DOI: 10.5061/dryad.2fqz612nq
title: Decoupling pioneering traits from latitudinal patterns in a North American bird experiencing a southward range shift

description: Eco-geographic rules describe spatial patterns in biological trait
 variation and shed light on the drivers of such variation. In animals, a
 consensus is emerging that ‘pioneering’ traits may facilitate range shifts
 via a set of bold, aggressive, and stress-resilient traits. Many of these
 same traits are associated with more northern latitudes, and most range
 shifts in the northern hemisphere indicate northward movement. As a
 consequence, it is unclear whether pioneering traits are simply
 corollaries of existing latitudinal variation, or whether they override
 other well-trodden latitudinal patterning as a unique eco-geographic rule
 of phenotypic variation. The tree swallow (Tachycineta bicolor) is a
 songbird undergoing a southward range shift in the eastern United States,
 in direct opposition of the poleward movement seen in most other native
 species’ range shifts. Because this organic range shift countervails the
 typical direction of movement, this case study provides for unique
 ecological insights on organisms and their ability to thrive in our
 changing world. We sampled female birds across seven populations,
 quantifying behavioral, physiological, and morphological traits. We also
 used GIS and field data to quantify a core set of ecological factors with
 strong ties to these traits as well as female performance. Females at more
 southern expansion sites displayed higher maternal aggression, higher
 baseline corticosterone, and more pronounced elevation of corticosterone
 following a standardized stressor, contrary to otherwise largely conserved
 latitudinal patterning in these traits. Microhabitat variation explained
 some quantitative phenotypic variation, but the expansion and historic
 ranges did not differ in openness, distance to water, or breeding density.
 This countervailing range shift therefore suggests that pioneering traits
 are not simply corollaries of existing latitudinal variation, but rather,
 they may override other well-trodden latitudinal patterning as a unique
 eco-geographic rule of phenotypic variation.
description: <em>Study sites and environmental data</em>
 Methods were approved by Appalachian State University IACUC
 #13-15 and US Master Banding Permit #23563. All animals were handled in
 such a way to reduce stress and avoid physical harm. All adults were
 released in their home territory. We conducted fieldwork during May-June
 2015. Historical sites included Saukville, Wisconsin (43.382 N, 88.023 W),
 Long Point, Ontario (42.623 N, 80.465 W), and Wolfville, Nova Scotia
 (45.107 N, 64.378 W). Expansion sites included Bloomington, Indiana
 (39.142 N, 86.602 W); Ames, Iowa (42.073 N, 93.635 W); Davidson, North
 Carolina (35.438 N, 80.697 W); and Boone, North Carolina (36.196 N, 81.783
 W). We recorded GPS coordinates at each nestbox (Garmin GPSmap
 78s). Sites were categorized as either historical or
 expansion based on prior publications (Lee, 1993; Shutler et al., 2012),
 bolstered by personal communications with local researchers and data from
 the Bird Breeding Survey (BBS; 1967 to 2017)(Sauer et al., 2017).
 Historical sites have abundant tree swallow breeding for &gt;100 years
 (Winkler et al., 2020), whereas expansion sites have increased abundance
 since the 1960s. In the first 10 years of the BBS, Nova Scotia, Ontario,
 and Wisconsin (historic sites) reported an annual average (± SE) of 458 ±
 87 breeding tree swallows, whereas Indiana, Iowa and North Carolina
 (expansion sites) reported only 2 ± 1. In the most recent 10 years,
 breeding numbers have increased by 40-fold in expansion sites (Figure 2b;
 visualized at the state-/province-level in Figure S1a). Although BBS data
 surely underestimate abundance, these trends reflect site-specific data
 (Lee, 1993; Shutler et al., 2012). Notably, not all expansion sites are at
 the southernmost range edge (i.e. Tennessee, the Carolinas), and some
 expansion sites (Davidson and Boone, North Carolina) have more recent
 histories than others (Iowa, Indiana). All expansion sites are nonetheless
 beyond the historic core of the species distribution.
