Identifying the drivers that shape the biodiversity of a region is important to predict the response of ecosystems to environmental changes. Macrofaunal distribution pattern in the North Indian Ocean has so far been mainly studied considering small geographical areas, and using taxonomic diversity. The present study is a functional trait based approach aimed to identify the environmental drivers of macrofaunal distribution and diversity pattern from the relatively unexplored southern Indian continental margin. A total of 53 stations were sampled along six transects from a depth range of 19–2639 m. Along with taxonomic diversity (α and β), functional structure was assessed by using Biological Trait Analysis (BTA) and multivariate functional indices (richness, evenness, dispersal, divergence and Rao's Entropy). Principal Component Analysis identified a clear depth-related variability in the measured environmental parameters. Alpha diversity of the macrofaunal community decreased with increasing depth, a trend driven by depth related changes in the environmental factors (dissolved oxygen, sediment organic carbon and texture). However, functional diversity was only partially explained by the studied environmental variables which may be due to different factors governing the taxonomic and functional diversity pattern of macrofauna. Among the functional indices, only functional richness and evenness showed a clear trend of increase from shallow to deeper depths indicating that biodiversity-environment relations are complex. Therefore, investigating the response of macrofaunal community using multiple measure of diversity (α, β- and, functional diversity) was useful for providing an understanding of macrofaunal diversity pattern. The results of our study contribute to the growing knowledge on large-scale diversity gradients for marine macroinvertebrates, which may be critical for understanding ecological patterns and its response to both habitat and climatic change.

确定影响一个地区生物多样性的驱动因素对于预测生态系统对环境变化的响应非常重要。迄今为止，主要是在考虑到较小的地理区域并利用分类学多样性的基础上研究了北印度洋的大型动物分布模式。本研究是一种基于功能性状的方法，旨在从相对未开发的印度南部大陆边缘识别宏观动物分布和多样性格局的环境驱动因素。在19-2639 m的深度范围内，沿着六个样面共采样了53个台站。除生物分类多样性（α和β）外，还通过生物特征分析（BTA）和多元功能指数（丰富度，均匀度，分散度，发散度和Rao熵）评估了功能结构。主成分分析确定了所测环境参数中与深度有关的明显变异性。大型动物群落的α多样性随着深度的增加而降低，这是由与深度相关的环境因素（溶解氧，沉积物有机碳和质地）变化驱动的趋势。然而，功能多样性仅由所研究的环境变量部分解释，这可能是由于控制大型动物的分类和功能多样性模式的因素不同所致。在功能指标中，只有功能的丰富性和均匀性呈现出从浅到深的明显增加趋势，这表明生物多样性与环境的关系是复杂的。因此，使用多样性的多种量度（α，β-和 功能多样性）有助于理解大型动物多样性模式。我们的研究结果有助于海洋大型无脊椎动物的大规模多样性梯度知识的增长，这对于理解生态模式及其对生境和气候变化的响应可能至关重要。