DOI: 10.5061/dryad.tmpg4f55d
title: Data from: Soilscapes of mortality risk suggest a Goldilocks effect for overwintering ectotherms

description: Changing climates are driving population declines in diverse animals
 worldwide. Winter conditions may play an important role in these declines
 but are often overlooked. Animals must not only survive winter but must
 also preserve body condition which is a key determinant of growing season
 success. We hypothesized that ectotherms overwintering in soil face a
 tradeoff between the risks of cold damage (including freezing) near the
 surface and of elevated energy use at deeper depths. To test this
 hypothesis, we developed landscapes of mortality risk across depth for
 overwintering bumble bee queens. These critical pollinators are in decline
 in part due to climate change, but little is known about how climate
 effects their overwintering mortality. We developed a mechanistic modeling
 approach combining measurements of freezing points and the temperature
 dependence of metabolic rates with hourly soil temperatures from across
 the US to estimate mortality risk across depth under historic conditions
 and under several climate change scenarios. We found that, under current
 climate conditions, overwintering queens face a Goldilocks effect:
 temperatures can be too cold at shallow depths due to substantial freezing
 risk, but too hot at deep depths where they risk prematurely exhausting
 lipid stores. Models suggest that increases in mean temperatures and in
 seasonal and daily temperature variation will increase risk of overwinter
 mortality. Our work suggests that ectotherms overwintering in the soil
 must select depths that balance competing risks of temperatures that are
 too cold or too hot. Better predictions of effects of changing climate on
 dormant ectotherms will require more measurements of physiological
 responses to temperature during dormancy across diverse taxa.
description: We provide cleaned soil temperature datasets based on the Soil
 Climate Analysis Network (SCAN), and physiology data combined with scripts
 to replicate analyses presented in this manuscript. The associated README
 file explains folder and file contents. 
description: **README for Waybright & Dillon Soilscapes code and data** Sarah A.
 Waybright* & Michael E. Dillon Department of Zoology and Physiology
 and Program in Ecology University of Wyoming, Laramie, WY 82071, USA
 *Corresponding author: *[swaybrig@uwyo.edu](mailto:swaybrig@uwyo.edu),
 (+12604387821)  In this study we developed a mechanistic modeling approach
 combining measurements of freezing points and the temperature dependence
 of metabolic rates with soil temperatures from across the US to estimate
 mortality risk across depth under historic conditions and under several
 climate change scenarios.  SAW and MED collaborated to write this code,
 while soil temperature data was sourced from the Soil Climate Analysis
 Network. All below code was run with *R* version 4.1.2, with the following
 additional packages loaded: maps_3.3.0, spData_0.3.10 , raster_3.5-2 , 
 sp_1.4-5, glmmTMB_1.1.3, mgcv_1.8-38, nlme_3.1-153, sf_1.0-2,
 lubridate_1.7.10, foreach_1.5.1, forcats_0.5.1, stringr_1.4.0,
 dplyr_1.0.7, purrr_0.3.4, readr_2.0.1, tidyr_1.1.3, tibble_3.1.3,
 ggplot2_3.3.5, and tidyverse_1.3.1. **Soilscapes.R**: main script loading
 data, doing calculations, running all statistical models. Everything is
 run and depends on this script. Follow this script and the embedded
 comments for the entire workflow. **Soilscapes-Figures.R**: script with
 code to make figures; depends on code in Soilscapes.R **cleanSCAN.csv**:
 raw temperature data from SCAN sites after cleaning described in
 Supplementary materials. Includes the following columns: depth: soil depth
 in cm temp: temperature (°C) datetime: date and time (year/month/day
 hours:minutes:seconds) site: unique SCAN site names state: state the site
 is located lat: latitude (°N) lon: longitude (°W) alt.m: elevation of the
 site (meters) obs.pre.clean: number of temperature records for each site
 and depth before data “cleaning” depth_ok: column with Y (yes) or N (no)
 in which we determined if the depth contained enough values to be included
 in the analysis notes: any additional notes about cleaning process
 **cleanSCANcc.RDS***: raw temperature data with additional columns of
 temperature data manipulated for four climate change scenarios; Note that
