Data for: Modeling the distribution of the endangered Jemez Mountains salamander (Plethodon neomexicanus) in relation to geology, topography, and climate
Cite this dataset
Bartlow, Andrew et al. (2022). Data for: Modeling the distribution of the endangered Jemez Mountains salamander (Plethodon neomexicanus) in relation to geology, topography, and climate [Dataset]. Dryad. https://doi.org/10.5061/dryad.9ghx3ffkz
The Jemez Mountains salamander (Plethodon neomexicanus; hereafter JMS) is an endangered salamander restricted to the Jemez Mountains in north-central New Mexico, United States. This strictly terrestrial species requires moist surface conditions for mating and foraging. Threats to its current habitat include fire suppression and ensuing severe fires, changes in forest composition, habitat fragmentation, and climate change. Forest composition changes resulting from reduced fire frequency and increased tree density suggest that its current aboveground habitat does not mirror its historically successful habitat regime. We hypothesized that geology and topography might play a significant role in the current distribution of the salamander. We modeled the distribution of the JMS using a machine learning algorithm to assess how geology, topography, and climate variables influence its distribution. Our habitat suitability map reveals low uncertainty in model predictions, and we found slight discrepancies between the designated critical habitat and the most suitable areas for the JMS. Because geological features are important to its distribution, we recommend that geological and topographical data are considered, both during survey design and in the description of localities of JMS records once detected.
We used ten variables in our distribution models [geologic: (1) unit classification based on 1:24,000 scale geologic maps produced by New Mexico Bureau of Geology and Mineral Resources, New Mexico Institute of Mining and Technology, (2) distance to the boundary of mapped geologic contacts within the Valles caldera region; topographic: (3) high-resolution elevation (10 m), (4) slope, (5) topographic characterization (i.e., curvature; change in slope, first derivative) from a LiDAR-derived digital elevation model (DEM); climatic: (6) total precipitation in summer, (7) total precipitation in winter, (8) maximum temperature in winter, (9) minimum temperature in winter, (10) minimum temperature in summer].
The climate variables were derived specifically for Southwestern United States from PRISM (PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu, data created 11 Sep 2013, accessed 29 Jul 2019) and are based on climate normals (1981-2010). Climate data that was at 800m resolution, and when needed (50% of our variables were downscaled), was downscaled to match our intended 5 m fine scale for our analysis by following the methodology of Lee et al. (2014).
Lee, T. R., De Wekker, S. F. J., & Wofford, J. E. B. (2014). Downscaling Maximum Temperatures to Subkilometer Resolutions in the Shenandoah National Park of Virginia, USA. Advances in Meteorology, 2014, 1–9. https://doi.org/10.1155/2014/594965
ESRI ArcGIS Pro
QGIS (open-source alternative to ArcGIS)
R with appropriate packages
LANL Seismic Hazards Program, currently under the Associate Laboratory Directorate for Facilities and Operations