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Williams et al . claim that the data used in Sabo et al . were improperly scaled to account for fishing effort, thereby invalidating the analysis. Here, we reanalyze the data rescaled per Williams et al . and following the methods in Sabo et al . Our original conclusions are robust to rescaling, thereby invalidating the assertion that our original analysis is invalid.

INTRODUCTION The Mekong River provides renewable energy and food security for a population of more than 60 million people in six countries: China, Myanmar, Lao PDR, Thailand, Vietnam, and Cambodia. Seasonal rains flood the river’s floodplain and delta. This flood pulse fuels what is likely the world’s largest freshwater fishery in Cambodia’s Tonle Sap Lake, with >2 million tonnes of annual harvest valued at ~$2 billion. Hydropower development is crucial to the region’s economic prosperity and is simultaneously a threat to fisheries and agriculture that thrived in the natural-flow regime. The Mekong is testament to the food, energy, and water challenges facing tropical rivers globally. RATIONALE We hypothesized that high fisheries yields are driven by measurable attributes of hydrologic variability, and that these relationships can be used to design and implement future flow regimes that improve fisheries yield through control of impending hydropower operations. Hydrologic attributes that drive strong fisheries yields were identified using a data-driven approach that combined 17 years of discharge and standardized harvest data with several time-series methods in the frequency and time domains. We then analyzed century-scale time series of discharge data on the Mekong and associated hydroclimate data sets to understand how current dams, independent of climate, have changed key drivers of the fishery since the early 1960s. Finally, we used estimated hydrologic drivers of the historical bag net, or “Dai,” fishery on the Tonle Sap River—the largest commercial fishery in the Mekong—to design better fisheries futures by comparing designed flows to current and pre-dam (natural-flow) regimes. RESULTS Our analysis identified several features of hydrologic variability that portend strong fisheries yield. These include two “high-level” descriptors: flood pulse extent (FPExt) and net annual anomaly (NAA). FPExt, which combines flood magnitude and duration, has long been hypothesized to drive fisheries yield in ecosystems subject to flood pulses, such as the Mekong. NAA is the annual sum of daily residual flows standardized to the long-term average hydrograph. Hence, NAA is a compact measure of hydrologic variance and can be further decomposed into nine shape “components.” Several of these components drive high fisheries yields, including a long low-flow period followed by a short, strong flood pulse with multiple peaks. All essential drivers of the flood pulse fishery have been changing since the closure of the first Mekong tributary dam and are independent of changes associated with climate observed over the past century. The direction of these changes is consistent with declining fisheries yield in the Tonle Sap. Projection of the fishery driven by a hypothetical “designer” hydrograph capturing the key shape features associated with strong yield improved harvest relative to current conditions; yield was projected to exceed that of the natural-flow regime by a factor of 3.7. This result was robust to the inclusion of density-dependent recruitment in our time-series model. CONCLUSION A data-driven approach reveals a new perspective on hydrologic drivers of fishery productivity in the Mekong. The extent of the flood pulse is paramount, as previous literature suggests, but so are other descriptors of hydrologic variation, including anomalous low flows. Variance is key—specifically, the sequence and timing of within-year anomalous high and low flows. A focus on variance shifts the conversation from “How much water do we need?” to “When do we need it the most, and when can we spare it?” Beneficial components of variance in the hydrograph can be described by a simple Fourier series—an asymmetric rectangular pulse train. A quantitative ecological objective function fills a critical gap in the balancing of fisheries harvest with other important objective functions including hydropower generation, rice production, and transportation. This opens the possibility of specifying and implementing flow regimes to manage rivers to satisfice trade-offs between fishery productivity and other ecosystem services provided by tropical rivers subject to flood pulses.