Search

Abstract Background Parental care is indispensable for the survival and development of dependent offspring, often requiring a delicate balance of time and energy allocation towards offspring by parents. Among ungulates employing a hider strategy, deciding when and where to provide care while also maintaining a sufficient distance to not reveal the offspring´s hiding place is likely crucial in determining their fate. Methods In this study, we analyzed the timing and spatial distribution of mother–offspring interactions in roe deer females (Capreolus capreolus L.). We fitted roe deer mothers and their neonates with GPS-collars combined with a proximity sensor in south Germany to address the spatial and temporal distribution of mother-fawn interactions during the first two months of the fawns’ lives. Results We observed variations in the distance between mother and fawn, which initially increased over the first month and then decreased as the fawns grew older. The timing of mother-fawn contacts was strongly linked with the circadian rhythm of the mother, aligning closely with their typical bimodal activity peaks at dawn and dusk. Furthermore, we observed differences in habitat use between mother and offspring, reflecting the mother's requirements for food and protection (e.g. greater use of forests, higher distances to roads), as well as the fawn's priority requirement for protection (e.g. higher use of unmown grassland). We documented variations over time, highlighting how these requirements changed as the fawn ages. Interestingly, during the initial two weeks, most of the contacts occurred in habitats that were particularly favored by mothers. However, as the fawns aged, contacts occurred increasingly often in habitats that were routinely used by fawns. Conclusions Understanding the timing, frequency, and spatial distribution of mother–offspring interactions provides valuable insights into the care strategies of hider ungulates. The observation that mothers leave their fawns in agricultural fields during the first few weeks of life has strong implications for wildlife management, as this behavior constitutes a kind of evolutionary trap under current agricultural practices and mowing regimes. Whether females can adjust their maternal care tactics to these novel selection pressures in human-altered landscapes is likely key to predicting the population dynamics of this obligate hider.

Abstract Wildlife tagging provides critical insights into animal movement ecology, physiology, and behavior amid global ecosystem changes. However, the stress induced by capture, handling, and tagging can impact post-release locomotion and activity and, consequently, the interpretation of study results. Here, we analyze post-tagging effects on 1585 individuals of 42 terrestrial mammal species using collar-collected GPS and accelerometer data. Species-specific displacements and overall dynamic body acceleration, as a proxy for activity, were assessed over 20 days post-release to quantify disturbance intensity, recovery duration, and speed. Differences were evaluated, considering species-specific traits and the human footprint of the study region. Over 70% of the analyzed species exhibited significant behavioral changes following collaring events. Herbivores traveled farther with variable activity reactions, while omnivores and carnivores were initially less active and mobile. Recovery duration proved brief, with alterations diminishing within 4–7 tracking days for most species. Herbivores, particularly males, showed quicker displacement recovery (4 days) but slower activity recovery (7 days). Individuals in high human footprint areas displayed faster recovery, indicating adaptation to human disturbance. Our findings emphasize the necessity of extending tracking periods beyond 1 week and particular caution in remote study areas or herbivore-focused research, specifically in smaller mammals.

Abstract Background Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. Methods We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where $$\sim 85\%$$ ∼ 85 % of the individuals have been directly monitored. Results Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. Conclusions The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.