ABSTRACT
To define whether the beta diversity of stream invertebrate communities in New Zealand exhibits geographical variation unexplained by variation in gamma diversity and, if so, what mechanisms (productivity, habitat heterogeneity, dispersal limitation, disturbance) best explain the observed broad-scale beta diversity patterns. We sampled 120 streams across eight regions (stream catchments), spanning a north-south gradient of 12° of latitude, and calculated beta diversity (with both species richness and abundance data) for each region. We explored through a null model if beta diversity deviates from the expectation of stochastic assembly processes and whether the magnitude of the deviation varies geographically. We then performed multimodel inference analysis on the key environmental drivers of beta diversity, using Akaike's information criterion and model and predictor weights to select the best model(s) explaining beta diversity. Beta diversity was, unexpectedly, highest in the South Island. The null model analysis revealed that beta diversity was greater than expected by chance in all eight regions, but the magnitude of beta deviation was higher in the South Island, suggesting differences in environmental filtering and/or dispersal limitation between North and South Island. Habitat heterogeneity was the predominant driver of beta diversity of stream macroinvertebrates, with productivity having a secondary, and negative, contribution. This is one of the first studies accounting for stochastic effects while examining the ecological drivers of beta diversity. Our results suggest that local environmental heterogeneity may be the strongest determinant of beta diversity of stream invertebrates, more so than regional- or landscape-scale variables.
ABSTRACT
This paper introduced the principles and applications of macroinvertebrate community index (MCI) and quantitative macroinvertebrate community index (QMCI) commonly used in New Zealand, and applied them to monitor and assess 53 sites in 40 rivers in Wellington region of the Country. Both MCI and QMCI were correlated with the rivers nutrients enrichment significantly, suggesting that it was possible to use the two indices to monitor and assess the nutrient pollution of water body. The MCI and QMCI could rapidly and easily assess water body quality, and the assessment showed that in Wellington region, the rivers water quality was overall good, but some of the rivers were poor in water quality. There was a significant correlation between MCI and QMCI, but, as compared with QMCI, MCI was better correlated with the rivers nutrients enrichment, and thus, recommended to be applied to assess water body quality. The aim of this paper was to introduce the related researchers in China how to use MCI and QMCI in monitoring and assessing water body quality, and gave some ideas on improving the development of biotic index with macroinvertebrates in rivers water quality assessment in China.
