北京不同古树类型和级别对土壤物理性质的影响

以北京的代表性古树白皮松、侧柏、油松和国槐为研究对象,分析不同树种类型和不同级别古树土壤理化特性以及土壤理化特性之间的关系,以期为古树土壤健康评价和复壮管理提供科学的依据。研究结果表明:研究区土壤含水量为10.62%,容重为1.20 g/cm3,紧实度为3.94 kg/m2,土壤孔隙度、毛管孔隙度和非毛管孔隙度分别为20.72%、11.77%和8.96%,各指标都呈中等变异;在6个物理指标中,土壤紧实度、土壤含水量和土壤容重与其他几个物理指标相关性较高;不同树种类型和不同古树级别对古树土壤物理特性影响并不显著。     

非感潮河流水污染带范围的估算

在河流污染带范围随机模拟方法的基础上提出了一个附加概率意义的污染带新定义，然后利用线性回归，分别采用３种曲线拟合和１个方面拟合两种方法，对污染带的范围进行估算，结果表明，能较真实地反映河流污染带的实际情况。     

Social capital as a key determinant of perceived benefits of community‐based marine protected areas

Globally, marine protected areas (MPAs) have been relatively unsuccessful in meeting biodiversity objectives. To be effective, they require some alteration of people's use and access to marine resources, which they will resist if they do not perceive associated benefits. Stakeholders’ support is crucial to ecological success of MPAs, and their support is likely to depend on their capacity to adapt to and benefit from MPAs. We examined the influence of social adaptive capacity (SAC) on perceived benefits of MPAs in Siquijor, Philippines, in the Coral Triangle. This region has substantial biodiversity and a population of over 120 million people, many of them dependent on marine resources for food and income. The region has many MPAs, most of which are managed under decentralized governance systems. We collected survey data from 540 households in 19 villages with associated MPAs. We evaluated the influence of multiple SAC variables (e.g., occupational multiplicity and social capital) on perceived benefits with decision trees (CHAID) and qualitatively analyzed this relationship with respect to types and recipients of benefits. Our models revealed the key role of social capital, particularly trust in leadership, in influencing perceptions of benefits (χ² = 14.762, p = 0.000). A path analysis revealed that perceptions of distributional equity were a key mechanism through which social capital affected perceived MPA benefits (root mean‐square error of approximation = 0.050). Building social capital and equity within communities could lead to more effective management of MPAs and thus to expenditure of fewer resources relative to, for example, regulation enforcement.     

西双版纳热带森林挥发性有机物的观测研究

2002年7月中旬利用异戊二烯通量测定系统对云南西双版纳热带森林异戊二烯的排放通量进行了连续测量,同时对光合有效辐射(PAR)、气温、湿度、风等参数进行了观测.结果表明,热带地区人工橡胶林区域湿季异戊二烯的排放有明显的日变化规律,中午前后为一天中的最大值,异戊二烯排放通量的日平均值在0.85～0.24mgC/(m²h)之间.对小时值和日平均值而言,异戊二烯的排放与PAR和气温的变化规律比较一致,但它对PAR比对气温更加敏感.不同的天气状况对异戊二烯的排放有非常明显的影响.橡胶林是一个单萜烯排放者,其单萜烯的排放速率明显依赖于PAR.橡胶林和热带雨林冠层上空异戊二烯和单萜烯的浓度也有明显的日变化规律,而且橡胶林区域的异戊二烯和单萜烯的浓度均明显高于热带雨林.     