 this is a large file (~750 MB) with 5 years of temperature data for each
 of over 100 stations at 4 depths under 4 climate scenarios in addition to
 the original data (~86 million rows). Includes the following columns:
 depth: soil depth in cm datetime: date and time (year/month/day
 hours:minutes:seconds) site: unique SCAN site names state: state the site
 is located lat: latitude (°N) lon: longitude (°W) alt.m: elevation of the
 site (meters) obs.pre.clean: number of temperature records for each site
 and depth before data “cleaning” obs.post.clean: number of temperature
 records for each site and depth after data “cleaning” depth_ok: column
 with Y (yes) or N (no) in which we determined if the depth contained
 enough values to be included in the analysis notes: any additional notes
 about cleaning process cc.scenario: soil temperature data manipulated to
 reflect one of 4 climate change scenarios: mean temperatures increase by
 3°C (M3A0D0), mean temperatures and annual temperature variation increase
 by 3°C (M3A3D0), mean temperatures and daily temperature variation
 increase by 3°C (M3A0D3), or mean temperatures, annual temperature
 variation, and daily temperature variation increase by 3°C (M3A3D3). temp:
 temperature (°C) **SCP.csv**: data on supercooling point measurements of
 Bombus from Keaveny et al., 2022,
 [https://www.sciencedirect.com/science/article/pii/S0306456522000109](https://www.sciencedirect.com/science/article/pii/S0306456522000109). Includes the following columns: bee.id: unique bee ID species: species of bumble bee caste: male, worker, or queen date: date of experiment (day/month/year) time.of.capture: time of capture (military time) capture.location: location of capture scp.type: type of supercooling point experiment (wet vs. dry) season: season bee was captured colony: colony name (if commercial species *B. impatiens*) mass.g: mass of bee (g) scp.c: supercooling point (°C) ramp.rate: ramp rate (°C/min) **MRTtempclean.csv**: data on metabolic rates as a function of temperature for *Bombus impatiens* queens. Includes the following columns: expid: unique bee ID settemp: set temperature (°C) temp: temperature (°C) VCO2m: volume of CO2 produced per min **groupedPROBs.RDS***: estimates of freezing and lipid depletion probabilities for all sites, depths, and climate change scenarios, where the dormancy transition threshold is 10°C. The code that generates these data is provided but these calculations are the most time-intensive part of the script, so the result is provided for those wanting to avoid many hours of computing time on a fast desktop computer. Includes the following columns: site: unique SCAN site names depth: soil depth in cm cc.scenario: soil temperature data manipulated to reflect one of 4 climate change scenarios: mean temperatures increase by 3°C (M3A0D0), mean temperatures and annual temperature variation increase by 3°C (M3A3D0), mean temperatures and daily temperature variation increase by 3°C (M3A0D3), or mean temperatures, annual temperature variation, and daily temperature variation increase by 3°C (M3A3D3). lat: latitude (°N) lon: longitude (°W) alt.m: elevation of the site (meters) prob.freeze: predicted probability of a queen bumble bee freezing wdays: dormancy duration (days) gfat.600mean: predicted mean of total lipid depleted by queen bumble bee gfat.600sd:predicted standard deviation of total lipid depleted by queen bumble bee prob.gt18: probability of a queen bumble bee depleting excess energy reserves when she started with 20% of her body mass as lipid prob.gt08: probability of a queen bumble bee depleting excess energy reserves when she started with 10% of her body mass as lipid temp.mean: extracted estimate of mean temperature for temperature series temp.tr: extracted trend of temperature for temperature series annamp: amplitude of annual temperature variation dayamp: amplitude of daily temperature variation **varSCAN.RDS***: site: unique SCAN site names depth: soil depth in cm cc.scenario: soil temperature data manipulated to reflect one of 4 climate change scenarios: mean temperatures increase by 3°C (M3A0D0), mean temperatures and annual temperature variation increase by 3°C (M3A3D0), mean temperatures and daily temperature variation increase by 3°C (M3A0D3), or mean temperatures, annual temperature variation, and daily temperature variation increase by 3°C (M3A3D3). temp.mean: extracted estimate of mean temperature for temperature series temp.tr: extracted trend of temperature for