This readme file was generated on 2022-09-12 by Raina Plowright GENERAL INFORMATION Title of Dataset: Data and scripts from: Pathogen spillover driven by rapid changes in bat ecology. Food shortage regression tree model. Recommended citation for this dataset: Eby, Peggy, Alison Peel, Andrew Hoegh, Wyatt Madden, John Giles, Peter Hudson, and Raina Plowright (2022) Data and scripts from: Pathogen spillover driven by rapid changes in bat ecology. Food shortage regression tree model. [Dataset]. Cornell University eCommons Digital Repository. https://doi.org/10.7298/rdbe-cy49 Author/Principal Investigator Information Name: Peggy Eby ORCID: https://orcid.org/0000-0001-5441-2682 Institution: University of New South Wales, Sydney, NSW, Australia; Griffith University, Nathan, Qld, Australia; Center for Large Landscape Conservation, Bozeman, MT, USA Email: peby@ozemail.com.au Author/Co-investigator Information Name: Andrew Hoegh ORCID: https://orcid.org/0000-0003-1176-4965 Institution: Montana State University, Bozeman, MT, USA Email: andrew.hoegh@montana.edu Author/Co-investigator Information Name: Wyatt Madden ORCID: https://orcid.org/0000-0002-9792-7077 Institution: Emory University, Atlanta, GA, USA Email: wyattgmadden@gmail.com Author/Corresponding Author Information Name: Raina Plowright ORCID: https://orcid.org/0000-0002-3338-6590 Institution: Cornell University, Ithaca, NY, USA Email: raina.plowright@cornell.edu Date of data collection: 1996-2020 Geographic location of data collection: Subtropical Australia Information about funding sources that supported the collection of the data: This research was developed with funding from the National Science Foundation (DEB-1716698), U.S. Defense Advanced Research Projects Agency (DARPA PREEMPT D18AC00031), and U.S. National Institute of Food and Agriculture (1015891). AJP was supported by an Australian Research Council DECRA fellowship (DE190100710). SHARING/ACCESS INFORMATION Licenses/restrictions placed on the data: This dataset is shared under a Creative Commons 1.0 Universal Public Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/). The material can be copied, modified and used without permission, but attribution to the original authors is always appreciated. Recommended citation for this dataset: Eby, Peggy, Alison Peel, Andrew Hoegh, Wyatt Madden, John Giles, Peter Hudson, and Raina Plowright (2022) Data and scripts from: Pathogen spillover driven by rapid changes in bat ecology. Food shortage regression tree model. [Dataset]. Cornell University eCommons Digital Repository. https://doi.org/10.7298/rdbe-cy49 Links to publications that cite or use the data: Eby, Peggy, Alison Peel, Andrew Hoegh, Wyatt Madden, John Giles, Peter Hudson, and Raina Plowright (2022) Pathogen spillover driven by rapid changes in bat ecology. Nature. https://doi.org/10.1038/s41586-022-05506-2. Links to other publicly accessible locations of the data: NA Links/relationships to ancillary data sets: Dataset D: Oceanic Niño Index (ONI). https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt Dataset K: Modelled pre-clearing vegetation types, southeast Queensland Bioregion. http://qldspatial.information.qld.gov.au/catalogue/custom/search.page?q=%22Biodiversity%20status%20of%20pre-clearing%20regional%20ecosystems%20%E2%80%93%20Queensland%22 Dataset L: Forests Cover. https://data.globalforestwatch.org/documents/gfw::tree-cover-2000/about Dataset M: Qld Statewide Landcover and Trees Study. https://www.data.qld.gov.au/dataset/statewide-landcover-and-trees-study-queensland-series Below is the list of datasets related to the 2022 Nature paper, Rapid changes in bat ecology drive the emergence of a fatal zoonotic virus, that are archived at eCommons, Cornell University. Data index: https://doi.org/10.7298/pjjb-3360 Regression model: https://doi.org/10.7298/rdbe-cy49 Bayesian model: https://doi.org/10.7298/y0nr-e545 Dataset A: https://doi.org/10.7298/3dbp-t721 Dataset B: https://doi.org/10.7298/kdht-sp38 Dataset C: https://doi.org/10.7298/ajmw-mp18 Dataset E: https://doi.org/10.7298/tb5p-dr98 Dataset F: https://doi.org/10.7298/j3q2-gw32 Dataset G: https://doi.org/10.7298/3vha-5m37 Dataset I: https://doi.org/10.7298/x71e-c660 Dataset J: https://doi.org/10.7298/rmhz-dc23 DATA & FILE OVERVIEW File