| Zhang Yunlei,Yu Huaming,Yu Haiqing,Xu Binduo,Zhang Chongliang,Ren Yiping,Xue Ying,Xu Lili. 2020. Optimization of environmental variables in habitat suitability modeling for mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent waters. Acta Oceanologica Sinica, 39(6):36－47 |
| Optimization of environmental variables in habitat suitability modeling for mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent waters |
| 海州湾及邻近海域口虾蛄栖息地适宜性建模的环境变量优化 |
| Received:April 29, 2019 |
| DOI：10.1007/s13131-020-1546-8 |
| Key words:habitat suitability index mantis shrimp generalized additive model boosted regression tree Haizhou Bay |
| 中文关键词: 栖息地适宜性指数 口虾蛄 广义可加模型 提升回归树 海州湾 |
| 基金项目:The National Key R&D Program of China under contract No. 2017YFE0104400; the National Natural Science Foundation of China under contract No. 31772852; the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) under contract No. 2018SDKJ0501-2. |
| Author Name | Affiliation | E-mail | | Zhang Yunlei | College of Fisheries, Ocean University of China, Qingdao 266003, China | | | Yu Huaming | College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China | | | Yu Haiqing | College of Fisheries, Ocean University of China, Qingdao 266003, China | | | Xu Binduo | College of Fisheries, Ocean University of China, Qingdao 266003, China | | | Zhang Chongliang | College of Fisheries, Ocean University of China, Qingdao 266003, China | | | Ren Yiping | College of Fisheries, Ocean University of China, Qingdao 266003, China
Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China | | | Xue Ying | College of Fisheries, Ocean University of China, Qingdao 266003, China | | | Xu Lili | Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China | xueying@ouc.edu.cn |
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| Abstract: |
| Habitat suitability index (HSI) models have been widely used to analyze the relationship between species abundance and environmental factors, and ultimately inform management of marine species. The response of species abundance to each environmental variable is different and habitat requirements may change over life history stages and seasons. Therefore, it is necessary to determine the optimal combination of environmental variables in HSI modelling. In this study, generalized additive models (GAMs) were used to determine which environmental variables to be included in the HSI models. Significant variables were retained and weighted in the HSI model according to their relative contribution (%) to the total deviation explained by the boosted regression tree (BRT). The HSI models were applied to evaluate the habitat suitability of mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent areas in 2011 and 2013-2017. Ontogenetic and seasonal variations in HSI models of mantis shrimp were also examined. Among the four models (non-optimized model, BRT informed HSI model, GAM informed HSI model, and both BRT and GAM informed HSI model), both BRT and GAM informed HSI model showed the best performance. Four environmental variables (bottom temperature, depth, distance offshore and sediment type) were selected in the HSI models for four groups (spring-juvenile, spring-adult, fall-juvenile and fall-adult) of mantis shrimp. The distribution of habitat suitability showed similar patterns between juveniles and adults, but obvious seasonal variations were observed. This study suggests that the process of optimizing environmental variables in HSI models improves the performance of HSI models, and this optimization strategy could be extended to other marine organisms to enhance the understanding of the habitat suitability of target species. |
| 中文摘要: |
| 栖息地适宜性指数（habitat suitability index, HSI）模型在海洋生物资源管理和物种分布与环境关系的研究中得到了广泛的应用。由于物种对环境变量的响应不同，栖息地适宜性也会随着生活史阶段和季节变化。因此，在HSI建模中确定环境变量的最优组合是十分重要的。本研究应用广义可加模型（general additive model, GAM）确定HSI模型中环境变量的最优组合，根据各环境变量对提升回归树（boosted regression tree，BRT）模型解释的总偏差的相对贡献率(%)，对优化的环境变量进行权重分配。并将该模型应用于2011年和2013-2017年海州湾及其邻近海域口虾蛄不同季节和不同生长阶段的栖息地适宜性评价。研究结果表明，四个分类组(春季-幼体、春季-成年、秋季-幼体、秋季-成年)的最佳HSI模型均由5个初始变量（底层水温、底层盐度、水深、离岸距离、底质类型）优化为4种环境变量(底层水温、水深、离岸距离、底质类型)；不同季节和不同生长阶段环境变量的权重不同；栖息地适宜性分布在幼体和成体之间呈现出相似的分布，但存在明显的季节变化。本研究表明，在HSI建模前对环境变量组合进行优化是提高HSI模型性能的关键，这将为类似模式的物种栖息地适宜性评价提供指导。 |
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