Wetland creation and reforestation of legacy surface mines in the Central Applachian Region (USA): A potential climate-adaptation approach for pond-breeding amphibians?

Habitat restoration and creation within human-altered landscapes can buffer the impacts of climate change on wildlife. The Forestry Reclamation Approach (FRA) is a coal surface mine reclamation practice that enhances reforestation through soil decompaction and the planting of native trees. Recently, wetland creation has been coupled with FRA to increase habitat available for wildlife, including amphibians. Our objective was to evaluate the response of pond-breeding amphibians to the FRA by comparing species occupancy, richness, and abundance across two FRA age-classes (2–5-year and 8–1—year reclaimed forests), traditionally reclaimed sites that were left to naturally regenerate after mining, and in mature, unmined forests in the Monongahela National Forest (West Virginia, USA). We found that species richness and occupancy estimates did not differ across treatment types. Spotted Salamanders (Ambystoma maculatum) and Eastern Newts (Notophthalmus viridescens) had the greatest estimated abu..., , , # Wetland creation and reforestation of legacy surface mines in the Central Applachian Region (USA): A potential climate-adaptation approach for pond-breeding amphibians?

[https://doi.org/10.5061/dryad.866t1g1zf](https://doi.org/10.5061/dryad.866t1g1zf)

## Description of the data and file structure

**R script and data used to estimate species’ occupancy, species richness, and abundance of pond-breeding amphibians in wetlands on reforested surface mines in the Monongahela National Forest, WV, USA.**

## **File list (files found within Sherman\_et\_al\_2024.zip)**

**Multi-species Occupancy Model**

            Sherman_et_al_2024_multispecies_occupancy.R

            aame.csv

            amac.csv

            hscu.csv

            hver.csv

            lcla.csv

            lpal.csv

            lsyl.csv

            nvir.csv

            pcru.csv

            SiteCovariates.csv

            SurveyCovariates.csv

**Abundance Model**

            Sherman_et_al_2024_abundance.R

       ...