DOI: 10.5061/dryad.5tb2rbpb3
title: Application of LiDAR to assess the habitat selection of an endangered small mammal in an estuarine wetland environment

description: Light detection and ranging (lidar) has emerged as a valuable tool for
 examining the fine-scale characteristics of vegetation. However, lidar is
 rarely used to examine coastal wetland vegetation or the habitat selection
 of small mammals. Extensive anthropogenic modification has threatened the
 endemic species in the estuarine wetlands of the California coast, such as
 the endangered salt marsh harvest mouse (Reithrodontomys raviventris;
 SMHM). A better understanding of SMHM habitat selection could help
 managers better protect this species. We assessed the ability of airborne
 topographic lidar imagery in measuring the vegetation structure of SMHM
 habitats in a coastal wetland with a narrow range of vegetation heights.
 We also aimed to better understand the role of vegetation structure in
 habitat selection at different spatial scales. Habitat selection was
 modeled from data compiled from 15 small mammal trapping grids collected
 in the highly urbanized San Francisco Estuary in California, USA. Analyses
 were conducted at three spatial scales: microhabitat (25 m2), mesohabitat
 (2,025 m2), and macrohabitat (10,000 m2). A suite of structural covariates
 was derived from raw lidar data to examine vegetation complexity. We found
 that adding structural covariates to conventional habitat selection
 variables significantly improved our models. At the microhabitat scale in
 managed wetlands, SMHM preferred areas with denser and shorter vegetation,
 and selected for proximity to levees and taller vegetation in tidal
 wetlands. At the mesohabitat scale, SMHM were associated with a lower
 percentage of bare ground and with pickleweed (Salicornia pacifica)
 presence. All covariates were insignificant at the macrohabitat scale. Our
 results suggest that SMHM preferentially selected microhabitats with
 access to tidal refugia and mesohabitats with consistent food sources. Our
 findings showed that lidar can contribute to improving our understanding
 of habitat selection of wildlife in coastal wetlands and help to guide
 future conservation of an endangered species.
description: # Application of lidar to assess the habitat selection of an endangered
 small mammal in an estuarine wetland environment *Hagani, J. S.1*, J. Y.
 Takekawa1, S. M. Skalos2,3, M. L. Casazza2, M. K. Riley4, S. A. Estrella4,
 L. M. Barthman-Thompson5, K. R. Smith6,7, K. J. Buffington8, and K. M.
 Thorne8* **1**Suisun Resource Conservation District, 2544 Grizzly Island
 Road, Suisun City, CA USA 94585 **2**USGS Western Ecological Research
 Center, Dixon Field Station, 800 Business Park Drive, Dixon, CA USA 95620
 **3**California Department of Fish and Wildlife, 1010 Riverside Parkway,
 West Sacramento, CA USA 95605 **4**California Department of Fish and
 Wildlife, 2825 Cordelia Road, Suite 100, Fairfield, CA USA 94534
 **5**California Department of Fish and Wildlife, 2109 Arch Airport Road,
 Stockton, CA USA 95206 **6**WRA, Inc., 2169-G Francisco Boulevard East,
 San Rafael, CA USA 94901 **7**Department of Wildlife, Fish and
 Conservation Biology, UC Davis, 1088 Academic Surge, 455 Crocker Lane,
 University of California Davis, Davis, CA USA 95616 **8**USGS Davis Field
 Station, 1 Shields Avenue, University of California Davis, Davis, CA USA
 95616 *Corresponding author: **R Code**
 ·        “SMHM Habitat Selection Models.R” o   Code for running
 generalized linear models (GLMs) to assess the importance of various lidar
 and/or conventional BASE covariates on the habitat selection of salt marsh
 harvest mice at any spatial scale. ·        “Lidar Processing.R” o   Code
 for normalizing a raw lidar point cloud using an external DEM and for
 extracting a suite of vegetation structure covariates from the resulting
 normalized dataset. **Datasets** ·        “All_Sites_5m.csv” o   Data at
 the trap-level (25 m2) spatial scale for every small mammal trap location
 analyzed in this study. GPS coordinates have been excluded for safety.
 Includes both BASE and habitat structure covariates. §  “Grid” = name of
 trapping grid §  “Trap” = trap number §  “RERA_CPUE” = catch per unit
 effort of SMHM §  “Type” = habitat type (managed wetland, tidal wetland,
 or upland area) §  “zmax” = maximum vegetation height (ft) §  “zmean” =
 mean vegetation height (ft) §  “zsd” = standard deviation of vegetation
 height (ft) §  “zskew” = skewness of vegetation height §  “zkurt” =
 kurtosis of vegetation height §  “pzabovezmean” = percentage of returns
 above mean vegetation height (%) §  “pzaboveX” = Percentage of returns
 above X (0.25, 0.50, 0.75) meters (%) §  “zqX” = The vegetation height at
 each 5% quantile (zq5, zq10…zq95).  (ft) §  “zpcumX” = Cumulative
 percentage of return of the Xth bin (zpcum1, zpcum2…zpcum9) (%) § 
 “pground” = percentage of lidar returns classified as bare ground
 (vegetation height = 0) (%) §  “Elevation” = average ground surface
 elevation (m) §  “Levee_Dist” = average distance to the nearest levee (m)
 §  “Urban_Dist” = average distance to the nearest urban area (m) § 
 “Vegetation” = code for the most dominant vegetation species ·       
 “All_Sites_Grids.csv” o   Data at the grid-level (~10,000 m2) spatial
 scale for every trapping grid analyzed in this study. Includes both BASE
 and habitat structure covariates. Height units are in U.S. Feet (ft)
 unless otherwise stated below. §  “Grid” = name of the trapping grid § 
 “Nights” = number of survey nights trapping occurred §  “Traps” = number
 of traps §  “RERA_Unique” = number of unique SMHM captured §  “RERA_CPUE”
 = catch per unit effort of SMHM §  “Type” = habitat type (managed wetland,
 tidal wetland, or upland area) §  “zmax” = maximum vegetation height (ft)
 §  “zmean” = mean vegetation height (ft) §  “zsd” = standard deviation of
 vegetation height (ft) §  “zskew” = skewness of vegetation height § 
 “zkurt” = kurtosis of vegetation height §  “pzabovezmean” = percentage of
 returns above mean vegetation height (%) §  “pzaboveX” = Percentage of
 returns above X (0.25, 0.50, 0.75) meters (%) §  “zqX” = The vegetation
 height at each 5% quantile (zq5, zq10…zq95) (ft) §  “zpcumX” = Cumulative
 percentage of return of the Xth bin (zpcum1, zpcum2…zpcum9) (%) § 
 “pground” = percentage of lidar returns classified as bare ground
 (vegetation height = 0) (%) §  “Elevation” = average ground surface
 elevation (m) §  “Levee_Dist” = average distance to the nearest levee (m)
 §  “Urban_Dist” = average distance to the nearest urban area (m) § 
 “Vegetation” = code for the most dominant vegetation species