Variation in selective regimes drives intraspecific variation in life-history traits and migratory behaviour along an elevational gradient
Item Type Article
Authors Lundblad, Carl G; Conway, Courtney J
Citation Lundblad, CG, Conway, CJ. Variation in selective regimes drives intraspecific variation in lifehistory traits and migratory behaviour along an elevational gradient. J Anim Ecol. 2020; 89: 397– 411. https://doi.org/10.1111/1365-2656.13134
DOI 10.1111/1365-2656.13134
Publisher WILEY
Journal JOURNAL OF ANIMAL ECOLOGY
Rights © 2019 The Authors. Journal of Animal Ecology © 2019 British Ecological Society.
Download date 01/10/2021 08:54:14
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Version Final accepted manuscript
Link to Item http://hdl.handle.net/10150/638070 Variation in Selective Regimes Drives Intraspecific Variation in Life History Traits and Migratory Behavior along an Elevational Gradient
CARL G. LUNDBLAD1,2, Arizona Cooperative Fish and Wildlife Research Unit, School of
Natural Resources and the Environment, 325 BioSciences East, University of
Arizona,
Tucson, AZ 85721, USA
COURTNEY J. CONWAY, U.S. Geological Survey, Idaho Cooperative Fish and Wildlife
Research Unit, 875 Perimeter Drive, University of Idaho, Moscow, ID 83844,
USA
1E-mail: [email protected]
2Current affiliation: Idaho Cooperative Fish and Wildlife Research Unit, 875 Perimeter
Drive, University of Idaho, Moscow, Idaho 83843, USA
1 ABSTRACT
Comparative studies, across and within taxa, have made important contributions
to our understanding of the evolutionary processes that promote phenotypic
diversity. Trait variation along geographic gradients provides a convenient
heuristic for understanding what drives and maintains diversity. Intraspecific trait
variation along latitudinal gradients is well-known, but elevational variation in the
same traits is rarely documented. Trait variation along continuous elevational
gradients, however, provides compelling evidence that individuals within a
breeding population may experience different selective pressures.
Our objectives were to quantify variation in a suite of traits along a continuous
elevational gradient, evaluate whether individuals in the population experience
different selective pressures along that gradient, and quantify variation in
migratory tendency along that gradient.
We examined variation in a suite of 14 life history, morphological, and behavioral
traits, including migratory tendency, of yellow-eyed juncos along a continuous
1000-m elevational gradient in the Santa Catalina Mountains of Arizona.
Many traits we examined varied along the elevational gradient. Nest survival and
nestling growth rates increased, while breeding season length, renesting
2 propensity, and adult survival declined, with increasing elevation. We documented
the migratory phenotype of juncos (partial altitudinal migrants) and show that
individual migratory tendency is higher among females than males and increases
with breeding elevation.
Our data support the paradigm that variation in breeding season length is a major
selective pressure driving life history variation along elevational gradients and
that individuals breeding at high elevation pursue strategies that favor offspring
quality over offspring quantity. Furthermore, a negative association between adult
survival and breeding elevation and a positive association between nest survival
and breeding elevation helps explain both the downslope and reciprocal upslope
seasonal migratory movements that characterize altitudinal migration in many
birds. Our results demonstrate how detailed studies of intraspecific variation in
suites of traits along environmental gradients can lend new insights into the
evolutionary processes that promote diversification and speciation, the causes of
migratory behavior, and how animal populations will likely respond to climate
change.
KEY WORDS altitudinal migration, clutch size, Junco phaeonotus, nest survival, phenotypic plasticity, season length, yellow-eyed junco
INTRODUCTION
Morphological, behavioral, and life history traits vary among and within species, and understanding the patterns and causes of such variation provides vital clues into the
3 ecological processes that promote diversification. Comparative studies of trait variation across species are common and have lent numerous insights into the processes that cause evolutionary changes. Phenotypic variation within species has received less attention, despite the fact that such variation is the raw material of natural selection, adaptation, and speciation (Fisher, 1930). Trait variation within populations demands explanation, given that stabilizing and directional selection should push traits towards their evolutionary optima (i.e., reduce variation). For example, traits that directly affect reproduction and survival should be under strong selection, yet we typically observe substantial intraspecific variation in such traits, including annual fecundity (Lack, 1947; Woodward,
1982; Young, 1994), parental care (Weatherhead & Mcrae, 1990; Duckworth, Badyaev, &
Parlow, 2003; Fontaine & Martin, 2006), pathogen resistance (Dufva & Allander, 1995),
and the intensity of anti-predator behaviors (Seghers, 1974; Winkler, 1992; Duffy, 2010).
Explaining the mechanisms that are responsible for maintenance of intraspecific variation
in such traits is required to gain a more complete understanding of the ecological
processes that drive trait evolution.
Patterns in species’ traits across geographic clines have long been used as space-
for-time substitutions to provide insights into pattern and process in trait evolution
because these clines provide convenient systems to study the ecological processes
responsible for trait variation. For example, latitudinal clines in species’ traits (e.g.,
clutch size, body size, limb length) are well known and numerous mechanistic
hypotheses have been proposed to explain them (Allen, 1877; Lack, 1947; Blackburn,
Gaston, & Loder, 1999). Indeed, studies designed to better understand latitudinal clines
4 in a particular trait, and the ubiquity of the latitudinal patterns, have advanced our general understanding of the processes responsible for trait variation (e.g., Jetz, Sekercioglu, &
Böhning-Gaese, 2008).
Elevational clines in species’ traits are much less well-documented compared to latitudinal patterns in those same traits. Trait variation along elevational clines, however, may provide even more insights given that individuals with different phenotypes often exist in close proximity to each other and probably experience greater gene flow. Hence, phenotypic variation along elevational clines suggests that individuals within a single population experience different selective pressures along that cline. Furthermore, some patterns observed across the two types of geographic gradients (latitudinal and elevational) are incongruous. For example, clutch size is positively associated with latitude but declines with increasing elevation among both temperate (Badyaev &
Ghalambor, 2001; Sandercock, Martin, & Hannon, 2005; Dillon & Conway, 2015) and tropical (Kleindorfer, 2007; Boyce, Freeman, Mitchell, & Martin, 2015) species. This pattern (lower fecundity at higher elevations) is operationally different than latitudinal clines (higher fecundity at higher latitudes) and seemingly contradicts some of the most popular mechanistic hypotheses invoked to explain the latitudinal pattern in fecundity.
