Spatial sampling to detect an invasive pathogen outside of an eradication zone

Area of Science:

• Plant Pathology
• Ecology
• Biosecurity

Background:

• Invasive pathogens pose significant threats to ecosystems.
• Early detection is critical for effective management of invasive species.
• Current sampling methods may not be optimal for pathogen detection.

Purpose of the Study:

• To introduce and evaluate optimized spatial sampling for invasive pathogen detection in plant pathology.
• To develop optimum sample schemes that maximize pathogen detection probability.
• To establish a baseline for comparing the accuracy of different sampling strategies.

Main Methods:

• Application of spatial simulated annealing for optimized spatial sampling.
• Integration of optimized spatial sampling with a spatially explicit epidemic model.
• Comparison of optimized spatial sampling against random, stratified, and simulation-based sampling methods.

Main Results:

• Optimized spatial sampling mathematically converges to the optimal allocation of sampling points.
• The method effectively maximizes the probability of detecting invasive pathogens.
• Optimized spatial sampling demonstrated superior performance compared to traditional methods in the case study.

Conclusions:

• Optimized spatial sampling is a powerful tool for enhancing invasive pathogen detection in plant pathology.
• This approach provides a robust framework for developing efficient and accurate disease surveillance strategies.
• The developed method serves as a benchmark for assessing the efficacy of various sampling techniques.