Skip to main content
NCBI home page
Conservation Physiology logoLink to Conservation Physiology
. 2018 Mar 1;6(1):coy010. doi: 10.1093/conphys/coy010

Elevated temperatures are associated with stress in rooftop-nesting Common Nighthawk (Chordeiles minor) chicks

Gretchen N Newberry 1,✉, David L Swanson 1
PMCID: PMC5830973  PMID: 29515805

Common Nighthawk (Chordeiles minor) nesting habitat is limited to urban rooftops in southeastern South Dakota. Regional climate has changed from 1948 to 2016, with increases in humidity and solar radiation. Chicks exposed to high temperatures showed high baseline plasma corticosterone and low stress responses, suggesting that current climate influences stress response capacity.

Keywords: Corticosterone, Common Nighthawk, nestling condition, micro-climate, regional climate, stress response

Abstract

Grasslands and riparian forests in southeastern South Dakota have been greatly reduced since historical times, primarily due to conversion to row-crop agriculture. Common Nighthawk (Chordeiles minor) nesting habitat includes grasslands, open woodlands and urban rooftops, but nesting sites in southeastern South Dakota are confined to rooftops, as natural nesting habitat is limited. Nighthawks nesting on exposed rooftop habitats may encounter thermal conditions that increase operative temperatures relative to vegetated land cover types. Mean humidity has increased and mean wind speed and cloud cover have decreased during the nighthawk breeding season from 1948 to 2016 in southeastern South Dakota. These changes might contribute to increasing operative temperatures at exposed rooftop nest sites and this could influence chick condition. We studied nest micro-climate and the plasma stress response for 24 rooftop-nesting nighthawk chicks from 17 nests during 2015 and 2016. High humidity prior to blood collection reduced both baseline and stress-induced plasma corticosterone (CORT). In contrast, high maximum temperatures during the day before sampling increased stress-induced CORT. The magnitude of the chick stress response was significantly negatively related to maximum wind speed for the week prior to CORT measurement. Other weather and micro-climate variables were not significant effectors of CORT metrics. Most chicks had low baseline CORT and were able to mount a stress response, but a subset of chicks (n = 4) showed elevated baseline CORT and a negative association between the magnitude of stress response and ambient temperature. For this subset, mean ambient temperature for the day before sampling was significantly higher (2.3°C) than for chicks with typical baseline CORT levels. These data suggest that regional climate change trends could affect the ability of nighthawk chicks to mount a stress response, which, in turn, might influence the susceptibility of nighthawk chicks to climate change in the Northern Prairie region.

Introduction

Since the 1980s, aerial insectivores have experienced among the most prominent population declines of any bird guild (Sauer et al., 2007; Nebel et al., 2010). Common Nighthawks are one such aerial insectivore and have a wide breeding distribution in North America, yet are subject to local population declines. North American Breeding Bird Survey data from 1980 to 2015 show a –1.5% annual population decline for nighthawks in South Dakota (Sauer et al., 2017). This population decline is coincident with a period of agricultural intensification (Wright and Wimberly, 2013) and warming winters that influence bird migration phenology (Swanson and Palmer, 2009) in the Northern Prairie region.

Due to changing land use practices, natural nighthawk nesting sites (i.e. open woodlands and grasslands) in the Northern Prairie region are in decline (Tallman et al., 2002; Wright and Wimberly, 2013). The study area, southeastern South Dakota, is currently dominated by row-crop agriculture, but was historically covered by grasslands (Spess Jackson et al., 1996; Tallman et al., 2002). Such conversion and loss of natural habitat is likely driving greater use of flat, gravel rooftops in urban areas for nesting by nighthawks (Brigham, 1989) and may contribute to population declines for this species. Nighthawk occupancy of exposed urban rooftop nesting sites is affected by rooftop characteristics, including thermal micro-climates (Viel, 2014). Because of higher wind speeds at rooftop than at ground sites, the convective advantages of rooftop sites might help mitigate the higher operative temperatures experienced at exposed rooftop sites compared to ground nest sites (Fisher et al., 2004; Fletcher et al., 2012). However, the characteristics of the parapet surrounding rooftop nest sites may also affect micro-climates and nest success, but not in a straightforward manner, as a trade-off between thermal micro-climate and falling danger likely exists. High parapets on rooftops will likely reduce wind movement resulting in warmer thermal micro-climates at the nest.

Urban habitats often function as ‘heat islands,’ with modified local climate due to reduced vegetation cover, impervious surfaces, and a high density of buildings, which lower evaporative cooling, store heat, and warm the surface air (Bonan, 2002). Exposed urban rooftop nest sites thus have the potential to produce thermally unfavorable conditions for nesting nighthawks, but no recent studies have examined nest micro-climates at urban rooftop nesting sites. Rooftop nesting micro-climates are typically warmer than micro-climates at natural sites, such as grasslands (Weller, 1958; Marzilli, 1989; Lohnes, 2010). If nighthawks are displaced to urban sites and climate change produces micro-climate temperatures that negatively impact nesting success at urban sites, this could negatively impact nighthawk populations, and leave them with few alternatives due to the reduction of natural nest site availability because of anthropogenic habitat disturbance.

Habitats can affect adult reproductive condition by influencing, among other traits, reproductive output, body condition, and the immune system (Wingfield et al., 1992, 1997). Perturbation of habitat can produce elevated baseline levels of corticosterone (CORT, the primary stress hormone in birds) and negatively impact nesting success in birds, although these relationships may be complex (Lucas et al., 2006; Bonier et al., 2007; Schoech et al., 2009). Moreover, elevated baseline corticosterone can also activate immunosuppressants in egg-laying females, which can be incorporated into the yolk, thereby impacting condition of the nestlings (Love et al., 2005) as well as the adults. Habitats also affect micro-climates experienced by breeding birds, which in turn impact physiological condition, including the stress response, which may be suppressed during the hottest days in some species (Wingfield et al., 1992). In addition, short-term temperature changes may be associated with increased baseline plasma corticosterone concentrations in some birds, but not others, especially in those species not specifically adapted to high temperatures (El-Halawani et al., 1973; Xie et al., 2017). Indeed, baseline CORT levels in nestling Common Nighthawks are sometimes positively correlated with exposure to high ambient nest temperatures (Lohnes, 2010). Such exposure could potentially lead to reduced chick survival and fitness by downregulating the immune system or mobilizing energy stores (Sapolosky et al., 2000; Love et al., 2005).

