Biology subjects

Fiderer, C.

Publications and source records attributed to Fiderer, C..

3 recordsLinked to original sources

bioRxiv2026

Continent-wide calibration of camera-trap metrics reveals low population densities in the European wildcat

Effective conservation depends on demographic metrics that reliably reflect species status, particularly population abundance. For elusive species occurring at low densities, however, such metrics remain difficult to obtain. Spatial capture-recapture (SCR) models are the standardized approach for estimating density in marked populations, but their data requirements, especially the need for multiple spatial recaptures across individuals, often limit applicability in small or data-poor populations. This constraint has resulted in knowledge gaps for some of the most vulnerable species, undermining evidence-based conservation planning and management. Using camera-trap data and SCR-derived density estimates from data-rich populations, we evaluated alternative, less data-demanding metrics and tested the hypothesis: Space to Event (STE), Mean Local Abundance (MLA), and Relative Abundance Index (RAI) exhibit predictable relationships with SCR-derived density; if supported, these metrics can reliably estimate density in populations where SCR models cannot be implemented. We applied this framework to the European wildcat (Felis silvestris), an elusive small felid with highly fragmented populations across Europe, for which density estimates are largely lacking despite growing conservation concern. Across 21 study areas spanning most of the species range, our results indicate that European wildcats generally occur at lower densities than previously reported. SCR-derived estimates (n=10) averaged 10.32 {+/-} 11.56 inds/100km2, while STE enabled density estimation in five additional data-poor areas (mean 5.52 {+/-} 5.33 inds/100km2). STE showed a strong linear relationship with SCR-derived density (R2=0.98), supporting its use as a viable alternative when SCR is infeasible, although it tended to underestimate compared to SCR, especially at higher densities. In contrast, MLA and RAI showed weaker and non-linear relationships with SCR-derived density (R2=0.65), indicating substantially lower explanatory power and suggesting their estimates are more strongly influenced by confounding processes. By explicitly calibrating alternative metrics across a wide density gradient throughout most of the species distribution, this study provides a transferable methodological framework for estimating density in low-density wildlife populations and the first continent-wide, standardized density assessment of a carnivore species. From a management perspective, our findings identify populations that may be most vulnerable, particularly those with the lowest densities, and highlight the need to prioritize absolute abundance monitoring.

ecology↗

bioRxiv2024

Prey attracting but not repelled by predators suggests an asymmetric investment in the encounter-avoid-escape sequence.

Understanding predator-prey interactions, particularly how species use space and time to influence encounter rates, is fundamental in community and behavioural ecology. However, for large, free-ranging animals, encounter rates are rarely quantified directly, because of logistical and methodological challenges associated with tracking both predators and prey simultaneously. Instead, studies commonly rely on proxies such as spatial or temporal overlap. While informative, these proxies provide only incomplete estimates of encounter rates because they typically consider only one dimension of the interaction process (spatial or temporal). Although camera traps cannot directly measure encounters among large free-roaming species, they offer the opportunity to quantify the proximal co-occurrence, i.e., the extent to which species tolerate or avoid one anothers proximity in space and time. We analysed data from a one-year study conducted across four German protected areas using 283 camera traps and applying recurrent event analysis to investigate interactions among three prey species, red deer (Cervus elaphus), roe deer (Capreolus capreolus), wild boar (Sus scrofa) and two large predators grey wolf (Canis lupus), and Eurasian lynx (Lynx lynx). Prey visitation rates were unaffected by predator presence, whereas wolves exhibited a strong attraction to prey, with visitation rates four to six times higher immediately after prey detections. The limited sample size prevented robust conclusions regarding Eurasian lynx responses. These findings point towards an asymmetry in the predation sequence (i.e., spatio-temporal proximity, encounter, ignorance or avoidance post-encounter, capture or escape from attack): Predators must succeed at every stage of the sequence to capture prey, while prey can avoid predation by disrupting the process at any single stage. Our results suggest that prey species do not necessarily reduce large-scale spatio-temporal proximity to predators and may instead rely on anti-predator responses occurring later in the predation sequence.

ecology↗

bioRxiv2021

imageseg: an R package for deep learning-based image segmentation

O_LIConvolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications. CNNs can perform very well in various tasks, especially for visual tasks and image data. Image segmentation (the classification of all pixels in images) is one such task and can for example be used to assess forest vertical and horizontal structure. While such methods have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. C_LIO_LIHere, we present R package imageseg which implements a workflow for general-purpose image segmentation using CNNs and the U-Net architecture in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with two models for forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 3288 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1468 understory vegetation images. C_LIO_LIOverall classification accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively), indicating robustness to variation in input images and good generalization strength across forest types and biomes. C_LIO_LIThe package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with multiple classes and based on color or grayscale images, e.g. in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond. C_LI

ecology↗