Aim of the study: Use LiDAR-derived vegetation and terrain characteristics to develop abundance and occupancy predictions for two terrestrial salamander species, Plethodon glutinosus and P. kentucki, and map abundance to identify vegetation and terrain characteristics affecting their distribution.
Area of study: The 1,550-ha Clemons Fork watershed, part of the University of Kentucky’s Robinson Forest in southeastern Kentucky, USA.
Materials and methods: We quantified the abundance of salamanders using 45 field transects, which were visited three times, placed across varying soil moisture and canopy cover conditions. We created several LiDAR-derived vegetation and terrain layers and used these layers as covariates in zero-inflated Poisson models to predict salamander abundance. Model output was used to map abundance for each species across the study area.
Main results: From the184 salamanders observed, 63 and 99 were identifdied as P. glutinosus and P. kentucki, respectively. LiDAR-derived vegetation height variation and flow accumulation were best predictors of P. glutinosus abundance while canopy cover predicted better the abundance of P. kentucki. Plethodon glutinosus was predicted to be more abundant in sites under dense, closed-canopy cover near streams (2.9 individuals per m2) while P. kentucki was predicted to be found across the study sites except in areas with no vegetation (0.58 individuals per m2).
Research highlight: Although models estimates are within the range of values reported by other studies, we envision their application to map abundance across the landscape to help understand vegetation and terrain characteristics influencing salamander distribution and aid future sampling and management efforts.
Digital Object Identifier (DOI)
National Institute of Food and Agriculture, U.S. Department of Agriculture, McIntire-Stennis
Project/Grant: KY009026 under accession 1001477
Contreras, Marco Antonio; Staats, Wesley A.; and Price, Steve J., "Predicting and Mapping Plethodontid Salamander Abundance Using LiDAR-Derived Terrain and Vegetation Characteristics" (2020). Forestry and Natural Resources Faculty Publications. 44.