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\begin{document}

\title{Factors affecting the nesting success of Swainson's thrush
(\emph{Catharus ustulatus}) along an elevational gradient}



\author[1]{Sarah Deckel}%
\author[2]{William DeLuca}%
\author[1]{Alexander Gerson}%
\author[1]{David King}%
\affil[1]{University of Massachusetts Amherst}%
\affil[2]{National Audubon Society}%


\vspace{-1em}



  
  \date{June 06, 2024}


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\selectlanguage{english}
\begin{abstract}
Montane birds experience a range of challenges that may limit their
breeding success, including nest predation and severe climactic
conditions. The continuing effects of climate change are causing shifts
in biotic and abiotic factors that may compound these threats to montane
bird species. In northeastern montane forests, many bird species are
shifting downslope, potentially as the result of increased precipitation
and temperature at higher elevations. Although lower elevations might be
more favorable in terms of climactic conditions, nest predation is
higher at lower elevations. Thus, montane birds might be faced with the
opposing pressures of adverse climactic conditions at higher elevations
and increased predation at lower elevations. We monitored nests of
Swainson's thrush (Catharus ustulatus) along an elevation gradient in
the White Mountain National Forest in New Hampshire in 2016, 2018, 2019
and 2021 to examine the effect of biotic and abiotic factors on nest
survival. We found a significant negative effect of rain intensity
(millimeters per hour per day) on daily nest survival, suggesting that
heavier rain per hour decreases Swainson's thrush daily daily nest
survival. Moreover, we found a negative interaction effect of elevation
in conjunction with minimum daily temperature and average daily
temperature, suggesting that at higher elevations, temperature,
specifically on colder days, decreases Swainson's thrush nest survival.
Our results provide evidence for a potential mechanism of how climate
change will affect nesting survival of montane breeding birds as heavier
precipitation events become more frequent and intense, a likely outcome
due to the changing climate within the White Mountains and other montane
ecosystems, putting other passerine species at risk in this system.%
\end{abstract}%



\sloppy


\textbf{Sarah C. Deckel\textsuperscript{1}, William V.
DeLuca\textsuperscript{1,2}, Alexander R. Gerson\textsuperscript{3},
David I. King\textsuperscript{1,4}}

\textsuperscript{1}University of Massachusetts, Amherst, Department of
Environmental Conservation, Amherst MA

\textsuperscript{2}National Audubon Society, Amherst MA

\textsuperscript{3}University of Massachusetts, Amherst, Department of
Biology, Amherst MA

\textsuperscript{4}Northern Research Station, USDA Forest Service,
Amherst MA

\emph{Contact information:}

\textbf{Sarah Deckel,} sdeckel@umass.edu, 401-871-0973

\textbf{Dr. William DeLuca,} william.deluca@audubon.org

\textbf{Dr. Alexander Gerson,} argerson@umass.edu, 413-545-2782

\textbf{Dr. David King,} daveking@umass.edu, 413-545-6795

\textbf{Abstract}

Montane birds experience a range of challenges that may limit their
breeding success, including nest predation and severe climactic
conditions. The continuing effects of climate change are causing shifts
in biotic and abiotic factors that may compound these threats to montane
bird species. In northeastern montane forests, many bird species are
shifting downslope, potentially as the result of increased precipitation
and temperature at higher elevations. Although lower elevations might be
more favorable in terms of climactic conditions, nest predation is
higher at lower elevations. Thus, montane birds might be faced with the
opposing pressures of adverse climactic conditions at higher elevations
and increased predation at lower elevations. We monitored nests of
Swainson's thrush (\emph{Catharus ustulatus} ) along an elevation
gradient in the White Mountain National Forest in New Hampshire in 2016,
2018, 2019 and 2021 to examine the effect of biotic and abiotic factors
on nest survival. We found a significant negative effect of rain
intensity (millimeters per hour per day) on daily nest survival,
suggesting that heavier rain per hour decreases Swainson's thrush daily
daily nest survival. Moreover, we found a negative interaction effect of
elevation in conjunction with minimum daily temperature and average
daily temperature, suggesting that at higher elevations, temperature,
specifically on colder days, decreases Swainson's thrush nest survival.
Our results provide evidence for a potential mechanism of how climate
change will affect nesting survival of montane breeding birds as heavier
precipitation events become more frequent and intense, a likely outcome
due to the changing climate within the White Mountains and other montane
ecosystems, putting other passerine species at risk in this system.

\emph{Keywords:} montane, nest survival, abiotic factors, White
Mountains

\subsection*{Introduction}

{\label{introduction}}

Montane bird species that breed within high elevation forests are
particularly vulnerable to the effects of climate change due to the
narrow elevational ranges they inhabit (Scridel et al., 2018), making
them susceptible to extirpation (Sekercioglu et al., 2008) if their
elevational ranges are compressed due to climate change and
anthropogenic land uses (Rodenhouse et al., 2008). Research within
northeastern forests of the United States confirms that the average
temperature within this system has increased over the last several
decades (Murray et al., 2021; Wason et al., 2017), which is likely
causing recently documented downslope shifts in montane birds (DeLuca \&
King, 2017) and high-elevation vegetation (Foster \& D'Amato, 2015;
Wason et al., 2017).

Coincident with these changes, the elevational distribution of montane
birds has also shifted downwards (DeLuca \& King, 2017), although the
motives for these shifts, and their consequences for montane breeding
birds, is unclear. Within northeastern high elevation forests,
populations of montane birds are declining (Ralston et al., 2015),
potentially because elevational range shifts are exposing birds to
unfamiliar resources and conditions along the elevation gradient that
could affect their breeding success. For example, breeding success of
blackpoll warblers (\emph{Setophaga striata} ), is lower at low
elevations, likely due to increased abundance of red-squirrels, a major
nest predator in this system (DeLuca, 2013). Similarly, dark-eyed juncos
(\emph{Junco hyemalis} ) at high elevations produced nestlings in better
condition and experienced higher survival rates than birds at lower
elevations (Bears et al., 2009). These examples raise the possibility
that lower elevations are less suitable for montane birds, and that the
documented downward shifts in their elevational ranges may expose these
species to elevated nest predation (Boyle 2008, Camfield et al., 2010;
DeLuca, 2013).

