摘要：Knowledge of spatial distribution of faunal species is crucial to prioritize conservation resources. While vulnerable or endangered species are usually well-protected, common species were always overlooked. Traditional survey represented species distribution by discrete points, which is precise but often limited in applicability for conservation analysis. This study combined remote sensing; geographical information analytical tools and well-established multivariate statistical modelling to predict habitats of fifty common and representative animal species. Reliable ground data records from terrestrial biodiversity survey were collected and ecological niche factor analysis was used to identify pseudo-absence sites essential for habitat statistical models. Binary logistic regression models and generalized additive models were used to predict the faunal habitats. Species-richness map was then produced to identify biodiversity hotspots and possible conservation gaps of the current conservation system. Results from gap analysis showed that only 1% of faunal species richness hotspots coincided with existing protected sites of special scientific interest (SSSI). 45% of species-rich sites were under-protected. The results not only identified deficiency of existing protection system, where extra conservation planning efforts should be considered, but also highlighted the direction for future development to minimize the land use conflicts. Integration of geoinformatics and statistical analysis with ecological knowledge assist timely conservation policy making.
关键词：Ecological Niche Factor Analysis; Gap Analysis; Generalized Additive Model; Logistic Regression; Species Richness
