Biology subjects

Seymour, M.

Publications and source records attributed to Seymour, M..

9 recordsLinked to original sources

bioRxiv2026

Energy quality shapes biodiversity across coastal oceans

Earth's biodiversity is distributed unevenly, typically peaking in warm, vegetated, and stable environments. This has frequently been linked to energy availability, which boosts productivity, bolsters populations, and facilitates coexistence. Here, we examine the relationship between energy and biodiversity across coastal oceans using particulate organic matter biogeochemistry and environmental DNA. We reveal that energy quantity (concentrations of carbon, nitrogen, and hydrolyzable amino acids) and salinity, two variables that co-vary with freshwater inflow, jointly predict species richness at regional and global scales. Biodiversity was lowest at sites and locations with high energy inflow and low salinity, suggesting that environmental filtering associated with the osmotic stress induced by freshwater inflow outweighs raw resource availability in governing species richness. Independent of this gradient, however, energy quality (defined via C:N ratios, {delta}C values, and amino acid profiles) was associated with higher biodiversity. Labile, protein-rich, marine-derived resources supported higher biodiversity across all taxa at the regional scale, and a larger number of planktonic consumer taxa at both regional and global scales. High energy quality sites were also enriched in key planktonic groups such as calanoid and cyclopoid copepods, suggesting that these sites act as hotspots of planktonic biodiversity across coastal seascapes. Our findings suggest that few species can directly harness the plentiful resources provided by terrestrial subsidies in coastal oceans because they arrive in low salinity waters. In turn, high-quality food attracts a diverse range of consumers, which may seed the patchy foraging hotspots that characterize open-water food webs. Altered coastal hydrodynamics and biogeochemistry may therefore affect nearshore biodiversity, food webs, and fisheries.

ecology↗

bioRxiv2026

Pollution and Anthropogenic Stressors Are Associate with Cetacean Vulnerability in Coastal Waters: Fine-Scale Diagnostics from eDNA and Multispecies Modeling

Indo-Pacific humpback dolphin (Sousa chinensis) and finless porpoise (Neophocaena phocaenoides) are increasingly threatened across their native range, yet the relative influence of multiple stressors in shaping their population dynamics remains unclear. Current conservation strategies for both species are limited by incomplete data and limited assessment of affecting factors. Here, we integrated eDNA metabarcoding with Joint Species Distribution Modeling (JSDM) to assess how environmental gradients, pollution, and trophic associations interactively influence cetacean distributions in Hong Kong waters. We show that degraded water quality and intensified human activity negatively associated with cetacean occurrence, with clear species-specific responses to different stressors. S. chinensis covaried most strongly with Secchi disc depth, and presence of vessels, while N. phocaenoides was negatively associated with nitrate nitrogen and microbial pollution (sewage). The JSDM variance partitioning analysis highlighted that the occurrence of S. chinensis was primarily associated with anthropogenic disturbances (30.04%), followed by water physical properties (26.63%), whereas N. phocaenoides was more strongly associated with physical (40.9%) and anthropogenic disturbances (35.2%). By integrating eDNA and JSDM, our approach provides fine-scale diagnostics of species-specific vulnerabilities, supporting adaptive conservation strategies and guiding the realignment of protected areas to mitigate biodiversity loss in urbanized marine ecosystems. Environmental ImplicationOur study demonstrates that hazardous water pollutants, including microbial contamination, nutrient enrichment, and chemical stressors, vessel pressure, show strong, species-specific impacts on resident cetaceans in Hong Kong. By integrating eDNA metabarcoding with joint species distribution models, we provide a diagnostic framework that directly links pollutant profiles to ecological risk. These findings highlight that conventional conservation strategies overlooking pollution drivers are insufficient for marine megafauna persistence. Our approach offers an early-warning system for monitoring hazardous pollutants in coastal ecosystems and supports adaptive management strategies to mitigate biodiversity loss in urbanized seascapes.

ecology↗

bioRxiv2026

Local IFNγ signaling contributes to the regenerative decline of aged alveolar progenitor cells

