Occupancy data improves parameter precision in spatial capture-recapture models.

Area of Science:

• Ecology
• Conservation Biology
• Wildlife Management

Background:

• Accurate population size estimation is crucial for species management and conservation.
• Spatially explicit capture-recapture (SCR) models are advanced tools for estimating population density.
• Current SCR methods face challenges with unmarked or partially marked species, limiting their application.

Purpose of the Study:

• To develop and evaluate an integrated model (SCR-IM) for estimating population density in species with limited individual identifiability.
• To assess the accuracy and precision of the SCR-IM compared to standard SCR models using simulations and a case study.
• To demonstrate the utility of SCR-IM for conservation of species like the stone marten (Martes foina).

Main Methods:

• Integrated three submodels: individual capture histories (tagging), occupancy data (camera traps), and telemetry data.
• Applied the SCR-IM to stone marten data, a species with partial natural markings.
• Conducted simulations to compare SCR-IM with standard SCR models under various scenarios.

Main Results:

• Estimated stone marten density at 0.352 (SD: 0.081) individuals/km².
• SCR-IM provided more accurate and precise population size estimates than standard SCR models in simulations.
• The SCR-IM approach increased precision by 37% in the stone marten case study.

Conclusions:

• The SCR-integrated model (SCR-IM) offers a significant advancement for estimating population density in partially marked or unmarked species.
• This integrated approach enhances precision and accuracy, crucial for effective wildlife management and conservation.
• SCR-IM reduces reliance on invasive sampling methods, making it a valuable tool for a wider range of species.