Swedish University of Agricultural Sciences

About: Swedish University of Agricultural Sciences is a education organization based out in Uppsala, Sweden. It is known for research contribution in the topics: Population & Soil water. The organization has 13510 authors who have published 35241 publications receiving 1414458 citations. The organization is also known as: Sveriges Lantbruksuniversitet & SLU.

Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar

Abstract: In this paper, a methodology for individual tree-based species classification using high sampling density and small footprint lidar data is clarified, corrected and improved. For this purpose, a well-defined directed graph (digraph) is introduced and it plays a fundamental role in the approach. It is argued that there exists one and only one such unique digraph that describes all four pure events and resulting disjoint sets of laser points associated with a single tree in data from a two-return lidar system. However, the digraph is extendable so that it fits an n-return lidar system (n>2) with higher logical resolution. Furthermore, a mathematical notation for different types of groupings of the laser points is defined, and a new terminology for various types of individual tree-based concepts defined by the digraph is proposed. A novel calibration technique for estimating individual tree heights is evaluated. The approach replaces the unreliable maximum single laser point height of each tree with a more reliable prediction based on shape characteristics of a marginal height distribution of the whole first-return point cloud of each tree. The result shows a reduction of the RMSE of the tree heights of about 20% (stddev=1.1 m reduced to stddev=0.92 m). The method improves the species classification accuracy markedly, but it could also be used for reducing the sampling density at the time of data acquisition. Using the calibrated tree heights, a scale-invariant rescaled space for the universal set of points for each tree is defined, in which all individual tree-based geometric measurements are conducted. With the corrected and improved classification methodology the total accuracy raises from 60% to 64% for classifying three leaf-off individual tree deciduous species (N=200 each) in West Virginia, USA: oaks (Quercus spp.), red maple (Acer ruhrum), and yellow poplar (Liriodendron tuliperifera).

The influences of forest stand management on biotic and abiotic risks of damage

Abstract: • This article synthesizes and reviews the available information on the effects of forestry practices on the occurrence of biotic and abiotic hazards, as well as on stand susceptibility to these damaging agents, concentrating on mammal herbivores, pest insects, pathogenic fungi, wind and fire. • The management operations examined are site selection, site preparation, stand composition, regeneration method, cleaning and weed control, thinning and pruning, and harvesting. For each of these operations we have examined how they influence the occurrence of biotic and abiotic damaging agents, the susceptibility of European forests, and describe the ecological processes that may explain these influences. • Overall, we find that the silvicultural operations that have the largest influence on both biotic and abiotic risks to European forest stands are closely related to species composition and the structure of the overstorey. Four main processes that drive the causal relationships between stand management and susceptibility have been identified: effect on local microclimate, provision of fuel and resources to biotic and abiotic hazards, enhancement of biological control by natural enemies and changes in individual tree physiology and development. • The review demonstrates an opportunity to develop silvicultural methods that achieve forest management objectives at the same time as minimising biotic and abiotic risks. Mots-cles : sylviculture / peuplement / occurrence / sensibilite /

Context dependent effects of ectomycorrhizal species richness on tree seedling productivity

Abstract: While there has been much recent interest about the relationships between plant diversity and plant productivity, much remains unknown about how the diversity of mycorrhizal fungi affects plant productivity. We investigated the effects of ectomycorrhizal fungal community composition and diversity on the productivity and growth characteristics of seedlings of two tree species (Pinus sylvetris and Betula pendula) as well as their interactions with each other. This involved setting up a mycorrhizal fungal diversity gradient from one to eight species using a design previously demonstrated to be able to separate diversity effects from compositional effects. We found that the eight mycorrhizal fungal species differed in their effects on seedling productivity and that the nature of effects was determined by the fertility of the substrate. Fungal species richness effects were also important in affecting seedling productivity over and above what could be explained by “sampling effect” but only in some situations. For B. pendula in a low fertility substrate there were clear positive causative effects between fungal species richness and productivity with the eight species treatment having over double the productivity of any of the eight monoculture treatments; no diversity effects were, however, detected in a high fertility substrate. For P. sylvestris in a high fertility substrate there were significant negative effects of fungal diversity on productivity while in a low fertility substrate no effects were apparent. The possible mechanistic bases for these results are discussed. The growth of P. sylvestris relative to that of B. pendula when grown in combination was unaffected by mycorrhizal treatments. Our results provide clear evidence that effects of mycorrhizal fungal diversity on productivity are context dependent and may be positive, negative or neutral depending on the situation considered.

A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels

Abstract: A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade‐offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross‐validation procedure involving separate data to establish which of these models performs best for the goal of the study.