Artificial Light at Night is Related to Broad-Scale Stopover Distributions of Nocturnally Migrating Landbirds along the Yucatan Peninsula, Mexico
Round 1
Reviewer 1 Report
I feel no competent to comment on the manuscript due to prevailingly biology content. My expertise is in physics, theoretical and numerical modeling, and experimentation having relation to ALAN. However, I have read the paper a few times and came to conclusion that it is not suitable to Remote Sensing. This has no relation to the quality of the work. I do not rule out that this work would be of high interest in other (more biologically oriented) journals. I found both the results and interpretations unconvincing and rather arbitrary because factors other than ALAN (e.g. biological factors, pollutants, natural factors having relation to the ambient environment) can be potentially decisive in different seasonal and spatial bird densities.
Author Response
This contribution is targeted for and is well aligned to the special issue on Radar Aeroecology, which: “invites original research papers focusing on concepts, methods, applications, and empirical findings associated with radar surveillance of animals in the airspace. The scope of these studies can include field or laboratory measurements of birds, bats, or insects made from atmospheric, marine, or purpose-built ecological radar units operating at millimeter to meter wavelengths.” (https://www.mdpi.com/journal/remotesensing/special_issues/radar_aeroecology; italics ours).
Ecology is not an exact science; it is typical to deal with uncertainty. The statistical methods that we used were selected because they have the ability to disentangle the potential effect of multiple variables. In our models, we include multiple predictors and report the relative importance of each of them in explaining the observed response. The cross-validation correlation, a goodness-of-fit measure for models, ranged between 0.47 and 0.86, which are accepted measures in our field. Those numbers do suggest that variables other than those we measured also affect bird distributions, but the results that we report derive from the data at hand, and the patterns and interpretations are informative of the pattern of bird distributions in the study area.
Reviewer 2 Report
Do not see any black dots on the Fig. 1., suggest putting a small cross instead.
Author Response
We added a small black cross to indicate the location in Fig. 1.
Reviewer 3 Report
This manuscript is very interesting as it first tested the relationship between the bird density and night light. However, the expression way does not match a remote sensing journal well, and I suggest the paper to be published after a major revision:
The legend of figure 1 should be labeled with a physical unit. “Since night-lights data are right skewed” in Line 218, I am surprised to know the light distribution in this area, and I would like to see the histogram of the data. The manuscript should introduce the Boosted Regression Tree models as many readers were not aware of them. I suggest the manuscript provide the bird density map with along with the night light images so that their relationship would be clearer from the vision. Some factors may affect the observed night-time light from satellite, which may bring some errors for the modeling, I suggest the authors to make a brief discussion on them. The factors include:
Atmosphere: Johnson, R.S., Zhang, J., Hyer, E.J., Miller, S.D., & Reid, J.S. (2013). Preliminary investigations toward nighttime aerosol optical depth retrievals from the VIIRS Day/Night Band. Atmospheric Measurement Techniques, 6, 1245-1255
Satellite observation angles: Li, X., Ma, R., Zhang, Q., Li, D., Liu, S., He, T., & Zhao, L. (2019). Anisotropic characteristic of artificial light at night – Systematic investigation with VIIRS DNB multi-temporal observations. Remote Sensing of Environment, 233, 111357
Author Response
This manuscript has been submitted for the special issue on Radar Aeroecology, which “invites original research papers focusing on concepts, methods, applications, and empirical findings associated with radar surveillance of animals in the airspace” (https://www.mdpi.com/journal/remotesensing/special_issues/radar_aeroecology; italics ours). Thus, we consider the topic of the manuscript well suited to this journal’s special issue.
We labeled Figure 1 with the actual light intensity values, expressed in units of nanoWatts per square centimeter per steradian (nW cm2 sr), and updated the figure caption accordingly.
Below is a histogram showing the distribution of ALAN intensities around Sabancuy (top row) and Cancun (bottom) before (left) and after log10 transforming (right). For the Cancun radar the distribution does not become normal but the skewness is greatly reduced.
We have included some additional details on the Boosted Regression Trees method and more references of the literature.