Exposed rooftop nesting sites with high thermal loads have the potential to become unfavorable for nighthawks if climate change produces even higher operative temperatures (Fletcher et al., 2012), particularly in urban areas as a result of the ‘heat island’ effect. Mean temperatures in the Great Plains are expected to increase by 3.6–6.1°C over the next 100 years (Ojima and Lackett, 2002). For example, mean summer temperatures and mean summer dew points at Sioux City, Iowa (an urban area and the nearest weather station to some of our field sites), are expected to increase by 6.3°C and 1.1°C, respectively, by the year 2100 (Kenward et al., 2014). As a result of this climate change, breeding ranges for Great Plains bird species are expected to be reduced by up to 35% (Peterson, 2003).

If sufficient natural nesting sites are available within a region, nighthawks prefer natural nesting sites to urban rooftops, suggesting that natural sites might provide better nesting conditions than rooftop sites (Brigham, 1989). If urban rooftop habitats provide lower-quality nesting habitat than natural habitats, then this may manifest as higher baseline plasma corticosterone (CORTB) levels and a reduced stress response for birds using rooftop habitats with hotter micro-climates. While adult Caprimulgids tolerate hot temperatures (Cowles and Dawson, 1951; Howell, 1959; Bartholomew et al., 1962; Lasiewski and Dawson, 1964; O’Connor et al., 2017), nest micro-climates that minimize heat loss in cold weather and maximize heat loss during higher temperatures are generally important to Caprimulgids (Kortner and Geiser, 1999). This may be particularly true for chicks, with less developed thermoregulatory capacities than adults. Thus, favorable micro-climates at nest sites for Common Nighthawks are likely to be important to nesting success.

In this study, we evaluated nest conditions (i.e. micro-climate) and plasma corticosterone of chicks at rooftop nest sites in southeastern South Dakota and examined correlations of these traits with a variety of climatic variables to determine which climate variables are most associated with CORT levels in nighthawk chicks. We hypothesized that baseline CORT levels of chicks will be positively associated with the maximum operative temperature (Te) encountered at the nest and to the duration of exposure to hot operative temperatures.

Material and methods

Nest searches

One to three months prior to the nesting season, we contacted property managers of 32 school, hospital and storefront rooftops in North Sioux City, Elk Point, Vermillion and Yankton, South Dakota (Fig. 1), and received permission to access 22 of them. Several property managers required an agreement indemnifying the property owner prior to granting rooftop access permission. We conducted systematic nest searches on these rooftops by laying out a grid network with 1 m × 1 m squares on graveled areas of the rooftop and walking all gridlines until adult birds flushed. When we flushed adults, we carefully searched the area where the adult flushed for eggs or chicks.

Figure 1:

Figure 1:

Open in a new tab

Cities with rooftop nest study sites (i.e. X) and NOAA weather stations (i.e. Circles) in South Dakota and Iowa.

Blood collection and corticosterone measurement

For CORT measurements, we collected blood samples (<100 µl) from 7- to 14-day-old chicks from the brachial vein (Wingfield et al., 1994) in heparinized capillary tubes. All blood samples were collected within 3 min after the female flushed from the nest. Following the first blood sample, we placed individual chicks in cloth bags in a shaded location for 30 min, after which we collected a second blood sample for measurement of the stress response (i.e. increase in corticosterone following handling stress) (Wingfield et al., 1994). We stored blood samples on ice in microcentrifuge tubes while in the field. Following blood collection, we banded chicks with a standard USFWS aluminum leg band, performed morphometric measurements, and released them back to their nest site. For morphometrics, we measured unflattened wing chord with calipers to the nearest 0.1 mm and measured mass to the nearest g with a Pesola spring balance.

Upon return to the laboratory, we centrifuged blood samples for 10 min at 3000 × g at 4°C, drew off the plasma, and stored plasma frozen at −70°C until later analyses. We assayed plasma CORT with commercially available spectrophotometric kits (ELISA kits ADI-900-097), as previously conducted in our laboratory (Liu and Swanson, 2014). We sampled 24 nestlings from 17 nests during the 2015 and 2016 breeding seasons.

Nest micro-climate

We collected nest micro-climate data using two methods. First, we placed iButton data loggers (DS1921G-F5# Thermochron) in the nest scrape next to the eggs or chicks and, once chicks hatched, moved them every week to where the chicks had relocated to record ambient ‘nest’ temperature (Ta). We programmed the iButtons to record nest temperature (°C) every 10 min from incubation to fledging (Ardia et al., 2006). Second, we deployed operative temperature (Te) thermometers within 1–2 m of the eggs or chicks at sites with similar exposure conditions to the nest site (e.g. shade prevalence and amount of gravel substrate) and, after hatching, moved them every week to where the chicks had relocated. This distance from the nest site was far enough away to avoid disturbing the birds (G. Newberry unpublished data). We designed operative temperature (Te) thermometers using 10 × 12 cm copper ovoid toilet floats (approximately the same volume as an adult nighthawk) with the outside surface painted flat gray. We cut a 2.5-cm hole in the copper ovoid to attach a 2-m Cu–Cn thermocouple probe connected to a Model UX120006M 4-channel analog input HOBO data logger (Hobo Instruments, Contoocook, NH), which we placed inside the copper ovoid. We recorded operative temperature once per minute from egg discovery date to chick fledging. During weekly nest visits, we also placed a CIH20DL Data Logging Hot Wire Anemometer and infrared thermometer (General Tools & Instruments, Secaucus, NJ) adjacent to the chicks or eggs for 30 s to record maximum wind speed (KPH) and maximum temperature to allow periodic estimation of the potential for convective heat loss.

Regional climate & roof characteristics

We collected 1948–2016 regional climate data for the three weather stations (Yankton and Sioux Falls, South Dakota, and Sioux City, Iowa; NOAA, 1948–2016) nearest to our study sites (Fig. 1) during the Common Nighthawk breeding season (15 May–15 August). We used hourly temperature (°C), relative humidity (%), dew point (°C), maximum wind gust (KPH), precipitation (cm), cloud cover (%) and wind speed (KPH) to calculate mean daily and weekly maximums for each metric prior to blood sampling. We calculated the roof height (m) with a Bushnell Yardage Pro Sport 450 laser rangefinder. We measured the height of the roof parapet with a tape measure or calipers to the nearest mm.

Statistics

To evaluate how temperature changed over time, we performed simple linear regression analyses of breeding season (15 May 15–August) means of maximum, minimum and average daily temperatures from regional climate recordings for 1948–2016 using the R 3.3.2 car package (Fox and Weisberg, 2011) with year as the predictor variable. To analyze how plasma CORT measurements changed with micro-climate, regional climate, rooftop and morphometric variables, we used multiple regression of CORT measurements as a function of these independent variables. We performed multiple regressions with three response variables, CORTB, stress-induced CORT (CORT30) and the magnitude of the stress response (CORT30–CORTB). We log10-transformed the residuals for all CORT variables (log[CORT/Mean+1]) to meet the assumptions of normality and equal variance. For cases where the stress response was ≤0 (10 out of 24 samples), we assigned a value of 1 for the magnitude of the stress response.

