DOI: 10.5061/dryad.sj3tx96gn
title: Data from: Wetland restoration: Predicting vegetation trajectories over 25 years

description: Worldwide wetland loss has made the conservation of these ecosystems a
 policy priority and led to the multiplication of restoration programs.
 However, the lack of long-term monitoring limits our understanding of the
 processes influencing the vegetation composition of restored wetlands and
 our ability to predict outcomes over multiple decades. Here, we assessed
 the extent to which hydrological regime and planting density of target
 species, two critical factors driving wetland vegetation and restoration
 success, can predict restoration outcomes. Using correlation analyses and
 generalized models, we assessed the role of target species planting
 density and analogous hydrological conditions (e.g. level, variation,
 seasonality) to reference wetlands for achieving and predicting restored
 vegetation similarity to reference plant communities in 12 sedge and/or
 willow dominated wetlands in Mountain Village, Colorado over 25 years
 post-restoration. We found a significant positive correlation between
 hydrological similarity and vegetation similarity, peaking at 15 years
 post-restoration (rho = 0.61). Similarly, planting density was positively
 correlated with vegetation similarity, peaking 5 years after restoration
 (rho = 0.75). For both variables, communities with the shallowest water
 table exhibited the strongest correlations. The similarity of restored
 vegetation to the reference community can be predicted using hydrological
 similarity and planting density. The models that combined these two
 variables outperformed single-variable models. However, the model accuracy
 decreased 25 years after restoration, making predictions over two decades
 inaccurate for most communities. Synthesis and applications: Hydrological
 similarity to a reference, combined with appropriate planting densities,
 reliably predicts restored wetland vegetation convergence towards
 reference communities over two-decades. Such models could provide managers
 with tools to assess failure risks across potential restoration sites,
 allowing them to select the most suitable locations and tailor planting
 efforts to maximize wetland restoration success.
description: ## **Wetland restoration: Predicting vegetation trajectories over 25
 years** **Dataset DOI**: 10.5061/dryad.sj3tx96gn\ **Associated article**:
 JAPPL-2024-01003.R2 (in press) ### **Description of the data and file
 structure** This dataset was collected to assess the long-term effects of
 hydrological restoration on wetland plant community trajectories over a
 25-year period. It includes both raw and derived data from a monitoring
 program conducted near Telluride, Colorado (USA). The dataset supports the
 analyses presented in the article *Wetland restoration: Predicting
 vegetation trajectories over 25 years*. ### **Content** The dataset
 includes: * **Vegetation data** from permanent plots surveyed in 2003,
 2013, and 2023, recording species presence and cover. * **Groundwater
 table depth measurements** taken weekly during the 2000 growing season. *
 **A synthesis table** aggregating derived indicators (e.g. Vegetation
 similarity, hydrological similarity, ...) ### **Files and variables** ####
 **File: `DATA_SYNTHESIS.csv`** Final synthesis including hydrological and
 vegetation similarity metrics for selected plots. **Variables:** * `PLOT`:
 Unique identifier for each restoration plot * `REFERENCE`: Reference plant
 community used for comparison * `TARGET`: Target community aimed for
 restoration * `X`: X coordinate of the plot (WGS84) * `Y`: Y coordinate of
 the plot (WGS84) * `VEG_SIM`: Vegetation similarity to the selected
 reference * `HYDRO_SIM`: Hydrological similarity to the selected reference
 * `PLANT_DENS`: Average planting density (m²) of target species within the
 plot * `MONIT_YEAR`: Year of vegetation monitoring * `MEAN_WATER_TABLE`:
 Mean water table depth (cm), May–August 2000 * `VAR_WATER_TABLE`: Mean
 variation in water table depth (cm), May–August 2000 --- #### **File:
 `HYDROLOGY_2001.csv`** Weekly groundwater level measurements during the
 2000 growing season. **Variables:** * `PLOT_ID`: Unique identifier for
 each restoration or reference plot * `HOW_ID`: Identifier for each
 monitoring well * `TYPE`: Plot type — Reference (`REF`) or Restoration
 (`REST`) * `TARGET`: Target community aimed for restoration * `Date` (e.g.
 `30/04/2000`): Water table depth (cm) on each date --- #### **File:
 `VEGETATION_2003.csv`** Raw vegetation survey data from 2003 (restored and
 reference plots). #### **File: `VEGETATION_2013.csv`** Raw vegetation
 survey data from 2013 (restored and reference plots). #### **File:
 `VEGETATION_2023.csv`** Raw vegetation survey data from 2023 (restored and
 reference plots). **Variables (shared across all vegetation files):** *
 `PLOT_ID`: Unique identifier for each plot * `HOW_ID`: Identifier for each
 monitoring well * `TYPE`: Plot type — Reference (`REF`) or Restoration
 (`REST`) * `TARGET`: Target community aimed for restoration * `[Species
 codes]` (e.g. `Achi_mill`): Percentage cover of each species (The file
 'PLANT_SPECIES_LIST.csv' provide the translation of each
 abbreviation) - `Date` (e.g. `30/04/2000`): Water table depth (cm) on each
 date --- #### **File: `PLANT_SPECIES_LIST.csv`** Correspondance between
 scientific name of the plant species and abbreviations used in the
 dataset. **Variables :** * `Abbrevation`: Shortened scientific names of
 species identified within the study area * `Scientific name`: Full
 scientific names of species identified within the study area ###
 **Code/software** All data are provided in `.csv` format, compatible with
 standard software such as R, Python, Excel, and LibreOffice. The R
 packages and methods used for data analysis and visualization are
 described in the associated manuscript
 : [https://doi.org/10.1111/1365-2664.70106](https://doi.org/10.1111/1365-2664.70106) The analytical code used to process and analyze the data is proprietary to Biotope and INRAE and is not publicly available.