Bayesian Structure Learning Reveals Disconnected Correlation Patterns Between Morphometric Traits and Blood Biomarkers in White Stork Nestlings

Environmental stressors, particularly agricultural pesticides, can influence both growth and physiology in developing birds, yet the relationship between morphometric condition indices and biochemical biomarkers remains poorly understood. We investigated body mass, beak length, tarsus length, and body condition index (BCI) alongside plasma and S9 biomarkers, including the activities of acetylcholinesterase (AChE), carboxylesterase (CES), and glutathione S-transferase (GST), as well as the levels of glutathione (GSH) and reactive oxygen species (ROS) in nestling White Storks (Ciconia ciconia) from Croatia. Bayesian undirected graphical model (BUGM) inferred a disconnected correlation structure composed of two communities, with a strong beak length–GSH association. Biomarkers further exhibited plasma-specific affinity: plasma markers reflected short-term adjustments, whereas S9 enzymes represented distinct metabolic pathways. Overall, morphometry and physiological status showed only limited integration, restricted mainly to plasma biomarkers, and residual body condition index did not serve as a reliable proxy for physiological stress. We conclude that integrated monitoring approaches, combining morphometric and biochemical profiling, provide a more nuanced assessment of nestling condition and strengthen the use of White Storks as sentinels of agroecosystem health.