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1.
Plant species composition and community structure were compared among four sites in an upland black spruce community in northwestern Ontario. One site had remained undisturbed since the 1930s and three had been disturbed by either logging, fire, or both logging and fire. Canonical correspondence ordination analyses indicated that herbaceous species composition and abundance differed among the disturbance types while differences in the shrub and tree strata were less pronounced. In the herb stratum Pleurozium schreberi, Ptilium crista-castrensis and Dicranum polysetum were in greatest abundance on the undisturbed forest site, while the wildfire and burned cutover sites were dominated by Epilobium angustifolium and Polytrichum juniperinum. The unburned harvested site was dominated by Epilobium angustifolium, Cornus canadensis and Pleurozium schreberi. Species richness was lower on the undisturbed site than on any of the disturbed sites while species diversity (H) and evenness (Hill's E5) were higher on the unburned harvested site than on the other sites. Results suggest that herb re-establishment is different among harvested and burned sites in upland black spruce communities and we hypothesize that differences in the characteristics of the disturbance were responsible, in particular, the impact of burning on nutrient availability. These differences need to be taken into account in determining the effects of these disturbances on biodiversity and long-term ecosystem management. 相似文献
2.
Daniela Lagomarsino V. Tofani S. Segoni F. Catani N. Casagli 《Environmental Modeling and Assessment》2017,22(3):201-214
Classification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling. 相似文献