Effects of chronic elevated ozone exposure on gas exchange responses of adult beech trees (Fagus sylvatica) as related to the within-canopy light gradient

The effects of elevated O₃ on photosynthetic properties in adult beech trees (Fagus sylvatica) were investigated in relation to leaf mass per area as a measure of the gradually changing, within-canopy light availability. Leaves under elevated O₃ showed decreased stomatal conductance at unchanged carboxylation capacity of Rubisco, which was consistent with enhanced δ¹³C of leaf organic matter, regardless of the light environment during growth. In parallel, increased energy demand for O₃ detoxification and repair was suggested under elevated O₃ owing to enhanced dark respiration. Only in shade-grown leaves, light-limited photosynthesis was reduced under elevated O₃, this effect being accompanied by lowered F_v/F_m. These results suggest that chronic O₃ exposure primarily caused stomatal closure to adult beech trees in the field regardless of the within-canopy light gradient. However, light limitation apparently raised the O₃ sensitivity of photosynthesis and accelerated senescence in shade leaves.     

Learning habitat models for the diatom community in Lake Prespa

Habitat suitability modelling studies the influence of abiotic factors on the abundance or diversity of a given taxonomic group of organisms. In this work, we investigate the effect of the environmental conditions of Lake Prespa (Republic of Macedonia) on diatom communities. The data contain measurements of physical and chemical properties of the environment as well as the relative abundances of 116 diatom taxa. In addition, we create a separate dataset that contains information only about the top 10 most abundant diatoms. We use two machine learning techniques to model the data: regression trees and multi-target regression trees. We learn a regression tree for each taxon separately (from the top 10 most abundant) to identify the environmental conditions that influence the abundance of the given diatom taxon. We learn two multi-target regression trees: one for modelling the complete community and the other for the top 10 most abundant diatoms. The multi-target regression trees approach is able to detect the conditions that affect the structure of a diatom community (as compared to other approaches that can model only a single target variable). We interpret and compare the obtained models. The models present knowledge about the influence of metallic ions and nutrients on the structure of the diatom community, which is consistent with, but further extends existing expert knowledge.     

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.     

Habitat-tree protection concepts over 200 years

The protection and sustainable management of habitat trees is an integral part of modern forest nature conservation concepts such as retention forestry. Bats, cavity-nesting birds, arboreal marsupials, and many different saproxylic species depend on habitat trees and their great variety of microhabitats and old-growth characteristics. With a focus on insights from temperate forests, we traced the development of habitat-tree protection over 200 years. The idea was first conceptualized by foresters and natural scientists in the early 19th century. At that time, utilitarian conservation aimed to protect cavity trees that provided roosts and nesting holes for insectivorous bats and birds. By the second half of the 19th century, habitat-tree protection was well known to foresters and was occasionally implemented. Knowledge of the protection of large old trees, a special kind of habitat tree, for sociocultural and aesthetic reasons developed similarly. But, many foresters of that time and in the following decades fundamentally rejected protection of habitat trees for economic reasons. Beginning in the 1970s, forest conservation and integrative forest management became increasingly important issues worldwide. Since then, the protection of habitat trees has been implemented on a large scale. Long-term views on the development of conservation concepts are important to inform the implementation of conservation today. In particular, historical analyses of conservation concepts allow the testing of long-term conservation outcomes and make it possible to study the resilience of conservation approaches to changing social or ecological conditions. We encourage all conservation ecologists to assess the practical and conceptual impact of the initial ideas that led to modern conservation concepts in terms of long-term biodiversity conservation.     

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

Detecting pattern in biological stressor response relationships using model based cluster analysis

Environmental monitoring of aquatic systems is needed to estimate the quality of the systems, to evaluate standards and to study stressor–response relationships. Monitoring programs often focus on the collection of biological, chemical and physical measures of the system. An important concern is the effect of chemical and physical stressors on the biological community. Evaluation of relationships may be difficult as the extent of the relationship is not known. From a management perspective, interest is on what factors affect the biological community and where these factors have an influence. The focus of this paper is on the use of regression based cluster analysis as a tool for finding relationships between a single biological response and a suite of environmental stressors. The approach to cluster analysis uses a penalized regression classification likelihood and Markov Chain Model Composition Monte Carlo. This approach allows for simultaneous development of regression models and clustering of the regression models. The method is applied to the analysis of a data set describing stressors/response relationship in Ohio.