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Article

Variation in Organ Biomass with Changing Climate and Forest Characteristics across Chinese Forests

by
Yiran Fang
1,
Xiaojun Zou
1,
Zhiyang Lie
1,2 and
Li Xue
1,*
1
College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China
2
South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
*
Author to whom correspondence should be addressed.
Forests 2018, 9(9), 521; https://doi.org/10.3390/f9090521
Submission received: 10 June 2018 / Revised: 24 August 2018 / Accepted: 27 August 2018 / Published: 29 August 2018
(This article belongs to the Section Forest Ecology and Management)
Versions Notes

Abstract

:
Forest biomass allocation patterns are important for understanding global carbon cycling and climate change, which might change with environmental conditions and forest characteristics. However, the effects of climate and forest characteristics on biomass allocation fractions (the fraction of total forest biomass distributed in organs) remains unknown. The authors use a large Chinese biomass dataset (1081 forests encompassing 10 forest types) to analyse the responses of biomass allocation fractions to biogeography, climate, and forest characteristics. The authors found that the stem mass fraction significantly increased with age and precipitation and significantly decreased with latitude and temperature. The branch mass fraction significantly decreased with age and density, but significantly increased with temperature and latitude. The leaf mass fraction significantly decreased with age and precipitation and significantly increased with temperature. The root mass fraction significantly increased with latitude and density, and significantly decreased with precipitation. The results suggest that latitude, temperature, precipitation, stand age and density are good predictors of biomass partitioning. These findings support the hypotheses that variation in resource availability constrains organ allocation and provides biogeographically explicit relationships between biomass allocation and both environmental and forest characteristics, which might be used for assessing the impact of changing environmental and forest characteristics on forest carbon dynamics and fixation.

1. Introduction

Forests play an important role in the global carbon cycle [1] because their biomass stores over 80% of the global aboveground carbon [2]. The plant biomass allocation pattern is an important topic in ecology [3,4], due to its importance in global carbon cycling [2,5]. Forest biomass can be divided into stems, branches, leaves and roots. The process of proportional allocation of new carbon (C) to these organs can be influenced by climate [6,7,8]. The proportional growth of trees results in the proportional distribution of standing including the stem, branches, leaves and roots. Such distribution has important implications for the accuracy of global C cycle modeling and accounting [8]. Biomass allocation fractions (BAFs), defined as the ratio of plant biomass of organs (the stem, branches, leaves and roots) to total plant biomass, are well grounded in plant growth theory [9,10].
Changes in environmental conditions can have rapid and profound effects on vegetation biomass allocation [11,12]. The resource balance/optimality theory assumes that plants try to maximize their growth by allocating biomass to the organ that takes up the most limiting resource [13]. That is, plants allocate more biomass to roots if the limiting factor for growth is below ground, whereas they allocate more biomass to shoots if the limiting factor is above ground [7]. Biomass distribution among tree organs can be influenced by climate due to the important influence of temperature, or precipitation, on biomass distribution for tree growth. Low temperatures in cold ecosystems limit many biological processes [14], whereas in dry environments plant growth is limited by water. Thus, it is reasonable to predict that a greater biomass allocation to roots and lower to stems and leaves should occur with decreasing temperature, and that precipitation would increase the uptake of water and nutrients. The fraction of total forest biomass in stems, branches and leaves should increase, and decrease in roots, as precipitation and/or temperature increases. During the past several decades, profound climatic change has occurred worldwide [15,16,17]. Increasing greenhouse gas emissions are expected to escalate the global atmospheric temperature by 2–7 °C by the end of this century due to the impacts of human population growth [18,19]. This temperature change is significantly altering the temporal variation of aboveground net primary productivity in terrestrial ecosystems [17], and the ecosystem carbon balance, because both ecosystem photosynthesis and respiration often increases with warming [19]. Many biological processes of trees are limited by low temperatures [14], which might affect forest growth. Conversely, precipitation is significantly altering the biomass of terrestrial ecosystems [17,20] due to its increase at high latitudes and decrease in most subtropical regions [16]. This, in turn, is expected to have a strong influence on the future of climate change [21,22,23]. Biomass allocation responses to large-scale spatial variations in temperature and precipitation has received increased attention due to its importance for several ecosystem processes, such as productivity, nutrient cycling, ecosystem fluxes and global carbon cycle modeling [21,24], and currently represents a major knowledge gap that hinders the understanding of carbon pool fluxes and below/above ground carbon storage at global scales. Consequently, researching biomass allocation responses to large-scale spatial variation in temperature and precipitation can strengthen the current understanding of responses to climate change.
Recently, many researchers have studied environmental effects on biomass allocation in many forest types with a focus on the root:shoot ratio, the allometric function relating root biomass and shoot biomass [14,25,26], or biomass allocation patterns to leaves, stems and roots [7,9,27,28,29,30], whereas research on BAFs has received little attention [8,10]. When different forest types are considered, the aforementioned studies have not been consistent, and consequently, relatively little is known about how the environment influences biomass allocation [26], especially on BAFs in forested ecosystems. Therefore, biomass allocation patterns in many forest types remains unclear. Thus, undertaking research to determine how environmental variation affects the shifts in forest biomass is required to understand the mechanisms of ecosystem adaptation and response to environmental change. Whether these allocation patterns vary systematically across environmental gradients remains unknown, and is a critical knowledge gap [24]. As a result, little is known about climate effects on tree organs. Moreover, the above studies have been focused on climate effects on tree organ allocation, but very little is known about climate effects on BAFs. Changes in BAFs that accompany climate changes include important implications for the global carbon cycle [8].
In addition, climatic factors, the growth environment, competition, plant age or size [31] also influence biomass allocation [7,28]. Although large data sets have been used to summarize the influence of forest characteristics on biomass allocation patterns, the understanding of BAFs in relation to effects of biogeography, environmental and forest characteristics is still unclear, and the large-scale pattern of BAFs in China’s forests remains poorly understood [10].
Focusing on China, alpine temperate Larix Mill. and alpine Picea abies (L.) Karst. forest, temperate deciduous broadleaved forest, Pinus tabulaeformis Carriere forest, mixed coniferous broadleaved forest, montane Populus L.-Betula L. deciduous forest, subtropical evergreen broadleaved forest, Pinus massoniana Lamb. forest, Cunninghamia lanceolata (Lamb.) Hook. forest; and subtropical montane Cupressus Linn. and Sabina Mill. forest form both monospecific and mixed forests under a broad variety of temperate and subtropical climates and site conditions. These forest types have complicated stand conditions (such as, age and density), being widely distributed, geographically, over land-cover types with an area of 114 million ha and occupying approximately 80% of the Chinese forested area [30,32]. This allowed the authors to investigate the effects of biogeography, climate and stand characteristics on biomass allocation patterns of these forest types. Although a large organ biomass dataset of China has been used to evaluate temperature effect on biomass allocation in ten forest types [30], information about the effects of biogeographical, environmental and forest characteristics on BAFs of these forest types is still lacking.
During this study, the authors analyze the effects of longitude, latitude, temperature, precipitation, stand age and density on BAFs based on a large organ biomass data set (1081 sites) of the above forest types, which will be useful for assessing biogeographical, environmental and forest characteristic impacts on these forest carbon dynamics. The authors hypothesized that organ allocation fractions will change with site conditions, climate, and forest characteristics, since the ability of forest trees to utilize available resources is affected by environmental and biological factors.

