Biology subjects

Erb, J.

Publications and source records attributed to Erb, J..

6 recordsLinked to original sources

bioRxiv2026

The centrality of ecotones: How scale, sex, and ontogeny shape the spatial ecology of a solitary carnivore

ContextAnimals balance resource acquisition with risk mitigation. These trade-offs are rarely uniform, being mediated by spatial scale, demographic traits, and environmental constraints. Understanding these divergent spatial behaviors is critical for management across human-dominated landscapes. ObjectivesWe investigated how sexual dimorphism and ontogeny interact with landscape structure to influence scale-dependent resource selection. Specifically, we sought to determine how these demographic factors mediate spatial trade-offs between optimal foraging habitats, top-down intraguild predation risk, and bottom-up severe winter weather. MethodsWe examined the spatial ecology of a solitary carnivore, the bobcat (Lynx rufus), across a heterogeneous, human-modified landscape in northern Minnesota, USA. Using spatial data derived from harvested adult and juvenile individuals, we evaluated multi-scale selection relative to land cover, structural ecotones, intraguild predator activity, and winter severity. ResultsHabitat selection was scale-dependent and partitioned demographically. Whereas bobcats universally selected for ecotones and avoided homogeneous open habitats at fine scales, responses to other features diverged by sex and age. Females actively avoided areas with high coyote activity and freezing temperatures; males exhibited high risk tolerance, apparently indifferent to coyote activity and tolerant of freezing temperatures. We identified a distinct ontogenetic spatial shift among females. Subordinate juveniles were competitively excluded from optimal natural ecotones, forcing them into riskier, anthropogenic agricultural edges. In contrast, adult females optimized foraging opportunities by selecting productive ecotones at the intersection of woody vegetation and semi-natural grasslands. ConclusionsOur findings demonstrate that habitat selection is not a static species-level trait, but instead a dynamic process resulting from the interaction between ontogeny, sex, and landscape heterogeneity. The reliance of vulnerable demographic groups on marginal or anthropogenic habitats highlights how human land-use changes can inadvertently produce ecological winners and losers within the same species. Consequently, landscape management and conservation planning for solitary carnivores must shift from broad, population-wide habitat prescriptions to strategies that explicitly accommodate the divergent spatial requirements of specific demographic cohorts.

ecology↗

bioRxiv2025

Prior Information Shapes Perceptual Evidence Accumulation Dynamics Differentially in Psychosis

Humans rely on prior information to navigate sensory uncertainty: Such priors could shape the decision process before sensory evidence is gathered (origin model), or could amplify sensory evidence dynamically (gain model). Dysfunctionalities in the utilisation of priors may underlie hallucinatory percepts and delusional ideation in psychosis, yet their impact on decision-making across sensory modalities has remained unclear. Using a perceptual target-detection task across auditory and visual domains in laboratory and online samples, we applied hierarchical drift diffusion modelling to examine how prior probabilities shape evidence accumulation in clinical and non-clinical populations. We show that in healthy individuals, prior information enhances sensory gain and decision flexibility, as represented by drift criterion and rate, consistent with the gain model. In contrast, individuals with psychosis exhibit diminished sensory gain, relying instead on pre-evidence biases, consistent with the origin model. Notably, greater positive symptom severity predicted a reduction in traditional criterion decision bias. These results suggest that sensory gain deficits may serve as a computational marker for psychosis progression, linking dysfunctional prior use to perceptual aberrancies. By demonstrating how prior information modulates evidence accumulation across sensory modalities, our study advances the understanding of psychotic perception and decision-making, offering insights for computational psychiatry and fine-tuned clinical diagnostics.

