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\begin{document}

\title{Effects of large carnivores, hunter harvest, and climate on the
mortality of moose calves in a partially migratory population}



\author[1]{Giorgia Ausilio}%
\author[2]{Hakan Sand}%
\author[2]{Camilla Wikenros}%
\author[3]{Malin Aronsson}%
\author[4]{Cyril Milleret}%
\author[5]{Kristoffer Nordli}%
\author[1]{Petter Wabakken}%
\author[1]{Ane Eriksen}%
\author[6]{Jens Persson}%
\author[5]{Erling Maartmann}%
\author[5]{Karen Mathisen}%
\author[5]{Barbara Zimmermann}%
\affil[1]{Inland Norway University of Applied Sciences}%
\affil[2]{Grimsö}%
\affil[3]{Swedish University of Agricultural Sciences}%
\affil[4]{Faculty of Applied Ecology and Agricultural Sciences}%
\affil[5]{Inland Norway University of Applied Sciences - Evenstad Campus}%
\affil[6]{SLU}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Survival among juvenile ungulates is an important demographic trait
affecting population dynamics. In many systems, juvenile ungulates
experience mortality from large carnivores, hunter harvest and
climate-related factors. These mortality sources often shift in
importance both in space and time. While wolves (Canis lupus) predate on
moose (Alces alces) throughout all seasons, brown bear (Ursus arctos)
predation and human harvest happen primarily during early summer and
fall, respectively. Hence, understanding how the mortality of juvenile
moose is affected by predation, harvest and climate is crucial to
adaptively managing populations and deciding sustainable harvest rates.
We used data from 39 female moose in south-central Scandinavia to
investigate the mortality of 77 calves in summer/fall and winter/spring,
in relation to carnivore presence (defined as wolf presence and bear
density), summer productivity, secondary road density, winter severity
and migratory strategy (migratory versus resident) using logistic
regressions. Summer mortality varied significantly between years but was
not correlated to any of our covariates. In winter, calf mortality was
higher with deeper snow in areas with wolves compared to areas without
and increased more strongly with an increasing proportion of
clearcuts/young forests in the presence of wolves compared to when
wolves were absent. Lastly, increasing hunting risk was associated with
higher calf mortality, and migratory females had higher calf mortality
compared to stationary ones. Our study provides useful insight into
mortality rates of moose calves coexisting with two large carnivores and
with an intensive harvest pressure. Increasing our understanding of the
mechanisms driving calf mortality both in summer and winter will become
increasingly important if the populations of wolves and bears continue
to expand and the moose population declines, and both summers and
winters become warmer.%
\end{abstract}%



\sloppy


\section*{Abstract}

{\label{abstract}}

Survival of juvenile ungulates represents an important demographic
parameter that influences population dynamics within ecosystems. In many
ecological systems, the mortality of juvenile ungulates is influenced by
various factors, including predation by large carnivores, human hunting
activities, and climate-related variables. The relative importance of
these mortality sources often exhibits spatial and temporal variation.
While wolves (\emph{Canis lupus} ) are known to predate on moose
(\emph{Alces alces} ) throughout all seasons, brown bears (\emph{Ursus
arctos} ) primarily engage in predation during early summer, while human
harvest activities primarily occur in autumn and early winter. Hence,
understanding the impacts of predation, harvest, and climate on the
survival of juvenile moose is crucial for adaptive population management
and the determination of sustainable harvest rates.

To investigate the summer and autumn-winter survival of moose calves in
relation to carnivore occurrence (wolf presence and bear density),
summer habitat productivity, winter severity, human harvest, and
migratory strategy (migratory versus resident), we analysed data
collected from 39 GPS-collared female moose in south-central
Scandinavia. Cox proportional hazard models were used to examine the
dataset, which included 77 observations of calf survival.

Our findings revealed significant interannual variation in summer
survival rates, which were negatively correlated with bear density.
Specifically, areas with high bear density exhibited calf mortality
rates twice as high as those in regions with low bear density. During
the autumn-winter period, calf survival was lowest in the presence of
wolves and deep snow, and it exhibited a negative correlation with the
proportion of clearcuts and young forests within the mother's home
range. Additionally, calf survival was negatively correlated with the
risk of human hunting, and calves of stationary females displayed ten
times higher survival rates compared to migratory individuals.

Our study provides valuable insights into the survival of moose calves
coexisting with two large carnivores and humans. As wolf and bear
populations continue to expand alongside declining moose populations,
intensifying hunting pressure, and the potential threat of warming
summers and winters, improving our understanding of the underlying
mechanisms driving calf survival during both summer and winter seasons
assumes increasing importance.

