The evolutionary consequences of behavioural plasticity

Domain: biology
Published: 2026-03-13
Editorial outcome: minor_revision

Abstract
When environments change and phenotypes become suboptimal, organisms often mitigate the costs of maladaptation with behavioural adjustments. Although this “buffering” of natural selection is widespread, its evolutionary consequences are poorly understood. Here, I use evolutionary simulations to explore how behavioural plasticity shapes the tempo and mode of adaptive evolution. My results indicate that this fast-acting form of plasticity enhances persistence under suboptimal conditions and facilitates the accumulation of genetic variation within populations. Over short timescales, or when opportunities for niche expansion are limited, the combined action of these effects leads to slower evolution of morphology and physiology. Over longer timescales, or when ecological opportunities abound, it can instead lead to a “Goldilocks” pattern, where evolution is fastest in moderately plastic lineages, whose medium buffering capacity enables persistence in suboptimal conditions without severely constraining subsequent optimizing selection. These findings expose key differences in scope and mechanism relative to other forms of phenotypic plasticity and suggest that behavioural plasticity may more broadly influence the evolutionary trajectories of individual lineages. More generally, they help clarify our understanding of how short-term flexibility shapes long-term adaptation and underscore the need to reassess current risk-assessment frameworks that equate slower evolutionary rates with greater vulnerability to environmental change.

Introduction
Adaptation to environmental change often unfolds over multiple generations and is constrained by the availability of suitable genetic variants, either already present in the population or arising through mutation. In the interim, behavioural plasticity can offer a more immediate mechanism for mitigating the fitness costs of phenotypic mismatch1–3. For example, when insulation becomes suboptimal, individuals may reduce thermal stress by selecting different microhabitats4, adjusting their activity patterns5, huddling6 or tolerating closer proximity to conspecifics7. Likewise, thermally mismatched individuals can offset the energetic costs of increased thermoregulation by modifying their foraging strategies to favour more energy-rich prey choices8. Similar behavioural buffers have been documented across a wide range of taxa and ecological contexts, but their evolutionary consequences, particularly in the context of adaptation to rapid environmental change9, remain poorly understood. As climates shift, urban footprints expand, and anthropogenic pressures accelerate environmental degradation10, clarifying the role of early behavioural plasticity on population persistence and genetic adaptation is both timely and essential1,3,11,12.

A key challenge in understanding the effects of behavioural plasticity on the evolutionary process is that behavioural responses to altered environments vary widely within and among species12. For example, some red squirrel populations, Tamiasciurus hudsonicus, have responded to climate change by shifting their phenology13, whereas others have increased instead their food hoarding and territoriality14. Such divergent responses to similar stressors raise questions about the equivalence and evolutionary implications of alternative behavioural strategies. Similarly striking qualitative differences in behavioural response are common among species that differ in morphology, habitat, or natural history, further complicating the derivation of general principles from individual case studies.

One practical way to circumvent the variability in observed behaviours is to focus our comparisons on the general capacity to buffer selection through behaviour (hereafter ‘behavioural buffering’) rather than on the specifics of individual response15–19. Two contrasting perspectives can help frame this discussion. The cognitive buffer hypothesis states that behavioural flexibility shields individuals from the fitness cost of phenotypic mismatch20–23 and therefore predicts a decrease in rates of genetic evolution due to weaker selection16. For example, the selective use of animal skins allowed early hominins with different levels of insulation to experience similar thermoregulation costs in cold environments, thereby weakening selection on traits like fur and body fat24. In contrast, the behavioural drive hypothesis15 notes that behavioural buffering rarely nullifies selection, and therefore makes the opposite prediction that rates of genetic evolution should be faster in more behaviourally plastic lineages because improved abilities to occupy new habitats and ecological niches are likely to provide more frequent opportunities for optimizing selection to act upon the phenotype15,18,25–28. The conditions under which behavioural buffering shifts from constraining to accelerating genetic evolution are poorly understood and remain a key unresolved challenge in cognitive ecology.

The contrasting views on the evolutionary consequences of behavioural plasticity echo long-standing debates on whether phenotypic plasticity generally accelerates or decelerates evolution3,11,12,29–36. Such similarity is not surprising because behaviour is part of the phenotype and behavioural flexibility is simply a form of phenotypic plasticity3,29. Nevertheless, behavioural adjustments can be typically expressed and reversed on much faster timescales than morphological and even physiological change9. Moreover, investments into behavioural plasticity (e.g., bigger brains) can more easily buffer selection in a greater variety of traits (even in entirely new contexts), whereas more structural forms of phenotypic plasticity tend to exert narrower, stage-dependent, or more context-specific effects. These key differences in both timing and scope suggest that behavioural plasticity may occupy a distinct position within the broader spectrum of phenotypic plasticity, with unique implications for how populations navigate and adapt to changing environments9,37–40.

Earlier theoretical work on phenotypic plasticity has provided important insights on its evolutionary consequences, but has not typically distinguished between behavioural and more structural forms of plasticity28,29,41–44. Likewise, the studies that have explicitly made such distinction have focused primarily on the evolution of plasticity itself rather than on its downstream consequences on the evolution of other components of the phenotype37,45,46. Here I integrate theoretical and empirical insights from animal behaviour, cognitive ecology, and evolutionary biology to identify conditions under which behavioural plasticity shapes genetic adaptation and influences the evolution of other phenotypic traits. Using evolutionary simulations and digital experiments, I show that increasing effectiveness in behavioural buffering is generally advantageous in changing environments, but that its downstream effects on genetic adaptation are complex and sometimes counterintuitive. I begin by showing that behavioural plasticity facilitates resilience to environmental change and promotes the accumulation of genetic variation within populations. I then show that although these dual effects lead to slower rates of morphological and physiological change over short timescales, on longer timescales they can result instead in a “Goldilocks” pattern47, where the rate of morphological and physiological change peaks at intermediate levels of behavioural flexibility. At that peak, intermediate plasticity facilitates evolution by enabling persistence in a wide range of suboptimal conditions without severely constraining the optimizing selection that will subsequently ensue. These findings help explain empirical discrepancies in the observed effects of behavioural plasticity on evolutionary rates and provide a better understanding of how behavioural flexibility influences genetic adaption in the natural world. They also provide new mechanistic insights that help explain the broad empirical associations that have been observed between brain size, environmental unpredictability48–50, and diversification potential15,17.

Results
Modelling Framework. To investigate the evolutionary consequences of behavioural plasticity, I developed an individual-based simulation model that tracks the evolution of a quantitative morphological trait, I, in a sexually reproducing population with non-overlapping generations and density-dependent regulation. Selection on I is stabilizing, such that fitness is maximized when trait values match local environmental conditions. For illustration purposes, I hereby frame the model in terms of thermal insulation: thick coats are favoured in cold habitats and impose costs in warm ones, whereas thin coats exhibit the opposite pattern. However, readers should note that this model is intentionally general and applies to many other traits under selection toward an environmentally defined optimum.

For each combination of parameter values, I simulated the evolution of insulation in 100 independent replicate populations. Each population was initialized with 5,000 individuals defined by a single continuous genetic trait, whose value directly determines the phenotype. Starting genotypes were randomly drawn from a uniform distribution on [0, 1]. Simulations proceeded through three sequential phases: (i) a 500-generation burn-in period with an initial environmental target equal to the mid-value of the seeded genotypes (E = 0.5, aimed at establishing realistic levels of standing genetic variation), (ii) a 1,000-generation evolutionary phase with a new environmental target of E = 0.3, and (iii) a 500-generation diaspora phase in which a small subset of randomly selected individuals experienced an extreme environmental change to an environmental target of E = -1.3. Absolute fitness was determined by the mismatch between an individual’s genotype, I, and its current environmental target, E, scaled by a population-wide buffering parameter, b (i.e., the lineage’s potential for behavioural plasticity). Specifically,

𝑊𝑖 = 𝑅0∙𝑒𝑑∙|𝐸−𝐼𝑖 |∙(1−𝑏), (1)

where R0 is the baseline reproductive output (assumed to be two offspring throughout), and d determines the steepness of fitness decline with increasing phenotypic mismatch (i.e., lower values = stronger selection). In accordance with the cognitive buffer hypothesis, this formulation produces shallower fitness gradients with increasing behavioural plasticity: when b = 0, phenotypic mismatches are strongly penalized, but as b approaches 1, variation in insulation levels becomes progressively less consequential (Fig. 1A).