Alternatively, abiotic factors, such as cold temperatures and increased
precipitation, may affect reproductive success of birds nesting at high
elevations (Pierce et al., 2019). Warmer temperatures promote larger egg
size (Johnson 2000; Lessells et al., 2002), increased clutch size
(Hargrove \& Rotenberry, 2011; Rotenberry \& Wiens, 1991), and improved
hatching and fledgling success (Martin, 1987; Reid et al., 2000), and
greater food availability (Bears et al., 2009; Svensson \& Nilsson,
1995). Increased precipitation may also negatively influence nesting
survival of breeding birds (Bordjan \& Tome, 2014; Dinsmore et al.,
2002; Dreitz et al., 2012; Fisher et al., 2015; McCain \& Colwell, 2011;
\selectlanguage{ngerman}Öberg et al., 2015; Schöll \& Hille, 2020), particularly during intense
rain events (Öberg et al., 2015; Sexson \& Farley, 2012). High elevation
sites in the Northeast U.S. are experiencing more frequent rain events
(Wright 2009; Karmalkar \& Bradley, 2017). Precipitation is known to
negatively affect avian nesting success and as precipitation intensities
are increasing in frequency, this raises concern for montane birds
nesting in this region.

Due to the reported vulnerability of montane birds and the threats that
a changing climate imposes on high elevation species (Ralston \& DeLuca,
2020; Ralston \& Kirchman, 2013), we located and monitored nests of a
representative montane bird species, the Swainson's Thrush
(\emph{Catharus ustulatus} ) within the White Mountains of New
Hampshire, USA. We considered three predictions: 1) nests at higher
elevations would be more likely to be successful due to lower predation
events (Boyle, 2008; Camfield et al., 2010), 2) average minimum daily
temperature, minimum daily temperature, or higher temperature range
(i.e., greater daily variability in temperature) would negatively affect
daily survival rate (Pierce et al., 2019), and 3) increased daily
cumulative precipitation, more intense rain bouts (i.e., more
millimeters of rain per hour), or heavy precipitation events would
negatively affect daily survival rate (Dinsmore et al., 2002; Dreitz et
al., 2012; Pierce et al., 2019). Documenting the abiotic effects on
reproductive success of birds along elevation gradients will contribute
to a better understanding of future climatic responses by species
(Martin, 2001; Tingley et al., 2012), something that is largely
understudied within northeastern montane forests.

\subsection*{Materials and Methods}

{\label{materials-and-methods}}

\textbf{Study area and study species.}

We researched nest survival of Swainson's thrush along an elevational
gradient in the White Mountains of New Hampshire, USA at Mt. Jefferson
(approximately 500-1,250m; 44.3045° N, 71.3176° W) and Bartlett
Experimental Forest (hereafter referred to as BEF; approximately
200-300m; 44.0556° N, 71.2973° W). The Swainson's Thrush is a Nearctic
Neotropical migratory songbird which is moderately common and breeds
along a wide elevation gradient within this system (Figure 1,
200-1,250m; Mack \& Yong, 2020). Though their populations were once
documented to be declining (Holmes \& Sherry, 2001; Robbins et al.,
1989), recent research has shown their ranges to be increasing (Ralston
et al. 2015) as well as shifting downslope (DeLuca and King, 2017),
suggesting population increases. Nest survival data were collected from
May through August 2016, 2018, 2019, and 2021 (field season canceled in
2020 due to COVID-19) in the White Mountains at Mt. Jefferson (all
years) and BEF (2018, 2019, and 2021). The surrounding plant community
on Mt. Jefferson below 700m in elevation consists of northern hardwood
forest dominated by American beech (\emph{Fagus grandifolia} ), paper
birch (\emph{Betula papyrifera} ), yellow birch (\emph{Betula
alleghaniensis} ), and sugar maple (\emph{Acer saccharum} ). Between
700m and 1,300m is a mixed forest of deciduous and coniferous of birch
trees and red spruce (\emph{Picea rubens} ), balsam fir (\emph{Abies
balsamea).} As elevation increases above 1,250m, high alpine boreal tree
species become more abundant before reaching treeline at
\textasciitilde{}1,350m. BEF ranges from 200-400m in elevation, though
to capture low elevation breeding populations of Swainson's thrush,
nests were searched only between 200-300m. Tree species within BEF are
primarily American beech, paper birch, red spruce and eastern hemlock
(\emph{Tsuga canadensis} ), though Swainson's thrush typically build
their nests in dense patches of small (\textless{}8 cm DBH) red spruce
and eastern hemlock trees.

Swainson's thrush typically arrive in New Hampshire between late May to
early June and their nesting period is 28 days (+/- 2) with four stages:
nest building (4 days), laying and incubation of the eggs (12 days), and
provisioning and caring for nestlings before fledgling (12 days). Female
Swainson's thrush typically have one brood, though they can renest, and
they build their nest in red spruce, balsam fir, and paper birch trees,
ranging anywhere from 3-10m in height (Mack and Yong, 2020). Nests are
typically built with bark from yellow birch or paper birch, as well as
miscellaneous tree branches, twigs, and moss, and can occasionally have
artificial materials in them (i.e., plastic bags, bottle caps).