 We quantified three key ecological factors at each site, using
 measurements in the field as well as satellite data. The purpose of these
 data was two-fold: First, we sought to test whether the expansion and
 historic range differed in a core set of parameters with strong ties to
 tree swallow success. Second, we wanted to assess whether habitat
 variation might confound any range-related differences in traits. To
 achieve these goals, we characterized land use/land cover (LULC) using
 ArcGIS 10.2 (ESRI, Redlands, California). Analyses focused on a typical
 foraging range of 300m around each nestbox (McCarty and Winkler, 1999).
 LULC data were obtained from USGS National Land Cover Dataset (2011) or
 Gouvernement du Canada Land Use (2010), at 30x30m resolution. We selected
 LULC categories based on their relevance to tree swallows, which require
 open, wet habitat for aerial foraging of insect prey (Winkler et al.,
 2020). As such, we focused on the percent of land with open habitat
 (pasture, open water, or barren), and distance to water (streams, rivers,
 ponds, wetlands). In the field, we estimated conspecific density as the
 number of nestboxes within a 50m radius that were defended by other tree
 swallows; previous studies have linked density with aspects of aggressive
 behavior (Bentz et al., 2013).  <em>Adult
 capture, morphology, and blood sampling</em>
 Females were captured between 09:00–16:30h, during incubation or
 chick-rearing (expansion: 44 incubating and 48 provisioning females;
 historic: 44 incubating and 36 provisioning). For provisioning females,
 average chick age was 6.3 days post-hatch (95%CI 5.3–7.3 days). We started
 a timer as soon as we captured each bird and collected an initial blood
 sample (~40–80uL) from the wing vein. Samples that took &gt;3min were
 not used because Cort can elevate rapidly after handling (Schoech et al.,
 2013). We measured wing length, tail length, and body mass, but excluded
 wing data due to inadvertent methodological differences among sites. Tail
 length is correlated with structural size (Bourret and Garant, 2017;
 Hainstock et al., 2010) and affects flying ability (Norberg, 1990), an
 important feature for aerial insectivores. We recorded age as “second year
 of life” (SY, or yearling) or “after second year of life” (ASY, or
 &gt;1 year) using plumage coloration (Hussell, 1983). All birds were
 banded with an aluminum leg band, and we marked each female across the
 breast with a colored marker to facilitate identification in during
 behavioral assays (Whittingham and Dunn, 2001). After processing, we
 placed each bird in an opaque paper bag until 30 min post-capture, a
 standardized restraint protocol to measure Cort elevation, or ΔCort. We
 collected a second blood sample (~40–80uL) and released the bird shortly
 thereafter. <em>Assay of maternal
 aggression</em> Nest defense is a key component
 of adaptive maternal investment. We measured clutch and brood size and
 found no difference between expansion and historic sites (linear mixed
 model, LMM, with population as random effect, clutch: β = 0.38 ±0.32 SE,
 F<sub>1,85</sub>=1.46, p=0.23; brood: β = -0.09 ±0.43 SE,
 F<sub>1,77</sub>= 0.05, p=0.84), and we therefore focused on a
 behavioral aspect of maternal investment. Specifically, we assayed
 maternal aggression against a simulated nest predator ~24h after blood
 sampling, using an assay modified from (Winkler, 1992). Decoys were
 commercially-manufactured models of the American crow
 (<em>Corvus</em> <em>brachyrhynchos</em>), a
 widespread nest predator. We randomly rotated among six exemplars to limit
 psuedoreplication. Ahead of time, we affixed a decoy via wire, dangling
 from a ~0.7m pole. Trials began by visually identifying the female and
 then deploying the decoy alongside crow calls from the Cornell Lab of
 Ornithology. The observer quickly slid the pole onto the existing nestbox
 and pole hardware, rapidly suspending the decoy above the box in a
 semi-natural flight position. The observer retreated to ~40m and began the
 behavioral assay. For 5 min, we measured the number of dives towards the
 model, within 1m of the nestbox. In tree swallows, nest defense is a
 highly repeatable trait (Betini and Norris, 2012).
