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\begin{document}

\title{Bottom-up rather than top-down mechanisms determine mesocarnivore
interactions in Norway}



\author[1]{Rocío Cano-Martínez}%
\author[2]{Neri Thorsen}%
\author[3]{Tim Hofmeester}%
\author[2]{John Odden}%
\author[1]{John Linnell}%
\author[1]{Olivier Devineau}%
\author[1]{Siow Yan Jennifer Angoh}%
\author[1]{Morten Odden}%
\affil[1]{Inland Norway University of Applied Sciences - Evenstad Campus}%
\affil[2]{Norwegian Institute for Nature Research Oslo}%
\affil[3]{Sveriges Lantbruksuniversitet Fakulteten for Skogsvetenskap}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Interactions among coexisting mesocarnivores can be influenced by
different factors such as the presence of large carnivores, land-use,
environmental productivity, or human disturbance. Disentangling the
relative importance of bottom-up and top-down processes can be
challenging, but it is important for biodiversity conservation and
wildlife management. The aim of this study was to assess how the
interactions among mesocarnivores (red fox Vulpes vulpes, badger Meles
meles, and pine marten Martes martes) are affected by large carnivores
(Eurasian lynx Lynx lynx and wolf Canis lupus), land cover variables
(proportion of agricultural land and primary productivity), and human
disturbance, as well as how these top-down and bottom-up mechanisms are
influenced by season. We analyzed three years (2018-2020) of camera
trapping data from Norway and used structural equation models to assess
hypothesized networks of causal relationships. Our results show that
land cover variables are stronger predictors of mesocarnivore activity
than large carnivores in Norway. This might be caused by a combination
of low density of large carnivores in an unproductive ecosystem with
strong seasonality. Additionally, all mesocarnivores showed positive
interactions among each other, which were stronger in winter. The
prevalence of positive interactions among predators might indicate a
tendency to use the same areas and resources combined with weak
interference competition. Alternatively, it might indicate some kind of
facilitative relationship among species. Human disturbance had
contrasting effects for different species, benefiting the larger
mesocarnivores (red fox and badger) probably through food subsidization,
but negatively affecting apex predators (wolf and lynx) and smaller
mesocarnivores (pine marten). In a human-dominated world, this
highlights the importance of including anthropogenic influences in the
study of species interactions.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

The study of interspecific interactions among mammalian carnivores is
fundamental to conservation biology (Linnell and Strand, 2000), but the
factors influencing these interactions can be complex and difficult to
disentangle. Interactions among coexisting species can potentially have
large consequences for community structure by influencing the
demography, distribution, and behavior of the species within the
community (de Satge et al., 2017, Grassel et al., 2015, St-Pierre et
al., 2006). The potential top-down effect of large carnivores and their
importance in maintaining ecosystem functioning has been widely
recognized (Estes et al., 2011, Ripple et al., 2014, Ritchie and
Johnson, 2009). Declines in the numbers and distribution of large
carnivores due to human persecution and habitat loss in many regions has
led to changes in species interactions and food webs, highlighting their
ecological role in ecosystems worldwide (Ripple et al., 2014). One
commonly proposed outcome following the loss of apex predators is the
increase in the abundance of mesopredators. This phenomenon is known as
``mesopredator release'' (Prugh et al., 2009), and it can potentially
have negative impacts on prey species through increased predation from
mesopredators, as well as on other smaller carnivores through intraguild
predation.

Even though negative interspecific interactions among carnivores seem to
be widespread, there is also a growing recognition of the importance of
positive interactions in structuring predator communities (Prugh and
Sivy, 2020). Large carnivores can facilitate mesocarnivores by providing
resource subsidies in the form of carrion (Pereira et al., 2014, Prugh
and Sivy, 2020). Scavenging is a widespread behavior that has clear
implications for food web structure and population dynamics (Mellard et
al., 2021). Carrion subsidies can be important food sources in areas
where several opportunistic mesocarnivores coexist (Sivy et al., 2018),
or when other food sources are scarce (Jedrzejewski and Jedrzejewska,
1992, Killengreen et al., 2011). Interactions between large carnivores
and mesocarnivores can therefore range from facilitation to suppression,
and both may even occur simultaneously (Prugh and Sivy, 2020, Wikenros
et al., 2017). In addition, the direction and strength of these
interactions may be scale dependent (Sivy et al., 2017).

Interactions among predators in human-dominated landscapes can be
different from those occurring in undisturbed habitats. Furthermore,
ecological phenomena such as mesopredator release can be difficult to
separate from land-use changes (Prugh et al., 2009). Mesopredator
numbers tend to increase in human-modified habitats, as a response to
higher resource availability, while apex predators are more likely to
disappear due to direct persecution and habitat loss (Prugh et al.,
2009). Unfortunately, large-scale experimental approaches that might
help disentangle the relative importance of top-down versus bottom-up
effects are rarely logistically possible (Nilsen et al., 2020). However,
modeling approaches based on large-scale observational data can be
useful (Dorresteijn et al., 2015, Elmhagen and Rushton, 2007).
Anthropogenic influence on food webs may operate through diverse
processes and influence multiple trophic levels simultaneously. For
example, humans can decrease predator density directly through hunting,
but they can also trigger behavioral responses at both spatial and
temporal scales (e.g., by changing predators' habitat use and activity
patterns) (Milner et al., 2007, Ordiz et al., 2012, Ordiz et al., 2021),
potentially causing top-down cascades that may affect species at lower
trophic levels. On the other hand, humans may also influence predators
through bottom-up processes via food subsidization (Gompper and Vanak,
2008, Newsome et al., 2015, Newsome et al., 2014), or by enhancing
forage availability of herbivores, thus increasing prey density (Muhly
et al., 2013). The effect of such food subsidies may be particularly
relevant for systems with low productivity (Melis et al., 2009).

