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Fire agencies across the United States must make complex resource allocation decisions to manage wildfires using a national network of shared firefighting resources. Firefighters play a critical role in suppressing fires and protecting vulnerable communities. However, they are exposed to health and safety risks associated with fire, smoke inhalation, and infectious disease transmission. The COVID-19 pandemic further complicated these risks, prompting fire agencies to propose resource management adaptations to minimize COVID-19 exposure and transmission. It is unclear if and how the pandemic may have operationally influenced wildland firefighting personnel resource use given compounding wildfire and COVID-19 risks. Therefore, we developed generalized linear mixed models that were fit using multiple integrated datasets to detect changes in personnel resource use for years prior to and during the COVID-19 pandemic, while controlling for historical fire and landscape conditions, societal risks, and management objectives. Analyses of observed and predicted firefighting resource use revealed reductions in the mean personnel resources used per wildfire per day during the pandemic for models developed across the western U.S. and for various western U.S. fire regions. Notably, the Northern California and the Great Basin Coordination Centers showed statistically significant reductions in ground personnel use during the COVID-19 pandemic. Learning from wildland fire management strategies and resource use trends that occurred during the COVID-19 pandemic, fire agencies can better anticipate resource constraints that may arise during the compounding threats of severe wildland fire activity and infectious disease outbreaks to proactively prepare and adapt suppression management strategies.

The U.S. Department of Energy has estimated that if the United States is to generate 20% of its electricity from wind, over 50 GW will be required from shallow offshore turbines. Hurricanes are a potential risk to these turbines. Turbine tower buckling has been observed in typhoons, but no offshore wind turbines have yet been built in the United States. We present a probabilistic model to estimate the number of turbines that would be destroyed by hurricanes in an offshore wind farm. We apply this model to estimate the risk to offshore wind farms in four representative locations in the Atlantic and Gulf Coastal waters of the United States. In the most vulnerable areas now being actively considered by developers, nearly half the turbines in a farm are likely to be destroyed in a 20-y period. Reasonable mitigation measures—increasing the design reference wind load, ensuring that the nacelle can be turned into rapidly changing winds, and building most wind plants in the areas with lower risk—can greatly enhance the probability that offshore wind can help to meet the United States' electricity needs.

Coral cover has been declining in recent decades due to increased temperatures and environmental stressors. However, the extent to which different stressors contribute both individually and in concert to bleaching and mortality is still very uncertain. We develop and use a novel regression approach, using non-linear parametric models that control for unobserved time invariant effects to estimate the effects on coral bleaching and mortality due to temperature, solar radiation, depth, hurricanes and anthropogenic stressors using historical data from a large bleaching event in 2005 across the Caribbean. Two separate models are created, one to predict coral bleaching, and the other to predict near-term mortality. A large ensemble of supporting data is assembled to control for omitted variable bias and improve fit, and a significant improvement in fit is observed from univariate linear regression based on temperature alone. The results suggest that climate stressors (temperature and radiation) far outweighed direct anthropogenic stressors (using distance from shore and nearby human population density as a proxy for such stressors) in driving coral health outcomes during the 2005 event. Indeed, temperature was found to play a role ~4 times greater in both the bleaching and mortality response than population density across their observed ranges. The empirical models tested in this study have large advantages over ordinary-least squares-they offer unbiased estimates for censored data, correct for spatial correlation, and are capable of handling more complex relationships between dependent and independent variables. The models offer a framework for preparing for future warming events and climate change; guiding monitoring and attribution of other bleaching and mortality events regionally and around the globe; and informing adaptive management and conservation efforts.

