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Title: Citizen Science and Public Health- Can eBird data inform relationships between public health and access to biodiversity?

Citation Type: Miscellaneous

Publication Year: 2022

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Abstract: The relationship between access to nature and public health outcomes has been well-studied and established in the literature. However, most studies use simple greenness indices as a proxy for access to nature, which ignores the “quality” of the nature since greenness indices are not able to predict biodiversity. My objective was to investigate the relationship between citizen scientist collected biodiversity data from the eBird platform, urban greenness and four human health outcomes (asthma, coronary heart disease, and self-assessed mental and physical health). I mapped and tested for correlations among eBird record species richness, greenness as NDVI and PLACES human health data in urban census tracts located in three metro areas/ecological zones (Albany, NY: eastern deciduous forest, Kansas City, MO: tallgrass prairie, and Phoenix, AZ: Sonoran Desert). eBird species richness was related to greenness, measures of urbanization and several human health factors; however, the correlations varied by metro area and in strength. Provided confounders are controlled for, eBird data could help to refine models surrounding relationships between public health and nature access. It is established in the literature that proximity and access to “nature” and green space is related to subjective wellness and health outcomes (Jennings et al. 2016, Mavoa et al. 2019). Greenness indices are often used to evaluate the availability of natural areas in urban landscapes, and while NDVI is often a good estimator of green spaces, in some situations it may be over-simplification to suggest that the health benefits of nature areas are simply a function of local greenness (Markevych et al. 2017, Reid et al. 2018, Jarvis et al. 2020). There is also evidence that exposure to biodiversity, that is, the “quality” of the green space and the variety of habitats it offers, influences health outcomes (Fuller et al. 2007, Hanski et al. 2012, Shanahan et al. 2015, Mills et al. 2020). It is often assumed that greenness and biodiversity are synonymous and yet a functional crop field can be low in biodiversity but have a high NDVI. While there have been many attempts to model diversity based on vegetative reflectance, effectiveness and portability of these methods vary (Wang and Gamon 2019, Leveau et al. 2020), and ground truthing biodiversity is costly. Investigation of the mechanisms behind the relationship between greenness and health outcomes continues (Markevych et al. 2017); this study proposes to further probe this question using citizen gathered data from the eBird platform (Cornell Lab of Ornithology 2020), particularly in urban areas. With rapid increases in data and data-driven decisions, citizen science has begun to play a larger role in scientific investigations, despite its inherent flaws. eBird, which was initiated in 2002, is a well-established, crowdsourced, georeferenced database to which more than 600,000 citizens and scientists submit bird sightings. The quantity of eBird data collected by citizen birders may be useful in further elucidating relationships among greenness, biodiversity, and human health. Urban development tends to homogenize bird communities, meaning that species richness and diversity decrease at high urban densities (Melles 2005, Tratalos et al. 2007), and trends in bird diversity and density may indicate biodiversity status in general. I may be able to use eBird contributor record species richness to provide a proxy measure of biodiversity and relate this to greenness and human health. My objectives were to: 1) Map eBird records of bird species and determine bird species richness at the census tract level, and 2) Relate eBird record species richness to census tract level NDVI, development intensity and local human health outcome measures to elucidate relationships.

Url: https://www.medrxiv.org/content/10.1101/2022.01.04.22268764v1.full.pdf

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Authors: LaFantasie, J.J.

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Data Collections: IPUMS NHGIS

Topics: Health, Population Data Science, Population Health and Health Systems

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