United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-73

June 2001

Physiological Attributes

of 11 Northwest Conifer

Species

Ronni L. Korol
Rocky Mountain Research Station

324 25th Street

Ogden, UT 84401

Abstract ________________________________________

Korol, Ronni L. 2001. Physiological attributes of 11 Northwest conifer species. Gen. Tech.

Rep. RMRS-GTR-73. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky

Mountain Research Station. 9 p.

The quantitative description and simulation of the fundamental processes that characterize

forest growth are increasing in importance in forestry research. Predicting future forest growth,

however, is compounded by the various combinations of temperature, humidity, precipitation, and

atmospheric carbon dioxide concentration that may occur. One method of integrating new

management objectives and potential climate scenarios is to model ecosystems mechanistically.

General application of ecosystem process models has been difficult. In particular, obtaining initial

physiological parameters from current techniques that rely on instantaneous gas exchange

measurements can be both expensive and challenging. Frequently, data necessary to parameterize ecosystem process models are not readily available. This report provides model parameters

for 11 conifer species of the Inland Northwest. Field measurements of A, Amax, g, gmax, ci

, predawn

water potentials, analysis of leaf nitrogen concentration, carbon isotope discrimination (∆), and

values of ci, and intrinsic water use efficiency (A/g) inferred from the carbon composition (δ13C) are

presented. The relationship of wet leaf weight to dry leaf weight is also presented. The data in this

report can be used to calibrate and constrain physiological parameters for modeling physiological

processes of 11 conifer species in the Inland Northwest.

Keywords: photosynthesis, stomatal conductance, gas exchange, model parameterization,

conifer, process models.

The use of trade or firm names in this publication is for reader information and does not

imply endorsement by the U.S. Department of Agriculture of any product or service.

The Author ______________________________________

Ronni L. Korol is a Research Physiologist with the Rocky Mountain Research Station

in Missoula, MT. She received her BS degree from the University of Montana, her MS

degree from the University of British Columbia, Vancouver B.C., Canada, and her Ph.D.

degree from the University of Montana. Her research interests include physiologically

based modeling and developing physiological parameters for use in such models.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001 1

Introduction ____________________

The biggest challenge facing ecophysiologists today

is predicting the ecosystems of the future. As forest

management objectives change from maximizing forest growth and yield to ecosystem management, the

tools used by management must broaden. Predictors

that incorporate multiple aspects of the ecosystem are

necessary (such as hydrology, pests, wildlife, climate,

and vegetation) to develop optimal management plans

and forecast forest growth. Overall, the quantitative

description and simulation of the fundamental processes that characterize forest growth are increasing

in importance in forestry research (Blake and others

1990), but predicting future forest growth is compounded by the various combinations of temperature,

humidity, precipitation, and atmospheric carbon dioxide concentration that may occur.

One method of integrating new management objectives and potential climate scenarios is to model ecosystems mechanistically (Fosberg 1990; Schimel 1993).

Ecosystem process models have been used to predict

the growth of forests under different climate regimes

and at various scales (tree to stand to landscape).

These models generally rely on estimating net photosynthesis, which quantifies the carbon available for

respiration and biomass production. Net photosynthetic rates depend both on the diffusion of CO2 into

the leaf and on the biochemical capacity for photosynthesis (see also Kirschbaum 1993; Running and

Coughlan 1988; Vasala and others 1996).

General application of ecosystem process models,

however, has been difficult. In particular, obtaining

initial physiological parameters from current techniques that rely on instantaneous gas exchange measurements can be both expensive and challenging.

Quite frequently, data necessary to parameterize ecosystem process models are not readily available.

In either case, species-specific knowledge of the

relationship between net photosynthetic rate (A), internal carbon concentration (ci), and conductance (g) is

integral to ecosystem process models. In particular,

maximum rates of net photosynthesis (Amax) and conductance (gmax) are valuable in constraining process

models because these values can represent optimum

Physiological Attributes of

11 Northwest Conifer Species

Ronni L. Korol

environmental conditions. Unfortunately, many environmental factors make it difficult to obtain these

variables (Yoder 1992). This report provides model

parameters for 11 conifer species of the Inland

Northwest.

Objective ______________________

This report presents field measurements of A, Amax,

g, gmax, ci, predawn water potentials, analysis of leaf

nitrogen concentration, carbon isotope discrimination

(∆), values of ci, and intrinsic water use efficiency (A/g)

inferred from the carbon composition (δ13C). The relationship of wet leaf weight to dry leaf weight is also

presented. These data, which were collected on 11

coniferous tree species located in the Inland Northwest, can be used to calibrate or parameterize process

models for application in that region.