Ecosystem productivity and seasonal change in resource availability can
determine the relative strength and direction of trophic interactions
(Elmhagen and Rushton, 2007, Ritchie and Johnson, 2009, Stoessel et al.,
2018). In particular, Stoessel et al. (2018) found that harsh winter
conditions and food scarcity increased the importance of bottom-up
constraints on species interactions as compared to top-down factors in
an arctic ecosystem. Furthermore, Elmhagen and Rushton (2007) found a
stronger degree of bottom-up rather than top-down control of red foxes
(\emph{Vulpes vulpes} ) in low productivity ecosystems in Sweden. These
studies highlight the importance of considering both top-down and
bottom-up processes when studying mesopredator interspecific
interactions in ecosystems with strong seasonality. The relevance of
such insights is of special importance in the face of climate change,
which will alter seasonal conditions and species interactions.

Several large carnivore species are now recovering across large parts of
Europe (Chapron et al., 2014), with potential cascading effects through
the entire carnivore community. In Southern Spain for example, the
recovery of the Iberian lynx (\emph{Lynx pardinus} ) exerted a strong
suppression control of two sympatric mesocarnivores (Burgos et al.,
2023). However, the strength of this top-down effect differed for
different mesocarnivore species, and it was modulated by prey
availability. In Scandinavia, the recovery of Eurasian lynx (\emph{Lynx
lynx} ) and wolves (\emph{Canis lupus} ) in human-modified ecosystems is
raising a key question regarding the ecological role that large
carnivores play in these anthropogenic landscapes and their importance
relative to the effects of humans on mesocarnivores (Dorresteijn et al.,
2015, Kuijper et al., 2016). There is some evidence that the recovering
lynx populations in Scandinavia significantly limit mesocarnivore
populations such as red fox in some areas (Elmhagen et al., 2010,
Fedriani et al., 1999, Helldin et al., 2006). This top-down effect has
also been observed at a continental scale in Eurasia (Pasanen-Mortensen
et al., 2013). However, the presence of lynx may also provide a stable
food supply in the form of carrion for red foxes (Helldin and
Danielsson, 2007) and other mesocarnivores, which can be vital during
winter (Needham et al., 2014). Mesocarnivores are also strongly
influenced by anthropogenic food supplies, and red fox and badger
(\emph{Meles meles} ) densities have been found to be higher, and their
home ranges smaller, in urban and suburban areas compared to
semi-natural habitats (\selectlanguage{polish}Š\selectlanguage{ngerman}álek et al., 2015). An increase in red fox
numbers might in turn have negative effects on smaller carnivore
species, like pine marten (\emph{Martes martes} ), through intraguild
predation (Lindström et al., 1995).

The aim of this study was to assess how interactions among
mesocarnivores are affected by large carnivores, land cover variables
(proportion of agricultural land and primary productivity), and human
disturbance, as well as how the relative strength of these top-down and
bottom-up mechanisms is influenced by season (summer vs winter). The
interactions among mesocarnivores were assessed using encounter rates
from a national level camera trapping study in Norway. We used three
years of camera trapping data to study a carnivore guild that included
lynx and wolves as apex predators, red foxes and badgers as dominant
mesocarnivores, and pine martens as a subordinate mesocarnivore.

\textbf{MATERIAL AND METHODS}

\textbf{Study area}

We conducted this study within nine different counties in Norway,
representing a gradient of landscape productivity and human influence
from Troms \& Finnmark County in the north (68° N), to Agder County in
the south (58° N) (Figure 1). The southern areas are in general more
fragmented, with forested areas intermixed with agricultural fields and
scattered human settlements, whereas the northern areas are less
productive with boreal forests and alpine tundra. Forests are dominated
by conifers, mainly Scots pine (\emph{Pinus sylvestris} ) and Norway
spruce (\emph{Picea abies} ), intermixed with deciduous species such as
birch (\emph{Betula pubescens} and \emph{B. pendula} ), rowan
(\emph{Sorbus aucuparia} ), aspen (\emph{Populus tremula} ), gray alder
(\emph{Alnus incana} ), and willow (\emph{Salix caprea} ), which are
more abundant in the south. Mean annual temperatures decrease with
latitude, being milder in the south (annual mean temperature 7.8°C in
Kristiansand) than in the north (annual mean temperature -0.2°C in
Tromsø) (no.climate-data.org), and winter severity (i.e. snow depth and
low temperatures) increases with latitude and altitude. Human population
densities range from 1642.7 inhabitants per km\textsuperscript{2} in
Oslo County to 3.4 inhabitants per km\textsuperscript{2} in Troms \&
Finnmark (www.ssb.no). Lynx can potentially be found across the entire
study area but in higher densities in the southernmost areas. Wolves are
restricted to the wolf management zone in southeastern Norway (Figure
1). Wolf density outside the wolf management zone is practically zero,
as wolves outside the zone are culled as part of management plans.

\textbf{Camera trapping}

We used three years of data (2018-2020) from 618 camera trap locations
as part of an ongoing long-term camera trapping study of lynx (SCANDCAM,
viltkamera.nina.no). The SCANDCAM project has volunteer-run camera traps
(HC500, HC600, PC800, PC850, PC900 and HP2X, Reconyx, Holmen, Wisconsin,
USA), which are distributed with one camera per 50 km\textsuperscript{2}
grid cell, covering 30,950 km\textsuperscript{2} in Norway (Figure 1).
Local volunteers, in cooperation with trained technicians, placed a
minimum of one camera trap inside each grid cell. To maximize the
probability of detecting lynx and other predators, the cameras were
preferably located on forest roads, trails or natural movement routes
for wildlife. Each camera trap was placed 60-120 cm above the ground and
aimed at the landscape feature of interest. Memory cards and batteries
were switched at least four times a year. All camera traps were set to
take a daily time-lapse image at 8 a.m., in addition to being activated
by an animal passing, in order to check if the unit functioned correctly
and if the field of view was clear. A deep convolutional neural network
trained with previous images from the SCANDCAM project was used to
classify all images using TensorFlow. All species identifications were
in addition manually verified by trained staff and students. All images
of humans and vehicles were automatically removed to conform to
Norwegian privacy regulations, but we retained information of their
passing. A detailed explanation of the pre-processing and classification
workflow can be found in Hofmeester et al. (2021).

