Roughly 10% of the Earth's surface is permanently covered by glaciers and ice sheets and in mountain ecosystems, this proportion of ice cover is often even higher. From an ecological perspective, ice-dominated ecosystems place harsh controls on life including cold temperature, limited nutrient availability, and often prolonged darkness due to snow cover for much of the year. Despite these limitations, glaciers, and perennial snowfields support diverse, primarily microbial communities, though macroinvertebrates and vertebrates are also present. The availability and mass balance of key elements [(carbon (C), nitrogen (N), phosphorous (P)] are known to influence the population dynamics of organisms, and ultimately shape the structure and function of ecosystems worldwide. While considerable attention has been devoted to patterns of biodiversity in mountain cryosphere-influenced ecosystems, the ecological stoichiometry of these habitats has received much less attention. Understanding this emerging research arena is particularly pressing in light of the rapid recession of glaciers and perennial snowfields worldwide. In this review, we synthesize existing knowledge of ecological stoichiometry, nutrient availability, and food webs in the mountain cryosphere (specifically glaciers and perennial snowfields). We use this synthesis to develop more general understanding of nutrient origins, distributions, and trophic interactions in these imperiled ecosystems. We focus our efforts on three major habitats: glacier surfaces (supraglacial), the area beneath glaciers (subglacial), and adjacent downstream habitats (i.e., glacier-fed streams and lakes). We compare nutrient availability in these habitats to comparable habitats on continental ice sheets (e.g., Greenland and Antarctica) and show that, in general, nutrient levels are substantially different between the two. We also discuss how ongoing climate warming will alter nutrient and trophic dynamics in mountain glacier-influenced ecosystems. We conclude by highlighting the pressing need for studies to understand spatial and temporal stoichiometric variation in the mountain cryosphere, ideally with direct comparisons to continental ice sheets, before these imperiled habitats vanish completely.

Significance: Persistently diverse microbial communities are one of biology’s great puzzles. Using a modeling framework that accommodates high mutation rates and a continuum of species traits, we studied microbial communities in which antagonistic interactions occur via the production of, inhibition of, and vulnerability to toxins (e.g., antibiotics). Mutation size and mobility enhanced microbial diversity and temporal persistence to extraordinarily high levels. These findings—including the discovery that the duration of the transient phase in community assembly provides a guide to equilibrial diversity—highlight the potentially critical role that antagonistic interactions play in promoting the diversity of bacterial systems. Such interactions, together with resource-driven interactions and spatial structure, may drive the enigmatic levels of biodiversity seen in microbial systems.

Plant–insect associations have been a significant component of terrestrial ecology for more than 400 Myr. Exploring these interactions in the fossil record through novel perspectives provides a window into understanding evolutionary and ecological forces that shaped these interactions. For the past several decades, researchers have documented, described, and categorized fossil evidence of these interactions. Drawing on powerful tools from network science, we propose here a bipartite network representation of fossilized plants and their herbivore-induced leaf damage to understand late Paleozoic plant–insect interactions at the local community level. We focus on four assemblages from north-central Texas, but the methods used in this work are general and can be applied to any well-preserved fossil flora. Network analysis can address key questions in the evolution of insect herbivory that often would be difficult to summarize using standard herbivory metrics.

Significance: Fire and logging reduce the carbon stored in Amazon forests, but the long-term impact of forest degradation on animal communities remains unclear. We recorded thousands of hours of ecosystem sounds to investigate the acoustic fingerprint of the animal community in degraded Amazon forests following fire and logging. The emergent 24-h patterns of acoustic activity differed between logged and burned forests, and we observed large and sustained shifts in acoustic community assembly after multiple fires. Soundscape differences among degraded forests were clearest during insect-dominated hours rarely sampled in field studies of biodiversity. These findings demonstrate that acoustic monitoring holds promise for routine biodiversity assessments, even by non-experts, to capture a holistic measure of sound-producing animals and track ecosystem changes over time.

The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein–protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8 782 166 protein–protein interactions, at different scales. We show the emergence of higher order ‘macroscales’ in these interactomes and that these biological macroscales are associated with lower noise and degeneracy and therefore lower uncertainty. Moreover, the nodes in the interactomes that make up the macroscale are more resilient compared with nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of eukaryota, as compared with prokaryota; these results hold even after sensitivity tests where we recalculate the emergent macroscales under network simulations where we add different edge weights to the interactomes. This points to plausible evolutionary adaptation for macroscales: biological networks evolve informative macroscales to gain benefits of both being uncertain at lower scales to boost their resilience, and also being ‘certain’ at higher scales to increase their effectiveness at information transmission. Our work explains some of the difficulty in understanding the workings of biological networks, since they are often most informative at a hidden higher scale, and demonstrates the tools to make these informative higher scales explicit.