Methods _______________________

Theory

Several process models describe the photosynthetic

carbon assimilation rate as:

A = gc(ca – ci) (1)

where A (μmol m–2s–1) is net photosynthetic rate, gc

(mol m–2s–1) is leaf diffusive conductance to CO2, ca

(ppm) is CO2 concentration of the air surrounding the

leaf, and ci (ppm) is intercellular CO2 concentration of

the leaves. Whereas ca values are readily available,

both ci and gc can be difficult to obtain.

Farquhar and others (1982) developed a theoretical

relationship between isotopic composition of leaves of

a C3 plant (δ13Cp) and the ratio of intercellular and

atmospheric CO2 concentrations. The carbon isotope

composition also depends on δ13Ca, the isotopic composition of CO2 in the atmosphere, so that the relationship is now conventionally expressed as one between

carbon isotope discrimination, ∆, and ci/ca:

∆ = a + (b-a) ci/ca (2)

where ∆ is calculated as:
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001

 

∆ = −

+

13 13

13 1

δ δ

δ

C C

Cp

a p

(3)

Current atmospheric conditions (δ13Ca) were assumed

to be –8‰; ‘a’ is the discrimination associated with

diffusion (4.4‰), and ‘b’ is a fitted parameter with a

value determined primarily by the discrimination of

the enzyme Ribulose bisphosphate carboxylase/oxygenase (27‰) (Farquhar and others 1989).

The ci/ca ratio can been calculated from measurements of D by substituting the parameter values

and rearranging equation (2) as:

ci/ca = (∆ - 4.4‰ ) / 22.6 (4)

Once carbon has been fixed, the ratio of the stable

carbon isotopes 13C and 12C remains constant unless

the carbon is subsequently used for further biological

reactions, which may further discriminate between

the different carbon isotopes.

Using equation (1), A can be calculated directly from

gas exchange measurements and measured values of

gc, ca and ci, or inferred from measurements of ∆ using

equation (4) and a priori knowledge of ca. Intrinsic

water use efficiency (TE) can also be calculated using

estimates of ci inferred from ∆:

TE = ( ca – ci) / (1.6v) (5)

where the term 1.6v is largely determined by climatic

conditions, and the term (ca – ci) is primarily determined by plant physiological adjustments (Korol and

others 1999).

Study Sites

Trees were sampled from the Flathead Indian Reservation and the Coram Experimental Forest in northern Montana, and the Priest River Experimental Forest in northern Idaho. Species sampled include

Douglas-fir (Pseudotsuga menziesii var glauca [Bessin]

Franco), ponderosa pine (Pinus ponderosas Laws),

lodgepole pine (Pinus contorta spp. latifolia Dougl.),

subalpine fir (Abies lasiocarpa (Hook.) Nutt.), grand

fir (Abies grandis (Dougl.) Lindl.), Engelman spruce

(Picea engelmannii Parry), western red cedar (Thuja

plicata Donn), western larch (Larix occidentalis Nutt.),

western white pine (Pinus monticola Dougl.), whitebark

pine (Pinus albicaulis Engelm), and western hemlock

(Tsuga heterophylla (Raf.) Sarg.). For all 11 species,

gas exchange data (including the maximum rates of

photosynthesis, Amax, and stomatal conductance, gmax)

were obtained on leaves that had fully elongated using

a Licor-6200 portable photosynthesis system. To obtain A and g, the same trees were sampled diurnally

and every 4 weeks over the growing season. Leaf

samples were obtained using a shotgun to detach

samples from the upper one-fourth of the canopy.

Previous work suggests that there is no immediate

influence of detachment on gas exchange (Zhang and

others 1993), except perhaps in western larch.

Canopy profiles were obtained on four species (Douglas-fir, western white pine, western larch, and grand

fir) at the Priest River Experimental Forest. Measurements included leaf nitrogen, ∆, and gas exchange

obtained at intervals of one-fourth the canopy length

of each tree. The measurements were collected from

intact samples using a cherry picker to reach the

samples. Branches were flagged so that the same

branch was measured repeatedly, both diurnally and

seasonally.

In addition, predawn water potentials were measured using standard pressure bomb techniques at the

Flathead Reservation site and at two sites in the

Priest River Experimental Forest. These measurements were taken in July 1996 on ponderosa pine,

western larch, grand-fir, and western white pine.

