Occurrence cubes: a new paradigm for aggregating species occurrence data

Damiano Oldoni; Quentin Groom; Tim Adriaens; Amy J.S. Davis; Lien Reyserhove; Diederik Strubbe; Sonia Vanderhoeven; Peter Desmet

doi:10.1101/2020.03.23.983601

Occurrence cubes: a new paradigm for aggregating species occurrence data

Damiano Oldoni, Quentin Groom, Tim Adriaens, Amy J.S. Davis, Lien Reyserhove, Diederik Strubbe, Sonia Vanderhoeven, Peter Desmet

Research output: Contribution to journal › Preprint

Abstract

In this paper we describe a method of aggregating species occurrence data into what we coined textquotedblleftoccurrence cubestextquotedblright. The aggregated data can be perceived as a cube with three dimensions - taxonomic, temporal and geographic - and takes into account the spatial uncertainty of each occurrence. The aggregation level of each of the three dimensions can be adapted to the scope. Built on Open Science principles, the method is easily automated and reproducible, and can be used for species trend indicators, maps and distribution models. We are using the method to aggregate species occurrence data for Europe per taxon, year and 1km2 European reference grid, to feed indicators and risk mapping/modelling for the Tracking Invasive Alien Species (TrIAS) project.

Original language	English
Number of pages	12
DOIs	https://doi.org/10.1101/2020.03.23.983601
Publication status	Published - 25-Mar-2020

Thematic List 2020

Invasive species
Data & infrastructure

Access to Document

10.1101/2020.03.23.983601Licence: CC BY

https://www.biorxiv.org/content/early/2020/03/25/2020.03.23.983601Licence: CC BY

1 Finished
1 Active

B-Cubed - Biodiversity Building Blocks for policy
Desmet, P. (Project leader), Adriaens, T. (Cooperator), Adriaenssens, V. (Cooperator), Delva, S. (Cooperator), Hillaert, J. (Cooperator), Langeraert, W. (Cooperator), Oldoni, D. (Cooperator), Onkelinx, T. (Cooperator), Reyserhove, L. (Cooperator), Van Calster, H. (Cooperator), Van Daele, T. (Cooperator), Van den Broeck, F. (Cooperator) & Vanderhaeghe, F. (Cooperator)
1/03/23 → 30/09/26
Project: Evinbo
TrIAS - Tracking Invasive Alien Species
Adriaens, T. (Project leader), D'hondt, B. (Cooperator), Delva, S. (Cooperator), Demolder, H. (Cooperator), Desmet, P. (Cooperator), Devisscher, S. (Cooperator), Oldoni, D. (Cooperator), Reyserhove, L. (Cooperator), Speybroeck, J. (Cooperator), Van Hoey, S. (Cooperator), Van Landuyt, W. (Cooperator) & Verzelen, Y. (Cooperator)
1/01/17 → 31/12/21
Project: Evinbo

3 Paper/Powerpoint/Abstract
1 Report not published by INBO
1 Poster
1 A2: Article in a journal with peer review, not included in A1

B-Cubed: Leveraging analysis-ready biodiversity datasets and cloud computing for timely and actionable biodiversity monitoring
Groom, Q., Abraham, L., Adriaens, T., Breugelmans, L., Clarke, D. A., Fernández, M. A., Hendrickx, L., Hui, C., Kumschick, S., Martini, M., McGeoch, M., Metodiev, T., Miller, J., Oldoni, D., Pereira, H. M., Preda, C., Robertson, T., Rocchini, D., Seebens, H. & Teixeira, H. & 5 others, Trekels, M., Wilson, J. R., Yovcheva, N., Zengeya, T. & Desmet, P., 9-Aug-2023, p. 1-3. 3 p.
Research output: Contribution to conference › Paper/Powerpoint/Abstract

Open Access
File

732 Downloads (Pure)
Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy. Contract - BR/165/A1/TrIAS. Final Report.
Groom, Q. J., Vanderhoeven, S., Eggermont, H., Coupremanne, M., Adriaens, T., Desmet, P., Reyserhove, L., Oldoni, D., Strubbe, D., Backeljau, T., Barbier, Y., Brosens, D., Davis, A., Lens, L., Monty, A., Mahy, G., Jacquemart, A.-L., Verleye, T., Van Hoey, G. & Van De Kerchove, R. & 6 others, Backeljau, T., Dispas, M., Vilain, A., De Troch, R., Van Schaeybroeck, B. & Termonia, P., 2022, Brussels, Belgium. 52 p.
Research output: Book/Report › Report not published by INBO

