Signals of stream fish homogenization revealed by AI-based clusters

Su Ting Cheng, Wen Ping Tsai, Tzu Chun Yu, Edwin E. Herricks, Fi John Chang

Research output: Contribution to journalArticlepeer-review


Risks of stream fish homogenization are attributable to multiple variables operating at various spatial and temporal scales. However, understanding the mechanisms of homogenization requires not only watershed-scale, but also exhaustive fish community structure shifts representing detailed local functional relationships essential to homogenization potentials. Here, we demonstrate the idea of applying AI-based clusters to reveal nonlinear responses of homogenization risks among heterogeneous hydro-chemo-bio variables in space and time. Results found that species introduction, dam isolation, and the potential of climate-mediated disruptions in hydrologic cycles producing degradation in water quality triggered shifts of community assembly and resulting structures producing detrimental conditions for endemic fishes. The AI-based clustering approach suggests that endemic species conservation should focus on alleviation of low flows, control of species introduction, limiting generalist expansion, and enhancing the hydrological connectivity fragmented by dams. Likewise, it can be applied in other geographical and environmental settings for finding homogenization mitigation strategies.

Original languageEnglish (US)
Article number15960
JournalScientific reports
Issue number1
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Signals of stream fish homogenization revealed by AI-based clusters'. Together they form a unique fingerprint.

Cite this