The lungs are highly susceptible to chronic disease in advanced age, likely due to the uniquely compromised repair function of alveolar type II (AT2) cells, facultative progenitor cells that maintain the gas exchange surface. Using aging mouse models, single-cell sequencing, and ex vivo organoid assays, we found that homeostatic aged AT2 cells exhibited an Interferon {gamma} (IFN{gamma}) response associated with IFN{gamma}+ CD8+ T cells in tertiary lymphoid structures (TLS). Aged AT2 cells exhibit impaired regeneration in organoid assays and lost markers of an IFN{gamma} response outside the lung microenvironment, demonstrating that elevated local IFN{gamma} influences the state of AT2 cells. Neutralization of IFN{gamma} signaling and immunoproteasome knockout mice with attenuated IFN{gamma} levels partially rescued aged AT2 cell regeneration. Our findings demonstrate that local IFN{gamma} signaling in aging lungs actively represses alveolar regeneration, establishing chronic inflammatory signaling as a cause of age-related decline in the lung. Halting chronic inflammatory processes restored alveolar regeneration and may provide a means to improve lung health in old age.

cell biology↗

bioRxiv2026

A conservation planning assessment of basin wide Unionid mussel assemblages using environmental DNA

Conservation planning for rare, threatened, and endangered species requires basic information for distribution and abundance. Often this information is lacking due to the nature of traditional survey methods which can be time and labor intensive and thus costly. Environmental DNA (eDNA) metabarcoding offers a promising approach for monitoring freshwater mussel assemblages, a taxonomic group that is both highly imperiled and difficult to survey using traditional methods. We evaluated the performance of eDNA metabarcoding across 30 km of Fish Creek, in Ohio and Indiana, U.S.. We compared results to visual surveys conducted at the same sites. eDNA detected 25 mussel species, including four species not observed alive visually, while visual surveys detected 22 live species. Both methods confirmed the presence of three federally protected species, and eDNA uniquely detected Simpsonaias ambigua, a species rarely encountered in conventional surveys. Incorporating detection repeatability improved congruence between methods: high-repeatability detections strongly aligned with visual presence, whereas moderate and low repeatability detections likely represented reach-scale occupancy. Overall, eDNA metabarcoding offers an efficient and sensitive tool for assessing mussel assemblages and can substantially enhance monitoring programs when integrated with species ecology and hydrological context.

ecology↗

bioRxiv2025

Environmental DNA Metabarcoding Effectively Detects Invasive Species, Pests, and Community Changes in Taiwan Rice Fields

Rice fields represent man-made semi-aquatic wetlands primed for invasive pests. Monitoring rice field biodiversity using conventional methods, however, is time-consuming and laborious. Environmental DNA (eDNA) methods can provide a fast and effective means to monitor rice field communities and inform management decisions. Our study provides proof-of-concept of rice field eDNA biodiversity assessments, with a focus on native and non-native pests across cultivation phases. We collected eDNA samples from locations in southern Taiwan during planting and harvesting, employing eDNA metabarcoding (COI) to detect diverse taxonomic groups. We assigned 77 ASVs across all sites to animal taxa, 34 of which were identified to species. Overall, 18 species were designated as native or non-native (83.3% and 16.6%, respectively), including three major rice pests, Chilo suppressalis (native), Coptotermes formosanus (native), and Pomacea canaliculata (non-native). Cultivation status affected overall diversity, with higher species richness during planting compared to harvesting. No significant differences were observed between native and non-native taxa between cultivation phases. Altogether, we detected a complex environment across trophic levels comprised of both native and non-native agricultural pests using limited sampling effort, demonstrating eDNA analysis as an efficient biomonitoring approach in rice agroecosystems with direct applications for pest, invasive species, and vector surveillance within Taiwan.

ecology↗

bioRxiv2025

Biodiversity modulates the size-abundance relationship in changing environments

Organismal abundance tends to decline with increasing body size. Metabolic theory links this size structure with energy use and productivity, postulating a size-abundance slope of -0.75 that is invariant across environments. We tested the robustness of this relationship across gradients of protist species richness (1-6 species), temperature (15-25 {degrees}C), and time. Using replicated microcosms, we provide an empirical test of how temperature and biodiversity jointly shape the cross-community scaling relationship (CCSR). While our results support the expected of slope -0.75, we also found interactive effects showing the relationship is not invariant. Warming altered abundance scaling with size depending on richness; in high-richness communities, temperature favoured small protists, steepening the CCSR slope. These context-dependent responses emerged over time, suggesting a role of size-dependent species interactions in shaping responses to environmental change. Our findings demonstrate that cross-community size scaling is not fixed but shifts dynamically with ecological context.

