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REVIEW 3 major objections 5 minor 4 references

Wind as Driver of Bird and Bat Abundance, Flight Direction, Altitude, and Speed on the North Atlantic Shelf

T0 review · 3 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read This paper claims that wind speed and direction, measured at flight height by co-located lidars, drive bird and bat presence, flight direction, altitude, and speed at an offshore site, with smaller animals riding tailwinds at varied heights

desk verdict New paired radar-lidar offshore dataset worth knowing; the size-group behavioral claims are partly a clustering artifact, but the wind-behavior relationships hold up. read the letter →

arxiv 2511.14983 v2 pith:I5SBI3YB submitted 2025-11-18 physics.bio-ph

Abigale Snortland , Jeff Clerc , Cris Hein , Emma Cotter This is my paper · ORCID
classification physics.bio-ph
keywords offshorewindbirdmigrationbatradarornithologylidarflightaltitudecollisionriskNorthAtlantic
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that wind is a driver of bird and bat presence, flight direction, flight height, and flight speed at an offshore site on the North Atlantic Shelf. Using an S-band radar to track individual animals and two profiling lidars to measure wind at flight height, the authors split the tracks into small and big size groups by radar reflectivity. Small animals mostly flew with tailwinds, in concentrated directions aligned with the wind, and across a wide range of altitudes; bigger animals flew in many directions but stayed mostly below 100 meters. The results imply that collision-risk models for offshore wind turbines, which often treat flight behavior as fixed, should treat wind-dependent behavior as a variable.

What carries the argument

The central mechanism is the coupling of an S-band avian radar with two profiling lidars on the same barge, letting each animal track be paired with the wind speed and direction at its own flight height. The analysis rests on two derived quantities: wind assistance (the component of wind in the animal's travel direction) and the altitude of maximum wind assistance. A hierarchical clustering of reflectivity bins splits the tracks into two approximate size groups at -8.54 dBsm, and generalized additive models quantify the contribution of wind and solar predictors to hourly abundance.

What would settle it

Place a second wind lidar or anemometer a kilometer from the barge and compare its wind vectors with the barge's during the same period; if the difference in direction or speed is large enough to change a track's tailwind/crosswind/headwind category often, the paper's behavioral classifications would need re-evaluating. Alternatively, compare the small/large cluster assignments against species composition from acoustic or camera surveys at the same site.

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Extended reading notes

Core claim

The central discovery is that wind speed and direction, measured at the animal's flight height by co-located lidars, drive bird and bat presence, flight direction, flight height, and flight speed at an offshore site. Hierarchical clustering split the tracks into two size groups: small animals (69% of tracks) flew with tailwinds 72.4% of the time, followed the wind's diurnal direction shift, and used a broad range of altitudes, often near the altitude of maximum wind assistance; big animals (31%) flew in more scattered directions (49.1% tailwinds), stayed mostly below 100 m (82.3%), and were more strongly deterred by high wind speeds. Abundance generally fell at high winds, and ground speed r

Load-bearing premise

The lidar-derived wind profile at the barge is assumed to represent the wind experienced by animals up to 1.1 km away; if the wind varies horizontally, tailwind/headwind classifications and wind-assistance values will be systematically misassigned.

