Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural div...Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets.Results: Here we developed an index of structural diversity based on National Forest Inventory(NFI) data of BadenWurttemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory(NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height(DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple,additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.Conclusions: The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.展开更多
利用森林资源二类调查数据及年度变更数据,测算林场森林生态系统的碳储量并分析其分布特征。结果显示,各树种碳储量差异显著。桉树面积占比25.33%,碳储量占29.64%,碳密度170.43 t CO_(2-e)/hm^(2),高于均值17.00%,贡献近1/3区域碳储量;...利用森林资源二类调查数据及年度变更数据,测算林场森林生态系统的碳储量并分析其分布特征。结果显示,各树种碳储量差异显著。桉树面积占比25.33%,碳储量占29.64%,碳密度170.43 t CO_(2-e)/hm^(2),高于均值17.00%,贡献近1/3区域碳储量;硬阔类碳密度最高,为232.10 t CO_(2-e)/hm^(2),面积仅占1.45%;阔叶混/软阔类面积占比超33.00%,碳密度120~122 t CO_(2-e)/hm^(2),低于均值145.66 t CO_(2-e)/hm^(2)。林龄是碳储分布的主导因素,近熟林、成熟林和中龄林的碳储量占总量的71.45%,其中,近熟林单位碳密度最高,为166.19 t CO_(2-e)/hm^(2);过熟林碳密度达173.06 t CO_(2-e)/hm^(2),面积有限;幼龄林碳积累能力最弱。混交林效率梯度,针阔混交林碳密度为131.19 t CO_(2-e)/hm^(2),较纯针叶林高6.40%,针叶混交林效率最低,为103.36 t CO_(2-e)/hm^(2)。同时提出管理建议,包括优先保护高碳密度近熟林,优化中龄林抚育措施,培育高潜力树种,并开发经济林种的碳汇潜力,以增加林场固碳能力。展开更多
Single trees sapwood scattering style and diameter classes diurnal water consumption rhythm were studied in a 48 years old Quercus variabilis stand at the east hill slope, located in the Forest Research Station of...Single trees sapwood scattering style and diameter classes diurnal water consumption rhythm were studied in a 48 years old Quercus variabilis stand at the east hill slope, located in the Forest Research Station of Beijing Forestry University in the water conservation area in Beijing (39°54′N, 116°28′E). Results showed that relation between trees sapwood area and diameter at breast height (DBH) was significant. Single trees daily water consumption ascended as DBH and sapwood area increased, and related significantly. Daily water consumption of different diameter class in September ascended steeply from the early morning and got the peak around 11:00 pm, and then descended till 18:00 when it got the valley slowly. Three-dimension model of daily-accumulated water consumption was acquired by scaling-up method from the typical Richards model and characteristic parameters of daily stand water consumption course were calculated from modulated Richards equation derivative: W d-it-j=(-7.147+1.174 d-i)[1-(-3 025.937+d 2.175 i)e (-0.011t-j)] 1/(1-d 0.242 i)(R=0.985 8).展开更多
基金supported by a grant from the Ministry of Science,Research and the Arts of Baden-Württemberg(7533-10-5-78)to Jürgen BauhusFelix Storch received additional support through the BBW ForWerts Graduate Program
文摘Background: The importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets.Results: Here we developed an index of structural diversity based on National Forest Inventory(NFI) data of BadenWurttemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory(NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height(DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple,additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.Conclusions: The forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.
文摘利用森林资源二类调查数据及年度变更数据,测算林场森林生态系统的碳储量并分析其分布特征。结果显示,各树种碳储量差异显著。桉树面积占比25.33%,碳储量占29.64%,碳密度170.43 t CO_(2-e)/hm^(2),高于均值17.00%,贡献近1/3区域碳储量;硬阔类碳密度最高,为232.10 t CO_(2-e)/hm^(2),面积仅占1.45%;阔叶混/软阔类面积占比超33.00%,碳密度120~122 t CO_(2-e)/hm^(2),低于均值145.66 t CO_(2-e)/hm^(2)。林龄是碳储分布的主导因素,近熟林、成熟林和中龄林的碳储量占总量的71.45%,其中,近熟林单位碳密度最高,为166.19 t CO_(2-e)/hm^(2);过熟林碳密度达173.06 t CO_(2-e)/hm^(2),面积有限;幼龄林碳积累能力最弱。混交林效率梯度,针阔混交林碳密度为131.19 t CO_(2-e)/hm^(2),较纯针叶林高6.40%,针叶混交林效率最低,为103.36 t CO_(2-e)/hm^(2)。同时提出管理建议,包括优先保护高碳密度近熟林,优化中龄林抚育措施,培育高潜力树种,并开发经济林种的碳汇潜力,以增加林场固碳能力。
文摘Single trees sapwood scattering style and diameter classes diurnal water consumption rhythm were studied in a 48 years old Quercus variabilis stand at the east hill slope, located in the Forest Research Station of Beijing Forestry University in the water conservation area in Beijing (39°54′N, 116°28′E). Results showed that relation between trees sapwood area and diameter at breast height (DBH) was significant. Single trees daily water consumption ascended as DBH and sapwood area increased, and related significantly. Daily water consumption of different diameter class in September ascended steeply from the early morning and got the peak around 11:00 pm, and then descended till 18:00 when it got the valley slowly. Three-dimension model of daily-accumulated water consumption was acquired by scaling-up method from the typical Richards model and characteristic parameters of daily stand water consumption course were calculated from modulated Richards equation derivative: W d-it-j=(-7.147+1.174 d-i)[1-(-3 025.937+d 2.175 i)e (-0.011t-j)] 1/(1-d 0.242 i)(R=0.985 8).
