Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Leveraging Delta Smelt Data for Juvenile Chinook Salmon Monitoring in the San Francisco Estuary

Version 1 : Received: 29 June 2020 / Approved: 30 June 2020 / Online: 30 June 2020 (08:31:48 CEST)
Version 2 : Received: 25 November 2020 / Approved: 26 November 2020 / Online: 26 November 2020 (12:25:53 CET)

A peer-reviewed article of this Preprint also exists.

Mahardja, B.; Mitchell, L.; Beakes, M.; Johnston, C.; Graham, C.; Goertler, P.; Barnard, D.; Castillo, G.; Matthias, B. Leveraging Delta Smelt Monitoring for Detecting Juvenile Chinook Salmon in the San Francisco Estuary. San Francisco Estuary and Watershed Science, 2021, 19. https://doi.org/10.15447/sfews.2021v19iss1art2. Mahardja, B.; Mitchell, L.; Beakes, M.; Johnston, C.; Graham, C.; Goertler, P.; Barnard, D.; Castillo, G.; Matthias, B. Leveraging Delta Smelt Monitoring for Detecting Juvenile Chinook Salmon in the San Francisco Estuary. San Francisco Estuary and Watershed Science, 2021, 19. https://doi.org/10.15447/sfews.2021v19iss1art2.

Abstract

Monitoring is an essential component in ecosystem management and leveraging existing data sources for multiple species of interest can be one effective way to enhance information when making management decisions. Here we analyzed juvenile Chinook Salmon (Oncorhynchus tshawytscha) bycatch data that has been collected by the recently established Enhanced Delta Smelt Monitoring program (EDSM), a survey designed to estimate the abundance and distribution of the San Francisco Estuary (estuary) endemic and endangered Delta Smelt (Hypomesus transpacificus). Two key aspects of the EDSM program distinguish it from other fish surveys in the estuary: a stratified random sampling design and the spatial scale of its sampling effort. We integrated the EDSM dataset with other existing surveys in the estuary and used an occupancy model to assess detection probability differences across gear types. We saw no large-scale differences in size selectivity, and while detection probability varied among gear types, cumulative detection probability for EDSM was comparable to other surveys due to the program’s use of replicate tows. Based on our occupancy model and sampling effort in the estuary during spring of 2017 and 2018, we highlighted under-sampled regions that saw improvements in monitoring coverage due to EDSM. Our analysis also revealed that each sampling method has its own benefits and constraints. Although the use of random sites with replicates as conducted by EDSM can provide more statistically robust abundance estimates relative to traditional methods, the use of fixed stations and simple methods such as beach seine may provide a more cost-effective way of monitoring salmon occurrence at certain regions of the estuary. Stronger inference on salmon abundance and distribution can be made by leveraging the strengths of each survey’s method. Careful consideration of these trade-offs and key monitoring objectives is crucial as the management agencies of the estuary continue to adapt and improve their monitoring programs.

Keywords

Chinook Salmon; monitoring; detection probability

Subject

Biology and Life Sciences, Ecology, Evolution, Behavior and Systematics

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