Local and landscape scale determinants of biodiversity and conservation value of macroinvertebrate assemblages in ponds across an urban land-use gradient

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • Massey Univ

Abstract

Urbanisation represents a growing threat to natural communities across the globe. Small aquatic habitats such as ponds are especially vulnerable and are often poorly protected by legislation. Many ponds are threatened by development and pollution from the surrounding landscape, yet their biodiversity and conservation value remain poorly described. Here we report the results of a survey of 30 ponds along an urban land-use gradient in the West Midlands, U.K. We outline the environmental conditions of these urban ponds to identify which local and landscape scale environmental variables determine the biodiversity and conservation value of the macroinvertebrate assemblages in the ponds. Cluster analysis identified four groups of ponds with contrasting macroinvertebrate assemblages reflecting differences in macrophyte cover, nutrient status, riparian shading, the nature of the pond edge, surrounding land-use availability of other wetland habitats. Pond conservation status varied markedly across the sites. The richest macroinvertebrate assemblages with high conservation value were found in ponds with complex macrophyte stands and floating vegetation with low nutrient concentrations and little surrounding urban land. The most impoverished assemblages were found in highly urban ponds with hard-engineered edges, heavy shading and nutrient rich waters. A random forest classification model revealed that local factors usually had primacy over landscape scale factors in determining pond conservation value, and constitute a priority focus for management.  

Details

Original languageEnglish
Pages (from-to)1065–1086
Number of pages22
JournalBiodiversity and Conservation
Volume26
Issue number5
Early online date17 Jan 2017
Publication statusPublished - Mar 2017

Keywords

  • Biodiversity, Urbanisation, Water quality, Machine learning, Aquatic ecology