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\begin{document}

\title{Juvenile survival of little owls decreases with snow cover}



\author[1]{Marco Perrig}%
\author[1]{Steffen Oppel}%
\author[1]{Matthias Tschumi}%
\author[2]{Herbert Keil}%
\author[3]{Beat Naef-Daenzer}%
\author[1]{Martin Grüebler}%
\affil[1]{Swiss Ornithological Institute}%
\affil[2]{Forschungsgemeinschaft zur Erhaltung einheimischer Eulen e.V.}%
\affil[3]{Schweizerische Vogelwarte}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Global environmental changes are predicted to lead to warmer average
temperatures and more extreme weather events thereby affecting wildlife
population dynamics by altering demographic processes. Extreme weather
events can reduce food resources and mortality, but the contribution of
such events to demographic processes are poorly understood. Estimates of
season-specific survival probabilities are crucial for understanding
mechanisms underlying annual mortality. However, only few studies have
investigated survival at sufficient temporal resolution to assess the
contribution of extreme weather events. Here, we analysed biweekly
survival probabilities of 307 radio-tracked juvenile little owls
(\emph{Athene noctua}) from fledging to their first breeding attempt in
the following spring. Biweekly survival probabilities were lowest during
the first weeks after fledging in summer and increased over autumn to
winter. The duration of snow cover in winter had a strong negative
effect on survival probability, while being well fed during the nestling
stage increased survival during the first weeks after fledging and
ultimately led to a larger proportion of birds surviving the first year.
Overall annual survival probability over the first year varied by 34.3
\% between 0.117 (95 \% credible interval 0.052 -- 0.223) and 0.178
(0.097 -- 0.293) depending on the severity of the winter, and up to
0.233 (0.127 -- 0.373) for well-fed fledglings. The season with the
lowest survival was the post-fledging period (0.508; 0.428 -- 0.594) in
years with mild winters, and the winter in years with extensive snow
cover (0.481; 0.337 -- 0.626). We therefore show that extreme weather
events reduced the proportion of first-year survivors. Increasingly
warmer winters with less snow cover may therefore increase annual
survival probability of juvenile little owls in central Europe, but
environmental changes reducing food supply during the nestling period
can have similarly large effects on annual juvenile survival and
therefore the viability of populations.%
\end{abstract}%



\sloppy


\textbf{1 Introduction}

Global environmental change is predicted to lead to warmer average
temperatures, and more extreme weather events (Stott 2016). If these
extreme weather events affect demographic processes, such as the
reproduction, survival or dispersal of individuals, environmental
changes may contribute directly to wildlife population dynamics (Shriver
2016; Saracco \& Rubenstein 2020). In birds, juvenile life stages are
often critical to the growth rate of populations (Newton 1989; Robinson
et al. 2004; Clark \& Martin 2007; Finkelstein et al. 2010), and to
understand the consequences of environmental change on populations, we
require a better understanding to what extent extreme events affect
juvenile survival.

The survival of juvenile birds from fledging to their first reproduction
is generally lower than the survival of adults (Maness \& Anderson 2013;
Naef-Daenzer \& Gr\selectlanguage{ngerman}üebler 2016; Newton et al. 2016). Because juveniles
are more susceptible to extreme weather events (Robinson et al. 2007),
juvenile survival can vary enormously among years partly due to
environmental conditions (Gaillard \& Yoccoz 2003; Harris et al. 2007;
Souchay et al. 2013).

The period from fledging to first reproduction in young birds includes
several distinct life-history stages such as the post-fledging period in
the natal home-range, dispersal and migration, wintering, and habitat
selection and settlement at the first breeding site. All these different
stages involve distinct challenges and therefore may impose differential
costs on juvenile survival (Robinson et al. 2004; Ward et al. 2004;
Grande et al. 2009; Grüebler et al. 2014a; Buechley et al. 2021).
Environmental conditions are known to affect survival during certain
life-history stages more than others (Reid et al. 2008; Dybala et al.
2013; Maness \& Anderson 2013), and the effects of anthropogenic
environmental changes on juvenile survival may therefore differ between
the life-history stages of the pre-recruiting phase. To understand which
life-history stages are particularly important for population dynamics,
and to predict the consequences of environmental changes on populations,
the contribution of environmental variation in each life-history stage
to pre-reproductive survival must be understood (Robinson et al. 2004;
Low \& Pärt 2009; Cox et al. 2014; Grüebler et al. 2014a).

The life-history transition of fledging generally results in high
mortality as fledged birds need to survive independently in unfamiliar
environments (Low \& Pärt 2009; Cox et al. 2014; Naef-Daenzer \&
Grüebler 2016). Following that period, young birds departing from their
natal site face two further significant challenges in consecutive
life-history stages. First, they move through and explore new,
unfamiliar, and potentially inhospitable environments during natal
dispersal (Robinson et al. 2004; Bowler \& Benton 2005; Low \& Pärt
2009; Clobert et al. 2012; Roque et al. 2021; Stillman et al. 2021).
Second, they face the reduced availability and accessibility of food,
and simultaneously increased thermoregulatory costs during winter
(Altwegg et al. 2006; Thorup et al. 2013; Rubá\selectlanguage{polish}č\selectlanguage{english}ov\selectlanguage{ngerman}á et al. 2021).
However, whether mortality is mainly associated with the post-fledging
and dispersal phases, or with the environmentally challenging period
during winter is poorly studied and may vary among species (Dybala et
al. 2013; Grüebler et al. 2014a).