Conventional environmental-health risk-assessment methods are often limited in their ability to account for uncertainty in contaminant exposure, chemical toxicity and resulting human health risk. Exposure levels and toxicity are both subject to significant measurement errors, and many predicted risks are well below those distinguishable from background incident rates in target populations. To address these issues methods are needed to characterize uncertainties in observations and inferences, including the ability to interpret the influence of improved measurements and larger datasets. Here we develop a Bayesian network (BN) model to quantify the joint effects of measurement errors and different sample sizes on an illustrative exposure-response system. Categorical variables are included in the network to describe measurement accuracies, actual and measured exposures, actual and measured response, and the true strength of the exposure-response relationship. Network scenarios are developed by fixing combinations of the exposure-response strength of relationship (none, medium or strong) and the accuracy of exposure and response measurements (low, high, perfect). Multiple cases are simulated for each scenario, corresponding to a synthetic exposure response study sampled from the known scenario population. A learn-from-cases algorithm is then used to assimilate the synthetic observations into an uninformed prior network, yielding updated probabilities for the strength of relationship. Ten replicate studies are simulated for each scenario and sample size, and results are presented for individual trials and their mean prediction. The model as parameterized yields little-to-no convergence when low accuracy measurements are used, though progressively faster convergence when employing high accuracy or perfect measurements. The inferences from the model are particularly efficient when the true strength of relationship is none or strong with smaller sample sizes. The tool developed in this study can help in the screening and design of exposure-response studies to better anticipate where such outcomes can occur under different levels of measurement error. It may also serve to inform methods of analysis for other network models that consider multiple streams of evidence from multiple studies of cumulative exposure and effects.

Water management policies in desert agricultural regions critically influence both crop choices and ecosystem dynamics, yet their cascading ecological impacts remain poorly understood. In particular, the complex interactions between water quality, agricultural practices, and wildlife responses require further investigation to inform sustainable management in desert landscapes. Here, we evaluate how water policy, particularly seawater desalination initiatives influencing irrigation and cropping practices, shapes ecological systems in a hyperarid region, the southern Arava Valley of Israel. We integrated community-level questionnaires, agricultural records, animal field observations, and spatially explicit scenario tools into a mixed-methods framework to model social–ecological cascades linking water policy to predator dynamics. Bayesian Belief Networks combined with Generalized Linear Models of predator abundance were used to assess how improved water quality affects cropping patterns and, in turn, regional predator populations. Our findings indicate that desalination is unlikely to alter the predominance of date orchards or the high abundance of range-expanding jackals associated with these systems. However, water quality-driven expansion of field crops corresponds to lower modelled fox abundance and shifts in predicted predator interactions, while jackal populations remain largely influenced by date orchard availability. Under business-as-usual scenarios with lower water quality, farmers are likely to reduce field crop areas, corresponding to further changes in regional predator abundance. These findings suggest that water policy decisions may generate cascading social–ecological responses on both agricultural practices and local desert ecosystems, emphasizing the need for strategies that balance agricultural productivity with ecological sustainability in arid landscapes.

We present a decision support framework for science-based assessment and multi-stakeholder deliberation. The framework consists of two parts: a DPSIR (Drivers–Pressures–States–Impacts–Responses) analysis to identify the important causal relationships among anthropogenic environmental stressors, processes, and outcomes; and a Decision Landscape analysis to depict the legal, social, and institutional dimensions of environmental decisions. The Decision Landscape incorporates interactions among government agencies, regulated businesses, non-government organizations, and other stakeholders. It also identifies where scientific information regarding environmental processes is collected and transmitted to improve knowledge about elements of the DPSIR and to improve the scientific basis for decisions. Our application of the decision support framework to coral reef protection and restoration in the Florida Keys focusing on anthropogenic stressors, such as wastewater, proved to be successful and offered several insights. Using information from a management plan, it was possible to capture the current state of the science with a DPSIR analysis as well as important decision options, decision makers and applicable laws with a the Decision Landscape analysis. A structured elicitation of values and beliefs conducted at a coral reef management workshop held in Key West, Florida provided a diversity of opinion and also indicated a prioritization of several environmental stressors affecting coral reef health. The integrated DPSIR/Decision landscape framework for the Florida Keys developed based on the elicited opinion and the DPSIR analysis can be used to inform management decisions, to reveal the role that further scientific information and research might play to populate the framework, and to facilitate better-informed agreement among participants.

