Urban birds' tolerance towards humans was largely unaffected by COVID-19 shutdown-induced variation in human presence

  1. Peter Mikula
  2. Martin Bulla
  3. Daniel T. Blumstein
  4. Yanina Benedetti
  5. Kristina Floigl
  6. Jukka Jokimäki
  7. Marja-Liisa Kaisanlahti-Jokimäki
  8. Gábor Markó
  9. Federico Morelli
  10. Anders Pape Møller
  11. Anastasiia Siretckaia
  12. Sára Szakony
  13. Michael A. Weston
  14. Farah Abou Zeid
  15. Piotr Tryjanowski
  16. Tomáš Albrecht

  • Curated by eLife

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    eLife assessment

    This useful paper examines changes (or lack thereof) in birds' fear response to humans as a result of COVID-19 lockdowns. The evidence supporting the primary conclusion is currently inadequate, because the model used does not properly account for many potentially confounding factors that could influence the study's outcomes. If the analytic approach were improved, the findings would be of interest to urban ecologists, behavioral biologists and ecologists, and researchers interested in understanding the effects of COVID-19 lockdowns on animals.

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Abstract

The coronavirus disease 2019 (COVID-19) pandemic and respective shutdowns dramatically altered human activities, potentially changing human pressures on urban-dwelling animals. Here, we use such COVID-19-induced variation in human presence to evaluate, across multiple temporal scales, how urban birds from five countries changed their tolerance towards humans, measured as escape distance. We collected 6369 escape responses for 147 species and found that human numbers in parks at a given hour, day, week or year (before and during shutdowns) had a little effect on birds’ escape distances. All effects centered around zero, except for the actual human numbers during escape trial (hourly scale) that correlated negatively, albeit weakly, with escape distance. The results were similar across countries and most species. Our results highlight the resilience of birds to changes in human numbers on multiple temporal scales, the complexities of linking animal fear responses to human behavior, and the challenge of quantifying both simultaneously in situ.

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  1. Version published to 10.1038/s42003-024-06387-z
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    Author Response

    eLife assessment

    This useful paper examines changes (or lack thereof) in birds' fear response to humans as a result of COVID-19 lockdowns. The evidence supporting the primary conclusion is currently inadequate, because the model used does not properly account for many potentially confounding factors that could influence the study's outcomes. If the analytic approach were improved, the findings would be of interest to urban ecologists, behavioral biologists and ecologists, and researchers interested in understanding the effects of COVID-19 lockdowns on animals.

    Many thanks for these supportive words. We did our best to improve our manuscript according to the reviewers and editor comments. Importantly, we regret being unclear in the Methods, as our models already controlled for most of the confounds (see below) discussed by the reviewers.

    For example, given that a single observer collected the data at most sites, site as a random intercept in the models controls also for the observer effects (which is one of the reasons why site is in the model). We added details to Methods (L352-356, see also “Statistical analyses” in the main text).

    The first reviewer asked us to use “some measure of urbanity (e.g. Human Footprint Index) that varies across the cities included here”. Our main results are now based on country-specific models and hence, the use of a single value predictor for each city is not appropriate. Please, see also below.

    The second reviewer is concerned about multicollinearity in our models because of the 0.95 correlation between Period and Stringency Index. However, these are key predictor variables of interest that have never been used within the same model as predictors. We now clearly explain this in the Methods (L458-538, 548-550) and within legend of Figure S2.

    The third reviewer suggested that our models would benefit from controlling for day in the species-specific breeding cycle. Although we don’t have precise city-specific information on the timing of breeding stages in the sampled populations of birds, we partly control for these effects by including a random intercept of day within each year and species. This random factor explained most of the variance (see Table S1-S2) – something that could have been expected. In other words, we do control for what the third reviewer asked for. Similarly, we account for habitat features that may influence escape distance by including site in the models. Site usually refers to a specific park (we assume that within-park heterogeneity is lower than between park variation) and hence partly addresses the reviewer’s concern. Again, we highlight this within the Methods (L466-476).

    Reviewer #1 (Public Review):

    This paper uses a series of flight initiation "challenges" conducted both prior to and during COVID-19-related restrictions on human movement to estimate the degree to which avian escape responses to humans changed during the "anthropause". This technique is suitable for understanding avian behavioral responses with a high degree of repeatability. The study collects an impressive dataset over multiple years across five cities on two continents. Overall the study finds no effect of lockdown on avian escape distance (the distance at which the "target" individual flees the approaching observer). The study considers the variable of interest as both binary (during lockdown or prior to lockdown) and continuous, using the Oxford Stringency Index (with neither apparently affecting escape distance). Overall this paper presents interesting results which may suggest that behavioral responses to humans are rather inflexible over "short" (~2 year) timespans. The anthropause represents a unique opportunity to disentangle the mechanistic drivers of myriad hypothesized impacts humans have on the behavior, distribution, and abundance of animals. Indeed, this finding would provide important context to the larger body of literature aimed at these ends.

    Thank you very much for your positive feedback.

    However, the paper could do more to carefully fit this finding into the broader literature and, in so doing, be a bit more careful about the conclusions they are able to draw given the study design and the measures used. Taking some of these points (in no particular order):

    Thank you. We did our best in addressing your comments (see below and updated Methods, Results and Discussion sections).

    1. Oxford Stringency Index is a useful measure of governmental responses to the pandemic and it's true that in some scenarios (including the (Geng et al. 2021) study cited by this paper) it can correlate with human mobility. However, it is far from a direct measure of human mobility (even in the Geng study, to my reading, the index only explained a minority of the variation). Moreover, particular sub-components of the index are wholly unrelated to human mobility (e.g. would changes to a country's public information campaign lead to concomitant changes in urban human mobility?). Finally, compliance with government restrictions can vary geographically and over time (i.e. we might expect lower compliance in 2021 than in 2020) and the index is calculated at the scale of entire countries and may not be very reflective of local conditions. Overall this paper could do more to address the potential shortcomings of the Oxford Stringency Index as a measure of human mobility including attempting to validate the effect on human mobility using other datasets (e.g. the google dataset and/or those discussed in (Noi et al. 2022). This is of critical importance since the fundamental logic of the experimental design relies on the assumption that stringency ~ mobility.

    Thank you for this comment. First, Oxford Stringency Index seemed to us as the best available index for our purposes, i.e to estimate people's mobility during the shutdown because restrictions surely influenced the possibility that people would be outside, and because the index is a country-specific estimate. However, in addition, we now checked all indices mentioned in Noi et al. 2022 and found useful only the Google Mobility Reports, which we now use, because (a) it is publicly available, (b) it is available also for territories outside US, and (c) provides data for each city included in our dataset as well as for urban parks where most of our data were collected. Note that some platforms are no longer providing their mobility data (e.g. Apple).

    However, Google Mobility provides day-to-day variation in human mobility, whereas we are interested in overall increase/decrease in human mobility. Nevertheless, we correlated the Google mobility index with the Stringency index and found that human mobility generally decreases with the strength of the anti-pandemic measures adopted in sampled countries (albeit the effect for some countries, e.g. Poland, is small; Fig. 5).

    Moreover, we also added analysis using # of humans collected directly in the field during escape trials (e.g. Fig. 6 and S6) and found that the link between # of humans and Stringency index or Google Mobility was weak and noise, 95%CIs widely crossing zero (Fig. 6).

    Importantly, if we use Google Mobility and # of humans, respectively, as predictors of escape distance, the results are qualitatively very similar to results based on Oxford Stringency Index (Fig. S6), or Period, with tiny effect sizes for both (95%CIs for Google Mobility -0.3 – 0.06, Table S5, for # of humans -0.12 – 0.02, Table S6) supporting our previous conclusions.

    Note that Google Mobility and the number of humans have their limitations (see our comment to the editor and the Methods section in the main manuscript, e.g. L418-433). The lack of Google Mobility data for years before the COVID-19 pandemic does not allow us to fully explore whether overall human activity decreased during COVID-19 or not (our test for period prior and during COVID-19). If the year 2022 reflects a return to “normal” (which is to be disputed due to COVID-19-driven rise in home office use) the 2020 and 2021 had on average lower levels of human activity (Fig. 4). Whether such a difference is biologically meaningful to birds is unclear given the immense day-to-day change in human mobility and presence (Fig 4). Moreover, the number of humans capture within- and between-day variation rather than long-term changes in human presence.

