The expansion and intensification of agriculture is a primary driver of declines in avian populations (Donald et al. 2001, Benton et al. 2003, Evans et al. 2007, Paquette et al. 2013). In eastern Canada, there has been a profound change in agricultural land use from pastoral to more intensive arable crop agriculture (Statistics Canada 2006). This trend is associated with a shift toward greater synthetic agrochemical use, which started in the 1970s (e.g., Boutin and Jobin 1998), and to a higher loss of natural and seminatural land covers, thus raising concern about how these changes have affected the avian community (Wilson et al. 2017, Endenburg et al. 2019). Numerous threatened species co-occur in agroecosystems, and the development of effective conservation and management strategies for these species requires that we understand how they respond to the expansion and intensification of agriculture (Caughley 1994, Guisan et al. 2013). Such information is aided by knowledge of three key components: (1) the tolerance of species to compositional change in preferred habitat (Lindenmayer et al. 2005, Swift and Hannon 2010, Suarez-Rubio et al. 2013), (2) whether the configuration of preferred habitat affects species, and (3) the spatial scales at which species are most responsive to habitat loss, which allows for the management of habitat at the scale most likely to be influential for species recovery (Jackson and Fahrig 2015, Farrell et al. 2019).
The Eastern Whip-poor-will (Caprimulgus vociferous) and the Common Nighthawk (Chordeiles minor) are two examples of nocturnal aerial insectivores that have experienced long-term population declines along with several other aerial insectivorous bird species (Nebel et al. 2010, Michel et al. 2016, Spiller and Dettmers 2019). In North America between 1970 and 2017, Eastern Whip-poor-will and Common Nighthawk populations declined by 74% and 56%, respectively (Smith et al. 2019), and under Canada’s Species at Risk Act, they are listed as Threatened and Special Concern, respectively (Environment and Climate Change Canada 2019). Several factors have been hypothesized as contributing to observed declines in Eastern Whip-poor-will populations, including reduced food availability and habitat loss on the breeding grounds (Boettner et al. 2000, Hallmann et al. 2014, Tozer et al. 2014, English et al. 2017), and habitat loss on the wintering grounds (Cink et al. 2017). Threats to Common Nighthawks are poorly known, in part because a large proportion of the Canadian population breeds in remote areas of the boreal forest where distribution and trend information is limited (Farrell et al. 2017, 2019).
The Eastern Whip-poor-will and Common Nighthawk are associated with semi-open habitats, which raises the possibility that individuals may use agricultural areas similar to their use of clearcut patches in forested landscapes (Brigham et al. 2011, Tozer et al. 2014, Cink et al. 2017, Farrell et al. 2017, 2019). Eastern Whip-poor-wills are strongly associated with open canopy forest and edge habitat for nesting, roosting, and increased visibility for foraging at night (Tozer et al. 2014, Akresh and King 2016, Cink et al. 2017, English et al. 2017). The importance of various components of landscape structure for Eastern Whip-poor-wills appears to be scale-dependent. For example, in relatively intact forested regions, forest amount has been found to influence Eastern Whip-poor-will presence at a broad landscape scale, but forest edge appears to be more important at finer spatial scales (Akresh and King 2016, English et al. 2017, Farrell et al. 2019). Common Nighthawks show variable habitat use, and often use open habitats such as agricultural areas (Brigham et al. 2011) and openings in forested landscapes created by wetlands, fires, or human activities (e.g., forestry) (Farrell et al. 2017).
In this study, we first measured occupancy of the two species within an agricultural region of eastern Ontario. We then assessed the effects of landscape composition (forest, agriculture, wetland, and urban land cover) and forest configuration (mean patch size, number of patches, distance to nearest patch) on Eastern Whip-poor-wills. While we intended to examine these same relationships between landscape composition/configuration and Common Nighthawk occupancy, we were unable to do so because of small sample sizes (i.e., models for Nighthawks were unreliable as indicated by standard errors and parameter estimates). For the Eastern Whip-poor-will, we first examined the relationship between occupancy and landscape composition at multiple spatial scales. We then examined if forest configuration and agriculture type (proportion of perennial forages and cropland) were better predictors of Eastern Whip-poor-will occupancy than landscape composition alone. Eastern Whip-poor-wills often select partially open landscapes (Tozer et al. 2014, Farrell et al. 2017, 2019); therefore, these combined objectives were intended to ask (1) whether Eastern Whip-poor-will occupancy of agroecosystems in eastern Canada is higher in more open landscapes with small forest patches or in more closed forested landscapes with larger forest patches, and (2) if occupancy is higher in areas where the agricultural habitat is dominated by perennial forages rather than arable crop. We predicted the latter because of higher food availability and greater habitat suitability in pasture compared to arable crop (Boutin and Jobin 1998, Evans et al. 2007).