Here we examined the first-year survival of little owls (\emph{Athene
noctua} ) at biweekly temporal resolution to determine season-specific
survival probabilities from fledging to the first reproductive attempt.
The little owl is a small generalist mesopredator, inhabiting various
open landscapes of Europe and Asia (Glue \& Scott 1980). Many
populations of little owls in central Europe have decreased in recent
decades, and harsh winters with extended periods of snow cover have
resulted in occasional population collapses (Van Nieuwenhuyse et al.
2023). Demographic analyses have indicated that juvenile survival and
immigration are key demographic factors explaining differences in
population growth rates (Schaub et al. 2006; Le Gouar et al. 2011).
Therefore, it is important to understand the critical bottlenecks in the
first year of the little owls' life (Thorup et al. 2013; Tschumi et al.
2019) and to identify the environmental factors affecting survival in
different juvenile life-history stages (Thorup et al. 2010; Le Gouar et
al. 2011; Perrig et al. 2014). We have previously shown that survival of
juvenile little owls was very low just after fledging, varied with
fledgling body condition associated with nestling food supply, and
increased over the first two months post-fledging (Perrig et al. 2017).
However, it is unclear whether the subsequent dispersal and wintering
stages impose an equal or different toll on the survival of juvenile
little owls, and what effect extreme winter weather events have on the
number of little owls surviving the first year.

In this study, we investigated two main hypotheses considering the
period between independence and the first breeding season. First, we
hypothesized that survival during autumn, when juveniles dispersed from
parental territories, would be lower than in winter and the following
spring because of the risk of exploring unfamiliar environments. Second,
we predicted that survival during winter would be reduced depending on
the severity of winter conditions because of limited access to food
resources (Altwegg et al. 2006; Le Gouar et al. 2011; Rubá\selectlanguage{polish}č\selectlanguage{english}ov\selectlanguage{ngerman}á et al.
2021). We estimated survival probabilities from the post-fledging period
to the first breeding season and could thus identify the most important
seasonal bottleneck within the first year of juvenile little owls. This
information will be critical to understand the potential future effects
of a changing climate on population dynamics of little owls.

\textbf{2 Methods}

\textbf{2.1 Study population}

The study was conducted in southwestern Germany (Landkreis Ludwigsburg,
Baden-Württemberg, 48°53'43\selectlanguage{english}"N, 9deg11'45\selectlanguage{english}"E) in an area of
\textasciitilde{}700 km\textsuperscript{2} at elevations ranging from
120 to 250 m above sea level. The landscape in the study area is
composed of intensively used agricultural fields, meadows, orchards, and
vineyards (56\%), forests (25\%), and urban settlements (17\%),
containing a high density and diversity of small structural elements and
management regimes (Perrig et al. 2014; Fattebert et al. 2018; Fattebert
et al. 2019; Hauenstein et al. 2019; Tschumi et al. 2020).

The little owl is a small (160 -- 250 g) nocturnal bird breeding in tree
cavities and nest boxes in orchards of traditional agricultural
landscapes (Van Nieuwenhuyse et al. 2023). Fledglings disperse from
their natal territory usually within 65 days after fledging (Perrig et
al. 2017; Fattebert et al. 2019). First reproduction normally occurs at
the age of one year, following the initial dispersal from the natal
territory, and a stationary period during winter (Exo 1992; Hauenstein
et al. 2019). Survival of adults and the quality of fledged offspring
are strongly linked to habitat quality and food availability (Schaub et
al. 2006; Thorup et al. 2010; Perrig et al. 2014; Michel et al. 2022).

\textbf{2.2 Monitoring survival}

From 2009 to 2011, 93 broods were closely monitored from egg laying
until fledging by conducting regular brood controls and using camera
traps (Perrig et al. 2014; Perrig et al. 2017). Prior to fledging, at an
average age of 28.7 days (+- 2.93 standard deviation), all 307 chicks of
these broods were tagged with VHF radio transmitters (Naef-Daenzer et
al. 2005) using backpack harnesses. The total tag mass (including
harness) was 6.9 -- 7.2 g (3-5\% of the birds' body mass), the expected
life span of the battery was 400 days and the detection range of the VHF
signals was up to 40 km (for details see Perrig et al. 2017).

We used hand-held antennas to locate all individuals at least three
times per week throughout the study period from May 2009 until May 2012,
except during four two-week intervals in winter 2009 and early spring
2010 when no radio-tracking could be carried out. In an additional six
two-week intervals (mid-winter 2009 and late winter 2010) the tracking
effort was reduced, and individuals were located less than three times
per week.

\textbf{2.3 Temporal, individual, and environmental variables affecting
survival}

To quantify seasonal variation in survival, we defined four discrete
seasons by date that correspond to differences in environmental
conditions and the typical behaviour of juvenile birds: (1) summer (15
May until 1 August): the post-fledging phase after juveniles first leave
the nest (previously analysed in Perrig et al. 2017); (2) autumn (2
August -- 23 October): the dispersal period when individuals permanently
depart from their natal territory, and environmental conditions change
to shorter days and cooler temperatures; (3) winter (24 October -- 12
March): the period when birds establish and occupy a winter home-range,
and endure occasionally cold winter weather during which food can become
inaccessible; (4) spring (13 March -- 15 June): the first breeding
period when birds acquire and occupy the first territory and breed
during gradually warming weather with longer day lengths. Note that our
season definition includes a deliberate overlap of 4 weeks between
subsequent years to accommodate the staggered fledging date of juveniles
and staggered onset of first reproduction.

Besides temporal variation, survival may also differ by age, sex, and
body mass (Le Gouar et al. 2011; Tschumi et al. 2019). We therefore
measured body mass (to the nearest 0.1 g) and tarsus length (to the
nearest 0.1 mm) at the day of tagging, and corrected these measurements
for the age of the bird at the time of measurement (Perrig et al. 2014).
We specified the age of juveniles in days based on standard pictures and
the known fledging date (Perrig et al. 2017). In addition, feather
samples were obtained for genetic sex determination of the nestlings
(Tschumi et al. 2019). In 2010 and 2011 roughly half of the monitored
broods were provided with supplementary food during the nestling stage
(Perrig et al. 2017), and we recorded whether individuals had benefitted
from supplementary feeding or not.