Editorial extensions

If this is right

  • Collision-risk models that treat flight direction, height, and speed as constants will mischaracterize risk; wind conditions should enter as a variable.
  • The fraction of animals inside the rotor-swept zone declines with wind speed (83.5% at <2.3 m/s vs 73.7% at >13.7 m/s), so the same turbine can pose very different risk at different times.
  • Because ground speed increases with wind assistance while airspeed stays roughly constant, airspeed alone or a fixed ground speed is not a reliable input for collision probability.
  • Size-group differences imply that species- and morphology-specific risk assessments should use wind-dependent behavioral parameters.
  • The paired radar-lidar processing pipeline is a reusable framework for other offshore sites and longer time series.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If small fliers are indeed long-distance migrants, their strong wind alignment suggests migration timing offshore may be modulated by wind forecasts; a testable extension is whether year-to-year wind direction changes shift the peak migration window.
  • The authors' use of a single barge-based wind profile to classify tracks up to 1.1 km away assumes horizontal wind uniformity; a two-point wind measurement would quantify how often this assumption fails and how much it biases tailwind/headwind statistics.
  • The reflectivity-based size split could be validated against species composition from acoustic or camera surveys; if validated, the clustering offers a low-cost way to estimate migrant-vs-resident behavior from radar alone.
  • The diurnal shift in small-animal flight directions following the wind suggests near-real-time wind cueing; a direct test is to compute the lag between wind direction change and track direction change within each hour.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper reports a five-week autumn 2024 deployment of an S-band avian radar and two profiling lidars on a research barge off southern Massachusetts (40.9° N, 70.79° W). After filtering, 67,410 tracks are analyzed. Each track is paired with co-located lidar wind measurements at flight height; the authors classify tailwind/crosswind/headwind, compute wind assistance and air speed, model hourly abundance with GAMs, and examine distributions of flight direction, altitude, and speed conditional on wind. A hierarchical clustering on reflectivity bins, using histograms of hourly abundance, flight height, flight direction, and flight speed as inputs, divides the data into 'small' and 'big' clusters at a reflectivity threshold of −8.54 dBsm. The paper claims that wind drives animal presence, flight direction, flight height, and flight speed, with smaller animals showing concentrated wind-aligned directions and a variety of altitudes, while bigger animals fly in wide directions but concentrate at low altitudes. Implications for wind-turbine collision-risk models are discussed.

Significance. The study's main strength is the co-located, high-resolution radar and lidar dataset, a genuine improvement over studies that rely on reanalysis wind fields. The methodological framework for pairing individual radar tracks with measured wind profiles, the explicit filtering criteria, and the transparent reporting of clustering input combinations are useful contributions. If the wind-behavior relationships hold, the implications for collision-risk modeling—treating flight direction, height, and speed as wind-dependent rather than constants—are meaningful and timely. However, the headline size-group behavioral contrasts are not independent evidence: the clustering objective uses the same behavioral histograms that are later compared between clusters, and the authors themselves acknowledge that radar detection limits compress the small-cluster altitude distribution. The paper would be considerably stronger if the size-group analysis were reframed as exploratory/descriptive rather than confirmatory, or if the clustering were redone using inputs not subsequently tested.

major comments (3)
  1. [§2.4, Table 1; §3.2–3.4, Figs. 9–13] The central size-group results are partly circular. The clustering inputs are histograms of hourly abundance, flight height, flight direction, and flight speed for each reflectivity bin, and the selected implementation is the one that maximizes Euclidean distance between clusters on those very inputs. The later comparisons of the two clusters on flight direction spread, flight altitude, and flight speed (§3.2–3.4, Figs. 9–13) therefore compare clusters on variables used to define them. The reflectivity divide is not robust across input combinations (Table 1 shows divides ranging from −20.16 to −9.23 dBsm). The wind-alignment result is less affected because wind direction is not a clustering input, but the direction-spread and altitude-range differences are inflated by construction. Please either (a) cluster on reflectivity alone, or on predictors not used in the subsequent behavioral com
  2. [§4.2, Fig. 5a] The paper acknowledges that radar detection range rolls off with reflectivity, so the small cluster's upper-altitude distribution—including the abstract's 'variety of altitudes'—is partly a detection artifact. Low-reflectivity targets (<−20 dBsm) were not detected above approximately 300 m, and the maximum detection altitude increases with reflectivity. Because the small cluster has lower reflectivity by construction, the altitude comparison between clusters is confounded. Please quantify the fraction of small-cluster tracks that lie near the reflectivity-dependent detection ceiling, and either restrict altitude comparisons to the reflectivity range where both clusters are detectable or attach this bias explicitly to every altitude claim.
  3. [§2.2] The wind experienced by an animal is assumed to equal the barge lidar profile at the track's flight height, but tracks range from 200 to 1100 m from the barge. Horizontal wind variability, especially in coastal and frontal conditions, could systematically misassign tailwind/headwind classifications and wind-assistance values for distant tracks. Since wind assistance underlies most of the paper's behavioral analyses, please add a sensitivity test—for example, recompute the key wind-behavior relationships using only tracks within a short range (e.g., <500 m), or restrict to periods when the lidar's spatial footprint is representative—and discuss whether the conclusions change.
minor comments (5)
  1. [§2.2] The phrase 'the spatial –before interpolation– and temporal resolution of the lidar dataset' contains a typographical formatting issue; please rephrase.
  2. [§3.4] The sentence 'This is consistent with the higher air speeds of animals in the big cluster (Figure 2)' appears to reference the wrong figure; the relevant support is in Figure 13b, not Figure 2 (species wing lengths).
  3. [Figure 7 caption] The caption says 'Wind turbine control regions I–III ... are denoted in (d) and (f)', but the text references wind-speed panels (c) and (f). Please correct the panel labels.
  4. [Table 2] The percent deviance explained by wind speed (1.2% small, 2.9% big) and wind direction (0.26% small, 0.77% big) is small relative to 'animals prior' (48.5% small, 36.5% big). The abstract's statement that 'wind is a driver of animal presence' should be calibrated to this modest effect size, especially since no significance or confidence intervals are given for the deviance contributions.
  5. [§2.4] The statement that 'two clusters were chosen because the resulting clusters delineated the data well' is not supported by an objective criterion. Please provide a quantitative justification (e.g., a silhouette score or a comparison against alternative numbers of clusters) or explicitly label this a pragmatic modeling choice.

