Functional Trait Analysis and the Multi-metric Integrity Model, Based on Stream Fish Indicators, and Their Relations to Chemical Water Quality

Abstract

Functional trait and biological integrity approaches in stream ecology enable the determination and prediction of aquatic community responses to a variety of environmental stressors, such as chemical pollution, habitat disturbance, and biological invasion. Here, we used multi-trait based functional groups (FGs) to predict the functional responses of fish assemblages to the physicochemical and ecological health gradients in a temperate stream. The multi-metric biological integrity model (mIBI model) was used to evaluate stream ecological health. The FGs were derived from the distance matrix of trophic, tolerance, and physical habitat traits among fish species. The leading water quality indicators (conductivity [EC], total suspended solids [TSS], and chlorophyll-a [CHL-a]) varied conspicuously with the stream gradient and anthropogenic pollution. The multi-metric water-pollution index (mWPI) showed differences in chemical health from upstream to downstream. Monsoon precipitation may have affected the variations in fish species and associated changes linked to irregular chemical health. The fish FGs varied more by space (longitudinal) than by season (premonsoon and postmonsoon). Functional metrics, which reflected trophic and tolerance traits, as well as vertical position preference, were strongly correlated with water quality degradation downstream. Changes were evident in FG (II, III, and IV) combinations from the upstream to downstream reaches. Furthermore, the structure of the fish assemblages from FG-II and FG-III was significantly correlated with chemical (R2 = 0.43 and 0.35, p < 0.001) and ecological health (R2 = 0.69 and 0.66, p < 0.001), as well as the metrics of mWPI. In conclusion, the results indicate significant variations in both trait-based FGs and biological integrity among stream-fish communities, influenced by chemical water quality gradients. The causes included longitudinal zones and intensifying degradation of water quality downstream. Therefore, multi-trait based FGs can facilitate ecological health assessment and develop the mIBI model based on fish assemblages by reflecting the prevailing chemical health status of streams and rivers.

Hydropower dam alters the microbial structure of fish gut in different habitats in upstream and downstream rivers

“…3A). TN, TOC and Chl-a are generally important indicators for evaluating eutrophication in water bodies, and Cyanobacteria is an important constituent ora of the algal bloom (Jargal et al, 2022;Xu et al, 2022). These results imply that the presence of hydropower plants increases the possibility of eutrophication in the downstream Qianjiang River.…”

Fish Tissue Contamination with Organic Pollutants and Heavy Metals: Link between Land Use and Ecological Health

“…The final scores were calculated by aggregating all scores for individual metrics to quantify each riverine site's overall biotic integrity. The final health status of the study sites was ranked based on the final IBI score as excellent (36)(37)(38)(39)(40), good (28)(29)(30)(31)(32)(33)(34), fair (20)(21)(22)(23)(24)(25)(26), poor (14)(15)(16)(17)(18), or very poor (8-13).…”

“…Environmental contaminants include a variety of harmful substances that enter the environment through anthropogenic and natural processes [15,16]. In general, these contaminants encompass heavy metals [17,18] and a variety of pesticides [19,20], which severely threaten the entire ecosystem by hindering its sustainable functions, biological diversity, and ecological health [21,22]. However, the relationship between environmental health assessment results and the impacts of various pollutants remains elusive in most parts of the world.…”

Ecological river health assessments, based on fish ordination analysis of ecological indicator entities and the biological integrity metrics, responding to the chemical water pollution

“…Habitat guild-based differences are locally associated with physical habitat conditions, such as meso-microhabitat diversity, flow regimes, and stream order (McCabe 2011 ; Spurgeon et al 2019 ). However, guild-based analysis or assessment based only on one identity, such as the abundance of sensitive or tolerant species, is insufficient to capture ecological diversity due to natural variability among species (Verdonschot and van der Lee 2020 ; Jargal et al 2022a ). Therefore, combining species with identical guild identities or traits into a cluster (hereafter an ecological entity) will help capture the nuances of ecological diversity and the dominance of trait combinations within communities.…”