Analysis and Interpretation of Ecological Field Data Using BACI Designs: Discussion

The Poisson regression model is widely used for count data. This model assumes equidispersion. In practice, equidispersion is seldom reflected in data. However, in real-life data, the variance usually exceeds the mean. This situation is known as overdispersion. Negative binomial distribution and other Poisson mix models are often used to model overdispersion count data. Another extension of the negative binomial distribution in another model for count data is the univariate generalized Waring. In addition, the model developed by Famoye can be used in the analysis of count data. When the count data contains a large number of zeros, it is necessary to use zero-inflated models. In this study, different generalized regression models are emphasized for the analysis of excessive zeros count data. For this purpose, a real data set was analysed with the generalized Poisson model, generalized negative binomial model, generalized negative binomial Famoye, generalized Waring model, and the foregoing zero-inflated models. Log-likelihood, Akaike information criterion, Bayes information criterion, Vuong statistics were used for model comparisons.

Pilot Randomized Controlled Trial of Spinal Versus General Anesthesia for Hip Fracture Surgery.

Recent developments of multivariate smoothing methods provide a rich collection of feasible models for non-parametric multivariate data analysis. Among the most popular are additive models. Here we interpret additive models as \(f(t)=\sum_{i=0}^{p}f_i(t)\) where the fi’s are individually interpretable but not necessarily univariate functions. Definitions of various models and algorithms for computing the models have been the main concern of existing literature in this area. Few results are available on the validation of computed fits, and many applications of nonparametric methods unfortunately end up interpreting the noise. In this article I propose and illustrate some simple geometric diagnostics to help data analysts in detecting possible aliasing effects in computed nonparametric fits and in building parsimonious models in an interactive fashion. For their ready availability, interaction splines are used in the illustrations.

Development of public toxicogenomics software for microarray data management and analysis

Repeated observations of experimental units are frequently used in many areas of ecological research. A special case is the replicated BACI (before after control impact) design, in which the units (plots) are sampled first before the experimental treatment is imposed on some of them. In this way, you obtain ‘baseline’ data, i.e. the data where differences between the sampling units are caused solely by random variability. After the treatment is imposed, the units are sampled once or several times to reveal the difference in the development (dynamics) of manipulated and control units.

In studies of wild animals, one frequently encounters both count and mark-recapture-recovery data. Here, we consider an integrated Bayesian analysis of ring¿recovery and count data using a state-space model. We then impose a Leslie-matrix-based model on the true population counts describing the natural birth-death and age transition processes. We focus upon the analysis of both count and recovery data collected on British lapwings (Vanellus vanellus) combined with records of the number of frost days each winter. We demonstrate how the combined analysis of these data provides a more robust inferential framework and discuss how the Bayesian approach using MCMC allows us to remove the potentially restrictive normality assumptions commonly assumed for analyses of this sort. It is shown how WinBUGS may be used to perform the Bayesian analysis. WinBUGS code is provided and its performance is critically discussed.

The community forestry (CF) program in Sri Lanka came forth in the early 1980 s following a global trend to conserve forest resources and provide benefits to the local community. However, very little is known about the role of CF on livelihoods in Sri Lanka. Therefore, this study evaluated the effect of the newly implemented CF program on community livelihoods in the intermediate zone of Sri Lanka by using a before-after control impact (BACI) design. Data were collected from 450 households and analyzed using the difference-indifference (DID) method. The results show that the CF program has improved livelihoods, with a significant (p < .001) positive effect on household conditions, income from livestock and non-timber forest products, savings, loans, state landholdings, informal education, trust, and collective aid actions. However, the increase in the number of CF members and the increase of demand for state land may threaten the future sustainability of the CF program. Despite, the CF program demonstrating very promising results in terms of livelihood improvement, more efforts are still needed to encourage alternative income sources to enable future generations to be less dependent on the forest.

As urban populations grow, conserving biodiversity within cities is increasingly vital and of global policy interest. However, urban environments pose unique challenges for understanding drivers of biodiversity change, as fragmented land ownership makes traditional biodiversity monitoring and randomized experiments logistically difficult. While participatory science platforms like iNaturalist offer a promising data source by providing extensive biodiversity data from urban areas, inferring causality remains challenging due to confounding factors in observational data. To leverage these data advances, we offer a framework that combines records from iNaturalist with synthetic control methods, a quasi-experimental approach. We demonstrate this approach in a case study assessing the impact of Hurricane Ida (2021) on bee biodiversity in Philadelphia, USA. The synthetic control estimated a 9.4% decline in bee abundance two years post-event. In contrast, three conventional ecological analyses—an interrupted time series regression, before-after comparison, and a before-after control impact (BACI) design—failed to detect this decline, with the before-after approach naively detecting an increase due to unaccounted temporal trends. Synthetic control methods offer a powerful tool for estimating citywide biodiversity responses to climate events and policy interventions, enhancing the utility of participatory science data for urban ecology.

