# Diagrammatic prevalence index: a new algorithm to evaluate pine wilt disease prevalence at the sub-compartment scale

## TL;DR

This paper introduces a new algorithm to better assess the spread of pine wilt disease in forests using detailed data from infected trees and sub-compartments.

## Contribution

A new diagrammatic prevalence index (DPI) is introduced for more accurate and reproducible evaluation of pine wilt disease at a sub-compartment scale.

## Key findings

- The DPI algorithm was applied in Hangzhou and showed consistent dynamic patterns and accuracy compared to existing metrics.
- The diagrammatic scale (DS) provides a semi-quantitative way to assess PWD prevalence within sub-compartments.
- The DPI and DS could improve the accuracy, precision, and repeatability of PWD prevalence assessments.

## Abstract

Pine wilt disease (PWD), caused by the nematode Bursaphelenchus xylophilus, has led to significant ecological and economic losses in pine forests worldwide. Historically, several metrics, including the number of PWD-infected trees, the proportion of PWD-infected pine sub-compartments, and the occurrence area, have been employed to evaluate the prevalence of PWD. However, these metrics are individual and limited in comprehensively representing the prevalence of PWD in extensive regions. This study introduces a new algorithm for evaluating PWD prevalence in Hangzhou, China, where the disease has been established for over two decades. The algorithm utilizes data on the information of PWD-infected trees and sub-compartments to develop a diagrammatic scale (DS) and diagrammatic prevalence index (DPI). The DS categorizes the natural logarithm of the number of PWD-infected trees per hectare into 12 levels, providing a scale for semi-quantifying prevalence status within a sub-compartment. The DPI summarizes the occurrence and status of PWD-infected sub-compartments PWD in the geographic regions. The application of DPI in analysis of PWD prevalence in Hangzhou from 2021 to 2023 revealed consistent dynamic patterns of and accuracy, compared to other metrics. The DS and DPI might contribute to the improvement of accuracy, precision, reproducibility and repeatability of PWD prevalence assessment.