2. Material and Methods

A large organ biomass dataset (comprising 1081 sites) was compiled from the literature, of which 916 sites were extracted from the database of Luo [33] and others collected from 21 sources [34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54]. The database was structured by geographic location, climate (temperature and precipitation), species, stand age and density. The sampled forests varied widely in age (from 3 to 317 years), density (from 125 to 20,535 trees ha−1) and size (with total forest biomass ranging from 3.86 to 657.30 Mg ha−1). The sampled forests also varied widely geographically (81°00′–134°00′ N, 18°70′–52°60′ E) and in mean annual temperature (MAT) (from −6.6 to 24.2 °C) and mean annual precipitation (MAP) (from 241.0 to 2989.1 mm) (Table 1).
The size of these plots varied with stand condition and different forest types. Generally, the area of sample plots was 400–1000 m2 for each forest with several replicates. Several “standard” trees within a plot were selected for felling and weighing of organs, then allometric equations were established, relating organ biomass to diameter at breast height and tree height. Organ mass was calculated using these allometric equations established for different organs in different plots. Organ biomass included stem, branch, leaf and root biomasses (Mstem, Mbra, Mleaf and Mroot) and total forest biomass (Mtot = Mstem + Mbra + Mleaf + Mroot) was estimated based on total tree numbers. The fraction of total biomass (Mtot) in the stem (Fstem = Mstem/Mtot), branches (Fbra = Mbra/Mtot), leaves (Flea = Mlea/Mtot), and roots (Froot = Mroot/Mtot) was calculated [7]. The spatial variation allowed the organ fraction of biomass allocation to be evaluated in relation to longitude, latitude, temperature, precipitation, stand age and stand density, potentially providing valuable information for understanding the effect of biogeography, climate and forest characteristics on biomass allocation patterns along temperature and precipitation gradients in China. This will enable better predictions of the regional carbon balance in response to future environmental change.
All data analyses were performed using statistical software SPSS 19.0 (SPSS Inc.: Chicago, IL, USA, 2004). Stepwise multiple regression (SMR) was used to identify the effects of climate (MAT and MAP), site condition (longitude, latitude) and forest characteristics (stand age and density) on BAFs. Pearson correlation analyses were performed to determine the relationships between the stem, branch, leaf, and root biomasses and site conditions (longitude and latitude).

3. Results

The average biomass of stems, branches, leaves and roots differed largely, ranging from 7.14 to 87.84 Mg ha−1 (Table 2), meaning that BAFs varied markedly among organs, accounting for 60%, 14%, 6% and 20% of total forest biomass for the stem, branches, leaves and roots, respectively.
All BAFs exhibited significant longitudinal trends (p < 0.01; Table 3). Fstem significantly increased from east to west (p < 0.01), whereas Fbra, Flea and Froot decreased from east to west (p < 0.01 or 0.05). Except for Flea, all BAFs exhibited significant latitudinal trends. Fstem and Fbra significantly decreased from south to north, whereas Froot significantly increased with increasing latitude (p < 0.01).
Stepwise multiple regression (SMR) analysis showed that the four BAFs exhibited different sensitivities to age (Table 4). Fstem was significantly positively correlated with age and precipitation and significantly negatively correlated with latitude and temperature. Fbra significantly decreased with age and density, but significantly increased with temperature and latitude. There were significant negative correlations between Flea and age, as well as between Flea and precipitation, and a significant positive correlation between Flea and temperature. Significant positive correlations were also present between Froot and latitude and density, and the negative correlation between Froot and precipitation was also significant.

4. Discussion

Significant changes in BAFs with latitude, temperature, precipitation and forest age and density were demonstrated in this study. These results support the hypothesis that different underlying environmental and biological factors affect the biogeographic patterns of biomass allocation. This study’s results were consistent with some previous research [8,10]. Temperature drives global patterns of forest biomass distribution in leaves, stems, and roots [8,10,28,30], for example, which supports the resource balance/optimality theory stating that plants try to maximize their growth by allocating biomass to the organ requiring the most limited resource [55]. This indicates that the resource balance/optimality theory might be used to analyze forest biomass allocation in response to environmental change.

4.1. Temperature Effects

During this study, Fbra and Flea exhibited significantly positive responses to warming. Many biological processes of trees are limited by low temperatures [14]. Low temperature also impairs plant functions, such as photosynthesis, nutrient uptake, and growth [56]. Moreover, low temperatures can limit water viscosity, membrane permeability and metabolic processes, which affects root and microbial activity, decomposition and mineralization of organic matter, resulting in a reduced supply of nitrogen and phosphorus [30]. Zhou et al. [57] reported that low temperatures decrease the number of leaves due to competition for light, nutrients and water. Reich et al. [8] also found that leaves are a smaller proportion of standing biomass in forests in colder climates. Increasing temperature accelerates the metabolic rates of trees, stimulates microbial activity, enhances biologically driven processes such as soil nutrient mineralization [19,58,59], enhances plant growth by stimulating plant photosynthesis due to increased soil nutrient mineralization [60], extends the growing season [61,62] which increases aboveground biomass [63] and compensates for increased respiratory C losses [60], resulting in increased forest biomass [64,65]. Thus, the branch and leaf mass fractions increase. Many studies have shown a stimulation in biomass with warming in cold-temperate regions [14,66]. Different responses in forest biomass allocation to temperature might be partly regulated by turnover rate. Reich et al. [8] found that forests probably allocate a smaller proportion of new biomass to leaves in low temperature environments due to low turnover rates, thus, increases in Fbra and Flea might result in a decrease in Fstem.