We used five different multiple regression models for these analyses: temporal (i.e. Year, Julian Date, Julian Date by Year interaction term, Time of Day [hours since midnight]), rooftop conditions (i.e. roof parapet height [cm] above roof surface, roof height [m] above ground), chick condition at blood collection (i.e. mass [g], wing chord [cm]), nest micro-climate (i.e. maximum anemometer temperature [°C] and wind speed [KPH] at blood collection, iButton temperature [°C] at blood collection, operative temperature [Te, °C] at blood collection), and regional (weather station) climate variables. For regional climate variables, we calculated maximum daily temperature (°C), maximum daily dew point (°C), mean daily maximum dew point (°C), maximum relative humidity (%), maximum wind speed (KPH), mean daily maximum wind speed (KPH), and mean daily maximum relative humidity (%) for 24-h and 7-day periods prior to blood sampling from the weather station (i.e. in Yankton, South Dakota or Sioux City, Iowa) nearest to the nest.

Because chick CORTB levels fell into two non-overlapping groups (CORTB < 25 ng/ml, n = 20; and CORTB > 50 ng/ml, n = 4), we compiled these five models for all chicks combined and for each group separately. Our total sample size was 24, but for all of these analyses, where there were nests with missing data due to equipment malfunction (i.e. n = 1 for roof characteristics, n = 2 for anemometer Ta and wind speed, n = 4 for Te, n = 15 for iButton Ta, as indicated in Tables 1–3 and S1–S2), thus invalidating the multiple regression approach, we performed simple linear regressions of predictor variables against plasma CORT. For any variables that were associated with CORT metrics in low or high CORTB groups but not in the other, to compare mean values between the two groups, we also performed two-tailed two-sample Welch’s t-tests because the variances were unequal.

Table 1:

Summary of regression analyses of interval means of daily summer (15 May–15 August, 1948–2016) environmental data (as dependent variables) from three surrounding weather stations (Yankton, SD, Sioux City, IA, and Sioux Falls, SD) with year as a predictor variable

Station Dependent variable F df Adj r2 P Coef
Yankton Maximum Temp (°C) 6.04 1,67 0.069 <0.05 −0.022
Mean Temp (°C) 0.5109 1,66 −0.007 0.48 0.007
Minimum Temp (°C) 4.204 1,67 0.045 <0.05 0.033
Maximum Dew Point (°C) 0.0095 1,42 −0.024 0.92 −0.001
Mean Dew Point (°C) 1.55 1,42 0.013 0.22 −0.024
Minimum Dew Point (°C) 1.84 1,42 0.019 0.18 −0.033
Maximum Humidity (%) 14.42 1,42 0.238 <0.001 0.238
Mean Humidity (%) 13.13 1,42 0.22 <0.001** 0.224
Minimum Humidity (%) 1.048 1,42 0.001 0.31 −0.070
Maximum Wind (KPH) 9.305 1,42 0.162 <0.05** 0.139
Mean Wind (KPH) 0.410 1,42 −0.014 0.53 −0.013
Precipitation (cm) 0.458 1,52 −0.010 0.50 0.010
Cloud Cover (%) 112.6 1,42 0.722 <0.001** −0.091
Sioux City 1 Maximum Temp (°C) 0.363 1,70 −0.009 0.55 0.005
Mean Temp (°C) 0.1168 1,70 −0.013 0.73 −0.001
Minimum Temp (°C) 4.402 1,70 0.0457 <0.05 −0.010
Maximum Dew Point (°C) 1.928 1,70 0.013 0.169 0.007
Mean Dew Point (°C) 2.085 1,70 0.015 0.15 0.008
Minimum Dew Point (°C) 1.96 1,70 0.013 0.17 0.008
Maximum Humidity (%) 2.614 1,70 0.022 0.11 0.036
Mean Humidity (%) 3.679 1,70 0.036 0.06 0.045
Minimum Humidity (%) 1.463 1,70 0.006 0.23 0.030
Maximum Wind (KPH) 3.901 1,70 0.039 0.052 0.033
Mean Wind (KPH) 11.61 1,70 0.13 <0.001** −0.030
Precipitation (cm) 2.632 1,68 0.023 0.109 −0.011
Cloud Cover (%) 7.817 1,44 0.132 <0.001** −0.026
Sioux Falls Maximum Temp (°C) 0.041 1,70 −0.014 0.84 0.002
Mean Temp (°C) 0.080 1,70 −0.013 0.78 0.002
Minimum Temp (°C) 0.104 1,70 −0.013 0.75 0.002
Maximum Dew Point (°C) 7.195 1,70 0.080 <0.001 0.015
Mean Dew Point (°C) 11.71 1,70 0.131 <0.001 0.020
Minimum Dew Point (°C) 15.18 1,70 0.167 <0.001 0.025
Maximum Humidity (%) 3.113 1,70 0.029 0.08 0.039
Mean Humidity (%) 5.499 1,70 0.060 <0.05** 0.062
Minimum Humidity (%) 7.096 1,70 0.080 <0.001 0.075
Maximum Wind (KPH) 4.115 1,70 0.042 <0.05** 0.029
Mean Wind (KPH) 13.19 1,70 0.147 <0.001** −0.026
Precipitation (cm) 0.010 1,67 −0.015 0.922 −0.200
Cloud Cover (%) 0.060 1,46 −0.020 0.81 0.002
Open in a new tab

Dependent variables with P < 0.05 at same direction of Coefficient at all three weather stations are denoted with *. Dependent variables with P < 0.05 at same direction of Coefficient at two weather stations are denoted with **.

Table 3:

Summary of regression analyses of log(CORTB/Mean+1), log(CORT30/Mean+1) and adjusted log(CORT30–CORTB/Mean+1) (as dependent variables) for chicks with CORTB > 50.00 ng/ml, the high CORTB group (n = 4)

Predictor variable model Predictor variable Dependent variable F df Adj r2 P Coef
Roof Characteristics Parapet Height (cm) CORTB 0.1832 2,1 −1.196 0.73 −0.0138
CORT30 0.4841 2,1 −0.524 0.52 0.0304
CORT30–CORTB 0.2500 2,1 −1 0.93 0.0016
Roof Height (m) CORTB 0.1832 2,1 −1.196 0.65 −0.3931
CORT30 0.4841 2,1 −0.524 0.54 0.5896
CORT30–CORTB 0.2500 2,1 −1 0.67 0.1744
Chick Condition Mass (g) CORTB 0.1538 2,1 −1.294 0.78 0.0198
CORT30 28.000 2,1 0.9474 0.09 0.0362
CORT30–CORTB 0.1916 2,1 −1.169 0.65 −0.0078
Wing Length (cm) CORTB 0.1538 2,1 −1.294 0.87 −0.0037
CORT30 28.000 2,1 0.9474 0.36 0.0054
CORT30–CORTB 0.1916 2,1 −1.169 0.86 −0.0018
Regional Climate Maximum Ta (°C) for Day CORTB 4.307 2,1 0.6879 0.23 0.6563
CORT30 0.7602 2,1 −0.1902 0.82 0.1780
CORT30–CORTB 1.332 × 1033 2,1 1 <0.001* −4.913 × 1016
Maximum Ta (°C) for Week CORTB 4.307 2,1 0.6879 0.65 0.0848
CORT30 0.7602 2,1 −0.1902 0.60 −0.2443
CORT30–CORTB 1.332 × 1033 2,1 1 <0.001* −1.946 × 1016
Open in a new tab

Dependent variables with P < 0.05 are denoted with *. Variables with no data were not included.

