Carried away by a moonlight shadow: activity of wild boar in relation to nocturnal light intensity

Abstract

An increase of nocturnal activity of ungulate species may represent a compensatory opportunity for energy intake, when activity in daylight is hindered by some disturbance events (e.g. hunting or predation). Therefore, mostly-diurnal and crepus-cular species may be active in bright moonlight nights whereas others may shift their diurnal activity towards darkest nights to limit their exposure to predators. In natural and undisturbed conditions, the wild boar may be active both during the day and the night, with alternating periods of activity and resting. In this work, we tested whether activity patterns of wild boar, a species with poor visive abilities, were dependent on moon phases and environmental lightening. We aimed to assess if nocturnal activity could be better explained by variations of the lunar cycle or by the variations of environmental lightening conditions, evaluated by means of different measures of night brightness. Data were collected through camera-trapping in Central Italy in 2019-2020. Despite the poor visive abilities of the wild boar, we observed that this ungulate significantly reduced their activity by avoiding the brightest nights. In our study area, the wild boar has to cope with both human pressure (i.e. mostly hunters and poachers) and predation by the grey wolf. Furthermore, the nocturnal activity of wild boar peaked in mid-Autumn, i.e. when hunting pressure is the highest and when leaf fall may bring wild boar to range for long distances to find suitable resting sites for diurnal hours.

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Mammal Research
https://doi.org/10.1007/s13364-021-00610-6
ORIGINAL ARTICLE
Carried away byamoonlight shadow: activity ofwild boar inrelation
tonocturnal light intensity
LorenzoGordigiani1· AndreaViviano2,3,4 · FrancescaBrivio5 · StefanoGrignolio5 · LorenzoLazzeri1 ·
AndreaMarcon6 · EmilianoMori4
Received: 12 July 2021 / Accepted: 16 November 2021
© The Author(s) 2021
Abstract
An increase of nocturnal activity of ungulate species may represent a compensatory opportunity for energy intake, when
activity in daylight is hindered by some disturbance events (e.g. hunting or predation). Therefore, mostly-diurnal and crepus-
cular species may be active in bright moonlight nights whereas others may shift their diurnal activity towards darkest nights
to limit their exposure to predators. In natural and undisturbed conditions, the wild boar may be active both during the day
and the night, with alternating periods of activity and resting. In this work, we tested whether activity patterns of wild boar,
a species with poor visive abilities, were dependent on moon phases and environmental lightening. We aimed to assess if
nocturnal activity could be better explained by variations of the lunar cycle or by the variations of environmental lightening
conditions, evaluated by means of different measures of night brightness. Data were collected through camera-trapping in
Central Italy in 2019–2020. Despite the poor visive abilities of the wild boar, we observed that this ungulate significantly
reduced their activity by avoiding the brightest nights. In our study area, the wild boar has to cope with both human pressure
(i.e. mostly hunters and poachers) and predation by the grey wolf. Furthermore, the nocturnal activity of wild boar peaked
in mid-Autumn, i.e. when hunting pressure is the highest and when leaf fall may bring wild boar to range for long distances
to find suitable resting sites for diurnal hours.
Keywords Activity rhythms· Closed habitats· Open habitats· Moonlight· Sus scrofa· Ungulates
Introduction
Predation avoidance is a pivotal factor shaping the noctur-
nal activity of wildlife, which has been modeled by evolu-
tion to local environmental variables (Lima and Dill 1990;
Ferrari etal. 2009; Monterroso etal. 2013). In this con-
text, prey species developed strategies to avoid predation
by developing survival tactics, whereas predators have to
learn how to overcome those tactics in a sort of arms race
(Monterroso etal. 2013). Although adapted to find prey in
darkness, most nocturnal carnivores improve their hunting
success on the brightest nights, i.e. in full moon and clear
sky (Lima Sábato etal. 2006; Harmsen etal. 2011; Cozzi
etal. 2012; Bhatt etal. 2021). In turn, prey species often
* Emiliano Mori
emiliano.mori@cnr.it
Lorenzo Gordigiani
gordazzoni@gmail.com
Andrea Viviano
a.viviano@studenti.unipi.it
Lorenzo Lazzeri
lazzerilorenzo12@gmail.com
1 Dipartimento di Scienze della Vita, Università di Siena, Via
P.A. Mattioli 4, 53100Siena, Italy
2 CREA Research Centre forPlant Protection andCertification,
Via di Lanciola 12⁄a, Cascine del Riccio, 50125Firenze, Italy
3 Dipartimento di Scienze Agrarie, Alimentari e
Agro-ambientali, Produzioni Agroalimentari e Gestione
degli Agroecosistemi, Università degli Studi di Pisa, Via del
Borghetto 80, 56124Pisa, Italy
4 Consiglio Nazionale delle Ricerche, Istituto di Ricerca
sugli Ecosistemi Terrestri, Via Madonna del Piano 10,
50019SestoFiorentino, Florence, Italy
5 Dipartimento di Medicina Veterinaria, Università di Sassari,
Via Vienna 2, 07100Sassari, Italy
6 ISPRA, Via Ca’Fornacetta 9, 40064Ozzanonell’Emilia,
Bologna, Italy

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decrease predator efficiency by moving in darkest nights, i.e.
