Mapping of Gynaephora alpherakii Inhabitability Area in the National Park of Qilian Mountain, China

Gynaephora alpherakii (G. alpherakii) is one of the dominant pests in the alpine meadow: its outbreak has substantial impacts on grass vegetation production and carrying capacity. The increasing frequency of G. alpherakii outbreaks will take place with global warming and human activities. Therefore, it is crucial to identify inhabitable areas to apply prevention measures efficiently and facilitate sustainable grassland management. In this study, field measurements of G. alpherakii (from 2010 to 2021) were used to explore the relationships between its density and environmental factors in the National Park of Qilian Mountain, China. Then, the inhabitability area was mapped. The results showed that (1) eight of the twenty-five factors studied (average and maximum value of solar radiation: minimum, standard deviation, and average value of normalized difference vegetation index (NDVI): maximum precipitation: and digital elevation model) had a greater influence on G. alpherakii density. (2) Among all of the estimation models, models based on the average of solar radiation, the minimum of NDVI, and the maximum precipitation were better than other factors, with a higher determination coefficient (R2) of 0.53&ndash:0.66 and a lower root mean square error (RMSE) of 40.54 head/m2&ndash:47.32 head/m2. Models based on rest factors had a lower accuracy, with R2 within 0.38&ndash:0.49 and an RMSE of 50.91 head/m2&ndash:58.68 head/m2. (3) The inhabitable area which was most suited for G. alpherakii growth, development, and frequent outbreaks was located in the northeast of the research area (24.72%), with inhabitability decreasing from southeast to northwest. This method is helpful for clarifying the distribution regions and occurrence dynamics of the G. alpherakii in the alpine meadow. The spatial distribution of G. alpherakii in the National Park of Qilian Mountain can also be clearly defined by using this method, which can provide data support for its prevention and control.