Social Structure Facilitated the Evolution of Care-giving as a Strategy for Disease Control in the Human Lineage

Humans are the only species to have evolved cooperative care-giving as a strategy for disease control. A synthesis of evidence from the fossil record, paleogenomics, human ecology, and disease transmission models, suggests that care-giving for the diseased evolved as part of the unique suite of cognitive and socio-cultural specializations that are attributed to the genus Homo. Here we demonstrate that the evolution of hominin social structure enabled the evolution of care-giving for the diseased. Using agent-based modeling, we simulate the evolution of care-giving in hominin networks derived from a basal primate social system and the three leading hypotheses of ancestral human social organization, each of which would have had to deal with the elevated disease spread associated with care-giving. We show that (1) care-giving is an evolutionarily stable strategy in kin-based cooperatively breeding groups, (2) care-giving can become established in small, low density groups, similar to communities that existed before the increases in community size and density that are associated with the advent of agriculture in the Neolithic, and (3) once established, care-giving became a successful method of disease control across social systems, even as community sizes and densities increased. We conclude that care-giving enabled hominins to suppress disease spread as social complexity, and thus socially-transmitted disease risk, increased

Individual-level movement bias leads to the formation of higher-order social structure in a mobile group of baboons

Sherpa Romeo green journal. Open access article. Creative Commons Attribution 4.0 International LIcense (CC BY 4.0) applies.In mobile social groups, influence patterns driving group movement can vary between democratic and despotic. The arrival at any single pattern of influence is thought to be underpinned by both environmental factors and group composition. To identify the specific patterns of influence driving travel decision-making in a chacma baboon troop, we used spatially explicit data to extract patterns of individual movement bias. We scaled these estimates of individual-level bias to the level of the group by constructing an influence network and assessing its emergent structural properties. Our results indicate that there is heterogeneity in movement bias: individual animals respond consistently to particular group members, and higher-ranking animals are more likely to influence the movement of others. This heterogeneity resulted in a group-level network structure that consisted of a single core and two outer shells. Here, the presence of a core suggests thatasetofhighlyinterdependentanimalsdroveroutinegroup movements. These results suggest that heterogeneity at the individual level can lead to group-level influence structures, and that movement patterns in mobile social groups can add to the exploration of both how these structures develop (i.e. mechanistic aspects) and what consequences they have for individual- and group-level outcomes (i.e. functional aspects).Ye

Sick and tired : sickness behaviour, polyparasitism and food stress in a gregarious mammal

Although sickness behaviour in response to non-lethal parasites has been documented in wild animals, it remains unclear how social and environmental stress might also shape an animal’s behavioural response to parasitism, nor do we know whether simultaneous infection with more than one parasite changes the way animals respond. Here, we combine physiological, environmental, behavioural and parasite measures to investigate behavioural responses to infection in wild vervet monkeys (Chlorocebus pygerythrus) living in a semi-arid region of South Africa. We quantified both activity budget and behavioural predictability to investigate the occurrence of sickness behaviour and infection with two non-lethal gastrointestinal parasite genera. Higher parasite load was linked to an increase in the time spent resting. However, the nature of the relationship with other behaviours was contingent on both the parasite genus in question and parasite species interacted, highlighting the importance of considering co-infection. Overall, food availability was the dominant predictor of behavioural change suggesting that, for monkeys living in a more extreme environment, coping with ecological stress may override the ability to modulate behaviour in response to other physiological stressors. Our findings provide insight into how animals living in harsh environments find ways to cope with parasite infection, avoidance and transmission. SIGNIFICANCE STATEMENT : Sickness behaviour is a suite of behaviours that occur in response to infection that may serve as an adaptive response to cope with infection. For wild animals, the ability to express sickness behaviour will be modulated by the presence of other competing stressors. Hence, the patterns shown are likely to be more complex than under captive conditions, which is where most of our knowledge of sickness behaviour comes from. Using physiological, environmental, behavioural and parasite measures, we demonstrate that although vervet monkeys (Chlorocebus pygerythrus) living in a semi-arid region of South Africa do exhibit sickness behaviours, this is contingent on the parasite genus in question. Further, food availability was the dominant predictor of behavioural change suggesting that, for monkeys living in a more extreme environment, coping with severe ecological stress may override the ability to express sickness behaviour in an adaptive fashion.National Research Foundation (South Africa), Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grants, the Canada Research Chairs Program, a Leakey Foundation Franklin Mosher Baldwin Memorial Fellowship and a Senior Post-doctoral Fellowship at the University of Pretoria.http://link.springer.com/journal/265hj2022Mammal Research Institut

