Research Paper

INTRODUCTION

Accurately estimating population trends is critical to understanding population dynamics, particularly for species of conservation concern (Sauer et al. 2003). Trends can be useful for identifying natural variation in a population (e.g., cyclical patterns in predator-prey relationships) or pronounced shifts in a population (e.g., large population decreases over the course of a decade), which would warrant immediate conservation attention. The lack of reliable population trends often limits conservation planning, especially for species that are secretive and/or occur in low densities and thus have low detection rates (Rayner et al. 2007, Mace et al. 2008, Barbraud et al. 2009). For these species, alternative or additional methods may be necessary to detect changes in local and regional populations. Long-term visible migration counts of actively migrating birds can be used for population monitoring and trend estimation, and these data have been increasingly integrated into monitoring efforts to supplement population trend estimates and to inform conservation efforts (Titus and Fuller 1990, Hoffman and Smith 2003, Farmer et al. 2007, Ruelas Inzunza et al. 2010, Martín et al. 2016). Although migration counts are commonly used for raptors and other taxa that use soaring flight (e.g., Hoffman and Smith 2003, Farmer et al. 2007, Martín et al. 2016), this technique could also be a useful tool for understanding migratory patterns and population dynamics of other diurnally migrating birds that use different migratory strategies (e.g., Ruelas Inzunza et al. 2010, Dunn et al. 2022). Monitoring a species effectively during migration requires basic information such as the identification of seasonal and daily periods of migration activity, an understanding of how local weather conditions influence migratory behavior, and identification of suitable count locations where migrants concentrate. This information is essential for establishing effective monitoring programs across different sites and for understanding annual variation in count data.

The relationship between weather and bird migration has been well studied for diurnal migrants (Richardson 1978, 1990, Liechti 2006). Although it is difficult to assign causality, there is broad agreement that wind speed and direction, temperature, and precipitation are among the most important predictors of migratory activity for diurnally migrating raptors and other soaring species (Richardson 1978, 1990, Liechti 2006). However, the relationships between weather conditions and daily migratory movement of other diurnally migrating groups have received considerably less attention. To control for variation in visible migration counts when estimating trends, it is vital to understand how weather influences the migratory behavior of species (Hussell 1981, Richardson 1990, Farmer and Hussell 2008, Shamoun-Baranes et al. 2010).

Common Nighthawks (Chordeiles minor) are crepuscular aerial insectivores that breed throughout North America and retreat to South America during the nonbreeding season (Brigham et al. 2020). Results from the North American Breeding Bird Survey (BBS) from 1966 to 2019 indicated that North American nighthawk populations have declined by 1.23% per year (Sauer et al. 2020). The species is listed as one of Special Concern in Canada (COSEWIC 2018) and designated as a Common Bird in Steep Decline by Partners in Flight (Rosenberg et al. 2016). However, confidence in BBS-derived population trends for Common Nighthawk are limited because survey methodology does not include evening counts when this species is most active and visible (Dunn et al. 2005, Brigham et al. 2020, Knight et al. 2021a). For this reason, some agencies have established nocturnal nightjar (i.e., birds in the Caprimulgidae family) surveys such as the Nightjar Survey Network (NSN; Wilson 2008) and the Canadian Nightjar Survey (CNS; Knight et al. 2019) during the breeding season to provide supplemental data for these species during the crepuscular period. Further, a substantial portion of the Common Nighthawk breeding range is in northern boreal forests which, because of the general remoteness and inaccessibility of these areas, are not well surveyed by BBS routes.

Large numbers of Common Nighthawks habitually concentrate and migrate along the western portion of Lake Superior during autumn migration where daily counts of over 40,000 birds have been documented (Hendrickson and Eckert 1991). With average annual counts of nearly 18,000 individuals, the airspace over the north shore of Lake Superior hosts the largest known concentration of migrating Common Nighthawks and provides critical airspace habitat for the conservation of Common Nighthawks during the post-breeding migratory period (Diehl 2013, Peterson et al. 2015). These individuals likely represent Common Nighthawk populations from northern Canada where roadside BBS routes are sparse. Currently, there are efforts to increase monitoring in remote areas using passive acoustic technology (e.g., CNS); visible migration counts could provide supporting evidence for BBS and CNS trend estimates.

