How Environment and Urbanization Shape Bird Diversity in Sri Lanka
2026-07-01 • Machine Learning
Machine Learning
AI summaryⓘ
The authors studied bird diversity in Sri Lanka by looking at where and when birds are seen along with environmental factors like land type, weather, pollution, and light at night. They analyzed bird species numbers at different map scales and adjusted for how much people watched birds to make fair comparisons over time. Their results showed that the type of land (like forest or city) is more important for bird variety than things like vegetation greenness or temperature alone. They also found cities tend to have fewer kinds of birds but more of a few common types. This work helps understand what affects bird diversity and can guide conservation efforts.
species richnessNormalized Difference Vegetation Index (NDVI)Artificial Light At Night (ALAN)Poisson Generalized Linear Models (GLMs)beta diversityland coverspatial thinningenvironmental driversoccupancy ratesrarefied richness
Authors
Dilusha Chandrasiri, Maneesha Herath, Yasith Hewarathna, Muditha Herath, Gishan Bandara, Madara Mendis, Nathali Athukorala, Nisansa de Silva, Sandareka Wickramanayake
Abstract
This study presents a comprehensive analysis of bird diversity across Sri Lanka by integrating spatial, temporal, and environmental data. Bird observation records were combined with environmental variables, including weather conditions, air pollution, the Normalized Difference Vegetation Index (NDVI), land cover, elevation, and Artificial Light At Night (ALAN), and rigorously preprocessed to ensure data quality. Spatial analyses were conducted on multiple grid scales (2 km, 5 km, 10 km) to evaluate patterns in species richness while minimizing sampling bias through spatial thinning. Temporal trends were assessed using effort-corrected metrics including rarefied richness and occupancy rates to account for variations in observation effort over time. Environmental drivers of bird diversity were examined using multivariate statistical models, including Poisson Generalized Linear Models (GLMs) and correlation analyses, to identify key associations between ecological factors and species richness. Additionally, community structure, dominance patterns, and beta diversity were analyzed to understand variations in species composition across regions and time. The study found that land-cover type is a stronger predictor of bird diversity than individual continuous variables such as NDVI or temperature alone. Urbanization, measured by ALAN, exhibits nuanced scale-dependent effects, supporting high abundances of a few generalist species while reducing overall richness. The findings provide actionable insights into the patterns and drivers of avian diversity in Sri Lanka, offering a scalable and reproducible framework for biodiversity research and conservation planning.