Data from: Establishing the plant component of a tallgrass prairie restoration using a remnant reference ecosystem model: A case study
Data files
Jan 29, 2025 version files 118.56 KB
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cover_data_2018_2020.xlsx
7.60 KB
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INAI_Meander_search_data_2021.xlsx
10.16 KB
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Plot_Sampling_data_2021.xlsx
84.05 KB
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Plot_sampling_data_name_codes.xlsx
13.22 KB
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README.md
3.53 KB
Abstract
Ecological restoration practitioners have debated the use of ecosystem models and reference ecosystems as targets for achieving ecological restoration of plant communities.
We used an ecosystem model based on nine tallgrass prairie remnants to establish high-quality tallgrass prairie plots in a prairie restoration. We planted 82 of the most common remnant species in five 10 x10m plots, aiming to replicate remnant-like species composition.
Most species required one to two planting attempts and established within the first few years. Within 5 years, we had planted all 82 species, most persisted and were still present in year six.
The most common clonal species in remnants became most abundant and spread rapidly beyond plot boundaries, while individual plot richness (64-72 species, mean 68.8) was similar to that found in remnant reference ecosystems (82 species).
Practical implication: Our results demonstrate the feasibility of replicating the plant composition and species richness of mesic prairie remnants through ecological restoration.
README: Data from: Establishing the plant component of a tallgrass prairie restoration using a remnant reference ecosystem model: A case study
https://doi.org/10.5061/dryad.vdncjsz4s
Description of the data and file structure
As part of a larger experiment examining additional questions, we randomized fifteen 10 x10m plots in a 3x5 plot grid within the 117-hectare restoration area. We randomly assigned each plot one of three species assemblages, with five replicate plots per assemblage. We focus here only on the establishment and analysis of the five plots planted with the INAI model assemblage.
All data were collected between 2018 and 2021. We made annual visual percent cover estimates of all species >1% (1m2) in the plots from 2018–2021. In 2021, we made cover estimates of all species in five randomly located, nested 0.25m2 and 1m2 quadrats (a 0.25m2 quadrat nested in the center of each 1.0 m2 quadrat) in each 10 x 10m plot. Total plot richness was determined using meander searches throughout the growing season.
Files and variables
File: INAI_Meander_search_data_2021.xlsx
Description: an excel spreadsheet that contains records of species found during meander searches in 2021.
INAI Remnant species | List of all species found |
---|---|
2 | presence of species found in plot 2 |
5 | presence of species found in plot 5 |
8 | presence of species found in plot 8 |
9 | presence of species found in plot 9 |
13 | presence of species found in plot 13 |
Sum | total number of plots each species was found in |
File: Plot_Sampling_data_2021.xlsx
Description: an excel spreadsheet that contains cover estimates of all species from 5 nested 0.25m and 1.0 m sq quadrats. There are separate tabs for each quadrat size. Missing data are represented by "?"
Variables
- plot - indicates which of the 5 plots sampled
- sample - indicates which of the 5 samples taken in each plot
- the remaining columns contain species name codes with cover of each; full names are found in the file: plot sampling data name codes
File: cover_data_2018_2020.xlsx
Description: an excel spreadsheet that contains records of species with visual estimates of cover greater than or equal to 1%.
Variables
variable | |
---|---|
Species | List of species |
year | Year 2018 or 2020 |
1 | Plot 1 of 15 |
2 | Plot 2 of 15 |
3 | Plot 3 of 15 |
4 | Plot 4 of 15 |
5 | Plot 5 of 15 |
6 | Plot 6 of 15 |
7 | Plot 7 of 15 |
8 | Plot 8 of 15 |
9 | Plot 9 of 15 |
10 | Plot 10 of 15 |
11 | Plot 11 of 15 |
12 | Plot 12 of 15 |
13 | Plot 13 of 15 |
14 | Plot 14 of 15 |
15 | Plot 15 of 15 |
* File: plot_sampling_data_name_codes.xlsx
Description: an excel spreadsheet that contains records of species with visual estimates of cover greater than or equal to 1%.
##### Variables
* Code - code for species name in other files
* Species name - binomial name for each code
Methods
All data were collected between 2018 and 2021. We made annual visual percent cover estimates of all species >1% (1m2) in the plots from 2018–2021, after the initial abundance of agricultural weeds declined and we could accurately estimate which species had become established. We defined an “established” species as one that was observed in 2018 and still present in 2021. In 2021, we made cover estimates of all species in five randomly located, nested 0.25m2 and 1m2 quadrats (a 0.25m2 quadrat nested in the center of each 1.0 m2 quadrat) in each 10 x 10m plot. Total plot richness was determined using meander searches throughout the growing season. To assess dispersal and spread beyond plot boundaries, we conducted meander searches in the buffer around each plot for the 42 species planted in the INAI plots but not in the buffer, and recorded the maximum distance that species unique to the INAI model seed mix had spread from each side of each plot.
Statistical analysis
We compiled species richness data at four scales: 0.25m2, 1m2, five 0.25m2 quadrats combined, and entire plots (100m2), comparing them to our INAI model. For 0.25m2 and 1m2 quadrats, species richness counts were made from the five quadrat cover estimates in each plot. We used the five nested quadrat samples from each plot to calculate cumulative species richness at the 0.25m2 and 1m2 scales. We used cumulative 0.25 m2 instead of m2 because INAI data use 0.25m2, making it more comparable. To do so, we combined the species from the five quadrat samples into a single list. The INAI data consist of 20-0.25m2 quadrats so we used a rarefaction curve to estimate richness for 5-0.25m2 quadrats. The rarefaction curve is a species-accumulation curve using all possible combinations of the 20-0.25m2 quadrats and resulting confidence intervals.