    We added details on the new analysis into the method and results sections (e.g. Fig. 4-6; L142-165, 418-438, 495-535) and Supplementary Information (Figs. S5-S9 and associated Tables) and discuss the problematic accordingly. Moreover, to enhance clarity about country specific effect (or their lack), we also add country specific estimates to the Results (Fig. 1 and Fig. S6 and respective Tables). Finally, our statistical design and random structure of the model allowed us to control for spatial and temporal variation in compliance with government restrictions.

    1. The interpretation of the primary finding (that behavioral responses to humans are inflexible) could use a bit more contextualization within the literature. Specifically, the study offers three potential explanations for the observed invariance in escape response: 1) these behaviors are consistent within individuals and this study provides evidence that there was no population turnover as a result of lockdowns; 2) escape response is linked to other urban adaptations such that to be an urban-dwelling species dictates escape response; and/or 3) these populations already exhibit maximum habituation and the reduction in human mobility would only have increased that habituation but that trait is already at a boundary condition. Some comments on each of these respectively:

    Thank for these comments. We incorporated them in the main text (L293-329). Your point 1) corresponds to our point (i): “Most urban bird species in our sample may be relatively inflexible in their escape responses because the species may be already adapted to human presence” (L293-306); your point 2) to our point (ii): “Urban environment might filter for bold individuals (Carrete and Tella, 2013, 2010; Sprau and Dingemanse, 2017). Thus, the lack of consistent change in escape behaviour of urban birds during the COVID-19 shutdowns may indicate an absence (or low influx) of generally shy, less tolerant individuals and species from rural or less disturbed areas into the cities…” (L307-314); your point 3) to our point (iii): “Urban birds might have been already habituated to or tolerant of variation in human presence, irrespective of the potential changes in human activity patterns” (L315-329). To distinguish between (ii) and (iii) or the two from (i), individually-marked birds and comprehensive genetic analyses are needed, which we now note in the Discussion (L330-348). Importantly, we also discuss that the lack of response might be due to relatively small changes in human activity (L253-292), which we unfortunately could not fully quantify.

    a) Even had these populations turned over as a result of a massive rural-to-urban dispersal event, it's not clear that the escape distance in those individuals would be different because this paper does not establish that these hypothetical rural birds have a different behavioral response which would be constant following dispersal. Thus the evidence gathered here is insufficient to tell us about possible relocations of the focal species.

    Thank you for this point. We address this point in the Introduction and Discussion (L92-101, 307-314). Rural bird populations/individuals are on average less tolerant of humans than urban birds (e.g. Díaz et al. 2013, PloS One 8:e64634; Tryjanowski et al. 2020, J Tropic Ecol 36:1-5; Mikula et al. 2023, Nat Commun 14:2146) and at the same time, bird individuals seem consistent in their escape responses (Carrete & Tella 2010, Biol Lett 23:167–170; Carrete & Tella 2013, Sci Rep 3:1–7).

    Additionally, the paper cites several papers that found no changes in abundance or movements of animals in response to lockdowns but ignore others that do. For example: (Wilmers et al. 2021), (Warrington et al. 2022) (though this may have been published after this was submitted...), and (Schrimpf et al. 2021).

    We added the papers (L89-91). Thank you!

    There is a missed opportunity to consider the drivers of some of these results - the findings in this paper are interesting in light of studies that did observe changes in space use or abundance - i.e. changes in space use could arise precisely because responses to humans are non-plastic but the distribution and activities of humans changed.

    Thank you. Indeed, we now address this in the Discussion (L303-306): “However, some studies reported changes in the space use by wildlife (Schrimpf et al., 2021; Warrington et al., 2022; Wilmers et al., 2021). and these could arise, as our results indicate, from fixed and non-plastic animal responses to humans who changed their activities”.

    To wit, the primary finding here would imply that the reaction norm to human presence is apparently fixed over such timescales - however, and critically, the putative reduction in human activity/mobility combined with fixed responses at the individual level might then imply changes in avian abundance/movement/etc.

    Unfortunately, we have not measured changes in avian abundance or movements. But, please, note that the change in human mobility in sampled cities might be not as dramatic as initially thought and we consider this scenario to be most plausible in explaining no significant differences in avian escape responses before and during the COVID-19 shutdowns (see Fig. 4). Nevertheless, we add your point into the Discussion: If our findings imply that in birds the reaction norm to human presence is fixed over the studied temporal scale, the putative changes in human presence might then imply changes in avian abundance or movement (L293 and text below it).

    b) If this were the case, wouldn't this be then measurable as a function of some measure of urbanity (e.g. Human Footprint Index) that varies across the cities included here? Site accounted for ~15% of the total variation in escape distance but was treated as a random effect - perhaps controlling for the nature of the urban environment using some e.g. remotely sensed variable would provide additional context here.

    Urbanity mirrors the long-term level of human presence in cities whereas we were interested mainly in the rather short-term effects of potential changes of human presence on bird behaviour. Thus, we are not sure how adding such variable will help elucidating the current results. Please, also note that we added the country-specific analysis. Site indeed accounted for considerable amount the total variance in escape distance and that is why it was included as random intercept, which controls for non-independents of data points from each city. This could partly help us to control for difference in habitat type (e.g. urbanization level) within cities.

    c) Because it's not clear the extent to which the populations tested had turned over between years, the paper could do with a bit more caution in interpreting these results as behavioral. This study spans several years so any response (or non-response) is not necessarily a measure of behavioral change because the sample at each time point could (likely does) represent different individuals. In fact, there may be an opportunity here to leverage the one site where pre-pandemic measures were taken several years prior to the pandemic. How much variance in the change in escape distance is observed when the gap between time points far exceeds the lifetime of the focal taxa versus measures taken close in time?

    We believe the initial Fig S4, now Figure 2, addresses this point. The between years temporal variation in FIDs exceeds the variation due to lockdowns. This is true both for measures taken in consecutive years, as well as for measures taken far apart.

    d) Finally, I think there are a few other potential explanations not sufficiently accounted for here:

    i) These behaviors might indeed be plastic, but not over the timescales observed here.

    We agree and have added this point (L301-303). Thank you.

    ii) Time of year - this study took place during the breeding season. The focal behavior here varies with the time of year, for example, escape distance for many of these species could be tied up in nest defense behaviors, tradeoffs between self-preservation and e.g. nest provisioning, etc.

    Please, note that we controlled for the date in our analyses. Date was used as a proxy for the progress in the breeding season (L463-464 and Fig. 1 caption). Note that we collected data only from foraging or resting individuals, and data were neither collected near the nest sites nor from individuals showing warning behaviours, which we now note (L400-401).

    iii) Escape behaviors from humans are adaptively evolved, strongly heritable, and not context dependent - thus we would only expect these behaviors to change on evolutionary timescales.

    We discussed this at L307-308 and 381-383. Escape behaviors from humans are highly consistent for individuals, populations, and species (Carrete & Tella 2010, Biol Lett 23:167–170; Díaz et al. 2013, PloS One 8:e64634; Mikula et al. 2023, Nat Commun 14:2146). Whether such behavior is consistent across contexts is less clear (e.g. Diamant et al. 2023, Proc Royal Soc B, in press; but see, e.g. Radkovic et al. 2019, J Ecotourism 18:100-106; Gnanapragasam et al. 2021, Am Nat 198:653-659). Escape distance is often not measured simultaneously, for example, with human presence. In other words, whereas general level of human presence may have no effect on escape distance, the day-to-day or hour-to-hour variations might. We need studies on fine temporal scales (day-to-day or hour-to-hour) using marked individual to elucidate this phenomenon.

    iv) See point one above - it's possible that the lockdown didn't modify human activity sufficiently to trigger a behavioral response or that the reaction norm to human behavior is non-linear (e.g. a threshold effect).

    We agree, now use also Google Mobility Reports and # of humans data to elucidated this phenomenon and have added such interpretations to L253-292 and, e.g. Fig. 4.

    LITERATURE CITED Geng DC, Innes J, Wu W, Wang G. 2021. Impacts of COVID-19 pandemic on urban park visitation: a global analysis. J For Res 32:553-567. doi:10.1007/s11676-020-01249-w

    Noi E, Rudolph A, Dodge S. 2022. Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework. Int J Geogr Inf Sci.

