In 2014, the Miistakis Institute hosted a Citizen Science Conference, exploring this diverse field with sessions on program design, evaluation, policy implications, technology and applications in parks and protected areas. This links to the website that chronicled that event.
Citizens Help with Automated Recording Units and Camera Traps to Support Conservation Science and Decision-making in Alberta
Citizen science can and have produced enormous amounts of data worldwide that are being used to address pressing conservation questions. The most common type of CS data is a documented detection of a species: a photo, an audio record, or a field note, often entered into a common database through an app. For obvious reasons, users almost never document the absence of the species, therefore the name, “presence- only” data. However, knowing the locations or times when a species is not detected is valuable for scientific analyses. Unlike presence-only data, presence-absence data sets inform about baseline prevalences and can correct for sampling biases due to the opportunistic and accessibility-driven nature of the data collection.
The Alberta Biodiversity Monitoring Institute (ABMI) collects data via automated recording units and camera traps that are deployed through the province each year. The audio and image files are stored and tagged through a system called WildTrax. The resulting data are used to describe habitat associations and spatial distribution of species. In our poster, we describe the data collection and data entry systems, and show the results from modeling. WildTrax is capable of storing data from CS projects (from units and cameras deployed by citizens), provide a standardized platform for computer aided and crowd sourced species identification, thus adding useful information on mammals and birds in Alberta, and ultimately benefiting conservation science and decision-making.
Speaker Bio: Péter Sólymos is a statistical ecologist with a research focus on developing and applying computational techniques for big data sets to better inform biodiversity conservation and natural resource management over large spatial scales. He is interested in understanding how cumulative effects of human development affect species. He is developing new algorithms and methodologies for more efficient data-information- knowledge pipelines.