Clinal trait variation along environmental gradients can reflect either phenotypic plasticity or genetic differentiation among populations in response to local adaptation to environmental conditions (Bradshaw & Holzapfel, 2006; Charmantier, McCleery, Cole,
Perrins, Kruuk, & Sheldon, 2008). Plasticity allows populations to persist under a range of environmental conditions without barriers to gene flow. However, recent evidence
5 suggests that phenotypic plasticity might actually facilitate adaptive genetic changes under certain conditions (Ghalambor et al., 2015). Hence, detailed descriptions of phenotypic variation along environmental gradients can help us understand and anticipate adaptive change regardless of whether such variation reflects genetic differences among populations or phenotypic plasticity.
Studies of trait variation along elevational gradients are relatively uncommon in birds. A few studies have examined elevational gradients in avian clutch size (Järvinen,
1989; Badyaev & Ghalambor, 2001; Boyce et al., 2015; Dillon & Conway, 2015, 2018), and even fewer have examined elevational gradients in other traits (but see Carey,
Thompson, Vleck, & James, 1982; Badyaev 1997; Sandercock et al. 2005; Camfield et al., 2010; Evans Ogden, Martin, & Martin, 2012). Many traits, however, have inherent trade-offs mediated by the principles of allocation and functional constraint. Hence, even greater insight into the ecological processes that generate trait variation can be gained by examining patterns of covariation in suites of traits along environmental and geographic gradients (Boyle, Sandercock, & Martin, 2016). Furthermore, most previous studies compared traits of populations that are widely separated in space, rather than along a continuous elevational gradient (but see Dillon & Conway, 2015, 2018; LaBarbera &
Lacey, 2018).
Life history variation along elevational gradients may be driven by the duration of conditions suitable for breeding (Martin & Wiebe, 2004; Grzybowski & Pease, 2005;
LaBarbera & Lacey, 2018). Breeding season duration at high elevations might be constrained directly by inclement weather, by the duration of the snow-free season, or by
6 the phenology of food resources (Körner, 1999; Thomas, Blondel, Perret, Lambrechts, &
Speakman, 2001; Bears, Smith, & Wingfield, 2003). Short breeding seasons provide fewer opportunities to raise multiple broods or renest following nest failure. Selection might favor small, high-quality broods and strategies that promote adult survival at high elevations (Badyaev & Ghalambor, 2001; Sandercock et al., 2005; Bears, Martin, &
White, 2009). Hence, we predicted that breeding season duration, renesting propensity, clutch size, annual fecundity, and developmental period duration would decline with increasing elevation. The same paradigm also predicts that adult survival, daily nest survival, and nestling growth rates should increase with increasing elevation. Nest survival should increase with increasing elevation either because: 1) high-elevation breeders invest more in each nesting attempt (prioritizing quality over quantity) and therefore exhibit stronger anti-predator behaviors and nest defense, or 2) simply because predation risk decreases with increasing elevation (as is often assumed, e.g., Fretwell,
1980; Boyle, 2008b). We predicted that migratory tendency would be associated with the severity of winter weather and seasonal food limitation and, hence, migratory tendency would increase with breeding site elevation. We predicted that hatching success would increase at higher elevations due to improved egg viability in cooler climates (Beissinger,
Cook, & Arendt, 2005). Finally, assuming that climactic interpretations of Bergmann’s well-known biogeographic rule (i.e., Mayr, 1956) are valid, we predicted that body size would increase with elevation.
We examined variation in 14 life history traits of a montane bird (yellow-eyed junco; Junco phaeonotus) along a 1000-m continuous elevational gradient in southeastern
7 Arizona: migratory tendency, adult survival, daily nest survival, clutch size, renesting propensity, annual fecundity, nesting season length, nestling growth rates, developmental period duration, hatching success, adult body size, dispersal probability, dispersal distance, and natal recruitment. Our objectives were to: 1) test the hypothesis that individuals living on different points of the elevational gradient are exposed to different sets of selective pressures, 2) quantify variation in each trait along a continuous elevational gradient, and 3) quantify elevational variation in the probability of altitudinal migration.
To our knowledge, this study is the first to examine intraspecific variation in a suite of >10 avian life history and behavioral traits along a single continuous elevational gradient that is not confounded by latitudinal, longitudinal, or phylogenetic differences among populations. A better understanding of the patterns and processes of trait variation along elevational gradients may lend novel insight into the causes of short-distance altitudinal migration (Boyle, Guglielmo, Hobson, & Norris, 2011; Boyle, 2017), help us anticipate the potential impacts of climate change on animal populations (Frederiksen,
Harris, Wanless, & Benton, 2005), improve our understanding of speciation, and advance our understanding of the causes of trait evolution.
MATERIALS AND METHODS
Study Species
The yellow-eyed junco is a small songbird with a restricted distribution, found primarily in the highlands of Mexico and Central America and in disjunct populations in isolated mountain ranges north to southeastern Arizona. Juncos subsist primarily on
8 invertebrates during the breeding season and switch to a granivorous diet during the fall, winter, and spring. Juncos frequently forage in loose flocks with conspecifics (Moore,
1972; Sullivan, 2018), and sometimes with congeneric dark-eyed juncos (Junco hyemalis), during the non-breeding season. Juncos typically build their nests on the ground under tufts of grass, logs, rocks, and mats of dead bracken fern (Pteridium aquilinum) or within dense stands of living ferns (Sullivan, 2018).
Field Methods
We studied yellow-eyed juncos in the Santa Catalina Mountains in Pima County,
Arizona (study area described in detail by Kirkpatrick & Conway, 2010). We monitored junco nests along a continuous elevational gradient that spanned the full elevational extent of their breeding range (1775 to 2791 m) from 2002 – 2012. We recorded the status of 702 nesting attempts every one to five days (more often when we anticipated transitions between nesting stages). We measured the tarsus length, wing chord, and body mass of each nestling on the eighth day after hatching in 2012. Eight days was the maximum age at which we felt confident handling the nestlings without the risk of forced early fledging.
We captured adult juncos at five sites during the 2011 and 2012 breeding seasons
(March-August). The elevation of these sites ranged from 1755 – 1810 m (Middle Bear
Canyon), 2110 – 2205 m (Rose Canyon Lake), 2230 – 2425 m (Marshall Gulch), 2335 –
2560 m (Upper Sabino Canyon), and 2690 – 2791 m (Lemmon Park). The vegetation at our study sites ranges from Madrean pine-oak woodlands at the lowest-elevation sites to mixed-coniferous woodlands at the upper sites. The vegetation at our study sites ranged
9 from open Madrean pine-oak woodlands with narrow riparian corridors at the lowest- elevation sites to closed canopy mixed riparian-coniferous woodland at middle elevations and open mixed-coniferous woodlands with a grassy understory at the upper sites. Except in extremely rare cases, juncos nested on the ground at all study sites.