    Schrimpf MB, Des Brisay PG, Johnston A, Smith AC, Sánchez-Jasso J, Robinson BG, Warrington MH, Mahony NA, Horn AG, Strimas-Mackey M, Fahrig L, Koper N. 2021. Reduced human activity during COVID-19 alters avian land use across North America. Sci Adv 7:eabf5073. doi:10.1126/sciadv.abf5073

    Warrington MH, Schrimpf MB, Des Brisay P, Taylor ME, Koper N. 2022. Avian behaviour changes in response to human activity during the COVID-19 lockdown in the United Kingdom. Proc Biol Sci 289:20212740. doi:10.1098/rspb.2021.2740

    Wilmers CC, Nisi AC, Ranc N. 2021. COVID-19 suppression of human mobility releases mountain lions from a landscape of fear. Curr Biol 31:3952-3955.e3. doi:10.1016/j.cub.2021.06.050

    Reviewer #2 (Public Review):

    Mikula et al. have a large experience studying the escape distances of birds as a proxy of behavioral adaptation to urban environments. They profited from the exceptional conditions of social distance and reduced mobility during the covid-19 pandemic to continue sampling urban populations of birds under exceptional circumstances of low human disturbance. Their aim was to compare these new data with data from previous "normal" years and check whether bird behavior shifted or not as a consequence of people's lockdown. Therefore, this study would add to the growing body of literature assessing the effect of the covid-19 shutdown on animals. In this sense, this is not a novel study. However, the authors provide an interesting conclusion: birds have not changed their behavior during the pandemic shutdown. This lack of effects disagrees with most of the previously published studies on the topic. I think that the authors cannot claim that urban birds were unaffected by the covid-19 shutdown. I think that the authors should claim that they did not find evidence of covid-19-shutdown effects. This point of view is based on some concerns about data collection and analyses, as well as on evolutionary and ecological rationale used by the authors both in their hypotheses and results interpretation. I will explain my criticisms point by point:

    We are grateful for your positive appraisal of our manuscript, as well as for your helpful critical comments. We toned down the discussion to claim, as suggested by you, that we did not find evidence for effects of covid-19-shutdowns on escape behaviour of birds in urban settings (see Results and Discussion sections). In general, we attempted to provide a more nuanced discussion and reporting of our findings. We also changed the manuscript title to “Urban birds' tolerance towards humans was largely unaffected by the COVID-19 shutdowns” and added validation using Google Mobility Reports (Fig. 5 & S6, Table S3a and S5) and the actual number of humans (Fig. 6 and S6; Table S3b-e and S6). Note however that there is only a single robust study on the topic of shutdown and animal escape distances (Diamant et al. 2023, Proc Royal Soc B, in press), i.e. the topic is largely unexplored (e.g. L99-101), whereas we discuss our finding in light of shutdown influences on other behaviours (L293-329).

    1. The authors used ambivalent, sometimes contradictory, reasoning in their predictions and results interpretation. Some examples:

    We tried to clarify our reasoning and increased consistency in our claims in the Introduction. Please, note that we simplified the Introduction and now provide one main expectation: FIDs of urban birds should increase with decreased human presence. This pattern is robustly empirically documented, regardless of the mechanism involved (e.g. Díaz et al. 2013, PloS One 8:e64634; Tryjanowski et al. 2020, J Tropic Ecol 36:1-5; Mikula et al. 2023, Nat Commun 14:2146). Please, see our revised Discussion for a more comprehensive discussion of mechanisms which could explain the patterns described in our study.

    1.1) The authors claimed that urban birds perceive humans as harmless (L224), but birds actually escape from us, when we approach them... Furthermore, they escape usually 5 to 20 m away. This is more distance that would be necessary just to be not trampled.

    We agree and have deleted mentions that humans are perceived as harmless.

    1.2) If we are harmless, why birds should spend time monitoring us as a potential threat (L102)? Indeed, I disagree with the second prediction of the authors. I could argue that reduced human activity should increase animal vigilance because real bird predators (e.g. raptors) may increase their occurrence or activity in empty cities. If birds should increase their vigilance because the invisible shield of human fear of their predators is no longer available, then I would expect longer escape distances.

    Thank you for this comment. We deleted this prediction and largely rewrote Introduction based on your comments and comments from the other reviewers.

    1.3) To justify the same escape behavior shown by birds in pre- and pandemic conditions from an adaptive point of view, the authors argued a lack of plasticity and a strong genetic determination of such behavior. This contravenes the plasticity proposed in the previous point or the expected effect of the stringency index (L112).

    We now attempted to write this more clearly while incorporating your suggestions. In the Discussion, we now propose various hypothesis that can, but need not be mutually exclusive. Please, note that we simplified the Introduction and now provide one main hypothesis: FIDs of urban birds should increase with decreased human presence.

    In my opinion, some degree of plasticity in the escape behavior would be really favorable for individuals from an adaptive perspective, as they may face quite different fear landscapes during their lives. Looking at the figures, one can see notable differences in the escape distance of the same species between sites in the same city. As I can hardly imagine great genetic differences between birds sampled in a park or a cemetery in Rovaniemi, for instance, I would expect a major role of plasticity to explain the observed variability. Furthermore, if escape behavior would not be plastic, I would not expect date or hour effects. By including them in their models, the authors are accepting implicitly some degree of plasticity.

    We regret being unclear. We do accept some degree of plasticity. Yet, our study design prohibits the assessment of the degree of individual plasticity because sampled birds were not individually marked and approached repeatedly. We tried to soften the statements in our Discussion to not fully dismiss a possibility that urban birds have some degree of plasticity in their antipredator behaviour (L293-329). Note however, that while our data collection was not designed to test how hour-to-hour changes in human numbers influence escape distance, the effect of the number of humans (i.e. hour-to-hour variation in human numbers) in our sample was tiny.

    The date and hour effect simply control for the particularities of the given day and hour (e.g. warm vs cold times or the time until sunset). In other words, the within species differences (even from the same park) may have little to do with individual plasticity, but instead may reflect between individual differences. We now add this issue to Methods (L471-476): “This approach enabled us to control for spatial and temporal heterogeneity and specificity in escape behaviour of birds (e.g. species-specific responses, changes in escape distances with the progress in the breeding season, spatial and temporal variation in compliance with government restrictions or particularities of the given day and hour)....”

    1. Looking at the figures I do not see the immense stochasticity (L156, Fig. S3, S5) claimed by the authors. Instead, I can see that some species showed an obvious behavioral change during the shutdown. For instance, Motacilla alba, Larus ridibundus, or Passer domesticus clearly reduced their escape distances, while others like the Dendrocopos major, Passer montanus, or Turdus merula tended to increase it.

    At L138-141 and 327-329 we discussed the within and between genera and cross-country variation and stochasticity in response to the shutdowns (Fig. 2). The reference to species-specific plots was perhaps a little bit misleading. We think that the essential figure, that we now reference at this point, is Figure 2 that shows the temporal trends and/or stochasticity that seem to have little in common with lockdowns. Please, also look at Figure 3 and S3-S4. These show that in all selected genera/species, the trends did not significantly deviate from central regression line which indicates no change in FID before and during the COVID-19 shutdowns.

    On the other hand, birds in Poland tended to have larger escape distances during the shutdown for most species, while in Rovaniemi there was an apparent reduction of escape distances in most cases. The multispecies and multisite approach is a strength of this study, but it is an Achilles' heel at the same time. The huge heterogeneity in bird responses among species and sites counterbalanced and as a result, there was an apparent lack of shutdown effects overall. Furthermore, as most data comes from a few (European) species (i.e. Columba, Passer, Parus, Pica, Turdus, Motacilla) I would say that the overall results are heavily influenced (or biased) by them. The authors realize that results are often area- or species-specific (L203), therefore, does a whole approach make sense?

    We are grateful for this valuable comment. We believe the general approach makes sense as there is a general expectation about how birds should respond to changes in human presence. That is why we control for non-independence of data points in our sample. Thus, although lots of data come from a few European species, this is corrected for by the model. Note that given the sheer number of sampled species, some site- or species-specific trends may have occurred by chance. Importantly, we believe that Figure 2, with species-site specific temporal trends, reveals that the between year stochasticity in escape distances seems greater that any effects of lockdowns. Nevertheless, we have further dealt with this issue in the revised manuscript by running country-specific models which again clearly showed no significant effect of Period on escape behaviour of birds (including, no effects in Poland and Finland).

    1. The previous point is worsened by the heterogeneity of cities and periods sampled. For instance:

    3.1) I can hardly imagine any common feature between a small city in northern Finland (Rovaniemi) and a megacity in Australia (Melbourne). Thus, I would not be surprised to find different results between them.

    3.2) Prague baseline data was for 2014 and 2018, while for the rest of the study sites were for 2018 and 2019. If study sites used a different starting point, you cannot compare differences at the final point.

    We are slightly confused by these comments.

