171 D/o Ajto-ir TEMPORAL CHANGES IN BIOMASS, SURFACE AREA, AND NET PRODUCTION FOR A PINUS STROBUS L. FOREST

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1 171 D/o Ajto-ir TEMPORAL CHANGES IN BIOMASS, SURFACE AREA, AND NET PRODUCTION FOR A PINUS STROBUS L. FOREST W. T. Swank, Coweeta Hydrologic Laboratory, Franklin, North Carolina, U.S.A. And H. T. Schreuder, Research Triangle Park, North Carolina, U.S.A. Southeastern Forest Experiment Station U.S. Department of Agriculture, Asheville, North Carolina, U.S.A. IUFRO Biomass Studies, International Union of Forest Research Organizations, Working Pprty on the Mensuration of the Forest Biomass, College of Life Sciences and Agriculture, University of Maine et Orono, 1973-

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3 173 TEMPORAL CHANGES IN BIOMASS, SURFACE AREA,,, AND NET PRODUCTION FOR A PINUS STROBUS L. FOREST-' Wayne T. Swank and Hans T. Schreuder Plant Ecologist, Coweeta Hydrologic Laboratory, Franklin, N. C. and Biometrician, Research Triangle Park, N. C. Southeastern Forest Experiment Station, Forest Service U. S. Department of Agriculture, Asheville, N. C. ABSTRACT Weighted, linear regression models were used to estimate biomass and surface area of foliage, branches, and stems from tree basal area for a planted white pine stand on a 16.1 hectare watershed. Estimates were made at stand ages 10, 12, and 15 years. During the 5-year period, the stand closure changed from partial to complete and model coefficients showed large changes, particularly for foliage. In February 1972, aboveground biomass for the population was 4,664, 22,825, and 42,110 kilograms per hectare for foliage, branches, and stems. The population of trees contained 9.9, 2.3, and 0.4 hectares of foliage, branches, and stems per hectare of land surface. Net primary production was estimated to be 13,500 kilograms per hectare per year, and foliage development for the pine population culminated when the stand was only 12 years old. Intra- and interspecies comparisons show that data are in the upper range of values reported for forests. Streamflow data for the watershed demonstrate the dynamic nature of changes in stand structure and hydrologic processes. INTRODUCTION A primary objective in the analysis of the structure and function of ecosystems in the Eastern Deciduous Forest Biome, IBP, is to relate productivity and nutrient cycling to the geographical patterns, age-size distributions, and trophic structure of forests. Part of the research effort is centered at the Coweeta Hydrologic Laboratory in a cooperative effort between the University of Georgia and the U. S. Forest Service. At Coweeta, a major research objective is to measure the annual and seasonal fluxes of selected nutrients in four experimental watersheds, each representative of a different vegetation type. A long-term objective of the study is to inventory the biotic and abiotic compartments as a basis for comparing the standing pool and turnover of nutrients for contrasting ecosystems. One of the vegetation types under investigation is a 15-year-old, white pine plantation. During the period of study, which spans 5 years of plantation development, the flux of water and nutrients has varied substantially compared to the mature, deciduous forests indigenous to the area. Conversion from hardwoods to pine resulted in a large reduction in streamflow (Swank and Miner, 1968) and a significantly lower loss of cations from the ecosystem (Johnson and Swank, 1973). Since hydrologic and nutrient cycling processes are coupled to the production of woody material, Information about changes in primary production is essential to an understanding of ecosystem response. Moreover, data on forest J/ Contribution No. 90 from the Eastern Deciduous Forest Biome US-IBP.