We calculated species encounter rate as the number of days in which an
animal (lynx, wolf, red fox, badger or pine marten) was detected by a
camera trap per year and season, corrected for camera effort (i.e.,
number of days during which the camera trap was active). This encounter
rate results from a combination of both local density and activity of
predators (Carbone et al., 2001). This is useful for our study because
it not only reflects the number of individuals present, but also the
intensity of use of a specific area.

\textbf{Covariates}

All covariates were extracted in ArcGIS (ESRI 2014). We used a Norwegian
and a Swedish vegetation map merged together to account for the cameras
along the border (Northern Research Institute's vegetation map, Norway,
30 x 30 m resolution merged with Swedish Corine land cover map
Lantmäteriet, Sweden, 25 x 25 m resolution into a 25 x 25-m resolution
raster, Mattisson et al., 2013, Ordiz et al., 2015), from which we
extracted the proportion of agricultural land. Additionally, we obtained
human density (inhabitants/km\textsuperscript{2}) from Statistics Norway
as a 250 m resolution raster (www.ssb.no). However, this raster only
contained data for urban areas. Given that the cameras were placed at
different distances from those areas, we used that data to calculate a
proxy for the direct human disturbance at the camera sites. For this, we
transformed the raster to a point layer, which we used to predict a
planar kernel density map set with bandwidth = 2,000 and cell size = 200
m. We then extracted these two variables (proportion of agricultural
land and human disturbance) to a 1-km radius circular buffer around each
camera trap.

In order to assess primary productivity, we downloaded the 1-km
resolution monthly EVI from the MOD13A3 V061 product (Didan, 2021) using
the AppEEARS application (AppEEARS-Team, 2022). We downloaded monthly
data for years 2016-2020 and deleted 11 raster cells because of low
quality and missing data. We then merged the monthly EVI map layers into
one final raster containing average value of overlapping cells. We
masked out water to include only terrestrial cells and calculated the
mean-EVI for a 10-km radius circular buffer around each camera trap. We
used a 10-km buffer for primary productivity in order to focus on the
wider region rather than on the micro camera site. Using an
environmental productivity variable as a covariate such as EVI can help
to correct for biases when considering multiple sites (Hofmeester et
al., 2019).

We divided the camera trap data into two six-month periods, from October
to March (winter; roughly the months with snow cover) and from April to
September (summer; snow free months in most of the study area). We also
created an 8-level ``study area'' covariate based on geographic clusters
of camera traps (Figure 1).

\textbf{Data analysis}

All statistical analyses were carried out in R version 3.6.1
(R-Core-Team, 2019). We standardized (scaled) all continuous covariates
by subtracting the mean and dividing by one standard deviation. We used
Pearson's correlation tests to check for collinearity among continuous
variables, with a limit of r \selectlanguage{english}[?] 0.6 (Zuur et al., 2010).

We used structural equation modeling (SEM) to test how interactions
among mesopredators (red fox, badger and pine marten) were affected by
large carnivores (wolf and lynx), land cover variables (proportion of
agricultural land and EVI), and human disturbance, as well as how these
interactions were affected by season (summer vs winter). SEM provides a
multivariate framework to develop and evaluate hypothesized networks of
causal relationships, estimating the relative strength of direct and
indirect paths within the system (Grace, 2006, Grace et al., 2012).

\emph{Model description}

We considered top-down and bottom-up pathways based on our predictions
and on documented predator interactions in boreal ecosystems (solid
lines in Figure 2, Model 1). Large carnivores (lynx and wolf) were
expected to limit the dominant mesocarnivores (red fox and badger)
through interference competition (Elmhagen and Rushton, 2007). Dominant
mesocarnivores, red foxes, were expected to limit pine martens through
the same process (Zalewska et al., 2021). We considered that bottom-up
factors (proportion of agricultural land and EVI) would affect all
species, but expected different strength for different seasons, with
bottom-up factors having a stronger effect than top-down factors in
winter than in summer (Elmhagen and Rushton, 2007, Stoessel et al.,
2018). We anticipated that large carnivores would not interfere with
each other, since their interactions appear to be negligible (Wikenros
et al., 2012, Schmidt et al., 2009). Humans were expected to limit large
carnivores through disturbance and culling (Dorresteijn et al., 2015),
and to have a positive effect on dominant mesocarnivores due to food
subsidization (Gompper and Vanak, 2008).

Additionally, we proposed three other alternative models to test against
Model 1. This is because there is a large potential for intraguild
competition between badger and the other two mesocarnivores (red fox and
pine marten). The three of them can occupy similar ecological niches,
share the same den sites (Macdonald et al., 2004, Mori et al., 2015)
and, being opportunistic generalists, may share foods such as
earthworms, smaller vertebrates, eggs and fruits (Macdonald, 1980,
Kauhala et al., 1998, Torretta et al., 2016, Prigioni et al., 2008).
However, differential use of time and space may enable coexistence
(Zalewska et al., 2021). Therefore, we added an additional pathway from
badger to red fox (Model 2 in Figure 2) and then a second pathway from
badger to pine marten (Model 3 in Figure 2). Moreover, pine martens are
considered to avoid urban areas (Goszczy\selectlanguage{polish}ń\selectlanguage{english}ski et al., 2007, Fusillo et
al., 2009), although recent research suggest that they can adapt to live
in areas with anthropogenic disturbances (Weber et al., 2018).
Therefore, we added a third pathway from humans to pine marten (Model 4
in Figure 2).