All leaf samples used for gas exchange measurements obtained were brought back to the laboratory,

and projected leaf area was determined with JAVA

video analysis software (Jandel Scientific), using a

Coho 4819-5000 high-resolution monochrome video

camera. Photosynthesis rates were expressed per unit

weight, and per unit leaf area (determined by analysis

of the perimeter of leaf cross-sections divided by their

width using photomicrographs and the image analysis

system), and the specific leaf area calculated. Regression equations were developed to predict leaf area

from dry leaf weight for the seven species (subalpine

fir, ponderosa pine, Douglas-fir, lodgepole pine, western hemlock, western red cedar, and spruce) that had

a large enough sample size (>15) to adequately develop the relationships. Leaf samples were sent to the

University of Waikato stable isotope facility in New

Zealand for analysis of ∂13C. A subsample of the leaves

had carbon, hydrogen, and leaf nitrogen concentrations analyzed at the Analytical Sciences Laboratory,

University of Idaho.

Results and Discussion __________

Gas Exchange Measurements

Species estimates of the maximum rates of photosynthesis (Amax) ranged from 3.1 μmol m–2s–1 for western hemlock to 14.1 μmol m–2s–1 for ponderosa pine

(fig. 1). Douglas-fir and the pine species, which tend to

be less shade tolerant, had higher rates of Amax than

the more shade-tolerant species. The measured rates

of gmax had a slightly different trend and ranged from

a low 0.045 mol m–2s–1 for western hemlock to a high

of 0.37 mol m–2s–1 for lodgepole pine (fig. 2). Whitebark

pine had one of the higher rates of Amax yet had one of

the lower rates of gmax (close to the rate measured for
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001 3

western red cedar). Figure 3 shows the relationship

between Amax and gmax.

Sensitivity of the assimilation rate to the ratio of ci/

ca, measured with LI-6200, varied considerably by

species (fig. 4a and 4b). Western white pine had large

changes in assimilation rates over a small range of the

ci/ca ratio (0.58 to 0.78). All other species had a wide

range (0.35 to 0.95) of measured ci/ca. Grand fir showed

little sensitivity of A to the ratio. Generally, rates of A

were higher when the ci/ca ratio was less then 0.65, and

significantly lower as the ratio increased above 0.65.

Ponderosa pine had the highest rates of A for a given

ratio, while Douglas-fir had the lowest rates. The lack

of a strong relationship between A and the ci/ca ratio

suggests that the trees are keeping ci/ca relatively

constant by adjusting g.

Generally, for all species the greater the difference

between ca and ci, the lower the measured rates of g

(fig. 5), which supports the assumption that there was

no immediate influence on gas exchange. This response appeared to be more species specific, however,

with Douglas-fir and western hemlock exhibiting lower

rates of g, and lodgepole pine and whitebark pine

exhibiting higher rates of g for a given (ca–ci). Subalpine fir appeared to be the most sensitive to changes in

(ca–ci).

2

4

6

8

10

12

14

16

DF PP LP WWP WBP AF GF ES WRC WL WH

Tree Species

0

A (u mol m^-2 s^-1)

a(max)

Key for Species Abbreviations

DF - Douglas-fir (Pseudotsuga menziesii var glauca [Bessin] Franco)

PP - ponderosa pine (Pinus ponderosas Laws)

LL - lodgepole pine (Pinus contorta spp. latifolia Dougl.)

AF - subalpine fir (Abies lasiocarpa (Hook.) Nutt.)

GF - grand fir (Abies grandis (Dougl.) Lindl.)

ES - Engleman spruce (Picea englemannii Parry)

WRC - western red cedar (Thuja plicata Donn)

WL - western larch (Larix occidentalis Nutt.)

WWP - western white pine (Pinus monticola Dougl.)

WBP - whitebark pine (Pinus albicaulis Engelm)

WH - western hemlock (Tsuga heterophylla (Raf.) Sarg.

Figure 1—Mean maximum assimilation rates

(μmol m–2 s–1) for different tree species measured every 4 weeks from May through October between 10:00 am and 1:00 pm, using gas

exchange techniques.

0.1

0.2

0.3

0.4

DF PP LP WWP WBP AF GF ES WRC WL WH

Tree species

g (mol m^-2 s^-1)

0

g(max)

Figure 2—Maximum stomatal conductances

(mol m–2s–1) for different tree species measured

every 4 weeks from May through October between 10:00 am and 1:00 pm, using gas exchange techniques.