Here we present the results of a 15-year visible migration monitoring program targeting Common Nighthawks and discuss the utility of this methodology for informing population trend estimates and conservation plans for this species. Our objectives were to (1) determine the relative importance of weather variables using standardized daily count data, (2) assess the influence of weather variables on the likelihood of high-count days, (3) develop trend estimates for annual counts of Common Nighthawks that move through the western Great Lakes region, and (4) explore the power of these counts to detect trends. We hypothesized that weather conditions that typically stimulate migratory movement in autumn—falling temperatures, tailwinds, and light winds—would be correlated with nighthawk migration.

METHODS

Study area and data collection

Migration counts were conducted daily from mid-August to early September 2008 to 2022 from an apartment rooftop (46.8387, -92.0073; Fig. A1) in Duluth, St. Louis County, Minnesota, USA. Duluth is located at the southwestern terminus of Lake Superior, a region that is well-known for concentrating migrating birds as they avoid crossing the lake. The rooftop count location provided an unobstructed view to the northeast, the direction from which migrant birds typically appear as they move parallel to the shore of Lake Superior. Although the exact dates of the survey varied among years, the count averaged 19.4 days (SD = 2.4) over the 15-year period. The short monitoring period used in this study was possible because of a combination of factors. Our counts targeted a single species unlike most efforts that target multiple species or guilds (e.g., migrating raptors), each of which have their own passage windows. Additionally, Common Nighthawk migratory passage in the study region is remarkably narrow. From 2008 to 2012, we explored the usefulness of counting nighthawks as early as 10 August and as late as 8 September but detected very few migrating nighthawks. These observations are validated by eBird data: less than 1% of all Common Nighthawks detected by observers in St. Louis County in August and September in all years were detected before 14 August or after 2 September (eBird Basic Dataset 2023; Fig. A2). Counts began at 16:00 CST and continued until sunset (approximately three hours; sunset ranged from 19:18 CST on 15 August to 18:48 CST on 1 September), coinciding with the peak diurnal movement period of Common Nighthawks (Hendrickson and Eckert 1991, Taylor 2009). The number of migrating nighthawks was recorded hourly by an experienced observer who actively scanned the sky with binoculars. On most days, nighthawks were counted individually, but during intense passage (> 2000 birds/hour), large flocks were counted by fives or tens. The counter recorded all nighthawks flying in a southwesterly direction. The possibility of double-counting migrants was low because of the straight-line flight exhibited by nighthawks during active migration.

Local weather variables

Weather covariates predicted to influence the migration of Common Nighthawks included temperature (°C), wind direction (polar degrees), wind speed (km/hr), and barometric pressure (mm Hg). These ground-level weather variables were obtained from the National Centers for Environmental Information (National Oceanic and Atmospheric Administration 2022) and were measured at the Duluth International Airport (Station ID: GHCND:USW00014913) which is located ~14 km west of the survey location. We included daily weather variables recorded at 15:55 CST, which most closely coincided with the start of the survey period. Weather variables were assessed for collinearity. Values ≥ 0.75 were considered highly correlated; if collinearity occurred, we chose the variable that was easiest to interpret and biologically relevant to include in the model. To constrain wind direction to values between -1 and 1, polar degrees were first converted into Cartesian degrees, then into radians, and transformed using sine and cosine transformations following Fisher (1993). To examine the effects of latitudinal (east/west) winds, cosine values range from W = -1 to E = 1. Likewise, to examine the effects of longitudinal (north/south) winds, sine values range from S = -1 to N = 1. All meteorological values were normalized by z-score prior to inclusion in models. Three interactions with possible biological significance identified a priori were also considered (latitudinal wind direction by wind speed, longitudinal wind direction by wind speed, and wind speed by temperature). These interactions were chosen based on anecdotal observations from nighthawk count observers; it often appeared that nighthawk flights could be expected on evenings that featured a combination of light winds, warmer temperatures, and westerly winds. The weather covariates and interactions described above were incorporated into the modeling efforts described below to assess the relationships between weather and nighthawk flights.