Population growth was regulated by Beverton–Holt density dependence51. Specifically, effective fitness, 𝑊′𝑖, was computed as

𝑊′𝑖 = 𝑅0∙𝑊𝑖 ⁄ (1+𝛼∙𝑛), (2)

where n is the current population size and  governs the strength of density-dependence.  was generally tuned by assuming a carrying capacity of 𝑁 = 5000 individuals (i.e., 𝛼 = (𝑅0 −1)⁄𝑁 = 0.0002).

The algorithm for sexual reproduction began by determining the number of offspring produced by each adult in the population, drawing these numbers from a Poisson distribution with a mean equal to the individual’s density-regulated fitness (𝑊′𝑖). For each offspring, a second parent was randomly drawn from the population, with selection probabilities proportional to effective fitness. Following standard quantitative genetic approaches for polygenic traits52,53, offspring genotypes were subsequently determined as the mid-parent value plus a small mutational change drawn from a normal distribution with mean zero and a standard deviation of 𝜇 = 0.05 (unless otherwise stated). This algorithm captures additive genetic inheritance without explicitly modelling individual loci, approximating with a single mutational change value the cumulative effect of many small and low-probability events across loci.

Evolutionary dynamics. Stable levels of standing genetic variation were attained in all simulation replicates across all parameter combinations by the end of the burn-in period (Fig. 1). The only exception was at b = 1, where a complete lack of selection led to evolutionary dynamics that approximated a random walk (e.g., green trajectory in Fig. 1B). After environmental changes were implemented in generation 500 (dashed vertical lines in Figs. 1B, 1D, and IF), the evolutionary dynamics of insulation became strongly correlated with the effectiveness of the behavioural buffer. Specifically, across parameter combinations, the mean population value of insulation phenotypes approached the new target more slowly in simulations with higher b.

Inter-population variability, measured as the spread among replicate trajectories, generally declined over time except for simulations with complete buffering (i.e., b = 1). This trend can be observed in the confidence intervals of Figs. 1B, 1D, and IF, where the gap between upper and lower bounds is widest in early generations and narrower thereafter. At equilibrium, lineages with higher b displayed greater residual variability, suggesting that more behaviourally plastic populations should exhibit stronger geographic variation in morphological and physiological response to environmental stress.

Standing genetic variation, as measured by the within-population variance in I at generation 1500 (𝑠2 𝐺1500), also increased with behavioural buffering (Fig. 2). This finding indicates that more behaviourally plastic populations will exhibit greater phenotypic diversity and could therefore be better equipped to respond to sudden environmental changes or new ecological opportunities.

Interactions with known drivers of the evolutionary process. To assess the robustness of my findings, I repeated these analyses under different selection strengths and mutation rates. As expected, stronger selection (smaller d) accelerated genetic evolution and reduced standing variation (Figs. 3A–B). Similarly, the pace of genetic adaptation continued to be negatively associated with behavioural buffering across d values, but the decline was considerably steeper in simulations with stronger selection (Fig. 3A). Likewise, the positive association between behavioural plasticity and genetic variance persisted across d values, but overall levels of variation decreased when selection was more intense (Fig. 3B). Close inspection of this pattern indicates that strength of selection had a pronounced effect at low b but minimal or no effect at high b. Consequently, the rate at which standing genetic variation rises with b is steeper when selection is stronger (Fig. 3C), underscoring the critical role that behavioural plasticity can play in enabling the accumulation of genetic variants that would otherwise be removed by selection.

The effects of mutation rate on the evolution of insulation were similarly affected by behavioural buffering (Fig. 3D-F). As expected, higher rates of mutation accumulation generally fuelled faster adaptation, but this effect weakened in lineages with stronger behavioural buffers (Fig. 3D). Similarly, standing genetic variation increased conspicuously with mutation rate (Fig. 3E) and continued to be dependent on b. Here too, the increase in standing genetic variation with b was more pronounced at higher mutation rates (Fig. 3F), suggesting that behavioural plasticity potentiates the effects of mutation by preventing the loss of extreme genetic variants during periods of stability.

Macro-evolutionary implications. The above analyses reveal that although behavioural plasticity can decelerate phenotypic evolution it can also increase the diversity of potentially adaptive variants before environments change. To explore the macro-evolutionary implications of these contrasting effects, I examined how small population diasporas fared when introduced to significantly different environments or ecological niches. Specifically, I randomly selected 100 individuals at generation 1,500 and allowed these nascent populations to evolve an additional 500 generations in a severely altered environment. Across model parameterizations, diasporas generally collapsed when behavioural buffering was not very effective but easily survived when it was (Fig. 4). Furthermore, population survival increased in a stepwise manner, reinforcing the idea that under extreme ∆E, population resilience to environmental shock relies largely on the pre-existence of genetic variants that are either already adaptive or at least sufficiently close to the new optimum to be meaningfully shielded by behavioural buffering. As expected, population survival was more likely when environmental change was less intense (Fig. 4A), selection was weaker (Fig. 4B), or mutations accumulated at a faster rate (Fig. 4C). With the possible exception of Fig. 4B, these effects were visibly weaker than those of behavioural plasticity, further suggesting that behaviour can play a critical role in protecting populations against collapse during rapid environmental change. Furthermore, the stepwise shifts observed in Fig. 4 from population collapse to long‑term persistence indicate that some relatively small increases in cognitive ability can substantially improve a lineage’s capacity for colonization, diversification, and long‑term viability.

