Environmental impact assessment and conservation planning of a Middle-Eastern River basin using a fish-based tolerance index.

The tolerance of aquatic organisms to stressors is widely used to monitor and evaluate the condition of freshwater ecosystems. Tolerance values (TV) derived from analyses of the relationship between species and their environment are considered to be more objective than those that rely on expert opinion. We used principal component analysis (PCA) to derive a generalized stressor gradient based on physicochemical characteristics and physical habitat quality and structure. Scores of the first principal component axis (PC1) were used to estimate TV for 37 fish species collected from 54 sites in the Karun River basin, Iran. PCA of 17 variables identified stressors that were influential such as total phosphate, total nitrogen, total coliform, and habitat and morphological score. The species were separated into three categories on the stressor gradient: sensitive (18.9%), semi-tolerant (48.6%), and tolerant species (32.4%). Based on these results we developed the Karun Fish Tolerance Index (KFTI) and demonstrated that it performed well in separating the least, moderate, and most disturbed sites in the study area. The discrimination efficiency of the KFTI was 82.5%, which makes it a robust management tool for the protection and conservation of streams and rivers in the Karun River watershed. TV developed here reflect objective characteristics of the sensitivity of fish species to the predominant stressors in the Karun and similar systems.

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.

Development of the Karun macroinvertebrate tolerance index (KMTI) for semi-arid mountainous streams in Iran.

The most robust approach to ecological monitoring and assessment is the use of regionally calibrated indicators. These should be calculated based on collocated biological (response) and physicochemical (stressor) variables and an objective rating and scoring system. In developing countries, a frequent lack of financial and technical resources for monitoring has led to many environmental problems being overlooked, such as the degradation of streams, rivers, and watersheds. In this paper, we propose the Karun Macroinvertebrate Tolerance Index (KMTI) for application to rivers in the Karun River basin, which is the largest watershed in Iran, draining semi-arid mountainous regions. The KMTI is the first biological index specifically developed and calibrated for Iranian water resources. Benthic macroinvertebrates, physical habitat, hydromorphic, and water quality data were collected and measured at 54 sites across four seasons in 2018 and 2019. A total of 101 families of benthic macroinvertebrates belonging to eight classes and 21 orders were identified, and tolerance values were determined for 95 families. The KMTI was found to be most efficient in identifying ecological degradation when data were used from winter samples with a discrimination efficiency (DE) 90% and a four-season mean of 84.3%. Also, the best DE of the water quality classification table based on the KMTI index was equal to 86.9%.

Defining a Disturbance Gradient in a Middle-Eastern River Basin.

A physical, chemical and biological characterization of river systems is needed to evaluate their ecological quality and support restoration programs. Herein, we describe an approach using water chemistry, physical structure and land use for identification of a disturbance gradient existing in the Karun River Basin. For this purpose, at each site, physical structure and physico-chemical data were collected once in each season for a total of 4 samples during the period (October 2018 - September 2019). Principal components analysis (PCA) of 17 variables identified five variables that were influential across all seasons: conductivity, total habitat score, stream morphology, clay & silt, and sand. Of the 54 sites, 14, 26 and 14 sites were classified as least, moderate and most disturbed sites, respectively. The metric Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa was used for validation of the classification. Results in different seasons showed that all the least disturbed sites (n=14) were significantly different from moderate and most disturbed sites (p < 0.01). In this study the validation process presented a good confirmation of a priori reference sites selection process, showing that the proposed criteria could be considered as appropriate tools for characterization of the existent disturbance gradient in the Karun River Basin.