See the tutorial link.
A short tutorial for spatial classification in Python through an example in species distribution modeling. Read on for more advanced applications of SDM modeling in ecological research, which, at heart, use the same basics as covered in the tutorial.
| Bioclimatic Variable | Coast redwood % Change | Giant sequoia % Change | Joshua tree % Change |
|---|---|---|---|
| Temperature Annual Mean | +22% | +47% | +24% |
| Temperature Annual Range | +5% | +4% | +2% |
| Precipitation Driest Month | -1% | -2% | -7% |
- SSP 370 CMIP6 models for the IPCC6 report.
- Bioclimatic Features from WorldClim2
- Species presences from GBIF and carefully cleaned
- Blending methods boosted model performances to ~ two-zero false negatives per species.
| Coast redwood SDM geo-classification (Sequoia sempervirens) | Standard deviations from multiple seeds/samples. |
|---|---|
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| Giant sequioa SDM geo-classification (Sequoiadendron giganteum) | Standard deviations from multiple seeds/samples. |
|---|---|
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| Joshua tree SDM geo-classification (Yucca brevifolia) | Standard deviations from multiple seeds/samples. |
|---|---|
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Python dependencies are listed in a requirements-py.txt file, including the library version numbers. You can replicate the environment your codebase needs by using virtualenv:
# This creates the virtual environment
cd $PROJECT-PATH
virtualenv ensemble-climate-projections
Then install the dependencies by referring to the requirements-py.txt:
# This installs the modules
pip install -r requirements-py.txt
# This activates the virtual environment
source ensemble-climate-projections/bin/activate