Pattern-Based Prediction of Population Outbreaks

TL;DR

The paper introduces the Pattern-Based Prediction (PBP) method for forecasting insect outbreaks using time series data, combining the Alert Zone Procedure with machine learning, and demonstrates its effectiveness and interpretability compared to other models.

Contribution

The paper presents the novel PBP method that improves outbreak prediction accuracy and interpretability, and provides open-source Python tools for ecologists and researchers.

Findings

Achieved 84.6% accuracy in simulations

Achieved 95.0% accuracy on real aphid data

Outperformed or matched state-of-the-art machine learning methods

Abstract

Insect outbreaks are biotic disturbances in forests and agroecosystems that cause economic and ecological damage. This phenomenon depends on a variety of biological and physical factors. The complexity and practical importance of the issue have made the problem of predicting outbreaks a focus of recent research. Here, we propose the Pattern-Based Prediction (PBP) method for predicting population outbreaks. It is based on the Alert Zone Procedure, combined with elements from machine learning. It uses information on previous time series values that precede an outbreak event as predictors of future outbreaks, which can be useful when monitoring pest species. We illustrate the methodology using simulated datasets and real time series data obtained by monitoring aphids in wheat crops in Southern Brazil. We obtained an average test accuracy of $84.6\%$ in the simulation studies implemented with stochastic models, and $95.0\%$ for predicting outbreaks using the real dataset. This shows the feasibility of the PBP method in predicting outbreaks in population dynamics. We benchmarked our results against established state-of-the-art machine learning methods, namely Support Vector Machines, Deep Neural Networks, Long Short Term Memory and Random Forests. The PBP method yielded a competitive performance, associated with higher true-positive rates in most comparisons, while being able to provide interpretability rather than being a black-box method. This is an improvement over current state-of-the-art machine learning tools, especially when being used by non-specialists, such as ecologists aiming to use a quantitative approach for pest monitoring. We provide open-source code to implement the PBP method in Python, through the \texttt{pypbp} package, which may be directly downloaded from the Python Package Index server or accessed through \url{https://pypbp-documentation.readthedocs.io}