| Summary: | 博士 === 國立臺灣大學 === 生態學與演化生物學研究所 === 98 === Climate change is the focus of a 21st- century global issue. Anthropogenic warming has caused, and will continue to cause, multi-faceted effects on ecosystems. Combining long-term observation data and species distribution models is helpful in understanding relationship between environment and species distribution and in recognizing biodiversity hotspots and currently protected situations. Analyzing various assumptions under possible climatic changes in order to predict future species distributions would provide further consideration of possible adaptation and mitigation strategies and help to achieve global sustainable development and utilization.
I used two bird inventories conducted in 1993–2004, and extracted data for 17 endemic bird species, with a spatial resolution of 1 km2, to identify individual and specific features of their distributions and to predict current and future potential distributions in Taiwan.
According to species’ occurrences, the 17 species were classified as common (present in >200 grids), uncommon (100–200 grids) or rare (<100 grids). The Mikado Pheasant (Syrmaticus mikado), as a rare species, had the lowest occurrence records, while the Taiwan Barbet (Megalaima nuchalis), as a common species, had the highest. Each species had a specific distribution range and habitat preference. In general, these 17 species occupied heterogeneous elevation and climatic conditions, and they favored habitats with high vegetation cover, dense forest and median-to-high Normalized Difference Vegetation Index (NDVI). In comparison with five species distribution models, including logistic regression (LR), multiple discriminant analysis (MDA), genetic algorithm for rule-set prediction (GARP), artificial neural network (ANN), and maximum entropy (MAXENT), the nonlinear models (GARP, ANN, LR, and MAXENT) provided better predictions than did the linear (MDA) model. Based on kappa, sensitivity, accuracy, and specificity values for each species and the three species categories (common, uncommon, and rare species), GARP and MAXENT were the most consistent models for predicting current distributions of the 17 endemic bird species. By overlapping species predictive distributions and defining areas with the upper 5% of species richness as biodiversity hotspots, three hotspot criteria were designated: CCA-based potential hotspots (CBPH), same-weighted hotspots (SWH), and differentiated-weighted hotspots (DWH). These potential hotspot zones of the endemic bird species can be used to assess the efficiency of protected areas. The SWH showed the most coverage (72%) of actual biodiversity hotspots where species richness is higher than 7 species, followed by the DWH and CBPH (61.6% and 35%, respectively). National Parks provided the greatest protection for the 17 endemic bird species, protecting 22-23% of hotspot areas, whereas nature reserves and wildlife refuges protected less than 6% areas. Most potential biodiversity hotspots were not protected adequately. The effects of climate change on species distributions showed that species would have both positive and negative responses (i.e. increases and decreases) that correlated highly with the median value of the species’ originally occupied elevations. The geographical patterns indicated that the negative-effect species would shift up in elevation, with decreased distribution over time while the positive-effect species would remain in the original habitats and expand to higher elevation.
Species predictive distributions are proved usefully to reflect their distributions both in the face of current environments and future increasing temperature. Long-term monitors, targeted field-based observations, and interdisciplinary experiments are necessary and helpful to resolve complicated problems across the natural systems.
|
|---|