Statistical analyses

Weather effects on daily counts

In the first analytical approach, we used generalized linear mixed effects models (GLMMs) to determine the relationship between daily counts and daily weather conditions using the lme4 package (Bates et al. 2015; Fig. A3). For this analysis we created a global model that included local weather variables, ordinal date, and a quadratic term (ordinal date²; because of the non-linear effect of date over the three-week monitoring period) as explanatory variables. Year was included as a random effect in all models. Because there is disagreement about whether raw or log-transformed count data is more appropriate to use when analyzing count data (O’Hara and Kotze 2010, Ives 2015), we conducted preliminary analyses using both. Preliminary results indicated that log-transformed count data with a normal distribution provided a better fitting model than the raw count data using a negative binomial distribution. Therefore, we used log-transformed data fit to a normal distribution to build the final models. We performed manual backward selection on the global model containing all predictors by sequentially removing non-significant terms until the optimized models remained. Akaike’s Information Criterion for small sample size (AICc) was used to rank and compare models based on delta AICc values (Burnham and Anderson 2002). Models with non-significant terms were retained when model delta AICc < 2.0. We considered a variable to have an effect on Common Nighthawk migration if it was included in the top model and the 95% confidence interval for the model coefficient failed to overlap zero.

Weather effects on high-count days

We also modeled the probability of a “high-count” day, defined here as an evening during which ≥ 1000 migrating nighthawks were counted. For this analysis, we included additional data from Hendrickson and Eckert (1991; Fig. A3) which consisted of counts of Common Nighthawks that were conducted periodically and opportunistically in Duluth, MN from 1985 to 2007. Incorporation of this dataset allowed for the inclusion of 21 additional flight days that were documented before the standardized counts were initiated in 2008. We used 1000 nighthawks as a cutoff between high-count and normal flight days because these historical counts often reported nighthawk flights only if they reached this threshold. To minimize issues associated with modeling raw counts from non-standardized surveys, we coded the occurrence of large flight days as a binary variable and used GLMM logistic regression models using the lme4 package (Bates et al. 2015) to determine the relationship between the incidence of large flight days and the weather, date, and year variables described above. We performed manual backward selection on the global model containing all predictors and used AICc model selection to rank and compare models using delta AICc values (Burnham and Anderson 2002).

Trend analysis

To estimate population trends for Common Nighthawk, count indices were developed using the daily migration count data collected from 2008 to 2022 (Fig. A3). Because count effort varied among years, a seasonal passage window of 95% was calculated and any counts occurring outside of this window were excluded from trend estimation. This 95% passage window included days during which the middle 95% of individual nighthawks were counted across the 2008–2022 monitoring period. This approach reduces the influence of annual variation in count periods on trend estimation (Farmer et al. 2007). To calculate trends, we used an arithmetic mean passage rate index and a geometric mean passage rate index following the methods described in Farmer et al. (2007). The arithmetic mean passage rate index for each year was calculated by summing nighthawk counts within the 95% passage window, dividing by the total number of observation hours during this window, and then multiplying by the number of observation hours in a standard count day (three). The geometric mean passage rate index was calculated from a GLMM that included only the year term and then back transformed to match the original scale of the nighthawk count. Additionally, to account for date and weather effects, we included a date-adjusted and weather-adjusted geometric mean daily passage rate following Hussell (1981) and Farmer et al. (2007). This passage rate index was calculated as the geometric mean passage rate above but with the addition of weather and date variables that were determined to be significant in the GLMM described above. Linear regression with standard t-tests was used to assess significance for both geometric mean passage rate and date-adjusted and weather-adjusted geometric mean daily counts.

Power analysis

To estimate the ability of the models to detect a trend, we performed a power analysis in which we created 10,000 simulation datasets with the residual standard error of the linear regression models derived from the arithmetic mean passage rate, geometric mean passage rate, and a date-adjusted and weather-adjusted geometric mean passage rate as described above. The output of these analyses was the slope (number of nighthawks per day for the arithmetic mean or number of nighthawks per season for the geometric mean passage rates) at which our model had an 80% chance of detecting a true significantly increasing or decreasing slope (i.e., type II error rate β = 0.2). We compared the model predicted slopes for each of the three linear models to this metric. We also simulated future datasets with an additional 5, 10, and 15 years of migration counts once again using the residual standard error of the linear regression model of the 15-year dataset. This allowed for exploration of a future dataset’s power to detect the same trend as predicted by our 15-year dataset if that same trend was seen given additional years of data. All statistical tests were performed in R v.4.0.2 (R Core Team 2022).

RESULTS

During the monitoring period (2008–2022), a total of 265,617 migrating Common Nighthawks were counted in 810.25 hours on 284 observation days, which included 56 high-count days. The average annual count was 18,271 and ranged from 2514 to 32,837. The largest flights documented during the 15-year study period included 14,081 on 21 August 2013 and 13,730 on 19 August 2018. At least one nighthawk was detected on 81% of all days during which counts were conducted. Days of at least 1000 nighthawks (high-count days) occurred throughout the survey period.