We acknowledge that factors such as atmospheric conditions and satellite observation angles affect the quality of satellite observations, and this should definitely be taken into account when using raw or un-preprocessed satellite-sensed data products. However, we used monthly image composites to estimate the intensity of artificial lights at night around each of the radars, and these composites were pre-processed before being made available for public use. Elvidge et al. (2017. VIIRS Night-Time Lights, https://doi.org/10.1080/01431161.2017.1342050) describes the steps involved in the production of these data. From their abstract: “There are a substantial number of steps involved in producing a product that has been cleaned to exclude background noise, solar and lunar contamination, data degraded by cloud cover, and features unrelated to electric lighting (e.g. fires, flares, volcanoes).” Furthermore, for analyses we estimated the average of night-lights intensity derived from the monthly satellite composites of bird migration months. Thus, we consider the potential effect of the factors suggested on our results negligible. We added a reference to Elvidge et al (2017) and wording to clarify that these data were pre-processed to address atmospheric and observation issues with the raw data in the second paragraph of subsection 2.2 Model covariates.
We have included a new figure showing bird densities in relation to artificial lights, available in the Supplemental materials - now provided at the end of the manuscript, after the references.
Reviewer 4 Report
2 title – since this is a correlative study I suggest to change word “influences” into “correlate with...”. Also “light pollution” should be changed into “artificial light at night” because this is what Authors used as a variable. I understand the need for using trendy word “pollution”, but it has a bit different meaning.
34 what does „other tall structures” mean? Be more specific, please.
34 what does „influence the behavior” mean? Behaviour has broad meaning – try to specify, please.
45 change “Artificial light at night” to ALAN
50-51 how is ALAN “novel” along migration routes compared to breeding and non-breeding grounds? It is unclear.
110 “Black dots” are not presented on the figure.
112 “migration months” – spring, fall or both seasons?
109 What is this illuminating object at the sea near Sabancuy radar? Top-left corner of the left-hand figure – out of the radar range.
126 what does „dates varied” mean? Explain, please.
206 Table 1 How are “bright lights” define? Add threshold value, please.
268 Provide justification for r value. It is quite large, while some papers indicate that less than 0.3 could be a problem. Since your land cover variables are proportion there will be a problem with correlation.
285 what is the correlation between distance to the coast and distance to bright lights? Considering the nature of these variables, I assume high r value. How this influence your results?
319 Please, unify the scale on X axis – it will be easier to compare results: distance to the coast and distance to bright lights have same units. [at least for each of the radar]
401 “increased mortality” could be a problem but these birds will be recorded by radar prior to collision. Right?
Author Response
Reviewer 4
Comment 1
2 title – since this is a correlative study I suggest to change word “influences” into “correlate with...”. Also “light pollution” should be changed into “artificial light at night” because this is what Authors used as a variable. I understand the need for using trendy word “pollution”, but it has a bit different meaning.
Response
We changed the title to “Artificial light at night is related to broad-scale stopover distributions of nocturnally migrating landbirds along the Yucatan Peninsula, Mexico”.
Comment 2
34 what does „other tall structures” mean? Be more specific, please.
Response
We now specify communication towers as examples of “other tall structures”.
Comment 3
34 what does „influence the behavior” mean? Behaviour has broad meaning – try to specify, please.
Response
We have replaced “influence the behavior” with “influence the flight trajectories [4], altitudes [5,6], orientation and distributions of migrating birds [7].”.
Comment 4
45 change “Artificial light at night” to ALAN
Response
Change made
Comment 5
50-51 how is ALAN “novel” along migration routes compared to breeding and non-breeding grounds? It is unclear.
Response
We considered ALAN to be novel along migration routes because of its greater intensity in the geographic ranges of migratory bird species, compared to their breeding and wintering ranges. However, we acknowledge that the wording was unclear. We changed the sentence to read
“Furthermore, light pollution is a relatively novel stimulus during migration and is brighter and more ubiquitous along migration routes compared to relatively dark stationary breeding and non-breeding grounds”
Comment 6
110 “Black dots” are not presented on the figure.
Response
Thank you for catchig this. We accidentally forgot to include those symbols in the figure. We corrected that and, following a suggestion from another reviewer, use crosses instead of dots.
Comment 7
112 “migration months” – spring, fall or both seasons?
Response
We now specify that we considered migration months from both seasons. The specific dates that we considered for each season were already provided in the following paragraph.
Comment 8
109 What is this illuminating object at the sea near Sabancuy radar? Top-left corner of the left-hand figure – out of the radar range.
