Pheromone patch

Pending Admin Approval. Cira Best Onion. Similar threads. Joza Perl Apr 16, Approved Technology. Replies 8 Views Apr 16, Cira. Locked Approved Tech Heartbeat Heels. Joza Perl Oct 23, Approved Technology. Replies 5 Views Oct 26, Allyson Locke. Saffron Oct 18, Approved Technology. Replies 3 Views Oct 19, Allyson Locke. All of our pheromones can be mixed, matched, and tailored to your specific social needs.

I would expect a mix containing these pheromones to create the effects of higher status, and higher levels of sociability. By that logic, it would seem they did really attempt to try and infuse pheromones into sticky skin patches. In fact, I quite like their simple, but very effective product line as standalones, as well as adding for combos for more complicated phero-concoctions. However, this attempt at recreating the Oceans 13 pheromone was a a bit of a reach, and probably did not spend enough time to truly develop something groundbreaking — not that we would expect such things from pheromones anyway.

It is nowhere near that effective, and even strong pheromone colognes and perfumes do not work in that way. In theory, yes. Applications on skin or clothing are likely to work much better than having pheromones applied on a small patch, and then applying it on your neck. Yes, True Pheromones is the only company currently selling their version of this product.

The only Gilroy Patch is a gimmick product from trusted vendor True Pheromones. The Gilroy Patch by True Pheromones review Effectiveness Range Complexity Scent Failed, But Good Attempt Although this product is impractical to use for various reasons, I feel like its always worth some credit when a company tries something NEW - after all, we never would have known the effects of pheromones until people studied them and through experimenting, discovered their effects.

I do not recommend this specific product, although the True Pheromones line up is well respected for their working and affordable products. Hey, Phero Joe here. Quick backstory - I've been a pheromone enthusiast for over 8 years now - and have tested countless pheromone products This non-stationary environment has received less attention than the previous factors.

It is typical of species with boom-and-bust life cycles such as C. It is interesting to note that the Evolutionary Stable Strategy found by our model does not give any benefit at the level of the species, and may even be deleterious. This Evolutionary Stable Strategy therefore emerges from intraspecific competition: Individuals benefit from paying the cost of switching to prevent being outcompeted by other individuals within the population, even if the end result is deleterious for the population as a whole.

An important evolutionary adaptation in this regard is that the C. We have identified associative learning as the most plausible phenomenon underpinning the change in pheromone preference. During feeding, worms learn to give a positive or negative preference to pheromones depending on the context in which they experience them, in particular the presence or absence of food Wyatt, A similar learning process occurs in bumblebees that, in their natural habitat, do not land or probe flowers that have been recently visited and marked by chemical footprints left by themselves or other bees.

It has been shown that only experienced foragers, that is those that learnt to associate the chemical footprints with the absence of nectar in marked flowers, can successfully avoid them and increase their overall nectar intake Ayasse and Jarau, This suggests that associative learning based on pairing pheromones or similar chemical signals with food availability might be frequently observed in animals feeding in groups, not only eusocial insects, as a strategy to increase food intake.

We have shown that dispersal of feeding stages of C. A mechanism based on the synergistic interaction between food and pheromones also regulates C. Our findings establish an interesting parallel between mechanisms promoting dispersal over short and long temporal scales and highlight the important role that non-dauer stages play in exploiting transient bacterial patches.

They also point to the emergence of interesting group dynamics promoted by this synergistic interaction between food and pheromones, adding to the wealth of studies addressing aggregation behaviors in C. Ascr 5 and icas 9 are potent signaling cues that are usually detected in C. The ability to assign a positive or negative preference to the pheromone blend through associative learning might depend also on other byproducts of worm metabolism or derive from the presence of multiple ascaroside molecules acting synergistically Srinivasan et al.

More studies are required to establish a link between associative learning and the composition of the pheromone blend, which is known to vary among developmental stages Kaplan et al.

As a final remark, our results suggest that C. In conclusion, our study establishes a link between learning and social signals, providing a framework for further analysis unravelling the neuronal origin of the observed behaviors. However, the experiments presented here were performed with the natural isolate MY1. Thus, it remains to be tested if the same responses occur in the canonical lab strain N2, whose social behavior has changed due to laboratory domestication Sterken et al.

