Translator Disclaimer
28 October 2015 Patterns of migratory connectivity in Vaux's Swifts at a northern migratory roost: A multi-isotope approach
Author Affiliations +

The strength of migratory connectivity between breeding, stopover, and wintering areas can have important implications for population dynamics, evolutionary processes, and conservation. For example, patterns of migratory connectivity may influence the vulnerability of species and populations to stochastic events. For many migratory songbirds, however, we are only beginning to understand patterns of migratory connectivity. We investigated the potential strength of migratory connectivity within a population of Vaux's Swifts (Chaetura vauxi). Like many aerial insectivores, this species is currently experiencing population declines. In 2012, a mass mortality event at a spring migratory roost on Vancouver Island, British Columbia, Canada, resulted in the deaths of >1,000 individuals (~2% of the British Columbia population). In these individuals, we examined variation in 3 stable isotopes (δ2H, δ13C, and δ15N) from claw samples to determine whether spring migrants showed inherent isotopic similarity in the habitats they used on their Mexican and Central American wintering grounds. Our results indicated the presence of 2 or 3 broad isotopic clusters, which suggests that Vaux's Swifts that migrated through Vancouver Island most likely originated from 2 or 3 overwintering locales or habitat types. We found no evidence of sex- or morphology-based segregation, which suggests that these groups likely share a similar overwintering ecology and, thus, may be equally vulnerable to stochastic events or habitat loss on the wintering grounds. Our results highlight the need for more studies on the nonbreeding-season ecology and migratory connectivity of this species.


Migratory birds move annually between breeding, wintering, and stopover sites that can be separated by hundreds or thousands of kilometers. Hence, they face an array of both natural and human-mediated environmental challenges. For their effective conservation, it is essential to take an annual-cycle approach that addresses factors that influence populations throughout the year and across broad geographic scales (Webster et al. 2002, Webster and Marra 2005). Stressors that occur during one phase of the annual cycle can have carryover effects to subsequent phases of the annual cycle, affecting both individual- and population-level dynamics (Marra et al. 1998, Sillett et al. 2000, Reudink et al. 2009a). Population-level carryover effects can be caused by density-dependent population regulation (Fretwell 1972, Ratikainen et al. 2008), large-scale climatic cycles (Sillett et al. 2000, Wilson et al. 2011), and habitat loss (Norris 2005). Examples of carryover effects that influence individual fitness include wintering-habitat-mediated differences in reproductive success (Reudink et al. 2009a) and natal dispersal (Studds et al. 2008). Understanding patterns of migratory connectivity, defined as the extent to which individuals from the same wintering site migrate to the same breeding site and vice versa (Marra et al. 2010), may be important for understanding species- or population-specific responses to anthropogenic disturbances (e.g., land-use change, agricultural development), as well as large-scale selective pressures such as climate change (Webster et al. 2002).

Migratory populations may be especially vulnerable to stochastic events when they display high migratory connectivity, particularly if population size is also small. For example, Kirtland's Warblers (Setophaga kirtlandii) exhibit extremely strong connectivity between breeding populations in Michigan, USA, and wintering populations in the Bahamas, Turks, and Caicos (Bocetti et al. 2014). As a consequence, the species experiences pronounced carryover effects, whereby drier winters delay arrival and nest initiation on the breeding grounds, ultimately resulting in fewer offspring fledged (Rockwell et al. 2012). Intensive management of the species on the breeding grounds has led to population increases, but continued long-term recovery likely depends on active management in the Bahamas as well (Wunderle et al. 2010), especially given that climate change is predicted to increase drought severity in the Caribbean (Rockwell et al. 2012).

For small migratory birds, making geographic connections for individuals and populations between different phases of their annual cycle can be exceedingly difficult. In recent years, information gleaned from traditional bird ringing has been greatly enhanced with the use of intrinsic markers such as genetic (e.g., Ruegg et al. 2014) and stable-isotope (Hobson 2011) techniques, as well as combinations of these techniques (Chabot et al. 2012, Rundel et al. 2013). Stable isotopes of hydrogen (δ2H), in particular, have been instrumental in migratory-connectivity research, owing to geographically predictable patterns of isotopic variation that are reflected in animal tissue and thus provide “markers of origin” that indicate where the tissue was grown (Hobson et al. 2012a). Such intrinsic markers have the advantage that birds need only be captured once, and they may be especially useful as a first step to understanding patterns of migratory connectivity in poorly studied species or in remote areas (Hobson et al. 2014, Pekarsky et al. 2015). The advent of new extrinsic marker technologies such as geolocators and GPS loggers has led to higher-resolution information on the migratory behavior of certain species (Stutchbury et al. 2009). However, use of these extrinsic markers is complicated by the need to recover data loggers and remains impractical, and often prohibitively expensive, for large-scale studies (Arlt et al. 2013, Bridge et al. 2013, Hobson et al. 2014). In addition, extrinsic markers cannot be used to infer historical patterns of migratory connectivity. By contrast, intrinsic markers can be extremely useful in forensic studies, including dietary reconstruction (Nocera et al. 2012, Blight et al. 2015) and inference of migratory connectivity from historical specimens (Hobson et al. 2010). Given the paucity of information on population- or region-specific migratory connectivity for many species of conservation concern, there is a pressing need to apply intrinsic and/or extrinsic marker approaches to evaluate factors that limit populations throughout their annual cycle (Hobson et al. 2014).

Like many other aerial insectivores, breeding populations of Vaux's Swift (Chaetura vauxi) have undergone significant declines (Nebel et al. 2010). Recent estimates from the North American Breeding Bird Survey suggest a population decline of −2.2% yr−1 in Canada since the 1970s (Environment Canada 2014). Vaux's Swifts breed throughout western North America but exhibit an unusual roosting strategy during migratory journeys between their western North American breeding grounds and their southern Mexican and Central American wintering grounds (Bull and Collins 2007). During migration, groups of Vaux's Swifts roost communally in tree cavities or, more commonly, in abandoned industrial chimneys, where they can remain for several days to weeks (Bull and Collins 2007). Migratory roosts may contain hundreds to thousands of individuals. During winter, Vaux's Swifts also appear to occupy large roost sites (Bull and Collins 2007). However, little is known about migratory connectivity in Vaux's Swift, and it is unknown whether (1) individuals from particular wintering areas migrate north together and stop at the same roost sites (strong migratory connectivity) or (2) migratory roost sites comprise individuals arriving from multiple locations across their winter range (weaker migratory connectivity). The degree of connectivity can have important implications for conservation (e.g., Sheehy et al. 2011), and if large concentrations of individuals winter together and move together throughout migration, they may be particularly vulnerable to stochastic events. Here, we use intrinsic markers to study putative migratory connectivity in migrating Vaux's Swifts. We sampled claws (grown during the overwintering period) from a large group of communally roosting Vaux's Swifts that accidentally suffocated at a spring migratory roost site on Vancouver Island, British Columbia, Canada. We used cluster analyses based on multiple stable-isotope markers (δ2H, δ13C, and δ15N) to examine support for the existence of a single versus multiple wintering origins within this population.