\emph{SEM analysis}

To construct the SEM, we used generalized linear mixed models with a
negative binomial distribution to model overdispersion, using the
Bayesian brms package (B\selectlanguage{ngerman}ürkner, 2017) in R version 3.6.1 (R-Core-Team,
2019). We used the number of species encounters (number of days in which
an animal was detected by a camera per year and season) as the response
variable, with the log-transformed number of active camera trap days as
an offset. Depending on the species, explanatory variables included: i)
large carnivores (wolf and lynx) encounters and human disturbance, and
ii) land cover variables (proportion of agricultural land and EVI). We
allowed the model to estimate different values for each pathway for
winter and summer by adding the interaction of season with all pathways.
We also included a varying intercept by study site and another by camera
location by including these two variables as random effects.

We included the default flat priors of brms and fitted the models using
3000 iterations on 3 chains. We used LOO values as indicators of
goodness of fit, and for model selection. We checked convergence by
looking at the trace plots of the MCMC chains, and with the Gelman and
Rubin convergence diagnostic \selectlanguage{romanian}\emph{Ȓ} (Gelman and Rubin, 1992). We also
calculated a Bayesian \emph{R} \textsuperscript{2} (Gelman et al., 2018)
for the best fitting model using the bayes\_R2 function in the brms
package (B\selectlanguage{ngerman}ürkner, 2017) to assess the variance explained by the main
factors. Below, we present the posterior median and associated 90\%
credible interval (CRI) for all parameters, which we discuss in terms of
non-overlapping CRI for convenience. The whole posterior distributions
can be found in Appendix 1 and 2. We also present the Probability of
Direction (\emph{pd} ) as defined in the bayestestR package (Makowski et
al., 2019), which represents the (un)certainty with which an effect is
either positive or negative, as well as the Region of Practical
Equivalence (ROPE; e.g. Kruschke, 2014), which assesses the magnitude
and importance of an effect (i.e., its ``significance'') (Makowski et
al., 2019). These values are in Appendix 3.

\textbf{RESULTS}

In three years of camera trapping we obtained 91,180 non-empty images,
of which 62.9\% were of people, 19.2\% were of red fox, 14.3\% were of
badger, 1.6\% were of pine marten, 1.2\% were of lynx and 0.6\% were
records of wolf.

We tested the baseline model (Model 1, solid lines in Figure 2) with
three other models by adding additional pathways. We selected Model 4,
which included all the tested pathways, as the best fitting model (Table
1). Bayesian \emph{R\textsuperscript{2}} values for Model 4 were 0.53
for red fox, 0.64 for badger, 0.55 for pine marten, 0.45 for lynx and
0.55 for wolf.

Below, we show coefficients whose 90\% CRI did not overlap zero. We
allowed the model to estimate different values for each pathway in
summer and winter, therefore we show one model for each season (Figure
3). We also present the \emph{pd} and ROPE for some of the main pathways
to ease interpretation. All posterior distributions, \emph{pd} and ROPE
can be found in Appendix 1, 2 and 3.

\textbf{Top-down vs bottom-up effects}

There was a positive relationship between lynx and badger in summer,
although the proportion of agricultural land and EVI were stronger
predictors of badger encounter rate (Figure 3). There was also a
positive association between wolf and red fox encounter rates in summer
(Figure 3), but this relationship was weaker (ROPE = 5.8; Appendix 3),
and red fox encounter rate was more associated with badger presence and
EVI (Figure 3). Both red fox and badger encounter rates were stronger
predictors of pine marten encounter rates than land cover variables in
winter (Figure 3). Indeed, all mesocarnivores (red fox, badger and pine
marten) showed positive interactions among each other, which were
stronger in winter (Figure 3).

Land cover variables had contrasting effects for different species and
seasons. The proportion of agricultural land was a positive predictor
for badger encounter rates in both seasons, and a negative predictor for
pine martens, wolves and lynx in summer (Figure 3). Additionally, EVI
was a positive predictor of lynx, red fox and badger encounter rates,
but only in summer (Figure 3). There was also some evidence of a
positive association between EVI and both wolf and pine marten encounter
rate. Even though the CRI for the median overlapped zero, only 13\% and
39\% of the posterior distribution was in the ROPE and \emph{pd} = 0.87
and \emph{pd} = 0.71, respectively for wolf and pine marten in summer
(Appendix 3).

\textbf{The effect of human disturbance}

Human disturbance had a positive association with badger encounter
rates, which was slightly stronger in winter (Figure 3). There was also
some evidence for a positive association between humans and red fox
encounter rates in summer, even though the CRI for the median overlapped
0, the probability of direction \emph{pd} = 0.94 indicates a 94\% chance
that the effect of humans on red fox encounter rates is positive (see
whole posterior distribution in Appendix 1). On the other hand, there
was some evidence for a negative association between human disturbance
and pine marten encounter rates, which was more certain in winter
(\emph{pd} = 0.75 in summer and \emph{pd} = 0.99 in winter), even though
the median CRI overlapped 0 (Appendix 1 and 3). Human disturbance had a
strong negative association with wolf encounter rates, which was
stronger in summer (Figure 3). There was also a high probability that
the association between humans and lynx would be negative, even though
the CRI for the median overlapped 0, only 18\% of the posterior
distribution was in the ROPE and \emph{pd} = 0.91 (Appendix 3).