\textbf{Nest monitoring.}

Fieldwork to locate breeding pairs and nests began approximately 25 May
(+/- 2) each year. Parental behavioral cues and rigorous nest searching
are both important techniques to find nest locations (Martin and Geupel,
1993). Searching effort was relatively evenly distributed among sites.
Once found, nests were marked with a GPS and flagged no less than 8m
away from the active nest so as not to disturb the nesting pair. We
monitored each nest every 2-3 days until they were close to fledging
(\textasciitilde{}11 days old), and then every day after until fledged.
If presumed fledged, we searched the area meticulously to take notes
such as provisioning behavior from parents (i.e., food in their bill,
alarm calling, etc.) or visual sightings. A successful nest yielded at
least one fledged bird, and a failed nest was depredated or abandoned.
Nests were considered depredated if an active nest was observed with
cracked or missing eggs, dead young nestlings (\textless{}11 days old)
and/or adults, or a nest that was pulled apart. On occasion, nests were
abandoned and confirmed as such if adults were absent for \selectlanguage{english}[?]3 visits. We
collected nesting data including initiation date (i.e., date the first
egg was laid), clutch size, hatch date (i.e., date the first egg
hatched), and nestling condition (i.e., how old the nestlings were at
the date checked and what their appearance was). When hatch date was
unknown, we back-calculated nest age based on the nestlings' condition.
Finally, if nest initiation date was unknown, we backdated from known or
estimated hatch date, assuming one egg was laid per day, incubation
occurred for 12 days, and nestlings were at least 11 days old before
fledgling (Ha\selectlanguage{polish}ł\selectlanguage{english}upka \emph{et al.} , 2018). Nests were binned in four
groups (``BEF'' = 200-300m; ``Low Jeff'' = 500-700m; ``Mid Jeff'' =
701-900m; ``High Jeff'' = 901-1,250m) to correspond with the matching
spatial precipitation and temperature data that was collected along the
elevation gradients.

\textbf{Environmental covariates.}

Hourly ambient temperature (\selectlanguage{ngerman}°C) was collected using Thermocron ® iButton
loggers (Maxim Integrated) from 25 May to 31 July 2016, 2018, and 18
May-3 August 2019 and 2021. Loggers were placed at long-term GPS
coordinates (Duclos et al., 2019) that represented the elevation
gradient we were interested in and corresponded with bins used for nests
(280m, ``BEF''; 550m, ``Low Jeff''; 800m, ``Mid Jeff; and 1,200m, ``High
Jeff''). Loggers were held in a Holden shield to protect the iButton
from direct solar exposure (Holden et al., 2013). Daily average
temperature, minimum daily temperature, and temperature range was
calculated and used to analyze the effect on daily nest survival.
Precipitation data were collected in 2019 and 2021 using Onset ® HOBO ®
RG3-M rain gauges. To capture the daily cumulative rainfall across the
elevation gradient, we placed three rain gauges at Mt. Jefferson at
locations similar to the iButton loggers (500m, ``Low Jeff''; 800m,
``Mid Jeff''; and 1,200m, ``High Jeff'') in open areas with no tree
canopy. We used precipitation data collected in BEF (250m, ``BEF'') from
the NSF National Ecological Observatory Network (NEON), an open-sourced
data platform. Within each rain gauge is a tipping bucket that measured
how much cumulative precipitation fell each day throughout the season.
Precipitation data were not collected in 2016 and 2018 and, therefore,
will not be analyzed with nest survival data from those years. Rain
intensity was quantified by how much rain (mm) was recorded per hour per
day.

Due to equipment malfunctions from the HOBO Rain Gauge placed at 500m
(i.e., ``low-Jeff), precipitation data from 1 July -- 3 August 2021 was
lost. To estimate the missing daily precipitation at this location for
the remainder of the 2021 season, we used the Random Forest (RF)
machine-learning algorithm. When tested against other algorithms to
estimate large amounts of missing precipitation data, the RF was the
most preferred method (Aguilera et al., 2020), in the context of our
data. The RF algorithm is a non-parametric imputation method that takes
observed values of each variable, and predicts the missing values,
without having any assumptions about the distribution of data or
imputation models. We followed the recommendations from Aguilla,
Guardiola-Albert and Serrano-Hidalgo (2020) and included dummy variables
to account for no rain days, a Hanssen-Kuipers (HK) score to distinguish
between occurrences and non-occurrences of a rain event (Hanssen and
Kuipers 1965), and an NRMSE (normalized root error mean squared error)
error metric to allow for the comparison of the average relative
differences between the observed and imputed missing values
(Supplementary Figure 1).

Dummy variables were produced by randomly assigning the missing data
matrix with presence (1) or absence (0) of rain days. For each day we
considered the dates where the dummy variable was 0 and input 0 for that
day, and only the days the dummy variable predicted a value of 1 did we
input the numeric value estimated from the RF algorithm. The HK score
ranges between -1 to +1, where 0 represents no skill or a random
estimate, and 1 indicates a perfect estimate, and has been widely used
to evaluate yes/no meteorological forecasts (Woodcock 1976, Teegavarapu
2014, Kim and Ryu 2016). We used the package \emph{missForest} in R
Studio (version 2022.07.0; R Core Team 2022) for the RF algorithm to
input missing precipitation values.

\subsection*{Statistical analysis.}

{\label{statistical-analysis.}}

\emph{Daily survival rate.} We measured the daily survival rate (DSR),
i.e., the probability that a nest survives one day (Dinsmore et al.,
2002; Mayfield, 1961) to better understand how abiotic factors
(temperature and precipitation), nest initiation date, elevation, and
other temporal variables influence nesting survival of Swainson's
thrush. We analyzed DSR using the RMark package (Laake et al., 2013) in
R Studio (v. 2022.07.0). This package calculates estimates of DSR using
a maximum likelihood estimator with the logit-link function with
predictor variables. The assumptions are that DSR is the same for all
nests on all days and for all nest ages (nest fates are independent and
identically distributed: \emph{iid} ; Rotella, 2006) though there is
currently no goodness-of-fit test for nest survival models in rMark
(Dinsmore et al., 2002; Laake et al., 2013), and rMark cannot run mixed
models (Rotella et al., 2004). Daily survival probabilities were
calculated by taking the beta parameter estimate and raising it to the
power of the number of nesting days (i.e., 28 for Swainson's thrush).
See Rotella et al. (2004) for a more in-depth description of how MARK
interprets the temporal covariates as an encounter history. We included
nests in our analysis when they met the following criteria: 1) known day
the nest was found, 2) the last day the nest was active, 3) the last day
the nest was checked, and 4) known nest fate (i.e., fledged or failed).
We standardized the ordinal dates to be the duration that nests were
first active (i.e., initiation date) which translated to 0 (1 June)
until the last known active nest date, 63 (2 August) over the four years
(number of occupancy days, NOCC = 63).

