Review of root dynamics in forest ecosystems grouped by climate, climatic forest type and species

Plant and Soil 187: 159-219, 1996. © 1996 KluwerAcademic Publishers. Printed in the Netherlands.

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Review of root dynamics in forest ecosystems grouped by climate, climatic forest type and species

Kristiina A. Vogt, Daniel J. Vogt, Peter A. Palmiotto, Paul Boon, Jennifer O ' H a r a and

Heidi Asbjornsen School of Forestry and Environmental Studies, 370 Prospect St., Yale University, New Haven, CT 06511, USA*

Received 8 January 1996. Accepted in revised form 27 July 1996

Key words: above- and belowground biomass and production, above- and belowground litter transfers, boreal forests, climatic variables, cold and warm temperate forests, forest floor accumulations, nutrients, soil organic matter, subtropical and tropical forests

Abstract

Patterns of both above- and belowground biomass and production were evaluated using published information from 200 individual data-sets. Data sets were comprised of the following types of information: organic matter storage in living and dead biomass (e.g. surface organic horizons and soil organic matter accumulations), above- and belowground net primary production (NPP) and biomass, litter transfers, climatic data (i.e. precipitation and temperature), and nutrient storage (N, P, Ca, K) in above- and belowground biomass, soil organic matter and litter transfers. Forests were grouped by climate, foliage life-span, species and soil order. Several climatic and nutrient variables were regressed against fine root biomass or net primary production to determine what variables were most useful in predicting their dynamics. There were no significant or consistent patterns for above- and belowground biomass accumulation or NPP change across the different climatic forest types and by soil order. Similarly, there were no consistent patterns of soil organic matter (SOM) accumulation by climatic forest type but SOM varied significantly by soil order- the chemistry of the soil was more important in determining the amount of organic matter accumulation than climate. Soil orders which were high in aluminum, iron, and clay (e.g. Ultisols, Oxisols) had high total living and dead organic matter accumulations - especially in the cold temperate zone and in the tropics. Climatic variables and nutrient storage pools (i.e. in the forest floor) successfully predicted fine root NPP but not fine root biomass which was better predicted by nutrients in litterfall. The importance of grouping information by species based on their adaptive strategies for water and nutrient-use is suggested by the data. Some species groups did not appear to be sensitive to large changes in either climatic or nutrient variables while for others these variables explained a large proportion of the variation in fine root biomass and/or NPP.

Introduction

The issue of global climate change has inspired a plethora of research efforts concerning the effects of climate change (e.g. elevated CO2) on plant growth rates and the ability of plants to sequester carbon (Anderson, 1991; Cannell and Dewar, 1994; Lands- berg et al., 1991, 1995). At the ecosystem level, this research has focused on the development and refine- ment of models which can be used to predict the impact

* Fax No: + 1 2 0 3 4 3 2 3 9 2 9 .

E-mail: KDVOGT@ YALEVM.CIS.YALE>EDU

of human induced stress on forests (Aber and Federer, 1992; Landsberg, et al., 1995; Rastetter et al., 1992; Running and Hunt, 1993). The development of these models has been limited by a poor understanding of the mechanisms which control carbon allocation to leaves, fine roots, stems, stored carbohydrates and secondary plant defensive chemicals (Waring, 1987), and how these relationships vary by plant species (Lambers and Poorter, 1992). The ability to effectively predict the impacts that climate change will have on the carbon cycle has been further hindered since half of the sys-

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tem's response may be occurring belowground, mak- ing these effects difficult to study (Vogt et al., 1993).

In order to predict the growth rate of trees in forests and the ability of these systems to sequester carbon under different climate scenarios, an understanding of the natural patterns and factors controlling carbon allocation within plants is required. Much research has been completed on how plants allocate carbon within aboveground vegetative structures since these compo- nents are relatively easy to monitor and measure (Baz- zaz et al., 1987; Chapin, 1991; Chapin et al., 1987; Coley 1988; Field, 1991; Lambers and Poorter, 1992; Reich et al., 1992; Waring, 1987). However, this contrasts with our poor understanding of the belowground processes because of the methodological difficulties and dynamic nature associated with studying this portion of the system. If the coarse structural roots con- tributed most significantly to the carbon storage and transfers occurring in the ecosystem, understanding the role of the belowground in ecosystems would be relatively simple since their dynamics can be predicted from aboveground parameters (e.g. diameter at breast heights of trees) (Santantonio, 1990). Furthermore, on a weight basis, coarse roots contribute more to the total ecosystem biomass (on average globally, about a quar- ter of total biomass) than fine roots (Vogt et al., 1987). However, biomass values are relatively static and do not reflect the annual changes that can occur belowground or how a system responds to disturbance. For example, even though fine roots may contribute less than 2% of the total ecosystem biomass, they may contribute up to 40% of the total ecosystem production (Vogt et al., 1990). This, means that when examining carbon allocation in forests, the fine root component (similar to the foliage in the aboveground) may be very sensitive to environmental change and thus may respond most strongly to a disturbance (Vogt et al., 1993).

The majority of model predictions have not included data from several belowground parts of ecosystems (e.g. fine roots, exudation, mycorrhizas, respiration, decomposer organisms - soil animals, bacteria, and fungi) since it is unclear how they should be included as part of the ecosystem response to stress. An addi- tional problem modelers have had is how to use data, from a few intensively studied sites scattered around the world, to identify general response variables that could be considered common to all sites (Brown and Lugo, 1982; Cole and Rapp, 1982; O'Neill and DeAn- gelis, 1982; Post et al., 1982; Schlesinger, 1977; Vogt et al., 1986, 1995; Vogt, 1991), For example, attempts

have been made to predict fine root biomass, production and turnover rates by using measurements taken from aboveground vegetative structures as a way of bridging this data gap (Marshal and Waring, 1985; Raich and Nadelhoffer, 1989; Vogt et al., 1985), however, these measurements are frequently site specific and not transferable to other sites.

The objectives of this paper were to generate broad- scale patterns of root growth variability to show how the grouping level of analysis are important to consid- er when attempting to predict root growth and senescence. Another objective was to identify at what scale fine root information was most sensitively predicted by a variety of abiotic and biotic factors. Many comparisons have been made at the level of comparing hardwoods to conifers or deciduous species to evergreen species (Reich et al., 1992) and then generalizing that information in models. This does not address the fact that these higher level analyses or lumping of information from several levels may minimize the ability to predict how growth or senescence is being controlled at the ecosystem level (Vogt et al., 1996). Nor does these groupings deal with the fact that differences in plant allocation of carbon may vary substantially within each group since different relative growth rates may exist within each broad-scale grouping category (Lam- bers and Poorter, 1992). It is important to identify which parameters may transcend as relevant predictive variables across different grouping levels at various spatial scales. This large data-set which includes research from the tropics to the boreal zones is quite useful to begin grouping information at different levels and to begin teasing apart where fine root biomass and production can be most effectively predicted. The relationships produced using this large data-set should be used to help design research that would further examine these patterns. This type of information is crucial to know when attempting to incorporate the belowground parameters into models of carbon dynamics in forest ecosystems. When determining carbon budgets, most large scale forest models do not incorporate variables which accurately portray the magnitude of carbon allocation to belowground processes.

Using a larger data-base (200 data-sets), we were interested in determining if the few variables identified in the 1970--80's (Aber et al., 1985; Vogt et al., 1986) were able to predict carbon allocation patterns for fine root biomass and production of ecosystems ranging from the boreal to the tropical zones. In addition to expanding the temperate zone data base, our analyses include a greater expanded data base from the

wet and dry tropics. The purpose of this paper was to explore the relationships between fine roots and abiotic variables which have been poorly studied in the past. Since fine roots can contribute a sizable proportion of ecosystem net primary production and few clear relationships have been developed for fine roots (Vogt et al., 1986), it is worthwhile exploring general patterns of root growth and what factors might be useful to predict this growth.

A p p r o a c h

The data-set synthesized in this paper includes only those studies for which fine root data were available (Appendix B). A large synthesized data base already exists for belowground biomass that is derived from estimates of coarse structural roots (see Cannell, 1982) and are not included in our data-base. The data-set in this paper also excluded fine root studies conducted in agroforestry systems as well as studies reporting fine root biomass values obtained from one sampling time where seasonal patterns of root growth were not available.

No attempt was made to standardize fine root data reported in the literature. Fine root data synthesized in Appendix B show the inconsistent and variable diam- eters selected by researchers to define their fine root category. In general, fine roots were classified as being <2 mm or <5 mm in diameter. Since the contribution to biomass by the >2-5 mm diameter roots is generally small (Vogt et al., 1989), comparing data with different diameter sizes is not unrealistic. In addition, the biomass data on fine roots do not include carbon lost from roots due to respiration or as exudation (Lambers and Poorter, 1992). The inability to account for these fluxes results in most carbon budgets underestimating the total carbon flows occurring in an ecosystem. If future studies verify the suggested carbon rhizodeposi- tion value of approximately 5% (Lambers and Poorter, 1992) for mature plants, not accounting for these fluxes may not be a problem since these levels are within the error term of the biomass values themselves.

The data were first analyzed as a whole using a four-way ANOVA GLM procedure SAS (Appendix A). This was followed by analyses (ANOVA) which used groupings of data by climate, climatic forest types, soil orders, and finally by species. Regressions were developed on log transformed data at the level of leaf life-span (i.e. evergreen, deciduous, semi deciduous), climatic forest type climatic forest types (cold

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temperate broadleaf deciduous, cold temperate needleleaf evergreen, warm temperate broadleaf deciduous, warm temperate needleleaf evergreen, mediterranean needleleaf evergreen, mediterranean broadleaf evergreen, subtropical broadleaf deciduous, subtropical broadleaf evergreen, tropical broadleaf deciduous and tropical broadleaf evergreen), soil order, climatic forest type x soil order, soil texture (14 classes were ordered by coarseness and given numeric values for regression analysis: sand, loamy sand, sandy loam, fine sand loam, very fine sandy loam, loam, silt loam, silt, sandy clay loam, silt loam, clay loam, sandy clay, silt clay, clay), and species (i.e. conifers excluding pines, pines (as a group followed by separating them into climate groupings: cold temperate, warm temperate + subtropical, tropical), oaks, hardwoods excluding oaks (as a group followed by separating them into climate grouping as above). The species groupings were selected because they are common species for which data has been collected by researchers around the world and prior analyses suggested that they respond as a group to abiotic variables.

In the analysis of the data-set (Appendix B), only stands that had reached canopy closure or were mature were included (in the tropics this consisted of stands older than 8 years old and in the remaining climatic zones this included all stands older than 20 years of age). Also, sites that had been manipulated (i.e. fertilized, irrigated or received applications of saw-dust or were spacing trials) were excluded from these analyses. As a result of this selection criteria most of the analyses were conducted on 173 of the 200 data points.

Variables utilized to determine what factors predicted fine root biomass and net primary production were selected because previous research had identified them as important across the diversity of climatic forest types. Only variables for which there existed plausible mechanistic explanations were used in these analyses. Factors analyzed in this paper were also limited by what data had been synthesized from the literature - the incompleteness of these data is apparent from an examination of Appendix B. For example, certain climatic data (precipitation; average mean, minimum and maximum temperatures) were frequently reported by authors in their papers however, information on soil water or nutrient availabilities were commonly lacking (Appendix B).

Water availability to plants was indexed using annual precipitation and soil texture data. Ideally, estimates of the actual evapotranspiration (AET) for each site would have been the best variable to deter-

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mine plant available water (Pallardy et al., 1995). Since only two sites out of the 200 study sites presented AET data, and climatic information to calculate this for the other sites was not readily available, we were unable to calculate AET for use in our analyses. Annual precipitation data was also used in this analysis although in many of the tropical areas, seasonal precipitation patterns are probably more important in controlling biomass accumulation and production than annual precipitation data. How- ever, because most studies presented annual precipitation values, comparisons were made at this level. The climatic variables included in this study consisted of mean annual temperature, minimum annual temperature, maximum annual temperature, annual precipitation, mean temperature/precipitation ratio, minimum temperature/precipitation ratio, maximum temperature/precipitation ratio. The temperature/precipitation ratios have been shown to be useful in explaining plant growth in the tropics and root biomass accumulation across large vegetative climatic groups (Lugo and Brown, 1982; Vogt et al., 1986).

Those nutrients which have been shown to control photosynthetic rates or for which fertilization additions have shown strong growth responses were included in these analyses (Chapin et al., 1987; Reich et al., 1992; Schulze, 1982; Vogt et al., 1990). Plant available nutrients were examined using parameters that have been shown to reflect the cycling of these nutrients on the site - the magnitude of tree cycling of nutrients in litterfall and by the accumulation rates and residence times of nutrients in surface organic horizons (N, P, Ca, K contents of aboveground litterfall; N/P, N/Ca and N/K ratios of aboveground litterfall; forest floor N, P, Ca, K contents and mean residence times).

Statistical analyses were performed using SAS (version, 6.11) for all data computations, linear regressions, one-way analysis of variance, four-way analysis of variance, and multiple comparisons of means using the Student-Newman-Keuls test at p - 0.10. Log trans- formations were use to normalize the distribution of the dependent variables. Correlations (Pearson) were ini- tially developed for individual parameters to determine how much of the variation in fine root biomass or production could be explained by one variable. Because plants are exposed to multiple stresses (Pallardy et al., 1995) it would be appropriate to use multiple regression techniques as a predictive tool. However, due to the incompleteness of the data published, the number of data-sets used and comparisons made in the multiple regression analysis were dramatically reduced.

Results and discussion

Large scale biomass and productivity comparisons by climatic forest type and soil order

Above- and belowground biomass At the largest scale of analysis where no grouping of data had occurred, a four-way ANOVA showed that aboveground and belowground biomass are significantly correlated to climate and there is a significant interaction term between climate and soil order (Appendix A). However, predicting how above- and belowground biomass accumulations vary by forest climatic type and within soil order groupings within each forest type is hampered by this grouping approach showing no significant differences in the amount of measurable above- and/or belowground biomass from the boreal to the tropics (climate groupings) (Table 1). High within group variation in recorded above- and belowground biomasses by climatic forest types makes it impossible to isolate significant group differences.

Separation of aboveground biomass data by soil order within climatic forest groupings did show that when high aboveground biomasses (> 300 Mg ha - l ) were recorded for forests that they tended to be found growing on Ultisols, Inceptisols, Oxisols, Alfisols and Spodosols in the cold temperate and tropical zones (Table 1). This does not mean that aboveground biomasses were always > 300 Mg ha - l within these groupings but the potential for measuring high biomasses did occur within these groupings. Above- ground biomasses > 300 Mg ha- 1 were not recorded in the boreal, warm temperate or subtropical zones (Table 1), Deciduous forests growing on the same soil series as the evergreen and semi-deciduous forests also tended to accumulate less aboveground (and total) biomass (Table 1).

Based on a much smaller data-base, Schulze (1982) noted that conifers growing in the boreal and temperate zone appeared to achieve much higher aboveground biomass values than the broad-leaved forests located in the temperate and tropical zones. The expanded data- set of this paper show that some tropical areas may have higher aboveground biomasses than previously documented. The low biomasses recorded in the subtropical forests are probably related to the dominant disturbance cycle which affects the forests where most of this data was collected. Hurricanes are common on several of the subtropical sites and appear to maintain these forests in early successional stages (i.e. stages

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Table 1. Above-, belowground and fine root biomass for climatic forest types separated by soil order. (Means (standard error; sample size in parentheses) followed by the same lower case letter are not significantly different within the same column using SNK multiple means comparison)

Aboveground Belowground Fine root % Total % Fine root

Climatic forest soil order biomass biomass biomass root of of total

type (Mg ha -1 ) (Mg ha -1 ) (gm -2) total biomass

anova (p<.001) anova (p<.003) anova (p<.001) biomass

Boreal Broadleaf deciduous

Needleleaf evergreen

Cold temperate

Broadleaf deciduous

Needleleaf deciduous


Warm temperate Broadleaf deciduous


Broadleaf deciduous+

needleleaf evergreen mix

Subtropical Broadleaf deciduous

Broadleaf evergreen


Tropical Broadleaf deciduous

Broadleaf semi-deciduous

Inceptisol 52 (45; 2) a 25 (19; 2) abc 129 (-; 1) bc 39 10

Entisol 28 (-; I) a 7(-; 1) abc 60 (-; 1) c 19 2

Histosol 81 (44; 3) a 22 (13; 3) abc 103 (38; 3) bc 20 1

Inceptisol 59 (31; 3) a 25 (13; 3) abc * 29 *

Spodosol 144 (23; 21) a 33 (5; 21) abc 165 (21; 21) bc 19 1

Alfisol 221 (1; 2) a 52 (8; 2) abc 546 (165; 6) abc 19 4

Inceptisol 217 (30; 7) a 45 (6; 7) abc 633 (189; 4) abc 25 5

Spodosol 175 (31; 7) a 25 (4; 6) abc 747 (88; 8) ab 17 7

Spodosol 169 (-; 1) a 38 (-; 1) abc * 18 *

Alfisol 373 (151; 4) a 61 (31; 4) abc 544 (179; 3) abc 13 4

Andisol 211 (120; 3) a 81 (57; 2) abc 912 (419; 3) abc 28 7

Histosol * * 97 (-; 1) bc * *

Inceptisol 360(54; 15)a 70(11; 13) ab 323 (118; 6) abc 19 0.4

Spodosol 244 (49; 10) a 47 (9; 10) abc 525 (47; 13) abc 22 5

Ultisol 554 (361; 2) a 113 (74; 2) a * 17 *

Alfisol * * 990 (-; 1) ab

Entisol 196 (-; 1) a * 243 (-; 1) abc * *

Ultisol 139 (12; 6) a 38 (4; 5) abc 803 (12; 5) ab 23 5

Entisol 233 (112; 2) a 34 (-; 1) abc 510 (280; 2) abc 22 5

Histosol 220 (-; 1) a * 500 (-; I) abc * *

Spodosol 60 (37; 2) a 31 (9; 2) abc 1,055 (159; 2) ab 41 13

Ultisol * * 945 (105; 2) ab * *

Oxisol 125 (-; 1) a 6 (-; 1)bc 321 (-; 1) abc 4 2

Ultisol 102 (-; 1) a 8 (-; 1) abc 297 (-; 1) abc 7 3

Histosol 229 (-; 1) a 37 (-; 1) abc 112 (-; 1) bc 14 0.4

Oxisol 80 (-; 1) a 21 (-, 1) abc 431 (-; 1) abc 20 4

Ultisol 126 (37; 3) a 45 (16; 4) abc 582 (80; 4) abc 19 4

Ultisol 171 (-; 1) a 20 (-; 1) abc 263 (193; 2) bc 10 2

Unknown 252 (68; 4) a 49 (1 I; 4) abc * * *

Ultisol * * 340 (89, 2) abc * *

Unknown 62 (11, 2) abc 31(1; 2) abc 623 (163; 3) abc 34 8 Inceptisol 398 (-; 1) a 36 (-; 1) abc 930 (-; 1) ab 8 2

Oxisol 305 (-; 1) a 54 (-; 1) ab 389 (109; 2) abc 15 1

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Table 1. Continued

Broadleaf evergreen Entisol

Inceptisol

MoUisol

Oxisoi

Spodosol

Ultisol

Needleleaf evergreen Unknown

Mediterranean

Broadleaf evergreen Unknown

Needleleaf evergreen Spodosol

69(-; l ) a

288 (62; 7) a

309 (65; 3) a 354 (-; 1) a

103 (25; 3) a

10 (-; 1) abc * 13 *

41 (-; 1) abc 749 (325; 4) abc * *

• 61 (-; 1) c * *

45 (4; 7) abc 937 (179; 9) ab 12 5

61 (4; 3) ab 2,270 (311; 4) a 14 3

27 (-; 1) abc 638 (306; 6) abc 7 *

25 (6; 3) abc * * *

269 (-; 1) a 5(-; 1) c * 1 *

• * 438 (-; 1) abc * *

* No data.

recovering from the previous hurricane) (Scatena and Lugo, 1995).

