Beyond the base: vertical bark profiles reveal differentiated allocation patterns among subtropical tree functional groups

Qian Liu , Jing Feng , Long Zhang , Hua Sun , Shisheng Long , Siqi Zeng , Huashun Xiao

Journal of Forestry Research ›› 2026, Vol. 37 ›› Issue (1) : 206

PDF
Journal of Forestry Research ›› 2026, Vol. 37 ›› Issue (1) :206 DOI: 10.1007/s11676-026-02147-3
Original Paper
research-article
Beyond the base: vertical bark profiles reveal differentiated allocation patterns among subtropical tree functional groups
Author information +
History +
PDF

Abstract

Bark thickness is a key functional trait influencing tree survival, particularly in fire-prone ecosystems. However, existing research relies heavily on single-point measurements at breast height, an approach that fails to capture how trees distribute defence along the stem in response to vertically uneven threats such as surface fire, which concentrates heat near the base. To address this gap, we introduce the concept of a “bark allocation pattern”, the vertical distribution of bark thickness along the stem, and apply a simplified Kozak taper model to quantify the bark-thickness profiles of 611 trees (from a felled sample of 626) from four functionally distinct groups in the subtropical forests of southern China. These groups comprise a fast-growing conifer (Cunninghamia lanceolata (Lamb.) Hook.), a fire-tolerant conifer (Pinus massoniana Lamb.), a fire-tolerant broadleaf group (Quercus spp.), and a Soft broadleaf group. We used linear mixed-effects models (LMMs) to examine how species, tree size (diameter at breast height, DBH), and topography are associated with these profiles. We identified two contrasting patterns. P. massoniana showed a pronounced “basal reinforcement” pattern, combining thick basal bark with steep upward thinning, whereas the fast-growing C. lanceolata showed an “economic investment” pattern, with thinner, more uniform bark along the stem. The two intermediate groups diverged in how their fire ecology was expressed: Quercus spp. matched P. massoniana in total bark investment but, like the non-fire-tolerant Soft broadleaf group, tapered only moderately, indicating that its fire tolerance was expressed through the amount rather than the vertical placement of bark. DBH was the strongest predictor: basal bark thickness increased steeply with DBH (χ2 = 560.64, P < 0.001), whereas the relative rate of basal-to-apical thinning declined with DBH in all groups (χ2 = 580.71, P < 0.001), indicating a shift from strong basal reinforcement in saplings towards more even allocation in larger trees. Responses to elevation were species-specific and sometimes opposing: P. massoniana built thicker basal bark at lower elevations, whereas Quercus spp. and C. lanceolata did so at higher elevations (significant Species × Elevation interaction for basal bark thickness, χ2 = 60.73, P < 0.001). By resolving how defence is distributed along the stem, the vertical bark profile captures survival-relevant variation that breast-height measurements alone cannot, consistent with a growth–defence trade-off and with niche differentiation among the four groups.

Keywords

Bark allocation / Vertical bark profile / Growth–defence trade-off / Fire resistance / Subtropical forest

Cite this article

Download citation ▾
Qian Liu, Jing Feng, Long Zhang, Hua Sun, Shisheng Long, Siqi Zeng, Huashun Xiao. Beyond the base: vertical bark profiles reveal differentiated allocation patterns among subtropical tree functional groups. Journal of Forestry Research, 2026, 37 (1) : 206 DOI:10.1007/s11676-026-02147-3

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw, 2015, 671148.

[2]

Berrill JP, Oara KL, Kichas NE. Bark thickness in coast redwood (Sequoia sempervirens (D.Don) Endl.) varies according to tree- and crown size, stand structure, latitude and genotype. Forests, 2020, 116637.

[3]

Bond WJ. What limits trees in C4 grasslands and savannas?. Annu Rev Ecol Evol Syst, 2008, 391641659.

[4]

Coley PD, Bryant JP, Chapin FS. Resource availability and plant antiherbivore defense. Science, 1985, 2304728895899.

[5]

Costa EA, Liesenberg V, Finger CAG, Hess AF, Schons CT. Understanding bark thickness variations for Araucaria angustifolia in southern Brazil. J for Res, 2021, 32310771087.

[6]

Dossa GGO, Schaefer D, Zhang JL, Tao JP, Cao KF, Corlett RT, Cunningham AB, Xu J, Cornelissen JHC, Harrison RD. The cover uncovered: bark control over wood decomposition. J Ecol, 2018, 106621472160.

[7]

Elzhov TV, Mullen KM, Spiess AN, Bolker B (2022) minpack.lm: R interface to the Levenberg–Marquardt nonlinear least-squares algorithm found in MINPACK, plus support for bounds. R package version 1.2-3. https://CRAN.R-project.org/package=minpack.lm

[8]

Fox J, Weisberg S. An R companion to applied regression, 20193rd ednThousand Oaks, CASage Publications

[9]

Franceschi VR, Krokene P, Christiansen E, Krekling T. Anatomical and chemical defenses of conifer bark against bark beetles and other pests. New Phytol, 2005, 1672353376.

[10]

Graves SJ, Rifai SW, Putz FE. Outer bark thickness decreases more with height on stems of fire-resistant than fire-sensitive Floridian oaks (Quercus spp.; Fagaceae). Am J Bot, 2014, 1011221832188.

