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   https://doi.org/10.1130/GES01523.1  

   Aslan, Andres; Boraas-Connors, Marisa; Sprinkel, Douglas A.; Becker, Thomas P.; Lynds, Ranie; Karlstrom, Karl E.; Heizler, Matt (November 2017, Geosphere)
2. alex-pk-sir/Siren---White-Mountains-Snowpack-Data: Siren---White-Mountains-Snowpack-Data

   https://doi.org/10.5281/zenodo.15306801  

   Siren, APK; Perkins, PJ; Kilborn, JR; Kanter, JJ; Sutherland, CS (April 2025, Zenodo)

   Snow depth and penetrability data from the northern White Mountains of New Hampshire (winter 2011-2012). -Overview of data and code - Data and associated code for modeling and plotting data from snowpack stations (n = 30) used for evaluating the influence of dormant season canopy cover (DSCC) on snow depth and penetrability during the 2011-2012 season. Stations were surveyed on 6 occasions over the course of the winter as part of a study to evaluate carnivore habitat use (Siren et al. 2017). -Response variables include snow depth and snow penetrability found in the depth.csv and penetrability.csv files. -Snow depth and penetrability measurements were taken 5 times at each site during each survey using a "research grade" avalanche probe and penetrometer, respectively. -There were 30 snow stations, 8 in open DSCC sites (Open), 4 in low DSCC sites (Low), 8 in medium DSCC sites (Medium), and 10 in high DSCC sites (High). These classifications were made using proprietary stand data provided by the landowners. For more information, please seen Siren et al. (2017). -The code was written using R Statistical software. Base R functions were used for linear modeling (lm) and the lubridate and ggplot2 packages were used for converting dates and plotting model predictions, respectively. -Structure of files - The nomenclature of the .csv files are the same for both .csv files. They are as follows: -site: name of the snow station -date: date of the snow survey -elevation: elevation of the snow station -canopy_density: DSCC classification of the site -elevation_s: scaled and centered elevation -date_f: categorical variable (created in R) -Structure of code - See R code. -Description of data collection and original use - Data were collected by Alexej Siren and originally used for evaluating the influence of snow on carnivores: Sirén, A. P. K., P. J. Pekins, J. R. Kilborn, J. J. Kanter, and C. S. Sutherland. 2017. Potential influence of high-elevation wind farms on carnivore mobility. Journal of Wildlife Management 81:1–8. https://doi.org/10.1002/jwmg.21317 

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3. Crustal structure across northern New England and the Adirondack Mountains

   Bourke, J; Link, F; Long, M; Webb, LE; Karabinos, P (January 2024, Geological Society of America Abstracts with Programs)
4. Vegetation structural complexity and biodiversity in the Great Smoky Mountains

   https://doi.org/10.1002/ecs2.3390  

   Walter, Jonathan A.; Stovall, Atticus E. L.; Atkins, Jeff W. (March 2021, Ecosphere)

   Abstract Vegetation structural complexity and biodiversity tend to be positively correlated, but understanding of this relationship is limited in part by structural metrics tending to quantify only horizontal or vertical variation, and that do not reflect internal structure. We developed new metrics for quantifying internal vegetation structural complexity using terrestrial LiDAR scanning and applied them to 12 NEON forest plots across an elevational gradient in Great Smoky Mountains National Park, USA. We asked (1) How do our newly developed structure metrics compare to traditional metrics? (2) How does forest structure vary with elevation in a high‐biodiversity, high topographic complexity region? (3) How do forest structural metrics vary in the strength of their relationships with vascular plant biodiversity? Our new measures of canopy density (Depth) and structural complexity (σDepth), and their canopy height‐normalized counterparts, were sensitive to structural variations and effectively summarized horizontal and vertical dimensions of structural complexity. Forest structure varied widely across plots spanning the elevational range of GRSM, with taller, more structurally complex forests at lower elevation. Vascular plant biodiversity was negatively correlated with elevation and more strongly positively correlated with vegetation structure variables. The strong correlations we observed between canopy structural complexity and biodiversity suggest that structural complexity metrics could be used to assay plant biodiversity over large areas in concert with airborne and spaceborne platforms. 

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5. CRUSTAL STRUCTURE ACROSS NORTHERN NEW ENGLAND AND THE ADIRONDACK MOUNTAINS

   https://doi.org/10.1130/abs/2024NE-397651  

   Bourke, James; Link, Frederik; Long, Maureen; Webb, Laura; Karabinos, Paul (March 2024, Geological Society of America)