Scholarly record
STOCHASTIC SIMULATION OF SELF-THINNING
Abstract
Understanding the principles of self-thinning forest ecosystems is essential for taking modern management techniques into practice. The spatial distribution of the surviving trees in a stand is influenced by a variety of factors, including tree mortality. In young forests, competition has a major role in determining spatial mortality; in older forests, random changes in the environment have a major role. The dynamics of the number of living and dead trees in the forests of central Lithuania will be addressed in this study. The Gompertz type mixed effect parameters univariate stochastic differential equations of the tree diameter, height, and occupied area combined with the normal copula function are used to derive new models for the number per ha of living and dead trees. The mean values of the dead tree size variables had much lower trajectories, which is especially apparent in mature stands, as demonstrated by the study of the individual tree size variables (height and diameter). The results are illustrated using experimental field studies carried out in Lithuania, in the municipality of Kazlu Ruda. The following information was gathered using 48 permanent test plots placed in mixed-species, uneven-aged stands: age, diameter at breast height (58,829 trees), tree position (58,829 trees), and height (10,796 trees). All results were implemented using the symbolic algebra system Maple.
Publication Impact Profile
- Captures
- Mendeley - Readers: 1
Publication details
References16
Di Crescenzo A., Iuliano A., Mustaro V., Verasani G. On the Telegraph Process Driven by Geometric Counting Process with Poisson-Based Resetting, Journal of Statistical Physics, vol. 190, 191, 2023. DOI: 10.1007/s10955-023-03189-1
Rupsys, P. Understanding the Evolution of Tree Size Diversity within the Multivariate Nonsymmetrical Diffusion Process and Information Measures, Mathematics, vol. 7, 761, 2019. DOI: 10.3390/math7080761
Balandier P., Marell A., Prevosto B., Vincenot L. Tamm review: Forest understorey and overstorey interactions: So much more than just light interception by trees, Forest Ecology and Management, vol. 526, 120584, 2022. DOI: 10.1016/j.foreco.2022.120584
Rupsys P. Univariate and bivariate diffusion models: computational aspects and applications to forestry, Stochastic Differential Equations: Basics and Applications, New York, pp 1-77, 2018. DOI: 10.1109/iccpcct.2018.8574223
Haq S.M., Khoja A.A., Lone F.A., Waheed M., Bussmann R.W., Mahmoud E.A. Elansary H.O. Floristic composition, life history traits and phytogeographic distribution of forest vegetation in the Western Himalaya, Frontiers in Forests Global Change, vol. 6, 1169085, 2023. DOI: 10.3389/ffgc.2023.1169085
Ito K. On stochastic processes, Japanese Journal of Mathematics, vol. 18, pp 261�301, 1942. DOI: 10.4099/jjm1924.18.0_261
Visalga G., Rupsys P., Petrauskas E. Influence of Noise on Decay Predictions in Standing Trees, AIP Conference Proceedings, vol. 1895, 030006, 2017. DOI: 10.1063/1.5007365
Bai Z., Wei H., Xiao Y., Song S., Kucherenko S. A Vine Copula-Based Global Sensitivity Analysis Method for Structures with Multidimensional Dependent Variables, Mathematics, vol. 9, 2489, 2021. DOI: 10.3390/math9192489
Nelsen R.B. An Introduction to Copulas, Springer, New York, 1999. DOI: 10.1007/978-1-4757-3076-0
Eklund J., Kim J.-M. Examining Factors That Affect Movie Gross Using Gaussian Copula Marginal Regression, Forecasting, vol. 4, pp 685-698, 2022. DOI: 10.3390/forecast4030037
Sklar A. Random variables, distribution functions, and copulas � a personal look backward and forward, IMS Lecture Notes � Monograph Series, vol. 28, pp 1�14, 1966. DOI: 10.1214/lnms/1215452606
Ai C. A Semiparametric Maximum Likelihood Estimator, Econometrica, vol. 65, pp 933�63, 1977. DOI: 10.2307/2171945
Rupsys P., Mozgeris G., Petrauskas E., Krikstolaitis R. (2023). A Framework for Analyzing Individual-Tree and Whole-Stand Growth by Fusing Multilevel Data: Stochastic Differential Equation and Copula Network. Forests, vol. 14, 2037, 2023. DOI: 10.3390/f14102037
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, vol. 32, pp 1515�1533, 2021. DOI: 10.1007/s11676-020-01196-6
Diao S., Sun H., Forrester D.I., Soares A.A.V., Protasio T.P., Jiang J. Variation in Growth, Wood Density, and Stem Taper Along the Stem in Self-Thinning Stands of Sassafras tzumu, Frontiers in Plant Science, vol. 13, 853968, 2022. DOI: 10.3389/fpls.2022.853968
Lu L., Zeng F., Zeng Z., Du H., Zhang C., Zhang H. Diversity and soil chemical properties jointly explained the basal area in karst forest. Frontiers in Forests and Global Change, vol. 6, 1268406, 2023. DOI: 10.3389/ffgc.2023.1268406
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.