\section*{Introduction}

{\label{introduction}}

Survival is a fundamental parameter influencing the demography and
population dynamics of ungulates (Gaillard et al., 1998). Variations in
survival rates can lead to fluctuations in population density (Loison
and Langvatn, 1998; Gaillard et al., 1998). Typically, ungulates exhibit
a hump-shaped pattern of age-specific survival, with the lowest and most
variable rates observed among juveniles, high and stable rates in prime
adulthood, and declining rates associated with senescence (Caughley,
1966; Gaillard et al., 1998, 2000; Loison et al., 1999). In the absence
of human hunting, adult survival rates remain relatively constant from
year to year, while juvenile survival rates show high variability
(Gaillard et al., 1998). Thus, the temporal variation and
unpredictability associated with juvenile survival make it a critical
demographic trait affecting population dynamics and trajectories
(Houston, 1982; Gaillard et al., 1997, 2000; Raithel et al., 2007).
Juvenile survival in ungulates is influenced by multiple factors,
including predation by predators (Linnell et al., 1995; Swenson et al.,
2007), exposure to pathogens (Grobler, 1981), access to food resources
(Eberhardt, 2002; Scornavacca et al., 2016), and climate conditions
(Singer et al., 1997; Ericsson et al., 2002; Ciach and P\selectlanguage{polish}ę\selectlanguage{english}ksa, 2019).
Predation often represents the primary source of mortality for neonate
ungulates during summer (Linnell et al., 1995; Swenson et al., 2007).
The availability of food resources and the nutritional status of
individuals strongly influence vital rates in ungulates, and these
factors are mostly determined by density-dependent effects associated
with the environment (Pettorelli et al., 2005; To\selectlanguage{ngerman}ïgo et al., 2006). In
boreal systems, ungulates frequently utilize clearcuts and young forests
early in the growing season due to higher forage density and the
presence of high-quality plants (Hjeljord et al., 1990; Boyce et al.,
2003; Månsson, 2009; Hebblewhite et al., 2008). Plant productivity has
been shown to positively affect the survival of both adult and juvenile
ungulates (Sims, 2017; Hurley et al., 2017).

Winter severity represents an important factor impacting the survival of
juvenile ungulates (Loison and Langvatn, 1998; Garrott et al., 2003).
Snow conditions and cold temperatures restrict movement and increase
energy expenditure, potentially making ungulates more vulnerable to
predation (Smith et al., 2004; Hebblewhite, 2005; Garrott et al., 2008).
In ungulate species subjected to human hunting, hunters often constitute
the most significant source of mortality for both adults and juveniles
(Festa\selectlanguage{english}-Bianchet et al., 2003; Apollonio et al., 2010). Hunting can have
substantial impacts on demography in harvested populations (Langvatn and
Loison, 1999; Ginsberg and Milner\selectlanguage{english}-Gulland, 1994). The effects of large
carnivores and hunting on ungulate population dynamics depend on whether
predation is additive or compensatory, the specific sex and age classes
targeted by these mortality sources, and the cumulative impact of
predation-induced mortality in relation to harvest (Bischof et al.,
2008). However, the effects of many of these factors remain largely
unknown. Therefore, despite the challenges associated with their
assessment (Caughley, 1977; Eberhardt, 1985; Lebreton et al., 1993),
survival rates are crucial parameters for understanding the mechanisms
affecting population dynamics and for effectively managing harvested
populations (Caughley, 1966; Raithel et al., 2007). Thus, investigating
changes in survival rates of juvenile ungulates and identifying the
factors influencing them is vital for population management, including
the estimation of sustainable harvest rates (Porath, 1980; Rohm et al.,
2007).

Moose (\emph{Alces alces} ) holds significant economic and recreational
value across Scandinavia (Storaas et al., 2001; Lavsund et al., 2003;
Boman et al., 2011). Over the past two decades, calf/cow ratios and
moose densities have generally declined in both areas with and without
large carnivores in Sweden (Wikenros et al., 2020; Tallian et al.,
2021). In areas with large carnivores, moose face multiple mortality
sources that vary in importance over space and time. Brown bear
(\emph{Ursus arctos} ) predation is often the primary cause of mortality
for neonatal moose in early summer (Swenson et al., 2007). As summer and
autumn progress, bear predation declines in significance, while hunting
becomes the primary mortality factor for both adult and juvenile moose
during early and late autumn (Cederlund and Sand, 1991; Lavsund et al.,
2003). Wolves (\emph{Canis lupus} ) also pose a significant mortality
risk to moose throughout the year, particularly to calves (Sand et al.,
2005; Sand et al., 2008). As expected, winter mortality of moose calves
in Norway was higher in wolf territories compared to areas lacking
wolves (Saether et al., 1996; Sivertsen et al., 2012). The spatial and
temporal variations in the presence of hunters and large carnivores
underscore the importance of investigating the impact of human-related,
abiotic, and biotic factors on moose calf mortality patterns. Given the
declining moose densities (Tallian et al., 2021), understanding the
factors that influence moose demography, such as calf survival, becomes
increasingly important as carnivore populations expand and in the
context of climate change, which may lead to increased thermoregulation
costs (Murray et al., 2006; Lenarz et al., 2009). This knowledge is
essential for providing management strategies that can mitigate the
effects of large carnivores and hunting mortality.

