Effects of Different Thinning Grades on the Spatial Structure of Pure Black Pine Stands

Abstract

Abstract

Black pine is very widely distributed in Turkey, with most stands managed by periodic harvesting to meet domestic wood demand. However, scientific knowledge about the spatial structure of stands after thinning is lacking. To correct this deficiency, 12 pure black pine stands in the Alaçam Mountains between 61 and 95 years of age were investigated. The stands were mapped according to spatial tree distribution and thinning grades were determined by both number of trees per hectare and basal area. The numbers of trees in the stands were between 590 and 2163 (before thinning) and between 269 and 1422 (after thinning). Spatial-structure analyses were carried out before and after thinning using a pair correlation function. Graphical illustrations were extracted and visually assessed. In addition, the total areas below and above the theoretical line (g=1) of the pair correlation functions were calculated, and the effects of thinning on these were determined. No change was observed in the slightly thinned sample stands (17, 35, 38, and 56) and only slight changes in the moderately thinned stands (18, 21, 49. and 53). The most striking differences were detected in the intensively thinned stands (59, 68, 76, and 78). Regular areas have increased in parallel with increased thinning applications. In other respects; clustered areas did not decrease along with the increased thinning. The pair correlation function provides a comprehensive explanation of the effects of thinning on stand spatial structure.

Keywords

• Black Pine, Function area, Pair correlation, Spatial point process, Thinning

EFFECTS OF DIFFERENT THINNING GRADES ON THE SPATIAL STRUCTURE OF PURE BLACK PINE STANDS

Abstract

Türkiye’de karaçam çok yaygın olmakla birlikte meşcerelerinin çoğunda yerel talebi karşılamak için dönemsel hasat yapılmaktadır. Yalnız aralama işleminden sonra meşcerenin mekânsal yapısı hakkında bilimsel bulgular yeterli olarak bulunmamaktadır. Bu eksikliği gidermek için Alaçam Dağlarında yaşları 61 ile 95 arasında bireyler bulunan 12 saf karaçam meşceresinde incelemeler yapılmıştır. Ağaçların mekânsal dağlımı ve aralama derecesine göre haritalandırılan meşcereler hem göğüs yüzeyi alanı hem de hektar başına düşen ağaç sayısına göre belirlenmiştir. Meşcerelerdeki ağaç sayısı aralama işleminden önce 590 ile 2163 iken, işlemden sonra 269 ile 1422’ye düşmüştür. Aralamadan önce ve sonra olmak üzere mekânsal yapı analizleri ikili korelasyon fonksiyonu kullanarak yapılmıştır. Grafiksel gösterimler üretilmiş ve görsel değerlendirmeleri yapılmıştır. Ayrıca ikili korelasyon teorik çizginin (g=1) altında ve üstünde olan toplam alanlar hesaplanmış ve aralamanın bunların üzerindeki etkileri belirlenmiştir. Aralamanın az yapıldığı meşcerelerde (17, 35, 38 ve 56) değişikliğe rastlanmayıp, orta düzeyde aralamanın yapıldığı meşcerelerde (18, 21, 49 ve 53) ise küçük değişikliğe tanık olunmuştur. En büyük fark ise aralamanın yoğun yapıldığı meşcerelerde (59, 68, 76 ve 78) görülmüştür. Düzenli (homojen dağılım gösteren) alanlar artan aralama uygulamasına paralel olarak artmıştır. Diğer bir deyişle kümelenmiş bölgeler seyreltmenin arttırılması ile azalmamıştır. İkili korelasyon fonksiyonu seyreltmenin meşcere konumsal yapısı üzerinde kapsamlı bilgi vermektedir.