Response
Offshore areas were not a part of our study but the illuminating object at sea near Sabancuy radar is an offshore oil field, the Cantarell Complex. It is possible that these, and the many other offshore lights in the Gulf of Mexico, play a role in the stopover patterns of birds but that was not a part of this study.
Comment 9
126 what does „dates varied” mean? Explain, please.
Response 9
We meant to say that the number of dates for which there was data available was different for each radar, because the data was sparsely archived. Aiming for better clarity, we rewrote and split this sentence. Now, at the end of the first paragraph of subsection 2.1. Radar data and processing, it says “We obtained all available data for dates within our defined migration periods from Mexico’s National Water Commission.” In addition, in the first paragraph of the Results, it says, “Data were sparsely archived by radar and year, hence the number of dates with data differ by radar.”
Comment 10
206 Table 1 How are “bright lights” define? Add threshold value, please.
Response
Bright lights and the threshold used are defined in the following paragraph as: “…areas where night-lights radiance is >=26, approximately equivalent to a sky brightness five times greater than natural light brightness (hereafter “bright areas”), the level of night sky brightness where the Milky Way is no longer visible to the human eye [55]”.
Comment 11
268 Provide justification for r value. It is quite large, while some papers indicate that less than 0.3 could be a problem. Since your land cover variables are proportion there will be a problem with correlation.
Response
We agree and variable correlation is typically considered high in ecology when r > |0.7| (Dormann et al. 2017). However, analytical methods similar to ours have proved the ability to identify the effect of highly correlated variables when large sample sizes are used (McLaren et al. 2018). Our models are based on 6000-30000 observations/radar (i.e. radar sample volumes), and the relationship of the predictors to our response variable did not change when excluding highly correlated variables from the models, hence we consider that incorporating variables correlated at r <= |0.8|does not constitute an issue in our case. We have included wording to justify how our methods account for high correlation among predictor variables (current lines 272 - 274).
We further specify the highly correlated variables in our data set in the last paragraph of the 2.3 Model Analysis subsection and include a figure in the Supplemental materials of correlation among all predictors. These were in the previous version but we learned that reviewers were not provided with that document. The handling editor advised us to add Supplemental materials at the end of the manuscript, which we have done. The figure of predictor correlations included in the Supplemental materials - now provided at the end of the manuscript, after the references, is also available in file attached to this response.
Comment 12
285 what is the correlation between distance to the coast and distance to bright lights? Considering the nature of these variables, I assume high r value. How this influence your results?
Response
For both radars and seasons, the correlation between distance to the coast and to bright lights ranged from 0.3 to 0.69 (see figure above from the supplementary materials). The specific relationship of these and all other predictors to bird densities by radar and season are provided in figures S2 to S5 (also available in the Supplemental materials located after the body of the manuscript, in the same file)
Comment 13
319 Please, unify the scale on X axis – it will be easier to compare results: distance to the coast and distance to bright lights have same units. [at least for each of the radar]
Response
We arrived at the present design of these plots after trying some other designs, including scaling X-axes to offer a homogeneous visualization of the effects across seasons and radars. However, we consider it more informative to represent the actual values in all X-axes. While this does not offer the cleanest view, it does show the actual relationship of the predictors with the response, and allows visualizing the differences in data availability not only between radars but also between seasons within each radar. We consider that the most important comparison is the seasonal relationship of each predictor between seasons. By giving the x-axis of each plot its own scale, this comparison becomes clearer.
Comment
401 “increased mortality” could be a problem but these birds will be recorded by radar prior to collision. Right?
Response
We meant that the observed avoidance in spring might result from high bird mortality in the fall, with mortality (cumulative throughout their migration ranges) acting as a selective force for individuals that are less attracted to ALAN. Thus, naïve ALAN-attracted individuals are selected out during their southwards migration in the fall, and a larger proportion of experienced or “ALAN-resistant” individuals return north in spring, resulting in the observed avoidance to lights. This is a hypothesis that we are posing here, and we rephrased for clarity (current lines 395 - 401).
