Ecophysiological 

Parameters for Pacific 

Northwest Trees

Amy E. Hessl, Cristina Milesi, Michael A. White, 

David L. Peterson, and Robert E. Keane

United States 

Department of 

Agriculture

Forest Service

Pacific Northwest 

Research Station

General Technical 

Report

PNW-GTR-618

September 2004
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Authors

Amy E. Hessl is an assistant professor, Department of Geology and Geography, 

West Virginia University, Morgantown, WV 26506; Cristina Milesi is a National 

Research Council Fellow at NASA Ames Research Center, Moffett Field, CA 

94035; Michael A. White is an assistant professor, College of Natural Resources, 

Utah State University, Logan, UT 84322; David L. Peterson is a research biologist, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station, Pacific Wildland Fire Sciences Laboratory, 400 N 34th Street, Suite 201, 

Seattle, WA 98103; and Robert E. Keane is a research ecologist, U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences 

Laboratory, P.O. Box 8089, Missoula, MT 59807.
Abstract

Hessl, Amy E.; Milesi, Cristina; White, Michael A.; Peterson, David L.; 

Keane, Robert E. 2004. Ecophysiological parameters for Pacific Northwest 

trees. Gen. Tech. Rep. PNW-GTR-618. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 14 p.

We developed a species- and location-specific database of published ecophysiological variables typically used as input parameters for biogeochemical 

models of coniferous and deciduous forested ecosystems in the Western United 

States. Parameters are based on the requirements of Biome-BGC, a widely used 

biogeochemical model that was originally parameterized for the forests of the 

Pacific Northwest. Several other ecosystem models, including Century 5, Daycent, 

TEM, and PnET, also use some of the inputs described here. This database provides 

a compendium of ecophysiological data for the Pacific Northwest that will provide 

easily accessible information for particular tree species, parameters, and ecosystems for application to simulation modeling. 

Keywords: Ecological modeling, ecophysiology, Pacific Northwest forests.
Summary

Ecosystem models use input parameters including physiology, biochemistry, 

structure, and allocation to describe processes and fluxes such as productivity, 

nitrogen cycling, and water relations. Many ecosystem models useful for investigating these interactions are grounded in ecophysiological relationships originally 

measured in the laboratory or field, typically at scales ranging from the leaf to the 

plot level. These lab- or field-based measurements serve as both parameterization 

and validation data sets for ecosystem models and therefore play a crucial role in 

current and future model development and implementation. 

Ecophysiological parameters for biogeochemical models have been measured 

for Pacific Northwest tree species on a variety of sites with multiple age classes. 

However, locating these parameter values in the literature can be difficult and 

time consuming especially when multiple species or community types are included 

in a model. Furthermore, these values may have been measured by using different 

methods or recorded in different units.

We developed a summary of critical ecophysiological values for biogeochemical model parameters through a search of the scientific literature and 

expert opinion. Parameters are based on the requirements of Biome-BGC, a 

widely used biogeochemical model that was originally parameterized for the 

forests of the Pacific Northwest. Having this information in an easily accessible 

database will make future modeling efforts with Biome-BGC and other models 

more efficient and consistent.
1

Ecophysiological Parameters for Pacific Northwest Trees

Introduction

Recent efforts to model rapid changes in global climate and atmospheric biochemistry require a detailed understanding of how biochemistry, biophysics, and plant 

responses interact across local, regional, and global scales (Waring 1993). Although 

some of these interactions can be measured at local scales, empirical estimates of 

these processes at regional and global scales are not yet tenable. Simulation modeling provides an essential tool for exploring these complex interactions at larger 

spatial scales (Running 1994). Ecosystem models use input parameters including 

physiology, biochemistry, structure, and allocation to describe processes and fluxes 

such as productivity, nitrogen cycling, and water relations. Many ecosystem models 

useful for investigating these interactions are grounded in ecophysiological relationships originally measured in the laboratory or field, typically at scales ranging from 

the leaf to the plot level. These lab- or field-based measurements serve as both 

parameterization and validation data sets for ecosystem models and therefore play 

a crucial role in current and future model development and implementation. 