Weather effect on daily counts

The top model from the GLMM regression analysis included latitudinal wind direction, wind speed, temperature, and the interaction between latitudinal wind direction and wind speed (Table 1; Table A1 contains all models with AICc < 2.0). Common Nighthawk counts were higher on days with westerly winds (β = -0.71, [95% CI: -0.88, -0.54]), lighter wind speeds (β = -0.28, [95% CI: -0.42, -0.15]), and higher temperatures (β = 0.28, [95% CI: 0.15, 0.41]). The best model also included the interaction between latitudinal wind and wind speed (β = -0.20, [95% CI: -0.38, -0.02]). Ordinal date (β = 23.6, [95% CI: 15.1, 32.2]) and ordinal date² (β = -23.8, [95% CI: -32.3, -15.2]) were also included in this model. Results suggest westerly winds (Fig. 1), lighter wind speeds, and higher temperatures (Fig. 2) were significantly associated with higher daily counts.

Weather effects on high-count days

The top model of the GLMM logistic regression analysis included latitudinal wind direction, wind speed, and temperature as significant weather covariates associated with the probability of a high-count day (Table 1; Table A1 contains all models with AICc < 2.0). Large flight days were more likely to occur on days with westerly winds (β = -1.35, [95% CI: -1.92, -0.78]), lighter wind speeds (β = -0.66, [95% CI: -1.01, -0.3]), and higher temperatures (β = 0.57, [95% CI: 0.21, 0.93]). Ordinal date (β = 70.7, [95% CI: 37.7, 104]), ordinal date² (β = -71, [95% CI: -104, -37.9]) were also included in this model. Like results of the daily counts model, westerly winds (Fig. 1), lighter wind speeds, and higher temperatures (Fig. 2) were significantly associated with high-count days.

Trend analysis

The 95% passage window for nighthawk migration fell between 15 August and 1 September for 2008–2022. Each method used to estimate trends (arithmetic mean, geometric mean, and date- and weather-adjusted geometric mean) suggested stable or non-significantly increasing trends for Common Nighthawks from 2008 to 2022 (Fig. 3). The arithmetic mean trend showed an annual increase of 48 [95% CI: -14, 110] nighthawks/day over the 15-year survey (869 nighthawks/year; r² = 0.18, p = 0.12). The geometric mean showed an annual increase of 320 [95% CI: 80, 560] nighthawks/day (r² = 0.12, p = 0.21), and the date- and weather-adjusted geometric mean showed an increase of 270 [95% CI:115, 425] nighthawks/day (r² = 0.19, p = 0.11) over this same period (Fig. 3).

Power analysis

The trends derived using each method (arithmetic mean, geometric mean, and date- and weather-adjusted geometric means) all currently lack the power to reliably detect trends (β = 0.35, β = 0.40, and β = 0.41, respectively). However, with additional years of count data, the power to detect a true increase or decrease in nighthawk counts grew, even with the most basic trend metric (Fig. 4). If the current trend is maintained, five more years of data would substantially improve the model’s ability to detect this trend, and an additional 10 or 20 years of data would provide strong support of this trend (Fig. 4).

DISCUSSION

Our study is one of the first to use visible migration counts to monitor Common Nighthawks in North America (see Taylor 2009). We determined that wind direction, wind speed, and temperature were associated with fluctuations in abundance of migrant nighthawks, and we did not detect a steeply declining trend for Common Nighthawks migrating through the western Great Lakes region. Inclusion of date and weather adjustments allowed increased trend estimate precision when compared to trends derived from simpler arithmetic or geometric means.

We acknowledge the scope of our study is limited because the data was collected at a single site over a relatively short period. However, our results indicate migration counts may be a promising tool for monitoring the status of this species and others with similar life-history traits. We demonstrate the importance of accounting for weather variables when estimating high-count passage days and provide valuable information that can be used to develop similar monitoring programs for nighthawks in North America. Our results can be used to predict likely high-count passage days, thus improving the efficiency and effectiveness of monitoring efforts. Contrary to our predictions, migratory counts of Common Nighthawks in this region did not mirror the declines reported from the BBS for Canada (Sauer et al. 2020) with or without weather adjustments. There is some indication that Common Nighthawk populations are stabilizing (Sauer et al. 2020), which is supported by the results of our study.