Literature cited
Dormann, Carsten F., Jane Elith, Sven Bacher, Carsten Buchmann, Gudrun Carl, Gabriel Carré, Jaime R. García Marquéz, et al. ‘Collinearity: A Review of Methods to Deal with It and a Simulation Study Evaluating Their Performance’. Ecography 36, no. 1 (1 January 2013): 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x. McLaren, James D., Jeffrey J. Buler, Tim Schreckengost, Jaclyn A. Smolinsky, Matthew Boone, E. Emiel van Loon, Deanna K. Dawson, and Eric L. Walters. ‘Artificial Light at Night Confounds Broad-Scale Habitat Use by Migrating Birds’. Ecology Letters 21, no. 3 (2018): 356–64. https://doi.org/10.1111/ele.12902.Author Response File: 
Author Response.docx
Reviewer 5 Report
This paper examines the influence of artificial town and city lighting on the distribution of stopover areas of migrating birds around two weather radar locations on the Yucatan Peninsula, Mexico. Unfortunately the authors provide no information that verifies the identity of the echoes that appeared on the radar screens near the beginning of darkness as birds other than the timing of the events. Because Supplementary information was not available in the manuscript, I cannot examine the results in detail.
A figure containing stopover areas in relation to light pollution levels would have been informative.
Abstract
Lines 14-15: The way this sentence is written implies that attraction to ALAN is related to concentrations of migrants near the coastlines of large water bodies, but attraction to ALAN occurs at inland locations as well, and concentrations of migrants frequently occur along coastlines of large bodies of water in areas without lighting as birds avoid overwater flight.
Lines 16-18: The authors say they quantified seasonal bird densities at migration exodus of nocturnally migrating landbirds during spring and fall of 2011 – 2015, using two weather radars, but in the paper the only validation provided that the echoes appearing on the radar were migrating birds was the timing of the events. They did confirm echoes from bats departing roosts for the Sabancuy radar, but the possibility of bats contributing to echoes at the Cancun was not mentioned.
Lines 23-25: The authors also found increased relative bird density with greater distance from lights, suggesting avoidance of bright areas, but it is also possible that the density of the echoes from purported migrating birds was influenced by habitat instead of avoiding lighted areas. See comment for lines 391-392 below and statement on lines 403-405 of manuscript.
Introduction
Lines 84-85: The authors cite Raymundo-Sanchez (2010) re: the decline in bird richness and abundance during fall migration with increasing urban development, but on lines 90-93 predict higher bird densities closer to bright areas and in relation to high ALAN intensities and add that the relationship is stronger and more evident in the fall. How do the authors resolve these potentially conflicting statements with respect to the locations of departing migratory birds and their predictions?
Line 110: Figure 1: I don’t see black dots showing location of radars
Material and Methods
Lines 152-153: radars provide reflectivity measures of migrating birds at exodus within a limited range (<50km), or with gaps within the radar coverage area. The “or with gaps” is a bit confusing. What does the “with gaps” statement mean?
Lines 154-157: Insect blooms occur at the same time as migrating bird departures and insects are also attracted to ALAN. Are the authors assuming that all blooms are birds? Could some of the blooms be mostly insects with very few or no birds? If this is a possibility it will certainly impact the results and conclusions of the paper. A C-band weather radar is more likely to detect insects than an S-band weather radar.
Line 157: This statement is only true if birds are departing. If only insects are climbing into the atmosphere the statement is false, and without information on wind speed and direction and speed and direction of movement of echoes, identity is problematic. Did the authors examine radar data outside the migration windows of spring and fall to see if exodus-like events occurred possibly attributable to insects and bats?
Line 174: how many nights in the subsample?
Results
Line 325-327: How does the index value relate to mean reflectivity values recorded for departing migrants? What were the values in terms of cm2 per km3. How did density vary between spring and fall migration between stations?
Figure3. Why is the distance to bright lights axis reversed for the spring and fall Sabancuy plots? Why are the axes for distance to bright lights and distance to coast reversed for spring and fall plots for Cancun? Comparisons would be easier if the axes were the same for all four plots.
Discussion
Line 378: northern
Lines 391-392: Could deviations in radar sensitivity (similar to STC) affect the pattern of higher densities at distance from the Sabancuy radar?
Conclusions
The conclusions follow from the results reported, but are potentially impacted by the quality of the radar data and the lack of information that confirms the identification of the echo patterns as being produce by birds departing on nocturnal migration.