Methods
Methodological details of the model structure, assumptions, parameterization, and variant-specific implementations are reported in the Results section, where they are introduced alongside the corresponding analyses. The model was implemented in R v.4.5.271. Parallelization of model runs relied on pbmcapply v1.5.172, nonlinear optimization and parameter estimation relied on minpack.lm v1.2-473, and post-processing and analysis of model outputs were carried out using dplyr v.1.1.474. Source code and scripts used to generate results and figures are provided in Supplementary Code 1.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "To the authors/editor:\nGeneral comments:\nThe author presents a model examining the evolutionary consequences of behavioral plasticity. While I find it extremely\nuseful to formalize how behavioral plasticity may shape organisms’ capacity to adjust to environmental changes across\nevolutionary timescales, I see scope for improving the present work as outlined in my comments below.\nI hope that my comments will help the authors to strengthen their manuscript.\nMajor comments:\nThe background provided in the introduction is relatively thin. In my specific comments, I point to potentially relevant\nliterature that the author may consider to incorporate. While the manuscript is generally well-written, the methods and results\nwould benefit from clearer structure and more details (see specific comments). My biggest concern is with the model itself:\nAs outlined in the specific comments, I am not sure whether I agree that this is really a model capturing the impact of\nbehavioral plasticity. Irrespective of this point, the paper lacks a transparent description of the model’s assumptions and\nlimitations. I find that both the results and discussion are fairly strongly worded given the limitations of the underlying model.\nSpecific comments:\n- Lines 43 following: Is the scope of this manuscript to only explore behavioral plasticity in response to stress? If so, could\nthis be made more explicit elsewhere? If not, could this example be made more general?\n- Lines 56 following: Could the authors explain why a shielding from negative consequences implies weaker selection\ngradients?\n- Lines 59 following: Similarly, could the authors explain in more detail why behavioral plasticity leads to accelerated genetic\nevolution? Is the explanation just that persistence in suboptimal conditions leads to more chance for evolution (the authors\nallude to this but the explanation could be more explicit)? Or, is there more to it?\n- Lines 78-97: In my opinion this paragraph provides unnecessary signposting. Briefly outlining what the reader can expect\nin this paper (i.e. a model of X implemented through Y) is valuable. But a detailed outline of the paper, including results, and\ndiscussion just blows up the introduction. I also feel that the introduction is quite ‘thin’ when it comes to discussing other\npotentially relevant work. I think the author could shorten this paragraph and use the space to add more background to the\nintroduction. Here is some potentially relevant literature (Burton et al., 2022; Fawcett et al., 2013; Mathot et al., 2012;\nRatikainen & Kokko, 2019; Stamps & Krishnan, 2017):\n-\nBurton, T., Ratikainen, I. I., & Einum, S. (2022). Environmental change and the rate of phenotypic plasticity. Global Change\nBiology, 28(18), 5337–5345. https://doi.org/10.1111/gcb.16291\nFawcett, T. W., Hamblin, S., & Giraldeau, L.-A. (2013). Exposing the behavioral gambit: The evolution of learning and\ndecision rules. Behavioral Ecology, 24(1), 2–11. https://doi.org/10.1093/beheco/ars085\nMathot, K. J., Wright, J., Kempenaers, B., & Dingemanse, N. J. (2012). Adaptive strategies for managing uncertainty may\nexplain personality‐related differences in behavioural plasticity. Oikos, 121(7), 1009–1020. https://doi.org/10.1111/j.1600-\n0706.2012.20339.x\nRatikainen, I. I., & Kokko, H. (2019). The coevolution of lifespan and reversible plasticity. Nature Communications, 10(1), 1–\n7. https://doi.org/10.1038/s41467-019-08502-9\nStamps, J. A., & Krishnan, V. V. (2017). Age-dependent changes in behavioural plasticity: Insights from Bayesian models of\ndevelopment. Animal Behaviour, 126, 53–67. https://doi.org/10.1016/j.anbehav.2017.01.013\n- Line 94: The mention of advanced cognition comes out of the blue. There is no other mention of it elsewhere in the\nmanuscript. Could the author remove it or integrate it into the manuscript?\n- I expected a model which produces patterns of evolution in line with either the cognitive buffer or behavioral drive\nhypothesis based on basic assumptions fed into the model. Instead the author build a model that intrinsically assumes\ncognitive buffering. The abstract and end of the introduction could be rewritten to better reflect the actual modeling.\n- Line 109: What is the justification for this model? What kind of assumptions does it make about behavioral buffering. How\ndoes this equation represent “general capacity to buffer selection through behaviour”? I struggle to relate to the dynamics:\nWhen b =1 and behavioral buffering is maximal, fitness will always be 1, irrespective of the phenotype-environment\nmismatch. How does this compare to behavioral buffering in real organisms? I expected a model in which organisms can\nadjust b (through trial and error) to change I and that b would be an evolvable parameter. I don’t expect a model to mimic\nreality with all its complexity but I do find it important that the consequences of simplifying model assumptions are made\nexplicit and, to some extent, justified.\n- Line 127: Rewrite the sentence to “In this model, the evolutionary dynamics of insulation phenotypes were strongly\ncorrelated with the effectiveness of the behavioural buffer (Figure 1).”\n- Lines 138-140: This information belongs in the discussion section not in the methods/results\n- Lines 145-150: idem\n- Lines 127-198: The results section would benefit from headers, which create more structure and make it easier for a reader\nto follow the type of result that is being described.\n- Line 167: The author should explain what mutation-selection balance means.\n- Lines 180-183: “The above analyses reveal that although behavioural plasticity can decelerate the evolution of morphology\nand physiology by reducing selection gradients, it can also accelerate it by increasing diversity in the reservoir of potentially\nadaptive variants before environments change.” I struggle with these kinds of sentences. Is it not directly baked into model\nthat higher b (which the author terms ‘behavioral plasticity’) reduces the selection gradient d? Regarding the second part of\nthe sentence, I don’t quite understand yet at what point and how standing genetic variation is quantified ( I could not find it in\nthe R code and the description in the manuscript is insufficient). Does the positive relationship between b and standing genetic\nvariation, at high b levels, not merely stem from the fact that high b values eliminate selection and thus do not constrain the\nspace of adaptive trait values for I? This would mean that this is not really a finding as much as a direct assumption of the model.\n- I find the methods hard to follow. I would expect to see a clearer explanation of what happens in each step of the\nsimulation. The information should describe what happens in the main loop of replicate.run in the R script. Some of the\ninformation is currently in the manuscript it is just spread out. Once that has been described sufficiently, I would describe the\nsimulation chronology.\n- Lines 202-204: “My analyses suggest that behavioural plasticity exerts dual and time-dependent effects on the tempo and\nmode of adaptive evolution: it weakens selection gradients, and it promotes the accumulation of genetic variation within\npopulations.” Based on my current understanding, these are not findings of the model but rather assumptions baked into the\nmodel (see above).\n- Lines 204 following: “On short evolutionary timescales—or when opportunities for niche expansion are severely\nconstrained—these effects lead to slower morphological and physiological evolution in an altered environment (Fig. 5).”\nWhere in the model did the author investigate the effect of timescales? Figure 5 is just a schematic figure and does not seem to be\nbased on modeling outcomes. The way the text is written suggests otherwise.\n- In my opinion, the discussion is too strongly worded given the simplicity of the model. This is corroborated by the fact that\nthe discussion lacks any mention of limitations of the current model.\n- In the discussion, I would have found it interesting to read something about the interplay between phenotypic plasticity in\nmorphological and behavioral traits and their combined impact on evolutionary dynamics.\n- It would have been nice to also see the R-code that generates the various figures in the paper.\nI hope my review will help the authors to strengthen their manuscript.\nI sign all of my reviews for accountability and transparency.\nWith kind regards,\nNicole Walasek\n(Remarks on code availability)\nThe code seemed fine and was appropriately commented. I did not try to run it but merely read through the txt file. I had no\nconcerns about it being able to run when looking through it. However, the code did not contain script to generate the figures\nfrom the manuscript which I found unfortunate.", "reviewer_2": "This paper explores a simple simulation model to understand how buffering of maladaptation affects the rate of evolution\nand the survival of subpopulations in stressful environments. I think this is an area of theory in which there is much left to\nlearn and that this scenario would interest a broad range of readers. However, I think that the modeling falls short of what I\nwould want to see in order to recommend this paper, though it certainly could be revised to be an excellent contribution.\nThe Introduction is excellent and lays out the central question and its motivation concisely and clearly. My only quibble\nwould be that the approach the paper will take is left quite vague in the Intro. This is OK, but I would like to have a little more\nabout the methodological novelty here. Has no one modeled this question, or is there a difference in the approach taken\nhere compared to prior work that can help explain why novel answers were found? It is fine if this topic is reserved for the\nDiscussion (though it isn’t much discussed there either), but some foreshadowing at least in the Intro would suit me.\nThe Results and Methods are presented together, with a later section giving more Methods. I think this is a fine approach in\ngeneral but confused me at first reading, as the earlier section seemed to have too little detail to be a complete Methods\nsection but also too much to be a concise overview of the model. We aren’t told important things like that this is (I think) a\nmodel of discrete, non-overlapping generations in either place. I don’t need to know the mutation rate (which varies anyway)\nbefore understanding basic aspects of the model like how demography works. The crucial information that population size is\nallowed to vary is mentioned only in the later Methods section, making the survival results in Figure 4 come out of left field\nfor me. This should be communicated earlier, and the constant k included in the the equation on line 109 so that it actually\ngives absolute fitness. I am still left confused about some aspects like density dependence–is there any? It seems like\nexcess individuals are discarded, but there isn’t any effect of density on reproduction otherwise. This seems like a very odd\nchoice as it doesn’t permit populations to stabilize at sizes below carrying capacity. I suspect that this is why the prob. of\nsurvival plots in Fig. 4 are so steep.\nBecause of the lack of density dependence (or detail on how it works, if it does), it’s hard to understand what role population\nsize might be playing here. This also plays in to the choice of asexual reproduction. In the right context, it can be fine to\nignore density dependence and to use this simpler, asexual form of reproduction. But, I’m not convinced that these questions\nbenefit from these choices, because the level of genetic variation seems so critical. I would expect feedback between fitness,\nas buffered by behavior, and population size to be very important for understanding how buffering affects adaptation.\nSimilarly, I would expect the ability of sexual populations to quickly generate extreme phenotypes to be critical to colonizing\nnew and stressful environments. I therefore don’t think that the model is ideally suited to investigating these questions,\nthough it could readily be extended to do so.\nI would also suggest that focusing the model on insulation isn’t quite the best level of abstraction, as it makes the model read\nas narrow when it doesn’t need to. There’s nothing specific to insulation as far as I can see, and certain aspects of the model\n(symmetrical loss of fitness if one is over- or under-insulated) seem abstract anyway. Similarly, there’s nothing about the\nbuffering mechanism which is specific to behavior in my view. This is both good and bad; the paper could apply much more\nbroadly, but also these results are perhaps less original when viewed in the context of general buffering mechanisms.\nI would recommend recasting the model in more general, abstract terms, positioning it more thoroughly in terms of prior\ntheory work, and modifying or extending it to allow for more reasonable demographic behavior, and possibly sexual\nreproduction. With these sorts of changes, I think this would be a very valuable paper.\n(Remarks on code availability)"}, "author_response": "REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nTo the authors/editor:\nGeneral comments:\nThe author presents a model examining the evolutionary consequences of behavioral plasticity.\nWhile I find it extremely useful to formalize how behavioral plasticity may shape organisms’\ncapacity to adjust to environmental changes across evolutionary timescales, I see scope for\nimproving the present work as outlined in my comments below.\nI hope that my comments will help the authors to strengthen their manuscript.\nThank you for your insights. The detailed feedback you provided has indeed helped me identify\nsome key areas that needed more clarity and/or work.\nMajor comments:\nThe background provided in the introduction is relatively thin. In my specific comments, I point to\npotentially relevant literature that the author may consider to incorporate. While the manuscript\nis generally well-written, the methods and results would benefit from clearer structure and more\ndetails (see specific comments).\nAs detailed below, I have incorporated the suggested citations and taken this opportunity to\nbetter connect my work to earlier theoretical papers that are now classics in this field. Following\nyour suggestion (and a similar one by Reviewer 2), I have also aggregated the Methods into a\nsingle section in the main text to provide more details and a clearer structure. Similarly, I added\npassages to the Results and Discussion describing potential limitations and next steps. Details\non these changes are provided in my responses to your specific comments and in my\nresponses to Reviewer 2.\nMy biggest concern is with the model itself: As outlined in the specific comments, I am not sure\nwhether I agree that this is really a model capturing the impact of behavioral plasticity.\nIrrespective of this point, the paper lacks a transparent description of the model’s assumptions\nand limitations. I find that both the results and discussion are fairly strongly worded given the\nlimitations of the underlying model.\nI appreciate the reviewer’s request for a better description of the assumptions and limitations of\nthe model. It was clearly needed and my hope is that the changes detailed below have fully\naddressed that concern. Having said that, I want to emphasize that this is absolutely a model\nthat captures the downstream evolutionary consequences of behavioural plasticity! By simply\noperationalizing the idea of a behavioural buffer into the fitness function, the model is able to\nrecreate the patterns predicted by both the cognitive and the behavioural drive hypotheses and\nclarifies the contexts in which acceleration and deceleration of genetic evolution are to be\nexpected.\nSpecific comments:\n- Lines 43 following: Is the scope of this manuscript to only explore behavioral plasticity in\nresponse to stress? If so, could this be made more explicit elsewhere? If not, could this example\nbe made more general?\n“Stress” is a very general term related to any kind of disruption to the normal state or\nenvironment of an organism. Nevertheless, in light of your comment, I have replaced the term\n“stress” for “altered environments” to avoid any confusion. The example you mention is simply\nused to illustrate why it has been so difficult to conceptualize and study the effects of\nbehavioural plasticity on evolution: behaviour can be very idiosyncratic even within a single\nspecies. The generality of this point is explicitly made in the first sentence of that paragraph\n(changes in version 2 are presented in bold):\n“A key challenge in understanding the effects of behavioural plasticity on the evolutionary\nprocess is that behavioural responses to altered environments vary widely within and among\nspecies”\n… and at the end of the same paragraph:\n“Similarly striking qualitative differences in behavioural response are common among species\nthat differ in morphology, habitat, or natural history, further complicating the derivation of\ngeneral principles from individual case studies.”\nRegarding the scope of this manuscript, please refer to:\n- Title: “The evolutionary consequences of behavioral plasticity”\n- Abstract: The goal of this manuscript is to “examine how behavioral plasticity influences\nthe tempo and mode of adaptive evolution”.\n- Introduction: “Here I integrate theoretical and empirical insights from animal behaviour,\ncognitive ecology and evolutionary biology to identify conditions under which behavioural\nplasticity shapes genetic adaptation and influences the evolution of other phenotypic\ntraits.”\nTo help readers better appreciate why this exercise is important, I also modified the last\nparagraph of the introduction as follows:\n“These findings help explain empirical discrepancies in the observed effects of behavioural\nplasticity on evolutionary rates and provide a better understanding of how behavioural\nflexibility influences genetic adaption in the natural world”\n- Lines 56 following: Could the authors explain why a shielding from negative consequences\nimplies weaker selection gradients?\nThank you for letting me know this was not clear. I have reworded the paragraph to more fully\nintroduce readers to the ideas behind the cognitive buffer hypothesis and the behavioural drive\nhypothesis. The new text clarifies the particular issue you mention as follows:\n“The cognitive buffer hypothesis states that behavioural flexibility shields individuals from the\nfitness cost of phenotypic mismatch20–23 and therefore predicts a decrease in rates of genetic\nevolution due to weaker selection16. For example, the selective use of animal skins allowed\nearly hominins with different levels of insulation to experience similar thermoregulation costs in\ncold environments, thereby weakening selection on traits like fur and body fat24”\n- Lines 59 following: Similarly, could the authors explain in more detail why behavioral plasticity\nleads to accelerated genetic evolution? Is the explanation just that persistence in suboptimal\nconditions leads to more chance for evolution (the authors allude to this but the explanation\ncould be more explicit)? Or, is there more to it?