“…2 and 3) whether in regards to all species or dominant speices. Again, similar observations were made in previous research (Chai et al, 2017;Li et al, 2012;Li et al, 2014;Zhang et al, 2014;Zhang et al, 2015b). This result indicates that the proportion of randomly distributed reference trees was highest in the communities across different forest types.…”
Section: Randomly Distributed Reference Trees Are Highest In the Commsupporting
confidence: 90%
“…SBFM can improve forest quality by changing species composition and structural diversity (Bettinger & Tang, 2015) and is an effective method of forest management. Under the SBFM concept, a healthy and stable forest is randomly distributed and with varied species and size differentiations (Chai et al, 2017). These three structural characteristics can be represented by a group of parameters based on the nearest neighborhood statistics: Uniform angle index (W), dominance (U), and mingling (M) (Hui et al, 2010).…”
Section: Introductionmentioning
confidence: 99%
“…Previous research on bivariate distribution of community structures in forests has mostly centered around combined probability for heights and diameters (Hafley & Schreuder, 1977;Rupšys & Petrauskas, 2010;Zucchini et al, 2001), and has yielded a wealth of useful information for forest management. Current studies investigating W, U, and M bivariate distribution (called "WUM bivariate distribution" from here on to distinguish it from the height-diameter bivariate distribution) are ongoing in Changbai Mountain (Li et al, 2012;Li et al, 2014;Li, 2013;Ni et al, 2014), Qinling Mountain (Chai et al, 2017;Zhang et al, 2015a;Zhang et al, 2014), Beijing Jiulong Mountain (Zhang et al, 2015b), and the coastline of Guangxi Province (Li et al, 2016).…”
Bivariate distribution is an effective approach to spatial structure analysis. Combined with two of the three structure parameters (uniform angle index (W), dominance (U) and mingling (M)), the structural bivariate characteristics of five different Pinus massoniana forests were explored in this study. Our objective was to provide a theoretical direction for structure-based and detailed management in P. massoniana forests. The bivariate traits showed that mixed artificial or secondary forests predominated by P. massoniana trees do not typically achieve the highest mingling level. Trees under extreme mingling conditions were rare and typically comprised of non-dominant species instead of dominant ones in P. massoniana forests; these trees were generally overtopped and randomly distributed. Management implications can be extracted comparing the bivariate traits between all species and dominant species. The original community structure, development stage, and mixed-tree species number affect the univariate M and, furthermore, the two bivariate U-M and W-M distributions in mixed forests. Forest type has little effect on all-species W-U traits compared to those of the dominant species. U-M traits should be adjusted first if the random frequency values are highest in W-U and W-M bivariate distribution, and it is necessary to determine whether these two bivariate traits shade the W univariate. Adjustments made based on bivariate distribution can reveal poor frequency combinations for foresters to target; this allows the simultaneous adjustment of dual aspects of forest structure based on one bivariate index. Our results show that bivariate distribution may provide very useful information for the management of P. massoniana and other forests.
“…2 and 3) whether in regards to all species or dominant speices. Again, similar observations were made in previous research (Chai et al, 2017;Li et al, 2012;Li et al, 2014;Zhang et al, 2014;Zhang et al, 2015b). This result indicates that the proportion of randomly distributed reference trees was highest in the communities across different forest types.…”
Section: Randomly Distributed Reference Trees Are Highest In the Commsupporting
confidence: 90%
“…SBFM can improve forest quality by changing species composition and structural diversity (Bettinger & Tang, 2015) and is an effective method of forest management. Under the SBFM concept, a healthy and stable forest is randomly distributed and with varied species and size differentiations (Chai et al, 2017). These three structural characteristics can be represented by a group of parameters based on the nearest neighborhood statistics: Uniform angle index (W), dominance (U), and mingling (M) (Hui et al, 2010).…”
Section: Introductionmentioning
confidence: 99%
“…Previous research on bivariate distribution of community structures in forests has mostly centered around combined probability for heights and diameters (Hafley & Schreuder, 1977;Rupšys & Petrauskas, 2010;Zucchini et al, 2001), and has yielded a wealth of useful information for forest management. Current studies investigating W, U, and M bivariate distribution (called "WUM bivariate distribution" from here on to distinguish it from the height-diameter bivariate distribution) are ongoing in Changbai Mountain (Li et al, 2012;Li et al, 2014;Li, 2013;Ni et al, 2014), Qinling Mountain (Chai et al, 2017;Zhang et al, 2015a;Zhang et al, 2014), Beijing Jiulong Mountain (Zhang et al, 2015b), and the coastline of Guangxi Province (Li et al, 2016).…”
Bivariate distribution is an effective approach to spatial structure analysis. Combined with two of the three structure parameters (uniform angle index (W), dominance (U) and mingling (M)), the structural bivariate characteristics of five different Pinus massoniana forests were explored in this study. Our objective was to provide a theoretical direction for structure-based and detailed management in P. massoniana forests. The bivariate traits showed that mixed artificial or secondary forests predominated by P. massoniana trees do not typically achieve the highest mingling level. Trees under extreme mingling conditions were rare and typically comprised of non-dominant species instead of dominant ones in P. massoniana forests; these trees were generally overtopped and randomly distributed. Management implications can be extracted comparing the bivariate traits between all species and dominant species. The original community structure, development stage, and mixed-tree species number affect the univariate M and, furthermore, the two bivariate U-M and W-M distributions in mixed forests. Forest type has little effect on all-species W-U traits compared to those of the dominant species. U-M traits should be adjusted first if the random frequency values are highest in W-U and W-M bivariate distribution, and it is necessary to determine whether these two bivariate traits shade the W univariate. Adjustments made based on bivariate distribution can reveal poor frequency combinations for foresters to target; this allows the simultaneous adjustment of dual aspects of forest structure based on one bivariate index. Our results show that bivariate distribution may provide very useful information for the management of P. massoniana and other forests.
“…Canopy cover, slope aspect, DBH, tree height, and species identity were recorded. To compare the structural differences among individuals within the same population, each tree was assigned a growth stage according to the size of the tree: juvenile (5 ≤ DBH < 10 cm), medium (10 ≤ DBH < 25 cm), and large (DBH ≥ 25 cm) (Chai et al 2016b). This work was conducted based on Forestry Standards “Observation Methodology for Long-term Forest Ecosystem Research” of People’s Republic of China (LY/T 1952–2011).Fig.…”
Section: Methodsmentioning
confidence: 99%
“…The annual sunshine hours are 2098 and the frost-free period is 185 days. The annual rainfall is 460–800 mm most of which falls from July to September (Chai et al 2016b ).…”
BackgroundUnderstanding interspecific associations in old-growth forests will help to reveal mechanisms of interspecific replacement in the process of forest development and provide a theoretical basis for vegetation restoration and reestablishment. In this study, we analyzed interspecific associations of eleven dominant tree populations of varying development stages in an old-growth oak forest stand in the Qinling Mountains, China. We examined overall interspecific associations (multiple species) and pairwise interspecific associations (two species).ResultsInterspecific competition was intense during forest development and was the main factor driving succession. Community structure appears to become more stable over time which supports the harsh-benign hypothesis that interspecific competition is more common in stable sites.ConclusionOld growth oak (Quercus spp.) forests are distributed widely around the world in part due to oak being a typical K-selected species. K-selected species produce fewer, high-quality offspring with higher survival rates, strong competitive ability, and longevity. The resulting distribution shifted from clumped to random, likely as a result of intense interspecific competition creating ecological niche differentiation.
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