A (

μmol m–2 s–1)

g (mol m–2s–1)
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001

2

4

6

8

10

12

14

16

A (umol m^-2 s^-1)

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

g (mol m^-2 s^-1)

DF

PP

LP WBPWWP

AF

GF

ES

WRC WL

WH

0.3 0.7 0.8

A (umol m^-2 s^-1) A (umol m^-2 s^-1)

16

14

12

10

8

6

4

2

0

0.2 0.4 0.5 0.6

Ci/Ca

PP WWP

0.3 0.7 0.8

16

14

12

10

8

6

4

2

0

0.2 0.4 0.5 0.6

Ci/Ca

DF GF WL

Ca-Ci

0.05 0.25

200

180

160

140

120

100

80

60

40

0 0.1 0.15 0.2

g(mol m^-2 s^-1)

AF ES DF GF LP WBP WH WL

Figure 3—The relationship between maximum

assimilation rate (A: μmol m–2 s–1) and maximum

stomatal conductances (g: mol m–2s–1) for different tree species measured every 4 weeks from

May through October between 10:00 am and 1:00

pm, using gas exchange techniques.

Figures 4a and 4b—The relationship between

assimilation rate (A: μmol m–2 s–1) and the ci

/ca

ratio for different tree species measured using

gas exchange techniques. Each data point shown

represents one sample.

Figure 5—The relationship between the difference of ca and ci and stomatal conductances

(g: mol m–2s–1) for different tree species measured using gas exchange techniques. Each

data point shown represents one sample.

Studies have shown correlations between conductance and hours since sunrise, vapor pressure deficit

(Jarvis and McNoughton 1986), low night temperatures, and relative humidity (Ball and others 1987).

Figures 6a and 6b show measured rates of stomatal

conductance for the vapor pressure deficits (vpd) as

measured in the cuvette. The vpd inside the cuvette

was maintained such that it was similar to the vpd

outside the cuvette. The data showed no discernible

relationship between stomatal conductance and photosynthetically active radiation (PAR), probably because the PAR recorded in the cuvette differed from

the PAR influencing the leaf before detachment. The

leaves were measured in the cuvette as soon as possible after detachment, possibly before the conductance was able to acclimatize to the new levels of PAR.

Carbon Isotope Analysis

The average carbon isotope discrimination (∆‰) for

conifer leaves is shown by species in figure 7. Western

red cedar had the lowest average value of ∆ at 17.5‰,

while grand fir had the highest at 22‰. A 1‰ change

in ∆ corresponds to approximately a 16 μmol mol–1 in

ci or a 12% change in transpiration efficiency (if initially ci = 200 μmol mol–1). This implies that western

red cedar would have a significantly higher transpiration efficiency than grand fir. However, figure 8 shows

that the average intrinsic transpiration efficiencies of

western red cedar and grand fir are similar, which

illustrates the difficulty in comparing the average

instantaneous (30 sec) point measurements of gas

g (mol m–2s–1)

ci/ca

ci/ca

ca–ci

g (mol m–2s–1)

A (

μmol m–2 s–1) A (

μmol m–2 s–1) A (

μmol m–2 s–1)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001 5

exchange data to the more integrated measurements

of A/g, inferred from leaf samples. The carbon isotope

discrimination of the leaf reflects the environmental

conditions surrounding the leaf for the growing season

(minus any translocated carbon) and is therefore an

integrated measure. Figure 9 illustrates the wide

range of intrinsic transpiration water efficiencies (point

measurements) that correspond to the different values of ∆ (integrated measurement). It is apparent that

western red cedar does tend to have higher intrinsic

transpiration efficiencies than grand fir. This is further

illustrated in figure 10, which shows the relationship

between ∆ and g.

0.05

0.1

0.15

0.2

0.25

012 3 4 5

vpd

0

g (mol m^-2 s^-1)

AF DF ES GF LP

0.05

0.1

0.15

0.2

0.25

123 4 5 6

vpd

0

g (mol m^-2 a^-1)

DFH ESH LPH GF ES WBP

5

10

15

20

25

30

WRC PP DF WBP ES WH WWP AF WL GF LP

Species

0

55

65

85

75

95

15 17 19 21 23 25

Delta

45

A/g

AF

DF ES

LP GF

35

25

WL

WRC PP

WH

WBP WWP

Figures 6a and 6b—The relationship between

stomatal conductances (g: mol m–2s–1) and vapor

pressure deficits (vpd: kPa) for different tree species measured using gas exchange techniques.

