Identification of hydrologic indicators related to fish diversity and abundance: A data mining approach for fish community analysis

[1] This paper develops a new approach to identify hydrologic indicators related to fish community and generate a quantitative function between an ecological target index and the identified hydrologic indicators. The approach is based on genetic programming (GP), a data mining method. Using the Shannon Index (a fish community diversity index) or the number of individuals (total abundance) of a fish community, as an ecological target, the GP identified the most ecologically relevant hydrologic indicators (ERHIs) from 32 indicators of hydrologic alteration, for the case study site, the upper Illinois River. Robustness analysis showed that different GP runs found a similar set of ERHIs; each of the identified ERHI from different GP runs had a consistent relationship with the target index. By comparing the GP results with those from principal component analysis and autecology matrix, the three approaches identified a small number (six) of common ERHIs. Particularly, the timing of low flow (D _min) seems to be more relevant to the diversity of the fish community, while the magnitude of the low flow (Q_b) is more relevant to the total fish abundance; large rising rates result in a significant improvement offish diversity, which is counterintuitive and against previous findings. The quantitative function developed by GP was further used to construct an indicator impact matrix (IIM), which was demonstrated as a potentially useful tool for streamflow restoration design.