Author response:
The following is the authors’ response to the original reviews.
eLife Assessment
This valuable work advances our understanding of the foraging behaviour of aerial insectivorous birds. Its major strength is the large volume of tracking data and the accuracy of those data. However, the evidence supporting the main claim of optimal foraging is incomplete.
We deeply appreciate the thoughtful review provided by the reviewers, including their valuable insights and meticulous attention to detail. Each comment has been thoroughly evaluated, leading to substantial improvements in the manuscript. Your constructive critique has been instrumental in refining our research and rectifying any oversights. We are confident that the revised article will make a substantial contribution to ecological research, particularly in advancing our understanding of foraging theories and the behaviors of aerial insectivores.
Public Reviews:
Reviewer #1 (Public Review):
This study tests whether Little Swifts exhibit optimal foraging, which the data seem to indicate is the case. This is unsurprising as most animals would be expected to optimize the energy income: expenditure ratio; however, it hasn't been explicitly quantified before the way it was in this manuscript.
The major strength of this work is the sheer volume of tracking data and the accuracy of those data. The ATLAS tracking system really enhanced this study and allowed for pinpoint monitoring of the tracked birds. These data could be used to ask and answer many questions beyond just the one tested here.
The major weakness of this work lies in the sampling of insect prey abundance at a single point on the landscape, 6.5 km from the colony. This sampling then requires the authors to work under the assumption that prey abundance is simultaneously even across the study region - an assumption that is certainly untrue. The authors recognize this problem and say that sampling in a spatially explicit way was beyond their scope, which I understand, but then at other times try to present this assumption as not being a problem, which it very much is.
Further, it is uncertain whether other aspects of the prey data are problematic. For example, the radar only samples insects at 50 m or higher from the ground - how often do Little Swifts forage under 50 m high?
Another example might be that the phrases "high abundance" and "low abundance" are often used in the manuscript, but never defined.
It may be fair to say that prey populations might be correlated over space but are not equal. It is this unknown degree of spatial correlation that lends confidence to the findings in the Results. As such, the finding that Little Swifts forage optimally is indeed supported by the data, notwithstanding some of the shortcomings in the prey abundance data. The authors achieved their aims and the results support their conclusions.
Thanks for this comment.
The basic assumption of this paper is that the abundance of insects bioflow in the airspace is correlated in space and varies over time. This has been demonstrated by different studies, see for example Bell et al. (Bell, J. R., Aralimarad, P., Lim, K. S., & Chapman, J. W. (2013). Predicting insect migration density and speed in the daytime convective boundary layer. PloS one, 8(1), e54202) in which positive correlation in insect bioflow is demonstrated between different sites that are more than 100 km away in Southern England. Given the much closer proximity of the colony and the radar site, as well as the large foraging distance of the swifts that often forage in the vicinity of the radar and beyond it, it is reasonable to assume that the radar was able to successfully capture between-day variation in the abundance of flying insects in the airspace, which is highly relevant for the foraging swifts. This is likely because meteorological variables such as temperature and wind, which tend to vary over a synoptic-system scale of several hundred kilometers, significantly influence the abundance of aerial insects. Furthermore, the direction of insect flight that has been recorded by the radar points to an overall south-north directionality of the insects during the period of the study (Werber et al. Under Review: Werber, Y., Chapman, J. W., Reynolds, D. R. and Sapir, N. Active navigation and meteorological selectivity drive patterns of mass intercontinental insect migration through the Levant). Hence, it is reasonable to assume that since the colony is positioned approximately 6.5 km south of the radar site, the radar is able to reliable estimate the between-day variation in aerial insect abundance experienced by the foraging swifts. Importantly, this between-day variation is very high, and detailed information regarding this variation is provided in the paper. We thank the reviewer for the comments on the wording and have corrected it accordingly so that it is explicitly stated that the spatial distribution of the flying insects is indeed not uniform, but is expected to be simultaneously affected by environmental variables creating spatially correlated bioflow of aerial insects.
The term "high abundance" or "low abundance" is relative to the variable being examined but throughout the manuscript we did not use these terms to describe an absolute amount or a certain threshold but rather to describe the ecological circumstances experienced by the birds on different days that substantially varied in abundance of insect recorded by the radar. However, we have improved the wording of the text so that it is now clear that we refer to relative and not to absolute values.
At its centre, this work adds to our understanding of Little Swift foraging and extends to a greater understanding of aerial insectivores in general. While unsurprising that Little Swifts act as optimal foragers, it is good to have quantified this and show that the population declines observed in so many aerial insectivores are not necessarily a function of inflexible foraging habits. Further, the methods used in this research have great potential for other work. For example, the ATLAS system poses some real advantages and an exciting challenge to existing systems, like MOTUS. The radar that was used to quantify prey abundance also presents exciting possibilities if multiple units could be deployed to get a more spatially-explicit view.
