Evolving nutritional strategies in the presence of competition: a geometric agent-based model

From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence.

Macronutrient intakes and the lifespan-fecundity trade-off: a geometric framework agent-based model

Lifespan and fecundity, the main components in evolutionary fitness, are both strongly affected by nutritional state. Geometric framework of nutrition (GFN) experiments has shown that lifespan and fecundity are separated in nutrient space leading to a functional trade-off between the two traits. Here we develop a spatially explicit agent-based model (ABM) using the GFN to explore how ecological factors may cause selection on macronutrient appetites to optimally balance these life-history traits. We show that increasing the risk of extrinsic mortality favours intake of a mixture of nutrients that is associated with maximal fecundity at the expense of reduced longevity and that this result is robust across spatial and nutritional environments. These model behaviours are consistent with what has been observed in studies that quantify changes in life history in response to environmental manipulations. Previous GFN-derived ABMs have treated fitness as a single value. This is the first such model to instead decompose fitness into its primary component traits, longevity and fecundity, allowing evolutionary fitness to be an emergent property of the two. Our model demonstrates that selection on macronutrient appetites may affect life-history trade-offs and makes predictions that can be directly tested in artificial selection experiments.

Macro-Nutritional Adaptive Strategies of Moose (Alces alces) Related to Population Density

The distribution area of moose in China has been shrinking back toward the north and northeast because of climate change and human disturbance, and the population number has been declining. Between 2011 and 2015, we studied moose at six sites in the northeast of China during the snowy seasons. We collected fecal samples and plant samples that were used to estimate population densities for moose, as well as their macro-nutrient selection. Out of a total of 257 fecal samples collected at six sites, we identified a total of 120 individual moose (57 females and 63 males). The population density (moose/km² ± SE) was highest at Hanma with 0.305 ± 0.064 moose/km² and lowest at Meitian with only 0.028 ± 0.013 moose/km². Forage availability was different among sites, with the lowest availability at Mohe (58.17 number/20 m²) and highest was Zhanhe (250.44 number/20 m²). Moose at Zhanhe, Hanma, and Nanwenghe had a balanced diet with higher N:C (1:7), while at Meitian, Shuanghe and Mohe the N:C was 1:8. Our results indicate that the southern areas had low forage quality and quantity and this may be the reason for the distribution of the population of moose shrinking northward.

Exploring Interactions between the Gut Microbiota and Social Behavior through Nutrition

Microbes influence a wide range of host social behaviors and vice versa. So far, however, the mechanisms underpinning these complex interactions remain poorly understood. In social animals, where individuals share microbes and interact around foods, the gut microbiota may have considerable consequences on host social interactions by acting upon the nutritional behavior of individual animals. Here we illustrate how conceptual advances in nutritional ecology can help the study of these processes and allow the formulation of new empirically testable predictions. First, we review key evidence showing that gut microbes influence the nutrition of individual animals, through modifications of their nutritional state and feeding decisions. Next, we describe how these microbial influences and their social consequences can be studied by modelling populations of hosts and their gut microbiota into a single conceptual framework derived from nutritional geometry. Our approach raises new perspectives for the study of holobiont nutrition and will facilitate theoretical and experimental research on the role of the gut microbiota in the mechanisms and evolution of social behavior.

Social nutrition: an emerging field in insect science

Nutrition is thought to be a major driver of social evolution, yet empirical support for this hypothesis is scarce. Here we illustrate how conceptual advances in nutritional ecology illuminate some of the mechanisms by which nutrition mediates social interactions in insects. We focus on experiments and models of nutritional geometry and argue that they provide a powerful means for comparing nutritional phenomena across species exhibiting various social ecologies. This approach, initially developed to study the nutritional behaviour of individual insects, has been increasingly used to study insect groups and societies, leading to the emerging field of social nutrition. We discuss future directions for exploring how these nutritional mechanisms may influence major social transitions in insects and other animals.