    3.1) The cities are expected to be different but (i) the difference may be smaller than imagined (e.g. park structures, managed grass cover, few shrubs and deciduous-dominated tree species) and (ii) we expect the effects of lockdowns to be similar across cities. Whether we have no people in Rovaniemi parks (which despite Rovaniemi’s small size are usually extremely well-visited) or no people in Melbourne parks should not make a difference in principle. Note however, that to avoid overconfident conclusions, we allow for different reaction norms within cities. Please, also note that we are now providing country-specific results which should identify whether shutdowns lead to different reaction in sampled countries. We found no strong effect of shutdowns in any of sampled countries/cities.

    3.2) Because of the possible between site differences at the starting point, we use study site as random intercept and control for the between site reaction norms by including the random slope of the period. In other words, such possible differences do not influence outcomes of our models. Regardless, our a priori expectation is that the human activity levels in a given park was similar prior to covid and hence in 2014, 2018, and 2019. Again, we are now providing country-specific results which identify whether shutdowns led to different reactions in sampled countries, which they mostly did not

    3.3) Due to the obvious seasonal differences between the northern and southern hemispheres, data collection in Australia began five months later than in the rest of the sites (Aug vs Mar 2020). There, urban birds faced already too many months of reduced human disturbances, while European birds were sampled just at the beginning of the lockdown.

    We agree that each city or even park within the city has its specific environmental conditions (here including the time point of lockdown). That is why we control for city and park location in the random structure of the model (see Method section). We now add results per country that shows no clear differences (e.g. Fig. 1).

    However, the aim of our study was to test for general, global effects of lockdowns, which are minimal. Note that we now specifically test for country-specific effects in separate models on each country (e.g. Fig. 1, Fig S6) but all country-specific effects are small and still centre around zero.

    3.4) Some cities were sampled by a single observer, while others by many of them. Even if all of them are skilled birders, they represent different observers from a statistical point of view and consequently, observer identity was an extra source of noise in your data that you did not account for.

    We agree. In Finland and Hungary, data were collected by two closely cooperating observers. In Poland, all data were collected by a single observer. In the Czech Republic and Australia, a single observer (P.M. and M.W., respectively) sampled 46 sites out of 56 and 32 sites out of 37, respectively. Each site was sampled by the same observer both before and during the shutdowns. We now clearly state it in the Methods (L352-356). In other words, our models already largely control for the possible observer confound by having site as a random intercept. Moreover, previous study showed that FID estimates do not vary significantly between trained observers (Guay et al. 2013, Wildlife Research, 40, 289-293).

    1. Although I liked the stringency index as a variable, I am not sure if it captured effectively the actual human activity every day. Even if restrictive measures were similar between countries, their actual accomplishment greatly depended on people's commitment and authorities' control and sanctions. I would suggest using a more realistic measure of human activity, such as google mobility reports.

    Thank you for this comment. We now validate the use of the stringency index with the Google Mobility Reports, showing that human mobility generally (albeit in some countries relatively weakly) decreases with the strength of governmental antipandemic measures. Please, note that our main research question is related to the general change in human outdoor activity and not to week-to-week, day-to-day or hour-to-hour changes captured by stringency index, Google Mobility or the number of humans during an escape trial data. Nevertheless, using Google Mobility and the number of humans as predictors led to the similar results as for stringency index and Period (Fig. 1 and S6). Please, see extended discussion on this topic in our manuscript (L270-292).

    1. The authors used escape trials from birds on the ground and perched birds. I think that they are not comparable, as birds on the ground probably perceive a greater risk than those placed some meters above the ground, i.e. I would expect shorter escape distances for perched birds. As this can be strongly dependent on the species preferences or sampling site (i.e, more or less available perches), I wonder how this mixture of observations from birds on the ground and perched birds could be affecting the results.

    We now added information that most birds were sampled when on the ground (79%). Importantly, previous studies have found that perch height has a minimum effect on FIDs (e.g. Bjørvik et al. 2015. J Ornithol 156:239–246; Kalb et al. 2019, Ethology 125:430-438; Ncube & Tarakini 2022, Afr J Ecol 60:533– 543; Sreekar et al. 2015,. Tropic Conserv Sci 8:505-512). We added this information to the Method section (L394-395).

    1. The authors did not sample the same location in the same breeding season to avoid repeated sampling of the same individuals (L331). This precaution may help, but it does not guarantee a lack of pseudoreplication. Birds are highly mobile organisms and the same individuals may be found in different places in the same city. This pseudoreplication seems particularly plausible for Rovaniemi, where sampling points must be necessarily close due to the modest size of this city.

    We appreciate your concern. We cannot fully exclude the possibility of sampling some individuals twice. However, we sampled during the breeding season within which most birds are territorial, active in the areas around the nests and hence an individual switching parks is unlikely. Also, most sampled birds in our study are passerines which have small territories (typically few hundred square meters). Some larger birds may have larger territories and move larger distance to forage (e.g. kestrels which often forage outside cities) but these birds represent a minority of our records and we have not sampled outside the cities.

    1. An intriguing result was that the authors collected data for 135 species during the shutdown, while they collected data only for 68 species before the pandemic. Such a two-fold increase in bird richness would not be expected with a 36% increase in sampling effort during 2020-21. I wonder if this could be reflecting an actual increase in bird richness in urban areas as a positive result of the shutdown and reduced human presence.

    There were 141 unique day-years during before COVID and 161 during COVID. So, the sampling effort as calculated by days does not explain the difference in species numbers. Whether the actual effort, which was 381 vs 463 h of sampling, explains the difference is unclear, which we now note in the Methods (L476-483). If not, your proposition is possible, but we would like to avoid any speculations on this topic in the manuscript as it is difficult to infer species diversity from FID sampling.

    1. The authors dismissed the multicollinearity problem of explanatory variables unjustifiably (L383). However, looking at fig. S1, I can see strong correlations between some of them. For instance, period and stringency index were virtually identical (r=0.95), while temperature and date were also strongly correlated.

    We are confused by this comment and think this reflects a misunderstanding. Period and stringency index are explanatory variables of interest that were never included in the same model and hence their correlation does not contribute to the within a model multicollinearity. To avoid further confusion, we note this within (Fig. S2) legend. However, we must be cautious when interpreting the results from the models on period, Google Mobility, # of humans and stringency index, as the four measure are similar.

    We discuss multicollinearity of explanatory variables within the manuscript (L458-538, 548-550) and noted that, with the exception of temperature and day within the breeding season (r = 0.48), the correlations among explanatory variables were minimal. We thus used only temperature as an explanatory variable (i.e. fixed factor; also because temperature reflects both season and variation in temperature across a season) whereas the day was included as a random intercept to control for pseudoreplication within day. Collinearity between all other predictors was low (|r| <0.36).

    1. The random structure of the models is a key element of the statistical analyses but those random factors are poorly explained and justified. I needed to look up the supplementary tables to fully understand the complex architecture of the random part of the models. To the best of my knowledge, random variables aim to account for undesirable correlations in the covariance matrix, which is expected in hierarchical designs, such as the present one. However, the theoretical violation of data independence may happen or not. As the random structure is usually of little interest, you should keep it as simple as necessary, otherwise random factors may be catching part of data variability that you would like to explain by fixed variables. I think that this is what is happening (at least, in part) here, as the authors included a too-complex random structure. For instance, if you include the year as a random factor, I think that you are leaving little room for the period effect. The authors simplified the random structure of the models (L387), but they did not explain how. Nevertheless, this model selection was not important at all, as the authors showed the results for several models. I assume, consequently, that the authors are considering all these models equally valid. This approach seems quite contradictory.

    The random structure of the model controls for possible pseudoreplication in the data, that is for the cases where we have multiple data points that may not be independent and hence technically represent one. Apart from that, random structure tells us about where the variance in the data lies. This is often of interest and your previous questions about city, site or species specificities can be answered with the random part of the model. To follow up on your example, year is included in the model because data from a single year are not independent (for example because of delayed breeding season in one year vs. in another).

    We regret being unclear about the model specification and have attempted to clarify the methods (L466-476). We first specified a model with an ideal random structure that necessarily was complex (perhaps too complex). We then showed that using models with simpler random structures did not influence the outcomes. We now use a simpler model within the main text, but do keep the alternative models to show that the results are not dependent on the random structure of the model (Fig. S1 and Table S2).

