Urban growth and intensification are projected to increase as the global human population increases. Historically, urban areas have been disregarded as suitable wildlife habitat, but it is now known that these areas can be biodiverse and that wildlife species can adapt to the environmental conditions. One such urban‐dwelling species is the Eurasian red squirrel Sciurus vulgaris, which has suffered population declines in several countries throughout its range in recent decades. The current published literature was systematically reviewed to determine whether or not urban habitats are suitable refugia for red squirrels, through identifying and discussing key topics regarding the urban ecology of red squirrels. Urban environments can support higher population densities of red squirrels than rural areas, probably due to the widespread and reliable provision of anthropogenic supplemental food alongside natural food sources. The availability and quality of urban greenspaces are important determinants of the suitability of urban habitats for red squirrels, as they provide natural food sources and nesting sites. Despite the barriers present in urban landscapes (e.g. roads), red squirrels can still disperse and maintain gene flow at the population level. Road traffic accidents appear to be a significant cause of mortality in some urban red squirrel populations, and seasonal peaks of mortality occur during the autumn months. Diseases (e.g. squirrelpox virus) can also be a significant cause of mortality, although effects differ between populations and depend on whether grey squirrels Sciurus carolinensis are present. Many of the predation events that affect red squirrels appear to be due to free‐ranging domestic and feral cats Felis catus, although there is currently little evidence to suggest that predation is a limiting factor for urban red squirrel populations. We conclude that urban areas can be suitable refugia for red squirrels, provided that high‐quality greenspaces are maintained. Mitigation measures may also be necessary to reduce population mortality and to prevent disease outbreaks.

We present a genome assembly from an individual male Sciurus carolinensis (the eastern grey squirrel; Vertebrata; Mammalia; Eutheria; Rodentia; Sciuridae). The genome sequence is 2.82 gigabases in span. The majority of the assembly (92.3%) is scaffolded into 21 chromosomal-level scaffolds, with both X and Y sex chromosomes assembled.

We present a genome assembly from an individual male Sciurus vulgaris (the Eurasian red squirrel; Vertebrata; Mammalia; Eutheria; Rodentia; Sciuridae). The genome sequence is 2.88 gigabases in span. The majority of the assembly is scaffolded into 21 chromosomal-level scaffolds, with both X and Y sex chromosomes assembled.

The mechanical properties of dietary items are known to influence skull morphology, either through evolution or by phenotypic plasticity. Here, we investigated the impact of supplementary feeding of peanuts on the morphology of red squirrels (Sciurus vulgaris) from five populations in Britain (North Scotland, Borders, Jersey and two temporally distinct populations from Formby (Merseyside)). Stable isotope analysis confirmed dietary ecology in 58 specimens. Geometric morphometrics were used to analyse three-dimensional and two-dimensional shape variation across 113 crania and 388 mandibles, respectively. Nitrogen isotope ratios (δ15N) were lower in the 1990s and 2010s Formby squirrels (suggesting a diet with an increased proportion of peanuts), and higher in other populations. Significant differences in cranio-mandibular shape were found between all populations, with 1990s Formby red squirrels exhibiting a morphology associated with reduced masticatory efficiency. This effect was partially reversed following a reduction in supplementary feeding of peanuts. We propose that these morphological changes are related to the reduced mechanical effort needed to process peanuts relative to naturally occurring food items. This could be an example of diet-induced plastic changes to the skeleton in non-muroid wild mammals, although further research is needed to exclude other driving factors such as genetics.

The widespread adoption of camera trap surveys for wildlife monitoring has generated a substantial volume of ecological data, yet processing constraints persist due to the time-consuming process of manual image classification and the reliability of automated systems. This study assesses the performance of Conservation AI’s UK Mammals model in classifying three species—Western European hedgehogs (Erinaceus europaeus), red foxes (Vulpes vulpes), and European badgers (Meles meles)—from a subsample of 234 records from camera trap images collected through a citizen science initiative across residential gardens. This analysis was repeated after retraining the model to assess improvement in model performance. Initial model outputs demonstrated high precision (>0.80) for foxes and hedgehogs but low recall (<0.50) for hedgehogs, with the lowest recall probability of 0.12 at the 95% confidence threshold (CT). Following retraining, model performance improved substantially across all metrics, with average F1-scores (weighted average of precision and recall across the three species tested) improving at all CTs, though discrepancies with human classifications remained statistically significant. Based on performance results from this study, we present a semi-automated, three-step workflow incorporating an artificially intelligent (AI) generalist object detector (MegaDetector), an AI species-specific classifier (Conservation AI), and manual validation. Where privacy concerns restrict citizen science contributions, our pipeline offers an alternative that accelerates camera trap data analysis whilst maintaining classification accuracy. The findings provide baseline performance estimates of Conservation AI’s UK Mammals model and present an approach that offers a practical solution to improve the efficiency of using camera traps in ecological research and conservation planning. We also highlight the importance of continuous AI model training, the value of citizen science in expanding training datasets, and the need for adaptable workflows in camera trap studies.