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Reproducible WiSDM : a workflow for reproducible invasive alien species risk maps under climate change scenarios using standardized open data

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2024

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Groom, Quentin
Adriaens, Tim
Vanderhoeven, Sonia
De Troch, Rozemien
Oldoni, Damiano
Desmet, Peter
Reyserhove, Lien
Lens, Luc
Strubbe, Diederik

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Frontiers in Ecology and Evolution. Frontiers. 2024, 12, 1148895. eISSN 2296-701X. Verfügbar unter: doi: 10.3389/fevo.2024.1148895

Zusammenfassung

Introduction: Species distribution models (SDMs) are often used to produce risk maps to guide conservation management and decision-making with regard to invasive alien species (IAS). However, gathering and harmonizing the required species occurrence and other spatial data, as well as identifying and coding a robust modeling framework for reproducible SDMs, requires expertise in both ecological data science and statistics. Methods: We developed WiSDM, a semi-automated workflow to democratize the creation of open, reproducible, transparent, invasive alien species risk maps. To facilitate the production of IAS risk maps using WiSDM, we harmonized and openly published climate and land cover data to a 1 km2 resolution with coverage for Europe. Our workflow mitigates spatial sampling bias, identifies highly correlated predictors, creates ensemble models to predict risk, and quantifies spatial autocorrelation. In addition, we present a novel application for assessing the transferability of the model by quantifying and visualizing the confidence of its predictions. All modeling steps, parameters, evaluation statistics, and other outputs are also automatically generated and are saved in a R markdown notebook file. Results: Our workflow requires minimal input from the user to generate reproducible maps at 1 km2 resolution for standard Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission representative concentration pathway (RCP) scenarios. The confidence associated with the predicted risk for each 1km2 pixel is also mapped, enabling the intuitive visualization and understanding of how the confidence of the model varies across space and RCP scenarios. Discussion: Our workflow can readily be applied by end users with a basic knowledge of R, does not require expertise in species distribution modeling, and only requires an understanding of the ecological theory underlying species distributions. The risk maps generated by our repeatable workflow can be used to support IAS risk assessment and surveillance.

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570 Biowissenschaften, Biologie

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uncertainty in SDMs, conformal prediction, spatial sampling bias, ecological models, confidence assessment, invasive alien species

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ISO 690DAVIS, Amy, Quentin GROOM, Tim ADRIAENS, Sonia VANDERHOEVEN, Rozemien DE TROCH, Damiano OLDONI, Peter DESMET, Lien REYSERHOVE, Luc LENS, Diederik STRUBBE, 2024. Reproducible WiSDM : a workflow for reproducible invasive alien species risk maps under climate change scenarios using standardized open data. In: Frontiers in Ecology and Evolution. Frontiers. 2024, 12, 1148895. eISSN 2296-701X. Verfügbar unter: doi: 10.3389/fevo.2024.1148895
BibTex
@article{Davis2024-02-09Repro-69573,
  year={2024},
  doi={10.3389/fevo.2024.1148895},
  title={Reproducible WiSDM : a workflow for reproducible invasive alien species risk maps under climate change scenarios using standardized open data},
  volume={12},
  journal={Frontiers in Ecology and Evolution},
  author={Davis, Amy and Groom, Quentin and Adriaens, Tim and Vanderhoeven, Sonia and De Troch, Rozemien and Oldoni, Damiano and Desmet, Peter and Reyserhove, Lien and Lens, Luc and Strubbe, Diederik},
  note={Article Number: 1148895}
}
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Methods: We developed WiSDM, a semi-automated workflow to democratize the creation of open, reproducible, transparent, invasive alien species risk maps. To facilitate the production of IAS risk maps using WiSDM, we harmonized and openly published climate and land cover data to a 1 km&lt;sup&gt;2&lt;/sup&gt; resolution with coverage for Europe. Our workflow mitigates spatial sampling bias, identifies highly correlated predictors, creates ensemble models to predict risk, and quantifies spatial autocorrelation. In addition, we present a novel application for assessing the transferability of the model by quantifying and visualizing the confidence of its predictions. All modeling steps, parameters, evaluation statistics, and other outputs are also automatically generated and are saved in a R markdown notebook file.
Results: Our workflow requires minimal input from the user to generate reproducible maps at 1 km&lt;sup&gt;2 &lt;/sup&gt;resolution for standard Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission representative concentration pathway (RCP) scenarios. The confidence associated with the predicted risk for each 1km&lt;sup&gt;2&lt;/sup&gt; pixel is also mapped, enabling the intuitive visualization and understanding of how the confidence of the model varies across space and RCP scenarios.
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