Predicting the distribution and abundance of bustards, storks, and harriers in Kenya using citizen science data - Ong’ondo, Frank Juma; Trevelyan, Rosie; Kuria, Anthony; Njoroge, Peter; Guchu, Samuel; Jackson, Colin (Frontiers in Ecology and Evolution, 2025)
Citizen science has the potential to advance scientific knowledge by producing large datasets from diverse landscapes. The Kenya Bird Map (KBM) has collected a large data set on Kenyan birds, yet it is largely untapped for scientific research. This study utilized data from KBM records (hereafter KBM data) to address specific questions regarding the distribution and abundance of grassland specialist birds (bustards) and grassland opportunist species (storks and harriers) within Laikipia County, Nairobi National Park and Masai Mara, Kenya. Our objectives were to predict these grassland bird species’ spatial distribution and abundance using KBM data and identify key landscape elements influencing their occurrence. Bird data were extracted from the KBM portal from 2013 - 2023, using only full protocol card records. Data on bustards, harriers, harrier-hawks, and storks were filtered, focusing on pentads with over four card submissions. We applied Sentinel-2B median imagery for December 2023, accessible through Google Earth Engine, alongside geographic information systems and remote sensing techniques to classify and characterize land cover types as explanatory variables. A linear mixed-effect model was used to predict grassland birds’ response. Our regression result showed that bustards responded positively to patch density but negatively to shrubland and woodland. Storks showed positive responses to grassland and woodland, while harriers showed negative responses to woodland. Storks had the highest number of records, while harriers had the least. Masai Mara had the highest number of records of the 16 species reported across the three regions, while Nairobi National Park had the least. For the first time, our study has recognized the importance of ongoing efforts to incorporate KBM data with complementary ecological datasets to deepen our understanding of bird communities and their responses to environmental changes. Our findings suggest that KBM data has substantial potential for identifying species distribution and monitoring temporal changes.