To investigate the effect of winter conditions on survival
probabilities, we extracted daily snow depths from the weather station
Sachsenheim (Germany, station ID: 04349, downloaded from:
https://opendata.dwd.de/climate\_environment/CDC/observations\_germany/climate/daily/kl/historical/,
accessed 16 Oct 2023). Snow depth is known to limit owl's access to food
and is therefore a primary indicator for winter harshness and food
availability that would influence survival (Altwegg et al. 2006; Le
Gouar et al. 2011; Thorup et al. 2013). We calculated the number of days
with a snow cover \selectlanguage{english}[?] 1 cm for each two-week encounter occasion.

\textbf{2.4 Estimating survival probabilities}

Radio-tracking data were aggregated into encounter histories of biweekly
intervals. The encounter histories were used in a Cormack--Jolly--Seber
(CJS) mark--recapture model estimating biweekly survival while
controlling for variation in detection probability and individual
variability (Lebreton et al. 1992; Kery \& Schaub 2012).

Because our key interest was to quantify seasonal differences in
survival, our survival model included a fixed intercept for each of the
four seasons (summer, autumn, winter, spring), as well as fixed effects
to account for the mass and sex of each fledgling (Tschumi et al. 2019),
and whether the fledglings came from broods that were provided with
supplementary food (Perrig et al. 2017). We also included a fixed effect
that explored whether duration of snow cover could explain variation in
survival. Because body mass and size were highly correlated, and the
variables age and body size did not affect survival in preliminary
explorations, we retained the most parsimonious combination of variables
(Hooten \& Hobbs 2015), and neither age nor size were retained in our
survival model. We fitted three models to include the variables body
mass and supplementary feeding in three alternative model formulations:
either by affecting survival only in the immediate post-fledging period
(Perrig et al. 2017), or by allowing body mass and supplementary feeding
to affect survival in every season over the first year of life (Catitti
et al. 2022; Nageli et al. 2022; Mainwaring et al. 2023).

To estimate detection probability, we included temporal variation in
tracking effort as an explanatory variable due to the unequal tracking
efforts across years and tracking periods. We specified that detection
probability was zero during four intervals when no tracking effort
occurred. For the remaining intervals we estimated two distinct
detection probabilities, one for those six biweekly periods with reduced
effort in winter 2009 and 2010, and another for the remaining 80 periods
with full tracking effort. We also included a random individual effect
to account for residual variability in detection probability among
individuals. Because severe winter weather may not only affect survival,
but may also lead to temporary escape movements to more benign areas
(Sonerud 1986; Mysterud 2016; Gura 2023), we included the same snow
cover variable that we assumed to affect survival also for detection
probability to account for possible temporary emigration and low
detection probability during severe winter weather.

We used a Bayesian approach for inference to include existing prior
information on the survival probability of little owls (Schaub et al.
2006; Le Gouar et al. 2011; Thorup et al. 2013). We fit the models in
software JAGS v. 3.3. (Plummer 2012) called from R 4.1.3 (R Core Team
2023) via the `runjags' library (Denwood 2016). We used a mildly
informative prior for the biweekly survival probability (beta
distribution with \selectlanguage{greek}α \selectlanguage{english}= 95 and \selectlanguage{greek}β \selectlanguage{english}= 10) given previous information on
little owl survival (Le Gouar et al. 2011; Thorup et al. 2013), and a
similarly informative prior for the detection probabilities during
occasions with normal (random uniform 0.7 -- 1) and reduced effort
(random uniform 0.3 -- 0.9). We used vague normally distributed priors
for all other parameters, and conducted a prior sensibility test to
ensure that biologically plausible survival estimates resulted from our
prior distributions (Banner et al. 2020). We ran three Markov chains for
3,500 iterations each, discarded the first 200 iterations and used every
sixth iteration for inference. Convergence of the three chains for all
monitored parameters was visually inspected using trace plots and tested
using the Gelman--Rubin diagnostic (Brooks \& Gelman 1998) to confirm
that all parameters had an R-hat of \textless{} 1.02. We implemented
posterior predictive checks to assess the goodness-of-fit of the
survival model (Gelman et al. 1996; K\selectlanguage{ngerman}éry \& Schaub 2012; Conn et al.
2018), and confirmed that there was no evidence for a lack of fit
(Bayesian p-value = 0.427). Code to replicate these analyses can be
found at https://github.com/Vogelwarte/LittleOwlSurvival and in the
Supplementary Material.

We present median parameter estimates (\selectlanguage{greek}\emph{β} \selectlanguage{english}) for covariates on the
logit scale with 95\% credible intervals. We also present posterior
estimates of biweekly survival probability with 95\% credible intervals
for each of the four seasons based on birds of average body mass that
did not receive supplementary food as nestlings. To facilitate
interpretation and comparison with other survival estimates, we
calculated season-specific survival by raising biweekly survival to the
power of the length of each season (summer: 4 periods, autumn: 6
periods, winter: 10 periods, spring: 6 periods). To predict survival in
severe winters, we used the maximum length of intense snow cover periods
during our study to decompose the 10 winter periods into 2 periods with
extreme snow cover, 3 periods each with high and intermediate snow
cover, and 2 periods without snow cover (resulting in 43\% of 140 winter
days experiencing snow cover), and we multiplied the respective survival
probabilities to estimate overwinter survival. To estimate annual
survival, we multiplied the four seasonal survival probabilities, which
represents the annual survival probability from 1 July to 30 June of the
following year. To visualise what proportion of juveniles survived over
the first year of life, we simulated the proportion of 100 juveniles of
average body mass that survived 26 fortnightly periods from one summer
to the next by multiplying the number of live birds by the
fortnight-specific survival probability. We present this proportion for
four scenarios, namely for birds that did and did not receive
supplementary food as nestlings during either a mild or a harsh winter.

