Biology subjects

Scheidegger, A.

Publications and source records attributed to Scheidegger, A..

8 recordsLinked to original sources

bioRxiv2026

Big data analysis of animal movements in aquatic ecosystems with acoustic telemetry

Underwater receiver networks (passive acoustic telemetry systems) are deployed to track animals in aquatic habitats all over the world, but remarkably limited attention has been given over to how we can strengthen the value of these networks through statistical and computational advances. Here, we upscale state-of-the-art methods of Bayesian inference to big animal-tracking datasets from acoustic telemetry, with the largest geolocation analysis in a sparse passive acoustic telemetry system (with non-overlapping receivers) to date. Using four years of data from 93 lake trout (Salvelinus namaycush) in North Americas Lake Champlain (657,360 timesteps per individual), we formulate and fit state-space models to reconstruct animal movements through time. Uniquely, we directly embed biological expertise and detailed complementary datasets from fine-scale positioning systems, accelerometry, swim-tunnel experiments and field range tests in our analysis. Using simulated and real-world datasets, we map movement patterns and estimate residency in distinct management zones. We quantify array precision and deliver maps and residency estimates with a median error and precision (standard error) below 1 %. These results strengthen the evidence base for management. This work takes us a step towards robust inference of movement patterns at scale in acoustic telemetry systems across the world. We can, and should, build on prior scientific progress and extend the value of hard-earned data beyond individual studies to refine inferences for ecology and management. Significance statementAcoustic receivers are deployed across the globe to track aquatic animals, but reconstructing detailed movement patterns from detections at receivers remains a considerable challenge. Here, we upscale state-of-the-art methods of Bayesian inference by two orders of magnitude to analyse big, real-world datasets, using an extensive case study of lake trout (Salvelinus namaycush) in Lake Champlain. By directly integrating diverse complementary datasets from animal-borne tags, swim-tunnel experiments, field studies and close-kin mark-recapture in our analysis, we resolve detailed movement patterns over a four-year period, with broad implications for ecology and management. This work provides a powerful framework for acoustic telemetry studies that strives to meet the challenges of big, real-world datasets from telemetry networks across the world.

ecology↗

bioRxiv2025

IFNAR1. Neutrophils Orchestrate Chronic Inflammatory Damage Through Mitochondrial Remodeling

Neutrophils are abundant innate effector cells that drive mucosal inflammation, yet the mechanisms by which they contribute to chronic inflammatory diseases across distinct tissues remain incompletely understood. Here, by reanalyzing single-cell RNA-seq datasets from patients with inflammatory bowel disease (IBD) and chronic obstructive pulmonary disease (COPD), we identify a shared neutrophil activation program enriched for type I interferon (IFN) signaling, nuclear factor-{kappa}B (NF-{kappa}B) and AP-1 transcriptional regulators, and effector pathways including NETosis, degranulation, and leukocyte trafficking. To interrogate these signatures, we established a CRISPR-compatible neutrophil differentiation platform from adult CD34 progenitors, which yielded cells closely resembling primary neutrophils at transcriptomic, proteomic, and functional levels. A targeted CRISPR-Cas9 screen revealed a central role for the mitochondrial iron transporter mitoferrin-1 (SLC25A37) in coordinating neutrophil oxidative phosphorylation, NET formation, and type I IFN production downstream of TLR9. Mechanistically, we show that NET-derived citrullinated histones activate an autocrine IFN-IFNAR1 loop, amplifying neutrophil inflammatory functions without impairing phagocytosis. Disruption of this loop, through IFNAR1 depletion or blockade, dampened neutrophil-driven tissue damage in human intestinal and alveolar organoid co-cultures as well as in murine models of colitis and cigarette smoke-induced lung inflammation. These findings uncover a conserved IFN-driven metabolic circuit in neutrophils that underpins pathology across chronic mucosal diseases and identify IFNAR1 as a therapeutic node to selectively disarm neutrophil-mediated tissue injury.