Results

Mean humidity and maximum wind speed increased significantly and cloud cover and mean wind speed decreased significantly over the 1948–2016 period for two of the three weather stations (Table 1). The only rooftop characteristic significantly associated with micro-climate variables was roof height, which was significantly positively correlated with iButton temperature (Table S1). However, this result was associated with a small sample size and can be explained by one particularly high and hot rooftop (Table S1). Removing this point from the regression eliminated the significant result.

Regional (weather station) climate variables were the only significant predictors of CORT metrics for all chicks pooled (Table S2). Baseline CORT was significantly negatively related to maximum dew point for the day of measurement and maximum humidity for the week prior to measurement. In addition, stress-induced CORT was significantly positively related to maximum Ta on the day of measurement and significantly negatively related to maximum dew point on the day of measurement. The magnitude of the stress response was significantly negatively related to maximum wind speed for the week prior to measurement. None of the other temporal, roof, chick condition, chick number, or other micro-climate or regional climate variables were significant predictors of CORT metrics.

Because chicks fell into two, non-overlapping groups based on their baseline CORT (Fig. 2), we also analyzed these two groups separately. Mean CORTB was 5.53 ± 1.54 (SE) ng/ml for the low CORTB group and 97.61 ± 19.66 (SE) ng/ml for the high CORTB group. These values differed significantly (Welch’s t3 = 4.670, P = 0.009). The low CORTB group had a mean stress response (CORT30–CORTB) of 15.40 ± 8.93 (SE) ng/ml, and the high CORTB group had a mean stress response of −24.04 ± 46.05 (SE) ng/ml.

Figure 2:

Figure 2:

Open in a new tab

Relationship between corticosterone (CORT) and time of blood sampling.

For the low CORTB group, there were no significant predictors for any CORT metric, except for a year effect on CORT30, with lower values in 2016 (Table 2). However, when we compared mean CORT30 between the 2 years with a t-test for the low CORTB group, there was no significant difference (Welch’s t18 = 0.682, P = 0.51). For the high CORTB group, the only significant result occurred for the magnitude of stress response, where maximum ambient temperature for the day and week prior to capture were both negatively associated with the magnitude of the stress response (Table 3). The maximum temperature for the day prior to blood sampling was significantly higher for the high CORTB group than for the low CORTB group (Welch’s t22 = −2.744, P = 0.01), but the mean maximum temperature for the week prior to blood sampling did not differ significantly between the two groups (Fig. 3).

Table 2:

Summary of regression analyses of log(CORTB/Mean+1), log(CORT30/Mean+1) and adjusted log(CORT30–CORTB/Mean+1) (as dependent variables) for chicks with CORTB < 25.00 ng/ml, the low CORTB group (n = 20)

Predictor variable model Predictor variable Dependent variable F df Adj r2 P Coef
Temporal Year CORTB 0.474 3,16 −0.091 0.31 −5.947
CORT30 2.873 3,16 0.228 0.02* −0.015
CORT30–CORTB 0.698 3,16 −0.050 0.68 −2.619
Time of Day CORTB 0.474 3,16 −0.091 0.61 0.013
CORT30 2.873 3,16 0.228 0.55 0.016
CORT30–CORTB 0.698 3,16 −0.050 0.63 0.013
Roof Characteristic Parapet Height (cm) CORTB 2.016 3,15 0.1448 0.45 0.003
CORT30 0.2904 3,16 −0.1341 0.83 −0.001
CORT30–CORTB 0.599 3,15 −0.0716 0.61 −0.002
Roof Height (m) CORTB 2.016 3,15 0.1448 0.05 0.029
CORT30 0.2904 3,16 −0.1341 0.46 0.015
CORT30–CORTB 0.599 3,15 −0.0716 0.46 0.012
Mean Gravel Diameter (cm) CORTB 2.016 3,15 0.1448 0.13 0.019
CORT30 0.2904 3,16 −0.1341 0.52 0.011
CORT30–CORTB 0.599 3,15 −0.0716 0.60 −0.007
Chick Condition Mass (g) CORTB 0.1432 2,17 −0.0999 0.68 0.002
CORT30 0.9971 2,17 −0.0003 0.40 0.004
CORT30–CORTB 2.458 2,17 0.1331 0.74 0.001
Wing Length (cm) CORTB 0.1432 2,17 −0.0999 0.68 0.001
CORT30 0.9971 2,17 −0.0003 0.40 −0.003
CORT30– CORTB 2.458 2,17 0.1331 0.06 −0.005
Regional Climate Maximum Ta (°C) for Day CORTB 5.000 12,7 0.7163 0.09 −0.283
CORT30 5.743 12,7 0.7497 0.96 −0.010
CORT30–CORTB 1.874 12,7 0.3556 0.71 0.094
Maximum Ta (°C) for Week CORTB 5.000 12,7 0.7163 0.12 0.150
CORT30 5.743 12,7 0.7497 0.14 0.170
CORT30–CORTB 1.874 12,7 0.3556 0.87 0.025
Maximum Dew Point (°C) for Day CORTB 5.000 12,7 0.7163 0.36 0.179
CORT30 5.743 12,7 0.7497 0.99 0.003
CORT30–CORTB 1.874 12,7 0.3556 0.80 0.030
Maximum Dew Point (°C) for Week CORTB 5.000 12,7 0.7163 0.65 −0.076
CORT30 5.743 12,7 0.7497 0.80 −0.334
CORT30–CORTB 1.874 12,7 0.3556 0.61 0.071
Maximum Humidity (%) for Day CORTB 5.000 12,7 0.7163 0.54 −0.069
CORT30 5.743 12,7 0.7497 0.94 0.010
CORT30–CORTB 1.874 12,7 0.3556 0.85 0.035
Maximum Humidity (%) for Week CORTB 5.000 12,7 0.7163 0.25 0.282
CORT30 5.743 12,7 0.7497 0.50 0.187
CORT30–CORTB 1.874 12,7 0.3556 0.94 0.030
Maximum Wind (KPH) for Day CORTB 5.000 12,7 0.7163 0.23 0.020
CORT30 5.743 12,7 0.7497 0.42 −0.016
CORT30–CORTB 1.874 12,7 0.3556 0.33 −0.027
Maximum Wind (KPH) for Week CORTB 5.000 12,7 0.7163 0.12 0.075
CORT30 5.743 12,7 0.7497 0.14 −0.083
CORT30–CORTB 1.874 12,7 0.3556 0.05 −0.163
Open in a new tab

Dependent variables with P < 0.05 are denoted with *. Variables with no data were not included.

