United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

Research Paper RMRS-RP-79

May 2010

Dynamic Phenotypic 

Plasticity in 

Photosynthesis and 

Biomass Patterns in 

Douglas-Fir Seedlings

Anita C. Koehn

Geral I. McDonald

David L. Turner

David L. Adams
Koehn, A.C.; McDonald, G.I.; Turner, D.L.; Adams, D.L. 2010. Dynamic 

phenotypic plasticity in photosynthesis and biomass patterns in Douglasfir seedlings. Res. Pap. RMRS-RP-79. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 14 p.

Abstract

As climate changes, understanding the mechanisms long-lived conifers use to adapt 

becomes more important. Light gradients within a forest stand vary constantly with 

the changes in climate, and the minimum light required for survival plays a major 

role in plant community dynamics. This study focuses on the dynamic plasticity 

of Douglas-fir (Pseudotsuga menziesii var. glauca [Beissn.] Franco) seedlings 

grown in contrasting light environments. Plasticity in Douglas-fir seedlings was 

primarily achieved by a combination of the physiological processes: maximum 

photosynthesis, quantum yield, Fv

/Fm, Km (the light constant), light compensation 

point, and the ratio of needle area to needle weight (specific leaf area). Specific 

leaf area was the most plastic of the biomass parameters measured.

Keywords: Pseudotsuga menziesii var. glauca (Beissn.) Franco, dynamic 

phenotypic plasticity, photosynthesis, Douglas-fir, biomass allocation, light 

constant

Authors

Anita C. Koehn is a Soil Scientist with the USDA-ARS Northwest Soils and 

Irrigation Laboratory in Kimberly, ID.

Geral I. McDonald (retired) is a Research Plant Pathologist with the USDA Forest 

Service, Rocky Mountain Research Station in Moscow, ID.

David L. Turner is a Mathematical Statistician with the Rocky Mountain Research 

Station in Logan, UT.

David L. Adams is a Professor Emeritus with the College of Natural Resources at 

the University of Idaho in Moscow, ID.

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Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rm/publications

Mailing address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526

Rocky Mountain Research Station

240 W. Prospect Road

Fort Collins, Colorado 80526

cover photo credits: Bill Cook, Michigan State University, Bugwood.org. (top right); 

Mary Ellen (Mel) Harte, Bugwood.org. (lower right).
Contents

Introduction........................................................................................................1

Materials and Methods......................................................................................2

Plant Material...............................................................................................2

Growth and Biomass Analysis .....................................................................3

Chlorophyll Fluorescence ............................................................................4

Oxygen Evolution.........................................................................................4

Data Calculations and Analysis....................................................................5

Results................................................................................................................5

O2

 Evolution and Chlorophyll Fluorescence.................................................5

Biomass Allocation Patterns ........................................................................7

Plasticity Index.............................................................................................9

Discussion........................................................................................................10

Physiology and Plasticity ...........................................................................10

Biomass Allocation and Physiology ...........................................................11

Conclusion .......................................................................................................12

Acknowledgments...........................................................................................12

References .......................................................................................................12

USDA Forest Service RMRS-RP-79. 2010. 1

Introduction

As climate changes, it becomes more important to understand its impact on 

long-lived conifers and their adaptation mechanisms. Changes in climate alter 

many aspects of plant growth and leaf area production simultaneously, which, 

in turn, alter the availability of light in plant communities (Valladares and 

Niinemets 2008). The light gradient in forest stands is a highly heterogeneous 

factor that influences a tree’s ability to survive and compete in the plant community. In addition to light gradients in a forest stand, there are also light gradients 

within the crown of a forest tree; therefore, all trees are exposed to some degree of shading in their lifetime (Valladares and Niinemets 2008). Although 

light is necessary for photosynthesis, both low and high sunlight can limit plant 

growth and survival. Plants can experience high heat, desiccation, excessive 

irradiance, and ultraviolet radiation stresses in high light (Demmig-Adams and 

Adams 2006). Trees must invest in protective and adaptive mechanisms in order 

to adjust to changing light gradients.

Adaptive mechanisms can be expressed as species’ plasticity, or the ability 

for species to adapt by changing biomass allocation patterns, leaf structure, and 

physiology (Ackerly and others 2000). Plasticity in the allocation of biomass 

to functionally distinct tissues such as roots, leaves, stems, and reproductive 

structures allows plants to enhance access to a specific resource that may be 

limited. For example, in response to low light availability, spotted lady’s thumb 

(Polygonum persicaria) increased the proportion of their tissue allocated to 

leaves, effectively maximizing leaf surface area for increased photon capture. 