immunology↗

bioRxiv2025

Animal geolocation with convolution algorithms in Julia and R via Wahoo.jl

O_LIAnimal geolocation is the core of movement ecology. In aquatic ecosystems, electronic tagging and tracking technologies, such as passive acoustic telemetry systems and biologging sensors, are widely deployed. However, statistical estimation of individual locations from these datasets can be challenging and computationally expensive. C_LIO_LIHere, we introduce Wahoo.jl, a Julia package that fits state-space models to animal-tracking data via convolution algorithms. Wahoo.jl supports passive acoustic telemetry (detection/non-detection) and biologging (i.e., depth) datasets; implements grid-based filtering, smoothing and sampling of trajectories; and exploits GPU acceleration. C_LIO_LIUsing simulations, we illustrate how to use Wahoo.jl from Julia and R to reconstruct movements for an example individual tagged with an acoustic transmitter and an archival depth tag. We also provide validation and sensitivity analyses. C_LIO_LIWahoo.jl fills a key gap in the animal-tracking toolbox. The package provides an accessible, flexible and performant interface for an inference methodology that reliably handles multimodal inference problems that challenge other approaches. We discuss the approachs pros and cons and provide guidance to readers on when to reach for Wahoo.jl. C_LI

ecology↗

bioRxiv2025

Animal tracking with particle algorithms for conservation

The movements of aquatic animals affect their exposure to threats and the efficacy of conservation measures, such as Marine Protected Areas (MPAs). However, many species movements remain poorly understood and difficult to reconstruct from available datasets, hampering conservation efforts. This is especially the case for species that rarely surface, for which data are often limited to observations from acoustic telemetry (detections) and ancillary sensors, such as archival tags. Here, we pioneer the use of state-of-the-art particle algorithms to model animal movement, integrate datasets and assess MPA design, using a case study of the Critically Endangered flapper skate (Dipturus intermedius) in Scotland. Our algorithms led to 5-fold improvements in maps of space use and 30-fold improvements in residency estimates (lower mean error) compared to prevailing heuristic methods. By formally integrating tracking datasets, we were uniquely able to examine movements beyond receivers into fished zones, MPA-scale residency and specific habitats beyond protected areas that may warrant protection. This work showcases a probabilistically sound modelling framework that is sufficiently fast, flexible and accessible to meet the demands of modern animal-tracking datasets in acoustic telemetry systems. This represents a marked advance for analyses of animal movements and MPA efficacy worldwide.

ecology↗

bioRxiv2024

Particle algorithms for animal movement modelling in autonomous receiver networks

1. Particle filters and smoothers are powerful sequential Monte Carlo algorithms used to fit non-linear, non-Gaussian state-space models. These algorithms are well placed to fit process-orientated models to animal-tracking data, especially in autonomous receiver networks, but to date they have received limited attention in the ecological literature. 2. Here, we introduce a Bayesian filtering-smoothing algorithm that reconstructs individual movements and patterns of space use from animal-tracking data, with a focus on passive acoustic telemetry systems. Within a sound probabilistic framework, the methodology uniquely integrates the movement process and the observation processes of disparate datasets, while correctly representing uncertainty. In a comprehensive simulation-based analysis, we compare the performance of our algorithm to the prevailing, heuristic methods used in passive acoustic telemetry systems and analyse algorithm sensitivity. 3. We find the particle smoothing methodology outperforms heuristic methods across the board. Particle-based maps consistently represent simulated movements more accurately, even in dense receiver networks, and are better suited to analyses of home ranges, residency and habitat preferences. 4. This study sets a new state-of-the-art for movement modelling in autonomous receiver networks. Particle algorithms provide a flexible and intuitive modelling framework with potential applications in many ecological settings.

