Harnessing Deep Learning in Ecology: An Example Predicting Bark Beetle Outbreaks.

Area of Science:

• Ecology
• Machine Learning
• Ecological Forecasting

Background:

• Global challenges like biodiversity loss and climate change necessitate enhanced ecological prediction.
• Advances in data, ecological understanding, and computing power support quantitative ecological approaches.
• Flexible frameworks are needed to leverage these advancements for ecological prediction.

Purpose of the Study:

• To demonstrate the application of deep learning, specifically deep neural networks (DNNs), for ecological prediction.
• To provide a reproducible example of designing, training, and applying DNNs in ecology.
• To evaluate DNN performance against conventional methods for ecological forecasting.

Main Methods:

• Utilized deep neural networks (DNNs), a type of artificial neural network with multiple layers and neurons.
• Developed and applied a reproducible framework for DNN design, training, and application.
• Used bark beetle outbreaks in conifer forests as a case study for prediction.

Main Results:

• DNNs accurately predicted short-term bark beetle infestation risk and long-term landscape-level outbreak dynamics.
• Deep learning models demonstrated superior performance compared to conventional approaches in predicting outbreak dynamics.
• The study provides a practical, code-based example of DNN implementation in ecological research.

Conclusions:

• Deep neural networks show significant potential for ecological prediction and forecasting.
• DNNs can serve as a foundational component for advanced disturbance forecasting systems.
• Increased adoption of DNNs is recommended for addressing diverse ecological challenges.