南岳森林群落生物多样性研究(Ⅷ)——藏经殿森林群落学特征

南岳藏经殿森林群落内有维管束植物63科、102属、129种,属可以划分为以北温带、泛热带和东亚为主的13种分布类型。各占总属数的22.73％、21.59％和17.05％,总的情况是温性质为主,热带性质成分为辅:种可以划分为18种分布类型,其中以华夏、东亚和中国一日本为主,具有明显的“华东—华中”区系过渡性质。群落木本植物和草本植物各占65.12％和34.88％,在木本植物中,常绿成分和落叶成分各占32.15％和67.95％,群落可明显划分为乔、灌、草三层,它是一种尚未报道过的新群落类型。即包石栎—锐齿槲栎—甜槠+箬竹+求米草群丛,在更新演替过程中、群落仍将保持常绿落叶阔叶混交林性质,其优势种,亚优势种很可能会枝云和新木姜、尾叶山茶、山姜、多脉青冈、雷公鹅耳枥和长叶石栎等取代。     

Extending the EU Renewable Energy Directive sustainability criteria to solid bioenergy from forests

Solid bioenergy from forests plays — and is expected to continue to play — a key role to fulfil the renewable energy targets at the European Union level. When the Renewable Energy Directive was enacted, sustainability criteria were incorporated solely for biofuels and bioliquids. Sustainability criteria for solid bioenergy are also needed in order to prevent wood and primary forest residues from posing additional environmental risks to ecosystems. Acknowledging this, the European Commission has been working on extending the biofuels and bioliquids provisions to solid biomass. An internal draft was circulated in August 2013 which addressed the ways to both balance and mitigate the risks in three main topics: biodiversity; sustainable forest management; and greenhouse gases. This paper presents a set of criteria and indicators, developed during workshops with experts from Governments, scientific institutions, businesses and NGOs, that may be considered by the EU to assure that solid biomass from forests is obtained in an environmentally sustainable way.     

退耕还林工程驱动下的土地利用变化合理性研究——以云南芒市为例

以滇西南中低山盆谷区的芒市为例,在探讨土地利用变化合理度的概念和内涵并构建土地利用变化合理度的评价指标、测算方法和分级标准体系基础上,定量地分析评价了芒市近8 a来退耕还林工程驱动下各个地类变化的合理度和总体土地利用变化的合理度,为推进山区土地资源的可持续利用和退耕还林工程的科学实施提供基础依据。评价结果表明,全市近8 a所有地类转化的总面积为77 602.80 hm²,转化的合理度(RD值)只有48.98%,属于"中度不合理"级。总体上,芒市近8 a来退耕还林工程驱动下的土地利用变化,既有合理的部分,如部分陡坡耕地的生态退耕、荒山造林和封山育林等,但其不合理性亦非常突出,主要体现在生态退耕地中约96%属于"不该退"的宜耕地。此外,在实施退耕还林工程的同时,却又出现较为突出的毁林开垦现象。这些问题的突出存在,使芒市近8 a土地利用变化的合理性程度很低。     

Modeling net primary production of a fast-growing forest using a light use efficiency model

As interest grows in the quantification of global carbon cycles, Light Use Efficiency (LUE) model predictions of the forest net primary production (NPP) are being developed at an accelerating rate. Such models can provide useful predictions at large scales, but evaluating their performance has been difficult. In this study, a remote sensing-based LUE model was established to estimate forest NPP. Using the forest inventory data (FID) from the regional forest inventory survey in China and established allometric biomass equations, we calculated the biomass, the biomass increment, and the NPP of Eucalyptus urophylla (E. urophylla) plantation plots in the forestry jurisdiction of the Leizhou Forestry Bureau, Southern China. The FID-based NPP and the NPP from LUE model predictions were then compared to each other. Results show that the NPP from model predictions at a spatial resolution of 30 m × 30 m varied from 0 to 265 gC/(m² month) and showed regional differences. In addition, the stand age had variable effects on the average individual biomass of the E. urophylla plantation plots. The average individual biomass of the young and mid-age forests increased exponentially and logarithmically with the stand age (R² = 0.9178 and R² = 0.8683), respectively. For young and mid-age E. urophylla plantation plots, the LUE model-predicted NPP was fairly consistent with the FID-based NPP, but the model predictions of the NPP were higher than the estimates from FID. Through the analysis of the causes of uncertainty and the possible reasons for the discrepancy between the model-based NPP and FID-based NPP, the FID-derived estimates provided a foundation for model evaluation.     

Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.     

Hierarchical Bayesian Spatial Models for Multispecies Conservation Planning and Monitoring

Abstract: Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence–absence data derived from regional monitoring programs to develop models with both landscape and site‐level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence–absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad‐scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km² hexagons), can increase the relevance of habitat models to multispecies conservation planning.     

Temporal limitations in the use of potassium dichromate as a blood preservative for the analysis of organohalogenated contaminants including polybrominated diphenyl ethers,dioxins and PCBs

Previous studies described the use of potassium dichromate (K₂Cr₂O₇) to successfully preserve whole blood for up to 34 days at room temperature (20–22°C) for analysis of chlorinated dioxins, dibenzofurans and PCBs. Potassium dichromate has been successfully employed as a preservative for cows’ milk and in a World Health Organization study of human milk. The use of two 100?mg tablets in 40 to 100?mL of whole blood in anticoagulant was found to provide almost identical levels of dioxins, dibenzofurans, PCBs and lipids as found in frozen comparison blood at??70°C which is generally regarded as the gold standard for blood preservation. Potassium dichromate was found to be preferable to 20 and 40% ethyl alcohol for this preservation. This finding opens opportunities for preservation under field conditions in developing countries where neither electricity nor dry ice is available. Not having to use dry ice for shipping also allows more flexibility in the choice of commercial carriers for transporting blood to an analytical laboratory. It is recommended considering a number of compounds in addition to those studied in our first report and extending the time beyond 34 days in order to determine if longer storage might also be feasible. Data on polybrominated diphenyl ethers (PBDEs), as well as polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and selected polychlorinated biphenyls (PCBs) are reported. For the present study 3 and 6 months were chosen in potassium dichromate at room temperature compared to freezing at ?70°C. Data indicate that these times exceed the limits of efficacy of potassium dichromate for whole blood preservation. This was attributed to degradation of the lipid component of the blood. Since dioxins and closely related structural analogues are traditionally reported as lipid normalized blood levels, this degradation of lipids provides erroneous results. Therefore, potassium dichromate is not recommended for long-term storage beyond 34 days at room temperature. It is planned to study the time period between one and three months and higher temperature preservation for shorter time periods, simulating conditions in tropical countries, to attempt to extend further the ease and convenience of chemical preservation of blood.     

多色强光手电筒在森林防火中的应用研究

本文介绍了一种用于实现林区夜晚信息互通的应急通信工具—多色强光手电筒,并对其构成、使用方法、适用范围及应用前景进行了详尽的分析研究。     

Effects of precipitation and temperature on rain-use efficiency of China's forests北大核心CSCD

Water plays an important role in the growth of plants. It is important to investigate the responses of different ecosystems to precipitation patterns, especially in terms of global changes involving extreme precipitation or drought. However, little is known about the rain-use efficiency (RUE) in China's forests and the correlation of the RUE with the precipitation and other environmental factors. Furthermore, it is unclear whether differences in the forest types or functional groups result in divergent RUE patterns. To investigate the RUE in China's forests, we analyzed the data from 1 266 sampling plots from 17 forest types. The results indicated that: (1) the mean RUE of China's forests is 1.21 g m-2 mm-1. The RUE and precipitation are negatively correlated in all the 17 forest types; (2) the average RUE of broadleaved forests (mainly angiosperms) is higher than that of coniferous forests (mainly gymnosperms), and the angiosperms are more adaptable to changes in the precipitation than gymnosperms; (3) the average RUE of deciduous forests is higher than that of the evergreen forests. The RUE of deciduous forests declines with increasing precipitation to a threshold value. However, the RUE of evergreen forests are unaffected by changes in the precipitation; and (4) the latitude and mean annual temperature are the major factors constraining the RUE under comprehensive environmental factors. We conclude that the mean RUE declines with increasing precipitation to a threshold value, and that the temperature is a crucial factor for RUE. © 2018 Science Press. All rights reserved.