Data from: Direction matching for sparse movement data sets: determining interaction rules in social groups

It is generally assumed that high-resolution movement data are needed to extract meaningful decision-making patterns of animals on the move. Here we propose a modified version of force matching (referred to here as direction matching), whereby sparse movement data (i.e., collected over minutes instead of seconds) can be used to test hypothesized forces acting on a focal animal based on their ability to explain observed movement. We first test the direction matching approach using simulated data from an agent-based model, and then go on to apply it to a sparse movement data set collected on a troop of baboons in the DeHoop Nature Reserve, South Africa. We use the baboon data set to test the hypothesis that an individual’s motion is influenced by the group as a whole or, alternatively, whether it is influenced by the location of specific individuals within the group. Our data provide support for both hypotheses, with stronger support for the latter. The focal animal showed consistent patterns of movement toward particular individuals when distance from these individuals increased beyond 5.6 m. Although the focal animal was also sensitive to the group movement on those occasions when the group as a whole was highly clustered, these conditions of isolation occurred infrequently. We suggest that specific social interactions may thus drive overall group cohesion. The results of the direction matching approach suggest that relatively sparse data, with low technical and economic costs, can be used to test between hypotheses on the factors driving movement decisions

The Gradient-Boosting Method for Tackling High Computing Demand in Underwater Acoustic Propagation Modeling

Agent-based models return spatiotemporal information used to process time series of specific parameters for specific individuals called “agents”. For complex, advanced and detailed models, this typically comes at the expense of high computing times and requires access to important computing resources. This paper provides an example on how machine learning and artificial intelligence can help predict an agent-based model’s output values at regular intervals without having to rely on time-consuming numerical calculations. Gradient-boosting XGBoost under GNU package’s R was used in the social-ecological agent-based model 3MTSim to interpolate, in the time domain, sound pressure levels received at the agents’ positions that were occupied by the endangered St. Lawrence Estuary and Saguenay Fjord belugas and caused by anthropomorphic noise of nearby transiting merchant vessels. A mean error of 3.23 ± 3.76(1σ) dB on received sound pressure levels was predicted when compared to ground truth values that were processed using rigorous, although time-consuming, numerical algorithms. The computing time gain was significant, i.e., it was estimated to be 10-fold higher than the ground truth simulation, whilst maintaining the original temporal resolution

The Gradient-Boosting Method for Tackling High Computing Demand in Underwater Acoustic Propagation Modeling

Agent-based models return spatiotemporal information used to process time series of specific parameters for specific individuals called “agents”. For complex, advanced and detailed models, this typically comes at the expense of high computing times and requires access to important computing resources. This paper provides an example on how machine learning and artificial intelligence can help predict an agent-based model’s output values at regular intervals without having to rely on time-consuming numerical calculations. Gradient-boosting XGBoost under GNU package’s R was used in the social-ecological agent-based model 3MTSim to interpolate, in the time domain, sound pressure levels received at the agents’ positions that were occupied by the endangered St. Lawrence Estuary and Saguenay Fjord belugas and caused by anthropomorphic noise of nearby transiting merchant vessels. A mean error of 3.23 ± 3.76(1σ) dB on received sound pressure levels was predicted when compared to ground truth values that were processed using rigorous, although time-consuming, numerical algorithms. The computing time gain was significant, i.e., it was estimated to be 10-fold higher than the ground truth simulation, whilst maintaining the original temporal resolution

Topographic and spectral data resolve land cover misclassification to distinguish and monitor wetlands in western Uganda