References
Line 634: reference 46 “eadar” should be radar
Author Response
We now include results that increase our certainty that our data came mostly from bird-dominated radar sweeps. The quantity of radial velocity data within each range ring did not allow for the preferred approach of determining ground vectors of flying animals by modeling radial velocity as a function of azimuth across ranges. However, there was enough radial velocity data for each radar sweep to estimate the localized standard deviation of radial velocity. Dokter et al. (2011) validated that radial velocity standard deviation > 2 is a discriminator of bird presence from insect presence in C-band radar data (same bandwidth as the radars in our study). All but one of the nights sampled have a standard deviation of radial velocity > 2. We have included details of this analysis in section 2.1. Radar data and processing.
We uploaded supplemental materials together with our manuscript in the first submission. However, we found that unfortunately, these materials were not facilitated to the reviewers during the first round of reviews. We called on the handling editor to provide some clarity as to why the supplemental materials were not provided to reviewers and were advised to add Supplemental materials at the end of the manuscript, in the same file. Except for Video S1 (a short animation showing the bloom of reflectivity around Cancun during exodus time), Supplemental materials are now available there. The handling editor said they would make Video S1 available to reviewers; if you do not get it with the manuscript then please request it from them.
Please find attached our point-by-point response to your specific comments.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Based on the author’s note that the paper is submitted to the special issue on Radar Aeroecology I reconsidered my original decision and am inclined to suggest the paper for publication in Remote Sensing Journal subject to a minor comment below.
The authors stated in lines 126-129 that the radar echoes are at a different tilt angles ranging from 0.5 degrees to 20 degrees, and, on the another place (lines 223-226) commented on the areas where night-lights radiance is >=26 (and the work [57] has been cited). However, the world atlas of sky glow is only for zenith brightness, while the information on ecological light pollution (for horizontal rather than vertical directions; e.g. J. Imaging 2019, 5(4), 46) is fairly unknown. If sky brightness is to act as some indicator for this study then the brightness at low elevation angles have to be important. Zenith brightness does not provide information on night-sky-brightness distribution, thus an extended model (such as e.g. MNRAS 490, 1953-1960, 2019) are needed to give a more reasonable estimate of ecological light pollution (especially the glow of a night sky near horizon).
Author Response
The product we used to estimate the intensity of artificial lights within radar sample volumes, as well as the distance to bright lights, are monthly composites from the VIIRS Day/Night band (DNB) sensor, not the world Atlas of sky glow. The VIIRS DNB composite product contains measures of radiance from electric lighting present on the surface of the planet, most of which is emitted by human settlements (Elvidge et al. 2017). Previous research has evaluated the effect of artificial lights from human settlements on the distribution of migrating birds using citizen-science (La Sorte et al 2017) and weather radar data (McLaren et al. 2018) in relation to similar VIIRS DNB data. We used the World atlas just to identify the brightest areas to mask these in the VIIRS DNB monthly composites, so that our definition of bright areas have a biological meaning (i.e. areas where night lights radiance >= 26 in the VIIRS-DNB correspond to areas where night sky brightness obscures the stars). Furthermore, McLaren et al (2018) found that the threshold of night-lights radiance >=26 produces the strongest relationship between bird stopover distribution and distance to bright light. We have reworded slightly this section for clarity, and added some wording in the methods with the justification of the night lights threshold selected (current lines 222 – 225).
Elvidge, Christopher D., Kimberly Baugh, Mikhail Zhizhin, Feng Chi Hsu, and Tilottama Ghosh. ‘VIIRS Night-Time Lights’. International Journal of Remote Sensing 38, no. 21 (2017): 5860–79. https://doi.org/10.1080/01431161.2017.1342050. La Sorte, Frank A., Daniel Fink, Jeffrey J. Buler, Andrew Farnsworth, and Sergio A. Cabrera-Cruz. ‘Seasonal Associations with Urban Light Pollution for Nocturnally Migrating Bird Populations’. Global Change Biology 23, no. 11 (1 November 2017): 4609–19. https://doi.org/10.1111/gcb.13792. McLaren, James D., Jeffrey J. Buler, Tim Schreckengost, Jaclyn A. Smolinsky, Matthew Boone, E. Emiel van Loon, Deanna K. Dawson, and Eric L. Walters. ‘Artificial Light at Night Confounds Broad-Scale Habitat Use by Migrating Birds’. Ecology Letters 21, no. 3 (2018): 356–64. https://doi.org/10.1111/ele.12902.
