DOI: 10.5061/dryad.31zcrjdwr
title: Evaluating restoration success using metric-based indicators of ecosystem recovery in tidal marshes along the northern Gulf of Mexico

description: Habitat restoration is commonly used to recover ecosystem services, but
 due to resource constraints, post-project monitoring often fails to fully
 evaluate the recovery of important ecosystem functions. Metric-based
 indicators use simple-to-measure variables to assess ecosystem health and
 function, thereby providing a time- and cost-effective method to improve
 monitoring. We used a tidal marsh dataset to develop metric-based
 indicators of ecosystem recovery. In 2021 and 2022, we surveyed eight
 restored/created and three natural reference tidal marshes in the northern
 Gulf of Mexico to assess recovery of ecosystem attributes [e.g., above-
 and below-ground biomass, soil organic matter (SOM), and sediment total
 carbon (C) and total nitrogen (N)]. To determine what combinations of
 variables best predicted recovery, we split our data into model training
 and testing datasets, used backward model selection, and then created and
 tested a metric-based indicator of ecosystem recovery. Recovery of plant
 above- and below-ground biomass and sediment structure (i.e., SOM, C, and
 N)—important measures of wetland carbon sink capacity and biogeochemical
 functioning—could be predicted through a combination of simpler-to-measure
 variables, such as time since restoration, percent plant cover, and
 sediment bulk density. The indicator constructed from these relationships
 was highly effective in predicting the development of ecosystem attributes
 (r = 0.85, p &lt; 0.001). This indicator approach provides an
 effective but simple method to assess the recovery of ecosystem attributes
 in tidal marshes, and it can be used to develop similar indicators in
 other ecosystems. By overcoming resource constraints of post-project
 monitoring, metric-based indicators of ecosystem recovery may serve as a
 key strategy to improve restoration outcomes.
description: <strong>Study Sites</strong><br><br>We
 conducted our study in the summer of 2021 and 2022 in three references
 (i.e., natural) and eight restored/created tidal marshes along the
 Mississippi-Alabama coast of the northern Gulf of Mexico, USA. In
 Mississippi, sites included two restored marshes (Deer Island-1 and Deer
 Island-2) located on Biloxi Bay and one restored marsh (Greenwood Island)
 on the Mississippi Sound. One natural (Fowl River Natural) and three
 created (Fowl River CON-0, CON-1, and CON-2) marshes were clustered along
 the West Fowl River on the western shore of Mobile Bay in Alabama. Helen
 Wood Park, a restored brackish marsh near the mouth of Dog River, was also
 located on the western shore of Mobile Bay. Two natural marshes (Pelican
 Point and Weeks Bay Natural) were in Weeks Bay on the eastern shore of
 Mobile Bay in Alabama. The final restored marsh was along Perdido Bay in
 Perdido Beach, Alabama.<br><br>The eight restored marshes
 varied in age (7-34 years since restoration), salinity, and vegetation and
 were developed using three distinct restoration strategies—habitat
 conversion, living shoreline, and beneficial use. Fowl River CON-0, CON-1,
 and CON-2 were created by converting coastal pine savanna to tidal marsh
 in 1987-1988 to mitigate impacts from a coal and grain facility by
 harvesting pine trees, excavating topsoil to an elevation of +0.3 m
 (NAVD88) relative to mean sea level, digging canals to create hydrological
 connectivity, and planting with <em>Spartina alterniflora
 </em>and <em>Juncus roemerianus</em>. The areas sampled
 in these three restored sites are now dominated by <em>J.
 roemerianus, </em>with a narrow fringe of <em>S. alterniflora
 </em>along the shoreline of the tidal canal<em>.
