MODELING SPATIAL DISTRIBUTION OF A RARE AND ENDANGERED PLANT SPECIES (Brainea insignis) IN CENTRAL TAIWAN
- 1Graduate student, Dept. of Forestry, Chung-Hsing University, Taiwan E-mail: firstname.lastname@example.org
- 2Specialist, EPMO, Chung-Hsing University, Taiwan E-mail: email@example.com
- 3Director, HsinChu FDO, Forest Bureau, Council of Agriculture, Taiwan
- 4250 Kuo-Kuang Road, Taichung, Taiwan
Keywords: Forestry, Ecology, Modeling, Prediction, Algorithms, Pattern, Performance, Accuracy
Abstract. With an increase in the rate of species extinction, we should choose right methods that are sustainable on the basis of appropriate science and human needs to conserve ecosystems and rare species. Species distribution modeling (SDM) uses 3S technology and statistics and becomes increasingly important in ecology. Brainea insignis (cycad-fern, CF) has been categorized a rare, endangered plant species, and thus was chosen as a target for the study. Five sampling schemes were created with different combinations of CF samples collected from three sites in Huisun forest station and one site, 10 km farther north from Huisun. Four models, MAXENT, GARP, generalized linear models (GLM), and discriminant analysis (DA), were developed based on topographic variables, and were evaluated by five sampling schemes. The accuracy of MAXENT was the highest, followed by GLM and GARP, and DA was the lowest. More importantly, they can identify the potential habitat less than 10% of the study area in the first round of SDM, thereby prioritizing either the field-survey area where microclimatic, edaphic or biotic data can be collected for refining predictions of potential habitat in the later rounds of SDM or search areas for new population discovery. However, it was shown unlikely to extend spatial patterns of CFs from one area to another with a big separation or to a larger area by predictive models merely based on topographic variables. Follow-up studies will attempt to incorporate proxy indicators that can be extracted from hyperspectral images or LIDAR DEM and substitute for direct parameters to make predictive models applicable on a broader scale.