Although the plants produced less biomass, the developmental shift raised the 

photosynthetic effectiveness of each gram of biomass so as to promote successful growth and reproduction despite limited light (Sultan 2003). This type of 

allocational plasticity may contribute to the species’ ability to inhabit a broad 

range of light habitats (Sultan 2003 and sources therein). An intriguing and important aspect of plastic response to environment that can describe acclimation 

or adaption to changing environments over time is dynamic plasticity, which 

has become meaningful in evaluating diversity and how plants develop and 

function in changing environments (Sultan 2004).

Conifers normally adjust their physiology repeatedly in nature to survive 

extremely heterogeneous environments. In the northern Rocky Mountains, the 

mean temperatures in January, the coldest winter month, range from -5.2 °C 

to 1.6 °C with an extreme low of -34.4 °C. In August, the warmest summer 

month, the mean low and high temperatures range from 9.8 °C to 28.1 °C, respectively, and temperatures have been measured as high as 42.8 °C (Hermann 

and Lavender 1990; http://snow.ag.uidaho.edu/; climate means from 1893 to 

2006). In the winter, conifers initially adjust to colder temperatures through 

frost-hardening at temperatures slightly above 0 °C. The second phase is winter 

stress, which occurs at subzero temperatures and includes changes in energy 

absorption and photochemical transformation through energy partitioning and 

accompanying changes in chloroplast metabolism, allocation, and partitioning. 

These plastic changes are reflected in decreased quantum yield of photosynthesis and a reduction in Fv

/Fm, the ratio of variable chlorophyll fluorescence to 

maximum fluorescence, for exposed needles (Öquist and Huner 2003). The effects of temperature fluctuations on photosynthesis and carbon gain eventually 

determine productivity, growth, and distribution of conifers on various spatial 
2 USDA Forest Service RMRS-RP-79. 2010.

scales, and the fluctuations may act as global sensors to environmental change 

(Ensminger and others 2006).

Douglas-fir, a major timber species with broad ecological amplitude, is native to the inland northwest region of the United States. It is physiologically 

attuned to its environment, and adaptive variation among populations is closely 

related to geographic, physiographic, and climatic variables (Rehfeldt 1983). 

Flexibility within the populations of Douglas-fir allows each population to produce progenies preadapted to a range of environmental conditions (Rehfeldt 

1978). Douglas-fir ranks intermediate in shade tolerance among its associates, 

being more tolerant than western larch, ponderosa pine, lodgepole pine, southwestern pine, and aspen. First-year seedlings grow best under light shade, and 

older seedlings require full sunlight (Hermann and Lavender 1990).

Few studies examine dynamic phenotypic plasticity in conifers. Douglas-fir 

is used in this study to observe natural processes of dynamic phenotypic plasticity in a conifer species with changing light environments. It was chosen for this 

purpose because it occupies a diverse range of habitats, retains its needles for 

extended periods of time, and must adjust to seasonal changes in temperature. 

This paper focuses on how contrasting light environments and seasonal changes 

affect the plasticity in photosynthetic physiology and biomass allocation patterns in Douglas-fir.

Materials and Methods

Plant Material

The seed source was a single population of Douglas-fir (Pseudotsuga menziesii var. glauca [Beissn.] Franco) collected from the Clearwater National 

Forest near Elk River, Idaho (116°07’ W, 45°50’ N; 884 m elevation). Seedlings 

were sown May 1988 in plastic containers filled with 650 cm3

 of potting soil 

and were grown in ambient light supplemented with 24-hour, 40-watt indoor 

fluorescent grow lights. Seedlings were moved outdoors to a shade house the 

first week of June 1988 and were given adequate water and fertilizer. During 

September and October 1988, the seedlings were redistributed in trays to every 

other cell to lessen shading of adjacent plants. The seedlings overwintered outdoors on the ground shaded with lath and mulched with wood chips to avoid 

root damage from freezing winter temperatures. Seedlings were brought into a 

fiberglass greenhouse April 10, 1989, and remained there throughout the measurement period. Variations in light intensity were provided for the seedlings in 

the greenhouse by combinations of shade cloth and cheese cloth to achieve light 

treatments of 24, 14, and 7 percent of full sunlight. A fourth treatment, 34 percent 

of full sunlight, did not include shade cloth; the greenhouse structure reduced 

full sunlight by 66 percent. Maximum ambient light intensities in Moscow, ID, 

in the summer are as high as 2000 μmol m-2 s-1. Light measurements were taken with a ceptometer (Decagon Devices, Pullman, WA) and quantum sensor 

(Omnidata Model ES-240, Omnidata International, Inc., Logan, UT). Minimum 

and maximum air temperatures (fig. 1) in the greenhouse were recorded using 

a hydrothermograph (Oakton Instruments, Vernon Hills, IL). Samples for O2

evolution, chlorophyll fluorescence, and biomass were taken over a two-week 

period once a month from September 1989 through August 1990.
USDA Forest Service RMRS-RP-79. 2010. 3

Growth and Biomass Analysis

Seedlings that were sampled for O2 evolution and chlorophyll fluorescence 

were harvested at the end of each month and were dried at 65 °C for 24 hours. 