Field Methods

On May 9, 2012, a mass mortality event occurred at a migratory roost in Cumberland, British Columbia, used annually by Vaux's Swifts during spring migration. Approximately 1,350 Vaux's Swifts died from suffocation after being trapped in the roost. That single event represented a loss of 1.5–2.7% of the published population-size estimates (Summers and Gebauer 1995, Partners in Flight Science Committee 2013) for Vaux's Swifts in British Columbia. From these mortalities, we randomly subsampled 98 individuals for analysis. On each individual, we measured wing length, tail length, and tail pin length, and we sexed each individual via laparoscopy. We then sampled 2 mm (0.30–0.40 mg) of tissue from the tip of the central claw, ensuring that we avoided the quick, of each foot from each individual for isotope analysis. Although claws grow continuously and, because of their conical growth pattern, incorporate materials grown at slightly different times, analysis of the claw tip should accurately reflect the isotopic environment over an extended period (weeks to months) preceding sampling (Mazerolle and Hobson 2005, Hahn et al. 2012). For instance, on the basis of changes in δ2H after arrival on the breeding grounds, Fraser et al. (2008) estimated that isotopic values in claws of parulids may reflect the nonbreeding area for 3–7 wk after the birds arrive on the breeding grounds. Hahn et al. (2014) also provided empirical evidence demonstrating that in Palearctic–African migratory passerines, the distal claw tip should reflect isotopic environments over a few months prior to sampling, with typical claw-growth rates of 0.03–0.05 mm day−1. Though data on the migration timing of Vaux's Swifts are sparse, major flight passages occur during mid-April to late May in California (Small 1994), with birds arriving in Oregon from late April to late May (Bull and Collins 2007, and references therein). Thus, claws sampled from birds that died during spring migration on May 9 should reflect wintering conditions, as has been demonstrated in passerines captured both on migration (Bearhop et al. 2004) and upon arrival on the breeding grounds (Reudink et al. 2009a, 2009b).

Stable Isotope Analysis

We analyzed the stable isotope ratios of 3 naturally occurring elements that are incorporated predictably into an animal's diet. First, we analyzed δ2H, which has been used extensively in studies of avian migration and connectivity because it is linked to both latitude and elevation, 2H being relatively less abundant at more northern latitudes and higher elevations (Hobson 2011). Second, we used δ13C, which varies in animal tissues with habitat: Individuals that occupy habitats with a higher proportion of C4 plants or under higher water stress exhibit less negative δ13C signatures (Lajtha and Marshall 1994, Cerling et al. 1997, Still and Powell 2010). Finally, we used δ15N, because 15N is preferentially incorporated into the tissues of consumers and thus biomagnifies with increasing trophic levels, leading to an increase in δ15N signatures (Post 2002, Poupin et al. 2011). In addition, δ15N may reflect the relative aridity of a biome because it is negatively correlated with rainfall and positively correlated with temperature (Sealy et al. 1987, Craine et al. 2009). All stable isotope analyses were conducted at the Smithsonian Institution OUSS/MCI Stable Isotope Mass Spectrometry Facility in Suitland, Maryland, USA. Claws were washed in a 2:1 chloroform:methanol solution, then air dried and allowed to acclimate to lab atmospheric conditions in a fume hood for 72 hr prior to sample preparation. Samples were pyrolyzed in a Thermo TC/EA elemental analyzer (Thermo Scientific, Waltham, Massachusetts, USA) at 1,350°C and analyzed using a Thermo Delta V Advantage isotope ratio mass spectrometer. For stable hydrogen isotope analysis, we ran 4 calibrated standards for every 10 samples, including the hydrogen standard International Atomic Energy Agency CH-7 and 3 additional standards (KHS: −54.1 ± 2.3‰; CBS: −197.3 ± 2.0‰; Spectrum keratin: −121.6 ± 2.5‰). Nonexchangeable δ2H values were corrected to keratin standards following Wassenaar and Hobson (2000) and were repeatable to within 3 ± 2‰ (mean ± SD; n = 10). For stable carbon and nitrogen analysis, we ran 2 in-house standards (acetanalide and urea) for every 10 samples. Stable isotope values are expressed in parts per thousand (‰) deviations from international standards VSMOW (hydrogen), PDB (carbon), and air (nitrogen) by the following equation:


where X is the isotope ratio of interest (δ2H, δ13C, or δ15N) and R is the corresponding ratio (2H:1H, 13C:12C, or 15N:14N). Carbon and nitrogen samples were repeatable to within ±0.2‰, based on repeated measurements of standards.

Statistical Analysis

There are many different methods available for determining the optimal number of clusters in a dataset. For this reason, we made use of the package “NbClust” in program R (R Development Core Team 2013), which provides the user with results from 30 indices aimed at determining the number of clusters into which the data should be split (Charrad et al. 2014). Clustering validity indices combine information such as intracluster and intercluster variation, geometric or statistical properties of the data, and dissimilarity or similarity measurements. For a detailed description of all 30 indices, see Charrad et al. (2014). Two widely used clustering algorithms are available in the NbClust package: k-means and hierarchical agglomerative clustering. In k-means clustering, observations are assigned to initial cluster centers, which are then iteratively updated until the cluster centers no longer change and the within-cluster sum of squares is minimized (MacQueen 1967). In hierarchical agglomerative clustering, each observation begins in its own cluster, and pairs of clusters are joined on the basis of a distance measure and an agglomeration criterion (Székely and Rizzo 2005). Because of the differences in these clustering approaches and a lack of a priori reasons to hypothesize a set number of clusters in our data, we used both clustering algorithms. We used a Euclidean distance (square distance between 2 vectors; Seber 1984) in both clustering algorithms and examined the relative support for division of the data into 2–10 clusters based on the 3 isotopic markers (δ2H, δ13C, and δ15N). For hierarchical agglomerative clustering, we used the Ward agglomeration method (Ward 1963), which minimizes the total within-cluster variance. We also repeated the above procedures after removing 6 multivariate outliers, assessed using the robust Mahalanobis distance (Varmuza and Filzmoser 2009).