Nonetheless, by working with a natural isolate rather than N2, we could provide insights into the ecological significance of the inversion in the preference for pheromones and respond to the pressing need to further our knowledge of C.

We obtained the crude pheromone blend by growing worms in liquid culture for 9 days at room temperature and shaking at rpm von Reuss et al. Individuals from one plate were washed and added to a 1-l flask with ml of S-medium inoculated with concentrated E.

Concentrated E. The pheromone blend was then obtained by collecting the supernatant and filter-sterilizing it twice. A new pheromone blend was produced every 3 months.

The control solvent for the pheromone blend is S-medium, while the control solvent for the pure ascarosides is an aqueous solution with the same amount of ethanol present in the ascaroside aqueous solution Srinivasan et al. It is a chemotaxis assay modified from Bargmann and Horvitz, and Saeki et al.

Worms are left to wander freely on the assay plate for 5 hr. The number of worms around the two spots was counted every hour and the chemotaxis index was calculated based on the number of new worms that reached the two spots during each hour. Animals, either naive or trained, are placed equidistant from the two spots and left to wander on the assay plate for 1 hr at room temperature Figure 1—figure supplement 1B. The average number of worms in each experiment is indicated in the figure captions.

The number of independent experiments performed in different days is indicated in each figure caption. For each experiment, we usually performed 10 replicated assays for each scenario. After that, animals are transferred to conditioning plates.

Animals spend 5 hr in the conditioning plates at room temperature before being assayed for chemotaxis to the pheromone blend. Animals stay in the conditioning plates for one hour at room temperature before being assayed for chemotaxis to the pheromone blend.

Worms spent 5 hr in the conditioning plates at room temperature, after which they are assayed for pheromone chemotaxis. The authors thank Sreekanth Chalasani and three anonymous reviewers for their constructive feedback on the paper; the members of the Gore lab for comments on the earlier versions of the manuscript; Ying K Zhang for assistance with the synthesis of ascarosides and Jonathan Friedman for feedback on the model.

The C. We assume that two types of food patches exist. Food patches marked with pheromones, which have a high average value they are capable of sustaining worm growth and are easy to find.

Unmarked food patches, which have a low average value as they are more difficult to find. Initially, individuals are distributed across the pheromone-marked patches. For simplicity, here we assume that all food patches are identical. We will show below that removing this assumption does not change our results qualitatively.

A 0 : Initial amount of food in the food patches. A i t : Amount of food at the i -th food patch at time t. A E : Effective amount of food in a food patch amount of food that worms will extract from the food patch, above what they would obtain by dispersing from the beginning.

G : Total food intake. H : Payoff total food intake minus cost. K : Number of food patches. In the ESS these numbers remain constant until worms start to disperse. At any time, individuals can take three possible actions: Remain in the current patch, switch to another pheromone-marked patch so they leave the patch and follow pheromones , or disperse and search for an unmarked patch so they leave the patch and avoid pheromones.

Let's start with the choice of dispersal. To model this decision we borrowed the results of classical foraging models from which the Marginal Value Theorem was derived Charnov, These models describe an individual depleting a food patch, whose environment contains other food patches that remain stationary i. Accordingly, we assume that the unmarked food patches remain stationary.

In these conditions, one can compute an average expected intake rate from dispersing and searching for unmarked patches, which we will call g D.

This average intake rate takes into account the average quality of the unmarked food patches and the time needed to find and consume them. The optimal strategy is to remain in the current food patch until the instantaneous feeding rate g t falls below g D Charnov, Following these models, we assume that any individual that disperses will experience a constant instantaneous feeding rate g D.

While we can use the formalism of classical foraging models for the dispersal decision, we cannot do the same for the switching decision, because the pheromone-marked food patches are non-stationary they all get depleted at roughly the same time, a feature characteristic of species with a boom-and-burst life cycle such as C. We will therefore model explicitly food depletion in all pheromone-marked patches. Therefore, instantaneous feeding rate in a food patch will be.