\nThat is certainly the case. Behavioural drives are very well known and have been used to\nexplain rapid evolution in a variety of systems, including high-profile examples like the\ndiversification of the Galapagos Finches. The basic idea is that behavioural plasticity facilitates\npersistence in suboptimal environments (or the exploration of new ecological niches) and that in\ndoing so, it provides more opportunities for optimizing selection to drive the evolution of new\nforms and species. There really is not much more to this idea! I have made the following\nchanges to the paragraph to clarify these issues:\n“In contrast, the behavioural drive hypothesis15 notes that behavioural buffering rarely nullifies\nselection, and therefore makes the opposite prediction that rates of genetic evolution should be\nfaster in more behaviourally plastic lineages because improved abilities to occupy new habitats\nand ecological niches are likely to provide more frequent opportunities for optimizing selection to\nact upon the phenotype15,18,25–28.”\nFor further context on this and the previous comment, I also added the following section to the\nend of that paragraph:\n“The conditions under which behavioural buffering shifts from constraining to accelerating\ngenetic evolution are poorly understood and remain a key unresolved challenge in cognitive\necology.”\n- Lines 78-97: In my opinion this paragraph provides unnecessary signposting. Briefly outlining\nwhat the reader can expect in this paper (i.e. a model of X implemented through Y) is valuable.\nBut a detailed outline of the paper, including results, and discussion just blows up the\nintroduction.\nThank you for sharing your perspective. Approaches to scientific writing vary among scholars,\nand it appears we simply hold different views on this matter.\nI also feel that the introduction is quite ‘thin’ when it comes to discussing other\npotentially relevant work. I think the author could shorten this paragraph and use the space to add more\nbackground to the introduction. Here is some potentially relevant literature (Burton et al., 2022;\nFawcett et al., 2013; Mathot et al., 2012; Ratikainen & Kokko, 2019; Stamps & Krishnan, 2017):\nThank you for these suggestions. They are indeed all excellent papers. I had originally chosen\nto give preference to citations that grounded my model on empirical work (only 70 citations are\nallowed in this journal) but I realize now that in doing so, I had neglected putting the model in\nthe proper context of existing theoretical work. To address that gap, I now cite the sources you\nsuggest plus classic work from Scheiner, West-Eberhardt, Witlock and others (see responses to\nR2). One more thing about the sources you suggest… all of them deal primarily with the\nevolution of plasticity itself rather than with its downstream consequences on the evolution of\nother aspects of phenotype (i.e., what my paper is really about). This commonality made me\nrealize that I needed to be more explicit about the key difference between my goals and those of\nother recent models on plasticity (behavioural or otherwise). To that end, and although I cite the\nsuggested sources in multiple sections of the manuscript, I’d like to call your attention to the\nfollowing passage in particular:\n“Earlier theoretical work on phenotypic plasticity has provided important insights on its\nevolutionary consequences, but has not typically distinguished between behavioural and more\nstructural forms of plasticity28,29,41–44. Likewise, the studies that have explicitly made such\ndistinction have focused primarily on the evolution of plasticity itself rather than on its\ndownstream consequences on the evolution of other components of the phenotype37,45,46.”\n- Line 94: The mention of advanced cognition comes out of the blue. There is no other mention\nof it elsewhere in the manuscript. Could the author remove it or integrate it into the manuscript?\nApologies for that. I included this term because the cognitive buffer hypothesis is primarily\ninvoked as a possible explanation for the evolution of “advanced cognition”. Given space\nconstraints, I chose to simply remove the offending term rather than explaining it.\n- I expected a model which produces patterns of evolution in line with either the cognitive buffer\nor behavioral drive hypothesis based on basic assumptions fed into the model. Instead the\nauthor build a model that intrinsically assumes cognitive buffering. The abstract and end of the\nintroduction could be rewritten to better reflect the actual modeling.\nThank you for making this important point. The reviewer correctly notes that behavioural\nbuffering is not an emergent property of my model but rather a built-in mechanism used to\noperationalize our most recent understanding of how behavioural plasticity influences selection.\nI must nevertheless point out that while there is broad agreement on what behavioural (or\n“cognitive”) buffering does to selection, there is still considerable disagreement and public\ndebate on what should happen as a result. That is still a major question in the field of cognitive\necology and the currently high rate of environmental change has made it relevant to all of\necology and evolution. Details on how I have clarified these issues in the revised version of my\nmanuscript are provided below.\n- Line 109: What is the justification for this model? What kind of assumptions does it make about\nbehavioral buffering. How does this equation represent “general capacity to buffer selection\nthrough behaviour”?\nI explain in the introduction that “One practical way to circumvent the variability in observed\nbehaviours is to focus our comparisons on the general capacity to buffer selection through\nbehaviour (hereafter ‘behavioural buffering’) rather than on the specifics of individual response”.\nThe model I propose here simply operationalizes this idea (i.e., the cognitive buffer hypothesis)\nby explicitly incorporating a buffering capacity into the fitness function. Figure 1A (cited later in\nthe same paragraph) unambiguously shows how varying b alters selection gradients. The ability\nto vary b in this simple formulation is what allows us to investigate later how different buffering\nlevels influence the evolution of a second trait in a variety of scenarios. The relevant text in the\nnew version has been modified as follows to make this clearer:\n“Absolute fitness was determined by the mismatch between an individual’s genotype, I, and its\ncurrent environmental target, E, scaled by a population-wide buffering parameter, b (i.e., the\nlineage’s potential for behavioural plasticity). Specifically,\n𝑊 = 𝑒𝑑∙|𝐸−𝐼𝑖 |∙(1−𝑏),\n𝑖\nwhere d determines the steepness of fitness decline with increasing phenotypic mismatch (i.e.,\nlower values = stronger selection). In accordance with the cognitive buffer hypothesis, this\nformulation produces shallower fitness gradients with increasing behavioural plasticity:\nwhen b = 0, phenotypic mismatches are strongly penalized, but as b approaches 1, variation in\ninsulation levels becomes progressively less consequential (Fig. 1A).”\nI struggle to relate to the dynamics: When b =1 and behavioral buffering is maximal, fitness will\nalways be 1, irrespective of the phenotype-environment mismatch. How does this compare to\nbehavioral buffering in real organisms?\nImagine an environment with temperatures close to absolute zero, no oxygen, high UV\nradiation, and no gravity. As far as we know it, no life form on Earth (even tardigrades!) can\noperate and thrive under those conditions. Yet, somehow, we humans have done it…\nspecifically, we have used our accumulated knowledge, material culture, and collaborative tools,\nto enable occupancy of low-Earth orbits for over 2 decades and have even set foot on the\nmoon. Our simple phenotypes may be relatively weak but the buffering our brains provides them\nhas allowed our bodies to explore space, the bottom of the ocean, visit the poles, live in the\nhottest and driest deserts… all in the absence of clear physical adaptations to exploit those\nenvironments. In these examples, the “behavioural buffer” is expressed in both physical form\n(i.e., in space suits/space station, scuba gear, air conditioning, aqueducts, etc.) and behavioural\nform (know-how, procedures, etc) but the result is close to 100% protection against selective\nforces that would otherwise kill us. Although it is true that in nature most behavioural buffers are\nunlikely to be 100% effective (as acknowledged in the passage about crows/ravens in my\nmanuscript), I believe it is still very useful to consider the full range of possible buffering\ncapabilities when developing a general theoretical framework that clarifies our thinking on these\nissues.\nI expected a model in which organisms can adjust b (through trial and error) to change I and\nthat b would be an evolvable parameter. I don’t expect a model to mimic reality with all its\ncomplexity but I do find it important that the consequences of simplifying model assumptions are\nmade explicit and, to some extent, justified.