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Female social relationships are often shaped by the distribution of dietary resources. Socioecological models predict that females should form strict linear dominance hierarchies when resources are clumped and exhibit more egalitarian social structures when resources are evenly distributed. While many frugivores and omnivores indeed exhibit dominance hierarchies accompanied by differential resource access, many folivores deviate from the expected pattern and display dominance hierarchies despite evenly distributed resources. Among these outliers, geladas (Theropithecus gelada) present a conspicuous puzzle; females exhibit aggressive competition and strict dominance hierarchies despite feeding primarily on non-monopolizable grasses. However, these grasses become scarce in the dry season and geladas supplement their diet with underground storage organs that require relatively extensive energy to extract. We tested whether female dominance hierarchies provide differential access to underground storage organs by assessing how rank, season, and feeding context affect aggression in geladas under long-term study in the Simien Mountains National Park, Ethiopia. We found that the likelihood of receiving aggression was highest when feeding belowground and that the inverse relationship between rank and aggression was the most extreme while feeding belowground in the dry season. These results suggest that aggression in geladas revolves around belowground foods, which may mean that underground storage organs are an energetically central dietary component (despite being consumed less frequently than grasses), or that even “fallback” foods can influence feeding competition and social relationships. Further work should assess whether aggression in this context is directly associated with high-ranking usurpation of belowground foods from lower-ranking females following extraction.

 

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.