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A fish-based multi-metric assessment index in the Karun River basin, Iran
Citation:
Zare-Shahraki, M., E. Ebrahimi-Dorche, Y. Keivany, K. Blocksom, A. Bruder, AND J. Flotemersch. A fish-based multi-metric assessment index in the Karun River basin, Iran. River Research and Applications. John Wiley & Sons Incorporated, New York, NY, 38(3):573-594, (2022). https://doi.org/10.1002/rra.3920
Impact/Purpose:
This manuscript provides an overview of the processes necessary to craft a multi-metric index for cold, mountainous rivers. Many things that have traditionally work in other systems did not apply to these systems. Therefore, the manuscript provides novel information for working in these system types.
Description:
Large river systems are one of the most important water resources for human societies. However, the ecological integrity of large rivers has been altered greatly by human activities. To monitor and manage these ecosystems, multimetric indices (MMI) are considered as efficient tools. This study aimed to develop and validate a fish-based multimetric index for the Karun River basin, Iran (Karun fish-based multi-metric index [KFMMI]). Eighteen rivers and 54 sites in the basin were sampled in July–August 2019, and physico-chemical and habitat characteristics were used to identify reference sites based on principal components analysis (PCA). Of the 54 sites, 14, 26, and 14 sites were classified as least, moderate, and most disturbed sites, respectively. Fifty-four candidate metrics were evaluated for range, responsiveness, and redundancy with other metrics. This resulted in the identification of eight metrics (relative abundance of native and endemic taxa, relative richness of migratory taxa, relative richness of Leuciscidae taxa, relative richness of herbivorous taxa, relative abundance of cyprinid taxa, relative richness of vegetative inhabitant taxa, relative abundance of slow water flow, and relative richness of edge inhabitant taxa) that informed on species richness and composition, migratory status, functional feeding groups, and habitat preferences. The KFMMI showed excellent performance in separating least, moderate, and most disturbed sites in our study area. Regarding water quality, the KFMMI was classified 16, 5, and 29 sites as good, moderate, and bad, respectively. The discrimination efficiency of KFMMI was 81.6%, which makes it an effective management tool for directing restoration actions at most disturbed sites and intensifying protection of least disturbed sites.