Ecophysiological relationships of forest ecosystems, especially in the Pacific 

Northwest of North America, have been studied extensively. Many critical ecophysiological parameters for biogeochemical models have been measured for individual 

species on a variety of sites with various age classes present. However, locating 

these parameter values in the literature can be difficult and time consuming, especially when multiple species or community types are included in a model run 

(Running 1994). Although data exist for many species, these values are difficult to 

locate and standardize for several reasons: (1) data were printed in older publications 

(pre-1980) that are not catalogued in online databases; (2) data were published in 

obscure journals or gray literature; (3) data collection methods and units differ 

substantially for some parameters, making standardization difficult. Despite these 

difficulties, it is critical that important parameter values and all references for these 

parameter values be provided for any model-based study (Aber 1997, White et al. 

2000).

In biome-based ecosystem models, commonly measured ecophysiological variables taken from a large number of observations of many communities and locations 

are typically averaged across broad vegetation classes (e.g., evergreen needleleaf, 

broadleaf deciduous, etc.) to generate default parameterization values (Neilson 

1995, White et al. 2000). These default values may include data collected from 

low-elevation to subalpine locations, mesic to xeric sites, and recently disturbed 

to old-growth forests. Thus, the average or default values include a high degree of 

variability even within these broad vegetation types. New parameterization data 

sets may be required to apply existing models to new locations, to parameterize 
2

General Technical Report PNW-GTR-618

new models, or to parameterize existing models more specifically to account for 

changes in the physical environment or species.

We developed a species- and location-specific database of published ecophysiological variables typically used as input parameters for biogeochemical 

models of coniferous and deciduous forested ecosystems in the Western United 

States. We selected parameters based on the requirements of Biome-BGC (White 

et al. 2000), a widely used biogeochemical model that was originally parameterized 

for the forests of the Pacific Northwest. Biome-BGC is a daily time step, spatially 

independent model that simulates the development of soil and plant carbon and 

nitrogen pools by using 43 parameters (table 1). Although the input parameters for 

this database were investigated based on the structure of Biome-BGC, several other 

ecosystem models, including Century 5, Daycent, TEM, VEMAP (1995), and PnET 

(Aber et al. 1996), use some of the inputs described here.

The purpose of this database is to provide a compendium of ecophysiological 

data for the Pacific Northwest that will provide easily accessible information for 

particular tree species, parameters, and ecosystems for application to simulation 

modeling. 

Methods

We used the databases Biosis and Agricola to conduct an extensive literature 

search for published ecophysiological data. We used the following keywords: 

Pacific Northwest, allocation, productivity, and ecophysiology. For species with 

few data, we used the common name and scientific name as keywords. Similarly, 

for parameters with few data, we searched by using keywords associated with that 

parameter specifically. We then used a “snowball” method by exploring the reference lists in sources with extensive literature citations. Finally we requested input 

from the University of Washington1 2 and the University of Montana3

 for additional 

references. Occasionally, when important values were unavailable for a species in 

the Pacific Northwest, we included values for similar species from other regions and 

sometimes other continents.

1

Hinckley, T. 2001, Personal communication. Professor, tree physiology, Center for 

Urban Horticulture, College of Forest Resources, University of Washington, Box 354115, 

Seattle, WA 98195-4115.

2 Edmonds, R. 2001. Personal communication. Professor, forest soil microbiology and 

forest pathology, College of Forest Resources, University of Washington, Box 352100, 

Seattle, WA 98195-2100.

