Occurrence Cubes

A new way of aggregating heterogeneous species occurrence data.
Presented at TDWG 2020 - A Virtual Conference
Conference abstract: https://biss.pensoft.net/article/59154/

Transcript

1. 23 October 2020, TDWG conference
   Occurrence Cubes
   A new way of aggregating heterogeneous
   species occurrence data
   Damiano Oldoni, Q Groom, P Desmet
   

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2. Hi!
   

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3. Why
   

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4. - Address the ongoing biodiversity crisis
   - Essential Biodiversity Variables (EBV)
   - Aggregated “data cubes” (taxonomic, spatial
   and temporal dimensions)
   - Repeatable? Scalable? Automated?
   Pereira et al. (2013): https://doi.org/10.1126/science.1229931
   Kissling et al. (2018): https://doi.org/10.1111/brv.12359
   Why
   

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5. What
   

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6. Occurrences are events in a 3-dimensional space
   Occurrences
   

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7. Occurrences are events in a 3-dimensional space
   - Taxonomic (what)
   Occurrences
   

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8. Occurrences
   Occurrences are events in a 3-dimensional space
   - Taxonomic (what)
   - Temporal (when)
   

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9. Occurrences are events in a 3-dimensional space
   - Taxonomic (what)
   - Temporal (when)
   - Spatial (where)
   Occurrences
   

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10. Aggregate occurrence data to
    partition the 3-dimensional space:
    - Taxonomic (e.g. species level)
    - Temporal (e.g. years)
    - Spatial (e.g. 1x1km cells of a
    reference grid)
    Occurrences → Occurrence cube
    

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11. year eea_cell_code speciesKey n min_coord_uncertainty
    2000 1kmE3809N3113 2889173 1 700
    2000 1kmE3809N3135 2889173 1 700
    ... ... ... ... ...
    2014 1kmE3886N3121 2889173 51 10
    2014 1kmE3886N3122 2889173 109 10
    ... ... ... ... ...
    2018 1kmE4047N3067 2889173 1 2828
    Occurrences cube: tabular representation
    

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12. How
    

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13. 1. Specify constraints
    (what, where, when) and
    granularity
    2. Harvest occurrences and
    assess data quality
    3. Solve uncertainty
    4. Aggregate
    Occurrences → Occurrence cube
    

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14. What, where, when
    GBIF Occurrence Download 2020-02-12: https://doi.org/10.15468/dl.aobecp
    Specify constraints 1
    

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15. 2000
    2001
    ...
    2017
    2018
    time
    taxonomic
    1km
    1km
    spatial
    Reynoutria Houtt.
    R.
    japonica
    R.
    bohemica
    R.
    sachalinensis
    Specify granularity
    https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2
    1
    

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16. Harvest occurrences
    GBIF Occurrence Download 2020-02-12: https://doi.org/10.15468/dl.aobecp
    2
    

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17. Assess data quality
    GBIF Occurrence Download 2020-02-12: https://doi.org/10.15468/dl.aobecp
    2
    

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18. issue_to_discard "ZERO_COORDINATE",
    "COORDINATE_OUT_OF_RANGE",
    "COORDINATE_INVALID",
    "COUNTRY_COORDINATE_MISMATCH"
    )
    Assess data quality 2
    

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19. occurrenceStatus_to_discard "absent",
    "excluded"
    )
    Assess data quality 2
    

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20. scientificName taxonRank species taxonomicStatus
    Reynoutria japonica Houtt. SPECIES Reynoutria japonica ACCEPTED
    Fallopia japonica (Houtt.) Ronse
    Decraene
    SPECIES Reynoutria japonica SYNONYM
    Fallopia compacta (Hook.fil.)
    G.H.Loos & P.Keil
    SPECIES Reynoutria japonica SYNONYM
    Fallopia japonica var. japonica VARIETY Reynoutria japonica DOUBTFUL
    Solve taxonomic uncertainty 3
    

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21. Trivial for most typical aggregation levels
    Solve temporal uncertainty 3
    

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22. Solve spatial uncertainty 3
    Directly assigning coordinates to grid can lead to
    huge spatial bias
    

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23. Solve spatial uncertainty 3
    Random assignment to grid within uncertainty
    circle
    

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24. synonyms,
    lower ranks
    trivial for most
    typical
    aggregation levels
    time
    taxonomic spatial
    random
    assignment
    Solve uncertainty 3
    

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25. year eea_cell_code speciesKey n min_coord_uncertainty
    2014 1kmE3886N3121 2889173 51 10
    2014 1kmE3886N3122 2889173 109 10
    ... ... ... ... ...
    2018 1kmE4047N3067 2889173 1 2828
    Aggregate 4
    Number of occurrences of a specific taxon in a
    specific cell and in a specific time interval
    

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26. Aggregate 4
    Number of occurrences of a specific taxon in a
    specific cell and in a specific time interval
    year eea_cell_code speciesKey n min_coord_uncertainty
    2014 1kmE3886N3121 2889173 51 10
    2014 1kmE3886N3122 2889173 109 10
    ... ... ... ... ...
    2018 1kmE4047N3067 2889173 1 2828
    

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27. Examples
    

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28. Oldoni et al. (2020): https://zenodo.org/record/3637911
    

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29. Oldoni et al. (2020): https://doi.org/10.5281/zenodo.3635510
    

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30. Applications
    

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31. Number of occurrences Area of occupancy
    Indicators
    

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32. Species modelling & risk mapping
    Davis et al. (2020): https://doi.org/10.3897/biss.4.59172
    Risk map for presence of Cyperus eragrostist
    

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33. Species interactions
    Groom (2020): https://github.com/AgentschapPlantentuinMeise/interactias
    

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34. Occurrences are events in a three-dimensional
    space
    Add additional dimensions:
    - Lifestage
    - Provenance
    - ...
    Extendable
    

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35. Oldoni D, Groom Q, Desmet P (2020) Occurrence cubes: A new way of aggregating
    heterogeneous species occurrence data. https://doi.org/10.3897/biss.4.59154
    @trias_project Tracking Invasive Alien Species
    trias-project.be
    @oscibio Open science lab for biodiversity
    oscibio.inbo.be
    @damianozingaro
    Thank you!
    

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