NEAFWA 2018

AUTHORS: Catherine Sun, Department of Natural Resources Cornell University, New York Cooperative Fish and Wildlife Research Unit; Angela Fuller, New York Cooperative Fish and Wildlife Research Unit; Jeremy Hurst, New York State Department of Environmental Conservation

ABSTRACT. Successful wildlife management requires knowledge about patterns of population abundance, distribution, and dynamics such as growth. However, limited resources for data collection make population inferences over large landscapes difficult. This is especially problematic for species that are long lived, have wide ranges of movement, occur at low densities, or are widely distributed. In New York, the black bear population has been growing and their range has been expanding since the mid-1990s. To collect presence-absence data on black bears across the state, traditional sampling methods must be supplemented by approaches that collect data across broad spatial and temporal extents. We developed a citizen science project called iSeeMammals that enlists volunteers to collect and submit opportunistic presence-absence data on black bears through three mechanisms: observations, hikes, and trail cameras. iSeeMammals launched in early 2017, and 712 users had registered by October 31, 2017. We received 290 observation in 38 counties, 43 hikes in 8 counties, and 4,552 camera days from 71 trail cameras in 12 counties. The 406 sets of spatial locations from iSeeMammals expanded the spatial extent of data collection in 2017 by 3.7 times and increased the number of camera days by 1.26 times, thereby increasing the total amount of data collected by 1.7 times. We discuss patterns in data filtering and data quality, and how iSeeMammals and similar citizen science initiatives can help address population-level research questions in wildlife and natural resource management.