A novel method for estimating pathogen presence, prevalence, load, and dynamics at multiple scales.

Area of Science:

• Ecology
• Wildlife disease
• Conservation biology

Background:

• Quantitative real-time PCR (qPCR) is widely used for pathogen monitoring.
• Existing quantitative frameworks often lack consideration for imperfect detection, pathogen dynamics, and load, especially in early disease stages.
• This limitation can lead to underestimation of pathogen presence and delayed intervention.

Purpose of the Study:

• To develop a novel hierarchical multi-scale dynamic occupancy hurdle model (MS-DOHM) for more robust pathogen dynamics estimation.
• To incorporate pathogen load and imperfect detection into occupancy modeling.
• To improve the understanding of Pseudogymnoascus destructans (Pd) dynamics in hibernating bats.

Main Methods:

• Developed and applied a hierarchical multi-scale dynamic occupancy hurdle model (MS-DOHM).
• Utilized qPCR data from Pseudogymnoascus destructans (Pd) sampling in bat hibernacula.
• Compared MS-DOHM results with a standard dynamic occupancy model and naïve detection/non-detection methods.

Main Results:

• MS-DOHM estimated site-level pathogen presence up to 35.7% higher than simpler models.
• Pathogen arrival was estimated two to three years earlier using MS-DOHM.
• The model indicated higher predicted pathogen loads and improved model fit and predictive ability.

Conclusions:

• Accounting for pathogen load and prevalence at multiple scales significantly alters the understanding of pathogen dynamics.
• The MS-DOHM framework allows for earlier detection and potentially earlier conservation interventions for diseases like white-nose syndrome.
• This multi-scale approach enhances the accuracy and predictive power of pathogen monitoring models.