Assessing the role of the hydrographic network structure and connectivity on the South American freshwater fish diversity

淡水生态系统被认为是生物多样性热点，因为它们只覆盖地球表面的一小部分，但蕴藏着大量物种。与此同时，淡水物种的数量在过去几十年里一直在迅速下降。为了减缓甚至扭转这一消极趋势，迫切需要关于淡水生物多样性现状的可靠基线，以便与变化进行比较。在这方面，空间淡水生物多样性科学在提供有关物种分布和区域保护规划的新信息方面走在了前列。淡水生态系统最独特的特征是水体的纵向连通性，但它同时也是淡水生物多样性分析中最突出的被忽视的特征。在拟议的项目中，我们将通过使用最新的图论和物种分布建模方法来解决这一缺点，以更好地了解南美洲淡水鱼动物群的物种和功能多样性模式。总体而言，南美鱼类区系约占全球鱼类种类的1/3，是一个理想和具有代表性的研究案例。具体地说，我们将(I)探索整个大陆的水文网络结构的空间模式，以评估水体是如何分布的，以及它们是如何连接的。这些信息将用于(Ii)利用空间网络结构的图形学习分布模型，并与一个广泛的地理鱼类记录数据库一起，将产生整个南美洲的物种多样性估计。最后，我们将(Iii)将功能特征信息与物种分布估计联系起来，以深入了解整个南美洲的空间功能多样性模式。该项目很有可能对整个大陆鱼类动物群的淡水生物多样性空间格局产生新的见解，以评估基本网络结构对鱼类多样性估计的作用。鉴于分析的空间梯度很大，预计结果将对大陆淡水鱼动物群的多样性模式提供高度概括性的见解，并为针对网络连接结构的区域信息保护措施奠定基石。

Freshwater ecosystems are considered biodiversity hotspots given that they cover only a small fraction of the Earth’s surface yet harbouring a large number of species. A the same time, freshwater species populations have been rapidly declining over the past decades. To slow down or even revert the negative trend, a solid baseline regarding the current status of freshwater biodiversity is urgently required, against which the changes can be compared to. In this regard, spatial freshwater biodiversity science is at the forefront to deliver new information regarding species distributions and for area-based conservation planning. The most unique feature of freshwater ecosystems is the longitudinal connectivity of water bodies, yet is it simultaneously the most prominently neglected feature in freshwater biodiversity analyses. In the proposed project we will address this shortcoming by employing latest graph-theory and species distribution modelling approaches to better understand the species and functional diversity patterns of the South American freshwater fish fauna. Taken together, the South American fish fauna comprises ca. 1/3 of the global fish species, thus serving as an ideal and representative study case. Specifically, we will (i) explore the spatial patterns of the hydrographic network structure across the continent to assess how the water bodies are distributed, and how they are connected. This information will be used in (ii) graph-learning distribution models that capitalize on the spatial network structure, and together with an extensive database of geographic fish records, will yield species diversity estimates across South America. Finally, we will (iii) link up functional trait information with the species distribution estimates to gain insights into spatial functional diversity patterns across South America. The project has the high potential to yield novel insights into spatial freshwater biodiversity patterns across a continental fish fauna in evaluating the role of the underlying network structure on fish diversity estimates. Given the large spatial gradient of the analyses, the results are expected to offer highly generalizable insights of the diversity patterns of a continental freshwater fish fauna, and to yield a cornerstone for area-based information conservation measures addressing the network connectivity structure.