3

Numerical Terradynamics Simulation Group. The University of Montana, College of 

Forestry and Conservation, Missoula, MT 59812.
3

Ecophysiological Parameters for Pacific Northwest Trees

Table 1—Input parameters and associated units required to run Biome-BGCa

Parameter description Units

Woody or nonwoody Flag

Evergreen or deciduous Flag

C3 or C4 grass Flag

Model-defined phenology or user-specified phenology Flag

Yearday to start new growth (user-specified phenology) Yrday

Yearday to end new growth (user-specified phenology) Yrday

Transfer growth period as a fraction of growing season Proportion

Litterfall as fraction of growing season Proportion

Annual leaf and fine root turnover fraction Proportion/yr

Annual live wood turnover fraction Proportion/yr

Annual whole-plant mortality fraction Proportion/yr

Annual fire mortality fraction Proportion/yr

Allocation new fine root C:new leaf C Ratio

Allocation new stem C:new leaf C Ratio

Allocation new live wood C:new total wood C Ratio

Allocation new root C:new stem C Ratio

Allocation current growth Proportion

C:N of leaves kg C/kg N

C:N of leaf litter, after translocation kg C/kg N

C:N of fine roots kg C/kg N

C:N of live wood kg C/kg N

C:N of dead wood kg C/kg N

Leaf litter labile DIM

Leaf litter cellulose DIM

Leaf litter lignin DIM

Fine root labile DIM

Fine root cellulose DIM

Fine root lignin DIM

Dead wood cellulose DIM

Dead lignin DIM

Canopy water interception coefficient 1/LAId

Canopy light extinction coefficient DIM

All-sided-area to projected-leaf-area ratio DIM

Canopy average specific leaf area (projected area basis) m

2

/kg C

Shaded SLA:sunlit SLA DIM

Fraction of leaf N in rubisco DIM

Maximum stomatal conductance (projected area basis) m/s

Cuticular conductance (projected area basis) m/s

Boundary later conductance (projected area basis) m/s

Leaf water potential:start of conductance reduction MPa

Leaf water potential:complete conductance reduction MPa

Vapor pressure deficit:start of conductance reduction -Pa

Vapor pressure deficit:complete conductance reduction -Pa

a Dimensionless values are denoted “DIM” .
4

General Technical Report PNW-GTR-618

If necessary, ecophysiological variables were converted to the International 

System of Units, and if possible, to the units used by Biome-BGC. We also recorded 

the species, location, elevation, and age (or time since disturbance) of the communities or populations studied whenever this information was available. 

Parameter Definitions

One of the difficulties in developing a parameterization database that covers 

multiple species and vegetation types is that different methods may have been 

used to collect data relating to the same variable or parameter. Below we describe 

the typical definitions and methods for collecting measurements of each parameter. 

Note, however, that methods were not always consistent, and the individual studies 

should be referenced if methods are a concern.

Biomass and Productivity

Biomass is the mass of vegetation per unit area and is reported here in kg · m-2. 

Methods for determining aboveground biomass usually involve “clipping” all 

aboveground matter, drying it, and weighing it. Where belowground biomass is 

measured, roots are typically excavated, dried, and weighed. 

Annual net primary productivity is the rate of carbon sequestered into dry 

matter per unit area over 1 year and is expressed here in kg · m-2 · yr

-1. Aboveground 

net primary productivity in forested ecosystems is measured in various ways, including allometric equations in which annual ring widths are used as indicators

of annual volume increment or repeated measurements of stem diameter (Waring 

and Running 1998).

Allocation Parameters

Allocation relationships between different plant pools (carbon, nitrogen) control 

how carbon is allocated throughout the ecosystem or biome. Data for these allocation ratios were typically collected separately, for example, as fine root, coarse root, 

stem, and leaf. Note that live wood and dead wood have a particular connotation in 

Biome-BGC. Live wood includes the actively respiring woody tissue, i.e., the lateral 

sheathing meristem of phloem tissue, plus any ray parenchyma extending radially 

into the xylem tissue. Dead wood consists of all the other woody material, including 

the heartwood, xylem, and bark. 

Carbon-to-Nitrogen Ratios

The mass ratio of carbon to nitrogen (C:N) in different plant components such 

as leaves, litter, roots, and live or dead wood is usually reported as the concentration of carbon divided by the concentration of nitrogen. If only the concentration of 
5

Ecophysiological Parameters for Pacific Northwest Trees

nitrogen is reported, the concentration of carbon is calculated from the dry weight 

of the plant component assuming that this is 50 percent carbon.