In this study, we used position data from 39 GPS-collared female moose
in south-central Scandinavia to investigate seasonal calf survival
during summer and autumn/winter. We examined the survival probability of
calves in relation to the presence of wolves, wolf predation risk, bear
density, human harvest and associated hunting risk, habitat
productivity, clearcuts and young forests (hereafter referred to as
young forests) and snow depth. During summer, we hypothesized that calf
survival would be higher in areas with greater productivity and a higher
proportion of young forests (\emph{forage opportunity hypotheses} ),
while it would be lower in the presence of wolves and with increasing
bear density (\emph{predation hypotheses} ). During autumn-winter, we
predicted that calf survival would decrease with increasing harvest and
hunting risk and would be lower in the presence of wolves and with
higher wolf predation risk (\emph{predation--hunting hypotheses} ). We
also hypothesized that calf survival would be lower in areas with
greater snow depth in the presence of wolves compared to areas without
wolves (\emph{predation*snow hypothesis} ). Additionally, we predicted
calf survival to be lower in areas with a higher proportion of young
forests, which are known to be risky habitats for moose during winter
(Gervasi et al., 2013; Ausilio et al., 2023) (\emph{habitat hypothesis}
).

Recent studies have shown that migration can influence neonatal ungulate
mortality by reducing the risk of predation for females and their calves
(White et al., 2014; Berg et al., 2019). Migratory behaviour allows
individuals to better utilize delayed green-up at higher altitudes and
experience less intraspecific competition by leaving winter
concentration areas (Van Moorter et al., 2021). Thus, we tested the
hypothesis that calves with migrating mothers would have higher survival
rates compared to those with stationary mothers, during both summer and
winter (\emph{migration hypothesis} ). Lastly, we predicted that calf
survival would vary across years due to annual climatic variations that
may affect moose forage availability and thermoregulation (\emph{climate
hypothesis} ).

By investigating these factors and their influence on moose calf
survival, we aim to contribute to an increased understanding of the
underlying factors shaping calf survival, which ultimately affect moose
population demography and dynamics.

\section*{Materials and methods}

{\label{materials-and-methods}}

\subsection*{Study area}

{\label{study-area}}

The study was carried out along the Swedish-Norwegian border
(60deg45'-61deg35'N, 11deg55' -- 12deg55'E) in the Norwegian municipalities of
Trysil, Elverum, Valer and Asnes and the Swedish counties of Varmland
and Dalarna (Fig. 1). The topography of the study area exhibits a
gradient from higher altitudes in the north to lower altitudes in the
south, with approximate elevations ranging from 900 to 400 meters above
sea level (masl) (Copernicus Land Monitoring, 2018). The northern part
of the study area has a continental climate and is dominated by bare
mountains, marsh complexes and deep valleys with dense old-growth
forests and vast heathlands (Jansson and Antonson 2011). The forest is
primarily composed of Scots pine (\emph{Pinus sylvestris} ), Norway
spruce (\emph{Picea abies} ) and birch (\emph{Betula} spp.) (Jansson and
Antonson 2011). The mean daily temperature in January and July are
--10deg\selectlanguage{english} C and 15deg\selectlanguage{english} C, respectively. The ground is typically covered with
snow from late October to early May, and the vegetation period spans
approximately 140-170 days (\emph{www.smhi.se};\emph{www.senorge.no}).
Annual precipitation in the area ranges from approximately 600 to 1000
mm. The southern part of the study area is mostly dominated by Scots
pine, Norway spruce and birch forests, which are extensively managed for
timber and pulp (Yrjola 2002; Lundmark et al., 2013). The mean
temperature in January and July is similar to the northern parts, but
snow mostly covers the ground between December and March. Due to
intensive silviculture practices, the study area features an extensive
network of forest gravel roads. Additionally, the presence of national
and regional roads contributes to a mean road density of 0.84 km/km2
within the study area.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\subsection*{Moose}

{\label{moose}}

\begin{enumerate}
\item ~
  \subsubsection*{Moose reproduction}

  {\label{moose-reproduction}}
\end{enumerate}

Between February and mid-March (2018-2021), we immobilized 39 adult
female moose from a helicopter using a dart gun injecting etorphine and
xylazine (Sandegren et al., 1987). The handling protocols adhered to the
ethical requirements for research on wild animals in both Sweden
(decision C281/6 and C315/6) and Norway (decision id 15370). Each female
moose was fitted with a GPS collar (Vectronics Aerospace GmbH, Berlin,
Germany) programmed to record a position every 2 hours.