The dried seedlings were stored and dried again at 65 °C for at least two hours 

and were then weighed immediately. The dried seedlings were partitioned into 

1990 stem and needles, 1989 stem and needles, 1988 shoot (included 1988 needles and 1988 stems), and roots; each biomass unit was weighed individually. 

Needle area was measured on a sample of 10 needles using a high-resolution 

monochrome TV camera, a PCVISIONplus Framegrabber, and Java video analysis software (Jandel Scientific, Corte Medera, CA). The total needle area for 

1989 was calculated from the sample used for O2 evolution as follows:

1989 sample area (mm2

) * total 1989 needle weight (gr) *1 m2

1989 sample weight (gr) 1,000,000 mm2.

Ten needles from the 1990 growth portion of the seedling were sampled 

to determine needle area and weight, and the total area was calculated using 

the same method for the 1989 needles. Total weight, needle weight ratio, stem 

weight ratio, root weight ratio, and specific leaf area (SLA) were calculated 

for each seedling. Needle, stem, and root weight ratios were the weights of the 

respective parts divided by the total seedling weight. SLA was calculated as:

leaf area (1989 + 1990 needles)

total needle weight (1989 + 1990 needles).

Figure 1. Daily minimum and 

maximum temperatures (°C) for 

August 1989 through August 1990 

recorded by a hydrothermograph 

placed in an open area in the 

greenhouse within the study 

treatments.
4 USDA Forest Service RMRS-RP-79. 2010.

Chlorophyll Fluorescence

A Hansatech fluorescence probe was inserted at a 40° angle into the side of 

the upper jacket of the LD-2 leaf disc electrode system (Hansatech Limited, 

UK). The probe was fitted with a 740-nm interference filter. The excitation light, 

Hansatech LS1, contained an array of 36 LEDs (light emitting diodes) and was 

positioned over the plexiglass window of the upper jacket. Needle samples 

were taken from the seedling’s terminal shoot between 0830 and 1400 and were 

immediately placed on moist filter paper in petri dishes and kept at room temperature. The needles were attached to tape and the ends were cut to fit into the 

LD-2 chamber. Needles were dark-adapted for one hour on moist filter paper in 

a petri dish covered with foil and were then transferred to the LD-2 chamber 

under weak, green light. Chlorophyll fluorescence measurements were taken in 

ambient air at 20 °C. Readings began 20 to 45 msec before the light was turned 

on, were recorded at one-msec intervals, and continued for a total of approximately two sec. Immediately after chlorophyll fluorescence measurements were 

completed, O2 evolution measurements were taken on the same needle sample. 

All needles measured for O2 evolution and chlorophyll fluorescence developed 

in the light treatments during the 1989 growing season.

Oxygen Evolution

Oxygen evolution was measured with a Hansatech leaf disc electrode system (Hansatech Limited, UK) following the procedures of Delieu and Walker 

(1981, 1983). A Polycorder 700 (Omnidata International, Inc., Logan, UT), a 

12-volt power supply, and an electrical circuit were used in this study to regulate 

the excitation voltage at 700 mvolts for the Clark-type oxygen electrode. The 

Polycorder 700 was programmed to record and display data and to regulate the 

lights for chlorophyll fluorescence measurements. Oxygen evolution was measured at 20 ± 1 °C and 5 percent CO2

. Temperature was controlled using water 

that circulated from a controlled-temperature water bath through the Hansatech 

cells. The CO2 source was a sodium carbonate/bicarbonate/borate buffer solution that wet capillary matting at the bottom of the chamber. The system was 

calibrated by injecting 1 cm3 of air into the chamber, recording the change in 

voltage, and correcting for standard temperature and pressure. Oxygen evolution measurements were taken at eight photon flux densities (PFD): 30, 50, 95, 

170, 230, 430, 820, and 1500 μmol m-2 s-1 (from September 1989 to January 

1990, O2

 evolution was measured at 170, 430, 820, and 1500 μmol m-2 s-1). Dark 

respiration was measured with all lights off at the beginning of the sampling 

period. Light was supplied by the Hansatech LS-2 light unit and intensities were 

varied using neutral density filters; photon flux density was measured with a 

quantum sensor. Twelve O2

 evolution readings from each needle sample were 

obtained over a three-minute period at one PFD. Oxygen evolution rate for each 

PFD was calculated as the slope from linear regression of O2

 evolution versus 

time (R2 for the linear regression analyses ranged from 0.83 to 0.99; Koehn and 

McDonald 1991). These values were used to obtain the light response curve 

used in data analysis.
USDA Forest Service RMRS-RP-79. 2010. 5

Data Calculations and Analysis

The light response curve was analyzed using Enzfitter (Leatherbarrow 1987). 