Figure 7—The average carbon isotope discrimination (∆‰) for different coniferous tree species.

Figure 8—The relationship between A/g and

average carbon isotope discrimination (∆‰) for

different tree species.

g (mol m–2

s–1)

vpd kPa

vpd (kPa)

∆ (‰)

g (mol m–2

s–1)

∆ (‰)
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001

Canopy Profiles

The canopy profile data for the four species sampled

at the Priest River Experimental Forest support the

findings of Ehleringer and others (1986) and

Broadmeadow and Griffiths (1993), who indicate that

∆ varies within a canopy of coniferous trees. The

influence of canopy position was such that ∆ tended to

be higher for foliage taken from the upper crown than

for foliage samples obtained from lower in the canopy

(fig. 11). Upper foliage was also found to have a lower ∆

for a given stomatal conductance (fig. 12) and, correspondingly, for a given vapor pressure deficit (fig. 13).

The within-canopy vpd differed significantly between foliage samples obtained from the top quarter of

the canopy and those obtained from the lower quarter

of the canopy (fig. 14). The top quarter of the canopy

had vpds of less than 3 kPa, while the lower quarter

had vpds of greater than 3 kPa. When comparing the

relationship of stomatal conductance with vpd, there

were two distinct data clusters, depending on the

canopy position (fig. 14). The range of stomatal conductances was only slightly higher for the upper canopy,

varying little from the range found for the lower

canopy. This result suggests that within-canopy stomatal conductance is not sensitive to within canopy

changes in vpd.

Predawn Water Potentials

Average predawn water potentials of selected species during the period of July 10 through 15, 1996, are

shown in figure 15. Ponderosa pine had the lowest

water potentials (ψ) at 3.4 kPa, while western white

pine had the highest ψ at 6 kPa. These data indicate

the type of water stress and the overnight recovery

that tree species experience in the Inland Northwest

and provide an insight into the site soil water balance.

Leaf Nitrogen Concentrations

The average leaf nitrogen concentrations ranged

from 1.09% for grand fir to 1.62% for western red cedar

(fig. 16). Farquhar and Von Caemmerer (1982) have

shown a strong correlation between photosynthesis

and the nitrogen held in rubisco. We found that assimilation, measured using gas exchange (point measurement), tended to increase with leaf nitrogen

(fig. 17), as did stomatal conductance. The ∆ also

tended to increase with leaf nitrogen concentration for

each species (fig. 18).

WRC WWP

80

60

100

17 18 19 20 21 22

Delta %

40

A/g

DF GF

20

PP WL

23 24

0

Figure 9—The range of carbon isotope discrimination (∆‰) for different values of A/g measured

using gas exchange techniques for various tree

species. Each point represents the A/g ratio

measured for a given leaf tissue that was then

analyzed for ∆.

Figure 10—The range of carbon isotope discrimination (∆‰) for different values of g measured using gas exchange techniques for various tree species.

21

20

22

0 0.05 0.1

g (mol m-2 sm-1)

19

Delta %

18

0.15 0.2

17

23

24

DF GF WL PP WRC WWP

∆ (‰)

∆ (‰)

g (mol m–2s–1)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001 7

20

15

30

DF WWP WL GF

10

Delta %

5

4

0

25

1 2 3

21

20

22

0.02 0.04 0.06 0.08 0.1

g (mol m^-2 s^-1)

19

Delta %

18

0.12

17

23

DF1 WWP1 DF4 WWP4

Figure 11—The range of average carbon isotope discrimination (∆‰) measured throughout

the crowns of four coniferous species. Position 1

refers to the lower one-fourth of the crown while

position 4 refers to the top one-fourth of the

crown.

Figure 12—The relationship between carbon isotope discrimination (∆‰) and stomatal conductances (g: mol m–2s–1) in the bottom one-fourth of

the crown (Position 1) and top one-fourth of the

crown (Position 4).

23

21

25

1.4 1.6 1.8 2.6 3.6

VPD (kPa)

Delta %

19

17

3.8

15

DF1 DF2 WWP3 WWP4

2 2.2 2.4 2.8 3 3.2 3.4

2

5

7

PP WWP WL GF DF

Species

Pre-dawns (avg in kPa)

0

July

6

4

3

1

Figure 13—The relationship between carbon isotope discrimination (∆‰) and vapor pressure deficit (vpd; kPa) in the bottom one-fourth of the crown

(low) and top one-fourth of the crown (high).