\textbf{DISCUSSION}

In this study, we assessed how the combined influences of top-down and
bottom-up factors affected the interactions among mesocarnivores in
Norway in two different seasons. In general, bottom-up factors were
stronger predictors of dominant mesocarnivores' encounter rates (red fox
and badger) than top-down factors. Contrary to what we expected, large
carnivores apparently had a positive effect on mesocarnivores, with a
positive association between lynx and badger and between wolf and red
fox in summer (although weak for the last pair). Previous studies also
found positive effects of large predators on mesopredators. For
instance, Sivy et al. (2017) found a local-scale positive association
between wolves and non-apex predators in Canada, which they attributed
to carrion facilitation. In Sweden, Wikenros et al. (2017) found a
positive effect of lynx on red foxes, which contrasted with previous
studies (Pasanen-Mortensen et al., 2013, Pasanen-Mortensen and Elmhagen,
2015). They suggested that this might be caused by a behavioral response
of foxes to lynx abundance, where foxes would be using olfatory cues
from lynx to increase their possibility to access carrion from prey
killed by lynx. Carcasses left by apex predators are known to be an
important food supply for smaller predators (Wikenros et al., 2014,
Selva et al., 2005, Prugh and Sivy, 2020). These remains can be an
important food source during winter, when other food sources are scarce
(Carricondo-Sánchez et al., 2016, Helldin and Danielsson, 2007, Needham
et al., 2014, Willebrand et al., 2017), or during spring, when animals
are usually reproducing (Wikenros et al., 2014). The slight positive
effect of wolf on red foxes in summer (which also includes spring months
April and May in our analysis) could be related to food provisioning
during reproduction time. Regarding the association between lynx and
badgers, their positive relationship in summer could be related to their
preference for productive environments during that season (Figure 3).
There are also no reports of aggressive interactions between these two
carnivores (Neal and Chesseman 1996, as cited in Fedriani et al., 1999),
and there are several studies documenting a predisposition of badgers to
coexist with other apex predators (Fedriani et al., 1999, Garrote and
Perez de ayala, 2019, Palomares et al., 1996).

Both red fox and badger were stronger predictors of pine marten
encounter rates than land cover variables in winter. Indeed, the three
mesocarnivores (red fox, badger and pine marten), showed positive
associations among each other, which were stronger in winter. Food
scarcity and challenging winter conditions may force species to use the
same resources and areas. Previous studies have found positive
associations among competing predators during times of food scarcity in
Scandinavia. For instance, Cano-Martínez et al. (2021) found a positive
association between red fox, pine marten and weasels (\emph{Mustela
nivalis} ) during periods of low rodent abundance. Similarly, Stoessel
et al. (2018) found a positive association between arctic fox
(\emph{Vulpes lagopus} ) and red fox during the low rodent phase.
However, there is evidence that red foxes kill pine martens (Lindström
et al., 1995), which is probably one of the reasons why pine martens
avoid open areas (Storch et al., 1990). Pine martens are often described
as being more habitat specialists in Scandinavia as compared to other
mesocarnivores. Also, pine martens are primarily associated with
spruce-dominated forests with large trees at fine spatial scales
(Brainerd and Rolstad, 2002), which enables them to avoid predation by
climbing trees (Storch et al., 1990). However, at the lanscape scale,
pine martens seem to be able to live in a mixed landscape of old forest
and other habitat types in Noway (Angoh et al., 2023), sharing that
landscape with other mesopredators. Pine martens might use small-scale
spatiotemporal mechanisms, i.e. avoiding sites only when there is a high
risk of encountering a potential enemy, in order to minimize
interactions with larger predators and facilitate coexistence (Zalewska
et al., 2021). Furthermore, the density of both red fox and pine marten
seems to be correlated in Finland, which indicates that the possible
effects of interspecific competition do not determine the spatial
abundance of pine martens at either a regional or a landscape level
(Kurki et al., 1998). This is in opposition to an increase in pine
marten density reported by Storch et al. (1990) following a decrease in
red fox density during an epizootic of sarcoptic mange (\emph{Sarcoptes
scabiei} ) in Sweden.

Regarding the positive association between badgers and red foxes,
Macdonald et al. (2004) recorded a series of non-aggressive interactions
between the two mesocarnivores where foxes seemed to seek the company of
badgers at setts. They argued for the possibility that foxes might
receive interspecific information from badgers, for example to find good
feedings spots. Also, red foxes and badgers are known to share
setts/dens (Macdonald 1987 as cited in Macdonald et al., 2004), which
indicates some kind of facilitative relationship. Positive associations
between competing species may be faciliated by landscape heterogeneity
and by mechanisms of niche separation such as temporal partitioning
(Bischof et al., 2014, Lesmeister et al., 2015, Viota, 2012). Badgers
are considered strictly nocturnal, while red foxes and pine martens are
considered facultative nocturnal, since they are more flexible in their
use of time (Torretta et al., 2016, Monterroso et al., 2014, Zalewska et
al., 2021).

Based on our predictions, we expected environmental productivity to have
a stronger positive effect on carnivore encounter rates in winter
because of food scarcity (Elmhagen and Rushton, 2007). However, we found
a positive association between EVI and some of the carnivores (lynx, red
fox and badger) only in summer. Nevertheless, even if the seasonal
effect of productivity was not as expected, mesocarnivores in Norway
seem to be more influenced by bottom-up rather than by top-down
mechanisms, as the effect of large carnivores was weak compared to the
effect of land cover. Wikenros et al. (2017) also found that bottom-up
processes were more influential on red fox abundance in Sweden than the
top-down effect of lynx and wolves, although they emphasized the
importance of considering spatial scale. Large carnivores in Norway may
occur at too low densities to have an effect on the encounter rates of
mesocarnivores at a large scale. The Scandinavian wolf population
declined strongly during the 19\textsuperscript{th} century and was
declared functionally extinct in 1966 (Wabakken et al., 2001). The
population started to recover in the early 1980s in south-central
Scandinavia and it has increased in number and geographical distribution
since then (Wabakken et al., 2001). However, of the 540 (95\% CI =
427-702) individuals estimated during the winter 2021/2022 in
Scandinavia, only 88-91 were counted in Norway (Wabakken, 2022).
Furthermore, the ``wolf zone'' only represents 5\% of the national
territory and is limited to southeastern Norway, and outside this area,
wolves are excluded via culling. Lynx numbers are also actively managed
through quota regulated hunting to limit their population densiy and
constrict their distribution (Linnell et al., 2010). A reduced predator
population exposed to hunting may not be able to play its full
ecological role, limiting both direct and indirect effects on other
predators and prey (Ordiz et al., 2013). In this kind of system, it is
predicted that bottom-up influence will be stronger than top-down
control of mesocarnivores (Pasanen-Mortensen and Elmhagen, 2015). This
implies that the abundance of smaller carnivores would be dependent on
prey availability, which ultimately is related to bioclimatic factors
such as environmental productivity and land use (Pasanen-Mortensen and
Elmhagen, 2015).

