Abstract Objectives We consider the relative contributions of maternal rank and sociability to the survival of infant vervet monkeys ( Chlorocebus pygerythrus ) to nutritional independence (~210 days) in a generally resource‐poor environment. Methods We analyzed survival data from 153 infants born to 60 mothers across three free‐ranging troops and 10 yearly birth cohorts at a South African research site experiencing general but variable resource scarcity. Results The population was characterized by a pre‐weaning mortality of 30% (Range: 9%–85%), with a median age at death of 50 days. In addition to the consequences of resource availability, increased infant survival was independently and equivalently positively associated with higher maternal rank and a greater number of maternal spatial partners. Discussion We use this outcome to suggest that apparent discrepancies across sites and species in the relative importance of different maternal attributes in determining reproductive outcomes may be resolved by considering more closely local sources of infant mortality.
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 that a set of highly interdependent animals drove routine group 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).
Vessel underwater noise (VUN) is one of the main threats to the recovery of the endangered St. Lawrence Estuary Beluga population (SLEB). The 1% yearly population decline indicates that the cumulative threats are already beyond sustainable limits for the SLEB. However, a potential threefold increase in shipping traffic is expected within its critical habitat in the coming years resulting from proposed port-industrial projects in the Saguenay River. Current data indicate that SLEB typically use multiple sectors within their summer range, likely leading to differential VUN exposure among individuals. The degree of displacement and spatial mixing among habitats are not yet well understood but can be simulated under different assumptions about movement patterns at the individual and population levels. Here, we propose using an agent-based model (ABM) to explore the biases introduced when estimating exposure to stressors such as VUN, where individual-centric movement patterns and habitat use are derived from different spatial behaviour assumptions. Simulations of the ABM revealed that alternative behavioural assumptions for individual belugas can significantly alter the estimation of instantaneous and cumulative exposure of SLEB to VUN. Our simulations also predicted that with the projected traffic increase in the Saguenay River, the characteristics making it a quiet zone for SLEB within its critical habitat would be nullified. Whereas spending more time in the Saguenay than in the Estuary allows belugas to be exposed to less noise under the current traffic regime, this relationship is reversed under the increased traffic scenario. Considering the importance of the Saguenay for SLEB females and calves, our results support the need to understand its role as a possible acoustic refuge for this endangered population. This underlines the need to understand and describe individual and collective beluga behaviours using the best available data to conduct a thorough acoustic impact assessment concerning future increased traffic.
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.
Spatial simulations are a valuable tool in understanding dynamic spatial processes. In developing these simulations, it is often required to make decisions about how to represent features in the environment and how events unfold in time. These spatial and temporal choices have been shown to significantly alter model outcomes, yet their interaction is less well understood. In this paper, we make use of a simple group foraging model and systematically vary how features are represented (cell size of the landscape) as well as how events unfold in time (order in which foragers take action) to better understand their interaction. Our results show similar nonlinear responses to changes in spatial representation found in the literature, and an effect of the order in which agents were processed. There was also a clear interaction between how features are represented and how events unfold in time, where, under certain environmental representations results were found to be more sensitive to the order in which individuals were processed. Furthermore, the effects of feature representation, scheduling of agents, and their interaction were all found to be influenced by the heterogeneity of the spatial surface (food), suggesting that the statistical properties of the underlying spatial variable will additionally play a role. We suggest that navigating these interactions can be facilitated through a better understanding of how these choices affect the decision landscape(s) on which agents operate. Specifically, how changes to representation affect aggregation and resolution of the decision surface, and thereby the degree to which agents interact directly or indirectly. We suggest that the challenges of dealing with spatial representation, scheduling, and their interaction, while building models could also present an opportunity. As explicitly including alternate representations and scheduling choices during model selection can aid in identifying optimal agent–environment representations. Potentially leading to improved insights into the relationships between spatial processes and the environments in which they occur.
Animal displays (i.e. movement-based signals) often involve extreme behaviours that seem to push signallers to the limits of their abilities. If motor constraints limit display performance, signal evolution will be constrained, and displays can function as honest signals of quality. Existing approaches for measuring constraint, however, require multiple kinds of behavioural data. A method that requires only one kind could open up new research directions. We propose a conceptual model of performance under constraint, which predicts that the distribution of constrained performance will skew away from the constraint. We tested this prediction with sports data, because we know a priori that athletic performance is constrained and that athletes attempt to maximize performance. Performance consistently skewed in the predicted direction in a variety of sports. We then used statistical models based on the skew normal distribution to estimate the constraints on athletes and displaying animals while controlling for potential confounds and clustered data. We concluded that motor constraints tend to generate skewed behaviour and that skew normal models are useful tools to estimate constraints from a single axis of behavioural data. This study expands the toolkit for identifying, characterizing, and comparing performance constraints for applications in animal behaviour, physiology and sports.
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.
Abstract Objectives To compare longitudinal weight gain in captive and wild juvenile vervet monkeys and conduct an empirical assessment of different mechanistic growth models. Methods Weights were collected from two groups of captive monkeys and two consecutive cohorts of wild monkeys until the end of the juvenile period (~800 days). The captive groups were each fed different diets, while the wild groups experienced different ecological conditions. Three different growth curve models were compared. Results By 800 days, the wild juveniles were lighter, with a slower maximum growth rate, and reached asymptote earlier than their captive counterparts. There were overall differences in weight and growth rate across the two wild cohorts. This corresponded to differences in resource availability. There was considerable overlap in growth rate and predicted adult weight of male and females in the first, but not the second, wild cohort. Maternal parity was not influential. While the von Bertalanffy curve provided the best fit to the data sets modeled together, the Logistic curve best described growth in the wild cohorts when considered separately. Conclusions The growth curves of the two captive cohorts are likely to lie near the maximum attainable by juvenile vervets. It may be helpful to include deviations from these rates when assessing the performance of wild vervet monkeys. The comparison of wild and captive juveniles confirmed the value of comparing different growth curve models, and an appreciation that the best models may well differ for different populations. Choice of mechanistic growth model can, therefore, be empirically justified, rather than theoretically predetermined.
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