Mapping Characteristics in Vaccinium uliginosum Populations Predicted Using Filtered Machine Learning Modeling

Forests Pub Date : 2024-07-18 DOI:10.3390/f15071252
Yadong Duan, Xin Wei, Ning Wang, Dandan Zang, Wenbo Zhao, Yuchun Yang, Xingdong Wang, Yige Xu, Xiaoyan Zhang, Cheng Liu
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Abstract

Bog bilberry (Vaccinium uliginosum L.) is considered a highly valued non-wood forest product (NWFP) species with edible and medicinal uses in East Asia. It grows in the northeastern forests of China, where stand attributes and structure jointly determine its population characteristics and individuals’ growth. Mapping the regional distributions of its population characteristics can be beneficial in the management of its natural resources, and this mapping should be predicted using machine learning modeling to obtain accurate results. In this study, a total of 60 stands were randomly chosen and screened to investigate natural bog bilberry populations in the eastern mountains of Heilongjiang and Jilin provinces in northeastern China. Individual height, canopy cover area, and fresh weight all increased in stands at higher latitudes, and shoot height was also higher in the eastern stands. The rootstock grove density showed a polynomial quadratic distribution pattern along increasing topographical gradients, resulting in a minimum density of 0.43–0.52 groves m−2 in stands in the southern part (44.3016° N, 129.4558° E) of Heilongjiang. Multivariate linear regression indicated that the bog bilberry density was depressed by host forest tree species diversity; this was assessed using both the Simpson and Shannon–Wiener indices, which also showed polynomial quadratic distribution patterns (with a modeling minimum of 0.27 and a maximum of 1.21, respectively) in response to the increase in latitude. Structural equation models identified positive contributions of tree diameter at breast height and latitude to shoot height and a negative contribution of longitude to the bog bilberry canopy area. Random forest modeling indicated that dense populations with heavy individuals were distributed in eastern Heilongjiang, and large-canopy individuals were distributed in Mudanjiang and Tonghua. In conclusion, bog bilberry populations showed better attributes in northeastern stands where host forest trees had low species diversity, but the dominant species had strong trunks.
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利用过滤式机器学习建模预测越橘种群的图谱特征
山桑子(Vaccinium uliginosum L.)在东亚被认为是一种具有食用和药用价值的高价值非木材林产品(NWFP)物种。它生长在中国东北部森林中,林分属性和结构共同决定了其种群特征和个体生长。绘制其种群特征的区域分布图有利于其自然资源的管理,而这种绘制应使用机器学习模型进行预测,以获得准确的结果。本研究在中国东北黑龙江省和吉林省东部山区随机选取并筛选了60个林分,调查水越橘的天然种群。结果表明,纬度较高的植株单株高度、冠层覆盖面积和鲜重均有所增加,东部植株的芽高也较高。砧木林密度沿地形梯度呈二次多项式分布,黑龙江南部(北纬 44.3016°,东经 129.4558°)的砧木林密度最小,为 0.43-0.52 m-2。多元线性回归结果表明,水越橘密度受寄主林树种多样性的影响;辛普森指数和香农-维纳指数也显示了多项式二次分布模式(建模最小值和最大值分别为0.27和1.21)。结构方程模型确定了胸径和纬度对芽高的正贡献,以及经度对水越橘树冠面积的负贡献。随机森林模型表明,黑龙江东部分布着高密度、重个体的种群,牡丹江和通化分布着大冠层个体。总之,水越橘种群在寄主林树种多样性低但优势树种树干粗壮的东北林分中表现出更好的属性。
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