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
2. Hi!
3. Why
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
5. What
6. Occurrences are events in a 3-dimensional space Occurrences
7. Occurrences are events in a 3-dimensional space - Taxonomic (what) Occurrences
8. Occurrences Occurrences are events in a 3-dimensional space - Taxonomic (what) - Temporal (when)
9. Occurrences are events in a 3-dimensional space - Taxonomic (what) - Temporal (when) - Spatial (where) Occurrences
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
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
12. How
13. 1. Specify constraints (what, where, when) and granularity 2. Harvest occurrences and assess data quality 3. Solve uncertainty 4. Aggregate Occurrences → Occurrence cube
14. What, where, when GBIF Occurrence Download 2020-02-12: https://doi.org/10.15468/dl.aobecp Specify constraints 1
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
16. Harvest occurrences GBIF Occurrence Download 2020-02-12: https://doi.org/10.15468/dl.aobecp 2
17. Assess data quality GBIF Occurrence Download 2020-02-12: https://doi.org/10.15468/dl.aobecp 2
18. issue_to_discard <- c( "ZERO_COORDINATE", "COORDINATE_OUT_OF_RANGE", "COORDINATE_INVALID", "COUNTRY_COORDINATE_MISMATCH" ) Assess data quality 2
19. occurrenceStatus_to_discard <- c( "absent", "excluded" ) Assess data quality 2
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
21. Trivial for most typical aggregation levels Solve temporal uncertainty 3
22. Solve spatial uncertainty 3 Directly assigning coordinates to grid can lead to huge spatial bias
23. Solve spatial uncertainty 3 Random assignment to grid within uncertainty circle
24. synonyms, lower ranks trivial for most typical aggregation levels time taxonomic spatial random assignment Solve uncertainty 3
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
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
27. Examples
28. Oldoni et al. (2020): https://zenodo.org/record/3637911
29. Oldoni et al. (2020): https://doi.org/10.5281/zenodo.3635510
30. Applications
31. Number of occurrences Area of occupancy Indicators
32. Species modelling & risk mapping Davis et al. (2020): https://doi.org/10.3897/biss.4.59172 Risk map for presence of Cyperus eragrostist
33. Species interactions Groom (2020): https://github.com/AgentschapPlantentuinMeise/interactias
34. Occurrences are events in a three-dimensional space Add additional dimensions: - Lifestage - Provenance - ... Extendable
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!