During the calving season (May-July), we monitored potential calving
events for each collared female using a rolling minimum convex polygon
(rMCP) method described by Nicholson et al. (2019). We employed a
12-point moving window to calculate the area of the rMCP within a
24-hour period. This timeframe allowed us to include both daytime and
nighttime positions and detect changes in movement and space use.
Potential parturition events were defined as the earliest date when the
mean of overlapping rMCPs remained below 1.7 hectares for approximately
72 hours. This criterion was based on the behaviour observed by McGraw
et al. (2014) in female moose in Minnesota, where extensive movements
over large distances occurred just before parturition, followed by a
period of minimal movement (presumably just after parturition; 1.72 \selectlanguage{ngerman}±
0.48 hectares for 7 days; McGraw et al., 2014). We plotted the
overlapping rMCPs for every day from the beginning of May and estimated
calving events by identifying spikes in movement prior to periods of
little movement (area \textless{} 1.7 ha) (Fig. 1 in Supplementary
Material). To identify the spike in movement before parturition, we set
a 25-ha threshold after which a search algorithm was initiated to detect
a 72-hour period where the average area of the 24-hour rMCPs was below
1.7 ha (see Nicholson et al., 2019 for details).

Once a calving event was identified, we located the female moose and
approached her silently to visually confirm the presence and number of
newborn calves. The approach was carried out on foot and using a
handheld VHF receiver to locate the female (RX98, Followit, Lindesberg,
Sweden). In some cases, drones were employed, flying to the last known
GPS position of the female moose and hovering over the area searching
for the moose. We waited a minimum of 2 days and a maximum of 7 days
from the assumed calving event before each approach. This interval
allowed time for the females to bond with their calves, reducing the
risk of calf abandonment. Additionally, it enabled us to collect calf
survival data within the first few days after parturition, when calf
mortality is typically high.

Once we had visual contact with the female moose, we waited until we had
visual confirmation of the number of calves present. In cases where the
reproductive status of a female was uncertain during the initial
approach, a second approach was conducted 2 to 7 days later to gather
additional information. Females that did not exhibit movement behavior
indicating calving were approached between the end of June and mid-July
to confirm the absence of calf/calves. We are aware that this method
does not determine calving success with absolute certainty, since
neonates could have died just after birth and prior to surveys;
nonetheless, it represents a reasonably unbiased method to measure the
relative production of calves in early summer. Throughout the study,
there were only two instances where female moose displayed calving
behaviour, but subsequent field checks revealed no presence of calves.

Within one month prior to the onset of the hunting season, each female
moose was re-approached to count the number of calves again using the
same procedure as in early summer. The hunting season in Norway starts
the 25\textsuperscript{th} of September, whereas it differs within the
Swedish part of the study area, starting either in the beginning of
September or on the second Monday of October. Accordingly, our approach
timing was strategically planned to coincide with the hunting initiation
in each respective area, ensuring that the estimates remained unaffected
by the onset of hunting activities. Lastly, a final verification of the
number of calves accompanying each female was performed before natal
dispersal, specifically in April of the following year. This procedure,
encompassing calf-checks after birth, prior to hunting, and prior to
dispersal, was carried out for the study years 2019-2020, 2020-2021, and
2021-2022.

\subsubsection*{Moose migratory strategy and home
ranges}

{\label{moose-migratory-strategy-and-home-ranges}}

To classify the migratory strategies of each female moose, we employed
the Net Squared Displacement method (NSD) (Bunnefeld et al., 2011; Singh
et al., 2016). This approach enables the differentiation of various
movement strategies, such as migration and residency (Bunnefeld et al.,
2011; Singh et al., 2016; Börger and Fryxell, 2012), by analysing the
displacement patterns of individual animals using non-linear mixed
effects models (Singh et al., 2016). The NSD method characterizes
migration as a double sigmoid or s-shaped function, which repeats within
a year and involves the animal returning to its departure location
(Singh et al., 2016).

We used GPS positions to estimate seasonal home ranges for each female
moose using 95\% minimum convex polygons (MCP). The MCPs were calculated
separately for the summer (1st of May -- 31st of August; mean = 39
km\textsuperscript{2}, range = 15 -- 88 km\textsuperscript{2}) and
autumn-winter (1st of September until 30th of April; mean = 42
km\textsuperscript{2}, range = 13 -- 100 km\textsuperscript{2}) periods
with the amt package in R (Signer et al., 2019). Using the start and end
date of migration (based on NSD), we excluded GPS positions during
migration from our MCP analysis.

\subsection*{Calf survival}

{\label{calf-survival}}

\begin{enumerate}
\item ~
  \subsubsection*{Harvest density}

  {\label{harvest-density}}
\end{enumerate}

We used harvest data from both Norway and Sweden at the moose management
area level (see Wikenros et al., 2020; for details regarding the moose
management systems in Norway and Sweden). We calculated harvest density
as the number of harvested moose per km\textsuperscript{2} (range = 0.11
-- 0.44 moose/km\textsuperscript{2}) and extracted the average harvest
density within the autumn-winter home range for each female moose.

