In:
Frontiers in Ecology and Evolution, Frontiers Media SA, Vol. 12 ( 2024-2-9)
Abstract:
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 km 2 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 2 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 2 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.
Type of Medium:
Online Resource
ISSN:
2296-701X
DOI:
10.3389/fevo.2024.1148895
DOI:
10.3389/fevo.2024.1148895.s001
DOI:
10.3389/fevo.2024.1148895.s002
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2024
detail.hit.zdb_id:
2745634-1