in new moon (Daly etal. 1992; Penteriani etal. 2013; Mori
etal. 2014) and/or in densely wooded/scrubland habitats
(Fattorini and Pokheral 2012; Prugh and Golden 2014). In
other cases, when diurnal species are brought to develop
nocturnal habits to limit encounters with humans or when
visual acuity is low, preys may also be mostly active in
bright moonlight to increase their ability to detect predators
(e.g. Brown etal. 2011; Carnevali etal. 2016; Grignolio
etal. 2018). Moonlight avoidance has been mostly recorded
in small prey species, including rodents, marsupials and
lagomorphs (Sutherland and Predavec 1999; Griffin etal.
2005; Mori etal. 2014; Viviano etal. 2021). Conversely,
this behaviour has been poorly assessed in ungulates (Medici
2010; Brown etal. 2011; Jasińska etal. 2021; Table1).
Nocturnal behaviour of ungulates is often reported as a
compensatory opportunity for energy intake when activity in
daylight is hindered by hunting or predation risk (Carnevali
etal. 2016; Visscher etal. 2017; Grignolio etal. 2018). There-
fore, in some cases (e.g. in the lowland tapir Tapirus ter-
restris and in the white-tailed deer Odocoileus virginianus),
also ungulates may increase their activity in brightest nights,
when their ability to detect predators is the highest (Medici
2010; Brown etal. 2011). Lashley etal. (2014) confirmed
that, when nocturnal visibility increases, ungulates may
increase their feeding activity by reducing vigilance time,
as predators can be better detected in full moon nights than
in dark nights. However, all these studies only tested for
the effects of moon phases on ungulate activity. In other
words, this kind of analysis only tells whether a lunar syn-
odic endogenous clock is present in animal species (Youthed
and Moran 1969; Kronfeld-Schor etal. 2013), but it does not
provide an actual estimation of the effect of environmental
lightening on their nocturnal activity. Studies on activity
rhythms of nocturnal small-sized mammals and other spe-
cies report that some of them tend to reduce their detectabil-
ity by limiting their activity on the brightest nights, which
includes both bright full moon and clear skies (Elangovan
and Marimuthu 2001; Jetz etal. 2003; Cozzi etal. 2012).
The wild boar Sus scrofa is the most widespread wild
ungulate in the world (Barrios-Garcia and Ballari 2012).