 <em>Hormone assays</em> We
 quantified plasma Cort levels using an enzyme immunoassay kit that has
 high accuracy and assay parallelism (Cayman ELISA #500655, see Rosvall et
 al., 2012). Briefly, we added 10uL plasma to 200uL ultrapure
 H<sub>2</sub>O, extracted 3x with ether, dried with N2, and
 reconstituted with 600uL assay buffer. Each plate contained up to 33
 samples in duplicate, an 8-point standard curve, blank, maximum binding,
 non-specific binding, and total activity controls, as well as 3 additional
 plasma pools used to calculate variability. Plates were balanced by site,
 breeding stage, and time point (baseline or 30-min). We read absorbance at
 412 nm and interpolated concentration using Gen5 software (v2.09, BioTek,
 Winooski, VT, USA). Inter-plate variability was 10.2%, and intra-plate
 ranged from 3.7–12.4% (mean: 7.7%). We did not calculate extraction
 efficiency at a sample-level; instead, we spiked a separate set of samples
 (n=10) with 20uL H3-Cort (~2500 CPM), extracted 3x, and found average
 efficiencies to be 98.1%. Values may therefore underestimate true Cort
 concentrations, though our data are typical for this species (e.g. Zimmer
 et al., 2019), suggesting this effect is minimal.
 <em>Data Analyses</em> Statistical
 analyses were performed in R (v. 3.4.3, R Core Team, 2017). We report
 results as mean ± one standard error, and α=0.05. This study included data
 on 172 birds and the habitat surrounding each bird’s nestbox; however, we
 did not collect all data from every bird, e.g. because not all birds could
 be captured or identified during behavioral observations, some could not
 be bled quickly enough, some did not have full LULC data. Thus, sample
 sizes vary from 97 to 147 birds (Table 1). To test
 whether ranges (expansion vs historical) differed in habitat parameters,
 we used a permutational multivariate analysis of variance (PERMANOVA)
 using the <em>adonis2</em> function in
 <em>vegan</em> (Oksanen et al., 2016). We selected PERMANOVA
 because this non-parametric test is insensitive to multicollearity, and we
 found moderate correlations among parameters
 (0.34&lt;|r|&lt;0.53). We visualized differences with a principal
 component analysis (PCA) using the <em>prcomp</em>
 function. We next tested how five putatively pioneering
 traits differ between historic and expansion ranges. A random effect of
 populations was included in all models. We performed separate analyses for
 each trait. LMM were used to analyze all traits, except for anti-predator
 aggression, which was over-dispersed count data better suited to a
 negative binomial generalized LMM. Models were run in
 <em>lme4</em> (Bates et al., 2015), and we tested residuals
 for normality with a Shapiro-Wilk test. Two variables required
 log-transformation to meet model assumptions: baseline Cort and ΔCort.
 There were 7 individuals (n=5 historic; n=2 expansion) that showed
 negative ΔCort values. We had no indication that they were disturbed by us
 or something else, but their responses are not the biological norm, and so
 we removed them from subsequent analyses under the assumption that they do
 not represent the typical stress-induced Cort elevation that we seek to
 understand. Two extreme outliers were detected for tail length (Grubb’s
 test, p&lt;0.05), and these points were removed prior to model
 comparison. For each trait, we ran a series of models,
 all of which contained range (historic vs. expansion) and all combinations
 of: stage (incubating vs. provisioning), a range*stage interaction, PC1 of
 the habitat parameters, age, and capture time, in addition to the
 intercept only (null) model. Capture time was unrelated to anti-predator
 aggression and was not included in those models. We used Akaike
 Information Criterion corrected for small sample sizes (AICc) to find the
 most appropriate model (Burnham and Anderson, 2002). Models within 2 ΔAICc
 of the top model are equally fit, and, when this occurred, we present the
 most parsimonious model. Model selection was conducted with maximum
 likelihood estimation, but variable significance of the top model was done
 using restricted maximum likelihood. Both marginal
 (R<sup>2</sup><sub>marg</sub>; variance explained
 by fixed effects) and conditional
 (R<sup>2</sup><sub>cond</sub>; variance explained
 by fixed and random effects) R<sup>2</sup> are estimated using
 <em>MuMIn</em> (Bartoń, 2019). See Supplementary Materials for
 greater detail on (a) how we accounted for spatial parameters, (b) how we
 evaluated range vs. latitude as predictors of trait variation, and (c) how
 we used a heterogeneity analysis to assess the degree of
 population-specificity of any range-related trait differences.
description: Excel