Education and advice contribute to increased density of broadleaved conservation trees, but not saplings, in young forest in Sweden

The effectiveness of different conservation policies is debated, but the policies are rarely evaluated quantitatively. A voluntary or 'soft' policy based mainly on education provides information about ecosystems and effects of land use, to encourage conservation action. Swedish forestry relies mainly on soft policy, with substantial resources for education and advice to more than 200,000 forest owners, while legal regulation is weak. Increased retention of broadleaved trees at clear-cutting, with environmental benefits in the conifer-dominated forestry, is important in the policy. We used the Swedish National Forest Inventory to analyse this policy for young forests in southern Sweden. Between 1983-1987 and 1998-2002 the policy had no positive effect on saplings (1.3m tall to 4.9 cm dbh) of birch, oak, beech and other species that mostly decreased in density, due to planting of conifers and browsing by ungulates. However, broadleaved conservation trees (>or=15 cm dbh) increased in density, e.g. to about one oak and six birches per ha in young coniferous forest in 1998-2002. The relative increase in density was higher for large (>or=20 cm dbh) than for small trees (15-20 cm dbh). The density of conservation trees was higher on forestland of high than of low productivity. Thus, the soft conservation policy did not influence regeneration of saplings in this type of forestry system, but large broadleaved trees were increasingly saved at 'clear-cuttings'. Advice and educational programmes probably contributed to this result. A continued increase in conservation trees at harvest may require economical support to forest owners.     

Effect of 3 years' free-air exposure to elevated ozone on mature Norway spruce (Picea abies (L.) Karst.) needle epicuticular wax physicochemical characteristics

We examined the effect of ozone (O₃) on Norway spruce (Picea abies) needle epicuticular wax over three seasons at the Kranzberg Ozone Fumigation Experiment. Exposure to 2× ambient O₃ ranged from 64.5 to 74.2 μl O₃ l⁻¹ h AOT40, and 117.1 to 123.2 nl O₃ l⁻¹ 4th highest daily maximum 8-h average O₃ concentration. The proportion of current-year needle surface covered by wax tubes, tube aggregates, and plates decreased (P = 0.011) under 2× O₃. Epistomatal chambers had increased deposits of amorphous wax. Proportion of secondary alcohols varied due to year (P = 0.004) and O₃ treatment (P = 0.029). Secondary alcohols were reduced by 9.1% under 2× O₃. Exposure to 2× O₃ increased (P = 0.037) proportions of fatty acids by 29%. Opposing trends in secondary alcohols and fatty acids indicate a direct action of O₃ on wax biosynthesis. These results demonstrate O₃-induced changes in biologically important needle surface characteristics of 50-year-old field-grown trees.     

Monitoring nitrogen accumulation in mosses in central European forests

In order to assess whether nitrogen (N) loads in mosses reflect different land uses, 143 sites in North Rhine-Westphalia, the Weser-Ems Region and the Euro Region Nissa were sampled between 2000 and 2005. The data were analysed statistically with available surface information on land use and forest conditions. N bioaccumulation in mosses in the Weser-Ems Region with high densities of agricultural land use and livestock exceeded the concentrations in the more industrialised Euro Region Nissa. In all three study areas agricultural and livestock spatial densities were found to be positively correlated with N bioaccumulation in mosses. In North Rhine-Westphalia, the N concentrations in mosses was also moderately correlated with N concentrations in leaves and needles of forest trees. The moss method proved useful to assess the spatial patterns of N bioaccumulation due to land use.     

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.     

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Introduction

The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study.

Method

Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it.

Results

The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals.

Impact

The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.     

Prioritizing the provision of urban ecosystem services in deprived areas,a question of environmental justice

The distribution of urban ecosystem services (UES) is often uneven across socioeconomic groups, leading to environmental justice issues. Understanding the distribution of UES across a landscape can help managers ensure an equitable distribution of services. While many past studies have focused on the distribution of green spaces in relation to socioeconomic variables, this research analyzes the distribution of UES provided by these green spaces. This research quantified air pollution removal, atmospheric carbon reduction, and surface runoff mitigation provided by urban trees in Strasbourg city (France). The provision of these three UES was studied at the census block scale by creating an index of UES delivery, which was contrasted with a constructed social deprivation index. Our results show that there is no significant association between the delivery of UES and social deprivation. Some deprived populations benefit from high UES delivery. Results also suggest that mapping associations between UES delivery and social deprivation should be integrated with future development plans to enhance the equitable distribution of UES. This study provides insights into the French context where studies about the distribution of UES at a small-area level remain lacking.     

Biodiversity conservation through faith and tradition in India: some case studies

SUMMARY

In India, since ancient times, several members of the flora and fauna have been identified with particular personalities of the Hindu pantheon and are worshipped. Many wild animals, such as tigers, lions and elephants, and birds such as the peacock, owl and swan have been revered as the vehicles of Gods and Goddesses and worshipped. Several trees, such as mango, coconut, khejri, emblica, tamarind, ashoka, madhuca and wood apple, and herbs such as basil are considered highly sacred and worshipped. This belief and these practices continue in one form or another even today in certain sections of Indian society, particularly the rural folk and the aboriginal tribes living in the forest. Nature worship is a form of tribal belief and the faith of these laymen has helped to conserve many natural ecosystems in India. They have preserved many pristine forests—called ‘sacred groves’ in their original forms. Sacred groves are rich in biodiversity. They are the last refuge of the rare flora and fauna which have otherwise disappeared from the modern world. Sacred groves have become part of the ‘biosphere reserves’ of India.     

鄱阳湖地区洪水灾害模式分析

在对多年水文数据进行分析的基础上，利用Kohonen自组织映射对鄱阳湖地区降水时序变化、长江来水和湖水水位变化进行模式识别。之后，将所得的模式作为解释变量，用CART方法对样本年份进行分类，建立了鄱阳湖区灾害模式判别树。借此揭示了鄱阳湖地区降水和长江来水对鄱阳湖洪涝灾害的影响。     

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.     

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.