Wetlands provide vital wildlife habitat and ecosystem services, but changes in human land use has made them one of the world’s most threatened ecosystems. Although wetlands are generally protected by law, growing human populations increasingly drain and clear them to provide agricultural land, especially in tropical Africa. Managing and conserving wetlands requires accurately monitoring their spatial and temporal extent, often using remote sensing, but distinguishing wetlands from other land covers can be difficult. Here, we report on a method to separate wetlands dominated by papyrus (Cyperus papyrus L.) from spectrally similar grasslands dominated by elephant grass (Pennisetum purpureum Schumach.). We tested whether topographic, spectral, and temperature data improved land cover classification within and around Kibale National Park, a priority conservation area in densely populated western Uganda. Slope and reflectance in the mid-IR range best separated the combined papyrus/elephant grass pixels (average accuracy: 86%). Using a time series of satellite images, we quantified changes in six land covers across the landscape from 1984 to 2008 (papyrus, elephant grass, forest, mixed agriculture/bare soil/short grass, mixed tea/shrub, and water). We found stark differences in how land cover changed inside versus outside the park, with particularly sharp changes next to the park boundary. Inside the park, changes in land cover varied with location and management history: elephant grass areas decreased by 52% through forest regeneration but there was no net difference in papyrus areas. Outside the park, elephant grass and papyrus areas decreased by 61% and 39%, mostly converted to agriculture. Our method and findings are particularly relevant in light of social, biotic, and abiotic changes in western Uganda, as interactions between climate change, infectious disease, and changing human population demographics and distribution are predicted to intensify existing agricultural pressure on natural areas

XY coordinates of individual baboons

This dataset consists of 74 days of full-day follows of a baboon troop (Papio hamadryas ursinus) at the DeHoop Nature Reserve in South Africa. Individual GPS points of all adult group members (N=14) were collected continuously throughout the day by an observer walking repeatedly from one end of the group to the other. A GPS point was taken on all adults present in the group by holding the GPS receiver above the animal (or as close as possible) to record its position and individual identit

Selection to outsmart the germs:the evolution of disease recognition and social cognition

The emergence of providing care to diseased conspecifics must have been a turning point during the evolution of hominin sociality. On a population level, such care may have minimized the costs of socially transmitted diseases at a time of increasing social complexity, although individual care-givers would have potentially incurred increased transmission risks while providing care. We propose that care-giving likely originated within kin networks where the costs of providing care may have been balanced by fitness increases obtained through caring for ill kin. We test a novel theory of hominin cognitive evolution in which disease may have selected for the cognitive ability to recognize when a conspecific is infected. Moreover, because diseases may produce symptoms that are likely detectable via the perceptual-cognitive pathways integral to social cognition, we suggest that disease recognition and social cognition may have evolved together. We use agent-based modeling to test 1) under what conditions disease can select for increasing disease recognition and care-giving among kin, 2) whether the strength of selection varies according to the disease’s characteristics, 3) whether providing care produces greater selection for cognition than an avoidance strategy, and 4) whether care-giving alters the progression of the disease through the population. We compare the selection created by diseases with different fatality rates (i.e., similar to Ebola, Crimean-Congo hemorrhagic fever, measles, and scabies) under conditions where agents provide care to kin and under conditions where they avoid infected kin. The greatest selection was produced by the measles-like disease which had lower risks to the care-giver and a prevalence that was low enough that it did not disrupt the population’s kin networks. When care-giving and avoidance strategies were compared, we found that care-giving reduced the severity of the disease outbreaks and subsequent population crashes. The greatest selection for increased cognitive abilities occurred early in the model runs when the outbreaks and population crashes were most severe. Therefore, we conclude that over the course of human evolution, repeated introductions of novel diseases into naïve populations could have produced sustained selection for increased disease recognition and care-giving behavior, leading to the evolution of increased cognition, social complexity, and, eventually, medical care in humans. Finally, we lay out predictions derived from our disease recognition hypothesis of hominin cognitive evolution that we encourage paleoanthropologists, bioarchaeologists, primatologists, and paleogeneticists to test.<br/