A theoretical exploration of dietary collective medication in social insects

Animals often alter their food choices following a pathogen infection in order to increase immune function and combat the infection. Whether social animals that collect food for their brood or nestmates adjust their nutrient intake to the infection states of their social partners is virtually unexplored. Here we develop an individual-based model of nutritional geometry to examine the impact of collective nutrient balancing on pathogen spread in a social insect colony. The model simulates a hypothetical social insect colony infected by a horizontally transmitted parasite. Simulation experiments suggest that collective nutrition, by which foragers adjust their nutrient intake to simultaneously address their own nutritional needs as well as those of their infected nestmates, is an efficient social immunity mechanism to limit contamination when immune responses are short. Impaired foraging in infected workers can favour colony resilience when pathogen transmission rate is low (by reducing contacts with the few infected foragers) or trigger colony collapse when transmission rate is fast (by depleting the entire pool of foragers). Our theoretical examination of dietary collective medication in social insects suggests a new possible mechanism by which colonies can defend themselves against pathogens and provides a conceptual framework for experimental investigations of the nutritional immunology of social animals.

Collective foraging in spatially complex nutritional environments

Nutrition impinges on virtually all aspects of an animal's life, including social interactions. Recent advances in nutritional ecology show how social animals often trade-off individual nutrition and group cohesion when foraging in simplified experimental environments. Here, we explore how the spatial structure of the nutritional landscape influences these complex collective foraging dynamics in ecologically realistic environments. We introduce an individual-based model integrating key concepts of nutritional geometry, collective animal behaviour and spatial ecology to study the nutritional behaviour of animal groups in large heterogeneous environments containing foods with different abundance, patchiness and nutritional composition. Simulations show that the spatial distribution of foods constrains the ability of individuals to balance their nutrient intake, the lowest performance being attained in environments with small isolated patches of nutritionally complementary foods. Social interactions improve individual regulatory performances when food is scarce and clumpy, but not when it is abundant and scattered, suggesting that collective foraging is favoured in some environments only. These social effects are further amplified if foragers adopt flexible search strategies based on their individual nutritional state. Our model provides a conceptual and predictive framework for developing new empirically testable hypotheses in the emerging field of social nutrition.This article is part of the themed issue 'Physiological determinants of social behaviour in animals'.

Automated multi-objective calibration of biological agent-based simulations

Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate simulations that generate more informative biological predictions.

Inter-individual variation in nutrient balancing in the honeybee (Apis mellifera)

The Geometric Framework approach in nutritional ecology postulates that animals attempt to balance the consumption of different nutrients rather than simply maximizing energetic gain. The intake target with respect to each nutrient maximizes fitness in a specific dimension and any difference between individuals in intake target therefore represents alternative behavioral and fitness maximization strategies. Nutritional interactions are a central component of all social groups and any inter-individual variation in intake target should therefore have a significant influence on social dynamics. Using the honeybee colony as an experimental model, we quantified differences in the carbohydrate intake target of individual foragers using a capillary feeder (CAFE) assay. Our results show that the bees did not simply maximize their net energetic gain, but combined sugar and water in their diet in a way that brought them to an intake target equivalent to a 33% sucrose solution. Although the mean intake target with respect to the nutrients sucrose and water was the same under different food choice regimens, there was significant inter-individual variation in intake target and the manner in which individuals reached this target, a variation which suggests different levels of tolerance to nutrient imbalance. We discuss our results in the context of how colony performance may be influenced by the different nutrient balancing strategies of individual members and how such nutritional constraints could have contributed to the evolution of sociality.

The Effect of Diet Mixing on a Nonselective Herbivore

The balanced-diet hypothesis states that a diverse prey community is beneficial to consumers due to resource complementarity among the prey species. Nonselective consumer species cannot differentiate between prey items and are therefore not able to actively regulate their diet intake. We thus wanted to test whether the balanced-diet hypothesis is applicable to nonselective consumers. We conducted a laboratory experiment in which a nonselective model grazer, the freshwater gastropod Lymnaea stagnalis, was fed benthic green algae as single species or as a multi-species mixture and quantified the snails’ somatic growth rates and shell lengths over a seven-week period. Gastropods fed the mixed diet were found to exhibit a higher somatic growth rate than the average of the snails fed single prey species. However, growth on the multi-species mixture did not exceed the growth rate obtained on the best single prey species. Similar results were obtained regarding the animals’ shell height increase over time. The mixed diet did not provide the highest growth rate, which confirms our hypothesis. We thus suggest that the balanced-diet hypothesis is less relevant for non-selective generalist consumers, which needs to be considered in estimates of secondary production.