Reviewer 3 Report
I think it is arbitrary to say the effect of atmospheric conditions and satellite observation angles are negligible, because Elvidge's monthly product didn't consider these factors, which resulted in abnormal changes of the light from my 8-year experience on nightlight remote sensing. However, I don't intend to topple the conclusion of this manuscript, but it is necessary to discuss potential factors that may impact the results. Some references are recommended for the above issues:
Román, Miguel O., Zhuosen Wang, Qingsong Sun, Virginia Kalb, Steven D. Miller, Andrew Molthan, Lori Schultz, et al. 2018. "NASA's Black Marble nighttime lights product suite." Remote Sensing of Environment 210:113-43.
Li, Xi, Ruiqi Ma, Qingling Zhang, Deren Li, Shanshan Liu, Tao He, and Lixian Zhao. 2019. "Anisotropic characteristic of artificial light at night – Systematic investigation with VIIRS DNB multi-temporal observations." Remote Sensing of Environment 233:111357.
Levin, Noam, Christopher C. M. Kyba, Qingling Zhang, Alejandro Sánchez de Miguel, Miguel O. Román, Xi Li, Boris A. Portnov, et al. 2020. "Remote sensing of night lights: A review and an outlook for the future." Remote Sensing of Environment 237:111443.
Author Response
We have included a new paragraph in the Discussion (shown below, also in current lines 466 – 478 of the manuscript) acknowledging the effect of satellite observation angles and atmospheric conditions on the satellite measurements of night-lights:
“Satellite measurements of night-lights are affected by factors such as atmospheric conditions and satellite observation angles. Unless night-light measurements are made exactly at the zenith, radiance levels recorded for the same geographic extent may differ with the angle of satellite observation, for example by capturing different facades of buildings, or because light sources may be hidden by obstacles from a specific viewpoint [76,77]. Aerosols, water vapor, and ozone are some of the factors that influence the radiance scattered by the atmosphere, ultimately affecting the satellite-measured radiance from artificial lights [78]. The night-lights data used for this research are monthly image composites averaging satellite observations that were subject to a series of algorithms to remove clouds and non-electric sources of lighting, and this averaging helps to minimize variations in night light measurements derived from different angles of satellite observation [79]. These monthly composites, however, do not account for atmospheric conditions and this is in fact a recognized area of improvement for such dataset [79]. Future investigations using atmospheric-corrected nights-lights data or improved models of night-sky brightness distribution [80] may be able to elucidate more subtle patterns between bird distributions and ALAN.”
Reviewer 5 Report
The authors have address all of my concerns.
I do have one additional point that the authors might mention in their methods, and it relates to the time of day in fall when trans-Gulf migrants arrive on the northern coast of Yucatan. They cite a telemetry study that reports 22.5 hours to cross the Gulf based on a few birds carrying transmitting devices, but William Buskirk observed purported trans-Gulf migrants arriving during daylight hours.
The point I wish to make is when trans-Gulf migrants arrive during daylight hours the distribution of stopover areas will not be influenced by ALAN, but the distribution of habitat should matter. The authors should also add that until ground surveys of migrants can be made in representative habitats in rural and urban areas, the conclusions about the distribution of migrants in different stopover areas near and away from the coast and close to or away from areas of ALAN based on weather radar should be tentative.
Author Response
We have added a new paragraph in the Discussion acknowledging that daytime arrival of migrants to the Peninsula during the fall migration season may affect the relationship of bird stopover distribution of and artificial lights. We also acknowledge that ground surveys would contribute valuable information to our knowledge of bird distributions in the Peninsula:
“There is likely some daytime trans-Gulf arrival of birds to the northern Yucatan coast during the fall season [29] when ALAN would not influence their stopover habitat selection. However, no studies have quantified the extent of daytime versus nighttime arrivals of trans-Gulf migrants in this region during the fall. Daytime landfall in the fall may help explain the weaker response of migrants to ALAN in Cancun and the difference between radars if daytime arrivals are more common at Cancun versus Sabancuy. Future studies, including ground surveys, would be valuable for assessing the distribution of migrating birds along the northern coast of the peninsula, and to test the bird distribution patterns in relation to the coast and ALAN reported here from weather radar observations.” (current lines 458 - 465).
