1. Dynamic management (DM) is a novel approach to spatial management that aligns scales of environmental variability, animal movement and human uses. While static approaches to spatial management rely on one-time assessments of biological, environmental, economic, and/or social conditions, dynamic approaches repeatedly assess conditions to produce regularly updated management recommendations. Owing to this complexity, particularly regarding operational challenges, examples of applied DM are rare. To implement DM, scientific methodologies are operationalized into tools, i.e., self-contained workflows that run automatically at a prescribed temporal frequency (e.g., daily, weekly, monthly). 2.Here we present a start-to-finish framework for operationalizing DM tools, consisting of four stages: Acquisition, Prediction, Dissemination, and Automation. We illustrate this operationalization framework using an applied DM tool as a case study.3. Our DM tool operates in near real-time and was designed to maximize target catch and minimize bycatch of non-target and protected species in a US-based commercial fishery. It is important to quantify the sensitivity of DM tools to missing data, because dissemination streams for observed (i.e., remotely sensed or directly sampled) data can experience delays or gaps. To address this issue, we perform a detailed example sensitivity analysis using our case study tool. 4. Synthesis and applications. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four-stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives. K E Y W O R D S dynamic management, ecological modelling, fisheries bycatch, near real-time, nowcast, operationalization, sensitivity analysis, spatial management This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Product: The end output of a DM tool that prescribes a management recommendation and optionally contains associated metadata Latency: The temporal delay in the dissemination of DM products or BEES data Contingency plan: A set of rules that govern DM tools' operational responses to missing or sparse BEES data | 461
Humans have substantially altered the thermal regimes of freshwater habitats worldwide, with significant environmental consequences. There is a critical need for a comprehensive modeling framework for forecasting the downstream impacts of two of the most common anthropogenic structures that alter river water temperatures: 1) dams that selectively release water from thermally stratified reservoirs, and 2) power generating stations and industrial plants that use river water for once-through cooling. These facilities change the thermal dynamics of the downstream waters through a complex interaction of water release volume and temperature and the subsequent exchange with the environment downstream. In order to stay within the downstream temperature limits imposed by regulatory agencies, managers must monitor not just release volumes and temperatures, but also need to be able to forecast the thermal impacts of their day-to-day operations on habitat which may be hundreds of kilometers downstream. Here we describe a coupled modeling framework that links mesoscale weather and ecological models to generate inputs for a physically-based water temperature model for monitoring and forecasting river temperatures downstream from these facilities at fine spatiotemporal scales. We provide an example of how this modeling framework is being applied to a water allocation decision support system (DSS) for the management of Endangered Species Act (ESA) listed salmon species in the Sacramento River in California.
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