This species is native to Eurasia and it has been introduced,
often with hybrid individuals with domestic pigs Sus scrofa
domestica, to most of America, Africa and several oceanic
islands (Barrios-Garcia and Ballari 2012). The wild boar
generates one of the most important conflicts with human
activities and wellness, mostly as being a crop pest (Mas-
sei etal. 1997; Apollonio etal. 2010; Ficetola etal. 2014;
Table 1 Summary of studies assessing the effect of moon phase on the activity of ungulate species
Species Study area Effect of moon phase Reference
Bovidae Eudorcas thomsoni Tanzania (open habitats) Activity peak in brightest nights Walther (1973)
Oryx gazella South Africa (open habitats) Activity peak in brightest nights Joubert and Eloff (1971)
Tragelaphus scriptus Uganda (open habitats) No effect Wronski etal. (2006)
Tragelaphus strepsiceros South Africa (open habitats) Activity peak in brightest nights Joubert and Eloff (1971)
Rupicapra rupicapra Italy (Italian Alps) Activity peak in brightest nights Carnevali etal. (2016);
Grignolio etal. (2018)
Cervidae Capreolus capreolus Italy (woodland) No effect Pagon etal. (2013)
Italy (rural area) No effect Viviano etal. (2021)
Poland (suburban forests) Activity peak in darkest nights Jasińska etal. (2021)
Capreolus pygargus Mongolia (steppe and mountain) No effect Mori etal. (2021a)
Cervus canadensis Oregon, USA (open habitats) No effect Woodside (2010)
Alberta, Canada (open habitats) Activity peak in brightest nights Visscher etal. (2017)
Odocoileus virginianus Pennsylvania, USA (open areas) Activity peak in brightest nights Brown etal. (2011)
Pennsylvania, USA (woodland) Activity peak in darkest nights Brown etal. (2011)
USA (open habitats) Activity peak in brightest nights Kie (1999)
USA (mix forests/open areas) No effect Webb etal. (2010)
Tapiridae Tapirus terrestris Brazil (scrubland) No effect Oliveira-Santos etal. (2010)
Brazil (forest) Activity peak in brightest nights Medici (2010)
Ecuador (forest) No effect Link etal. (2012)
Tapirus terrestris Argentina (forest) No effect Cruz etal. (2014)
Tapirus pinchaque Colombia (mountain forests) Activity peak in brightest nights Lizcano and Cavelier (2000)
Suidae Phacochoerus aethiopicus South Africa (open habitats) Activity peak in brightest nights Shortridge (1934)
Sus scrofa Central Italy (woodland) Activity peak in brightest nights Brivio etal. (2017)
Germany (open habitats) No effect Johann etal. (2020)

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Laurenzi etal. 2016). Wild boar activity lasts typically
6–12h a day (Boitani etal. 2003; Lemel etal. 2003). Sea-
sonal variation in activity patterns is usually scarce (Keuling
etal. 2013; Mori etal. 2020), although some daily adjust-
ments may occur as a response to changes in temperature,
photoperiod, precipitation and humidity (Brivio etal. 2017).
In natural and rural conditions, wild boar usually alternates
periods of activity and resting both during daylight and night
hours (Podgórski etal. 2013; Brivio etal. 2017; Mori etal.
2020; Rossa etal. 2021; Zanni etal. 2021). Conversely, in
human-dominated landscapes, wild boar is mostly nocturnal
to reduce interference with humans, independently of the
seasonal changes in photoperiod (Keuling etal. 2013; Brivio
etal. 2017). As other primarily diurnal species (Carnevali
etal. 2016; Grignolio etal. 2018), the nocturnal activity
of the wild boar mostly occurs on bright moonlight nights,
when environmental lighting should be the highest, particu-
larly where natural predators occur (Brivio etal. 2017).
On the brightest nights, the number of collisions between
wild boar and vehicles also increases, as a result of the
increased movements of ungulates in areas of highest visibil-
ity, e.g. paved road (Colino-Rabanal etal. 2018). Conversely,
Johann etal. (2020) detected no effect of moon phase on
activity patterns in rural areas where natural predators are
absent. Theuerkauf etal. (2003) reported that hunting suc-
cess of wolves (Canis lupus) is the highest in bright full
moon night. Although it may be surprising to detect the
highest activity of a prey species overlapping with that of
its main predator (cf., Brivio etal. 2017), the poor visual
acuity of the wild boar may imply that ranging movements
would be mostly concentrated when environmental visibility
is good enough. Thus, being active in bright nights may rep-
resent a profitable trade-off for wild boar, which may reduce
their visibility to some predators and hunters and may, at
the same time, detect potential others. Conversely, Rossa
etal. (2021) showed that in the Mediterranean scrubland,
i.e. a concealed habitat, the activity of wild boar was not
influenced by that of wolf.
Given the seasonality of hunting periods, the activity of
wild boar may seasonally change not only following the sea-
sonal differences in night and day duration, but also in light
of different risk perception (Boitani etal. 2003). Accord-
ingly, when nights are shorter (e.g. at the start of the spring),
wild boar may compensate by being active also in some
diurnal hours (cf. Brivio etal. 2017). As well, particularly
during the hunting period (i.e. in late autumn-early winter),
wild boar may avoid humans by being more active in night-
time. In this study, we compared the performance of some
competitive models using different variables describing the
moon cycle or estimating the actual nocturnal brightness
on the ground, to find which one better explain the activity
probability(AP) of wild boar during night. In this way, we
investigated whether a lunar synodic endogenous clock is the
most powerful driver in determining wild boar activity pat-
terns or if the actual brightness of the night is a more impor-
tant factor affecting their activity. We took also into account
the potential effect of Julian night (i.e. a proxy of seasonal-
ity) in determining nocturnal activity. Given the local hunt-
ing pressure, we predicted that wild boar would increase
their nocturnality in autumn and winter to limit encounters
with humans. However, some nocturnal behaviour could be
maintained throughout the year to limit visibility to preda-
tors (i.e. wolves) and potential poachers. We also predicted
that wild boar would reduce their detectability by reducing
activity in the brightest nights throughout the year.
