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Seafloor geomorphic features as an alternative approach into modelling the distribution of cetaceans
Ecological Informatics ( IF 5.1 ) Pub Date : 2020-04-10 , DOI: 10.1016/j.ecoinf.2020.101092
Bruno Claro , Sergi Pérez-Jorge , Silvia Frey

Bathymetric proxies tend to indicate the relationships between the distribution of cetaceans and the seafloor in species distribution models (SDMs). Usually, seafloor features are described arbitrary to explain these relationships. Currently, a global seafloor geomorphic features dataset is available, with an objective identification and full representation for each seafloor geomorphic feature. Models based on common bathymetric proxies were compared with models with seafloor predictors, by a standardized ensemble SDM framework. Occurrences of two species with different foraging strategies and relationships with the seabed were selected: the sperm whale (Physeter macrocephalus) and the striped dolphin (Stenella coeruleoalba).. For sperm whales, GLM models had a significant better performance with seageomorphic features than with the common bathymetric proxies. In GAM and MARS models, the performance was similar for both seabed features and bathymetric proxies' models. In relation to striped dolphins, all models obtained had a significant better performance with seafloor features than with bathymetric proxies, regardless the modelling technique. The models showed preference of sperm whales for areas distant from the continental shelf and continental rise areas which consists of submarine canyons with adjacent submarine fans. Striped dolphins showed a clear preference for continental slope areas. The modelling based on seafloor geomorphic features support the hypothesis that the use of this type of predictors can improve the modelling performance and provide a complementary understanding of the relationship between cetaceans and the seafloor.



中文翻译:

海底地貌特征作为模拟鲸类分布的一种替代方法

测深代理倾向于在物种分布模型(SDM)中指示鲸类的分布与海底之间的关系。通常,海底特征被任意描述以解释这些关系。当前,可获得全球海底地貌特征数据集,其中包含每个海底地貌特征的客观识别和完整表示。通过标准化的集成SDM框架,将基于常见测深代理的模型与具有海底预测器的模型进行了比较。选择了两种觅食策略不同且与海床相关的物种:抹香鲸(Physeter macrocephalus)和条纹海豚(Stenella coeruleoalba)对于抹香鲸,GLM模型在具有海地貌特征方面的性能要明显优于普通的测深代理。在GAM和MARS模型中,海底特征和测深代理模型的性能相似。关于条带海豚,无论采用哪种建模技术,所获得的所有模型均具有海底特征,而比测深代理具有更好的性能。模型显示出抹香鲸偏爱远离大陆架和大陆上升区的区域,后者由具有相邻海底扇形的海底峡谷组成。条纹海豚对大陆斜坡地区表现出明显的偏爱。

更新日期:2020-04-10
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