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的热点，因为它们只覆盖地球表面的一小部分，但却拥有大量物种。与此同时，淡水物种的数量在过去几十年中迅速下降。为了减缓甚至扭转这一消极趋势，迫切需要有一个关于淡水生物多样性现状的坚实基线，以便与变化进行比较。在这方面，空间淡水生物多样性科学处于最前沿，提供了有关物种分布的新信息，并用于基于地区的保护规划。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明显被忽视的特征。在拟议的项目中，我们将采用最新的图论和物种分布建模方法，以更好地了解南美洲淡水鱼类动物的物种和功能多样性模式，以解决这一缺点。南美洲的鱼类包括。全球1/3的鱼类，是一个理想的、具有代表性的研究案例。具体而言，我们将（i）探索整个大陆的水文网络结构的空间模式，以评估水体如何分布，以及它们如何连接。这些信息将用于（二）利用空间网络结构的图形学习分布模型，并与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）连接功能性状信息与物种分布估计，以深入了解整个南美洲的空间功能多样性模式。该项目具有很高的潜力，以产生新的见解空间淡水生物多样性模式在整个大陆鱼类动物区系在评估鱼类多样性估计的基础网络结构的作用。鉴于大的空间梯度的分析，结果预计将提供高度概括的见解的多样性模式的大陆淡水鱼类动物，并产生一个基石，以区域为基础的信息保护措施，解决网络连接结构。

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.