Decomposition Constant

The decomposition constant is based on an exponential pattern of loss and is calculated according to Olson (1963) based on litter bag loss, with the formula:

mt/mo = e

-kt ,

where mt

 is the weight of the litter at time t, mo is the initial weight of the litter, k is 

the decomposition constant and t is time. Solving for k, the decomposition constant 

is obtained as: 

k = -ln(mt/mo)/t .

Efficiencies

Nutrient use efficiency is the proportion of organic matter produced for each unit of 

nutrient taken up. For long-lived plants, it is also a measure of how long a nutrient is 

retained in the plant to be used for carbon fixation. 

Nitrogen and phosphorous use efficiency values are calculated here as the ratio 

of aboveground annual productivity to unit of nutrient uptake and are unitless. Two 

values are reported as dry mass · N litterfall-1. Production use efficiency is defined as 

the proportion of aboveground net primary productivity relative to standing biomass.

Leaf Area Indices—Morphological Parameters

Leaf area index (LAI) is defined as the total leaf area on one surface of the leaf over 

a unit ground area and is expressed as m2 · m

-2. Specific leaf area (SLA) defines leaf 

area per unit of leaf carbon mass (m2 · kg-1). Projected leaf area includes the leaf 

area projected horizontally on the ground surface, whereas all-sided leaf area includes the total surface area of leaves. The all-sided-area to projected-leaf-area ratio 

can be used to convert projected leaf area to all-sided leaf area, which is important 

for some physiological approximations such as canopy water interception (Barclay 

1998). The LAI can be measured with a wide range of techniques including radiation transmittance (Chen et al. 1997), sapwood allometrics (Sampson and Smith 

1993), and foliage biomass. 

The canopy light extinction coefficient is the Beer’s law extinction coefficient, 

which controls the attenuation of radiation from the top of the canopy to the ground 

owing to leaf absorption and reflection. Canopy light extinction coefficients are typically estimated with a radiation measuring device such as a sunfleck ceptometer.
6

General Technical Report PNW-GTR-618

Nitrogen Distribution

Nitrogen distribution among the different biomass components (aboveground, 

belowground, litter, and forest floor) is determined by chemical analysis of samples 

by using standard procedures and is expressed as kg N · m-2. Soil nitrogen is usually 

determined separately for each horizon, and the total value is reported here as kg 

N · m-2. 

Nitrogen Input

Nitrogen input represents an estimate of atmospheric nitrogen deposition, usually 

through precipitation or dust. The value is reported in kg N · m-2.

Carbon Proportions: Labile, Cellulose, Lignin

Typically, lab techniques are used to determine proportions of carbon allocated to 

each class (labile, cellulose, or lignin) (White et al. 2000). Labile pools include the 

easily decomposed fractions, such as carbohydrates that are soluble in hot water 

and alcohol. Cellulose is the fraction soluble in an acid bath, after extraction of the 

labile fraction. The remainder is calculated as the lignin pool. The labile, cellulose, 

and lignin fractions must sum to 1.

Leaf and Fine Root Turnover

Turnover refers to the proportion of the carbon pool replaced each year and is the 

inverse of the mean residence time (White et al. 2000). For deciduous species, leaf 

and fine root turnover is set to 1. For evergreen trees, turnover is calculated as the 

inverse of leaf longevity.

We did not include data for all the ecophysiological variables needed to parameterize Biome-BGC in the database either because they do not change significantly 

among biomes or because data are extremely sparse, and we were not able to find 

them for tree species of the Pacific Northwest. For these variables, refer to White 

et al. (2000).

Results

The literature search yielded ecophysiological information for 18 evergreen 

needleleaf, 2 deciduous broadleaf, and 2 deciduous needleleaf tree taxa common 

in the Pacific Northwest (table 2). Species-specific data on 37 variables (table 3), 

including 587 values critical for model parameterization, are recorded in a Microsoft Access®4

 relational database. 