\textbf{3 Results}

Of the 307 individuals that left their nest, 46 (15\%) survived to the
end of the brood-rearing stage in the following year. Our survival model
estimated that biweekly survival was lowest in the summer post-fledging
season (0.844; 0.809 -- 0.878), and substantially higher in autumn
(0.936; 0.916 -- 0.953), snow-free winter (0.970; 0.954 -- 0.981), and
the following spring (0.945; 0.921 -- 0.964; Fig. 1) seasons. Accounting
for the different duration of the seasons, the overall survival was
lowest for the summer post-fledging season despite its short duration
(0.508), with the autumn dispersal seasons (0.673) having marginally
lower survival than mild snow-free winters (0.734) and the following
springs (0.712; Table 1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1. Estimated median survival probabilities of juvenile little owls
of average body mass during the four seasons across their first year of
life. Survival probability is scaled to biweekly encounter occasions,
and therefore comparable across seasons despite the different duration
of each season. Error bars represent 95\% credible intervals.

Biweekly survival decreased strongly with an increasing duration of snow
cover (\selectlanguage{greek}\emph{β} \selectlanguage{english}= -0.389; 95\% credible interval -0.571~ -\selectlanguage{english}0.201; Fig. 1,
Fig. S1). By contrast, we found only a weak negative effect of snow
cover on detection probability (\selectlanguage{greek}\emph{β} \selectlanguage{english}= -0.229; -0.468~ \selectlanguage{english}0.022; Fig.
S1), and detection probability was very high (in periods with normal
effort: 0.994; 0.989~ \selectlanguage{english}0.996; in periods with reduced effort: 0.803;
0.625~ \selectlanguage{english}0.892). In winters with extreme snow cover, biweekly survival
decreased from 0.971 to 0.879 (Fig. 1), and the survival over an entire
harsh winter season was therefore even lower than during the
post-fledging summer (0.481; Table 1). In total, the annual survival
probability of little owls during their first year of life ranged from
0.117 to 0.178 depending on the severity of the winter (Table 1). Thus,
in years with long periods of snow cover, the winter period reduced
first-year survival by 34.3\% compared to a snow-free winter (Fig. 2).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2. Estimated median proportion (and 95\% confidence intervals) of
juvenile little owls surviving from fledging in May until the first
breeding season in the following year depending on the harshness of
winter (left panel: harsh winter with 60 days of snow cover, right
panel: mild winter with no snow cover), and whether they received
supplementary feeding as nestlings (blue: unfed natural nestlings; red:
nestlings provided with supplementary food).

Besides the effect of snow cover, we also found evidence that survival
increased with body mass (\selectlanguage{greek}\emph{β} \selectlanguage{english}= 0.480; 0.290~ \selectlanguage{english}0.670), and with
supplementary food provided to nestlings (\selectlanguage{greek}\emph{β} \selectlanguage{english}= 0.345; -0.073~
\selectlanguage{english}0.758), Survival was only marginally higher for males than for females
(\selectlanguage{greek}\emph{β} \selectlanguage{english}= 0.153; -0.256~ \selectlanguage{english}0.521; Fig. S1). We found that the effects of
body mass (Fig. S2) and food supplementation (Fig. S3) on survival only
occurred during the post-fledging period, but not later in the first
year of juvenile little owls. However, those differences resulted in a
23.5\% lower proportion of juveniles surviving the first year depending
on whether nestlings had received supplementary food (annual survival
0.233; 0.127 -- 0.373) or not, solely due to the difference in
post-fledging survival (Fig. 2). Out of all the juveniles that died
during the first year, the greatest proportion (39.7\% -- 59.9\%)
perished during the post-fledging period, while even harsh winters
accounted for only 11.1\% - 27.3\% of annual mortality (Table 2).
Extrapolated seasonal survival estimates for birds of specific mass,
sex, and feeding status are provided in Table S1.

Table 1. Estimated survival probabilities of juvenile little owls of
average body mass in southwestern Germany during the four seasons of
their first year, and cumulative annual survival. Mild winter refers to
winters without snow cover, harsh winter refers to 60 winter days
experiencing snow cover of [?] 1 cm.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Season & Duration (weeks) & Survival probability (mild winter) &
Survival probability (harsh winter)\tabularnewline
\midrule
\endhead
Summer & 8 & 0.508 (0.428 - 0.594) & 0.508 (0.428 -
0.594)\tabularnewline
Autumn & 12 & 0.673 (0.592 - 0.748) & 0.673 (0.592 -
0.748)\tabularnewline
Winter & 20 & 0.734 (0.624 - 0.823) & 0.481 (0.337 -
0.626)\tabularnewline
Spring & 12 & 0.712 (0.611 - 0.801) & 0.712 (0.611 -
0.801)\tabularnewline
Annual Survival & 52 & 0.178 (0.097 - 0.293) & 0.117 (0.052 -
0.223)\tabularnewline
\bottomrule
\end{longtable}

Table 2. Seasonal contribution to the overall annual mortality over the
first year of life of little owls. Numbers represent the proportion of
juveniles that died in a given season out of all birds that died during
the first year for mild and harsh winters and for birds that received
supplementary food as nestlings or not. Mild winter refers to winters
without snow cover, harsh winter refers to 60 winter days experiencing
snow cover of [?] 1 cm.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Season & natural & natural & supplementary fed & supplementary
fed\tabularnewline
\midrule
\endhead
& mild winter & harsh winter & mild winter & harsh winter\tabularnewline
Summer & 59.9 & 55.7 & 43.9 & 39.7\tabularnewline
Autumn & 20.2 & 18.8 & 28.3 & 25.6\tabularnewline
Winter & 11.1 & 20.1 & 15.5 & 27.3\tabularnewline
Spring & 8.8 & 5.4 & 12.3 & 7.3\tabularnewline
\bottomrule
\end{longtable}

\textbf{4 Discussion}

We show that winter survival in juvenile little owls was highly
dependent on the number of days with snow cover, but that annual
survival in years with mild winters was characterized by remarkably
equal survival probabilities throughout seasons once individuals had
survived the first weeks after fledging. Overall, only a fifth of
juvenile little owls survived to the age of one year, and harsh winters
with extended periods of snow cover reduced annual survival probability
by \textgreater{} 30\%. However, the most important mortality bottleneck
was the month after fledging, which accounted for 40-60\% of the total
first year mortality. Supplementary food provided to nestlings increased
annual survival by 23.5\% due to its beneficial effect on post-fledging
survival. A changing climate and food supply during the nestling stage
may therefore have similarly large effects on the survival of juvenile
little owls during their first year of life.

