Variation in the abundance of animals affects a broad range of ecosystem processes. However, patterns of abundance for large mammals, and the effects of human disturbances on them are not well understood because we lack data at the appropriate scales. We created eMammal to effectively camera-trap at landscape scale. Camera traps detect animals with infrared sensors that trigger the camera to take a photo, a sequence of photos, or a video clip. Through photography, camera traps create records of wildlife from known locations and dates, and can be set in arrays to quantify animal distribution across a landscape. This allows linkage to other distributed networks of ecological data.
Through the eMammal program, we demonstrate that volunteer-based camera trapping can meet landscape scale spatial data needs, while also engaging the public in nature and science. We assert that camera surveys can be effectively scaled to a macrosystem level through citizen science, but only after solving challenges of data and volunteer management.
We present study design and technology solutions for landscape scale camera trapping to effectively recruit, train and retain volunteers while providing efficient data workflows and quality control.
Our initial work with > 400 volunteers across six contiguous U.S. states has proven that citizen scientists can deploy these camera traps properly (94 % of volunteer deployments correct) and tag the photos accurately for most species (67–100 %). Using these tools we processed 2.6 million images over a 2 year period. The eMammal cyberinfrastructure made it possible to process far more data than any participating researcher had previously achieved. The core components include an upload application using a standard metadata format, an expert review tool to ensure data quality, and a curated data repository.
Macrosystem scale monitoring of wildlife by volunteer-run camera traps can produce the data needed to address questions concerning broadly distributed mammals, and also help to raise public awareness on the science of conservation. This scale of data will allow for linkage of large mammals to ecosystem processes now measured through national programs.
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McShea, W.J., Forrester, T., Costello, R. et al. Volunteer-run cameras as distributed sensors for macrosystem mammal research. Landscape Ecol 31, 55–66 (2016). https://doi.org/10.1007/s10980-015-0262-9
- Camera traps
- Standard metadata
- Citizen science