Abstract—

Mean growing stock across different wood species and age groups of forests based on the State Forest Inventory 2013 data were correlated by means of regression analysis with climate variables averaged over 1981–2000. The following species categories were predefined: all species, conifers, hardwoods, softwoods, and others. The following age groups were predefined: all ages, young growth, middle-aged, pre-mature, with old-growth forests. The correlation and regression analyses were performed for each of the 20 age and species groups of forests. Mean annual temperature, difference between mean July and mean January temperatures, and annual precipitation were independent variables. Mean stock positively correlates with the mean annual temperature and humidity, and negatively correlates with the difference between mean July and mean January temperature. The correlation coefficient between the mean stock and mean annual temperature had a pronounced upward trend across the species groups, as follows by their value: other species, conifers, hardwoods, and softwoods. Multiple polynomial regression equations, which included all possible combinations of independent variables and their quadratic effects, were found for each species and age groups. Determination coefficients had the highest values (R² > 0.7) for all species, hardwood species, and mature and middle-aged forests of all species and hardwood species. Other wood species and all young growth were the least predictable (R² < 0.5). The regression equations could be used to forecast changes in the mean growing stock under changing climate for forests overall and their groups of different age and species composition.

中文翻译：

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气候参数对俄罗斯森林平均生长种群的影响

摘要—

根据2013年国家森林清单数据，不同木材物种和年龄组的平均生长种群通过回归分析与1981-2000年的平均气候变量相关联。预定义了以下物种类别：所有物种，针叶树，硬木，软木等。预先定义了以下年龄组：所有年龄，年轻人的成长，中年，早熟和老龄森林。对20个年龄和森林物种组中的每一个进行了相关和回归分析。年平均温度，7月和1月平均温度之间的差异以及年降水量是自变量。平均库存与年平均温度和湿度成正相关，与七月和一月平均温度之间的差呈负相关。平均种群和年平均温度之间的相关系数在所有物种组中都有明显的上升趋势，其值如下：其他物种，针叶树，硬木和软木。对于每个物种和年龄组，发现了多个多项式回归方程，其中包括自变量的所有可能组合及其二次效应。测定系数具有最高值（R ² > 0.7）适用于所有物种，阔叶树种以及所有物种和硬木树种的成熟和中年森林。其他木种和所有幼木生长的可预测性最差（R ² <0.5）。回归方程可用于预测整体气候及其不同年龄和物种组成的森林在气候变化下的平均生长种群的变化。