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Supporting Information for:
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Habitat preference facilitates successful early breeding in an open-cup nesting songbird
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Ryan R. Germain, Richard Schuster, Kira E. Delmore, and Peter Arcese
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Appendix S1: Long-term patterns of early season grid cell preference
To evaluate whether female song sparrows selected grid cells non-randomly during the
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early breeding season, we used Pearson’s chi-square to compare the distribution of observed cell
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occupancy versus expected values of random cell occupancy from a Poisson distribution
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following Germain & Arcese (2014). For the 632 cells included in the analysis (see Methods),
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the mean frequency of overlap was 6.80 ± 5.04 (SD) nesting attempts per grid cell. We pooled
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the frequency of overlap (cell overlapped by at least one 100m2 buffer) between early season
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nesting attempts and a given grid cell into 13 categories (<2, 3, 4,..., 12, 13, >14) with expected
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frequencies greater than 5 to meet the assumptions of the χ2 statistic. We determined that grid
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cells were selected non-randomly during the early season (χ212 = 1921.72, p < 0.0001), with
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certain cells occupied more frequently than expected and many cells occupied only 1-3 times
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over the long-term study (Fig. S1).
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Fig. S1. Observed versus expected (given Poisson distribution) pattern of occupation by nesting
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song sparrows in 632 grid cells over 38 years.
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Appendix S2: Measuring incubation behaviour
Selection of monitored nests was based on the availability of un-deployed temperature
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loggers and the stage of incubation when the nest was discovered, with higher priority given to
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nests found earlier in the incubation period. In each instance, we inserted the temperature probe
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into the lining of the nesting cup when the female left the nest to forage, and allowed 30 minutes
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to pass before beginning to record temperature to account for any disturbance effects on female
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behaviour. Once loggers were removed from the nest, we used Rhythm 1.0 (Cooper & Mills
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2005) to identify transitions between incubation on/off bouts based on changes in nest
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temperature (off bouts determined by decrease in temp by 1.0˚C in 5 min). We then visually
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inspected transition selections using Raven Pro 1.4 (Bioacoustics Research Program 2011) to
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ensure consistent selection criteria in delineating transitions between incubation on/off bouts.
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For each measure of incubation behaviour (constancy and average off-bout duration) we
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limited our observation period to between 2-11 days before hatching (average Mandarte song
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sparrow incubation period = 13 days). Because of this nine day span in measured incubation
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behaviour, we extracted studentized residuals from a linear regression between the behaviour
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(cubed-root transformation) and number of days before hatch to remove variation in incubation
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behaviour due to stage of embryo development (Deeming 2006): constancy- (R2 = 0.07, F1, 67 =
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5.10, p = 0.03); average off duration- (R2 = 0.08, F1, 67 = 5.94, p = 0.02). We found no significant
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relationships between incubation behaviour and either Julian date or clutch size (80% of nests
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monitored contained 4 eggs), and thus excluded both from further analyses.
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Appendix S3: Assignment of time periods to account for sun location throughout the day
To determine appropriate cut-off points in our separation of cell-specific micro-climate
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by time of day, we plotted all measures of air temperature (°C) during the early breeding season
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(March 9-April 21, 2011-2013) versus ‘decimal time’ (time of day expressed as a value between
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0 [00:01] and 1 [23:59]). Next, we fit a cubic spline with Gaussian error structure through the
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data with a smoothing parameter (λ) that minimized the generalized cross-validation (GVC)
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score, indicating optimal separation of a smooth function from white noise (Fig. S2). We then
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visually inspected the output and classified four distinct time periods which represented the
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general state of temperature throughout the day (i.e., increasing, peak, decreasing, stable)
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independent of wind-speed. These time periods (represented by decimal time with Pacific
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Daylight Time in square brackets) consisted of: Morning (0.31 – 0.46 [7:30 – 11:00]), Mid-Day
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(0.48 – 0.63 [11:30 – 15:00]), Evening (0.65 – 0.83 [15:30 – 20:00]), and Overnight (0.85 – 0.0
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[20:30 – 24:00] + 0.0-0.29 [0:00 – 7:00]).
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Fig. S2. Cubic regression spline based on ‘best smoother’ (λ = 0.000026, R2 = 0.49, n = 22857)
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of relative temperature versus time of day expressed as a value between 0-1 (00:00-23:59).
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Vertical black lines represent cut-off points delineating the four time periods of overnight (a),
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morning (b), mid-day (c), and evening (d).
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Appendix S4: Modelling relative micro-climate of grid cells
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Detailed measures of the slope and aspect of the entire surface of Mandarte Island were
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retrieved via remote sensing, using aerial flyovers of Mandarte and surrounding islands and
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Light Detection and Ranging (LiDAR) sensor technology following (Jones, Coops & Sharma
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2010; Jones et al. 2013). LiDAR sensors produce pulses of near-infrared light which can
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penetrate vegetation canopies and retrieve information on ground surface elevation and
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orientation (Wehr & Lohr 1999). For each grid cell, detailed measures of vegetation
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characteristics previously found to be significant positive predictors of site preference (total
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shrub cover [m2], linear distance of shrub/grass interface [‘edge’, m], and soil depth [mm],
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Germain & Arcese 2014) were calculated by averaging two island-wide vegetation surveys
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conducted in 1986 and 2006. Although shrub species composition of individual grid cells has
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changed over time, the percent change in total island-wide shrub cover is estimated to be only
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7% between these two vegetation surveys (Crombie, Germain, Arcese, unpublished data). Daily
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summaries of local weather (average temperature [°C] and total precipitation [mm]) were
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obtained from the National Climate Archive of Environment Canada
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(http://climate.weather.gc.ca) for the Victoria International Airport station (48°38'50.010" N,
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123°25'33.000" W). Daily average temperature and total precipitation were highly positively
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correlated (r > 0.7), thus only precipitation was included in our predictive models of cell-specific
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micro-climate.