4

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. 
7

Ecophysiological Parameters for Pacific Northwest Trees

Table 2—Species code, scientific name, and common name of tree species 

included in the databasea

Species code Scientific name Common name

ABAM Abies amabilis Dougl. ex Forbes Pacific silver fir

ABCO Abies concolor (Gord. & Glend.) Lindl. ex Hildebr. White fir

ABGR Abies grandis (Dougl. ex D. Don) Lindl. Grand fir

ABLA Abies lasiocarpa (Hook.) Nutt. Subalpine fir

ABPR Abies procera Rehd. Noble fir

ALRU Alnus rubra Bong. Red alder

JUOC Juniperus occidentalis Hook. Western juniper

LALY Larix lyallii Parl. Alpine larch

LAOC Larix occidentalis Nutt. Western larch

LIDE Libocedrus decurrens Torr. Incense-cedar

PIEN Picea engelmannii Parry ex Engelm. Engelmann spruce

PISI Picea sitchensis (Bong.) Carr. Sitka spruce

PIAL Pinus albicaulus Engelm. Whitebark pine

PICO Pinus contorta Dougl. ex Loud. Lodgepole pine

PILA Pinus lambertiana Dougl. Sugar pine

PIPO Pinus ponderosa Dougl. ex Loud. Ponderosa pine

POTR Populus tremuloides Michx. Quaking aspen

PSME Pseudotsuga menziesii (Mirb.) Franco Douglas-fir

TABR Taxus brevifolia Nutt. Pacific yew

THPL Thuja plicata Donn ex D. Don Western redcedar

TSHE Tsuga heterophylla (Raf.) Sarg. Western hemlock

TSME Tsuga mertensiana (Bong.) Carr. Mountain hemlock

a

Species authorities not included in the database.

With the exception of leaf area parameters (evergreen needleleaf) and C:N ratio 

of dead wood (deciduous broadleaf), all the mean parameter values for each leaf 

type are within one standard deviation of those reported elsewhere (White et al. 

2000) (tables 4, 5, 6, 7, and 8). For example, we report a mean of 0.26 (SD = 0.10, 

n = 50) for leaf and fine root turnover for evergreen needleleaf trees of the Pacific 

Northwest. This compares with a mean of 0.26 (SD 0.15, n = 129) for evergreen 

needleleaf species from other temperate regions (White et al. 2000). 

Mean values for leaf characteristics described here highlight critical differences 

between the evergreen needleleaf forests of the Pacific Northwest and those studied 

elsewhere. For example, mean SLA for evergreen needleleaf trees of the Pacific 

Northwest is much higher and more variable (mean = 20.66, SD = 15.01, n = 24) 

(table 8) than reported for evergreen needleleaf trees from other regions (White 

et al. 2000 report mean = 8.2, SD = 3.6, n = 39). Species-level means indicate that 

several species (especially Tsuga heterophylla, Abies amabilis, Pseudotsuga menziesii, and A. procera) occurring in low- to mid-elevation west-side environments 
8