Enzfitter uses Michaelis-Menton enzyme kinetics (a nonlinear regression analysis) to calculate maximum photosynthetic capacity (Pmax) and the light constant, 

Km (the light intensity at one-half Pmax). Quantum yield was calculated as the 

slope from linear regression using gross O2 evolution rates from the first three 

PFD (30, 50, and 95 μmol m-2 s-1) where the O2 evolution rate was linear (dark 

respiration rates were added to O2

 evolution rates for each sample). Quantum 

yield for September through January was estimated using linear regression on 

the values measured at 0 and 170 μmol m-2 s-1. The light compensation point (the 

photon flux density were oxygen evolution equals respiration) was calculated as 

the x-intercept from linear regression on oxygen evolution rate at the first three 

PFD and the dark respiration rate versus PFD for each measurement.

The ratio of variable chlorophyll fluorescence to maximum fluorescence, Fv/

Fm, was derived from the Kautsky curve (Kautsky and Hirsch 1931). Initial chlorophyll fluorescence, Fo

, was the first mvolt reading recorded by the polycorder 

after the light was turned on. Maximum chlorophyll fluorescence, Fm, was the 

maximum mvolt value measured by the polycorder, and variable fluorescence 

was calculated as Fv = Fm - Fo. Using this method to estimate chlorophyll fluorescence, we were able to obtain Fv

/Fm values of 0.80 to 0.83 during periods 

of no winter stress; this data is comparable to data published by Björkman and 

Demmig (1987).

Similar to methods described by Valladares and others (2000), an index of 

plasticity for each treatment was calculated as the difference between the minimum and maximum of the mean values (the mean value for each replication 

in each month was used to calculate the minimum and maximum overall mean 

values) calculated for each light treatment divided by the maximum mean value 

for the specific light treatment. The light compensation point and dark respiration sometimes had zero values, so the plasticity index had little meaning for 

these variables and was not reported here.

The experimental design was a randomized complete block design with one 

species (Douglas-fir), four replications, and four light treatments. A total of 960 

seedlings were included in the study. Each month for 12 months, five seedlings 

were randomly selected from each treatment in each replication. Data were analyzed using Proc Mixed (SAS) where treatment was a fixed effect, replication 

was the random effect, and month was a repeated measure. Because of the significance of the month by treatment interaction, treatments for each month were 

analyzed separately. The Tukey-Kramer adjustment from Proc Mixed was used 

to determine significant differences between treatments with adjusted P≤0.05.

Results

O2 Evolution and Chlorophyll Fluorescence

Each physiological variable we measured graphically displayed a different rhythmic pattern in the adjustment of the Douglas-fir seedlings to seasonal 

environment and light treatment. Significant fluctuations in Pmax occurred in 

December, February, and May when the seedlings were undergoing physiological adjustments to cold hardiness, deep winter dormancy, and active growth, 
6 USDA Forest Service RMRS-RP-79. 2010.

respectively (fig. 2A). In the wintertime (December, January, and February), 

there was a 36 percent reduction (from 12.02 μmol m-2 s-1 to 7.69 μmol m-2 s-1) in 

Pmax for the seedlings grown in the 34 percent light treatment. Fv

/Fm responded 

to treatments and seasonal changes with significant differences primarily in the 

winter months of December, January, February, and March when the sun needles 

experienced photoinhibition (fig. 2B). There was a Fv

/Fm reduction of 20 percent 

(from 0.80 to 0.60) in the winter for the 34 percent treatment seedlings compared to the summer, whereas Fv

/Fm remained near 0.80 for the seedlings in the 

7 percent treatment (fig. 2B). Quantum yield, an indication of the efficiency of 

O2

 evolved per μmol m-2 s-1 of light, generally followed a similar pattern to Pmax 

with the exception of the apparent adjustment in Pmax that occurred between 

October and November (fig. 2C). There was a distinctive pattern of respiration 

for all treatments during the year, with the highest dark respiration rates measured in February for 7 and 14 percent treatments and in March for seedlings in 

the 24 and 34 percent light treatments (fig. 2D). Significant differences occurred 

between treatments in dark respiration in April and September. Overall, respiration rates in the winter were 2.2 to 2.9 times higher than the average annual 

respiration rates for seedlings in each light treatment (fig. 2D). Km, the light 

constant that reflects the affinity of the enzyme complexes of photosynthesis for 

Figure 2. Maximum photosynthetic capacity (Pmax) for oxygen evolution (A), Fv/Fm (B), quantum yield (C), and dark respiration 

(D) for Douglas-fir seedlings from September 1989 through August 1990. The units on the radial arms for Fv

/Fm (B) are 

relative units. The y-values are represented as months with the significant differences between treatments listed adjacent to 

each month (α = 0.05, n = 20).