After the optimal number of clusters was identified by each clustering approach, we examined the partitioning of observations (individual birds) into clusters. We performed multivariate analysis of variance (MANOVA) to verify significant differences among the selected clusters in δ2H, δ13C, and δ15N ratios. We then used a Pearson's chi-square test to examine whether the proportion of claws from male and female birds differed among clusters, and we used a t-test or analysis of variance (ANOVA) to examine whether wing length and tail length varied among clusters.

Finally, we used a model-based hierarchical clustering procedure within the package “mclust” in R (Fraley et al. 2012) to determine whether the optimal number(s) of clusters selected by the above procedures was preferred over no clustering at all. Package “mclust” uses an expectation-maximization (EM) algorithm to estimate the finite mixture models that correspond to different numbers of clusters, and uses the Bayesian Information Criterion (BIC) to select the best number of clusters. Importantly, a single cluster can also be considered. We evaluated the support for 1–10 clusters.

The δ2H, δ13C, and δ15N isotope ratios were standardized prior to analysis by subtracting by their mean and dividing by their standard deviation. Standardization is recommended in cluster analysis when variables are on different scales, to minimize the effects of outliers and so that cluster formation is not overly influenced by variables with greater absolute variation (Milligan and Cooper 1988). Although isotope ratios represent the ratio of heavy to light isotopes in a sample divided by the ratio of a standard and are always expressed in the same units (‰), differences in the natural abundances and fractionation of heavy and light isotopes among elements lead to differences in the magnitude of the range of typical δ values (Fry 2006). Thus, standardization was useful in giving equal weight to variation among the 3 isotope ratios. All analyses were performed in R version 3.1.1, and results are shown as means ± SD.


Stable isotopic values (δ2H, δ13C, and δ15N) were available from claws of 98 individuals. All 3 elements showed considerable variation in all 3 isotopes (Table 1). Mean isotope ratios were −37.9 ± 12.4‰ for δ2H, −21.6 ± 0.6‰ for δ13C, and 7.3 ± 0.9‰ for δ15N. Correlations among isotope ratios were r = −0.09 (P = 0.37) for δ2H and δ13C, r = −0.14 (P = 0.17) for δ2H and δ15N, and r = 0.53 (P < 0.001) for δ13C and δ15N. Of those 98 individuals with isotope data, we were unable to determine sex for 1 individual. There was a nearly even number of males and females in our sample (n = 47 females, n = 50 males). Males and females did not differ in δ2H (t95 = −0.02, P = 0.98), δ13C (t95 = −0.98, P = 0.33), or δ15N (t95 = −0.18, P = 0.86). Mean wing length was 113.5 ± 2.8 mm and did not differ between males and females (males: 113.4 ± 2.7, females: 113.7 ± 2.8; t95 = −0.46, P = 0.65). Wing length was not correlated with δ2H (r = −0.06, P = 0.59), δ13C (r = 0.16, P = 0.12), or δ15N (r = 0.16, P = 0.12). Mean tail length was 36.2 ± 2.3 mm and did not differ between males and females (males: 35.8 ± 1.8, females: 36.5 ± 2.7; t95 = −1.53, P = 0.13). Tail length was not correlated with δ2H (r = 0.00, P = 0.99) or δ13C (r = −0.07, P = 0.49), but it was negatively correlated with δ15N (r = −0.23, P = −0.02).


Sex, age, morphometrics (mm), and stable isotope values (‰) for 98 Vaux's Swift claws from a roost site on Vancouver Island, British Columbia, Canada.


When using the full dataset, 11 of 30 clustering validity indices (37%) proposed 2 as the optimal number of clusters in the k-means clustering procedure. The next highest-ranked numbers of clusters were 3 and 4, which received support from 6 (20%) and 5 (17%) clustering indices, respectively. Three was deemed the optimal number of clusters by the majority (10, or 33%) of indices in the hierarchical agglomerative procedure, but a large proportion of indices (9, or 30%) also supported 2 clusters. The only alternative clustering that received support was a 10-cluster solution with support from 5 (17%) indices. Results of the k-means clustering procedure were similar when outliers were removed, with 2 deemed the optimal number of clusters by the majority (9, or 30%) of indices. For the hierarchical agglomerative procedure, 2 was identified as the optimal number (8, or 27% of indices) and 3 received the second-most support (5, or 17% of indices). Overall, these results suggest that the isotope data grouped most naturally into 2 or 3 clusters, and thus we used the full dataset for the remainder of the analyses. Although the selection of the optimal number of clusters was not unanimous among indices, similar results are obtained from simulated datasets even when distinct, non-overlapping clusters are used (Milligan and Cooper 1985, Charrad et al. 2014). Furthermore, according to the BIC, the model-based clustering procedure suggested that the optimal number of clusters was 2 (Table 2).


Bayesian Information Criterion (BIC) from model-based hierarchical clustering procedure using the package “mclust” in R. BIC values are shown for 1–10 clusters for δ2H, δ13C, and δ15N isotopes from Vaux's Swift claws.


We examined the classification of individual claws into 2 or 3 clusters using the results from k-means clustering or hierarchical agglomerative clustering, respectively. The clusters were significantly different from one another for all 3 isotope ratios simultaneously when partitioned into 2 (MANOVA, F = 52.5, df = 3 and 93, P < 0.001) or 3 clusters (F = 36.4, df = 3 and 93, P < 0.001). The 2-cluster solution resulted from a split between individuals with higher δ15N and δ13C ratios and those with lower δ15N and δ13C ratios, with little influence of δ2H on clustering (Table 3). The 3-cluster solution suggested an additional division of the individuals with low δ15N and δ13C ratios into those with higher or lower δ2H ratio (Figures 1 and 2). There was no association between sex and cluster membership when the claws were grouped in 2 (i0010-5422-117-4-670-ilm01.gif, P = 1) or 3 (i0010-5422-117-4-670-ilm02.gif, P = 0.42) clusters. There was no association between wing length and cluster membership for 2 (t96 = 0.62, P = 0.54) or 3 (ANOVA, F = 0.70, df = 2 and 95, P = 0.50) clusters. Similarly, there was no association between tail length and cluster membership for 2 (t96 = −0.48, P = 0.64) or 3 (ANOVA, F = 1.60, df = 2 and 95, P = 0.21) clusters.


Mean (± SD) stable isotope ratios of Vaux's Swift claws partitioned into 2 or 3 clusters based on k-means or hierarchical agglomerative clustering, respectively. Cluster numbers (1–3) corresponds to those shown in Figures 1 and 2.



Variation in claw stable isotopes of hydrogen (δ2H: A, B), carbon (δ13C: C, D), and nitrogen (δ15N: E, F) in Vaux's Swifts from a roost site on Vancouver Island, British Columbia, Canada. Claws were clustered into 2 (A, C, E) or 3 (B, D, F) clusters. The median, interquartile range (IQR), and outliers (>1.5 × IQR) are shown.



