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Predictive Models for Biodiversity in Flora Text Documents from Royal Botanic Gardens

Explore how to create predictive models anticipating biodiversity loss by clustering habitats and classifying flora traits from textual data at Kew Gardens, London. Initial experiments show promising results in predicting plant traits directly from flora data.

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Predictive Models for Biodiversity in Flora Text Documents from Royal Botanic Gardens

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  1. Extracting Predictive Models from Flora Free-Text Documents at The Royal Botanic Gardens, Kew, London Joanna Pawlik, PhD Candidate

  2. Context • Habitat and Flora data from Kew Gardens, London, • Textual Data clustered and classified, • Analysis using R, • Main motivation: to create predictive models that, anticipate biodiversity loss.

  3. First Experiment

  4. Habitat Clustering

  5. Habitat Classification

  6. First Experiment Outcome

  7. Second Experiment Our experiment has two stages: • Habitat ClusteringWe have explored probabilistic clustering to the habitat in order to identify different types of habitat. • Classification from flora traits using habitat cluster as the main predictor. • We used cross-validation to validate accuracy and confusion matrix to perform sensitivity test.

  8. Initial Findings

  9. Conclusion • Our experiment demonstrated predictive capabilities between flora traits and habitat cluster. • Our model proved that it is possible to predict plant’s traits directly from flora. • This means that we can predict families and species. The project will continue with other experiments such as identification of factors (including taxonomic and environmental) that influence biodiversity and stability of ecosystems.

  10. Thank you for your attention

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