Digital technologies offer a powerful toolkit to protect our planet's biodiversity. From satellite imagery to citizen science apps, they can help us monitor ecosystems, track species, and understand the impact of human activities. However, challenges like data access and environmental costs must be addressed. By working together, scientists, policymakers, communities, and technology experts can use digital solutions to safeguard our planet's biodiversity and ensure a sustainable future.

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.

We quantify the extent to which forested Natura 2000 sites are affected by tree species distribution shifts in a changing climate. As ecosystems of any type are highly dynamic, climate change can lead to additional severe pressure on statically defined conservation goals and management activities associated with this. We utilised 26 bio-climatic variables and analysed the climate-induced change of tree species distribution at Natura 2000 sites, the largest conservation area network worldwide.

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.

We evaluated shifts in plant–pollinator communities between two years with different precipitation patterns on a mountain gradient in Arizona. We found that plant richness decreased, pollinator generalization increased at the highest life zone, and plant and pollinator activity was less consistent in the dry year. As drought is expected to increase worldwide, understanding local-scale plant–pollinator interactions may highlight which species will be critical in times of lower precipitation.

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.

Insects are the most abundant and diverse animals on Earth, playing a key role in ecosystem stability. Their success is partly due to mutualistic relationships with microorganisms, which provide nutrients inaccessible due to their limited enzymatic capacity and specialized diets. Microorganisms also produce antimicrobial compounds that protect insects from pathogens. Understanding these interactions is crucial for the sustainable management of insects and their microbial partners.

We used simulations to analyze the dynamics of an intertidal predator and its prey, which also engage in non-trophic interactions. These interactions create strong dependencies between predator and prey, shifting dynamics from being driven mainly by new individuals arriving from external sources to an interplay between regional and local processes. Our findings highlight the importance of considering multiple interaction types to understand population and community structure.

Braided rivers are very dynamic ecosystems. Arthropods inhabiting these areas use various strategies to survive floods. This study showed a different repertoire of survival strategies during floods and a significantly altered distribution of the studied species by floods and highlights the crucial role of elevated areas and riverbanks as refuges for arthropods during floods.