Our surveys were conducted during the breeding season (6 June to 19 July 2016) to coincide with the peak in vocal activity of Eastern Whip-poor-wills and Common Nighthawks in eastern Ontario, Canada. We used a spatial road layer in ArcGIS (Esri Inc. 2016) to randomly allocate a sample of survey locations (latitude range: 44°92′ to 45°51′ N; longitude range: 74°41′ to 76°73′ W) on any accessible road except for primary highways. We placed a constraint on the location of each potential site such that they were at least 4 km apart. We obtained 205 potential sites of which we identified 127 that represented a gradient along two land cover axes at a 1-km radius: (1) high proportion agriculture (range 0.00–0.96) to high proportion forest (range 0.02–0.93), and (2) within the agricultural land cover class, a high proportion of arable crop types (range 0.00–0.99) to a high proportion of perennial forages (range 0.00–1.00).
We used SM2+ autonomous recording units (ARUs [Wildlife Acoustics Inc.]) to sample for Eastern Whip-poor-wills and Common Nighthawks at the 127 selected sites. The ARUs were placed at approximately chest height on a fence post or a tree along the roadside with the microphones extending perpendicular to the road. The ARUs were programmed to make three recordings per day (sunset, 1 h after sunset, and 1 h before sunrise) over seven days at each site. Using multiple recordings per site allowed us to estimate occupancy of the two species while accounting for temporal variation and the effects of background noise on detectability (MacKenzie et al. 2002). After the field season, a single observer selected four days per site with the lightest wind conditions and little-to-no rain, and listened to the first 5 min of each 10-min recording for each of the three daily time periods. Thus, with this sampling schedule, we had a total of 12 replicate surveys per site (i.e., three recordings per day over four days). Detections of all Eastern Whip-poor-wills or Common Nighthawks heard on the recordings were noted. The observer recorded multiple individuals of a species when it was possible to determine that two or more were calling; however, for consistency, only occurrence information (e.g., detected = 1, or not detected = 0) was subsequently used in the analysis. During each recording, the listener noted any significant disturbances (e.g., vehicle, equipment malfunction) that might influence detectability of calling individuals. Noise, wind, and rain were all classified as binomial variables (included as 1/0 categorical variables), while vehicular disturbances were quantified as a count of the number of passing vehicles. We included recordings with low wind or light drizzle.
We obtained the landscape cover types from the 2016 Agriculture and Agri-Food Canada (AAFC) annual crop inventory (Fisette et al. 2013), which includes major land cover types in Canada mapped at a 30-m spatial resolution. The AAFC inventory includes 66 land cover classes representing natural, agricultural, and urban land covers. We grouped together various land cover types to create forest and agricultural land covers. The forest land cover consisted of three classes: mixedwood, broadleaf, and coniferous forest. Agricultural land cover consisted of 35 land cover classes, including all grains, vegetables, fruits, grasses, crop fields, vineyards, and greenhouses. Cropland consisted of a subset of these: 20 classes of annual row crops, which were predominantly corn, soy, and wheat, but also included vegetables and fruits. Perennial forages included periodically cultivated land with tame grasses and other perennial crops such as alfalfa and clover grown alone or as mixtures for hay, pasture, or seed (Statistics Canada 2011). We also used the wetland and urban land covers from the AAFC inventory to quantify the amount of wetland and urban cover in the landscape.
To assess scale of effect (sensu Jackson and Fahrig 2015; see scale of effect analysis in Appendix 1), we measured the amount of each land cover type at 11 spatial scales from the survey point using circular buffers with the following radii: 500 m, 1000 m, 2000 m, 3000 m, 4000 m, 5000 m, 6000 m, 7000 m, 8000 m, 9000 m, and 10000 m. For landscape composition measures (i.e., amount of forest, agriculture, wetland, urban land cover), we divided the amount of land cover by the total landscape area to calculate the proportion of each land cover type. To assess how the proportion of different agriculture types (perennial forages versus row crops) affects occupancy, we divided the amount of perennial forages and cropland by the total amount of agriculture in the landscape (Put et al. 2019).