 </em>Helen Wood Park and Perdido Beach are living shorelines that
 are under 20 years old and were planted with and currently dominated by,
 <em>S. alterniflora</em> and <em>J.
 roemerianus</em>, respectively. Finally, Deer Island 1, Deer Island
 2, and Greenwood Island were created 7-19 years ago by the Mississippi
 Department of Marine Resources by re-establishing the marsh platform
 through the beneficial use of dredged sediments and allowing <em>S.
 alterniflora </em>or <em>S. patens</em> to naturally
 colonize the sites.<br><br>Each of the restored sites was
 paired apriori with one of the three reference sites based on a
 combination of factors, including geographic proximity (preferably within
 the same watershed), site salinity, and vegetation. When marshes in close
 proximity were not a good match in terms of other site characteristics, we
 paired sites with similar vegetation, as with Deer Island 1 and Weeks Bay
 Natural. When designating these site pairings, we also considered the
 original restoration objectives and management targets, as they could
 affect the potential for ecosystem attribute recovery. For example, we
 opted to pair Fowl River Natural with Fowl River CON-0, CON-1, and CON-2,
 as opposed to other sites that also had similar vegetation and salinity,
 because it served as the original reference for these constructed
 marshes.<br><br><strong>Data
 Collection</strong><br><br>In May 2021, we randomly
 established three sampling locations within the dominant plant zone of
 each site. We collected data during peak growing season (July-August) in
 2021 and 2022 from 1 m<sup>2</sup> quadrats, which were
 positioned on opposite sides between years to minimize disturbance and
 avoid resampling the same area. We surveyed areal plant cover within each
 1 m<sup>2</sup> plot (n = 3 plots/marsh) to characterize plant
 community composition. All plants were identified to species level;
 vouchers for plants that could not be identified on-site were collected
 from outside the plots for subsequent identification. Once cover was
 estimated, standing aboveground biomass was collected from a randomly
 placed 0.25 m<sup>2</sup> subplot within the 1
 m<sup>2</sup> plots. Plants were clipped at the sediment
 surface, bagged, and transported to the lab, where they were sorted into
 live components by species and total dead and then dried at 60 °C to a
 constant mass (g m<sup>-2</sup>). We collected a sediment core
 (7.9 cm diameter x 20 cm deep) from within the clipped subplot to assess
 belowground biomass. This core was sub-sectioned in the field into two,
 10-cm sections. In the lab, subsections were rinsed over a sieve (1 mm),
 and biomass was sorted by size class (i.e., fine, coarse, rhizome) and
 dried at 60 °C to a constant mass (g m<sup>-2</sup> by depth
 interval). Roots were not sorted by species; therefore, samples represent
 community belowground biomass. We did not harvest above- and below-ground
 biomass at the Perdido Beach living shoreline site, as is it small and we
 wanted to minimize disturbance. For the indicator, we only included
 biomass data from the upper 0-10 cm because we were unable to physically
 core the full 20 cm interval at several of the restored sites.
 To quantify sediment attributes [i.e., soil organic matter (SOM),
 sediment total carbon (C), and nitrogen (N)], we collected duplicate
 sediment cores (5 cm diameter x 20 cm depth) from each plot. Cores were
 sub-sectioned in the field into four, 5-cm sections. In the lab,
 subsections were dried at 60 °C to a constant mass to calculate the bulk
 density (g cm<sup>-3</sup>). Homogenized subsamples were
 combusted in a muffle furnace at 550 °C for six hours to determine % SOM
 by loss-on-ignition. Another set of subsamples was analyzed for C and N
 content at the Alabama Stable Isotope Laboratory. For data analysis, we
 obtained mean sediment attribute values for bulk density, SOM, C, and N
 from each plot per year. As with belowground biomass, we only included
 data from the upper 0-10 cm (i.e., 0-5 and 5-10 cm intervals) because we
 were unable to physically core the full 20 cm interval at several of the
 restored sites. To characterize pH and salinity, commonly measured site
 characteristics that can influence or reflect variation in plant community
 structure, productivity, and biogeochemistry, porewater was extracted from
 each plot at 15 cm below the sediment surface using sipper tubes. We
 measured pH and salinity using a Thermo Orion Star pH meter (model A211)
 and a YSI conductivity and salinity instrument (model 3100),
 respectively.<br><br><strong>Model Selection
 Procedure</strong> We first classified response
 variables as either proxies or ecosystem attributes. Proxies, like site
 age, plant percent cover, bulk density, pH, and salinity, represent
 simpler-to-measure components that, alone or in combination with other
 proxies, can explain or predict some broader, often harder-to-measure,
 attribute. In this case, proxies represented variables that can be
 measured at lower costs than many attributes and from relatively small
 areas to minimize disturbance to, and negative impacts of research
 activities on, recently restored habitats. Ecosystem attributes represent
 factors and functions critical to succession from young (e.g., restored)
 to mature (e.g., references) ecosystems, and that may be either driven by
 or correlated with proxies. For tidal wetlands, examples include above-
 and below-ground standing biomass, SOM, and sediment C and N, which should
 increase with increasing time since restoration. Specifically, we expect
 plant biomass, SOM, and sediment C and N to increase with increasing site
 age, as plant cover increases, and as bulk density decreases, reflecting
 the accumulation of plant biomass, root penetration into the sediments,
 and greater inputs of plant-derived OM, C, and N to the sediments.