4 174 structural characteristics, such as surface area of tree organs which influence transpiration, interception, and photosynthesis, are needed to interpret streamflow and nutrient flux results. The objectives of this paper are (1) to describe changes in biomass, surface area, and net production for the aboveground tree components of a white pine forest over a five-year period; (2) to compare results with data reported for other forests and (3) to discuss the impact of changing forest structure on hydrologic processes as reflected in streamflow response. METHODOLOGY AND DATA DESCRIPTION The primary study area is a white pine forest located on. a 16.1 hectare watershed (Watershed 1) at the Coweeta Hydrologic Laboratory in western North Carolina, U.S.A. Pines were planted on the watershed in 1957 after the mixed hardwood forest was cut and burned. The watershed slopes steeply to the south and elevations range from 706 meters to 988 meters. Precipitation averages 1727 millimeters annually with at least 100 mm each month. Another white pine plantation of the same age on a north-facing watershed (Watershed 17) has been used as a secondary sampling site. Ten growing seasons after the pines were planted on Watershed 1, the stand was inventoried (February 1967) by measuring the breast height (1.37 m) diameter of each tree on 20, randomly located, 0.08 ha plots. The plots were reinventoried in February 1969 and During the five-year period, basal area increased from 7.3 m 2 /ha to 23.4 m 2 /ha with a loss of only 30 trees/ha (Table 1). An inventory on Watershed 17 showed almost identical basal area and stand density. Table 1. Summary of white pine stand inventory on Watershed 1 beginning in 1967 when the stand was 10 years old. Year Basal Area (m 2 /ha) Mean DBH (cm) No. Trees (per/ha) Based on the diameter distribution obtained from the inventory plots, seven diameter classes were designated. Twenty trees were selected by stratified random sampling from the watershed population in February This sampling period was selected because biomass changes are most static during late winter, thus providing a base time for comparative measurements in succeeding years. Sample trees were felled, measured for physical dimensions, and all branches were cut and transported to the laboratory. Foliage was separated from the branches by node, dried at 70 C, and weighed. Stem sections were taken from each tree to determine biomass and surface area. Subsampling techniques were used to estimate surface area of foliage and branches. A detailed description of sampling methods and statistical analysis of the data is given elsewhere (Swank and Schreuder, 1973). Six additional sample trees were collected in

5 175 February 1970 for complete analysis and 14 trees were sampled for stem area. In February 1972, 13 trees (8 from Watershed 17) were collected and measured as described above. The mean and range of sample tree dimensions are given in Table 2. The data are presented in two groups, and 1972, based on a statistical analysis of regression models for the various tree components. Table 2. The mean and range of tree dimensions for 26 (1968 and 1970) and 13 (1972) eastern white pine trees sampled from a young forest plantation. Tree Dimension Basal Area, Breast Height (cm 2 ) Total Height (m) Foliage Weight (kg) Branch Weight (kg) Stem Weight (kg) Foliage Area (m 2 ) Branch Area (m 2 ) Stem Area (m 2 ) 1968 Mean & 1970 Range , Mean Range Litter collections were made on Watershed 17 in 1969, 1970, and 1971 (Cromack, 1972). Similar collections on Watershed 1 were made in Data Analysis RESULTS Initial analysis of the tree data consisted of simple linear regression analysis where biomass and surface area for foliage, branches, and stems were related to basal area. The-1968 and 1970 models for branch and foliage variables were not significantly different at the five-percent level and the data were pooled (n=26) for the two sample dates. However, the regression lines for stem weight and area differed in 1968 and 1970, but the 1970 and 1972 data were not significantly different and were pooled, resulting in n=19 for stem weight and n=27 for stem area. The regression lines for the pooled data for branches and foliage were found to be significantly different from those for 1972 (n=13). In the second stage of analysis, the data were used to compare statistical models as described by Schreuder and Swank (1971). From an array of seven models, a weighted linear model was selected: Y = a + gx + e, E ^ N(0,a 2 X k ) where Y is biomass or surface area for the tree component of interest, X is tree basal area in cm 2, and k is a weighting factor estimated from the data. Model coefficients and other statistics for each tree component are shown in Table '3.

6 176 Table 3. Weighted regression models used to estimate white pine tree components from basal area. Data collected in three different years. Tree Component and Year(s) of a B k r 2 SE Sample Collection Foliage wt. (g) 1968 and 70 Foliage wt. (g) 1972 Branch wt. (g) 1968 and 70 Branch wt. (g) 1972 Stem wt. (kg) 1968 Stem wt. (kg) 1970 and 72 Foliage area (m 2 ) 1968 and 70 Foliage area (m 2 ) 1972 Branch area (m 2 ) 1968 and 70 Branch area (m 2 ) 1972 Stem area (dm 2 ) 1968 Stem area (dm 2 ) 1970 and m The biomass and area of foliage and branches in relation to basal area exhibited a downward shift in slope with an increase in stand age (Figure 1). This decrease in quantity of crown components for trees of similar basal area coincides with a change from partial to complete stand closure, and represents the net effect of competition. In contrast, the slope of stem biomass and area showed an upward shift; this change was associated with an increase in tree height from 1968 to for a given tree diameter. Biomass, Surface Area, Net Production Estimates of biomass and surface area for aboveground tree components for the different stand ages are shown in Table 4. The total tree biomass at age 10 was 18,086 kg/ha and increased to 69,599 kg/ha by age 15. Stem contributions to total biomass over the five-year period increased from 44 percent to 60 percent. Branch biomass showed an increase over the five-year period but the annual rate of increment decreased. Of greatest significance, foliage biomass development for the population culminated when the stand was only 12 years old.