Figure 3:

Figure 3:

Open in a new tab

Difference in ambient temperature means between low and high baseline CORT groups.

Discussion

Because Common Nighthawks nest in an open scrape on flat, gravel rooftops in our study area, they are relatively exposed to weather and might be expected to be particularly susceptible to climate change. Over the past 68 years, regional climate changes include stable summer temperatures, but increasing humidity, declining cloud cover and decreasing mean wind speed (Kenward et al., 2014). All of these changes could affect the thermal environment used by nighthawks by increasing radiative heat gain, decreasing convective heat loss, and reducing the ability for effective evaporative cooling during the hottest parts of the day. As a consequence, we hypothesized that the thermal environment of the rooftop nest sites might be stressful for nighthawks, including nighthawk chicks. This, in turn, might function to impair chick growth rates and survival at such thermally exposed sites.

The positive relationship between roof height and nest site iButton temperatures in this study offers tentative support for the idea that temperatures increase with roof height, but this relationship was driven by a single outlier rooftop. In addition, roof height was not a significant predictor for any of the CORT metrics in chicks. Thus, rooftop height does not appear to influence nest micro-climate sufficiently to serve as a prominent factor affecting chick condition in rooftop-nesting Common Nighthawks.

Nighthawk chicks in this study fell into two groups based on their baseline CORT metrics. One group showed low (i.e. CORTB < 25 ng/ml, n = 20) and the other high (i.e. CORTB > 50 ng/ml, n = 4) baseline CORT levels. The low baseline group had CORTB levels (5.53 ± 1.54 (SE) ng/ml) within the range of variation for other semi-precocial chick species. For example, CORTB in 21-day-old Red-legged Kittiwake (Rissa brevirostris) chicks was less than 4 ng/ml (Kitaysky et al., 2001) and was less than 5 ng/ml in 4- to 18-day-old Tufted Puffin (Fratercula cirrhata) chicks (Kitaysky et al., 2005), but ranged from 10 to 18 ng/ml in 10- to 20-day-old Ring-Billed Gull (Larus delawarensis) chicks (Chin et al., 2013). In addition, the low CORTB group in the present study showed a mean stress response of 15.40 ± 8.93 (SE) ng/ml, indicating that these chicks were capable of mounting a stress response. The high baseline CORT group in this study, however, had much higher CORTB levels (97.61 ± 19.66 (SE) ng/ml) and showed a negative association between the stress response and maximum ambient temperatures for the day and week prior to blood sample collection (Table 3). Moreover, mean ambient temperature for the day of sampling was significantly elevated in the high CORTB relative to the low CORTB group (Fig. 3). This suggests that short-term weather might affect both baseline CORT and the ability of some chicks to mount a stress response. Collectively, these data suggest that the majority of our study population does not show a compromised ability to cope with environmental stressors, but for some individual chicks, short-term weather conditions might be thermally stressful. Consistent with this idea, we observed several instances of chick die-offs after heat waves accompanied by high humidity (G.N., personal observation).

Our data highlight some associations between regional climate conditions for the day and week prior to measurement and baseline corticosterone or the magnitude of the stress response (i.e. maximum humidity, wind speed and dew point). However, these results do not occur consistently in a direction signifying heat stress under hot, humid conditions, so high CORTB and a compromised stress response do not appear to be general responses of nighthawk chicks to potentially unfavorable thermal conditions on rooftops. As mentioned previously, chicks in the high CORTB group did show a compromised ability to mount a stress response, and repeated extreme stochastic weather events predicted under future climate change scenarios (Meehl et al., 2000) might have negative impacts on development or survival for at least some chicks.

We had predicted some associations between rooftop micro-climate variables and the CORT metrics, but found none. This suggests that rooftop micro-climate variables are not major effectors of baseline CORT or the stress response in nighthawk chicks or that sufficient variation among rooftops was not present to detect micro-climate effects. Perhaps this is due to the ability of the semi-precocial nighthawk chicks to seek favorable micro-climates (e.g. shady or windy sites) shortly after hatching. Their increased mobility relative to altricial species might alleviate some of the thermal load at the exposed nesting sites, and thus, chicks may be able to overcome some of the more extreme conditions reflected in the regional climate variables.

Corticosterone levels may vary in nestlings based upon their developmental strategy. For example, altricial nestlings, like barn owls, Tyto alba (Almansi et al., 2009) and passerines (Tilgar et al., 2017) have less developed hypothalamic–pituitary–adrenal (HPA) functionality at hatching and thus a lower stress response to poor environmental conditions, presumably due to allocation of resource investment to body growth. In contrast, chicks of semi-precocial species, such as Black-Legged Kittiwakes (Rissa tridactyla) (Kitaysky et al., 2003), Red-legged Kittiwakes (R. brevirostris) (Kitaysky et al., 2001), Tufted Puffins (F. cirrhata) (Kitaysky et al., 2005), Ring-Billed Gulls (L. delawarensis) (Chin et al., 2013), and Grey-faced Petrels (Pterodroma macroptera gouldi) (Adams et al., 2008), have the capacity to mount a stress response. Because nighthawks are also semi-precocial (Brigham et al., 2011), it might be expected that chicks are also capable of mounting a stress response. Indeed, the majority of nighthawk chicks in this study did mount a stress response to handling restraint. However, frequent exposure to extreme weather events can trigger a delayed emergency life history stage in which birds do not mount a stress response upon exposure to stressful conditions (Wingfield and Kitaysky, 2002). For a semi-precocial species that experiences frequent and unpredictable environmental perturbations, such as those at exposed rooftop nest sites of nighthawks, successive exposures to hot, humid conditions might elevate baseline CORT and reduce the capacity to mount a stress response to additional thermal challenges. For example, a stress response for a mobile second-week nestling that is left alone at night when the parents are feeding, might facilitate movement to find shelter from a thunderstorm, thereby prioritizing immediate survival over long-term growth. The low baseline CORT and capacity for stress responses in the majority of the chicks in our study, however, suggest that low metabolic cost alternatives, such micro-climate selection, gular fluttering, reduced activity and torpor (Lasiewski and Dawson, 1964; Brigham et al., 1995; Fletcher et al., 2004), rendered rooftop conditions as not thermally stressful to chicks.

