Predicting Risk to Estuary Water Quality and Patterns of Benthic Environmental DNA in Queensland, Australia using Bayesian Networks

Predictive modeling can inform natural resource management by demonstrating stressor-response pathways and quantifying the effects on selected endpoints. This study develops a risk assessment model using the Bayesian network-relative risk model (BN-RRM) approach, and, for the first time, incorporates eukaryote environmental DNA data as a measure of benthic community structure into an ecological risk assessment context. Environmental DNA sampling is a relatively new technique for biodiversity measurements that involves extracting DNA from environmental samples, sequencing a region of the 18s rDNA gene, and matching the sequences to organisms. Using a network of probability distributions, the BN-RRM model predicts risk to water quality objectives and also the richness of benthic taxa in the Noosa, Pine, and Logan Estuaries in South East Queensland (SEQ), Australia. The model is more accurate at predicting Dissolved Oxygen than it is the Chlorophyll-a water quality endpoint, and it predicts photosynthesizing benthos more accurately than heterotrophs. Results of BN-RRM modeling indicate that the water quality and benthic assemblages of the Noosa are relatively homogenous across all sub risk regions, and that the Noosa has a high probability (73 - 92% probability) of achieving water quality objectives, which indicates low relative risk. On the other hand, the Middle Logan, Middle Pine, and Lower Pine regions are much less likely to meet objectives (15 – 55% probability), indicating a relatively high risk to water quality in those regions. The benthic community richness patterns associated with low relative risk in the Noosa are high Diatom relative richness and low Green Algae richness. The only benthic pattern consistently associated with high relative risk to water quality is the high Fungi richness state. The BN-RRM predicts current conditions in SEQ based on available monitoring data, and provides a basis for future predictions and adaptive management at the direction of resource managers. As new data are made available or more questions are asked, this BN-RRM model can be updated and improved

Breakdown of biomass for energy applications using microwave pyrolysis: A technological review

The agricultural industry faces a permanent increase in waste generation, which is associated with the fast-growing population. Due to the environmental hazards, there is a paramount demand for generating electricity and value-added products from renewable sources. The selection of the conversion method is crucial to develop an eco-friendly, efficient and economically viable energy application. This manuscript investigates the influencing factors that affect the quality and yield of the biochar, bio-oil and biogas during the microwave pyrolysis process, evaluating the biomass nature and diverse combinations of operating conditions. The by-product yield depends on the intrinsic physicochemical properties of biomass. Feedstock with high lignin content is favourable for biochar production, and the breakdown of cellulose and hemicellulose leads to higher syngas formation. Biomass with high volatile matter concentration promotes the generation of bio-oil and biogas. The pyrolysis system's conditions of input power, microwave heating suspector, vacuum, reaction temperature, and the processing chamber geometry were influence factors for optimising the energy recovery. Increased input power and microwave susceptor addition lead to high heating rates, which were beneficial for biogas production, but the excess pyrolysis temperature induce a reduction of bio-oil yield

Using Bayesian Networks to Predict Risk to Estuary Water Quality and Patterns of Benthic Environmental DNA in Queensland

Predictive modeling can inform natural resource management by representing stressor-response pathways in a logical way and quantifying the effects on selected endpoints. This study demonstrates a risk assessment model using the Bayesian network-relative risk model (BNRRM) approach to predict water quality and; for the first time, eukaryote environmental DNA (eDNA) data as a measure of benthic community structure. Environmental DNA sampling is a technique for biodiversity measurements that involves extracting DNA from environmental samples, amplicon sequencing a targeted gene, in this case the 18s rDNA gene which targets eukaryotes, and matching the sequences to organisms. Using a network of probability distributions, the BN-RRM model predicts risk to water quality objectives and the relative richness of benthic taxa groups in the Noosa, Pine, and Logan estuaries in South East Queensland (SEQ), Australia. The model predicts Dissolved Oxygen more accurately than the Chlorophyll-a water quality endpoint, and photosynthesizing benthos more accurately than heterotrophs. Results of BN-RRM modeling given current inputs indicate that the water quality and benthic assemblages of the Noosa are relatively homogenous across all sub risk regions, and that the Noosa has a 73 – 92 percent probability of achieving water quality objectives, indicating a low relative risk. Conversely, the Middle Logan, Middle Pine, and Lower Pine regions are much less likely to meet objectives (15 – 55 percent probability), indicating a relatively higher risk to water quality in those regions. The benthic community richness patterns associated with risk in the Noosa are high Diatom relative richness and low Green Algae relative richness. The only benthic pattern consistently associated with the relatively higher risk to water quality is high richness of fungi species. The BN-RRM model provides a basis for future predictions and adaptive management at the direction of resource managers

Parks Canada’s adaptation framework and workshop approach: Lessons learned across a diverse series of adaptation workshops

In 2017, the Canadian Parks Council Climate Change Working Group, a team of federal, provincial, and territorial representatives, developed a Climate Change Adaptation Framework for Parks and Protected Areas, guiding practitioners through a simple, effective five-step adaptation process. This framework was adapted by Parks Canada into a two-day adaptation workshop approach, with 11 workshops subsequently held from September 2017 to May 2019 at Parks Canada sites in the Yukon, Quebec, Manitoba, Alberta, Nova Scotia, British Columbia, Newfoundland, and Ontario. Lessons learned from each workshop have been integrated into the approach, with the development of tools and guidance for each phase of the process, and a shareable, visual “placemat” that describes each step of the framework, acting as a map for those navigating the process

Assessing the Effects of Chemical Mixtures using a Bayesian Network-Relative Risk Model (BN-RRM) Integrating Adverse Outcome Pathways (AOPs)

There are long-standing uncertainties about toxicity of chemical mixtures to populations. Laboratory toxicity tests have confirmed synergistic and antagonistic effects to individuals, but not to populations.We will conduct a regional scale ecological risk assessment by evaluating the effects chemical mixtures to populations with a new Bayesian Network- Relative Risk Model (BN-RRM) incorporating an Adverse Outcome Pathway (AOP). We started applying this new BN-RRM framework in a case study with organophosphate pesticide mixtures (diazinon, chlorpyrifos, and malathion). Acetylcholinesterase inhibition (AChE) was chosen the molecular initiating event and the Puget Sound Chinook salmon (Oncorhynchus tshawytscha) and Coho salmon (Oncorhynchus kisutch) Evolutionary Significant Units (ESU) were chosen as population endpoints. Dose-response equations will be generated from the mixtures, integrated into the new BN-RRM framework and then overall risk will be calculated for the populations. Preliminary results indicate that organophosphate pesticide mixtures act synergistically and impair olfactory function that lead to loss of antipredator, homing and reproductive behavior which lead to changes in population age structure and patch dynamics. Assessing mixtures through this new BN-RRM framework is an innovative method of predicting effects to populations. This research will demonstrate a probabilistic approach to synthesize the effects of mixtures and predict impacts to populations

Dataset for the Environmental Risk Assessment of Chlorpyrifos to Chinook Salmon in four Rivers of Washington State, United States

Data files available below. This data set is in support of Landis et al (in press) The integration of chlorpyrifos acetylcholinesterase inhibition, water temperature and dissolved oxygen concentration into a regional scale multiple stressor risk assessment estimating risk to Chinook salmon in four rivers in Washington State, USA. DOI: 10.1002/ieam.4199. In this research We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT) and dissolved oxygen concentrations (DO) in four watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle -Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN-RRM) was used to conduct this ecological risk assessment and was modified to contain an AChE inhibition pathway parameterized using data from chlorpyrifos toxicity datasets. The completed BN-RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling run up to 50 year timeframes. There were 4 primary conclusions drawn from the model building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65 to 85 percent. Environmental conditions contributed to a larger proportion of the risk compared to chlorpyrifos. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential that CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management-goal, we calculated the conditions that would increase in juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. This research was supported by USEPA STAR Grant RD-83579501. Excel spreadsheet, model in Netica

Quantum interference of electromechanically stabilized emitters in nanophotonic devices

Photon-mediated coupling between distant matter qubits may enable secure communication over long distances, the implementation of distributed quantum computing schemes, and the exploration of new regimes of many-body quantum dynamics. Nanophotonic devices coupled to solid-state quantum emitters represent a promising approach towards realization of these goals, as they combine strong light-matter interaction and high photon collection efficiencies. However, the scalability of these approaches is limited by the frequency mismatch between solid-state emitters and the instability of their optical transitions. Here we present a nano-electromechanical platform for stabilization and tuning of optical transitions of silicon-vacancy (SiV) color centers in diamond nanophotonic devices by dynamically controlling their strain environments. This strain-based tuning scheme has sufficient range and bandwidth to alleviate the spectral mismatch between individual SiV centers. Using strain, we ensure overlap between color center optical transitions and observe an entangled superradiant state by measuring correlations of photons collected from the diamond waveguide. This platform for tuning spectrally stable color centers in nanophotonic waveguides and resonators constitutes an important step towards a scalable quantum network

Integration of Chlorpyrifos Acetylcholinesterase Inhibition, Water Temperature, and Dissolved Oxygen Concentration into a Regional Scale Multiple Stressor Risk Assessment Estimating Risk to Chinook Salmon

We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT), and dissolved oxygen concentration (DO) in 4 watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle–Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN‐RRM) was used to conduct this ecological risk assessment and was modified to contain an acetylcholinesterase (AChE) inhibition pathway parameterized using data from CH toxicity data sets. The completed BN‐RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling runs up to 50‐y timeframes. There were 3 primary conclusions drawn from the model‐ building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65% to 85%. Environmental conditions contributed to a larger proportion of the risk compared to CH. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential than CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management goal, we calculated the conditions that would increase juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. Integr Environ Assess Manag 2020;16:28–42. © 2019 SETA

Central nervous system inflammation induces muscle atrophy via activation of the hypothalamic–pituitary–adrenal axis

Systemic and CNS-delimited inflammation triggers skeletal muscle catabolism in a manner dependent on glucocorticoid signaling