Individuals that switch pay a cost c for switching. We assume that switching is fast compared to the depletion rate of the food patches, so switching is instantaneous in our model. Individuals that switch will then arrive to any pheromone-marked food patch with equal probability including their initial one. We now consider the total food intake, which is the integral of the instantaneous intake rate g t over a long period of time. The exact length of this period does not actually matter, because in all relevant cases we will be comparing strategies that end with dispersal, and therefore get the same intake rate at the end.

We will always work with differences between the payoffs of these strategies, so these final periods will cancel out. Finally, we assume that individuals have strong sensory constraints: They only perceive their instantaneous feeding rate g t , not having information about any of the other parameters number of patches, number of individuals per patch, etc.

However, their behavior can be adapted to be optimal with respect to the average values of these parameters over the species' evolutionary history.

Then they all remain in the food patches until their instantaneous feeding rate falls below g D , at which point they disperse. In the experiment we observed C. This difference is due to the simplicity of the model, which neglects factors such as inter-individual differences and stochasticity. Despite this simplicity, the model captures the key feature that individual worms leave their initial food patch before it is depleted switching behavior , which is a counterintuitive idea in the context of optimal foraging.

We will prove each part of the Evolutionary Stable Strategy separately. Individuals will not disperse until the occupied food patches are nearly depleted. Dispersing gives an instantaneous average payoff of g D , so individuals should never disperse if their instantaneous intake rate is above g D i.

We define. Let m 1 , m 2 … m K be the number of individuals in each food patch after the switch. These numbers are related to the initial distribution as. Here we are assuming that worms that switch have equal probability to arrive to any occupied food patch including the initial one.

After the switch, all individuals will remain in their new food patch until the instantaneous feeding rate reaches g D. From Equation S1 , this will happen at time. We are ready to compute the total intake over a period T for an individual at the i -th patch.

It's convenient to split this in the two periods before and after dispersal, so we have. Solving these two integrals and replacing t D , i for its expression in Equation S4 gives. We then have. Now we can compute the expected payoff for each decision H , which is the expected total food intake minus any costs incurred by the behavior.

Individuals that switch have an equal probability of ending up in any of the food patches, so their expected payoff is simply the average of the payoffs across the food patches minus the cost of switching:. Note that this benefit of switching is identical to what we would obtain using Equations 3a and 3b in the main text instead of Equations S9a and S9b. For this reason, this model and the simplified model presented in the main text are mathematically equivalent.

We also substitute m i according to Equation S3 , getting. Therefore, if switching is costless all individuals should switch regardless of the values of the other parameters. In the equilibrium, no mutant has an incentive to deviate from its strategy, since both switching and remaining give the same payoff. A new switch will leave these probabilities unchanged—we assume that the population is large enough so that a single mutant does not alter the distributions significantly. Therefore, individuals have no incentive to switch more than once.

Individuals must disperse once their current food patch is depleted. Therefore, worms should not remain. And neither they should switch, as we saw in sections 3 and 4. Therefore, they should disperse. The above calculations assume that all food patches are identical, having an initial density A 0 and the same sizes. In reality, food patches will differ both in their initial density and their size.

Differences in the initial density A 0 lead to effects that have been thoroughly discussed in the optimal foraging literature Stephens and Krebs, Higher density translates into higher feeding rate, which can be detected by the worms as they exploit the patches. Worms that estimate that their current patch has lower quality than the environmental average should leave it and search for a more profitable one, and the key challenge is the estimation of this environmental average quality. Part of the merit of our work is to show that even in the absence of these differences in initial density, worms may benefit from leaving a non-depleted food patch.

Differences in patch size are more interesting from our point of view. A larger food patch will not give a higher instantaneous feeding rate, but will be depleted more slowly.

So the amount of food in the patch will be. We assume that worms cannot detect the size of their current food patch the only way to do so would be by measuring depletion rate, which is more difficult than measuring instantaneous feeding rate and requires waiting for a long enough period of time for depletion to be significant.

