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Aquatic insects in subtropical streams: the role of different grassland ecosystems and local environmental descriptors.

Tainara R Menna-BarretoAna Emília SieglochSandro SantosTiago Gomes Dos SantosMarcos André P VellosoMarcia Regina Spies
Published in: Anais da Academia Brasileira de Ciencias (2024)
Organisms can respond to environmental gradients from local to landscape features. Aquatic insects are particularly affected by watershed peculiarities due to their dependence on microhabitat conditions. However, these relationships are poorly understood in lotic ecosystems of subtropical grasslands, limiting water resources management and bioassessment proposals. Here, we investigated how local stream environment and variations in landscape types affect the assemblage structure of a bioindicator insect group, face to the spatial proximity of the sampled locations. We sampled immatures of Ephemeroptera, Plecoptera, and Trichoptera in streams along the Brazilian Pampa biome, recording environmental descriptors in different grassland ecosystem types. The structure of aquatic insect assemblages differed across grassland types, with specific dominant genera associated with each landscape. Spatially-structured water physicochemical descriptors explained a significant amount of variation in assemblage data. Our findings suggest that grassland ecosystem type delimitations capture ecological attributes, influencing watershed features important to EPT assemblage structuration. Moreover, we highlight the importance of niche-based process structuring EPT assemblages along grassland ecosystem types of Pampa biome. In addition, we encourage using aquatic insects in bioassessment of lotic waters to assess local and landscape environmental impacts. We strongly recommend considering the grassland ecosystem schedule for water resources management and bioassessment proposals.
Keyphrases
  • human health
  • risk assessment
  • climate change
  • single cell
  • life cycle
  • zika virus
  • mass spectrometry
  • big data
  • multidrug resistant
  • aedes aegypti
  • gram negative
  • artificial intelligence
  • data analysis