To assess landscape configuration with respect to forest cover, we measured forest patch configuration metrics using Patch Analyst 5 (Rempel et al. 2012), an ESRI ArcMap extension (Esri Inc. 2016). The configuration metrics included mean patch size (in hectares), total number of patches, and distance to nearest forest patch (distance in meters to patches larger than 1800 m2). All forest patch configuration metrics were calculated at 2000-m radius buffers around each site, which corresponded to the most significant scale of effect for Eastern Whip-poor-will response to forest composition (see scale of effect analysis in Appendix 1). We calculated and summarized statistics for the mean, minimum, maximum, and standard deviation of each landscape variable (Table 1).
We conducted analyses in R Version 3.5.2 (R Core Team 2017) and used single-season occupancy models from the package “unmarked” (Fiske and Chandler 2020). We examined occupancy and detectability for both species, and then developed more complex models to test the effects of potentially influential predictor variables, including landscape variables, on Eastern Whip-poor-will occupancy while accounting for detection probability (MacKenzie et al. 2002). We evaluated the bivariate correlation (Pearson correlation coefficient [PCC]) between predictor variables to ensure that variables included in the same model were not highly correlated. Pairs of predictor variables were considered correlated if the PCC value was greater than 0.7 or less than -0.7. Correlated predictor variables were not included in the same models but were evaluated separately to determine which variable had the most explanatory power.
Small-sample Akaike’s information criterion (AICc) was used to evaluate the support of models containing different variables (Table 2). We first tested for effects on detectability with a comparison among models that included the time of the recording as a categorical variable (i.e., 1 h after sunset and 1 h before sunrise), date, number of passing cars, and presence of wind, rain, or other noise during the survey; an intercept-only model was used on the occupancy component for these comparisons. Based on the detectability model that had the most support, we proceeded to examine support for models with variables hypothesized to influence occupancy. Our first step in this process was to conduct the scale of effect analysis on each of our land cover composition variables of interest (proportion of forest, agriculture, wetland, urban, cropland, and perennial forages) in order to identify the spatial scale at which composition most strongly influenced occupancy (Appendix 1). Each scale of effect was then used to compare models that included land cover proportions (forest, agriculture, wetland, urban) along with the type of agriculture (i.e., proportion of arable crops versus proportion of perennial forages) and the forest cover configuration metrics.
We examined model fit for our analyses using a parametric bootstrap on the most parameterized model with the highest support in the candidate set (Model 2: mean patch size, wetland, and urban) (Table 2) (Fiske and Chandler 2020). We simulated 200 data sets from the model, and for each, we refit the model to the data and computed a Chi-square fit statistic. We then calculated the Chi-square statistic from our observed data. For a well-fit model, the observed value should not be extreme relative to the values from the bootstrapped distribution, and we used p > 0.05 to indicate adequate fit (Fiske and Chandler 2020). The goodness-of-fit indicated that the observed values did not differ significantly from the simulated bootstrapped distribution for the Eastern Whip-poor-will (p = 0.42), which indicated that the models adequately fit the data.
We detected Eastern Whip-poor-wills at 29 of the 127 sites and Common Nighthawks at 8 the 127 sites (Fig. 1). The mean probability of detection at occupied sites for Eastern Whip-poor-wills and Common Nighthawks respectively was 0.255 (95% confidence interval [CI]: 0.208, 0.302) and 0.273 (95% CI: 0.179, 0.367) (Fig. 2). The mean probability of occupancy across the study area was 0.244 (95% CI: 0.168, 0.320) for Eastern Whip-poor-wills and 0.064 (95% CI: 0.021, 0.107) for Common Nighthawks (Fig. 2).
Time of day had the largest influence on detectability of Eastern Whip-poor-wills. Detectability was highest 1 h before sunrise, with a detection probability of 0.433 (95% CI: 0.34, 0.52), followed by 1 h after sunset, with a probability of 0.332 (95% CI: 0.25, 0.42). We did not detect any Eastern Whip-poor-wills at sunset, and therefore removed the sunset period from the analysis. There was weak support for a negative effect of wind but no effects of date, rain, noise, or number of passing cars (Table 2). Based on these findings, we retained time of day and wind in subsequent models that examined the influence of landscape composition and configuration on Eastern Whip-poor-wills.
