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Response to Comments on "Designing river flows to improve food security futures in the Lower Mekong Basin".

Gordon William HoltgrieveMauricio Eduardo AriasA RuhiVittoria L ElliottSo NamPeng Bun NgorTimo RäsänenJohn L Sabo
Published in: Science (New York, N.Y.) (2018)
Sabo et al presented an empirically derived algorithm defining the socioecological response of the Tonle Sap Dai fishery in the Cambodian Mekong to basin-scale variation in hydrologic flow regime. Williams suggests that the analysis leading to the algorithm is flawed because of the large distance between the gauge used to measure water levels (hydrology) and the site of harvest for the fishery. Halls and Moyle argue that Sabo et al's findings are well known and contend that the algorithm is not a comprehensive assessment of sustainability. We argue that Williams' critique stems from a misunderstanding about our analysis; further clarification of the analysis is provided. We regret not citing more of the work indicated by Halls and Moyle, yet we note that our empirical analysis provides additional new insights into Mekong flow-fishery relationships.
Keyphrases
  • machine learning
  • deep learning
  • climate change
  • risk assessment
  • ultrasound guided
  • water quality