\subsubsection*{Large carnivores}

{\label{large-carnivores}}

Wolves

{\label{wolves}}

Wolves belonging to four packs, all having cross border territories
along the Swedish-Norwegian border, were located on snow, and
immobilized by darting from helicopter (see Sand et al. 2006; Arnemo and
Evans 2017). Handling protocols fulfilled the ethical requirements for
research on wild animals in Sweden (decision C281/6 and C315/6) and
Norway (The Norwegian Food Safety Authority, decision id 15370). The
collars were programmed to acquire one position every four hours. We
created 95\% minimum convex polygons (MCPs) using GPS positions from the
scent-marking adult breeders to represent the territory of each wolf
pack during summer (May -- August) and autumn-winter (September --
April) with the package \emph{amt} in R (Signer et al., 2019). In cases
where multiple adult wolves were collared within a wolf pack during our
study period, we prioritized data from the individual with the most
extensive collar operation during the specific time interval (referred
to as the ''main individual''). For periods when location data from the
main individual were unavailable, we supplemented the dataset with
relocations from the other collared adult. We checked for overlap
between moose and wolf home ranges during both summer and autumn-winter
and categorized each moose home range to be overlapping (entirely or
partially) or not overlapping with the wolf home range (0=no overlap;
1=overlap), depending on whether they were inside or outside a wolf
territory.

Bears

{\label{bears}}

To estimate bear density within the study area, we used density raster
maps provided by Bischof et al. (2020). These maps provided estimates of
bear density as the number of bears per square kilometer. By overlaying
the bear density raster maps with the summer home ranges of each female
moose, we extracted the average bear density for each moose's respective
summer home range.

\subsubsection*{Environmental covariates}

{\label{environmental-covariates}}

We obtained data on young forests from the Corine Land Cover (CLC)
inventory (Copernicus Land Monitoring, 2018). Using this inventory, we
calculated the proportion of each female moose's home range covered by
young forests for each season (summer and autumn/winter) and year (mean
= 15 ± 7; range = 0 -- 36\%).

We used cumulative average winter snow depth from October to March of
each study year as a proxy for winter severity. The data was obtained
from the Norwegian Water Resources and Energy Directorate (NVE) (for
more information on the interpolation method used see Saloranta 2012).
For each female moose home range in autumn-winter in each year we then
extracted average snow depth.

Normalized Difference Vegetation Index (NDVI) is a measure of
photosynthetic activity at landscape scales that is often used to as a
proxy for plant productivity and nutritional status (Pettorelli et al.
2011). We obtained data on NDVI from Copernicus Global Land Service (300
m resolution raster) and used weekly NDVI cell values to calculate the
mean cumulative summer NDVI for each moose home range (mean = 0.73 ±
0.08; range = 0.53 -- 0.86).

\subsubsection*{Hunting and wolf predation risk
metrics}

{\label{hunting-and-wolf-predation-risk-metrics}}

We used previously estimated relative risks of human hunting and wolf
predation for moose in our study area, based on locations of wolf-killed
and hunter-killed moose during autumn-winter (September-April; Ausilio
et al., 2022). Ausilio et al. (2022) modelled hunting and wolf predation
risk separately using logistic regressions with the relative probability
of a location being a kill site or a random location as a function of
different landscape features (distance to bogs, young forests, main and
secondary roads, elevation, building density and terrain ruggedness).
The estimates represent the odds ratio (relative risk) of being killed
by wolves or hunters (hereafter, relative hunting risk and relative wolf
predation risk). The odds ratios for each given location within the
study area were then plotted as raster layers (25x25 m). For more
details on the methods used to estimate wolf predation risk and human
hunting risk, see Ausilio et al., (2022). We extracted the odds ratio of
hunting and wolf predation risk for a subset (n = 31 females) of moose
home ranges that overlapped with the risk maps from Ausilio et al.
(2022).

\subsection*{Survival analyses}

{\label{survival-analyses}}

To investigate the factors influencing calf survival during summer and
autumn-winter, we used Cox Proportional-Hazard models. These models
allowed us to analyse the probability of survival (0 = survived; 1 =
died) while considering the repeated checks of individual moose by
including moose ID as a covariate. For each season, we initially
constructed a full model that included all predictors and interactions
based on our hypotheses. To refine the models, we employed stepwise
backwards selection, eliminating interactions with p \textgreater{} 0.10
and individual predictors with p \textgreater{} 0.05.

The summer model included the additive and linear effects of the
following predictors: year (3-level factor), wolf presence, bear
density, proportion of young forests, migratory strategy of the mother
(migratory versus stationary) and NDVI.

The autumn-winter model included the additive and linear effects of year
(3-level factor), harvest density, wolf presence, snow depth, proportion
of clear-cuts/young forests and the migratory strategy. We included the
interaction between wolf presence and snow depth to test whether calf
survival was lower with deeper snow in the presence of wolves compared
to the absence of wolves.

Lastly, we used a subset of female moose to test the effect of hunting
and wolf predation risk on autumn-winter calf survival. This
supplementary analysis allowed us to evaluate whether the mortality risk
metric developed by Ausilio et al. (2022) correlates with calf survival,
but also to account for smaller spatial-scale variation in hunting risk
compared to overall harvest density, which might be too coarse to detect
changes in survival at the individual-level. For this subset of the
data, we assumed that calf survival was a function of the additive and
linear effects of hunting risk, wolf predation risk and year (3-level
factor). All continuous variables were scaled prior to analysis to have
mean~=~0 and standard deviation~=~1. We could not use all female moose
for this analysis because we lacked data about wolf predation risk for
2021/22, hence why we selected only a subset of moose overlapping in
time with our previously estimated metric of wolf predation risk
(Ausilio et al., 2022).