Our results are consistent with previous findings that harsh winters
affect survival of little owls (Le Gouar et al. 2011; Thorup et al.
2013; Michel 2016), but our study was able to disentangle mortality
during the post-fledging period in summer, during dispersal in autumn,
and in winter. We show that winters with constant snow cover over two
months result in less than half of juveniles surviving a winter (Table
1). Snow cover and frosty conditions are widely known to prevent owls
and other resident bird species from accessing food, thus leading to
reduced body condition and increased foraging efforts, which ultimately
result in increased mortality rates (Sonerud 1986; Kostrzewa \&
Kostrzewa 1991; Altwegg et al. 2006; Riegert \& Fuchs 2011; Naef-Daenzer
\& Gr\selectlanguage{ngerman}üebler 2016). Young owls may also become more vulnerable to
predators when they struggle to find suitable shelters and suffer from
excessive thermoregulation costs (Bock et al. 2013; Grüebler et al.
2014b; Naef-Daenzer \& Grüebler 2016). As juvenile survival represents a
key factor in little owl population dynamics (Schaub et al. 2006; Le
Gouar et al. 2011), our results suggest strong population effects of
extended periods of snow cover in this species, which is consistent with
past evidence of population collapses after harsh winters from the last
century (summarized in Van Nieuwenhuyse et al. 2023).

We found that survival increased for food supplemented and for heavier
birds, but these effects occurred only in the post-fledging period and
were not detectable in subsequent seasons. A previous study revealed
that early-life effects associated with nestling food supplementation
affected departure decisions from the parental home-range, but not
movement decisions during natal dispersal after departure (Fattebert et
al. 2019). Similarly, early-life conditions affected survival primarily
during the post-fledging period, but not afterwards, and we speculate
that the selection imposed by early post-fledging mortality reduces the
influence of early-life conditions at later stages in life (Sergio et
al. 2014; Sergio et al. 2019). Nonetheless, given the magnitude of the
difference in survival between food-supplemented and un-supplemented
(natural) little owl fledglings during the post-fledging period, 23.5\%
more food-supplemented fledglings survived the first year (Fig. 2).
Thus, favourable natal conditions in our study species may affect
population level processes mainly by non-random selection of juveniles
in the nestling and post-fledging period, with an effect of similar
magnitude as that imposed by harsh winters.

Increased predation risk of inexperienced birds is widely known as a
main factor for lower juvenile than adult survival probability during
the first year of life in general (Clutton-Brock et al. 1985; Sunde
2005; Maness \& Anderson 2013; Naef-Daenzer \& Grüebler 2016), and in
little owls in particular (Naef-Daenzer et al. 2017; \selectlanguage{polish}Š\selectlanguage{ngerman}álek et al. 2019).
Juvenile predation rate is often further increased under poor food
conditions (Rohner \& Hunter 1996; Coles \& Petty 1997) and when
juveniles explore unknown areas (Bélichon et al. 1996; Lima 1998; Yoder
et al. 2004). Elevated costs during natal dispersal have led to the
theory that the dispersal stage is a bottleneck with respect to survival
and evolutionary ecology (Bowler \& Benton 2005; Benard \& McCauley
2008; Barto\selectlanguage{polish}ń \selectlanguage{english}et al. 2012; Bonte et al. 2012; Hardouin et al. 2012; V\selectlanguage{ngerman}äli
et al. 2021). Our results show that in little owls the survival during
the autumn dispersal season was not noticeably reduced. However, because
we focused on a fixed temporal definition of the autumnal dispersal
season, we were not able to investigate survival during the actual
dispersal movement of individuals. Main natal dispersal movements are
generally of short duration in little owls (median = 10 days; Fattebert
et al. 2019). Mortality might be considerably increased during the few
days of active dispersal without affecting our overall survival
probability of the season. More detailed investigations of survival in
relation to individual movements may reveal more nuanced patterns in
survival probability and will illuminate the costs of dispersal in
little owls (Yoder et al. 2004). Nestling food supply and its effects on
post-fledging survival may therefore be a more important factor for
juvenile survival and the growth rate of populations than the dispersal
or wintering stages (Low \& Pärt 2009; Cox et al. 2014; Grüebler et al.
2014a; Naef-Daenzer \& Grüebler 2016; Martin et al. 2018).

We found only a relatively small and uncertain effect of increased snow
cover on detection probability, which was generally very high in our
study. Thus, while some temporary emigration is possible and accounted
for by our model, we can be confident that our estimate of lower
survival in harsh winters is not affected by little owls temporarily
leaving the study area during periods with extensive snow cover. We
cannot exclude the possibility that little owls performed permanent
escape movements that have been recorded in other owl species during
severe winter conditions (Sonerud 1986; Mysterud 2016; Gura 2023),
because permanent emigration out of the monitored area and mortality are
confounded in our data set. However, permanent emigration would be most
likely during the autumn dispersal phase (Hauenstein et al. 2019; Van
Nieuwenhuyse et al. 2023), but we did not find a reduction in apparent
survival during this season with the highest mobility of juveniles. We
therefore suggest that permanent emigration likely only accounts for a
small proportion of the estimated mortality.

