Assessing drivers of benthic macroinvertebrate community structure in African highland streams: An exploration using multivariate analysis

Sci Total Environ. 2017 Dec 1:601-602:1340-1348. doi: 10.1016/j.scitotenv.2017.06.023. Epub 2017 Jun 9.

Abstract

Understanding the drivers of community structure is fundamental for adequately managing ecosystems under global change. Here we used a large dataset of eighty-four headwater stream sites in three catchments in the Eastern Highlands of Zimbabwe, which represent a variety of abiotic conditions and levels of impairment, to examine the drivers of benthic macroinvertebrate community structure. We focused our assessment on macroinvertebrate family level community composition and functional feeding group classifications. Taxonomic richness was weakly positively correlated with ammonium, phosphates and pH, and weakly negatively correlated with detrital cover and dissolved oxygen. Measured abiotic variables, however, had limited influence on both macroinvertebrate diversity and functional feeding group structure, with the exception of ammonium, channel width and phosphates. This reflected the fact that many macroinvertebrate families and functional feeding guilds were well represented across a broad range of habitats. Predatory macroinvertebrates were relatively abundant, with collector-filterers having the lowest relative abundances. The findings of the study suggest that for certain ecological questions, a more detailed taxonomic resolution may be required to adequately understand the ecology of aquatic macroinvertebrates within river systems. We further recommend management and conservation initiatives on the Save River system, which showed significant impact from catchment developmental pressures, such as urbanisation, agriculture and illegal mining.

Keywords: Abiotic; Benthic macroinvertebrate; Biodiversity; Community structure; Functional feeding groups; Highland; Multivariate analysis.

MeSH terms

  • Agriculture
  • Animals
  • Biodiversity*
  • Ecosystem*
  • Environmental Monitoring
  • Invertebrates*
  • Mining
  • Multivariate Analysis
  • Rivers / chemistry*
  • Urbanization
  • Zimbabwe