Functional traits driving species role in the structure of terrestrial vertebrate scavenger networks

Species assemblages often have a non-random nested organization, which in vertebrate scavenger (carrion-consuming) assemblages is thought to be driven by facilitation in competitive environments. However, not all scavenger species play the same role in maintaining assemblage structure, as some species are obligate scavengers (i.e., vultures) and others are facultative, scavenging opportunistically. We used a database with 177 vertebrate scavenger species from 53 assemblages in 22 countries across five continents to identify which functional traits of scavenger species are key to maintaining the scavenging network structure. We used network analyses to relate ten traits hypothesized to affect assemblage structure with the role of each species in the scavenging assemblage in which it appeared. We characterized the role of a species in terms of both the proportion of monitored carcasses on which that species scavenged, or scavenging breadth (i.e., the species normalized degree), and the role of that species in the nested structure of the assemblage (i.e., the species paired nested degree), therefore identifying possible facilitative interactions among species. We found that species with high olfactory acuity, social foragers, and obligate scavengers had the widest scavenging breadth. We also found that social foragers had a large paired nested degree in scavenger assemblages, probably because their presence is easier to detect by other species to signal carcass occurrence. Our study highlights differences in the functional roles of scavenger species and can be used to identify key species for targeted conservation to maintain the ecological function of scavenger assemblages

Economic modelling fit for the demands of energy decision makers

Decision makers need sector-specific, policy-focused, dynamic economic models with rich representations of technological progress. These allow them to understand how the energy transition is likely to unfold with different policies and what its impacts might be. A new generation of models is emerging to meet these demands, but more action is needed

Predicting bacterial community assemblages using an artificial neural network approach

Understanding the interactions between the Earth's microbiome and the physical, chemical and biological environment is a fundamental goal of microbial ecology. We describe a bioclimatic modeling approach that leverages artificial neural networks to predict microbial community structure as a function of environmental parameters and microbial interactions. This method was better at predicting observed community structure than were any of several single-species models that do not incorporate biotic interactions. The model was used to interpolate and extrapolate community structure over time with an average Bray-Curtis similarity of 89.7. Additionally, community structure was extrapolated geographically to create the first microbial map derived from single-point observations. This method can be generalized to the many microbial ecosystems for which detailed taxonomic data are currently being generated, providing an observation-based modeling technique for predicting microbial taxonomic structure in ecological studies