\emph{Environmental covariates.} We used cumulative daily precipitation
(mm; 2019, 2021), categorical rain events denoted as either no rain (0),
``light'' rain (0.1-6.9mm) or ``heavy'' rain (\textgreater{}7mm), and
rain intensity (mm per hour) as predictor variables in our daily
survival models. Rain event parameters were determined by examining the
data in a frequency plot and categorizing ``light'' and ``heavy'' rain
days accordingly. Average daily temperature, minimum daily temperature,
and daily temperature range (°C; 2019, 2021) were separately modeled
against daily survival to better understand how temperature in the White
Mountains affects daily survival probability. Nests from 2016 and 2018
were excluded from precipitation and temperature analysis because we did
not collect climate data from those years. Nests were binned in four
groups (``BEF'' = 200-300m; ``Low Jeff'' = 500-700m; ``Mid Jeff'' =
701-900m; ``High Jeff'' = 901-1,250m) to correspond with the matching
spatial precipitation and temperature data that was collected along the
elevation gradients.

\emph{Model selection.} We began the model building stage by testing
pairwise correlation comparisons in our predictor variables for our nest
survival data (elevation, nest age, initiation date) and the
environmental covariates (elevation, daily average temperature, minimum
daily temperature, temperature range, daily cumulative precipitation,
and rain intensity). This comparison revealed a negative correlation
coefficient with elevation and daily temperature range (\emph{p} =-0.46)
and, therefore, these variables were not included in the same model.

We created 14 \emph{a priori} DSR univariate models that examined the
effects of temporal variables including nest age, julian date, and year,
as well as initiation date, elevation, and our weather covariates on the
daily nest survival of Swainson's thrush (Table 1; models 2-3, 5-13). We
also included an intercept model (model 1) in which only daily survival
rate was estimated, and a quadratic time trend (model 4), which allows
for nest survival to fit a curvilinear pattern, since survival over time
is not always linear. We included several other models (Supplementary
Table 1) in the initial stages of model building to test for any
relevant additive or interaction effects, including a global model, but
because they did not perform well, they were removed from our final
analysis.

After evaluating variables \emph{a priori} models above, and removing
necessary models that performed poorly, we added two additional models
with interaction effects (year and elevation) with seasonal time. This
predictor variable performed the best against DSR (Table 1; models 15
and 16). We then built four more models that included biological
interaction and additive effects we were interested in (models 17-23) to
explore all potentially important relationships between our variables
and DSR. In the final stage of model building, we used AIC (Akaike's
Information Criterion) to find support for the most parsimonious model
that best described DSR (Anderson \& Burnham, 2002) and compared models
1-5 (Table 1) between all years, and models 6, 8-13, and 19-20 for 2019
and 2021 (Table 1). We considered the best model to have a
\selectlanguage{english}[?]AIC\textsubscript{c} value of zero, variables in models that were
within 4 \selectlanguage{english}[?]AIC\textsubscript{c} of the best model with 95\% confidence
intervals not including zero to be strongly supported (Arnold, 2010),
and variables in models within 4 \selectlanguage{english}[?]AICc with 90\% confidence intervals to
have some support (Hein et al., 2008; Long et al., 2008). Model outputs
are reported with 95\% confidence intervals otherwise noted.

\subsection*{Results}

{\label{results}}

\textbf{Data summary.}

A total of 58 nests were found and included in the initial stage of
analyses between 2016 (n=5), 2018 (n=6), 2019 (n=21), and 2021 (n=26)
from 31 May to 2 August. Out of these 58 nests, 24 succeeded (42\%) and
34 nests failed (58\%), either due to nest predation (71\%) or
abandonment (29\%). We found 10 nests at ``BEF'', 14 nests at ``Low
Jeff'', 19 nests at ``Mid Jeff'', and 15 nests at ``High Jeff''. Mean
daily temperature (Fig. 1A) and precipitation (Fig. 1B) varied depending
on the year and elevation. Across both study sites throughout the
season, average minimum daily temperature was 11.4degC (\emph{se} =0.21),
and average temperature range was 11.5degC (\emph{se} =0.25). When we
examined the effects of initiation date, precipitation, and temperature
from 2019 and 2021, 44 nests were included in the analysis. After
backdating for initiation and hatch date, we removed 3 nests from the
analysis because nests were found in the incubation stage but failed
before hatch date. Average initiation date was June
17\textsuperscript{th} (+/- 2 days), and average clutch size was 3.1
eggs in 2019 and 3.2 in 2021. The RF algorithm calculated 34 missing
days of precipitation data (NRMSE = 0.078, HK = 0.39) for the ``Low
Jeff'' Rain Gauge (1 July -- 3 August 2021).