\nThank you for letting me know what you expected. This is very helpful. Understanding the forces\nthat drive the evolution of behavioural plasticity is indeed a very interesting question. However,\nthe model I present here explores a different yet (I would argue) equally interesting question:\nhow does existing behavioural plasticity shape the (genetic) evolution of other aspects of the\nphenotype? Since this is the first attempt to explicitly address this question, I believe it is\nimportant not to confound cause and effect. Nevertheless, please note that one of the changes I\nmade to this new version of my manuscript is the inclusion of the following statement suggesting\nthat the two approaches could be linked in future work:\n“Future extensions could relax these assumptions by allowing plasticity itself to evolve,\nincorporating energetic or opportunity costs of flexible behaviour, modelling effects on multiple\ntraits with conflicting selective demands, or embedding the process in spatially structured or\ntemporally autocorrelated environments. Such extensions could further refine our understanding\nof the limits of behavioural buffering.”\n- Line 127: Rewrite the sentence to “In this model, the evolutionary dynamics of insulation\nphenotypes were strongly correlated with the effectiveness of the behavioural buffer (Figure 1).”\nThe results section has been substantially edited to incorporate changes to the model that were\nsuggested by R2 (results and main take-home messages remain unchanged). As a result, the\nsentence you highlighted here now reads:\n“After environmental changes were implemented in generation 500 (dashed vertical lines\nin Figs. 1B, 1D, and IF), the evolutionary dynamics of insulation became strongly correlated\nwith the effectiveness of the behavioural buffer.”\n- Lines 138-140: This information belongs in the discussion section not in the methods/results\n- Lines 145-150: idem\nThe unique structure of research articles in Nature Communications and the intended generality\nof their audience puts a premium on describing the work as if the author is telling a story. To do\nthat, I have found it very helpful in the past to interpret findings as they are presented and to\nexplain how one finding may have motivated additional downstream analyses. I stand by these\nwriting choices.\n- Lines 127-198: The results section would benefit from headers, which create more structure\nand make it easier for a reader to follow the type of result that is being described.\nThank you for this suggestion. Headers have now been added.\n- Line 167: The author should explain what mutation-selection balance means.\nThis passage is no longer part of the manuscript given the edits that accommodated the\nchanges in model architecture.\n- Lines 180-183: “The above analyses reveal that although behavioural plasticity can decelerate\nthe evolution of morphology and physiology by reducing selection gradients, it can also\naccelerate it by increasing diversity in the reservoir of potentially adaptive variants before\nenvironments change.” I struggle with these kinds of sentences. Is it not directly baked into\nmodel that higher b (which the author terms ‘behavioral plasticity’) reduces the selection\ngradient d? Regarding the second part of the sentence, I don’t quite understand yet at what\npoint and how standing genetic variation is quantified ( I could not find it in the R code and the\ndescription in the manuscript is insufficient). Does the positive relationship between b and\nstanding genetic variation, at high b levels, not merely stem from the fact that high b values\neliminate selection and thus do not constrain the space of adaptive trait values for I? This would\nmean that this is not really a finding as much as a direct assumption of the model.\nLots to unpack here! You are correct in noting that behavioural plasticity (b) enters explicitly into\nthe model by scaling the degree to which phenotypic mismatch translates into fitness loss. This\noperationalization follows directly from the verbal logic of the cognitive buffer hypothesis. In that\nsense, the buffering effect of b is not an emergent feature of the model, but a deliberate\ntranslation of a hypothesis into a formal fitness function. This, by the way, is part of the novelty\nof my approach.\nAs for the difference between assumptions and results, it is important to distinguish\nbetween how plasticity is implemented and what follows from that implementation over\nevolutionary time. While higher b necessarily reduces instantaneous selection gradients acting\non the focal trait, the downstream evolutionary consequences are not trivially assumed in my\nmodel. In particular, the model does not prescribe a priori how changes in selection strength\ninteract with mutation, drift, density dependence, population persistence, and demographic\nstochasticity across multiple ecological contexts and timescales. Slower short-term evolutionary\nchange, the accumulation of standing genetic variation, and the emergence of a non-monotonic\n“Goldilocks” relationship between plasticity and long-term evolutionary rates are all observed\noutcomes rather than baked-in assumptions. All of these outcomes are emergent properties,\nand as I note in the next paragraph, not all of them are immediately obvious.\nIt is true that very high values of b weaken stabilizing selection and thereby permit the\naccumulation of broader genotypic variance. That mechanism is well understood in theory and\nis the basis for my prediction for the cognitive buffer hypothesis. What is not known, however, is\nthat such variance will necessarily translate into accelerated adaptation following environmental\nchange. Indeed, my simulations show that excessive buffering can constrain subsequent\nadaptive evolution by weakening selection gradients to the point of no selection. Intermediate\nbuffering, however, enables both persistence under suboptimal conditions and relatively rapid\npost-change optimizing selection. The key result is therefore not that plasticity “eliminates\nselection,” but that the balance between buffering and selection strength produces qualitatively\ndifferent evolutionary regimes! That finding may now seem somewhat obvious (thanks to the\nmodel itself!) but it has certainly not been part of the academic discussion that has been taking\nplace over the last two decades.\nIn that context, I’d like to point out that debates over whether phenotypic plasticity (generally\ndefined) accelerates or decelerates evolution have persisted for decades precisely because\nintuitive verbal arguments can be made in both directions! The value of the present model lies in\nformalizing an influential verbal hypothesis and demonstrating that it can help us understand\nwhy the evolutionary consequences of behavioural flexibility appear to be contingent, scale-\ndependent, and non-linear. The fact that you now find the mechanisms you mention quite\ntransparent should therefore not be viewed as a weakness of the model, but as evidence that it\nis doing exactly what it needs to do: provide conceptual clarity about when, why, and over what\ntimescales behavioural plasticity will speed up or slow down genetic evolution!\nRe your confusion about how SGV was computed, please consider that in the first version of my\nmanuscript I say that:\n“Standing genetic variation in I also increased with behavioural buffering (Fig. 2). Specifically,\nwithin-population variance at generation 500 (𝒔𝟐 ) rose sharply at low b…”\n𝑮𝟓𝟎𝟎\nAfter incorporating the revisions to the model architecture suggested by Reviewer 2, the\ntimepoint for comparison has been changed to generation 1500 but other details remained\nunchanged. In the new version, I tried to clarify the definition by restating it as follows:\n“Standing genetic variation, as measured by the within-population variance in I at generation\n1500 (𝑠2 ),…”\n𝐺1500\nFinally, please note that the code for computing these variances was already provided in the\nfirst version of the supplementary software file, in the statement:\nthese.results$GenotypicVarianceBaseline <- var(my.pop[,'Genotype'])\n… although I understand that this may have not been entirely obvious because the code to\nproduce figure 2 was not shared in my original submission. The new version of this file contains\nthis instruction in line 177, which takes place immediately after finishing “phase 2” (i.e., the 1000\ngenerations allowed for evolution in response to a change in environmental target from 0.5 to\n0.3), and the code for Figure 2 now transparently shows where the data are coming from.\n- I find the methods hard to follow. I would expect to see a clearer explanation of what happens\nin each step of the simulation. The information should describe what happens in the main loop\nof replicate.run in the R script. Some of the information is currently in the manuscript it is just\nspread out. Once that has been described sufficiently, I would describe the simulation\nchronology.\nApologies for that. I have revised the manuscript to include a full description of the model,\nits assumptions and main steps right at the beginning of the Results section. The changes are too\nextensive to quote here, but I do note that, per your suggestions, a description of every step in\nthe main loop of the simulation code is now available in a single location.\n- Lines 202-204: “My analyses suggest that behavioural plasticity exerts dual and time-\ndependent effects on the tempo and mode of adaptive evolution: it weakens selection gradients,\nand it promotes the accumulation of genetic variation within populations.” Based on my current\nunderstanding, these are not findings of the model but rather assumptions baked into the model\n(see above).\nPlease see my response to comment on Lines 180-183 (above).\n- Lines 204 following: “On short evolutionary timescales—or when opportunities for niche\nexpansion are severely constrained—these effects lead to slower morphological and\nphysiological evolution in an altered environment (Fig. 5).” Where in the model did the author\ninvestigate the effect of timescales? Figure 5 is just a schematic figure and does not seem to be\nbased on modeling outcomes. The way the text is written suggests otherwise.