DOI: 10.5061/dryad.69p8cz991
title: Data from: Seasonal macro-demography of North American bird populations revealed through citizen science monitoring

description: Avian population sizes fluctuate and change over vast spatial scales, but
 the mechanistic underpinnings remain poorly understood. A key question is
 whether spatial and annual variation in avian population dynamics is
 driven primarily by variation in breeding season recruitment or by
 variation in overwinter survival. We present a method using large-scale
 eBird citizen-science data to develop species-specific indices of net
 population change as proxies for survival and recruitment gain, based on
 twice-annual, rangewide snapshots of relative abundance in spring and
 fall. We demonstrate the use of these indices by examining spatially
 explicit annual variation in survival and recruitment gain in two
 well-surveyed nonmigratory North American species, Carolina Wren
 (Thryothorus ludovicianus) and Northern Cardinal (Cardinalis cardinalis).
 We show that, while interannual variation in both survival and recruitment
 gain is slight for Northern Cardinal, eBird abundance data reveal strong
 and geographically coherent signals of interannual variation in the
 overwinter survival of Carolina Wren. As predicted, variation in
 wintertime survival dominates overall interannual population fluctuations
 of wrens and is correlated with winter temperature and snowfall in the
 northeastern United States, but not the southern United States. This study
 demonstrates the potential of volunteer-collected big datasets like eBird
 for inferring variation in demographic rates and introduces a new
 complementary approach towards illuminating the macrodemography of North
 American birds at comprehensive continental extents.
description: ## Seasonal macro-demography of North American bird populations revealed
 through citizen science monitoring
 [https://doi.org/10.5061/dryad.69p8cz991](https://doi.org/10.5061/dryad.69p8cz991) ## Description of the data and file structure This zip file contains underlying dataset, R script and R package that accompany the following project: Socolar, J., B. Galtbalt, A. Johnston, F. L. Sorte, O. Robinson, K. Rosenberg, and A. Dokter. 2024. Seasonal macro-demography of North American bird populations revealed through citizen science monitoring. Ecography. The zipped folder (data_code) contains:    * "carwre.rds" and "norcar.rds" - the bootstrapped abundance data for Carolina wren and Northern cardinal derived from eBird Basic Dataset (EBD).  * "tmax_winter-tmax_summer-swe.rds" - the daymet weather dataset, extracted from: [https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_DAYMET_V4](https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_DAYMET_V4) * "residents.R" script specifying the workflow * "macrodemography" R package containing the necessary functions to run the code.  The raw eBird data are freely available online from the Cornell Lab of Ornithology.  ## Sharing/Access information Links to other publicly accessible locations of the data: * https://science.ebird.org/en/use-ebird-data/download-ebird-data-products Data was derived from the following sources: * https://science.ebird.org/en/use-ebird-data/download-ebird-data-products ## Code/Software Datasets: "*carwre.rds*" and "*norcar.rds*" contains the data to run the workflow, "residents.R", starting from the section named "Calculate demographic indices" after loading the necessary packages. This section in the script loads the data as per below. It is recommended to put these species data inside the output directories as specified below: ``` file_species &lt;- paste0(params$output_path, "/abun_data/", species_code , ".rds") ``` ``` data &lt;- readRDS(file_species)  ``` Both datasets contain a large list with two components ("grid" and "abun"). Only component "abun" (i.e., abundance) is used for downstream analyses. The abundance data for all hexagon cells for each season and year is structured as individual dataframe within the list components of year, and contains following variables: * *cell* : Large hexagon cell ID * *tot_small*: total number of small hexagon cells within a large hexagon cell * *mean*: mean abundance estimate for the focal species in given large hexagon cell for given year and season * *rep_1 - 100*: Hundred replicates of bootstrapped abundance estimate from which above mean is calculated.  The dataset "*tmax_winter-tmax_summer-swe.rds*" contains the weather data to run weather regressions as specified in "residents.R", and loaded by calling: ``` weather_file &lt;- paste0(params$input_path, "weather/",paste0(params$daymet$label, collapse = "-"),".rds")  ``` ``` data_daymet &lt;- readRDS(weather_file) ``` This weather data contains following variables: * *cell*: Large hexagon cell ID * *year*: respective year of the weather variables * *tmax_winter*: maximum winter temperature in given cell and year (Celsius) * *tmax_summer*: maximum summer temperature in given cell and year (Celsius) * *swe*: snow water equivalent, the amount of water contained within the snowpack, kg/m^2 R is required to run "*residents.R*" script which installs the package "*macrodemography*" as part of the workflow; the script was created using version 4.4.0. Annotations are provided throughout the script through 1) defining parameters and input/output paths, 2) library loading, 3) importing raw data, 4) bootstrapping abundance, 5) calculating the demographic indices, 6) comparing recruitment and mortality variances, 7) running weather regressions, 8) smoothing regression estimates using ICAR (Intrinsic Auto-Regressive) models. Empty folders present are generated during the analysis.