4.2. Precipitation Effects

Forest organ biomass varies across a large scale as a result of precipitation gradients [67]. During this study, Fstem significantly increased, whereas Flea and Froot significantly decreased with increasing MAP, suggesting that the precipitation gradient is the critical control on Fstem, Flea and Froot. McCarthy and Enquist [31] found that stem mass increased with increased precipitation. Increased precipitation is favorable for plant growth [25]. Stem biomass increasing at the expense of root biomass will be favorable for competing for light resources, indicating that light is more limiting than water and nutrients in a warm and rainy climate. Decreased precipitation not only suppresses plant biomass and physiological processes, but also reduces nutrient availability due to water limitations on soil microbial processes [68,69].
Varying MAP did not alter the branch mass fraction. This result implies that precipitation is less important for limiting branch biomass in these forest types, and low temperature is more critical to limiting plant growth in cold-temperate regions [70,71]. Therefore, branch allocation patterns are fairly rigid and do not allow for acclimation in response to precipitation change. Inflexible allocation patterns might restrict the spatial distribution of branches, since they are incapable of adjusting biomass allocation to maximize resource acquisition in response to external factors [72].
Evidence for precipitation control of roots at a large scale is scarce [57,73]. A significantly negative correlation between Froot and MAP in this study suggested that trees in these forest types allocate a lower proportion of biomass to roots as water availability increases. This agrees with reports in other studies [5,13,74]. Mokany et al. [75] also showed that roots:shoot (stem + branches + leaves) was negatively related to precipitation for forests and woodlands worldwide [49]. These results are in line with the generally accepted idea that root biomass allocation is strongly correlated with available soil moisture, and with the common prediction that the root mass fraction increases with decreasing MAP [76] because soil water stress from low precipitation causes greater biomass allocation to roots [5,77]. The observed trend of root mass fraction in the Chinese forest types, along the precipitation gradient, is consistent with the prediction of the resource balance/optimality theory [7,13,28].

4.3. The Effects of Forest Characteristics

Biomass allocation patterns are controlled by forest characteristics, such as, age and competition caused by high density [10,51,78]. During this study, Fstem significantly increased, whereas Flea and Fbra significantly decreased, with age (Table 4). Plants invest proportionally more in support tissues with growth [7,27]. Stems, as support tissues, increase with tree growth [79]. Zewdie et al. [80] also concluded that Fstem increased, whereas Flea decreased, with increasing stand age. Resource allocation from leaves and branches to stems with increasing stand growth has also been reported for Pinus densiflora [81] and Eucalyptus urophylla stands [82]. Considering tree growth, wood (dead cells) continuously accumulates in the stem, whereas branch and leaf biomass decreases as early death of lower branches, due to mutual shading, results in a lower amount of foliage [83,84]. Following canopy closure, Fstem continues to increase, whereas Flea and Fbra decreases as trees grow. This is because older and taller trees entail greater expenditures for their construction and maintenance and expend more energy supplying leaves with the same amount of water, than do younger and shorter trees [82,85].
Fbra decreases and Froot increases with stand density (Table 4). Canopy closure first occurs in the high-density stand, so its canopy growth is impeded. Conversely, the canopy of the low-density stand fully develops due to the broad growth space. The number of lateral branches gradually decreases and their length becomes shorter with increasing stand density [83], which results in Fbra decrease with increasing density. Weiner et al. [86] and Weiner and Fishman [87] also highlighted that branches are fewer in highly crowded stands than in low density stands. Zhang et al. [28] also observed low root biomass under low stand density. Resource acquisition and use is influenced by forest density. Increasing stand density on a site might increase the acquisition and use of soil nutrients and water. Therefore, Froot increases with stand density even though the root growth rate of individual trees decreases.

4.4. Latitude Effects

In this study, BAFs showed different latitude trends. Fstem revealed a significant negative longitudinal trend, whereas Fbra and Flea displayed a significant positive latitudinal trend, which indicates that climate shapes the biogeographic patterns of forest biomass [29,78,79,80,81,82,83,84,85,86,87,88,89]. Climatically, the north-to-south gradients in China reflect shifts from cold and dry to warm and moist conditions, a phenomenon that appears to be consistent with latitudinal trends since temperature decreases with increasing latitude [10]. Forests in high-latitude conditions are influenced by low temperature, low water availability, and low nutrient supply [90,91]. Low MAT and low soil nutrients might result in an increase of root biomass, and low MAP might cause dramatic increases in root biomass at the expense of the stem [28].
Generally, biomass allocation displays a linear relationship with single climatic factors, such as, precipitation or temperature, though sometimes it shows a curvilinear relationship with the climatic factor [84]. When several influence factors were considered, SMR was generally used as the analysis method [10,28]. In the future, more feasible methods need to be developed to increase understanding of the ecological significance of climate and the effect of forest characteristics on patterns of forest biomass allocation.

5. Conclusions

This study analyzed changes of biomass allocation fractions with biogeography, climate, and forest characteristics. The results suggest that latitude, temperature, precipitation, stand age and density have significant effects on biomass partitioning, which provide biogeographically explicit relationships between biomass allocation and environment, as well as forest characteristics. This might influence the ability to improve management of forest ecosystems, understand regional patterns of biomass allocation and reduce the uncertainties in predicting forest carbon fixation.

Author Contributions

Y.F. and X.Z. collected the data and prepared tables and wrote some sections of manuscript, and Z.L. analyzed the data, L.X. designed this study and wrote the main manuscript text. All authors reviewed the manuscript.