\textbf{Daily survival rate.}

\emph{All years.} The first round of model building included temporal
variables with nests from all years (2016, 2018, 2019, 2021; NOCC=63;
Table 2). Daily survival probability for Swainson's thrush across the
four-year sampling period was 23\%. We did not find support that
elevation affected daily survival probability (\emph{b} =0.0005, 95\% CI
{[}-0.0008, 0.0019{]}, 90\% CI {[}0.0017, -0.0005{]}). The model with
the most support suggested that nests have a higher probability of
failing as the season progresses (Table 2; \emph{b} =-0.02, 95\% CI
{[}-0.05, 0.0092{]}). When we examined linear time within 90\%
confidence intervals, the model suggests some support (\emph{b} =-0.02,
90\% CI {[}-0.003, -0.051{]}). We examined all models using AIC and
found two models that were within 2 \selectlanguage{greek}Δ\selectlanguage{english}AICc values of one another and
demonstrated the strongest support above the null model, which were the
quadratic time trend and linear time trend (Table 3). Finally, we ran an
interaction effect between time and elevation (\emph{b} = -0.00004, 95\%
CI {[}-0.0001, 0.00006{]}) but this model did not perform well and was
removed from our model comparison output.

\emph{2019 and 2021.} In the second stage of our analysis, we built
models for nests from 2019 and 2021 (Table 2) and included temperature
and precipitation covariates, as well as effects with initiation date
against daily survival rate. There was no evidence that nest survival
was affected by either initiation date (\emph{b} = -0.007, 95\% CI
{[}-0.047, 0.032{]}) or elevation (\emph{b} = 0.0003, 95\% CI
{[}-0.0009, 0.0016{]}). Nest survival was negatively related to linear
time (\emph{b} =-0.03, 90\% CI {[}-0.002, -0.057{]}), indicating some
support that nest survival declined over the 2019 and 2021 nesting
season.

We ran an interaction effect between elevation and several other
meaningful parameters, including initiation date (\emph{b} = -0.00003,
95\% CI {[}-0.0001, 0.00008{]}), linear time (\emph{b} = -0.00003, 95\%
CI {[}-0.0003, 0.00006{]}) and nest age (\emph{b} = 0.00004, 95\% CI
{[}-0.00008, 0.00016{]}) but we did not find support for these
relationships. We also included an interaction between linear time and
year (Table 1, model 15) because precipitation varied markedly among
years (Fig. 1A\&B), and we were interested in evaluating year for other
interannual variation, but this relationship did not demonstrate an
effect (Table 2; \emph{b} = -0.002, 95\% CI {[}-0.07, 0.06{]}).

\emph{Environmental covariates (2019 and 2021 only).} Our models did not
show support that average daily temperature (\emph{b} = -0.02, 95\% CI
{[}-0.15, 0.11{]}), minimum daily temperature (\emph{b} = -0.02, 95\% CI
{[}-0.13, 0.08{]}) or daily temperature range (\emph{b} =0.06, 95\% CI
{[}-0.04, 0.16{]}) affected daily nest survival in Swainson's thrush
(Table 2). We found strong support that an interaction between elevation
and minimum daily temperature effected daily survival rate (\emph{b} =
-0.007, 95\% CI {[}-0.001, -0.0002{]}). We also found strong support for
an interaction between elevation and average daily temperature (\emph{b}
= -0.0006, 95\% CI {[}-0.001, -0.000009{]}). Because our other
temperature variable (i.e., daily temperature range) demonstrated
collinearity with elevation, we did not test interaction effects of
these relationships.

We examined ``light'' and ``heavy'' rain events and found support that
light rain (i.e., 0-6.9mm) negatively affected the daily survival rate
of Swainson's thrush nests (\emph{b} = -1.39, 95\% CI {[}-2.72,
-0.05{]}) but we did not find an effect of ``heavy'' rain events
(\textgreater{}7mm; \emph{b} = 3.51, 95\% CI {[}-144.70, 151.73{]})
(Table 2). We further investigated our rain metrics by quantifying rain
intensity as millimeters per hour and found a strong negative
relationship on daily survival rate (\emph{b} =-0.09, 95\% CI {[}-0.18,
-0.01{]}; Figure 3). We did not find an effect of cumulative daily
precipitation on daily nest survival probability (\emph{b} = 0.01, 95\%
CI {[}-0.09, 0.11{]}). We did not find any support for an interaction
effect between rain intensity and cumulative daily precipitation
(\emph{b} = 0.004, 95\% CI {[}-0.02, 0.03{]}), rain intensity and nest
age (\emph{b} = 0.003, 95\% CI {[}-0.007, 0.015{]}), and elevation and
rain intensity (\emph{b} = -0.00005, 95\% CI {[}-0.0007, 0.0006{]}).

In the final stage of our analysis for nests from 2019 and 2021, we
compared models of interest using AIC. This revealed two competing
models against DSR, the most parsimonious model to be the interaction
between elevation and minimum daily temperature against daily survival
rate (Table 3). We found some support for the second most competitive
model, suggesting that as rain intensity increases, the daily survival
rate of Swainson's thrush decreases (\selectlanguage{greek}\textbf{Δ} \selectlanguage{english}AICc \textless{}4; Table
3), though it should be noted that this model was within 2\selectlanguage{greek}\textbf{Δ}
\selectlanguage{english}AICc values of the null model.

\textbf{Discussion}

Our results demonstrate that within these northeastern forests, nest
survival of passerine birds is negatively influenced by abiotic factors
(i.e., intense rain bouts and minimum and average daily temperature) and
nests have a lower probability of surviving later in the breeding
season. These findings suggest important implications for montane
breeding birds as warmer temperatures and more frequent precipitation
events are likely to occur due to climate change (Murray et al., 2021;
Westra et al., 2014), both factors that have been documented to effect
species' populations in this region (Duclos et al. 2019).