animal behavior and cognition↗

bioRxiv2024

patter: particle algorithms for animal tracking in R and Julia

O_LIIn the field of movement ecology, state-space models have emerged as a powerful modelling framework that represents individual movements and the processes that connect movements to observations. However, fitting state-space models to animal tracking data is often difficult and computationally expensive. C_LIO_LIHere, we introduce patter, a package that provides particle filtering and smoothing algorithms that fit Bayesian state-space models to tracking data, with a focus on data from aquatic animals in autonomous receiver arrays. patter is written in R, with a high-performance Julia backend. Package functionality supports data simulation, preparation, filtering, smoothing and mapping. C_LIO_LIIn two worked examples, we demonstrate how to implement patter to reconstruct the movements of a tagged animal in an acoustic telemetry system from acoustic detections and ancillary observations. With perfect information, the particle filter reconstructs the true (unobserved) movement path (Example One). More generally, particle-based methods represent an individuals possible location probabilistically as a weighted series of samples ( particles). In our illustration, we resolve an individuals (unobserved) location every two minutes during one month in minutes and use particles to visualise movements, map space use and quantify residency (Example Two). C_LIO_LIpatter facilitates robust, flexible and efficient analyses of animal tracking data. The methods are widely applicable and enable refined analyses of home ranges, residency and habitat preferences. C_LI

ecology↗

bioRxiv2022

Bayesian multi-level calibration of a process-based maize phenology model

Plant phenology models are important components in process-based crop models, which are used to assess the impact of climate change on food production. For reliable model predictions, parameters in phenology models have to be accurately known. They are usually estimated by calibrating the model to observations. However, at regional scales in which different cultivars of a crop species may be grown, not accounting for inherent differences in phenological development between cultivars in the model and the presence of model deficits lead to inaccurate parameter estimates. To account for inherent differences between cultivars and to identify model deficits, we used a Bayesian multi-level approach to calibrate a phenology model (SPASS) to observations of silage maize grown across Germany between 2009 and 2017. We evaluated four multi-level models of increasing complexity, where we accounted for different combinations of ecological, weather, and year effects, as well as the hierarchical classification of cultivars nested within ripening groups of the maize species. We compared the calibration quality from this approach to the commonly used pooled approach in which none of these factors are considered. Our approach proved successful in improving calibration quality by incorporating the hierarchical classification of cultivars within ripening groups of maize. Our findings have implications for regional model calibration and data-gathering studies, since it emphasizes that ripening group and cultivar information is essential. Furthermore, we found that if this information is not available, at least weather, ecological regions and year effects should be taken into account. Our results can facilitate model improvement studies since we identified possible model limitations related to temperature effects in the reproductive (post-flowering) phase and to soil-moisture. We demonstrate that Bayesian multi-level calibration of a phenology model facilitates the incorporation of hierarchical dependencies and the identification of model limitations. Our approach can be extended to full crop models at different spatial scales.

plant biology↗

bioRxiv2018

DNA Methylation by Restriction Modification Systems Affects the Global Transcriptome Profile in Borrelia burgdorferi

Prokaryote restriction modification (RM) systems serve to protect bacteria from potentially detrimental foreign DNA. Recent evidence suggests that DNA methylation by the methyltransferase (MTase) components of RM systems can also have effects on transcriptome profiles. The causative agent of Lyme disease, Borrelia burgdorferi, encodes two RM systems with N6-Methyladenosine (m6A) MTase activity. The specific recognition and/or methylation sequences have not been identified for either B. burgdorferi MTase, and it is not currently known whether these RM systems influence transcriptome profiles. In the current study, Single Molecule Real Time sequencing was utilized to map genome-wide m6A sites, and to identify consensus modified motifs in wild-type B. burgdorferi as well as isogenic MTase mutants. Four conserved m6A motifs were identified, and were fully attributable to the presence of specific MTases. Whole-genome transcriptome changes were observed in conjunction with the loss of MTase enzymes, indicating that DNA methylation by RM systems has effects on gene expression in B. burgdorferi. The results of this study provide a comprehensive view of the DNA methylation pattern in B. burgdorferi, and the accompanying gene expression profiles add to the emerging body of research on RM systems and gene regulation in bacteria.\n\nIMPORTANCELyme disease is the most prevalent vector-borne disease in North America, and is classified by the Centers for Disease Control and Prevention (CDC) as an emerging infectious disease with an expanding geographical area of occurrence. Previous studies have shown that the causative bacterium, Borrelia burgdorferi, methylates its genome using restriction modification systems that allow for the distinction of self from foreign DNA. Although much research has focused on the regulation of gene expression in B. burgdorferi, the effects of DNA methylation on gene regulation has not been evaluated. The current study characterizes the patterns of DNA methylation by restriction modification systems in B. burgdorferi, and evaluates the resulting effects on gene regulation in this important pathogen.

microbiology↗