Journal of Agricultural Biological and Environmental Statistics最新文献

Training set optimization is a crucial factor affecting the probability of success for plant breeding programs using genomic selection. Conventionally, the training set optimization is developed to maximize Pearson’s correlation between true breeding values and genomic estimated breeding values for a testing population, because it is an essential component of genetic gain in plant breeding. However, many practical breeding programs aim to identify the best genotypes for target traits in a breeding population. A modified Bayesian optimization approach is therefore developed in this study to construct training sets for tackling such an interesting problem. The proposed approach is based on Monte Carlo simulation and data cross-validation, which is shown to be competitive with the existing methods developed to achieve the maximal Pearson’s correlation. Four real genome datasets, including two rice, one wheat, and one soybean, are analyzed in this study. An R package is generated to facilitate the application of the proposed approach. Supplementary materials accompanying this paper appear online.

The use of statistical methods informed by partial differential equations (PDEs) and in particular reaction–diffusion PDEs such as ecological diffusion equations (EDEs) has been studied and used to model spatiotemporal processes. In this paper, we consider a stochastic extension of the EDE (SEDE) and discuss its interpretation and main differences from the deterministic EDE. We then leverage a non-stationary extension of the diffusion-based Gaussian Matérn field and show that this extension has SEDE-like behavior. The elucidated connection enables us to find a finite element approximated solution for SEDEs by means of the stochastic partial differential equation (SPDE) Bayesian method. For illustration, we analyze the evolution of white-nose syndrome (WNS) in the continental USA, comparing two models: stationary SEDE and a non-stationary pseudo-SEDE. Our results demonstrate the importance of non-stationarity in wildlife disease modeling and identify spatial explanatory variables for the non-stationarity in the WNS process. Finally, a simulation study is conducted to assess the deviance information criterion for differentiating from the two models, as well as the identifiability of the model parameters.Supplementary materials accompanying this paper appear online.

A Hawkes point process describes self-exciting behaviour where event arrivals are triggered by historic events. These models are increasingly becoming a popular choice in analysing event-type data. Like all other inhomogeneous Poisson point processes, the waiting time between events in a Hawkes process is derived from an exponential distribution with mean one. However, as with many ecological and environmental data, this is an unrealistic assumption. We, therefore, extend and generalise the Hawkes process to account for potential under- or overdispersion in the waiting times between events by assuming the Weibull distribution as the foundation of the waiting times. We apply this model to the acoustic cue production times of sperm whales and show that our Weibull–Hawkes model better captures the inherent underdispersion in the interarrival times of echolocation clicks emitted by these whales.

Spatially correlated data with an excess of zeros, usually referred to as zero-inflated spatial data, arise in many disciplines. Examples include count data, for instance, abundance (or lack thereof) of animal species and disease counts, as well as semi-continuous data like observed precipitation. Spatial two-part models are a flexible class of models for such data. Fitting two-part models can be computationally expensive for large data due to high-dimensional dependent latent variables, costly matrix operations, and slow mixing Markov chains. We describe a flexible, computationally efficient approach for modeling large zero-inflated spatial data using the projection-based intrinsic conditional autoregression (PICAR) framework. We study our approach, which we call PICAR-Z, through extensive simulation studies and two environmental data sets. Our results suggest that PICAR-Z provides accurate predictions while remaining computationally efficient. An important goal of our work is to allow researchers who are not experts in computation to easily build computationally efficient extensions to zero-inflated spatial models; this also allows for a more thorough exploration of modeling choices in two-part models than was previously possible. We show that PICAR-Z is easy to implement and extend in popular probabilistic programming languages such as nimble and stan.

Humans have recorded the arrival dates of migratory birds for millennia, searching for trends and patterns. As the first arrival among individuals in a species is the realized tail of the probability distribution of arrivals, the appropriate statistical framework with which to analyze such events is extreme value theory. Here, for the first time, we apply formal extreme value techniques to the dynamics of bird migrations. We study the annual first arrivals of Magnolia Warblers using modern tools from the statistical field of extreme value analysis. Using observations from the eBird database, we model the spatial distribution of observed Magnolia Warbler arrivals as a max-infinitely divisible process, which allows us to spatially interpolate observed annual arrivals in a probabilistically coherent way and to project arrival dynamics into the future by conditioning on climatic variables. Supplementary materials accompanying this paper appear online.

Climate change impacts ecosystems variably in space and time. Landscape features may confer resistance against environmental stressors, whose intensity and frequency also depend on local weather patterns. Characterizing spatio-temporal variation in population responses to these stressors improves our understanding of what constitutes climate change refugia. We developed a Bayesian hierarchical framework that allowed us to differentiate population responses to seasonal weather patterns depending on their “sensitive” or “resilient” states. The framework inferred these sensitivity states based on latent trajectories delineating dynamic state probabilities. The latent trajectories are composed of linear initial conditions, functional regression models, and additive random effects representing ecological mechanisms such as topological buffering and effects of legacy weather conditions. Further, we developed a Bayesian regularization strategy that promoted temporal coherence in the inferred states. We demonstrated our hierarchical framework and regularization strategy using simulated examples and a case study of native brook trout (Salvelinus fontinalis) count data from the Great Smoky Mountains National Park, southeastern USA. Our study provided insights into ecological processes influencing brook trout sensitivity. Our framework can also be applied to other species and ecosystems to facilitate management and conservation.

