Emergent regulation of ant foraging frequency through a computationally inexpensive forager movement rule
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eLife assessment
This study is of relevance to the field of collective animal behavior. The proposed crop-cue-based motion-switching rules provide a welcome alternative to other models that assume far more deliberative abilities of ants, and it will be valuable to add this example to the collective motion and collective decision-making literature. There were several major issues that need addressing, including: overly simplistic models, no connection to similar phenomena in motion ecology and statistical mechanics, potential deficiences in the stochastic modeling approach, as well as some confusing terms and curious citations of the literature.
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Abstract
Ant colonies regulate foraging in response to their collective hunger, yet the mechanism behind this distributed regulation remains unclear. Previously, by imaging food flow within ant colonies we showed that the frequency of foraging events declines linearly with colony satiation (Greenwald et al., 2018). Our analysis implied that as a forager distributes food in the nest, two factors affect her decision to exit for another foraging trip: her current food load and its rate of change. Sensing these variables can be attributed to the forager’s individual cognitive ability. Here, new analyses of the foragers’ trajectories within the nest imply a different way to achieve the observed regulation. Instead of an explicit decision to exit, foragers merely tend toward the depth of the nest when their food load is high and toward the nest exit when it is low. Thus, the colony shapes the forager’s trajectory by controlling her unloading rate, while she senses only her current food load. Using an agent-based model and mathematical analysis, we show that this simple mechanism robustly yields emergent regulation of foraging frequency. These findings demonstrate how the embedding of individuals in physical space can reduce their cognitive demands without compromising their computational role in the group.
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eLife assessment
This study is of relevance to the field of collective animal behavior. The proposed crop-cue-based motion-switching rules provide a welcome alternative to other models that assume far more deliberative abilities of ants, and it will be valuable to add this example to the collective motion and collective decision-making literature. There were several major issues that need addressing, including: overly simplistic models, no connection to similar phenomena in motion ecology and statistical mechanics, potential deficiences in the stochastic modeling approach, as well as some confusing terms and curious citations of the literature.
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Reviewer #1 (Public Review):
The authors tackle an interesting problem: how do ant colonies regulate foraging in response to their collective hunger? In previous work, the authors related the colony's response to individual ants sensing their own food levels and its temporal dynamics. Looking more carefully at the spatial dynamics of ants, the authors now find that foragers tend to move toward the depth of the nest when their food load is high and toward the nest exit when it is low. This is an elegant and computationally inexpensive set of rules that explains the spatiotemporal dynamics of the system.
Overall, the paper is written clearly, the methods are sound, and I agree with the interpretation of the results.
I do have a few comments and suggestions:
How exactly are the inward outward directions defined? Is it simply, away, or …
Reviewer #1 (Public Review):
The authors tackle an interesting problem: how do ant colonies regulate foraging in response to their collective hunger? In previous work, the authors related the colony's response to individual ants sensing their own food levels and its temporal dynamics. Looking more carefully at the spatial dynamics of ants, the authors now find that foragers tend to move toward the depth of the nest when their food load is high and toward the nest exit when it is low. This is an elegant and computationally inexpensive set of rules that explains the spatiotemporal dynamics of the system.
Overall, the paper is written clearly, the methods are sound, and I agree with the interpretation of the results.
I do have a few comments and suggestions:
How exactly are the inward outward directions defined? Is it simply, away, or towards the entrance? It is not clear from the text, and since this system is not symmetric (cubic with entrance at one of the corners) the authors should clarify.
To analyze the biased random walk analysis of the ants, the authors "coarse-grained" the steps as being "inwards" "outwards" and "stay". It's not clear how this level of granulation is justified. Since the authors have access to the actual trajectories and all trophallaxis events, why not just calculate the actual turning angles between consecutive steps the ants take? This would give an actual assessment of both the bias and the noise imposed on the random walks, which the authors could then use directly in their models.
It would be important to better connect the author's previous mechanism (relating the colony's response to individual ants sensing their own food levels and its temporal dynamics) to the new mechanism (spatial-temporal dynamics). Are they mutually exclusive? It would be useful to elaborate on this in the Discussion.
It would be useful to add a few supplementary movies from the experiments, showing ants moving toward the entrance with low food loads, and moving away from the entrance with high food loads.