Funding

This research was funded by [the National Natural Science Foundation of China] grant number [31600353].

Acknowledgments

We are grateful that Luo Tianxiang provided the precious information for our study. The study was partially supported by the National Natural Science Foundation of China (Grant No. 31600353) and Forestry Technology Popularization Demonstration Project of the Central Government of China (2015-GDTK-07).

Conflicts of Interest

The authors have declared that no competing interests exist.

References

  1. Zhang, K.; Xu, X.N.; Wang, Q.; Liu, B. Biomass, and carbon and nitrogen pools in a subtropical evergreen broad-leaved forest in eastern China. J. For. Res. 2010, 15, 274–282. [Google Scholar] [CrossRef]
  2. Houghton, R.A.; Hall, F.; Scott, J.G. Importance of biomass in the global carbon cycle. J. Geophys. Res. 2009, 114, 1–13. [Google Scholar] [CrossRef]
  3. Shipley, B.; Meziane, D. The balance-growth hypothesis and the allometry of leaf and roots biomass allocation. Funct. Ecol. 2002, 16, 326–331. [Google Scholar] [CrossRef]
  4. Weiner, J. Allocation, plasticity and allometry in plants. Perspect. Plant Ecol. Evol. Syst. 2004, 6, 207–215. [Google Scholar] [CrossRef]
  5. Roa-Fuentes, L.L.; Campo, J.; Parra-Tabla, V. Plant biomass allocation across a precipitation gradient: An approach to seasonally dry tropical forest at Yucatán, Mexico. Ecosystems 2012, 15, 1234–1244. [Google Scholar] [CrossRef]
  6. Vogel, J.G.; Bond-Lamberty, B.P.; Schuur, E.A.G.; Gower, S.T.; Mack, M.C.; O’connell, K.E.B.; Valentine, D.W.; Ruess, R.W. Carbon allocation in boreal black spruce forests across regions varying in soil temperature and precipitation. Glob. Chang. Biol. 2008, 14, 1503–1516. [Google Scholar] [CrossRef]
  7. Poorter, H.; Niklas, K.J.; Reich, P.B.; Oleksyn, J.; Poot, P.; Mommer, L. Biomass allocation to leaves, stems and roots: Meta-analyses of interspecific variation and environmental control. New Phytol. 2012, 193, 30–50. [Google Scholar] [CrossRef] [PubMed]
  8. Reich, P.B.; Luo, Y.J.; Bradford, J.B.; Poorter, H.; Perry, C.H.; Oleksyn, J. Temperature drives global patterns in forest biomass distribution in leaves, stems, and roots. Proc. Natl. Acad. Sci. USA 2014, 111, 13721–13726. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Poorter, H.; Niinemets, Ü.; Poorter, L.; Wright, I.J.; Villar, R. Causes and consequences of variation in leaf mass per area (LMA): A meta-analysis. New Phytol. 2009, 182, 565588. [Google Scholar] [CrossRef]
  10. Zhang, H.; Song, T.; Wang, K.; Wang, G.; Liao, J.; Xu, G.; Zeng, F. Biogeographical patterns of forest biomass allocation vary with climate, soil and forest characteristics in China. Environ. Res. Lett. 2015, 10, 044014. [Google Scholar] [CrossRef]
  11. Maherali, H.; DeLucia, E.H. Influence of climate-driven shifts in biomass allocation on water transport and storage in ponderosa pine. Oecologia 2001, 129, 481–491. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Fan, J.W.; Wang, K.; Harris, W.; Zhong, H.P.; Hu, Z.M.; Han, B.; Zhang, W.Y.; Wang, J.B. Allocation of vegetation biomass across a climate-related gradient in the grasslands of Inner Mongolia. J. Arid Environ. 2009, 73, 521–528. [Google Scholar] [CrossRef]
  13. Zerihun, A.; Montagu, K.D.; Hoffmann, M.B.; Bray, S.G. Biomass in Eucalyptus populnea woodland communities of Northeast Australia along a rainfall gradient. Ecosystems 2006, 9, 501–515. [Google Scholar] [CrossRef]
  14. Sebastià, M.-T. Plant guilds drive biomass response to global warming and water availability in subalpine grassland. J. Appl. Ecol. 2007, 44, 158–167. [Google Scholar] [CrossRef]
  15. Hansen, J.; Sato, M.; Ruedy, R.; Lo, K.; Lea, D.W.; Medina-Elizade, M. Global temperature change. Proc. Natl. Acad. Sci. USA 2006, 103, 14288–14293. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Intergovernmental Panel on Climate Change (IPCC). Summary for policymakers. In Proceedings of the Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva, Switzerland, 1 January 2007; Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2007. [Google Scholar]
  17. Ma, W.H.; Liu, Z.L.; Wang, Z.H.; Wang, W.; Liang, C.Z.; Tang, Y.H.; He, J.S.; Fang, J.Y. Climate change alters interannual variation of grassland aboveground productivity: Evidence from a 22-year measurement series in the Inner Mongolian grassland. J. Plant Res. 2010, 123, 509–517. [Google Scholar] [CrossRef] [PubMed]
  18. Xiao, C.-W.; Zhou, G.-S.; Ceulemans, R. Effects of elevated temperature on growth and gas exchange in dominant plant species from Maowusu sandland, China. Photosynthetica 2003, 41, 565–569. [Google Scholar] [CrossRef]
  19. Wu, Z.T.; Dijkstra, P.; Koch, G.W.; Peñuelasa, J.; Hungate, B.A. Responses of terrestrial ecosystems to temperature and precipitation change: A meta-analysis of experimental manipulation. Glob. Chang. Biol. 2011, 17, 927–942. [Google Scholar] [CrossRef]
  20. Zhou, G.; Peng, C.; Li, Y.; Liu, S.; Zhang, Q.; Tang, X.; Liu, J.; Yan, J.; Zhang, D.; Chu, G.A. Climate change-induced threat to the ecological resilience of a subtropical monsoon evergreen broad-leaved forest in Southern China. Glob. Chang. Biol. 2013, 19, 1197–1210. [Google Scholar] [CrossRef] [PubMed]