Clustering of stable isotope ratios of Vaux's Swift claws from a roost site on Vancouver Island, British Columbia, Canada, into (A) 2 clusters or (B) 3 clusters based on k-means or hierarchical agglomerative clustering, respectively.



Patterns of migratory connectivity in long-distance migratory birds can have important implications for population dynamics, evolutionary processes, and effective conservation strategies (Webster and Marra 2005). Using multiple stable isotopes (δ13C, δ15N, and δ2H) from claw samples of Vaux's Swifts at a spring migratory roost, our goal was not to assign individuals to a particular overwintering locality or localities, which would have been exceedingly difficult given the poor resolution in isoscapes of Mexico and Central America (Bowen et al. 2005) and our lack of known-origin tissues from these areas. Rather, we asked whether Vaux's Swifts at a single migratory roost likely came from a single winter location, which would indicate stronger migratory connectivity, or multiple winter locations, which would indicate weaker migratory connectivity. We found evidence in support of 2 or possibly 3 broad isotopic clusters on the overwintering grounds from the single migratory roost on Vancouver Island. While alternative explanations for our observations are possible (see below), we tentatively interpret our findings as likely demonstrating differences in overwintering locations used by Vaux's Swifts in our sample.

Understanding patterns of migratory connectivity in small, long-distance migratory birds has been exceedingly challenging, but the use of stable isotope analysis over the past 2 decades has revolutionized our ability to establish patterns of connectivity (Hobson 2005). Generally, this approach relies on the assignment of tissues to spatially explicit isoscapes generated from precipitation isoscapes (Bowen and West 2008) or known-origin individuals sampled across their range (e.g., Hobson et al. 2009b, 2012b). Recently, these methods have been enhanced through the use of Bayesian statistical methods and GIS-based models of precipitation isoscapes, as well as additional information such as band recoveries, to improve assignments by creating probabilistic regions of origin (Hobson et al. 2009a, 2009c, Van Wilgenburg and Hobson 2011). However, most studies have been focused on assigning breeding origins to winter-captured individuals, rather than vice versa, because breeding-ground isoscapes, particularly in North America, are better delineated than wintering-ground isoscapes (Bowen et al. 2005). This poses a problem for assigning winter origins to individuals sampled within breeding or migratory populations, as in our study. Furthermore, the particular tissue chosen for sampling must reflect the area of origin that is of interest to the researcher. This too poses difficulties for assigning winter origins because many North American bird species, including Vaux's Swifts, molt their feathers on or near the breeding grounds (Pyle 1997; but see, e.g., Kelly et al. 2008, Quinlan and Green 2011). Thus, we chose to sample claws because they reflect conditions several weeks prior to sampling, a time during which the birds should have been present on the wintering grounds (Bearhop et al. 2004, Reudink et al. 2009a, 2009b).

Despite the relatively high variation in all 3 isotopic signatures (δ2H range: 66‰; δ15N range: 5.8‰; δ13C range: 3.5‰; Table 1), our analysis provides support for Vaux's Swift claws fitting into 2 or 3 isotopically distinct clusters (Figure 2), with strongest support for 2 clusters when outliers were removed. When partitioned into 2 clusters, the claws separated into those with higher δ13C and δ15N and those with lower δ13C and δ15N. Variation in δ13C and δ15N is most likely related to broad-scale habitat differences, given that food-web δ13C signatures are strongly related to the distribution of C3 and C4 vegetation (Lajtha and Marshall 1994), and food-web δ15N signatures are associated with soil exposure and climate (Nadelhoffer and Fry 1994). Although we cannot exclude the possibility that birds were separated, in part, on the basis of dietary shifts and niche specialization, broad-scale differences in δ13C, δ15N, and δ2H appear to be consistent with Vaux's Swift using habitats that follow climatic–moisture gradients. Regardless of the clustering algorithm, birds associated with cluster 2 were enriched in 13C by ~0.9‰ over individuals from cluster 1. Simultaneously, birds associated with cluster 2 were also enriched in 15N by ~1.6 ‰ compared with birds from cluster 1 (Table 3). Assuming that δ13C follows gradients similar to those reported elsewhere (e.g., Marra et al. 1998), these data appear to suggest that birds in cluster 2 originated from hotter and drier habitats than birds in cluster 1.

The agglomerative clustering approach suggested that birds could be further partitioned into a third cluster on the basis of splitting claws that had both low δ13C and δ15N values, based on those with higher (cluster 3) and lower (cluster 1) δ2H values (see Table 3). Latitudinal and altitudinal gradients in δ2H are well established and among the strongest of isotopic gradients (Hobson 2005), and our results thus suggest that individuals that had low δ13C and δ15N were likely further segregated along a latitudinal or, possibly, an altitudinal gradient. Although a species-specific δ2H claw-tissue isoscape is lacking for the Vaux's Swift overwintering range, a δ2H feather isoscape developed for House Sparrows (Passer domesticus) in Mexico showed more positive δ2H signatures in the northeast of the country and more negative signatures in the south and in high-altitude regions (Hobson et al. 2009b). Overall, our results indicate that individuals from the migratory population of Vaux's Swifts on Vancouver Island may have originated from a small number of different latitudes and/or habitats.

Breeding populations that are more strongly connected to specific wintering areas, particularly those experiencing degradation, may be most vulnerable to population declines (Dolman and Sutherland 1995, Webster and Marra 2005), although a recent study found that the strength of connectivity in breeding populations of Barn Swallows (Hirundo rustica) in North America was unrelated to their population trend (García-Pérez and Hobson 2014). Our data suggest that further research to estimate the strength of connectivity across breeding populations of Vaux's Swifts is now warranted and might help explain patterns of decline if extrinsic markers could be deployed along a gradient of declining versus increasing populations (sensu Fraser et al. 2012, Hobson et al. 2015).

We also asked whether individuals in our sample showed evidence of segregation based on sex or morphology during the overwintering period, as has been observed in several other passerines, such as Hooded Warblers (Setophaga citrina; Morton 1990), Prairie Warblers (S. discolor; Latta and Faaborg 2001), and American Redstarts (S. ruticilla), which segregate on the basis of habitat; and White-throated Sparrows (Zonotrichia albicollis; Mazerolle and Hobson 2007) and Hermit Thrushes (Catharus guttatus; Stouffer and Dwyer 2003), which segregate on the basis of latitude. For example, American Redstarts exhibit marked age- and sex-biased habitat segregation, with older males disproportionately inhabiting high-quality mangrove forests (Parrish and Sherry 1994, Marra 2000)—a characteristic also easily discerned via stable isotope analysis of muscle (Marra et al. 1998), blood (Norris et al. 2004), and claw samples (Reudink et al. 2009a, 2009b). Furthermore, larger females were more likely to inhabit high-quality mangrove territories because of dominance-mediated habitat segregation (Marra 2000). Latitudinal segregation, on the other hand, may be related to trade-offs between body size or wing size/shape and migration distance (García Peiró 2003, Mazerolle and Hobson 2007). Our results suggest no evidence for sex- or morphology-based segregation, at least when considering wing and tail length, and indeed there was no evidence of differences in wing size between males and females, which suggests that male and female Vaux's Swifts likely share a similar overwintering ecology and, thus, may be equally vulnerable to stochastic events or habitat loss.