Peak Common Nighthawk movement along the north shore of Lake Superior was highly synchronous. All high-counts occurred between 14 August and 2 September with a steep ramp-up and drop-off of birds counted on either side of these dates. This suggests a strong entrainment of migratory timing for this species. Additional research focused on endogenous and exogenous controls associated with timing of migration are needed (Newton 2007). The distribution of nighthawk passage was highly clumped on days with certain weather characteristics: specifically, Common Nighthawks preferentially migrated on days with warmer temperatures. Generally, during fall migration, high migration counts occur after the passage of a cold front. Our results highlight not only a unique migration strategy but the importance of understanding species-specific weather effects on migration (Richardson 1990, Newton 2007). Common Nighthawks showed a preference for westerly winds and lacked a strong preference between northerly and southerly winds in most models, a finding that is contradictory to the general pattern of migrating birds avoiding headwinds (Richardson 1990). Regional geography may partially explain the strong preference for wind with a westerly component (i.e., avoidance of crossing a large body of water) but it does not explain the lack of a strong preference between northerly and southerly winds. Southerly winds may be slightly preferred because they are often associated with warmer temperatures. Light wind speeds are often associated with larger migratory movements of non-raptors (Bowlin and Wikelski 2008), and this holds true for Common Nighthawks migrating in this region. Future studies could explore the lagged influence of weather on nighthawk migration.

Common Nighthawks are aerial insectivores and obligate fly-and-forage migrants, and this behavior may help explain their migratory behaviors. Weather conditions associated with major flights of Common Nighthawks in the study area are also conducive to the presence of aerial insects in the migratory airspace (e.g., Finlay 1976). A positive correlation between temperature and aerial insect abundance has been well established (Hardy and Milne 1938, Glick 1939, Taylor 1963), and the abundance of flying insects decreases with increasing wind speed (Glick 1939, Møller 2013). Richardson (1978) reviewed observations of upwind movements of aerial insectivores that were presumably caused by aerial prey availability or accessibility. These observations, combined with results from our study, suggest the importance of aerial insects for migrating nighthawks. Common Nighthawks may constrain the timing of their migratory movements along the north shore of Lake Superior each season to maximize foraging efficiency on days with large numbers of aerial insects. Migratory flight requires deposition of subcutaneous fat and thus a marked increase in food consumption (Newton 2007). Past reports have noted large numbers of swarming insects in the guts of migrating Common Nighthawks, indicating the importance of insect hatches for migratory movements (Blem 1972). Future studies should focus on identifying and quantifying the insect prey consumed by this species during migration.

The catchment area and thus the population or populations of Common Nighthawks migrating through the Great Lakes region during the post-breeding season is poorly known. Recent tracking of Common Nighthawks indicates that individuals breeding as far north and west as Alberta and British Columbia have substantial easterly components to their post-breeding migrations that direct them much closer to the Great Lakes than previously known (Ng et al. 2018, Knight et al. 2021b). Using tracking data to help identify additional migration hotspots and to enhance the utility of migration counts at key monitoring locations to bolster trend estimation is a priority. Also, additional identification of major molt migration regions outside of North America is needed (Cockle et al. 2023).

As bird populations continue to decline throughout North America, considering alternative survey methods that may help us understand changes in population dynamics is necessary. Our current dataset lacks power to identify population trends at our site, but additional years of count data will likely increase our confidence in the trend. Just as raptor population trends are not derived from data from a single migration count location, the power to detect nighthawk population trends could be enhanced by the formation of additional nighthawk migration monitoring sites across North America. It is possible that many or most of these sites already host raptor monitoring stations and the implementation of systematic migration counting during the evening hours is all that would be necessary to establish a substantial network of nighthawk migration sites. Developing population indices based on counts of migrants from multiple sites across a large geographic range is common practice for raptor population estimation, and methods and analytical approaches have been well established (Farmer et al. 2007). We suggest the use of migration counts as not only a cost-effective method for monitoring species that are poorly surveyed during the breeding season, such as Common Nighthawk, but also to compare or provide additional evidence for population estimates derived from other long-term monitoring programs, some of which may provide contrasting population estimates for species that are challenging to detect because of survey methodologies.

