Capitalizing on opportunistic citizen science data to monitor urban biodiversity: A multi-taxa framework
Biological Conservation(2020)
Abstract
Monitoring urban biodiversity is increasingly important, given the increasing anthropogenic pressures on biodiversity in urban areas. While the cost of broad-scale monitoring by professionals may be prohibitive, citizen science (also referred to as community science) will likely play an important role in understanding biodiversity responses to urbanization into the future. Here, we present a framework that relies on broad-scale citizen science data - collected through iNaturalist - to quantify (1) species-specific responses to urbanization on a continuous scale, capitalizing on globally-available VIIRS night-time lights data; and (2) community-level measures of the urbanness of a given biological community that can be aggregated to any spatial unit relevant for policy-decisions. We demonstrate the potential utility of this framework in the Boston metropolitan region, using > 1000 species aggregated across 87 towns throughout the region. Of the most common species, our species-specific urbanness measures highlighted the expected difference between native and non-native species. Further, our biological community-level urbanness measures - aggregated by towns - negatively correlated with enhanced vegetation indices within a town and positively correlated with the area of impervious surface within a town. We conclude by demonstrating how towns can be 'ranked' promoting a framework where towns can be compared based on whether they over- or under-perform in the urbanness of their community relative to other towns. Ultimately, biodiversity conservation in urban environments will best succeed with robust, repeatable, and interpretable measures of biodiversity responses to urbanization, and involving the broader public in the derivation and tracking of these responses will likely result in increased bioliteracy and conservation awareness.
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Key words
Citizen science,Community science,Participatory science,Community-based monitoring,Urban ecology,Urban tolerance,Sampling biases,iNaturalist, species occurrence data
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