If one compares the proportion of total living biomass in the belowground across climatic forest type and by soil order, all forests had broad ranges of allocation to roots with no clear trends at this scale of analysis: boreal = 19-39% (n = 30), cold temperate -- 13-28% (n=46), warm temperate -- 22-0-41% (n = 8), subtropics = 4-20% (n = 13) and tropics = 7-34% (n -- 20) (Table 1). There was no consistent pattern for how much of total biomass was allocated to roots based on climate, forest type or foliage life-span groupings. The only pattern revealed by this data-base was that introduced plantation species (both deciduous and evergreen) had a lower proportion of total living biomass belowground (4-10%) than broadleaf evergreen forests (19-20%) growing in the subtropics (Appendix B).

A four-way ANOVA showed fine root biomass was significantly correlated with climate, soil order and foliage type (Appendix A). However, except for a few notable exceptions, mean fine root biomasses were not significantly different when examined by climatic forest type and soil order groupings (Table 1). Again this is due to the high variability in fine root biomass within each grouping and this grouping approach does not reflect the variables which conlrol the amount of fine root biomass maintained in an ecosystem. However, the groupings did identify the extreme values where fine root biomass exceeded 900 g m -2. The highest mean fine root biomass (2,270 g m - E ) w a s reported for three tropical broadleaf evergreen forests growing on Spodosols (Table 1). Irrespective of the climate and leaf life-span, when high fine root biomasses were recorded for a forest, the soil orders present were Spo- dosols, Oxisols and Ultisols (exception of one site on

an Alfisol). Most of these high fine root biomasses occurred on sites where the soil chemistry was dominated by A1 and Fe. This link between these soil elements, the potential mycorrhizal status of these species (i.e. ectomycorrhizas) and plant tolerance to AI needs to be pursued further (Dahlgren et al., 1991; Vogt et al., 1987) as variables which indicate where high fine root biomasses should be found.

This data-base suggested that some climatic forest types located on particular soil orders appeared to accumulate high total biomasses that were not achiev- able in the other climatic and soil order groupings (Table 1) - whether these patterns are upheld as the data-base increases needs to be examined in the future. For example, total living biomass values in the boreal, warm temperate and subtropical climatic zones in general never exceeded 300 Mg ha-1 for both evergreen and deciduous species. This contrasts with the tropical and cold temperate climatic zones that consistently had total living biomass values greater than 300 Mg ha- l for evergreen and semi-deciduous dominated sites (Table 1). Total living biomass values were notably high in some cold temperate needle-leaf evergreen forests growing on Alfisols (434 Mg ha-l) , Inceptisols (430 Mg ha- l) and Ultisols (667 Mg ha- 1). Similarly in the tropics, total living biomass values were high for some broadleaf semi-deciduous forests growing on Inceptisols (434 Mg ha - l ) and Oxisols (359 Mg ha- l) and for broadleaf evergreen forests on Ultisols (381 Mg ha - l ) and Spodosols (370 Mg ha-l) . The high biomasses recorded in the tropics are contrary to what one would expect since the consistently high temperatures here should result in higher respiration rates (Sprugel et al., 1994), leading to lower biomass accumulation rates. Furthermore, most of the soils in the tropical areas are lower in available nutrients

(Vitousek and Sanford, 1986) which should preclude high production rates. Despite this, high biomasses were recorded for forests on those soil orders characterized by high accumulation of soil organic matter (Vogt et al., 1995) as evidenced by the high production trends observed on Ultisols and Spodosols. The high accumulations of organic matter in the soil may result in better nutrient and water relationships for the forests growing on these sites. The high biomasses recorded in the cold-temperate zone can be explained by evergreen tree species having lower respiration rates due to the cooler annual maximum temperatures and most tree growth occurring during the winter (Waring and Franklin, 1979), a lower turnover rate of tissues (Aerts, 1995; Vogt et al., 1986) and a more efficient use of nutrients (Lambers and Poorter, 1992).

Net primary production

Similar to the large scale biomass comparisons, above- and below production values could be significantly grouped using climate and soil order (also foliage type for aboveground NPP) (Appendix A). However, a combined climatic forest types and soil order grouping was not useful in identifying clear patterns of above- and belowground NPP (Table 2). The range of variation in above- and belowground NPP were, in general, so broad within a grouping that few significant differences could be identified. However, higher total NPP values (> 1,500 g m 2 yr -1) were recorded for both deciduous and evergreen forests in the warm temperate and cold temperate zones (Table 2). Unlike the biomass comparisons where evergreen species accumulated more total biomass, no significant differences between evergreen and deciduous forests were recorded for NPP (Table 2). The patterns recorded for aboveground NPP did not occur for belowground NPP - high aboveground NPP was not associated with a significantly lower belowground NPP or vise-versa. This trend reflects the fact that plant responses to their environment are expressed as shifts in carbon allocation between the above- and belowground (see later discussion on nutrient controls on C allocation).

The proportion of total productivity found belowground varied considerably (3-54%) without any consistent pattern by climatic forest type or soil order (Table 2). Those few sites with the highest allocations of carbon to the belowground (> 40% of total NPP) were: broadleaf evergreen forests in the tropics growing on Oxisols (49%) and subtropics on Ultisols (44%); broadleaf deciduous forests on Ultisols (46%) in the

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warm temperate zones; broadleaf deciduous forests on Spodosols (41%) and needleleaf evergreen forests on Andisols (54%) in the cold temperate zone. Even though these sites had high belowground productivities and allocated a large proportion of total productivity to fine roots, they also had a low percentage (6-7%) of total living biomass in fine roots (Table I). Again, there is no apparent relationship between the biomass of roots maintained by a forest and the amount of carbon allocated annually to maintaining the growth and function of fine roots. The lowest allocation to belowground production (5%) was recorded for a subtropical needle-leaf evergreen forest which was an introduced conifer species growing in a plantation (Lugo, 1992).

There is a general pattern which suggests that the highest allocations to belowground production are found in soils where high levels of A1 and Fe may limit plant growth - again this follows the pattern found for fine root biomass. Higher annual allocations of carbon to root growth may be a mechanism which enables these plants to avoid A1 toxicity (Dahlgren et al., 1991). The tropical forests with high annual allocations to roots are also characterized by having surface root mats and apogeotrophic roots (Benzing, 1991) - both typically found where nutrient availability to plants is low (Sanford, 1987).

In contrast to the patterns found for above- and belowground biomass accumulations by forest climatic type and soil orders, high total productivities (> 2,000 g m 2 yr -1) were recorded in the warm temperate and subtropical (not including plantations) climatic zones (Tables 1 and 2). Even though high total productivities were recorded in these forests, biomass accumulations were not high due to annual losses of production to the detrital pools (i.e. droughts, hurricanes etc.) (Vogt et al., 1986). The only exception to this pattern of high productivity and low biomass accumulation occurred in three broadleaf evergreen forests in the subtropics and in two cold temperate needle-leaf evergreen forests both growing on Ultisols (Tables 1 and 2).

Detrital transfers, organic matter accumulations by climatic forest type and soil order groupings Our earlier analyses indicated that knowledge of allocation patterns for both above- and belowground biomass does not enable prediction of ecosystem production. Part of this is due to the fact that the turnover rate of above- and belowground tissues did not vary in a predictable manner across different forest types; the losses of annual production to litter inputs varied

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Table 2. Above-, belowground net primary production for climatic forest types separated by soil order. (Means (standard deviation; sample size in parentheses) followed by the same lower case letter are not significantly different within the same column)

Aboveground Belowground % Belowground NPP of

Climatic forest Soil order NPP NPP total NPP type (gm -2 yr -1) (gm -2 yr -1 )

Boreal

Broadleaf deciduous


Inceptisol 348 (169; 2) de 238 (-; 1) abedef 32

Entisol 287 (-; 1) de 43 (-; 1) f 13

Histosol 394 (19; 3) cde 60 (4; 3) def 13

Inceptisol 131 (19; 3) e 56 (12; 3) ef 30

Spodosol 602 (61; 19) bed 97 (11; 19) cdef 14

Cold temperate

Broadleaf deciduous



Alfisol 1,267 (172; 3) abed 314 (64; 6) abedef 27

lnceptisol 1,180 (99; 7) abed 265 (62; 7) abedef 18

Spodosol 855 (120; 6) abed 498 (105; 6) abed 41 Spodosol 1,449 (-; 1) abe 264 (-; 1) abedef 15

Alfisol 844 (197; 4) abed 277 (110; 4) abedef 24

Andisol 332 (100; 2) de 875 (328; 3) ab 54

Histosol * 378 (146; 3) abede *

Incepfisol 1,086 (111; 9) abed 399(67; 12) abede 20

Spodosol 877 (126; 10) abed 441 (46; 13) abed 38

Ultisol 2,605 (615; 2) ab 410 (140; 2) abede 13

Warm temperate

Broadleaf deciduous Entisol 1,815 (-; 1) abe 59 (-; 1) def 3

Ultisol 1,008 (224; 6) abed 516 (146; 6) abed 46

Needleleaf evergreen Entisol 1,272 (596; 2) abed 254 (54; 2) abedef 18

Histosol 1,952 (-; 1) ab 366 (-; I) abode 16 Spodosol 2,156 (825; 2) ab 409 (207; 2) abcde 18

Subtropical

Broadleaf deciduous

Broadleaf evergreen


Oxisol 1,910 (-; 1) ab * *

Ultisol 1,660 (-; 1) abe * *

Histosol 690 (-; 1) abed * *

Oxisol 1,230 (-; 1) abed * *

Ultisol 1,130(175; 4) abed 1,317(-; 1)a 44

Ultisol 1,660 (150; 2) abe 110 (-; 1) bedef 6

Unknown 950 (88; 4) abed 217 (36; 4) abedef *


Broadleaf evergreen


Uttisol 1,258"* 488 (104; 2)abe * Unknown * 500 (77; 2) abe *

Oxisol 761 (288; 5) bed 379 (74; 5) abede 49

Spodosoi * 619 (499; 2) abede * Ultisol * 111 (-; 1) bedef * Unknown 2,802 (-; 1) a * *

Mediterranean

Broadleaf evergreen Unknown 710 (-; 1) abed * *

* N o data. ** Only total NPP and belowground NPP given, calculated as a difference.

167

Table 3. Above- and belowground litter transfers, forest floor accumulations and soil organic matter by climatic forest types separated by soil order. Means (standard error;, sample size in parentheses) followed by the same lower case letter are not significantly different within the same column)

Aboveground Belowground Forest floor Soil organic Climatic forest Soil order litterfall litter transfers biomass matter content type (gm -2 yr -~ ) (gin -2 yr -1) (Mgha - l ) (Mgha -1 )

Boreal Broadleaf deciduous


Inceptisol 246 (-; 1) abcd * 37 (31; 2) abed 207 (73; 2) ab

Entisol 140 (-; 1) d * 19 (-; 1) abed *

Histosol 162 (10; 3) cd * 47 (14; 2) abe 120 (-; 1) ab

Inceptisol 32 (11; 3) e * 113 (14; 3) a 41 (3; 3) b

Spodosol 256 (24; 19) abed * 27 (3; 16) abed 127 (48; 3) ab

Cold temperate Broadleaf deciduous



Alfisol 420 (61; 5) abed 326 (65; 5) 6 (1; 2) bed 141 (-; 1) ab

Inceptisol 436 (29; 8) abed 439 (-; 1) 14 (5; 7) abed 213 (24; 5) ab

Spodosol 628 (195; 6) abed 371 (127; 4) 34 (9; 4) abed 145 (37; 6) ab

Spodosot 359 (-; 1) abed * 14 (-; 1) abed * Alfisol 276 (64; 4) abed 534 (-; 1) 45 (10; 4) abed 96 (25; 4) ab

Andisol 185 (34; 2) bed 823 (316; 3) 99 (51; 2) ab 247 (27; 2) ab

Histosol 406 (196; 2) abed 520 (-; 1) 46 (-; 1) abc *

lnceptisol 255 (39; 13) abed 685 (89; 5) 38 (9; 10) abcd 139 (25; 10) ab

Ultisol 657 (43; 2) abed 310 (190; 2) 28 (6; 2) abed 773 (3; 2) a

Spodosol 290 (45; 9) abed 315 (71; 5) 41 (10; 7) abed 145 (55; 3) ab

Warm temperate Broadleaf deciduous


Entisol 658 (-; 1) abed 249 (-; 1) 7 (-; 1) bed *

Ultisol 453 (46; 6) abed 673 (3; 2) 17 (4; 6) abed 130 (24; 5) ab

Entisol 509 (169; 2) abed 110 (-; 1) 27 (20; 2) abed *

Histosol 757 (-; 1)abed 211 (-; 1) 7 (-; 1) *

Spodosol 526 (16; 2) abed 692(241;2) 26 (7; 2) abed 111 (-; l ) ab

Subtropical Broadleaf deciduous

Broadleaf evergreen


Oxisol 1,250 (-; 1)a * 6(-; t )bcd 90(-; 1)ab

Ulfisol 1,117(-; 1)ab * 9(-; 1) abcd 186(-; 1)ab

Histosol 660 (-; 1) abed * 11 (-; 1) abed 250 (-; 1) ab

Oxisol 1,057 (-; 1) abe * 6 (-; 1) bed 161 (-; 1) ab

Ultisol 649 (148; 4) abed 250 (-; 1) 5 (1; 4) cd 139 (16; 3) ab

Ultisol 839 (528; 2) abed * 11 (2; 2) abed 177 (-; 1) ab

Unknown * * * *


Broadleaf semi-deciduous

Broadleaf evergreen


Ultisol 289 (-; 1) abed * 6 (-; 1) bed * Unknown 535 (0; 2) abed * * *

Inceptisol * * 9 (-; l) abed 199 (-; 1) ab

Oxisol 1,050 (-; 1) abe * 2 (-; l) d 76 (-; 1) ab Entisol * * 3 (-; 1) cd 41 (-; 1) b Inceptisol 1,070 (-; 1) abe * 7 (-; 1) abed *

Oxisol 844 (101; 9) abed * 16 (7; 6) abed 300 (180; 6) ab Spodosol 737 (103; 4) abed * 34 (12; 2) abed 216 (-; 1) ab

Ulfisol 750 (85; 7) abed * 9 (4; 4) bed 90 (-; 1) ab

Unknown * * 14 (5; 3) abed *

168

Table 3. Continued.

Mediterranean

Broadleaf evergreen Unknown

Needleleaf evergreen Spodosol

384 (-; 1)abcd * 11 (-; 1)abcd *

697 (-; 1) abcd * * *

* No data.

significantly within each climatic forest type grouping (Table 3). Even the four-way ANOVA showed that litterfall was not correlated to climate but did suggest that litterfall was significantly correlated to both soil order and leaf type (Appendix A).

Despite no clear patterns across the climatic zones, only in the tropics and subtropics did aboveground litterfall exceed 1,000 g m 2 yr -1 (Table 3). Earlier analyses (Vogt et al., 1986) had shown a relatively good relationship between aboveground litterfall and latitude, most likely a'reflection of the temperature and precipitation regimes. The use of soil order groupings inadequately reflected the available soil water for plant growth which determines the leaf biomass maintained by a plant and its turnover rate. In general, soil texture is a better indicator of plant available water (see later discussion).

Patterns of surface organic layer accumulations were primarily controlled by temperature (through its control of decomposer activity) and leaf tissue chemistries (deciduous litter decomposing faster than evergreen litter) (Swift et al., 1979). The four-way ANOVA showed climate to significantly explain the variations in surface organic matter accumulations (Appendix A). However, the high variability in the amount of surface organic accumulations within each grouping when using the climatic forest type grouping level resulted in only a few significant differences for most forest groups. A previous analysis of a large data- set showed significant differences in surface organic matter accumulations (Vogt et al., 1995) that could not be shown for this data-set since that information was infrequently included. This data-base did allow for the identification of those groups with the lowest and the highest accumulations. For example, those forests with lower mean annual temperatures and dominated by evergreen species had the higher forest floor accumulations (Table 3). Higher surface litter accumulations were observed for needle-leaf evergreen forests in the boreal zone and in the high elevations of the cold temperate zone, while the lowest accumulations occurred in the deciduous forests in the warm temperate and tropical zones (Table 3).

