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Temporal Variability of Macroinvertebrate Assemblages in a Mediterranean Coastal Stream: Implications for Bioassessment.

Pablo FierroRobert M HughesClaudio Valdovinos
Published in: Neotropical entomology (2021)
Macroinvertebrates from a small forest stream in central-south Chile were sampled monthly from September 2017 to August 2018 to assess temporal variability in the assemblage and the effects of that variability on ecological indicators. Higher precipitation and flows occurred in winter months, and water quality varied among months. We collected 59 macroinvertebrate taxa, finding higher taxa richness and abundances in summer months than in winter months. Four taxa demonstrated marked seasonality, being abundant in some months and then decreasing in following months (Limnoperla jaffueli (Navás), Nousia maculata (Demoulin), Smicridea sp. (McLachlan), Chironomidae spp.). The scores of the family Hilsenhoff Biotic Index (HBI), Hilsenhoff Species-level Biotic Index (HSBI), Multimetric Macroinvertebrate Index (MMI), and Chilean Biological Monitoring Working Party (ChBMWP) index varied throughout the year reflecting natural variability. However, only HBI and HSBI scores were significantly different among seasons, ranging across three water quality classes (excellent, very good, and good), showing the lowest water classes in spring, coinciding with higher abundances of tolerant species. The MMI and ChBMWP indicated good and very good site conditions throughout the year, respectively. Shannon-Weaver diversity ranged between 2.59 (April) and 1.78 (February); however, Pielou evenness had high values throughout the year (> 0.62), except in February. Changes in macroinvertebrates composition throughout the year were explained primarily by discharge, water temperature, and conductivity. Our findings indicate that natural monthly variability in macroinvertebrate assemblages influences the scores of biological indices throughout the year. Therefore, we recommend that natural stream variability be accounted for in biomonitoring programs. We also emphasize the need to use caution when interpreting biological index scores to avoid misinterpretations in stream quality classification.
Keyphrases
  • water quality
  • climate change
  • machine learning
  • deep learning
  • public health
  • genetic diversity
  • heat stress