A fish-based multimetric index for Brazilian savanna streams

Abstract

Multimetric indices (MMI) have been widely used to assess ecosystem conditions because they are low-cost, employ a rapid field method, and can incorporate various biological metrics at different levels of biological organization. Our objective was to create a fish-based multimetric index applicable to all streams of the Brazilian savanna biome (Cerrado), the second largest biome in Brazil and deemed a global biodiversity hotspot. We evaluated 156 sites in two river basins (Paraná and São Francisco) and selected metrics capable of distinguishing stream-sites across a gradient of anthropogenic disturbances. We employed two different MMI approaches to determine if an MMI based on natural variation-adjusted metrics performed better than one based on unadjusted metrics. In addition, we assessed the performance of the two final MMIs and their sensitivity to anthropogenic pressures at local (LDI), catchment (CDI) and both scales integrated (IDI). Finally, we employed the power of a probability sample survey design to infer headwater stream conditions across a hydrologic region of approximately 47,000 km2. Our final MMI for Brazilian savanna streams included six metrics: % common species; % characiform individuals; % loricariid individuals; % trichomycterid individuals; % invertivore species, and % Poecilia reticulata individuals. MMI1 (unadjusted metrics) performed better than MMI2 (natural variation-adjusted metrics) in discriminating least- and most-disturbed sites, but MMI2 distinguished intermediate from most-disturbed sites better than MMI1. Both indices were negatively correlated with the CDI scores; however, only MMI2 was negatively correlated with the IDI scores. We inferred that 709 km (9.35%) of streams in the studied hydrologic region were in good condition, 8115 km (82.73%) were intermediate, and 641 km (7.91%) were in poor condition. We conclude that the MMIs proposed in this study have great potential for widespread application because they integrate data from two of the most important Brazilian river basins included in a biome that represents more than 20% of the country. Furthermore, the metrics retained in the indexes are easy to access with a rapid low-cost field method. However, their feasibility in areas influenced by mining, as well as in different biomes, should be tested.