Predicting and mapping the spatial distribution of Chamaecyparis formosensis in central Taiwan in a GIS with species distribution models

Analyzing the relationship between species and environment is always a focal question of ecological research. In recent years species distribution models (SDMs) has been widely used to predict the spatial distribution of species. SDMs are numerical tools that combine observations and species occurrence or abundance with environmental variables to predict the spatial distribution of species across landscapes, sometimes requiring extrapolation in space and time. Chamaecyparis formosensis (Taiwan red cypress, TRCs) is a coniferous species endemic to Taiwan, where it natural grows in the central mountains at moderate to high altitudes of 800–2800m, and most stands in the range of 1500–2150m. It is threatened by habitat loss and over-cutting for its valuable timber. To preserve TRCs species and achieve sustainable use of biological resources, we choose TRCs as a target for the study to predict its distribution in central Taiwan.The pure forests of TRCs in the study area were mainly located in Pachsienshan (P), Shouchentashan (S) and Baigou Mountain (B) in central Taiwan, and the distribution data were originally obtained by The Third Survey of Forest Resources and Land Use in Taiwan. Elevation, slope, aspect, and three vegetation indices were derived from both SPOT-5 satellite images and DEM. GIS technique was used to overlay those factors. Discriminant analysis (DA), decision tree (DT) and maximum entropy (MAXENT), three commonly used SDMs, were applied based on above-mentioned six variables to predict the suitable habitat of TRCs, and to evaluate which the best model is in terms of accuracy and efficiency. Three experiment designs (ED1, ED2 and ED3) with different combinations of samples were used for model building and validation. The 200 target samples were collected from the site P–B, B–S and P–S for model building under ED1, ED2 and ED3 respectively, while the 100 samples were collected from the site S, P and B for model validation. All experiment designs had same 1350 background samples. The results showed that the overall accuracy and kappa coefficient of DT (96%, 0.88) was higher than that of MAXENT (91%, 0.70), and their accuracies were better than that of DA (84%, 0.58). All the three models were highly efficient in implementation of model construction and evaluation, while the DT model was difficult for generating the entire predicted map of potential habitat due to its complex conditional sentence. Vegetation indices derived from SPOT-5 satellite images could not improve model accuracy because of its insufficiency of spectral resolution and spatial resolution. High spatial resolution and spectral resolution remotely sensed imagery should be used in our future research to improve model performance and reliability.