Modeling Habitat Shifts: Integrating Convolutional Neural Networks and Tabular Data for Species Migration Prediction

TL;DR

This paper introduces a hybrid modeling approach combining CNNs and tabular data to predict bird habitat presence amid climate-driven range shifts, achieving high accuracy and scalability.

Contribution

It presents a novel integration of satellite imagery and environmental data with neural networks for habitat modeling, improving prediction accuracy for species migration.

Findings

Achieved 85% average prediction accuracy.

Effectively captures landscape features influencing bird presence.

Provides a scalable method for habitat and migration analysis.

Abstract

Due to climate-induced changes, many habitats are experiencing range shifts away from their traditional geographic locations (Piguet, 2011). We propose a solution to accurately model whether bird species are present in a specific habitat through the combination of Convolutional Neural Networks (CNNs) (O'Shea, 2015) and tabular data. Our approach makes use of satellite imagery and environmental features (e.g., temperature, precipitation, elevation) to predict bird presence across various climates. The CNN model captures spatial characteristics of landscapes such as forestation, water bodies, and urbanization, whereas the tabular method uses ecological and geographic data. Both systems predict the distribution of birds with an average accuracy of 85%, offering a scalable but reliable method to understand bird migration.