Analytical Bayesian models to quantify pest eradication success or species absence using zero-sighting records

Area of Science:

• Ecology
• Conservation Biology
• Statistical Ecology

Background:

• Establishing definitive population absence is challenging with imperfect zero-sighting data.
• Management decisions often require robust inference of absence, especially post-eradication or for conservation.

Purpose of the Study:

• To develop accessible Bayesian formulations for inferring population absence using zero-sighting survey data.
• To provide versatile analytical tools supporting management decisions regarding population status.

Main Methods:

• Utilized Bayesian methods to formulate analytical inferred distributions and statistics.
• Incorporated stochastic processes including prior population size, growth, and imperfect detection into a flexible distribution.
• Derived analytical solutions for key statistics like probability of absence and detection probability.

Main Results:

• Developed analytical solutions for inferred mean and variance of population size/infested units.
• Formulated probabilities of absence, conditional negative surveys, and first detection.
• Provided explicit thresholds to aid management decision-making.

Conclusions:

• The Bayesian framework offers an efficient and powerful method for assessing population absence from zero-sighting data.
• The formulations are applicable to pest eradication, threatened species status, trade requirements, and inferring population size upon first detection.