The utility of triple-isotope isoscapes for assigning origins to South American and African wintering birds has recently been demonstrated (Hobson et al. 2012b, García-Pérez and Hobson 2014), and our results indicate that the approach has potential for identifying Mexican and Central American wintering clusters as well. Taken together, our results highlight the need for additional research on the overwintering ecology and behavior of this poorly studied long-distance migrant and the need for calibrating tissue-specific isoscapes to better assign Nearctic–Neotropical migratory birds to wintering localities. Our data suggest that birds migrating through Vancouver Island likely overwinter in 2 or 3 isotopically similar regions or habitat types, and these predictions should now be validated. Wintering-ground sampling would facilitate nominal assignment (sensu Wunder 2012) to 2 or 3 spatially delineated geographic regions or, alternatively, might facilitate spatially explicit, multivariate assignment approaches (e.g., García-Pérez and Hobson 2014, Veen et al. 2014).

Recent work has highlighted the potential for keratinous tissues to vary isotopically with diet (Fraser et al. 2011, Soto et al. 2013, Voigt et al. 2013, 2015) and microhabitat (Fraser et al. 2011). Thus, although we interpret our results primarily in the context of migratory connectivity, we cannot preclude the possibility that multivariate differences in the isotopic composition of claws could stem from variation in microhabitat, behavior, or dietary preferences among individuals. Given the high motility of Vaux's Swifts while foraging on the wing (Bull and Collins 2007), it is plausible that our results could also stem from variation in the degree of foraging over aquatic versus terrestrial habitats, or over forested versus grassland or agricultural habitats. Like other aerial insectivores, Vaux's Swifts forage broadly over forest canopy, meadows, and open water (Bull and Collins 2007), and their diets likely reflect the composition of aerial plankton from major insect emergences from both the aquatic and terrestrial environments. Within their wintering grounds, these major habitat types should be relatively isotopically distinct, owing to major isotopic differences in δ13C and δ15N between forest canopy, which primarily uses a C3 phytosynthetic pathway, and grasslands–pastures, which are dominated by C4 plants (Still and Powell 2010). It is important to note that the distribution of forest versus grassland, pasture, and agricultural lands displays a great deal of geographic structure (Ellis and Ramankutty 2008, Dixon et al. 2014) and, thus, it is probable that isotopic differences due to differences in habitat selection would also covary with any spatial segregation. Regardless of whether the patterns we observed are attributable solely to differences in migratory connectivity or to variation in local habitat selection, our results point to relatively limited variation in overwintering niches of Vaux's Swifts. Thus, a better understanding of the overwintering ecology of Vaux's Swifts is necessary to inform conservation. Wintering-ground work on habitat use and diets of Vaux's Swifts or multi-isotope assays on claws from birds fitted with miniaturized GPS tags (Hallworth and Marra 2015) would shed light on the mechanistic explanations for our observations.

Like most migratory aerial insectivores (Nebel et al. 2010), Vaux's Swifts are in decline. Given the unique roosting ecology of Vaux's Swifts, they are particularly susceptible to stochastic events as exemplified by this 1 mass mortality, which may represent a loss of about 1.5–2.7% of the British Columbia population of Vaux's Swifts in a single evening. This exceptional vulnerability, coupled with increasing habitat loss and alteration in the tropics (Hansen et al. 2013), indicates a crucial need for further research to determine patterns of migratory connectivity and identify critical winter and migratory roost sites for future conservation planning.


We thank S. Joly for assistance in data collection.

Funding statement: Funding was provided by a Natural Sciences and Engineering Research Council of Canada Discovery Grant, a Thompson Rivers University Internal Research Fund Award to M.W.R., and a National Science Foundation grant to P.P.M.

Ethics statement: Birds were collected under a Canadian salvage permit (BC-SA-0049-12).


  1. D. Arlt M. Lowand T. Pärt (2013). Effect of geolocators on migration and subsequent breeding performance of a long-distance passerine migrant. PLOS One 8:e82316. Google Scholar

  2. S. Bearhop G. M. Hilton S. C. Votierand S. Waldron (2004). Stable isotope ratios indicate that body condition in migrating passerines is influenced by winter habitat. Proceedings of the Royal Society of London, Series B 271 (Supplement 4):S215–S218. Google Scholar

  3. L. K. Blight K. A. Hobson T. K. Kyserand P. Arcese (2015). Changing gull diet in a changing world: A 150-year stable isotope (δ13C, δ15N) record from feathers collected in the Pacific Northwest of North America. Global Change Biology 21:1497–1507. Google Scholar

  4. C. I. Bocetti D. M. Donnerand H. F. Mayfield (2014). Kirtland's Warbler (Setophaga kirtlandii). In Birds of North America Online ( A. Poole Editor). Cornell Lab of Ornithology, Ithaca, NY, USA. Google Scholar

  5. G. J. Bowen L. I. Wassenaarand K. A. Hobson (2005). Global application of stable hydrogen and oxygen isotopes to wildlife forensics. Oecologia 143:337–348. Google Scholar

  6. G. J. Bowenand J. B. West (2008). Isotope landscapes for terrestrial migration research. In Tracking Animal Migration with Stable Isotopes ( K. A. Hobsonand L. I. Wassenaar Editors). Academic Press, Amsterdam, The Netherlands. Google Scholar

  7. E. S. Bridge J. F. Kelly A. Contina R. M. Gabrielson R. B. MacCurdyand D. W. Winkler (2013). Advances in tracking small migratory birds: A technical review of light-level geolocation. Journal of Field Ornithology 84:121–137. Google Scholar

  8. E. L. Bulland C. T. Collins (2007). Vaux's Swift (Chaetura vauxi). In Birds of North America Online ( A. Poole Editor). Cornell Lab of Ornithology, Ithaca, NY, USA. Google Scholar

  9. T. E. Cerling J. M. Harris B. J. MacFadden M. G. Leakey J. Quade V. Eisenmannand J. R. Ehleringer (1997). Global vegetation change through the Miocene/Pliocene boundary. Nature 389:153–158. Google Scholar

