Arbuscular mycorrhizal (AM) and ectomycorrhizal (EcM) associations are critical for host-tree performance. However, how mycorrhizal associations correlate with the latitudinal tree beta-diversity remains untested. Using a global dataset of 45 forest plots representing 2,804,270 trees across 3840 species, we test how AM and EcM trees contribute to total beta-diversity and its components (turnover and nestedness) of all trees. We find AM rather than EcM trees predominantly contribute to decreasing total beta-diversity and turnover and increasing nestedness with increasing latitude, probably because wide distributions of EcM trees do not generate strong compositional differences among localities. Environmental variables, especially temperature and precipitation, are strongly correlated with beta-diversity patterns for both AM trees and all trees rather than EcM trees. Results support our hypotheses that latitudinal beta-diversity patterns and environmental effects on these patterns are highly dependent on mycorrhizal types. Our findings highlight the importance of AM-dominated forests for conserving global forest biodiversity.
Climate is widely recognised as an important determinant of the latitudinal diversity gradient. However, most existing studies make no distinction between direct and indirect effects of climate, which substantially hinders our understanding of how climate constrains biodiversity globally. Using data from 35 large forest plots, we test hypothesised relationships amongst climate, topography, forest structural attributes (stem abundance, tree size variation and stand basal area) and tree species richness to better understand drivers of latitudinal tree diversity patterns. Climate influences tree richness both directly, with more species in warm, moist, aseasonal climates and indirectly, with more species at higher stem abundance. These results imply direct limitation of species diversity by climatic stress and more rapid (co-)evolution and narrower niche partitioning in warm climates. They also support the idea that increased numbers of individuals associated with high primary productivity are partitioned to support a greater number of species.
Terrestrial laser scanning is a powerful technology for capturing the three-dimensional structure of forests with a high level of detail and accuracy. Over the last decade, many algorithms have been developed to extract various tree parameters from terrestrial laser scanning data. Here we present 3D Forest, an open-source non-platform-specific software application with an easy-to-use graphical user interface with the compilation of algorithms focused on the forest environment and extraction of tree parameters. The current version (0.42) extracts important parameters of forest structure from the terrestrial laser scanning data, such as stem positions (X, Y, Z), tree heights, diameters at breast height (DBH), as well as more advanced parameters such as tree planar projections, stem profiles or detailed crown parameters including convex and concave crown surface and volume. Moreover, 3D Forest provides quantitative measures of between-crown interactions and their real arrangement in 3D space. 3D Forest also includes an original algorithm of automatic tree segmentation and crown segmentation. Comparison with field data measurements showed no significant difference in measuring DBH or tree height using 3D Forest, although for DBH only the Randomized Hough Transform algorithm proved to be sufficiently resistant to noise and provided results comparable to traditional field measurements.
- MeSH
- algoritmy MeSH
- automatizace MeSH
- lesy * MeSH
- zobrazování trojrozměrné * MeSH
- Publikační typ
- časopisecké články MeSH
