Analysing interaction effects in forests using the mark correlation function

doi:10.3832/ifor0449-0010034

The spatial distribution of trees in forests can be described and modelled by point processes where the points are given by the locations (coordinates) of the trees. Further properties of a tree like height or mean crown radius can be interpreted as so called marks of the considered point process characterising the points or trees in some way. The so called mark correlation function describes the spatial correlation of these marks in the observed point pattern. In this paper we introduce a special mark, the overlapping or crown index. We show that mark correlation functions for the considered marks help to understand interaction effects of forest trees.

Keywords

Forestry statistics, Marked point process, Interaction, Crown index

Authors’ address

(1)

Institute for Stochastics, TU Bergakademie Freiberg (Freiberg University of Mining and Technology), Prüferstrasse 9, D-09599, Freiberg (Germany)

(2)

TU Dresden (Dresden University of Technology), Institute for Cartography, D-01062, Dresden (Germany)

Corresponding author

K Wälder
waelder@math.tu-freiberg.de

Citation

Wälder K, Wälder O (2008). Analysing interaction effects in forests using the mark correlation function. iForest 1: 34-38. - doi: 10.3832/ifor0449-0010034

Paper history

Received: May 15, 2007
Accepted: Sep 02, 2007

First online: Feb 28, 2008
Publication Date: Feb 28, 2008
Publication Time: 5.97 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 52083
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 42711
Abstract Page Views: 2527
PDF Downloads: 5391
Citation/Reference Downloads: 132
XML Downloads: 1322

Web Metrics
Days since publication: 6111
Overall contacts: 52083
Avg. contacts per week: 59.66

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

Total number of cites (since 2008): 12
Average cites per year: 0.75

(no Plumx Metrics available for this paper)

Publication Metrics

by Dimensions ^©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

(1)

Albers D, Migge S, Schaefer M, Scheu S (2004)
Decomposition of beech leaves (Fagus sylvatica) and spruce needles (Picea abies) in pure and mixed stands of beech and spruce. Soil Biology and Biochemistry 36: 155-164.
CrossRef | Gscholar

(2)

Aussenac G (2000)
Interactions between forest stands and microclimate: Exophysiological aspects and consequences for silviculture. Annales of forest science 57: 287-301.
CrossRef | Gscholar

(3)

Canham CD, Finzi AC, Pacala SW, Burbank DH (1994)
Causes and consequences of resource heterogeneity in forests: interspecific variation in light transmission by canopy trees. Canadian Journal of Forest Research 24,:337-349.
CrossRef | Gscholar

(4)

Harper JL (1994)
Population Biology of Plants (9^th edn). Academic Press Inc., London.
Gscholar

(5)

Lancaster J, Downes BJ (2004)
Spatial point pattern analysis of available and exploited resources. Ecography 27: 94-102.
CrossRef | Gscholar

(6)

Lancaster J (2006)
Using neutral landscapes to identify patterns of aggregation across resource patches. Ecograpy 29: 385-395.
CrossRef | Gscholar

(7)

McEwan RW, Muller RN (2006)
Spatial and temporal dynamics in canopy dominance of an old-growth central Appalachian forest. Canadian Journal of Forest Research 36: 1536-1550.
CrossRef | Gscholar

(8)

Näther W, Wälder K (2003)
Experimental design and statistical inference for cluster point processes. Biometrical Journal 45: 1006-1022.
Gscholar

(9)

Näther W, Wälder K (2007)
Applying fuzzy measures for Considering interaction effects in root dispersal models. Fuzzy Sets and Systems 158: 572-582.
Gscholar

(10)

Parrott L, Lange H (2004)
Use of interactive forest growth simulation to characterise spatial stand structure. Forest Ecology and Management 194: 29-47.
CrossRef | Gscholar

(11)

Penttinen A, Stoyan D, Henttonen HM (1992)
Marked point processes in forest statistics. Forest Science 38: 806-824.
Gscholar

(12)

Prescott CE (2002)
The influence of the forest canopy on nutrient cycling. Tree Physiology 22: 1193-1200.
Gscholar

(13)

Pukkala T, Kolström TA (1992)
Stochastic Spatial Regeneration Model for Pinus sylvestris. Scandinavian Journal of Forest Research 7: 377-385.
Gscholar

(14)

Scheu S (2005)
Linkages between tree diversity, soil fauna and ecosystem processes. In: Forest diversity and function Ecological Studies, No.176 (Scherer-Lorenzen M, Körner C, Schulze ED eds), Springer Verlag, Berlin, Heidelberg, New York, pp. 211-233.
CrossRef | Gscholar

(15)

Stoyan D, Stoyan H (1994)
Fractals, Random Shapes and Point Fields. John Wiley & Sons, Chichester, UK.
Gscholar

(16)

Stoyan D, Kendall WS, Mecke J (1995)
Stochastic Geometry and its Applications. John Wiley & Sons, New York, USA.
Gscholar

(17)

Stoyan D, Wagner S (2001)
Estimating the fruit dispersion of anemochorous forest trees. Ecological Modelling 145: 35-47.
CrossRef | Gscholar

(18)

Wälder O, Stoyan D (1996)
On variograms in point process statistics. Biometrical Journal 38: 895-905.
Gscholar

(19)

Wagner S, Wälder K, Ribbens E, Zeibig A (2004)
Considering directionality in fruit dispersal models for anemochorous forest trees. Ecological Modelling 179: 487-498.
CrossRef | Gscholar