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Title: Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size
Abstract

Population size is a key metric for management and policy decisions, yet wildlife monitoring programmes 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 programme can be combined with regional monitoring efforts by the US 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 with 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 US 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 programmes 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.

 
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Award ID(s):
1939187
NSF-PAR ID:
10468889
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;  ;  ;  
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
Journal of Applied Ecology
ISSN:
0021-8901
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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