  21. Clark, D.A.; Piper, S.C.; Keeling, C.D.; Clark, D.B. Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984–2000. Proc. Natl. Acad. Sci. USA 2003, 100, 5852–5857. [Google Scholar] [CrossRef] [PubMed]
  22. Baker, T.R.; Phillips, O.L.; Malhi, Y.; Almeida, S.; Arroyo, L.; Di Fiore, A.; Erwin, T.; Higuchi, N.; Killeen, T.J.; Laurance, S.G.; et al. Increasing biomass in Amazonian forest plots. Phil. Trans. R. Lond. B 2004, 359, 353–365. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Malhi, Y.; Phillips, O.L. Tropical forests and global atmospheric change: A synthesis. Phil. Trans. R. Lond. B 2004, 359, 549–555. [Google Scholar] [CrossRef] [PubMed]
  24. Rötzer, T.; Seifert, T.; Pretzsch, H. Modelling above and below ground carbon dynamics in a mixed beech and spruce stand influenced by climate. Eur. J. For. Res. 2009, 128, 171–182. [Google Scholar] [CrossRef]
  25. Wang, R.Z.; Gao, Q. Climate-driven changes in shoot density and shoot biomass in Leymus chinensis (Poaceae) on the North-east China Transect (NECT). Glob. Ecol. Biogeogr. 2003, 12, 249–259. [Google Scholar]
  26. Stegen, J.C.; Swenson, N.G.; Enquist, B.J.; White, E.P.; Phillips, O.L.; Jørgensen, P.M.; Weiser, M.D.; Mendoza, A.M.; Vargas, P.N. Variation in above-ground forest biomass across broad climatic gradients. Glob. Ecol. Biogeogr. 2011, 20, 744–754. [Google Scholar] [CrossRef]
  27. Xue, L.; Jacobs, D.F.; Zeng, S.C.; Liu, B.; Yang, Z.Y.; Gu, S.H. Relationship between aboveground biomass allocation and stand density index in Populus × euramericana stands. Forestry 2012, 85, 611–619. [Google Scholar] [CrossRef]
  28. Zhang, H.; Wang, K.; Xu, X.; Song, T.; Xu, Y.; Zeng, F. Biogeographical patterns of biomass allocation in leaves, stems, and roots in China’s forests. Sci. Rep. 2015, 5, 15997. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Zhang, H.; Song, T.; Wang, K.; Yang, H.; Yue, Y.; Zeng, Z.; Peng, W.; Zeng, F. Influences of stand characteristics and environmental factors on forest biomass and root-shoot allocation in southwest China. Ecol. Eng. 2016, 91, 7–15. [Google Scholar] [CrossRef]
  30. Lie, G.W.; Xue, L. Biomass allocation patterns in forests growing different climatic zones of China. Trees 2016, 30, 639–646. [Google Scholar] [CrossRef]
  31. McCarthy, M.C.; Enquist, B.J. Consistency between an allometric approach and optimal partitioning theory in global patterns of plant biomass allocation. Funct. Ecol. 2007, 21, 713–720. [Google Scholar] [CrossRef] [Green Version]
  32. Wang, B.; Liu, M.C.; Zhang, B. Dynamics of net production of Chinese forest vegetation based on forest inventory data. For. Res. Manag. 2009, 1, 35–43, (In Chinese with English abstract). [Google Scholar]
  33. Luo, T.X. Patterns of Net Primary Productivity for Chinese Major Forest Types and Their Mathematical Models. Ph.D. Thesis, Chinese Academy of Sciences, Beijing, China, April 1996. (In Chinese with English abstract). [Google Scholar]
  34. Tian, D.L.; Pan, W.C. Preliminary discussion on biomass timber differentiation and density effect of Masson’s pine pole stands. Chin. J. Plant Ecol. 1986, 10, 294–301, (In Chinese with English abstract). [Google Scholar]
  35. Hui, G.Y.; Tong, S.Z.; Liu, J.F.; Luo, Y.W. Study on the afforestation density of Cunninghamia lanceolata. I. The effect of density on biomass of young growth of C. lanceolata. For. Res. 1988, 1, 410–417, (In Chinese with English abstract). [Google Scholar]
  36. Tian, D.L. Studies on nutrient elements cycling and density effect of pole stage of Pinus massoniana stand. Sci. Sil. Sin. 1989, 25, 106–112, (In Chinese with English abstract). [Google Scholar]
  37. Ding, G.J.; Wang, P.C.; Yang, R.F. Studies on the dynamic change of the commercial biomass of Masson pine pulpwood stands and its modeling. Sci. Sil. Sin. 1998, 34, 33–41, (In Chinese with English abstract). [Google Scholar]
  38. Ding, G.J. Study on change laws of biomass and productivity of Masson pine forest plantation. III. Biomass and productivity of different site. J. Mt. Agric. Biol. 2000, 19, 411–417, (In Chinese with English abstract). [Google Scholar]
  39. Ding, G.J. Study on biomass and productivity of Masson pine planting stand. I. Biomass and density effect of different planting density. J. Fujian Coll. For. 2003, 23, 34–38, (In Chinese with English abstract). [Google Scholar]
  40. Chen, Z.X.; He, Y.J.; Bai, F.M.; Zhang, J.H.; Li, Z.H. Effects of stand density on the biomass and productivity of Pinus massoniana air-sowing stands. J. Cen. South Univ. For. Technol. 2001, 21, 44–47, (In Chinese with English abstract). [Google Scholar]
  41. Ding, G.J.; Wang, P.C. Study on change laws of biomass and productivity of Masson pine forest plantation. II. Biomass and productivity of stand at different ages. For. Res. 2001, 15, 54–60, (In Chinese with English abstract). [Google Scholar]
  42. Fang, Y.T.; Mo, J.M. Study on carbon distribution and storage of a pine forest ecosystem in Dinghushan Biosphere Reserve. Guihaia 2002, 22, 305–310, (In Chinese with English abstract). [Google Scholar]
