Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 8626
Title: Abundance and distribution of sturgeon feeding pits in a Washington estuary
Author: Mary L. Moser, Kim Patten, Steve C. Corbett, Blake E. Feist, Steven T. Lindley
Publication Year: 2017
Journal: Environmental Biology of Fishes
Volume: 100
Issue: 5
Pages: 597-609
Keywords: green sturgeon, habitat, foraging, conservation,
Abstract:

Sturgeon diet and feeding habitats are notoriously difficult to document.  We mapped the locations of feeding pits in Willapa Bay, Washington, to characterize estuarine habitats used by sub-adult and adult sturgeon for infaunal feeding.  Monthly summer surveys of intertidal plots revealed that feeding pit density was highest in July and August, when sturgeon occupy Willapa Bay.  The ephemeral nature of feeding pits and high daily densities (> 1000 pits/ha) indicated intensive sturgeon feeding over unvegetated littoral mud flats during high tide.  Feeding pit density was lowest in subtidal areas, over sand (grain sizes primarily >63 μ), and at sites with dense stands of non-indigenous seagrass, Zostera japonica.  Sub-adult and adult sturgeon apparently used these habitats significantly less than would be predicted based on their availability.  Feeding pit formation was negatively correlated with Z. japonica shoot dry weight and positively correlated with the abundance of thalassinid shrimp burrows.  Experimental removal of Z. japonica resulted in increased sturgeon feeding, but experimental removal of burrowing shrimp did not significantly affect feeding pit formation.  Aquaculture activities that harden substrate and proliferation of invasive seagrass both appear to produce estuarine substrates that are unsuitable for benthic feeding by sturgeon.

URL1: The next link will exit from NWFSC web site http://dx.doi.org/10.1007/s10641-017-0589-y
Theme: Recovery and rebuilding of marine and coastal species
Foci: Develop methods to use physiological, biological and behavioral information to predict population-level processes.