List: 1. Eby_et_al_2022_food_shortage_model_inputdata.csv a. Number of variables: 35 b. Number of cases/rows: 170 c. Variable List: i. oni - Running 3-month average Oceanic Niño Index, a measure of the El Niño-Southern Oscillation. ii. year - Years from 2006 to 2020 iii. month - Months from January 2006 to February 2020 iv. shortage - Binary response variable indicating nectar shortage based on observations and practices of commercial apiarists v. rehab_num - Monthly count of flying foxes in the records of NR WIRES, excluding heat event outliers. vi. lag_one_rehab_num - One-month lag of rehab_num vii. mean_mass_bff_female - Mean monthly adult BFF female mass (BFF = black flying fox) viii. min_mass_bff_female - Minimum monthly adult BFF female mass ix. quant_25_mass_bff_female - Lower 0.25 quantile monthly BFF adult female mass x. mean_wt_to_fa_bff_female - Mean monthly BFF adult female mass/forearm xi. min_wt_to_fa_bff_female - Minimum monthly BFF adult female mass/forearm xii. quant_25_wt_to_fa_bff_female - Lower 0.25 quantile monthly BFF adult female mass/forearm xiii. mean_mass_bff_male - Mean monthly adult BFF male mass xiv. min_mass_bff_male - Minimum monthly adult BFF male mass xv. quant_25_mass_bff_male - Lower 0.25 quantile monthly BFF adult male mass xvi. mean_wt_to_fa_bff_male - Mean monthly BFF adult male mass/forearm xvii. min_wt_to_fa_bff_male - Minimum monthly BFF adult male mass/forearm xviii. quant_25_wt_to_fa_bff_male - Lower 0.25 quantile monthly BFF adult male mass/forearm xix. mean_mass_ghff_female - Mean monthly adult GHFF female mass (GHFF = grey-headed flying fox) xx. min_mass_ghff_female - Minimum monthly adult GHFF female mass xxi. quant_25_mass_ghff_female - Lower 0.25 quantile monthly GHFF adult female mass xxii. mean_wt_to_fa_ghff_female - Mean monthly GHFF adult female mass/forearm xxiii. min_wt_to_fa_ghff_female - Minimum monthly GHFF adult female mass/forearm xxiv. quant_25_wt_to_fa_ghff_female - Lower 0.25 quantile monthly GHFF adult female mass/forearm xxv. mean_mass_ghff_male - Mean monthly adult GHFF male mass xxvi. min_mass_ghff_male - Minimum monthly adult GHFF male mass xxvii. quant_25_mass_ghff_male - Lower 0.25 quantile monthly GHFF adult male mass xxviii. mean_wt_to_fa_ghff_male - Mean monthly GHFF adult male mass/forearm xxix. min_wt_to_fa_ghff_male - Minimum monthly GHFF adult male mass/forearm xxx. quant_25_wt_to_fa_ghff_male - Lower 0.25 quantile monthly GHFF adult male mass/forearm xxxi. boolum_perc_w_young - Booyong/Lumley roost annual Dec/Jan measurements percent adult females with young applied to months of April through March xxxii. rotpk_perc_w_young - Rotary Park roost annual Dec/Jan measurements percent adult females with young applied to months of April through March xxxiii. min_perc_w_young - Minimum annual Dec/Jan measurements percent adult females with young (between Booyong/Lumley and Rotary Park) applied to months of April through March xxxiv. mean_perc_w_young - Mean annual Dec/Jan measurements percent adult females with young (between Booyong/Lumley and Rotary Park) applied to months of April through March xxxv. spring_and_after_oni_peak - Indicator if the month is Oct/Nov/Dec and if an ONI of greater than or equal to 0.8 occurred within last 11 months. 2. Eby_et_al_2022_food_shortage_model_outputdata.csv a. Number of variables: 7 b. Number of cases/rows: 170 c. Variable List: i. year - Years from 2006 to 2020 ii. month - Months from January 2006 to February 2020 iii. rehab_num - Monthly count of flying foxes in the records of NR WIRES, excluding heat event outliers. iv. min_perc_w_young - Minumum annual Dec/Jan measurements percent adult females with young (between Booyong/Lumley and Rotary Park) applied to months of April through March v. shortage - Categorical variable indicating whether the month was identified as a nectar shortage based on observations and practices of commercial apiarists vi. model_shortage - Categorical variable indicating whether the month was identified as a nectar shortage by the regression model 3. Eby_et_al_2022_food_shortage_model_runcode.R a. R script for replicating food shortage analysis. b. Code executed in R version 4.2.1 c. Requires tidyverse, rpart, and rattle R packages Are there multiple versions of the dataset? NO