A Floristic Quality Assessment Methodology for Citizen Science

The Floristic Quality Assessment (FQA) is a widely accepted method of gauging the health of native plant communities in a natural habitat. Rather than relying on a qualitative, subjective judgement on the health of a habitat, FQA derives a quantitative, standardized, objective metric, called the Floristic Quality Index (FQI) (Swink and Wilhelm 1979, 1994) to grade the quality of plant life, allowing for measurable results. It does so by assigning a numerical coefficient of conservatism (CoC), to each plant species within an ecological zone, based upon a plant species' fidelity to that zone, and then combining conservatism coefficient values (C) of all species with species richness at a given assessment site.

My objectives for this project are to demonstrate how the iNaturalist online platform, as part of a new methodology, can be used as the vehicle for data collection and analysis for a Floristic Quality Assessment, to offer an additional tool for restoration managers to use, and to enable citizen scientists participate in floristic quality assessments. This paper presents the methodology I developed and how it was applied to a complete floristic quality assessment.

Two online tools are used for this methodology. iNaturalist is an online community that allows naturalists to connect and share their observations of nature. Users, as citizen scientists, can upload photos and iNaturalist's image recognition software assists in identification. These identifications become part of an ever-growing database of wild organisms and become research grade when other naturalists corroborate the findings. iNaturalist can be accessed through their website (iNaturalist.org) or by using their smartphone app. Pictures taken by smartphone or Global Positioning System (GPS) enabled cameras contain location and timestamp meta-data that add value to the observation. iNaturalist is the platform used in this methodology to create assessment areas and make, record, track, and extract observations of plant life.

A traditional way to create assessment areas is to go into the field and hammer stakes into the ground in a rectilinear fashion to define a boundary for the assessment area, typically a square (WHEP 2015). With iNaturalist one can create virtual assessment areas of any size and any shape on any internet-connected computer. One can use iNaturalist to create FQA assessment areas with boundaries by using the iNaturalist feature "Add a New Place". By defining its "Place type" as "Open Space", the drawn boundary can be viewed as an overlay on a displayed map.

Using the iNaturalist smartphone app in the field, one can view a map of the area to be assessed and can overlay the virtual boundary of the assessment area onto the map. This can be done by using the "Explore" Feature, selecting the "Search" icon, then entering the name of the assessment area and selecting "Find locations named". In this way individuals can go into the field, view their location with respect to the assessment area, enter the assessment area and view the observations that have already been made.

With each observation made iNaturalist records photos, GPS location, and time which is then stored in its database. Observation data and its distribution can be viewed on a map by category, date, quality grade, identification type and place. By viewing the distribution of observation data on a map one can analyze what observations were made, their exact location and identify areas needing additional observational coverage. To involve citizen scientists in collecting data using iNaturalist, it is possible to create an iNaturalist project for the FQA and then invite citizen scientists to join. Once a part of the project, they can be invited to participate in a BioBlitz, a citizen science event that allows the intensive recording of a large amount of data in a short amount of time.

High quality of observational data is supported in several ways. iNaturalist employs artificial intelligence and image recognition coupled with a powerful database to suggest identifications. As its database grows, these suggested identifications become ever more accurate. An FQA should only use research grade data. Observational identification can become research grade only after the identification has been verified by two or more individuals from the community of citizens...