The logic behind this approach is that many high avian mortality days are correlated with these factors . Substantial mortality during clear and calm weather during the migration season has also been documented  ,  Figure 1. For these reasons we used raw data from two studies that carried out daily carcass searches — WCTV Florida tower data from — initiated by Herbert L. The Florida estimates were averaged over the 10 years of sampling during which height of tower and predator control were the same; the North Dakota estimates are for two years of sampling.
When the estimate was partially based on sampling outside the migratory period as defined , we used the Florida dataset, which had continuous, year-round sampling. We did not, however, correct upward all kill estimates to account for the trickle of kills recorded in the non-migratory seasons.
We believe, therefore, that our estimates are conservative. To control for differences between spring and fall migration we developed estimates for both spring and fall separately. Raw data from Crawford and Engstrom were used to plot daily bird fatalities against the mean free airspace between the top of the tower and the cloud ceiling each day.
Days with maximum ceiling were excluded. To adjust for the kills between sampling days during the migratory seasons we resampled with replacement daily mortality data from the Florida and North Dakota datasets within each of the spring and fall migration periods by randomly selecting a subset of days and summing avian mortality for the selected days. We calculated average bird mortality for 5, iterations and then used the ratio of the average bird mortality from the 5, iterations to the total number of birds killed during either spring or fall migration or outside of the migration period to adjust mortality estimates for studies without daily sampling.
We averaged estimates between the Florida and North Dakota datasets. This adjustment was applied to studies where researchers sampled on bad weather days see below and to those with weekly sampling outside the migration period. For studies that did not provide complete details on their sampling design, we made simplifying assumptions see below. If more than one sampling strategy was used, we developed estimates for each and used the sum as our overall estimate. We defined the spring and fall migration periods as a day window before and after the migration peak for both spring and fall for each dataset, recognizing that for some recent studies e.
We determined the peak for the Florida and North Dakota datasets by plotting the number of birds killed from the raw data against Julian date for all years of data combined and using negative exponential smoothing. Some investigators reported the total number of days sampled during one or both migration periods and sometimes outside the migration periods. When the sampling interval e.
If no sampling interval was defined, selection was random. Usually no other information was provided to define bad weather or the number of days when bad weather occurred. High bird mortality at communication towers is correlated with bad weather days  ,  , . Days where maximum free airspace was recorded were excluded from analysis because measurements did not vary for total ceiling greater than m 2, ft. Mortality for days with mean ceiling at the maximum was 4. Considering these remaining points, a linear regression reveals a highly significant effect of mean free airspace, but also low explanatory power Figure 1.
Based on these data, we used days with mean free airspace equal to or below m 1, ft as an index of bad weather days because mortality was significantly lower on days with airspace greater than m For some studies, the only information provided was the number of days sampled and the timing of sampling during migration or all year.
For these studies we assumed that researchers sampled on bad weather days during migration when large bird kills at communication towers were expected, given that this was the response obtained when we were able to contact researchers to ask about papers where this detail was not provided e. The typical daily trickle of dead birds for the Florida dataset over the — period was five. We therefore defined big kills as six or more birds located after any given night. We investigated the sensitivity of our results to our assumptions about sampling effort by varying these assumptions for the 13 studies in our dataset that either did not indicate the number of days sampled or did not provide a definition of sampling design, or did neither.
Some researchers had indicated that they had sampled on overcast or bad weather days or following bad weather days. For all of these studies and for those that did not mention anything specific, we made the conservative assumption that towers were sampled on bad weather days. We plotted either raw carcass counts or mortality estimates corrected for either sampling effort or search efficiency and scavenging, or both, against tower height and looked for improvements in the regression coefficient as an indication that the corrections improved the model.
The FCC data are freely available and we purchased a license for the Canadian obstruction data for the limited purpose of this study. We did considerable quality control on the tower data, confirming from independent sources that all towers greater than m existed. Full details of the quality assurance are available from the authors. We therefore relied on data from the FCC and TowerMaps datasets and assumed that lighting and guy wire use was similar in both countries for towers of the same height class, an assumption supported by the similarity in marking and lighting standards between the two countries.
The U. Avian mortality was estimated with the antilogarithm of the regression of the log transformed variables, which was adjusted for transformation bias using the smearing estimator after testing to confirm homoscedasticity of variance in the regression  , . Most recorded tower kill events take place at guyed towers, and steady-burning lights increase the probability of large tower kills  , . Bird Conservation Regions are divisions defined by habitat and topography that have been delineated for the purpose of bird conservation by the North American Bird Conservation Initiative and are endorsed by a range of bird conservation organizations and government agencies.