  10. A. A. Chabot K. A. Hobson S. L. Van Wilgenburg G. J. McQuatand S. C. Lougheed (2012). Advances in linking wintering migrant birds to their breeding-ground origins using combined analyses of genetic and stable isotope markers. PLOS One 7:e43627. Google Scholar

  11. M. Charrad N. Ghazzali V. Boiteauand A. Niknafs (2014). NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software 61:1–36. Google Scholar

  12. J. M. Craine A. J. Elmore M. P. M. Aidar M. Bustamante T. E. Dawson E. A. Hobbie A. Kahmen M. C. Mack K. K. McLauchlan A. Michelsen G. B. Nardoto et al . (2009). Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytologist 183:980–992. Google Scholar

  13. A. P. Dixon D. Faber-Langendoen C. Josse J. Morrisonand C. J. Loucks (2014). Distribution mapping of world grassland types. Journal of Biogeography 41:2003–2019. Google Scholar

  14. P. M. Dolmanand W. J. Sutherland (1995). The response of bird populations to habitat loss. Ibis 137:S38–S46. Google Scholar

  15. E. C. Ellisand N. Ramankutty (2008). Putting people in the map: Anthropogenic biomes of the world. Frontiers in Ecology and the Environment 6:439–447. Google Scholar

  16. Environment Canada (2014). North American Breeding Bird Survey—Canadian Trends Website, Data-version 2012. Environment Canada, Gatineau, Quebec, Canada. Google Scholar

  17. C. Fraley A. E. Raftery T. B. Murphyand L. Scrucca (2012). mclust version 4 for R: Normal mixture modeling for model-based clustering, classification, and density estimation. Technical Report 597. Department of Statistics, University of Washington, Seattle, WA, USA. Google Scholar

  18. K. C. Fraser T. K. Kyser R. J. Robertsonand L. M. Ratcliffe (2008). Season patterns in hydrogen isotopes of claws from breeding wood-warblers (Parulidae): Utility for estimating migratory origins. Avian Conservation and Ecology 3(1):2. Google Scholar

  19. K. C. Fraser E. A. McKinnon A. W. Diamondand L. Chavarría (2011). The influence of microhabitat, moisture and diet on stable-hydrogen isotope variation in a Neotropical avian food web. Journal of Tropical Ecology 27:563–572. Google Scholar

  20. K. C. Fraser B. J. M. Stutchbury C. Silverio P. M. Kramer J. Barrow D. Newstead N. Mickle B. F. Cousens J. C. Lee D. M. Morrison T. Shaheen et al . (2012). Continent-wide tracking to determine migratory connectivity and tropical habitat associations of a declining aerial insectivore. Proceedings of the Royal Society of London, Series B:rspb20122207. Google Scholar

  21. S. D. Fretwell (1972). Populations in a Seasonal Environment. Princeton University Press, Princeton, NJ, USA. Google Scholar

  22. B. Fry (2006). Stable Isotope Ecology. Springer, New York, NY, USA. Google Scholar

  23. I. García Peiró (2003). Intraspecific variation in the wing shape of the long-distance migrant Reed Warbler Acrocephalus scirpaceus: Effects of age and distance of migration. Ardeola 50:31–37. Google Scholar

  24. B. García-Pérezand K. A. Hobson (2014). A multi-isotope (δ2H, δ13C, δ15N) approach to establishing migratory connectivity of Barn Swallow (Hirundo rustica). Ecosphere 5:art21. Google Scholar

  25. S. Hahn B. J. Hoye H. Korthalsand M. Klaassen (2012). From food to offspring down: Tissue-specific discrimination and turn-over of stable isotopes in herbivorous waterbirds and other avian foraging guilds. PLOS One 7:e30242. Google Scholar

  26. M. T. Hallworthand P. P. Marra (2015). Miniaturized GPS tags identify non-breeding territories of a small breeding migratory songbird. Scientific Reports 5:art11069. Google Scholar

  27. S. Hahn D. Dimitrov S. Rehse E. Yohannesand L. Jenni (2014). Avian claw morphometry and growth determine the temporal pattern of archived stable isotopes. Journal of Avian Biology 45:202–207. Google Scholar

  28. M. C. Hansen P. V. Potapov R. Moore M. Hancher S. A. Turubanova A. Tyukavina D. Thau S. V. Stehman S. J. Goetz T. R. Loveland A. Kommareddy et al . (2013). High-resolution global maps of 21st-century forest cover change. Science 342:850–853. Google Scholar

  29. K. A. Hobson (2005). Stable isotopes and the determination of avian migratory connectivity and seasonal interactions. The Auk 122:1037–1048. Google Scholar

  30. K. A. Hobson (2011). Isotopic ornithology: A perspective. Journal of Ornithology 152 (Supplement 1):S49–S66. Google Scholar

  31. K. A. Hobson R. Greenberg S. L. Van Wilgenburgand C. Mettke-Hofmann (2010). Migratory connectivity in the Rusty Blackbird: Isotopic evidence from feathers of historical and contemporary specimens. The Condor 112:778–788. Google Scholar

  32. K. A. Hobson K. J. Kardynal S. L. Van Wilgenburg G. Albrecht A. Salvadori M. D. Cadman F. Liechtiand J. W. Fox (2015). A continent-wide migratory divide in North American breeding Barn Swallows (Hirundo rustica). PLOS One 10:e0129340. Google Scholar

  33. K. A. Hobson H. Lormée S. L. Van Wilgenburg L. I. Wassenaarand J. M. Boutin (2009a). Stable isotopes (δD) delineate the origins and migratory connectivity of harvested animals: The case of European woodpigeons. Journal of Applied Ecology 46:572–581. Google Scholar

  34. K. A. Hobson S. L. Van Wilgenburg K. Larsonand L. I. Wassenaar (2009b). A feather hydrogen isoscape for Mexico. Journal of Geochemical Exploration 102:167–174. Google Scholar

  35. K. A. Hobson S. L. Van Wilgenburg J. Faaborg J. D. Toms C. Rengifo A. Llanes Sosa Y Aubryand R. Brito Aguilar (2014). Connecting breeding and wintering grounds of Neotropical migrant songbirds using stable hydrogen isotopes: A call for an isotopic atlas of migratory connectivity. Journal of Field Ornithology 85:237–257. Google Scholar

  36. K. A. Hobson S. L. Van Wilgenburg L. I. Wassenaarand K. Larson (2012a). Linking hydrogen (δ2H) isotopes in feathers and precipitation: Sources of variance and consequences for assignment to isoscapes. PLOS One 7:e35137. Google Scholar

