Analytics on Bird Migration

Abstract

Global warming presents an immediate danger to all life on Earth. Recent changes in the climate has been linked to changes in the migration phenologies of avian species around the world. The aim of this thesis is to study the migration behaviour of short distant migrants in India. Two parameters are chosen to study migration: the day of first arrival and the day of last departure. These migration days are extracted and evaluated with data from citizen science project eBird for the time period between 2014 and 2019. Using linear regression, the relationship between the migration days and the climatic conditions present at the both the breeding and wintering grounds of the species is studied. The climatic predictors chosen represent the temperature, total precipitation, amount of vegetation greenness, and the species’ level of urban tolerance. 62.5% of the species are shown to have a clear relationship between the predictors used in the study and the arrival days. For 25% of the study species a relationship between the predictors and departure days is established. Of all the predictors, those relating to temperature had the best model performance. For modelling the days of first arrival at the wintering ground of the species, temperature predictors had the best predictive power for 62.5% species, followed by urban tolerance for 25% species and vegetation index for 12.5% species. For modelling the days of last departure from the wintering grounds, temperature variables had best performance for 50% species, followed by precipitation and vegetation index both for 25% of the study species. As the impact of global climate change is slowly but surely been felt, its important to establish clear connections between the climate and migration phenologies of bird species in India, so that appropriate conservation action can be undertaken to mitigate the impact of the changing bird behaviour and the climate.