The proportion of agricultural land also had an important effect on all
carnivores, except for red foxes. Actually, agricultural fields in
Norway have been found to sustain the lowest abundance of small mammals
(Panzacchi et al., 2010), which would explain the lack of effect of this
type of landscape on red fox encounter rates. On the other hand, the
negative effect of agricultural land found on lynx, wolf and pine marten
was expected given that these are predominantly forest species. This
negative effect of agriculture was weaker in winter. Agricultural fields
vary in usage level by humans over the year, with high activity during
late spring and summer, and less activity occurring during autumn and
winter (Bunnefeld et al., 2006). This might explain the observed
seasonal variation and the strong negative effect of agriculture fields
in summer for lynx and wolf. Furthermore, this seasonal variation could
also be related to seasonal movements of prey. Moose (\emph{Alces alces}
) and roe deer (\emph{Capreolus capreolus} ) migrate to areas of lower
elevation during winter, in search of higher food availability provided
by lower snow depth and artificial feeding sites (Bunnefeld et al.,
2006, Singh et al., 2012, Mysterud, 1999). The migration of moose and
roe deer to areas of lower elevation during winter could relax the
negative effect of fields on lynx and wolf in winter.

Regarding the effect of human disturbance, humans were positively
associated with the two dominant mesocarnivores (red fox and badger) in
summer. Previous studies have documented positive relationships between
red fox abundance and human settlements (e.g., Jahren et al., 2020,
Panek and Bresi\selectlanguage{polish}ń\selectlanguage{english}ski, 2002), although they tend to avoid highly urbanized
areas (\selectlanguage{polish}Č\selectlanguage{english}ervinka et al., 2014). Human population density has been
suggested to be a good proxy for anthropogenic food subsidies (Oro et
al., 2013), which are used by red foxes and badgers (Rosalino et al.,
2010, Gomes et al., 2019). Manlick and Pauli (2020) found strong dietary
responses by a large variety of generalist carnivores to human
disturbance, suggesting substantial use of human subsidies. These human
resources can potentially increase dietary overlap (S\selectlanguage{ngerman}évêque et al.,
2020), which may increase the probability of interspecific competition
and intraguild predation in human-dominated landscapes (Manlick and
Pauli, 2020, Newsome et al., 2015). However, these potential effects
were not strong enough to prevent both red foxes and badgers from being
positively associated with these areas. Pine marten, on the other hand,
showed a tendency to avoid areas of high human disturbance mostly in
winter. This negative association between humans and pine marten is in
line with previous studies (e.g. Fusillo et al., 2009, Goszczy\selectlanguage{polish}ń\selectlanguage{english}ski et
al., 2007), suggesting that pine marten is sensitive to human
disturbance.

Human disturbance had a negative effect on both large carnivore species,
but stronger on wolf in both seasons. Wolves in Norway are strongly
managed and restricted to the so-called ``wolf zone'', which is located
in an area with low human population density. Wolf distribution is
therefore strongly influenced by politics and management restrictions.
Furthermore, lynx populations in Norway are heavily managed, and
human-caused sources of mortality such as hunting, poaching and vehicle
collisions are high (Andr\selectlanguage{ngerman}én et al., 2006). All those factors may explain
the observed negative effect of humans on both wolves and lynx in our
study. Yet, it seems that both species are less negatively influenced by
humans in winter, which could be related to prey seasonal movements, as
explained above. Additionally, high roe deer densities often occur in
agricultural, fragmented and disturbed areas (Basille et al., 2009),
which are generally associated with higher human densities. Thus, wolves
and lynx distribution may be determined by a trade-off between the
availability of food and the risk of human presence. A higher
availability of main prey closer to more populated areas in winter, when
alternative prey is difficult to find, could be attracting both lynx
(Bunnefeld et al., 2006, Basille et al., 2009) and wolves to these areas
(Carricondo-Sanchez et al., 2020), thus reducing the negative effect of
humans. Furthermore, local-scale avoidance and temporal adjustments in
habitat use might facilitate large predators sharing the landscape with
humans (Thorsen et al., 2022).

It is important to highlight the potential weakness of the use of
by-catch data from camera traps from a study designed to target a single
species to study multiple species and their interactions (Hofmeester et
al., 2019). However, Hofmeester et al. (2021) showed high detection
probability of multiple carnivore species at lynx-targeted camera traps
in boreal ecosystems, including high detectability of wolf, red fox and
badger. Furthermore, all the camera traps used in this study were
deployed with the same sampling design. Therefore, we argue that
comparing trapping rates of different species from camera traps within
this study design should not result in biased estimates of species
interactions.

\textbf{Conclusion}

Land cover variables were stronger predictors of mesocarnivore activity
than large carnivores in Norway. This might be caused by a combination
of low density of large carnivores in an unproductive ecosystem with
strong seasonality. Positive intraguild interactions were predominant in
our system, which might indicate a tendency of multiple species to use
the same areas and resources (probably facilitated by mechanisms of
niche separation such as temporal partitioning and relatively low
degrees of interference). Alternatively, these positive interactions
might indicate some kind of facilitative relationship between species.
Further studies with a more experimental aproach and finer scales might
help determine the mechanisms behind these positive relationships.