BCRs are based on the North American ecoregions developed to promote international conservation efforts . For each height class within each BCR we calculated the average number of birds killed per year, using the tower height—mortality regression adjusted for sampling effort, search efficiency, and scavenging as described above. For purposes of calculating total mortality we included all towers in the continental portions of the United States and Canada. Although most literature on tower mortality in North America describes studies from east of the Rocky Mountains, we included the West as well for purposes of estimating total mortality, a decision supported by records of tower mortality in Colorado  , New Mexico  , and Alaska  , in addition to documented kills at lighthouses in California and British Columbia  , .
We acknowledge that local habitat factors may influence mortality at particular towers, but because only To illustrate the contribution of each part of our adjustment to the final estimate of mortality, we calculated the extrapolated mortality estimates for the unadjusted mortality data, with the sampling correction only, with the search efficiency and scavenging corrections only, and corrected for all factors. Towers used in the height—mortality regression were located throughout the eastern United States Figure 2.
We were able to confirm from original sources and personal communications that The median R 2 values of the resampled distributions are similar to those obtained from using all of the available studies Figure 4 , Table 6 and are not sensitive to the addition or elimination of a few or a set of studies. The results of the resampling procedure for subsets of 18 studies a little under half of the studies and for 37 studies 1 fewer than the total show the range of influence that study inclusion could have on the regression line Table 6.
Correcting for search efficiency and scavenging losses appeared to provide the best improvement to the overall model Table 5. Most towers in the United States dataset 31,; Some towers had strobe lights during the day but red flashing and red solid lights at night so these were included as having solid lights. Combination of the tower height—mortality regression with estimates of reduced mortality at towers without guy wires or steady-burning lights produced a matrix of mortality by height class and tower characteristics. These estimates, already adjusted for sampling effort, search efficiency, and scavenging, ranged from zero for short unguyed towers to over 20, birds per year for the tallest guyed towers with steady-burning lights.
The back-transformed tower height—mortality regression, adjusted for bias smearing estimator and applied to towers in the continental United States and Canada, produced an annual mortality estimate of 6. Extrapolation from the unadjusted data yielded an estimate of 1. These results are sensitive to the assumptions that were made about these factors. As an illustration, we calculated total mortality while assuming a constant search efficiency equal to the average of the measured search efficiency from those towers where this was measured Applying the average scavenging rate Using both averages for scavenging and search efficiency yielded an estimate of 6.
For the sampling effort adjustments, recalculated mortality estimates for the three scenarios applied to studies with unknown sampling schemes were: 5. Finally, if we recalculate mortality after omitting all towers selected with prior knowledge of any mortality on site Our estimates of mortality vary by region, influenced both by the size of the region and the number and height distribution of towers Figure 6 ; Table 8.
The number of towers in each BCR does not directly correlate with estimated annual mortality because of differing numbers and heights of towers. As a result, Peninsular Florida is associated with more mortality than all of Canada; even though fewer towers are reported in Peninsular Florida, they are on average much taller.
Canadian mortality accounts for only a fraction of the total approximately 3. High mortality estimates in Peninsular Florida and Southeastern Coastal Plain reflect the more numerous and taller communication towers in these regions. Although we extended mortality estimates to all towers in Canada and the continental United States, few studies are available from the West Figure 2. This may be a function of a higher number of nocturnal migrants in the East, different patterns of migration, different weather patterns, or it may simply reflect the fewer and shorter towers in the West as a whole.
We investigated the effect of location on annual mortality by regressing the residuals of our height regression against longitude and also by testing the residuals for spatial autocorrelation. The resulting plot showed slightly higher mortality in the East, but the relationship was not significant and was largely driven by a single data point in Colorado.
More comprehensive surveys of towers in the West are needed to see if the lower mortality at the site in Colorado represents an anomaly or a different pattern of mortality in the West. Pending such further analysis, extrapolation of mortality at towers in the western portions of the United States and Canada should be regarded as provisional. Contour lines indicate regions above and below the regression line. Although exhibiting a geographically variable pattern, the residuals are not significantly spatially autocorrelated. Our total mortality estimate of 6. Our results do not support the suggestion that mortality might be an order of magnitude higher  ,  , which had been made before this type of synthetic analysis had been attempted.