In summary, the survival of little owls during the first year can be
characterized by two bottlenecks, differing in the underlying
mechanisms. Juveniles first encounter a survival bottleneck in summer
immediately after fledging from the nest, and another bottleneck in
winter if environmental conditions reduce the accessibility of food.
Contrary to the general hypothesis of elevated costs during natal
dispersal, our results indicate that the autumn dispersal season in
little owls is not more hazardous than other seasons during the first
year of life. Increasingly warmer winters with less snow cover, which
are expected under climate change, will therefore likely have beneficial
effects on the juvenile survival probability and thus, on population
dynamics of little owls.

\textbf{DATA AVAILABILITY STATEMENT}

The data and R code that support the findings of this study are openly
available in GitHub (https://doi.org/10.5281/zenodo.10714914).

\textbf{AUTHOR CONTRIBUTIONS}

\textbf{Marco Perrig:} Conceptualization (Equal)

Data curation (Equal)

Formal analysis (Lead)

Methodology (Lead)

Software (Equal)

Writing -- original draft (Lead)

Writing -- review \& editing (Equal)

\textbf{Steffen Oppel:} Formal analysis (Equal)

Writing -- review \& editing (Equal)

\textbf{Matthias Tschumi:} Data curation (Equal)

Investigation (Equal)

Project administration (Equal)

Writing -- review \& editing (Equal)

\textbf{Herbert Keil:} Conceptualization (Equal)

Funding acquisition (Supporting)

Investigation (Equal)

Methodology (Supporting)

Project administration (Equal)

Validation (Equal)

Writing -- review \& editing (Supporting)

\textbf{Beat Naef-Daenzer:} Conceptualization (Lead)

Funding acquisition (Equal)

Investigation (Equal)

Methodology (Equal)

Project administration (Lead)

Resources (Equal)

Supervision (Lead)

Writing -- original draft (Equal)

Writing -- review \& editing (Equal)

\textbf{Martin U. Grüebler:} Conceptualization (Equal)

Formal analysis (Supporting)

Funding acquisition (Lead)

Investigation (Equal)

Methodology (Equal)

Project administration (Equal)

Resources (Equal)

Supervision (Equal)

Writing -- original draft (Equal)

Writing -- review \& editing (Equal)

\textbf{Acknowledgments}

We thank all our assistants and volunteers, in particular S. Bartholdi,
A. Berger, S. Blatter, M. Büttel, E. Christen, N. Erath, M. Fader, L.
Fischer, A. Grendelmeier, H. Greter, M. Hauptvogel, M. Hertach, F.
Hoffet, J. Huber, L. Huber-Eustachi, A. Koller, C. Labhart, M. Lörcher,
E. Lutz, S. Meier, S. Meyer, L. Michel, C. Mischler, J. Naef, F.
Nyffeler, C. Portmann, H. Reers, O. Rickenbach, A. Schärer, S.
Schmieder, J. Schoop, M. Spinelli, J. Staggenborg, I. Stützle, S. M,
Treffler, D. Trobe and I. Weinberger for their effort in the field, and
E. Keil for her kind hospitality. Many thanks also to V. Michel for
coordinating much of the field work. We are very grateful to L. Jenni
and G. Pasinelli for their valuable comments on earlier drafts of the
manuscript. W. Fiedler from the Max Planck Institute of Ornithology
(today Max Planck Institute of Animal Behaviour) provided logistic and
administrative support. Capture and tagging of little owls were
permitted by the regional council of Baden-Württemberg, Germany (permit
No 35-9185.81/0288). Radio-tracking was conducted under license of
Deutsche Bundesnetzagentur (license No 37 55 5413). This work was funded
by the Swiss National Science Foundation (Grant 3100A 132951/1 to Beat
Naef-Daenzer and Martin Grüebler), the Hirschmann Foundation, and the
Karl Mayer Foundation.

\textbf{CONFLICT OF INTEREST STATEMENT}

The authors declare no competing interests.

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

\end{center}
\end{figure}

Fig. S1: Parameter estimates of the Cormack-Jolly-Seber model estimating
biweekly survival and detection probability of radio-tracked little owls
in Germany between 2009 -- 2011. Mean.phi are survival probability
intercepts for summer {[}1{]}, autumn {[}2{]}, winter {[}3{]}, and
spring {[}4{]}, mean.p are detection probability intercepts for periods
with full effort {[}1{]} and reduced effort {[}2{]}, and `beta' are
parameter estimates for the linear predictors of winter snow cover
(win), sex (male), body mass (mass), and supplementary feeding during
the nestling phase (feed) on survival probability, and for the linear
predictor of winter snow cover on detection probability (p.win).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. S2: Parameter estimates of a Cormack-Jolly-Seber model estimating
biweekly survival and detection probability of radio-tracked little owls
in Germany between 2009 -- 2011, assuming that body mass affected
survival in all seasons. Mean.phi are survival probability intercepts
for summer {[}1{]}, autumn {[}2{]}, winter {[}3{]}, and spring {[}4{]},
and `beta' are parameter estimates for the linear predictors of body
mass (mass, for summer {[}1{]}, autumn {[}2{]}, winter {[}3{]}, and
spring {[}4{]}), and supplementary feeding during the nestling phase
(feed) on survival probability.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig. S3: Parameter estimates of a Cormack-Jolly-Seber model estimating
biweekly survival and detection probability of radio-tracked little owls
in Germany between 2009 -- 2011, assuming that supplementary feeding
affected survival in all seasons. Mean.phi are survival probability
intercepts for summer {[}1{]}, autumn {[}2{]}, winter {[}3{]}, and
spring {[}4{]}, and `beta' are parameter estimates for the linear
predictors of body mass (mass), and supplementary feeding during the
nestling phase (feed, , for summer {[}1{]}, autumn {[}2{]}, winter
{[}3{]}, and spring {[}4{]}) on survival probability.