0

5

10

15

20

25

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

Pmax

(μmol m2 s-1)

L-7%

L-14%

L-24%

L-34%

7 vs 14, 24, 34

7 vs 24, 34

14 vs 34

7 vs 14, 34

24 vs 34

0

0.02

0.04

0.06

0.08

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

Quantum Yield

(μmol m2 s-1 O2 per 

μmol m2 s-1 photons )

L-7%

L-14%

L-24%

L-34%

7 vs 34

14 vs 34

24 vs 34

7 vs 24, 34

14 vs 34

24 vs 34

7 vs 34

7 vs 14, 24, 34

14 vs 34

-0.8

-0.6

-0.4

-0.2

0

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

Dark Respiration

(μmol m2 s-1)

L-7%

L-14%

L-24%

L-34%

7 vs 14

7 vs 34

14 vs 34

0

0.5

1

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

Fv/Fm

L-7%

L-14%

L-24%

L-34%

7 vs 24, 34

14 vs 34

24 vs 34

7 vs 24, 34

14 vs 34

24 vs 34

7 vs 34

7 vs 24, 34

14 vs 34

(A) (B)

(C) (D)
USDA Forest Service RMRS-RP-79. 2010. 7

the substrate light, varied significantly between the 7 and 34 percent light treatments in January, February, May, and September. There was also a difference 

in Km between the 7 and 24 percent treatments in February (fig. 3A). The light 

compensation point, or the light intensity where O2

 evolution equals respiration, was similar for all treatments for the entire year except in February when 

the light compensation point for the seedlings in the 34 percent treatment was 

4.3 times higher than the average for the 12-month period (fig. 3B). Although 

there are differences in January and March, they are insignificant because of the 

variation in the measurements as the physiology of the enzyme complexes adjusted to the changing temperatures and seasons (Laisk and Eichelmann 1989).

Figure 3. Km, the light constant, 

(A) and the light compensation 

point (B) for Douglas-fir seedlings 

from September 1989 through 

August 1990. For Km, significant 

differences between the 7% 

and 34% light treatments occur 

for months Feb-90, May-90, 

and Sep-90, and treatments 7% 

and 24% light are significantly 

different in the month of Feb-90 

(α = 0.05, n = 20). For the light 

compensation point, treatments 

7% and 34% differed significantly 

in Feb-90.

0

5

10

15

20

25

30

Jan90Feb90Mar90Apr90May90Jun90Jul90Aug90Sep89Oct89Nov89Dec89

Light Compensation Point (μmol m2 s-1)

L - 7%

L - 14%

L - 24%

L - 34%

0

100

200

300

400

500

600

700

Jan90Feb90Mar90Apr90May90Jun90Jul90Aug90Sep89Oct89Nov89Dec89

Km, Light Constant

(μmol m2 s-1)

L - 7%

L - 14%

L - 24%

L - 34%

(A)

(B)

Biomass Allocation Patterns

Biomass allocation patterns were generally more concentric circles with less 

crossing-over between treatments than was found in the physiological parameters. Total weight of the seedlings followed the expected pattern: seedlings 

receiving the most light energy (34 percent treatment) had the greatest biomass accumulation and it decreased with decreasing light (fig. 4A). The shade 

seedlings (7 percent light treatment) at the end of the study accumulated an 
8 USDA Forest Service RMRS-RP-79. 2010.

average of 44 percent of the total weight (or had a 56 percent reduction in total 

weight) of the sun seedlings (34 percent light treatment) with only a 27 percent 

reduction in light energy. The seedlings in the 14 percent treatment weighed 81 

percent of the sun seedlings, and the 24 percent treatment seedlings weighed 

96 percent of the sun seedlings. Because the weights of the seedlings in the 

7 percent treatment were reduced at a higher percent than the percent reduction 

in light quantity, our interpretation is that Douglas-fir may be maladapted at the 

lower light intensity. The root/shoot ratio appears to be one of the most plastic 

of the biomass parameters because it fluctuates more with season (especially for 

seedlings in the 34 percent light treatment) than the other biomass parameters 

except specific leaf area (SLA) (fig. 4B). The root weight ratio, stem weight 

ratio, and needle weight ratio parameters changed depending on the light treatment (figs. 4C, 4D, and 5A, respectively). Sun seedlings allocated more to roots 

than stems or needles, whereas shade seedlings allocated more to the supporting 

stem tissues and slightly more to needles.