Similarly, few significant differences in soil organic matter (SOM) could be identified within the climatic and soil order groupings- again due to the high within group variability. Only two cold temperate needleleaf evergreen forests growing on Ultisols had significant SOM accumulation (773 Mg ha -1) compared to the other groups (41-300 Mg ha-l) (Table 3).

Even though this data-set showed no relationship between carbon sequestration in the soil by climatic and soil order groupings, another study with a large data-set for SOM suggested a relationship between carbon sequestration and soil chemical factors (Vogt et al., 1995). The SOM accumulations occurring in the Ultisol, Oxisol and Andisol soil orders have higher levels of A1 cycling and high clay contents which result in strong organic-aluminum-clay complexes. These complexes diminish the ability of microbes to decompose organic material (Dahlgren et al., 1991; Merckx et al., 1985). Merckx et al. (1985) showed a strong pattern of soil texture controlling the turnover rate of root derived C by soil microbes. They recorded a rapid turnover rate of root derived C in sandy soils while decay rates were slow in clay soil. Interestingly, the soil orders with higher soil organic matter accumulation rates were also those that had higher total living biomasses recorded in the temperate and tropical zones (Table 1 and 3) and, at least in the warm and cold temperate zones, had some of the highest total NPP values recorded (> 1,500 g m 2 yr-1).

Using our current data-base, no relationships were found between the magnitude of either above- or belowground litter inputs and the amount of carbon sequestered in the soil and surface organic layers (Table 3); another study (Vogt et al., 1995) supported the ineffectiveness of utilizing litter inputs to predict the ability of forest ecosystems to sequester and store carbon in the soil or surface organic layers. In that study (Vogt et al., 1995), less than 10% of the variation in surface organic matter accumulations or in the amount of organic matter accumulating in the soil was explained by using above- and belowground litter transfers. When the amount of nitrogen in aboveground litterfall was used to predict forest floor biomass and SOM accumulation, it became evident that there is a

significant relationship between aboveground litterfall N and forest floor biomass (even though only 23% of the variation was explained) (Vogt et al., 1995). In contrast, there appeared to be no relationship between aboveground litterfall N and SOM for that data-set (Vogt et al., 1995).

Factors predicting carbon allocation patterns

Aboveground Past studies have shown good relationships between aboveground biomass or productivity and climatic variables (see O'Neill and DeAngelis, 1982). How- ever, few significant relationships were found between these variables with the data in Appendix B. For example, mean annual temperature explained the highest proportion of the variation in aboveground biomass and even though the correlation was significant (p = 0.009) it only explained 6% of this variation. In contrast some of the nutrient pools and fluxes explained significantly more, albeit low percentage (12-47%), of the variation in aboveground biomass. For example, forest floor nutrient contents were negatively correlated with aboveground biomass while litterfall cycling of nutrients were positively correlated with aboveground biomass (i.e. mean residence time of forest floor K (r = -0.57, p = 0.003, n = 25); mean residence time of forest floor N (r = -0.56, p = 0.003; n = 38); mean residence time of forest floor Ca (r = --0.48, p = 0.014; n =2); mean residence time forest floor P (r = -0.41, p - 0.003; n - 38); aboveground litterfall N (r = 0.41, p = 0.001; n = 57); aboveground litterfall P (r = 0.41, p = 0.005; n =45); aboveground litterfall K (r = 0.37, p = 0.016; n = 4); aboveground litterfall Ca (r = 0.37, p = 0.017; n = 42). The negative correlations with forest floor nutrient contents probably reflect the fact that as the forest floor accumulates less nutrients are available to trees because nutrients are immobilized in the surface organic layers (Vogt et al., 1986). In contrast, litterfall nutrient contents indirectly reflect the amount of nutrients being cycled within the ecosystem (Cole and Rapp, 1981) so that a positive relationship should be expected.

In contrast to the relationships obtained with aboveground biomass, using the entire dataset to examine how aboveground productivity was related to climatic and nutrient factors showed both variable groupings to be effective and better at predicting changes in aboveground production. For example, mean annual temperature (r = 0.61, p = 0.0001, n =91), minimum

169

annual temperature (r = 0.56, p = 0.0001, n = 54), and mean annual temperature/precipitation ratio (r ,- 0.47, p = 0.0001, n = 91) as individual variables were able to explain a significant portion of the variability in aboveground production. The best predictive relationships were obtained with nutrient variables even though the sample sizes were lower for these comparisons - again the mean residence time of forest floor nutrients were negatively correlated and litterfall nutrients were positively correlated to aboveground productivity. Forest floor nutrient contents and their mean residence times (i.e. mean residence time of forest floor N [r = -0.82, p = 0.0001, n = 36]; mean residence time forest floor K [r = -0.66,p = 0.001, n = 24]) were effective at explaining the recorded variations in aboveground production. Similarly, litterfall nutrient contents as individual variables were able to explain about a third of the variation in aboveground production (aboveground litterfall Ca [r = 0.60, p = 0.001; n = 57]; aboveground litterfall P [r ~ 0.41, p = 0.005; n = 45]; aboveground litterfall K [r = 0.37, p - 0.016; n - 41]).

Belowground Studies from the 1980's successfully predicted fine root biomass and production in several ecosystems using mainly climatic and nutrient data (Gower, 1987; Nadelhoffer et al., 1985; Vogt et al., 1986, 1990). These studies were conducted in the cold temperate zone where the following variables appeared to explain between one-half to three-fourths of the variation in fine root biomass or fine root turnover: mean annual temperature, mean annual temperature/precipitation ratio, soil nitrogen mineralization rates and nitrate production rates, litterfall N, litterfall Ca, and forest floor N mean residence time. Based on these earlier small data-sets, maximum monthly temperatures explained 65% of the variation in fine root biomass for needle-leafed evergreen forests but not for broadleaf deciduous forests (Vogt et al., 1986). Temperature variables by themselves were unable to explain any of the variation in fine root dynamics in broadleaf forests; however, a mean annual temperature/precipitation ratio explained 79% of the variation in fine root turnover. This contrasted with the needle- leafed forests where a temperature/precipitation ratio did not increase the ability of predicting fine root dynamics (Vogt et al., 1986). Variables reflecting N cycling in ecosystems explained approximately 75% of the variation in fine root turnover in cold temperate

170

Table 4. S u m m a r y o f re la t ionsh ips b e t w e e n log t r a n s f o r m e d f ine roo t b i o m a s s and log t r a n s f o r m e d f ine roo t ne t p r i m a r y p roduc t ion ( N P P ) data,

and abiot ic and b io t ic va r i ab le s

Dependent Independent Boreal Cold Cold Warm Warm Subtropical Tropical Tropical

variable variable needleleaf temperate temperate temperate temperate broadleaf bmadleaf broadleaf

Y - x - evergreen broadleaf needleleaf broadleaf needleleaf evergreen deciduous evergreen

deciduous evergreen deciduous evergreen

Fine root Soil texture R 2 - 0.73 r 2 - 0.84

biomass (coarse to p < 0.041 p < 0.054

f i ~ ) a l 7.402688 ot - 8.02457

3 ---0.1619887 3 --0.131649

n - 0 n - 1 0 * n - 2 1 * n - 7 * n - 5 n - 4 * * n - 5" n - 14"

Fine root Annual re - O. 15 r e - 0.28

biomass precipitation p < 0.033 p < 0.017

c~ - 3.963352 c~ - 7.733787

/3 " 0.001246 3 " -0 .001476

n - 2 5 n - 17 n - 2 2 " n - 6 * n - 5 * n - 6 * n - 5 * n - 2 1 *

Fine root Mean annual

biomass air

temperature

n - 2 5 * n - 13" n - 13" n - 6 * n - 5 *

re - 0.83

p < 0.008

a - 1.754699

/3 - 0.20579

n - 6 n-3*

r 2 - 0.39

p < 0.003

o~ - 2.759455

/~ 0.164558

n-18

Fine root Maximum

biomass annual air

temperature

n - 1 n - 11" n - 2 0 * n - 4 * n - l * n - 6 *

r2 - 0.69 re - 0 . 8 1

p < 0.051 p < 0.001

ot - 9.444893 a - 1.469007

/3 - --0.096564 3 - 0.200584

n - 5 n - 1 3

Fine root Minimum

biomass annual air

temperature

n - 1 n - 12"

r 2 - 0 . 1 1

p < 0.069

a - 5.829313

3 - -0.053195

n - 22 n - 4* n - 2* a - e * n - 5 *

re - 0.29

p < 0.034

a - 5.245706

/3 - 0.085052

n - 13

Fine root Aboveground re - 0.95

biomass litteffall P p < 0.101

a - 6.170377

/3 - -0.602439

n - 3 * * n - 4 *

r 2 - 0.64

p < 0.034

a - 8.568653

/3 - -1.822383

n - 6 n - 6 * n - 3 * n - 3 * n - 0

re - 0.53

p < 0.003

ot - 8.148794

/3 - -0.384467

n - 1 3

Fine root Aboveground

biomass litterfall K

n - 3 *

r 2 - 0.97 rre - 0.74

p < 0 . 0 1 1 p < 0.092

c~ - 4.726906 a - 8.136369

3 - 0 . 1 1 4 5 1 6 3 - - 0 . 4 4 2 4 6 2

n - 4 n - 4 * * n - 4 * n - 1 n3* n - O

- 0.34

p < 0.036

- 8.192298

3 - -0 .051365

n - 11

Fine root Abovegronnd r 2 - 0.99

biomass litterfall N p < 0.014

a - 6.498713

3 - -0.078648

n - 3 n - 12" n - 8" n - 6" a - a * n - 4 * n - 0 n - 17"

Firm root Aboveground

biomass litteffall NP

ratio

n - 3* n - 4 * n - 5* n 6* n - 3* n - 3 * n - 0

re - 0.19

p < 0.076

a - 5.726211

3 - 0.027873

n - 13

171

Table 4. Continued

Fine root Forest floor N

biomass

n - 0

r 2 - 0 . 5 3

p < 0.025

a - 2.478602

/3 - 0.000396

n - 4 * n - 8 n g 4 * n - O n - 4 * n - O n - 7 *

Fine root Mean r 2 - 0.63

biomass residence p < 0.067

time forest a - 2.571277

floor N fl - -0 .006219

n - 5 n = 0 n - 0 n m 4 * n - O n - 4 * n - O n - 7 "

Fine root Soil texture

NPP (coarse to

fine)

r im2* n - 8* n - 26"

1"2 - 0.75

p < 0.007

oL - 6.417555

/3 - 0.572938

n-7 rim4* n - O n - O n-6*

Fine root Annual

NPP precipitation

n - 26"

:-o.13 p < 0.024

a - 5.37181

fl - 0.000313

n - 19" n - 33 n - 7 * n - 5 * n=l n-4* n - 6 *

Fine root Mean annual r 2 - 0.19

air p < 0.016

temperature o~ - 4.134077

/3 = 0.112126

n = 26 n - 17 n - 2 4 n - 7

r 2 - 0.61 NPP

p < 0.073

ot - 3.585646

/3 - 0.134347

n - 5 n - I n - 3 * n - 4 *

Fine root Maximum

NPP annual air r 2 - 0.83

temperature p < 0.06

c~ - -4 .393725

/3 = 0.523916

n=4** n - 10" n - 30* r im4* n - 1 n - 1 n - 4 * n - 3 *

Fine root Minimum r 2 - 0.83

NPP annual air p < 0.06

temperature a - 6.057023

fl - 0 .082724

n = 4 * * n - 11" n - 2 9 " n - 4 * n - 2 n - I n - 4 * n - 3 *


NPP Litterfall N

n - 4 *

r 2 - 0 . 1 5

P < 0.066

oz = 6.58257

fl 1 - 0 . 0 2 3 0 1 3

n - 14" n - 17 n n 7 * n - 3 * n - O n - O n - 4 *


NFP litteffail N/P

ratio

n - 1 n - 6* n - 14" n - 6 * h i 3 *

r 2 - 0.99

p < 0.004

ot - 3.963198

fl - 0.052116

n - 0 n - 0 n - 4

Fine root Forest floor

NPP N content

n m3* n - 6* n - 15"

r 2 - 0.66

p < 0.059

ct - 3.018691

fl - -0 .002421

n - 5 n-0 n-0 n-0 n-I

172

Table 4. Cont inued

Fine root Mean r 2 = 0.99

residence p < 0.070

time forest ct = 2.128776

floor N 13 = -0.001133

n = 3 n l 6 * n - 12"

r 2 - 0.65 NPP

p < 0.062

ct - 2.996559

/3 = -0.082811

n - 5 n = O n = O n = O n - 1

* No s ignif icant re la t ionshop (p < 0.10).

** Data clustered.

needle-leaf and broadleaf deciduous forests (Nadelhof- fer et al., 1985; Vogt et al., 1988b).

These earlier studies identified temperature and N cycling variables as being useful for predicting fine root biomass and turnover. However, these earlier analyses were conducted with small data-sets from only a few sites. It is important to determine whether the same relationships identified earlier will be maintained as the size of the data-set increases and as more data is collected from a broader range of sites. The following sections synthesize the results from the 200 data-sets that were used in our analyses (Appendix B ) - a data-set was only included if either fine root biomass or NPP data was available. The statistical analyses which used a variety of climatic and nutrient indices to predict fine root biomass and production resulted in three general patterns which are further discussed below: (1) different variables predicted fine root biomass and fine root NPP, (2) predictive variables changed depending on the scale at which analyses were conducted (i.e. by evergreen compared to deciduous forests, across climatic forest types and by general species groups (i.e. oaks, pines, conifers minus pines, and deciduous species minus oaks), and (3) an effective variable predicting fine root biomass or fine root NPP at one grouping level (i.e. climatic forest type or species grouping) is not transferable- indicating that adaptive strategies vary significantly by functional or species groups (Tables 4 and 5).

Evergreen versus deciduous grouping. Climatic variables did not explain any of the variation in fine root biomass and root production in the deciduous grouping when analyzing the data-set at the evergreen versus deciduous grouping level. In the deciduous grouping level, a low (12%) but significant level (p - 0.10, n -24) of fine root biomass changes could be explained by soil texture. At the deciduous grouping level, nutrients (especially K) in litterfall and in the forest floor explained a large portion of the variability in fine root biomass (aboveground litterfall K (r - 0.96, p - 0.0001, n - 10); forest floor K mean residence time [r = -0.82, p - 0.05, n - 6]; aboveground litterfall N/K ratio (r =

-0.82, p = 0.004, n = 10); aboveground litterfall N/Ca ratio (r = -0.68, p = 0.02, n = 11)). For this data-set, only forest floor mean residence time of P (r - -0 .60 ,p = 0.09, n - 9) and soil N contents (r - -0.57, p - 0.03, n = 14) explained any of the variation in root NPP in the deciduous category.

In the evergreen category level, climatic and nutrient variables were significant in explaining changes in fine root biomass and root NPP. For example, the following individual climatic variables explained some of the variation in fine root biomass: mean annual temperature (r ~ 0.61, p = 0.0001, n - 70); mean annual precipitation (r = 0.42, p = 0.0001, n - 82); minimum annual temperature (r - 0.30, p - 0.04, n - 47); and soil texture (r - -0.36, p - 0.02, n = 45). The following nutrient variables explained a low but significant proportion of the changes in fine root biomass in the evergreen category: aboveground litterfall N/P ratio (r = 0.48,p = 0.009, n - 28); soil Ca contents (r - 0.45,p - 0.08, n = 16); aboveground litterfall P (r = -0.39, p = 0.04, n = 29); and aboveground litterfall N/Ca ratio (r = 0.36, p = 0.09, n = 24). For the evergreen category, aboveground litterfall was able to explain 20% of the variation in fine root biomass (p = 0.0002, n = 67).

