Prey noticing a predator may freeze (turn immobile) to avoid detection. Freezing is generally thought to precede predator–prey contact, but this may not hold for species of limited sensory abilities. The snail Agaronia detects its prey Pachyoliva, another snail, only when it is right before the predator, while Pachyoliva bumping into Agaronia's side avoids detection by freezing. This post-contact freezing reminds us to consider sensory capabilities when interpreting predator–prey interactions.

We explored the relationship between environmental variables and bird color diversity in urban parks. Color diversity increased with green-area size, but richness-corrected color diversity ceased to be related to area size. Therefore, the increase of color size with area size was mediated by increases in species richness. Color diversity was clustered in parks with the highest human disturbance, favoring the abundance of grey species which probably took advantage of food discarded by humans.

Road traffic is a key threat to global animal biodiversity, causing numerous fatalities. In Vienna, we gathered vertebrate roadkill data via citizen science, bicycle, and foot monitoring. Only 4 roadkill incidents were observed during monitoring; citizens reported 84 cases. Hedgehogs and urban birds were frequent roadkill. Our findings indicate low urban roadkill rates, highlighting the importance of citizen science and expanding monitoring routes.

We studied rates of litter decomposition in natural and disturbed vegetation on elevation gradients of Mount Kilimanjaro to disentangle effects of climate and disturbance. Decomposition was slower in disturbed than in natural forests, but we did not find a negative effect of disturbance for non-forest vegetation. Decomposition slowed down with increasing land-use intensity, but only in the warm wet season. Temperature and humidity were the most important drivers of decomposition in all analyses.

This study investigates classical scrapie (CS) and the risk of interspecies prion transmission by using presence data from wild cervids and infected small ruminant flocks. Employing remote sensing technologies, it derives vegetative and biophysical satellite indices to represent habitat features. A species distribution model integrates these data to identify suitable areas for CS and its hosts. The resultant consensus map and overlapping suitable areas create a detailed infection risk matrix.

The study examined how terrestrial mammals' diets vary across geography. It found that herbivory is more prevalent in cold and desert areas, while frugivory and insectivory dominate in tropical regions. Granivory dominates in temperate environments, while carnivory is more common in the Northern Hemisphere and towards the poles. Terrestrial mammals' diets show conspicuous geographical patterns across the globe, partially explained by climate.

Using eDNA to study fish communities in river–lake systems is a new, cost-effective method. Our study found that water eDNA might miss some fish living in the sediment. Fish communities in rivers and lakes are quite similar, showing how connected these systems are. The environment, especially temperature, influences the genetic signals found in water and sediment. This research sheds light on the effectiveness of eDNA in understanding aquatic ecosystems.

Plant-feeding insects can have large impacts on the photosynthetic rate of their host plants. Through reducing photosynthesis, and thus carbon assimilation by the plant, these impacts might have large-scale influences on ecosystem carbon cycling. Nevertheless, these effects are rarely considered in ecosystem-level studies. Here we propose an approach to incorporating these changes in plant physiology into estimates of ecosystem productivity.

Machine learning models outperform simple mechanistic models in predicting pollinator visitation rates. We use deep learning to infer rules from land cover maps to estimate pollination services globally. Results suggest deep learning can improve predictions by identifying complex patterns in landscape composition, especially in data-rich but knowledge-poor areas. The challenge is to make deep learning algorithms more interpretable so that experts can validate prediction rules for pollination.

Ranavirus infections in amphibians, especially anurans, have been increasingly documented, raising concerns over their role in population declines. This integrative review examines the prevalence, epidemiology, and global distribution of ranavirus in anurans over the past decade. The study concludes with recommendations for enhancing global surveillance and risk assessment strategies, particularly in biodiversity hotspots, where vulnerable species face a high risk of extinction.

The results of a recent paper on the modeling of a honey bee colony have been supplemented and strengthened by showing that the domain of biological validity of a mathematical model with unstable singularity removed is contained in the domain of attraction of positive stable equilibrium.