Spatial distribution and its limiting environmental factors of native orchid species diversity in the Beipan River Basin of Guizhou Province, China

Abstract

To understand the distribution of biodiversity and its determinants, particularly that of ecologically sensitive ones, has long been intriguing to the science community and will help formulate conservation strategies under future climate changes. To this end, we conducted extensive field surveys on the distribution of orchid flora in the Beipan River Basin in Guizhou Province, which is one of the biodiversity conservation priorities in China. The data we acquired, together with those published previously, were converted into orchid species richness for each of the 3 km × 3 km grid cells covering the study region. Redundancy analysis (RDA) and geographically weighted regression (GWR) were then applied to determine which of the 30 environmental factors are potentially critical for the spatial distribution of orchid flora we have observed. Despite a moderate spatial extent, we found that the Beipan River Basin harbors about 249 native orchid species belonging to 74 genera, equivalent to 14.5% of orchid flora of China. Orchid species richness in this area follows a descending gradient from the southeast to the northwest, 70.41% of its variation among grid cells can be explained by environmental factors and spatial variables, and spatial variables accounted for 63.90% of the spatial variation of orchid distribution, indicating that spatial variables played a dominant role in the distribution of wild Orchidaceae species richness. In addition, the main environmental driver is the mean temperature of the wettest quarter. Our study provides a good example for revealing the main drivers of orchid distribution characteristics and has a certain reference value for the development of orchid conservation strategies.

Utilizing Gis and Remote Sensing for Modeling the Spatial Distribution of Wild Orchid Species in Phu Faek Forest Park

“…Spatial regression analysis was conducted to explore the relationships between wild orchids distribution and various key parameters, including elevation, NDWI, basin density, forest type, and aspect. The objective was to gain a comprehensive understanding of how these environmental factors influenced the occurrence and abundance of wild orchids populations within the study area (Ye et al, 2022;Martinis et al, 2018). In the analysis, spatial regression analysis was accounted for to capture the inherent spatial dependencies among the wild orchids data points (Aroonsri and Sangpradid, 2021).…”

“…By considering spatial autocorrelation within the data, the spatial regression model accommodates intricate spatial arrangements and facilitates the representation of intricate spatial structures (Sangpradid, 2023). A spatial regression model, specifically geographically weighted regression (GWR), was then applied to evaluate the potential influence of different environmental factors on the spatial distribution of orchid flora (Ye et al, 2022). This analysis illuminated the complex relationships between environmental factors, spatial variables, and the patterns of orchid distribution.…”