Improving the performance of macroinvertebrate based multi-metric indices by incorporating functional traits and an index performance-driven approach.

Human-driven multiple pressures impact freshwater ecosystems worldwide, reducing biodiversity, and impacting ecosystem functioning and services provided to human societies. Multi-metric indices (MMIs) are suitable tools for tracking the effects of anthropogenic pressures on freshwater ecosystems because they incorporate various biological metrics responding to multiple pressures at different levels of biological organization. However, the performance and applicability of MMIs depend on their metrics' selection and their calibration against natural environmental gradients. In this study, we aimed to unravel i) how incorporating functional trait-based metrics affects the performance of MMIs, ii) how disentangling the natural environmental gradients from anthropogenic pressures effects affects the performance of MMIs, and iii) how the performance of MMIs developed using a metric performance-driven approach compares with MMIs developed using an index performance-driven approach. We carried out a field survey measuring abiotic and biotic variables at 53 sites in the Karun River basin (Iran) in 2018. For functional trait-based metrics, we used 15 macroinvertebrate traits and calculated community-weighted mean trait values and functional diversity indices. We used random forest modeling to account for the effect of natural environmental gradients on each metric. Based on our results, incorporating functional traits increased the MMI performance significantly and facilitated ecological interpretation of MMIs. Both taxonomic and functional components of macroinvertebrate assemblages co-varied strongly with natural environmental gradients, and accounting for these covariations improved the performance of MMIs. Finally, we found that index performance-driven MMIs performed better in terms of precision, bias, sensitivity, and responsiveness than metric performance-driven MMIs.

Revised Iranian Water Quality Index (RIWQI): a tool for the assessment and management of water quality in Iran.

Water quality indices use biological, chemical, and physical data and information to classify the condition of surface waters, ultimately contributing to their management. We used multicollinearity and principal components analyses to develop the Revised Iranian Water Quality Index (RIWQI) as an indicator of agricultural and urban effects in the Karun River Basin of southwestern Iran. Seasonal sampling and analysis of water quality parameters from 54 sites across 18 rivers of the Karun River Basin include fecal coliform, total dissolved solid, phosphate, biological and chemical oxygen demand, nitrate, dissolved oxygen saturation, turbidity, pH, and water temperature. This study updates the previous version of Iranian Water Quality Index (IWQI) by differentially weighting individual variables, refining the main sub-indices, adding phosphate (PO4-), biological oxygen demand (BOD), chemical oxygen demand (COD), and temperature (T), and improving the aggregation calculation. Sensitivity testing of the RIWQI resulted in a mean value for discrimination efficiency (DE) > 85.6%, the highest of other indices calculated with the same dataset.

A fish-based multi-metric assessment index in the Karun River basin, Iran.

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.

Fish Species Composition, Distribution and Community Structure in Relation to Environmental Variation in a Semi-Arid Mountainous River Basin, Iran.

We analyzed spatial variation in fish species richness and community composition in the Karun River basin, Iran. Knowledge about fish diversity in the basin is incomplete and varies widely along spatial and temporal scales: The Karun is the longest river in Iran (950 km) with the largest drainage area (about 67,000 km2). Fish samples were collected from 54 sites from July through August 2019 using a backpack electro-fisher. Physico-chemical and habitat parameter data collected at each site included pH, conductivity (µS/cm), dissolved oxygen (mg/L), water temperature (°C), turbidity (NTU), stream width (m), stream depth (m), water velocity (m/s) and elevation (m). In total, 37 species were collected (5241 individuals weighing 110.67 kg). The species collected represented 12 families and 27 genera. A total of 13 endemic species (35.14%), 16 native species (43.24%), and eight non-native species (21.62%) were recorded. Diversity indices were calculated and used to measure the spatial variation in community composition. Relationships between native and endemic species assemblage structure and environmental descriptors were assessed using canonical correspondence analysis (CCA). The first two axes of the canonical correspondence analysis explained 62.57% of the variation in the data. Of the nine environmental descriptors analyzed, eight significantly affected species distribution; however, electrical conductivity and elevation were most influential. Our study provides up-to-date status information on the distribution of freshwater fishes in the Karun River basin. This information is essential for developing conservation and management strategies to support the long-term sustainability of fish populations in the Karun River basin.