Annual precipitation has a bimodal pattern with the bulk of precipitation falling during the summer “monsoon” season and during infrequent winter storms. Winter precipitation varies among years (more so than at other seasons; McDonald, 1956) in accordance with the El Nino—Southern Oscillation cycle and is positively associated with elevation. Average annual precipitation increases from <30cm on the valley floor at
806m in Tucson, to >75cm at the summit (where our highest elevation site was located;
Western Region Climate Center, 2019). We systematically deployed temperature-logging iButtons (model DS1923, Maxim Integrated, San Jose) throughout each site from May
2012 – April 2013. We programmed the iButtons to record the ambient temperature every
60 minutes.
We measured tarsus length, wing chord length, tail length, head length, head width, body mass, and fat content of 355 adult juncos. We used the presence of cloacal protuberances, brood patches, and behavioral observations (i.e., of nesting or singing birds) to determine the sex of each adult junco. We fitted each captured junco with an aluminum U.S. Geological Survey leg band and a unique combination of colored plastic leg bands.
We classified the migratory behavior of color-banded juncos through intensive area searches (Ralph, Geupel, Pyle, Martin, & DeSante, 1993) of each site over each of
10 two winters. We surveyed each site every 7 – 14 days during the winters of 2011 – 2012 and 2012 – 2013 to determine the migratory behavior of marked juncos. On each winter visit, we surveyed a study site for as long as needed until the observer believed that they had detected all of the juncos present. Juncos forage on the ground and are vocal birds that are relatively easy to detect during winter months, so we felt confident that we detected most juncos with this survey method. During major storm events (n=4) at our 2 highest study sites, we spread bait piles (each consisting of ~3 – 5kg of commercial bird seed) to see whether they attracted juncos that would have otherwise gone undetected
(i.e., increased detection). We also opportunistically monitored nearby feeders (at private residences). These efforts did not produce any new detections.
Analytical Methods
We used ArcGIS v.10 (ESRI, Redlands, CA, USA) to extract the elevation of each nest from a 10-m digital elevation model. We assumed that individual juncos that were not associated with a known nest were breeding at the elevation where they were captured (we only captured and banded juncos during the breeding season). We used R
(R Core Team, Vienna, 2018) for all statistical analyses. We used package RMark (Laake,
2013) when selecting among models of adult and daily nest survival, and based model selection on AICc (Burnham & Anderson, 2002), implemented with the Multi-model
Inference (MuMin) package in R (Barton, 2017) for all other analyses. We considered models with ΔAICc <2.0 to be competing models, and when competing models were present, we based our inferences on parameter estimates averaged across all candidate models to account for model uncertainty (Lukacs, Burnham, & Anderson, 2010).
11 Migratory Tendency. — Yellow-eyed juncos are generally resident within individual mountain ranges (Sullivan, 2018) and, hence, we expected that migration in this study system would be spontaneous and incremental rather than synchronized (i.e., typical of facultative short-distance migrants; Terrill & Able, 1988). We classified migratory behavior only for juncos present on the same site during two consecutive breeding seasons. By doing so, we focused explicitly on migratory movements and eliminated movements of juncos that may have died, emigrated permanently, or immigrated (i.e., banded somewhere other than their breeding site). We assumed return
(i.e., spring) migration began in February (Horvath & Sullivan, 1988; pers. obs.). We assigned a migration score (an index of migratory tendency, equal to an integer from 0 to
4) to each color-banded junco corresponding to the number of “winter” months (defined as October to January) that the bird was absent from the study site where it was captured and bred. We compared eight models to explain migratory tendency including all combinations of year, sex, and the standardized linear and quadratic effects of breeding elevation. Although each junco was assigned a migration score corresponding to a discrete integer, we modeled migration score as a continuous variable because intermediate values are biologically realistic. We applied a variance-stabilizing natural log transformation to the response variable (migration score) to minimize heteroscedasticity. We used AICc to compare a global model with a Gaussian error distribution and log-transformed response variable to a model with a Poisson error distribution and non-transformed response variable. The Gaussian model performed substantially better (ΔAICc = 457) than the Poisson model, and the two models yielded
12 identical model rankings and similar ΔAICc values among the competing models. Hence,
we used the Gaussian model for the final analysis.
Nesting Season Duration. — We calculated the middle 90th percentile of nest
initiation dates in each of four equally spaced elevation bins (1755 – 2014 m, 2015 –
2273 m, 2274 – 2532 m, and 2533 – 2791 m) for the 2011 and 2012 breeding seasons. We
only included 2011 – 2012 because our nest-searching effort was evenly distributed
across the elevational gradient in those years. By restricting the data set to the 90th percentile, we excluded outliers that may be sensitive to differences in junco density
(Miller-Rushing, Lloyd-Evans, Primack, & Satzinger, 2008).
Adult Survival. — We used Cormack-Jolly-Seber models, implemented in the
RMark package (Laake, 2013), to determine whether annual survival of 355 color- marked adult juncos from 2011 – 2013 varied with elevation. We compared 121 models including all combinations of sex, elevation, year, and the 2-way interactions of elevation with sex and year, for both survival and detection probability.
Daily Nest Survival. — We used nest survival models, implemented in the RMark package (Laake, 2013), to examine the association between elevation and the daily survival probability of junco nests from 2002 – 2012 (n = 630) (Dinsmore, White, &
Knopf, 2002). We compared 20 candidate models including the linear and quadratic effects of elevation, linear and quadratic effects of nest initiation date, year, and the interaction between elevation and initiation date.
Clutch Size. — We used general linear regression to examine the relationship between junco clutch size and elevation for the 441 nests where the clutch size was
13 known with confidence. We first used AICc to compare a global model with a Gaussian
distribution to one with a Poisson distribution (the Gaussian distribution performed
substantially better than the Poisson version, consistent with Kery & Royle (2016) and
Dillon & Conway (2018)). We compared 32 candidate models including all combinations
of year, the linear and quadratic effects of standardized elevation and standardized
initiation date, and the interaction between elevation and initiation date.
