Habitat Suitability and Environmental Niche Differentiation of Two Commercially Important Solenocera Shrimp Species in the Southern Yellow Sea and East China Sea (2018–2026)

Solenocera crassicornis (H. Milne Edwards, 1837) and Solenocera koelbeli De Man, 1911 are two commercially important shrimp species in the coastal waters of China; however, their inter-annual variation, seasonal spatial habitat use, and environmental niche differentiation remain poorly understood. Based on fishery-independent bottom-trawl surveys conducted over 21 cruises from November 2018 to January 2026 in the Southern Yellow Sea and East China Sea, we integrated Tweedie generalized additive models (GAMs) and boosted regression trees (BRTs) to generate habitat suitability index (HSI) model outputs and examine the observed spatiotemporal patterns and environmental niches of the two species. Model outputs yielded hold-out AUC-ROC values > 0.75; the models showed moderate to good temporal generalization but weaker spatial generalization, particularly for S. crassicornis. Field observations showed that S. crassicornis is a nearshore species whose distribution is primarily driven by sea surface temperature, sea bottom temperature and sea bottom salinity; it exhibited seasonal distributional shifts, with higher occurrence at northern latitudes (32–35° N) in summer and more southerly distribution in winter. S. koelbeli was a deep-water, high-salinity species mainly associated with sea bottom salinity and depth, found at 50–100 m and virtually absent from low-salinity estuarine waters, with high occurrence in autumn–winter and late spring. Both shrimp species shared core habitats (Zhoushan, Yushan and Wentai) but displayed temporal, spatial, and environmental niche partitioning according to our observations and model outputs. These findings provide scientific references for resource conservation and fishing ground management. Potential seasonal protection windows for broodstocks (June–September) and recruitment stocks (December–February) are proposed based on field observations and correlative model outputs, although these require further empirical validation.