Back

Functional insights on chemical communication of fishing cats: a strategic mechanism for adaptation in semi-aquatic habitat

Das, S.; Das, P.; Manna, S.; Poddar Sarkar, M.

2024-11-20 zoology
10.1101/2024.11.19.624236 bioRxiv
Show abstract

Chemical communication system of fishing cats, focusing on volatile organic compounds present in their urine (UR) and marking fluid (MF) has been explored. Using solid-phase microextraction guided gas chromatography mass spectrometry analysis, a diverse range of functional groups from UR and MF were detected, including ketones, aldehydes, sulfur- and nitrogen-containing compounds, among which 3-Mercapto-3-methylbutanol (3MMB) and its related sulfur compounds were found to be sex-specific, predominantly present in males. The unique behavior of fishing cats to urinate frequently in water when experimentally tested with synthetic 3MMB, was aligned for its retention in aqueous medium upto 21 days. This suggests that fishing cats may employ an energy-efficient scent-marking strategy by leveraging aquatic environments for prolonging chemical signalling process. The analysis of less-volatile compounds, such as triglycerides and fatty acids, suggests that these compounds could be acted as fixatives, helping to stabilize and prolong the intensity of chemical messages coded to highly volatile molecules. The combination of highly volatile, less volatile compounds and lipids may create complex synergy to facilitate interindividual communication, potentially serving as pheromones. This research enhances our understanding of the fishing cats adaptation to semi-aquatic environments and its unique behavioural pattern in marshy landforms, thus concentrated with high population density in deltaic South Bengal.

Published in Biology Bulletin · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.