Quantitative spatial upscaling of categorical information: The multi‐dimensional grid‐point scaling algorithm

Forest habitats and plant communities strongly predicts Megachilidae bee biodiversity

Megachilidae is one of the United States' most diverse bee families, with 667 described species in 19 genera. Unlike other bee families, which are primarily ground nesters, most megachilid bees require biotic cavities for nesting (i.e., wood, pithy stems, etc.). For this group, the availability of woody-plant species may be as important as nectar/pollen resources in maintaining populations. We studied Megachilidae biodiversity in the continental United States. We confirmed that the highest species richness of Megachilidae was in the southwestern United States. We examined the relationship between species richness and climate, land cover, tree species richness, and flowering plant diversity. When examining environmental predictors across the conterminous United States, we found that forested habitats, but not tree diversity, strongly predicted Megachilidae richness. Additionally, Megachilidae richness was highest in areas with high temperature and low precipitation, however this was not linearly correlated and strongly positively correlated with flowering plant diversity. Our research suggests that the availability of nesting substrate (forested habitats), and not only flowering plants, is particularly important for these cavity-nesting species. Since trees and forested areas are particularly susceptible to climate change, including effects of drought, fire, and infestations, nesting substrates could become a potential limiting resource for Megachilidae populations.

Fusing multidimensional hierarchical information into finer spatial landscape metrics

One of the core issues of ecology is to understand the effects of landscape patterns on ecological processes. For this, we need to accurately capture changes in the fine landscape structures to avoid losing information about spatial heterogeneity. The landscape pattern indicators (LPIs) can characterize the spatial structures and give some information about landscape patterns. However, researches on LPIs had mainly focused on the horizontal structure of landscape patterns, while few studies addressed vertical relationships between the levels of hierarchical landscape structures. Thus, the ignorance of the vertical hierarchical relationships may cause serious biases and reduce LPIs' representational ability and accuracy. The hierarchy theory about the landscape pattern structures could notably reduce the loss of hierarchical information, and the information entropy could quantitatively describe the vertical status of landscape units. Therefore, we established a new multidimensional fusion method of LPIs based on hierarchy theory and information entropy. Here, we created a general fusion formula for commonly used simple LPIs based on two-grade land use data (whose land use classification system contains two grades/levels) and derived 3 fusion landscape pattern indicators (FLIs) with a case study. The results show that the information about fine spatial structure is captured by the fusion method. The regions with the most differences between the FLIs and the traditional LPIs are those with the largest vertical structure such as the ecological ecotones, where vertical structure was ignored before. The FLIs have a finer spatial representational ability and accuracy, not only retaining the main trend information of first-grade land use data, but also containing the internal detail information of second-grade land use data. Capturing finer spatial information of landscape patterns should encourage the application of fusion method, which should be suitable for more LPIs or more dimensional data. And the increased accuracy of FLIs will improve ecological models that rely on finer spatial information.