\section*{Results}

{\label{results}}

Across our three-year study period, we obtained data from 39 female
moose producing a total of 77 calves. The survival rates of calves from
birth to dispersal were 42\% for the 2019/20 season, 58\% for the
2020/21 season, and 52\% for the 2021/22 season.

\subsection*{Summer survival}

{\label{summer-survival}}

The best model explaining variation in the survival probability of
calves during summer included bear density and year (Table 1 in
Supplementary Materials), with the probability of survival decreasing
with increasing bear density (Fig. 2). Summer calf survival varied
significantly between years, with survival being highest in 2019/20
(92\%), followed by 2021/22 (69\%) and 2020/21 (58\%) (Table 1).\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Summer & 2019/20 & 2020/21 & 2021/22\tabularnewline
\midrule
\endhead
Total number of females & 26 & 27 & 32\tabularnewline
Females that gave birth & 22 & 21 & 26\tabularnewline
Females with twins & 2 & 3 & 3\tabularnewline
Number of calves born & 24 & 24 & 29\tabularnewline
Calf mortality & 8\% & 31\% & 42\%\tabularnewline
\bottomrule
\end{longtable}

Table 1: Summary table showing the total number of female moose equipped
with GPS collars, the number of females that gave birth, the number of
females that gave birth to twins, the total number of calves born and
the overall summer calf mortality (\%) for three years.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Autumn-winter & 2019/20 & 2020/21 & 2021/22\tabularnewline
\midrule
\endhead
Females with calves & 21 & 12 & 20\tabularnewline
Number of calves & 22 & 14 & 20\tabularnewline
Calf mortality & 37\% & 36\% & 30\%\tabularnewline
Annual survival & 10/24 & 14/24 & 15/29\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\subsection*{Autumn-winter survival}

{\label{autumn-winter-survival}}

The best model explaining variation in the survival probability of
calves during autumn-winter included the following covariates: wolf
presence, snow depth, the interaction \emph{wolf*snow} , proportion of
young forests, and the migratory strategy of the mother (Table 2 in
Supplementary Materials). Calf mortality did not vary between years:
37\%, 36\% and 30\% of all calves died during autumn-winters 2019/20,
2020/21 and 2021/22, respectively (Table 1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

The interaction \emph{wolf*snow} was significant, indicating that in the
presence of wolves, snow depth decreased the probability of survival
(Fig. 3). However, the probability of calf survival was higher with
increasing snow depth in the absence of wolves (Fig. 3). The probability
of survival was negatively correlated to the proportion of young forests
present within the mother's home range. Harvest density was not related
to autumn-winter calf survival, but survival probability was higher for
calves of stationary females compared to calves of migrating females
(Fig. 4).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4: Predicted autumn-winter survival probability of moose calves
with migratory versus stationary mothers in south-central Scandinavia.
Calf survival was investigated by performing field checks on
GPS-collared female moose and their calves. The behavioural strategy of
the mother (migratory or stationary) was estimated using Net Squared
Displacement.

The separate analyses on a subset of female moose showed that calf
survival was negatively correlated to hunting risk (Fig. 5) but was not
significantly related to wolf predation risk (Table 3 in Supplementary
materials).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5: Predicted autumn-winter survival probability of moose calves
in relation to hunting risk (expressed as odds ratios) by humans in
south-central Scandinavia. Odds ratio can be interpreted as relative
hunting risk, where a value of 1.5 indicates a kill probability 1.5
times greater than average. Autumn-winter moose calf survival was
estimated with field checks on GPS-collared female moose after summer
(Sep-Oct) and at the end of winter (April of the following year).
Hunting risk was obtained from previous research from the same study
area (see Ausilio et al., 2022 for further details on how hunting risk
was estimated).

\section*{Discussion}

{\label{discussion}}

In this study, we found that calf survival during both summer and winter
was influenced by several factors including the presence of predators,
landscape and climatic features, human activities such as hunting, and
behavioural traits such as the migratory strategy of the mother.
Specifically, we observed that summer calf survival decreased with
increasing bear density and varied significantly between years, giving
support to one of the two \emph{predation} \emph{hypotheses} and to
the\emph{climate} \emph{hypothesis} . During the summer season, brown
bears are one of the main predators of moose calves. Previous research
has shown that moose calves can constitute a significant portion,
ranging from 36\% to 44\% of the total energy content in the diet of
brown bears (Opseth, 1998). In south-central Sweden, studies estimated a
predation rate of 26\% for moose calves by brown bears, with individual
female bears killing an average of 6.8 calves per year (Swenson et al.,
2007). The majority (92\%) of brown bear predation on moose calves
occurs when the calves are less than one month old (Swenson et al.,
2007; Rauset et al., 2012). Our study revealed that the risk of calf
mortality is more than twice as high in areas with high bear density
compared to areas with low bear density. These findings align with a
previous study that found a negative association between bear density
and calf/cow ratio (i.e., the number of observed calves per female moose
in autumn) in Sweden (Tallian et al., 2021). The lower summer survival
rates and decreased autumn calf/cow ratios observed with increasing bear
density likely reflect the impact of bear predation on neonate moose.

