Ensemble habitat suitability modeling of stomatopods with Oratosquilla oratoria as an example

文献类型： 外文期刊

第一作者： Guan, Lisha

作者： Guan, Lisha;Jin, Xianshi;Yang, Tao;Shan, Xiujuan;Guan, Lisha;Jin, Xianshi;Yang, Tao;Shan, Xiujuan

作者机构：

关键词： habitat suitability; stomatopod; coastal fisheries; predictor selection; ensemble model

期刊名称：ACTA OCEANOLOGICA SINICA （ 影响因子：1.4； 五年影响因子：1.6 ）

ISSN： 0253-505X

年卷期： 2023 年 42 卷 4 期

页码：

收录情况： SCI

摘要： Stomatopods are better known as mantis shrimp with considerable ecological importance in wide coastal waters globally. Some stomatopod species are exploited commercially, including Oratosquilla oratoria in the Northwest Pacific. Yet, few studies have published to promote accurate habitat identification of stomatopods, obstructing scientific management and conservation of these valuable organisms. This study provides an ensemble modeling framework for habitat suitability modeling of stomatopods, utilizing the O. oratoria stock in the Bohai Sea as an example. Two modeling techniques (i.e., generalized additive model (GAM) and geographical weighted regression (GWR)) were applied to select environmental predictors (especially the selection between two types of sediment metrics) that better characterize O. oratoria distribution and build separate habitat suitability models (HSM). The performance of the individual HSMs were compared on interpolation accuracy and transferability. Then, they were integrated to check whether the ensemble model outperforms either individual model, according to fishers' knowledge and scientific survey data. As a result, grain-size metrics of sediment outperformed sediment content metrics in modeling O. oratoria habitat, possibly because grain-size metrics not only reflect the effect of substrates on burrow development, but also link to sediment heat capacity which influences individual thermoregulation. Moreover, the GWR-based HSM outperformed the GAM-based HSM in interpolation accuracy, while the latter one displayed better transferability. On balance, the ensemble HSM appeared to improve the predictive performance overall, as it could avoid dependence on a single model type and successfully identified fisher-recognized and survey-indicated suitable habitats in either sparsely sampled or well investigated areas.

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