Recommending plant taxa for supporting on-site species identification

Wittich, Hans Christian; Seeland, Marco GND; Wäldchen, Jana GND; Rzanny, Michael Carsten GND; Mäder, Patrick

Background: Predicting a list of plant taxa most likely to be observed at a given geographical location and time is useful for many scenarios in biodiversity informatics. Since efficient plant species identification is impeded mainly by the large number of possible candidate species, providing a shortlist of likely candidates can help significantly expedite the task. Whereas species distribution models heavily rely on geo-referenced occurrence data, such information still remains largely unused for plant taxa identification tools. Results: In this paper, we conduct a study on the feasibility of computing a ranked shortlist of plant taxa likely to be encountered by an observer in the field. We use the territory of Germany as case study with a total of 7.62M records of freely available plant presence-absence data and occurrence records for 2.7k plant taxa. We systematically study achievable recommendation quality based on two types of source data: binary presence-absence data and individual occurrence records. Furthermore, we study strategies for aggregating records into a taxa recommendation based on location and date of an observation. Conclusion: We evaluate recommendations using 28k geo-referenced and taxa-labeled plant images hosted on the Flickr website as an independent test dataset. Relying on location information from presence-absence data alone results in an average recall of 82%. However, we find that occurrence records are complementary to presence-absence data and using both in combination yields considerably higher recall of 96% along with improved ranking metrics. Ultimately, by reducing the list of candidate taxa by an average of 62%, a spatio-temporal prior can substantially expedite the overall identification problem.

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Wittich, Hans Christian / Seeland, Marco / Wäldchen, Jana / et al: Recommending plant taxa for supporting on-site species identification. 2018.

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