Table S1: Seasonal survival probabilities of radio-tracked little owls
in Germany given as the median and 95\% credible interval of survival
estimates derived from a Cormack-Jolly-Seber model. Because the model
accounted for sex, size, winter severity and supplementary feeding,
survival estimates are provided for all possible parameter combinations.
Food supplementation was provided during the chick-rearing period only,
for details see (Perrig et al. 2017)\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Food Suppl. & Sex & Mass & Season & Survival probability (mild winter) &
Survival probability (harsh winter)\tabularnewline
\midrule
\endhead
No & Female & light & Summer & 0.196 (0.109 - 0.317) & 0.196 (0.109 -
0.317)\tabularnewline
No & Female & light & Autumn & 0.661 (0.586 - 0.732) & 0.661 (0.586 -
0.732)\tabularnewline
No & Female & light & Winter & 0.723 (0.615 - 0.812) & 0.465 (0.324 -
0.608)\tabularnewline
No & Female & light & Spring & 0.701 (0.601 - 0.789) & 0.701 (0.601 -
0.789)\tabularnewline
No & Female & average & Summer & 0.494 (0.421 - 0.567) & 0.494 (0.421 -
0.567)\tabularnewline
No & Female & average & Autumn & 0.661 (0.586 - 0.732) & 0.661 (0.586 -
0.732)\tabularnewline
No & Female & average & Winter & 0.723 (0.615 - 0.812) & 0.465 (0.324 -
0.608)\tabularnewline
No & Female & average & Spring & 0.701 (0.601 - 0.789) & 0.701 (0.601 -
0.789)\tabularnewline
No & Female & heavy & Summer & 0.752 (0.636 - 0.838) & 0.752 (0.636 -
0.838)\tabularnewline
No & Female & heavy & Autumn & 0.661 (0.586 - 0.732) & 0.661 (0.586 -
0.732)\tabularnewline
No & Female & heavy & Winter & 0.723 (0.615 - 0.812) & 0.465 (0.324 -
0.608)\tabularnewline
No & Female & heavy & Spring & 0.701 (0.601 - 0.789) & 0.701 (0.601 -
0.789)\tabularnewline
No & Male & light & Summer & 0.222 (0.122 - 0.345) & 0.222 (0.122 -
0.345)\tabularnewline
No & Male & light & Autumn & 0.684 (0.604 - 0.757) & 0.684 (0.604 -
0.757)\tabularnewline
No & Male & light & Winter & 0.743 (0.638 - 0.829) & 0.497 (0.354 -
0.636)\tabularnewline
No & Male & light & Spring & 0.723 (0.623 - 0.808) & 0.723 (0.623 -
0.808)\tabularnewline
No & Male & average & Summer & 0.523 (0.444 - 0.605) & 0.523 (0.444 -
0.605)\tabularnewline
No & Male & average & Autumn & 0.684 (0.604 - 0.757) & 0.684 (0.604 -
0.757)\tabularnewline
No & Male & average & Winter & 0.743 (0.638 - 0.829) & 0.497 (0.354 -
0.636)\tabularnewline
No & Male & average & Spring & 0.723 (0.623 - 0.808) & 0.723 (0.623 -
0.808)\tabularnewline
No & Male & heavy & Summer & 0.771 (0.652 - 0.858) & 0.771 (0.652 -
0.858)\tabularnewline
No & Male & heavy & Autumn & 0.684 (0.604 - 0.757) & 0.684 (0.604 -
0.757)\tabularnewline
No & Male & heavy & Winter & 0.743 (0.638 - 0.829) & 0.497 (0.354 -
0.636)\tabularnewline
No & Male & heavy & Spring & 0.723 (0.623 - 0.808) & 0.723 (0.623 -
0.808)\tabularnewline
Yes & Female & light & Summer & 0.355 (0.192 - 0.544) & 0.355 (0.192 -
0.544)\tabularnewline
Yes & Female & light & Autumn & 0.661 (0.586 - 0.732) & 0.661 (0.586 -
0.732)\tabularnewline
Yes & Female & light & Winter & 0.723 (0.615 - 0.812) & 0.465 (0.324 -
0.608)\tabularnewline
Yes & Female & light & Spring & 0.701 (0.601 - 0.789) & 0.701 (0.601 -
0.789)\tabularnewline
Yes & Female & average & Summer & 0.652 (0.554 - 0.742) & 0.652 (0.554 -
0.742)\tabularnewline
Yes & Female & average & Autumn & 0.661 (0.586 - 0.732) & 0.661 (0.586 -
0.732)\tabularnewline
Yes & Female & average & Winter & 0.723 (0.615 - 0.812) & 0.465 (0.324 -
0.608)\tabularnewline
Yes & Female & average & Spring & 0.701 (0.601 - 0.789) & 0.701 (0.601 -
0.789)\tabularnewline
Yes & Female & heavy & Summer & 0.845 (0.767 - 0.901) & 0.845 (0.767 -
0.901)\tabularnewline
Yes & Female & heavy & Autumn & 0.661 (0.586 - 0.732) & 0.661 (0.586 -
0.732)\tabularnewline
Yes & Female & heavy & Winter & 0.723 (0.615 - 0.812) & 0.465 (0.324 -
0.608)\tabularnewline
Yes & Female & heavy & Spring & 0.701 (0.601 - 0.789) & 0.701 (0.601 -
0.789)\tabularnewline
Yes & Male & light & Summer & 0.387 (0.214 - 0.568) & 0.387 (0.214 -
0.568)\tabularnewline
Yes & Male & light & Autumn & 0.684 (0.604 - 0.757) & 0.684 (0.604 -
0.757)\tabularnewline
Yes & Male & light & Winter & 0.743 (0.638 - 0.829) & 0.497 (0.354 -
0.636)\tabularnewline
Yes & Male & light & Spring & 0.723 (0.623 - 0.808) & 0.723 (0.623 -
0.808)\tabularnewline
Yes & Male & average & Summer & 0.676 (0.579 - 0.766) & 0.676 (0.579 -
0.766)\tabularnewline
Yes & Male & average & Autumn & 0.684 (0.604 - 0.757) & 0.684 (0.604 -
0.757)\tabularnewline
Yes & Male & average & Winter & 0.743 (0.638 - 0.829) & 0.497 (0.354 -
0.636)\tabularnewline
Yes & Male & average & Spring & 0.723 (0.623 - 0.808) & 0.723 (0.623 -
0.808)\tabularnewline
Yes & Male & heavy & Summer & 0.857 (0.781 - 0.912) & 0.857 (0.781 -
0.912)\tabularnewline
Yes & Male & heavy & Autumn & 0.684 (0.604 - 0.757) & 0.684 (0.604 -
0.757)\tabularnewline
Yes & Male & heavy & Winter & 0.743 (0.638 - 0.829) & 0.497 (0.354 -
0.636)\tabularnewline
Yes & Male & heavy & Spring & 0.723 (0.623 - 0.808) & 0.723 (0.623 -
0.808)\tabularnewline
\bottomrule
\end{longtable}