With a service area population exceeding four million people and with close to 90 % of the water supply being imported from sources outside the city, the Los Angeles water system is subject to multiple stressors, including climate change and population growth. The influence of various factors on water demand in Los Angeles was evaluated through development and application of multiple linear regression models for residential, commercial, industrial, and governmental water demand categories from 1970 to 2014 in the service area of the Los Angeles Department of Water and Power. Performance of the models in describing historical water demand was compared using the coefficient of determination, mean average percent error, and normalized root mean square error. Overall, the results of the linear regression models demonstrated that each water demand category is affected by different parameters. However, price and population were found to have the most significant impact on all categories. The seasonality of residential water demand was well described with the model based on monthly data, with precipitation and temperature being highly significant factors. Fitting of the residential data furthermore revealed that price and conservation have significantly counteracted the impact of population growth on water demand.

Proper characterizations of background soil CO 2 respiration rates are critical for interpreting CO 2 leakage monitoring results at geologic sequestration sites. In this paper, a method is developed for determining temperature-dependent critical values of soil CO 2 flux for preliminary leak detection inference. The method is illustrated using surface CO 2 flux measurements obtained from the AmeriFlux network fit with alternative models for the soil CO 2 flux versus soil temperature relationship. The models are fit first to determine pooled parameter estimates across the sites, then using a Bayesian hierarchical method to obtain both global and site-specific parameter estimates. Model comparisons are made using the deviance information criterion (DIC), which considers both goodness of fit and model complexity. The hierarchical models consistently outperform the corresponding pooled models, demonstrating the need for site-specific data and estimates when determining relationships for background soil respiration. A hierarchical model that relates the square root of the CO 2 flux to a quadratic function of soil temperature is found to provide the best fit for the AmeriFlux sites among the models tested. This model also yields effective prediction intervals, consistent with the upper envelope of the flux data across the modeled sites and temperature ranges. Calculation of upper prediction intervals using the proposed method can provide a basis for setting critical values in CO 2 leak detection monitoring at sequestration sites.

Current energy efficiency policy and incentive programs tend to target economic motivations, which may misalign with other potentially important motivations arising from situational factors, individual differences, and social context. Thus, in this research, we review areas of work that have focused on psychological and social influences to energy efficiency adoption in commercial buildings. We then conduct an empirical scoping study interviewing 10 commercial building owners/managers (decision makers) and 10 experts/consultants (decision influencers) regarding perceived motives and barriers to energy efficient investments, decision-maker attributes, and the social context of the decision. Potential factors that emerge from the interviews, which are not yet extensively discussed in the energy efficiency literature, include owners/managers’ resistance to change and the influence of investment funding origins on the decision. Our results also suggest potential heterogeneity in energy efficiency decision-making philosophies between the two groups. Interviewed owners/managers prioritize corporate social responsibility (CSR) and prefer internal consulting (e.g., building engineers). Conversely, experts/consultants do not emphasize CSR and are more concerned with external policies. These findings suggest that accounting for the decision maker and the social context in which decisions are made could enhance the design of commercial sector energy efficiency programs.

A number of high-profile seismic events have occurred in recent years, with a wide variation in the resulting economic damage and loss of life. This variation has been attributed in part to the stringency of seismic building codes implemented in different regions. Using the HAZUS Earthquake Model, a benefit–cost analysis was performed on varying levels of standard buildings codes for Haiti and Puerto Rico. The methodology computes expected loss assuming a Poisson event process with lognormally distributed event magnitude and idealized damage–magnitude response functions. The event frequency and magnitude distributions are estimated from the historical record, while the damage functions are fit using HAZUS simulation results for events with systematically varying magnitudes and different seismic code levels. To validate the approach, a single-event analysis was conducted using alternative building codes and mean magnitude earthquakes. A probabilistic analysis was then used to evaluate the long-term expected value for alternative levels of building codes. To account for the relationship between lives saved and economic loss, the implicit cost of saving a life is computed for each code option. It was found that in the two areas studied, the expected loss of life was reduced the most by use of high seismic building code levels, but lower levels of seismic building code were more cost-effective when considering only building damages and the costs for code implementation. The methodology presented is meant to provide a basic framework for the future development of an economic-behavioral model for code adoption.