  43. Guo, C.Y.; Wu, J.L.; Tian, Y.L.; Guo, L.S. A study on the biomass of Pinus tabulaeformis forest on the different densities on Daqing Mountain. J. Inner Mongolia Agric. Univ. 2006, 27, 29–33, (In Chinese with English abstract). [Google Scholar]
  44. Peng, L.F. Preliminary study on the biomass of Phoebe bournei plantation with different density and site condition. J. Fujian For. Sci. Technol. 2008, 35, 15–18, 23, (In Chinese with English abstract). [Google Scholar]
  45. Li, B.B.; Jian, W.H.; Qin, Y.; Zhang, Y.Z.; Wang, Z.B. Relations of stand density and the biomass of the artificial young Larix principis-upprechtii forest. Hebei J. For. Orchard Res. 2009, 24, 244–247, (In Chinese with English abstract). [Google Scholar]
  46. Xie, W.D.; Ye, S.M.; Yang, M.; Zhao, L.J. Biomass and distribution pattern of Pinus massoniana plantation in southeast area of Guanxi. J. Beihua Univ. 2009, 10, 68–71, (In Chinese with English abstract). [Google Scholar]
  47. Deng, H.P.; Geng, G.; Wang, Z.C. Carbon storage and allocation of 35-years-old Pinus massoniana forest ecosystem in southern of Henan province. J. Cent. South Univ. For. Technol. 2010, 30, 6–9, (In Chinese with English abstract). [Google Scholar]
  48. Li, Y. Carbon Storage of Cunninghamia lanceolata Mature Plantation in Shaowu, Fujian Province. Ph.D. Thesis, Chinese Academy of Forestry, Beijing, China, May 2010. (In Chinese with English abstract). [Google Scholar]
  49. Li, Z.; Zhou, W.; Guan, Q.W.; Wei, W.; Dong, P.; Zhang, H.N. Biomass and its influencing factors of Platyclatdus orientalis plantation in the limestone mountains of Xuzhou. J. Anhui Agric. Univ. 2010, 37, 669–674, (In Chinese with English abstract). [Google Scholar]
  50. Tan, J.S. Biomass Dynamics in Successive Rotations of Chinese Fir Plantation in Huitong. Ph.D. Thesis, Central South University of Technology, Changsha, China, October 2010. (In Chinese with English abstract). [Google Scholar]
  51. Zhang, S.G.; Liu, J.; Huang, K.Y.; Liang, R.L.; Lan, X. Biomass and distribution patterns of Pinus massoniana plantation in Northwest Guangxi. Guangxi For. Sci. 2010, 39, 189–192, (In Chinese with English abstract). [Google Scholar]
  52. Cai, Q.L. Research on the biomass of Pinus massoniana Plantations. J. Anhui Agric. Univ. 2011, 39, 7122–7124, (In Chinese with English abstract). [Google Scholar]
  53. Wang, X.Y.; Sun, Y.J.; Ma, W. Biomass and carbon storage distribution of different density in Larix olgensis plantation. J. Fujian Coll. For. 2011, 31, 221–226, (In Chinese with English abstract). [Google Scholar]
  54. Xiang, W.H.; Liu, S.H.; Deng, X.W.; Shen, A.H.; Lei, X.D.; Tian, D.L.; Zhao, M.F.; Peng, C.H. General allometric equations and biomass allocation of Pinus massoniana trees on a regional scale in southern China. Ecol. Res. 2011, 26, 697–711. [Google Scholar] [CrossRef]
  55. Aikio, S.; Rämö, K.; Manninen, S. Dynamics of biomass partitioning in two competing meadow plant species. Plant Ecol. 2009, 205, 129–137. [Google Scholar] [CrossRef]
  56. Lambers, H.; Chapin, F.S.; Pons, T.L. Plant Physiological Ecology; Springer: New York, NY, USA, 2008. [Google Scholar]
  57. Zhou, G.; Wang, Y.; Jiang, Y.; Yang, Z. Estimating biomass and net primary production from forest inventory data, a case study of China’s Larix forests. For. Ecol. Manag. 2002, 169, 149–157. [Google Scholar] [CrossRef]
  58. Miller, A.E.; Schimel, J.P.; Sickman, J.O.; Meixner, T.; Doyle, A.P.; Melack, J.M. Mineralization responses at near-zero temperatures in three alpine soils. Biogeochemistry 2007, 84, 233–245. [Google Scholar] [CrossRef]
  59. Lin, D.L.; Xia, J.Y.; Wan, S.Q. Climate warming and biomass accumulation of terrestrial plants: A meta-analysis. New Phytol. 2010, 188, 187–198. [Google Scholar] [CrossRef] [PubMed]
  60. Melillo, J.M.; Steudler, P.A.; Aber, J.D.; Newkirk, K.; Lux, H.; Bowles, F.P.; Catricala, C.; Magill, A.; Ahrns, T.; Morrissaeua, S. Soil warming and carbon-cycle feedbacks to the climate system. Science 2002, 298, 2173–2176. [Google Scholar] [CrossRef] [PubMed]
  61. Majdi, H.; Ohrvik, J. Interactive effects of soil warming and fertilization on root production, mortality, and longevity in a Norway spruce stand in Northern Sweden. Glob. Chang. Biol. 2004, 10, 182–188. [Google Scholar] [CrossRef]
  62. Zhou, X.H.; Fei, S.F.; Sherry, R.; Luo, Y.Q. Root biomass dynamics under experimental warming and doubled precipitation in a tallgrass prairie. Ecosystems 2012, 15, 542–554. [Google Scholar] [CrossRef]
  63. Sullivan, P.F.; Arens, S.J.T.; Chimner, R.A.; Welker, J.M. Temperature and microtopography interact to control carbon cycling in a high arctic fen. Ecosystems 2008, 11, 61–76. [Google Scholar] [CrossRef]
  64. Peñuelas, J.; Prieto, P.; Beier, C.; Cesaraccio, C.; de Angelis, P.; de Dato, G.; Emmett, B.A.; Estiarte, M.; Garadnai, J.; Gorissen, A.; et al. Response of plant species richness and primary productivity in shrublands along a north–south gradient in Europe to seven years of experimental warming and drought, reductions in primary productivity in the heat and drought year of 2003. Glob. Chang. Biol. 2007, 13, 2563–2581. [Google Scholar] [CrossRef]
