DOI: 10.5061/dryad.7h44j103t
title: Modeled annual seed production estimates for Pinus edulis 1900-2024

description: These data are gridded estimates of annual seed production (or mast
 seeding intensity) in a conifer species of the southwestern United States.
 The extent of these predictions cover the range of Pinus edulis in the
 U.S. states of Colorado, New Mexico, Arizona, and Utah between 1900-2024.
 Also included is a map of the variance in bootstrapped predictions for the
 covered range, and the unpublished training data used in model validation.
 All other data sources are publically available and free to use. 
description: These data were collected with the purpose of validating an
 established forecast model of cone production in this species. The model
 was parameterized with existing data on seed production from Wion et al.
 2020 (Ecography), and these known relationships between seed production
 and weather were extrapolated across the range of this species and
 hindcasted into the past. These data may be used to understand where
 climatic conditions were most likely favorable to mast seed production in
 this species for a given year, and where those mast years are likely to
 have occurred.
description: This data is associated with the publication Mast Hindcasting reveals
 pervasive effects of extreme drought on a foundational conifer species, in
 New Phytologist, 2025. **Description of the data and file structure** Data
 descriptions can be found in the associated metadata. Validation Data
 contains a csv file, containing testing data used in model validation.
 These data were collected for this study and have not been previously
 published elsewhere.  These data are associated with this data release by
 relation to the publication and associated analyses but should be
 considered auxiliary to the primary components. *The columns in the
 Validation Data are as follows:* site - unique ID year - year of a given
 cone scar as observed on a given branch.  This must be adjusted +1 to
 reflect cone production in the year of maturation.   cwd - 30 year average
 annual climatic water deficit, a measurement of cumulative drought stress,
 derived from TerraClimate mon - 30 year average annual proportion of
 monsoon precipitation falling between July-Sep, derived from PRISM
 cone_total - the number of cone scars counted multiplied by the number of
 cone bearing branches fert_vpd - z-score scaled vapor pressure deficit
 during the year of cone pollination (year of cone scar observation, -1
 from maturation) aug_vpd - z-score scaled vapor pressure deficit during
 the year of cone initiation (-1 year of cone scar observation, -2 from
 maturation) x - Longitude (EPSG:4326) y - Latitude (EPSG:4326)
 scaled_cones - a relativized measure of annual cone production at a given
 site compared to all years measured.  These values are standardized
 between 0-1 to reflect low cone years (closer to zero) and high cone years
 (closer to one).  Predictor variable used in most/all models.
 \___________________________________________________________________________________________________\_ Raw predictions - the raw model output and associated bootstrapped confidence intervals Prediction percentiles - transformed model predictions, constrained to 0-1 representing the relative ranking of a given mast year at a given pixel relative to all other years at that same location. Raw predictions includes a file - model out, which is the primary datafile associated with this release. The folder "bootstrapped CI" contains the 5th and 95th percentiles of bootstrapped predictions, as well as the confidence interval width for each year, and the mean CI width for all years. Prediction percentiles includes a similar file - scaled, which is the secondary datafile associated with this release. Each raster layer contains values between 0-1. These values represent the rank-order mast year relative to all other years at a given pixel. The bootstrapped CI's are identical to those above, but again, transformed to values between 0-1. Email [awion@usgs.gov](mailto:awion@usgs.gov) or [andreas.wion@gmail.com](mailto:andreas.wion@gmail.com) with data quality issues or questions.