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Comparing Multi-Criteria Analysis and Species Distribution Models for Identifying Locust Suitable Habitats in
Sijie Cui1,2, Jianghua Zheng1,2, Jun Lin3
1College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830046, China.
Biology
|May 26, 2026
Summary
This study compared multi-criteria analysis (MCA) and species distribution models (SDMs) for identifying locust habitats. Both methods effectively pinpointed suitable areas, suggesting they are complementary tools for locust management.
Area of Science:
- Ecology
- Environmental Science
- Pest Management
Background:
- Locust outbreaks significantly disrupt arid and semi-arid grassland ecosystems.
- Accurate identification of locust habitats is crucial for effective regional monitoring and management strategies.
- Direct comparative studies of multi-criteria analysis (MCA) and species distribution models (SDMs) for locust habitat suitability are limited.
Purpose of the Study:
- To compare the performance of MCA and SDMs in identifying suitable habitats for dominant locust species in Xinjiang, China.
- To evaluate the consistency and spatial agreement between different MCA and SDM approaches.
- To assess the complementary roles of MCA and SDMs in locust monitoring and management.
Main Methods:
- Utilized identical environmental variables and occurrence records for all models.
- Applied three MCA methods: Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Ordered Weighted Averaging (OWA).
- Employed four SDMs: Generalized Linear Model (GLM), Maximum Entropy (MaxEnt), Extreme Gradient Boosting (XGBoost), and an ensemble model.
Main Results:
- Species distribution models (SDMs) demonstrated superior performance with higher Area Under the Receiver Operating Characteristic Curve (AUC) and True Skill Statistic (TSS) values compared to MCA.
- Both MCA and SDMs successfully identified locust-suitable habitats, showing high spatial agreement (Jaccard indices 0.88-0.92) in moderately and highly suitable areas.
- Consistently identified core suitable areas including the northern Tianshan Mountains, Ili River Valley, and Junggar Basin margins.
Conclusions:
- SDMs generally outperform MCA in predictive accuracy for locust habitat suitability.
- Despite performance differences, both MCA and SDMs are effective and complementary approaches for locust habitat identification.
- The findings support the integrated use of MCA and SDMs for enhanced locust monitoring and management in arid and semi-arid regions.
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