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Recently, much work has been put into improving species distribution models, especially for systematic conservation planning for important ecosystems. Understanding fish spawning sites requires a thorough examination of ichthyoplankton. These examinations usually produce sparse counts contaminated by inaccurate detection, making it impossible to directly infer the abundance or occurrence from observational data, which could lead to inaccurate model predictions. A flexible modeling framework for estimating and inference about the abundance of eggs with ensemble models that include the presence/absence and abundance components is described in this study. The generalized linear model, generalized additive model, integrated nested Laplace approximations, and random forest habitat modeling approaches were compared within this framework to those currently being used for fish conservation planning at regional scales. Additionally, the distribution of suitable habitats for small yellow croaker (Larimichthys polyactis) spawning stocks were mapped based on the ensemble model. Furthermore, the promotion ability of ensemble models with different weighting methods was evaluated. The outcomes demonstrated that machine learning algorithms performed better than statistical models, and the geometric weighted ensemble model further increased prediction accuracy. However, there was no significant difference compared to the optimal individual model (p > 0.05). The predicted distributions of the four models can be divided into two groups. The central sea of Jiangsu was recognized as the most suitable area by the GAM with a fixed effect for each year and INLA models, while the GLM was similar to the RF with spatial effect (RF-LL) and demonstrated Haizhou Bay as the most suitable area. The ensemble model discovered several areas of highly suitable habitat that dominated areas in the two groups of models and revealed many finer-scale patterns in the egg distribution. According to the ensemble model, although 5.37% of the area could be suitable habitat, only 0.12% was highly suitable. It is suggested that examining small yellow croaker spawning aggregation areas would benefit from using an ensemble modeling approach to identify and prioritize conservation areas. • Using a multi-models and ensemble algorithm for sparse egg data. • Random forest model outperformed individual base models. • Identify abiotic factors contributed to spawning sites selection. • Proposed the locations and areas for priority protected areas. [ABSTRACT FROM AUTHOR]