SLA, the ratio of needle area to needle weight, is a key trait in the maintenance of a species’ growth rate under low light conditions. There are two 

distinctive phases in the annual SLA pattern in seedlings grown in the low light 

Figure 4. Total plant dry weight (A), root/shoot ratio (B), root weight ratio (C), and stem weight ratio (D) for Douglas-fir 

seedlings from September 1989 through August 1990. The y-values are represented as months with the significant 

differences between treatments listed adjacent to each month (n = 20, except for November 1989: α = 0.05, n = 8).

0

10

20

30

40

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

Total Plant Weight 

(gr)

L-7%

L-14%

L-24%

L-34%

7 vs 14, 24, 34

7 vs 14, 24, 34

14 vs 24

7 vs 14, 24, 34

14 vs 24

7 vs 14, 24, 34

14 vs 34

7 vs 14, 24, 34

14 vs 24, 34

7 vs 14, 24, 34

14 vs 24, 34

7 vs 14, 24, 34

7 vs 14, 24, 34

14 vs 34

7 vs 24, 34

7 vs 14, 24. 34

14 vs 24, 34

24 vs 34

α = 

7 vs 14, 24, 34

14 vs 34

7 vs 14, 24, 34

14 vs 24, 34

0

0.5

1

1.5

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

Root/Shoot Ratio

L-7%

L-14%

L-24%

L-34%

7 vs 34

7 vs 24, 34

14 vs 24

7 vs 14, 24, 34

14 vs 34

7 vs 24, 34

14 vs 24, 34

7 vs 34

14 vs 34

7 vs 24, 34

14 vs 34

7 vs 14, 24, 34

7 vs 24, 34

14 vs 34

24 vs 34

7 vs 34

14 vs 34

7 vs 24, 34

7 vs 34

7 vs 24, 34

0

0.2

0.4

0.6

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

Root Weight Ratio

L-7%

L-14%

L-24%

L-34%

7 vs 34

7 vs 24, 34

14 vs 24

7 vs 14, 24, 34

14 vs 34

7 vs 24, 34

14 vs 24, 34

7 vs 34

14 vs 34

7 vs 14, 24, 34

14 vs 34 7 vs 14, 24, 34

7 vs 24, 34

14 vs 34

24 vs 34

7 vs 34

14 vs 34

7 vs 24, 34

7 vs 34

7 vs 24, 34

0

0.1

0.2

0.3

0.4

0.5

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

L-7%

L-14%

L-24%

L-34%

7 vs 24, 34

7 vs 24, 34

7 vs 14, 24, 34

7 vs 14, 24, 34

14 vs 34

7 vs 24, 34

14 vs 24

24 vs 34

7 vs 14, 24, 34

14 vs 24

24 vs 34

7 vs 34

Stem Weight Ratio

(A) (B)

(C) (D)
USDA Forest Service RMRS-RP-79. 2010. 9

Figure 5. Needle weight ratio (A) and specific leaf area (SLA) (B) for Douglas-fir seedlings from 1989 through August 1990. The 

y-values are represented as months with the significant differences between treatments listed adjacent to each month (n = 

20, except for November 1989: α = 0.05, n = 8).

Table 1. The plasticity index is calculated as the maximum value minus the minimum value divided by the maximum value. 

The maximum and minimum values are calculated from the average for each replication for each month (4 replications * 

12 months; n = 48).

Annual plasticity index

Total Stem Needle Root

Light Quantum plant Root/shoot weight weight weight

treatment Pmax yield Fv

/Fm Km weight ratio ratio ratio ratio SLA

7% 0.82 0.74 0.08 0.90 0.74 0.61 0.49 0.61 0.45 0.70

14% 0.82 0.75 0.14 0.94 0.65 0.55 0.33 0.54 0.37 0.71

24% 0.72 0.80 0.25 0.93 0.70 0.64 0.44 0.54 0.43 0.61

34% 0.85 0.85 0.39 0.94 0.65 0.64 0.39 0.50 0.42 0.82

Difference 0.13 0.11 0.31 0.04 0.09 0.09 0.16 0.11 0.08 0.21

treatment in this study. The first phase occurred in the fall with a decrease in 

SLA from September to October; the second phase occurred in the spring with 

an increase in SLA with budburst from March to April (fig. 5B). Generally, 

seedlings in the higher light treatment showed the increase in SLA at budburst 

but did not show the decrease in the fall.

Plasticity Index

The plasticity index values were highest for physiological variables compared to biomass parameters. The exception was Fv

/Fm, which had a very low 

plasticity index (0.08 to 0.39) but had the greatest difference in plasticity between treatments (table 1). Pmax and quantum yield plasticity indices ranged 

from 0.72 to 0.85, Km had the highest index of 0.90 to 0.94, and the biomass 

ratios had indices of 0.61 or below. One interpretation of the plasticity index 

is that some parameters varied with season (Pmax and Km) while other factors 

were influenced more by light treatment (Fv

/Fm).