General Technical Report PNW-GTR-618

Table 3—Ecophysiological variables and their associated units included 

in the databasea

Parameter Units

Biomass—aboveground kg·m

-2

Biomass—belowground kg·m

-2

Biomass—total kg·m

-2

Productivity—aboveground NPP kg·m

-2·yr-1

Productivity—belowground NPP kg·m

-2·yr-1

Productivity—total NPP kg·m

-2·yr-1

Carbon allocation—coarse root C:stem C DIM

Carbon allocation—new fine root C:new leaf C DIM

Carbon allocation—new live wood C:new total wood C DIM

Carbon allocation—new stem C:new leaf C DIM

C:N ratio—dead wood kg C/kg N

C:N ratio—fine root kg C/kg N

C:N ratio—leaf kg C/kg N

C:N ratio—litter kg C/kg N

Decomposition—annual constant DIM

Efficiency—nitrogen—use kg ANPP/kg N/yr

b

Efficiency—phosphorous—use kg drymass/kg litterfall N

Leaf area—all sided:projected m·m

-2

Leaf area index m·m

-2

Leaf area—light extinction coefficient DIM

Leaf area—specific m

2

·kg-1 C

Nitrogen—aboveground kg·m-2

Nitrogen—belowground kg·m-2

Nitrogen—forest floor kg·m-2 c

Nitrogen—input kg·m-2·yr-1

Nitrogen—litter kg·m-2·yr-1

Nitrogen—soil kg·m-2

Proportion—dead wood—cellulose DIM

Proportion—dead wood—labile DIM

Proportion—dead wood—lignin DIM

Proportion—fine root—cellulose DIM

Proportion—fine root—labile DIM

Proportion—fine root—lignin DIM

Proportion—litter—cellulose DIM

Proportion—litter—labile DIM

Proportion—litter—lignin DIM

Proportion—annual turnover—leaf and root 1/yr

a

Dimensionless values are denoted “DIM”.

b

Two values are recorded as drymass/litter fall N.

c

Three values are recorded as kg·m-2 ·yr-1.
9

Ecophysiological Parameters for Pacific Northwest Trees

Table 4—Biomass of forest stands in this database

Leaf type Biomass Mean Minimum Maximum SD N

Kilograms per square meter

Deciduous broadleaf Total 21.00 21.00 21.00 NA 1

Aboveground 14.73 7.80 24.00 6.17 6

Belowground 3.46 1.08 5.80 2.36 3

Evergreen needleleaf Total 66.89 7.60 117.00 34.92 12

Aboveground 45.67 2.00 149.20 35.27 46

Belowground 22.58 3.30 80.60 27.91 17

NA = not applicable.

Table 5—Net primary productivity (NPP) of forest stands in this database

Leaf type Productivity Mean Minimum Maximum SD N

Kilograms per square meter per year

Deciduous broadleaf Total NPP 1.71 1.46 1.95 .35 2

Aboveground NPP 1.35 1.03 1.70 .34 3

Belowground NPP .17 .12 .24 .06 3

Deciduous needleleaf Aboveground NPP .10 .10 .10 NA 1

Evergreen needleleaf Total NPP 1.58 .79 3.62 .96 7

Aboveground NPP 1.47 .12 10.5 2.12 44

Belowground NPP .50 .20 1.22 .28 20

NA = Not applicable.

Table 6—Carbon allocation ratios of forest stands in this database

Leaf type Carbon allocation Mean Minimum Maximum SD N

Deciduous broadleaf Coarse root C:stem C .21 .15 .27 .09 2

New live wood C:new total wood C .10 .10 .10 NA 1

New stem C:new leaf C 2.40 1.69 3.07 .56 6

Deciduous needleleaf New live wood C:new total wood C .10 .10 .10 NA 1

Evergreen needleleaf Coarse root C:stem C .27 .19 .44 .07 10

New fine root C:new leaf C 2.89 .67 10.81 2.71 14

New live wood C:new total wood C .07 .06 .07 .01 2

New stem C:new leaf C 1.92 .20 3.32 .86 36

NA = not applicable.

contribute to this high mean (table 9). Based on recent work, differences in SLA 

may be critical for modeling productivity (Reich et al. 1999, White et al. 2000). As 

a result, these input parameters should be defined with close attention to variability 

within and among species.

We found data about mean C:N for dead wood for only two deciduous broadleaf species of Pacific Northwest trees, i.e., Populus tremuloides and Alnus rubra. 
10

General Technical Report PNW-GTR-618

Table 8—Leaf area parameters of forest stands in this database

Leaf type Leaf area Mean Minimum Maximum SD N

Deciduous broadleaf Leaf area index 4.45 2.50 6.40 2.76 2

Specific leaf area (m

2

· kg-1) 35.40 24.20 46.60 15.84 2

Deciduous needleleaf Specific leaf area (m

2

·kg-1) 21.04 15.20 26.36 4.81 5

Evergreen needleleaf All-sided: projected leaf area 2.42 2.32 2.57 .08 7

Leaf area index 14.46 2.00 46.43 12.35 14

Specific leaf area (m

2

· kg-1) 20.66 2.08 42.26 15.01 24

NA = not applicable.