A minority of chicks exposed to higher mean ambient temperature on the day prior to sampling, however, showed elevated baseline CORT, suggesting that the environment was stressful for these individuals. Stress responses to high temperatures in these chicks could be due to direct effects of temperature or to increased levels of dehydration resulting from increased evaporative cooling at high temperatures. Chicks did engage in gular flutter during laboratory studies at ambient temperatures above about 42°C and we also observed gular flutter in chicks on rooftops at high temperatures (Newberry and Swanson, unpubl. data). Because chicks do not have regular access to water on rooftops, high rates of evaporative cooling at high temperatures might be expected to result in dehydration, and dehydration (Arad and Skadhauge, 1984; Klingbeil, 1985) or hot, dry conditions (Treen et al., 2015) may lead to increased baseline CORT in birds. Further study is needed to determine if the CORT responses to high temperatures documented for nighthawk chicks in this study result from direct effects of temperature, indirect effects of dehydration, or both.

While adult nighthawks and other nightjars in arid conditions are adaptable to extremely high temperatures (Cowles and Dawson, 1951; Howell, 1959; Bartholomew et al., 1962; Lasiewski and Dawson, 1964; O’Connor et al., 2017), increasing humidity in non-desert areas, such as our study sites, in the future (Kenward et al., 2014) could negatively affect evaporative cooling capacity by lowering evaporative water loss rates in birds (Lasiewski et al., 1966; Richards, 1976; Webster and King, 1987; Powers, 1992). Such a limitation on evaporative cooling might particularly impact chicks, with their less-developed capacities for evaporative cooling (Kirkham and Montevecchi, 1982; Speich and Manuwal, 1974). Low capacities for evaporative cooling might trigger elevated baseline corticosterone levels, which could potentially compromise growth, the immune system and survival of chicks (Sapolosky et al., 2000; Love et al., 2005). The chicks in the present study, however, did not show such a response to high humidities or high dew points, suggesting that current conditions of temperature and humidity at rooftop nest sites are not particularly stressful to nighthawk chicks. More research is needed to more fully quantify the relationships among nest site thermal conditions, and growth, survival and fitness for nighthawk chicks, which face challenges from both climate and land use change.

Some nighthawk chicks in the present study showed elevated baseline CORT associated with exposure to high ambient temperatures prior to blood sampling. The CORT-fitness hypothesis proposes that high baseline CORT can negatively impact survival or reproduction (Bonier et al., 2009a) and although the hypothesis is controversial (e.g. Bonier et al., 2009b; MacDougall-Shackleton et al., 2009; Rivers et al., 2012, 2017; Love et al., 2014), some support for such a relationship exists for birds (e.g. Lanctot et al., 2003; Buck et al., 2007; Angelier et al., 2010; Jaatinen et al., 2013). If exposure to high temperatures routinely increases baseline CORT, either through direct temperature effects or indirect effects on dehydration, and high baseline CORT is negatively related to chick survival, this raises the possibility that future temperatures associated with climate change trends could make rooftops unsuitable for nighthawk nesting. If nighthawks are attracted to urban habitats as refugia in response to reductions of available natural habitats (Brigham et al., 2011), increasing temperatures and the ‘urban heat island’ effect could render the rooftop nesting strategy as untenable. If so, nighthawks could be eliminated as a breeding species from this region and other similar regions where agricultural landscapes have reduced open natural habitats to low levels on the landscape.

Supplementary Material

Supplementary Data
Click here for additional data file. (45.3KB, docx)

Acknowledgements

We wish to thank Dr Michael J. Watt of University of South Dakota for assistance with CORT assay procedures and the many property managers and owners who permitted access to their rooftops for this study.

Supplementary material

Supplementary material is available at Conservation Physiology online.

Funding

This work was supported by grants from the South Dakota Game, Fish and Parks Wildlife Diversity Small Grant Program, Prairie Biotic Inc., the Department of Biology, University of South Dakota, the University of South Dakota Graduate Student Research and Creativity Grant Program, the University of South Dakota College of Arts & Sciences John W. Carlson Research Program, and the South Dakota Ornithologists’ Union Nathanial R. Whitney Memorial Research Grant Program. No funding organization influenced or required approval of the manuscript.