Montane birds face several tradeoffs when breeding along an elevation
gradient. For example, DeLuca (2013) documented that blackpoll warblers
are more likely to have a successful nest and overall higher annual
fecundity at high elevation breeding sites. However, high-elevation
breeding pairs of dark-eyed juncos (\emph{Junco hyemalis} ) had overall
lower nest success, yet their offspring were in better condition than
their low elevation population (Bears et al. 2009), potentially because
of higher parental investment in these conditions (Badyaev \& Ghalambor,
2001; Bears et al., 2009). We predicted that Swainson's thrush nest
survival would have a higher probability of success at high elevations
due to the lower predator effects at high elevations (Boyle, 2008;
Camfield et al., 2010), and higher success from evidence DeLuca (2013)
found in blackpoll warblers, however, our model did not support this
prediction. Another purpose for examining this effect was because nest
depredation within the White Mountains has historically been affected by
red squirrel (\emph{Tamiasciurus hudsonicus} ) densities on biennial
cone cycles (Townsend et al., 2015), creating challenges for nesting
bird species when these fir trees produce mass crops. Specifically, red
squirrel densities negatively affected nest survival of American
redstarts at a site approximately 64 kilometers west of our study site
(Hubbard Brook Experimental Forest, Sherry et al., 2015), raising the
possibility that high nest predation is possible during these
fluctuating seed-bearing trees (Bergeron et al., 2011; Holmes, 2011).
Furthermore, predation from birds may be more active in warmer
temperatures (Cox et al., 2013), potentially resulting in unusually
higher predation events in warmer, mass crop years. If montane species'
ranges continue to shift upslope (DeLuca \& King, 2017; Van Tatenhove et
al., 2019), likely in response to climatic influences, they may avoid
the increased predation effects at lower elevations but consequently,
will be at risk of high elevation abiotic influences. Though we didn't
monitor predator density due to equipment and time constraints, further
investigation of predator abundances and mass crops of fir trees along
the elevation gradient would help inform how influential predation could
be on nest survival of passerine species.

Extreme precipitation events have been shown to negatively influence
nest survival of several passerine species, including in great tits
(\emph{Parus major} ; Bordjan \& Tome, 2014; Sch\selectlanguage{ngerman}öll \& Hille, 2020),
northern wheatears (\emph{Oenanthe oenanthe;} Öberg et. al 2015), and in
mountain plovers (\emph{Charadrius montanus;} Dinsmore et al., 2002;
Dreitz et al., 2012). Additionally, due to the orographic effect (i.e.,
colder high elevation air forcing clouds to release water), higher
montane elevations are likely to receive more rain, prompting us to
investigate the relationship between precipitation and nest survival. We
found a negative effect of light rain days (0.1-6.9mm daily cumulative,
Table 2) and rain intensity on DSR (Table 2, Figure 3) supporting
previously documented research and suggesting that rain bouts have
negative consequences on DSR of Swainson's thrush. Also, we documented
large accumulations of precipitation between 30 June-4 July 2021 (Figure
2b) and had several nest failures throughout the elevation gradient (4
depredated, 4 abandoned) during this time. We examined an interaction
effect between seasonal time and daily cumulative precipitation to
evaluate if there were more nest failures after this rain event as well
as an interaction between nest age and rain intensity to determine if
the nest stage was affected by the amount of daily rain, but we did not
find support for these relationships. Nests that failed between 30
June-4 July 2021 had nestlings \textasciitilde{}5+ days old, and perhaps
parents were unable to maintain their own energetic demands in addition
to caring for their nestlings (Martin, 1987; Martin, 1995) and chose to
renest after the inclement weather passed, though this is risky in that
fledglings born later in the season are less likely to survive (Shutler
et al., 2006). As the climate continues to warm, more intense
precipitation events will become more common (Trenberth, 2011; Westra et
al., 2014), putting these montane breeding birds at risk of these
effects.

Colder temperatures have been shown to negatively influence nest
survival of high elevation breeding bird species (Pierce et al. 2019)
and can delay spring insect emergence(Bears et al., 2009; Forrest \&
Thomson, 2011), especially at higher elevation sites where there may be
persisting spring snow (Hahn et al., 2004). Additionally, warm
temperatures can promote arthropod abundance (Reneerkens et al., 2011;
Tulp \& Schekkerman, 2008) as well as higher hatching success (Martin,
1987) and fledgling success (Reid et al., 2000). Despite our first
prediction, these studies raise the possibility that lower elevation
breeding populations may have an advantage over the colder, wetter high
elevation nesting pairs. We investigated several different temperature
metrics after predicting that minimum daily temperature would have a
negative effect on DSR and found strong support for this relationship,
in addition to a negative effect of average daily temperature on DSR,
but only in conjunction with elevation. This supports our prediction
that there are negative effects of temperature on DSR, and further
suggests a lower probability of Swainson's thrush nest survival with
increasing elevation and colder temperatures. However, Dreitz et al.
(2012) reported higher nest success in mountain plovers in cooler
temperatures, and perhaps is preferable for high elevation ground
nesting breeding birds, since some predators may be more active when
temperatures are warmer (Cox et al., 2013).

Warming temperatures and inclement weather events are likely a product
of the changing climate (Hamburg et al., 2013; IPCC, 2014; Seidel et
al., 2009), resulting in negative direct and indirect effects on species
that breed in the White Mountains (Duclos et al., 2019) and causing both
upslope and downslope shifts in several montane bird species (DeLuca \&
King 2017; Van Tatenhove et al., 2019). Although previous literature has
documented positive relationships with nest survival and warmer
temperatures (Hargrove \& Rotenberry, 2011; Martin, 1987; Reid et al.,
2000), these climatic effects can trigger major phenological changes and
mismatches within bird populations (Leech \& Crick, 2007). This could be
a disadvantage for single brood species, such as the Swainson's thrush
(Mack \& Yong, 2020) who rely on certain timing of events (i.e., insect
emergence, habitat availability, food abundance) during the breeding
season. It should be noted that this phenological phenomenon was not
documented at a nearby experimental forest within the White Mountains
(black-throated blue warblers, \emph{Setophaga caerulescens} in Hubbard
Brook Experimental Forest; Lany et al., 2016). However, Swainson's
thrush breed at higher elevations and occupy sensitive habitat, perhaps
putting them (and other high-elevation breeding birds) at greater risk
of climatic and phenological influences as a result of the warming
temperatures.

