DOI: 10.5061/dryad.4j0zpc8pk
title: The vulnerability of overwintering insects to loss of the subnivium

description: Aim: Winter climate change threatens the subnivium (i.e., the microhabitat
 that exists between the snowpack and the ground), and the community of
 species that depends on it for overwintering survival. One group of
 species that will likely exhibit an array of responses to subnivium loss
 are overwintering insects because they vary in their cold tolerance
 strategies and lower thermal limits. For an assemblage of eight insect
 species that range in their cold tolerance strategies and include both
 pollinators and pests, we investigated species-specific vulnerabilities to
 shifting subnivium conditions. Location: Great Lakes region of North
 America Methods: We applied information on each insect’s supercooling
 point to spatially- and temporally-explicit models of minimum subnivium
 temperatures generated from active-warming experiments and comprising
 three scenarios: current conditions (i.e., control), +3°C, and +5°C.
 Results: Although species varied in their vulnerabilities, our predictions
 indicated that exposure to lethal temperatures generally decreased under
 warming of 3°C, but increased under warming of 5°C, indicating that once
 enough warming happens, a tipping point is reached. We also found that
 freeze-tolerant species (i.e., species that can survive at temperatures
 below their supercooling point) possess a more cryptic vulnerability to
 winter climate change because sustained below-freezing temperatures were
 sufficient to induce vulnerability (i.e., predicted mortality), even when
 temperatures were above the supercooling point. Main conclusions: This
 work provides a better understanding of the vulnerability of different
 insect species to winter climate change, which is critical because
 overwintering survival and the fitness consequences incurred during
 overwintering likely represent important bottlenecks for the population
 dynamics of subnivium-dependent species.
description: Active-warming Experiments
 <em>Experimental Design</em> In
 the fall of 2016-17, we installed three micro-greenhouses (2.5 m x 2.5 m x
 3 m) at each study site for a total of 27 greenhouses. Each greenhouse had
 an aluminum frame with walls of corrugated plastic, and was equipped with
 a pair of heaters and vents positioned on opposite walls. At each site,
 greenhouses were separated by a minimum of 4.5 m, and consisted of three
 temperature treatments: control (GH<sub>control</sub>,
 internal temperature = ambient temperature), 3°C warmer than ambient
 (GH<sub>+3°C</sub>), and 5°C warmer than ambient
 (GH<sub>+5°C</sub>). We also monitored the environment
 external to all greenhouses to capture current conditions (hereafter
 external). Inside each greenhouse, we attached a
 temperature probe within a radiation shield (Davis Instruments Corp
 External Temperature Sensor), an anemometer (Davis Instruments Corp
 Anemometer), and a snow depth sensor (HRXL-Max Sonar WRS Series Ultrasonic
 Snow Depth Sensor, typical accuracy of 1%). Temperature (°C) and wind
 speed (m/s) were measured every minute, while snow depth (cm) was measured
 every five minutes. We also established three weather stations at each
 site and paired each station with a greenhouse. Weather stations had a
 heated rain gauge for measuring liquid precipitation (mm, Davis
 Instruments Corp Rain Collector and Rain Collector Heater), an anemometer
 (m/s, Davis Instruments Corp Anemometer, mounted at mean height of 1.8 ±
 0.3 meters), and a temperature probe within a radiation shield (°C, Davis
 Instruments Corp External Temperature Sensor, mounted at mean height of
 1.6 ± 0.2 meters). Measurements from these instruments were also recorded
 every minute. At one of the three weather stations at each site we also
 attached a snow depth sensor to measure the snow depth (cm) external to
 the greenhouses, at five minute intervals. All instruments recorded data
 from December 2016 through March 2017.
 <em>Temperature and Precipitation
 Regulation</em> All instruments on the weather
 station and inside the greenhouse were connected to a central control box.
 This box monitored temperatures from the greenhouse (i.e., experimental
 temperature) and the weather station (i.e., ambient temperature) every
 minute and increased heating or venting within the greenhouse to maintain
 a set experimental temperature relative to ambient conditions. In addition
 to temperature controls, greenhouse automation included a retractable roof
 to capture all precipitation events during the winter season. Within the
 rain gauges at each weather station, a wetness sensor logged voltage
 measurements at 1-minute intervals. Once a positive voltage was recorded,
 the roof of the greenhouse opened to allow precipitation to fall inside.
