do stem form differences mask responses to silvicultural treatment? doug maguire department of...
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Do stem form differences mask responses to silvicultural
treatment?
Doug Maguire
Department of Forest Science
Oregon State University
Typical responses monitored during silvicultural trials
-Dbh
-Height
-Height to crown base?
-Upper stem diameters??
-Branch diameters??
Monitor Dbh and Ht (perhaps crown size), but do regional or subregional volume/taper equations adequately estimate tree volumes?
How would you test statistically for silvicultural treatment effects on stem form?
Lennette thesis – Effects of stand density regime on stem form in larch
Garber thesis – Effects of initial spacing and species mix on tree and stand productivity
Scott Ketchum, Robin Rose – Does relative stem profile respond to early control of competing vegetation?
Mark Gourley et al. – Are Swiss needle cast and/or nutrient amendments changing stem form in Douglas-fir?
Wider spacing
Larger crowns
(length and width)
Change in relative stem profile?
Influence on distribution of bole increment
Are any responses in stem form accounted for by monitoring treatment
effects on crown size (length)?
Andy Lennette. 1999. Twenty-five-year response of Larix occidentalis stem form to five stand density regimes in the Blue Mountains of eastern Oregon. M.S. Thesis, Oregon State University
Lexen (1943): bole surface area as measure of growing stock
(Approximation of cambial surface area on which wood accrues)
=>Measurement of bole surface area to regulate stocking
Catherine Creek Levels-of-growing-stock study
Stocking regulated by bole surface area
=>Accomplished with Barr and Stroud optical dendrometer
=>Many upper stem measurements over time
Growing stock levels
1: 5,000 ft2/ac
2: 10,000 ft2/ac
3: 15,000 ft2/ac
4: 20,000 ft2/ac
5: 25,000 ft2/ac
35 yrs old in 1966 at start of study
Thinned twice, ages 45 and 65 (last thinning in 1965
Last measurement in 1991 – upper stem diameters retrieved for 25-29 trees per treatment
On average 10 d.o.b.s per tree
35
40
45
50
55
60
65
70
75
0 1 2 3 4 5 6
GSL
Cro
wn
Rat
io(%
)C
row
n ra
tio
Increasing thinning intensity
Analysis:
Kozak variable exponent model
Dob/DBH = XC
where X = [1-(h/H)0.5] / [1-(4.5/H)0.5]
C = a1sin-1(h/H) + a2(h/H)2
Fitted to each individual tree, then SUR for
a1 = f( GSL or tree attributes (eD/H) )
a2 = g( GSL or tree attributes (CR) )
Conclusions:
Relative stem profile was significantly different between the 2 most intensive thinning treatments, and these 2 were significantly different than the 3 least intensive thinnings
There was no marginal effect of treatment beyond its effect on D/H and crown ratio
Production analysis requires development of taper/volume functions
(without attempt at explicit test of treatment effects on stem profile)
Sean Garber. 2002. Crown structure, stand dynamics, and production ecology of two species mixtures in the central Oregon Cascades. M.S. Thesis, Oregon State University
Ponderosa pine/lodgepole pine mixed species spacing trial, planted in 1967
Grand fir/ponderosa pine mixed species spacing trial, planted in 1974
Both sampled in fall 2001 (34 and 27 yrs old, respectively)
Upper stem measurements from trees felled outside of permanent spacing trials
Analysis based on Kozak variable exponent model:
Dob/DBH = XC
where X = [1-(h/H)0.5] / [1-(4.5/H)0.5]
C = f(h, H, and D)
Objective was NOT to test for spacing and species effects on stem form, but rather on relative productivity. BUT needed a reliable volume or taper function for the site.
Rather than two-stage approach, can a mixed-effects model be applied ?
Is a random tree effect sufficient to eliminate autocorrelation among observations within a tree?
Grand fir
Ponderosa pine
Lodgepole pine
Ponderosa pine
Subtle spacing effects on relative stem profile
(but estimated adequately from D/H)
Average tree in each spacing
Spacing effect was not tested explicitly in taper model since trees were felled off the plots
Instead profiles were plotted for the tree of average dbh and height within each spacing-species combination
Effect of species composition was even more subtle
Conclusions:
Random tree effect dramatically reduced the order of autocorrelation, but did not eliminate it.
A first-order continuous autoregressive error process eliminated the remaining autocorrelation.
Conclusions (continued):
The taper functions had <3% bias in almost all cases.
Regional volume equations (Cochran 1985) differed from the taper equation estimates by 20-30% for grand fir, 20-60% for lodgpole pine, and 2-10% for ponderosa pine.
Rose, Ketchum, & Hanson. 1999. Three-year survival and growth of Douglas-fir seedlings under various vegetation-free regimes. Forest Science 45:117-126.
8 treatments, 3 reps/trt @ each of 2 sites
Area of herbaceous and woody control (1st two growing seasons):
0, 4, 16, 36, 64, 100 ft2
+ 100 ft2 woody only
+ 100 ft2 herbaceous only
Planted in February 1993 with 1+1 Douglas-fir
Rose et al. (1999) present 3-yr results:
Maximum growth response under the largest (Summit) or 2 largest (Marcola) areas of treatment (height, D2H, basal diameter)
Greater growth under herbaceous only, not under woody only, relative to controls
Winter 2001-2002, stem d.o.b. measurements
Does the intensity of early weed control affect stem profile beyond the effect on diameter and height?
Do existing volume equations accurately predict stem volume of weeded plantations?
Difference = observed - predicted
0
5
10
15
20
25
30
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
Difference (in)
Hei
ght (
ft)
Marcola
Summit
Difference = observed - predictedSummit and Marcola averaged
0
5
10
15
20
25
30
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2
Difference (in)
Heig
ht (f
t)
4
16
36
64
100
0
herb
woody
Difference = observed - predictedMarcola site only
0
5
10
15
20
25
30
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6
Difference (in)
Hei
ght (
ft)
4
16
36
64
100
0
herb
woody
Difference = observed - predictedSummit site only
0
5
10
15
20
25
30
-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2
Difference (in)
Hei
ght (
ft)
4
16
36
64
100
0
herb
woody
Potential for systematic bias by treatment
To test for treatment effects on stem profile,
mixed-effects linear and non-linear models
start
finish
Analysis:
Kozak variable exponent model
Dob/DBH = XC
where X = [1-(h/H)0.5] / [1-(4.5/H)0.5]
C = b1(h/H) + b2(h/H)2
Fitted to each individual tree, then SUR for
b1 = f( site, treatment, tree attributes )
b2 = g( site, treatment, tree attributes )