Biology subjects

Thron, C.

Publications and source records attributed to Thron, C..

3 recordsLinked to original sources

bioRxiv2024

Estimation of Uncertainty and Location-specific Variation in Regional Food Web Interactions

Food-web interactions are commonly aggregated across sampling locations and represented by a single interaction strength for an entire region. This practice implicitly treats interaction strengths as fixed regional constants, even though ecological interactions may vary substantially among locations because of local environmental conditions, species abundances, and habitat structure. We argue that food-web interaction strengths should instead be viewed as spatially distributed latent variables whose regional distributions must be inferred from observations collected at a limited number of sites. To estimate this spatial variation, we develop a Bayesian framework based on transect observations from multiple locations within a region. Species-species interaction measurements at each location are modeled as Poisson random variables whose means are proportional to local interaction strengths. Preliminary analyses suggest that location-dependent interaction strengths may be simply approximated by a uniform distribution on an interval. Under this assumption, we estimate the endpoints of the interval and thereby characterize both the mean interaction strength and its spatial variability. For the case of three sampling locations, we derive graphical representations of the resulting estimates and their uncertainties. Among other results, we show that the average of three observed interaction frequencies systematically underestimates the true regional mean interaction strength under the uniform-distribution model. We apply the framework to avian frugivore-plant interaction data collected at three locations within each of six regions in Ecuador. The estimated distributions of interaction strength are used to test the hypothesis that species within the same genus spatially substitute for one another. The results do not support this hypothesis. Instead, species within the same genus tend to exhibit elevated interaction strengths in the same locations, indicating positive spatial association rather than substitution. We also find evidence that habitat fragmentation may reduce within-region variability in bird-plant interactions even when species diversity is maintained. These results suggest that explicitly modeling spatial variation can reveal ecological patterns that are obscured by conventional food-web representations.

ecology↗

bioRxiv2023

A Simple Bias Reduction Algorithm for RNA Sequencing Datasets

RNA sequencing (RNA-seq) is the conventional genome-scale approach used to capture the expression levels of all detectable genes in a biological sample. This is now regularly used in the clinical diagnostic space for cancer patients. While the information gained is intended to impact treatment decisions, numerous technical and quality issues remain. This includes inaccuracies in the dissemination of gene-gene relationships. For such reasons, clinical decisions are still mostly driven by DNA biomarkers, such as gene mutations or fusions. In this study, we aimed to correct for systemic bias based on RNA-sequencing platforms in order to improve our understanding of the gene-gene relationships. To do so, we examined standard pre-processed RNA-seq datasets obtained from three studies conducted by two consortium efforts including The Cancer Genome Atlas (TCGA) and Stand Up 2 Cancer (SU2C). We particularly examined the TCGA Bladder Cancer (n = 408) and Prostate Cancer (n = 498) studies as well as the SU2C Prostate Cancer study (n = 208). Using various statistical tests, we detected expression-level dependent, per-sample biases in all datasets. Using simulations, we show that these biases corrupt the results of t-tests designed to identify expression level differences between subpopulations. Importantly, these biases introduce large errors into estimates of gene-gene correlations. To mitigate these biases, we introduce Local Leveling as a novel mathematical approach that transforms count level data and corrects these observed biases. Local Leveling specifically corrects for the bias due to the inherent differential detection of transcripts that is driven by differential expression levels. Based on standard forms of count data (Raw counts, transcripts per million, fragments per kilobase of exon per million), we demonstrate that local leveling effectively removes the observed per-sample biases, and improves the accuracy in simulated statistical tests. Importantly, this led to systemic changes of gene-gene relationships when examining the correlation of key oncogenes, such as the Androgen Receptor, with all other detectable genes. Altogether, Local Leveling improves our capacity towards understanding gene-gene relationships, which may lead to novel ways to utilize the information derived from clinical tests.

genomics↗

bioRxiv2023

Improved Estimation of Bird Abundance and Energy Consumption by Combining Point and Transect Observations

Point counts and transect surveys are both common methods for estimating bird abundance (by species, genus, or family) in a given environment. Sometimes both methods are used in the same study. Point counts reflect abundance directly, while transect observations of bird feeding instances reflect both the bird abundance and the mean energy consumption rate per bird. In this research we develop an improved estimator of bird abundance based on both sets of measurements, and derive error bars for these abundance estimates. The improved estimator is a linear combination of the two individual abundance estimates obtained from the two data types. The combination weights are obtained through a comparison of two separate energy estimates. The mathematical methods developed are applied to a dataset consisting of point counts and transect surveys of Ecuadorian frugivore bird species in six different types of environment (three elevations and two degrees of forestation). The improvement in accuracy achieved by this combined estimator is verified by simulations applied to bird family data. We also obtain 90% confidence intervals for the bird family relative abundance estimates, and verify the confidence intervals using simulations.

ecology↗