Therefore, the behaviors available to the worms are the same as before, and they still have an equal probability of arriving to any food patch when they decide to switch. Repeating the calculations made above but using Equation S15 as instantaneous density, the benefit of switching becomes. In fact, it is possible to start in a situation in which switching is detrimental: If large patches contain more individuals at the beginning, remaining in the patches will produce a more balanced depletion than switching to equalize the number of individuals per patch.

However, this good match between patch size and number of individuals is unlikely. In nature one would expect little or no correlation between patch size and initial number of individuals. In this case, the average result is very similar to the case with identical food patches Figure 2—figure supplement 2.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Competing interests No competing interests declared. Author contributions Conceptualization, Data curation, Formal analysis, Visualization, Writing - original draft.

Conceptualization, Formal analysis, Funding acquisition, Writing - original draft. Conceptualization, Supervision, Funding acquisition, Writing - original draft. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses. This paper will be of interest to scientists in the field of animal behaviour, especially those working on foraging, navigation and the integration of sensory cues.

Experimental data obtained with C. The authors interpret this behaviour as an optimal foraging strategy and suggest a model that might represent a first step towards a theoretical understanding of these observations. Thank you for submitting your article "Inversion of pheromone preference optimizes foraging in C. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Christian Rutz as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this decision letter to help you prepare a revised submission. Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.

Our expectation is that you will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife , either of which would be linked to the original paper. The authors present an analysis of the foraging dynamics of C. Navigational preference for these sensory cues is found to change from attractive to repulsive depending on the time at which worms leave a food patch, and additional experiments that condition worms under different combinations of conditions indicate that associative learning is involved in this inversion of preference.

This behavior is illuminated by a mathematical model that points to the conclusion that this inversion represents an optimal, evolutionarily stable foraging strategy. Overall, the results are interesting and clearly presented but could be better supported by experiments and modeling. The paper is of high quality and would be of interest to the readership of eLife provided that the following crucial remarks on experiments and modeling are duly addressed by a major revision.

Regarding the interpretation of the data in Figure S2, which was designed to test for associative learning in the preference for specific pheromone molecules synthetic ascarosides. You state in the main text that the results in Figure 3 of associative learning experiments with the 'pheromone blend' i.

Thus, it appears as if the preference for these pheromones might be explained by feeding status only, without associative learning. Have we missed something here? The results of Figure 3 are still consistent with associative learning, but the results of Figure S2 appear to suggest that the cues involved in the associative learning are different from the specific pheromones tested within Figure S2, and must in fact override the feeding-state dependent preference to these specific ascaroside molecules.

This leaves us wondering whether it is fair to conclude that the sensory cues involved in the associative learning are really pheromone molecules. Can you rule out that the learning is a response to other substances in the supernatant, e. You may want to consider conducting additional experiments to find other pheromone molecules that do demonstrate evidence for associative learning , or to just change relevant wording to avoid committing to what appears to be at present a weakly supported conclusion.

Although conceptually appealing, the modeling appears to be an add-on to the experimental results. It also seems to suffer from weaknesses that should be addressed as detailed below. First, it would seem that at least some of the analysis results e.

This does seem a strong assumption if the model is to be considered relevant to natural ecology. Which of the model-based conclusions are still valid if this assumption is removed? Second, how reasonable is the assumption that all worms start in all patches at the same time? Naively, it would seem more realistic that a small founder population would arrive at a patch, and that these foraging dynamics would play out in a context that involves not only food depletion but also population growth.

Would the main conclusions of the model about optimal foraging and evolutionary stability still hold in a more realistic model that considers such expected natural population dynamics? Third, the key conceptual assumption is that all patches are either colonized together or left unoccupied.

It would be helpful to provide references supporting this assumption. Fourth, the statement at line that "worms have no way to tell if they are in the overcrowded or under-crowded environment" seems at first sight to be inconsistent with the very idea of pheromone signalling. Can you please clarify? Since the mathematical model -- currently relegated to the supplement -- forms an important part of the manuscript, we think that it should be clearly summarized in the main text with an accompanying figure, taking into account all the concerns raised above.

