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The availability of sample data, together with detailed environmental factors, has fueled a rapid increase in predictive modeling of species geographic distributions and environmental requirements. We founded that MaxEnt model has provided different descriptions of potential distributions based on different sample size, sample accuracy and environmental background. We used six combinations based on three sample data set and two kinds of environmental variables to estimate the potentially suitable areas of Brown Eared Pheasant (Crossoptilon mantchuricum) in MaxEnt model. The results show that the complex variables provided the higher AUC value and accurate potential distribution than simple variables based on the same size of samples. Complicated environmental factors combined with moderate size and accurate sample, can predict better results. The model results were scabrous based on simple environmental factors. Furthermore, big sample size and simple prediction environmental factors will reduce the prediction accuracy, whereas small samples provided a conservative description of ecological niche. Here, we highlighted that considering the big size and high accuracy of sample and many environmental factors of a species to minimize error when attempting to infer potential distributions from current data in MaxEnt model.
Brown Eared-pheasant (Crossoptilon mantchuricum Swinhoe, 1863) is an endangered species endemic to China. Habitat management for Brown Eared-pheasant has to compensate the loss of natural forests and to improve their habitat quality. In this study, we applied a Habitat Suitability Index (HSI) model to analyze the habitat suitability and quality for the breeding populations of this species in Xiaowutaishan National Nature Reserve, North China. The Reserve (area 21833 ha) is located at 800–2882 m a.s.l. and high mountain and deep valley habitats as well as different types of mountain forest and alpine meadows occur there. We used six variables (vegetation type, canopy cover, altitude, exposure, slope gradient and position of slope) both in field observation and in the analysed model. According to the utilization rates of different habitat types by Brown Eared-pheasants, these variables were categorized into five classes (unsuitable, less suitable, moderately suitable, suitable, and highly suitable). Our results show that highly suitable, suitable, less suitable and unsuitable habitat accounted for 13.5%, 53%, 32%, and 1.5% of the entire reserve area, respectively. Our HSI model is well validated by the observed data. We expect this study can be useful as an example of successful model applying to the habitat management or population manipulation of the endangered species.
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