Description

Active revegetation is often necessary to recover native plant communities in disturbed or degraded wetlands. However, active revegetation has been pursued less in wetlands than other ecosystems and thus, there are few established best practices to guide implementation. Additionally, wetland revegetation is challenging due to dynamic hydrology becoming more extreme and high invasive plant propagule pressure that can limit native plant recruitment and favor invasive plant dominance. In this study, we tested the efficacy of varied revegetation approaches on native plant reassembly and invasion resistance in semi-arid wetlands subject to seasonal hydrologic fluctuations. In a Great Salt Lake wetland in Utah, USA, we introduced two functionally distinct native plant mixes, varied their method of introduction via seeding or planting, and varied their order of introduction over two planting dates. Unfortunately, there were no treatment effects on native or invasive cover, likely due to extreme drought in the first growing season followed by excessive flooding in the second. However, there were compositional shifts from drought-tolerant to flood-tolerant species throughout the study. Community compositional changes underscore the importance of including species with divergent and wide-ranging environmental tolerances in revegetation mixes to enhance native establishment across variable environmental conditions. This bet-hedging approach runs counter to the current focus on precision restoration—the method prioritizing the “right seed in the right place at the right time”—but may be an effective strategy to combat unpredictable conditions given increasingly extreme site conditions.

Document Type

Dataset

DCMI Type

Dataset

File Format

.zip, .r, .csv, .txt

Publication Date

9-5-2024

Funder

Environmental Protection Agency (EPA)

Utah Agricultural Experiment Station

Publisher

Utah State University

Award Number

EPA 95810500; UAES 9801

Methodology

Raw data and code for the field experiment which occurred July 7, 2022 - Sept 13, 2023.

The "Raw_Data" folder contains unaltered .csv files of biweekly species percent cover and groundwater well water level data collected during the 2022 and 2023 field seasons. There is also a .csv titled Unknown_Tracker_2022 which tracks any unknown plants observed in the field.

The "Processed_Data" folder contains .RData objects of cleaned data which we used for analyses and figures. Cleaned dataframe objects always begin with "DF_" The code used to clean the raw data is also housed in this folder and begins with "Code_". After analysis, we saved model objects here ("Model-Objects_") to then quickly load for contrast tables and model graphs.

The main folder contains analysis code which begins with "Model_" and figure code (graphs and tables) which begins with "Figures_" or "Graphs_".

- Experiment_Random_Assignment is the code used to assign treatments to blocks.

- Function_round is a script for consistent rounding across output tables.

- NMDS is the NMDS and PERMANOVA analysis script.

We use 4-letter plant codes (First two letters of genus, first two letters of species) as shorthand for species names. Species names are described in Table 2 of the manuscript.

Treatments:

C = Control (no seeding or planting).

F = Forb seed mix at initial planting, no follow up planting.

G = Graminoid seed mix at initial planting, no follow up planting.

F&G = Combined forb and graminoid seed mix at initial planting, no follow up planting.

FtG = Forb seed mix at initial planting, graminoid seed mix at secondary planting.

FtGp = Forb seed mix at initial planting, graminoid plug mix at secondary planting.

NtGp = No initial planting, graminoid plug mix at secondary planting.

All data and statistical methods are described in detail in the Manuscript methods section.

Scientfic Names

Phragmites australis

Start Date

7-7-2022

End Date

9-13-2023

Location

Great Salt Lake, Utah, United States

Language

eng

Code Lists

see README

Disciplines

Aquaculture and Fisheries | Plant Sciences

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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