The use of citizen science data to predict the winter distribution of the snowy owl in Ontario

Abstract

As an Anishinaabe person and environmental scientist, I feel I have an inherent duty to be a steward of the environment. I chose to conduct research on Snowy Owls after learning about the conservation issues affecting this species, namely habitat loss due to climate change. I hypothesized that using citizen science data and bioclimatic (BIOCLIM) variables would effectively predict the winter distribution of Snowy Owls in the province of Ontario s. I created a species distribution model (SDM) based upon occurrence data from the eBird and iNaturalist citizen science databases and bioclimatic variables using the computer software Maxent. The occurrence data was cleaned to a scale of 1-km2 using QGIS to mitigate impacts of sampling biases in citizen science data. Though it had a relatively high AUC of 0.848, the final SDM was inaccurate in predicting the winter distribution of Snowy Owls in Ontario. Sampling biases inherent in citizen science data, possibly exacerbated by the permutation importance of the chosen BIOCLIM variables, were found to heavily skew the results of the SDM. The SDM indicated that Snowy Owls are more populous within Southern Ontario than Northern Ontario, and within developed areas highly populated by humans. Careful consideration of sampling biases and their magnitude of impact is recommended when developing an SDM based upon data acquired via citizen science databases. Further research with alternative methodologies is required to develop an appropriate SDM of winter occurrence of Snowy Owls in Ontario.