Table 9—Evergreen needleleaf species with reported mean specific leaf areas 

>25.0

Species Mean Minimum Maximum SD N

Square meters of leaf per kilogram carbon

Tsuga heterophylla 35.88 21.00 42.26 8.12 6

Abies amabilis 30.40 30.40 30.40 NA 1

Pseudotsuga menziesii 26.67 8.94 38.88 13.83 6

Abies procera 26.38 26.38 26.38 NA 1

NA = not applicable.

Table 7—Plant component ratio of forest stands in this database

Leaf type Plant component Mean Minimum Maximum SD N

Deciduous broadleaf Dead wood 149.35 107.30 196.00 41.01 4

Fine root 56.80 56.80 56.80 NA 1

Leaf 22.56 18.40 29.40 3.80 7

Litter 49.65 23.80 80.00 22.71 7

Deciduous needleleaf Dead wood 270.00 270.00 270.00 NA 1

Leaf 29.12 24.40 35.00 4.50 5

Evergreen needleleaf Dead wood 854.71 139.00 1400.00 335.50 14

Fine root 67.37 48.00 90.90 17.34 7

Leaf 46.06 35.70 75.80 9.21 16

Litter 85.09 49.20 135.00 23.23 18

NA = not applicable.

Their dead wood C:N ratio was much lower than for deciduous trees in other regions, indicating a larger availability of nitrogen for microbial decomposition. These 

higher nitrogen values suggest that dead wood will decompose quickly and will 

provide more nitrogen to be cycled in the system relative to other deciduous forests. 

Although mean ecophysiological parameters for Pacific Northwest trees may 

be similar to the mean values for trees of the same leaf type from other regions, 
11

Ecophysiological Parameters for Pacific Northwest Trees

there is great variability among species and location. For example, total biomass 

(aboveground and belowground) for evergreen needleleaf forests ranged between 

7.6 kg·m

-2 for Abies amabilis at Findley Lake, Washington (Vogt et al. 1982), and 

117 kg·m

-2 for Pseudotsuga menziesii at the H.J. Andrews Forest, Oregon (Grier 

and Logan 1977). Similarly, aboveground annual productivity ranges between 

0.10 kg·m

-2·yr-1 for Larix occidentalis on Chumstick Mountain, Washington

(Gower et al. 1989), and 10.5 kg·m

-2·yr-1 for P. menziesii in the interior Coast 

Range of Oregon (Gholz 1982). For P. menziesii alone, aboveground productivity 

ranges between 0.51 kg·m-2·yr

-1 for the Thompson Research Center, Washington 

(Turner and Long 1975), and the aforementioned 10.5 kg·m-2·yr

-1 in the interior 

Coast Range, Oregon. This extreme variation suggests careful application of mean 

parameter values in topographically and biologically diverse regions like the Pacific 

Northwest.

Our results also point to a number of serious deficiencies in empirical data. 

We did not locate any references describing leaf nitrogen in rubisco, the key enzyme leading to photosynthesis that is one of the more sensitive parameters used 

in Biome-BGC (White et al. 2000). In addition, few data exist on high-elevation 

species such as Larix lyallii (three values), Pinus albicaulis (two values), and 

Tsuga mertensiana (eight values).

Conclusions

Our results suggest that morphological parameters critical for model input are 

available for most major tree species in the Pacific Northwest and should be carefully defined in simulation models. In addition, the variability of parameters within 

and between species indicates a need to consider location and species composition 

in model runs when possible.