CONCLUSION

The utility of visible migration counts for population monitoring is most obvious for diurnal migrants that are difficult to monitor with traditional methods such as the rapidly declining aerial insectivores (swifts, swallows, other nighthawks; Nebel et al. 2010), and northerly-breeding species like Rusty Blackbird (Euphagus carolinus). Migration counts can often be conducted by skilled volunteers and require minimal cost to implement (Bildstein 1998). We advocate for locations that reliably detect migrating Common Nighthawks to implement or continue nighthawk monitoring, akin to the hawk migration network that has been established throughout North and Central America (Bildstein 1998). We especially encourage sites to monitor migrating nighthawks throughout the early post-breeding migration season and during the last four hours of daylight until site- or region-specific passage is understood. Using the study of migratory raptors as a guide, an integrated approach that combines data from breeding, nonbreeding, and migration surveys will enhance the reliability of population trends (Dunn and Hussell 1995, Paprocki et al. 2017). Given time and an expanded geographic scope, migration counts of Common Nighthawks could be used to supplement breeding bird surveys to enhance our understanding of the population status of this species.

LITERATURE CITED

Barbraud, C., K. Delord, C. Marteau, and H. Weimerskirch. 2009. Estimates of population size of White-chinned Petrels and Grey Petrels at Kerguelen Islands and sensitivity to fisheries. Animal Conservation 12:258-265. https://doi.org/10.1111/j.1469-1795.2009.00248.x

Bates, D., M. Mächler, B. M. Bolker, and S. C. Walker. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67(1):1-48. https://doi.org/10.18637/jss.v067.i01

Bildstein, K. L. 1998. Long-term counts of migrating raptors: a role for volunteers in wildlife research. Journal of Wildlife Management 62:435-445. https://doi.org/10.2307/3802318

Blem, C. R. 1972. Stomach capacity in the Common Nighthawk. Wilson Bulletin 84:492-493.

Bowlin, M. S., and M. Wikelski. 2008. Pointed wings, low wingloading and calm air reduce migratory flight costs in songbirds. PLoS ONE 3:e2154. https://doi.org/10.1371/journal.pone.0002154

Brigham, R. M., J. Ng, R. G. Poulin, and S. D. Grindal. 2020. Common Nighthawk (Chordeiles minor), version 1.0. In A. F. Poole, editor. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.comnig.01

Burnham, K., and D. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Second edition. Springer, New York, New York, USA.

Cockle, K. L., O. Villalba, N. Fariña, A. Bodrati, L. G. Pagano, E. D. Stein, and A. R. Norris. 2023. Non-breeding ecology of a Neotropical-Nearctic migrant, the Common Nighthawk (Chordeiles minor): habitat, activity patterns, molt, and migration. Journal of Field Ornithology 94(3):8. https://doi.org/10.5751/JFO-00293-940308

Committee on the Status of Endangered Wildlife in Canada (COSEWIC). 2018. COSEWIC Assessment and Status Report on the Common Nighthawk (Chordeiles minor) in Canada 2018. COSEWIC, Gatineau, Québec, Canada. https://www.canada.ca/en/environment-climate-change/services/species-risk-public-registry/cosewic-assessments-status-reports/common-nighthawk-2018.html

Diehl, R. H. 2013. The airspace is habitat. Trends in Ecology and Evolution 28:377-379. https://doi.org/10.1016/j.tree.2013.02.015

Dunn, E., A. Dreelin, P. Heveran, L. Goodrich, D. Potter, A. Florea, B. Ewald, and J. Gyekis. 2022. Community science reveals biennial irruptive migration in the White-breasted Nuthatch (Sitta carolinensis). Journal of Field Ornithology 93(2):2. https://doi.org/10.5751/JFO-00091-930202

Dunn, E. H., B. L. Altman, J. Bart, C. J. Beardmore, H. Berlanga, P. J. Blancher, G. S. Butcher, D. W. Demarest, R. Dettmers, W. C. Hunter, E. E. Iñigo-Elias, A. O. Panjabi, D. N. Pashley, C. J. Ralph, T. D. Rich, K. V. Rosenberg, C. M. Rustay, J. M. Ruth, and T. C. Will. 2005. High priority needs for range-wide monitoring of North American landbirds. Partners in Flight Science Committee, Laurel, Maryland, USA.

Dunn, E. H., and D. J. T. Hussell. 1995. Using migration counts to monitor landbird populations: review and evaluation of current status. Current Ornithology 12:43-88. https://doi.org/10.1007/978-1-4615-1835-8_2

eBird Basic Dataset. 2023. Version: EBD_relDec-2023. Cornell Lab of Ornithology, Ithaca, New York, USA.