Human disturbance had contrasting effects on different species,
benefiting the dominant mesocarnivores probably through food
subsidization, but negatively affecting large carnivores and smaller
mesocarnivores. Given that ecological interactions are now happening in
a human-dominated world, there is a clear need to include anthropogenic
influences when studying species interactions.

\textbf{ACKNOWLEDGMENTS}

We want to thank the students and volunteers that helped with fieldwork
and classification of the camera trap images. We are also extremely
grateful to all volunteers from the Norwegian Association of Hunters and
Anglers who helped us maintain a large number of camera traps.

\textbf{FUNDING INFORMATION}

SCANDCAM was funded by the Viltvardsfonden (Wildlife Management Fund) of
the Swedish Environmental Protection Agency as part of the SCANDCAM
(NV-00695-17) project, the Norwegian Environment Agency, the Research
Council of Norway (grant 281092), and the Nature Protection Division of
the County Governor's Office for Innlandet County, Viken County,
Vestfold \& Telemark County, Trondelag County, More \& Romsdal County,
Nordland County, and Troms \& Finnmark County. RCM and MO received
funding from Inland Norway University of Applied Sciences. TH received
funding from the Wildlife Management Fund of the Swedish Environmental
Protection Agency (NV-2020-00088).

\textbf{CONFLICTS OF INTEREST}

The authors declare no conflicts of interest.

\textbf{AUTHOR CONTRIBUTIONS}

\textbf{Rocio Cano-Martinez:} Conceptualization (equal); Data curation
(supporting); Formal analysis (lead); Investigation (lead); Methodology
(equal); Visualization (lead); Writing - original draft preparation
(lead);Writing - review and editing (lead). \textbf{Neri Horntvedt
Thorsen} : Conceptualization (equal); Data curation (supporting);
Investigation (supporting); Methodology (equal); Writing - review and
editing (supporting). \textbf{Tim Hofmeester} : Conceptualization
(equal); Data curation (supporting); Funding acquisition (supporting);
Investigation (supporting); Methodology (equal); Writing - review and
editing (supporting). \textbf{John Odden} : Conceptualization (equal);
Data curation (lead); Funding acquisition (lead); Methodology
(supporting); Project administration (supporting); Supervision
(supporting); Writing - review and editing (supporting). \textbf{John
Linnell} : Conceptualization (equal); Data Curation (supporting);
Writing - review and editing (supporting). \textbf{Olivier Devineau:}
Formal analysis (supporting); Methodology (equal); Writing - review and
editing (supporting). \textbf{Siow Yan} \textbf{Jennifer Angoh} :
Investigation (supporting); Writing - review and editing (supporting).
\textbf{Morten Odden} : Conceptualization (equal); Funding acquisition
(supporting); Methodology (equal); Project administration (lead);
Supervision (lead); Writing - review and editing (supporting).

\textbf{DATA AVAILABILITY STATEMENT}

Data and code are availale from the Zenodo repository:
https://doi.org/10.5281/zenodo.10005330

\textbf{ORCID}

Rocío Cano-Martínez: https://orcid.org/0000-0001-7335-5863

Neri Horntvedt Thorsen: https://orcid.org/0000-0002-3472-0546

Tim Hofmeester: https://orcid.org/0000-0003-2101-5482

John Odden: https://orcid.org/0000-0002-6275-8648

John Linnell: https://orcid.org/0000-0002-8370-5633

Olivier Devineau: https://orcid.org/0000-0002-7625-2816

Jennifer Angoh: https://orcid.org/0000-0002-3791-0150

Morten Odden:

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\textbf{APPENDIX 1}

Posterior median parameter estimates for the final structural equation
model (Model 4 in the text) explaining direct associations and
interactions of mesopredators (red fox, badger and pine marten) with
humans, large predators (wolf and lynx), and bottom-up variables
(Agriculture and EVI), together with their associated 50\% (thick blue)
and 80\% (light blue) credible intervals.

\textbf{APPENDIX 2}

Posterior median parameter estimates for the final structural equation
model (Model 4 in the text) explaining direct associations and
interactions of lynx and wolf with humans and bottom-up variables
(Agriculture and EVI), together with their associated 50\% (thick blue)
and 80\% (light blue) credible intervals.

