A Rule-Based Species Predictive Model for the Vulnerable Fairy Pitta (Pitta nympha) in Taiwan

• Main Authors: Chia-Ying Ko, Pei-Fen Lee, Mei-Ling Bai, Ruey-Shing Lin
• Format: Article
• Language: English
• Published: National Taiwan University 2009-03-01
• Series: Taiwania
• Subjects: Niche; rule-based model; spatial sampling; vulnerable species; macrohabitat.
• Online Access: http://tai2.ntu.edu.tw/taiwania/abstract.php?type=abstract&id=857
• ISSN: 0372-333X; 0372-333X

A fundamental step in biodiversity conservation is to identify potential distribution and quality habitat for a desired species, especially when the target is rare and difficult to detect. We demonstrated a GIS application in developing a quick predictive model to study the globally vulnerable Fairy Pitta (Pitta nympha) in Taiwan. We compiled sighting data between 1982 and 2000, established a rule-based model to predict its distribution, and applied the predictive map to design a sampling protocol, conduced field surveys to evaluate the accuracy of the model and to obtain hotspots. The results showed that most known distribution of the Fairy Pitta occurred in low elevation, hilly and forested areas. The map predicted 21.6% areas of Taiwan suitable for the Fairy Pitta and 78% of them occurred in western Taiwan. A total of 511 pittas were detected during the 2001 survey that covered 4% areas of Taiwan or 14.3% of predictive areas, with a mean of 30.2% detection probability per grid cell (2 × 2 km in resolution). The adjusted data indicated that the overall accuracy of our model was increased to 40.3% with 290 qualified cells. Most of the new sightings of the Fairy Pitta arising from the 2001 field survey fell in our predictive areas with most of them occurring in western Taiwan. The probability of detecting pitta was highest in the active selection cells within predictive areas. Based on mean number of pitta detected per cell, the hotspots of the Fairy Pitta in Taiwan included three regions: the watershed of Shimen Reservoir within Hsinchu and Taoyuan County, Linnei of Yulin County and the watershed of Wusanto Reservoir in Tainan County. We concluded that the model provides quick and effective predictions for planning conservation strategies and is particularly useful for rare species.