Ruffed Grouse Occurrence Predictions for Pennsylvania Using Acoustic and LiDAR Data

A multi-year passive acoustic monitoring dataset paired with LiDAR-derived forest structure metrics predicts Ruffed Grouse occurrence probability across Pennsylvania. The dataset, authored by Randy Koleck and last updated on 2026-04-22, was created to identify areas for targeted habitat management. The analysis indicates high-elevation, well-connected hardwood forests with some conifers and developed understories have the highest predicted probability of grouse occurrence.

Use Cases

• Predicting species occurrence probability based on LiDAR-derived forest structure metrics.
• Identifying priority areas for habitat management based on landscape context and forest features.
• Modeling the relationship between forest understory development and wildlife presence.
• Assessing the effectiveness of passive acoustic monitoring for building large detection datasets.

Strengths

• Dataset pairs a multi-year acoustic detection dataset with fine-scale LiDAR data.
• Analysis covers a statewide geography (Pennsylvania).
• License is CC0-1.0, allowing for unrestricted reuse.

Limitations

• Row count and column definitions are unknown, limiting suitability assessment.
• File size is listed as 0.0 B, suggesting the actual data files may need to be located separately.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Collection Method

Data gathered via passive acoustic monitoring (autonomous recording units) and LiDAR remote sensing.

Time Range

Multi-year, but specific years are not provided.

Freshness

Last updated 2026-04-22 06:38:27; freshness should be verified.

Geography

Pennsylvania, USA.