The correlation between variables in the top occupancy models (Table 2) ranged between -0.7 and 0.7. The proportion of forest and mean forest patch size were strongly and positively correlated (PCC = 0.7) and were not included together in any subsequent models. The proportion of wetland was not correlated with mean forest patch size (PCC = 0.05) but was moderately positively correlated with proportion of forest (PCC = 0.47). The proportion of urban area showed a slight negative correlation with proportion of forest (PCC = -0.25), mean forest patch size (PCC = -0.2), and proportion of wetlands (PCC = -0.21). Finally, mean forest patch size and the number of patches were negatively correlated (PCC = -0.75).
For Eastern Whip-poor-wills, variables in the most supported occupancy model included mean forest patch size and wetland amount (Table 2). Occupancy varied positively with mean patch size (βpatch size = 0.27 ± 0.07 [SE]), which was the most supported configuration variable. More specifically, the relationship between mean patch size and estimated occupancy predicted occupancy estimates of between 0.8 and 1.0 for patches of approximately 15–25 ha (Fig. 3a). Occupancy also varied positively with forest cover at 2000 m (βforest= 4.72 ± 1.19) and negatively with the number of forest patches (βpatch number = -0.012 ± 0.004). Compared to the intercept-only occupancy model, the addition of the two variables individually led to a reduction in AICc of 17.8 and 5.2, respectively. However, mean patch size was the strongest predictor, and because of the correlation among the three variables, we did not include all three in the top model. Nevertheless, our results taken together show that Eastern Whip-poor-will occupancy tends to be higher in landscapes with greater forest cover in the form of larger patches rather than more open landscapes with a greater number of small forest patches. Eastern Whip-poor-will occupancy also increased with increasing amount of wetland at the 6000-m scale (βwetland = 7.38 ± 4.14), with the estimate ranging from approximately 0.2 in landscapes without wetlands to 0.4 in landscapes where wetlands represented 25% of the land cover (Fig. 3b). Occupancy was negatively correlated with urban land cover at the 2000-m scale when considered only with mean patch size (βurban = -11.24 ± 8.86; Model 3 in Table 2) but was not supported after accounting for the influence of wetlands (βurban = -8.18 ± 8.66; Model 2 in Table 2). While controlling the total proportion of forest in the landscape, we found no evidence that Eastern Whip-poor-will occupancy was related to the type of agriculture, opposite to our prediction (Table 2).
Our study provides novel estimates of mean occupancy for two declining aerial insectivore bird species in an agroecosystem in eastern Canada, and presents important results on the influence of landscape composition and configuration on the occupancy of the Eastern Whip-poor-will. Mean site occupancy for both species in this region was lower than results from studies conducted elsewhere in boreal or eastern hardwood regions, possibly due to the overall openness of the region due to past agricultural and urban expansion. For the Eastern Whip-poor-will, we also found that landscapes with larger forest patches had higher occupancy relative to more open landscapes with small forest patches and that occupancy was greater in areas with higher wetland amount at large spatial scales.
Eastern Whip-poor-wills regularly select semi-open habitats on the edge of burns, wetlands, and clearcuts in forested landscapes that provide adjacent foraging and nesting habitat (Wilson 2003, Wilson and Watts 2008, Tozer et al. 2014, Farrell et al. 2017). We tested whether a similar pattern would occur in semi-open agricultural areas in eastern Ontario. However, our results suggest that Eastern Whip-poor-wills preferentially occupy larger forest patches in this region. Edge habitat and small natural or anthropogenic openings may still be beneficial at finer spatial scales, but if so, those effects did not translate to the broad spatial scales we examined (English et al. 2017, Farrell et al. 2019). It is possible that open agricultural land cover does not represent beneficial foraging habitat because of a reduction in insect prey if agrochemicals are applied to agricultural fields (Stoate et al. 2009, Stanton et al. 2018). Alternatively, small forest patches in areas with higher amounts of agriculture may be riskier habitats if potential nest or adult predators such as corvids, foxes, or coyotes are attracted to such landscapes (Bollinger et al. 1990, Gehring and Swihart 2003, Schmidt 2003, Perlut et al. 2006, Stanton et al. 2018). Thus, from a landscape perspective, maintaining larger forest patches appears to be most important in agroecosystems where considerable forest area has already been lost to agriculture.
