DOI: 10.5061/dryad.31zcrjdvn
title: Test of a mountain pine beetle winter mortality model

description: Winter mortality of mountain pine beetle larvae caused by unexpectedly low
 winter temperatures is an important determinant of mountain pine beetle
 population dynamics. We tested a widely used mountain pine beetle winter
 mortality model developed by researchers at the Canadian Forest Service
 and the United States Forest Service. lt predicts larval survival
 probabilities given temperature time series over the winter season. To
 validate the model, we compared its predictions to observed lower lethal
 temperature thresholds and cold-associated mortality. Model predictions
 were biologically reasonable--especially when the model was driven using
 observed under-bark temperatures. However, when the model was driven using
 predicted under-bark temperatures, its predictions were slightly biased
 due in part to inaccurate translation of air temperature data to
 temperatures under the bark where larvae develop. Relative mortality
 predicted across the study area in Banff National Park was not well
 predicted by the model. We speculate that the poor predictive performance
 in this mountainous study region is likely because the topography presents
 a difficult prediction challenge for the model. We hope that our results
 and data will inform users of this model of its constraints and how to
 optimize the accuracy of model predictions.
description: At our detailed study site, we collected hourly time series of
 under-bark temperature in mountain pine beetle-infested trees on the north
 and south sides. In addition, we measured larval supercooling temperatures
 to quantify their level of cold hardiness, and assessed larval winter
 survival by pre and post-winter population surveys within each of our
 study trees. Observations were compared to model predictions when the
 winter mortality model was driven using under-bark temperatures.
 At our spatially distributed sites, we assessed population
 success by quantifying the number of parents, and surviving progeny in the
 spring (after winter). Survival estimates based on these data were
 compared to model predictions of winter survival probability made using
 the mountain pine beetle winter mortality model as implemented in the
 publicly available BioSIM software.
description: # Test of a Mountain Pine Beetle Winter Mortality Model
 [https://doi.org/10.5061/dryad.31zcrjdvn](https://doi.org/10.5061/dryad.31zcrjdvn) The data described below are analyzed and discussed in a manuscript to be submitted: "An Evaluation of a Winter Mortality Model for Mountain Pine Beetles authored by Devin W. Goodsman, Jim D Weber, and Katherine P. Bleiker. The data and code included in the Dryad data submission called "Test of a Mountain Pine Beetle Winter Mortality Model" are described in detail below. The data are divided into five folders based on their type. The code is divided among five scripts. The code in each of these scripts work best if run line-by-line (interactively) so that the user can visualize the graphical outputs separately. The scripts also contain relevant statistical analyses and running them prints statistical summaries to the R console. All code was written by Devin W. Goodsman. ## Description of the data and file structure ### **Files that Pertain to Banff Sites BioSIM Output** 1. BanffRegBentz1920Daily.csv, which has the following column headers * KeyID: meaningless * Name: The name of the Hobo temperature logger on the the experimental tree (each tree has a logger with a unique name)-each logger has two channels * Latitude: in decimal degrees * Longitude: in decimal degrees * Elevation: in m above sea level * P: always one (BioSIM output) * Replication: The number of replications if stochastic replications are employed (always one) * Year: The year in which beetles colonized host trees (they emerge in the following year) * Month: self-explanatory * Day: day of month * Tmin: The minimum daily under-bark temperature in degrees Celcius predicted by the Regniere and Bentz (2007) winter mortality model * Tmax: The maximum daily under-bark temperature in degrees Celcius predicted by the Regniere and Bentz (2007) winter mortality model * P1: The percentage of larvae in the least cold-hardened state predicted by the Regniere and Bentz (2007) winter mortality model * P2: The percentage of larvae in the moderately cold-hardened state predicted by the Regniere and Bentz (2007) winter mortality model * P3: The percentage of larvae in the most cold-hardened state predicted by the Regniere and Bentz (2007) winter mortality model * Ct: The Level of cold-hardiness (unitless) predicted by the Regniere and Bentz (2007) winter mortality model * LT50: The temperature at which half of all individuals are expected to die predicted by the Regniere and Bentz (2007) winter mortality model * Psurv: The cumulative minimum survival probability expressed as a percentage predicted by the Regniere and Bentz (2007) winter mortality model * Pmort: One hundred minus Psurv predicted by the Regniere and Bentz (2007) winter mortality model ### **Files that Pertain to Mountain Pine Beetle Life Table Data** 1. BanffPostWinter2020.txt, which has the following column headers * Sample: Sample identifier for the tree. The last letter indicates the side of the tree from which the sample was taken. * Hobo: The last four letters of the unique data logger ID (Hobo) and the side of the tree in which the temperature probe was placed * Holes: count of entrance holes made by colonizing parent mountain pine beetles * MaternalGals: The count of mountain pine beetle maternal galleries * LarvalGals: The count of mountain pine beetle larval galleries * DeadLarvae: The count of dead mountain pine beetle larvae * LivingLarvae: The count of living mountain pine beetle larvae * Unknown: The count of mountain pine beetle larvae that may be dead or alive (damaged during sampling or otherwise ambiguous) 2. BanffPreWinter2019.txt, which has the following column headers * Sample: Sample identifier for the tree. The last letter indicates the side of the tree from which the sample was taken. * Holes: count of entrance holes made by colonizing parent mountain pine beetles * LiveBeetles: The count of live mountain pine beetles from the parent generation * DeadBeetles: The count of dead mountain pine beetles from the parent generation * MaternalGals: The count of * LarvalGals: The count of mountain pine beetle larval galleries * DeadLarvae: The count of dead mountain pine beetle larvae * LivingLarvae: The count of living mountain pine beetle larvae ### **Files that Pertain to Observed Survival Data** 1. BanffRegBentz1718Site.csv, which has the following column headers * KeyID: Empty column * Name: Empty column * Latitude: In decimal degrees * Longitude: In decimal degrees * Elevation: In m above sea level * ObjectID: Integer * Site: Descriptor/identifier * Year: Year in which sample was taken. Mountain pine beetles colonized the tree in the previous year * LivePropTree: The observed proportion of all mountain pine beetle larvae that were living averaged at the tree level. * LivePropSite: The observed proportion of all mountain pine beetle larvae that were living averaged at the site level. * P: always one (BioSIM output) * Replication: The number of replications if stochastic replications are employed (always one) * Year: The year in which the samples used to calculate average larval survival proportions were taken * Tmin: The annual minimum under-bark temperature experienced by mountain pine beetles predicted by the Regniere and Bentz (2007) winter mortality model * Psurv: The cumulative minimum survival probability expressed as a percentage predicted by the Regniere and Bentz (2007) winter mortality model 2. BanffRegBentz1819Site.csv, which has the same column headers as described for BanffRegBentz1718Site.csv ### **Files that Contain Supercooling Data** 1. Goodsman 4-Mar-20 SCP.csv, which has the following column headers * Assessor: Initials of technician who performed the laboratory work * Scope: Identifier for microscope * Sample Date: Date of analysis (one day after larvae were sampled and shipped from AB to BC) * Time Run Initiated: self evident * Run Number: integer * Start Temp. for SCP: in degrees Celcius * Site: Province of origin (AB-Alberta) * Tree: Tree ID number in this case corresponds to the last four digits of the Hobo logger on each tree * Peel/ Emerged: Did the sample insect emerge naturally or was it extracted by peeling the bark and removing juvenile insects? * Temp Treatment: Descriptor--field means temperatures were not manipulated * SCP Tube #: integer * Cold Spike (SCP) (oC): temperature at which larvae froze (exothermic reaction was observed) * Magn. (Adult X25 or Larvae X40): Magnification setting for microscope used to measure head capsule width * PN/HC width (Units): Pronotum/head capsule width measured in microscope * PN/HC width (mm): Pronotum/head capsule width measurement converted to mm * Life stage: Larval life stage (instar) inferred from head capsule width (LI, LII, LIII, LIV) * Comments: Description of state of each insect * Page: page of spreadsheet ### **Files that Contain Temperature Logger Data** 1. H8560DailyTemps2019to2020.csv, which has the following column headers * Dates: YYYY-MM-DD (Year-Month-Day) * Ch1min: Minimum daily temperature in degree Celcius recorded on channel one * Ch1max: Maximum daily temperature in degree Celcius recorded on channel one * Ch2min: Minimum daily temperature in degree Celcius recorded on channel two * Ch2max: Maximum daily temperature in degree Celcius recorded on channel two 2. H9969DailyTemps2019to2020.csv, which has the same column headers as H8560DailyTemps2019to2020.csv 3. H9970DailyTemps2019to2020.csv, which has the same column headers as H8560DailyTemps2019to2020.csv 4. H9971DailyTemps2019to2020.csv, which has the same column headers as H8560DailyTemps2019to2020.csv ## Code/Software To more easily run the following R scripts, the user should either save their workspace in R to the folder location wherein the aforementioned data have been downloaded. Alternatively, the user can save an R project to the location where the data are saved, which has the same effect. If neither of these is favourable, the user will need to alter the directory paths used in the R scripts to read in the raw data. 1. The R script called LifeTableBanffFig2.R contains code for reading the life table data, visualizing them and conducting basic statistical tests. The script uses the ggplot2 and cowplot R packages. 2. The R script called RBMortMod3.R contains the R version of the Regniere and Bentz (2007) winter mortality model that has been written as a function by Devin W. Goodsman. Model input requirements are described in the script itself. The original research on which the R function is based is Regniere, J.; Bentz, B. Modeling Cold Tolerance in the Mountain Pine Beetle, Dendroctonus Ponderosae. J. Insect Physiol. 2007, 53, 559–572, doi:10.1016/j.jinphys.2007.02.007. 3. The R script called RBMortModBanffFig3.R contains code for comparison of the predictions of the Regniere and Bentz (2007) winter mortality model to observed winter survival data and to measurements of supercooling points when the model is driven using observed under bark temperatures. The script uses the ggplot2 and cowplot R packages as well as the RBMortMod3.R script described above. 4. The R script called RBMortModBanffFig4.R contains code for comparison of the predictions of the Regniere and Bentz (2007) winter mortality model driven using observed under-bark temperatures to predictions of the same model deployed in BioSIM and driven using weather station air temperature data. Statistical analyses (regressions) are conducted to compare under-bark temperatures predicted by BioSIM to observed under-bark temperatures. The script uses the ggplot2, cowplot, and lmodel2 R packages as well as the RBMortMod3.R script described above. 5. The R script called RBMortModBanffFig5.R contains code for comparing predictions of the Regniere and Bentz (2007) winter mortality model deployed in BioSIM and driven using weather station air temperature data to observed survival proportion data across Banff National Park in the 2017/2018 and 2018/2019 seasons. Regressions are used to assess the quality of predictions across seasons as well as across space within seasons. The script uses the ggplot2, cowplot, and lmodel2 R packages.