To improve the context of this work, it is worth noting that the authors suggest that this work is important because it has never been done before for an aerial insectivore; however, that justification is untrue as it has been assessed in several flycatcher and swallow species. A further justification is that this research is needed due to dramatic insect population declines, but the magnitude and extent of such declines are fiercely debated in the literature. Perhaps these justifications are unnecessary, and the work can more simply be couched as just a test of optimality theory.
We appreciate the reviewer's helpful comment. A flycatcher is indeed an aerial insect eater, but its foraging strategy is very different from that of swifts. A comparison with the foraging strategy of the swallow is much more relevant. However, the methods used to quantify bird movement in the airspace in previous articles limited the ability to examine the optimal foraging theory in detail. Following the comment, we revised the text to better describe the uniqueness of our research. Further, since we studied insectivores, it is important to provide a broad context to potentially significant threats to the birds, albeit being debatable
Reviewer #2 (Public Review):
Summary:
Bloch et al. investigate the relationships between aerial foragers (little swifts) tracked with an automated radio-telemetry system (Atlas) and their prey (flying insects) monitored with a small-scale vertical-looking radar device (BirdScan MR1). The aim of the study was to test whether little swifts optimise their foraging with the abundance of their prey. However, the results provided little evidence of optimal foraging behaviour.
Strengths:
This study addresses fundamental knowledge gaps on the prey-predator dynamics in the airspace. It describes the coincidence between the abundance of flying insects and features derived from tracking individual swifts.
Weaknesses:
The article uses hypotheses broadly derived from optimal foraging theory, but mixes the form of natural selection: parental energetics, parental survival (predation risks), nestling foraging, and breeding success.
While this study explores additional behavioral theories alongside optimal foraging theory, its findings unequivocally support the latter. The highly statistically significant observed reduction in flight distance from the breeding colony in elation to increasing insect abundance (supporting predictions 1 and 2) coupled with an increased rate of colony visits (supporting prediction 5) demonstrate the Little Swifts' adeptness at optimizing their aerial foraging behavior. This behavior manifests in an enhanced frequency of visits to the breeding colony, underscoring their food provisioning maximization.
Results are partly incoherent (e.g., "Thus, even when the birds foraged close to the colony under optimal conditions, the shorter traveling distance is not thought to not confer lower flight-related energetic expenditure because more return trips were made.", L285-287),
Thanks for the comment. We have corrected this sentence.
and confounding factors (e.g., brooding vs. nestling phase) are ignored.
The breeding stage may indeed affect food provisioning properties but this factor is not confounded since insect abundance, and the consequent changes in bird foraging properties, fluctuated between sequential days while brooding and nestling phases take place over a period of several weeks, each. Further, despite the possible influence of breeding stages on bird behavior, variability in reproductive stages is expected among pairs in a breeding colony occupying dozens of pairs, despite some coordination in nesting initiation. Practically, the narrow and concealed nest openings hindered direct observation of the nests, posing challenges in determining the precise reproductive stage of each pair. Anyway, we added a short description of the dense colony structure to the Methods section.
Some limits are clearly recognised by the authors (L329 and ff).
See above the response about the distribution of insects in space.
To illustrate potential confounding effects, the daily flight duration (Prediction 4) should decrease with prey abundance, but how far does the daily flight duration coincide with departure and arrival at sunrise and sunset (note that day length increases between March and May), respectively, and how much do parents vary in the duration of nest attendance during the day across chick ages?
We added the following explanation to the Methods section:
To standardize the effect of day length on daily foraging duration, we calculated and subtracted the day length from the total daily foraging time (Day duration - Daily foraging duration = Net foraging duration). The resulting data represent the daily foraging duration in relation to sunrise and sunset, independent of day length.
To conclude, insufficient analyses are performed to rigorously assess whether little swifts optimize their foraging.
We disagree. See our responses above.
Filters applied on tracking data are necessary but may strongly influence derived features based on maximum or mean values. Providing sensitivity tests or using features less dependent on extreme values may provide more robust results.
Thank you for highlighting the importance of considering the impact of data filtering on derived features. In our analysis, we employed rigorous filtering methods to emphasize central data tendencies while mitigating the influence of extreme values. These methods, validated through consultation with experts in tracking data analysis, follow established practices in the literature. Detailed descriptions of our filtering procedures can be found in the Methods section, with citations to relevant published studies.
Radar insect monitoring is incomplete and strongly size-dependent. What is the favourite prey size of swifts? How does it match with BirdScan MR1 monitoring capability?
We added an explanation to the Methods section to address this comment:
The Radar Cross Section (RCS) quantifies the reflectivity of a target, serving as a proxy for size by representing the cross-sectional area of a sphere with identical reflectivity to water, whose diameter equals the target's body length. Recent findings indicate that the BirdScan MR1 radar can detect insects with an RCS as low as 3 mm², enabling the detection of insects with body lengths as small as 2 mm. These capabilities make the radar suitable for locating the primary prey of swifts, which typically range in size from 1 to 16 mm.