We found that Eastern Whip-poor-will occupancy was positively related to wetland amount. Wetlands have been shown to be important habitat for this species in the boreal forest of northern Ontario, and they were stronger predictors of landscape-level occupancy than clearcuts, which are also selected at local scales (Farrell et al. 2017, 2019). This relationship with wetlands may be because they provide open areas for foraging and high insect abundance (Spitzer and Danks 2006, COSEWIC 2009). A close relative of the Eastern Whip-poor-will, the European Nightjar, has also been shown to forage more frequently in wetlands than in other open habitat types (Alexander and Cresswell 1990). However, Eastern Whip-poor-will association with wetland habitat is not often reported or emphasized in the literature (e.g., COSEWIC 2009, Tozer et al. 2014, Slover and Katzner 2016, Cink et al. 2017). In eastern Ontario, approximately 72% of presettlement wetlands have been reduced, and wetlands continue to be threatened (Ducks Unlimited Canada 2010). Our study highlights the importance of considering wetlands in Eastern Whip-poor-will recovery plans.
Our estimate of Eastern Whip-poor-will site occupancy was 0.24, which is lower than other empirical estimates in the literature. For example, occupancy estimates were 0.76 in clearcut areas in sites in central Ontario (Tozer et al. 2014) and 0.75 along the Eastern Gulf Coastal Plains of Mississippi, based on the Breeding Bird Survey (Twedt 2015). In northern Ontario, occupancy estimates were 0.47, 0.40, and 0.36 in burned stands, open wetlands, and clearcuts, respectively (Farrell et al. 2017), and 0.36 in clearcuts in landscapes devoid of burns (Farrell et al. 2019). Our lower occupancy estimates by comparison are likely because our study region contains smaller patches of forest and lower amounts of wetland (relative to these other study areas), and the open habitat is dominated by agricultural land cover, which Eastern Whip-poor-wills have been found to avoid elsewhere (Slover and Katzner 2016). Our surveys were conducted along roads, which may have resulted in lower occupancy than at off-road sites if the species shows any road bias (Harris and Haskell 2007). Future research on Eastern Whip-poor-wills in agroecosystems could include on-road and off-road surveys to test this possibility. It is also possible that because we surveyed in only one year, low occupancy estimates may have been further exacerbated by year effects. For example, habitat degradation or poor climatic conditions during the nonbreeding season may have resulted in lower than average population sizes on the breeding grounds during our study year (e.g., Wilson et al. 2011, Rushing et al. 2016).
The low Common Nighthawk occupancy (0.064) we observed in agricultural areas was surprising because other studies have shown that Common Nighthawks use a wide variety of habitats for nesting (Brigham et al. 2011, COSEWIC 2018) and foraging, including agricultural areas, wetlands, water, and urban centers, if food is available (Brigham 1989, Knight et al. 2018). The mean occupancy for Common Nighthawks was only about one-third that of Eastern Whip-poor-wills, while detection estimates were similar (Fig. 2), which suggests that low Common Nighthawk occupancy was not a result of a detectability bias. Results from a study in the boreal forest of northern Ontario showed higher occupancy estimates of 0.44 (Farrell et al. 2017) and 0.67 (Farrell et al. 2019) where forest cover is greater at a regional scale. Additionally, a study in the native Great Plains grasslands in the United States reported an estimated Common Nighthawk occupancy of 0.43 (Stenger et al. 2011). Common Nighthawks were detected primarily in the western region of our study area, which is more forested and has a different geology, including more exposed bedrock, limited drainage, and subsequently more wetlands than other areas in the study region. Because wetlands may provide important foraging habitat, their loss and currently low abundance across much of our study area may have negatively affected Common Nighthawk occupancy (Spitzer and Danks 2006, COSEWIC 2018, Farrell et al. 2019).
Our study region represents a landscape fragmented by urban and agricultural activities, similar to many other landscapes within the hardwood forest ecosystems of eastern North America. Management strategies in these types of fragmented agricultural landscapes should emphasize wetland protection and the maintenance of large forest patches; the latter can be encouraged by protecting existing patches and promoting forest regeneration on abandoned lands. This strategy would provide foraging opportunities during early stages of regeneration as well as habitat for roosting and nesting as stands age and reach maturity.
Thanks to Christine Eberl at the National Wildlife Research Center for her assistance and to Lenore Fahrig for providing constructive criticism on early versions of the manuscript. Also, thanks to the Geomatics and Landscape Ecology Lab (GLEL) discussion group for providing helpful comments. Funding for this project was provided by Environment and Climate Change Canada.
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