In the evergreen category, changes in root NPP were explained mainly by the mean residence time of nutrients in the forest floor in contrast to litterfall nutrients that appeared to explain a greater proportion of fine root biomass changes. For example, mean residence time of forest floor N explained most of the variation (53%) in root NPP (p - 0.001, n = 1 6 ) - this was a negative correlation which showed that increased retention of N in the forest floor resulted in lower root NPP. Oth- er individual nutrient variables were also effective in explaining root NPP changes: forest floor P contents (r - -0.73, p - 0.005, n - 13); forest floor mean residence time for K (r = -0.55, p = 0.06, n = 12); soil Ca contents (r = 0.55, p = 0.05, n = 13); forest floor P mean residence time (r = -0.62, p = 0.03, n = 12). Aboveground litterfall (r - 0.31, p - 0.01, n - 67) and forest floor mass mean residence time (r = -0.37, p 0.007, n - 53) were also able to significantly explain

Table 5. Summary of relationships between log transformed fine root biomass and log transformed

fine root NPP, and abiotic and biotic variables for oaks, pines, broadleaf deciduous species minus

oaks (deciduous), coniferous evergreen species without pines (conifers)

1 7 3

Dependent Independent Conifers Hardwood

variable variable Pines

Y - x - (w/o Pines) (w/o Oak)

Oaks

Fine root

biomass

Soil texture r 2 - 0 . 1 7

p < 0.004

a - 6.867929

13 - 4) .076464

n - l l * n17* n - 4 0 n - 5 *

Fine root

biomass

Annual

precipitation

m m

- 0.44

p < 0.001

a - 4.128603

/3 - 0 . 0 0 1 0 3 8

n - 38 n - 15" n - 55* n - 7"

Fine root

biomass

Maxiumum

annual

temperature

o C

r 2 - 0,20

p < 0.061

a - 8.954426

g - 4) . 169167

n - 1 4 n - 11" n - 37* n - 5 *

Fine root

biomass

Minimum

annual air

temperature,

o C

- 0.17

p < 0.066

a - 5.932532

/3 - - 0 . 0 9 5 2 1 1

n - 16 n - 12" n - 37* n - 6*

Fine root

biomass

Mean

annual air

temperature/

precipitation

ratio

r 2 - 0.27

p < 0.001

a - 6.348545

- - 1 . 6 5 4 5 3 7

n - 32 n - l l * n - 4 6 " n - 7 *

Fine root

biomass

Maximum

annual air

temperature/

precipitation

ratio

- 0.37

p < 0.013

a - 7.180704

/3 - - 0 . 7 7 3 7 0 6

n - 14 n - 12" n - 37" n - 5*

Fine root

biomass

Minimum

annual air

temperature/

precipitation

ratio

r 2 - 0 . 1 6

p < 0.070

a - 6.880636

/3 - 4) .495288

n - 1 6 n - 12" n - 39* n - 6*

Fine root

biomass

Forest floor

N content

r 2 - 0 . 7 3

p < 0.042

- 6.083229

/3 - 0.0O0714

n - 5 n - 4*

r 2 - 0 . 1 9

p < 0.029

cz - 6.187128

/3 - 0.001525

n - 2 1

r 2 - 0.99

p < 0.037

- 6.825373

/3 - 4) .000486

n - 3**

Fine mot

biomass

Aboveground

litterfall P

r 2 - 0.69

p < 0 . O 1 2

a - 8.19545

/3 - -1.507838

n - 7 r t - 4*

r 2 - 0.23

p < 0.007

a - 7.300949

- -0 .24095

n - 27 r / - 4 *

174

Table 5. Continued

Fine root Forest floor P

biomass m e a n

residence

time

n - 4" n - 1

r 2 - 0 . 3 4

p < 0.028

a - 2.679806

3 - 0.045833

n - 1 2

r 2 - 0.99

p < 0.051

a - 3.055979

3 - -O.02O266

/ / - 3 * *

Fine root Abovegmtmd

biomass litterfall Ca

r 2 - 0 . 5 4

p < 0.099

a - 9.080567

3 - -0.14001

n - 5 n - 3 * n - 23* / / - 4 *


biomass litterfall N/P

ratio

r / - a * n m 4 *

r 2 - 0 . 2 1

p < 0.009

a - 5.63396

3 - 0.026626

n - 27 / /m4*

Fine root Forest floor N

biomass mean

residence

time

n - 4 * n - 2 n - 19"

r 2 - 0 . 9 9

p < 0.023

a - 2.983728

3 - -0 .009626

n - 3 * *

Fine root Annual

NPP precipitation,

m m

r 2 - 0.23

p < 0.0002

a - 4.345186

- 0.000597

n - 54

r 2 - 0.25

p < 0.0379

a - 6.105269

3 - - 0 . 0 0 0 3 1 2

n - 1 4 n - 30" n - 10"

Fine root Mimimum r 2 - 0.33 r 2 - 0.34

NPP annual iar p < 0.001 p < 0.0219

temperature, a - 5.848291 a - 5.700991

°C 3 - 0.057023 3 - -0 .031009

n - 2 7 n - 1 3 n - 1 6 " / / - 8 *

Fine root Mean

NPP annual air

temperature

r 2 - 0 . 4 4

p < 0.0001

ct - 4.268831

3 - 0.149055

n - 4 8 n - l l * n - 25* n - 10"

Fine Root Forest floor P r 2 - 0.33

NPP mean p < 0,031

residence a - 2.572408

time 3 - -0 .004582

n - 1 2 n - 0 / / - 5 * / /m4*

Fine root Forest floor

NPP N content

n - 16" n - 2

r 2 - 0 . 3 8

p < 0.0465

a - 6.104104

3 - - 0 . 0 0 1 4 0 2

n - 9

r 2 - 0.79

p < 0.0767

ct - 5.027503

3 - 0.001364

n - 4

175

Table 5. Continued

Fine root Forest floor NPP P content

rE -0.49

p < 0.0048

ct - 6.297092 a - 6.297092 B - -0.013525 n - 1 3 n - 0 n - 5 *

Fine root Aboveground NPP litterfall N

n - 20" n - 2 n - 20*

Fine root Aboveground NPP litterfall K

n - 14" n - 1 n - 10"

Fine root Aboveground NPP Litterfall P

n 16" n - 1 n - 13"

Fine root NPP

Maximum annual air

temperature/ precipitation ratio n - 28*

rE - 0.33 p < 0.0298 a - 4.836896

B - 0.410323 n - 12 n - 16"

Fine root Mean NPP annual air

temperature/ precipitation

ratio n - 4 8 " n - 11" n - 25*

* No significant relationship (p < 0.10). * * Data clustered.

/ / -4*

rE -0.31

p < 0.0859 ct - 6.768667

B - -0.025366 n - 8

r E - 0.58

p < 0.0828 a - 4.270154

B - 0.056890 n - 5

r 2 -0.45

p < 0.0875

a - 3.747428 B - 0.591437 n - 6

n - 6 "

rE - 0.47

p < 0.0174 a - 6.624322

B --0.927168 n - 10

some o f the var ia t ion in root N P E In the everg reen

category, only mean annual temperature (r - 0.47, p

= 0.0001, n = 65) and m e a n annual precipi ta t ion (r =

0.35, p = 0.002, n = 76) were able to expla in a low

propor t ion o f the changes in root N P E

Di f fe rences in nutr ient cyc l ing and where a plant

needs to obtain its nutrients requi red for growth should

affect the impor tance o f these nutrients in control l ing

carbon al locat ion patterns. Di f fe rences be tween ever-

green and dec iduous species were genera l ized in the

past (Aerts, 1995) but these di f ferences may not be

so clear. For example , in a study conduc ted by Son

and G o w e r (1991), eve rg reen species were requi red to

take up 7 2 - 7 4 % o f their annual N required for growth

f rom the soil or decaying litter compared to the two

dec iduous species where re t ranslocat ion accounted for

7 6 - 7 7 % of the annual N requirement . In the past it had

been assumed that eve rg reen species re t ranslocate N

and P more eff iciently than dec iduous species (Chapin

and Kedrowski , 1983; Vitousek, 1982; Waring and

Schlesinger , 1985), however , this relat ionship is prob-

ably not this genera l and may vary by cl imat ic forest

type and by species grouping. Therefore , it could be

hypothes ized that i f a tree is more dependen t on taking

up a large propor t ion o f its nutrients f rom the soil or

surface organic layers, the nutrient l imi ta t ion o f a site

may more strongly control C a l locat ion patterns and

may be a useful parameter to monitor.

176

Climatic forest type. At the grouping level of the climatic forest type, climatic variables were ineffective in explaining variations in fine root biomass or NPP (Table 4). Climatic variables were best at explaining variations in belowground processes at the extreme ends of the temperature gradients: boreal, subtropical and tropical climatic zones. Precipitation explained a low proportion of the variation in fine root biomass only in the boreal and cold temperate zones. The proportion of the changes in fine root biomass and NPP explained by precipitation were lower than what was obtained using the larger scale grouping categories of evergreen and deciduous.

Analyzing the data set by climatic forest type and then separately by species showed that some climatic variables were better predictors of fine root biomass across several climatic forest types and in some species groupings but they were poor predictors of fine root NPP (Table 4). The different abilities to predict changes in fine root biomass compared to fine root NPP is highlighted by examining the role of mean annual precipitation in explaining changes in fine root dynamics in the cold temperate broadleaf deciduous forests (Table 4). While annual precipitation significantly explained 28% of the variation in fine root biomass in this climatic forest type, it explained none of the variation in fine root NPP. Similarly, maximum annual air temperature explained 81% of the variation in fine root biomass but none of the variation in fine root NPP for tropical broadleaf evergreen forests. Whether these relationships are sustained as the data- base is expanded will be important to monitor since these relationships are presently formed from small data-sets.

Even though the data base is quite small, strong correlations of fine root biomass and NPP with aboveground litterfall transfers of N, P, or K were obtained in climatic forest types where these nutrients limit their growth (Cuevas and Medina, 1986; Husni Mohd. Shar- iff and Miller 1991; Lugo and Scatena, 1995; Vitousek, 1982; Vitousek and Sanford, 1986). Nitrogen in litterfall correlated with fine root NPP in cold (r 2 - 0.44) and warm temperate (r 2 - 0.81) needleleafed forests, and in cold temperate broadleaf deciduous forests (r 2 - 0.59). In tropical broadleaf evergreen forests where both N and P can limit tree growth (Cuevas and Medina, 1986), a ratio of N/P in aboveground litterfall explained 99% of the variation in fine root NPP (Table 4). Sim- ilarly, 53% of the variation in fine root biomass was explained by aboveground litterfall P in the tropical broadleaf evergreen forests and 34% of the variation

by aboveground litterfall K in the tropical broadleaf evergreen forests (Table 4).

Species groupings. Climatic variables at the species grouping level were better at explaining the variations in fine root dynamics than using the climatic forest type grouping (Tables 4 and 5). At the species grouping level for conifers, annual precipitation explained 44% of the variation in fine root biomass and 23% of the variation in fine root NPP (Table 5). This contrasts the cold temperate needleleaf evergreen forests where none of the variation in fine root biomass and 13% of the variation in root production was explained by mean annual precipitation.

When the conifer group did not include Picea spp., the variability in fine root biomass explained by precipitation increased to 78% while 31% of variation in fine root NPP was explained. This shows the importance of the grouping level in determining how much of the variability in fine root dynamics can be explained by individual climatic variables. The predictive relationship between fine root biomass and precipitation for the coniferous species (Table 5) is similar to that obtained in the earlier analyses (Vogt et al., 1986). This relationship between fine root biomass and annual precipitation in the coniferous forests did not exist far the pine, oak and hardwood groupings (Table 5).

The low ability to predict fine root dynamics using precipitation may be related to species drought adaptive strategies which would confer on these species high water-use-efficiencies (Valentini et al., 1992) and the fact that precipitation does not reflect the water storage capacity of the soil. Research has shown strong relationships between site soil water potential and the leaf area supported by a stand when comparisons were made regionally for conifers growing on sites with very different water balances (Grier and Run- ning, 1976). Analyzing within the climatic forest type groupings, soil texture had strong correlations with fine root biomass but not fine root NPP in warm temperate and tropical evergreen sites where it explained a high proportion (73 to 84%) of the variability in fine root biomass (Table 4). The lack of a correlation with precipitation but strong correlations with soil texture demonstrate the importance of water availability in controlling tree growth and the fact that precipitation by itself may not reflect the water holding capacity of a site.

A general pattern has been suggested that tree growth is lower as precipitation decreases and the leaf area decreases and root growth is favored (Schulze,

1982). Few comparative studies of changing water budgets for evergreen and deciduous forests have been reported that would allow for a speculation of the patterns obtained with precipitation and biomass accumulation or growth (Schulze, 1982). Conifers have been shown to intercept annually 15-20% (up to 66% for Pseudotsuga menziesii) more water at the leaf surface than broad-leaved trees resulting in conifers having less water totally available on the site (Schulze, 1982). How the differential ability of forests to retain precipitation on the site affects plant access to water is not clear and needs to be examined in relationship to plant adaptive strategies for acquiring water and their water-use-efficiencies.

The lack of a good relationship between soil texture or precipitation and fine root biomass for the oaks is probably due to the oaks being more drought tolerant than most other hardwood species (Abrams, and Kubiske, 1990). Sclerophyllous leaves with thick cuticular layers common to oaks help reduce water loss and enhance resistance to drought (Mooney and Dunn, 1970). If the oaks are able to adapt to varying soil water levels, any variable reflecting site water availability will not be effective in predicting oak growth. For example, the capacity of white oak to increase the number of root tips, and in turn, the root system biomass, despite moisture and nutrient constraints which limit root elongation rates, may be an important mechanism for drought resistance which can compen- sate for fluctuations in water availability (Teskey and Hinckley, 1981).

Comparison of different grouping levels. At the evergreen and deciduous grouping level, temperature variables were ineffective in explaining any of the changes in fine root biomass or NPP for deciduous species while mean annual temperature did explain 38%, of the variation in fine root biomass and 22% of the variation in root NPP. At the level of the individual climatic forest type, temperature variables were better at explaining changes in root dynamics than in the evergreen and deciduous grouping. In the climatic forest type groupings, the extreme ends of the climatic gradient (i.e. boreal, tropics) had temperature variables effectively explaining changes in fine root dynamics. In the climatic forest groupings, mean and maximum annual temperatures were effective in explaining from 39 to 83% of the variation found in the amount of fine root biomass but not fine root NPP maintained by broadleaf forests of the tropics and subtropics (Table 4). In the boreal needleleaf evergreen forests, maximum annu-

177

al temperature appeared to explain a high proportion (83%) of the variation in root NPE A combination of temperature and precipitation as an independent variable to predict fine root biomass or NPP were ineffective in improving our ability to predict fine root dynamics. Even though research conducted by Luxmoore et al. (1995) suggested pines should be sensitive to maximum mean annual temperatures, we were unable to tease this relationship apart with our data-set.

There is a strong influence of soil nutrients on carbon allocation patterns in plants especially when the availability of these nutrients limits plant growth (Ingestad and Agren, 1992). In contrast to the studies using climatic variables to predict fine root dynamics, studies have shown good relationships between nutrient availability indices and fine roots in a few sites localized in the warm and cold temperate climatic zones. In the cold temperate zone, several different N availability indices have been shown to have some utility for predicting root biomass and/or turnover rates (Aber et al., 1985; Gower et al., 1992; Vogt et al., 1986). In 13 forests located in Wisconsin and Mas- sachusetts, soil nitrification rates predicted 78% of the variation in fine root biomass - fine root biomass decreased as the nitrification rates increased (Aber et al., 1985). The relationship with nitrification rates did not occur with N mineralization rates suggesting that nitrate levels had a very different effect on root biomass compared to ammonium levels. Similarly, in cold-temperate needle-leafed forests, 76-77% of the variation in fine root biomass and fine root turnover was explained by the mean residence time of nitrogen in the forest floor and the amount of nitrogen cycled in aboveground litterfall, respectively (Vogt et al., 1986). In broad-leaved forests of the cold temperate zone, litterfall nitrogen explained even a higher proportion of fine root turnover (88%) than in the needle-leafed forests (Vogt et al., 1986). The number of data points used for the forest floor mean residence time and litterfall nitrogen comparisons with fine roots was small (e.g. 5-6) so it is not clear if these patterns are generally transferable to other ecosystems or are unique to the region where the data was collected.

At the species grouping level, the importance of forest floor nutrient contents versus litterfall cycling of nutrients were not as clear (Table 5) except for the fact that the pine category had none of the nutrient variables explaining any of the variation in root growth and biomass accumulation. Again, a greater number of nutrient variables were capable of explaining changes in fine root biomass than for root NPP (Table 5).

178

The species level grouping also appears to need further subdivision to effectively predict root dynamics. For example, pines appear to have 3 basic adaptive strategies of phenological patterns for carbon allocation that varies by climate - 1 strategy for cold temperate and boreal species (i.e. determinate growth), a 2nd strategy for warm temperate and subtropical pines (i.e. semi-determinate growth) and a 3rd strategy for tropical pines (i.e. indeterminate growth) (Gower et al., 1995). Our data-set lends support for separating pines into smaller groups by climate type instead of lumping pines as one group. Data were analyzed separately for the pines by cold temperate and boreal, and warm temperate and subtropical. Pines in the warm temperate and subtropical zones had mean annual temperature and soil texture explaining 52% and 47%, respectively, of the variation in root NPP. When the pines were combined as a group, 33% of the variation in root NPP was explained by mean annual temperature and none of the variation could be explained by soil texture. When pines were analyzed separately for the cold temperate and boreal zones as a group, no variables were able to explain root NPP. Similarly for fine root biomass, grouping of pines by zones showed that cold temperate and boreal pines were the only ones that had maximum annual temperature ( r - 0.76, p - 0.05, n - 7) explaining over half of the variation in fine root biomass. Similar- ly for the warm temperate and subtropical pines, soil texture (r - -0.81, p -- 0.03, n = 7) and precipitation (r - -0.80, p - 0.10, n - 5) were able to significantly predict fine root biomass. The importance of maximum mean annual temperature in controlling pine growth as suggested by Luxmoore et al. (1995) was teased apart in this smaller grouping of data but could not be shown when larger data groupings were used.

When the hardwood species were separated into smaller groups using the climate grouping approach (i.e. boreal + cold temperate; warm temperate + subtropical; tropical), other variables were effective at predicting root dynamics. The cold temperate and boreal hardwoods had climatic variables explaining a significant portion of the variability in fine root biomass (mean annual temperature explaining 68% of the variation, precipitation 61% and maximum temperature/precipitation ratio explaining 44% of the variation). Root NPP was better explained by nutrients than climatic variables. The tropical hardwoods had SOM (r - -0.76, p - 0.08, n - 6) and the other nutrients identified in the climatic forest groupings explaining a significant portion of fine root biomass.

The multiple regressions developed for predicting fine root biomass and NPP have many of the vari-

ables identified earlier explaining a significant portion of this variability across climatic forest types and soil types. With a sample size of 15, 69% of the variation in fine root biomass was predicted using precipitation, aboveground litterfall N/Ca ratio and soil Ca contents (p - 0.004). This contrasted root NPP where mean annual temperature and soil Ca contents were best able to predict NPP with an R 2 = 0.62 (p - 0.0003, n ~ 20). The multiple regressions developed for the belowground contrast that produced for the aboveground which incorporated the following variables: forest floor Ca contents, soil N contents, aboveground litterfall and mean residence time of forest floor N (R 2

0.90,p = 0.0001, n - 21). These regressions combine climatic and nutrient variables and utilize similar variables in the equations so that the distinctions between biomass and productivity are not as clear.

Conclusions

In the process of attempting to predict the role of the belowground in ecosystem response to disturbances, it becomes apparent that variables that reflect shifts in carbon allocation between above- and belowground biomass are important response variable to monitor. Analysis of this data-base suggested that climatic variables and nutrient pools are important controlling factors in determining the amount of fine root biomass maintained on a site while nutrient fluxes are important in predicting the amount of roots produced annually (NPP). Interestingly, the variables that were effective at predicting fine root biomass were poor at predicting fine root NPP and viceversa. Depending on the scale at which data were grouped, climatic variables increased in their ability to predict root dynamics as the groupings became more specific (i.e., evergreen and deciduous, climatic forest grouping to species and species groups by climatic zones). The high variability in either root biomass or root NPP by climatic forest type showed that variables other than climate are important in determining the amount of carbon that is allocated to roots in many forest groupings. Fine root biomass was significantly predicted by either the N, P, or K contents or N/P ratio of aboveground litterfall - again variations in fine root NPP were not as well explained by a nutrient being cycled in aboveground litterfall. In contrast root NPP was better explained by the nutrients stored or being cycled in the surface organic layers. The data showed the importance of identifying functional groups at different scales, for example at the level of a climatic forest type and/or species groupings (e.g. pines vs.

oaks) based on mechanistic physiological differences which strongly influence carbon allocation to roots and NPP.

