Biology subjects

Brockmann, D.

Publications and source records attributed to Brockmann, D..

4 recordsLinked to original sources

bioRxiv2025

Origins of the Fittest: Clonal Interference in Heterogeneous Networks.

In asexual populations, clonal interference, the competition between strains carrying different beneficial mutations, plays a crucial role in shaping evolutionary outcomes. This study investigates how this phenomenon unfolds in complex heterogeneous networks, demonstrating that network structure significantly affects the origin and spread of high-fitness strains. Using computational modeling and a novel analytical approach, we demonstrate that the interplay between mutation rates and network topology creates distinct evolutionary regimes. We observe that the mutations driving adaptation originate from network locations that facilitate mutation spread within a time-frame given by the mutation rate. If the window widens or narrows, the contribution of different network regions grows or diminishes. Using analytical approaches from epidemic modeling and network geometry, our analysis efficiently captures this evolutionary dynamic in arbitrary network configurations. This work provides new insights into how spatial heterogeneity shapes evolutionary trajectories and indicates that attempts to alter the speed of adaptation through network modifications will succeed or fail depending critically on the prevailing evolutionary regime--insights relevant to epidemiology, conservation biology, and beyond.

evolutionary biology↗

bioRxiv2025

Large-scale wearable data reveal spatiotemporal organization of annual sleep patterns

Sleep is fundamental to health, yet large-scale, objective data on how geography shapes sleep behavior remain scarce. We analyzed over 45 million nights of sensor data from 105,741 German adults wearing consumer-grade wearables across 2.7 years. Sleep timing displayed a continuous east-west gradient, with later onset, midsleep, and offset in western regions, consistent with solar progression. This effect was strongest on weekends and in rural areas, where midsleep was delayed by 2.2 minutes per degree longitude and sleep duration increased by 1.0 minute. A north-south gradient also emerged. Weekday midsleep advanced by 0.9 minutes per degree latitude, while weekend midsleep was delayed by 0.2 minutes, resulting in greater social jetlag in the north. Sleep duration declined toward higher latitudes across both day types. Seasonal analyses revealed consistent annual rhythms. Sleep duration increased by 24.7 minutes in winter relative to summer, and sleep offset closely followed sunrise. These patterns highlight the joint influence of solar and social time on sleep, with implications for regionally tailored public health strategies.

neuroscience↗

bioRxiv2022

Rhythm of relationships in a social fish over the course of a full year in the wild

Animals are expected to adjust their social behaviour to cope with challenges in their environment. Therefore, for fish populations, in temperate regions with seasonal and daily environmental oscillations, characteristic rhythms of social relationships should be pronounced. To date, most research concerning fish social networks and biorhythms has occurred in artificial laboratory environments or over confined temporal scales of days to weeks. By contrast, little is known about the social networks of wild, freely roaming fish, including how seasonal and diurnal rhythms modulate social networks over the course of a full year. The advent of high-resolution acoustic telemetry enables us to quantify detailed social interactions in the wild over time-scales sufficient to examine seasonal rhythms at whole-ecosystems scales. Our objective was to explore the rhythms of social interactions in a social fish population at various time-scales over one full year in the wild by examining high-resolution snapshots of dynamic social network. To that end, we tracked the behaviour of 36 adult common carp, Cyprinus carpio, in a 25 ha lake and constructed temporal social networks among individuals across various time-scales, where social interactions were defined by proximity. We compared the network structure to a temporally shuffled null model to examine the importance of social attraction, and checked for persistent characteristic groups ("friendships") over time. The clustering within the carp social network tended to be more pronounced during daytime than nighttime throughout the year. Social attraction, particularly during daytime, was a key driver for interactions. Shoaling behavior substantially increased during daytime in the wintertime, whereas in summer carp interacted less frequently, but the interaction duration increased. Characteristic groups were more common in the summer months and during nighttime, where the social memory of carp lasted up to two weeks. We conclude that social relationships of carp change diurnally and seasonally. These patterns were likely driven by predator avoidance, seasonal shifts in lake temperature, visibility, forage availability and the presence of anoxic zones. The techniques we employed can be applied generally to high-resolution biotelemetry data to reveal social structures across other fish species at ecologically realistic scales.

animal behavior and cognition↗

bioRxiv2020

Social networks predict the life and death of honey bees

In complex societies, individuals roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individuals social network that can be obtained without interfering with the colony. This network age accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function.

animal behavior and cognition↗