Renesting Propensity. — We evaluated the renesting propensity of juncos along
our elevational gradient by modeling the number of nests detected per marked junco per
season including both nesting attempts after failure and subsequent attempts after
successfully fledging a brood. We first ran and compared (with AICc) global models with
Gaussian and Poisson distributions. The Gaussian distribution model performed better
than the Poisson, so we used a Gaussian distribution in the final analysis. We repeated
each analysis with all marked individuals for which at least one nest was known (n =
237) and for females only (n = 108). We compared 12 models including all combinations
of the linear and quadratic effects of standardized elevation, year, and study site area
(km2). We also obtained similar results with a logistic regression analysis, where the
response variable was the probability a junco had >1 nest per season.
Annual Fecundity. — We used general linear regression with a Gaussian distribution to model the number of offspring fledged, across all detected nests per female in a given season, as a function of elevation. For each marked female for whom we found at least one nest, we summed the total number of known fledglings produced per year from 2011-2012 (the only years during which we monitored nests of marked individuals).
14 Only a small number of individuals (n = 7) had nests detected both years, so we treated
those as independent samples. We compared 12 models including all combinations of the
linear and quadratic effects of standardized elevation, year, and study site area (km2).
Nestling Growth Rate. — We used general linear regression with a Gaussian distribution to model nestling growth as a function of elevation. We averaged wing chord length, tarsus length, and body mass across 8-day-old nestlings within each brood. We then modeled variation in each body size measurement, individually, by comparing sets of 18 models including all combinations of the linear and quadratic effects of standardized elevation, the linear and quadratic effects of standardized initiation date, and the estimated time elapsed since the nest began to hatch (measurements occurred 8 – 8.5 days after hatching).
Development Period Duration. — We used general linear regression with a
Gaussian distribution to model the duration of the incubation and nestling periods as a function of elevation. We assumed that incubation began on the penultimate egg
(Weathers & Sullivan, 1989) and that one egg was laid each day (pers. obs.). We included those nests for which both transition dates (i.e., nest initiation and hatching for incubation period, hatching and fledging for nestling period) were known to within one day (n = 51 for incubation period and n = 89 for the nestling period). For each period, we considered six models including all combinations of the linear and quadratic effects of standardized elevation and standardized initiation date.
Hatching Success. — We used Poisson regression to examine whether the number of unhatched eggs per nest was associated with elevation. We included nests where the
15 clutch size was known with confidence and was either: 1) observed during the first half
(six days) of the nestling period, or 2) observed later in the nestling period, but the brood
size matched the clutch size (implying 100% hatching success). We also included nests
for which the clutch size was unknown, but >1 unhatched egg remained after the nest
fledged. We compared models including all combinations of the linear and quadratic
effects of standardized elevation and standardized initiation date. We obtained similar
results with logistic regression, where the response variable was the probability of >1 egg
in a nest failing to hatch.
Adult Body Size. — We used principal component analysis (PCA) to generate a multivariate index of body size that pooled variation in the five structural body size
measurements for 318 adult juncos. The first axis of the PCA (PC1) was the only
principal component associated with each univariate measurement in the same direction.
We used individual values of PC1 as our index of overall body size. We used general
linear regression with a Gaussian distribution to examine the association between body
size PC1 and elevation. We compared 16 models to explain variation in body size (PC1),
including all combinations of sex, year, date measured, the linear and quadratic effect of
standardized elevation, and the interaction of elevation with sex. We also compared sets
of 16 models to explain body mass and fat content as a function of sex, elevation, capture
date, and the interaction between elevation and capture date.
Breeding Dispersal Probability and Distance. — We used logistic regression with
a binomial distribution to model the probability of breeding dispersal as a function of
elevation. We included all individuals for which >1 nest was found during two different
16 breeding seasons (n = 14). We considered an individual to have dispersed if the straight- line distance between nests in subsequent years was greater than 56 meters, the radius of territory size in juncos (Sullivan, 2018). We considered 8 different models to explain the probability of breeding dispersal, including an intercept-only null model, the linear and quadratic effects of elevation, sex, and an interaction between sex and elevation. We then used general linear regression with a Gaussian distribution to model breeding dispersal distance as a function of elevation. For each marked adult for which we located >1 nesting attempt during two different breeding seasons, we calculated the distance between the nest location in year t and year t+1. When an individual had >1 known nesting attempt in either year, we randomly selected one nest location. We considered 8 different models to explain distance including an intercept-only null model, the linear and quadratic effects of elevation, sex, and an interaction between sex and elevation.
Natal Recruitment. — We calculated the proportion of marked juveniles, whether banded as nestlings or as fledglings, that we detected during the subsequent breeding season.
RESULTS
We caught and color-marked 355 adult yellow-eyed juncos (159 males and 82 females) across two breeding seasons (2011 and 2012, respectively), and we monitored
702 yellow-eyed junco nesting attempts from 2002 – 2012. We caught, marked, and measured 358 nestling juncos at 122 nests and 145 juveniles captured away from nests.
We recorded the migratory scores for 286 color-banded juncos (70 had scores for both years): 128 during the winter of 2011 – 2012 and 228 during the winter of 2012 – 2013.
17 We obtained useable data from 108 iButtons across our 5 study sites: 18 at Middle Bear
Canyon, 12 at Rose Canyon Lake, 29 at Marshall Gulch, 23 at Upper Sabino Canyon, and
20 at Lemmon Park. Average monthly temperatures across our study sites peaked in
June – August, reached their minimum in January – February, and decreased with increasing elevation (Fig. 1).
Migratory Tendency. — We observed direct evidence of altitudinal migration: seven color-marked juncos made confirmed round-trip seasonal movements (i.e., bred at one site, wintered at another, and then returned to their breeding site the subsequent breeding season). Furthermore, all juncos temporarily vacated our highest elevation study sites following major snowstorms. We detected zero juncos (marked or unmarked) on 16 of the 129 winter-season surveys (i.e., a single survey of one of our five sites on a single date), across both winters, and we detected zero of our color-marked juncos on five additional surveys. We did not detect any unbanded juncos on most of the winter surveys when we detected zero color-marked juncos, which suggests that our detection rates were high.
Sex, breeding elevation, and year explained variation in migratory behavior. A single model (Migration Score ~ Year + Sex + Elevation + Elevation2) received 98% of total model weight, and models including elevation received 100% of total model weight
(Table 1). Migratory tendency (migration score) was lower among males than females (β
= െ0.63, SE = 0.18, 95% C.I. = െ0.99 – െ0.27), lower during the winter of 2012 – 2013
(β = െ1.05, SE = 0.19, 95% C.I. = െ1.43 – െ0.68), and was positively and non-linearly associated with breeding elevation (Fig. 2). Males remained on their breeding sites for an
18 average of 0.63 months longer than females, and all juncos remained on their breeding
site for an average of 1.05 months longer in the winter of 2012 – 2013 than in 2011 –
2012.