 Additionally, because plant structure and productivity have been shown to
 be influenced by, or correspond to, changes in porewater chemistry,
 variation in site characteristics like salinity or pH may also be useful
 predictors of ecosystem attributes. Thus, proxies represent the easier,
 cheaper, and less destructive variables to measure that are also related
 to, or predictive of, other more difficult, expensive, or destructive
 variables to measure (i.e., attributes).  <br><br>We paired
 each restored marsh with a reference based on a combination of geographic
 proximity, plant community composition, and salinity, as described above.
 Using the site pairings, we calculated response ratios for each plot (n =
 3) in every restored marsh using the following equation:
 RR<sub>A</sub> =
 (A<sub>R</sub>/A<sub>N</sub>)
 where RR<sub>A</sub> is the response ratio of an
 ecosystem attribute in a restored site relative to its reference,
 A<sub>R</sub> is the value of the attribute calculated at the
 plot level in the restored marsh and is the value of the attribute
 calculated at the paired reference marsh. This ratio indicates the
 recovery trajectory of a given attribute for a restored site relative to
 its reference. A response ratio &lt; 1 indicates that the restored
 marsh is underperforming relative to its reference, while a response ratio
 ≥ 1 indicates that the restored marsh is at least equivalent to the
 reference. We set the maximum response ratio value to 1, which gave all
 attributes a shared range of values and facilitated indicator development
 (described below). In a few instances, restored marshes did outperform
 their reference (RR &gt; 1), as detailed in the published
 dataset. Data from 2021 and 2022 were used to develop
 two separate datasets (training and testing). The training dataset
 included two randomly selected plots per site from both years (66% of the
 total data, n = 30). The testing dataset included the remaining plot from
 each site per year (n =15) and was withheld from indicator development for
 subsequent validation (described below). Specifically, we used least
 squares regressions with backward model selection, with p-value as the
 stepping method criteria, to determine what combinations of proxies (i.e.,
 explanatory covariates) were most strongly correlated with restoration
 response ratios for the ecosystem attributes. The top model for each
 attribute was then used to generate metrics for the
 indicator.<br><br><strong>Indicator Creation and
 Validation</strong> To create metrics for
 ecosystem attributes, we followed previously established approaches for
 IBIs using calculated metrics relating proxies to attribute recovery.
 First, we created scatterplots relating each proxy (x-axis) to its
 response ratio (y-axis). Second, we split our gradient of interest
 (y-axis) into three even groups. Third, we “binned” points along the
 x-axis intentionally so that most points within each bin fell within the
 group. Lastly, we assigned a “score” to each bin, the bounds of which
 constituted the scoring range for each metric. Specifically, we used the
 absolute value of each proxy’s standardized coefficients to determine the
 proportional contribution of each proxy to the overall metric. This
 proportion was used as the total possible score value for a given proxy
 within the metric, with a maximum score of 1 for a given metric. For
 example, if a metric included both site age and percent plant cover as
 proxies and the absolute values of their standardized coefficients within
 the top model were 0.75 and 0.25, respectively, then the maximum score
 value for site age within the metric would be 0.75 and the maximum score
 value for percent plant cover within the metric would be 0.25, for a
 possible maximum metric of 1. This process was repeated for all proxies in
 each top model for every ecosystem attribute to create the indicator. To
 predict recovery, the indicator was then applied to the testing dataset,
 and recovery scores were compared to the combined restoration ratios to
 determine effectiveness using Pearson correlations. We opted to use
 univariate models for this process because they are simpler to construct
 and interpret, thereby facilitating use by diverse audiences with varying
 levels of expertise, and they allow for a more straightforward binning
 approach, which provides a simple scoring system to describe the relative
 recovery of a restored site.