    Reviewer #3 (Public Review):

    This study examined the changes in fear response, as measured by the flight initiation distances (FID), of birds living in urban areas. The authors examined the FIDs of birds during the pandemic (COVID-19 lockdown restrictions) compared to FIDs measured before the pandemic (mostly in 2018 & 2019). The main study justification was that human presence changed drastically during the pandemic lockdowns and the change in human presence might have influenced the fear response of birds as a result of changing the "landscape of fear". Human presence was quantified using a 'stringency' index (government-mandated restrictions). Urban areas were selected from within five different cities, which included four European cities (Czech Republic - Prague, Finland - Rovaniemi, Hungary - Budapest, Poland - Poznan), and one city in the global south (Australia - Melbourne). Using 6369 flight initiation distances across 147 different bird species, the authors found that FIDs were not significantly different before the pandemic versus during the pandemic, nor was the variation in FID explained by the level of 'stringency'.

    Major strengths: There are several strengths to this study that allows for understanding the variety of factors that influence a bird's response to fear (measured as flight initiation distances). This study also demonstrates that FIDs are highly variable between species and regions.

    Specifically,

    1. One of the major strengths of this paper is the focus on birds living in urban areas, a habitat type that is hypothesized to have changed drastically in the 'landscape of fear' experienced by animals during the pandemic lockdown restrictions (due to the presumed decrease in human presence and densities). Maintaining the focus on urban birds allowed for a deeper examination of the effect of human behaviour changes on bird behaviour in urban habitats, which are at the interface of human-wildlife interactions.
    1. This study accounted for several variables that are predicted to influence flight initiation distances in birds including species, genus, region (country), variability between years, pandemic year (pre- versus during), the strictness of government-mandated lockdown measures, and ecological factors such as the human observer starting distance, flock size, species-specific body size, ambient air temperature (also a proxy of the timing during the breeding season), time of day, date of data collection (timing within the regional [Europe or Australia] breeding season), and categorization of urban site type (e.g. park, cemetery, city centre).
    1. This study examined FIDs in two years previous to the pandemic (mostly 2018 and 2019, one site was 2014) which would account for some of the within- and between-year FID variation exhibited prior to the pandemic.
    1. This study uses strong statistical approaches (mixed effect models) which allows for repeat sampling, and a post hoc analysis testing for a phylogenetic signal.

    Thank you for your supportive and positive comments.

    Major weaknesses: The authors used government 'stringency' as a proxy for human presence and densities, however, this may not have been an accurate measure of actual human presence at the study sites and during measurements of FIDs. Furthermore, although the authors accounted for many factors that are predicted to influence fear response and FIDs in birds, there are several other factors that may have contributed to the high level of variation and patterns in FIDS observed during this study, thus resulting in the authors' conclusion that FIDs did not vary between pre- and during pandemic years.

    Thank you for your suggestions. We agree. To capture the general human presence in parks, we now incorporated an analysis using Google Mobility Reports (Fig S6b) that directly measures human mobility in each of sampled cities and specifically in urban parks where most our data were collected, and also address your further concerns that you detail below. Albeit not the main interest of our study, we now also incorporated an analysis using actual # of humans during an escape trial (Fig. S6c).

    Moreover, we think that including further possible confounds should not influence our conclusions. In other words, including further confounds will decrease the variance that can be explained by shutdowns and thus such shutdown effects (if any) would be tiny and hence likely not biologically meaningful.

    Specifically,

    1. The authors used "government stringency" as a measure of change in human activity, which makes the assumption that the higher the level of 'stringency', the fewer humans in urban areas where birds are living. However, the association between "stringency" and actual human presence at the study sites was not measured, nor was 'stringency' compared to other measures of human presence such as human mobility.

    Thank you for this essential comment. Initially, we viewed Oxford Stringency Index as the best available index for our purposes. However, we now further acknowledge its limitations (L) and validate the Oxford Stringency Index with the Google Mobility Reports data, showing that both indices are generally negatively (albeit sometimes weakly) correlated across sampled cities (i.e. human mobility decreases with the increasing stringency index). Although other human presence indices were used in the past, e.g. Cuebiq, Descartes Labs and Maryland Uni index, Apple (see Noi et al. 2022, Int J Geograph Info Sci, 36, 585-616), we used only the Google Mobility index because (a) it is publicly available, (b) is available also for territories outside US, and (c) provides data for urban parks within each city included in our dataset. Note however that Google Mobility data are inappropriate to answer our primary question, i.e. whether changes in human presence outdoors due to the COVID-19 shutdowns had any effect on avian tolerance towards humans. First, Google Mobility was available only for 2020-22, i.e. the baseline pre-COVID-19 data for 2018-2019 were unavailable. Thus, there was no way to check whether the human activity levels really changed during the COVID-19 years. Second, Google Mobility data are calculated as a change from 2020 January–February baseline for each day of the week for each city and its location (here we used parks). In other words, the data are not comparable between days and cities, albeit we attempted to correct for this within the random structure of the mixed model. Also, the data may be influenced by extreme events within the 2020 Jan–Feb baseline period (see here). Third, the Google Mobility varies greatly between days and across season (see Fig 4 & S5 or the first figure in these responses), likely more than the possible change due to shutdowns. Nevertheless, we found that results based on Google Mobility are qualitatively very similar to results based on stringency index. Moreover, we showed that the relationships between # of humans and both Google Mobility or Stringency index (Figure 6) are weak and noise with 95%CIs widely overlapping zero (Table S3b-e). Also, similarly to other predictors of human presence, # of humans only poorly predicted changes in avian escape distances. We added details on the new analysis into the Methods and Results and Supplement (L134-165 and associated figures and tables, L415-535).

    1. There was considerable variation in FID measurements, which can be seen in the figures, indicating that most of the variation in FID was not accounted for in the authors' models.

    We are confused by this statement. The fact that the FIDs varied does not translate directly to that our models did not account for the variation. Nevertheless, we do control for most of the discussed confounds (see further answers below). Importantly, it is unclear how including further possible confounds should influence our conclusions, unless the lockdowns effects are tiny, in which case those might not be biologically meaningful.

    Factors that may have contributed to variation in FIDs that were not accounted for in this study are as follows:

    a. The authors accounted for the date of data collection using the 'day' since the start of the general region's breeding season (Europe: Day 1 = 1 April; Australia: Day 1 = 15 August). Using 'day' since the breeding season started probably was an attempt to quantify the effect of the breeding stage (e.g. territory establishment, nest young, fledgling) on FIDs. However, breeding stages vary both within- and between species, as well as between sub-regions (e.g. Finland vs. Hungary). As different species respond to predation or human presence differently depending on the stage during their breeding cycle, more specificity in the breeding cycle stage may allow for explaining the observed variation and patterns in FID.

    We agree. Although we don’t have a precise city-specific information on the timing of breeding stages in sampled populations of birds, we partly control for these effects by including a random intercept of day within each year and species. This random factor explained relatively high portion of the variance in our data (see Table S1 and S2) - perhaps something you expected.

    b. Variation in species-specific FIDs may also vary with habitat features within urban sites, such as the proximity of trees and other protective structures (e.g. perches and cover), the openness of the area, and the level of stressors present (e.g. noise pollution, distance to roads). Perhaps accounting for this habitat heterogeneity would account for the FID variation measured in this study.

    We agree. We don’t have such fine-scale data, but we included site identity (typically within a particular park or cemetery) which should account for the habitat heterogeneity among localities. Depending on the model, site explained relatively little variance (1-6%), indicating low heterogeneity between localities in these undescribed characteristics. Also note that park structure may be quite similar both within and between cities, i.e. managed green grass areas, with only a few shrubs and deciduous trees. Therefore, the possible minor habitat heterogeneity should not have any great impacts on our results.

    c. The authors accounted for species and genus within their models, however, FIDs may vary with other species-specific (or even specific populations of a species) characteristics such as whether the species/population is neophobic versus neophilic, precocial versus altricial, and the level of behavioural plasticity exhibited. These variables were not accounted for in the analysis.

    We agree that FIDs can be correlated with many possible factors. Here, we were interested in general patterns, while controlling for FID differences between species, as well as for possible species-specific reaction norms to lockdowns. Whether neophobic vs neophilic population or precocial versus altricial species react differently to lockdowns might be of interest, but it is beyond the scope of this study. However, that population and population specific reaction norms explain little variation (Table S2a, 0-6% of variation) so such a confound should not substantially influence our conclusion much. We do not have fine-scale data on the level of neophobia, but the effects of lockdowns seem similar for precocial (see Anas, Larus, Cygnus) and altricial (the remaining, mostly passerine) species in our dataset (see Fig. 3 and S3-S4). Please, note that we sampled mainly adults (L386). Moreover, the effects for clades, which may differ in their cognitive skills, are also similar (e.g. Corvids vs. Anas or Cygnus; Fig. 3).

    d. Three different methods of measuring the distances between flight and the observer location were used, and FIDs were only measured once per bird, such that there were no measures of repeatability for a test subject. Thus, variation surrounding the measurement of FIDs would have contributed to the variation in FIDs seen during this study.

    While all observers were trained, the three methods may add some noise to the FID estimates. However, the FID estimates from a single method may still slightly differ between observers (so do well standardized morphology measurements; Wang, et al. 2019, PLoS Biology, 17, e3000156). Importantly, FID estimates are highly replicable among skilled observers (Guay et al. 2013, Wildlife Research 40:289-293), and we previously validated this approach and showed that distance measured by counting steps did not differ from distance measured by a rangefinder (Mikula 2014, Ardea 102:53-60), which we now explicitly state (L391-394). Importantly, we control for observer bias by specifying locality as a random intercept (see further details in our response to the Editor). Moreover, each site was sampled by the same observer both before and during the shutdowns.

    1. The sample design of this study may have influenced the FID variability associated with specific species, and specific populations of species. A different number of species were sampled across the time periods of interest; 68 species were sampled before the pandemic versus 135 species after the pandemic. However, the authors do not appear to have directly compared the FIDs for the same species before the pandemic compared to during the pandemic (e.g. the FIDs of Eurasian blackbirds before the pandemic versus during the pandemic). Furthermore, within the same country-city, it is unclear whether the species observed before the pandemic were observed at the same location (e.g. same habitat type such as the same park) during the pandemic. As a species' FID response may be influenced by population characteristics and features specific to each site (e.g. habitat openness), these factors may have influenced the variability in FID measurements in this study.

    We regret being unclear in our methods. Our full model uses all data, but alternative models (see e.g. Fig. S1) used data with ≥5 as well as ≥10 observations before and during lockdowns for a given species. Importantly, Figure 2 and 3 depict data for species sampled at specific sites. We now clarify this within the Methods (L460-483) and the Results (L125-133 and associated figures) and in the figure legends (Fig. S1).

    1. The models in this study accounted for many factors predicted to affect FIDs (see the section on major strengths), however, the number of fixed and random factors are large in number compared to the total sample size (N =6369), such that models may have been over-extended.

    The number of predictors and random effects is well within the limits for the given sample size (Korner-Nievergelt et al. 2015. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan). Importantly, simpler models give similar results as the more complex ones (Fig. S1) and the visual (model free) representations of our raw and aggregated data confirm our model results. This, we suggest, makes our findings robust and convincing.

    Overarching main conclusion

    Overall, this study examines factors influencing FIDs in a variety of bird species and concludes that FIDs did not differ during the pandemic lockdowns compared to before the pandemic (2019 and earlier). Furthermore, FIDs were not influenced by the strictness of government-mandated restrictions. Although the authors accounted for many factors influencing the measurement of FIDs in birds, the authors did not achieve their aim of disentangling the effects of pandemic-specific ecological effects from ecological effects unrelated to the pandemic (such as habitat heterogeneity).

    We find this statement confusing. We accounted for most relevant confounding factors and found little evidence for the strong effect of pandemic. Moreover, we now added country-specific analyses that confirm the lack of evidence, highlight the Figure 3 that shows no clear shutdown effect and also explore how levels of human presence changed over and within the years. Adding more possible confounds (albeit note that not many are left to add) might only further reduce the variation that could be explained by pandemic and hence such hypothetical effects of pandemic will be if anything small and thus likely not biologically meaningful.

    Their findings indicate that FIDs are highly variable both within- and between- species, but do not strongly support the conclusion that FIDs did not change in urban species during the pandemic lockdown. Therefore, this study is of limited impact on our understanding of how a drastic change in human behaviour may impact bird behaviour in urban habitats.

    It is unclear why you think our study lacks support for the conclusion that FIDs changed little during pandemic, if all results show no such effects. However, we toned down our Discussion and highlighted also potential issues linked to our approach (e.g. that sampled individuals were not marked and hence we cannot distinguish between various mechanisms that might explain the described pattern (L293-329) or that human presence may not have changed (L253-269). For further details see our previous response.

    Overall, the study demonstrates the challenges in using FIDs as a general fear response in birds, even during a pandemic lockdown when fewer humans are presumably present, and this study illustrates the large degree of variation in FIDs in response to a human observer.

    We appreciate and agree that our study demonstrates the challenges in quantifying human activity to understand bird escape distance and we added a paragraph on this topic to the discussion (L270-292).

    Nevertheless, we hope that our above responses clarify and address most of the issues you had with our manuscript. We tried to show that (a) most of your proposed controls are indeed included in our study design, models, and visualisations, and that (b) multiple evidence (from models and visualisation of raw and aggregated data) support the no overall effect conclusion. We further emphasize the temporal and between- and within-species variability in FIDs in the Results and now specifically indicate that lockdowns did not influenced FIDs above such variability (Fig. 2-3, Fig. S3). In other words, the natural (e.g. temporal) variation in FIDs seems far greater that potential effects of lockdowns (Fig. 2). We believe that even if lockdowns would have tiny effects that could have been detected with more. stringent experimental design (e.g. individually tagged birds) or even more complex models, such effects would be far from being biologically meaningful.

  3. eLife

    eLife assessment

    This useful paper examines changes (or lack thereof) in birds' fear response to humans as a result of COVID-19 lockdowns. The evidence supporting the primary conclusion is currently inadequate, because the model used does not properly account for many potentially confounding factors that could influence the study's outcomes. If the analytic approach were improved, the findings would be of interest to urban ecologists, behavioral biologists and ecologists, and researchers interested in understanding the effects of COVID-19 lockdowns on animals.

  4. eLife

    Reviewer #1 (Public Review):

    This paper uses a series of flight initiation "challenges" conducted both prior to and during COVID-19-related restrictions on human movement to estimate the degree to which avian escape responses to humans changed during the "anthropause". This technique is suitable for understanding avian behavioral responses with a high degree of repeatability. The study collects an impressive dataset over multiple years across five cities on two continents. Overall the study finds no effect of lockdown on avian escape distance (the distance at which the "target" individual flees the approaching observer). The study considers the variable of interest as both binary (during lockdown or prior to lockdown) and continuous, using the Oxford Stringency Index (with neither apparently affecting escape distance).

    Overall this paper presents interesting results which may suggest that behavioral responses to humans are rather inflexible over "short" (~2 year) timespans. The anthropause represents a unique opportunity to disentangle the mechanistic drivers of myriad hypothesized impacts humans have on the behavior, distribution, and abundance of animals. Indeed, this finding would provide important context to the larger body of literature aimed at these ends. However, the paper could do more to carefully fit this finding into the broader literature and, in so doing, be a bit more careful about the conclusions they are able to draw given the study design and the measures used. Taking some of these points (in no particular order):

    1. Oxford Stringency Index is a useful measure of governmental responses to the pandemic and it's true that in some scenarios (including the (Geng et al. 2021) study cited by this paper) it can correlate with human mobility. However, it is far from a direct measure of human mobility (even in the Geng study, to my reading, the index only explained a minority of the variation). Moreover, particular sub-components of the index are wholly unrelated to human mobility (e.g., would changes to a country's public information campaign lead to concomitant changes in urban human mobility?). Finally, compliance with government restrictions can vary geographically and over time (i.e., we might expect lower compliance in 2021 than in 2020) and the index is calculated at the scale of entire countries and may not be very reflective of local conditions. Overall this paper could do more to address the potential shortcomings of the Oxford Stringency Index as a measure of human mobility including attempting to validate the effect on human mobility using other datasets (e.g., the google dataset and/or those discussed in (Noi et al. 2022). This is of critical importance since the fundamental logic of the experimental design relies on the assumption that stringency ~ mobility.

    2. The interpretation of the primary finding (that behavioral responses to humans are inflexible) could use a bit more contextualization within the literature. Specifically, the study offers three potential explanations for the observed invariance in escape response: 1) these behaviors are consistent within individuals and this study provides evidence that there was no population turnover as a result of lockdowns; 2) escape response is linked to other urban adaptations such that to be an urban-dwelling species dictates escape response; and/or 3) these populations already exhibit maximum habituation and the reduction in human mobility would only have increased that habituation but that trait is already at a boundary condition. Some comments on each of these respectively:

    a) Even had these populations turned over as a result of a massive rural-to-urban dispersal event, it's not clear that the escape distance in those individuals would be different because this paper does not establish that these hypothetical rural birds have a different behavioral response which would be constant following dispersal. Thus the evidence gathered here is insufficient to tell us about possible relocations of the focal species. Additionally, the paper cites several papers that found no changes in abundance or movements of animals in response to lockdowns but ignore others that do. For example: (Wilmers et al. 2021), (Warrington et al. 2022) (though this may have been published after this was submitted...), and (Schrimpf et al. 2021). There is a missed opportunity to consider the drivers of some of these results - the findings in this paper are interesting in light of studies that *did* observe changes in space use or abundance - i.e., changes in space use could arise precisely *because* responses to humans are non-plastic but the distribution and activities of humans changed. To wit, the primary finding here would imply that the reaction norm to human presence is apparently fixed over such timescales - however, and critically, the putative reduction in human activity/mobility combined with fixed responses at the individual level might then imply changes in avian abundance/movement/etc.

    b) If this were the case, wouldn't this be then measurable as a function of some measure of urbanity (e.g. Human Footprint Index) that varies across the cities included here? Site accounted for ~15% of the total variation in escape distance but was treated as a random effect - perhaps controlling for the nature of the urban environment using some e.g., remotely sensed variable would provide additional context here.

    c) Because it's not clear the extent to which the populations tested had turned over between years, the paper could do with a bit more caution in interpreting these results as behavioral. This study spans several years so any response (or non-response) is not necessarily a measure of behavioral change because the sample at each time point could (likely does) represent different individuals. In fact, there may be an opportunity here to leverage the one site where pre-pandemic measures were taken several years prior to the pandemic. How much variance in the change in escape distance is observed when the gap between time points far exceeds the lifetime of the focal taxa versus measures taken close in time?

    d) Finally, I think there are a few other potential explanations not sufficiently accounted for here:

    i) These behaviors might indeed be plastic, but not over the timescales observed here.
    ii) Time of year - this study took place during the breeding season. The focal behavior here varies with the time of year, for example, escape distance for many of these species could be tied up in nest defense behaviors, tradeoffs between self-preservation and e.g., nest provisioning, etc.
    iii) Escape behaviors from humans are adaptively evolved, strongly heritable, and not context dependent - thus we would only expect these behaviors to change on evolutionary timescales.
    iv) See point one above - it's possible that the lockdown didn't modify human activity sufficiently to trigger a behavioral response or that the reaction norm to human behavior is non-linear (e.g. a threshold effect).

    LITERATURE CITED
    Geng DC, Innes J, Wu W, Wang G. 2021. Impacts of COVID-19 pandemic on urban park visitation: a global analysis. J For Res 32:553-567. doi:10.1007/s11676-020-01249-w

    Noi E, Rudolph A, Dodge S. 2022. Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework. Int J Geogr Inf Sci.

    Schrimpf MB, Des Brisay PG, Johnston A, Smith AC, Sánchez-Jasso J, Robinson BG, Warrington MH, Mahony NA, Horn AG, Strimas-Mackey M, Fahrig L, Koper N. 2021. Reduced human activity during COVID-19 alters avian land use across North America. Sci Adv 7:eabf5073. doi:10.1126/sciadv.abf5073

    Warrington MH, Schrimpf MB, Des Brisay P, Taylor ME, Koper N. 2022. Avian behaviour changes in response to human activity during the COVID-19 lockdown in the United Kingdom. Proc Biol Sci 289:20212740. doi:10.1098/rspb.2021.2740

    Wilmers CC, Nisi AC, Ranc N. 2021. COVID-19 suppression of human mobility releases mountain lions from a landscape of fear. Curr Biol 31:3952-3955.e3. doi:10.1016/j.cub.2021.06.050

  5. eLife

    Reviewer #2 (Public Review):

    Mikula et al. have a large experience studying the escape distances of birds as a proxy of behavioral adaptation to urban environments. They profited from the exceptional conditions of social distance and reduced mobility during the covid-19 pandemic to continue sampling urban populations of birds under exceptional circumstances of low human disturbance. Their aim was to compare these new data with data from previous "normal" years and check whether bird behavior shifted or not as a consequence of people's lockdown. Therefore, this study would add to the growing body of literature assessing the effect of the covid-19 shutdown on animals. In this sense, this is not a novel study. However, the authors provide an interesting conclusion: birds have not changed their behavior during the pandemic shutdown. This lack of effects disagrees with most of the previously published studies on the topic. I think that the authors cannot claim that urban birds were unaffected by the covid-19 shutdown. I think that the authors should claim that they did not find evidence of covid-19-shutdown effects. This point of view is based on some concerns about data collection and analyses, as well as on evolutionary and ecological rationale used by the authors both in their hypotheses and results interpretation. I will explain my criticisms point by point:

    1. The authors used ambivalent, sometimes contradictory, reasoning in their predictions and results interpretation. Some examples:
      1.1) The authors claimed that urban birds perceive humans as harmless (L224), but birds actually escape from us, when we approach them... Furthermore, they escape usually 5 to 20 m away. This is more distance that would be necessary just to be not trampled.
      1.2) If we are harmless, why birds should spend time monitoring us as a potential threat (L102)? Indeed, I disagree with the second prediction of the authors. I could argue that reduced human activity should increase animal vigilance because real bird predators (e.g., raptors) may increase their occurrence or activity in empty cities. If birds should increase their vigilance because the invisible shield of human fear of their predators is no longer available, then I would expect longer escape distances.
      1.3) To justify the same escape behavior shown by birds in pre- and pandemic conditions from an adaptive point of view, the authors argued a lack of plasticity and a strong genetic determination of such behavior. This contravenes the plasticity proposed in the previous point or the expected effect of the stringency index (L112). In my opinion, some degree of plasticity in the escape behavior would be really favorable for individuals from an adaptive perspective, as they may face quite different fear landscapes during their lives. Looking at the figures, one can see notable differences in the escape distance of the same species between sites in the same city. As I can hardly imagine great genetic differences between birds sampled in a park or a cemetery in Rovaniemi, for instance, I would expect a major role of plasticity to explain the observed variability. Furthermore, if escape behavior would not be plastic, I would not expect date or hour effects. By including them in their models, the authors are accepting implicitly some degree of plasticity.

    2. Looking at the figures I do not see the immense stochasticity (L156, Fig. S3, S5) claimed by the authors. Instead, I can see that some species showed an obvious behavioral change during the shutdown. For instance, Motacilla alba, Larus ridibundus, or Passer domesticus clearly reduced their escape distances, while others like the Dendrocopos major, Passer montanus, or Turdus merula tended to increase it. On the other hand, birds in Poland tended to have larger escape distances during the shutdown for most species, while in Rovaniemi there was an apparent reduction of escape distances in most cases. The multispecies and multisite approach is a strength of this study, but it is an Achilles' heel at the same time. The huge heterogeneity in bird responses among species and sites counterbalanced and as a result, there was an apparent lack of shutdown effects overall. Furthermore, as most data comes from a few (European) species (i.e., Columba, Passer, Parus, Pica, Turdus, Motacilla) I would say that the overall results are heavily influenced (or biased) by them. The authors realize that results are often area- or species-specific (L203), therefore, does a whole approach make sense?

    3. The previous point is worsened by the heterogeneity of cities and periods sampled. For instance:
      3.1) I can hardly imagine any common feature between a small city in northern Finland (Rovaniemi) and a megacity in Australia (Melbourne). Thus, I would not be surprised to find different results between them.
      3.2) Prague baseline data was for 2014 and 2018, while for the rest of the study sites were for 2018 and 2019. If study sites used a different starting point, you cannot compare differences at the final point.
      3.3) Due to the obvious seasonal differences between the northern and southern hemispheres, data collection in Australia began five months later than in the rest of the sites (Aug vs Mar 2020). There, urban birds faced already too many months of reduced human disturbances, while European birds were sampled just at the beginning of the lockdown.
      3.4) Some cities were sampled by a single observer, while others by many of them. Even if all of them are skilled birders, they represent different observers from a statistical point of view and consequently, observer identity was an extra source of noise in your data that you did not account for.

    4. Although I liked the stringency index as a variable, I am not sure if it captured effectively the actual human activity every day. Even if restrictive measures were similar between countries, their actual accomplishment greatly depended on people's commitment and authorities' control and sanctions. I would suggest using a more realistic measure of human activity, such as google mobility reports.

    5. The authors used escape trials from birds on the ground and perched birds. I think that they are not comparable, as birds on the ground probably perceive a greater risk than those placed some meters above the ground, i.e. I would expect shorter escape distances for perched birds. As this can be strongly dependent on the species preferences or sampling site (i.e, more or less available perches), I wonder how this mixture of observations from birds on the ground and perched birds could be affecting the results.

    6. The authors did not sample the same location in the same breeding season to avoid repeated sampling of the same individuals (L331). This precaution may help, but it does not guarantee a lack of pseudoreplication. Birds are highly mobile organisms and the same individuals may be found in different places in the same city. This pseudoreplication seems particularly plausible for Rovaniemi, where sampling points must be necessarily close due to the modest size of this city.

    7. An intriguing result was that the authors collected data for 135 species during the shutdown, while they collected data only for 68 species before the pandemic. Such a two-fold increase in bird richness would not be expected with a 36% increase in sampling effort during 2020-21. I wonder if this could be reflecting an actual increase in bird richness in urban areas as a positive result of the shutdown and reduced human presence.

    8. The authors dismissed the multicollinearity problem of explanatory variables unjustifiably (L383). However, looking at fig. S1, I can see strong correlations between some of them. For instance, period and stringency index were virtually identical (r=0.95), while temperature and date were also strongly correlated.

    9. The random structure of the models is a key element of the statistical analyses but those random factors are poorly explained and justified. I needed to look up the supplementary tables to fully understand the complex architecture of the random part of the models. To the best of my knowledge, random variables aim to account for undesirable correlations in the covariance matrix, which is expected in hierarchical designs, such as the present one. However, the theoretical violation of data independence may happen or not. As the random structure is usually of little interest, you should keep it as simple as necessary, otherwise random factors may be catching part of data variability that you would like to explain by fixed variables. I think that this is what is happening (at least, in part) here, as the authors included a too-complex random structure. For instance, if you include the year as a random factor, I think that you are leaving little room for the period effect. The authors simplified the random structure of the models (L387), but they did not explain how. Nevertheless, this model selection was not important at all, as the authors showed the results for several models. I assume, consequently, that the authors are considering all these models equally valid. This approach seems quite contradictory.

  6. eLife

    Reviewer #3 (Public Review):

    This study examined the changes in fear response, as measured by the flight initiation distances (FID), of birds living in urban areas. The authors examined the FIDs of birds during the pandemic (COVID-19 lockdown restrictions) compared to FIDs measured before the pandemic (mostly in 2018 & 2019). The main study justification was that human presence changed drastically during the pandemic lockdowns and the change in human presence might have influenced the fear response of birds as a result of changing the "landscape of fear". Human presence was quantified using a 'stringency' index (government-mandated restrictions). Urban areas were selected from within five different cities, which included four European cities (Czech Republic - Prague, Finland - Rovaniemi, Hungary - Budapest, Poland - Poznan), and one city in the global south (Australia - Melbourne). Using 6369 flight initiation distances across 147 different bird species, the authors found that FIDs were not significantly different before the pandemic versus during the pandemic, nor was the variation in FID explained by the level of 'stringency'.

    Major strengths: There are several strengths to this study that allows for understanding the variety of factors that influence a bird's response to fear (measured as flight initiation distances). This study also demonstrates that FIDs are highly variable between species and regions.
    Specifically,

    1. One of the major strengths of this paper is the focus on birds living in urban areas, a habitat type that is hypothesized to have changed drastically in the 'landscape of fear' experienced by animals during the pandemic lockdown restrictions (due to the presumed decrease in human presence and densities). Maintaining the focus on urban birds allowed for a deeper examination of the effect of human behaviour changes on bird behaviour in urban habitats, which are at the interface of human-wildlife interactions.
    2. This study accounted for several variables that are predicted to influence flight initiation distances in birds including species, genus, region (country), variability between years, pandemic year (pre- versus during), the strictness of government-mandated lockdown measures, and ecological factors such as the human observer starting distance, flock size, species-specific body size, ambient air temperature (also a proxy of the timing during the breeding season), time of day, date of data collection (timing within the regional [Europe or Australia] breeding season), and categorization of urban site type (e.g. park, cemetery, city centre).
    3. This study examined FIDs in two years previous to the pandemic (mostly 2018 and 2019, one site was 2014) which would account for some of the within- and between-year FID variation exhibited prior to the pandemic.
    4. This study uses strong statistical approaches (mixed effect models) which allows for repeat sampling, and a post hoc analysis testing for a phylogenetic signal.

    Major weaknesses: The authors used government 'stringency' as a proxy for human presence and densities, however, this may not have been an accurate measure of actual human presence at the study sites and during measurements of FIDs. Furthermore, although the authors accounted for many factors that are predicted to influence fear response and FIDs in birds, there are several other factors that may have contributed to the high level of variation and patterns in FIDS observed during this study, thus resulting in the authors' conclusion that FIDs did not vary between pre- and during pandemic years.
    Specifically,

    1. The authors used "government stringency" as a measure of change in human activity, which makes the assumption that the higher the level of 'stringency', the fewer humans in urban areas where birds are living. However, the association between "stringency" and actual human presence at the study sites was not measured, nor was 'stringency' compared to other measures of human presence such as human mobility.
    2. There was considerable variation in FID measurements, which can be seen in the figures, indicating that most of the variation in FID was not accounted for in the authors' models. Factors that may have contributed to variation in FIDs that were not accounted for in this study are as follows:
      a. The authors accounted for the date of data collection using the 'day' since the start of the general region's breeding season (Europe: Day 1 = 1 April; Australia: Day 1 = 15 August). Using 'day' since the breeding season started probably was an attempt to quantify the effect of the breeding stage (e.g. territory establishment, nest young, fledgling) on FIDs. However, breeding stages vary both within- and between species, as well as between sub-regions (e.g. Finland vs. Hungary). As different species respond to predation or human presence differently depending on the stage during their breeding cycle, more specificity in the breeding cycle stage may allow for explaining the observed variation and patterns in FID.
      b. Variation in species-specific FIDs may also vary with habitat features within urban sites, such as the proximity of trees and other protective structures (e.g. perches and cover), the openness of the area, and the level of stressors present (e.g. noise pollution, distance to roads). Perhaps accounting for this habitat heterogeneity would account for the FID variation measured in this study.
      c. The authors accounted for species and genus within their models, however, FIDs may vary with other species-specific (or even specific populations of a species) characteristics such as whether the species/population is neophobic versus neophilic, precocial versus altricial, and the level of behavioural plasticity exhibited. These variables were not accounted for in the analysis.
      d. Three different methods of measuring the distances between flight and the observer location were used, and FIDs were only measured once per bird, such that there were no measures of repeatability for a test subject. Thus, variation surrounding the measurement of FIDs would have contributed to the variation in FIDs seen during this study.
    3. The sample design of this study may have influenced the FID variability associated with specific species, and specific populations of species. A different number of species were sampled across the time periods of interest; 68 species were sampled before the pandemic versus 135 species after the pandemic. However, the authors do not appear to have directly compared the FIDs for the same species before the pandemic compared to during the pandemic (e.g. the FIDs of Eurasian blackbirds before the pandemic versus during the pandemic). Furthermore, within the same country-city, it is unclear whether the species observed before the pandemic were observed at the same location (e.g. same habitat type such as the same park) during the pandemic. As a species' FID response may be influenced by population characteristics and features specific to each site (e.g. habitat openness), these factors may have influenced the variability in FID measurements in this study.
    4. The models in this study accounted for many factors predicted to affect FIDs (see the section on major strengths), however, the number of fixed and random factors are large in number compared to the total sample size (N =6369), such that models may have been over-extended.

    Overarching main conclusion
    Overall, this study examines factors influencing FIDs in a variety of bird species and concludes that FIDs did not differ during the pandemic lockdowns compared to before the pandemic (2019 and earlier). Furthermore, FIDs were not influenced by the strictness of government-mandated restrictions. Although the authors accounted for many factors influencing the measurement of FIDs in birds, the authors did not achieve their aim of disentangling the effects of pandemic-specific ecological effects from ecological effects unrelated to the pandemic (such as habitat heterogeneity). Their findings indicate that FIDs are highly variable both within- and between- species, but do not strongly support the conclusion that FIDs did not change in urban species during the pandemic lockdown. Therefore, this study is of limited impact on our understanding of how a drastic change in human behaviour may impact bird behaviour in urban habitats. Overall, the study demonstrates the challenges in using FIDs as a general fear response in birds, even during a pandemic lockdown when fewer humans are presumably present, and this study illustrates the large degree of variation in FIDs in response to a human observer.

  7. Version published to 10.1101/2022.07.15.500232 on bioRxiv