Nesting Season Duration. — The nesting season was longest in our second lowest
elevation bin (2015 – 2273 m), but then declined with increasing elevation (Fig. 3).
Adult Survival. — The probability of overwinter survival declined with elevation
for both adult males and females (Fig. 4a). The top model to explain variation in adult
survival included additive effects of sex and elevation, and models including elevation
received 100% of the total model weight (Table 2). At the average elevation along the
gradient (2474 m), annual survival was 87.0% (SE = 3.5, 95% C.I. = 78.5 – 92.5%) for
adult males and 72.2% (SE = 5.1, 95% C.I. = 61.2 – 81.1%) for adult females. Annual
survival probability declined by 16.1% and 29.5% (males and females, respectively)
across a 1000-meter increase in breeding elevation (Fig. 4a).
Daily Nest Survival. — Daily nest survival was positively correlated with
elevation (β = 0.0007, SE = 0.0002, 95% C.I. = 0.0002 – 0.001; Fig. 4b). The top model
to explain variation in nest survival included the linear effects of elevation and nest
initiation date, and models including elevation received 95% of total model weight (Table
3). Daily nest survival declined over the course of the nesting season (β = െ0.007, SE =
0.003, 95% C.I. = െ0.012 – െ0.001). The daily probability of nest survival increased by
2.3% across a 1000-m increase in elevation, or from 94.4% at our lowest elevation to
97.0% at our highest elevation site, when initiation date was held at its mean. Therefore, cumulative nest survival increased from 22.3% at 1775 m to 45.5% at 2791 m, across a
19 26.2-day junco nesting period (the average for successful nests in our study).
Clutch Size. — The top model to explain variation in clutch size (n = 441)
included the linear and quadratic effects of elevation but received only 41.9% of total
model weight (Table 4). Models including elevation received 77% of total model weight.
The model-averaged parameter estimates suggested small negative effects of elevation (β
= െ0.02, SE = 0.03, 95% CI = െ0.09 – 0.03) and elevation2 (β = െ0.04, SE = 0.03, 95%
CI = െ0.10 – െ0.01) on clutch size.
Resenting Propensity. — Renesting propensity was negatively correlated with elevation. The top model to explain the number of nests located per individual per season included the linear effect of elevation, year, and study site area, and models including elevation received 100% of the total model weight (Table 5). The number of nesting attempts detected per season declined among all juncos (i.e., both sexes; β = െ0.24, SE =
0.05, 95% CI = െ0.34 – െ0.14) and among females only (β = െ0.28, SE = 0.04, 95% CI
= െ0.45 – െ0.11) from 2.15 at the lowest elevation to 1.18 at the highest elevation (Fig.
4c).
Annual Fecundity. — The total number of young fledged annually per female, across all detected nests, varied non-linearly with elevation, and annual productivity was lowest at middle elevations (Fig. 4d). The top model included year and the linear and quadratic effects of elevation (Table 6). Models including elevation received 74% of total model weight.
Nestling Growth Rate. — Nestling growth was positively associated with elevation. The top model to explain nestling wing chord length included elevation and
20 days since hatching, and models including elevation received 85% of the total model
weight (Table 7). Wing chord length was 1.36mm (or 4.2%) longer at our highest
elevation than at our lowest elevation (β = 0.54, SE = 0.35, 95% C.I. = 0.06 – 1.21; Fig.
4e). The top models for explaining variation in tarsus length and body mass did not
include elevation, but the model-averaged parameter estimates for elevation across all
candidate models were positive (i.e., further indication that nestlings at higher elevations
grew faster) for tarsus length and body mass.
Development Period Duration. — Duration of the development period did not
vary with elevation. For the incubation period, the top model included only the additive
effects of clutch size and initiation date. A negative linear effect of elevation appeared in
competing models (best among them; ΔAIC = 0.74), the best models that included elevation performed better than the intercept-only null model (ΔAIC = 3.6), but the model-averaged confidence intervals for elevation overlapped zero. For the nestling period, the top model was the intercept-only null model, but a positive linear effect of elevation appeared in competing models (best among them; ΔAIC = 0.40).
Hatching Success. — The proportion of each clutch that hatched successfully was not strongly correlated with clutch size (Pearson’s R = 0.05) and did not vary with elevation. The top model to explain the number of eggs that failed per nest was the intercept-only null model.
Adult Body Size. — PC1 accounted for 31.8% of the total variance in adult body size, and the loadings of all five measurements on PC1 were positive, indicating that each was associated with PC1 in the same direction. The top model to explain variation in
21 adult body size included the linear effect of elevation, sex, and year captured, but only
received 20.7% of total model weight. Model-averaged parameter estimates indicate that
body size was slightly negatively associated with elevation (β = െ0.09, SE = 0.08, 96%
C.I. = െ0.27 – െ0.60). Neither body mass nor fat content varied by elevation (top models did not include elevation, for either parameter).
Breeding Dispersal Probability and Distance. — The top model to explain breeding dispersal probability (ΔAICc = 2.5 better than the second best model, which was the null model) included only the linear effect of elevation, and the 95% confidence interval excluded zero. The probability of dispersal was positively associated with elevation and the predicted probability of dispersal increased from 15.1% at 2137m to
86.7% at 2793m. Breeding dispersal distance was also positively associated with elevation, and the 95% confidence interval excluded zero (Fig. 4f). The model including only the linear effect performed better (ΔAICc = 4.0) than the quadratic version and better than the intercept-only null model (ΔAICc = 4.2). Among those individuals that dispersed, 7 moved to nest sites at lower elevations (mean = 22.6m), 6 moved to nest sites at higher elevations (mean = 15.0m), and 1 renested at the same elevation.
Natal Recruitment. —Recruitment was positively associated with elevation. The percent of 2011 juveniles recruited in 2012 was (ordered by increasing elevation): 0.0% at Rose Canyon Lake (n = 4), Marshall Gulch (n = 1), and Upper Sabino Canyon (n =
13), and 11.8% at Lemmon Park (n = 85) between 2011 and 2012. The percent of 2012 juveniles recruited in 2013 was: 0.0% at Middle Bear Canyon (n = 34), 1.3% at Rose
Canyon Lake (n = 76), 4.3% at Marshal Gulch (n = 46), 1.5% at Upper Sabino Canyon (n
22 = 68), and 1.7 % at Lemmon Park (n = 175).
