GRM-118 Analysis of Climate Change Effects on Bird Migration Patterns Using Long-Term Data

This project examines the impact of climate change on bird migration patterns by integrating bird observation data from eBird with climate data from the NOAA Global Historical Climate Network (GHCN). Focusing on species such as the Arctic Tern, the study analyzes changes in migration timing, routes, and population trends over recent decades. Migration paths were visualized using QGIS, while MODIS land cover data helped assess habitat changes along these routes. Temporal analysis revealed noticeable shifts in migration timing, with earlier arrivals in some regions correlating with rising temperatures and changing precipitation patterns. For future predictions, CHELSA climate data was combined with machine learning models, including Gradient Boosting and Random Forest, to forecast migration behavior under different climate scenarios. The results highlight critical stopover sites increasingly threatened by habitat loss, emphasizing the need for targeted conservation efforts to support migratory species adapting to a changing climate.

Publication Details

Journal
DigitalCommons - Kennesaw State University (Kennesaw State University)
Published
2025-04-15
Primary Topic
Bioinformatics and Genomic Networks
Type
article
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article

GRM-118 Analysis of Climate Change Effects on Bird Migration Patterns Using Long-Term Data

DigitalCommons - Kennesaw State University (Kennesaw State University)
Bioinformatics and Genomic Networks
article

GRM-118 Analysis of Climate Change Effects on Bird Migration Patterns Using Long-Term Data

article en

Abstract

This project examines the impact of climate change on bird migration patterns by integrating bird observation data from eBird with climate data from the NOAA Global Historical Climate Network (GHCN). Focusing on species such as the Arctic Tern, the study analyzes changes in migration timing, routes, and population trends over recent decades. Migration paths were visualized using QGIS, while MODIS land cover data helped assess habitat changes along these routes. Temporal analysis revealed noticeable shifts in migration timing, with earlier arrivals in some regions correlating with rising temperatures and changing precipitation patterns. For future predictions, CHELSA climate data was combined with machine learning models, including Gradient Boosting and Random Forest, to forecast migration behavior under different climate scenarios. The results highlight critical stopover sites increasingly threatened by habitat loss, emphasizing the need for targeted conservation efforts to support migratory species adapting to a changing climate.

DigitalCommons - Kennesaw State University (Kennesaw State University)
Climate action
Openalex Percentile: Top 50%
Bioinformatics and Genomic Networks
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GRM-118 Analysis of Climate Change Effects on Bird Migration Patterns Using Long-Term Data · DigitalCommons - Kennesaw State University (Kennesaw State University) (2025) | TGRS Research Map | TGRS