IDEAS home Printed from https://ideas.repec.org/a/caa/jnljfs/v68y2022i6id10-2022-jfs.html
   My bibliography  Save this article

Spatial relationships of trees in middle taiga post-pyrogenic pine forest stands in the European North-East of Russia

Author

Listed:
  • Ivan N. Kutyavin
  • Alexei V. Manov

    (Department of Forest Science, Institute of Biology of Federal Research Centre Komi Science Centre of the Ural Branch of the Russian Academy of Sciences, Syktyvkar, Russia)

Abstract

Information on the structural organization of forest stands obtained on sample plots is the basis for long-term monitoring of post-fire pine forest structure and dynamics in the European North-East. These data can be used as a marker of native pine stands of the European taiga. Here, we studied vertical and horizontal structure in the post-pyrogenic pine forests of Vacciniosum, Vaccinioso-cladinosum and Myrtillosum site types in the boreal forest of the Komi Republic. The type of horizontal structure of uneven-aged forest stands changed with age from grouped to random one. Large trees were randomly distributed on the plot. We observed the weak aggregation of undergrowth trees (natural tree regeneration) in stands at distances of 2-6 m. Undergrowth individuals were characterized by group distribution at smaller distances than 1-2 m. Spatial relationships between large, small and codominant trees demonstrated random distribution in most cases. Undergrowth individuals did not show any competitive relations. However, we revealed a "taking off" effect between mature trees of pine and pine undergrowth. The direction of the displacement of tree crown centre projections relative to the bases of their trunks was ambiguous. The shift of the crown space towards the maximum solar radiation was detected in a thinned stand with old age and big size of trees. In other site types, no one-sided orientation of the tree crown development emerged.

Suggested Citation

  • Ivan N. Kutyavin & Alexei V. Manov, 2022. "Spatial relationships of trees in middle taiga post-pyrogenic pine forest stands in the European North-East of Russia," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 68(6), pages 228-240.
    • Handle: RePEc:caa:jnljfs:v:68:y:2022:i:6:id:10-2022-jfs
    DOI: 10.17221/10/2022-JFS
    as

    Download full text from publisher

    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/10/2022-JFS.html
    Download Restriction: free of charge
    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/10/2022-JFS.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/10/2022-JFS?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Grabarnik, Pavel & Särkkä, Aila, 2009. "Modelling the spatial structure of forest stands by multivariate point processes with hierarchical interactions," Ecological Modelling, Elsevier, vol. 220(9), pages 1232-1240.
    2. Grabarnik, Pavel & Myllymäki, Mari & Stoyan, Dietrich, 2011. "Correct testing of mark independence for marked point patterns," Ecological Modelling, Elsevier, vol. 222(23), pages 3888-3894.
    3. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lister, Andrew J. & Leites, Laura P., 2018. "Modeling and simulation of tree spatial patterns in an oak-hickory forest with a modular, hierarchical spatial point process framework," Ecological Modelling, Elsevier, vol. 378(C), pages 37-45.
    2. Mari Myllymäki & Tomáš Mrkvička & Pavel Grabarnik & Henri Seijo & Ute Hahn, 2017. "Global envelope tests for spatial processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 381-404, March.
    3. O. Cronie & M. N. M. Van Lieshout, 2015. "A J -function for Inhomogeneous Spatio-temporal Point Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 562-579, June.
    4. Genet, Astrid & Grabarnik, Pavel & Sekretenko, Olga & Pothier, David, 2014. "Incorporating the mechanisms underlying inter-tree competition into a random point process model to improve spatial tree pattern analysis in forestry," Ecological Modelling, Elsevier, vol. 288(C), pages 143-154.
    5. Arii, Ken & Caspersen, John P. & Jones, Trevor A. & Thomas, Sean C., 2008. "A selection harvesting algorithm for use in spatially explicit individual-based forest simulation models," Ecological Modelling, Elsevier, vol. 211(3), pages 251-266.
    6. Jiao Jieying & Hu Guanyu & Yan Jun, 2021. "A Bayesian marked spatial point processes model for basketball shot chart," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 77-90, June.
    7. Frank Davenport, 2017. "Estimating standard errors in spatial panel models with time varying spatial correlation," Papers in Regional Science, Wiley Blackwell, vol. 96, pages 155-177, March.
    8. Leandro, Camila & Jay-Robert, Pierre & Mériguet, Bruno & Houard, Xavier & Renner, Ian W., 2020. "Is my sdm good enough? insights from a citizen science dataset in a point process modeling framework," Ecological Modelling, Elsevier, vol. 438(C).
    9. Vijay Rajagopal & Gregory Bass & Cameron G Walker & David J Crossman & Amorita Petzer & Anthony Hickey & Ivo Siekmann & Masahiko Hoshijima & Mark H Ellisman & Edmund J Crampin & Christian Soeller, 2015. "Examination of the Effects of Heterogeneous Organization of RyR Clusters, Myofibrils and Mitochondria on Ca2+ Release Patterns in Cardiomyocytes," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-31, September.
    10. Christoph Lambio & Tillman Schmitz & Richard Elson & Jeffrey Butler & Alexandra Roth & Silke Feller & Nicolai Savaskan & Tobia Lakes, 2023. "Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln," IJERPH, MDPI, vol. 20(10), pages 1-22, May.
    11. Liao, Jinbao & Li, Zhenqing & Quets, Jan J. & Nijs, Ivan, 2013. "Effects of space partitioning in a plant species diversity model," Ecological Modelling, Elsevier, vol. 251(C), pages 271-278.
    12. Abdollah Jalilian, 2017. "Modelling and classification of species abundance: a case study in the Barro Colorado Island plot," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2401-2409, October.
    13. Herguido Sevillano, E. & Lavado Contador, J.F. & Schnabel, S. & Pulido, M. & Ibáñez, J., 2018. "Using spatial models of temporal tree dynamics to evaluate the implementation of EU afforestation policies in rangelands of SW Spain," Land Use Policy, Elsevier, vol. 78(C), pages 166-175.
    14. Athanasios C. Micheas & Jiaxun Chen, 2018. "sppmix: Poisson point process modeling using normal mixture models," Computational Statistics, Springer, vol. 33(4), pages 1767-1798, December.
    15. Eric Marcon & Florence Puech, 2012. "A typology of distance-based measures of spatial concentration," Working Papers halshs-00679993, HAL.
    16. Raphaël Jauslin & Bardia Panahbehagh & Yves Tillé, 2022. "Sequential spatially balanced sampling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    17. Davies, Tilman M. & Jones, Khair & Hazelton, Martin L., 2016. "Symmetric adaptive smoothing regimens for estimation of the spatial relative risk function," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 12-28.
    18. Catherine Linard & Marius Gilbert & Robert W Snow & Abdisalan M Noor & Andrew J Tatem, 2012. "Population Distribution, Settlement Patterns and Accessibility across Africa in 2010," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
    19. D'Angelo, Nicoletta & Adelfio, Giada & Mateu, Jorge, 2023. "Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    20. J. Bednařík & V. Čada & K. Matějka, 2014. "Forest succession after a major anthropogenic disturbance: a case study of the Jewish Forest in the Bohemian Forest, Czech Republic," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 60(8), pages 336-348.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnljfs:v:68:y:2022:i:6:id:10-2022-jfs. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.