Adaptive collective foraging in groups with conflicting nutritional needs

Collective foraging, based on positive feedback and quorum responses, is believed to improve the foraging efficiency of animals. Nutritional models suggest that social information transfer increases the ability of foragers with closely aligned nutritional needs to find nutrients and maintain a balanced diet. However, whether or not collective foraging is adaptive in a heterogeneous group composed of individuals with differing nutritional needs is virtually unexplored. Here we develop an evolutionary agent-based model using concepts of nutritional ecology to address this knowledge gap. Our aim was to evaluate how collective foraging, mediated by social retention on foods, can improve nutrient balancing in individuals with different requirements. The model suggests that in groups where inter-individual nutritional needs are unimodally distributed, high levels of collective foraging yield optimal individual fitness by reducing search times that result from moving between nutritionally imbalanced foods. However, where nutritional needs are highly bimodal (e.g. where the requirements of males and females differ) collective foraging is selected against, leading to group fission. In this case, additional mechanisms such as assortative interactions can coevolve to allow collective foraging by subgroups of individuals with aligned requirements. Our findings indicate that collective foraging is an efficient strategy for nutrient regulation in animals inhabiting complex nutritional environments and exhibiting a range of social forms.

Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach

Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.

Drosophila females trade off good nutrition with high quality oviposition sites when choosing foods

Animals, from insects to human, select foods to regulate their acquisition of key nutrients in amounts and balances maximising fitness. In species where the nutrition of juveniles depends on parents, adults must make challenging foraging decisions that simultaneously address their own nutrient needs as well as those of the progeny. Here we examined how fruit flies Drosophila melanogaster, a species where individuals eat and lay eggs in decaying fruits, integrate feeding decisions (individual nutrition) and oviposition decisions (offspring nutrition) when foraging. Using cafeteria assays with artificial diets varying in concentrations and ratios of protein to carbohydrates, we show that Drosophila females exhibit complex foraging patterns, alternating between laying eggs on high carbohydrate foods and feeding on foods with different nutrient contents depending on their own nutritional state. Although larvae showed faster development on high protein foods, both survival and learning performances were higher on balanced foods. We suggest that the apparent mismatch between the oviposition preference of females for high carbohydrate foods and the high performances of larvae on balanced foods reflects a natural situation where high carbohydrate ripened fruits gradually enrich in proteinaceous yeast as they start rotting, thereby yielding optimal nutrition for the developing larvae. Our findings that animals with rudimentary parental care uncouple feeding and egg-laying decisions in order to balance their own diet and provide a nutritionally optimal environment to their progeny reveals unsuspected levels of complexity in the nutritional ecology of parent-offspring interactions.

An Overlooked Consequence of Dietary Mixing: A Varied Diet Reduces Interindividual Variance in Fitness

The determinants of diet breadth are of interest to nutritionists, ecologists, and evolutionary biologists. A recent synthesis addressing this issue found conflicting evidence for the relationship between diet breadth and mean individual fitness. Specifically, it found that while, on average, a mixed diet does increase mean fitness, in some instances, a single food provides equal (or higher) fitness than a mixed diet. Critical to ecological and evolutionary considerations of diet, however, is not only mean fitness but also variance in fitness. We combine contemporary meta-analytic methods with models of nutritional geometry to evaluate how diet affects between-individual variance in fitness within generalist consumers from a range of trophic levels. As predicted by nutritional geometry, we found that between-individual variance in fitness-related traits is higher on single-food than mixed diets. The effect was strong for longevity traits (57% higher) and reproductive traits (37%) and present but weaker for size-related traits (10%). Further, the effect became stronger as the number of available foods increased. The availability of multiple foods likely allows individuals with differing nutritional optima to customize intake, each maximizing their own fitness. Importantly, these findings may suggest that selection on traits correlated with nutritional requirements is weak in heterogeneous nutritional environments.