  37. K. A. Hobson S. L. Van Wilgenburg L. I. Wassenaar R. L. Powell C. J. Stilland J. M. Craine (2012b). A multi-isotope (δ13C, δ15N, δ2H) feather isoscape to assign Afrotropical migrant birds to origins. Ecosphere 3:art44. Google Scholar

  38. K. A. Hobson M. B. Wunder S. L. Van Wilgenburg R. G. Clarkand L. I. Wassenaar (2009c). A method for investigating population declines of migratory birds using stable isotopes: Origins of harvested Lesser Scaup in North America. PLOS One 4:e7915. Google Scholar

  39. J. F. Kelly M. J. Johnson S. Langridgeand M. Whitfield (2008). Efficacy of stable isotope ratios in assigning endangered migrants to breeding and wintering sites. Ecological Applications 18:568–576. Google Scholar

  40. K. Lajthaand J. D. Marshall (1994). Sources of variation in isotopic composition in plants. In Stable Isotopes in Ecological and Environmental Sciences ( K. Lathjaand J. D. Marshall Editors). Oxford University Press, London, UK. pp. 1–21. Google Scholar

  41. S. C. Lattaand J. Faaborg (2001). Winter site fidelity of Prairie Warblers in the Dominican Republic. The Condor 103:455–468. Google Scholar

  42. J. B. MacQueen (1967). Some methods for classication and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1:281–297. Google Scholar

  43. P. P. Marra (2000). The role of behavioral dominance in structuring patterns of habitat occupancy in a migrant bird during the nonbreeding season. Behavioral Ecology 11:299–308. Google Scholar

  44. P. P. Marra K. A. Hobsonand R. T. Holmes (1998). Linking winter and summer events in a migratory bird by using stable-carbon isotopes. Science 282:1884–1886. Google Scholar

  45. P. P. Marra C. E. Studdsand M. Webster (2010). Migratory connectivity. In Encyclopedia of Animal Behavior, vol. 2 ( M. D. Breedand J. Moore Editors). Academic Press, Oxford, UK. pp. 455–461. Google Scholar

  46. D. F. Mazerolleand K. A. Hobson (2005). Estimating origins of short-distance migrant songbirds in North America: Contrasting inferences from hydrogen isotope measurements of feathers, claws, and blood. The Condor 107:280–288. Google Scholar

  47. D. F. Mazerolleand K. A. Hobson (2007). Patterns of differential migration in White-throated Sparrows evaluated with isotopic measurements of feathers. Canadian Journal of Zoology 85:413–420. Google Scholar

  48. G. W. Milliganand M. C. Cooper (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika 50:159–179. Google Scholar

  49. G. W. Milliganand M. C. Cooper (1988). A study of standardization of variables in cluster analysis. Journal of Classification 5:181–204. Google Scholar

  50. E. S. Morton (1990). Habitat segregation by sex in the Hooded Warbler: Experiments on proximate causation and discussion of its evolution. The American Naturalist 135:319–333. Google Scholar

  51. K. J. Nadelhofferand B. Fry (1994). Nitrogen isotope studies in forest ecosystems. In Stable Isotopes in Ecology and Environmental Science ( K. Lajthaand R. H. Michener Editors). Blackwell Scientific, Oxford, UK. pp. 22–44. Google Scholar

  52. S. Nebel A. Mills J. D. McCrackenand P. D. Taylor (2010). Declines of aerial insectivores in North America follow a geographic gradient. Avian Conservation and Ecology 5:1. Google Scholar

  53. J. J. Nocera J. M. Blais D. V. Beresford L. K. Finity C. Grooms L. E. Kimpe K. Kyser N. Michelutti M. W. Reudinkand J. P. Smol (2012). Historical pesticide applications coincided with an altered diet of aerially foraging insectivorous Chimney Swifts. Proceedings of the Royal Society of London, Series B 279:3114–3120. Google Scholar

  54. D. R. Norris (2005). Carry-over effects and habitat quality in migratory populations. Oikos 109:178–186. Google Scholar

  55. D. R. Norris P. P. Marra T. K. Kyser T. W. Sherryand L. M. Ratcliffe (2004). Tropical winter habitat limits reproductive success on the temperate breeding grounds in a migratory bird. Proceedings of the Royal Society of London, Series B 271:59–64. Google Scholar

  56. J. D. Parrishand T. W. Sherry (1994). Sexual habitat segregation by American Redstarts wintering in Jamaica: Importance of resource seasonality. The Auk 111:38–49. Google Scholar

  57. Partners in Flight Science Committee (2013). Population Estimates Database, version 2013. Google Scholar

  58. S. Pekarsky A. Angert B. Haese M. Werner K. A. Hobsonand R. Nathan (2015). Enriching the isotopic toolbox for migratory connectivity analysis: A new approach for migratory species breeding in remote or unexplored areas. Diversity and Distributions 21:416–427. Google Scholar

  59. D. M. Post (2002). Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83:703–718. Google Scholar

  60. N. Poupin C. Bos F. Mariotti J.-F. Huneau D. Toméand H. Fouillet (2011). The nature of the dietary protein impacts the tissue-to-diet 15N discrimination factors in laboratory rats. PLOS One 6:e28046. Google Scholar

  61. P. Pyle (1997). Identification Guide to North American Birds, part 1: Columbidae to Ploceidae. Slate Creek Press, Bolinas, CA, USA. Google Scholar

  62. S. P. Quinlanand D. J. Green (2011). Variation in deuterium (δD) signatures of Yellow Warbler Dendroica petechia feathers grown on breeding and wintering grounds. Journal of Ornithology 152:93–101. Google Scholar

  63. R Development Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Google Scholar

  64. I. I. Ratikainen J. A. Gill T. G. Gunnarsson W. J. Sutherlandand H. Kokko (2008). When density dependence is not instantaneous: Theoretical developments and management implications. Ecology Letters 11:184–198. Google Scholar

  65. M. W. Reudink P. P. Marra T. K. Kyser P. T. Boag K. M. Langinand L. M. Ratcliffe (2009a). Non-breeding season events influence sexual selection in a long-distance migratory bird. Proceedings of the Royal Society of London, Series B 276:1619–1626. Google Scholar

  66. M. W. Reudink C. E. Studds P. P. Marra T. K. Kyserand L. M. Ratcliffe (2009b). Plumage brightness predicts non-breeding season territory quality in a long-distance migratory songbird, the American Redstart Setophaga ruticilla. Journal of Avian Biology 40:34–41. Google Scholar