We hope these data will motivate additional modeling, empirical data collection, laboratory analysis, and database development in the Pacific Northwest 

and in other regions. Although some Pacific Northwest species (e.g., Pseudotsuga 

menziesii) and some ecophysiological parameters (e.g., aboveground biomass) have 

been studied extensively, others have only a few references for a few species (e.g., 

decomposition constant). The database of parameters has deficiencies in the following areas: percentage of leaf nitrogen in rubisco (no data listed here), decomposition 

constants for most species, nitrogen use efficiencies for most species, and data on 

most parameters for subalpine species. We hope that additional data collection in 

these areas will supplement existing knowledge. It is the careful collection of field 

and laboratory data that allows scientists to parameterize and validate ecosystem 

models at all scales, from local ecosystem models to global biogeochemical models.
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General Technical Report PNW-GTR-618

Acknowledgments

We thank Paige Eagle for developing the relational database included in a CD-ROM 

attached to this report.

English Equivalents

When you know: Multiply by: To find:

Kilograms per square meter (kg· m

-2) 0.20 Pounds per square foot

Square meters (m2) 10.76 Square feet

Square meters per kilogram (m2 · kg-1) 4.95 Square feet per pound

Milligrams per gram (mg· g

-1) 1,000 Parts per million

Mega Pascal (Mpa) 20,900 Pounds per square foot

Pascal (Pa) .0209 Pounds per square foot

Meters per second (m· s

-1) 3.28 Feet per second

Literature Cited

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Society of America. 78: 232–233.

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to the canopy: a generalized model of forest photosynthesis compared with 

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Gholz, H. 1982. Environmental limits on aboveground net primary production, 

leaf area, and biomass in vegetation zones of the Pacific Northwest. Ecology. 

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by Larix occidentalis and Pinus contorta in the Washington Cascades, USA. 

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Grier, C.; Logan, R. 1977. Old-growth Pseudotsuga menziesii communities of 

a western Oregon watershed: biomass distribution and production budgets. 

Ecological Monographs. 47: 373–400.
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Ecophysiological Parameters for Pacific Northwest Trees

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net primary production and nutrient cycling in Abies amabilis ecosystems in 

western Washington. Ecology. 63: 370–380.

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Waring, R.H.; Running, S.W. 1998. Forest ecosystems: analysis at multiple 

scales. San Diego, CA: Academic Press. 370 p.

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Parameterization and sensitivity analysis of the Biome-BGC terrestrial 

ecosystem model: net primary productivity controls. Earth Interactions. 

4: 1–85.
14

General Technical Report PNW-GTR-618

Appendix A: Instructions

Microsoft Access® Users

One search form, built into the database, allows users to search for all data by 

species, parameter, and location, then allows users to view and print the parameter 

values and citations to a file. This file can be printed as text or downloaded to other 

applications (Microsoft Excel®). The data also can be accessed as a series of text 

files that can be used with a variety of applications. Advanced Microsoft Access®

users may wish to forgo the search form and create their own queries on the database by using Microsoft Access® query tools.

Text or Other Database Users

Seven comma-delimited text files also have been included on the CD-ROM 

attached to this publication to allow researchers who are using software other 

than Microsoft Access® to use the database. These files are entitled:

• Parameters.txt—includes a list of parameters and definitions

• Parameter-Location.txt—links parameters with locations

• Parameter-Species.txt—links parameters with species

• ParameterValues.txt—includes parameter values, species, site, and reference information (this is the critical table)

• References.txt—includes the list of references for the parameter values

• Species.txt—defines the species, scientific name, four-letter acronym, and 

common name for all species included 

• Location.txt—defines the abbreviation for place names used in 

ParameterValues
Pacific Northwest Research Station

Web site http://www.fs.fed.us/pnw

Telephone (503) 808-2592

Publication requests (503) 808-2138

FAX (503) 808-2130

E-mail pnw_pnwpubs@fs.fed.us

Mailing address Publications Distribution

 Pacific Northwest Research Station

 P.O. Box 3890

 Portland, OR 97208-3890
U.S. Department of Agriculture

Pacific Northwest Research Station

333 SW First Avenue

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Official Business

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