References

  1. Adams NJ, Cockrem JF, Candy EJ, Taylor GA (2008) Non-precocial grey-faced petrel chicks (Pterodroma macroptera gouldi) show no age-related variation in corticosterone responses to capture and handling. Gen Comp Endocr 157: 86–90. [DOI] [PubMed] [Google Scholar]
  2. Almansi B, Roulin A, Jenni-Eirman S, Breruner CW, Jenni L (2009) Regulation of free corticosterone and CBG capacity under different conditions in altricial nestlings. Gen Comp Endocr 164: 117–124. [DOI] [PubMed] [Google Scholar]
  3. Angelier F, Wingfield JC, Weimerskirch H, Chastel O (2010) Hormonal correlates of individual quality in a long-lived bird: a test of the corticosterone-fitness hypothesis. Biol Lett 6: 846–849. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Arad Z, Skadhauge E (1984) Plasma hormones (arginine vasotocin, prolactin, aldosterone, and corticosterone) in relation to hydration state, NaCl uptake, and egg laying in fowls. J Exp Zool 232: 707–714. [DOI] [PubMed] [Google Scholar]
  5. Ardia DR, Perez JH, Clotfelter EH (2006) Nest box orientation affects internal temperature and nest site selection by Tree Swallows. J Field Ornithol 77: 339–344. [Google Scholar]
  6. Bartholomew GA, Hudson JW, Howell TR (1962) Body temperature, oxygen consumption, evaporative water loss, and heart rate in the poor-will. Condor 64: 117–125. [Google Scholar]
  7. Bonan GB. (2002) Ecological Climatology. Cambridge University Press, Cambridge, UK. [Google Scholar]
  8. Bonier F, Martin PR, Moore IT, Wingfield JC (2009. a) Do baseline glucocorticoids predict fitness? Trends Ecol Evol 24: 634–642. [DOI] [PubMed] [Google Scholar]
  9. Bonier F, Martin PR, Sheldon KS, Jensen JP, Foltz SL, Wingfield JC (2006) Sex-specific consequences of life in the city. Behav Ecol 18: 121–129. [Google Scholar]
  10. Bonier F, Moore IT, Martin PR, Robertson RJ (2009. b) The relationship between fitness and baseline glucocorticoids in a passerine bird. Gen Comp Endocr 163: 208–213. [DOI] [PubMed] [Google Scholar]
  11. Brigham RM. (1989) Roost and nest sites of Common Nighthawks: are gravel roofs important? Condor 91: 722–724. [Google Scholar]
  12. Brigham RM, Morgan KH, James PC (1995) Evidence that free-ranging Common Nighthawks may enter torpor. Northwest Nat 76: 149–150. [Google Scholar]
  13. Brigham RM, Ng J, Poulin RG, Grindal SD (2011) Common Nighthawk (Chordeiles minor) In Poole A, ed. The Birds of North America Online. Cornell Lab of Ornithology, Ithaca: 10.2173/bna.213. [DOI] [Google Scholar]
  14. Buck CL, O’Reilly KM, Kildaw SD (2007) Interannual variability of black-legged kittiwake productivity is reflected in baseline plasma corticosterone. Gen Comp Endocr 150: 430–436. [DOI] [PubMed] [Google Scholar]
  15. Chin EH, Quinn JS, Burness G (2013) Acute stress during ontogeny suppresses innate, but not acquired immunity in a semi-precocial bird (Larus delawarensis). Gen Comp Endocr 193: 185–192. [DOI] [PubMed] [Google Scholar]
  16. Cowles RB, Dawson WR (1951) A cooling mechanism of the Texas Nighthawk. Condor 53: 19–22. [Google Scholar]
  17. El-Halawani ME, Waibel PE, Appel JR, Good AL (1973) Effects of temperature stress on catecholamines and corticosterone of male turkeys. Am J Physiol 224: 384–388. [DOI] [PubMed] [Google Scholar]
  18. Fisher RJ, Fletcher QE, Willis CKR, Brigham RM (2004) Roost selection and roosting behavior of male Common Nighthawks. Am Midl Nat 151: 79–87. [Google Scholar]
  19. Fletcher QE, Fisher RJ, Willis CKR, Brigham RM (2004) Free-ranging Common Nighthawks use torpor. J Therm Biol 29: 9–14. [Google Scholar]
  20. Fletcher RJ Jr, Orlock JL, Robertson BA (2012) How the type of anthropogenic change alters the consequences of ecological traps. Proc R Soc Lond B Bio 279: 2546–2552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fox J, Weisberg S (2011) An {R} Companion to Applied Regression, Ed 2 Sage, Thousand Oaks. [Google Scholar]
  22. Howell TR. (1959) A field study of temperature regulation in young least terns and common nighthawks. Wilson Bull 71: 19–32. [Google Scholar]
  23. Jaatinen K, Seltmann MW, Hollmén R, Atkinson S, Mashburn K, Öst M (2013) Context dependency of baseline glucocorticoids as indicators of individual quality in a capital breeder. Gen Comp Endocr 191: 231–238. [DOI] [PubMed] [Google Scholar]
  24. Kenward A, Yawitz D, Sanford T, Wang R (2014) Summer in the City: Hot and Getting Hotter. Climate Central, Princeton, pp 1–29. [Google Scholar]
  25. Kirkham IR, Montevecchi WA (1982) Growth and thermal development of Northern Gannets (Sula bassanus) in Atlantic Canada. Colon Waterbirds 5: 66–72. [Google Scholar]
  26. Kitaysky AS, Kitaiskaia EV, Piatt JF, Wingfield JC (2003) Benefits and costs of increased levels of corticosterone in seabird chicks. Horm Behav 43: 140–149. [DOI] [PubMed] [Google Scholar]
  27. Kitaysky AS, Kitaiskaia EV, Wingfield JC, Piatt JF (2001) Dietary restriction causes chronic elevation of corticosterone and enhances stress response in red-legged kittiwake chicks. J Comp Physiol B 171: 701–709. [DOI] [PubMed] [Google Scholar]
  28. Kitaysky AS, Romano MD, Piatt JF, Wingfield JC, Kikuchi M (2005) The adrenocortical response of tufted puffin chicks to nutritional deficits. Horm Behav 47: 606–619. [DOI] [PubMed] [Google Scholar]
  29. Klingbeil C. (1985) Effects of chronic changes in dietary electrolytes and acute stress on plasma levels of corticosterone and aldosterone in the duck (Anas platyrhynchos). Gen Comp Endocrinol 58: 10–19. [DOI] [PubMed] [Google Scholar]
  30. Kortner G, Geiser F (1999) Roosting behaviour of the tawny frogmouth (Podargusstrigoides). J Zool 248: 501–507. [Google Scholar]
  31. Lanctot RB, Hatch SA, Gill VA, Eens M (2003) Are corticosterone levels a good indicator of food availability and reproductive performance in a kittiwake colony? Horm Behav 43: 489–502. [DOI] [PubMed] [Google Scholar]
  32. Lasiewski RC, Acosta AL, Bersntein MH (1966) Evaporative water loss in birds—I. A modified method for determination by direct weighing. Comp Biochem Physiol 19: 459–470. [Google Scholar]
  33. Lasiewski RC, Dawson WR (1964) Physiological responses to temperature in the common nighthawk. Condor 66: 477–490. [Google Scholar]
  34. Liu M, Swanson DL (2014) Stress physiology of migrant birds during stopover in natural and anthropogenic woodland habitats of the Northern Prairie region. Conserv Physiol 2: 1–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lohnes RG. (2010). Nest site selection and nest thermal properties of Common Nighthawks on the tallgrass prairie of Kansas. MSc thesis. Cornell University, Ithaca, NY, USA.
  36. Love OP, Chin EH, Wynne-Edwards KE, Williams TD (2005) Stress hormones: a link between maternal condition and sex-biased reproductive investment. Am Nat 166: 751–766. [DOI] [PubMed] [Google Scholar]
  37. Love OP, Madliger CL, Bourgeon S, Semeniuk CAD, Williams TD (2014) Evidence for baseline glucocorticoids as mediators of reproductive investment in a wild bird. Gen Comp Endocr 199: 65–69. [DOI] [PubMed] [Google Scholar]