7 X o yj o LEGE ND f -8" TREES O 1972 TREES (CM 2 ) 360 Figure 1. The relationship between foliage weight and basal area for white pine sample trees shows a reduction in slope as the stand changed from partial (1968 to 1970) to complete closure (1972).

8 178 Table 4. Estimates of biomass and surface area by tree components for a 16.1 ha white pine plantation at ages 10, 12, and 15 years. Stand Age (years) Biomass (kg/ha) Surface Area (m 2 /m 2 ) A Foliage Branches Stems Total Foliage Branches Stems Total ,547 4,667 4,664 7,626 15,314 22,825 7,913 23,493 42,110 18,086 43,474 69, In contrast to the biomass data, the contribution of tree components to total surface area is in reverse order. Foliage comprised 84 percent of the 6.36 surface area index at age 10, followed by branches and stems. By age 12, total surface area index had reached 11.49; because foliage development culminated, the index only increased to in the three subsequent years. Estimates of foliage surface area for a given year represent the minimum value since sample trees were collected in February, long after major leaf-fall had occurred. However, it is possible to estimate maximum foliage area based on phenological characteristics of white pine and separation of foliage on sample trees by age classes. Vegetative bud break for white pine at Coweeta begins about the last week of April and foliage expansion is complete about the first week of July. Leaf-fall begins the middle of September and is largely complete by the latter part of November. Foliage on the six sample trees collected in February 1970, was separated into age classes and showed that current-year foliage accounted for 80 percent of the total foliage biomass with the remainder in 1-year-old foliage. Thus, a reasonable estimate of the maximum foliage surface area for the preceding summer can be made by increasing the dormant season estimate by 80 percent. On this basis, the leaf area index for the pine stand in the summer of 1971 was estimated to be Net primary production can be estimated with the formula: P n = + L + 6 (after Newbould, 1967) where P. n is net production by the community, B is biomass change, L is plant losses by death and shedding, and G is plant losses by consumer organisms during the time period of interest. Estimates of these terms for the period are shown in Table 5. Studies are in progress to derive estimates of losses by consumer organisms but results are incomplete at this time. Stems contributed about 50 percent to the annual net production of 13,529 kg/ha, with the remainder about equally divided between foliage and branches. White pine retains dead branches for a long period of time and deterioration is slow. Thus, foliage is the dominant contributor to plant losses in this young stand.

9 Table 5. Parameter B L G Total 179 Summary of values used to calculate aboveground net primary production for eastern white pine over the period Foliage 2,117 18,162 Branches kg/ha 15,199 UNKNOWN 15,258 Stems 34, / 34,224 Total 51,513 16,131 67,644 V Derived from litter collections; 2/ derived from litter collections and tree mortality; 3/ derived from tree mortality. DISCUSSION Comparison with Other Forest Stands The data in this study represent a specific combination of stand structure, climate, soil, and genetic factors, and it is desirable to compare estimates with similar data for white pine in different locations and also to provide interspecies comparisons. The most appropriate intraspecies comparison can probably be obtained from biomass data. For trees with the same d.b.h., the foliage biomass in the Coweeta study is two to five times greater than values reported for trees on two different sites in Vermont (Adams, after Kittredge 1944). In a study in Maine (Young, et al., 1964), foliage and branches were collected as a single component and a comparison for trees of similar diameter showed that the biomass at Coweeta is about sixfold greater for the same tree components. Individual stem biomass at Coweeta is only about one-half the value reported for Maine. When the aboveground components are combined, total tree biomass at the two locations is similar. These contrasts cannot be rigorous because either a description of stand characteristics, sampling date, or methods are not contained in the literature. Art and Marks (1971) summarized biomass, net annual primary production, and leaf area index for a wide range of forest types from throughout the world. The annual production of 13,500 kg/ha for the white pine plantation exceeds many values reported for other conifer plantations.(abies grandis, Picea abies, Pinus densiflora, Pinus nigra, and Pseudotsuga menziesii) of comparable age and tree components. Productivity data for plantations of Pinus radiata in Australia and Pinus sylvestris in the United Kingdom tend to be larger than values for white pine at Coweeta. The white pine productivity data are two-to-five times greater than values for plantations of deciduous hardwoods, such as Alnus. Betula, Fagus, and Quercus. Information on surface area of tree components is generally unavailable,.although Kittredge (1948) reported leaf area indices of 14.4, 16.6, and 15.9 for well stocked, 21-, 53-, and 70-year-old white pine stands in Switzerland. The dormant season LAI of 9.9 for white pine exceeds most conifer values summarized by Art and Marks (1971), and the maximum LAI of 17.8 is greater than all values