While we do not have information on cause-specific mortality for each
moose calf included in the study, we were able to confirm at least two
documented bear predation events during the first study year: one in the
northernmost part of the study area, where a pair of twin calves were
killed by bears, and one in the easternmost part where the female moose
and her calf were observed being followed by a female bear and her two
yearling cubs, and the calf was not seen again the following day.
Overall, these findings highlight the significant influence of bear
predation on the survival of moose calves during the summer season.

In ungulates, the survival of adult females is a crucial driver of
population growth, but calf survival can account for up to 75\% of the
variation in population growth rates (Gaillard et al., 2000, Raithel et
al., 2007). Previous research from Sweden asserted that bear predation
was not an influential driver of moose population dynamics (Haglund
1974) but that study was made during a time with a much lower brown bear
population size and did not have access to the modern tools of
GPS-technology for field checks. Instead, our study supports the notion
that bear predation on moose neonates is likely an influential factor
driving population dynamics of moose (Swenson et al. 2007, Rauset et al.
2012, Tallian et al. 2021). This is likely to become even more relevant
in the future as the brown bear population has been expanding in their
southern range (Kindberg et al. 2011; Kindberg 2010) and now overlaps
with the core areas of the wolf population.

In Scandinavia, wolves are important predators of new-born moose calves,
which make up approximately 90\% of wolf kills during the summer season
(Sand et al., 2008, Tallian et al., 2017). Surprisingly, we did not
detect a relationship between wolf presence and summer calf survival.
The lack of such an association might be related to the spatial location
of the collared female moose and wolf space use and behaviour during
denning time. During the summer season, breeding wolves exhibit more
restricted space use compared to winter, focusing their activities
around the den area where they care for their pups (Fritts and Mech,
1981; J\selectlanguage{polish}ę\selectlanguage{english}drzejewski et al., 2001; Walton et al., 2001; Zimmermann et al.,
2019). During the denning period, Scandinavian female and male wolves
had a mean attendance at the den site of 68\% and 70\%, respectively
(Alfr\selectlanguage{ngerman}éeden, 2006). Consequently, the likelihood of a moose calf being
killed by wolves may vary within wolf territories, with higher predation
risk closer to the den site compared to farther away.

In our study, we characterized wolf presence based on whether a moose
home range was inside or outside a wolf home range. By using this broad
classification, we might have overlooked the spatial variation in
predation risk within wolf home ranges. This could have resulted in a
failure to detect a correlation between wolf presence and calf survival,
which may have been influenced by a bias introduced by the sampling of
collared females. In other words, our data might have included an
overrepresentation of collared female moose that utilized areas with low
wolf activity during the summer season.

We offer several potential \emph{ad hoc} explanations for the lack of
support for our \emph{forage opportunity hypotheses} (habitat
productivity and proportion of young forests) in our analysis of summer
survival. Previous studies have indicated that during the summer season,
the composition of moose home ranges tends to resemble the overall
landscape, suggesting that moose utilize a greater variety of habitats
compared to winter (Hjeljord et al., 1990; Nikula et al., 2004). This
may indicate that although clearcuts and young forests are important
habitats for moose, the more homogenous distribution of high-quality,
nutritional plant forage during summer allows moose to find optimal
feeding patches in different habitats, also explaining why productivity
was unrelated to calf mortality. Moreover, Nicholson et al. (2014) found
that during summer, females with calves had lower selection strength for
young forests and tended to select older forests, which may also explain
why we did not find a correlation between calf mortality and the
proportion of young forests.

The observed annual variation in summer survival of neonates is likely
an effect of differing climatic conditions. Neonate growth and survival
can be negatively affected by hot and dry summers due to nutritional
constraints (Cook et al. 2004), but also by the environmental conditions
experienced by their mothers in the preceding summer and during
pregnancy in the preceding winter and spring (Forchhammer et al. 2001;
Eacker et al. 2016; Bastille\selectlanguage{english}-Rousseau et al. 2016).

