Biology subjects

Schulke, O.

Publications and source records attributed to Schulke, O..

3 recordsLinked to original sources

bioRxiv2025

Age-trajectory of mother-infant relationships in wild Assamese macaques

Maternal care is ubiquitous in mammals, yet its degree and duration vary across taxa. Primate mothers provide extended care, with similar developmental transitions of the mother-infant relationship, though with different paces of change. Ecological conditions can influence the trajectory of this relationship, but data from the wild are still scarce. We used methods from growth studies to quantitatively describe the non-linear age-trajectory of the mother-infant spatial relationship, and the transition from dependent to independent feeding and locomotion in wild Assamese macaques (M. assamensis). We also explored sex differences in the development of the mother-infant relationship. We used a modified Gompertz function to model the combined effect of infant age and sex on mother and infant behaviors extracted from focal observations of 58 infants. Newborns were fully dependent on their mothers for feeding and transportation, with mothers maintaining close proximity. A transitional phase emerged between 1 and 3 months of infant age, marked by a noticeable reduction in the spatial proximity with the mother and a shift in the responsibility for the infants feeding and transportation. During the second half of infancy, the decrease in proximity time slowed down, with infants achieving near-complete locomotion independence, spending the majority of time away from their mothers and feeding independently. No sex differences were found. Our models provided a robust fit for most variables, but we recommend future exploration of alternative nonlinear functions. We interpret the early infant independence observed in our population in the context of the species reproductive strategy.

animal behavior and cognition↗

bioRxiv2024

PriMAT: A robust multi-animal tracking model for primates in the wild

O_LIDetection and tracking of animals is an important first step for automated behavioral studies using videos. Animal tracking is currently done mostly using deep learning frameworks based on keypoints, which show remarkable results in lab settings with fixed cameras, backgrounds, and lighting. However, multi-animal tracking in the wild presents several challenges such as high variability in background and lighting conditions, complex motion, and occlusion. C_LIO_LIWe propose a multi-animal tracking model, PriMAT, for nonhuman primates in the wild. The model learns to detect and track primates and other objects of interest from labeled videos or single images using bounding boxes instead of keypoints. Using bounding boxes significantly facilitates data annotation and robustness. Our one-stage model is conceptually simple but highly flexible, and we add a classification branch that allows us to train individual identification. C_LIO_LITo evaluate the performance of our model, we applied it in two case studies with Assamese macaques (Macaca assamensis) and redfronted lemurs (Eulemur rufifrons) in the wild. We show that with only a few hundred frames labeled with bounding boxes, we can achieve robust tracking results. Combining these results with the classification branch for the lemur videos, our model shows an accuracy of 84% in predicting lemur identities. C_LIO_LIOur approach presents a promising solution for accurately tracking and identifying animals in the wild, offering researchers a tool to study animal behavior in their natural habitats. Our code, models, training images, and evaluation video sequences are publicly available1, facilitating their use for animal behavior analyses and future research in this field. C_LI

animal behavior and cognition↗

bioRxiv2023

MacaqueNet: big-team research into the biological drivers of social relationships

O_LIFor many animals, social relationships are a key determinant of fitness. However, major gaps remain in our understanding of the adaptive function, ontogeny, evolution, and mechanistic underpinnings of social relationships. There is a vast and ever-accumulating amount of social behavioural data on individually recognised animals, an incredible resource to shed light onto the biological basis of social relationships. Yet, the full potential of such data lies in comparative research across taxa with distinct life histories and ecologies. Substantial challenges impede systematic comparisons, one of which is the lack of persistent, accessible, and standardised databases. C_LIO_LIHere, we advocate for the creation of big-team collaborations and comparative databases to unlock the wealth of behavioural data for research on social relationships by introducing MacaqueNet (https://macaquenet.github.io/). C_LIO_LIAs a global collaboration of over 100 researchers, the MacaqueNet database encompasses data from 1981 to the present on 14 species and is the first publicly searchable and standardised database on affiliative and agonistic animal social networks. With substantial inter-specific variation in ecology and social structure and the first published record on macaque behaviour dating back to 1956, macaque research has already contributed to answering fundamental questions on the biological bases and evolution of social relationships. Building on these strong foundations, we believe that MacaqueNet can further promote collaborative and comparative research on social behaviour. C_LIO_LIWe believe that big-team approaches to building standardised databases, bringing together data contributors and researchers, will aid much-needed large-scale comparative research in behavioural ecology and beyond. We describe the establishment of MacaqueNet, from starting a large-scale collective to the creation of a cross-species collaborative database and the implementation of data entry and retrieval protocols. As such, we hope to provide a functional example for future endeavours of large-scale collaborative research into the biology of social behaviour. C_LI

animal behavior and cognition↗