DISCUSSION
Behavioral and life history traits of yellow-eyed juncos varied along a continuous
1000-m elevational gradient (Fig. 4). The gradient in phenotypes associated with elevation was notable because all our study sites were <12 km apart along a continuous elevational gradient following a single mountain slope. Hence, individual juncos could easily and quickly move between the highest and lowest study sites (and sometimes did).
Most extreme was one individual that bred at our highest-elevation site and was detected during winter at our lowest elevation site approximately 12km away and approximately
1000m lower in elevation. Despite this close proximity, birds at the upper end of the gradient were faced with a different suite of selective pressures and employed distinctly different strategies, compared to their lower-elevation counterparts within the same population. Our findings indicate that the selective pressures experienced by individuals, even within the same population, can vary across relatively compact spatial scales. Our results are the first to show variation in so many traits across such a small elevational gradient and provide evidence that individuals within the same population experience different selective regimes. This microscale variation in life history strategies in response to different selection regimes can lend insight into the processes that maintain phenotypic diversity generally, account for life history variation within populations, and may help to explain migratory behavior along those same gradients.
Studies of phenotypic variation along environmental gradients provide useful space-for-time substitutions for anticipating the future responses of populations to
23 climate change. Such variation in life history traits could reflect genetic-based microevolutionary adaptations of different populations but more likely reflects plastic responses to different sets of environmental pressures (Bradshaw & Holzapfel, 2006;
Charmantier et al., 2008). Distinguishing between these two mechanisms of adaptation
(plasticity and microevolution) was beyond the focus of this study and generally requires either common garden experiments or long-term studies of marked populations (e.g.,
Charmantier et al., 2008; Vedder, Bouwhuis, & Sheldon, 2013). Existing empirical evidence suggests that populations respond to environmental variation and change via both genetic-based local adaptation (Balanyá, Oller, Huey, Gilchrist, & Serra, 2006;
Jonzén et al., 2006) and phenotypic plasticity (Przybylo, Sheldon, & Merilä, 2000; Réale,
McAdam, Boutin, & Berteaux, 2003; Charmantier et al., 2008). Furthermore, phenotypic plasticity has been speculated to both constrain and facilitate adaptive genetic change
(Ghalambor et al., 2015). Therefore, regardless of the underlying mechanism of adaptation, studies of trait variation along geographic and environmental gradients will help us anticipate how populations will be able to respond to temporal variation like that induced by climate change.
Breeding season length and renesting propensity in our system declined with increasing elevation and these results support the paradigm that season length is among the selective factors driving life history variation along elevational gradients (Martin &
Wiebe, 2004; Grzybowski & Pease, 2005; LaBarbera & Lacey, 2018). Our results suggest that high-elevation breeders are time-limited and prioritize offspring quality over quantity. The probability of renesting declined with elevation and the number of
24 fledglings produced in a season was lowest at middle elevations, but nest survival and nestling growth were both positively associated with elevation. The cumulative probability of a junco nest surviving the entire nesting cycle doubled from our lowest elevation site to our highest elevation site. Previous studies have reported both negative
(Evans Ogden et al., 2012; Altamirano, Ibarra, de la Mazza, Navarrete, & Bonacic, 2015;
LaBarbera & Lacey, 2018) and positive (Badyaev & Ghalambor, 2001; Camfield,
Pearson, & Martin, 2010; Dillon & Conway, 2018, among Red-faced Warblers
(Cardellina rubrifrons) in our study area) associations between nest survival and elevation. Previous reports of increased nest mortality at higher elevations (e.g.,
LaBarbera & Lacey, 2018) may be driven by an increased incidence of severe weather at high elevations (Boyce & Perrins, 1987). Severe weather is uncommon in our study area from April – June, but weather-induced nest failure does occur during rare spring storms
(Decker & Conway, 2009). The primary cause of nest mortality in our study, however, was predation. Hence, increased nest survival at high elevations could reflect either differences in ambient predation risk or variation in the intensity of adult anti-predator behaviors. If juncos with fewer renesting opportunities (those experiencing shorter nesting seasons, at high elevations) invest more in individual nesting attempts than those with greater renesting opportunities, then they might employ more-intense predation avoidance strategies. Our finding, that nestling growth rates increased with increasing elevation, supports the idea that juncos invest more in each reproductive attempt when season length is limited, but food availability also increases with elevation in our system
(Dillon & Conway, 2018).
25 Clutch size has been the primary focus of many studies of life history variation
along geographic gradients, and high-elevation populations often lay smaller clutches than those at lower elevations (Bears et al., 2009; Martin, Camfield, & Martin, 2009;
Wilson & Martin, 2011) including among Red-faced Warblers breeding in our study area
(Dillon & Conway, 2015). We found only a slight and non-significant trend for yellow- eyed junco clutch size to decline with increasing elevation. Yellow-eyed juncos, however, are multi-brooded, and elevational patterns in fecundity might be obscured if multiple nesting attempts are not accounted for. These results illustrate the importance of measuring a full suite of life history traits and accounting for the number of broods produced annually. The non-linear relationship we observed between annual fecundity and elevation also illustrates that the direction of observed relationships between life history traits and elevation might depend on which portion of the gradient is sampled,
and that only sampling a portion of an elevational gradient might lead to incomplete or
misleading conclusions. Moreover, these non-linear relationships emphasize that we need
to understand the underlying mechanisms that drive elevational patterns in phenotypic
traits because doing so will likely explain the non-linearity.
Adult survival of yellow-eyed juncos decreased (and territory turnover increased)
with increasing elevation, contrary to most previous studies (Sandercock et al., 2005;
Camfield et al., 2010; Bears et al., 2009; Martin et al., 2009; Wilson & Martin, 2011) and
counter what would be expected if short breeding seasons at high elevations select for
strategies that prioritize survival (as suggested by Sandercock et al., 2005). We were
unable to distinguish mortality from emigration, but yellow-eyed juncos have high
26 breeding site fidelity and low rates of long-distance dispersal (Sullivan, 2018).
Furthermore, an advantage of examining apparent survival along a continuous elevational
gradient is the ability to control for (and detect) elevational and site-specific variation in
dispersal and emigration. Our results are more likely to reflect real differences in
survival, compared to other studies, given that our highest-elevation sites were <12 km
apart from our lowest-elevation sites, we monitored sites at intermediate elevations
between the extremes, and all our sites are likely part of a single breeding population (we
documented movement among them).
Variation in adult survival may reflect the climatic gradient along our elevational
gradient. Greater snowfall at higher elevations could limit access to predictable winter
food, or colder temperatures might directly exceed the physiological tolerance of small-
bodied birds. High-elevation residents therefore risk starvation (Boyle, 2008a; Boyle et
al., 2011) or hypothermia (Ketterson & Nolan, 1976). Facultative altitudinal migration
allows individuals to quickly reach less-challenging winter conditions, but competition
for high-quality breeding territories may be an incentive for individuals to remain at or
near high elevations during the winter (Ketterson & Nolan, 1976; Fretwell, 1980).