10 180 reported. An even sharper contrast in surface area can be found between hardwoods and conifers. Whittaker and Woodwell (1967) summarized data for closed deciduous forests in the eastern United States and found a range in foliage area of 3 to 6, branch area 1.2 to 2.2, and stem area 0.3 to 0.6. Thus, foliage area of the young white pine is nearly three times the upper limits for temperate deciduous forests, while branch area is close to the upper extreme and stem area is in the middle of the hardwood range. In the comparison of foliage area, we should point out that values for white pine include all surfaces and data for hardwoods are for one surface. Streamflow and Stand Structure Dynamics Differences in productivity and surface area dimensions between forest types have important consequences to other ecosystem responses. The original purpose of collecting data for the white pine stand was to test a hypothesis related to streamflow, j_.e_., that the rapid streamflow reduction resulting from hardwood to pine conversion would tend to level-off when foliage area development culminated and thereafter decline gradually as the total plant surface area slowly increased with stand age. This seemed to be a plausible hypothesis because surface area is a structural characteristic closely related to the major evaporative processes of interception and transpiration. Sufficient data are now available to test part of this hypothesis (Figure 2). Points plotted between years of actual foliage measurement were derived by interpolation. The upper part of the figure shows the annual reduction in streamflow for the pine cover expressed as a percent of the expected streamflow from a mature hardwood stand. A detailed description of watershed experimental methods has been given elsewhere (Swank and Miner, 1968). The streamflow reductions shown represent substantial quantities of water. For example, the 14 percent reduction in 1967 is equivalent to 9..4 area-centimeters and the 23 percent reduction in 1972 is 17.5 area-centimeters. In the last three years, the streamflow decline tended to level-off and this trend is closely associated with the culmination of LAI development shown in the lower part of the figure. Total plant surface area for the same period has tended to increase gradually but additional streamflow results will be required to determine time trends. This study has shown the dynamic nature of changes in stand structure and the strong streamflow response to these changes. We will continue to document net production, surface area dimensions, and streamflow on the.watershed. This information will be examined with respect to other data which have been collected for ecosystem processes, such as decomposition and nutrient cycling. We expect that substantially greater evapotranspiration and net production of pine, as. compared with hardwood forests, will contribute to large contrasts in biogeochemical cycles between forest types.

11 181 z o u 3 O IAI YEAR 1970 Figure 2. Time trends of stream-flow reductions, leaf area Index (LAI) and total aboveground surface area Index (TSAI) for a plantation of eastern white pine from age 10 to 15.

12 182 LITERATURE CITED Art, H. W. and P. L. Marks A summary table of biomass and net annual primary production in forest ecosystems of the world. J_n Forest Biomass Studies. Life Sciences and Agr, Exp. Sta., U. of Maine at Orono: Crornack, K Litter production and litter decomposition in a mixed hardwood watershed and in a white pine watershed at Coweeta Hydrologic Station, North Carolina. Ph. D. Dissertation, U. of Georgia, Athens, Ga. 137 pp. Johnson, P. L. and W. T. Swank Studies of cation budgets in the southern Appalachians on four experimental watersheds with contrasting vegetation. Ecology 54: Kittredge, J Estimation of the amount of foliage of trees and stands. J. Forestry 42: _ Forest Influences. McGraw-Hill Book Company, New York: Newbould, P. J Methods for estimating the primary production of forests. IBP Handbook No. 2, Blackwell Scientific Pub!., Oxford and Edinburgh. 62 pp. Schreuder, H. T. and W. T. Swank A comparison of several statistical models in forest biomass and surface area estimation. _In_ Forest Biomass Studies. Life Sciences and Agr. Exp. Sta., U. of Maine at Orono: Swank, U. T. and N. H. Miner Conversion of hardwood covered watersheds to white pine reduces water yield. Water Resources Res. 4: _ and H. T. Schreuder Comparison of Three Methods of Estimating Surface Area and Biomass for a Forest of Young Eastern White Pine. In Press. Forest Science. Whittaker> R. H. and G. M. Woodwell Surface area relations of woody plants and forest communities. Amer. J. Bot. 54(8): Young, H. E., L. Strand and R. Altenberger Preliminary fresh and dry weight tables for seven tree species in Maine. Maine Agr. Exp. Sta,, Tech. Bull. 12, 76 pp.