Joint inference for telemetry and spatial survey data.

Area of Science:

• Spatial ecology
• Ecological modeling
• Wildlife telemetry

Background:

• Data integration is crucial for spatial ecology, but combining telemetry and survey data is challenging due to different analytical needs.
• Step selection functions (SSFs) for telemetry and habitat selection functions (HSFs) for surveys have distinct specifications and results.
• A unified approach is needed to leverage the strengths of both data types for robust ecological inference.

Purpose of the Study:

• To develop a novel joint likelihood function for integrating telemetry and spatial survey data.
• To establish a relationship between SSFs and HSFs by linking animal movement to population distributions.
• To provide guidelines for optimal effort allocation between telemetry and survey methods in ecological studies.

Main Methods:

• Developed a joint statistical framework linking microscopic animal movement (SSFs) to macroscopic distributions (HSFs).
• Implemented the integrated analysis using maximum likelihood estimation.
• Conducted systematic simulation studies to assess estimation precision and explore effort allocation strategies.

Main Results:

• Joint analysis of telemetry and survey data significantly improves inferential precision and accuracy compared to using either data type alone.
• Complementarities in spatial coverage and resolution between datasets yield substantial benefits.
• Optimal effort allocation depends on total effort and environmental heterogeneity, but integrated analysis is generally preferable.

Conclusions:

• Integrating telemetry and spatial survey data is statistically powerful, computationally efficient, and feasible using traditional methods.
• Joint analysis overcomes previous limitations, offering a more comprehensive understanding of species distributions and habitat associations.
• The developed methodology provides a robust framework for advancing spatial ecology research.