Signals of local bioclimate-driven ecomorphological changes in wild birds

Abstract

AbstractAcross disciplines—biological, ecological, evolutionary, or environmental—researchers increasingly recognize the importance and the need for cost-effective, non-invasive techniques for in-situ morphological measurements of organisms in diverse research contexts. By applying a non-invasive technique using digital images taken under field conditions, we successfully measured the body sizes of wild Painted Storks (Mycteria leucocephala) in two different biogeographic regions of India, spatially separated by 20° of latitude. We have used the wild Painted Storks as model species for measuring their morphometrics using a non-invasive technique that could easily be applied to similar species, rare, endemic, colonial, aquatic, and even those with cultural taboos. Our results satisfactorily classify and predict the sexes of the species and their biogeographic origin based on independent morphological variables using Machine Learning algorithms. The BayesNet yielded the correct classification instances (Receiver Operating Characteristic (ROC) = 0.985), outperforming all the other tested classifying algorithms. A strong relationship was observed between the local bioclimatic conditions and the morphological variations in wild Painted Storks reflecting clear eco-geographic patterns. Without this non-invasive technique, it would be almost impossible to collect morphological measurements at a large scale from live birds under field conditions. Our study is a testimony to the effectual use of the non-invasive digital method for in-situ measurements from free-living wild species in the field, assuming significance, especially from climate change perspectives, biology, ecology, and conservation.