Acknowledgements

We thank Kris H Johnson and Oswald Schmitz for con- sultations on statistical questions and John Ranciato for assistance with library research. The ideas developed in this paper occurred while conducting research supported by the National Science Foundation Grant # BSR-8811902 (Long-term Ecological Research Pro- gram in the Luquillo Experimental Forest, Puerto Rico) and DEB9306758 to Yale University, and from the USDA Forest Service Insect and Disease Laborato- ry, Northeast Forest Experiment Station and Northeast Global Change Program.

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Section editor: H Lambers

184

Appendix A. Relationships between various dependent 'log transformed' and independent variables (defined in the text) are analyzed using four-way ANOVA's. Results are derived after non-significant sources are dropped from the initial model. For consistency all main effects are listed; however, interactions are listed only when significant (p < 0.I)

Source DF MS F p

a) Above.ground bionmss (n - 125)

Model 23 1.75 3.56 <0.001

Error 101 0.49

Climate 5 3.15 6.41 <0.001

Soil order 8 0.40 0.81 0.597

Foliage life

Foliage life

Climate x soil order 10 1.97 4.01 <0.001

b) Below-ground biomass (n - 118)

Model 22 1.20 2.86 <0.001

Error 95 0.42

Climate 5 2.20 5.25 0.003*

Soil order 8 0.36 0.85 0.559 Foliage type

Foliage life

Climate x soil order 9 1.07 2.55 0.011

c) Fine root biomass (n - 127)

Model 15 4.33 9.82 <0.001

Error 84 0.19

Climate 5 6.48 14.69 <0.001

Soil order 9 2.09 4.75 <0.001

Foliage type 1 2.21 5.01 0.027

Foliage life

d) Above-ground NPP (n - 105)

Model 20 1.57 8.43 <0.001

Error 84 0.19

Climate 5 2.75 14.77 <0.001

Soil order 8 0.95 5.10 <0.001" Foliage type

Foliage life 1 0.96 5.17 0.026

Climate x soil order 6 1.95 10.45 <0.001

185

Appendix A. Continued

e) Below-ground NPP (n - 115)

Model 12

Error 102

Climate 4

Soil order 8

Foliage type

Foliage life

3.95 9.60 <0.001

0.41

9.16 22.26 <0.001

0.76 1.84 0.078

f) Litter fall (n - 128)

Model 29

Error 98

Climate 5

Soil order 8

Foliage type Foliage life 2

Climate x soil order 10

Climate x foliage life 4

1.89 8.28 <0.001

.23

0.24 1.04 0.396

0.47 2.03 0.050

1.18 5.14 0.008*

1.02 4.45 <0.001

0.99 4.33 <0.0003

g) Below.ground litter transfers (n - 35)

Model 9

Error 25

Climate

Soil order

Foliage type

Foliage life

0.58 1.5 0.203

0.39

h) Forest floor mass (n - 108)

Model 5

Error 102

Climate 5

Soil order

Foliage type

Foliage life

6.19 8.93 <0.001

0.69

6.19 8.93 <0.001

i) Soil organic matter (n - 67)

Model 14

Error 52

Climate

Soil order

Foliage type

Foliage life

0.60 1.13 0.354

0.53

* This p value for the independent variable is significant, however sig- nificance difference is mostly due to one outlying mean value which has n < 3 .

186

Appendix B.

Climatic

forest

type a Forest type Location Longitude Latitude

1 BOBLDE

2 BOBLDE

3 BONLEV

4 BONLEV

5 BONLEV

6 BONLEV

7 BONLEV

8 BONLEV

9 BONLE~ r

10 BONLEV

11 BONLEV

12 BONLEV

13 BONLEV

14 BONLEV

15 BONLEV

16 BONLEV

17 BONLEV

18 BONLEV

19 BONLEV

20 BONLEV

21 BONLEV

22 BONLEV

23 BONLEV

24 BONLEV

25 BONLEV

26 BONLEV

27 BONLEV

28 BONLEV

29 BONLEV

30 BONLEV

31 B ONLEV

32 CTBLDE

33 CTBLDE

34 CTBLDE

35 CTBLDE

36 CTBLDE

37 CTBLDE

38 C'I~LDE

39 CTBLDE 40 CTBLDE

P etula papyrifera

Salix reticulata

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea abies

Picea ables

Picea abies

Picea excelsa

Picea mariana, feather moss, no permafrost

Picea mariana, muskeg, no permafrost

Picea mariana, muskeg, permafrost 55 cm

Picea rubens, dry

Picea rubens, fresh

Picea rubens, moist

Picea rubens, wet

Pinus sylvestris

Pinus sylvestris

Pinus sylvestris

Acer saccharum

Acer saccharum-hardwood mix, northem

Acer saccharum-hardwood mix, southern Alnus rubra

Betula sp.

Fagus silvatica

Fagus sylvatica

Fagus sylvatica

Fagus sylvatica

Alaska 148 W 64 N

Yukon Territory, Canada 139 W 61 N

Karelia, USSR 34 E 62 N

















Sweden 13 10 E 55 59 N

Finland 29 E 66 22 N

Alaska 1 48 W 64 N

Alaska 148 W 64 N

Alaska 148 W 764 N

Ontario, Canada 78 23 W 45 14 N




Finland 30 58 E 62 47 N

Finland 30 58 E 62 47 N

Finland 30 58 E 62 47 N

Wisconsin 89 24 W 43 02 N

Michigan 44 23 E 85 50 N

Michigan 43 40 E 86 09 N

Washington 122 W 47 23 N

Wisconsin 89 24 W 43 02 N

Germany 9 E 51 45 N

Denmark 10 29 E 56 18 N

Belgium 6 6 E 50 35 N

Germany 9 35 E 51 49 N

187

Appendix B. Continued

41 CTBLDE Fagus sylvatica 42 CTBLDE Fagus sylvatica 43 CTBLDE Fagus/Acer/Betula mix

44 CTBLDE Fagus/Acer/Betula mix

45 CTBLDE Fagus/Acer/Betula mix

46 CTBLDE Hardwood mixture (sugar maple, etc)

47 CTBLDE Northern hardwoods

48 CTBLDE Populus tremuloides 49 CTBLDE Populus tremuloides 50 CTBLDE Populus tremuloides 51 CTBLDE Quercus alba 52 CTBLDE Quercus prinus

53 CTBLDE Quercus robus-tilia cordata 54 CTBLDE Quercus rubra 55 CTBLDE Quercus rubra

56 CTBLDE Quercus-Acer 57 CTBLDE Quercus-Betula-Fraxinus 58 CTBLDE Quercus-mixed 59 CTNLEV Abies amabilis 60 CTNLEV Abies amabilis 61 CTNLDE Larix leptolepis 62 CTNLEV Picea abies 63 CTNLEV Picea abies 64 CTNLEV Picea abies

65 CTNLEV Picea abies 66 CTNLEV Picea abies 67 CTNLEV Picea abies

68 CTNLEV Picea abies 69 CTNLEV Picea abies

70 CTNLEV Picea engelmannii/Abies lasiocarpa 71 CTNLEV Picea sitchensis 72 CTNLEV Picea sitchensis

73 CTNLEV Picea sitchensis 74 CTNLEV Pinus contorta, mesic

75 CTNLEV Pinus contorta, mesic

76 CTNLEV Pinus contorta, xeric

77 CTNLEV Pinus contorta, xeric

78 CTNLEV Pinus koraiensis

79 CTNLEV Pinus ponderosa 80 CTNLEV Pinus radiata

Sweden

Sweden

New Hampshire

New Hampshire

New Hampshire

New York

New Hampshire

Wisconsin

Wisconsin

Wisconsin

Missouri

Georgia

Sweden

Wisconsin

Wisconsin

Massachusetts

England

Belgium

Washington

Washington

Japan

Belgium

France

France

France

Germany

Germany

Sweden

Wisconsin

Colorado

Scotland

Scotland

Scotland

British Columbia, Canada




China

Arizona

New Zealand

13 55E

13 10 E

71W

71W

71W

74 13 W

71 30W

8945W

8945W

8945 W

92 12W

83 26 W

1620E

89 24 W

91.2W

254E

121 35 W

121 35 W

141E

6 5 E

935E

935E

13 10E

89 24 W

128 6E

176 14E

55 45 N

55 59 N 44N

44N

44N

4400N

4400N

4545N

4545N

4545N

3440N

35 02 N

60 19N

43 02 N

44.1N

42N

5413N

50 4N

47 19N

47 19N

3945 N

50 32 N

5149N

51 49N

55 59 N

43 02 N

55 N

55 N

55 N

50 N

50 N

50 N

50 N

4225N

38 25 S

188

Appendix B.

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116 117

118

119 120

Continued

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV

CTNLEV CTNLEV

CTNLEV

CTNLEV

WTBLDE

Pinus radiata Pinus resinosa Pinus resinosa Pinus resinosa Pinus strobus Pinus sylvestris Pinus sylvestris Pinus sylvestris Pinus sylvestris Pinus sylvestris, density 10,000 ha-1

Pinus sylvestris, density 40,000 ha- 1

Pseudotsuga menztesii Pseudotsuga menztesii Pseudotsuga menziesii, dry site

Pseudotsuga menztesii, moderate site

Pseudotsuga menztesii, wet site

Pseudotsuga menztesii Pseudotsuga menz~esii Pseudotsuga menztesii Pseudotsuga menszesii Pseudotsuga menztesii Pseudotsuga menztesii Pseudotsuga menztesii Pseudotsuga menztesii Pseudotsuga menztesii Pseudotsuga menzzesii Pseudotsuga menztesii Pseudotsuga menztesii Pseudotsuga menz~esii Pseudotsuga menztesii Pseudotsuga menztesii Pseudotsuga menztesii Rocky Mt. mixed conifer

Rocky Mt. mixed conifer

Rocky Mt. mixed conifer

Rocky Mt. Mixed conifer Rocky Mt. mixed conifer

Tsuga heterophylla/Picea sitchensis Tsuga heterophylla/Picea sitchensis Acer rubrum/Quercus prinus

New Zealand

Wisconsin

Wisconsin

Massachusetts

Georgia

Sweden

Sweden

Sweden

Sweden Sweden Sweden Oregon Oregon Oregon Oregon Oregon Washington Washington Washington Washington Washington Washington Washington Washington

Washington

Washington

Washington

Washington

Washington

Washington Washington Washington New Mexico New Mexico

New Mexico

New Mexico

New Mexico Oregon

Oregon

North Carolina

176 14E

89 40W

89 40W

83 26 W

16 30 E

16 30E

16 30E

16 30E

20 30 E

20 30E

122 13 W

122 20W

122 13 W

122 13 W

122 13 W

127 E

127W

122 W

122 W

122 W

122 W

122 W

122 W

122 W

122 W

122 W

122 W

122 W

122 W

122 W

122 W

107 34 W

107 34 W

107 34 W

107 34 W 107 34 W

122 W

122 W

83 26W

38 25 S

46 10N

46 10N

42N

35 02 N

6049N

6049N

6049N

6049N

6454N

6454N

4414N

4415N

4414N

4414N

44 14N

47N

47N

47N

47N

47N

47N

47N

47N

47N

47 23N

47 23N

47N

47N

47N

46N

46N

35 15N

35 15 N

35 15 N

35 15N 35 15N

45N

45N

35 03 N

189


121 WTBLDE

122 WTB LDE

123 WTBLDE

124 WTBLDE

125 WTBLDE

126 WTBLDE

127 WTBLDE

128 WTNLEV/BLDE

129 WTNLEV

130 WTNLEV

131 WTNLEV

132 WTNLEV

133 WTNLEV

134 WTNLEV

135 WTNLEV

136 MEBLEV

137 MENLEV

138 STBLDE

139 STBLDE

140 STBLEV

141 STBLEV

142 STBLEV

143 STBLEV

144 STBLEV

145 STBLEV

146 STBLEV

147 STBLEV

148 STNLEV

149 STNLEV

150 STNLEV

151 STNLEV

152 STNLEV

153 STNLEV

154 STNLEV

155 STNLEV

156 TRBLDE

157 TRBLDE

158 TRBLDE

159 TRBLDE

160 TRBLDE

Hardwood mix, bottomland

Hardwood mixture

Liriodendron tulipifera

Liriodendron tulipifera Nyssa aquatica, Acer rubrum, N. sylvatica Quercus alba/Carya spp.

Quercus prinus Piner taeda-hardwood mix, upland

Pinus elliottii Pinus elliottii Pinus taeda-hardwood mix, upland

Pinus taeda Pinus echinata

Chamaecyparis thyoides Taxodium distichum

Quercus ilex Pinus pinea Swietenia macrophylla

Swietenia macrophylla/ S. mahagoni Montane evergreen broadleaved

Quercus langinosa/ Q. floribtmda Montane rain forest, ridge

Old field successional forest

Secondary forest

Secondary forest

Secondary forest

Tabonuco forest

Pinus caribaea var. hondurensis Pinus caribaea var. hondurensis Pinus caribaea var. hondurensis Pinus roxburghii/Myrica esculenta Pinus elliotti

Pinus elliottii Pinus elliottii Pinus elliottii

Deciduous dry forest

Deciduous dry forest

Populus deltoides, 2 × 2 spacing



Louisiana

Virginia

Tennessee

Tennessee

Virginia

Tennessee

Tennessee

Louisiana

Florida

Florida

Louisiana

North Carolina

Tennessee

Virginia

Virginia

France

France

Puerto Rico

Puerto Rico

China

Himalaya, India

Jamaica

Puerto Rico

Puerto Rico

Puerto Rico

Puerto Rico

Puerto Rico

Puerto Rico

Puerto Rico

Peurto Rico

Himalaya, India

India

India

India

India

Mexico

Mexico

India

India

India

92 W

84 17W

80 77W

84 17W

84 17W

92W

82W

82W

82W

79W

84 17W

2e

65 60W

65 60W

77W

65 50W

65 50W

65 50W

65 50W

65 50W

65 50W

65 50W

65 50W

75 46E

75 46E

75 46E

32 N

35 58 N

35 55 N

35 58 N

35 58 N

32 N

30 W

30 W

32 N

36 N

35 58 N

43 42N

48N

18 18 N

18 18 N

30 N

18N

18 18 N

17 17 N

17 17 N

17 17N

18 18 N

18 18 N

18 18 N

18 18 N

30 N

3021N

3021N

3021N

3021N

19 20 N

19 30 N

29 10N

29 10 N

29 10 N

190


161 TRBLDE

162 TRBLDE

163 TRBLDE

164 TRBLDE

165 TRBLEV

166 BRBLEV

167 TRBLEV

168 TRBLEV

169 TRBLEV

170 BRBLEV

171 TRBLEV

172 TRBLEV

173 TRBLEV

174 TRBLEV

175 TRBLEV

176 TRBLEV

177 TRBLEV

178 TRBLEV

179 TRBLEV

180 TRBLEV

181 TRBLEV

182 TRBLEV

183 TRBLEV

184 TRBLEV

185 TRBLEV

186 TRBLEV

187 TRBLEV

188 TRBLEV

189 TRBLEV

190 TRBLEV

191 TRBLEV

192 TRBLEV

193 TRBLEV

194 TRBLSDE

195 TRBLSDE

196 TRBLSDE

197 TRNLEV 198 TRNLEV

199 TRNLEV

200 TRNLEV

Shorea robusta/Mallotus philippensis

Tectonia grandis, monsoon

Tropical mixed dry deciduous, hill top

Tropical mixed dry deciduous, slope

Primary wet tropical forest

Secondary wet tropical forest

Secondary wet tropical forest

Rain forest, campina

Tropical moist

Tropical broadleaf evergreen



Tropical evergreen forest



Montane wet forest

Tropical wet

Tropical wet

Wet forest, premontane



Tropical dry evergreen forest

Tropical dry evergreen forest

Tropical premontane, Banco

Tropical premontane, Yapo

Lowland Dipterocarp

Lower montane rain forest

Montane rain forest

Montane rainforest

Tropical dry forest

Low Bana

Tall Caatinga

Tierra Firme

Semi-everygreen seasonal floodplain

Tropical moist forest

Tropical semi-deciduous forest

Pinus caribaea Pinus caribaea

Pinus caribaea

Pinus caribaea

India

India

India

India

Brasil

Brasil

Brasil

Brasil

Brasil

Colombia

Colombia

Columbia

Columbia

Columbia

Columbia

Costa Rica

Costa Rica

Costa Rica

Costa Rica

Costa Rica

Costa Rica

India

India

Ivory Coast

Ivory Coast

Malaysia

New Guinea

New Guinea

Panama

Thailand

Venezuela

Venezuela

Venezuela

Venezuela

Ghana

Panama

Nigeria

Nigeria

Nigeria

Nigeria

83 03 E

82 37 E

82 37 E

8448W

83 59W

83 59W

83W

82W

83W

79 55 E

79 55 E

402W

406W

102 18W

82 14W

67 03 W

67 O3 W

67 03 W

70 55 W

055W

80 09 W

355 E

3 55E

355E

355E

30 N

24 52 N

25 02 N

25 02 N

3S

3S

3S

3S

3S

8N

8N

8N

10 18 N

1026N

10 26 N

10N

10N

10N

1211N

1211N

523N

542N

259N

8 43 N

1 56N

1 56N

1 56N

7 26N

609N

907 N

722N

7 22N

722N

722N

191


Elevation

m Soil type

Soil order b

Soil mxmre c

Forest

origin a

Sim quality e

Stand

age

(years)