Figure 14—The relationship stomatal conductances

(g: mol m–2s–1) and vapor pressure deficit (vpd;

kPa) in the bottom one-fourth of the crown (low)

and top one-fourth of the crown (high).

g (mol m–2s–1)

∆ (‰) ∆ (‰)

∆ (‰) Pre-dawns (kPa)
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001

2

5

7

PP WWP WL GF DF

Species

Pre-dawns (avg in kPa)

0

July

6

4

3

1

Figure 15—Average predawn water potentials

(kPa) for different coniferous tree species taken in

July 1996.

Figure 16—Average leaf nitrogen concentrations

(%) for different coniferous tree species.

Figure 17—The relationship between stomatal

conductances (g: mol m–2s–1) and leaf nitrogen

concentration (%) for different coniferous tree

species.

Figure 18—The relationship between carbon

isotope discrimination (∆‰) and leaf nitrogen

concentration (%) for different coniferous tree

species.

2

4

6

8

10

12

Leaf nitrogen (%)

0

A (umol m-2 s-1)

DF GF PP WL WRC WWP

1 1.2 1.4 1.6 1.8 2

0.05

0.1

0.15

0.2

Leaf nitrogen (%)

0

g (mol m-2 s-1)

DF GF PP WL WRC WWP

1 1.2 1.4 1.6 1.8 2

Leaf nitrogen (%)

∆ %

DF GF PP WL WRC WWP

1 1.2 1.4 1.6 1.8 2

17

18

19

20

21

22

23

24

Leaf Area/Leaf Weight Relationships

Table 1 lists the regression equations for predicting

leaf area from dry leaf weight. The highest correlation

coefficients calculated (r2) were for western hemlock

(r2 = 0.88), western red cedar (r2 = 0.90), and spruce

(r2 = 0.81). Separating the data by aspect (north and

south) increased the correlation coefficients for subalpine fir, Douglas-fir, and lodgepole pine.

Conclusion_____________________

Several models of stand and tree growth currently

exist and more are being developed. A large number of

these models rely on relationships developed at the

leaf level (for example, Farquhar and von Caemmerer

1982) to predict growth at the stand level. As the use

of such models to predict carbon uptake and sequestration or stand growth increases, the need for

Pre-dawns (kPa)

∆ (‰)

A (

μmol m–2 s–1)

g (mol m–2s–1)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001 9

physiological data to calibrate the models becomes

increasingly important. Leaf-level physiological data

are both difficult and expensive to obtain, and the

results can be quite variable due to the multitude of

environmental effects. The real data presented in this

report can be used to calibrate and constrain physiological parameters for 11 conifer species in the Inland

Northwest.

References _____________________

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Table 1—Leaf area equations (leaf area (cm/cm) = a + (leaf weight * b)). Species with large samples in different aspects were

stratified by aspect.

Species n r2 see a (se) b (se) leaf wt (gr)

Subalpine fir 68 0.58 3.224 3.41 (0.991) 33.54 (3.545)

north 49 0.69 1.935 3.53 (0.656) 26.26 (2.575)

south 19 0.73 1.726 9.31 (1.256) 27.29 (4.042)

Ponderosa pine 24 0.67 3.167 5.72 (2.676) 33.12 (5.123)

Douglas-fir 46 0.64 1.895 2.46 (1.028) 34.539 (3.920)

north 18 0.89 1.212 4.96 (0.924) 30.007 (2.926)

south 28 0.70 1.321 1.05 (1.662) 39.639 (4.277)

Lodgepole pine 23 0.67 2.544 6.10 (1.754) 24.46 (3.737)

north 12 0.93 1.008 0.40 (1.419) 30.08 (2.926)

south 11 0.85 1.543 0.87 (2.292) 39.64 (4.277)

Western hemlock 8 0.88 0.145 4.55 (0.503) 43.44 (6.379)

Western red cedar 15 0.90 3.154 1.82 (1.989) 41.84 (3.786)

Spruce 43 0.81 2.105 0.21 (0.844) 39.25 (2.990)

north 30 0.82 1.4329 1.63 (1.027) 32.03 (2.872)

south 13 0.85 1.4687 1.23 (0.987) 35.87 (2.910)

Whitebark pine 15 0.87 1.077 2.88 (0.859) 22.13 (2.330)

Grand-fir 36 0.76 2.355 5.01 (1.061) 31.20 (2.998)
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-73. 2001

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