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.

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. Gelada monkeys mainly eat grass, over which they do not compete, but dominant females are more aggressive toward subordinates who are extracting underground food sources. Dominance hierarchies are not expected in species who feed on abundant, non-monopolizable resources like grass, but our study on wild geladas in the Simien Mountains National Park, Ethiopia, suggests that valuable but hard-to-reach “fallback” foods that become particularly important during the dry season may partially explain the existence of geladas’ stable dominance hierarchies.

Group hunting is common among social carnivores, and mechanisms that promote this behavior are a central topic in evolutionary biology. Increased prey capture success and decreased losses from competitors are often invoked as factors promoting group hunting. However, many animal societies have linear dominance hierarchies where access to critical resources is determined by social rank, and group-hunting rewards are shared unequally. Despite this inequality, animals in such societies cooperate to hunt and defend resources. Game theoretic models predict that rank and relative rewards from group hunting vs. solitary hunting affect which hunting strategies will evolve. These predictions are partially supported by empirical work, but data needed to test these predictions are difficult to obtain in natural systems. We use digital evolution to test how social rank and tolerance by dominants of subordinates feeding while sharing spoils from group hunting influence which hunting strategies evolve in digital organisms. We created a computer-simulated world to reflect social and hunting dynamics of spotted hyenas (Crocuta crocuta). We found that group hunting increased as tolerance increased and as the relative payoff from group hunting increased. Also, top-ranking agents were more likely to group hunt than lower-ranking agents under despotic sharing conditions. These results provide insights into mechanisms that may promote cooperation in animal societies structured by dominance hierarchies.