Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation

This approach readily identifies traits predictive of ecoregion origin, and thus the functional traits most likely to be responsible for contrasting ecological strategies across the landscape. This type of approach can be used to parse large plant trait datasets in a wide range of contexts, including explicitly testing the applicability of interspecific paradigms at intraspecific scales.

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Publication Details

Journal
Applications in Plant Sciences
Published
2025-05-01
DOI
https://doi.org/10.1002/aps3.70015
Citations
1
Primary Topic
Plant and animal studies
Type
article
Field-Weighted Citation Impact
3.03

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article

Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation

Sambadi Majumder, Chase M. Mason
1 citations
Applications in Plant Sciences
Plant and animal studies
3.03
article

Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation

Sambadi Majumder, Chase M. Mason
article en
1 citations

Abstract

This approach readily identifies traits predictive of ecoregion origin, and thus the functional traits most likely to be responsible for contrasting ecological strategies across the landscape. This type of approach can be used to parse large plant trait datasets in a wide range of contexts, including explicitly testing the applicability of interspecific paradigms at intraspecific scales.

Applications in Plant SciencesVol. 13(3)
University of Central Florida (US), University of British Columbia (CA), Okanagan University College (CA)
University of Central Florida, National Institute on Aging
Life in Land
Openalex Percentile: Top 10%
Plant and animal studies
3.03
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