Modelling individual tree maximum basal area growth rates of five tall eucalypt species growing in even-aged forests

P. W. West

Article ID: 2738
Vol 6, Issue 2, 2023

VIEWS - 117 (Abstract) 70 (PDF)

Abstract


Inventory plot data were available providing over 87,000 individual tree basal area growth rates from even-aged native forests of three ash eucalypts (Eucalyptus regnans, E. obliqua, and E. delegatensis), from temperate regions, and two other species from more sub-tropical climes (E. grandis and E. pilularis). Models were developed relating maximum observed growth rates for these species in relation to tree size when, presumably, trees were under ideal environmental conditions and without competition from neighbours for site growth resources. These maximum growth rates increased with increasing tree size to a maximum of their own and then declined as tree size (hence age) increased further. The tree sizes, at which these maximum growth rates reached their maxima, were much greater for the ash eucalypts than for the other two species. It is hypothesised that the ash eucalypts may have evolved physiological characteristics that make them more efficient in compensating for the well-known physiological constraints imposed on growth rates as trees grow to great heights and ages.

Keywords


growth model; maximum growth; basal area; tree height; growth efficiency

Full Text:

PDF


References


1. Weiskittel AR, Hann DW, Kershaw JA, Vanclay JK. Forest Growth and Yield Modelling. Wiley-Blackwell; 2011. doi: 10.1002/9781119998518

2. Burkhart HE, Tomé M. Modeling Forest Trees and Stands. Springer Dordrecht; 2012. doi: 10.1007/978-90-481-3170-9

3. Pretzsch H, Biber P. Size-symmetric versus size-asymmetric competition and growth partitioning among trees in forest stands along an ecological gradient in central Europe. Canadian Journal of Forest Research. 2010; 40(2): 370−384. doi: 10.1139/X09-195

4. Mattay JP, West PW. A Collection of Growth and Yield Data from Eight Eucalypt Species Growing in Even-aged Monoculture Forest. Division of Forestry, User Series No 18. CSIRO Publishing; 1994.

5. West PW, Mattay JP. Yield prediction models and comparative growth rates for six eucalypt species. Australian Forestry. 1993; 56(3): 211−225. doi: 10.1080/00049158.1993.10674609

6. Coomes DA, Allen RB. Effects of size, competition and altitude on tree growth. Journal of Ecology. 2007; 95(5): 1084−1097. doi: 10.1111/j.1365-2745.2007.01280.x

7. Pommerening A, LeMay V, Stoyan D. Model-based analysis of the influence of ecological processes on forest point pattern formation—A case study. Ecological Modelling. 2011; 222(3): 666–678. doi: 10.1016/j.ecolmodel.2010.10.019

8. Bošeľ̋a M, Petráš R, Šebeň V, et al. Evaluating competitive interactions between trees in mixed forests in the Western Carpathians: Comparison between long-term experiments and SIBYLA simulations. Forest Ecology and Management. 2013; 310: 577−588. doi: 10.1016/j.foreco.2013.09.005

9. Pommerening A, Särkkä A. What mark variograms tell about spatial plant interactions. Ecological Modelling. 2013; 251: 64–72. doi: 10.1016/j.ecolmodel.2012.12.009

10. Pommerening A, Maleki K. Differences between competition kernels and traditional size-ratio based competition indices used in forest ecology. Forest Ecology and Management. 2014; 331: 135−143. doi: 10.1016/j.foreco.2014.07.028

11. Häbel H, Myllymäki M, Pommerening A. New insights on the behaviour of alternative types of individual-based tree models for natural forests. Ecological Modelling. 2019; 406: 23−32. doi: 10.1016/j.ecolmodel.2019.02.013

12. Tian D, Bi H, Jin X, Li F. Stochastic frontiers or regression quantiles for estimating the self-thinning surface in higher dimensions? Journal of Forestry Research. 2021; 32: 1515–1533. doi: 10.1007/s11676-020-01196-6

13. Koenker R, Hallock KF. Quantile regression. Journal of Economic Perspectives. 2002; 15(4): 143−156.

14. Cade BS, Noon BR. A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment. 2003; 1(8): 412−420. doi: 10.2307/3868138

15. Bi H, Turvey ND. A method of selecting data points for fitting the maximum biomass-density line for stands undergoing self-thinning. Australian Journal of Ecology. 1997; 22(3): 356−359. doi: 10.1111/j.1442-9993.1997.tb00683.x

16. Bi H, Wan G, Turvey ND. Estimating the self-thinning boundary line as a density-dependent stochastic biomass frontier. Ecology. 2000; 81(6): 1477–1483. doi: 10.1890/0012-9658(2000)081[1477:ETSTBL]2.0.CO;2

17. Zhang L, Bi H, Gove JH, Heath LS. A comparison of alternative methods for estimating the self-thinning boundary line. Canadian Journal of Forest Research. 2005; 35(6): 1507–1514. doi: 10.1139/x05-070

18. West PW. Modelling maximum stem basal area growth rates of individual trees of Eucalyptus pilularis Smith. Forest Science. 2021; 67(6): 633–636. doi: 10.1093/forsci/fxab047

19. West PW. Quantifying effects on tree growth rates of symmetric and asymmetric inter-tree competition in even-aged, monoculture Eucalyptus pilularis forests. Trees. 2023; 37: 239−254. doi: 10.1007/s00468-022-02341-w

20. West PW. Effects of site productive capacity on individual tree maximum basal area growth rates of Eucalyptus pilularis Smith in subtropical Australia. Journal of Forestry Research. 2023; 34: 1659–1668. doi: 10.1007/s11676-023-01623-4

21. Smith WR, Farrar Jr. RM, Murphy PA, et al. Crown and basal area relationships of open-grown southern pines for modeling competition and growth. Canadian Journal of Forest Research. 1992; 22(3): 341–347. doi: 10.1139/x92-044

22. Pienaar LV, Turnbull KJ. The Chapman–Richards generalization of von Bertalanffy’s growth model for basal area growth and yield in even-aged stands. Forest Science. 1973; 19(1): 2−22. doi: 10.1093/forestscience/19.1.2

23. Hahn JT, Leary RA. Potential diameter growth functions. In: A generalized Forest Growth Projection System Applied to The Lake States Region. General Technical Report NC−49. U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station; 1979. pp. 22−26.

24. Teck RM, Hilt DE. Individual-tree Diameter Growth Model for the Northeastern United States. Research Paper NE−649. U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station; 1991. doi: 10.2737/NE-RP-649

25. Bragg DC. Potential relative increment (PRI): A new method to empirically derive optimal tree diameter growth. Ecological Modelling. 2001; 137(1): 77−92. doi: 10.1016/S0304-3800(00)00433-6

26. Schröder J, Soalleiro RR, Alonso GV. An age-independent basal area increment model for maritime pine trees in northwestern Spain. Forest Ecology and Management. 2002; 15(1–3): 55–64. doi: 10.1016/S0378-1127(00)00657-5

27. Canham CD, LePage PT, Coates KD. A neighborhood analysis of canopy tree competition: Effects of shading versus crowding. Canadian Journal of Forest Research. 2004; 34(4): 778−787. doi: 10.1139/x03-232

28. Lamonica D, Pagel J, Valdés-Correcher E, et al. Tree potential growth varies more than competition among spontaneously established forest stands of pedunculate oak (Quercus robur). Annals of Forest Science. 2020; 77: 80. doi: 10.1007/s13595-020-00981-x

29. Pommerening A, Sterba H, West P. Sampling theory inspires quantitative forest ecology: The story of the relascope kernel function. Ecological Modelling. 2022; 467: 109924. doi: 10.1016/j.ecolmodel.2022.109924

30. Gower ST, McMurtrie RE, Murty D. Aboveground net primary production decline with stand age: Potential causes. Trends in Ecology & Evolution. 1996; 11(9): 378−382. doi: 10.1016/0169-5347(96)10042-2