\nAs described in the main text, the experiments simulating a small diaspora that is exposed to a\ndramatically different environment intend to approximate the process of allopatric divergence in\nmacro-evolutionary timescales (in contrast with the first section, which was concerned with\ngradual in situ macro-evolution). The reason this passage was placed in the Discussion is\nbecause it is integrating the totality of my findings into a single framework to give us a global,\ncohesive understanding of the likely effects of behavioural plasticity. I appreciate, however, that\nI can be more explicit in my distinction between results and interpretation, so in response to the\nreviewer’s comment I have revised the text as follows:\n“On short evolutionary timescales, or when opportunities for niche expansion are limited, my\nresults suggest that these effects are likely to translate into reduced rates of morphological and\nphysiological evolution (Fig. 5). However, over longer timescales, or when opportunities for\necological diversification abound, they suggest instead that the differential abilities to persist\nunder suboptimal conditions will produce a Goldilocks pattern in which the pace of evolutionary\nchange peaks at intermediate levels of behavioural plasticity (Fig. 5).”\n- In my opinion, the discussion is too strongly worded given the simplicity of the model.\nI have included a new paragraph in the Discussion to cover the model’s main limitations. The\nnew text reads:\n“The model developed here is intentionally simple, a choice that entails both limitations and\nadvantages. Some of its limitations include the assumption that behavioural plasticity is cost-\nfree and non-evolving, and the examination of evolutionary consequences on a single\nquantitative trait under stabilizing selection. Future extensions could relax these assumptions by\nallowing plasticity itself to evolve, incorporating energetic or opportunity costs of flexible\nbehaviour, modelling effects on multiple traits with conflicting selective demands, or embedding\nthe process in spatially structured or temporally autocorrelated environments. Such extensions\ncould further refine our understanding of the limits of behavioural buffering.”\n- In the discussion, I would have found it interesting to read something about the interplay\nbetween phenotypic plasticity in morphological and behavioral traits and their combined impact\non evolutionary dynamics.\nI agree that could be interesting too. However, please note that this suggestion is beyond the\nscope of this manuscript.\n- It would have been nice to also see the R-code that generates the various figures in the\npaper.\nI added R-code to reproduce all figures and reported stats at the end of the supplementary\nsoftware file.\nI hope my review will help the authors to strengthen their manuscript.\nI sign all of my reviews for accountability and transparency.\nWith kind regards,\nNicole Walasek\n############################################################################\n###############\nReviewer #2 (Remarks to the Author):\nThis paper explores a simple simulation model to understand how buffering of maladaptation\naffects the rate of evolution and the survival of subpopulations in stressful environments. I think\nthis is an area of theory in which there is much left to learn and that this scenario would interest\na broad range of readers. However, I think that the modeling falls short of what I would want to\nsee in order to recommend this paper, though it certainly could be revised to be an excellent\ncontribution.\nThank you for your positive comments and for the detailed and useful suggestions provided\nbelow. I have fully addressed your primary concerns about the model by incorporating explicit\ndensity dependence via Beverton-Holt dynamics and enabling sexual reproduction. These\nchanges have strengthened the modelling framework without substantially altering the\nqualitative nature of my findings. Details on the changes made and their consequences are\nprovided below…\nThe Introduction is excellent and lays out the central question and its motivation concisely and\nclearly.\nCommunicating complex ideas to a general audience is often a careful balancing act. Your\ncomment is a highly appreciated counterpoint to Reviewer 1’s characterization of the same\nsection as ‘thin’.\nMy only quibble would be that the approach the paper will take is left quite vague in the Intro.\nThis is OK, but I would like to have a little more about the methodological novelty here. Has no\none modeled this question, or is there a difference in the approach taken here compared to prior\nwork that can help explain why novel answers were found? It is fine if this topic is reserved for\nthe Discussion (though it isn’t much discussed there either), but some foreshadowing at least in\nthe Intro would suit me.\nThank you for pointing this out. I added the following paragraph to highlight the main\ndifferences:\n“Earlier theoretical work on phenotypic plasticity has provided important insights on its\nevolutionary consequences, but has not typically distinguished between behavioural and more\nstructural forms of plasticity28,29,41–44. Likewise, the studies that have explicitly made such\ndistinction have focused primarily on the evolution of plasticity itself rather than on its\ndownstream consequences on the evolution of other components of the phenotype37,45,46.”\nAs for why novel answers were found… the simple answer is that this model takes a very\ndifferent approach to modelling plasticity: the operationalization of our current understanding of\nthe explicit mechanism by which behavioural flexibility affects selection.\nThe Results and Methods are presented together, with a later section giving more Methods. I\nthink this is a fine approach in general but confused me at first reading, as the earlier section\nseemed to have too little detail to be a complete Methods section but also too much to be a\nconcise overview of the model. We aren’t told important things like that this is (I think) a\nmodel of discrete, non-overlapping generations in either place. I don’t need to know the mutation rate\n(which varies anyway) before understanding basic aspects of the model like how demography\nworks. The crucial information that population size is allowed to vary is mentioned only in the\nlater Methods section, making the survival results in Figure 4 come out of left field for me. This\nshould be communicated earlier, and the constant k included in the the equation on line 109 so\nthat it actually gives absolute fitness.\nApologies for that. I can see how the earlier structure was confusing, and I appreciate that key\ndetails from my approach had not been described. To address these deficiencies, I completely\nrestructured the paper, providing a full description of the methods, assumptions and limitations\nin the first lines of the Results section. Relevant new text includes:\n“Modelling Framework. To investigate the evolutionary consequences of behavioural plasticity,\nI developed an individual-based simulation model that tracks the evolution of a quantitative\nmorphological trait, I, in a sexually reproducing population with non-overlapping generations and\ndensity-dependent regulation. Selection on I is stabilizing, such that fitness is maximized when\ntrait values match local environmental conditions.”\nFurther revisions made in response to this comment are detailed below, including the addition of\nsections explaining how density dependence and sexual reproduction were incorporated into\nthe model.\nI am still left confused about some aspects like density dependence–is there any? It seems like\nexcess individuals are discarded, but there isn’t any effect of density on reproduction otherwise.\nThis seems like a very odd choice as it doesn’t permit populations to stabilize at sizes below\ncarrying capacity. I suspect that this is why the prob. of survival plots in Fig. 4 are so steep.\nYou are absolutely right… I can and should do better on that front. The algorithm I used to\nenforce a carrying capacity in the first version of my model was admittedly not the most elegant\nimplementation of density dependence. In this new version of my model, I have replaced it with\nBeverton-Holt dynamics. The process is now described in the main text as follows:\n“Population growth was regulated by Beverton–Holt density dependence51. Specifically,\neffective fitness, 𝑊′, was computed as\n𝑖\n𝑊′ = 𝑊𝑖 ,\n𝑖 (1+𝛼∗𝑛)\nwhere n is the current population size and  governs the strength of density-dependence.  was\ngenerally tuned by assuming a baseline reproductive output of 𝑅 = 2 offspring and a carrying\n0\ncapacity of N=5000 individuals (i.e., 𝛼 = (𝑅 −1)⁄𝑁 = 0.0002).”\nunderstand what role population size might be playing here. This also plays in to the choice of\nasexual reproduction. In the right context, it can be fine to ignore density dependence and to\nuse this simpler, asexual form of reproduction. But, I’m not convinced that these questions\nbenefit from these choices, because the level of genetic variation seems so critical. I would\nexpect feedback between fitness, as buffered by behavior, and population size to be very\nimportant for understanding how buffering affects adaptation. Similarly, I would expect the ability\nof sexual populations to quickly generate extreme phenotypes to be critical to colonizing new\nand stressful environments. I therefore don’t think that the model is ideally suited to investigating\nthese questions, though it could readily be extended to do so.\nThank you as well for these insightful comments. Your intuition is largely correct. I implemented\nsexual reproduction through the following algorithm (description taken from the revised\nmanuscript):\n“The algorithm for sexual reproduction began by determining the number of offspring produced\nby each adult in the population, drawing these numbers from a Poisson distribution with a mean\nequal to the individual’s density-regulated fitness (𝑊′). For each offspring, a second parent was\n𝑖\nrandomly drawn from the population, with selection probabilities proportional to effective fitness.