Ecophysiological Parameterization

To Database for Pacific Northwest Trees

Introduction  Methods  Parameter Definitions  Results  Conclusions  Authors  Query Data  Download Zip Data File

Introduction
Data and text from: Hessl, Amy E.; Milesi, Cristina; Keane, Robert; Peterson, David L. Ecophysiological Parameterization Database for Pacific Northwest Trees or Forests. General Technical Report, GTR-PNW, Portland, OR. US Department of Agriculture, Forest Service, Pacific Northwest Research Station. In press.

Recent predictions of 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). Though 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 a variety of 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). While 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 methodologies 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 level 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 (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 communities. Thus, the average or default values include a high degree of variability even within these broad vegetation types. New parameterization datasets may be required to apply existing models to new locations, to parameterize 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, 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 using 34 parameters (Table 1). Though the input parameters for this database were investigated based on the structure of Biome-BGC, several other ecosystem models, including Century 5, Daycent, TEMS, (VEMAP 1995) and PnET (Aber et al. 1992) 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 conducted an extensive literature search to find published ecophysiological data using the databases Biosis and Agricola. We used the following keywords: Pacific Northwest, allocation, productivity, and ecophysiology. For species with little data, we used the common name and scientific name as keywords. Similarly, for parameters with little data, we searched 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 Tom Hinckley (College of Forest Resources, University of Washington, Seattle), Robert Edmonds (College of Forest Resources, University of Washington, Seattle), and the Numerical Terradynamics Simulation Group (School of Forestry, University of Montana, Missoula) 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.

If necessary, ecophysiological variables were converted to SI units and if possible, to the units used by Biome-BGC. We also recorded the species, location, elevation, and the age (or time since disturbance) of the communities or populations studied whenever this information was available.

Following initial data entry, two authors (A.H. and C.M.) quality controlled the data by returning to the original publications and checking the values, units and any necessary conversions.

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 methodologies for collecting measurements of each parameter. It should be noted however, that methodologies were not always consistent and if methodologies are a concern, the individual studies should be referenced.

Biomass and Productivity
Biomass is the mass of vegetation per unit area and is reported here in kg m-2. Methods for determining above-ground biomass usually involve "clipping" all above ground matter, drying it, and weighing it. Where below ground 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 one year and is expressed here in kg m-2 yr-1. Aboveground net primary productivity in forested ecosystems is measured in a variety of ways, including allometric equations where 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 NPP. It is important to note that livewood and deadwood have a particular connotation in Biome-BGC and are defined as follows. Livewood includes the actively respiring woody tissue, that is, the lateral sheathing meristem of phloem tissue, plus any ray parenchyma extending radially into the xylem tissue. Deadwood consists of all the other woody material, including the heartwood, the xylem, and the bark.

Carbon to Nitrogen Ratios
The ratio of carbon to nitrogen (C:N) in different plant components such as leaves, litter, roots and live or dead wood is usually measured as milligrams of nitrogen per gram of dry weight or percent carbon. It is assumed that total dry weight is 50% carbon.

Decomposition Constant
The decomposition constant is based on an exponential pattern of loss and is calculated accordingly to Olson (1963) based on litter bag loss, with the formula:
ml(t) c1-kt
where ml is the fractional weight loss over time t, c1 is the initial weight of needles, and k is the decomposition coefficient. When k is set to unity to correspond to the initial weight, then k -(ln m1)/t.

Efficiencies
Nutrient use efficiency is the ratio of organic matter produced to 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 here calculated as the ratio of aboveground annual productivity to unit of nutrient uptake and are reported in m-2 yr-1. Two values are reported as dry mass N-1litterfall. Production use efficiency is defined as the ratio of ANPP to standing biomass.

Leaf Area Indices-Morphological Parameters
Leaf area index (LAI) is defined as the total leaf area on one surface 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 while all-sided leaf area includes the total surface area of leaves. The all-sided 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). 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 due to leaf absorption and reflection. Canopy light extinction coefficients are typically estimated with a radiation measuring device such as a sunfleck ceptometer.

Nitrogen Distribution
Nitrogen distribution among the different biomass components (aboveground, belowground, litter and forest floor) is determined by chemical analysis of samples 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 here reported 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.

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 1.0.

Leaf and Fine Root Turnover
Turnover refers to the percent 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 our 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 the 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 21 variables (Table 3) including 587 values, critical for model parameterization, are recorded in a Microsoft Access© relational database.

With the exception of leaf area parameters (evergreen needle leaf) and carbon to nitrogen ratio of dead wood (deciduous broadleaf), all of 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 and 7). For example, we report a mean of 0.26 (st. dev. 0.10, n 50) for leaf and fine root turnover for evergreen needle leaf trees of the Pacific Northwest. This compares with a mean of 0.26 (st. dev. 0.15, n 129) for evergreen needleleaf species from other temperate regions (White et al. 2000).

Mean values for morphological parameters described here highlight critical differences between the evergreen needle leaf forests of the Pacific Northwest and those studied elsewhere. For example, mean specific leaf area for evergreen needle leaf trees of the Pacific Northwest is much higher and more variable (mean 20.66, st. dev. 15.01, n 24)(Table 8) than reported for evergreen needle leaf trees from other regions (White et al. 2000 reports mean = 8.2, st. dev. 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 contribute to this high mean (Table 9). Based on recent work, differences in specific leaf area 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 between species.

Mean carbon to nitrogen ratio of dead wood in the deciduous broad leaf species was much lower than for other regions. We found data for only two species, Populus tremuloides and Alnus rubra. As a nitrogen fixing species, we expect A. rubra wood to contain high levels of nitrogen, dictating a small ratio. However, the C:N ratio for P. tremuloides is also low.

Though mean ecophysiological parameters for Pacific Northwest trees may be similar to the mean values for trees of the same leaf type from other regions, there is great variability between species and location. For example total biomass (above- and below-ground) for evergreen needle leaf forests ranged between 7.6 kg m2 for Abies amabilis at Findley Lake, Washington (Vogt 1982) and 117 kg m-2 for Pseudotsuga menziesii at HJ Andrews, Oregon (Grier and Logan 1977). Similarly, aboveground annual productivityvaries 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 Pseudotsuga menziesii in the interior coast range of Oregon (Gholz 1982). For Pseudotsuga menziesii alone, aboveground productivity varies 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, one of the more sensitive parameters used in Biome-BGC (White et al. 2000). In addition, little 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, may be unique in the Pacific Northwest and should be carefully defined in simulation models. In addition, the variability within and between species indicates a need to consider place and species composition in model runs, when possible.

We hope these readily available data will motivate additional modeling, field collection, laboratory analysis, and database development in the Pacific Northwest and in other regions. While some Pacific Northwest species and some ecophysiological parameters have been studied extensively (above ground biomass and Pseudotsuga menziesii, respectively) others have only a few references for a few species (i.e. decomposition constant). The results of this study, (the database itself), point to deficiencies in the following areas: percent 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.

Authors
Amy E. Hessl is Assistant Professor, Department of Geology and Geography, West Virginia University, Morgantown, WV, 26506; Cristina Milesi is a Graduate Associate, School of Forestry, University of Montana, Missoula, MT, 59812; Mike White is Assistant Professor, Geography and Earth Resources, Utah State University, Logan, UT, 80307; Robert Keane is a Research Ecologist, US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, PO Box 8089, Missoula MT, 59807; David L. Peterson is a Professor, College of Forest Resources, University of Washington, Seattle, WA 98112.