During autumn-winter, calf survival exhibited a negative association
with increasing snow depth in the presence of wolves
(\emph{predation*snow hypothesis} ) and an increasing proportion of
young forests in the mother's home range (\emph{habitat hypothesis} ).
In addition, calf survival was higher for stationary moose calves
compared to migratory calves, contrary to our initial prediction
(\emph{migration hypothesis} ). Deep snow can hinder the movement of
both wolves and moose, but since the former have a lighter foot loading,
they can often travel on top of the snow crust (Peterson 1974). Hence,
deeper snow generally increases the vulnerability of moose to predation
and therefore increases the hunting success of wolves (Kolenosky 1972;
Peterson and Allen 1974; Haber 1977) and kill rate (Nelson and Mech
1986; Huggard 1993; Post et al. 1999). Contrary to expected, we found
that deeper snow was positively correlated with calf survival the
absence of wolves. Although surprising, this unexpected result may be
confounded by spatial variations in snow depth within our study area: in
the northernmost part, where wolves are absent, snow depth averaged 45
cm, whereas average snow depth in the central and southern parts (where
wolves are present) was only 22cm. Thus, the absence of wolves and the
generally higher snow depths in the northernmost region might be
contributing to the observed positive relationship between calf survival
and snow depth.

As food resources become scarcer during winter, moose commonly aggregate
in young forest plantations to feed (Gundersen 2003), with Scots pine
being the quantitatively most important food source (Mansson, 2007). In
our study, we observed a negative relationship between calf survival and
the proportion of young forests during autumn-winter. This finding
supports the notion that clearcuts and young forests represent riskier
habitats for moose, both in terms of predation by wolves (Gervasi et
al., 2013; Ausilio et al., 2022) and hunting by humans (Ausilio et al.,
2022).

Game harvesting has become the leading cause of mortality in many
ungulate species (Allendorf et al. 2008; Darimont et al. 2015), with
moose calves accounting for about 40\% of total harvest of moose in
Scandinavia (www.algdata.se; www.ssb.no). We did not find a relationship
between harvest density and moose calf survival, but we found a
significant negative correlation between hunting risk and autumn-winter
survival: calves exposed to high hunting risk had twice the risk of
mortality compared to calves exposed to low hunting risk. The lack of a
relationship between harvest density and survival is most likely a
result of the spatial scale at which both harvest statistics and harvest
density were calculated, which is the moose management unit (MMU). Moose
management units are larger (average +-\selectlanguage{english} SE = 1830 +- 140
km\textsuperscript{2},Wikenros et al. (2020)) than an average moose home
range (68 +-\selectlanguage{english} 7 km\textsuperscript{2}), which means that one MMU can
include several hundreds of moose home ranges. Moose management units
are in turn made up by many hunting teams. Unfortunately, harvest
density at the hunting team level was not available, so we decided to
average harvest density at the MMU-level, which may be a too coarse
spatial scale to identify a significant correlation between harvest
density and calf survival. Hunting risk was, on the other hand,
estimated at a much finer spatial scale (hunting team level, Ausilio et
al 2022) and was negatively correlated to calf survival, providing
support for the hunting part of our \emph{predation-hunting hypothesis}
.

In our study area, the moose population is partially migratory, with
some individuals moving between high-altitude summer ranges and
low-altitude winter ranges. Migration is usually the product of
balancing the costs and benefits of remaining in a range all year-round
or moving to new areas. We found that the migratory strategy of females
was unrelated to summer calf survival, while during autumn-winter,
migratory females were associated with a lower probability of calf
survival compared to stationary ones. This outcome was opposite to
our\emph{migration hypothesis} . A possible explanation is that
migratory moose in our study area moved from summer ranges, that were
often located in Norway where the hunting season stopped already in
December, to winter ranges in Sweden where the hunting season continued
to February. It is also possible that migratory moose were forced to
select sub-optimal habitats that increased their exposure to hunting and
wolf predation compared to stationary ones, which can select optimal
habitats based on the perceived risk of hunting and foraging needs.

Both the wolf and brown bear populations have increased during the last
decades and expanded their geographical distribution in Scandinavia
(Liberg et al., 2012; Wabakken et al., 2011; Kindberg et al., 2011), and
in many areas these two predators have overlapping ranges (Kindberg et
al. 2011). A future scenario likely to arise is therefore increased
predation pressure on moose, which will intensify the competition with
human hunters for this shared prey (Jonzen et al. 2013). In areas where
wolves and bears coexist with human hunters, the additive effects of two
large carnivores and human harvest will most likely result in greatly
reduced calf survival, which may influence population dynamics. In order
to ensure a sustainable harvest of the moose population, calf survival
is a crucial demographic parameter to take into consideration. The
coexistence of wolves, brown bears, and human hunters presents
challenges that can lead to reduced calf survival, ultimately
influencing population dynamics. To ensure a balanced and sustainable
harvest of the moose population, comprehensive management strategies
need to account for the intricate interplay between predation, hunting,
habitat conditions, and climatic factors.

\section*{Author Contributions}

{\label{author-contributions}}

MA, AE, KMM, JP, HS, PW, CW, and BZ secured funding. GA, BZ, CW, HS, AE,
and PW conceived the idea of the study. GA led and conducted fieldwork
with the help of Per Larsson, KN, EM, EF and BZ. GA compiled and
analysed the data and led the writing of the manuscript. MA created the
script to estimate rMCP and CM contributed to the survival analysis. All
authors contributed critically to the drafts and gave final approval for
publication.

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