Study area andsampling design
Our survey was carried out in the North-Eastern part of the prov-
ince of Grosseto, Central Italy (“Poggi di Prata”, about 1400ha,
43.08° N 10.99° E; 1350ha; 475–903m a.s.l.: Battocchio etal.
2017; Viviano etal. 2021), throughout 2019 and 2020. About
67% of the study site was covered by deciduous mixed oak-
woods (mostly Quercus cerris L., Castanea sativa Miller,
Ostrya carpinifolia Scop., Carpinus betulus L., Fraxinus
ornus L. and Robinia pseudoacacia L.). A belt of scrubland
(Juniperus communis L., Rubus ulmifolius Schott. and Spar-
tium junceum L.: 1.7%) occurred around woodlands. Fal-
lows count for 19.5%, cultivations (sunflowers, cereals and
vegetable gardens) for 7.8%. Coniferous woodlands (Pinus
nigra Arnold and Cupressus arizonica Greene) and human
settlements covered the remaining part of the study area.
A map of the study area could be seen in Fig.1 (cf. Mori
etal. 2021b; Viviano etal. 2021). Three brooks and some
ponds fed by rainfall are present. The local climate shows
sub-montane features: during our survey, the average annual
rainfall was 850mm and the average annual temperature
was 15°C. Drive-hunt to wild boar is conducted throughout
the study area, between the 1st of November and the 31st of
January. Some poaching is known to occur in the surround-
ings of farmlands and other cultivated areas (e.g. vegetable
gardens). The grey wolf was present in the study area and the
wild boar represented the main prey species in the study area
(44% of relative frequency over a total of 117 wolf scats:
Battocchio etal. 2017).
Camera traps (N tot = 7) were placed at 25 stations
(Fig.1) across all habitat types of the study area, in propor-
tion to their local availability. Stations were separated one-
another by at least 500m (Battocchio etal. 2017; Greco etal.
2021). Although home range size of the wild boar in Central
Italy can be larger in size (Boitani etal. 1994; Massei etal.
1997), presence of fences around open areas as well as short
duration of each camera-trap deployment (14 nights) may
have limited the occurrence of the same wild boar groups
at different stations during the same deployment (cf. Greco
etal. 2021). Cameras were distributed in all habitat types,

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including forest patches and areas over the tree-line level,
depending on their accessibility. Camera traps were placed
at an average height of 80cm from ground level (Mori etal.
2020; Greco etal. 2021) on random points. We used Multipir
12 cameras, triggered by Passive InfraRed sensor (PIR). The
cameras were furnished with eight 1.5V alkaline batteries
and 16GB SD cards. The cameras were set to record videos
of 60s with a minimum time interval between one video
and the next of about 5s. Camera trigger time was about
1.2s. The camera traps had a detection range of up to 20m
(15m at night) and a horizontal field of view of 90°. Camera
traps were activated for 24h a day and checked once every
14days to download data and replace dead batteries. Camera
traps were randomly rotated between stations once every 14
nights, so that each station was sampled for 45–62days per
season (TableS1 in Supplementary Material 1). No camera
failure occurred during our survey.
Pattern ofactivity rhythms
For each detection of wild boar, we reported on a dataset
the date and the solar time of capture, which is directly
shown on each camera trap record. Hours were converted
into radians before the statistical analysis on the package
overlap (Meredith and Ridout 2014) for the software R 3.6.1
(R Core Team 2013). Records occurring at the same camera-
trap location within less than 30min were removed from
the dataset by keeping only one intermediate hour between
the first and the last detection, to limit pseudo-replication
(Meredith and Ridout 2014). So, all the detections included
in the final dataset were considered “independent.” Records
were classified following astronomical seasons: spring
(21st March–20th June), summer (21st June–20th Septem-
ber), autumn (21st September–20th December) and winter
(21st December–20th March). Seasonal patterns of activity
rhythms and associated 95% confidence intervals (hereafter,
CIs) were calculated with the package overlap. Dawn and
dusk times were calculated through the R package NightDay
(Hughes-Brandl 2018). We estimated all the coefficients of
temporal overlapping (Δ) between all pairwise combinations
of the four seasons. The coefficient of overlapping ranges
between 0 (no overlap) and 1 (total overlap: Meredith and
Ridout 2014). We calculated the Δ4 estimator and its 95%
confidence intervals (hereafter, CIs) as also the smallest
sample of the pairwise comparison was over 75 records
(Meredith and Ridout 2014).