description: # Evaluating restoration success using metric-based indicators of
 ecosystem recovery in tidal marshes along the northern Gulf of Mexico
 [https://doi.org/10.5061/dryad.31zcrjdwr](https://doi.org/10.5061/dryad.31zcrjdwr) ## Description of the data and file structure The data is provided as a csv file.  --- ### Files and variables #### File: Dybiecetal\_responseratios\_06072024.csv **Description:** Full dataset including the raw data collected from the processing of plant and soil samples as well as the calculated recovery ratios for each ecosystem attribute. NA represents samples that were unable to be collected due to limited porewater, highly compacted sediments (preventing deep-sediment coring), or agreements with landowners to minimize site impacts.  ##### Variables * site: The site code associated with the tidal marsh where the samples were collected * year: The year (2021 or 2022) in which the data was collected * replicate: The individual replicate associated with the data at each site (all sites had three replicates; A, B, C).  * dataset: The dataset (train or test) to which the data were assigned for the model selection procedure.  * age: The time since restoration (in years) for each restored or created site at the time of sampling.  * type: The type of marsh (restored or reference) that all study sites were assigned; all marshes for which response ratios were calculated were of the restored type.  * cover_%: The total aerial percent cover of the emergent vascular plant community.  * bd_0-5_g/cm3: Bulk density of sediments in g/cm3 collected from a depth of 0-5 cm.  * bd_5-10_g/cm3: Bulk density of sediments in g/cm3 collected from a depth of 5-10 cm.  * salinity: Porewater salinity in parts per thousand (ppt) collected from a depth of 15 cm.  * pH: Porewater pH collected from a depth of 15 cm.  * om_0-5_%: Percent organic matter of sediments collected from a depth of 0-5 cm.  * om_5-10_%: Percent organic matter of sediments collected from a depth of 5-10 cm.  * abg_g: Aboveground plant biomass in g per plot.  * bgb_0-10_g: Belowground plant biomass in g per core collected from a depth of 0-10 cm.  * n_0-5_%: Percent total nitrogen of sediments collected from a depth of 0-5 cm.  * n_5-10_%: Percent total nitrogen of sediments collected from a depth of 5-10 cm.  * c_0-5_%: Percent total carbon of sediments collected from a depth of 0-5 cm.  * c_5-10_%: Percent total carbon of sediments collected from a depth of 5-10 cm.  * om_0-5_rr: Recovery response ratio (rr) calculated for organic matter collected from a depth of 0-5 cm.  * om_5-10_rr: Recovery response ratio (rr) calculated for organic matter collected from a depth of 5-10 cm.  * abg_rr: Recovery response ratio (rr) calculated for aboveground biomass.  * bgb_0-10_rr: Recovery response ratio (rr) calculated for belowground biomass collected from a depth of 0-10 cm. * n_0-5_rr: Recovery response ratio (rr) calculated for total nitrogen collected from a depth of 0-5 cm.  * n_5-10_rr: Recovery response ratio (rr) calculated for total nitrogen collected from a depth of 5-10 cm.  * c_0-5_rr: Recovery response ratio (rr) calculated for total carbon collected from a depth of 0-5 cm.  * c_5-10_rr: Recovery response ratio (rr) calculated for total carbon collected from a depth of 5-10 cm.  * NA: cells containing NA represent samples unable to be collected for various purposes (e.g., porewater samples could not be collected) ## Code/software NA ## Access information NA