Circularity Check

1 steps flagged · score 5.0 of 10

Size-group behavioral contrasts are partly constructed by the clustering procedure, but the core wind-driver relationships retain independent support.

  1. self definitional [Section 2.4 (Clustering, Table 1); results in Section 3.2–3.4 and abstract]
    "The clustering inputs for each reflectivity bin were all possible combinations of four probability histograms for the tracks in the bin: hourly abundance (number of tracks per hour of the year), flight height, flight direction, and flight speed. ... The best performing clustering implementations were determined to be the combinations of inputs that maximized the euclidean distance between clusters (i.e., the two clusters were the most distinct from each other). ... Of these, the implementation using flight height, flight direction, and flight speed is carried forward for the rest of the analys"

    The 'small' vs 'big' grouping is produced by clustering reflectivity bins using histograms of flight height, flight direction, and flight speed, selecting the split that maximizes between-cluster Euclidean distance on those same variables. The paper then presents the same variables as discovered behavioral differences: small animals had concentrated flight directions and varied altitudes, while big animals had wide directions and low altitudes (Figs. 9–13, abstract). Those contrasts are partly guaranteed by the clustering objective rather than independently measured. Wind variables were not clustering inputs, so the wind-alignment and wind-conditional analyses are less affected, but the headline size-specific behavioral claims in the abstract are partly constructed.

full rationale

The central wind-driver claim is not wholly circular: wind speed and direction were not used in the clustering, the wind-assistance decomposition is a kinematic identity rather than a fitted result, and the GAM is explicitly in-sample ('not meant to be predictive'), with no load-bearing self-citations. The substantive circularity is confined to the size-cluster analysis: the reflectivity threshold is chosen by clustering on flight height, direction, and speed, maximizing separation on those variables, and the same variables are then reported as the main behavioral differences between clusters. This makes the abstract's concrete size-group contrasts partly an artifact of the clustering selection. The wind-behavior relationships retain partial independence, so a score of 5 reflects partial circularity limited to the size-group contrast rather than the entire derivation.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper introduces one fitted reflectivity threshold (-8.54 dBsm) that defines all size-group results, plus GAM hyperparameters and subjective track filters. The central wind-behavior findings rely on unstated assumptions about wind spatial homogeneity and unbiased radar detection. No new physical entities are introduced.

