Biology subjects

Hebert, K.

Publications and source records attributed to Hebert, K..

3 recordsLinked to original sources

bioRxiv2025

Tracking community change via network coherence

Understanding how ecological communities respond to environmental change remains a key challenge for biodiversity monitoring. To characterize such responses, we need tools that capture how coherently species respond across a community, and to predict their consequences, we must account for ecological interactions. We first introduce the Ecological Coherence (EC) framework, which describes how species co-responses are structured within a community. Building on this foundation, we extend it to Ecological Network Coherence (ENC), which embeds co-responses within the network of interactions by restricting them to interacting species. Both are expressed through two complementary representations: a response correlation matrix and the distribution of its values. The first can reveal aspects such as coherent or incoherent modules and the roles species play in shaping coherence, whereas the second provides a profile whose shape may serve as an early-warning indicator of instability. These can be applied to both intrinsic responses (environmental performance) and realized responses (abundance dynamics), derived from currently available monitoring data. We illustrate this approach in two empirical systems: a tropical pollination network, where interacting mutualists were more coherent in their temperature responses than the broader community, and a marine food web, where coherence in abundance trends shifted during collapse. Using a Lotka-Volterra model, we further show that ENC distributions with higher variance--reflecting stronger positive and negative co-responses--increase the risk of instability or amplification in dynamics. We also find that species influential in both the correlation matrix and the interaction matrix are key drivers of major dynamic shifts. These results point to the importance of further exploring ENC distributions as potential early-warning indicators of ecological disruption.

ecology↗

bioRxiv2025

Ecological coherence in abundance dynamics across terrestrial and marine assemblages

Understanding how communities respond to environmental change requires assessing not just overall variability but also the structure of co-variation among taxa. We frame this idea under the Ecological Coherence (EC) framework, which generalizes previous notions such as community synchrony or coherence. EC captures the structure of co-responses among taxa within assemblages and can be expressed through two complementary objects: (1) the co-response matrix C, which contains all pairwise correlations between taxa and can be used to identify clusters of taxa with coherent responses as well as the contributions of individual species to community-wide coherence; and (2) the EC distribution, which summarizes the overall profile of co-responses by capturing their shape, spread, and central tendency across the community. By combining these two views, EC moves beyond single summary metrics and provides a richer picture of how coherence is organized within communities. Analyzing the EC distribution across 341 terrestrial and 105 marine assemblages worldwide, we found a general prevalence of weak correlations and a few strong, directional correlations. We also found that it varies with community composition, sampling effort, and biogeographic region. Moreover, the C matrix consistently identified a small subset of taxa with strong correlations to many others, suggesting a promising path to detecting those that may play central roles in amplifying or buffering community responses to environmental change. Our findings on EC pave the way for deeper investigations into what drives the diversity of ecological responses to environmental changes and how it shapes community dynamics, while also underscoring the need for strategically distributing ecological monitoring across trophic guilds and functional roles.

ecology↗

bioRxiv2024

Combining NeuroPainting with transcriptomics reveals cell-type-specific morphological and molecular signatures of the 22q11.2 deletion

Neuropsychiatric conditions pose substantial challenges for therapeutic development due to their complex and poorly understood underlying mechanisms. High-throughput, unbiased phenotypic assays present a promising path for advancing therapeutic discovery, especially within disease-relevant neural tissues. Here, we introduce NeuroPainting, a novel adaptation of the Cell Painting assay, optimized for high-dimensional morphological phenotyping of neural cell types, including neurons, neuronal progenitor cells, and astrocytes derived from human stem cells. Using NeuroPainting, we quantified cell structure and organelle behavior across various brain cell types, creating a public dataset of over 4,000 cellular traits. This extensive dataset not only sets a new benchmark for phenotypic screening in neuropsychiatric research but also serves as a gold standard for the research community, enabling comparisons and validation of results. We then applied NeuroPainting to identify morphological signatures associated with the 22q11.2 deletion, a major genetic risk factor for schizophrenia. We observed profound cell-type-specific effects of the 22q11.2 deletion, with significant alterations in mitochondrial structure, endoplasmic reticulum organization, and cytoskeletal dynamics, particularly in astrocytes. Transcriptomic analysis revealed reduced expression of cell adhesion genes in 22q11.2 deletion astrocytes, consistent with recent post-mortem findings. Integrating the RNA sequencing data and morphological profiles uncovered a novel biological link between altered expression of specific cell adhesion molecules and observed changes in mitochondrial morphology in 22q11.2 deletion astrocytes. These findings underscore the power of combined phenomic and transcriptomic analyses to reveal mechanistic insights associated with human genetic variants of neuropsychiatric conditions.

neuroscience↗