This readme file was generated on 2022-09-12 by Raina Plowright GENERAL INFORMATION Title of Dataset: Data and figure from Pathogen spillover driven by rapid changes in bat ecology. Bayesian network model Recommended citation for this dataset: Eby, Peggy, Alison Peel, Andrew Hoegh, Wyatt Madden, John Giles, Peter Hudson, and Raina Plowright (2022) Data and figure from Pathogen spillover driven by rapid changes in bat ecology. Bayesian network model [Dataset]. Cornell University eCommons Digital Repository. https://doi.org/10.7298/y0nr-e545 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/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: 1994-2020 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 figure from Pathogen spillover driven by rapid changes in bat ecology. Bayesian network model. [Dataset]. Cornell University eCommons Digital Repository. https://doi.org/10.7298/y0nr-e545 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, Pathogen spillover driven by rapid changes in bat ecology, 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_fissioned_roosts.csv a. Number of variables: 4 b. Number of cases/rows: 19 c. Variable List: i. roost_year: year stored as 4 digit value ii. n: number of new roosts formed in the roost_year iii. food_shortage_year: binary variable indicating whether a food shortage occurred during the roost_year. (1 indicates a food shortage) iv. winter_flower_pulse: variable indicating whether a winter flowering pulse occurred. (1 indicates a winter flowering pulse, 0 indicates no winter flowering pulse, and uk indicates it is not known if a winter flowering pulse occurred). 2. Eby_et_al_2022_roost_spillover_data.csv a. Number of variables: 7 b. Number of cases/rows: 2352 c. Variable List: i. roost_name: name of roost as character string ii. roost_year: year stored as 4 digit value iii. spillover: binary variable indicating whether a spillover occurred at the roost_name during the roost_year. iv. winter_flower_pulse: variable indicating whether a winter flowering pulse occurred during that roost_year. (1 indicates a winter flowering pulse, 0 indicates no winter flowering pulse, and uk indicates it is not known if a winter flowering pulse occurred). v. food_shortage_prev: binary variable indicating whether a food shortage occurred during the previous roost_year. (1 indicates a food shortage) vi. land_type: land type classification of feeding area for roost. Possible values are “Crop”, “Urban”, and “Forest” vii. ONI_2years: binary variable indicating whether an elevated ONI value was observed two years preceeding the roost_year. 3. Figure All 64 models assessed with multi scale Bayesian network models.pdf File contains graphic describing the 64 model structures considered with the multi scale network. Are there multiple versions of the dataset? NO