Statistical modeling of Peromyscus maniculatus (deer mouse) amounts per trap with spatiotemporal data

• Published in: Japanese journal of statistics and data science Vol. 6; no. 2; pp. 847 - 860
• Main Authors: Huang, Hsin-Hsiung, He, Qing
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.11.2023
• Subjects: Chemistry and Earth Sciences; Computer Science; Economics; Finance; Health Sciences; Humanities; Insurance; Law; Management; Mathematics and Statistics; Medicine; Original Paper; Physics; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Climate; Precipitation; Temperature; Gaussian process; Habitat; Biodiversity; Deer mouse; Spatial and temporal correlation
• DOI: 10.1007/s42081-023-00212-3
• ISSN: 2520-8756

The North American deer mice (Peromyscus maniculatus) have been used as an environmental change indicator in North America. Since precipitation and temperature changes affect plant productivity and deer mouse habitats, they are substantial factors of deer mouse population radical variations. Therefore, modeling their association is important for monitoring dynamic changes of the deer mouse amounts per trap and relationships among weather variables such as precipitation, maximum and minimum temperatures. We acquired the National Ecological Observatory Network (NEON) data of deer mouse monthly amounts in traps for 2013 through 2022 in the contiguous United States from long-term study sites maintained for monitoring spatial differences and temporal changes in populations. We categorize the contiguous United States into six regions associated with climates. The proposed method identifies important factors of temperature and precipitation seasonal patterns with the month and year temporal effect interacting with the proposed climate-related regions.