A two-stage hierarchical Bayesian model is developed and implemented to estimate forest biomass density and total given sparsely sampled LiDAR and georeferenced forest inventory plot measurements. The model is motivated by the United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) objective to provide biomass estimates for the remote Tanana Inventory Unit (TIU) in interior Alaska. The proposed model yields stratum-level biomass estimates for arbitrarily sized areas. Model-based estimates are compared with the TIU FIA design-based post-stratified estimates. Model-based small area estimates (SAEs) for two experimental forests within the TIU are compared with each forest’s design-based estimates generated using a dense network of independent inventory plots. Model parameter estimates and biomass predictions are informed using FIA plot measurements, LiDAR data that are spatially aligned with a subset of the FIA plots, and complete coverage remotely detected data used to define landuse/landcover stratum and percent forest canopy cover. Results support a model-based approach to estimating forest parameters when inventory data are sparse or resources limit collection of enough data to achieve desired accuracy and precision using design-based methods. Supplementary materials accompanying this paper appear on-line

Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is motivated by the KAUST Competition on Large Spatial Datasets 2023, which tasked participants with estimating spatial covariance-related parameters and predicting values at testing sites, along with uncertainty estimates. We compared various statistical and deep learning approaches through cross-validation and ultimately selected the Vecchia approximation technique for model fitting. To overcome the constraints in the R package GpGp, which lacked support for fitting zero-mean Gaussian processes and direct uncertainty estimation—two things that are necessary for the competition, we developed additional R functions. Besides, we implemented certain subsampling-based approximations and parametric smoothing for skewed sampling distributions of the estimators. Our team DesiBoys secured the first position in two out of four sub-competitions and the second position in the other two, validating the effectiveness of our proposed strategies. Moreover, we extended our evaluation to a large real spatial satellite-derived dataset on total precipitable water, where we compared the predictive performances of different models using multiple diagnostics.

To analyze tree growth statistically through annual ring widths measured in 2-D horizontal trunk sections, we propose two tests of significance defined under a linear-circular regression model with fixed trigonometric effects and normal random errors with a variance-covariance structure from the symmetric circulant family. The associated von Mises distribution has a preferred direction parameter. Accordingly, the first test aims to assess the presence of a preferred direction in the radial growth of a tree from the center of its trunk in a given year. Assuming there is a preferred direction of radial growth for the tree in two years, the second test extends the first one by assessing the equality of tree radial growth in the two preferred directions. Both tests of significance are modified F-tests with the denominator df adjusted for the presence of autocorrelation. Their validity is analyzed for two autoregressive symmetric circulant correlation structures, as a function of the number (n) of angular data and the autocorrelation parameter value. Effects of the inter-year correlation coefficient value are also studied in the two-year case. The performance of REstricted Maximum Likelihood as estimation method is scrutinized in an extensive Monte Carlo study, and the power of the tests is analyzed when valid. The new testing procedures are applied with (n = 32, 64) ring widths per year for a white spruce tree during 18 years of growth until its harvest. R codes are available. Conclusions and perspectives for future research are given. Supplementary materials accompanying this paper appear on-line.

The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar instrument that collects near-global measurements of forest structure. While expansive in scope, GEDI samples are spatially sparse and cover a small fraction of the land surface. Converting the sparse samples into spatially complete predictive maps is of practical importance for a number of ecological studies. A complicating factor is that GEDI collects measurements over forested and non-forested land alike, with no automatic labeling of the land type. Such classification is important, as it categorically influences the probability distribution of the spatial process and the ecological interpretation of the observations/predictions. We propose and implement a spatial mixture model, separating the observations and the greater spatial domain into two latent classes. The latent classes are governed by a Bernoulli spatial process, with spatial effects driven by a Gaussian process. Within each class, the process is governed by a separate spatial model, describing the unique probabilistic attributes. Model predictions take the form of scalar predictions of the GEDI observables as well as discrete labeling of the class membership. Inference is conducted through a Bayesian paradigm, yielding rich quantification of prediction and uncertainty through posterior predictive distributions. We demonstrate the method using GEDI data over Wollemi National Park, Australia, using optical data from Landsat 8 as model covariates. When compared to a single spatial model, the mixture model achieves much higher posterior predictive densities on the true value. When compared to a random forest model, a common algorithmic approach in the remote sensing community, the random forest achieves better absolute prediction accuracy for prediction locations far from observed training data locations, but at the expense of location-specific assessments of uncertainty. The unsupervised binary classifications of the mixture model appear broadly ecologically interpretable as forest and non-forest when compared to optical imagery, but further comparison to ground-truth data is required.