AI Article Synopsis
- Trace metals in fish structures, like otoliths and scales, can indicate the water composition where they lived and help assess fish population dynamics and migration patterns.
- This study evaluates the variability of metal-to-calcium ratios (Me:Ca) in the St. Lawrence River and its tributaries to improve the accuracy of classifications based on elemental composition.
- Findings reveal that while element ratios show stability over time, and classification accuracy is high with the right elements, errors often occur in closely located tributaries, suggesting that understanding regional variations is key for better trace metal analysis.
Article Abstract
Trace metals found in the calcified structures of fish (i.e. otolith, scales and vertebrae) serve as proxies for the ambient water composition at the time of mineralization, and these trace metals are increasingly used as a tool for assessing population structure and the migratory patterns of fish. However, the appropriate scale (e.g. resolution) for such applications can be uncertain because of a poor understanding of the spatiotemporal variations of metal-to-calcium ratios (Me:Ca) in the studied watersheds. This study aims to assess Me:Ca spatiotemporal variability within the St. Lawrence River and nine major tributaries and evaluate the ability of random-forest models to correctly identify rivers on the basis of their elemental composition. We tested the influence of daily discharge on four measured ratios (Sr:Ca, Ba:Ca, Mg:Ca and Mn:Ca) to document local and regional trace element sources and dynamics. The four element ratios displayed a low spatiotemporal variation, reflecting a marked stability over time. We observed that most element- and tributary-specific concentration-discharge relationships were either not significant or showed a weak influence, thereby confirming a stable point source dynamic. The classification performance based on a four-element model (Sr:Ca, Ba:Ca, Mg:Ca and Mn:Ca) produced a classification accuracy of 92.5%, which correspond to a small decrease of accuracy compared to the full model (25 elements, 96.6% of correct classification). A classification based on two elements (Sr:Ca and Ba:Ca) produced a lower classification accuracy (72.6%). Classification errors related mainly to tributaries in close proximity, a problem tempered by grouping these geochemically similar watersheds. Our results show that surveys of the elemental fingerprint of regional tributaries within a given region can provide critical information to determine the appropriate scale (tributary or watershed) for trace metal analysis of the hard-calcified parts of fish.
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| http://dx.doi.org/10.1016/j.scitotenv.2020.143320 | DOI Listing |
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