Our approach to estimating total avian mortality at towers uses far more data than previous efforts. Our method incorporates evidence from 38 towers to establish the relationship between tower height and avian mortality. Notwithstanding the sources of uncertainty in our estimate, the method improves previous efforts, is transparent, and can be revised in conjunction with additional field studies. If only these studies ending after are used in the regression, the total mortality estimate decreases to 4.
The residuals of the tower height—mortality regression, however, are not significantly explained by the ending year of the survey results not shown so we did not exclude the older studies from our final regression. Even if the decline in number of birds killed at towers is a real phenomenon, the effect of these kills on sensitive species could still be substantial if populations have declined by a greater proportion. Estimated tower mortality increases exponentially with tower height  , which makes our results sensitive to the use of the height classes.
The use of the height classifications was necessary for ease of calculation and because attributes of the Canadian towers that were not known had to be assigned probabilistically. We used log transformations of both variables to normalize the distributions and because the total volume of airspace occupied by guy wires increases far more rapidly than does height.
The increasing length of guy wires provides a mechanistic explanation for the exponentially increasing probability of avian collisions as tower height increases. We also considered using separate regressions for towers less than and greater than m, given the break in the data, but found that doing so had little effect on the overall estimate and we could not formulate a functional explanation why the tower height—mortality relationship should change in this manner.
Further research is needed on the mortality rates at the tallest towers i. These data are needed to confirm that the tower height—mortality relationship is exponential . The nature of this relationship is important because it leads directly to a policy recommendation of focusing on the tallest towers first for mitigation. If more extensive tower datasets show a different relationship e. Producing this estimate of avian mortality at towers required many assumptions, the implications of which we have explored to the degree possible with the data available.
By undertaking this exercise, we have reaffirmed what elements should be included in tower studies going forward — explicit measurement of search efficiency, scavenging rates, and the effect of sampling schemes for any study, as well as investigation of geographic variation in mortality and inclusion of towers representative of the extremes of the height distribution. Such research will help refine our regionalized mortality estimates. In , the Exxon Valdez oil spill killed approximately , birds in what has become the benchmark for a major environmental disaster .
Our estimates show that communication towers are responsible for bird deaths equivalent to more than 27 Exxon Valdez disasters each year. Our estimate of the number of birds killed annually by communication towers is 2—4 times greater than the estimate for annual fatalities from lead poisoning before lead shot was phased out for hunting waterfowl .
Previous efforts e. Data on per species mortality would provide even more clarity about the biological significance of avian mortality at communication towers. In a companion manuscript, we estimate species-specific losses based on total losses estimated here and species-specific casualty reports for Bird Conservation Regions following methods we developed previously . But even without such estimates, the aggregate mortality numbers developed here should lead policymakers to pursue mitigation measures to reduce this source of chronic mortality.
Mitigation of avian mortality at communication towers could most practicably be achieved by implementing several measures: 1 concomitant with permission from aviation authorities, remove steady-burning red lights from towers, leaving only flashing not slow pulsing red, red strobe, or white strobe lights  ,  ,  ,  ; 2 avoid floodlights and other light sources at the bases of towers, especially those left on all night  ; 3 avoid guy wires where practicable  ,  ; 4 minimize the number of new towers by encouraging collocation of equipment owned by competing companies; and 5 limit height of new towers when possible.
The authors acknowledge the outstanding contribution of Herbert L. We thank Charles Francis for his help in extracting weather records used in Figure 1 and Martin Raillard for his support. Young for productive discussions about this research and access to unpublished reports. Competing Interests: The authors have declared that no competing interests exist.
The Canadian Wildlife Service www. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Center for Biotechnology Information , U. PLoS One. Published online Apr Bert , 3 Lauren M.
Sullivan , 4 Erin Mutrie , 3 Sidney A. Gauthreaux, Jr , 5 Michael L. Avery , 6 Robert L. Crawford , 7 Albert M. Manville, II , 8 Emilie R. Travis , 9 and David Drake 9. Daniel G. Lauren M. Sidney A. Gauthreaux, Jr. Michael L. Robert L. Albert M. Manville, II. Emilie R. Martin Krkosek, Editor. Author information Article notes Copyright and License information Disclaimer. Received Jul 28; Accepted Feb Copyright This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
The work is made available under the Creative Commons CC0 public domain dedication. This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. This article has been cited by other articles in PMC. Abstract Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern.
Methods We assigned average mortality values to tower height classes every 30 m using a regression of tower height by annual mortality following . Sensitivity of Tower Height—mortality Regression We collected as many studies of bird mortality at communication towers as possible from the literature and, when necessary, obtained raw data from study authors.
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