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Coastal recreation and water quality are major contributors to human well-being in coastal regions. They can also interact, creating opportunities for ecosystem based management, ecological restoration, and water quality improvement that can positively affect people and the environment. Yet the effect of environmental quality on human behavior is often poorly quantified, but commonly assumed in coastal ecosystem service studies. To clarify this effect we investigate a water quality dataset for evidence that environmental condition partially explains variation in recreational visitation, our indicator of human behavior. In Puget Sound, WA, we investigate variation in visitation in both visitation rate and fixed effects (FE) models. The visitation rate model relates the differences in annual recreational visitation among parks to environmental conditions, park characteristics, travel cost, and recreational demand. In our FE model we control for all time-invariant unobserved variables and compare monthly variation at the park level to determine how water quality affects visitation during the summer season. The results of our first model illustrate how visitation relates to various amenities and costs. In the FE analysis, monthly visitation was negatively related to water quality while controlling for monthly visitation trends. This indicates people are responding to changes in water quality, and an improvement would yield an increase in the value of recreation. Together, these results could help in prioritizing water quality improvements, could assist the creation of new parks or the modification of existing recreational infrastructure, and provide quantitative estimates for the expected benefits from potential changes in recreational visitation and water quality improvements. Our results also provide an example of how recreational visitation can be quantified and used in ecosystem service assessments.

In the Santa Cruz Watershed, located on the Arizona-Sonora portion of the U.S.-Mexico border, an international wastewater treatment plant treats wastewater from cities on both sides of the border, before discharging it into the river in Arizona. These artificial flows often subsidize important perennial surface water ecosystems in the region. An explicit understanding of the benefits of maintaining instream flow for present and future generations requires the ability to assess and understand the important trade-offs implicit in water-resource management decisions. In this paper, we outline an approach for modeling and visualizing impacts of management decisions in terms of rare terrestrial and aquatic wildlife, vegetation, surface water, groundwater recharge, real-estate values and socio-environmental vulnerable communities. We identify and quantify ecosystem services and model the potential reduction in effluent discharge to the U.S. that is under scrutiny by binational water policy makers and of concern to stakeholders. Results of service provisioning are presented, and implications for policy makers and resource managers are discussed. This paper presents a robust ecosystem services assessment of multiple scenarios of watershed management as a means to discern eco-hydrological responses and consider their potential values for future generations living in the borderlands.

Ecosystem restoration in south Florida is a state and national priority centered on the Everglades wetlands. However, urban development pressures affect the restoration potential and remaining habitat functions of the natural undeveloped areas. Land use (LU) planning often focuses at the local level, but a better understanding of the cumulative effects of small projects at the landscape level is needed to support ecosystem restoration and preservation. The South Florida Ecosystem Portfolio Model (SFL EPM) is a regional LU planning tool developed to help stakeholders visualize LU scenario evaluation and improve communication about regional effects of LU decisions. One component of the SFL EPM is ecological value (EV), which is evaluated through modeled ecological criteria related to ecosystem services using metrics for (1) biodiversity potential, (2) threatened and endangered species, (3) rare and unique habitats, (4) landscape pattern and fragmentation, (5) water quality buffer potential, and (6) ecological restoration potential. In this article, we demonstrate the calculation of EV using two case studies: (1) assessing altered EV in the Biscayne Gateway area by comparing 2004 LU to potential LU in 2025 and 2050, and (2) the cumulative impact of adding limestone mines south of Miami. Our analyses spatially convey changing regional EV resulting from conversion of local natural and agricultural areas to urban, industrial, or extractive use. Different simulated local LU scenarios may result in different alterations in calculated regional EV. These case studies demonstrate methods that may facilitate evaluation of potential future LU patterns and incorporate EV into decision making.

Using respective strengths of the biological, physical, and social sciences, we are developing an online decision support tool, the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM), to help promote the use of information relevant to water allocation and land management in a binational watershed along the U.S.-Mexico border. The SCWEPM will include an ES valuation system within a suite of linked regional driver-response models and will use a multicriteria scenario-evaluation framework that builds on GIS analysis and spatially-explicit models that characterize important ecological, economic, and societal endpoints and consequences that are sensitive to climate patterns, regional water budgets, and regional LULC change in the SCW.

