新《森林法》背景下的补种树木责任承担方式

补种树木是森林生态环境修复的有效途径,而我国立法对补种树木责任承担方式的规定未形成体系,亟待完善.在新《森林法》出台的背景下,运用比较分析法对新旧《森林法》的修订变化进行阐释和探析,可得出:补种树木责任承担方式在执法与司法领域适用的过程中,遇到原地与异地补种的适用顺位不明、补种树木执法主体的确立不明确、缺乏补种树木执法...     

Does defoliation induce chemical and morphological defenses in the leaves of silver birch seedlings under changing climate?

Summary.  We examined the effects of defoliation con-currently with elevated temperature and CO₂ on some chemical and morphological characteristics in the leaves of silver birch seedlings (Betula pendula). We also analyzed the consequent changes in the palatability of leaves for adult blue alder leaf beetles (Agelastica alni). Under the different climatic treatments, the seedlings were subjected to three fertilizer treatments (0 kg, 130 kg and 270 kg N ha⁻¹) and defoliation treatments (0%, 25% and 50% of the total leaf area). In each climatic treatment, fertilization increased the nitrogen content in the leaves, but decreased total concentrations of soluble phenolics, detected by high-performance liquid chromatography (HPLC), and insoluble condensed tannins. Defoliation, both independently and in combination with elevated temperature and CO₂, decreased the concentrations of the phenolics. Compared to the intact controls, the leaves of the defoliated seedlings were smaller and tougher. Under elevated temperature, the beetles consumed a smaller amount of the leaves of plants subjected to the high fertilization, while under ambient climatic conditions, fertilization increased the feeding. The total leaf consumption was higher under the ambient climatic conditions than under elevated temperature, elevated CO₂ or the combination of elevated temperature and CO₂.     

Assessing local knowledge use in agroforestry management with cognitive maps

Small-holder farmers often develop adaptable agroforestry management techniques to improve and diversify crop production. In the cocoa growing region of Ghana, local knowledge on such farm management holds a noteworthy role in the overall farm development. The documentation and analysis of such knowledge use in cocoa agroforests may afford an applicable framework to determine mechanisms driving farmer preference and indicators in farm management. This study employed 12 in-depth farmer interviews regarding variables in farm management as a unit of analysis and utilized cognitive mapping as a qualitative method of analysis. Our objectives were (1) to illustrate and describe agroforestry management variables and associated farm practices, (2) to determine the scope of decision making of individual farmers, and (3) to investigate the suitability of cognitive mapping as a tool for assessing local knowledge use. Results from the cognitive maps revealed an average of 16 ± 3 variables and 19 ± 3 links between management variables in the farmer cognitive maps. Farmer use of advantageous ecological processes was highly central to farm management (48% of all variables), particularly manipulation of organic matter, shade and food crop establishment, and maintenance of a tree stratum as the most common, highly linked variables. Over 85% of variables included bidirectional arrows, interpreted as farm management practices dominated by controllable factors, insofar as farmers indicated an ability to alter most farm characteristics. Local knowledge use on cocoa production revealed detailed indicators for site evaluation, thus affecting farm preparation and management. Our findings suggest that amid multisourced information under conditions of uncertainty, strategies for adaptable agroforestry management should integrate existing and localized management frameworks and that cognitive mapping provides a tool-based approach to advance such a management support system.     

Effects of pollution on hormone content of ornamental trees

Elm and black locust samples were collected in the city of Thessaloniki, Greece from a number of locations with different pollutant concentrations and the hormonal content was determined using radioimmunoassay tests. In both tree species, a considerable decrease in endogenous cytokinins was detected in symptomless leaves from the polluted city center, suggesting latent senescence and thus latent injury. Similarly, cytokinin content was found to be lower in seedlings exposed to SO₂‐O₃ fumigation than in controls. Thus cytokinin levels may serve as useful indicators of the onset of pollution stress and/or of the degree of stress experienced by urban trees.

Several environmental stresses were reported to promote an ABA increase. An increased ABA content in black locust samples from the polluted city center was also detected. This observation did not hold in elm, where comparable ABA levels were detected in trees in the city center, the suburbs, and in controls grown in filtered air. Hence ABA content cannot serve as an unequivocal indicator of pollution‐induced latent plant senescence.     

Learning to predict channel stability using biogeomorphic features

Current human land use activities are altering many components of the river landscape, resulting in unstable channels. Instability may have serious negative consequences for water quality, aquatic and riparian habitat, and for river-related human infrastructure such as bridges and roads. Resource management agencies have developed rapid bioassessment surveys to help assess stability in a fast and cost-effective way. While this assessment can be done for a single site fairly rapidly, it is still time-consuming to apply over large watersheds and assessment activities must be prioritized. We constructed a system that employs commonly available map data as inputs to cost-sensitive variants of decision tree algorithms to predict the relative channel stability of different sites. In particular, we use bagged lazy option trees (LOTs) and bagged probability estimation trees (PETs) to identify all unstable channels while making the smallest number of errors of classifying stable channels as unstable, thereby minimizing cost and maximizing safety. We measured the performance of the classifiers using ROC curves and found that the PETs performed better than the LOTs in situations where the number of instances of the stable and unstable classes were relatively balanced, but the LOTs did better where unstable examples were relatively rare compared to stable, perhaps due to the LOTs’ ability to focus on individual examples.     

Effects of sample survey design on the accuracy of classification tree models in species distribution models

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys.