U.S. Geological Survey, Western Fisheries Research Center, Columbia River Research Laboratory, Cook, Washington, United States of America.
U.S. Geological Survey, Upper Midwest Environmental Sciences Center, La Crosse, Wisconsin, United States of America.
PLoS One. 2022 Apr 29;17(4):e0267113. doi: 10.1371/journal.pone.0267113. eCollection 2022.
Management actions intended to benefit fish in large rivers can directly or indirectly affect multiple ecosystem components. Without consideration of the effects of management on non-target ecosystem components, unintended consequences may limit management efficacy. Monitoring can help clarify the effects of management actions, including on non-target ecosystem components, but only if data are collected to characterize key ecosystem processes that could affect the outcome. Scientists from across the U.S. convened to develop a conceptual model that would help identify monitoring information needed to better understand how natural and anthropogenic factors affect large river fishes. We applied the conceptual model to case studies in four large U.S. rivers. The application of the conceptual model indicates the model is flexible and relevant to large rivers in different geographic settings and with different management challenges. By visualizing how natural and anthropogenic drivers directly or indirectly affect cascading ecosystem tiers, our model identified critical information gaps and uncertainties that, if resolved, could inform how to best meet management objectives. Despite large differences in the physical and ecological contexts of the river systems, the case studies also demonstrated substantial commonalities in the data needed to better understand how stressors affect fish in these systems. For example, in most systems information on river discharge and water temperature were needed and available. Conversely, information regarding trophic relationships and the habitat requirements of larval fishes were generally lacking. This result suggests that there is a need to better understand a set of common factors across large-river systems. We provide a stepwise procedure to facilitate the application of our conceptual model to other river systems and management goals.
旨在使大河流域鱼类受益的管理措施可能会直接或间接地影响多个生态系统组成部分。如果不考虑管理对非目标生态系统组成部分的影响,意外后果可能会限制管理效果。监测可以帮助阐明管理措施的效果,包括对非目标生态系统组成部分的效果,但前提是收集的数据要能够描述可能影响结果的关键生态系统过程。来自美国各地的科学家聚集在一起,制定了一个概念模型,以帮助确定监测信息,从而更好地了解自然和人为因素如何影响大河流域的鱼类。我们将该概念模型应用于美国四条主要河流的案例研究。该概念模型的应用表明,该模型具有灵活性,与不同地理环境和具有不同管理挑战的大河流域相关。通过直观地展示自然和人为驱动因素如何直接或间接地影响级联生态系统层次,我们的模型确定了关键的信息差距和不确定性,如果能够解决这些问题,将有助于了解如何最好地实现管理目标。尽管河流系统在物理和生态背景方面存在很大差异,但这些案例研究还表明,需要更好地了解这些系统中胁迫因素如何影响鱼类的大量数据具有相当大的共性。例如,在大多数系统中,都需要并提供有关河流流量和水温度的信息。相反,关于营养关系和幼鱼栖息地需求的信息通常缺乏。这一结果表明,需要更好地了解大河流域系统之间的一组共同因素。我们提供了一个逐步的程序,以促进将我们的概念模型应用于其他河流系统和管理目标。