Keywords

• Karaçam, İkili korelasyon, Mekânsal nokta süreci, Aralama

References

1. Aguirre O, Hui G, Gadow KV, Jimenez J. An analysis of spatial forest structure using neighbourhood-based variables. Forest Ecology and Management 2003,183: 137€“145.
2. Akkemik U, Yılmaz H, Oral D, Kaya A. Pinus. In: Yaltırık F,
3. Akkemik U, editors. Türkiye'nin Doğal Gymnospermleri (Açık Tohumlular). OGM Press: Ankara; 2011.
4. Antonovics J, Levin DA. The ecological and genetic consequences of density-dependent regulation in plants. Annual Review of Ecology and Systematics 1980,11:411€“52.
5. Baddeley A, Turner R. Spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software 2005;12(6),1€“42. ISSN: 1548-7660. URL: www.jstatsoft.org.
6. Barbeito, I.; Cañellas, I. & Montes, F. Evaluating the behaviour of vertical structure indices in Scots pine forests. Ann. For. Sci. 2009, 66 (7) 710-10
7. Biber P, Weyerhaeuser H. Numerical methods for characterizing structure and diversity applied to a natural tropical forest and to an even-aged teak stand. In: Mies, E., editor, Natural and Socioeconomic Analysis and Modelling of Forest and Agroforestry Systems in Southeast Asia. Zentralstelle für
8. Ernährung und Landwirtschaft, Feldafing, und Zschortau; 1998, 83€“104.

