DOI: 10.5061/dryad.51c59zw66
title: Analytic dataset informing prediction of subterranean cave and mine ambient temperatures

description: Caves and other subterranean features provide unique environments for many
 species. The importance of cave microclimate is particularly relevant at
 temperate latitudes where bats make seasonal use of caves for hibernation.
 White-nose syndrome (WNS), a fungal disease that has devastated
 populations of hibernating bats across eastern and central North America,
 has brought renewed interest in bat hibernation and hibernaculum
 conditions. A recent review synthesized current understanding of cave
 climatology, exploring the qualitative relationship between cave and
 surface climate with implications for hibernaculum suitability. However, a
 more quantitative understanding of the conditions in which bats hibernate
 and how they may promote or mediate WNS impacts is required. We compiled
 subterranean temperatures from caves and mines across the western United
 States and Canada to: a) quantify the hypothesized relationship between
 mean annual surface temperature (MAST) and subterranean temperature and
 how it is influenced by measurable site attributes, and b) use readily
 available gridded data to predict and continuously map the range of
 temperatures that may be available in caves and mines. Our analysis
 supports qualitative predictions that subterranean winter temperatures are
 correlated with MAST, that temperatures are warmer and less variable
 farther from the surface, and that even deep within caves temperatures
 tend to be lower than MAST. Effects of other site attributes (e.g.,
 topography, vegetation, precipitation) on subterranean temperatures were
 not detected. We then assessed the plausibility of model-predicted
 temperatures using knowledge of winter bat distributions and preferred
 hibernaculum temperatures. Our model unavoidably simplifies complex
 subterranean environments, and is not intended to explain all variability
 in subterranean temperatures. Rather, our results offer researchers and
 managers improved broad-scale estimates of the geographic distribution of
 potential hibernaculum conditions compared to reliance on MAST alone. We
 expect this information to better support range-scale estimation of winter
 bat distributions and projection of likely WNS impacts across the West. We
 suggest that our model predictions should serve as hypotheses to be
 further tested and refined as additional data become available. 
description: <em>Subterranean microclimate
 data</em> We compiled
 temperature data collected from caves and mines (139 unique sites,
 including 75 caves and 64 mines) across western North America (9 U.S.
 states, 2 Canadian provinces) between 2006 and 2019 from several data
 providers and one published source (see publication Acknowledgements and
 Cited Literature). We only included datasets for which site location was
 provided with positional error &lt; 10 km. Data were collected using
 temperature loggers (iButton, Onset HOBO loggers), which recorded
 temperature at subdaily intervals that varied by site. Many sites
 contained multiple loggers, placed at a range of distances from the site
 entrance (479 total loggers, mean of 3.4 per site). Some datasets were
 accompanied by metadata describing key attributes that may influence
 temperature at the logger placement site (e.g., distance from site
 entrance, description of site size or shape, site geology), or other
 logger placement details (e.g., wall, crevice, or ceiling). Although many
 loggers recorded humidity as well as temperature, logger saturation, a
 phenomenon in which the logger fails to accurately record humidity after
 reaching readings of 100% humidity, precluded the use of data from the
 majority of loggers. Therefore, we focused only on temperature
 here.  We first screened all logger
 temperature data for deployment errors, particularly evident recording of
 surface temperature prior to and following placement of the logger at the
 site. We restricted our analysis to data recorded during the core winter
 months of December - February to restrict analysis to a consistent time
 frame that can be considered winter at all sites across the broad
 latitudinal range of our study. For loggers deployed over multiple
 winters, we summarized each winter period separately. Winter periods
 during which fewer than 14 days of readings were recorded were excluded
 from analysis (resulting in 131 sites, 427 loggers, and 699
 logger-winters). We further restricted our dataset to loggers for which
 distance from the site entrance was provided, or for which cave maps were
 provided that could be used to generate a logger placement distance (54
 sites, 156 loggers, 202 logger-winters). Recorded distances ranged from 0
 to 324 m from the site entrance (median = 61 m). 
 Because the compiled and filtered raw logger dataset contained
 &gt;1.2 million observations, which were highly variable in their
 distribution across loggers as well as their frequency and regularity of
 recording, we conducted all analyses using a stratified random sample from
 the raw logger temperature dataset. We selected 250 records from each
 unique logger so as to retain all sites and loggers; for loggers with
 fewer than 250 records (19 loggers; 5%), all records were selected. Model
 results were insensitive to this choice of sample size. All logger data
 processing and analysis was conducted in R (version 3.4.1).
 <em>Surface climate and landscape
 data</em> We derived MAST and
 predictors representing key landscape attributes using Google Earth
 Engine, a cloud-based computing platform supporting large-scale analysis
 on an extensive catalog of remotely sensed, climatological, and other
 geospatial datasets. We estimated MAST at each cave site using the DayMet
 Version 3 dataset, which provides gridded daily surface temperature at
 1-km resolution (1980-2018). Daily mean temperature was first calculated
 as the mean of daily minimum and maximum temperature. We then estimated
 MAST as the 20-year (1998 - 2018) mean of daily mean
 temperatures.  We also derived site-level landscape
 predictors that are believed to impact winter cave temperatures (Perry
 2012, <em>Environmental Reviews </em>21:28). We extracted
 elevation of each site from the Shuttle Radar Topography Mission (SRTM)
 digital elevation model at 30-m resolution. Based on elevation, we derived
 a multiscale topographic position index (TPI), in which canyon and valley
 bottoms have low position and peaks and ridges have high position. Using a
 moving-window approach, TPI was calculated as the elevation of a focal
 raster cell minus the mean elevation within a given neighborhood
 surrounding the focal cell. We calculated TPI for three neighborhood sizes
 (500 m, 5 km, and 25 km squares), then averaged these to produce a
 multiscale index. We extracted Continuous Heat-Insolation Load Index
 (CHILI), a surrogate for effects of solar insolation and topographic
 shading (e.g., due to slope aspect) on evapotranspiration, from the Global
 ALOS CHILI product at 90-m resolution. Percent tree cover was extracted
 from the Terra MODIS Vegetation Continuous Fields product, which estimates
 percent tree cover within 250-m resolution pixels. We estimated the mean
 annual number of snow days at 500-m resolution from the MODIS Snow Cover
 dataset (V6) by averaging the number of days per year with at least 10%
 snow cover over the most recent 5-year period available (2014-2018), as
 well as mean annual snowpack (snow water equivalent (SWE) on April 1) for
 the same time period at 1-km resolution from DayMet (V3). We sampled
 groundwater table depth estimated from compiled point observations
 gap-filled with a mechanistic groundwater flow model (Fan et al. 2013,
 <em>Science</em> 339:940) as a proxy for the likelihood of
 water flow within caves or mines, assuming that flow was more likely at
 sites with shallower (i.e., closer to the surface) groundwater.
description: See uploaded ReadMe file.