I

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39 40

2000

130

200

170

130

150

160

100

80

130

110

140

130

120

140

110

110

120

120

270

470

167

465

495

480

503

145

145

145

274

210

274

470

1-28

450 500

Pergelic cryaquept, silt liam

Glacial till, calcareous sedimentary rocks

Humus iron podzol, sand moraine

Eluvium debris, geology - crystaline base

Humus iron podzol

Humus iron podzol

Sand podzol

Sand podzol

Peat, sand moraine

Peat, peat on clay moraine






Humus iron podzol

Humus iron podzol

Humus iron podzol

Humus iron podzol

Podzol

Podzol

Pergelic cryaquept, silt loam

Pergelic cryaquept, silt loam

Histic pergelic cryaquept

Lithic ferro-humic podzol

Ortho ferr-humic podzol

Orthic ferro-humic podzol

Hydric humisol

Podzol

Podzol

Podzol

Alfisol. gtasial deposits, silt clay loam

Alfic and typic haplorthods

Entic and typic haplorthods

Typic haplorthod, alderwood gravelly sandy loam

Alfisol, glasial depostis, silt loam

Brown forest soil (acid)

Mollic hapludalf, grey brown podzol Inceptisol, loamy brown acid Brown forest soils (acid)

5

5

8

7

8

8

8

9

4

4

8

8

8

8

8

8

8

8

8

8

8

5

5

5

8

8

8

4

8

8

8

2

8

8

8

2

5

2 5

5

15

15

15

10

4.0

50

138

37

45

39

43

38

42

41

22

37

45

54

68

82

98

109

126

60

260

130

55

51

84

246

212

130

15

35

100

35

74

78

36

35

80

90 90

122

192


41 150

42 120 43 300

44 300

45 300

46 530

47 550-710

48

49

50 51 240

52 760

53 30 54 274

55

56 57 45 58 245

59 1150

60 1150

61 360

62 550

63 1050-1100

64 1050-1100

65 1050-1100 66 390

67 440

68 120

69 70 3100--4000

71

72 355

73 74 1370

75 1380

76 1300 77 1300

78 800

79 80 580-600

Podzol 8 1

Brown forest soil (acid) 5 2 Typic fragiorthod, coarse loam 8 6 1

Typic fragiorthod, coarse loam 8 6 1

Typic fragiorthod, coarse loam 8 6 1 Typic haplorthod, Becket bouldery fine sandy loam 8 4 1

Podzolic haplorthod, boulders, glacial till 8 1

Entic haplorthod, loamy sand, glacial outwash 8 2 2



Aq uic Hapludalf, fine-silty, mixed, mesic-weldon silt loam 2 7 1

Huvaquentic dystrochrept, fine-loamy, mixed, mesic 5 6 1

Brown forest soil, gley type 5 1

Alfisol, glacial deposits, silt loam 2 7 1 Typic hapludalf, loam 2 6 1

Spodosol, entic haplorthods, stony, glacial origin 8 1

Glacial drift and brown earths 5 1

Calcareous brown soil 5 6 1

Andisol 3 3 1

Andisol 3 3 1

Black soil of volcanic ash origin 8 2

Inceptisol, brown acid forest soil, loamy 5 6 1

Brown forest soil, ocreux 5 2

Brown forest soil, acid and ocreux 5 2

Brown forest soil (acid) 5 2




Alfisol, glacial deposits, silt loam 2 7 2

Cryoboralfs 2 1 Peaty gley soils 4 15 2

Peaty gley soils 4 15 2

Peaty gley soils 4 15 2 Podzol, loam 8 6 1

Podzol, silty clay 8 13 1

Podzol, loamy sand 8 2 1 Podzol, loamy sand 8 2 1

Dark brown, volcanic ash 8 3 Typic rhodustalf 2 1

Yellow brown pumice, Oruanui silty sand types, sandy loam 3 4 2

100

45-130

80

110

10

20

32

105 Mature

125-190

35 Mature

80 80

80

23

180

39

35

10

30

85

34

115 60

35

200-500

17

70

78

70 70

150

56 12

193


81 580-600 Yellow brown pumice, Oruanui silty sand types, sandy loam

82 500 Sandy, mixed frigid entic haplotthod, loamy fine sand

83 500 Sandy, mixed frigid entic haplorthod, loamy fine sand

84 Spodosol, entic haplorthods, stony, glacial origin

85 760 Fuvaquentic dystrochrept, fine-loamy, mixed mesic

86 185 Spodosol, sandy sediments




90 Spodosol, sandy

91 Spodosol, sandy

92 Bohannon gravely loam

93 430-670 Inceptisol

94 610 Typic dystrochrept, loam, glacial terrace

95 790 Entic haplumbrept, loam, overlaying clay loam

96 520 Typic haplohumult, clay loam overlaying loam clay 97 URic haploxeralfs, fine-loamy, mixed mesic

98 Dystric zerochrepts, sandy-skeletal, mixed mesic

99 Typic xerumbrepts, fine-loamy, mixed mesic



102 Typic xerumbrepts, fine-loamy, mixed mesic 103 Dystric entic durochrepts, medial, mesic

104 Dystric entic durochrepts, medial, mesic

105 Dystric entic durochrepts, medial, mesic

106 210 Typic haplorthod, Everett gravelly sandy loam

107 210 Typic haplorthod, Everett gravelly sandy loam

108 Typic haplorthod, Everett gravelly sandy loam

109 Typic haplorthod, Everett gravelly sandy loam

110 Mixed mesic duric haplorthods, over sandy or sandy-skeletal 111 320 Typic haplorthod, glacial outwash, gravelly loamy sand 112 320 Wilkeson series, colluvial soil with lake sediments

113 2900 Mollic paleoboralf, clayey-skeletal




117 2900 Mollic paleoboralf, clayey-skeletal 118 200 Ultisol

119 200 Ultisol

120 726--993 Typic hapludult, loamy, saluda stony loam

3 4 2

8 2 2 8 2 2

8 2

5 2 8 1 2

8 1 2

8 1 1 8 1 1

8 1 2

8 1 2

5 6 1

5 6 1

5 6 1 5 6 1

8 11 1 2 6 1

5 2 1

5 6 1

5 6 1

5 6 1

5 6 1

5 1

5 1

5 1

8 3 2

8 3 1

8 3 1

8 3 1

8 3 1

8 3 1 5 6 1

2 14 1

2 14 1

2 14 1

2 14 1

2 14 1

6 1

6 I

6 6 1

12

31

31

53

18

20

120

20

20

16

16

35-50 2.5 450

3.0 70

3.0 170

3.0 120 2.0 150

4.0 70

4.0 1

4.0 10

4.0 40

4.0 150

2.0 1

2.0 10

2.0 70

4.0 36

4.0 9

4.0 40 4.0 40

2.0 40 4O

40

50 5O

5O

50

5O

1.0 121

1.0 26 6O--2OO

194


121 122 123 265-360

124 225

125 126 265-360

127 265-360

128 129

130

131 132 144

133 265-360

134

135

136 180

137

138 170

139 200

140

141

142 1550

143 200-230

144 500-600

145 170 146 200

147 250-500 1 48 200-230

149 550--600

150 550--600

151 152 640

153 640

154 640

155 640

156 500 157 158 215

159 215 160 215

Thermic typic glossaqualfs, fine-silty, mixed 2 2 1

Typic ochraquult, fine, loamy, mixed thermic 6 6 1 Ultisol, typic paleudult, deep alluvial emory silt loam 6 7 1

Ultisol, typic paleudult, deep alluvial emory silt loam 6 7 1

Thermic histic fluvaquent mix, fine, silty 7 8 1 Ultisol, typic paleudult, deep alluvial emory silt loam 6 7 1

Ultisol, typic paleudult, deep alluvial emory silt loam 6 7 1

Thermic typic paleudults, fine-loamy, siliceous 6 2 1

Ultic haplaquod, poorly drained 8 1 2

Ultic haplaquod, porly drained 8 1 2

Thermic typic paleudults, fine-loamy, siliceous 6 2 1

Red-yellow podsols, sandy acid loams 8 5 2

Typic paleudults, derived dolomitic residium 7 7 2

Dysic, thermic, typic medisaprist 4 15 1

Typic fluvaquent, silty clay loam clayey, mixed acid 7 10 1

Brunified Mediterranean red soil 1

Spodosol 8 2

Typic tropohumults, isohyperthermic, clayey, kaolinitic 6 14 2

Tropeptic haplorthox, clayey, oxidic, isohyperthermic 9 14 2

Ultisol, acid, from granite, dark colour and fine texture 6 1

Ultisol, dolomite limestone and conglomeratic sandstone 6 1

Peat podzol 4 1 Eqiaquic palehumults, isothermic, clayey 6 14 1

Epiaquic palehumults, isothermic, clayey 6 14 1 Typic tropohumults, isohyperthermic, clayey, kaolinitic 6 14 1

Tropeptic haplorthox, oxidic, isohyperthermic, clayey 9 14 1

Ultisols, typic trophumults 6 10 1

Epiaquic palehumults, clayey, mixed isothermic 6 14 2 Epiaquic palehumults, clayey, mixed isothermic 6 14 2

Epiaquic palehumults, clayey, mixed, isothermic 6 14 2

Ultisol, slates and dolomite 6 1

Alluvial, non calcareous, ferruginous, sandy loam to loam 3 2




Sandy loam with gravely structure 3 1 Sandy loam with gravely structure 3 1

Typic camborthids, sandy loams 10 3 2

Typic camborthids, sandy loams 10 3 2 Typic camborthids, sandy loams 10 3 2

Mature

78 30-80

48

52 30-80

30-80

Mature

7

27

Mature

16

3O

57

86

150

17

49 100

Mature

Mature

3-8

15-30

15-30

>50 Mature

4

11

18.5 Mature

10

20

30 40

195


161

162

163

164

165

166

167

168

169 170

171

172

173 174

175

176

177

178 179

180

181

182

183

184

185

186

187

188

189

190

191

192 193

194

195 196

197

198

199

200

190

1550

50-70

50-70

50

70 100

2500

1200

119

119

119

I00

60

Ultisol, sandstone associated with slates 6

Residual, reddish brown, well drained, sandy loam

Ultisols, sandstones derived, coarse sandy loam 6

Ultisols, sandstones derived, coarse sandy loam 6

Oxisols 9

Oxisols 9

Oxisols 9 Pale yellow latosol 9

Pale yellow latosol 8 Oxyaquic dystropept, Inceptisol, floodplain, fine loam 5

Ultisol/oxisol, upland, well drained, sandy loam 6

Spodosols, upland, white sand soils 8

Infertile acid, surface water gleys 9



Typic dystrandept 5

Fluvaquentic hapludoll 1

Oxic Dystrandept 5

Ultisols 6 Ultisols 6

Ultisols 6

Red ferralitic, alluvial, sandy loam 6 Red ferralitic, alluvial, sandy loam 6

Ferrallitique fortement desature, well drained 9

Ferrallitique fortement desature, gravelly, poor drainage 9

Ultisol, sandy clay loam-clay 6 Humic brown, deeply weathered clay 9

Humic brown, deeply weathered clay 9

Inceptisol 5

Deep sandy soils 7

Oxisols 9

Spodosols, alluvial quartizitic sands 8

Spodosols, well drained 8

Alfic dystropepts, eutrophic alluvial, sandy loam 5

Reddish yellow latosols 9 Oxisol 9

3

3

3

6 4

I

6

3

3 1

1

9 14

14

3

2

1

1

1

4

9

Mature

19 Mature

Mature

Mature

Young

50

Mamm Mature

Mature

Mature

16 Mature

Mature

Mature Mature

Mature Mature

Mature

Mature

Mature

Mature

Mature

Mature

Mature

Mature

Mature

Mature

50 Mature

6

8

10

10

196


Precip- Air temp. Air temp. Air temp. Above

itation mean minimum maximum biomass

(ram) (°C) (°C) (°C) (Mgha -1)

Below Fine root Total Above/

mass[diam.] biosmass production (Mgha -1) (gm -2 [man]) (Mgha - l ) biomass

Above

production (gm2yr -1)

1 268O -3.4

2

3 650 2.2

4 650 2.2

5 650 2.2

6 650 2.2

7 650 2.2

8 650 2.2

9 650 2.2

10 650 2.2

11 650 2.2

12 650 2.2

13 650 2.2

14 650 2.2

15 650 2.2

16 650 2.2

17 650 2.2

18 650 2.2

19 650 2.2

20 800 4

21 500 0

22 2680 -3.4

23 2868 -3.4

24 2688 -3.4

25 465

26 1243 4

27 1243 7

28 1243 7

29 690 1.4

30 690 1.4

31 690 1.4

32 953 6.90

33 810 5.8

34 850 7.6

35 1322 9.3

36 953

37 1063 5.9

38 660 7.1

39 1450 6.1 40 1065 6.1

-25 16

7.1

-6 19

-25 16

-25 16

-25 16

-5.2

2.1

-5.2

-3.8

21.3

15.2

21.3

18.2

97.4 44.3 141.7 0.69 516

6.4 129 (<I) 13.5 0.53 179

200.5 47.5 201 (<5) 248.0 0.81 255

28.3 6.6 60 (<5) 34.9 0.81 287

45.9 10.1 60 (<5) 56.1 0.82 478

69.7 14.6 70 (<5) 84.3 0.83 629

79.4 16.8 67 (<5) 96.1 0.83 716

87.5 18.3 75 (<5) 105.8 0.83 779

34.5 8.0 70 (<5) 42.5 0.81 368

41.8 9.5 60 (<5) 51.2 0.82 432

24.9 6.2 57 (<5) 31.1 0.80 350

62.0 14.1 87 (<5) 76.1 0.81 539

78.2 15.8 101 (<5) 94.0 0.83 585

98.1 21.6 124 (<5) 119.7 0.82 621

132.6 29.1 152 (<5) 161.7 0.82 617

144.2 33.2 175 (<5) 177.4 0.81 570

185.3 41.0 194 (<5) 226.3 0.82 481

192.3 45.0 192 (<5) 237.3 0.81 409

208.7 46.0 202 (<5) 254.7 0.82 309

308.0 59.0 200 (<5) 367.0 0.84 1370

101.9 37.6 132 (<5) 139.5 0.73 421

121.5 51.7 173.2 0.70 159

30.3 10.4 40.7 0.74 140

25.2 12.5 37.7 0.67 94

121.5 28.0 95 (<5) 149.5 0.81 451

263.9 62.0 209 (<5) 325.9 0.81 870

462.2 104.6 353 (<5) 566.8 0.82 990

169.2 47.5 178 (<5) 216.7 0.78 383

16.2 9.3 243 (<2) 25.5 0.64

44.7 15.0 420 (<2) 59.7 0.75

125.2 22.7 295 (<2) 147.8 0.85

428 (<3) 932

281.4 689 (<2) 530

240.4 790 (<2) 545

183.7 25.2 209.0 0.88 1278 318 (<3)

158.8 22.1 180.8 0.88 965

221.3 43.2 1322 (<5) 264.5 0.84 1499

152 (<5) 275.0 37.1 707 (<5) 312.1 0.88 1123

197


41 900 6

42 800 7 -6

43

44

45

46 1060 -8.7

47 1395 5.6 -8.7

48 800 -11 49 800 -11

50 800 -11

51 939 13.0 -0.9

52 1800 13 3.0

53 644 7.5

54 953 6.90 -5.2

55 792 6.9 -1.7

56 1070 -2 20

57 1115 7.8

58 952 8.5 3.3

59 2730 5.4 -3.2

60 2730 5.4 -3.2

61 1806 10.2

62 1450 6.1 3.8

63 1600 6 5

64 1600 6 5

65 1600 6 5

66 1063 5.9

67 1063 5.9

68 800 7

69 953 5.2

70 I000 1.5 -7.6

71 771 10 4.9

72 1800

73 771 10 4.9

74

75

76

77

78 671 2.2 -18.6

79 570 7.4

80 1491 4.00

19

18.8

18.8

19

19

19

25.6

20.0

21.3

22.2

10.4

14.4

14.4

18.2

21.2

12.3

15.7

27

15.7

19.9

16

226.0

324.0

134.0

40.3

52.2

105.0

138.5

201.0

220.0

128.4

291.9

52.3

445.7

169.4

97.6

197.1

298.6

142.7

233.0

288.8

124.0

214.6

313.1

119.4

116.5

265.0

134.0

36.6

51.0

28.6

21.7

15.9

19.6

52.5

39.0

60

75.2

35.3

24.7

137.7

37.9

12.2

43.8

43.3

34.6

74.9

59.0

27.0

53.9

78.6

36.6

44.2

19.0

600 (<5)

1246 (<-3)

1229

2711 (<-3)

540 (<-3)

452 (<2)

1013 (<-3)

741 (<-3)

909 (<-3)

298 (<2)

(<2.5)

270 (<3)

640 (<2)

610 (<3)

1072 (<5)

924 (<2)

1633 (<2)

157 (<5)

174 (<5)

112 (<5) 46 (<5)

333 (<3)

900 (<2)

97 (<5)

430 (<5)

590 (<5)

640 (<5)

640 (<5)

508 (<2)

180 (<2)

262.6

83.4

162.6

62.0

68.1

124.6

191.0

240.0

280

203.6

327.2

78.0

585. I

207.4

109.9

240.9

341.9

177.3

307.9

347.8

151.0

268.5

391.7

156.0

160.7

284.0

0.86

3.88

0.82

0.65

0.77

0.84

0.73

0.84

0.63

0.89

0.67

0.76

0.82

0.89

0.82

0.87

0.81

0.76

0.83

0.82

0.80

0.80

0.77

0.72

0.93

1060

1540

789

875

1290

1371

930

992

1478

432

232

1449

850

652

1380

748

366

640

740

350

330

1486

198

AppendixB, Continued

81 1491 ~00 16

82 3246 -10.4 19,5

83 3246 -10.4 19.5

84 1070 -2,0 20

85 1800 13 3.0 20.0

86 600 3.8 -6.0 16

87 600 3,8 -6,0 16

88 600 3.8 -6 16

89 600 3.8 -6 16

90

91

92 1905 2.7 18.5

93 2300 8.5 1 21

94 1800 8 1 17

95 2000 1 15

96 2290 1 14

97 860 11 2 19

98 1355 11.4 1.1 21.8

99 860 13.5 -1.6 28.6

100 860 14.7 0.4 29.1

101 1355 12.3 2.8 21.9

102 860 13.9 3.8 24,1

103 1510 13,4 -1.9 28.2

104 1185 12.2 -3 27.4

105 1510 12.1 1.2 23

106 1322 9.3 2,1 16.9

107 1322 9.3 2.1 15,2

108 1000 2.4 18,1

109 1000 2.4 18.1

110 1510 9,3 -1.6 20.1

111 1000 2.4 18.1

112 1000 2.4 18,1

113 2080 4 -10.5 17

114 2080 4 -10.5 17

115 2080 4 -10.5 17

116 2080 4 -10.5 17

117 2080 4 -10.5 17

118 3420 10.1 4.6 15.2

119 3420 10.1 4.6 15.2

120 1945 12.6

59.0

95.5

13.8

36.7

51.8

47.4

300.8

717.9

464,0

627.0

548.0

811,5

256.4

4.0

6,6

170.0

763.2

3.8

49.1

378.9

172.6

16.8

386,1

430.7

306.2

248.6

466,7

293.1

915.6

192.7

137,5

6.8

6

60.3

7.1

8.9

67.4

152.5

153,5

44.3

0.2

7.0

29.5

140.8

1.6

8.7

66.5

33.0

3.5

94.3

100.5

55.8

57.6

88,1

44.9

186.7

38.4

52.5

75 (<2)

431 (<5)

370 (<5)

510 (<3)

200 (<2)

342 (<2)

400 (<2)

248 (<2)

1571 (?)