Seasonal Hermans–Rasson tests were computed
through the R package circMLE (Fitak 2020), to evaluate
whether a random activity pattern was exhibited over the
24h (Landler etal. 2019). This test evaluates if activity
data collected through camera-trapping are drawn from a
uniform distribution or they are concentrated around one
or more preferred directions (i.e. hours of the day). The
Mardia–Watson–Wheeler test (W) was computed to esti-
mate interseasonal overlaps of activity rhythms. Bootstrap
tests were used to obtain a probability test that two sets of
Fig. 1 Map of the study area, with camera trap stations and main habitat types

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circular observations (i.e. from two seasons) belonged to
the same distribution, for all season pairs, with the function
compareCkern() of the R-package activity (Rowcliffe etal.
2008, 2014).
Nocturnal activity
To assess the effect of night brightness on wild boar activity
patterns, we estimated the probability to detect active wild
boar during the night depending on different measures of
night brightness. To this aim, we prepared a new dataset
in which the sampling period of each camera trap (i.e. the
actual days when the camera trap was active) was split into
two-hour intervals. For each two-hour interval, we defined
a new variable named “activity probability” (hereafter, AP),
which assumes value 1, when at least one wild boar was
detected by the camera trap during the corresponding two
hours, and 0, when no wild boar was detected (ratio of 0 to
1 = 0.97). Then, we focused on nocturnal records only: all
two-hour intervals were classified as nocturnal if at least
50% of interval time was before dawn or after dusk.
We considered different measures of night brightness
which consider natural (i.e. lunar) and anthropogenic light
sources, as well as cloud cover of the sky which may have
affected the illumination level of the moon. Thus, we com-
puted through a visual assessment every night at midnight
in an open area: (1) moon phase (phase 1, from new moon
to ¼; phase 2, from ¼ to ½; phase 3, from ½ to ¾ and phase
4, over ¾), (2) lunar age (i.e. 0–29days of epact), (3) moon
visibility (i.e. veiled, covered, or fully visible), (4) lightening
(at ground level and at every camera trap station, including
anthropogenic light sources, computed by the © KHTSXR
Luxmeter App for Android smartphones: Zozzoli etal.
2018), (5) cloud cover (in percentage with a 10% accuracy),
and (6) a sky brightness index, i.e. an indicator resulting
from the joint effects of moon phase and sky cloudiness
preventing moon visibility (Luzi etal. 2021). To calculate
it, we multiplied the moon phase per moon visibility (0.1,
when moon was completely or almost hidden by clouds;
0.5, when moon was partially veiled by clouds; 1, when sky
was clear and moon fully visible) to obtain this index, rang-
ing from 0.1 (maximum sky darkness) to 4 (maximum sky
brightness).
To analyse the influence of night brightness on wild boar
activity, we modelled AP by using Generalized Additive
Models (GAMs) with binomial distribution. GAMs were
implemented within the mgcv package in R (Wood 2017).
As the six different measure of night brightness were highly
correlated (see Supplementary materials S1), we fitted six
alternative GAMs one for each measure of night brightness
(moon phase, lunar age, moon visibility, sky cover, lighten-
ing, and sky brightness index) to evaluate which one better
explain the activity probability of wild boar during night
(Table2). In each GAM, we included the sampling time
(two-hour interval) and the date (Julian night), to account for
daily and seasonal variations in wild boar activity rhythms.
The effect of the date was modelled as a cyclic cubic regres-
sion spline, to take into account the circularity of this vari-
able: thus, we ensured that the value of the smoother at the
far-left point (1 January) was the same as the one at the
far-right point (31 December). Camera station ID and year
of data collection were fitted as random factors to control
for the influence of camera-related factors (e.g. vegetation
cover, distance to water) and year-related environmental
conditions (e.g., weather, food availability), by declaring
them in the GAMs formulas using “re” terms and smoother
linkage (Wood 2013). Predictors were screened for collin-
earity (Pearson correlation matrix) and multicollinearity
(Variance Inflation Factor), with thresholds set to |rp|= 0.5
and VIF = 3, respectively. