Kotta Jonne, Oganjan Katarina, Lauringson Velda, Pärnoja Merli, Kaasik Ants, Rohtla Liisa, Kotta Ilmar, Orav-Kotta Helen
University of Tartu, Estonian Marine Institute, Department of Marine Biology, Mäealuse 14, 12618 Tallinn, Estonia.
University of Tartu, Institute of Ecology and Earth Sciences, Chair of Zoology, Vanemuise 46, 51014, Tartu, Estonia.
PLoS One. 2015 Aug 28;10(8):e0136949. doi: 10.1371/journal.pone.0136949. eCollection 2015.
Benthic suspension feeding mussels are an important functional guild in coastal and estuarine ecosystems. To date we lack information on how various environmental gradients and biotic interactions separately and interactively shape the distribution patterns of mussels in non-tidal environments. Opposing to tidal environments, mussels inhabit solely subtidal zone in non-tidal waterbodies and, thereby, driving factors for mussel populations are expected to differ from the tidal areas. In the present study, we used the boosted regression tree modelling (BRT), an ensemble method for statistical techniques and machine learning, in order to explain the distribution and biomass of the suspension feeding mussel Mytilus trossulus in the non-tidal Baltic Sea. BRT models suggested that (1) distribution patterns of M. trossulus are largely driven by separate effects of direct environmental gradients and partly by interactive effects of resource gradients with direct environmental gradients. (2) Within its suitable habitat range, however, resource gradients had an important role in shaping the biomass distribution of M. trossulus. (3) Contrary to tidal areas, mussels were not competitively superior over macrophytes with patterns indicating either facilitative interactions between mussels and macrophytes or co-variance due to common stressor. To conclude, direct environmental gradients seem to define the distribution pattern of M. trossulus, and within the favourable distribution range, resource gradients in interaction with direct environmental gradients are expected to set the biomass level of mussels.
底栖悬浮取食贻贝是沿海和河口生态系统中的一个重要功能类群。迄今为止,我们缺乏关于各种环境梯度和生物相互作用如何单独以及相互作用地塑造非潮汐环境中贻贝分布模式的信息。与潮汐环境不同,贻贝仅栖息在非潮汐水体的潮下带,因此,贻贝种群的驱动因素预计与潮汐区域不同。在本研究中,我们使用了增强回归树建模(BRT),这是一种统计技术和机器学习的集成方法,以解释非潮汐波罗的海悬浮取食贻贝——三角帆蚌的分布和生物量。BRT模型表明:(1)三角帆蚌的分布模式在很大程度上由直接环境梯度的单独效应驱动,部分由资源梯度与直接环境梯度的交互效应驱动。(2)然而,在其适宜栖息地范围内,资源梯度在塑造三角帆蚌的生物量分布方面发挥了重要作用。(3)与潮汐区域相反,贻贝在与大型植物的竞争中并不占优势,其模式表明贻贝与大型植物之间存在促进性相互作用,或者是由于共同压力源导致的协变。总之,直接环境梯度似乎决定了三角帆蚌的分布模式,并且在有利的分布范围内,与直接环境梯度相互作用的资源梯度预计会设定贻贝的生物量水平。