representative of the types of responses you would expect in old, open-grown trees at high elevation. Moreover, the climatic responses are necessarily weak because they are dominated by the up-down pattern of crown expansion and canopy closure over a 30-year period.

(3) The statistical method of mixed models, while applicable, is not likely to overturn any result in dendroclimatology because all of the nested effects estimated are either trivial, or never come into play, or are easy to correct for. Yes, it would be nice to see a side-by-side comparison. That would even be publishable, because it would satisfy curiosities like yours. But the bottom line is that nothing would turn on this. [Ok, well, there is the sad case of the strip-bark bristlecone pines that have inconsistent readings across various radii. There would be some effect in those cases.] Finally, what dendro is going to destructively sample an entire tree so that the needs of this particular model can be satisfied? A practical model has to accomodate non-destructive sampling involving nothing more than a couple of cores. No biomass measures as covariates.

From near the top of the problem page:

Content last modified 2009-01-16

Source of climate data added 2009-09-02

A bit strange for a no-longer-active case study. Statisticians like things to be complete. 😉

]]>What relevance would an analysis of very young trees growing in a closed stand have for paleoclimatologists that favor the use of old trees growing in open stands? This statistical method is specially designed for a situation that paleoclimatologists specifically try to avoid.

Bender, this was the proposition from the introduction to this thread:

To what extent do climate, position on the tree bole, and current foliar biomass explain cross-sectional area increment and proportion of early and late wood?

I would suppose that very old trees were once young and I assume counted in very old proxies. Perhaps you can weigh in on that.

I also am assuming that the reason that Roman chose to post this study was as much or more from the statistical methods used as what from the analysis could specifically be applied to dendrochronology.

I think you need to give more details on what you think could be useful from the students’ analysis to dendrochronoligists from either a statistical perspective or the explanatory variables effecting tree ring growth. The analysis was, after all, presented as an assignment to test the students statistical skills and my point has been what we can we learn from their work – given that I have not been that impressed from the published papers by dendros that I have seen analyzed here at CA and for reasons given above.

Oh, and please do not sugar coat your reply – I hate that.

]]>This would mean that “tracking foliage” was apparently not done in the study so I don’t know how it can apply to all of the rings.

Exactly. So here we are at the usual impasse – wondering what exactly was done, wondering why the plotted data don’t line up with the raw data in the source datsets. And so on.

]]>Each year, a tree lays down an annual ring of wood in a layer under the bark. Pressler’s hypothesis states that area of wood laid down annually (measured by the cross-sectional area increment) increases linearly from the top of the tree to the base of the crown (the location of the lowest live branches) with the assumption that it is proportional to the amount of foliage above the point of interest.

This would imply to me that a cumulative value for the foliage variable would be calculated for each level. There is also another statement that I find somewhat confusing in respect of the analysis:

Measurements of the last year of growth and wood density are often unreliable because of proximity to the bark and difficulties of sample preparation. However, it is for this ring only that we have measures of the amount of foliage.

This would mean that “tracking foliage” was apparently not done in the study so I don’t know how it can apply to all of the rings.

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Look at the actual pattern being modeled here:

Ring width starts low in the mid- 1970s, when the trees are establishing, goes up, peaks in ~1990 and comes down, troughing in 2004 at the end of the study as the canopy presumably starts to close up. Meanwhile, branch biomass is low for young branches, peaks for medium-aged branches, and drops for the oldest branches. Wait a sec …

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How did these authors get from

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Even if they did the biomass calculation correctly … nuanced climatic fluctuations in the 30 year period from 1975 ot 2004 are not going to explain much for what is essentially a brand new plantation starting from seed. Of course the pattern of radial growth is going to track the process of canopy growth and closure. ]]>