Farmer, C. J., and D. J. T. Hussell. 2008. The raptor population index in practice. Pages 165-177 in K. L. Bildstein, J. P. Smith, E. Ruelas Inzunza, and R. R. Veit, editors. State of North America’s birds of prey. Series in Ornithology, no. 3. Nuttall Ornithological Club, Cambridge, Massachusetts, and American Ornithologists’ Union, Washington, D.C., USA.

Farmer, C. J., D. J. T. Hussell, and D. Mizrahi. 2007. Detecting population trends in migratory birds of prey. Auk 124:1047-1062. https://doi.org/10.1093/auk/124.3.1047

Finlay, J. C. 1976. Some effects of weather on Purple Martin activity. Auk 93:231-244.

Fisher, N. I. 1993. Statistical analysis of circular data. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511564345

Glick, P. A. 1939. The distribution of insects, spiders, and mites in the air. Technical Bulletin No. 673. United States Department of Agriculture, Washington, D.C., USA.

Hardy, A. C., and P. S. Milne. 1938. Studies in the distribution of insects by aerial currents. Journal of Animal Ecology 7:199-229. https://doi.org/10.2307/1156

Hendrickson, M., and K. Eckert. 1991. A record movement of Common Nighthawks. Loon 63:68-69.

Hoffman, S. W., and J. P. Smith. 2003. Population trends of migratory raptors in western North America, 1977-2001. Condor 105:397-419. https://doi.org/10.1093/condor/105.3.397

Hussell, D. J. T. 1981. The use of migration counts for monitoring bird population levels. Studies in Avian Biology 6:92-102.

Ives, A. R. 2015. For testing the significance of regression coefficients, go ahead and log-transform count data. Methods in Ecology and Evolution 6:828-835. https://doi.org/10.1111/2041-210X.12386

Knight, E. C., K. C. Hannah, R. M. Brigham, J. McCracken, G. Falardeau, M.-F. Julien, J.-S. Guénette, A. Manthorne, A. Sidler, G. J. Foley, J. McKnight, K. S. Laurent, P. Sinclair, and R. Pankratz. 2019. Canadian Nightjar survey protocol. WildResearch, Vancouver, British Columbia, Canada.

Knight, E. C., A. Harrison, A. L. Scarpignato, S. L. van Wilgenburg, E. M. Bayne, J. W. Ng, E. Angell, R. Bowman, R. M. Brigham, B. Drolet, W. E. Easton, T. R. Forrester, J. T. Foster, S. Haché, K. C. Hannah, K. G. Hick, J. Ibarzabal, T. L. Imlay, S. A. Mackenzie, A. Marsh, L. P. McGuire, G. N. Newberry, D. Newstead, A. Sidler, P. H. Sinclair, J. L. Stephens, D. L. Swanson, J. A. Tremblay, and P. P. Marra. 2021b. Comprehensive estimation of spatial and temporal migratory connectivity across the annual cycle to direct conservation efforts. Ecography 44:665-679. https://doi.org/10.1111/ecog.05111

Knight, E. C., A. C. Smith, R. Mark Brigham, and E. M. Bayne. 2021a. Combination of targeted monitoring and Breeding Bird Survey data improves population trend estimation and species distribution modeling for the Common Nighthawk. Ornithological Applications 123:duab005. https://doi.org/10.1093/ornithapp/duab005

Liechti, F. 2006. Birds: blowin’ by the wind? Journal of Ornithology 147:202-211. https://doi.org/10.1007/s10336-006-0061-9

Mace, G. M., N. J. Collar, K. J. Gaston, C. Hilton-Taylor, H. R. Akçakaya, N. Leader-Williams, E. J. Milner-Gulland, and S. N. Stuart. 2008. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conservation Biology 22:1424-1442. https://doi.org/10.1111/j.1523-1739.2008.01044.x

Martín, B., A. Onrubia, A. de la Cruz, and M. Ferrer. 2016. Trends of autumn counts at Iberian migration bottlenecks as a tool for monitoring continental populations of soaring birds in Europe. Biodiversity and Conservation 25:295-309. https://doi.org/10.1007/s10531-016-1047-4

Møller, A. P. 2013. Long-term trends in wind speed, insect abundance and ecology of an insectivorous bird. Ecosphere 4:1-11. https://doi.org/10.1890/ES12-00310.1

National Oceanic and Atmospheric Administration. 2022. National centers for environmental information. NOAA, Washington, D.C., USA. https://www.ncei.noaa.gov/

Nebel, S., A. Mills, J. D. McCracken, and P. D. Taylor. 2010. Declines of aerial insectivores in North America follow a geographic gradient. Avian Conservation and Ecology 5(2):1. https://doi.org/10.5751/ACE-00391-050201

Newton, I. 2007. The migration ecology of birds. Academic, London, UK.