Would the results change with a different strain? Would it be feasible to consider a mutant with blocked pheromone sensing as a null control? Could you please explain the seemingly contradictory statements? The following individual involved in the review of your submission has agreed to reveal their identity: Sreekanth H Chalasani Reviewer 4.

The reviewers concur that this article offers an interesting conclusion regarding optimal foraging and chemosensory valence. However, they also agree that it would benefit from a second round of revision, aiming at an improved precision of language and a better discussion of the assumptions of the model and experimental conclusions.

The revised version is still unsatisfactory from this point of view. For more detail about these points, please read the Recommendations by the Reviewers. Many aspects of the manuscript have improved in this revision but unfortunately, I feel that the authors still haven't adequately incorporated the model details into the manuscript.

I do feel strongly that one shouldn't show model predictions Figure 2 without a summary of the model and its most important parameters. Such a summary is necessary so that readers can reasonable judge the modeling choices. I also disagree with the authors own assessment "The model is now extensively described in the main text lines ". One cannot read these lines and easily recreate any sense of the model.

There are many more details given in the "Foraging model" section of the supplement, but these details are also not presented in such a way so that the model architecture is clear. Line 22 consider removing "model" or changing to "animal model" to distinguish from the computational model. The authors present experiments that demonstrate how C. A mathematical model is provided to argue that this inversion represents an optimal foraging strategy that is also evolutionarily stable.

I am satisfied that the edits made by the authors sufficiently addressed concerns I raised in the previous round. The authors use the nematode C.

Specifically, they show that C. The authors using a behavioral model to suggest that the switch from attraction to repulsion is likely due to a change in learning.

This study links learning with social signals providing a framework for further analysis into the underlying neuronal pathways. Decision-making in C. If there is a decision to be made regarding exploitation of a food patch, what are the possible actions that the animal can choose to take? It is unclear whether or not the use of N2 in these experiments would change the results.

If these results do not hold in N2, this is of note because it could lead to interesting follow-up experiments aiming to identify the biology underlying the MY1-specific behavior.

The use of MY1 is cause for concern in truly placing these results within the full body of C. It seems prudent that the use of this strain be further addressed in the discussion. How far do pheromones diffuse to within the detectable limit of a C. Is it clear that the decision to leave a patch is made without knowledge of other food patches?

Or is it possible that the animals are receiving pheromonal cues about other patches even while residing on a food patch bathed in pheromones. This distinction seems important to both the conclusions of the paper and the model and are not discussed in the body of the text. Can the diffusion of pheromones be experimentally defined or modeled to support the assumptions of the foraging model? Is this foraging model ethological? Would worms not have a much lower payoff of leaving their patch in their natural boom-and-bust environment?

Specifically, would patch density not be significantly lower in a natural environment such that switching patches is not beneficial until much later in food depletion which could presumably be generations later? Consider the spatial scale of a C. Line This brings up an interesting discussion point. The model and experiments assume stationarity of the pheromone blend over time with an inversion of valence occurring due to associative learning and satiety.

However, would the pheromone blend not change throughout the course of the experiment? Could the specific combination of pheromones in the blend possibly be cause for the valence change? Overall, I think this paper offers an interesting conclusion regarding optimal foraging and chemosensory valence. However, I do think it would benefit from precision of language and better discussion of the assumptions of the model and experimental conclusions.

Thank you for resubmitting your work entitled "Inversion of pheromone preference optimizes foraging in C. This paper will be of interest to scientists in the field of animal behaviour with a focus on foraging, navigation and the integration of sensory cues. The authors interpret this behaviour as an optimal foraging strategy but the modelling in support of this conclusion could be improved. Del Bello et al. The paper offers an interesting conclusion regarding optimal foraging and chemosensory valence that is mostly supported by the data.

However, the modelling part is comparatively weaker. Below is a list of the issues that still need to be addressed:. This distinction seems important to both the conclusions of the paper and the model and is not discussed in the main text. Can the diffusion of pheromones be experimentally defined or modeled, to support the assumptions of the foraging model? I do note a typo in the caption of Figure 2: "During the first phase, worms equalize occupancy the occupied patches.

Gore et al.