\textbf{APPENDIX 3}

Summary of posterior distributions for the final structural equation
model, including the posterior median parameter estimate (Median), the
90\% credible interval (90\% CRI), the percentage probability of
direction (\% pd) and the percentage in the Region of Practical
Equivalence (\% in the ROPE).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Response & Parameter & Median & 90\% CRI & 90\% CRI & \% pd & \% in
ROPE\tabularnewline
\midrule
\endhead
Fox & Badger & 0.2 & 0.14 & 0.25 & 1 & 0\tabularnewline
Fox & Wolf & 0.09 & 0.04 & 0.15 & 1 & 0.58\tabularnewline
Fox & Lynx & 0.06 & -0.02 & 0.13 & 0.9 & 0.85\tabularnewline
Fox & Humans & 0.1 & -0.01 & 0.22 & 0.94 & 0.49\tabularnewline
Fox & Agriculture & -0.04 & -0.15 & 0.07 & 0.71 & 0.84\tabularnewline
Fox & EVI & 0.18 & 0 & 0.35 & 0.95 & 0.22\tabularnewline
Fox & Badger:Winter & 0.27 & 0.17 & 0.38 & 1 & 0\tabularnewline
Fox & Wolf:Winter & -0.06 & -0.14 & 0.02 & 0.89 & 0.8\tabularnewline
Fox & Lynx:Winter & 0 & -0.08 & 0.08 & 0.53 & 1\tabularnewline
Fox & Humans:Winter & 0.02 & -0.05 & 0.08 & 0.64 & 1\tabularnewline
Fox & Agriculture:Winter & -0.03 & -0.11 & 0.05 & 0.73 &
0.96\tabularnewline
Fox & EVI:Winter & 0.02 & -0.07 & 0.1 & 0.62 & 0.98\tabularnewline
Badger & Wolf & 0.03 & -0.05 & 0.12 & 0.74 & 0.92\tabularnewline
Badger & Lynx & 0.21 & 0.11 & 0.3 & 1 & 0\tabularnewline
Badger & Humans & 0.25 & 0.11 & 0.38 & 1 & 0.01\tabularnewline
Badger & Agriculture & 0.26 & 0.12 & 0.39 & 1 & 0.01\tabularnewline
Badger & EVI & 0.31 & 0.06 & 0.56 & 0.98 & 0.06\tabularnewline
Badger & Wolf:Winter & 0.01 & -0.12 & 0.13 & 0.54 & 0.86\tabularnewline
Badger & Lynx:Winter & -0.03 & -0.13 & 0.06 & 0.73 & 0.9\tabularnewline
Badger & Humans:Winter & 0.07 & 0 & 0.13 & 0.94 & 0.81\tabularnewline
Badger & Agriculture:Winter & 0.13 & 0.05 & 0.21 & 1 &
0.23\tabularnewline
Badger & EVI:Winter & -0.01 & -0.16 & 0.14 & 0.55 & 0.75\tabularnewline
Marten & Fox & 0.06 & -0.03 & 0.14 & 0.87 & 0.8\tabularnewline
Marten & Badger & 0.3 & 0.2 & 0.39 & 1 & 0\tabularnewline
Marten & Humans & -0.07 & -0.23 & 0.09 & 0.75 & 0.61\tabularnewline
Marten & Agriculture & -0.19 & -0.38 & -0.01 & 0.96 &
0.18\tabularnewline
Marten & EVI & 0.1 & -0.2 & 0.39 & 0.71 & 0.39\tabularnewline
Marten & Fox:Winter & 0.3 & 0.17 & 0.43 & 1 & 0\tabularnewline
Marten & Badger:Winter & 0.13 & -0.01 & 0.28 & 0.93 &
0.38\tabularnewline
Marten & Humans:Winter & -0.34 & -0.74 & -0.07 & 0.99 &
0.06\tabularnewline
Marten & Agriculture:Winter & -0.05 & -0.22 & 0.12 & 0.69 &
0.64\tabularnewline
Marten & EVI:Winter & 0.09 & -0.1 & 0.3 & 0.79 & 0.49\tabularnewline
Lynx & Humans & -0.3 & -0.8 & 0.05 & 0.91 & 0.18\tabularnewline
Lynx & Agriculture & -0.53 & -0.82 & -0.26 & 1 & 0\tabularnewline
Lynx & EVI & 0.49 & 0.1 & 0.88 & 0.98 & 0.03\tabularnewline
Lynx & Humans:Winter & 0 & -0.43 & 0.43 & 0.5 & 0.33\tabularnewline
Lynx & Agriculture:Winter & 0.43 & 0.22 & 0.65 & 1 & 0\tabularnewline
Lynx & EVI:Winter & -0.04 & -0.26 & 0.18 & 0.63 & 0.55\tabularnewline
Wolf & Humans & -1.01 & -2.26 & -0.05 & 0.96 & 0.04\tabularnewline
Wolf & Agriculture & -0.95 & -1.47 & -0.49 & 1 & 0\tabularnewline
Wolf & EVI & 0.39 & -0.17 & 0.99 & 0.87 & 0.13\tabularnewline
Wolf & Humans:Winter & 0.51 & -0.67 & 1.69 & 0.76 & 0.09\tabularnewline
Wolf & Agriculture:Winter & 0.07 & -0.44 & 0.56 & 0.59 &
0.27\tabularnewline
Wolf & EVI:Winter & 0.5 & 0.05 & 0.95 & 0.97 & 0.05\tabularnewline
\bottomrule
\end{longtable}

\textbf{FIGURE LEGENDS}

Figure 1. Location of the camera traps in Norway, study areas based on
geographic clusters of camera traps, and the wolf zone.

Figure 2. A priori structural equation models describing all
hypothesized top-down and bottom-up interactions evaluated. Model 1
(baseline model) is represented by solid lines between variables. Models
2, 3 and 4 are as Model 1, but with added paths from badger to red fox
(Model 2, dashed line), from badger to pine marten (Model 3, dotted
line), and from humans to pine marten (Model 4, dashed-dotted line).

Figure 3. Structural equation model (SEM) with the best fit showing
top-down and bottom-up pathways for large carnivores and mesocarnivores
in summer (left figure) and winter (right figure). Values along arrows
represent the relative magnitudes of positive (black) and negative (red)
standardized path coefficients whose 90\% CRI did not overlap zero.
Arrows with no coefficients represent those pathways included in the
model whose 90\% CRI overlap 0. However, we still indicate positive
(black) and negative (red) for those pathways that are discussed in the
text given their significant \emph{pd} and ROPE values. Grey arrows
represent pathways included in the model whose 90\% CRI overlap 0 but
with very weak evidence according to their \emph{pd} and ROPE.

\textbf{TABLES}

Table 1. Comparison of the four tested models, ranked by relative
predictive accuracy (Expected Log-Posterior Density, elpd\_diff), and
associated uncertainty (se\_diff). Model 4 is ``best'', as per Vehtari
et al. (2017) and (Sivula et al., 2020). Model 1 corresponds to the
baseline model, to which an extra pathway was added for each of models
2-4.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
elpd\_diff\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
se\_diff\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Added pathways\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Model 4\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Humans -\textgreater{} Pine marten Badger -\textgreater{} Pine marten
Badger -\textgreater{} Red fox\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Model 3\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-2.23\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
4.03\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Badger -\textgreater{} Pine marten Badger -\textgreater{} Red fox\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Model 2\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-23.06\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6.75\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Badger -\textgreater{} Red fox\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Model 1\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-77.99\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
10.38\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Baseline\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
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