Performance of imputation-based models in predicting breeding population trend of a near-threatened bird in changing water regime: A 36-year long-term case study of Painted Stork, Mycteria leucocephala

Abstract

The breeding population of birds are dynamic and are affected by multiple factors including climate and local environmental conditions. However, often to understand such relations requires long-term data modelling. Such long-term population data is either lacking or has data gaps. This study demonstrates the use of Multiple Imputation Chained Equation (MICE) to overcome the problem of missing data population census. This is also the first comprehensive study, modelling the 36-year (1980-2015) long-term breeding population data of a near-threatened bird, Painted Stork, from Keoladeo National Park, India. It tests the effect of local water availability, i.e., water released to the park, and regional rainfall, i.e, climatic condition, on the breeding population using Generalised Additive Model (GAM). Both imputation and observed data series-based GAM models identified the local water availability as the most important factor influencing the breeding population of Painted Stork. More than 80% population decline was observed, despite a slight increase in the rainfall at regional scale, suggesting local hydrological conditions are limiting to the breeding population and not the climate. According to the visual assessment of partial plot of GAM, minimum 200-300 million cubic feet of water is needed each nesting season to ensure  sustenance of breeding population. Post-1989, the breeding population was unable to match the long-term mean (~726) except in 1992, 1995, and 1996. The maximum decline was observed between 2000-2009, a decade of frequent droughts. The breeding population was stable until the end of this study, but it was far below the long term mean.