temperature series annamp: amplitude of annual temperature variation dayamp: amplitude of daily temperature variation **SCAN2047.csv, SCAN2117.csv**: example of raw temperature data before cleaning, used for Supplemental figure 2. Includes the following columns: depth: soil depth in cm temp: temp: temperature (°C) date: date and time (year/month/day hours:minutes:seconds) **topo2.tiff**: Topo map downloaded from US Geological Survey website, used for figures which include maps. **lipids.fall.spring.csv**: total lipid measurements and wet masses of queen bumble bees collected in the Fall of 2019 and Spring of 2020 in Laramie, WY. Includes the following columns: bee_num: unique bee ID trt: season bee was captured capture_date: date of capture capture_location: location of capture capture_mass.g: mass at capture (g) capture_notes: additional notes about bee at capture wet_weight_g: wet mass of bee (g) wet_head_g: wet mass of head (g) dry_head_g: dry mass of head (g) wet_thorax_g: wet mass of thorax (g) dry_thorax_g: dry mass of thorax (g) wet_abd_g: wet mass of abdomen (g) dry_abd_g: dry mass of abdomen (g) total_ug_H: total mass of head (μg) total_ug_T: total mass of thorax (μg) total_ug_A: total mass of abdomen (μg) Std_Curve_R2: R2 of standard curve total.fat.g: total lipid (g) total.dry: total wet mass (g) total.wet : total dry mass (g) prop.fat.dry: proportion of dry mass as fat prop.fat.wet: proportion of wet mass as fat **groupedPROBs.cutoff.8.RDS***: estimates of freezing and lipid depletion probabilities for all sites, depths, and climate change scenarios, where the dormancy transition threshold is 8°C. The code that generates these data is provided but these calculations are the most time-intensive part of the script, so the result is provided for those wanting to avoid many hours of computing time on a fast desktop computer. Includes the following columns: site: unique SCAN site names depth: soil depth in cm lat: latitude (°N) lon: longitude (°W) alt.m: elevation of the site (meters) prob.freeze: predicted probability of a queen bumble bee freezing wdays: dormancy duration (days) gfat.600mean: predicted mean of total lipid depleted by queen bumble bee gfat.600sd: predicted standard deviation of total lipid depleted by queen bumble bee prob.gt08: probability of a queen bumble bee depleting excess energy reserves when she started with 10% of her body mass as lipid **groupedPROBs.cutoff.12.RDS***: estimates of freezing and lipid depletion probabilities for all sites, depths, and climate change scenarios, where the dormancy transition threshold is 12°C. The code that generates these data is provided but these calculations are the most time-intensive part of the script, so the result is provided for those wanting to avoid many hours of computing time on a fast desktop computer. Includes the following columns: site: unique SCAN site names depth: soil depth in cm lat: latitude (°N) lon: longitude (°W) alt.m: elevation of the site (meters) prob.freeze: predicted probability of a queen bumble bee freezing wdays: dormancy duration (days) gfat.600mean: predicted mean of total lipid depleted by queen bumble bee gfat.600sd: predicted standard deviation of total lipid depleted by queen bumble bee prob.gt08: probability of a queen bumble bee depleting excess energy reserves when she started with 10% of her body mass as lipid **var.thresh.grouped.RDS***: estimates of freezing and lipid depletion probabilities for all sites, depths, and climate change scenarios, where the dormancy transition threshold creates a counter-gradient with latitude. The code that generates these data is provided but these calculations are the most time-intensive part of the script, so the result is provided for those wanting to avoid many hours of computing time on a fast desktop computer. Includes the following columns: site: unique SCAN site names depth: soil depth in cm lat: latitude (°N) lon: longitude (°W) alt.m: elevation of the site (meters) prob.freeze: predicted probability of a queen bumble bee freezing wdays: dormancy duration (days) gfat.600mean: predicted mean of total lipid depleted by queen bumble bee gfat.600sd:predicted standard deviation of total lipid depleted by queen bumble bee prob.gt08: probability of a queen bumble bee depleting excess energy reserves when she started with 10% of her body mass as lipid Data file not necessary as the calculations that generate the data are in the script but provided to save processing time.