This work develops a decision analytical approach to water quality management at the watershed scale through a mercury Total Maximum Daily Load (TMDL) development case study. This approach treats the key environmental variables as causally-related random variables that may be influenced through mitigation actions (interventions) to an uncertain degree. Starting from the perspective that water quality management falls under the rubric of \"decision-making under uncertainty\", I explore the application of state of the art probabilistic tools for decision support. This work goes beyond the current deterministic paradigm in which conservative modeling choices are used to deal with predictive uncertainty. The proposed decision model frames the TMDL setting process as a set of regulatory decisions that may involve large uncertainties (limited data bases and incomplete knowledge) and that is subject to tight regulatory deadlines and small decision process budgets. Probabilistic source analysis and linkage analysis models based on the available data, standard environmental science and engineering theory, and mercury biogeochemistry expertise were created for the case study mercury TMDL decision situation. Discrete conditional probability distributions based on these models and expertise were incorporated in a Bayesian network model, a tool for solving prediction and inference queries. In conjunction with a parametric value model, this mercury Bayesian network serves as the basis of a mercury TMDL decision model for the case study. This decision model demonstrates a formal context for considering the importance of uncertainty in TMDL decisions, for prioritizing information collecting activities, for considering trade-offs between compliance uncertainty and mitigation costs, and for considering and representing hypotheses within a TMDL decision-modeling framework. Sensitivity analysis using the Bayesian network is used to demonstrate approaches for prioritizing information collection activities and for estimating the value of perfect information on variables of interest. As demonstrated, future information activities should be based on preliminary models of the uncertain relationships between possible interventions and environmental targets. Very importantly, the Bayesian perspective of decision analysis allows decision participants to interpret new information (monitoring and knowledge) in light of previous information and knowledge, which is a good basis for an adaptive management framework.

Due to cyanobacterial toxin (cyanotoxin) contamination issues in 2018, the city of Salem, Oregon, issued a 33-day do-not-drink advisory for vulnerable people among the 200,000 residents. After the incident, the state of Oregon put in place drinking water rules to require the routine testing of raw water, as well as finished water, in cases where the raw water cyanotoxin concentrations exceeded trigger values. The United States Environmental Protection Agency (EPA) total microcystins drinking water health advisory level (HAL) for small children is 0.3 µg/L. This is equivalent to the minimum reporting level (MRL) for EPA Method 546. Consequently, there was no ability to provide early warnings via toxin testing for total microcystins using the EPA method. In this study, we performed a comparison of the precision and accuracy of the enzyme-linked immunosorbent assay (ELISA) described in the EPA method to a more sensitive assay, the Streptavidin-enhanced Sensitivity (SAES) assay. Based on these precision and accuracy studies and quantitation limit determinations and confirmations, the EPA Office of Ground Water and Drinking Water (OGWDW) has concluded the SAES kit meets the requirements of EPA Method 546. With an MRL that is one-third of the original concentration, the new kit provides a small but critical window for identifying early warnings. Challenges remain with providing early warnings due to the variability in bloom dynamics; however, the new MRL allowed Oregon to lower the trigger level for susceptible systems, thereby providing an additional early warning.

Robot decision-making in partially observable, real-time, dynamic, and multi-agent environments remains a difficult and unsolved challenge. Model-free reinforcement learning (RL) is a promising approach to learning decision-making in such domains, however, end-to-end RL in complex environments is often intractable. To address this challenge in the RoboCup Standard Platform League (SPL) domain, we developed a novel architecture integrating RL within a classical robotics stack, while employing a multi-fidelity sim2real approach and decomposing behavior into learned sub-behaviors with heuristic selection. Our architecture led to victory in the 2024 RoboCup SPL Challenge Shield Division. In this work, we fully describe our system's architecture and empirically analyze key design decisions that contributed to its success. Our approach demonstrates how RL-based behaviors can be integrated into complete robot behavior architectures.