ecology↗

bioRxiv2023

The small protein MntS evolved from a signal peptide and acquired a novel function regulating manganese homeostasis in Escherichia coli

Small proteins (< 50 amino acids) are emerging as ubiquitous and important regulators in organisms ranging from bacteria to humans, where they commonly bind to and regulate larger proteins during stress responses. However, fundamental aspects of small proteins, such as their molecular mechanism of action, downregulation after they are no longer needed, and their evolutionary provenance are poorly understood. Here we show that the MntS small protein involved in manganese (Mn) homeostasis binds and inhibits the MntP Mn transporter. Mn is crucial for bacterial survival in stressful environments, but is toxic in excess. Thus, Mn transport is tightly controlled at multiple levels to maintain optimal Mn levels. The small protein MntS adds a new level of regulation for Mn transporters, beyond the known transcriptional and post-transcriptional control. We also found that MntS binds to itself in the presence of Mn, providing a possible mechanism of downregulating MntS activity to terminate its inhibition of MntP Mn export. MntS is homologous to the signal peptide of SitA, the periplasmic metal-binding subunit of a Mn importer. Remarkably, the homologous signal peptide regions can substitute for MntS, demonstrating a functional relationship between MntS and these signal peptides. Conserved gene-neighborhoods support that MntS evolved from an ancestral SitA, acquiring a life of its own with a distinct function in Mn homeostasis. SignificanceThis study demonstrates that the MntS small protein binds and inhibits the MntP Mn exporter, adding another layer to the complex regulation of Mn homeostasis. MntS also interacts with itself in cells with Mn, which could prevent it from regulating MntP. We propose that MntS and other small proteins might sense environmental signals and shut off their own regulation via binding to ligands (e.g., metals) or other proteins. We also provide evidence that MntS evolved from the signal peptide region of the Mn importer, SitA. Homologous SitA signal peptides can recapitulate MntS activities, showing that they have a second function beyond protein secretion. Overall, we establish that small proteins can emerge and develop novel functionalities from gene remnants.

microbiology↗

bioRxiv2019

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi- omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.

cancer biology↗

bioRxiv2017

Dynamic species classification of microorganisms across time, abiotic and biotic environments - a sliding window approach

1. Technological advances have greatly simplified to take and analyze digital images and videos, and ecologists increasingly use these techniques for trait, behavioral and taxonomic analyses. The development of techniques to automate biological measurements from the environment opens up new possibilities to infer species numbers, observe presence/absence patterns and recognize individuals based on audio-visual information.\n\n2. Streams of quantitative data, such as temporal species abundances, are processed by machine learning (ML) algorithms into meaningful information. Machine learning approaches learn to distinguish classes (e.g., species) from observed quantitative features (phenotypes), and in-turn predict the distinguished classes in subsequent observations. However, in biological systems, the environment changes, often driving phenotypic changes in behaviour and morphology.\n\n3. Here we describe a framework for classifying species under dynamic biotic and abiotic conditions using a novel sliding window approach. We train a random forest classifier on subsets of the data, covering restricted temporal, biotic and abiotic ranges (i.e. windows). We test our approach by applying the classification framework to experimental microbial communities where results were validated against manual classification. Individuals from one to six ciliate species were monitored over hundreds of generations in dozens of different species combinations and over a temperature gradient. We describe the steps of our classification pipeline and systematically explore the effects of the abiotic and biotic environments as well as temporal effects on classification success.\n\n4. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, because phenotypic differences driven by environmental change could be captured in the learning algorithm. Importantly, automatic classification showed comparable success compared to manual identifications.\n\n5. Our framework allows for reliable classification even in dynamic environmental contexts, and may help to improve long-term monitoring of species from environmental samples. It therefore has application in disciplines with automatic enumeration and phenotyping of organisms such as eco-toxicology, ecology and evolutionary ecology, and broad-scale environmental monitoring.

ecology↗