use of random forest for species distribution modeling
Description
We use random forest models in R as part of an ensemble species distribution modelling workflow. Random forests have been applied to model the distribution of Joshua trees, as well as other species in the Mojave Desert in support of the BLM's Mojave Desert Native Plant Program (interagency partnership). Traditional machine learning methods, including random forests and maxent, will continue to feature in our development of species distribution models.
Detailed example
The output of AI from machine learning that we use are predictive and places bounds on species distributions that are then used by regulatory agencies to provide guidance.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The use of AI in this application improves species distribution models, reduces error, and makes models more precise which provides more accurate and useful information to decision-makers atInterior agencies.
Controls / human review
ATO: No; PIA: https://doi.org/10.5066/P9NZMDLL
Data needed
We used research data sets of Joshua trees that were collected in house and owned by USGS (public) The data have been published through proper procedures