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Assessing habitat suitability of protected migratory birds in coastal wetlands with multi-source data and a probability-averaging ensemble

Xue Sha,Huacheng Xie,Yi Zeng

Published 2026 in Frontiers in Environmental Science

ABSTRACT

Habitat-suitability modelling supports conservation planning for protected migratory birds in dynamic coastal wetlands, yet single species distribution models (SDMs) can be unstable when presence records are scarce and class imbalance is strong. Here we present a dual-model probability averaging (DMPA) framework that ensembles two standard SDMs--logistic regression and random forest--by simply averaging their predicted occurrence probabilities to improve robustness. We apply the framework to the Yancheng coastal wetlands (eastern China) using a pooled presence-background dataset comprising 18 bird species (56 presence records) and multi-source climatic, topographic, and distance-based predictors, with covariates screened for collinearity (|r| > 0.95) and missing values imputed by variable means. Model performance is assessed using cross-validation with held-out predictions, and binary suitability maps are derived using an F1-based operating threshold selected across folds. Quantitatively, the DMPA ensemble achieves strong discrimination (ROC-AUC = 0.899; PR-AUC = 0.617) and substantially improves classification performance relative to single models (F1 = 0.643 vs. 0.474 for logistic regression and 0.034 for random forest, which collapses under F1-based thresholding due to extreme class imbalance), while maintaining competitive probabilistic accuracy (Brier = 0.036, compared with 0.057 and 0.034) and moderate calibration (ECE = 0.061, compared with 0.082 for logistic regression). Spatial projections concentrate higher suitability along the coastal wetland corridor, and feature-importance analysis highlights distance to coastline/rivers and key bioclimatic variables as leading predictors. Overall, DMPA provides a simple and practical ensemble strategy that improves PR-AUC and F1 under class imbalance without sacrificing overall discrimination, supporting suitability screening and mapping in fast-changing coastal wetlands.

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