Biology subjects

Zbinden, R.

Publications and source records attributed to Zbinden, R..

4 recordsLinked to original sources

bioRxiv2025

Multi-scale neural networks enhance species distribution modelling across predictors and taxonomic groups

Ecological processes and patterns are scale-dependent, as reflected in how relationships between species occurrence and the environment vary across scales. In species distribution models (SDMs), spatial scale can strongly affect model predictions and performance. Despite this, the choice of scale is often overlooked. The development of SDMs using deep learning models has enabled spatially structured data, such as patches of environmental raster data, to be considered as model inputs, making the question of scale even more crucial. Here, we evaluate convolutional neural network-based multi-species SDMs considering different scales on a dataset from Switzerland, including more than 8 million observations for 2390 plant and 1006 animal species comprising amphibians, butterflies, beetles, and mammals. We investigate how scale affects model performance and compare single- and multi-scale models. Our results reveal stronger scale effects for remote sensing indices than for bioclimatic and edaphic variables. In single-scale models, we find that plant species perform better with smaller spatial scales, while the opposite is true for animal species. Multi-scale models consistently improve predictive performance and reduce sensitivity to arbitrary scale selection across all modalities and taxonomic groups. The effect of scale is nonetheless smaller than the increase in performance achieved by considering multiple predictor groups. To improve the interpretability of such complex models, we use attribution methods to compute the relative contribution of different scales and predictor groups. Our study demonstrates the importance of accounting for scale and the potential of deep learning methods to integrate ecological complexity with spatial data across scales. HighlightsO_LIDeep learning SDMs can use spatial environmental data as model predictors C_LIO_LIThe spatial extent of the predictors affects model performance C_LIO_LIMulti-scale models improve performance across predictors and taxonomic groups C_LIO_LIMulti-scale modelling reduces sensitivity to arbitrary scale selection C_LIO_LIExplainable AI methods provide contribution scores for scales and predictor groups C_LI

ecology↗

bioRxiv2025

Migrating in a warming world: A deep learning approach to predict pan-American seasonal shifts in the monarch butterfly niche

Climate change is driving biodiversity loss, disrupting ecosystem functioning, and altering species distributions. Migratory species, whose range varies across seasons depending on specific climatic conditions, are particularly sensitive to environmental changes and serve as indicators of ecosystem health. However, current species distribution models often fail to capture the temporal dynamics critical for migratory species, limiting their ability to provide accurate future range estimations. In this study, we address this gap by developing a time-aware deep learning species distribution model for the monarch butterfly (Danaus plexippus), an iconic species for biodiversity conservation. Using monarch occurrence records across the Americas gathered from scientific and citizen science sources, we embed the effect of monthly climatic variables in a sequential framework. We compare the performance of our seasonal model to conventional time-static baselines, showing not only better performance in the present, where the models have been trained and validated, but also in the past. Our findings show that climatic factors such as humidity, temperature, precipitation and cloud coverage strongly influence the ecological niche of the monarch butterfly, with notable seasonal and spatial variability. Applying our model under climate change scenarios, we predict a northwestward shift in the monarch range by the end of the XXI century, with expansion in Canada and significant contraction in California and Central America, key sites for overwintering that also host resident monarch populations. These changes could severely impact the species migratory cycle and population stability. Using Shapley values, an explainable AI technique, we identify the decrease in precipitation and humidity as important environmental drivers responsible for the contraction of overwintering sites. By focusing on a species of high ecological relevance through a time-aware modeling approach, this work brings novel insights for the conservation of migratory species in the face of the challenges posed by climate change.

ecology↗

bioRxiv2022

Engineered reporter phages for rapid detection of Escherichia coli, Klebsiella spp., and Enterococcus spp. in urine

The rapid detection and species-level differentiation of bacterial pathogens facilitates antibiotic stewardship and improves disease management. Here, we develop a rapid bacteriophage-based diagnostic assay to detect the most prevalent pathogens causing urinary tract infections: Escherichia coli, Klebsiella spp., and Enterococcus spp. For each uropathogen, two virulent phages were genetically engineered to express a nanoluciferase reporter gene upon host infection. Using 206 patient urine samples, reporter phage-induced bioluminescence was quantified to identify bacteriuria and the assay was benchmarked against conventional urinalysis. Overall, E. coli, Klebsiella spp., and Enterococcus spp. were each detected with high sensitivity (68%, 78%, 85%), specificity (99%, 99%, 99%), and accuracy (90%, 94%, 96%) at a resolution of [>=]103 CFU/ml within 5 h. We further demonstrate how bioluminescence in urine can be used to predict phage antibacterial activity, demonstrating the future potential of reporter phages as companion diagnostics that guide patient-phage matching prior to therapeutic phage application.

microbiology↗

bioRxiv2021

Assessing antibiotic tolerance of Staphylococcus aureus derived directly from patients by the Replica Plating Tolerance Isolation System- REPTIS

Antibiotic tolerant Staphylococcus aureus pose a great challenge to clinicians as well as to microbiological laboratories and are one reason for treatment failure. Antibiotic tolerant strains survive transient antibiotic exposure despite being fully susceptible in vitro. Thus, fast and reliable methods to detect tolerance in the routine microbiology laboratory are urgently required. We therefore evaluated the feasibility of the replica plating tolerance isolation system (REPTIS) to detect antibiotic tolerance in S. aureus isolates derived directly from patients suffering from different types of infections and investigated possible connections to clinical presentations and patient characteristics. One hundred twenty-five S. aureus isolates were included. Replica plating of the original resistance testing plate was used to assess regrowth in the zones of inhibition, indicating antibiotic tolerance. Bacterial regrowth was assessed after 24 and 48 hours of incubation and an overall regrowth score (ORS) was assigned. Regrowth scores were compared to the clinical presentation. Bacterial regrowth was high for most antibiotics targeting protein synthesis and relatively low for antibiotics targeting other cellular functions such as DNA-replication, transcription and cell wall synthesis, with the exception of rifampicin. Isolates with a blaZ penicillinase had lower regrowth in penicillin and ampicillin. Low ORSs were more prevalent among isolates recovered from patients with immunosuppression or methicillin-resistant S. aureus (MRSA) isolates. In conclusion, REPTIS is useful to detect antibiotic tolerance in clinical microbiological routine diagnostics. Rapid detection of antibiotic tolerance offers a new diagnostic readout that might allow more tailored treatments in the future.

microbiology↗