Previous studies that found a positive association between elevation and survival involved species that were either obligate migrants or larger-bodied non-passerines
(Sandercock et al., 2005; Camfield et al., 2010; Bears et al., 2009; Martin et al., 2009;
Wilson & Martin, 2011). Large-bodied birds may be less susceptible than small-bodied passerines to climatic extremes due to their smaller surface area to volume ratio (Reiss
1989) and their greater capacity for storing fat (Calder, 1974; Ketterson & Nolan, 1976).
27 Species that are obligate migrants to lower latitudes or elevations during the winter are also buffered from the harsh conditions of high-elevation winters. The winter weather in our study area is strongly influenced by the El Niño-Southern Oscillation, the amount of winter precipitation is highly variable (Swetnam & Betancourt, 1990), and some winters remain relatively snow free while others have substantial and persistent snow cover.
Yellow-eyed juncos are short-distance altitudinal migrants and might therefore be able to occupy high-elevation territories throughout a mild winter but be forced to migrate downslope in years when winter weather is harsh. Indeed, we observed inter-annual variation in the average migratory tendency of yellow-eyed juncos. The reproductive advantages of prior occupancy or first arrival on the breeding grounds (Fretwell, 1980), combined with unpredictable winter weather, might promote risky strategies in short- distance facultative migrants like yellow-eyed juncos and help explain our observation that annual survival declined with elevation.
Body size of adult yellow-eyed juncos was weakly and negatively associated with elevation. Body size variation along elevational gradients might arise from limits to thermal tolerance, fasting ability, or foraging opportunities (Ketterson & Nolan, 1976;
Boyle, Norris, & Guglielmo, 2010). A negative association between elevation and body size is opposite what might be predicted by climatic interpretations of Bergmann’s rule
(e.g., Mayr, 1956). Altitudinal migration may be an alternative evolutionary mechanism that allows birds breeding at higher elevations to cope with (i.e., avoid) the colder climate without changes to body size. Indeed, juncos at higher elevations were more likely to migrate during the winter and juncos were completely absent from our high-elevation
28 sites during inclement weather.
Female juncos were more likely to migrate than males, corroborating results of many previous studies and in accordance with predictions made by all the major hypotheses proposed to explain intraspecific variation in migratory tendency (Ketterson
& Nolan, 1976). Even our least migratory individuals at our highest elevation sites temporarily migrated facultatively downslope following winter storms. Surveys when we detected zero juncos followed major snowfall events, usually when snow cover was at or near 100%. Major snowfalls require granivorous birds to undergo a period of fasting or temporary migration. The average fasting endurance of dark-eyed juncos was estimated to be 43 hours (range 34 – 67 hours; Stuebe & Ketterson, 1982), implying that a junco might be able to fast for most of two days and one night, but they might not survive a second night without food. Hence, short-term facultative movements downslope might be required by even the least migration-prone juncos during prolonged snow cover (Boyle et al., 2010).
The mechanisms driving the uphill and downhill components of altitudinal migrations may not be the same (Boyle, 2008b). Understanding the different costs and benefits of occupying different elevations, across the annual cycle, may reveal the selective forces that cause these reciprocal movements. Two of our most striking findings are that nest survival increased with elevation while apparent survival of adults decreased with elevation. The abundance of lepidopteran larvae and growth rates of red-faced warbler nestlings were also positively associated with elevation in our study area (Dillon
& Conway, 2018). Variation observed in nest survival, nestling growth rates, and food
29 availability suggests that the most productive breeding territories might be at the highest elevations and that the owners of those territories may benefit from maintaining and defending them over the winter (Ketterson & Nolan, 1976). Attempting to overwinter on a high-elevation territory, however, may expose birds to greater risks than migrating down-slope or simply occupying a lower-elevation territory year-round. Our results demonstrate that decisions regarding where to nest along a continuous elevational gradient include implications for migration behavior and influence avian life history strategies.
Our results highlight the fact that species traits can vary substantially along a single elevational gradient, and the individuals along that gradient are exposed to different selective pressures. Hence, such systems provide a relatively untapped opportunity to better understand how and why variation in species traits are maintained within species. Additional long-term studies along continuous elevational gradients would enhance our understanding of trait evolution and the ecological processes that create and maintain that variation.
Quantifying the seasonal costs and benefits of residing at different elevations can also lend new insights into the causes of altitudinal migration and the effects of climate change. For example, if migratory behavior is driven by seasonal changes in resource availability and climatic conditions, then changes to those seasonal dynamics under a changing climate may in turn alter migration dynamics. An improved understanding of the diversity in (and drivers of) migratory patterns is therefore a critical step towards constructing mechanistic models of how climate change may alter animal migrations.
30 ACKNOWLEDGEMENTS
A. Boyle and R. Duckworth provided invaluable feedback and support throughout the development and implementation of this study. C. Kirkpatrick and D. LaRoche provided field assistance. K. Martin and 2 anonymous reviewers provided suggestions that improved the manuscript. CGL was supported by National Science Foundation
Graduate Research Fellowship No. DGE-1143953. Arizona Field Ornithologists, T&E
Inc., and Western Bird Banding Association provided funding. This study was performed under the University of Arizona IACUC protocol #11-272. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the
U.S. Government.
AUTHOR CONTRIBUTIONS
C. Lundblad and C. Conway jointly conceived the ideas, designed the methodology, collected the data, analyzed the data, and wrote the manuscript. Both authors contributed critically to the draft and gave approval for final publication.
DATA ACCESSIBILITY
Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.2547d7wmj (Lundblad and Conway 2019).
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46 47 Figure 1. Monthly mean temperatures from May 2012 – April 2013 decreased with increasing elevation at five study sites in the Santa Catalina Mountains of Arizona.
48 Figure 2. Migratory tendency of yellow-eyed juncos was positively correlated with breeding elevation, was higher among females (dotted line, n = 82) than males (solid line, n = 159), and was higher during the winter of 2011 – 2012 (a) than 2012 – 2013 (b).
Shaded regions represent 95% confidence intervals and lower migration scores indicate
49 more time spent on the breeding grounds during winter.
50 51 Figure 3. The length of the yellow-eyed junco nesting season was shorter and the season began later at high elevation than at lower elevation, based on the middle 90th percentile of nests.
