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Alternate Title

Vegetation Species and Bed Position May Drive Epifaunal but Not Nekton Habitat Use in Estuarine Submerged Vegetation in the Mobile-Tensaw River Delta, Alabama

Document Type

Short Communication

Abstract

Submerged aquatic vegetation (SAV) supports nekton and epifaunal macroinvertebrates, sustaining food webs and ecosystem services in estuaries. Invasive SAV can alter these dynamics by displacing native vegetation and restructuring communities. In Alabama’s Mobile-Tensaw Delta (MTD), Eurasian milfoil (Myriophyllum spicatum) is rapidly expanding and outcompeting native wild celery (Vallisneria americana). This study compared nekton and epifaunal assemblages between M. spicatum and V. americana, focusing on edge versus interior positions. Nekton surveys identified 22 taxa (M. spicatum, n = 13; V. americana, n = 18), but assemblages did not differ significantly between vegetation types nor positions, suggesting motile nekton use both habitats similarly. Seventeen epifauna taxa were identified (M. spicatum, n = 16; V. americana, n = 14), with these communities showing strong vegetation-specific and some within-bed spatial patterns. A significant interaction between habitat and position revealed differences between M. spicatum and V. americana, with edge-interior contrasts occurring only in M. spicatum, potentially due to higher structural complexity of this SAV. Moreover, M. spicatum supported higher epifaunal richness, abundance, and diversity; SIMPER analyses showed that ~72% of the community differences between V. americana and M. spicatum were driven by Gammarus sp., Chironomus sp., damselfly taxa, and Hydrobiidae. Similarly, the same 4 taxa explained ~72% of the edge–interior dissimilarity, with all but Hydrobiidae more abundant at edges. Overall, nekton assemblages appear resilient across SAV types, while epifaunal communities respond strongly to vegetation structure and bed position, highlighting potential shifts in some community dynamics with expansion of invasive SAV.

First Page

SC16

Last Page

SC22

Supplemental Table S1 for Vroljik et al.pdf (89 kB)
Supplemental Table S1

Supplemental Table S4 for Vroljik et al.pdf (166 kB)
Supplemental Table S4

Supplemental Table S5 for Vroljik et al.pdf (166 kB)
Supplemental Table S5

Supplemental Table S3 for Vroljik et al.pdf (146 kB)
Supplemental Table S3

Supplemental Table S2 for Vroljik et al.pdf (101 kB)
Supplemental Table S2

Supplemental Figure S1 for Vroljik et al.pdf (252 kB)
Supplemental Figure S1

Artificial Intelligence (AI) Use Statement

No artificial intelligence (AI) was used in the preparation of this manuscript

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