31. Ryan MG, Binkley D, Fownes JH. Age-related decline in forest productivity: Pattern and process. Advances in Ecological Research. 1997; 27: 213−262. doi: 10.1016/S0065-2504(08)60009-4

32. Davis LS, Johnson KN, Bettinger P, Howard T. Forest Management, 4th ed. McGraw-Hill; 2000.

33. West PW. Tree and Forest Measurement, 3rd ed. Springer Cham; 2015. doi: 10.1007/978-3-319-14708-6

34. Binkley D. Acorn review: The persistent mystery of declining growth in older forests. Forest Ecology and Management. 2023; 538: 121004. doi: 10.1016/j.foreco.2023.121004

35. Ryan MG, Phillips N, Bond BJ. The hydraulic limitation hypothesis revisited. Plant, Cell & Environment. 2006; 29(3): 367–381. doi: 10.1111/j.1365-3040.2005.01478.x

36. Koch GW, Sillett SC, Jennings GM, Davis SD. The limits to tree height. Nature. 2004; 428: 851–854. doi: 10.1038/nature02417

37. West PW. Do increasing respiratory costs explain the decline with age in forest growth rate? Journal of Forestry Research. 2020; 31(3): 693−712. doi: 10.1007/s11676-019-01020-w

38. Boland DJ, Brooker MIH, Chippendale GM, et al. Forest Trees of Australia, 5th ed. CSIRO Publishing; 2006.

39. Florence RG (editor). Ecology and Silviculture of Eucalypt Forests. CSIRO Publishing; 1996.

40. West PW. Comparative growth rates of several eucalypts in mixed-species stands in southern Tasmania. [Eucalyptus regnans, E. obliqua, hybrids of these two, and E. globulus]. New Zealand Journal of Forestry Science. 1981; 11: 45−52.

41. Sillett SC, Van Pelt R, Koch GW, et al. Increasing wood production through old age in tall trees. Forest Ecology and Management. 2010; 259(5): 976−994. doi: 10.1016/j.foreco.2009.12.003

42. Sillett SC, Van Pelt R, Kramer RD, et al. Biomass and growth potential of Eucalyptus regnans up to 100 m tall. Forest Ecology and Management. 2015; 348: 78−91. doi: 10.1016/j.foreco.2015.03.046

43. Koch GW, Sillett SC, Antoine ME, Williams CB. Growth maximization trumps maintenance of leaf conductance in the tallest angiosperm. Oecologia. 2015; 177: 321–331. doi: 10.1007/s00442-014-3181-6

44. Wensel LC, Meerschaert WJ, Biging GS. Tree height and diameter growth models for northern California conifers. Hilgardia. 1987; 55: 1−20. doi: 10.3733/HILG.V55N08P020

45. Belcher DW, Holdaway MR, Brand GJ. A Description of STEMS: The Stand and Tree Evaluation and Modeling System. General Technical Report NC−79. U.S. Department of Agriculture, Forest Service; 1982. doi: 10.2737/NC-GTR-79

46. Lessard VC, McRoberts RE, Holdaway MR. Diameter growth models using Minnesota forest inventory and analysis data. Forest Science. 2001; 47(3): 301−310. doi: 10.1093/forestscience/47.3.301

47. Bragg DC, Roberts DW, Crow TR. A hierarchical approach for simulating northern forest dynamics. Ecological Modelling. 2004; 173(1): 31−94. doi: 10.1016/j.ecolmodel.2003.08.017

48. West PW. Estimation of height, bark thickness and plot volume in regrowth eucalypt forest. Australian Forest Research. 1979; 9(4): 295−308.

49. West PW. Functions to estimate tree height and bark thickness of Tasmanian regrowth Eucalypts. Australian Forest Research. 1982; 12(3): 183−190.

50. Paul K, Roxburgh SH, Chave J, et al. Testing the generality of above-ground biomass allometry across plant functional types at the continent scale. Global Change Biology. 2016; 22(6): 2106−2124. doi: 10.1111/gcb.13201




DOI: https://doi.org/10.24294/sf.v6i2.2738

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 P.W. West

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

This site is licensed under a Creative Commons Attribution 4.0 International License.