  38. Lucas JR, Freeberg TM, Egbert J, Schwabl H (2006) Fecal corticosterone, body mass, and caching rates of Carolina chickadees (Poecile carolinensis) from disturbed and undisturbed sites. Horm Behav 49: 634–643. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. MacDougall-Shackleton SA, Dindia L, Newman AEM, Potvin DA, Stewart KA, MacDougall-Shackleton EA (2009) Stress, song and survival in sparrows. Biol Lett 5: 746–748. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Marzilli V. (1989) Up on the roof. Maine Fish Wildl 31: 25–29. [Google Scholar]
  41. Meehl GA, Zwiers F, Evans J, Knutson T, Mearns L, Whetton P (2000) Trends in extreme weather and climate events: Issues related to modeling extremes in projections of future climate change. B Am Meteorol Soc 81: 427–436. [Google Scholar]
  42. Nebel S, Mills A, McCracken J, Taylor P (2010) Declines of aerial insectivores in North America follow a geographic gradient. Avian Conserv Ecol 5(2): 1. [Google Scholar]
  43. NOAA (2016) National Oceanic and Atmospheric Administration: National Centers for Environmental Information Local Climatological Data with Comparative Data 1948–2016, Yankton and Sioux Falls, South Dakota, Sioux City, Iowa. https://www.ncdc.noaa.gov/ghcn/comparative-climatic-data (last accessed 12 March 2017).
  44. Ojima DS, Lackett JM (2002) Preparing for a changing climate: the potential consequences of climate variability and change—Central Great Plains. Central Great Plains Steering Committee and Assessment Team. US Global Change Research Program, Fort Collins, CO, USA, pp 9–15. [Google Scholar]
  45. O’Connor RS, Wolf BO, Brigham RM, McKechnie AE (2017) Avian thermoregulation in the heat: efficient evaporative cooling in two southern African nightjars. J Comp Physiol B 187: 477–491. [DOI] [PubMed] [Google Scholar]
  46. Peterson AT. (2003) Projected climate change effects on Rocky Mountain and Great Plains birds: generalities of biodiversity consequences. Glob Change Biol 9: 647–655. [Google Scholar]
  47. Powers DR. (1992) Effect of temperature and humidity on evaporative water loss in Anna’s hummingbird (Calypte anna). J Comp Physiol B 162: 74–84. [Google Scholar]
  48. Richards SA. (1976) Evaporative water loss in domestic fowls and its partition in relation to ambient temperature. J Agric Sci 87: 527–532. [Google Scholar]
  49. Rivers JW, Liebl AL, Owen JC, Martin LB, Betts MG (2012) Baseline corticosterone is positively related to juvenile survival in a migrant passerine bird. Funct Ecol 26: 1127–1134. [Google Scholar]
  50. Rivers JW, Newberry GN, Schwarz CJ, Ardia DR (2017) Success despite the stress: violet-green swallows increase glucocorticoids and maintain reproductive output despite experimental increases in flight costs. Funct Ecol 31: 235–244. [Google Scholar]
  51. Sapolosky RM, Romero LM, Munck AU (2000) How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr Rev 21: 55–89. [DOI] [PubMed] [Google Scholar]
  52. Sauer JR, Hines JE, Fallon JE (2007). The North American breeding bird survey, results and analysis 1966–2006, Version 10.13.2007 (Archived). Laurel, MD: USGS Patuxent Wildlife Research Center. https://www.mbr-pwrc.usgs.gov/bbs/ (last accessed 4 April 2017).
  53. Sauer JR, Niven DK, Hines JE, Ziolkowski DJ Jr, Pardieck KL, Fallon JE, Link WA (2017). The North American Breeding Bird Survey, results and analysis 1966–2015. Version 2.07.2017 Laurel, MD: USGS Patuxent Wildlife Research Center. https://www.mbr-pwrc.usgs.gov/bbs/ (last accessed 28 August 2017).
  54. Schoech SJ, Rensel MA, Bridge ES, Boughton RK, Wilcoxon TE (2009) Environment, glucocorticoids, and timing of reproduction. Gen Comp Endocr 163: 201–207. [DOI] [PubMed] [Google Scholar]
  55. Speich S, Manuwal DA (1974) Gular pouch development and population structure of Cassin’s Auklet. The Auk 91: 291–306. [Google Scholar]
  56. Spess Jackson L, Thompson CA, Dinsmore JJ (1996) The Iowa Breeding Bird Atlas. University of Iowa Press, Iowa City. [Google Scholar]
  57. Swanson DL, Palmer JS (2009) Spring migration phenology of birds in the Northern Prairie region is correlated with local climate change. J Field Ornithol 80: 351–363. [Google Scholar]
  58. Tallman DA, Swanson DL, Palmer JS (2002) Birds of South Dakota. South Dakota Ornithologists’ Union, Aberdeen, pp i–vii, 197. [Google Scholar]
  59. Tilgar V, Lind M, Lodjak J, Moks K (2017) Corticosterone response as an age-specific mediator of nestling body mass in a wild passerine. Physiol Biochem Zool 90: 512–521. [DOI] [PubMed] [Google Scholar]
  60. Treen GD, Hobson KA, Marchant TA, Bortolotti GR (2015) Large-scale spatial variation in feather corticosterone in invasive house sparrows (Passer domesticus) in Mexico is related to climate. Ecol Evol 5: 3808–3817. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Viel JM. (2014) Habitat preferences of the Common Nighthawk (Chordeiles Minor) in cities and villages in southeastern Wisconsin. MSc thesis. University of Wisconsin, Milwaukee, WI, USA.
  62. Webster MD, King JR (1987) Temperature and humidity dynamics of cutaneous and respiratory evaporation in pigeons, Columba livia. J Comp Physiol B 157: 253–260. [DOI] [PubMed] [Google Scholar]
  63. Weller MW. (1958) Observations of the incubation behavior of a Common Nighthawk. The Auk 75: 48–59. [Google Scholar]
  64. Wingfield JC, Hunt K, Bruener C, Dunlap K, Fowler GS, Freed L, Lepsom J (1997) Environmental stress, field endocrinology and conservation biology In Clemmons JR, Buchholz R, eds. Behavioral Approaches to Conservation in the Wild. Cambridge University Press, Cambridge, UK, pp 95–131. [Google Scholar]
  65. Wingfield JC, Kitaysky AS (2002) Endocrine responses to unpredictable environmental events: Stress or anti-stress hormones? Integr Comp Biol 42: 600–609. [DOI] [PubMed] [Google Scholar]
  66. Wingfield JC, Suydam CR, Hunt K (1994) The Adrenocortical responses to stress in Snow Buntings (Plectrophenax nivalis) and Lapland Longspurs (Calcarius lapponicus) at Barrow, Alaska. Comp Biochem Phys C 108: 299–306. [Google Scholar]
  67. Wingfield JC, Vleck CM, Moore MC (1992) Seasonal changes of the adrenocortical response to stress in birds of the Sonoran Desert. J Exp Zool 264: 419–428. [DOI] [PubMed] [Google Scholar]
  68. Wright CK, Wimberly MC (2013) Recent land use change in the Western Corn Belt threatens grasslands and wetlands. Proc Natl Acad Sci USA 110: 4134–4139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Xie S, Romero LM, Htut ZW, McWhorter TJ (2017) Stress response of heat exposure in three species of Australian desert birds. Physiol Biochem Zool 90: 348–358. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Data
Click here for additional data file. (45.3KB, docx)

Articles from Conservation Physiology are provided here courtesy of Oxford University Press

RESOURCES