A comment on priors for Bayesian occupancy models.

Area of Science:

• Ecology
• Ecological modeling
• Statistical ecology

Background:

• Species occurrence patterns are fundamental to ecology.
• Occupancy modeling accounts for imperfect detection in surveys.
• Bayesian modeling is increasingly used in ecology, offering prior information incorporation.

Purpose of the Study:

• To investigate how "vague" normally distributed priors in Bayesian occupancy models can unintentionally influence parameter estimation.
• To highlight potential compromises in species-habitat relationship inference due to informative priors.
• To provide guidance for researchers using Bayesian occupancy models.

Main Methods:

• Analysis of simulated and empirical data.
• Examination of the impact of "vague" Gaussian priors on model coefficients.
• Comparison with less informative prior distributions (e.g., logistic, t-distributions).

Main Results:

• "Vague" Gaussian priors can be unintentionally informative, affecting parameter estimation.
• This influence can compromise inference about species-habitat relationships.
• The extent of influence is data-dependent.

Conclusions:

• Researchers using Bayesian occupancy models must be cautious about prior specification.
• Conducting sensitivity analyses is crucial to validate inference.
• Employing less informative priors or exploring alternative distributions is recommended for robust results.