1913 (?)

(<5)

582(<5)

769(<5)

744(<5)

562 (<2)

135 (<2)

830 (<-2)

270 (<-2)

(<5)

399 (<-5)

(<5)

(<5)

(<5)

155.8

21.0

45.6

368.2

870.4

964.9

300.7

4.2

13.5

200.3

903.9

5.4

57,8

445.4

205.5

20.3

480.4

531.2

362.0

306.2

555.8

338.0

1102.3

231.1

190.0

0.61

0.66

0.80

0.82

0.82

0.84

0.85

0,95

0.49

0,85

0.84

0.70

0.85

0.85

0.84

0.83

0.80

0.81

0.85

0.81

0.84

0.87

0.83

0.83

0.72

980

797

956

885

396

454

908

1354

375

1358

1581

1436

965

1259

1115

730

1370

1155

1306

1452

1410

1716

1991

3220

875

199


121 990 (<-3)

122 1170 15 194.6 794 (<2) 2098

123 1400 13.3 0.5 22.6 108.6 33.3 790 (<5) 141.9 0.77 608

124 1265 13.3 0.5 22.6 133.5 36.0 850 (<5) 169.5 0.79 742

125 1170 15 195.7 243 (<2) 1815

126 1400 13.3 0.5 22.6 121.6 33.3 790 (<5) 154.9 0.79 775

127 1400 13.3 0.5 22.6 137.3 33.3 790 (<5) 170.6 0.81 952

128 840 (<-3)

129 1320 24 8.3 11.7 14.0 806 (<5) 25.7 0.46 448

130 1450 21 8.3 23.0 40.2 1214 (<5) 63.2 0.36 1331

131 1050 (<-3)

132 1150 15.6 4.00 25.3 96.1 22.4 896(<1) 118.5 0.81 2980

133 1400 13.3 121.4 33.5 790 (<2) 154.9 0.78 676

134 1170 15 220.4 500 (<2) 1952

135 1170 15 345.3 230 (<2) 1867

136 987 12.4 269.3 4.5 314.3 0.86 710

137 648 14.2 3.2 17.6 438 (<10)

138 2330 22.3 19.3 25.3 102.0 8.1 297 (<2) 110.1 0.93 1660

139 3920 22.3 19.3 25.3 125.0 5.7 321 (<2) 130.7 0.96 1910

140 1744 19.6 ~5.1 38.8 73.5 482 (<3) 1643

141 2488 15 415.1

142 2500 15 8.5 24 229.0 37.0 112 (<2) 266.0 0.86 690

143 3920 22.3 19.3 25.3 32.0 10.9 449 (<2) 42.9 0.75 790

144 3920 22.3 19.3 25.3 109.0 21.3 489 (<2) 130.3 0.84 1000

145 2330 22.3 19.3 25.3 72.0 12.5 537 (<2) 84.5 0.85 850

146 3920 22.3 19.3 25.3 80.0 20.5 431 (<2) 100.5 0.80 1230

147 3500 23 21 24 198.0 73.0 819 (<5) 271.0 0.73 1030

148 3920 22.3 19.3 25.3 40.0 2.8 157 (<2) 42.8 0.93 1140

149 3920 22.3 19.3 25.3 70 (<2) 95.6 1810

150 3920 22.3 19.3 25.3 171.0 19.8 455 (<2) 190.8 0.90 1510

151 2185 15.8 289.0

152 2060 20 13 28 136.9 31.8 1114

153 2060 20 13 28 189.2 40.3 879

154 2060 20 13 28 232.6 43.8 735

155 2060 20 13 28 448.5 80.7 1070

156 748 24.9 14.8 32 51.0 32.0 749 (<5) 83.0 0.61

157 707 20 27 73.6 30.9 820 (<5) 104.5 0.70

158 510 5.2 42 205.9 45.6 1380 (<2) 251.5 0.82

159 510 5.2 42 88.0 29.3 1550 (<2) 117.3 0.75

160 510 5.2 42 40.7 30.8 2300 (<2) 71.5 0.57

200

Append~B. Continued

161 2076 23

162 992 10.1 37

163 821 24 17 38

164 821 24 17 38

165

166

167 168 1500 27.2 26 27

169 1771 27.2 26 27

170 3060 26.2 22 30.9

171 3060 26.2 22 30.9

172 3060 26.2 22 30.9

173 3000 27.5 26 28

174 3000 27.5 26 28

175 3000 27.5 26 28

176 2000

177 3800 20

178 3800 20 ..

179 2700 22 19 22

180 2700 22 19 22

181 2700 22 19 22

182 1254

183 1254

184 2095 26.7

185 1739 26.7

186 1800 26

187 4000 13 5 22

188 2985 13 5 22

189 3900 19

190 1100 26

191 3500 26 22 31.5

192 3500 26 22 31.5

193 3500 26 22 31.5

194 1800 25

195 1651 25 22.3 28.4

196 2567 27 197 1330 19 35

198 1330 19 35

199 1330 19 35

200 1330 19 35

406.0

326.0

179.0

203.0

510.0

450.0

353.6

310.0

69.0

37.0

185.0

335.0

398.0

305.0

62.0

57.7

108.0

144.4

67,0

55.0

30.0

34.0

40.8

49.0

49.0

26.5

40.0

10.0

60.9

60.6

54.6

35.8

54.0

15.6

14.0

25.6

34.1

299 (<2)

251 (<5)

429 (<5)

740 (?)

1140(.'?) 1030 (?)

843 (<2)

1373 (<6)

1600 (<-5)

2100 (<5)

2400 (<-5)

881 (<2) 61 (<-2)

114 (<-2) 129 (<5)

587 (<5)

645 (<5)

134 (<-2)

234 (<-2)

960 (<5)

880 (<5)

280 (<5) 400 (<5)

400 (<2)

2156 (<5)

2805 (<5)

2501 (<5)

930 (<5)

498 (<6.4)

280 (<2)

523.8

178.0

79.0

190.0

473.0

381.0

209.0

237.0

550.0

499.0

380.1

350.0

79.0

433.8

359.0

0.86

0.86

0.86

0.86

0.93

0.90

0.93

0.89

0.87

0.92

0.85

380

270 260

1647

1250

2802

201


Below Total Below/ Forest Forest floor Soil organic SOM Above litter

production production Total floor mass MRT f matter [SOM] depth transfers (gm-2yr -1) (gm-2yr -1) productivity (Mgha -1) (years) (Mgha -1) (cm) (gm-2yr -1)

1 238 754 0.32

2

3 45 300 0.15

4 43 330 0.13

5 70 548 0.13

6 93 722 0.13

7 104 820 0.13

8 117 896 0.13

9 52 420 0.12

10 63 495 0.13

11 65 415 0.16

12 90 629 0.14

13 104 689 0.15

14 110 731 0.15

15 116 733 0.16

16 104 674 0.15

17 92 573 0.16

18 72 481 0.15

19 56 365 0.15

20 260 1630 0.16

21 20 441 0.05

22 80 239 0.33

23 48 188 0.26

24 41 135 0.30

25 72 523 0.14

26 106 976 0.11

27 156 1146 0.14

28 65 448 0.15

29 201

30 614

31 593

32 402 1410 0.29

33 730 1260 0.58

34 808 1353 0.60

35

36 324 1000 0.32

37 63 1028 0.06

38 375 1874 0.20

39

40 250 1373 0.18

68.8 26.0

6.0

39.0 18.0

18.7 13.4

19.3 8.8

19.8 7.6

20.6 7.2

20.0 6.4

33.8 22.4

61.0 33.7

17.4 12.2

18.5 8.O

19.2 5.2

22.0 8.1

24.7 8.6

26.0 9.0

29.6 11.3

32.8 13.2

35.5 15.8

18.5 3.2

67.6 226.8

119.2 223.3

133.3 459.5

86.3 603.5

5.1 1.8

45.6 2.9

39.0 9.6

7.5 2.6

29.7 7.9

280 100 246

134 35

47 48

35 50

40 55

31 13

187 30

163 25

120 18

46

42

159

212

141

60

60

217

140

22O

261

285

312

151

181

143

231

370

273

286

288

263

248

225

571

3O

53

29

14

145

257

243

154

410

478

485

1597

284

405

292

340

375

202


41 170 1230 0.14

42 240 1790 0.13

43

44

45

46 230

47 508 1297 0.39

48

49

50

51 118

52 600 1475 0.41

53 230 1520 O. 15

54 524 1910 0.27

55 140

56 540 1470 0.37

57 269 1261 0.21

58 200 1678' 0.12

59 1182 1614 0.73

60 1223 1455 0.84

61 264 1713 0.15

62 408

63

64

65

66 159 1009 0.16

67 85 737 O. 12

68 260 1640 0.16

69 160 908 0.18

70 151 517 0.29

71 86

72 528

73 520

74 550 1190 0.46

75 450 1190 0.38

76 430 780 0.55

77 630 960 0.66

78 625 2312 0.36

79

80 220 4800 0.05

8.2

5.2

48.0

6.1

6.1

33.5

6.1

4.8

47.8

149.5

13.9

52.0

111.0

18.5

68.0

45.7

49.0

2.1

0.9

12.4

1.4

1.3

7.6

1.7

0.9

31.7

68.6

3.9

17.8

36.1

3.2

165

207

175

288

282

138

220

220

273

190

251

207

126

124

70

70

45

60

30

60

60

70

60

383

569

387

518

425

477

596

440

370

529

151

218

359

240

292

308

572

383

170

601

210

394

203

Append~ B. Continued

81 190

82 260

83 100

84 410

85

86 356

87 217

88

89

90

91

92 916

93 290

94 650

95 630

96 480

97 191

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

3100

1400

1087

1147

305 1190

34 488

343 1250

433 1788

0.06

0.29

0.27

1.17

0.26

0.07

0.27

0.24

293 1651 0.18

303 1884 0.16

363 1799 0.20

504 1636 0.31

304 1816 0.17

263 1378 0.19

1540

1780

371 1526 0.24

607 1913 0.32

330 1782 0.19

444 1854 0.24

398 2114 0.19

270 2261 0.12

550 3775 0.15

797 600 1.33

41.5 7.8

78.0 37.1

27.0

78.0 37.1

252

7.0 2.6 88 100

51.2 11.9 113 100

29.0 14.5 251 83

12.6

31.1

20.0

28.0

10.0

16.0 80.0

27.0 32.9

30.0 11.4

33.0

18.0 39.1

23.0 22.8

16.4 5.1

3.9 1.6

23.2 11.6

22.8 7.1

23.0 20.0

110

70

80

80

50

50

250

110

120

112

97

70

15

15

15

15

15

15

15

15

15

60

60

15

420

512

530

321

210

210

268

429

200

150

210

159

90

20

82

263

46

101

320

242

200

320

115

357

42.2 10.8 23 30 390

286

297

281

34.0 5.5 776 70 614

22.0 3.1 770 70 700

9.5 2.2 137 80 437

204


121

122 989

123 200

124 710

125 59

126 200

127 200

128

129 490

130 616

131

132 202

133 200

134 366

135 308

136

137

138

139

140 1317

141

142

143

144

145

146

147

148

149 I10

150

151

152 318

153 201

154 146

155 202

156 423

157

158

159

160

3087

808

1452

1874

975

1152

938

1947

4112

876

2318

2175

2960

1925

1920

2150

0.32

0.25

0.49

0.03

0.21

0.17

0.52

0.32

0.05

0.23

0.16

0.14

0.44

0.06

9.0

23.4

5.6

6.5

27.0

25.0

8.1

33.0

18.4

46.5

6.6

6.8

11.4

9.2

5.9

5.1

11.1

4.4

4.0

3.6

5.5

6.0

6.2

12.9

9.3

1.4

6.3

1.7

1.0

5.6

5.6

3.1

6.5

3.4

13.7

0.9

1.0

3.0

0.8

0.5

2.4

1.7

0.8

0.6

0.5

0.5

0.7

1.5

0.9

3.0

189

159

47

116

183

111

186

90

250

78

171

121

161

126

88

177

6O

6O

100

100

100

I00

30

45

100

100

100

100

30

100

100

30

652

370

331

658 480

445

264

510

.541

340

757

678

384

697

1117

1250

213

660

537

723

790

1057

869

420

1367

311

535 535

205

AppendixB. Continued

161 162 577

163 384

164 591

165 370

166 570

167 520

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182 104

183 117

184

185 200

186

187

188

189

190

191 235

192 120

193 1117

194

195

196

197

198

199

200

1745

750

840

780

1450

1400

0.49

" 0.68

0.67

0.14

5.8

45.0

11.3

7.0

18.0

38.0

2.1

3,2

4,0

3.2

12.6

20.7

5.5

3.4

17.8

52.5

9.4

2.3

3.7

4.6

17.3

19.7

2.0

6.1

1.5

0.7

2.6

6.1

0.4

0.3

0.3

0.3

1.5

2.7

0.9

7.3

9.4

0.2

216

95

162

152

78

116

90

1198

41

199

76

30

100

50

50

50

100

45

100

100

100

100

30

289

740

740

1070

680

623

1202

946

874

460

770

890

510

1115

1240

961

822

756

635

243

561

1025

1050

206


Below Forest Forest floor Soil Above litter Below litter Forest Forest floor Soil

transfers floor N N MRT f N transfer N transfer N floor Ca CA MRT f Ca (g m-2 yr - t ) (gin -2) (years) (gin -2) (kgha - t yr -1) (kgha -1 yr -1) (gin -2) (years) (gin -2)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

2O

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

54.8 30.4 287.9 18.0 48.9 14.2 754.6

10.0 241.9 15.1 707.0

24.0

71.0 197.2 236.2 3.6

65.7 410.6 219.8 1.6

48.6 441.8 68.9 1.1

102.1 2.2

120.9 6.0

208.9 10.1

402 175.0 22.7

484 112.5 54.6

668 140.8 49.3 71.1 8.2 544.7 87

324 206.0 24.7 10.2

105.0 19.3 945.2 54.3

9.4

439 58.5 15.1

81.0 16.5 49.0

45.2 110.3 505.2

23.1 57.8 60.9

39 .6 3596.4 169.2

39.1 5.8

11.8 6.6

85.2

144.0

93.9

28.0

207


41

42

43

44

45

46 231

47 100

48

49

50

51 220

52

53

54 524

55 160

56

57

58

59 998

60 1262

61

62 701

63

64

65

66

67

68

69

70

71

72

73 530

74

75

76

77

78 216

79

80 210

8.6

12.6

84.6

4.4

68.6

197.0

143.0

226.0

24.5

76.6

43.0

1.2

2.3

20.6

0.7

49.0

98.5

34.5

103.7

780.0

590.0

259.0

448.0

198.0

232.0

665.0

706.0

690.0

175.0

393.0

69.0

54.2

68.3

29.0

29.6

41.0

63.5

50.0

14.0

20.0

28.8

41.5

43.3

58.0

27.9

8.9

34.0

23

22.4

42.0

19.4

29.0

60.0

110.0

3.4

18.0

10.1

10.7

52.8

48.4

7.2

25.8

4.8

51.0

1.1

4.4

1.2

1.0

48.0

28.5

4.8

16.5

25.4

17.5

176.6

1360.0

36.7

73.7

26.9

17.0

16.0

15.0

58.0

208


81 200

82

83 84 410

85 86 210

87 188

88

89

90

91

92 916

93 367

94 720

95 720

96 550

97

98

99

100

101

102

103

104

105 106

107

108

109

110

111

112

113 452

114 534

115 116

116 250 117 280

118 120

119 500

120

84.6

38.4

38.4

4.8

25.6

20.6

3.4 661.8

5.1 372.4

734.0

41.0

27.0

13.9

50.5

16.1 11.8 280.9 13.6

0.6 0.4 379.0 14.8

31.3 14.0 501.2 22.4

30.6

37.5 39.9 198.1 9.4

26.5 7.4 3490.0 35.8

17.1 5.7 3378.0 30.2

11.0 3.2 33.9

24.0

21.0

102.0

21.0

73.0

73.0

56.0

71.0

25.0

5.8

61.9

11.2

3.1

24.4

34.7

13.0

2.3

15.3

I0.1

1.2

16.3

2.9

661.9

204.0

9881.6

83.7

132.2

160.0

209

Append~ B. Continued

121

122 670

123

124 675

125 249

126

127

128

129 214

130 451

131

132 932

133

134 211

135 10

136

137

138

139

140

141

142

143

144

145

146

147 250

148

149

150

151

152

153

154

155

156

157

158

159

160

18.7

7.8

33.4

29.8

12.5

11.3

6.6

11.8

4.3

4.3

5.8

7.0

7.0

3.7

10.2

14.0

5.2

1.8

9.2

8.7

3.8

3.4

1.5

1.6

1.2

0.7

0.5

0.6

0.7

2.6

1.2

2.5

730.0

765.0

450.0

470.0

811.0

548.0

633.1

900.0

518.0

802.0

844.0

888.0

890.0

892.0

1163.0

474.4

78.0

36.2

31.3

79.0

37.0

34.0

26.0

22.0

96.0

86.0

32.8

33.0

43.0

72.2

35.0

61.0

109.0

117.0

103.0

14.0

86.0

56.9

76.0

6.6

31.3

2.3

29.4

10.0

51.7

31.8

36.1

5.0

1.3

10.5

7.1

5.7

630.0

813.0

360.0

360.0

201.0

1372.0

3.0

594.0

1216.0

113.0

314.0

314.0

294.0

363.0

210

Append~B. Continued

161 11.3

162

163

164

165

166

167

168 44.4

169 28.0

170 15.0

171 40.0

172 72.0

173

174

175

176

177

178

179

180

181

182

183

184

185 3.5

186

187 17.0

188

189

190

191

192

193

194 13.7

195 3.5

196 197 1.8

198

199 200 8.9

1.6

4.2

3.1

0.8

4,0

10.6

0.3

1.9

0.2

440.1

930.0

1095.0

1920.0

1033.4

495.0

69.8

106.0

89.0

178

101

68

165.0

119.0

74.0

144.0

169.0

170.0

113.0

91.0

91.0

12.1

27.9

121.1

199.0

6.0

68.0

6.4

1.0

4.7

15.8

17.8

0.7

3.9

1.7

679.0

25.0

375.0

453.7

258.0

211


Above litter Below litter Forest Forest floor Soil Above litter Forest Forest floor Soil Above litter

transfer Ca transfer Ca floor P P MRT P transfer P floor K K MRT f K transfer K (kgha -1 yr -1) (kgha -1 yr -1) (gin -2) (years) (gin -2) (kgha - l yrl) (gm -2) (years) (gm -2) (kgha -1 yr -1)

