Biology subjects

Wild, J.

Publications and source records attributed to Wild, J..

3 recordsLinked to original sources

bioRxiv2026

Data-driven identification of the ecologically relevant climatic windows and climate summaries with StatClim model

Climate affects biological processes and organisms. It is extensively recorded, and its effects are frequently studied. Climatic variables are recorded as time series of measurements, but these are often summarized as long-term averages, throwing away biologically relevant information. Subsequent analyses can therefore miss important climate effects or suffer from low predictive power. Here, we propose a new method, StatClim, inferring the most informative climatic summary from the data. Instead of taking a fixed arbitrary summary of a time series, StatClim automatically estimates the beginning and width of a climatic window and simultaneously calculates data aggregation, which best predicts a response variable. We implemented the proposed approach as a Bayesian hierarchical model in Stan. We illustrated the potential of StatClim on published data on mast seeding of Fagus sylvatica. StatClim correctly identified known climatic windows relevant for this tree species, but also uncovered that mast seeding is triggered by high temperatures within these windows. We demonstrated flexibility of the model to account for complex data structures, we ran power analysis showing the ability of the model to identify climatic effects even with modest datasets, and we evaluated its false positive rate, which is lower than 0.01. We discuss the advantages and limitations of StatClim. Compared to existing alternatives, it is suitable for hypothesis testing and offers important advances, such as estimation of the type of aggregation. In combination with existing methods, StatClim will facilitate data-driven use of climatic and microclimatic data and therefore discovering so far unknown climatic effects on biological processes and ecosystem functioning.

ecology↗

bioRxiv2022

The relationship between spectral and plant diversity: disentangling the influence of metrics and habitat types

Biodiversity monitoring is crucial for ecosystem conservation, yet field data collection is limited by costs, time, and extent. Remote sensing represents a convenient approach providing frequent, near-real-time information over wide areas. According to the Spectral Variation Hypothesis (SVH), spectral diversity (SD) is an effective proxy of environmental heterogeneity, which ultimately relates to plant diversity. So far, studies testing the relationship between SD and biodiversity have reported contradictory findings, calling for a thorough investigation of the key factors (e.g., metrics applied, ecosystem type) and the conditions under which such a relationship holds true. This study investigates the applicability of the SVH for plant diversity monitoring at the landscape scale by comparing the performance of three different types of SD metrics. Species richness and functional diversity were calculated for more than 2000 cells forming a grid covering the Czech Republic. Within each cell, we quantified SD using a Landsat-8 "greenest pixel" composite by applying: i) the standard deviation of NDVI, ii) Raos Q entropy index, and iii) richness of "spectral communities". Habitat type (i.e., land cover) was included in the models describing the relationship between SD and ground biodiversity. Both species richness and functional diversity show positive and significant relationships with each SD metric tested. However, SD alone accounts for a small fraction of the deviance explained by the models. Furthermore, the strength of the relationship depends significantly on habitat type and is highest in natural transitional areas. Our results underline that, despite the stability in the significance of the link between SD and plant diversity at this scale, the applicability of SD for biodiversity monitoring is context-dependent and the factors mediating such a relationship must be carefully considered to avoid drawing misleading conclusions. HighlightsO_LIPlant species richness and functional diversity show significant and positive relationships with spectral diversity C_LIO_LISpectral diversity alone explains a small fraction of the total variability in ground biodiversity C_LIO_LISlight differences among the performances of the spectral diversity metrics tested C_LIO_LIThe relationship between spectral and plant diversity is context-dependent C_LI

ecology↗

bioRxiv2020

Compressive Big Data Analytics: An Ensemble Meta-Algorithm for High-dimensional Multisource Datasets

Health advances are contingent on continuous development of new methods and approaches to foster data driven discovery in the biomedical and clinical health sciences. Open-science offers hope for tackling some of the challenges associated with Big Data and team-based scientific discovery. Domain-independent reproducibility, area-specific replicability, curation, analysis, organization, management and sharing of health-related digital objects are critical components. This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA). Applied to high-dimensional data, CBDA identifies salient features and key biomarkers for reliable and reproducible forecasting of binary or multinomial outcomes. The method relies on iterative subsampling, combines function optimization and statistical inference, and generates ensemble predictions of observed univariate outcomes. In this manuscript, we extend the CBDA technique by (1) efficiently handling extremely large datasets, (2) generalizing the internal and external validation steps, (3) expanding the set of base-learners for joint ensemble prediction, (4) introduce an automated selection of CBDA specifications, and (5) provide mechanisms to assess CBDA convergence, evaluate the prediction accuracy, and measure result consistency. We validated the CBDA 2.0 technique using synthetic datasets as well as a population-wide census-like study, which grounds the mathematical models and the computational algorithm into translational health research settings. Specifically, we empirically validated the CBDA technique on a large-scale clinical study (UK Biobank), which includes imaging, cognitive, and clinical assessment data. The UK Biobank archive presents several difficult challenges related to the aggregation, harmonization, modeling, and interrogation of the information. These problems are related to the complex longitudinal structure, feature heterogeneity, multicollinearity, incongruency, and missingness, as well as violations of classical parametric assumptions that require novel health analytical approaches. Our results showcase the scalability, efficiency and potential of CBDA to compress complex data into structural information leading to derived knowledge and translational action. The results of the real case-study suggest new and exciting avenues of research in the context of identifying, tracking, and treating mental health and aging-related disorders. Following open-science principles, we share the entire end-to-end protocol, source-code, and results. This facilitates independent validation, result reproducibility, and team-based collaborative discovery.

bioinformatics↗