  67. S. M. Rockwell C. I. Bocettiand P. P. Marra (2012). Carry-over effects of winter climate on spring arrival date and reproductive success in an endangered migratory bird, Kirtland's Warbler (Setophaga kirtlandii). The Auk 129:744–752. Google Scholar

  68. K. C. Ruegg E. C. Anderson K. L. Paxton V. Apkenas S. Lao R. B. Siegel D. F. DeSante F. Mooreand T. B. Smith (2014). Mapping migration in a songbird using high-resolution genetic markers. Molecular Ecology 23:5726–5736. Google Scholar

  69. C. W. Rundel M. B. Wunder A. H. Alvarado K. C. Ruegg R. Harrigan A. Schuh J. F. Kelly R. B. Siegel D. F. DeSante T. B. Smithand J. Novembre (2013). Novel statistical methods for integrating genetic and stable isotope data to infer individual-level migratory connectivity. Molecular Ecology 22:4163–4176. Google Scholar

  70. J. C. Sealy N. J. van der Merwe J. A. Lee-Thorpand J. L. Lanham (1987). Nitrogen isotopic ecology in southern Africa: Implications for environmental and dietary tracing. Geochimica et Cosmochimica Acta 51:2707–2717. Google Scholar

  71. G. A. F. Seber (1984). Multivariate Observations. Wiley, New York, NY, USA. Google Scholar

  72. J. Sheehy C. M. Taylorand D. R. Norris (2011). The importance of stopover habitat for developing effective conservation strategies for migratory animals. Journal of Ornithology 152 (Supplement 1):S161–S168. Google Scholar

  73. T. S. Sillett R. T. Holmesand T. W. Sherry (2000). Impacts of a global climate cycle on the population dynamics of a migratory songbird. Science 288:2040–2042. Google Scholar

  74. A. Small (1994). California Birds: Their Status and Distribution. Ibis, Temecula, CA, USA. Google Scholar

  75. D. X. Soto L. I. Wassenaarand K. A. Hobson (2013). Stable hydrogen and oxygen isotopes in aquatic food webs are tracers of diet and provenance. Functional Ecology 27:535–543. Google Scholar

  76. C. J. Stilland R. L. Powell (2010). Continental-scale distributions of vegetation stable carbon isotope ratios. In Isoscapes: Understanding Movements, Pattern and Process on Earth through Isotope Mapping ( J. B. West G. J. Bowen T. E. Dawsonand K. P. Tu Editors). Springer, New York, NY, USA. pp. 179–194. Google Scholar

  77. P. C. Stoufferand G. M. Dwyer (2003). Sex-biased winter distribution and timing of migration of Hermit Thrushes (Catharus guttatus) in eastern North America. The Auk 120:836–847. Google Scholar

  78. C. E. Studds T. K. Kyserand P. P. Marra (2008). Natal dispersal driven by environmental conditions interacting across the annual cycle of a migratory songbird. Proceedings of the National Academy of Sciences USA 105:2929–2933. Google Scholar

  79. B. J. Stutchbury S. A. Tarof T. Done E. Gow P. M. Kramer J. Tautin J. W. Foxand V. Afanasyev (2009). Tracking long-distance songbird migration by using geolocators. Science 323:896. Google Scholar

  80. K. Summersand M. Gebauer (1995). Status of the Vaux's Swift in British Columbia. Wildlife Working Report WR-67. Ministry of Environment, Lands and Parks, Victoria, British Columbia, Canada. Google Scholar

  81. G. J. Székelyand M. L. Rizzo (2005). Hierarchical clustering via joint between–within distances: Extending Ward's minimum variance method. Journal of Classification 22:151–183. Google Scholar

  82. S. L. Van Wilgenburgand K. A. Hobson (2011). Combining stable-isotope (δD) and band recovery data to improve probabilistic assignment of migratory birds to origin. Ecological Applications 21:1340–1351. Google Scholar

  83. K. Varmuzaand P. Filzmoser (2009). Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, USA. Google Scholar

  84. T. Veen M. B. Hjernquist S. L. Van Wilgenburg K. A. Hobson E. Folmer L. Fontand M. Klaassen (2014). Identifying the wintering grounds of hybrid flycatchers using a multi-isotope (δ2H, δ13C, δ15N) assignment approach. PLOS One 9:e98075. Google Scholar

  85. C. C. Voigt D. Lehmannand S. Greif (2015). Stable isotope ratios of hydrogen separate mammals of aquatic and terrestrial food webs. Methods in Ecology and Evolution 6. In press. Google Scholar

  86. C. C. Voigt K. Schneebergerand A. Luckner (2013). Ecological and dietary correlates of stable hydrogen isotope ratios in fur and body water of syntopic tropical bats. Ecology 94:346–355. Google Scholar

  87. J. H. Ward Jr (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58:236–244. Google Scholar

  88. L. I. Wassenaarand K. A. Hobson (2000). Stable-carbon and hydrogen isotope ratios reveal breeding origins of Red-winged Blackbirds. Ecological Applications 10:911–916. Google Scholar

  89. M. S. Websterand P. P. Marra (2005). The importance of understanding migratory connectivity and seasonal interactions. In Birds of Two Worlds: The Ecology and Evolution of Migration ( R. Greenbergand P. P. Marra Editors). Johns Hopkins University Press, Baltimore, MD, USA. pp. 199–209. Google Scholar

  90. M. S. Webster P. P. Marra S. M. Haig S. Benschand R. T. Holmes (2002). Links between worlds: Unraveling migratory connectivity. Trends in Ecology & Evolution 17:76–83. Google Scholar

  91. S. Wilson S. L. LaDeau A. P. Tøttrupand P. P. Marra (2011). Range-wide effects of breeding- and nonbreeding-season climate on the abundance of a Neotropical migrant songbird. Ecology 92:1789–1798. Google Scholar

  92. M. B. Wunder (2012). Determining geographic patterns of migration and dispersal using stable isotopes in keratins. Journal of Mammalogy 9:360–367. Google Scholar

  93. J. M. Wunderle Jr D. Currie E. H. Helmer D. N. Ewert J. D. White T. S. Ruzycki B. Parresoland C. Kwit (2010). Kirtland's Warblers in anthropogenically disturbed early-successional habitats on Eleuthera, the Bahamas. The Condor 112:123–137. Google Scholar

© 2015 Cooper Ornithological Society.
Matthew W. Reudink, Steven L. Van Wilgenburg, Lauren S. Steele, Andrew G. Pillar, Peter P. Marra, and Ann E. McKellar "Patterns of migratory connectivity in Vaux's Swifts at a northern migratory roost: A multi-isotope approach," The Condor 117(4), (28 October 2015).
Received: 11 May 2015; Accepted: 1 August 2015; Published: 28 October 2015

Back to Top