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We incorporated year, location of the weather meter (Lat, Long) and relative vegetation
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cover (0-5, see Methods) surrounding the weather meter as random effects in our analysis. We
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further accounted for temporal autocorrelation by using a first order autocovariate, which
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estimated the dependence between observations at time t and t-1. We generated separate models
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for measured wind-chill temperature (°C) for each of our four time periods (see Appendix S3),
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using all possible combinations of fixed effects (decimal time, Julian date, total daily
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precipitation, slope, aspect, total shrub cover, edge, and soil depth) following an information
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theoretic approach and averaging all models within ΔAICc of 7 from the top model (Table S1).
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We chose ΔAICc ≤ 7 here (as opposed to ΔAICc ≤ 2 as used for models of reproduction and
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incubation behaviour) to ensure that estimates of the relative influences on wind-chill
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temperature were as conservative and inclusive as possible. We then used the averaged models to
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generate a surface of relative differences in cell-specific micro-climate (‘relative micro-climate’)
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across the island to be used in further analyses, and found no spatial autocorrelation in this
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metric beyond 20m (i.e., roughly the diameter of two adjacent cells; Moran’s I < 0.2 for
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increments over 20m) for any of the four time periods.
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Table S1. Final averaged models for cell-specific wind-chill temperature (°C) across four time periods on Mandarte Island.
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Significant predictors (where SEs do not overlap zero) are depicted in bold. Total models ran for each time period = 256, s = number
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of top models in ΔAICc ≤ 7 subset, n = number of observations in each time period. Blank cells occur where a predictor was not
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present in ΔAICc ≤ 7 subset.
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Time period
Intercept
Overnight
s =4, n = 15976
Aspect
Decimal
Time
Date
Edge
(m)
-3.17
(1.01)
0.44
(0.03)
0.08
(0.008)
-0.001
(0.001)
Morning
s = 4, n = 5554
-12.00
(1.97)
39.37
(4.18)
0.06
(0.02)
-0.0007
(0.001)
-0.01
(0.01)
-0.02
(0.005)
Mid-Day
s = 6, n = 5776
13.28
(1.48)
3.27
(0.75)
0.001
(0.002)
-0.0008
(0.002)
-0.29
(0.02)
-0.03
(0.008)
Evening
s = 6, n = 7459
30.96
(2.41)
-36.18
(2.09)
0.06
(0.01)
-0.0004
(0.001)
-0.18
(0.02)
-0.003
(0.003)
0.0004
(0.0003)
Daily
Precip.
(mm)
Shrub
Cover
(m2)
Slope
Soil
Depth
(mm)
0.001
(0.001)
-0.0004
(0.001)
-0.001
(0.002)
-0.0001
(0.001)
-0.001
(0.001)
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The predicted micro-climate of each grid cell was highly correlated with the vegetation
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characteristics of the cell, indicating that areas of the island with greater vegetation structure
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(total shrub cover, deeper soil) were relatively cooler overall (Figs. S3, S4). Linear distance of
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edge exhibited a quadratic relationship with micro-climate, as cells with minimal/no edge could
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represent either tall grass meadow or areas completely immersed in vegetative cover.
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Fig. S3. Predicted relative micro-climate (shown as °C) for 632 cells versus total shrub cover
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(shown as percentage of cell under shrub cover), linear distance of edge (m) and soil depth (mm)
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of the cell, averaged across two vegetation surveys conducted in 1986 and 2006.
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Fig. S4. Predicted relative micro-climate (shown as °C) for 632 cells across Mandarte Island. Areas of increased shrub cover (central
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line of the island) are relatively cooler overall throughout the Morning, Mid-Day, and Evening periods. Grid cells are nearly uniformly
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cool during the Overnight period (note small scale bar).
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Appendix S5: Shrub selection criteria for assessments of food availability
We sampled bud phenology and food availability on the two most abundant deciduous
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shrub species present on Mandarte Island (Snowberry [Symphoricarpos albus] and Nootka rose
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[Rosa nutkana]). The selection of individual shrubs from which food/phenology measures were
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taken was independent of the system of grid cells, and based solely on the distribution of
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snowberry and rose in the local area following a 2006 vegetation survey of the island using
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400m2 plots (Germain & Arcese 2014). Individual shrubs were selected using a stratified random
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sampling design, where four pairs of locations where chosen for each species from a map of the
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local area depicting the extent of total shrub cover (but not shrub species/height, etc), and the
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four sampling locations were determined via coin flip from the original eight selected. Once at
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one of the four sampling locations, two shrubs from each species were selected and the sample
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shrub was again determined via coin flip, and the locations of the sampled shrubs were mapped.
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Over 95% of surveys were conducted by RRG, with supplemental sampling conducted under the
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supervision of RRG.
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To estimate differences in plant phenology and insect abundance across grid cells, we
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quantified cell-level deviation from the island-wide mean for each measure (studentized
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residuals), following a series of mixed effects models with shrub species as a random effect and
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Julian date as a fixed effect, weighted by the number of shrubs sampled in each cell.
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(Version 1.4). The Cornell Lab of Ornithology. URL: http://www.birds.cornell.edu/raven.
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Cooper, C.B. & Mills, H. (2005) New software for quantifying incubation behavior from timeseries recordings. Journal of Field Ornithology, 76, 352–356.
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Deeming, D.C. (2006) Behviour patterns during incubation. Avian Incubation: Behaviour,
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