The potential natural vegetation (PNV) is a useful benchmark for the restoration of large river floodplains because very few natural reference reaches exist. Expert-based approaches and different types of ecological models (static and dynamic) are commonly used for its estimation despite the conceptual differences they imply. For natural floodplains a static concept of PNV is not reasonable, as natural disturbances cause a constant resetting of succession. However, various forms of river regulation have disrupted the natural dynamics of most large European rivers for centuries. Therefore, we asked whether the consideration of succession dynamics and time dependent habitat turnover are still relevant factors for the reconstruction of the PNV.

To answer this we compared the results of a simulation of the vegetation succession (1872–2016) of a segment of the upper Rhine river after regulation (damming, straightening and bank protection) to different statistic and expert-based modelling approaches for PNV reconstruction. The validation of the different PNV estimation methods against a set of independent reference plots and the direct comparison of their results revealed very similar performances. We therefore conclude that due to a lack of large disturbances, the vegetation of regulated large rivers has reached a near-equilibrium state with the altered hydrologic regime and that a static perception of its PNV may be justified. Consequently, statistical models seem to be the best option for its reconstruction since they need relatively few resources (data, time, expert knowledge) and are reproducible.

潜在的自然植被（PNV）是恢复大型河流漫滩的有用基准，因为几乎没有自然参考范围。尽管基于专家的方法和概念不同，但通常使用不同类型的生态模型（静态和动态）进行估算。对于自然洪泛区，PNV的静态概念是不合理的，因为自然干扰会导致连续不断的重置。然而，几个世纪以来，各种形式的河流调节扰乱了大多数欧洲大型河流的自然动力。因此，我们询问是否考虑继承动力学和时间相关的栖息地周转率仍然是重建PNV的相关因素。

为了回答这个问题，我们将调节（大坝，拉直和河岸保护）调节后莱茵河上游一段植被演替（1872-2016年）的模拟结果与针对PNV重建的不同统计和基于专家的建模方法进行了比较。针对一组独立参考图对不同PNV估计方法的验证以及其结果的直接比较显示出非常相似的性能。因此，我们得出的结论是，由于缺乏大的干扰，受管制的大河的植被已随着水文状况的变化达到了接近平衡的状态，因此对其PNV的静态感知可能是合理的。因此，统计模型似乎是重建模型的最佳选择，因为它们需要的资源相对较少（数据，时间，