Ng, J. W., E. C. Knight, A. L. Scarpignato, A. L. Harrison, E. M. Bayne, and P. P. Marra. 2018. First full annual cycle tracking of a declining aerial insectivorous bird, the common nighthawk (Chordeiles minor), identifies migration routes, nonbreeding habitat, and breeding site fidelity. Canadian Journal of Zoology 96:869-875. https://doi.org/10.1139/cjz-2017-0098

O’Hara, R. B., and D. J. Kotze. 2010. Do not log-transform count data. Methods in Ecology and Evolution 1:118-122. https://doi.org/10.1111/j.2041-210X.2010.00021.x

Paprocki, N., D. Oleyar, D. Brandes, L. Goodrich, T. Crewe, and S. W. Hoffman. 2017. Combining migration and wintering counts to enhance understanding of population change in a generalist raptor species, the North American Red-tailed Hawk. Condor 119:98-107. https://doi.org/10.1650/CONDOR-16-132.1

Peterson, A. C., G. J. Niemi, and D. H. Johnson. 2015. Patterns in diurnal airspace use by migratory landbirds along an ecological barrier. Ecological Applications 25:673-684. https://doi.org/10.1890/14-0277.1

R Core Team. 2022. R: A language and environment for statistical computing. Version 4.0.2. R Foundation for Statistical Computing, Vienna, Austria.

Rayner, M. J., M. N. Clout, R. K. Stamp, M. J. Imber, D. H. Brunton, and M. E. Hauber. 2007. Predictive habitat modelling for the population census of a burrowing seabird: a study of the endangered Cook’s Petrel. Biological Conservation 138:235-247. https://doi.org/10.1016/j.biocon.2007.04.021

Richardson, W. J. 1978. Timing and amount of bird migration in relation to weather: a review. Oikos 30:224-272. https://doi.org/10.2307/3543482

Richardson, W. J. 1990. Timing of bird migration in relation to weather: updated review. Pages 78-101 in E. Gwinner, editor. Bird migration. Springer Verlag, Berlin, Germany. https://doi.org/10.1007/978-3-642-74542-3_6

Rosenberg, K. V., J. A. Kennedy, R. Dettmers, R. P. Ford, and D. Reynolds. 2016. Partners in Flight Landbird Conservation Plan: 2016 revision for Canada and Continental United States. Cornell Lab of Ornithology, Ithaca, New York, USA.

Ruelas Inzuza, E., L. J. Goodrich, and S. W. Hoffman. 2010. North American population estimates of waterbirds, vultures and hawks from migration counts in Veracruz, México. Bird Conservation International 20:124-133. https://doi.org/10.1017/S0959270909990293

Sauer, J., W. Link, and J. Hines. 2020. The North American Breeding Bird Survey, analysis results 1966-2019. U.S. Geological Survey data release. https://doi.org/10.5066/P96A7675

Sauer, J. R., J. E. Fallon, and R. Johnson. 2003. Use of North American Breeding Bird Survey data to estimate population change for bird conservation regions. Journal of Wildlife Management 67:372-389. https://doi.org/10.2307/3802778

Shamoun-Baranes, J., W. Bouten, and E. E. van Loon. 2010. Integrating meteorology into research on migration. Integrative and Comparative Biology 50:280-292. https://doi.org/10.1093/icb/icq011

Taylor, L. R. 1963. Analysis of the effect of temperature on insects in flight. Journal of Animal Ecology. 32:99-117. https://doi.org/10.2307/2520

Taylor, P. 2009. Late-summer feeding and migration behaviour and numerical trends of Common Nighthawks, Chordeiles minor, near Pinawa, Manitoba, 1976-2009. Canadian Field-Naturalist 123:338-345. https://doi.org/10.22621/cfn.v123i4.1003

Titus, K., and M. R. Fuller. 1990. Recent trends in counts of migrant hawks from northeastern North America. Journal of Wildlife Management 54:463-470. https://doi.org/10.2307/3809660

Wilson, M. D. 2008. The Nightjar Survey Network: program construction and 2007 southeastern Nightjar survey results. Technical Report Series CCBTR-08-001. Center for Conservation Biology, Williamsburg, Virginia, USA.