[11]

Jang W, Keyes CR, Graham JM. Evaluation of predictive models for Douglas-fir bark thickness at breast height following 12 biomass harvest treatments. Biomass Bioenergy, 2016, 84: 118-123.

[12]

Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, Bowman DM. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun, 2015, 617537

[13]

Kidd KR, Varner JM. Differential relative bark thickness and aboveground growth discriminates fire resistance among hardwood sprouts in the southern Cascades, California. Trees, 2018, 331267277.

[14]

Kozak A. My last words on taper equations. For Chron, 2004, 804507515.

[15]

Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest package: tests in linear mixed effects models. J Stat Softw, 2017, 8213126.

[16]

Lawes MJ, Midgley JJ, Clarke PJ. Costs and benefits of relative bark thickness in relation to fire damage: a savanna/forest comparison. J Ecol, 2012, 1012517524.

[17]

Li J, You J, Xu Y, Yu X, Deng M, Wu L, Qi C. Vegetation research in Hunan Province: vegetation types, composition, and distribution pattern. Sci Sin Vitae, 2020, 513275288.

[18]

Lindenmayer DB, Franklin J. A major shift to the retention approach for forestry can help resolve some global forest sustainability issues. Conserv Lett, 2012, 56421431.

[19]

Liu N. Wildland surface fire spread: mechanism transformation and behavior transition. Fire Saf J, 2023, 141. 103974

[20]

Loehle C. Tree life history strategies: the role of defenses. Can J for Res, 1988, 182209222.

[21]

Long S, Zeng S, Shi Z, Yang S. Estimating the self-thinning boundary line for oak mixed forests in central China by using stochastic frontier analysis and a proposed variable density model. Ecol Evol, 2022, 129e9321

[22]

MacKinney AL. Some factors affecting the bark thickness of second-growth longleaf pine. J for, 1934, 324470474.

[23]

Menning KM, Stephens SL. Fire climbing in the forest: a semiqualitative, semiquantitative approach to assessing ladder fuel hazards. West J Appl for, 2007, 2228893.

[24]

Muhairwe CK. Bark thickness equations for five commercial tree species in regrowth forests of Northern New South Wales. Aust for, 2000, 6313443.

[25]

Nie W, Liu Y, Tan C, Wang Y, Liu J, Zhao X, Jiang Z, Jia Z. Characteristics and factors driving the variations in bark thickness of major woody plants in China. Ecol Indic, 2022, 144. 109447

[26]

Nolan RH, Rahmani S, Samson SA, Simpson-Southward HM, Boer MM, Bradstock RA. Bark attributes determine variation in fire resistance in resprouting tree species. For Ecol Manage, 2020, 474. 118385

[27]

Or D, Furtak-Cole E, Berli M, Shillito R, Ebrahimian H, Vahdat-Aboueshagh H, McKenna SA. Review of wildfire modeling considering effects on land surfaces. Earth Sci Rev, 2023, 245. 104569

[28]

Paine CET, Stahl C, Courtois EA, Patiño S, Sarmiento C, Baraloto C. Functional explanations for variation in bark thickness in tropical tree communities. Funct Ecol, 2010, 24612631271.

[29]

Pausas JG. Bark thickness and fire regime. Funct Ecol, 2015, 293315327.

[30]

Pinheiro JC, Bates DM. Mixed-effects models in S and S-PLUS, 2000, New York: Springer

[31]

Polle A, Rennenberg H. Physiological responses to abiotic and biotic stress in forest trees. Forests, 2019, 109711.

[32]

Poorter H, Niklas KJ, Reich PB, Oleksyn J, Poot P, Mommer L. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol, 2011, 19313050.

[33]

R Core Team (2023) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

[34]

Rosell JA. Bark thickness across the angiosperms: more than just fire. New Phytol, 2016, 211190102.

[35]

Schafer JL, Breslow BP, Hohmann MG, Hoffmann WA. Relative bark thickness is correlated with tree species distributions along a fire frequency gradient. Fire Ecol, 2015, 1117487.

[36]

Shi Z, Deng X, Zeng L, Shi S, Lei L, Xiao W. Acclimation strategy of Masson pine (Pinus massoniana) by limiting flavonoid and terpenoid production under low light and drought. Int J Mol Sci, 2022, 23158441

[37]

Singh RD, Gumber S, Singh SP, Singh P. Bark thickness and related parameters of tree species along an elevation transect leading to treeline in Central Himalaya. Environ Monit Assess, 2024, 1965441.

[38]

Sultan SE. Phenotypic plasticity for plant development, function and life history. Trends Plant Sci, 2000, 512537542.

[39]

Tan C, Feng Z. Mapping forest fire risk zones using machine learning algorithms in Hunan Province, China. Sustainability, 2023, 1576292

[40]

van Mantgem PJ, Schwartz M. Bark heat resistance of small trees in Californian mixed conifer forests: testing some model assumptions. For Ecol Manage, 2003, 1783341352.

[41]

Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Villar R. The worldwide leaf economics spectrum. Nature, 2004, 4286985821827.

[42]

Yang SI, Radtke PJ. Predicting bark thickness with one- and two-stage regression models for three hardwood species in the southeastern US. For Ecol Manage, 2022, 503. 119778

Rights & permissions

Northeast Forestry University

PDF

0

Accesses

0

Citation

Detail

Sections
Recommended

/

〈 〉