9. Bivand R. spgrass6: Interface between GRASS 6 and R. R package version 0.7-10; 2012.
10. Brumelis G, Elferts D, Liepina L, Luce I, Tabors G, Tjarve D. Age and spatial structure of natural Pinus sylvestris stands in Latvia. Scandinavian Journal of Forest Research 2005;20:471€“80.
11. Bussotti F. Pinus nigra Arnold. In: CABI Publishing (Hrsg.), Pine of silvicultural importance. CABI Publishing; 2002, pp. 266€“86.
12. Clark PJ, Evans FC. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 1954;35:445€“453.
13. Dessard H, Picard N, Pelissier R, Colliner-Vautier F. Spatial patterns of the most abundant tree species. In: [Gourlet-Fleury Sylvie , Guehl Jean-Marc, Laroussinie Olivier ] . Ecology and management of a neotropical rainforest. Elsevier SAS; 2004, pp. 177€“189.
14. Eichhorn MP. Pattern reveals process: spatial organization of a Kamchatkan stone birch forest. Plant Ecology & Diversity 2010a;3(3):281€“8.
15. Eichhorn MP. Spatial organization of a bimodal forest stand. J For Res 2010b;15:391€“7.
16. Fard YE, Feghhi J, Zobeiri M, Namiranian M. Comparison of two distance methods for forest spatial pattern analysis (case study: Zagros forests of Iran). Journal of Applied Sciences 2008;8(1):152€“7.
17. Getzin S, Dean C, He F, Trofymow JA, Wiegand K, Wiegand T. Spatial patterns and competition of tree species in a Douglas-fir chronosequence on Vancouver Island. Ecography 2006;29:671€“82.
18. GRASS Development Team. Geographic Resources Analysis Support System (GRASS) Software. Open Source Geospatial Foundation Project. http://grass.osgeo.org; 2011.
19. Hanewinkel M, Pretzsch H. Modelling the conversion from even-aged to uneven-aged stands of Norway spruce (Picea abies L. Karst.) with a distance-dependent growth simulator. Forest Ecology and Management 2000;134:55€“70.
20. Hanewinkel M. Spatial patterns in mixed coniferous even-aged, uneven-aged and conversion stands. Eur J Forest Res 2004;123:139€“55.
21. He F, Duncan RP. Density-dependent effects on tree survival in an old-growth Douglas fir forest. J Ecol 2000;88:676€“88.
22. Kalıpsız A. Orman Hasılat Bilgisi. İstanbul Üniversitesi, Orman Fakültesi, Yayın No: 3052/328, İstanbul, S: 349; 1982.
23. Kenkel NC. Pattern of self-thinning in jack pine: testing the random mortality hypothesis. Ecology 1988;69:1017€“24.
24. Kint V, Robert DW, Noel L. Evaluation of sampling methods for the estimation of structural indices in forest stands. Ecological Modelling 2004;180:461€“76.
25. Laar AV, Akça A. Forest mensuration. Springer; 2009.
26. Law R, Janine I, Burslem DFRP, Gratzer G, Gunatilleke CVS,
27. Gunatilleke IAUN. Ecological information from spatial patterns of plants: insights from point process theory. Journal of Ecology 2009;97:616€“28.
28. Li L, Wei S, Huang Z, Ye W, Cao H. Spatial patterns and interspecific associations of three canopy species at different life stages in a subtropical forest, China. Journal of Integrative Plant Biology 2008;50(9):1140€“50.
29. Longuetaud F, Seifert T, Leban JM, Pretzsch H. Analysis of long-term dynamics of crowns of sessile oaks at the stand level by means of spatial statistics. Forest Ecology and Management 2008;255:2007€“19.
30. Montes F, Canellas I, Rio MD, Calama R, Montero G. The effects of thinning on the structural diversity of coppice forests. Ann For Sci 2004;61:771€“9.
31. Odabaşı T Silvikültürde Gelişmeler ve Aralama. İ.Ü.Or. Fak. Der. 1985;35(B)4:55€“72.
32. Odabaşı T, Çalışkan A, Bozkuş HF. Orman Bakımı. İstanbul Üni. Yayın Nu: 4458, Orman Fak. Yayın Nu: 474, ISBN:975-404-703-0, 192; 2004
33. OGM. Orman Varlığımız. Turkish Ministry of Environment and Forestry, Ankara; 2006.
34. Pommerening A. Approaches to quantifying forest structures. Forestry 2002;75:305€“24.
35. Pretzsch H. Analysis and modeling of spatial stand structures: methodological considerations based on mixed beech-larch stands in Lower Saxony. Forest Ecology and Management 1997;97:237€“53.
36. Pretzsch H. Structural diversity as a result of silvicultural operations. Lesnictvi-Forestry 1998;44(10):429€“39.
37. Pretzsch H. A unified law of spatial allometry for woody and herbaceous plants. Plant Biol 2002;4:159€“66.
38. Pretzsch H, Biber P. A re-evaluation of Reineke's rule and stand density index. Forest Science 2005;51(4):304-320.
39. Pretzsch H. Forest dynamics, growth and yield. Berlin and Heidelberg: Springer-Verlag, 664 pages, ISBN: 978-3-540-88306-7; 2009.
40. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org; 2008.
41. Roberts SD, Harrington CA. Individual tree growth response to variable-density thinning in coastal Pacific Northwest forests. Forest Ecology and Management 2008;255:2771€“81.
42. Saatçioğlu F. Orman kaynaklarımızdan optimal faydalanma bakımından üretimin artırılmasına ilişkin silvikültürel tedbirler ve imkanlar. İ.Ü.Or. Fak. Der. 22(B)1:124€“49; 1972.
43. Saatçioğlu F, Odabaşı T. Türkiye ormancılığında bakım sorunları, bazı doğal ve yapay kızılçam (Pinus brutia Ten.) genç meşçerelerinde yapılan bakım müdahalelerine ait bulgular. İ.Ü.Or. Fak. Der. 29(B)1:51 €“ 82; 1979.
44. Sekretenko OP, Gavrikov VL. Characterization of tree spatial distribution in small plots using the pair correlation function. Forest Ecology and Management 1998;102:113€“20.
45. Sevgi O, Yılmaz OY, Carus S, Dündar T, Kavgacı A, Tecimen HB. Alaçam Dağları'nda karaçam ormanlarının yükseltiye göre beslenme - büyüme modelleri ve odununun teknolojik özellikleri. TÜBİTAK tarafından 104 O 551 numaralı projenin kesin raporu; 2010.
46. Stoyan D, Penttinen A. Recent applications of point process methods in forestry statistics. Statistical Science 2000;15(1):61€“78.
47. Uuttera J, Haara A, Tokola T, Maltamo M. Determination of the spatial distribution of trees from digital aerial photographs. Forest Ecology and Management 1998;110:275€“82.
48. Wälder K, Wälder O. Analysing interaction effects in forests using the mark correlation function. iForest, SISEF - Italian Society of Silviculture and Forest Ecology, 2008-02-28, 1, 34-38
49. Wiegand T, Moloney KA. Rings, circles, and null-models for point pattern analysis in ecology. Oikos 2004;104:209€“29.
50. Wiegand T, Gunatilleke S, Gunatilleke N, Okuda T. Analyzing the spatial structure of a Sri Lankan tree species wıth multiple scales of clustering. Ecology 2007;88(12):3088€“102.
51. Yu H, Wiegand T, Yang, X, Ci L. The impact of fire and density-dependent mortality on the spatial patterns of a pine forest in the Hulun Buir sandland, Inner Mongolia, China. Forest Ecology and Management 2009;257:2098€“210.
52. Zenner EK. Do residual trees increase structural complexity in Pacific Northwest coniferous forests? Ecological Applications 2000;10(3),800€“10.
53. Zenner EK, Hibbs DE. A new method for modeling the heterogeneity of forest structure. Forest Ecology and Management 2000;129:75€“87.