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Quantify risk to specific viticultural regions
Example usage of create_risk_report() to create reports of key viticultural areas
Initialize scari
Initialization of renv package for dependencies
1. Retrieve and tidy input data for MaxEnt
Bioclim variable rasters: CHELSA
SLF occurrence records: GBIF, lydemapR, literature
2. SDM modeling pipeline
Setup for global-scale MaxEnt model
Run global-scale MaxEnt model
Setup for regional-scale MaxEnt models
Run 'Ri.NAmerica' model trained on SLF invaded region in North America
Run 'Ri.Asia' model trained on SLF invaded range in Japan and South Korea
Run 'Rn' model trained on SLF native range in east Asia
3. Ensemble Regional-scale SDMs
Run an Extrapolation Detection (NT2) analysis and calculate MIC
Ensemble regional-scale models using a weighted mean
4. Quantify SLF risk and model fit
Create risk maps of each model scale and intersect predictions
Plot shift in SLF risk to important viticultural regions under climate change
Infer stability of known SLF populations due climate change effects
Analyze biological realism of model response curves
Check model goodness of fit via AUC, TSS and confusion matrices
Calculate model fit using confusion matrices
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Reference
Changelog
Changelog
Source:
NEWS.md
scari 0.2.0
Release Notes
built on R 4.4.1
retired used of
taxize
,
humboldt
,
rgdal
, and
rgeos