  65. Luo, Y.J.; Wang, X.K.; Zhang, X.Q.; Booth, T.H.; Lu, F. Root:shoot ratios across China’s forests: Forest type and climatic effects. For. Ecol. Manag. 2012, 269, 19–25. [Google Scholar] [CrossRef]
  66. Rustad, L.E.; Campbell, J.L.; Marion, G.M.; Norby, R.J.; Mitchell, M.J.; Hartley, A.E. A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming. Oecologia 2001, 126, 543–562. [Google Scholar] [CrossRef] [PubMed]
  67. Houghton, R.A. Aboveground forest biomass and the global carbon balance. Glob. Chang. Biol. 2005, 11, 945–958. [Google Scholar] [CrossRef]
  68. De Dato, G.; Pellizzaro, G.; Cesaraccio, C.; Costantino, S.; De Angelis, P.; Duce, P. Effects of warmer and drier climate conditions on plant composition and biomass production in a Mediterranean shrubland community. Forests 2006, 3, 511–526. [Google Scholar] [CrossRef]
  69. Sardans, J.; Penuuelas, J.; Estiarte, M.; Prieto, P. Warming and drought alter C and N concentration, allocation and accumulation in a Mediterranean shrubland. Glob. Chang. Biol. 2008, 14, 2304–2316. [Google Scholar] [CrossRef] [Green Version]
  70. Shaver, G.R.; Canadell, J.; Chapin, F.S., III; Gurevitch, J.; Harte, J.; Henry, G.; Ineson, P.; Jonasson, S.; Melillo, J.; Pitelka, L.; et al. Global warming and terrestrial ecosystems: A conceptual framework for analysis. Bioscience 2000, 10, 871–882. [Google Scholar] [CrossRef]
  71. Peñuelas, J.; Gordon, C.; Llorens, L.; Nielsen, T.; Tietema, A.; Beier, C.; Bruna, P.; Emmett, B.; Estiarte, M.; Gorissen, A. Nonintrusive field experiments show different plant responses to warming and drought among sites, seasons, and species in a north-south European gradient. Ecosystems 2004, 7, 598–612. [Google Scholar] [CrossRef]
  72. Perkins, S.R.; Owens, M.K. Growth and biomass allocation of shrub and grass seedlings in response to predicted changes in precipitation seasonality. Plant Ecol. 2003, 168, 107–120. [Google Scholar] [CrossRef]
  73. Wang, X.P.; Fang, J.Y.; Zhu, B. Forest biomass and root–shoot allocation in northeast China. For. Ecol. Manag. 2008, 255, 4007–4020. [Google Scholar] [CrossRef]
  74. Zhou, X.H.; Talley, M.; Luo, Y.Q. Biomass, litter, and soil respiration along a precipitation gradient in Southern Great Plains, USA. Ecosystems 2009, 12, 1369–1380. [Google Scholar] [CrossRef]
  75. Mokany, K.; Raison, R.J.; Prokushkin, A.S. Critical analysis of root:shoot ratios in terrestrial biomes. Glob. Change Biol. 2005, 12, 84–96. [Google Scholar] [CrossRef]
  76. Chapin, F.S., III; Autumn, K.; Pugnaire, F. Evolution of suites of traits in response to environmental stress. Am. Nat. 1993, 142, S78–S92. [Google Scholar] [CrossRef]
  77. Sanford, R.L., Jr.; Cuevas, E. Root growth and rhizosphere interactions in tropical forests. In Tropical Forest Plant Eecophysiology; Mulkey, S.S., Chazdon, R.L., Eds.; Chapman and Hall: New York, NY, USA, 1996; pp. 268–300. [Google Scholar]
  78. Zhang, W.P.; Jia, X.; Bai, Y.Y.; Wang, G.X. The difference between above- and below-ground self-thinning lines in forest communities. Ecol. Res. 2011, 26, 819–825. [Google Scholar] [CrossRef]
  79. Enquist, B.J.; Niklas, K.J. Global allocation rules for patterns of biomass partitioning in seed plants. Science 2002, 295, 1517–1520. [Google Scholar] [CrossRef] [PubMed]
  80. Zewdie, M.; Olsson, M.; Verwijst, T. Above-ground biomass production and allometric relations of Eucalyptus globulus Labill. coppice plantations along a chronosequence in the central highlands of Ethiopia. Biomass Bioenergy 2009, 33, 421–428. [Google Scholar] [CrossRef]
  81. Xue, L.; Feng, H.F.; Chen, F.X. Time-trajectory of mean component weight and density in self-thinning Pinus densiflora stands. Eur. J. For. Res. 2010, 129, 1027–1035. [Google Scholar] [CrossRef]
  82. Xue, L.; Pan, L.; Zhang, R.; Xu, P.B. Density effects on the growth of self-thinning Eucalyptus urophylla stands. Trees 2011, 25, 1021–1031. [Google Scholar] [CrossRef]
  83. Xue, L.; Hagihara, A. Density effects on tree organs in self-thinning Pinus densiflora Sieb. et Zucc. stands. Ecol. Res. 2008, 23, 689–695. [Google Scholar] [CrossRef]
  84. Lie, Z.; Xue, L.; Jacobs, D.F. Allocation of forest biomass across broad precipitation gradients In China’s forests. Sci. Rep. 2018, 8, 10536. [Google Scholar] [CrossRef] [PubMed]
  85. Zeide, B. A relationship between size of trees and their number. For. Ecol. Manag. 1995, 72, 265–272. [Google Scholar] [CrossRef]
  86. Weiner, J.; Berntson, G.M.; Thomas, S.C. Competition and growth form in a woodland annual. J. Ecol. 1990, 78, 459–469. [Google Scholar] [CrossRef]
  87. Weiner, J.; Fishman, L. Competition and allomery in Kochia scoparia. Ann. Bot. 1994, 73, 263–271. [Google Scholar] [CrossRef]
  88. Hui, D.; Wang, J.; Le, X.; Shen, W.; Ren, H. Influences of biotic and abiotic factors on the relationship between tree productivity and biomass in China. For. Ecol. Manag. 2012, 264, 72–80. [Google Scholar] [CrossRef]
  89. Liu, M.X.; Xu, X.L.; Sun, A.Y.; Wang, K.L.; Liu, W.; Zhang, X.Y. Is southwestern China experiencing more frequent precipitation extremes? Environ. Res. Lett. 2014, 9, 064002. [Google Scholar] [CrossRef] [Green Version]
