Estimating deer density and abundance using spatial mark-resight models with camera trap data.

Area of Science:

• Wildlife ecology
• Population dynamics
• Conservation biology

Background:

• Accurate wild deer population estimates are crucial for conservation, hunting, and damage mitigation.
• Traditional survey methods often struggle to provide precise estimates in difficult terrains or for elusive species.

Purpose of the Study:

• To evaluate the effectiveness of combining camera trap data with spatial mark-resight (SMR) models for estimating deer density.
• To assess the utility of this method across diverse deer species and habitats.

Main Methods:

• Utilized camera trap data from 13 wild deer populations (four species) across nine sites.
• Applied Bayesian spatial mark-resight (SMR) models to estimate population densities and abundances.
• Analyzed data from 12 surveys, with seven yielding density estimates with coefficients of variation (CVs) ≤ 0.25.

Main Results:

• Successfully estimated deer densities ranging from 0.3 to 24.6 deer km⁻².
• Achieved precise density estimates (CV ≤ 0.25) in seven out of twelve surveys.
• Demonstrated the flexibility of the camera trap and SMR model approach for various deer populations.

Conclusions:

• Camera trap surveys combined with SMR models provide a powerful and flexible method for estimating deer density.
• This approach is effective in situations where traditional methods are not feasible.
• Recommended deployment guidelines: at least 30 cameras, 500-1,000 m spacing, for 90 days, for reliable estimates in data-poor situations.