Citizen science data enhance spatio-temporal extent and resolution of animal population studies

Abstract

Informed management and conservation decisions for animal populations often require data at sufficient geographic, temporal, and demographic resolutions for precise and unbiased estimates of parameters including population size and demographic rates. Recently developed integrated population models estimate such parameters by unifying population presence-absence and demographic data, and we demonstrate how citizen science offers a cost-efficient mechanism to collect such data. We describe the early results of iSeeMammals, a citizen science project that collects opportunistic data on the black bear population in New York State by enlisting volunteers to collect data through observations, hikes, and trail cameras. In 10 months, iSeeMammals increased the spatio-temporal extent of data collection by approximately fourfold and reduced cost by 83% compared to systematic sampling. In combination with other datasets in integrated population model frameworks, large, spatiotemporally extensive datasets from citizen science projects like iSeeMammals can help improve inferences about population-level structure and dynamics.