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Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method.

ABSTRACT: We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates of uncertainty. We illustrate our method using tree data for the eastern United States and from a tropical successional chronosequence. The model is able to detect pervasive declines in the oak community in Minnesota and Indiana, potentially due to fire suppression, increased growing season precipitation and herbivory. The chronosequence analysis is able to delineate clear successional trends in species composition, while also revealing that site-specific factors significantly impact these successional trajectories. The proposed method provides a means to decompose and track the dynamics of species assemblages along temporal and spatial gradients, including effects of global change and forest disturbances.

SUBMITTER: Valle D

PROVIDER: S-EPMC4240467 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method.

Valle Denis D Baiser Benjamin B Woodall Christopher W CW Chazdon Robin R

Ecology letters 20141017 12

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates of uncertainty. We illustrate our method using tree data for the eastern United States and from a tropical successional chronose ...[more]

PMID: 25328064

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Project description:Interactions between stressed organisms and their microbiome environments may provide new routes for understanding and controlling biological systems. However, microbiomes are a form of high-dimensional data, with thousands of taxa present in any given sample, which makes untangling the interaction between an organism and its microbial environment a challenge. Here we apply Latent Dirichlet Allocation (LDA), a technique for language modeling, which decomposes the microbial communities into a set of topics (non-mutually-exclusive sub-communities) that compactly represent the distribution of full communities. LDA provides a lens into the microbiome at broad and fine-grained taxonomic levels, which we show on two datasets. In the first dataset, from the literature, we show how LDA topics succinctly recapitulate many results from a previous study on diseased coral species. We then apply LDA to a new dataset of maize soil microbiomes under drought, and find a large number of significant associations between the microbiome topics and plant traits as well as associations between the microbiome and the experimental factors, e.g. watering level. This yields new information on the plant-microbial interactions in maize and shows that LDA technique is useful for studying the coupling between microbiomes and stressed organisms. Author summary Host-microbe interaction may be an important factor determining the performance and survival of an organism under stress. Understanding how microbiomes influence organisms under stress is a challenging new area of research because microbiomes are complex with the potential for complex responses and adaptations to stress that influence their interactions with the other stressed organisms. We show the use of LDA, a data-science technique in the context of environmental microbial datasets to break down the thousands of microbes present in samples into groups called topics; each topic is a group of organisms that occur together as a common pattern in the dataset. We show that this technique, combined with correlation analyses, provides a way to view a very large set of microbes in a sample as a smaller, more manageable set of communities of microbial taxa related to experimental conditions and plant traits. In this way, LDA helps to unravel complex interactions between organisms and their microbiome, which could help to better predict the behavior of real-world ecological systems, and perhaps support them through many challenges brought by changing climate and environment.

Project description:BackgroundIn most industrialized societies, regulations, inspections, insurance, and legal options are established to support workers who suffer injury, disease, or death in relation to their work; in practice, these resources are imperfect or even unavailable due to workplace or employer obstruction. Thus, limitations exist to identify unmet needs in occupational safety and health information.ObjectiveThe aim of this study was to explore hidden issues related to occupational accidents by examining social network services (SNS) data using topic modeling.MethodsBased on the results of a Google search for the phrases occupational accident, industrial accident and occupational diseases, a total of 145 websites were selected. From among these websites, we collected 15,244 documents on queries related to occupational accidents between 2002 and 2018. To transform unstructured text into structure data, natural language processing of the Korean language was conducted. We performed the latent Dirichlet allocation (LDA) as a topic model using a Python library. A time-series linear regression analysis was also conducted to identify yearly trends for the given documents.ResultsThe results of the LDA model showed 14 topics with 3 themes: workers' compensation benefits (Theme 1), illicit agreements with the employer (Theme 2), and fatal and non-fatal injuries and vulnerable workers (Theme 3). Theme 1 represented the largest cluster (52.2%) of the collected documents and included keywords related to workers' compensation (ie, company, occupational injury, insurance, accident, approval, and compensation) and keywords describing specific compensation benefits such as medical expense benefits, temporary incapacity benefits, and disability benefits. In the yearly trend, Theme 1 gradually decreased; however, other themes showed an overall increasing pattern. Certain queries (ie, musculoskeletal system, critical care, and foreign workers) showed no significant variation in the number of queries.ConclusionsWe conducted LDA analysis of SNS data of occupational accident-related queries and discovered that the primary concerns of workers posting about occupational injuries and diseases were workers' compensation benefits, fatal and non-fatal injuries, vulnerable workers, and illicit agreements with employers. While traditional systems focus mainly on quantitative monitoring of occupational accidents, qualitative aspects formulated by topic modeling from unstructured SNS queries may be valuable to address inequalities and improve occupational health and safety.

Dataset Information

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method.

Publications

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method.

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