Open Access
File
A pipeline to feed headline indicators on the state of invasions and to prioritize emerging alien species for risk assessment
Adriaens, T., Van Daele, T., Groom, Q., Vanderhoeven, S., Davis, A., Strubbe, D., Reyserhove, L., Desmet, P. & Oldoni, D., 15-Sept-2020. 1 p.
Research output: Contribution to conference › Poster

Open Access
File

Cite this

@article{f1ce892b8d6b410c80c3078c9fce3dc8,

title = "Occurrence cubes: a new paradigm for aggregating species occurrence data",

abstract = "In this paper we describe a method of aggregating species occurrence data into what we coined textquotedblleftoccurrence cubestextquotedblright. The aggregated data can be perceived as a cube with three dimensions - taxonomic, temporal and geographic - and takes into account the spatial uncertainty of each occurrence. The aggregation level of each of the three dimensions can be adapted to the scope. Built on Open Science principles, the method is easily automated and reproducible, and can be used for species trend indicators, maps and distribution models. We are using the method to aggregate species occurrence data for Europe per taxon, year and 1km2 European reference grid, to feed indicators and risk mapping/modelling for the Tracking Invasive Alien Species (TrIAS) project.",

author = "Damiano Oldoni and Quentin Groom and Tim Adriaens and Davis, {Amy J.S.} and Lien Reyserhove and Diederik Strubbe and Sonia Vanderhoeven and Peter Desmet",

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TY - JOUR

T1 - Occurrence cubes: a new paradigm for aggregating species occurrence data

AU - Oldoni, Damiano

AU - Groom, Quentin

AU - Adriaens, Tim

AU - Davis, Amy J.S.

AU - Reyserhove, Lien

AU - Strubbe, Diederik

AU - Vanderhoeven, Sonia

AU - Desmet, Peter

PY - 2020/3/25

Y1 - 2020/3/25

N2 - In this paper we describe a method of aggregating species occurrence data into what we coined textquotedblleftoccurrence cubestextquotedblright. The aggregated data can be perceived as a cube with three dimensions - taxonomic, temporal and geographic - and takes into account the spatial uncertainty of each occurrence. The aggregation level of each of the three dimensions can be adapted to the scope. Built on Open Science principles, the method is easily automated and reproducible, and can be used for species trend indicators, maps and distribution models. We are using the method to aggregate species occurrence data for Europe per taxon, year and 1km2 European reference grid, to feed indicators and risk mapping/modelling for the Tracking Invasive Alien Species (TrIAS) project.

AB - In this paper we describe a method of aggregating species occurrence data into what we coined textquotedblleftoccurrence cubestextquotedblright. The aggregated data can be perceived as a cube with three dimensions - taxonomic, temporal and geographic - and takes into account the spatial uncertainty of each occurrence. The aggregation level of each of the three dimensions can be adapted to the scope. Built on Open Science principles, the method is easily automated and reproducible, and can be used for species trend indicators, maps and distribution models. We are using the method to aggregate species occurrence data for Europe per taxon, year and 1km2 European reference grid, to feed indicators and risk mapping/modelling for the Tracking Invasive Alien Species (TrIAS) project.

U2 - 10.1101/2020.03.23.983601

DO - 10.1101/2020.03.23.983601

M3 - Preprint

JO - bioRxiv

JF - bioRxiv

ER -

Occurrence cubes: a new paradigm for aggregating species occurrence data

Abstract

Thematic List 2020

Access to Document

Fingerprint

Projects

B-Cubed - Biodiversity Building Blocks for policy

TrIAS - Tracking Invasive Alien Species

Research output

B-Cubed: Leveraging analysis-ready biodiversity datasets and cloud computing for timely and actionable biodiversity monitoring

Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy. Contract - BR/165/A1/TrIAS. Final Report.

A pipeline to feed headline indicators on the state of invasions and to prioritize emerging alien species for risk assessment

Cite this