0

0.1

0.2

0.3

0.4

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

Needle Weight Ratio

L-7%

L-14%

L-24%

L-34%

7 vs 24

7 vs 24

14 vs 24

7 vs 24, 34

14 vs 34

7 vs 34

7 vs 34

7 vs 24

0

5

10

15

20

Jan-90

Feb-90

Mar-90

Apr-90

May-90

Jun-90

Jul-90

Aug-90

Sep-89

Oct-89

Nov-89

Dec-89

Specific Leaf Area 

(m2gr-1) x 10-3

L-7%

L-14%

L-24%

L-34%

7 vs 14,24, 34

7 vs 14, 24, 34

7 vs 14, 24, 34

14 vs 34

7 vs 14, 24, 34

14 vs 24, 34

7 vs 14, 24, 34

14 vs 34

7 vs 14, 24, 34

7 vs 34

7 vs 24

7 vs 14, 24, 34

14 vs 34

(A) (B)
10 USDA Forest Service RMRS-RP-79. 2010.

Discussion

Physiology and Plasticity

The dynamic plasticity we described in this study is represented primarily 

by Douglas-fir’s physiological response to the combination of light acclimation 

and season. For instance, the changes in Pmax, Fv

/Fm, quantum yield, and dark 

respiration (fig. 2) indicate physiological rearrangements that take place with 

seasonal changes in temperature, and these changes vary with light environment. This ability to make different physiological adjustments in response to 

environmental changes over time is important in allowing forest trees to cope 

with variations in light conditions within a forest canopy. Conifers use strategies that involve down-regulating photosynthesis by inactivating photosystem 

II reaction centers and by re-organizing pigment-protein complexes that harvest 

light during the growing season to complexes that quench light energy during autumn and winter (Öquist and Huner 2003; Ensminger and others 2006). 

Recent research by Verhoeven and others (2009) in balsam fir compares the 

changes in light harvesting protein complexes (LPC) between sun and shade 

needles in the winter. The shade needles maintained most of their LPC in the 

winter, whereas sun needles had reduced amounts, indicating that the reorganization of LHC during acclimation to winter stress in balsam fir varied depending 

on the light environment to which the needles were exposed. The consequences 

of these strategies are reflected in the reduction of Fv

/Fm, the maximum yield 

of primary photochemistry (Kitajima and Butler 1975). The sun-grown seedlings in our study demonstrated an adjustment to winter temperatures measured 

by changes in Fv/Fm; however, there was little change in the Fv/Fm amplitude 

with chilling temperatures in shade-grown seedlings (fig. 2B). A reduction in 

quantum yield (the efficiency of O2 produced per photons of light absorbed) 

occurred in February and March in shade seedlings, but it was less than with 

sun needles (0.039 versus 0.023 for the 7 percent and 34 percent treatments, 

respectively; fig. 2C). Quantum yield fully recovered in early summer in sun 

needles to a maximum of 0.057, whereas the shade needles remained at a lower efficiency (fig. 2C). There are physiological costs in the re-organization of 

pigment-protein complexes, and shade seedlings in our study evidently did not 

need to expend the energy in the process of reorganizing complexes (Öquist 

and Huner 2003). Ecologically, Douglas-fir from the Intermountain West of the 

United States ranks intermediate in shade tolerance and is considered both a 

seral and climax species depending on the moisture and temperature regime of 

the forest site (Hermann and Lavender 1990). It has evolved to be very plastic in 

the reorganization of the pigment-protein complexes in response to differences 

in light environment and temperature changes.

The pattern of respiration rates in the Douglas-fir seedlings in this study indicated higher rates of O2

 uptake (higher dark respiration) in February in the shade 

needles and in March in the sun needles. The increase in respiration rates in sun 

needles in March coincides with a greater demand for photosynthates as sun 

needles begin the re-organization and repair of the pigment-protein complexes 

of photosynthesis. Because the capacity for photosynthesis in the growing season was greater than with dark respiration in the winter, spring maintenance 

energy costs were not at an overall deficit for the Douglas-fir seedlings and 
USDA Forest Service RMRS-RP-79. 2010. 11

growth was positive. Photosynthesis and respiration in conifers can continue 

throughout the winter, especially when temperatures rise above 5 °C (Perry 

1971). In Siberian Scots pine, there was photometabolic activity measured by 

variable amounts of reaction center D1 proteins or changes in Fv

/Fm, which were 

linked to the variable climate during the cold season (Ensminger and others 

2004). In this respect, cold hardiness in conifers allows for dynamic physiological processes that balance light absorption, dissipation of excess light energy, 

and light utilization during winter months (Ensminger and others 2004).