Code S1: JAGS model code for the Cormack-Jolly-Seber model estimating
biweekly survival and resighting frequency of radio-tracked little owls
in Germany between 2009 -- 2011. Data and R code to run this model are
available at https://github.com/Vogelwarte/LittleOwlSurvival

model \{

\# Priors and constraints

for (i in 1:nind)\{

for (t in f{[}i{]}:(n.occasions))\{

logit(phi{[}i,t{]}) \textless{}- mu{[}season{[}t{]}{]} +

beta.mass*weight{[}i{]}*pf{[}t{]} +

beta.feed*feeding{[}i{]}*pf{[}t{]} +

beta.win*env{[}year{[}i{]},t{]} +

beta.male*sex{[}i{]}

logit(p{[}i,t{]}) \textless{}- mu.p{[}recap.mat{[}i,t{]}{]} +
beta.p.win*env{[}year{[}i{]},t{]} + epsilon.p{[}i{]}

\} \#t

\} \#i

for (i in 1:nind)\{

epsilon.p{[}i{]} \textasciitilde{} dnorm(0, tau.p)

\}

for (s in 1:4)\{ \#\#\# baseline for the 4 seasons summer, autumn,
winter, spring

mean.phi{[}s{]} \textasciitilde{} dbeta(95, 10) \# Prior for mean
biweekly survival from Thorup et al. 2013, converted to beta

mu{[}s{]} \textless{}- log(mean.phi{[}s{]} / (1-mean.phi{[}s{]})) \#
Logit transformation

\}

mean.p{[}1{]} \textasciitilde{} dunif(0.7, 1) \# Prior for mean
recapture during full effort periods

mean.p{[}2{]} \textasciitilde{} dunif(0.3, 0.9) \# Prior for mean
recapture during reduced effort periods

for (y in 1:2) \{

mu.p{[}y{]} \textless{}- log(mean.p{[}y{]} / (1-mean.p{[}y{]})) \# Logit
transformation

\}

mu.p{[}3{]} \textless{}- -999999999999999999 \# recapture probability of
zero on logit scale

sigma.p \textasciitilde{} dunif(0, 2) \# Prior for standard deviation
for random detection effect

tau.p \textless{}- pow(sigma.p, -2)

beta.mass \textasciitilde{} dnorm(0, 1) \# Prior for mass effect

beta.male \textasciitilde{} dnorm(0, 1) \# Prior for sex effect (for
males, females are 0)

beta.win \textasciitilde{} dunif(-2, 2) \# Prior for winter weather
effect, which we know is negative

beta.p.win \textasciitilde{} dnorm(0, 1) \# Prior for winter weather
DETECTION effect

beta.feed \textasciitilde{} dnorm(0, 1) \# Prior for effect of
supplementary feeding

\# Likelihood

for (i in 1:nind)\{

\# Define latent state at first capture

z{[}i,f{[}i{]}{]} \textless{}- 1

z.rep{[}i,f{[}i{]}{]} \textless{}- 1 \# replicate z (true state)

y.rep{[}i,f{[}i{]}{]} \textless{}- 1 \# replicate y (data)

for (t in (f{[}i{]}+1):n.occasions)\{

\# State process

z{[}i,t{]} \textasciitilde{} dbern(phi{[}i,t-1{]} * z{[}i,t-1{]})

z.rep{[}i,t{]} \textasciitilde{} dbern(phi{[}i,t-1{]} *
z.rep{[}i,t-1{]}) \# replicate z (true state)

\# Observation process

y{[}i,t{]} \textasciitilde{} dbern(p{[}i,t{]} * z{[}i,t{]})

y.rep{[}i,t{]} \textasciitilde{} dbern(p{[}i,t{]} * z.rep{[}i,t{]}) \#
replicate y (observations)

\} \#t end

\#Derived parameters

\#\# GOODNESS OF FIT TEST SECTION

\#\# Discrepancy observed data

E.obs{[}i{]} \textless{}- pow((sum(y{[}i,(f{[}i{]}+1):n.occasions{]}) -
sum(p{[}i,(f{[}i{]}+1):(n.occasions){]} *
z{[}i,(f{[}i{]}+1):n.occasions{]})), 2) /
(sum(p{[}i,(f{[}i{]}+1):n.occasions{]} *
z{[}i,(f{[}i{]}+1):n.occasions{]}) + 0.001)

\#\# Discrepancy replicated data

E.rep{[}i{]} \textless{}-
pow((sum(y.rep{[}i,(f{[}i{]}+1):n.occasions{]}) -
sum(p{[}i,(f{[}i{]}+1):(n.occasions){]} *
z.rep{[}i,(f{[}i{]}+1):n.occasions{]})), 2) /
(sum(p{[}i,(f{[}i{]}+1):(n.occasions){]} *
z.rep{[}i,(f{[}i{]}+1):n.occasions{]}) + 0.001)

\} \#i end

fit \textless{}- sum(E.obs{[}{]})

fit.rep \textless{}- sum(E.rep{[}{]})

\}

\selectlanguage{english}
\FloatBarrier
\end{document}