neuroscience↗

bioRxiv2020

Predicting speech from a cortical hierarchy of event-based timescales

How can anticipatory neural processes structure the temporal unfolding of context in our natural environment? We here provide evidence for a neural coding scheme that sparsely updates contextual representations at the boundary of events and gives rise to a hierarchical, multi-layered organization of predictive language comprehension. Training artificial neural networks to predict the next word in a story at five stacked timescales and then using model-based functional MRI, we observe a sparse, event-based "surprisal hierarchy". The hierarchy evolved along a temporo-parietal pathway, with model-based surprisal at longest timescales represented in inferior parietal regions. Along this hierarchy, surprisal at any given timescale gated bottom-up and top-down connectivity to neighbouring timescales. In contrast, surprisal derived from a continuously updated context influenced temporo-parietal activity only at short timescales. Representing context in the form of increasingly coarse events constitutes a network architecture for making predictions that is both computationally efficient and semantically rich.

neuroscience↗

bioRxiv2020

Aberrant perceptual judgements on speech-relevant acoustic features in hallucination-prone individuals

Hallucinations constitute an intriguing model of how percepts are generated and how perception can fail. Here, we investigate the hypothesis that an altered perceptual weighting of the spectro-temporal modulations that characterize speech contributes to the emergence of auditory verbal hallucinations. Healthy adults (N=168) varying in their predisposition for hallucinations had to choose the more speech-like of two presented ambiguous sound textures and give a confidence judgement. Using psychophysical reverse correlation, we quantified the contribution of different acoustic features to a listeners perceptual decisions. Higher hallucination proneness covaried with perceptual down-weighting of speech-typical, low-frequency acoustic energy while prioritising high frequencies. Remarkably, higher confidence judgements in single trials depended not only on acoustic evidence but also on an individuals hallucination proneness and schizotypy score. In line with an account of altered perceptual priors and differential weighting of sensory evidence, these results show that hallucination-prone individuals exhibit qualitative and quantitative changes in their perception of the modulations typical for speech. Author summaryHallucinations -- that is, percepts in the absence of an external stimulus -- are prevalent in psychotic disorders such as schizophrenia, but also occur in the general population. To date it is unknown whether the emergence of hallucinations is rooted in an altered perception of sounds. Fusing the psychophysical technique of reverse correlation with concepts from computational psychiatry, this research reveals alterations of sensory processing in hallucination-prone adults. We show that the higher nonclinical adults predisposition to hallucinations, the more they prioritise the sound features atypical for speech such as higher frequencies. At the same time, they express higher confidence in their perceptual judgements. The present approach may contribute to improving early diagnosis and prevention strategies in individuals at risk for psychosis.

neuroscience↗

bioRxiv2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

O_LIMotion-activated wildlife cameras (or "camera traps") are frequently used to remotely and non-invasively observe animals. The vast number of images collected from camera trap projects have prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. C_LIO_LIWe used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the "species model," and one that determines if an image is empty or if it contains an animal, the "empty-animal model." C_LIO_LIOur species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36-91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91-94% on out-of-sample datasets from different continents. C_LIO_LIOur software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths. C_LI

ecology↗

bioRxiv2020

Temporal selectivity declines in the aging human auditory cortex

Current models successfully describe the auditory cortical response to natural sounds with a set of spectra-temporal features. However, these models have hardly been linked to the ill-understood neurobiological changes that occur in the aging auditory cortex. Modelling the hemodynamic response to a rich natural sound mixture in N=64 listeners of varying age, we here show that in older listeners auditory cortex, the key feature of temporal rate is represented with a markedly broader tuning. This loss of temporal selectivity is most prominent in primary auditory cortex and planum temporale, with no such changes in adjacent auditory or other brain areas. Amongst older listeners, we observe a direct relationship between chronological age and temporal-rate tuning, unconfounded by auditory acuity or model goodness of fit. In line with senescent neural dedifferentiation more generally, our results highlight decreased selectivity to temporal information as a hallmark of the aging auditory cortex. Impact StatementAge-dependent broadening of tuning to temporal modulation rate in older humans cortical sound representations highlights decreased temporal selectivity as an anatomically and functionally specific hallmark of the aging auditory cortex.

neuroscience↗