Next-Generation Remote Sensing Data at Multiple Spatial Scales Improves Understanding of Habitat Selection by a Small...

Area of Science:

• Ecology
• Remote Sensing
• Animal Behavior

Background:

• Small mammal habitat selection (HS) is complex and occurs across multiple scales.
• Integrating fine-scale remote sensing (RS) with GPS tracking data offers new possibilities for studying HS.
• Few studies have combined advanced RS data with detailed movement data for small mammals.

Purpose of the Study:

• To investigate fox squirrel (Sciurus niger) habitat selection using multi-scale approaches.
• To determine if next-generation RS variables enhance single-scale HS models.
• To assess if multi-scale HS models outperform single-scale models.

Main Methods:

• Utilized GPS tracking data from 45 individual fox squirrels.
• Developed HS models at three spatial scales: 4 ha, 0.09 ha, and 0.01 ha.
• Incorporated traditional and next-generation RS data into the HS models.

Main Results:

• The best single-scale model (4 ha) explained 58% of HS variations using both data types.
• Next-generation RS data improved HS model performance.
• The multi-scale HS model was the most informative, explaining 68% of HS variations.

Conclusions:

• Next-generation RS data are valuable for quantifying small mammal habitat selection.
• Multi-scale analyses are crucial for a comprehensive understanding of HS in small mammals.
• This study highlights the synergy between advanced RS and GPS tracking for ecological research.