Recommendations for the authors:
Reviewer #1 (Recommendations For The Authors):
Lines 53-59 - major run-on sentence
Thanks for the comment. Done.
Line 133 - describe better. Attached where? Were feathers clipped or removed?
Thanks for the comment. Done.
Line 153 - shouldn't be a new paragraph
Done.
Line 157 - justify choosing four
To ensure a robust analysis of swifts' behavior relative to food abundance across multiple individuals simultaneously, we opted to exclude data from instances where only 3 tags were active. This decision was motivated by the fact that these instances accounted for only 2.9% of the data, and their exclusion minimally impacted overall data volume while enhancing data quality. In contrast, instances with 4 tags, comprising 16.2% of the data, provided substantial insights. Omitting these instances would have resulted in significant data loss. Thus, setting a threshold of 4 simultaneous tags represents a balance between maintaining adequate data quantity and ensuring high data quality for meaningful analysis.
It took me a long time to determine whether the average and maximum flight distance was actual or Euclidean. It was only in the Results that I grasped it was actual. Define up front in the Methods.
Thanks for the comment. Done.
In my public review, I mention that optimal foraging has been assessed in other aerial insectivores. Here are some of the papers I was referring to:
• Davies (1977) Prey selection and the search strategy of the spotted flycatcher (Muscicapa striata): A field study on optimal foraging. Animal Behaviour 25: 1016-1022.
• Lifjeld & Slagsvold (1988) Effects of energy costs on the optimal diet: an experiment with pied flycatchers Ficedula hypoleuca feeding nestlings. Ornis Scandinavica 19: 111-118.
• Quinney & Ankney (1985) Prey size selection by tree swallows. Auk 102: 245-250.
• Turner (1982) Optimal foraging by the swallow (Hirundo rustica, L): Prey size selection. Animal Behaviour 30:862-872.
Lastly, in terms of the work not being spatially-explicit, I do note that in lines 323-324 you acknowledge that prey populations can be patchy, then ten lines later, you provide citations to say that patchiness is not a problem because of spatial correlations. This is a bit overly dismissive, in my view, and to suggest (lines 336-337) that "patches of high insect concentration...might not exist at all" is certainly incorrect (and misleading). I do note the valiant attempt to address the spatial shortcoming in the remainder of the paragraph - although addressing it does not make the problem go away.
Thanks for the comment.
We revised the text to make it more coherent.
Reviewer #2 (Recommendations For The Authors):
L161: typo > missing space in 'meanof'
Corrected.
L192-193: Did the authors use the timing of sunrise and sunset to determine daytime?
Yes. The daytime was calculated in relation to sunrise and sunset.
Did the authors calculate the MTR from sunrise to sunset, or averaging the hourly MTR?
If using hourly MTR, specify the criteria to assign an hourly MTR to daytime when sunset/sunrise is happening during that hour.
A simplified terminology for "Average daily insect MTR" might be useful, in particular for the result section (insect MTR).
Average daily insect MTR is calculated for a fixed period from 5 am to 8 pm local time. An explanation has been added to the Methods section, and the terminology in the text has been simplified as suggested
Note that the 'M' of MTR stands for migration, which may not be appropriate in this context, and simply using "insect traffic rate" may be a better terminology.
Thanks for the comment. The 'M' of MTR can also stand for movement, as the insects detected by the radar move in the airspace. This is how this term has been defined in the paper (e.g. in line 23 of the Summary section). Therefore, we did not change the terminology to “insect traffic rate”, which is a term not used in other studies.
Considering the large number of predictions (10!), it would be appropriate to list them in the results (e.g., "on the daily average flight distance from the breeding colony (Prediction 3)").
We added prediction numbers to the Results and the Discussion.
Note that the terminology varies; e.g., in the introduction "overall daily flight distance" (L75), in the results "average length of the daily flight route" (L236), and further confusion with "daily average flight distance from the breeding colony" (L232).
Thanks for the comment. fixed.
The terminology - average daily 'air/flight' distance (L74-76) - needs clarification.
Done.
Results: Use only a relevant and consistent number of decimals to report on the effect size and p-values.
Done.
The authors are citing non-peer-reviewed publications:
21. Bloch I, Troupin D, Sapir N. Movement and parental care characteristics during the nesting season of 468 the Little Swift (Apus affinis) [Poster presentation]. 12th European Ornithologists' Union Congress. Cluj Napoca, Romania. 2019.
62. Zaugg S, Schmid B, Liechti F. Ensemble approach for automated classification of radar echoes into functional bird sub-types. In: Radar Aeroecology. 2017. p. 1. doi:10.13140/RG.2.2.23354.80326
It is acceptable to cite non-peer-reviewed sources if they have a significant contribution to the background of the article without a critical impact on the core of the research.
