In this manuscript, the authors develop an experimental methodology to assess the presence of deterministic Allee effect in laboratory controlled growing populations of parasitoids. As stated by the authors, determining whether a population exhibit an Allee effect or not is correlated with an increased extinction risk at low population numbers. Determining whether Allee effects are present or not in growing populations has important implications for the development of populations and their potential survival. Given my area of expertise, I can evaluate the statistical methodology and the significance of the results that come out of the analysis. I cannot evaluate, however, the experimental design and the adequacy of the empirical assumptions used by the authors. Overall, I think that the manuscript is well written, concise and has a clear-cut objective. My global impression is that I can recommend this preprint to be published by PCI Ecology once a couple of questions about the statistical analyses are responded by the authors. 1. Fig. 2. Non-linear fits show clear evidence of Allee effect in three populations. However, there is a population (cac8) for which per-capita growth rate data seem to show an increase for small populations, but the presented fit does not seem to account for that fact (other populations like sem4 clearly account for that initial increase). Please, explain why and add a bit of discussion about this. 2. Given that experimental conditions are controlled and remain the same across populations, I find intriguing why all the cacoeciae populations do not exhibit Allee effects at all except for one population (cac_8). Do the authors have any idea of why this population is singular compared with the remaining cacoeciae? Please discuss this point a bit in the manuscript.

The preprint by Vercken et al presents results from an experiment where the growth rates of Trichogramma populations and their density-dependence were determined. The authors find three out of 20 cases where an Allee effect would be present.

I enjoyed reading the preprint but have doubts concerning the conclusions and would appreciate that the Allee-like effects discussed by e.g. Lande (1998) are treated from the introduction and more seriously considered. I would personally prefer reserving the term "Allee effects" for growth rate effects due to lack of social interactions at low densities. That does happen in the discussion but insufficiently so in general. In the introduction Allee effects are equated with positive density-dependence, which is a good choice as well, but then all potential mechanisms have to be introduced immediately. The abstract should define Allee effects as positive density dependence in that case as well. What you call "Allee-like" effects are there because of stochastic variability, interactions of means and variances. If you can attribute your results to them, please do so clearly. Lande (1998) calls them a type of Allee effect, so why not categorize different causes which can make one conclude from a time series analysis that there is positive density dependence and call all of them Allee effects?

I am unconvinced by the way Allee effects are detected: that happens just in the predicted time series, as if these predictions are perfect and without any uncertainty. You should consider the confidence bands for the predictions and detect an Allee effect from them.

Specific comments: Introduction: Plead add a citation to the first paper where the Allee effect was introduced. The papers added as references for the modified logistic model in fact refer to several different modified models, so there are "modified versions" of the logistic model. The terminology of Boukal and Berec is more precise than "weak" and "strong" Allee effects so please introduce it and use it. Maximum per capita growth rates are smaller with an Allee effect: does -A have to be smaller than K for this, please be more precise. Please state in the introduction how an Allee effect is detected in an actual population dynamical time series, so how its presence can be determined. Explain whether this then automatically implies that other statistics of the population dynamics will correlate with the strength of an Allee effect estimated from a time series or not. The introduction now gives us a feeling for what Allee effects do in models, but not how they emerge from time series analysis. I would already interpret a conclusion potentially differently here: did a time series with an extinction increase the likelihood of detecting an Allee effect of the kind Lande 1998 studied?

Methods: I don't agree with removing some of the time series from the analysis. You removed your true replicates. A mixed model analysis can deal with it easily: you did fit random line effects. There seemingly is an error in one of the sentences: one T. cordubensis population was removed for which there were several replicates? In the list the number (3) only goes down to (2). The text has to be written more clearly and state much earlier that replicates were initiated with different densities. You could drop the correlation tests on small population sizes, it's not treated in the results. The statistical analysis of per capita growth rate: Please use a Poisson model with an exponential link function. Your data are counts. Figure 2 suggest that your analysis might be too optimistic and underestimate expected demographic noise. I also don't see the need for the transformation in Eqn. 1. Use the density of the previous time step t as an offset. Then the model fits ln(lambda): in "R" a simple model version reads: glm(Nt+1 ~ 1 + offset(ln(Nt)), family = poisson). The intercept is ln(lambda). Fixing the spline parameter at k = 8 in the presentation: first of all, which method was used to fit the models, and second, the figure doesn't give the impression k was fixed. Was it allowed to be simplified automatically? Figure two needs to show confidence bands of the predictions. Glmm analysis: there are three response variables. Three population sizes are converted into a maximum value and a mean, which is hardly any data reduction. The expected variability of the mean will depend on the length of the time series, so this should be accounted for. Since the response is multivariate, a multivariate model should be fitted with each replicate as unit of observation, and an overall anova test done of the Allee effect on these responses. In a way, however, this test is a bit strange, because the presence of an Allee effect is derived from grouped growth rates i.e. population density data (is this circular or not?) and as if the Allee effect is a lineage property which is perfectly known. However, you probably don't and you need to keep this in mind for the discussion. AIC are not good for hypothesis testing, because they tend to select models which are too rich in parameters (That's why they are very efficient at predicting). So simply use an anova applicable to your mixed models, which will probably be a parametric bootstrap for example. Interpreting the p-value of the fixed effect estimate is strange, because you already went through a frequentist procedure (the way you apply AIC) before looking at these. In the equation for RI, what is "C_8"? If extinction occurs, isn't replicability automatically lower? Is it reasonable to use this measure, because when the difference is large after one generation, do you expect further large differences or again small ones? Do the densities at all time points contribute the same information? Maybe just t=1 is most informative?

Results: Figure two: connect the three points per replicate by a line. Confidence bands please for the predicted curves. Describe how you derive that an Allee effect is present from inspecting these confidence bands. If k changes automatically due to the model fitting procedure, indicate that. AIC: if you use AICc which is more pertinent, which values do you get? I find the p-values for the fixed effect parameters in two cases on the edge. What are the confidence intervals for these parameters? RI: what does a randomization test produce as p-value? The extinction probability of populations assigned to Allee is about 50%. Maybe you are just detecting population extinctions with your Allee effect. What happens to the splines when you re-run the same three lineages, but without the replicates which went extinct?

Discussion: I cite from Lande 1998 on the Allee-like effect: " . For example, if the infinitesimal mean is positive and the infinitesimal variance increases with population size, it may nevertheless be true that starting from a given size most population trajectories tend to decrease, but that a small proportion of population trajectories undergo relatively large increases so that the expected population size increases with time" Please try to come up with a manner to falsify this hypothesis in your data. I believe you should give more attention to variability in the amounts of stochasticity between lineages, not just by calculating the RI index. The discussion, second paragraph, already suggest that differences in the amounts of stochasticity determine what you observed. If they do, you actually did a study detecting stochastic aspects of population dynamics. Please make it very clear what the difference is between weak Allee effects and stochastics producing the same patterns as Allee-effects (here they seem to be defined as effects due to the lack of interactions again).