Correlations between Covid-19 Policy and Great Horned Owl Sightings

By Vincent Wilson

Great Horned Owl Population Centers (01-2019)-(05-2020)

Determined with K-Means clustering

The COVID-19 pandemic has warranted various responses from states such as business closures, social distancing rules and mandatory mask regulations. Datasets such as a policy summary from the National Governor's Association demonstrate that some states have employed closures on nearly nine distinct categories of business (Adams and Locke 2020). These policies may contribute to lowered levels of human activity during the COVID-19 pandemic.

EBird, an online birding platform published by the Cornell Lab of Ornithology contains user submitted data on bird sightings. Birds within EBird such as the Great Horned Owl are non-migratory and avoid human contact. An assumption of this project's hypothesis is that the frequency of Great Horned Owl sightings accurately reflect the level of human activity.


Because of the COVID-19 Pandemic, Great Horned Owl sightings will increase because of lower levels of human activity as a result of state lockdown policy.


  1. Analyze Ebird data with Python and a Jupyter Notebook (here)

  2. Identify which birds are most likely to be impacted by lowered human activity. I chose the Great Horned Owl ( Bubo virginianus) because of it's non-migratory behavior. That way, sightings will not be impacted by the seasonal movement of owl populations.

  3. Find which states have the greatest number of sightings. Employ a metric that takes into account the number of birders to lessen the impact of greater birder activity. As can be seen on the graph to the right, birder activity varies drastically on a monthly basis.

  4. Compare those states which have the highest number of bird sightings to state policy. Use a Pearson correlation to determine relationship.

This graph shows the number of birders per month over the course of over a year. The red line represents the beginning of the pandemic (2020-02-29). The number of birders varies drastically from month to month, so any analysis will need to account for these shifts.


Bird Sighting Data(Sourced from Ebird):

NOTE: These graphs may take a while to appear, since they are sourced from here. Data only applies only to the Continental US

The total Great Horned Owl sightings in the given time frame. California (41,819) has approximately twice as many sightings as the second most frequent sighting state, Colorado (20,148).

The month to month sightings of Great Horned Owl in each state. California remains the state with the highest total in every month.

The percentage change in Great Horned Owl sightings from month to month.

This graph represents the average number of birds seen per birder within the time frame. Note that while California had the highest number of sightings, Vermont, Utah and Indiana have a much greater bird/birder score. Therefore, California's large number of sightings can be explained in part by the state's number of active birders.

State Policy Data(Sourced from National Governor's Association):

The table on the left represents the COVID-19 closure policies of each state for the following categories:

  • Restaurants and Bars for Dine-In
  • Retail
  • Outdoor Recreation (e.g. golf courses, parks etc.)
  • Gyms and Fitness Centers
  • Places of Worship
  • Personal Services (e.g. massage services, nail salons, tattoo parlors etc.)
  • Entertainment (e.g. bowling alleys, movie theaters, sporting venues, etc.)
  • Manufacturing and Distribution
  • Construction
  • Office Environments (in-person)

If a state had a policy banning or restricting any of these categories, an arbitrary score of 1 was given to that category, if not a 0. The sum of any single state's categories is the State's score, seen in the right-most column.

Correlating the State Policy Score and Bird Sighting Data:

The Graph below displays the data for the first 15 states. The data "BIRDS PER BIRDER" and "% INCREASE IN SIGHTINGS" refer to the average of those values from March 1 to May 31 (the months of the pandemic within the dataset).



In both graphs, it is evident that there is a low correlation between either the birds/birder metric or increase in sightings. This is made even more evident by the low Pearson correlation score. A score of 0 means no correlation, 1 means positive-linear and -1 means negative-linear:


The main finding of the project was that correlation between increased bird sightings and birds seen per birder and the lockdown policy of a state is low (see above Pearson Correlation). Although my work disproved my original hypothesis, I think I learned a lot from the experience of performing data analysis on a research project of my own design. From what I could find, no other study has attempted to determine a quantitative relationship between increases in wildlife sightings as a result of lowered lockdown activity, so my project was an exploration of uncharted waters. In the future, I hope to refine this research with a more nuanced policy scoring system to reassess the data. Thanks for checking out my project!


Thank you to:

  • Claudia Ludwig

  • Rachel Calder

  • The Computational Modeling Workgroup

  • The Institute for Systems Biology

Code: (Jupyter Notebook in HTML Format)

Data Citations:

Adams, Locke: summary of actions addressing business re-openings [Internet]. Washington (DC): National Governor's Association; c2020 [updated 2020 Oct 16; cited 2020 Oct 20]. Available from:

Ebird: USA Great Horned Owl Sightings 01-2019, 05-2020 [Internet]. Ithaca (NY): Cornell Lab of Ornithology; c2020 [updated 2020 May 31; cited 2020 Oct 20]. Available from: (Data by request only)

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