Evidence within the White Mountains overwhelmingly signals that the
climate is changing, leading to more frequent precipitation events in
the future (IPCC, 2014; Murray et al., 2021; Wason et al., 2017). Duclos
et al. (2019) found that Swainson's thrush populations were directly
affected by precipitation, and indirectly influenced by changing
vegetation composition due to warming temperatures, though they were
uncertain what underlying mechanisms were driving these relationships.
Our results provide insight on this matter, indicating that
precipitation and minimum daily temperature negatively influenced nest
survival of Swainson's thrush, thus, potentially affecting their
populations. Additionally, Swainson's thrush rely on specific vegetation
composition that includes coniferous tree species such as red spruce and
balsam fir (Mack \& Yong, 2020). Changes in vegetation composition have
occurred in this region (Foster \& D'Amato, 2015), though red spruce may
be recovering, and thus expanding, due to shifts in land use within this
region (Battles et al., 2003). These climatic consequences to Swainson's
thrush populations have both indirect and direct effects (Duclos et al.,
2019), and our results provide guidance on the potential underlying
mechanistic drivers these effects have.

This study provides evidence that Swainson's thrush daily nest survival
is negatively affected by precipitation (light rain events and rain
intensity), as well as minimum and average daily temperature along an
elevational gradient. Several factors that were not measured in this
study could contribute to nest survival, including habitat structure,
predator abundance, and food availability. It is worth noting that other
studies have assessed the effects of temporal and abiotic variables on
nest survival and had considerably larger sample sizes than us (Dinsmore
et al., 2002; Dreitz et al., 2012; Johnson et al., 2018; Pierce et al.,
2019) and thus, could be one of the limitations this study faces. As the
climate continues to warm and cause more frequent rain bouts, as well as
shifts in plant and animal communities that may expose species to
factors they are not accustomed to, breeding birds will be at risk of
these shifts (Dunn \& Winkler, 2010; Parmesan, 2007), especially for
species that occupy such restricted niches, like montane birds.
Specifically, one species of concern in northeastern forests is the
Bicknell's thrush (\emph{Catharus bicknelli} ), a rare and vulnerable
species that is facing population declines (Hill et al., 2019; Lambert
et al., 2008) and has already been negatively impacted due to climatic
effects (Lambert et al., 2008; Rodenhouse et al., 2008). (Freeman \&
Montgomery, 2015) have documented dominant interspecific aggression from
Swainson's towards Bicknell's thrush, suggesting that Swainson's thrush
may follow the ``push'' hypothesis and force Bicknell's thrush out of
their climatic niche, a likely occurrence due to species range shifts
(DeLuca \& King, 2017). Our inspiration for this study was considered
due to the vulnerability the Bicknell's thrush faces, as well as the
finding that Duclos et al. (2019) reported. As we continue to better
understand the mechanistic drivers that influence these sensitive
montane species, this baseline knowledge will help inform land managers
about how passerine birds and their population sizes will cope with the
effects of climate change.

\textbf{Data accessibility statement}

Raw data, supplementary materials, and R scripts can be found on Dryad
(doi to be determined upon acceptance). Data includes nest survival and
daily weather data, supplementary figures and tables, and an rMarkdown
of script used to analyze daily survival rate.

\textbf{Acknowledgements}

This study was funded by the USDA U.S. Forest Service (USFS), the
University of Massachusetts, Amherst, and the National Science
Foundation (NSF) Graduate Research Fellowship. We thank our hardworking
field technicians (Hunter Trowbridge, Lucas Bobay, Kali Loughlin, Hannah
Wait, Eden Smith and Brandon Maiersperger) and volunteers (Cory Elowe,
Mariamar Gutierezz, Beth Rogers) who participated in data collection and
kept moral high during cold, rainy days and hot sweltering conditions
hiking up the Jefferson Notch Road before it was open for the season.
Christine Costello has been integral in assisting our team for several
years in permitting access, housing availability, and lab space within
the Bartlett Experimental Forest, and without her expertise our work
would have been much more difficult. We want to acknowledge that though
this study area is now referred to as the White Mountain National Forest
and is owned by federal agencies, the land this research was conducted
on is part of the cultural patrimony of Abenaki Penacook and other
Wabanaki people, before colonization by European Americans occurred in
this region. Finally, we want to thank the anonymous reviewers as well
as Toni Lyn Morelli for their helpful comments on the final versions of
this manuscript.

\textbf{Conflicts of interest}

None declared.

\textbf{Author Contributions}

Sarah C. Deckel contributed to conceptual design of the study,
organizing, and executing the data collection/field season, all analysis
(including interpretation), and writing the draft for this manuscript.
William V. DeLuca assisted with conceptualization of the study design,
developing field methods and field work, and contributed to final review
and editing of the manuscript. Alexander R. Gerson contributed expertise
to the manuscript draft and analysis interpretation, as well
conceptualization of the study design. David I. King contributed to
input on data collection methods, funding opportunities,
conceptualization of study design, as well as expertise on the
manuscript drafts. All authors gave final approval for this version to
be published.

\textbf{Tables and Figures}

\emph{Table 1.} A list of predictor variables included in analyses of
Swainson's thrush nest survival in the White Mountains, NH. Annotations
denote the year it was used: \textsuperscript{a}all
years,\textsuperscript{b}2019 and 2021 only. ``DSR'' is the response
variable, daily survival rate.