1

2

3 4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

34.5 7.9 15.8 2.62 5.0 9.90 11.9 18.60 8.30

1.0 0.40 2.10 6.90

28

4.1

4.0

0. I

2.0

5.3

8.7 218.5 0.44

15.2 0.54

10.6 75.4 0.20

2.6

118.70 0.2

155.40 0.4

106.05 0.6

10.60

67.0 0.7 1.1 6.2 9.10 2.3 39.00

17.8 7.2 16.7 231.0 4.3 12.30 7.0 55.00 17.50

18 62.00 2.7 1.80

46.80 0.50

60.50 0.80

39.10 1.50

12.21 174.4 28.60 0.70

14.54 484.7 16.90 0.30

5.97 19990.0 12.40 0.03

212


41

42 31.7

43 44

45

46

47 40.7

48

49

50 51 75.7

52

53

54

55

56 57 83.3

58 110

59 11.0

60 17.0

61

62

63

64

65 66 14.9

67 15.6

68

69

70 20.1

71

72

73

74

75

76 77

78

79

80

22

30.0

30.0

146

0.6

0.3

0.2

0.1

0.3

19.9

9.8

1.5

5.4

1.2

.13

0.8

1

2.5

56.9

26.5

3.2

60

3.80

151.20

92.00

159.20

159.20

102.40

266.00

153.00

14.60

5.0 10.40

2.9

4.2

7.2

2.6 1.20 0.6

2.4 1.70 0.8

1 12.~ 64.5

1.2 28.~ 96.3

1.6

3.5 10.50 87.5

3.7 11.50 98.5

1.50 1.4 6.50

1.2

0.9 7.80 26.9

2.3

4.4

5.60

154.30

15.70

58.90

58.90

52.30

34.00

34.20

10.70

12.00

14.40

10.10

19.00

21.00

2.00

3.00

6.70

1.20

1.20

2.90

213


81

82

83

84

85

86

87

88

89

90

91

92 25.3

93 40.4

94

95

96

97

98

99

100

101

102

103

104

105

106 11.1

107 26.0

108 19.8

109

110

111

112

113

114

115

116

117

118 21.3

119

120 44.5

18.7

25.5 5.0

2.6

6.5

4.2

21.

8.0

18.8

3.7

3.40

1.30 1.40

4.0

2.3

3.2

2.8

5.2

11.4

5.63

3.78

11.80

2.90

0.7

3.9

9.1

1.6

66.00

72.00

3.50

9.70

12.90

18.10

214


121

122 86.0

123 58.3

124 77.7

125 77.0

126 49.1

127 45

128

129 28.0

130 18.0

131

132

133

134 114.0

135 83.0

136 63.9

137

138

139

140

141 89.2

142

143

144

145

146

147 63.2

148

149

150 151 74.2

152

153

154

155

156 157

158

159 160

8.1

1.1

0.5

2.2

1.8

0.4

0.4

0.3

0.2

0.2

0.3

0.2

0.2

0.4

4.1

1.9

8.1

7.5

1.4

3.45

1.73

1

0.67

0.54

0.68

2.57

1.52

140.00

284.00

120.00

120.00

4.3

2.7

2.8

4.5

2.70

2.4

1.6

1.5

5.6

5.8

2.8

1.1

1.5

1.8

3.0

5.2

3.1

0.7

2.5

1.40

0.92

2.60

2.60

0.7 3600.00 19.10

0.5 3896.00 19.00

1.02

0.92

0.60

0.59

0.58

0.56

0.67

0.65

0.98

1.3 3800.00 19.80

1.6 3800.00 16.40

0.6

5.50

4.00

16.20

5.20

5.00

8.10

18.90

16.50

16.30

3.40

9.60

215

Append~B. Continued

161 80.7

162

163

164

165

166

167

168

169 18.0

170 124.0

171 6.0

172 12.0

173

174

175

176

177 71

178 51

179

180

181

182

183

184 61.0

185 105.0

186

187 94.7

188 95

189

190

191 15.2

192 31.4

193 13.0

194

195

196

197

198

199

200

0.4

0.7

1.8

1.0

1.0

0.0

0.2

1.03

6.36

16.36

2.0

12.40

0.60

721.00

2.1

3.9

1.1

1.1

7.6

5.2

8

4.2

0.4

2.0

2.1

7.3

0.90

1.40

3.10

3.30

2.00

0.89

4.73

1.2

110.70

13.00

36.00

10.00

19.00

28.00

26.00

27.80

28.00

9.90

8.00

15.40

216


References

1 Cole, Rapp, 1981; Viereck et al., 1983 2 Grier, BaUard, 1981 3 DeAngelis et al., 1981 4 DeAngelis et al., 1981 5 DeAngelis et al., 1981 6 DeAngelis et al., 1981 7 DeAngelis et al., 1981 8 DeAngelis et al., 1981

9 DeAngelis et al., 1981 10 DeAngelis et al., 1981 11 DeAngelis et al., 1981 12 DeAngelis et al., 1981 13 Cole, Rapp, 1981; DeAngelis et al., 1981 14 DeAngelis et al., 1981 15 DeAngelis et al., 1981 16 DeAngelis et al., 1981 17 DeAngelis et al., 1981 18 DeAngelis et al., 1981

19 DeAngelis et al., 1981 20 DeAngelis et al., 1981

21 Kimmins, Hawkes, 1978; DeAngelis et al., 1981

22 Cole, Rapp, 1981; DeAngelis et al., 1981; Viereck et al., 1983 23 Cole, Rapp, 1981; DeAngelis et al., 1981; Viereck et al., 1983 24 Cole, Rapp, 1981; DeAngelis et al., 1981; Viereck et al., 1983 25 DeAngelis et al., 1981 26 DeAngelis et al., 1981

27 DeAngelis et al., 1981 28 DeAngelis et al., 1981 29 Helmisaari, 1995 30 Helmisaari, 1995

31 Helmisaari, 1995

32 Nadelhoffer et al., 1983, 1985; McClaugherty et al., 1984; Aber et al., 1985 33 Hendrick, Pregitzer, 1993 34 Hendrick, Pregitzer, 1993 35 Tumer, 1975 36 Nadelhoffer et al., 1983, 1985

37 Cole, Rapp, 1981 38 DeAngelis et al., 1981

39 Van Praag et al., 1988 40 DeAngelis et al., 1981; Vogt et al., 1986 41 DeAngelis et al., 1981 43 Safford, 1974 44 Safford, 1974 45 Safford, 1974 46 Burke, Raynal, 1995

47 Covington, 1976, 1981; Bormann, Likens, 1979; Cole, Rapp, 1981; Huntington et al., 1989; Fahey, Hughes, 1994 48 Ruark, Bockheim, 1987 49 Ruark, Bockheim, 1987 50 Ruark, Bockheim, 1987 51 Hoslin, Henderson, 1987 52 Cromack, 1973; McGinty, 1976; Swank, Crossley, 1988 53 DeAngelis et al., 1981

54 NadeUaoffer et al., 1983, 1985; Aber et al., 1985


55 Yin et al., 1989

56 McClaugherty et aL, 1982, 1984; Vitousek et al., 1982; Nadelhoffer et al., 1983, Pastor et al., 1984; Aber et al., 1985

57 Cole, Rapp, 1981; DeAngelis et al., 1981 58 Cole, Rapp, 1981; Vogt et al., 1986 59 Grier et a1.,1981; Meier, 1981; Vogt et al., 1982; Vogt, 1991

60 Grier et al., 1981; Meier, 1981; Vogt et al., 1982; Vogt, 1991 61 DeAngelis et al., 1981

62 Van Praag et al., 1988 63 Goaster et al., 1991 64 Goaster et al., 1991 65 Goaster et al., 1991

66 Cole, Rapp, 1981 67 Cole, Rapp, 1981 68 DeAngelis et al., 1981 69 Nadelhoffer et al., 1983, 1985

70 Arthur, Fahey, 1992 71 Owen, 1954; Carey, Farrell, 1978; Blyth, MacLeod, 1981; Cannell, 1982; Alexander, Fairley, 1983

72 Deans, 1979, 1981 73 Carey, Farrell, 1978; Blythe, MacLeod, 1981; Cannell, 1982; Vogt et al., 1986

74 Comeau, Kimmins, 1989 75 Comeau, Kimmins, 1989

76 Comeau, kimmins, 1989 77 Comeau, Kimmins, 1989 78 Jianping et al., 1993

79 Welch, Klemmedson, 1975 80 Santantonio, Santantonio, 1987; Samtantonio, Grace, 1987

81 Santantonio, Santantonio, 1987; Santantonio, Grace, 1987 82 Haynes, Gower, 1995

83 Haynes, Gower, 1995 84 McClaugherty et al., 1982, 1984; Aber et al., 1985; Vogt et al., 1986

85 Cromack, 1973; Malkonen, 1975; Swank, Crossley, 1988 86 Persson, 1978; Axelsson, Brakenhielm, 1980 87 Persson, 1978; Axelsson, Brakenhielm, 1980 88 Axelsson, Brakenhielm, 1980; Linder, Axelsson, 1982 89 Axelsson, Brakenhielm, 1980; Linder, Axelsson, 1982

90 Nilsson, Albrektson, 1993 91 Nilsson, Albrektson, 1993

92 Fogel, Hunt, 1979, 1983

93 Grier, Logan, 1977; Cromack et a1.,1979; Sollins et al., 1980; Cole and Rapp, 1981 94 Spycer et a1.,1983; Santantonio, Hermann, 1985; Vogt, 1991 95 Santantonio, Hermann, 1985 96 Santantonio, Hermarm, 1985 97 Vogt, 1987 98 Vogt, 1987 99 Vogt, 1987

100 Vogt, 1987 101 Vogt, 1987 102 Vogt, 1987 103 Vogt, 1987 104 Vogt, 1987 105 Vogt, 1987 106 Turner, 1975; DeAngelis et al., 1981; Johnson et al., 1982 107 Tumer, 1975 108 Cole, Gessel, 1968; Vogt et al., 1990 109 Vogt et al., 1990 110 Vogt, 1987

217

218


111 Keyes, Orier, 1981 112 Keyes, Cn-ier, 1981 I 13 Gower et al., 1992 i 14 Homer, 1987; Homer et al., 1987; White et al., 1988; Gower et ai.,1992

115 Oower et al., 1992 116 Gower et al., 1992 117 Gower et al., 1992 118 Grier, 1976; Cole, Rapp, 1981

I19 Grier, 1976 120 DeAngelis et ai.,1981 121 Farrish, 1991 122 Gomez Day, 1982; Megonigal, Day, 1988; Day, 1982, 1984; Powell Day, 1991 123 DeAngleis et aI,, 1981 124 Harris et al., 1975; Kinerson et al., 1977; Cox et al., 1978; DeAngelis et al., 1981; Cole, Rapp, 1981

125 Gomez, Day, 1982; Megonigal, Day, 1988; Day, 1982, 1984; Powell, Day, 1991 126 Climate of the States, 1980; Cole, Rapp, 1981; DeAngelis et al., 1981 127 Cole, Rapp, 1981; DeAngelis et al., 1981 128 Farrish, 1991 129 Gholz et al., 1968; Ewel et al., 1987ab 130 Gholz et al., 1986; Ewel et al., 1987ab 131 Fardsh, 1991 132 Kinerson et al., 1977 133 DeAngelis et al., 1981 134 Gomez, Day, 1982; Megonigal, Day, 1988; Day, 1982, 1984; Powell, Day, 1991 135 Gomez, Day, 1982; Megonigal, Day, 1988; Day, 1982, 1984; Powell, Day, 1991

136 Cole, Rapp, 1981

137 Cabanettes, 1979 138 Lugo, 1992 139 Lugo, 1992 140 Zhanghe et al., 1994 141 Singh, Singh, 1987 142 Gmbb, Tanner, 1976; Tanner, 1977, 1980ab, 1985; Brown, Lugo, 1982; Cannell, 1982

143 Lugo, 1992 144 Lugo, 1992

145 Lugo, 1992

146 Lugo, 1992 147 Odum, 1970; Ovington, Olson, 1970; Jordon, 1971; Brown, Lugo, 1982; Lodge et al., 1991; Kangas, 1992;

Lugo, 1992; Vogts, unpublished 148 Lugo, 1992

149 Cuevas et al., 1991 150 Lugo, 1992 151 Singh, Singh, 1987 152 Kaul et a1.,1982 153 Kaul et a1.,1982 154 Kaul et al., 1982 155 Kaul et al., 1982 156 Kummerow et al., 1990 157 Castellanos et al., 1991 158 Puri et al., 1994; Singh, 1994 159 Purl et al., 1994; Singh, 1994 160 Puri et al., 1994; Singh, 1994 161 Singh, Singh, 1987 162 Srivastava et al., 1986


163 Roy, Singh, 1995

164 Roy, Singh, 1995

165 Priess, Ft~lster, 1994

166 Priess, FOlster, 1994

167 Priess, F6lster, 1994

168 Huttel, 1969; Klinge, 1973, 1975; Klinge, Herrera, 1978; Edwards, 1982

169 Klinge, Rodriguez, 1968; Klinge et al., 1875; Brown, Lugo, 1982; Vogt et al., 1986

170 Duivenvoorden, Lips, 1995



173 F~lster et al., 1976

174 F61ster et al., 1976

175 F~lster et al., 1976

176 Vance, Nadkarni, 1992

177 Gower, 1987

178 Gower, 1987

179 Ewel, 1976; Berish, 1982

180 Ewel, 1976; Berish, 1982

181 Ewel, 1976; Stark, Spratt, 1977; Berish, 1982

182 Visalakshi, 1994

183 Visalakshi, 1994 184 Huttel, Bernhard-Reversat, 1975; Huttel, 1975; Brown, Lugo, 1982; Cannell, 1982; Vislakshi, 1994

185 Huttel, bernhard-Reversat, 1975; Huttel, 1975; Brown, Lugo, 1982; Cannell, 1982; Visalakshi, 1994

186 DeAngelis et al., 1981

187 Edward, Grubb, 1977, 1982; Edwards, 1977, 1982

188 Edwards, 1977; Edwards, 1982; Edwards, Grubb, 1982

189 Cavelier, 1992

190 Ogawa et al., 1965; Yoda, Kira, 1969

191 Klinge, Herrera, 1978; Cuevas, Medina, 1988; Sanford, 1989

192 Klinge, Herrera, 1978; Vuevas, Medina, 1988; Sanford, 1989

193 Klinge, Herrera, 1978; Cuevas, Medina, 1988; Sanford, 1989

194 Hase, F~lster, 1982

195 Greenland, Kowal, 1960; Nye, 1961; Lawson et al., 1970; Edwards, Grubb, 1982; Mabberley, 1992

196 Cavelier, 1992

197 Egunjobi, Bada, 1979




219

aClimatic forest type: BOBLDE - Boreal broadleaf deciduous; BONLEV - Boreal needleleaf evergreen; CTBLDE - cold temperate broadleaf deciduous; CTBLEV - cold temperate broadleaf evergreen; CTNLEV - cold temperate needleleaf evergreen; WTBLDE - warm temperate broadleaf deciduous; WTBLEV - warm temperate broadleaf evergreen; WTNLEV -. warn temperate needleleaf evergreen; STBLDE - subtropical broadleaf deciduous; STBLEV - subtropical broadleaf evergreen; STNLEV - subtropical needleleaf evergreen; TRBLDE - tropical bmadleaf deciduous; TRBLSDE - tropical broadleaf semi-deciduous; TRBLEV - tropical bmadleaf evergreen; TRNLEV - tropical needleleaf evergreen; MENLEV- mediterranean needleleaf evergreen; MEBLEV - mediterranean broadleaf evergreen. bSoil order - 1 - Mollisol; 2 - Alfisol; 3 - Andisol; 4 - Histosol; 5 - Inceptisol; 6 - Ultisol; 7 - Entisol; 8 - Spodosol; 9 - Oxisol; 10 - Aridisol; 11 - Vertisol;. unknown. cSoil texture - 1 - sand; 2 - loamy sand; 3 - sandy loam; 4 - fine sandy loam; 5 - vry fine sandy loam; 6 - loam; 7 - silt loam; 8 - silt; 9 - sandy clay loam; 10 - silt clay loam; 11 - clay loam; 12 - sandy clay; 13 - silty clay; 14 - clay;, unknown. aForest origin. eSite quality. fMRT - mean residence time.

Review of root dynamics in forest ecosystems grouped by climate, climatic forest type and species

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Transcript of Review of root dynamics in forest ecosystems grouped by climate, climatic forest type and species