free parameters (4)
  • Reflectivity cluster threshold = -8.54 dBsm
    Selected from the hierarchical clustering variant with maximum Euclidean distance between clusters; all 'small' vs 'big' classifications depend on this threshold.
  • GAM smoothing parameter λ = 1000
    Chosen by pyGAM grid search minimizing generalized cross-validation score; affects smoothness of abundance-environment relationships.
  • GAM number of basis functions = 20
    Fixed number of spline basis functions for all predictors in abundance GAMs.
  • Track filters (min detections, max speed, reflectivity interval, range) = >=5 detections, <=35 m/s, reflectivity [-25.5, 10] dBsm, range [200, 1100] m
    Subjective thresholds applied to remove insects/spurious tracks and account for detection rate variability; these choices affect all downstream analyses.
assumptions (4)
  • domain assumption Radar reflectivity is a monotonic proxy for animal body size over the considered range
    Used to infer size classes from reflectivity; the paper notes reflectivity also depends on orientation and water content (Sec 2.4), so this is approximate.
  • domain assumption The wind profile measured at the barge is representative of the wind experienced by animals across the 200–1100 m radar range
    All track-wind pairings use lidar at the barge's location; no spatial wind variability correction is applied.
  • domain assumption Radar detection probability is constant across the altitude range of interest, or biases are small enough not to invert qualitative trends
    Sec 4.2 acknowledges detection roll-off with range and lower reflectivity, so this assumption is violated for the small cluster at high altitude.
  • domain assumption The proprietary DeTect tracking software correctly associates detections into individual animal tracks and excludes non-animal targets
    All track data depend on this proprietary processing; no validation is provided.

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Cite this review

Pith. "Pith review of Wind as Driver of Bird and Bat Abundance, Flight Direction, Altitude, and Speed on the North Atlantic Shelf." pith.science (2026). https://pith.science/paper/I5SBI3YB

@misc{pith2026251114983,
  author       = {Pith},
  title        = {Pith review of: Wind as Driver of Bird and Bat Abundance, Flight Direction, Altitude, and Speed on the North Atlantic Shelf},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/I5SBI3YB}},
  note         = {Machine review of arXiv:2511.14983}
}
read the original abstract

Quantifying the collision risk of birds and bats with offshore wind turbines requires an understanding of the drivers of flying animal behavior at offshore wind sites. An omnidirectional S-band radar system was deployed on a research barge on the Northeastern Shelf of the United States (40.9 deg N, 70.79 deg W) and collected data for a 5-week window during the 2024 autumn bird and bat migration. The barge also supported two profiling lidar systems that measured the wind speed and direction. This study presents a first methodological approach for analyzing radar and lidar data together, providing a framework for future analyses of offshore bird and bat movements that can be used to improve collision risk models. Coupling the radar animal tracks with measured wind speed profiles revealed that wind is a driver of animal presence, flight direction, flight height, and flight speed. Further, a hierarchical clustering methodology was developed to investigate behavior by approximate animal size. For example, smaller animals had concentrated flight direction distributions aligned with the wind and flew at a variety of altitudes, whereas bigger animals flew in a wide variety of directions but were concentrated at low altitudes. Our results provide the first insights into animal behavior at offshore wind sites with paired radar and lidar data.

Figures

Figures reproduced from arXiv: 2511.14983 by the authors.

Figure 1
Figure 1. (a) Location of barge deployment south of Massachusetts, USA. Map was generated using the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Species expected at the study location in autumn. Categorized by migration preference and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Data analysis flow chart inertial measurement unit did not function properly until 24 August, limiting motion compensation capabilities. The system was fully functional from 24 August to 28 September 2024. This time period forms the basis of our analysis. Bird and bat detections were automatically processed onboard the radar using proprietary tracking software developed by DeTect. The software output latitude, longi… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: (a) Flight speed and direction rose for all tracks. Note: flight direction is the direction the tracks [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Bivariate histograms of reflectivity with (a) flight height, (b) flight direction, (c) flight speed, and [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: (a) Histogram of small and big cluster tracks with respect to hour of the day. The range of sun [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Partial dependence plots (PDP) for the top four predictors in the GAM abundance models for the [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: (a) Weekly wind speed and direction polars for wind conditions at flight height. (b) Weekly flight [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: (a) Histograms of difference between wind and flight direction for the two size clusters. Cumulative [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: Cumulative distributions conditional on hour of the day for (a) wind direction at 100 m, (b) small [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: (a) Flight height distribution histograms for each size cluster. (b,c) Cumulative distribution [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: (a,b) Height of maximum wind assistance (WA) histogram for the small cluster and the big [PITH_FULL_IMAGE:figures/full_fig_p015_12.png]
Figure 13
Figure 13. Figure 13: Violin plots of the relationship between (a) ground speed and (b) air speed with WA. Distributions [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

4 extracted references · 1 linked inside Pith

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