Depth distribution of ostracods in a large fresh-water lake on the Qinghai–Tibet Plateau and its ecological and palaeolimnological significance

We analyze 31 surface-sediment samples from the depths of 2–33 m in the large fresh-water Lake Ngoring on the northeastern Tibet–Qinghai Plateau to provide depth-preference information of ostracods valuable for palaeo-bathymetric reconstruction. Among the nine species discovered, Tonnacypris estonic...

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Bibliographic Details
Main Authors: Ji, M. (Author), Li, X. (Author), Wang, Q. (Author), Wen, R. (Author), Zhai, D. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
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Online Access:View Fulltext in Publisher
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Summary:We analyze 31 surface-sediment samples from the depths of 2–33 m in the large fresh-water Lake Ngoring on the northeastern Tibet–Qinghai Plateau to provide depth-preference information of ostracods valuable for palaeo-bathymetric reconstruction. Among the nine species discovered, Tonnacypris estonica and Ilyocypris echinata show clear preferences to shallow waters while Leucocythere sp. 1 and Cytherissa lacustris are confined to depths exceeding 22 m. Ilyocypris sp., Candoninae sp., and Leucocythere sp. 2 are slightly more abundant in deeper parts of the lake, while Candona candida and Fabaeformiscandona sp. tend to be more abundant in the shallow area. Such information can be used to reconstruct qualitatively the past lake level. Meanwhile, based on 23 forward-selected samples with over 200 valve counts, three water-depth transfer functions are established, which have generally good and comparable performances judged from their determination coefficients and predictive errors. We propose that future studies should endeavor to investigate the distribution of more ostracod species across wider depth ranges from various lakes to encompass the large changes in ostracod assemblage and depth in the geologic past, and that datasets from different lakes can be synthesized into ‘mega-transfer functions’ to improve palaeolimnological reconstruction. © 2021 The Authors
ISBN:1470160X (ISSN)
DOI:10.1016/j.ecolind.2021.108019