DOI · DOI-0161 · record R2601

use of random forest for species distribution modeling

Department of the Interior · USGS

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2023-12-14 (day precision)Source: 12/14/2023
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

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.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: No
Public source code: doi.org ↗

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: doi.org ↗
Authorization to Operate: No
Demographic features: k) None of the above
Training and evaluation data: 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
Federal Data Catalog: Not reported

Governance

5 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewReported in placeSource: a) Yes – by another appropriate agency office or reviewer not directly involved in the AI’s development

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeNot applicableSource: b) Not applicable

Appeal processReported in progressSource: c) Establishment of an appropriate appeal process is in-progress

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported