Article activity feed

  1. Author Response

    Reviewer #2 (Public Review):

    The authors compiled camera-trap datasets from across North America to test hypotheses about how animal species adjust their daily activity cycle to urban development and human activity. They found that multiple species adjust their diel cycle, with human activity clearly supported as a driver.

    The paper is very well-written. It is clear, concise, and the narrative is lovely to follow. The background information provides a strong foundation. I do note that previous research on diel activity from camera traps is a little sparse, selecting only a couple cursory examples. I have no other suggestions for the Introduction, which is a nice read.

    Methods:

    The methods used are novel, interesting, and suitable to the questions at hand.

    How might the different sample sizes in different cities, impact the results? Were there any correlations between sample size and the attributes you measured, such that bigger, more intensely developed and used cities were sampled more than smaller, less developed cities? I appreciated the information presented in Table S4, and I note that cities varied widely in the various metrics. Some correlation tables and variance inflation factor (VIF) estimation should also be presented to assure the reader that the highly unbalanced sampling design necessarily arising from UWIN did not influence the results and hence conclusions.

    This is a great point and something we did not address. In regards to uneven sample size, we now have group mean centered each covariate by the respective city and scaled by the respective covariates global standard deviation. This scaling eases parameter interpretation and makes parameter estimates less sensitive to unequal sample size among cities (Fidino et al., 2021; Milliren et al., 2018). Doing did change our results slightly, but did not change our results in any significant way. Therefore, we have updated our figures, tables, and relevant areas of the results, but did not make any changes in the discussion. We also added the following information to improve clarity.

    “All predictor variables were group mean centered by the respective city and scaled by the global standard deviation for each variable. This scaling eases parameter interpretation and makes parameter estimates less sensitive to unequal sample size among cities (Fidino et al., 2021; Milliren et al., 2018).”

    In regards to correlation among predictor variables, our LASSO regularization allows for models to contain correlated variables. We have added more context in our methods to describe how this approach is appropriate when you have collinearity between variables.

    Why did you choose to discretize animal detections into these bins, rather than using continuous time as several animal activity packages allow? The use of Ridout and Linkie (2009)'s R packages is very common in diel activity pattern analysis and I wonder why you chose this categorical approach instead? I suspect it has to do with the necessary sample sizes, which are restrictive, but I would like to see your rationale here.

    With the Ridout and Linkie kernel density approach one is unable to put continuous covariates on the activity pattern. We are only able to compare activity patterns between two categorical variables at a time. Here we have shown that you can use a multinomial model like a resource selection function on time and estimate the influence of continuous variables on the probability that an animal is active in a particular time category. In theory, with enough data and appropriate biological reasoning, you could slice your bins thinner and thinner and estimate more fine scale temporal patterns.

    We now included the following caveat in the discussion recognizing past research on activity patterns but further explaining the novelty of our work.

    “A variety of methods have been developed to study animal activity patterns and temporal behavior using time-stamped camera data (see Frey et al., 2017 and references within). However, very little work has been done to quantify changes in temporal behavior across continuous independent variables (Cox et al., 2021; Gaston, 2019). Here, we built upon Farris et al. (2015) and developed an analytical approach to quantify temporal resource selection across continuous environmental gradients. Although we have developed a new analytical tool to measure temporal selection, a theoretical context for temporal habitat selection is needed and a further understanding of disproportional selection relative to the number of hours available is a promising avenue for future animal biology research.”

    Line 501: You used city as a random effect, which makes sense. However, did you consider using cityscale attributes as fixed effects? A random effect is essentially a bin for unexplained variation, but there are several attributes of city (Table S4) that might explain some variation expressed in this level of the sampling hierarchy. This relates to my comment above about whether within-city attributes might be masking (or amplifying) some of the fixed effects that you did model. Notwithstanding these comments, the analysis selected is appropriate to the question and conducted properly.

    We did not model city-specific attributes as a fixed effect in this model. As our UWIN network partners grow, we add more cities and sites, and we obtain more data we can better fit these complex multi-level models. In this manuscript, we acknowledge and account for among city variation via a random effect and present those results in Figure 1, so that the reader can interpret that variation as they please. Please see comment above about handling among city variation and differences in sample size between cities.

    Results The Results are clear, concise, and well-presented. I have no comments or suggestions for improvement.

    Discussion This is also well-written and clear, following an enjoyable logical narrative. The conclusions follow soundly from the results. I have very little to offer to the Discussion, despite my best efforts. I might suggest that some context of the importance of urban wildlife here might be useful in your closing sentences. This paper will have a strong impact in the field, but this is not fully conveyed therein. Critics might doubt the importance of urban wildlife, given that most of wildlife occurs outside urban areas. The growth of urban areas globally, and the projections for the future, signal that urban areas encroaching on wildlife ranges will only grow, requiring that we plan human spaces that can also accommodate wild spaces.

    Thank you for this more impactful mic drop. We have added the following two sentence to the end of our discussion

    “Future projections of urban growth signal that urban areas will continually encroach on wildlife habitat. Therefore, it is imperative that we consider animal behavioral responses to urbanization as we plan human spaces that can also accommodate wildlife.”

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  2. Evaluation Summary:

    This study will be of interest to wildlife ecologists and conservation practitioners. The authors took a collaborative approach and collated a large dataset of wildlife camera trap recordings across cities in the USA. The analyses reveal variability in diel activity among species and cities, providing important insights into the effects of urbanization.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their name with the authors.)

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  3. Reviewer #1 (Public Review):

    Gallo et al. use camera trap data collected across a range of cities in the US to explore how mammals adapt the timing of their activity to a human-dominated activity. They found that activity patterns are highly flexible, varying across and within species, but overall were able to determine that mammals in urban areas can adapt their activity to reduce risk. The diversity of species involved means that no overall pattern emerged, but the species-specific patterns discussed by the authors are justified by the data.

    The argument in the introduction needs tightening, and further clarification and information are required in the methods. The terminology also needs greater consideration. Artificial light at night and the skyglow that it generates means that there is often light well above natural levels throughout the night, and as such the term the 'darkest hour' should be revised.

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  4. Reviewer #2 (Public Review):

    The authors compiled camera-trap datasets from across North America to test hypotheses about how animal species adjust their daily activity cycle to urban development and human activity. They found that multiple species adjust their diel cycle, with human activity clearly supported as a driver.

    The paper is very well-written. It is clear, concise, and the narrative is lovely to follow. The background information provides a strong foundation. I do note that previous research on diel activity from camera traps is a little sparse, selecting only a couple cursory examples.
    I have no other suggestions for the Introduction, which is a nice read.

    Methods:

    The methods used are novel, interesting, and suitable to the questions at hand.

    How might the different sample sizes in different cities, impact the results? Were there any correlations between sample size and the attributes you measured, such that bigger, more intensely developed and used cities were sampled more than smaller, less developed cities? I appreciated the information presented in Table S4, and I note that cities varied widely in the various metrics. Some correlation tables and variance inflation factor (VIF) estimation should also be presented to assure the reader that the highly unbalanced sampling design necessarily arising from UWIN did not influence the results and hence conclusions.

    Why did you choose to discretize animal detections into these bins, rather than using continuous time as several animal activity packages allow? The use of Ridout and Linkie (2009)'s R packages is very common in diel activity pattern analysis and I wonder why you chose this categorical approach instead? I suspect it has to do with the necessary sample sizes, which are restrictive, but I would like to see your rationale here.

    Line 501: You used city as a random effect, which makes sense. However, did you consider using city-scale attributes as fixed effects? A random effect is essentially a bin for unexplained variation, but there are several attributes of city (Table S4) that might explain some variation expressed in this level of the sampling hierarchy. This relates to my comment above about whether within-city attributes might be masking (or amplifying) some of the fixed effects that you did model. Notwithstanding these comments, the analysis selected is appropriate to the question and conducted properly.

    Results:

    The Results are clear, concise, and well-presented. I have no comments or suggestions for improvement.

    Discussion:

    This is also well-written and clear, following an enjoyable logical narrative. The conclusions follow soundly from the results. I have very little to offer to the Discussion, despite my best efforts. I might suggest that some context of the importance of urban wildlife here might be useful in your closing sentences. This paper will have a strong impact in the field, but this is not fully conveyed therein. Critics might doubt the importance of urban wildlife, given that most of wildlife occurs outside urban areas. The growth of urban areas globally, and the projections for the future, signal that urban areas encroaching on wildlife ranges will only grow, requiring that we plan human spaces that can also accommodate wild spaces.

    Was this evaluation helpful?