Research communities show interest in in-situ measurements using digital photography. We measured the body parts of Painted Storks, through a non-invasive method, with their digital images taken under field conditions at two different biogeographic regions of India to test the Ecogeographic rules and sexual dimorphism. Our results show a significant difference in the morphological measurements between the sexes. The Nested ANOVA of the principal component scores indicated an apparent sexual dimorphism with a substantial variation in the leg morphology of the Painted Storks. Further, we have classified and predicted the Painted Storks' sexes and the region from the independent morphological variables through Machine Learning algorithms. Without the non-invasive method, it would be almost impossible to collect morphological measurements at a large scale from live Painted Storks under field conditions. Besides, the non-invasive sex identification of birds under field conditions assumes importance from animal welfare and conservation perspective.

Digital images have become cheap, yet its perspective issues cause hindrance for size and shape estimations. Here, we quantified shape distortions due to projective issues using Cardboard model fishes. We identified that Generalized Procrustes Analysis preserves shape homogeneity where objects differ in angle up to 20° are grouped as similar ones. For 2D shape analysis, the proposed novel filter criterion aids in profitable use of images with minor projective issues. Using the proposed filter criterion images of free ranging Painted Storks (Mycteria leucocephala), taken under field conditions, were subjected for shape analysis. Our result indicates that a subtle difference was noted in the mandibular shape of Painted Storks between two habitats viz., Delhi Zoo and Keoladeo National Park, which indicates a cue of intra and interspecific competition. This study deserve merits as the entire exercise has been done without disturbing or capturing birds which highlights the value of animal welfare.