An interesting oscillation of Pmax and Km was observed in the fall. In studies 

on oscillations in photosynthesis, Laisk and Eichelmann (1989) observed that 

minimums in photosynthesis that represent a reduction in the stability of the 

photosynthetic apparatus occur, followed by an increase in photosynthesis rates 

because of regulation processes in the photosynthetic apparatus. The adjustments in Pmax from October to November coincided with changes in Km. In 

enzyme systems, Km indicates changes in enzyme affinity for substrates (Mohr 

and Schopfer 1995). In this study, light is the substrate for the enzyme-complex 

reactions of photosynthesis; therefore, the affinity of these enzyme complexes 

must have readjusted between October and November (figs. 2A and 3A). These 

adjustments allowed the seedlings to continue photosynthesis at higher levels longer into the fall season despite a decrease in optimum temperatures for 

photosynthesis.

Biomass Allocation and Physiology

SLA, or the increase in leaf surface area relative to leaf weight that will 

capture light energy under low photon flux densities, is known to enhance lightharvesting efficiency (Björkman 1981) and is a key trait in the maintenance of 

species growth rate under low irradiance levels (Poorter 2001). The Douglas-fir 

seedlings varied SLA with season and treatment, but seedlings in the low light 

treatment typically had a higher SLA (fig. 5B). These results agree with a study 

by Chen (1997) that compared the plasticity of allocation patterns of Douglasfir, Engelmann spruce, and ponderosa pine. Chen (1997) found Douglas-fir had 

the highest SLA plasticity and the highest ratio of aboveground to belowground 

biomass of the three species. Considering that Douglas-fir is a shade-tolerant 

species, especially in the early seedling stage, it seems a beneficial trait for 

Douglas-fir to be able to dynamically change SLA as the tree matures and canopy light conditions change.

The sun seedlings allocated more growth to roots than shoots compared to 

the shade seedlings, but the shade seedlings allocated more growth to the stem 

portion of the plant (fig. 4). This is true in other sun/shade studies of forest 

species such as Pacific silver fir (Abies amabilis), in which shade seedlings allocated more biomass to stems. The increased investment in the trunk of the 

saplings helped support branches that were restricted to the top of a flat crown 

(Mori and others 2008). Mori and others (2008) also found slower foliage turnover in shade saplings, which would compensate for the increased investment 

in support organs or stems. The increased needle weight ratio of shade seedlings 

in our study agrees with Chan and others (2003) on growth allocation of interior 

Douglas-fir in contrasting environments. In their study, Douglas-fir also adapted 

to reduced light by producing more needles per unit branch and stem.
12 USDA Forest Service RMRS-RP-79. 2010.

Conclusion

Our study with Douglas-fir seedlings demonstrates there are different patterns 

in plasticity between physiological variables and biomass allocation patterns 

related a seedling’s light environment. Some of the variables we measured 

changed with environment and the change was constant with time (for example, 

needle weight ratio), while others were more plastic (for example, Pmax, Fv/Fm, 

SLA, and root/shoot ratio) and changed with environment and time. Seasonal 

photosynthesis patterns such as the hardening and dehardening processes and 

their modulation by abiotic factors are becoming important issues in predicting tree responses to anticipated climate change (Saxe and others 2001). The 

concern for forest tree species with respect to climate change is that the natural 

processes of evolution in forest ecosystems will not respond quickly enough 

to the predicted rate of climate change (Rehfeldt and others 2006). Therefore, 

understanding the magnitude and timing of plastic responses to environmental 

changes may be helpful in managing long-lived forest tree species in the future 

(Sultan 2003). Also, if models are to be used in a dynamic sense over long 

periods of time, a description of carbon allocation patterns with changing environments is important (Landsberg and Gower 1997).

This study on individual morphological and physiological development of 

Douglas-fir in different light environments is an initial probe into understanding 

developmental mechanisms that are important to the processes of evolutionary 

change. Important cues of rapid response to abiotic factors identified in this 

study are Pmax, SLA, Fv/Fm, and root/shoot ratio. Shifts in seasonal physiology and biomass allocation patterns will likely become more important to the 

survival of the long-lived Douglas-fir species in response to forecasted rapid 

changes in the environment (Sultan 2004). This study provides forest managers 

with information on the resiliency of Douglas-fir, and results are important in 

future forest management strategies.

Acknowledgments

This research was a part of the senior author’s Ph.D. dissertation at the 

University of Idaho. Financial support for the study was provided by the USDA 

Forest Service, Rocky Mountain Research Station in Moscow, Idaho, and by 

Stillinger Funds from the University of Idaho. Excellent technical support was 

provided by Duane Andrews, Stephen P. Wells, and Ann Kelly.

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