Peer Reviewed

Molecular ecology regularly requires the analysis of count data that reflect the relative abundance of features of a composition (e.g., taxa in a community, gene transcripts in a tissue). The sampling process that generates these data can be modelled using the multinomial distribution. Replicate multinomial samples inform the relative abundances of features in an underlying Dirichlet distribution. These distributions together form a hierarchical model for relative abundances among replicates and sampling groups. This type of Dirichlet-multinomial modelling (DMM) has been described previously, but its benefits and limitations are largely untested. With simulated data, we quantified the ability of DMM to detect differences in proportions between treatment and control groups, and compared the efficacy of three computational methods to implement DMM-Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo. We report that DMM was better able to detect shifts in relative abundances than analogous analytical tools, while identifying an acceptably low number of false positives. Among methods for implementing DMM, HMC provided the most accurate estimates of relative abundances, and VI was the most computationally efficient. The sensitivity of DMM was exemplified through analysis of previously published data describing lung microbiomes. We report that DMM identified several potentially pathogenic, bacterial taxa as more abundant in the lungs of children who aspirated foreign material during swallowing; these differences went undetected with different statistical approaches. Our results suggest that DMM has strong potential as a statistical method to guide inference in molecular ecology.

Genetic counseling in direct-to-consumer exome sequencing: a case report.

ACQUIRED HEMOPHILIA IN OLDER PEOPLE: A POOR PROGNOSIS DESPITE INTENSIVE CARE

In this thesis, we study two random models with various applications in data analysis. For our first model, we investigate subspaces spanned by biased random vectors. The underlying random model is motivated by applications in computational biology, where one aims at computing a low-rank matrix factorization involving a binary factor. In a random model with adjustable expected sparsity of the binary factor, we show for a large class of random binary factors that the corresponding factorization problem is uniquely solvable with high probability. In data analysis, such uniqueness results are of particular interest; ambiguous solutions often lack interpretability and do not give an insight into the structure of the underlying data. For proving uniqueness in this random model, small ball probability estimates are a key ingredient. Since to the best of our knowledge, there are no such estimate suitable for our application, we prove an extension of the famous Lemma of Littlewood and Offord. Hereby, we also discover a connection between the matrix factorization problem at hand and the notion of Sperner families. In the second part of this thesis, we will investigate a model for randomized ultrasonic data in nondestructive testing. Here, we aim at accelerating the data acquisition process by superposing ultrasonic measurements with random time shifts. To this end, we will first study the effects of randomized ultrasonic measurements in the context of the Synthetic Aperture Focusing Technique (SAFT), a widely used defect imaging method. By adapting SAFT to our random data model, we will significantly improve its performance for randomized data. In this way, for sparse defects and with high probability, we achieve better defect reconstructions as with SAFT applied to deterministic ultrasonic data acquired in the same amount of time.

Artificial neural network models are typically useful in pattern recognition and extraction of important features in large data sets. These models are implemented in a wide variety of contexts and with diverse type of input-output data. The underlying mathematics of supervised training of neural networks is ultimately tied to the ability to approximate the nonlinearities that are inherent in network’s generalization ability. The quality and availability of sufficient data points for training and validation play a key role in the generalization ability of the network. A potential domain of applications of neural networks is in analysis of subjective data, such as in consumer science, affective neuroscience and perception of chemical senses. In applications of ANN to subjective data, it is common to rely on knowledge of the science and context for data acquisition, for instance as a priori probabilities in the Bayesian framework. In this paper, we discuss the circumstances that create challenges for success of neural network models for subjective data analysis, such as sparseness of data and cost of acquisition of additional samples. In particular, in the case of affect and perception of chemical senses, we suggest that inherent ambiguity of subjective responses could be offset by a combination of human-machine expert. We propose a method of pre- and post-processing for blind analysis of data that that relies on heuristics from human performance in interpretation of data. In particular, we offer an information-theoretic smoothing (ITS) algorithm that optimizes that geometric visualization of multi-dimensional data and improves human interpretation of the input-output view of neural network implementations. The pre- and post-processing algorithms and ITS are unsupervised. Finally, we discuss the details of an example of blind data analysis from actual taste-smell subjective data, and demonstrate the usefulness of PCA in reduction of dimensionality, as well as ITS.

Several models which analyse count data have been proposed in econometric literature. These models allow the discrete, nonnegative nature of specific phenomena of interest to be gathered in a appropriate way and can be useful for the explanation of specific preference structures among individuals. In this work, an analysis of the number of wine types consumed by residents of Tenerife is carried out, with an aim to observe which characteristics determine the exclusivity in its consumption, given the current context of increased competition in this sector. The specific characteristics of the considered variable allow the study to cover two aspects. The first is methodological, and is seen by the variety of models that may be considered in this case. This focus consists in comparing several possibilities, which fit the type of count data involved. The second aspect is clearly empirical, and is based on the description of not only the most appropriate decision-making mechanism for the study but in the identification of those factors that explain the diversity in wine consumption.