Sample 5-column table

Model name (DSR\textasciitilde{})

Model notation

Constant DSR Year Linear time trend Quadratic time trend

Intercept only\textsuperscript{a} Year\textsuperscript{a}
TimeTrend\textsuperscript{a} TT\textsuperscript{a}

Nest Age Elevation Initiation date

NestAge\textsuperscript{a} Elev\textsuperscript{a}
Init\textsuperscript{b}

Daily average temperature Daily minimum temperature Daily temperature
range

MeanTemp\textsuperscript{b} MinTemp\textsuperscript{b}
TempRange\textsuperscript{b}

Daily cumulative precipitation

Precip\textsuperscript{b}

Rain Events Rain Intensity Yes/No Rain (Binary) Linear time trend * Year
Linear time trend * Elevation Elevation * Initiation Date Elevation *
Rain Intensity Elevation * Daily Minimum Temp Elevation * Daily Average
Temp Nest Age * Rain Intensity Nest Age * Elevation Elevation +
TimeTrend

RainEvent\textsuperscript{b} Intensity\textsuperscript{b}
Binary\textsuperscript{b} TimeTrendYear\textsuperscript{b}
TimeTrendElev\textsuperscript{a} ElevInit\textsuperscript{b}
ElevInten\textsuperscript{b} ElevMin\textsuperscript{b}
ElevMeanT\textsuperscript{b} NestAgeInten\textsuperscript{b}
NestAgeElev\textsuperscript{b} ElevTime\textsuperscript{b}

\emph{Table 2.} Parameter estimates, standard error (SE), and confidence
intervals for daily nest survival for Swainson's thrush in the White
Mountains, NH for all years (\emph{n=} 58; 2016, 2018, 2019, 2021) and
for just 2019 and 2021 (\emph{n=} 44). Supported models have 95\% CI
significance and are denoted when the model's name is in bold, and
models that have some support have 90\% significance and are denoted in
italics.

\textbf{All years (2016, 2018, 2019, 2021)}

Sample 5-column table

\emph{Model}

\emph{Beta (b)}

\emph{SE}

\emph{Lower CI}

\emph{Upper CI}

\emph{TimeTrend} \emph{TT} \textbf{Constant DSR (intercept)}

-0.027 -0.0003 0.952

0.014 0.0002 0.007

-0.056 -0.0007 0.934

0.0009 0.00004 0.965

Elev NestAge

0.0005 0.003

0.0007 0.017

-0.0008 -0.031

0.001 0.038

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\emph{Model}

\emph{Beta (b)}

\emph{SE}

\emph{Lower CI}

\emph{Upper CI}

\textbf{Intensity} \textbf{ElevMin} \textbf{ElevMeanT} \emph{TimeTrend}
TT DSR (intercept only) RainEvent \textbf{Light} Heavy TempRange Elev
MinTemp Init MeanTemp Precip NestAge

-0.099 -0.0007 -0.0006 -0.030 -0.0003 0.952 --- -1.390 3.519 0.063
0.0003 -0.023 -0.007 -0.021 0.010 0.003

0.044 0.0002 0.0001 0.016 0.0002 0.008 --- 0.679 75.622 0.054 0.0006
0.056 0.020 0.067 0.053 0.020

-0.185 -0.001 -0.001 -0.057 -0.0008 0.931 --- -2.722 -144.70 -0.043
-0.0009 -0.133 -0.047 -0.153 -0.094 -0.036

-0.013 -0.0002 -0.000009 -0.002 0.00008 0.967 --- -0.058 151.73 0.169
0.001 0.086 0.032 0.111 0.115 0.043

\par\null

\emph{Table 3.} A summary of nest survival model selection results using
AIC (Akaike's Information Criterion) model comparison for Swainson's
thrush in the White Mountains, NH separated between all years (\emph{n}
= 58) and just 2019 and 2021 (\emph{n=} 44). 95\% CI significance is
denoted when the model's name is in bold, 90\% denoted in italics.

\textbf{All years (2016, 2018, 2019, 2021)}

Sample 5-column table

\emph{Model (DSR\textasciitilde{})}

\emph{Model (DSR\textasciitilde{})}

\emph{Model (DSR\textasciitilde{})}

\emph{Parameters}

\emph{AICc}

\selectlanguage{greek}\textbf{Δ\emph{AICc}}\selectlanguage{english}

\emph{wi}

\emph{Deviance}

\emph{TimeTrend} TT \textbf{DSR (intercept only)} Year

\emph{TimeTrend} TT \textbf{DSR (intercept only)} Year

\emph{TimeTrend} TT \textbf{DSR (intercept only)} Year

2 2 1 2

189.34 190.10 190.81 191.48

0.00 0.76 1.46 2.41

0.34 0.23 0.16 0.11

185.32 186.08 188.80 187.46

Elev NestAge

Elev NestAge

Elev NestAge

2 2

192.18 192.77

2.84 3.43

0.08 0.06

188.16 188.76

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\textbf{2019 and 2021 only}

\emph{Model (DSR\textasciitilde{})}

\emph{Model (DSR\textasciitilde{})}

\emph{Model (DSR\textasciitilde{})}

\emph{Parameters}

\emph{AICc}

\selectlanguage{greek}\textbf{Δ\emph{AICc}}\selectlanguage{english}

\emph{wi}

\emph{Deviance}

\textbf{ElevMin} \textbf{Intensity} DSR (intercept only)
\textbf{RainEvent} TempRange ElevMeanT Elev MinTemp MeanTemp Precip

\textbf{ElevMin} \textbf{Intensity} DSR (intercept only)
\textbf{RainEvent} TempRange ElevMeanT Elev MinTemp MeanTemp Precip

\textbf{ElevMin} \textbf{Intensity} DSR (intercept only)
\textbf{RainEvent} TempRange ElevMeanT Elev MinTemp MeanTemp Precip

4 2 1 3 2 4 2 2 2 2

151.49 154.98 155.44 155.71 155.93 156.96 157.18 157.29 157.36 157.41

0.00 3.48 3.94 4.22 4.43 5.46 5.69 5.79 5.86 5.92

0.54 0.09 0.07 0.06 0.05 0.03 0.03 0.03 0.02 0.02

143.42 150.96 153.43 149.67 151.91 148.89 153.16 153.27 153.34 153.39\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

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