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• W4283395030 startingPage "110058" @default.
• W4283395030 abstract "Monitoring is usually among the first actions taken to help inform recovery planning for declining species, but these data are rarely used formally to inform conservation decision making. For example, Central Valley Chinook salmon were once abundant, but anthropogenic activities have led to widespread habitat loss and degradation resulting in significant population declines. Monitoring data suggest survival through the southern Sacramento-San Joaquin River Delta, in particular, may be a limiting factor for juvenile Chinook salmon outmigrating from the San Joaquin River and its tributaries. However, survival and routing monitoring data have not been formally used to inform water management in a decision analytic framework. Here, we illustrate how estimates derived from disjunct monitoring data can be used to inform water management and as a basis for adaptively managing flows. We aggregated a meta-analysis of Chinook salmon smolt survival and routing estimates through the south Delta with other sources of data to develop a survival and routing simulation model to estimate optimal flows for the San Joaquin River during smolt outmigration from February–May. We found that large flow pulses at predictable times during the spring are projected to be optimal for increasing Chinook salmon smolt survival to the San Francisco Bay and that optimal scenarios differed somewhat with water year type. Sensitivity analysis revealed temperature and smolt outmigration timing are driving optimal pulse distribution and that water allocation changes little with parameter uncertainty. This case study highlights the utility of the decision-analytic framework for solving conservation problems." @default.
• W4283395030 created "2022-06-25" @default.
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• W4283395030 date "2022-09-01" @default.
• W4283395030 modified "2023-10-03" @default.
• W4283395030 title "Integrating monitoring and optimization modeling to inform flow decisions for Chinook salmon smolts" @default.
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• W4283395030 doi "https://doi.org/10.1016/j.ecolmodel.2022.110058" @default.
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