\nFollowing standard quantitative genetic approaches for polygenic traits52,53, offspring genotypes\nwere subsequently determined as the mid-parent value plus a small mutational change drawn\nfrom a normal distribution with mean zero and a standard deviation of μ=0.05 (unless otherwise\nstated). This modelling approach captures additive genetic inheritance without explicitly\nmodelling individual loci, approximating with a single mutational change value the cumulative\neffect of many small and low-probability events across loci.”\nAs you predicted, the introduction of sex increases the rate at which new phenotypic variants\nare introduced to the population and alters evolutionary dynamics. While the main findings\nremain unchanged, there are some notable differences in the findings from the new approach:\n- Standing genetic variation now increases linearly with behavioural buffering (Fig. 2). For\nreference, this increase was best described by a cubic spline in the first version of the\nmodel.\n- The rate at which mutations accrue is now much more consequential than in the initial\nmodel (see new Fig. 3E). Nevertheless, it is still true that behavioural buffering enables\nthe accumulation of genetic diversity beyond what is possible from mutation alone (see\nFig. 3F). It is also now evident that the positive effects of behavioural plasticity on the\naccumulation of genetic variance are strongest when selection gradients are more\nintense (see new Fig. 3C). The relevant passages in the new text are:\n“As expected, stronger selection (smaller d) accelerated genetic evolution and reduced\nstanding variation (Figs. 3A–B). Similarly, the pace of genetic adaptation continued to be\nnegatively associated with behavioural buffering across d values, but the decline was\nconsiderably steeper in simulations with stronger selection (Fig. 3A). Likewise, the positive\nassociation between behavioural plasticity and genetic variance persisted across d values,\nbut overall levels of variation decreased when selection was more intense (Fig. 3B). Close\ninspection of this pattern indicates that strength of selection had a pronounced effect at low\nb but minimal or no effect at high b. Consequently, the rate at which standing genetic\nvariation rises with b is steeper when selection is stronger (Fig. 3C), underscoring the critical\nrole that behavioural plasticity can play in enabling the accumulation of genetic variants that\nwould otherwise be removed by selection.”\n… and for mutation:\n“The effects of mutation rate on the evolution of insulation were similarly affected by\nbehavioural buffering (Fig. 3D-F). As expected, higher rates of mutation accumulation\ngenerally fuelled faster adaptation, but this effect weakened in lineages with stronger\nbehavioural buffers (Fig. 3D). Similarly, standing genetic variation increased conspicuously\nwith mutation rate (Fig. 3E) and continued to be dependent on b. Here too, the increase in\nstanding genetic variation with b was more pronounced at higher mutation rates (Fig. 3F),\nsuggesting that behavioural plasticity potentiates the effects of mutation by facilitating the\naccumulation of genetic variants produced during periods of stability.”\n- One last difference. As you predicted, transitions from total collapse to long-term survival\nare now more gradual in my diaspora (macro-evolutionary) simulations (see new Fig. 4).\nNevertheless, the general pattern is still largely a stepwise change, indicating that\nrelatively small changes in cognitive ability (or behavioural buffering) could have\nprofound implications for population survival and diversification potential. The relevant\ntext here is:\n“As expected, population survival was more likely when environmental change was less\nintense (Fig. 4A), selection was weaker (Fig. 4B), or mutations accumulated at a faster rate\n(Fig. 4C). With the possible exception of Fig. 4B, these effects were visibly weaker than\nthose of behavioural plasticity, further suggesting that behaviour can play a critical role in\nprotecting populations against collapse during rapid environmental change. Furthermore,\nthe stepwise shifts observed in Fig. 4 from population collapse to long‑term persistence\nindicate that some relatively small increases in cognitive ability can substantially improve a\nlineage’s capacity for colonization, diversification, and long‑term viability.”\nI would also suggest that focusing the model on insulation isn’t quite the best level of abstraction,\nas it makes the model read as narrow when it doesn’t need to. There’s nothing specific to insulation as far as I can see, and certain aspects of the model\n(symmetrical loss of fitness if one is over- or under-insulated) seem abstract anyway.\nThank you for noticing that the model is broadly framed and that its findings are not limited to\nthe evolution of insulation. It has been my intention from the beginning to make it as general as\npossible. I am nevertheless of the opinion that having a concrete example can help readers\nconceptualize the issues and develop their intuition. Yet, your comment made me realize that\nmy initial wording did not convey generality as explicitly as I’d like. To address that gap, I added\nthe following statement the model description:\n“For illustration purposes, I hereby frame the model in terms of thermal insulation: thick coats\nare favoured in cold habitats and impose costs in warm ones, whereas thin coats exhibit the\nopposite pattern. However, readers should note that this model is intentionally general and\napplies to many other traits under selection toward an environmentally defined optimum.”\nSimilarly, there’s nothing about the buffering mechanism which is specific to behavior in my\nview. This is both good and bad; the paper could apply much more\nbroadly, but also these results are perhaps less original when viewed in the context of general buffering mechanisms.\nThis is a great insight, thank you sharing it. I have incorporated it into the manuscript and have\nsuggested some likely buffering agents that could affect evolution in similar ways. The relevant\ntext is:\n“There are also, however, some important conceptual benefits to the model’s generality. Given\nthat behavioural plasticity was broadly abstracted as a general buffering agent that helps reduce\nthe fitness costs of environmental mismatch, the model’s insights can potentially be extended\nfar beyond behaviour alone. For example, rapid physiological adjustments (e.g., acclimation),\nhuman‑mediated interventions (such as conservation actions), and even ecological\ninteractions44 (e.g., microbiome shifts or symbioses) can presumably buffer the costs of\nphenotypic mismatch in similar ways, and could therefore result in similar evolutionary\ndynamics.”\nI would recommend recasting the model in more general, abstract terms, positioning it more\nthoroughly in terms of prior theory work, and modifying or extending it to allow for more\nreasonable demographic behavior, and possibly sexual reproduction. With these sorts of\nchanges, I think this would be a very valuable paper.\nI want to thank the reviewer once more for their constructive critiques and insightful\nsuggestions. Most of the issues raised in this last summary paragraph have been addressed in\nmy responses to earlier comments so I will focus here on the ones I have not already covered.\nPrior theory work. I expanded the introduction to better differentiate my model from earlier\ntheoretical studies on plasticity (both behavioural and broadly defined). I have added both\nsources suggested by Reviewer 1 and what I consider to be some of the classics in the field.\nThe literature on this topic is vast, and difficult decisions had to be made re what should be\nincluded and what was less relevant. Please let me know if I missed something vital.\nModel simplification. Given that changes to reproduction and density dependence algorithms\ninvolved re-running all simulations, I took the opportunity to further simplify the model. Some of\nthese changes were practical (e.g., I vectorized the code to help it run much faster) but other\nwere structural. Specifically, rather than allowing populations to evolve twice in response to an\nenvironmental change, I formally established a 500 generation burn-in period at E = 0.5 (i.e., the\nmid-value of starting genotypes) to allow populations to establish natural levels of standing\ngenetic variation prior to introducing the first environmental change. This structural change\nsimplified the model description and eliminated a paragraph from the results, giving me some\nextra space to accommodate the changes requested by reviewers."}, {"round": 2, "reviewer_comments": {"reviewer_1": "To the author / editor:\nI really appreciate the respectful and thoughtful responses by the author. I found that they not only addressed my concerns\nbut also were quite interesting to read and engage with.\nI have no further comments.\nKind regards,\nNicole Walasek\n(Remarks on code availability)", "reviewer_2": "This is a very thorough revision that fixed many of my issues and improved on the earlier draft’s strengths. The Intro has\ngone from good to great in my estimation; I’ve read a lot of plasticity papers and actually found it quite stimulating in this new\nversion. The model has been revised considerably in actual function and presentation. The addition of a clear model of\ndensity dependence is excellent, as is the presence now of recombination. The balance of abstraction and specificity of the\nmodel is also now more artfully presented and, to me, works. I really have no further issues, and recommend acceptance.\n(Remarks on code availability)"}, "author_response": ""}]

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