Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a...

Area of Science:

• Wildlife ecology
• Conservation biology
• Spatial modeling

Background:

• Resource selection functions (RSFs) are crucial for understanding wildlife habitat use.
• Multi-level and multi-scale analyses enhance RSFs by considering nested organizational levels and varying spatial extents.
• Integrating diverse data types for resistance modeling is an emerging area in wildlife studies.

Purpose of the Study:

• To extend the multi-level, multi-scale framework to model wildlife resistance by combining genetic and movement data.
• To develop a combined multi-scale resistance surface representing different hierarchical processes.
• To identify resource use patches and resistant corridors for pumas in a developed landscape.

Main Methods:

• Utilized telemetry and genetic data from a long-term puma study in southern California.
• Developed a multi-level, multi-scale resource selection function and resistance surface.
• Combined resistance surfaces derived from genetic (broad-scale dispersal) and movement (fine-scale travel) data.

Main Results:

• Pumas avoided urban, agricultural, and road areas, preferring riparian zones and rugged terrain across all levels.
• Habitat selection varied among hierarchical levels and scales for specific landscape features.
• Identified critical resource use patches and resistant corridors for puma movement.

Conclusions:

• Combining genetic and movement data provides a comprehensive understanding of wildlife resistance across scales and levels.
• The developed multi-level, multi-scale approach is effective for conservation planning, particularly for isolated populations.
• This framework is adaptable for modeling resistance and informing conservation for various wildlife species.