DOI: 10.5061/dryad.dfn2z357x
title: Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size

description: Population size is a key metric for management and policy decisions, yet
 wildlife monitoring programs are often limited by the spatial and temporal
 scope of surveys. In these cases, citizen science data may provide
 complementary information at higher resolution and greater extent. We
 present a case study demonstrating how data from the eBird citizen science
 program can be combined with regional monitoring efforts by the U.S. Fish
 and Wildlife Service to produce high-resolution estimates of golden eagle
 abundance. We developed a model that uses aerial survey data from the
 western United States to calibrate high-resolution annual estimates of
 relative abundance from eBird. Using this model, we compared regional
 population size estimates based on the calibrated eBird information to
 those based on aerial survey data alone. Population size estimates based
 on the calibrated eBird information had strong correspondence to estimates
 from aerial survey data in two out of four regions, and population
 trajectories based on the two approaches showed high correlations. We
 demonstrate how the combination of citizen science data and targeted
 surveys can be used to (a) increase the spatial resolution of population
 size estimates, (b) extend the spatial extent of inference, and (c)
 predict population size beyond the temporal period of surveys. Findings
 based on this case study can be used to refine policy metrics used by the
 U.S. Fish and Wildlife Service and inform permitting regulations (e.g.,
 mortality/harm associated with wind energy development). Policy
 implications. Our results demonstrate the ability of citizen science data
 to complement targeted monitoring programs and improve the efficacy of
 decision frameworks that require information on population size or
 trajectory. After validating citizen science data against survey-based
 benchmarks, agencies can harness strengths of citizen science data to
 supplement information needs and increase the resolution and extent of
 population size predictions.
description: # Leveraging the strengths of citizen science and structured surveys to
 achieve scalable inference on population size
 [https://doi.org/10.5061/dryad.dfn2z357x](https://doi.org/10.5061/dryad.dfn2z357x) ## Description of the data and file structure This file contains information and explanation for the data and code that accompany the following project: Stillman, A.N., P.E. Howell, G.S. Zimmerman, E.R. Bjerre, B.A. Millsap, O.J. Robinson, D. Fink, E.F. Stuber, and V. Ruiz-Gutierrez. 2023. Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size. Journal of Applied Ecology. This .README file accompanies the archived data for this project. Two files marked with the word "Script:" provide R code for two Bayesian models described in the main text. Two files marked with the word "Dataset:" provide the necessary data to run the models. Data from the eBird Status and Trends program are freely available online from the Cornell Lab of Ornithology. Software versions are recorded in the references section of the manuscript. ## Sharing/Access information Links to other publicly accessible locations of the data: * [https://science.ebird.org/en/status-and-trends](http://...) ## Code/Software ##### Dataset: jags\_data.rds This file contains the data to run the model in PopEstSide.mdl. Reading the file into R using the line "mod.data &lt;- readRDS("jags\_data.rds")" will return a large list with the following components: * Detection rates used in FWS Golden Eagle population size updates, developed by Western EcoSystems Technology, Inc., Cheyenne, Wyoming, USA. mndet = Mean for normal distribution (from Western EcoSystems Technology) sedet = SD for normal distribution (from Western EcoSystems Technology) ndetcls = Number of detection classes fixedyear\_W = Median Year (for centering), same for both perched and flying birds nOVstrata = BCR (strata) nyears\_W = Number of years A = Area from NABCI layers n.YrBCRTrn = Number of unique Year-BCR-Transect combinations * Indexing for summing subtransect counts to the transect level for perched (P) and flying (F) birds startP stopP startF stopF * Data for perched birds ncounts\_P = Number of observations areaP = Transect area (one side) in km^2 detcls\_P = Detection classes strat\_P = BCRs (strata; 9, 10, 16, 17) year\_P = Year obser\_P = BCR-Transect count\_P = Number of perched eagles observed along a Year-BCR-Transect-Elev-Side nobservers\_P = Number of BCR-Transects Trn\_AP = Transect area (both sides) in km^2 * Data for flying birds ncounts\_F = Number of observations areaF = Transect area (one side) in km^2 detcls\_F = Detection classes strat\_F = BCRs (strata) year\_F = Year obser\_F = BCR-Transect count\_F = Number of eagles observed along a Year-BCR-Transect-Elev-Side nobservers\_F = Number of BCR-Transects Trn\_AF = Transect area (both sides) in km^2 ##### Dataset: LinearModel\_Data.csv BCRTrn\_year = Unique ID for each transect flown during the aerial survey, in the format: [BCR number].[transect number].[year]. BCR = Bird Conservation Region (BCR) where each transect is located. year = Year in which the aerial transect survey occured. mean.D = Estimated density of Golden Eagles within the surveyed area, in eagles/km^2. These numbers come from the model of aerial survey data. eBirdMean = The mean relative abundance value for all \~3x3 gridcells that overlap the flight track during a single transect survey. Relative abundance values are annualized to match the year of the survey. ##### Script: PopEstSide.mdl This is the model code for the hierarchical Poisson model in Methods Section 2.2. The input data are contained in jags\_data.rds. Outputs from this model include (1) population size estimates for each BCR in every year, and (2) density estimates (eagles/km^2) for each transect in every year. ##### Script: Run\_LinearModel.txt This is the model code for the hierarchical linear model in Methods Section 2.3. The input data are contained in LinearModel\_Data.csv.