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Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that datamore »cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.

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The purpose of this study was to learn if a convenient 1H NMR method could be developed to serve as a tool for estimating the propensity of a given lactone to participate in ring-opening transesterification polymerization (ROTEP). The methanolysis of each of 18 lactones was initially examined in CD3OD solution in the presence of sulfuric acid as a Brønsted catalyst at ambient temperature. Once equilibrium was established, the ratio of remaining lactone to the ring-opened methyl ester/alcohol could be readily measured by NMR spectroscopy. The observed thermodynamic driving force observed for the methanol ring openings is roughly in line with the extent of ROTEP for the various classes of lactones. This is the case even though the reaction conditions for these methanolyses versus ROTEP reactions are substantially different. Qualitative evaluations of the rates of the ring-opening methanolyses were also performed, and several non-obvious relative reactivities were observed. Finally, employing this simple NMR methanolysis using low concentrations of methanol in CDCl3 is recommended as the preferred protocol for the initial evaluation of the polymerizability of any new lactone monomer that researchers may prepare in the future.

A method for imaging an acoustic standing wave in the presence of flowing gas is described. The optical power at the acoustic frequency in each pixel of a series of high-speed transmission electronic speckle pattern interferograms is used to map the steady-state pressure variations of an acoustic standing wave. The utility of the process is demonstrated by imaging the standing wave inside a transparent organ pipe.

We report the results from follow-up observations of two Roche-lobe filling hot subdwarf binaries with white dwarf companions predicted to have accretion discs. ZTF J213056.71+442046.5 (ZTF J2130) with a 39-min period and ZTF J205515.98+465106.5 (ZTF J2055) with a 56-min period were both discovered as subdwarf binaries with light curves that could only be explained well by including an accretion disc in their models. We performed a detailed high-resolution spectral analysis, using Keck/ESI to search for possible accretion features for both objects. We also employed polarimetric analysis using the Nordic Optical Telescope (NOT) for ZTF J2130. We did not find any signatures of an accretion disc in either object, and placed upper limits on the flux contribution and variation in degree of polarization due to the disc. Owing to the short 39-min period and availability of photometric data over 6 yr for ZTF J2130, we conducted an extensive O − C timing analysis in an attempt to look for orbital decay due to gravitational wave radiation. No such decay was detected conclusively, and a few more years of data paired with precise and consistent timing measurements were deemed necessary to constrain $\dot{P}$ observationally.

Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.

Community-based public health interventions often rely on representative, spatially referenced outcome data to draw conclusions about a finite population. To estimate finite-population parameters, we are posed with two challenges: to correctly account for spatial association among the sampled and nonsampled participants and to correctly model missingness in key covariates, which may be also spatially associated. To accomplish this, we take inspiration from the preferential sampling literature and develop a general Bayesian framework that can specifically account for preferential non-response. This framework is first applied to three missing data scenarios in a simulation study. It is then used to account for missing data patterns seen in reported annual household income in a corner-store intervention project. Through this, we are able to construct finite-population estimates of the percent of income spent on fruits and vegetables. Such a framework provides a flexible way to account for spatial association and complex missing data structures in finite populations.