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Zool."],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Incomplete morphological trait data pose major hurdles for trait\u2010based analyses, particularly when missing values, multicollinearity, and sparse sampling constrain inference. These issues limit our ability to quantify trait variation and explore broad patterns of functional differentiation across taxa. Here, we introduce FS\u2010DeepRBFNet, which overcomes these pitfalls through integrating correlation\u2010based feature selection with a dual\u2010layer adaptive radial basis function (RBF) network. This end\u2010to\u2010end approach effectively reduces noise and captures both linear allometric trends and nonlinear morphological relationships. We tested the framework on a large species\u2010level morphological trait dataset of Chinese birds and further validated its cross\u2010taxon transferability using the Amphibian Database (Caudata). FS\u2010DeepRBFNet consistently outperformed conventional methods such as KNN, Random Forest, and XGBoost, demonstrating superior predictive accuracy across multiple traits. Beyond improvements, the model revealed biologically interpretable trait associations and stable cross\u2010taxon generalization. These results demonstrate that FS\u2010DeepRBFNet provides a robust and biologically grounded solution for morphological trait prediction, enabling reliable imputation for comparative phylogenetics, functional ecology, and biodiversity forecasting in data\u2010limited situations.<\/jats:p>","DOI":"10.1111\/1749-4877.70178","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T13:39:57Z","timestamp":1789565997000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Deep Model Framework for Morphological Trait Imputation Across Taxonomic Groups"],"prefix":"10.1111","author":[{"given":"Yuang","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Artificial Intelligence Southwest Minzu University  Chengdu China"},{"name":"Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station Southwest Minzu University  Chengdu 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