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Interest in modeling contemporary crime trends, a task that has historically been considered valuable to the public, researchers, and policymakers, is resurging. Advancements in criminology have made it clear that understanding crime trends necessarily involves understanding trends in how likely individuals are to report crimes to the police, as well as how likely the police are to accurately record those crimes. In this paper, we use dynamic linear models to simultaneously model the time series for several crime types in order to gain insight into trends in crime and crime reporting. We analyze crime data from Chicago spanning 2007 through 2016 and show how correlations in the way crime trends evolve may contain information about drivers of crime and crime reporting. We provide evidence of substantial differences in the relationships between the trends of crimes of different types depending on whether crimes are violent or nonviolent and whether or not crimes are tracked in the FBI's Uniform Crime Report.

Removal and distance modeling are two common methods to adjust counts for imperfect detection in point-count surveys. Several recent articles have formulated models to combine them into a distance-removal framework. We observe that these models fall into two groups building from different assumptions about the joint distribution of observed distances and first times to detection. One approach assumes the joint distribution results from a Poisson process (PP). The other assumes an independent joint (IJ) distribution with its joint density being the product of its marginal densities. We compose an IJ+PP model that more flexibly models the joint distribution and accommodates both existing approaches as special cases. The IJ+PP model matches the bias and coverage of the true model for data simulated from either PP or IJ models. In contrast, PP models underestimate abundance from IJ simulations, while IJ models overestimate abundance from PP simulations. We apply all three models to surveys of golden-crowned sparrows in Alaska. Only the IJ+PP model reasonably fits the joint distribution of observed distances and first times to detection. Model choice affects estimates of abundance and detection but has little impact on the magnitude of estimated covariate effects on availability and perceptibility.

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

Process-based agroecosystem models are powerful tools to assess performance of managed landscapes, but their ability to accurately represent reality is limited by the types of input data they can use. Ensuring these models can represent cropping field heterogeneity and environmental impact is important, especially given the growing interest in using agroecosystem models to quantify ecosystem services from best management practices and land use change. We posited that augmenting process-based agroecosystem models with additional field-specific information such as topography, hydrologic processes, or independent indicators of yield could help limit simulation artifacts that obscure mechanisms driving observed variations. To test this, we augmented the agroecosystem model Agricultural Production Systems Simulator (APSIM) with field-specific topography and satellite imagery in a simulation framework we call Foresite. We used Foresite to optimize APSIM yield predictions to match those created from a machine learning model built on remotely sensed indicators of hydrology and plant productivity. Using these improved subfield yield predictions to guide APSIM optimization, total N O 3 − N loss estimates increased by 39% in maize and 20% in soybeans when summed across all years. In addition, we found a disproportionate total amount of leaching in the lowest yielding field areas vs the highest yielding areas in maize (42% vs 15%) and a similar effect in soybeans (31% vs 20%). Overall, we found that augmenting process-based models with now-common subfield remotely sensed data significantly increased values of predicted nutrient loss from fields, indicating opportunities to improve field-scale agroecosystem simulations, particularly if used to calculate nutrient credits in ecosystem service markets.

Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of corn (R3) as well as other new agronomic signals from SMOS VOD. We compare our estimates of the timing of R3 to United States Department of Agriculture (USDA) survey data for the years 2011, 2012, and 2013. We find that 87%, 70%, and 37%, respectively, of our model estimates of R3 timing agree with USDA district-level observations. We postulate that since the satellite estimates can be directly linked to a physiological state (the maximum amount of plant water, or water contained within plant tissue per ground area) it is more accurate than the USDA data which is based upon visual observations from roadways. Consequently, SMOS VOD could be used to replace, at a finer resolution than the district-level USDA reports, the R3 data that has not been reported by the USDA since 2013. We hypothesize the other model parameters contain new information about soil and crop management and crop productivity that are not routinely collected by any federal or state agency in the Corn Belt.

Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs). MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM 2 ): an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM) algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM 2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods. Conclusions This work provides a novel, accelerated version of a likelihood-based parameter estimation method that can be readily applied to stochastic biochemical systems. In addition, our results suggest opportunities for added efficiency improvements that will further enhance our ability to mechanistically simulate biological processes.

Riparian buffers can improve water quality, but watershed‐scale evaluations of riparian buffering opportunities are rare. A landscape discretization tool called riparian catchments, part of the Agricultural Conservation Planning Framework (ACPF) version 3, was applied to evaluate functional riparian settings for 32 headwater watersheds representing three major land resource areas (MLRAs) in Iowa. Riparian settings of 250‐m length were classified based on height above channel and upslope contributing area to show where to place buffers primarily designed to intercept runoff, treat nitrate in shallow groundwater, and/or protect streambanks. Riparian zones found below small riparian catchments were common, typically occupying >50% of streambank lengths in MLRA 103 (northern Iowa) and MLRA 108 (southeast Iowa). In these settings, narrow (6–10 m wide) buffers provide a buffer/contributing area ratio of >0.02 to filter surface runoff, while providing streambank protection. This similarity occurred despite these two MLRAs having contrasting landscapes. Whereas the narrow buffers suggested are associated with ditches and flat terrain in MLRA 103, they occur below short slopes along streams that have well dissected the watersheds in MLRA 108. In MLRA 104 of east‐central Iowa, headwater alluvial streams often had broad low‐lying riparian zones, where wide buffers (>25 m) may be placed to help mitigate nitrate transport in shallow groundwater. The ACPF riparian catchments approach enabled cross‐watershed analyses of riparian settings, while providing spatial data to inform watershed‐scale riparian planning efforts. Core Ideas Thirty‐two Iowa watersheds were delineated into riparian catchments. Riparian zones were classified using the Agricultural Conservation Planning Framework. Differences in riparian classes were observed among three major land resource areas. Multiwatershed comparison of riparian classes could assist regional planning efforts.

Heterosis, or hybrid vigor, is the enhancement of the phenotype of hybrid progeny relative to their inbred parents. Heterosis is extensively used in agriculture, and the underlying mechanisms are unclear. To investigate the molecular basis of phenotypic heterosis, researchers search tens of thousands of genes for heterosis with respect to expression in the transcriptome. Difficulty arises in the assessment of heterosis due to composite null hypotheses and nonuniform distributions for p-values under these null hypotheses. Thus, we develop a general hierarchical model for count data and a fully Bayesian analysis in which an efficient parallelized Markov chain Monte Carlo algorithm ameliorates the computational burden. We use our method to detect gene expression heterosis in a two-hybrid plant-breeding scenario, both in a real RNA-seq maize dataset and in simulation studies. In the simulation studies, we show our method has well-calibrated posterior probabilities and credible intervals when the model assumed in analysis matches the model used to simulate the data. Although model misspecification can adversely affect calibration, the methodology is still able to accurately rank genes. Finally, we show that hyperparameter posteriors are extremely narrow and an empirical Bayes (eBayes) approach based on posterior means from the fully Bayesian analysis provides virtually equivalent posterior probabilities, credible intervals, and gene rankings relative to the fully Bayesian solution. This evidence of equivalence provides support for the use of eBayes procedures in RNA-seq data analysis if accurate hyperparameter estimates can be obtained. Supplementary materials for this article are available online.