 When the voltage measurement returned to zero, indicating that there was
 no more precipitation, the roof closed.
 <em>Subnivium Temperature    </em> 
 We positioned temperature strings containing 20 individual
 temperature probes at each greenhouse to measure subnivium temperatures.
 Each probe was separated by 0.3 m, and was staked to be flush with the
 ground. We arranged the temperature strings so that 16 probes were
 fastened to the ground inside of each greenhouse, while 4 probes were
 fastened to the ground outside of each greenhouse, leading to a total of
 16 subnivium probes for each of the greenhouse treatments and 12 probes
 for external subnivium conditions. Subnivium temperatures were recorded at
 five-minute intervals from December 2016 through March 2017.
 To derive a daily subnivium temperature for each treatment, we
 extracted the daily minimum ground temperature from each sensor for the
 period of December 1, 2016, to March 31, 2017 and then calculated the mean
 of those minimum temperatures for each treatment (environmental control, n
 = 12; greenhouse treatments, n = 16) and each day.
description: # The vulnerability of overwintering insects to loss of the subnivium
 [https://doi.org/10.5061/dryad.4j0zpc8pk](https://doi.org/10.5061/dryad.4j0zpc8pk) ## Description of the data and file structure ## Data provided include: * Minimum Subnivium Temperature CSV files for the daily minimum ground temperature at each site for each treatment (9 files) plus a csv that combines this data into one file (Master_Min_Subnivium_Temps.csv). Files include A_Min_Subnivium_Temps.csv, HH_Min_Subnivium_Temps.csv, C_Min_Subnivium_Temps.csv, L_Min_Subnivium_Temps.csv, MTD_Min_Subnivium_Temps.csv, MTOP_Min_Subnivium_Temps.csv, M_Min_Subnivium_Temps.csv, TH_Min_Subnivium_Temps.csv, and SW_Min_Subnivium_Temps.csv. NA values indicate that there were not values from which to calculate minimum subnivium temperature for that day. * Columns in CSV files * Date (mm/dd/yyyy) * Treat: Treatment which can be ext (external), 0 (0°C warmer than the external environment), 3 (3°C warmer than the external environment), 5 (5°C warmer than the external environment) * Loc: Location which can be A (lower latitude, deciduous forest), SW (lower latitude, open), M (lower latitude, conifer forest), HH (mid latitude, deciduous forest), TH (mid latitude, open), L (mid latitude, conifer forest), MTD (upper latitude, deciduous forest), MTOP (upper latitude, open), C (upper latitude, conifer forest) * min.subniv: Minimum subnivium temperature (°C) * Wide Form String Data CSV files for the raw ground temperature data measured in 15-minute interval at each site (9 files) plus a .docx file that contains metadata for the column headings of the CSV files. Files include A_string_wide.csv, C_string_wide.csv, HH_string_wide.csv, L_string_wide.csv, M_string_wide.csv, MTD_string_wide.csv, MTOP_string_wide.csv, TH_string_wide.csv, and SW_string_wide.csv. Metadata for these data are provided in Metadata_for_String_Data.docx. NA values indicate no temperature was recorded at that time point. * Columns in CSV files * Date (mm/dd/yyyy) * GHID: Unique ID that combines Location, Treatment, and land-cover type * Loc: Location which can be A (lower latitude, deciduous forest), SW (lower latitude, open), M (lower latitude, conifer forest), HH (mid latitude, deciduous forest), TH (mid latitude, open), L (mid latitude, conifer forest), MTD (upper latitude, deciduous forest), MTOP (upper latitude, open), C (upper latitude, conifer forest) * Treat: Treatment which can be 0 (0°C warmer than the external environment), 3 (3°C warmer than the external environment), 5 (5°C warmer than the external environment) * Cov: Land-cover type which can be Dec (deciduous forest), Con (conifer forest), or Open (open) * Year: yyyy * Month: mm * Day: dd * Hour: hh * Minute: mm * S1 – S16: Temperature (°C) for each of 16 temperature probes distributed on the ground inside of each greenhouse * Lat: Latitude in decimal degrees * Long: Longitude in decimal degrees * Stacks_1_km_res_snodas_for_depth.7z contains raster stacks of windspeed, max air temp, min air temp, median snow depth, and snow density for each day in the period of December 1, 2016, through March 31, 2017 * Blank_resampling_raster_1km_resolution.tif is used to get spatial predictor tif files at the same resolution in conjunction with Bounding_Box_Coordinates.csv. * Bounding Box Coordinates.csv provides latitudinal and longitudinal coordinates to create a bounding box around the study area * Columns in CSV * coordinates: either xmax, xmin (for Longitude) or ymax, ymin for Latitude * albers: longitudinal and latitudinal values in meters according to the Albers Equal Area Projection * wgs84: longitudinal and latitudinal values in degrees according to the WGS84 Projection * Data_from_Lam_and_Pedigo_LLT_at_0C.csv: CSV file containing data extracted from Lam and Pedigo (2000) on time until mortality and the percentage mortality in a population of bean leaf beetles (Ceratoma trifurcata) held at a constant temperature of 0C. * Columns in CSV * Hours: Hours since treatment * Percent.Mortality: percent mortality * CI: confidence interval around percent mortality * Great_Lakes.shp Shapefile of the Great Lakes used in making figures. Include auxiliary files Great_Lakes.dbf, Great_Lakes.shx, Great_Lakes.prj, Great_Lakes.cpg. * Great_Lakes_States.shp Shapefile of the states in the Great Lakes Region used in making figures. Includes auxiliary files Great_Lakes_States.shx, Great_Lakes_States.prj, Great_Lakes_States.sbn, Great_Lakes_States.sbx, and Great_Lakes_States.dbf. * Latitude_degrees.tif Raster of latitudinal ranges in the Great Lakes Region used in making figures. * Subniv_Temps_and_Predictors_complete.csv: CSV containing experimental data that were used in models generated from active-warming experiments in the Great Lakes Region. * Columns in CSV files * Date (mm/dd/yyyy) * Treat: Treatment which can be 0 (0°C warmer than the external environment), 3 (3°C warmer than the external environment), 5 (5°C warmer than the external environment) * Loc: Location which can be A (lower latitude, deciduous forest), SW (lower latitude, open), M (lower latitude, conifer forest), HH (mid latitude, deciduous forest), TH (mid latitude, open), L (mid latitude, conifer forest), MTD (upper latitude, deciduous forest), MTOP (upper latitude, open), C (upper latitude, conifer forest) * Presence: 0 when subnivium is not present, 1 when subnivium is present * Julian: Julian date ranging from 1 (12/01/2016) to 121 (03/31/2017) * Tairmin: Minimum daily air temperature (°C) * Tairmax: Maximum daily air temperature (°C) * Snowmed: Median snow depth (cm) * Wind: Average daily wind speed (m/s) * Cov: Land-cover type which can be Dec (deciduous forest), Con (conifer forest), or Open (open) * density: Daily average snow density (g/cm^3) * mean.subniv: Daily mean ground-surface temperature (°C) * median.subniv: Daily median ground-surface temperature (°C) * min.subniv: Daily minimum ground-surface temperature * Not used in analyses but provided is also a shapefile of the Great Lakes States and surrounding Canadian provinces: Great_Lakes_States_and_Prov.dbf, Great_Lakes_States_and_Prov.shx, Great_Lakes_States_and_Prov.shp, Great_Lakes_States_and_Prov.prj. ## Data openly available to reproduce results include: * Daily air temperature data available from Daymet ([https://daymet.ornl.gov/](https://daymet.ornl.gov/)) * Land-cover data were available from the United States Geological Survey’s National Land Cover Database ([https://www.usgs.gov/centers/eros/science/national-land-cover-database](https://www.usgs.gov/centers/eros/science/national-land-cover-database)) * Daily snow depth and snow water equivalent data available from the National Snow and Ice Data Center’s Snow Data Assimilation System Product (SNODAS, [https://nsidc.org/data/g02158/versions/1#anchor-data-access-tools](https://nsidc.org/data/g02158/versions/1#anchor-data-access-tools)) * Daily wind speed data available from the National Centers for Environmental Protection’s North American Regional Reanalysis product (NARR, [https://psl.noaa.gov/data/gridded/data.narr.html](https://psl.noaa.gov/data/gridded/data.narr.html)) * Data on insect cold tolerances were extracted from published literature and are summarized in Table 1 of the preprint. Although these data are openly available, we have included the daily raster stacks of these predictors with which we generated our spatial predictions.