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.
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- undergraduate theses [325]