Alterations in the infrared spectral signature of avian feathers reflect potential chemical exposure: a pilot study comparing two sites in Pakistan

Abstract

Chemical contamination of ecosystems is a global issue with evidence that pollutants impact on living organisms in a harmful fashion. Developing sensor approaches that would allow the derivation of biomarkers or signatures of effect in target sentinel organisms and monitor environmental chemical contamination in a high throughput manner is of utmost importance. As biomolecules absorb infrared (IR), signature vibrational spectra related to structure and function can be derived. In light of this, we tested the notion that IR spectra of bird feathers might reflect environmental chemical contaminant exposure patterns. Feathers were collected from monospecific heronries of cattle egret based in two independent locations (Trimu vs. Mailsi) in the Punjab province of Pakistan; these sites were found to differ in their chemical contamination patterns. Feather samples were chemically analyzed for polychlorinated biphenyls, polybrominated diphenyl ethers, organochlorines and heavy metals. Attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy was employed to derive a spectral signature of individual feathers. Resultant IR spectra were then subjected to canonical correspondence analysis (CAA) to determine whether feather spectral signatures correlate to chemical exposure. Additionally, we explored if principal component analysis (PCA) and linear discriminant analysis (LDA) could be applied to distinguish site-specific differences; linear discriminant function (LDF) was also applied to classify sites. The sampled feathers varied in their chemical exposure patterns depending on whether they were sourced from one site associated with heavy metal exposure or the other which suggested high organic pollutant exposures. CCA of chemical and spectral data showed a correlation between spectral signatures and chemical exposure. PCA-LDA readily distinguished feathers from the two different sites. Discriminating alterations were identified and these were associated with protein and lipid regions in IR spectra. Additionally, LDF showed that the classification rate of spectral categories correlated well with the two chemical exposure patterns (93.6% for Trimu feathers and 91.77% for Mailsi feathers). This pilot study suggests that IR spectra derived from feathers reflect background chemical exposure and points to a novel monitoring tool for contamination.