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Reviewer #2 (Public Review):
Because individuals in most colonies of eusocial insects (i.e., ants, social bees, social wasps, and termites) cannot directly reproduce, theory suggests that natural selection will shape the behavior and physiology of such individuals to be hyper-sensitive to the needs of their colony. In the context of foraging, an individual should make decisions of how often to search for new food based on the "hunger" of the colony that she belongs to. In fact, in previously published work, the authors of this manuscript have confirmed empirically that the frequency of foraging events for individual workers in colonies of _Camponotus sanctus_ carpenter ants is correlated with the amount of food stored within the collection of ants within the nest -- as the colony "satiated" (i.e., the communal stomach of the average …
Reviewer #2 (Public Review):
Because individuals in most colonies of eusocial insects (i.e., ants, social bees, social wasps, and termites) cannot directly reproduce, theory suggests that natural selection will shape the behavior and physiology of such individuals to be hyper-sensitive to the needs of their colony. In the context of foraging, an individual should make decisions of how often to search for new food based on the "hunger" of the colony that she belongs to. In fact, in previously published work, the authors of this manuscript have confirmed empirically that the frequency of foraging events for individual workers in colonies of _Camponotus sanctus_ carpenter ants is correlated with the amount of food stored within the collection of ants within the nest -- as the colony "satiated" (i.e., the communal stomach of the average nest ant became full), the foraging frequency would decrease (and vice versa). In that work, the authors showed that an individual's decision to leave a nest to return to foraging was predictable from her own communal stomach ("crop") level and how quickly it was being depleted by nest ants receiving it. From that observation, the authors previously suggested that a cognitive process within each individual ant could monitor these two internal variables (crop level and rate of change) and lead an ant to make a decision as to if and when to leave a nest. In the current work, the authors suggest an alternative mechanism that exports the discrete decision making into the nest cavity itself and only requires an individual forager to adjust her movement pattern based on her current level of crop load. In particular, they use computational and mathematical models to show that spatiotemporal statistics similar to real ants emerge when hypothetical modeled foragers move deeper into a nest when their crop level is above a certain threshold and instead move toward the nest exit when their crop level is below that threshold (leaving the nest when randomly encountering it). This simple crop-based rule does not require estimation of depletion rate nor require an ant to deliberate over when to exit. Foragers in "hungry" colonies have shallow penetration in their nests before turning around and quickly returning to foraging while foragers in "satiated" colonies have deeper penetration and may remain in their nests for long periods of time. This proposed mechanism provides the adaptive foraging patterns observed in real carpenter ants with significantly reduced assumptions about individual cognitive abilities when compared to previous mechanistic explanations of this behavior. Broadly speaking, it (combined with other recent work from these authors and others) helps to demonstrate proof of concept of cognitive hypotheses that are embodied in the physical environment around the individual apparently making the decision.
The movement rule proposed by the authors is elegantly simple and produces trajectories that are, at least to the human eye, a good match to the stereotyped trajectories from real ant colonies in terms of their directionality and duration, and the length of these trajectories is modulated by colony hunger-state in exactly the same way as the real ant trajectories. Although the authors do not provide statistics on multiple runs of the simulation (they provide examples of single runs), they do complement their simulation work with both deterministic and stochastic models of statistics of the modeled paths and show that those statistics have the same qualitative relationship to colony hunger-state as the statistics of the real ants. Consequently, the paper provides a compelling argument via the use of multiple types of models for a novel behavioral rule that answers an important question in collective decision making in confined physical spaces.
Much of the authors' argument rests on trajectories and statistics generated from a two-dimensional computational simulation that may be overly simplistic. The computational model simulates a single forager (as opposed to multiple foragers) arriving to a nest that is partitioned into a grid of squares with an immobile ant in the center of every square. Foragers move in discrete steps from square to square, with the guarantee of an interaction in each step. This "grid world" model of ant nest movement is significantly different than the experience of real foraging ants returning to the nest, and the authors even admit that deviations between the empirical data and the computational model may be due to nest-ant clumping and interaction sparsity in the paths of real ants. Continuous-motion agent-based models are commonly used to investigate collective-motion hypotheses, and so the choice of a grid world model instead seems notable and weakens the authors' arguments. Furthermore, whereas the deterministic mathematical model of grid-world forager trajectories seems too simplistic, the stochastic model buried in the appendix that is meant to validate the deterministic model's results seems to have some potential flaws and is itself not validated experimentally against replicated simulation data. Instead of perfecting these models, the authors could have bolstered their arguments using more familiar approaches from statistical mechanics that might help explain the likely depth an ant "diffuses" into such a nest. In the current form of the manuscript, the mathematical models do not add much beyond the simulation models (and the lack of replication of the simulated data may make some readers wonder if the example trajectories are representative).
There are also a few questionable parameters that the authors have chosen in their model, likely for analytical tractability. For example, the authors assume that at each interaction between a forager and a nest ant, the forager offloads enough food to fill 15% of the crop space remaining in the receiving ant. One can assume that this parameter is something like the 63.21% associated with an exponential time constant or may be based on empirical measurements of transfer in real ants, but the actual justification is not completely clear from the manuscript. Because the mathematical models make predictions that depend upon these parameters, their existence (and plausible values) is itself an important assumption that needs to be defended for the argument to be truly compelling.
Beyond these methodological issues, the behavioral model described by the authors assumes that ants are able to choose a direction toward their nest's entrance at any time. This within-nest path-integration ability does not seem cognitively inexpensive, which narrows the cognitive distance between the behavioral model they propose here and the one they proposed in their prior work and weakens the argument for the relevance of this new model. The authors failed to place their work within the context of other simple cue-based motion-switching behaviors discussed in the literature for other taxa - such as "running" and "tumbling" in E. coli bacteria - but if they had, they might have envisioned an alternative crop-based motion rule that would have the same effect as their current rule (i.e., movement toward the entrance on low crop state) without having to assert that the ant moves directly back toward the entrance.
Focusing on the explanatory power of this model specifically for (some) ants, the authors do not address how to empirically reconcile the ambiguity between the more cognitive mechanisms proposed in their previous work (where ants "decide" to exit a nest) and the current proposal (where the nest cavity "decides" when the ant will exit). For this new hypothesis to be useful, it must be empirically discriminable from the previous hypothesis. At first glance, it is difficult to imagine an experiment that would lead to different predicted behavior from the two different hypotheses. In other words, at the moment, it seems impossible to tell whether the "ant decide" or the "nest decide" model is a better predictor of real ant behavior/cognitive architectures. The lack of discriminability becomes even more problematic when considering that the current version of the model actually increases some cognitive demands by assuming (as described above) that ants keep track of the position of the entrance over the trajectory within the nest.
The arguments in the current form of this manuscript could be strengthened by adding realism, connections to related literature in collective motion and motion ecology, and more general models from statistical mechanics, and it is important for the authors to identify potential ways to empirically discriminate between the model introduced here and the behavioral model suggested in their prior work. That said, the salient features of the basic crop-cue-based two-motion-primitive model proposed by the authors are elegant and novel and help to further demonstrate how cognition can be embodied in the physical spaces it is embedded within. The authors focus on a particular example in ants, but it is easy to imagine extending the same model to a variety of other scales and application spaces. For example, there may be microbiological examples of coordination among collectives where individuals face even more stringent cognitive constraints. Moreover, the same methods might be used to build artificial swarms in engineering contexts that allocate to tasks based on demand without significant communication or sensing requirements. Even in industrial organization, there may be ways to use methods like these to ensure an emergent adaptive re-allocation of human workers to tasks based on need. In general, this manuscript provides a new example of how spatiotemporal properties of decision making long thought to be associated with cognitive processes endogenous to individuals can be alternatively generated by simple cue-based behaviors interacting in a non-trivial environment. This is a relatively new perspective that may be useful in both the analysis of natural systems as well as the design of artificially intelligent systems. With the right framing, the example from this manuscript could be very useful not only to ant biologists but to scientists and engineers interested in collective decision making more broadly.
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Reviewer #3 (Public Review):
This work adds to our understanding of the many diverse ways that different species of social insects organize the regulation of foraging behavior. This work compares model results with data previously collected on Camponotus sanctus, an ant species that collects nectar. Unlike other species in which foragers collect prey, seeds or other items that they do not ingest, in nectar-feeding species such as this one, the foragers drink nectar and then must unload it by regurgitating to other workers at the nest. This work presents a model that suggests that, like honey bees who also collect nectar, a C. sanctus forager's decision to exit the nest on its next trip depends on when it can unload the nectar, which is linked to the amount of nectar currently held by other workers.
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