  90. Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain. Ecosystems 1999, 1, 1–13. [Google Scholar]
  91. Aerts, R.; Chapin, F.S. The mineral nutrition of wild plants revisited: A re-evaluation of processes and patterns. Adv. Ecol. Res. 2000, 30, 1–67. [Google Scholar]
Table
Table 1. Site conditions (longitude, latitude), climate factors (mean annual temperature (MAT), mean annual precipitation (MAP)) and forest characteristics (stand age, stand density) of the studied forest types.
Table 1. Site conditions (longitude, latitude), climate factors (mean annual temperature (MAT), mean annual precipitation (MAP)) and forest characteristics (stand age, stand density) of the studied forest types.
Forest TypeLatitude (°N)Longitude (°E)Mean Annual Temperature (°C)Mean Annual Precipitation (mm)Stand Age (years)Stand Density (trees ha−1)
Larix forest28.62–52.6086.40–131.80−6.2–12.9357–127430–195219–20,535
Alpine Picea abies forest26.14–52.6081.10–131.80−6.6–13.9370–193746–317125–3967
Temperate Pinus tabulaeformis forest32.60–42.60103.79–129.502.9–18.7380–117315–95146–8506
Temperate mixed coniferous-broadleaved forest40.88–50.70123.90–133.50−0.4–16.0300–83820–238200–3920
Temperate typical deciduous broadleaved forest28.25–51.70103.00–134.00−3.3–18.6410–114220–157166–8326
Temperate/subtropical montane Populus–Betula deciduous forest25.75–52.5085.20–134.00−5.5–17.4241–128325–222149–7302
Subtropical montane Populus spp. and Betula spp. forest25.50–33.5985.20–113.102.7–18.2370–19378–220376–3450
Subtropical evergreen broadleaved forest20.70–30.2585.20–120.173.5–25.4637–23233–200150–9257
Subtropical Pinus massoniana forest21.72–32.70105.08–120.6012.2–24.01020–200615–101392–6700
Subtropical Cunninghamia lanceolata forest18.7–32.33103.37–121.579.4–22.4720–298916–551018–4978
Table
Table 2. Organ biomass and forest biomass (Mg ha−1) and organ mass fraction.
Table 2. Organ biomass and forest biomass (Mg ha−1) and organ mass fraction.
OrganForest
StemBranchesLeavesRoots
BiomassRange1.96–534.240.54–211.880.53–31.450.52–232.533.86–657.30
Mean ± SD87.84 ± 63.9319.82 ± 18.827.14 ± 4.2327.70 ± 19.38142.31 ± 93.06
Organ/ForestRange0.34–0.870.02–0.370.01–0.280.06–0.45
Mean ± SD0.60 ± 0.080.14 ± 0.060.06 ± 0.030.20 ± 0.06
Data were taken from Luo (1996) and others.
Table
Table 3. Pearson correlations between four biomass allocation fractions (BAFs) and site conditions.
Table 3. Pearson correlations between four biomass allocation fractions (BAFs) and site conditions.
Site ConditionsFstemFbraFleaFroot
Longitude (E, °C)−0.259 **0.145 **0.081 *0.185 **
Latitude (N, °C)−0.176 **−0.118 **0.0190.394 **
Note: (1) Fstem, stem mass fraction; Fbra, branch mass fraction; Flea, leaf mass fraction; Froot, root mass fraction; (2) * and ** represent p < 0.05 and p < 0.01, respectively.
Table
Table 4. Stepwise multiple regressions (SMR) between four biomass allocation fractions (BAFs) with stand characteristics and environmental factors in China’s forests.
Table 4. Stepwise multiple regressions (SMR) between four biomass allocation fractions (BAFs) with stand characteristics and environmental factors in China’s forests.
BAFSModelsEquationspR2
Fstem1Stem biomass fractions = 0.0010age + 0.5550.000 0.136
2Stem biomass fractions = 0.0010age − 0.003latitude + 0.6490.000 0.195
3Stem biomass fractions = 0.0010age − 0.005latitude − 0.030temperature + 0.7510.000 0.202
4Stem biomass fractions = 0.0010age − 0.005latitude − 0.040temperature + 0.000023precipitation + 0.7260.000 0.207
Fbra1Branch biomass fractions = −0.0004age + 0.170.000 0.086
2Branch biomass fractions = −0.0005age − 0.000008density + 0.1870.000 0.115
3Branch biomass fractions = −0.0004age − 0.000008density + 0.010temperature + 0.1690.000 0.130
4Branch biomass fractions = −0.0003age − 0.000008density + 0.040temperature + 0.030latitude + 0.0520.000 0.146
Flea 1Leaf biomass fractions = −0.0002age + 0.0720.000 0.132
2Leaf biomass fractions = −0.0002age − 0.000009precipitation + 0.0820.000 0.151
3Leaf biomass fractions = −0.0004age − 0.000017precipitation + 0.010temperature + 0.0820.000 0.157
Froot 1Root biomass fractions = 0.003latitude + 0.0980.000 0.154
2Root biomass fractions = 0.003latitude + 0.000008density + 0.0880.000 0.197

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Fang, Y.; Zou, X.; Lie, Z.; Xue, L. Variation in Organ Biomass with Changing Climate and Forest Characteristics across Chinese Forests. Forests 2018, 9, 521. https://doi.org/10.3390/f9090521

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Fang Y, Zou X, Lie Z, Xue L. Variation in Organ Biomass with Changing Climate and Forest Characteristics across Chinese Forests. Forests. 2018; 9(9):521. https://doi.org/10.3390/f9090521

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Fang, Yiran, Xiaojun Zou, Zhiyang Lie, and Li Xue. 2018. "Variation in Organ Biomass with Changing Climate and Forest Characteristics across Chinese Forests" Forests 9, no. 9: 521. https://doi.org/10.3390/f9090521

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