52 53 Figure 4. With increasing elevation, a) apparent annual survival of adult yellow-eyed juncos decreased, and more steeply among females (dotted line) than among males (solid line), b) daily nest survival increased (after controlling for initiation date), c) the number of annual nesting attempts per female junco declined, d) annual fecundity was lowest at intermediate elevations, e) wing chord length of nestlings 8 days post-hatching increased, and f) dispersal distance increased. Shaded regions represent 95% confidence intervals
Figure 5. Yellow-eyed juncos are exposed to different selection pressures along a single
54 1000-meter elevational gradient and exhibited different life history and migratory strategies, despite their close proximity. Individuals breeding at high elevations were more likely to undertake altitudinal migration, had shorter breeding seasons, fewer renesting opportunities, higher nest survival, faster nestling growth rates, and lower adult survival, compared to their low-elevation counterparts.
Table 1. Altitudinal migration tendency of yellow-eyed juncos during the winters of
2011 – 2012 and 2012 – 2013, was associated with sex, year, and the linear and quadratic effects of elevation.
Model K ΔAICc W
Migratory Tendency ~ Elevation + Elevation2 + Sex + Year 5 0.0 0.98
Migratory Tendency ~ Elevation + Elevation2 + Year 4 7.4 0.03
Migratory Tendency ~ Elevation + Elevation2 + Sex 4 28.0 0.00
Migratory Tendency ~ Elevation + Elevation2 3 35.0 0.00
Migratory Tendency ~ Sex + Year 3 40.5 0.00
Migratory Tendency ~ Year 2 44.2 0.00
Migratory Tendency ~ Sex 2 79.8 0.00
Migratory Tendency ~ Intercept 1 82.8 0.00
55 Table 2. Annual survival of adult yellow-eyed juncos was explained by sex and the linear effect of elevation. Detection probability varied by year (p = 0.96 during the winter of
2011 – 2012; p = 0.77 during the winter of 2012 – 2013) but not as a function of sex or elevation.
Model K ΔAICc W
Φ(sex + elevation)p(year) 6 0.0 0.16
Φ(sex + year + elevation + sex*elevation)p(.) 8 1.3 0.08
Φ(sex + year + elevation)p(.) 6 1.4 0.08
Φ(sex + elevation + sex*elevation)p(year) 8 1.9 0.06
Φ(sex + year + elevation)p(year) 7 2.1 0.06
Φ(sex + elevation)p(sex + year) 8 2.7 0.04
Φ(sex + year + elevation + sex*elevation)p(elevation) 9 3.1 0.03
Φ(sex + year + elevation)p(sex) 8 3.1 0.03
Φ(.)p(.) 2 32.0 0.00
56 Table 3. Daily survival rate (DSR) of yellow-eyed junco nests was associated with elevation and nest initiation date.
Model K ΔAICc W
57 DSR ~ Elevation + Initiation Date 3 0.0 0.26
DSR ~ Elevation + Initiation Date + Nest Age 4 0.6 0.19
DSR ~ Elevation + Initiation Date + Elevation* Initiation Date 4 1.8 0.11
DSR ~ Elevation + Elevation2 + Initiation Date 4 2.0 0.10
DSR ~ Elevation + Initiation Date + Year 11 2.2 0.09
DSR ~ Elevation + Initiation Date + Nest Age + Year 12 2.3 0.08
DSR ~ Elevation + Nest Age 3 3.7 0.04
DSR ~ Elevation + Elevation2 + Initiation Date + 5 3.8 0.04
Elevation* Initiation Date
DSR ~ Elevation 2 4.0 0.03
DSR ~ Initiation Date + Nest Age 3 5.1 0.03
DSR ~ Initiation Date 2 5.5 0.02
DSR ~ Elevation + Elevation2 3 5.9 0.01
DSR ~ Intercept 1 10.0 0.00
Table 4. Yellow-eyed junco clutch sizes were a function of elevation, initiation date, and year.
Model K ΔAICc
58 Clutch Size ~ Elevation + Elevation2 + Initiation Date + Initiation Date2 + Year 6 0.0
Clutch Size ~ Elevation + Elevation2 + Initiation Date + Initiation Date2 + Year + 7 1.2
Elevation*Initiation Date
Clutch Size ~ Initiation Date + Initiation Date2 + Year 4 1.3
Clutch Size ~ Elevation + Initiation Date + Initiation Date2 + Year 5 3.3
Clutch Size ~ Elevation + Initiation Date + Initiation Date2 + Year + Elevation*Initiation Date 6 4.6
Clutch Size ~ Elevation + Elevation2 + Initiation Date + Initiation Date2 5 8.2
Clutch Size ~ Elevation + Elevation2 + Initiation Date + Year 5 9.4
Clutch Size ~ Intercept 1 51.7
Table 5. Number of nesting attempts detected per female yellow-eyed junco was associated with elevation, year, and study site area.
Model K ΔAICc W
# Attempts ~ Elevation + Site Area + Year 4 0.0 0.67
# Attempts ~ Elevation + Elevation2 + Site Area + Year 5 2.1 0.24
# Attempts ~ Elevation + Site Area 3 4.6 0.07
# Attempts ~ Elevation + Elevation2 + Site Area 4 6.5 0.03
# Attempts ~ Intercept 1 39.7 0.00
59 Table 6. Number of fledglings produced per female yellow-eyed junco was explained by the linear and quadratic effects of elevation and a year effect.
Model K ΔAICc W
# Fledged ~ Elevation + Elevation2 + Year 4 0.0 0.25
# Fledged ~ Elevation + Elevation2 + Year + Site Area 5 0.6 0.19
# Fledged ~ Year 2 1.5 0.12
# Fledged ~ Elevation + Year 3 1.9 0.10
# Fledged ~ Intercept 1 2.5 0.07
60 Table 7. Size of yellow-eyed junco nestlings was associated with elevation. The top model to explain nestling wing chord length on the 8th day after hatching included elevation and brood age to the nearest half day (range 8 – 8.5 days).
Model K ΔAICc W
Wing Chord ~ Elevation + Age 3 0.0 0.98
Wing Chord ~ Elevation + Elevation2 + Age 4 0.6 0.03
Wing Chord ~ Elevation + Initiation Date + Initiation Date2 + Age 5 1.1 0.00
Wing Chord ~ Elevation + Elevation2 + Initiation Date + 6 1.2 0.00
Initiation Date2 + Age
Wing Chord ~ Elevation + Initiation Date + Age 4 1.7 0.00
Wing Chord ~ Intercept 1 20.1 0.00
61