Outstanding challenges in the transferability of ecological models

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.Katherine L. Yates ... Alice R. Jones ... et al

Allowing for the effect of data binning in a Bayesian Normal mixture model

The usual Gibbs sampling framework of the Bayesian mixture model is extended to account for binned data. This model involves the addition of a latent variable in the model which represents simulated values from the believed true distribution at each iteration of the algorithm. The technique results in better model fit and recognition of the more subtle aspects of the density of the data.

A new method for calculating the volume of primary tissue types in live sheep using computed tomography scanning

Interest in the use of computed tomography (CT) scanning in animal experimentation has increased markedly over the last decade due to the benefits of studying tissue in live subjects over time. In these experiments, the non-carcass components of the scan are removed from the collected data, allowing scientists to study the carcass of a live animal without the need to slaughter the individual. However, there is not yet a consensus regarding the most appropriate manner in which to convert the CT numbers into a meaningful estimate of area, volume or proportion of tissue present in a carcass at the time of scanning. In this paper we use a Bayesian mixture model to estimate the area of each of three tissue types of interest, fat, muscle and bone present in individual CT scan slices. We then use the Cavalieri principle to estimate the volume and proportion of the carcass attributable to each of these tissues. The approach is validated by analysis of experimental sheep carcasses

Bayesian mixture models: A blood-free dissection of a sheep

The use of computed tomography (CT) scanning to measure attributes of tissue composition in animal experiments has grown steadily since the early 1990s. This technology is used on a range of experiments, such as nutrition trials for live animals, as well as on carcases after slaughter. A CT scan returns measurements averaged over a pixel area that represent the denseness of the tissue. This tissue denseness is related to tissue type, with fat being generally less dense then muscle and bone being the most dense tissue we study. However, tissue denseness is not well separated, leading to a large overlap on the boundaries between types. Normal mixture models have proved to be an efficient analytical technique for estimating the proportion of tissue types in individual CT scans, with MCMC output providing measures of variability that are unavailable in the standard cut-point modelling approach. These models are then used in conjunction with integration techniques to estimate the tissue volumes within a carcase. In this paper we initially model individual scan data using a hierarchical mixture model, where skewed tissue densities are represented by the addition of two or more components.The mixture model is then extended to account for some of the spatial information using a Markov random field represented by a Potts model in terms of the allocation vector. A scheme for choosing starting values for component parameters is presented. The paper concludes with the use of the Cavalieri approach to combine individual scan estimates in order to estimate the carcase volume.No Full Tex

Investigation of Bayesian spatial models

The popularity of Bayesian disease mapping is increasing, as is the variety of available models. The most commonly used prior for enabling spatial correlation within a Bayesian model is the intrinsic conditional autoregressive (CAR) distribution. This approach allows for local smoothing of estimates over neighbouring areas, but it assumes a common variance for the smoothing term over the whole region. This is applicable if there is a smooth spatial trend over the region, which may not be valid for large, spatially heterogeneous areas. The aim of this report is to critically review alternative Bayesian models, especially those that enable local variation in the smoothing

Perfect Slice Samplers for Mixtures of Distributions

This paper extends the result of Hobert et al. (1999) to the case of general finite mixtures of distributions, under conjugate priors, that is, when either the p i 's, the ` i 's or both are unknown, by proposing a dioeerent approach to the problem. The foundation of the technique used here relies on the facts that, under conjugate priors, the marginal posterior distribution of the latent variables z is known in closed form, up to a constant, as exhibited and exploited for importance sampling in Casella et al. (1999), and that, moreover, a slice sampler can be implemented for this distribution. We can thus use the results of Mira and Roberts (1999), who show how a general perfect sampler can be adapted to (univariate) slice samplers, by taking advantage of the fact that the slice sampler is naturally monotone for the order induced by the distribution of interest. Indeed, a naive implementation of the slice sampler in the parameter space is impossible, given the complexity of the posterior distribution. The "slice region"..

Evaluating health facility access using Bayesian spatial models and location analysis methods

Background Floating catchment methods have recently been applied to identify priority regions for Automated External Defibrillator (AED) deployment, to aid in improving Out of Hospital Cardiac Arrest (OHCA) survival. This approach models access as a supply-to-demand ratio for each area, targeting areas with high demand and low supply for AED placement. These methods incorporate spatial covariates on OHCA occurrence, but do not provide precise AED locations, which are critical to the initial intent of such location analysis research. Exact AED locations can be determined using optimisation methods, but they do not incorporate known spatial risk factors for OHCA, such as income and demographics. Combining these two approaches would evaluate AED placement impact, describe drivers of OHCA occurrence, and identify areas that may not be appropriately covered by AED placement strategies. There are two aims in this paper. First, to develop geospatial models of OHCA that account for and display uncertainty. Second, to evaluate the AED placement methods using geospatial models of accessibility. We first identify communities with the greatest gap between demand and supply for allocating AEDs. We then use this information to evaluate models for precise AED location deployment. Methods Case study data set consisted of 2802 OHCA events and 719 AEDs. Spatial OHCA occurrence was described using a geospatial model, with possible spatial correlation accommodated by introducing a conditional autoregressive (CAR) prior on the municipality-level spatial random effect. This model was fit with Integrated Nested Laplacian Approximation (INLA), using covariates for population density, proportion male, proportion over 65 years, financial strength, and the proportion of land used for transport, commercial, buildings, recreation, and urban areas. Optimisation methods for AED locations were applied to find the top 100 AED placement locations. AED access was calculated for current access and 100 AED placements. Priority rankings were then given for each area based on their access score and predicted number of OHCA events. Results Of the 2802 OHCA events, 64.28% occurred in rural areas, and 35.72% in urban areas. Additionally, over 70% of individuals were aged over 65. Supply of AEDs was less than demand in most areas. Priority regions for AED placement were identified, and access scores were evaluated for AED placement methodology by ranking the access scores and the predicted OHCA count. AED placement methodology placed AEDs in areas with the highest priority, but placed more AEDs in areas with more predicted OHCA events in each grid cell. Conclusion The methods in this paper incorporate OHCA spatial risk factors and OHCA coverage to identify spatial regions most in need of resources. These methods can be used to help understand how AED allocation methods affect OHCA accessibility, which is of significant practical value for communities when deciding AED placements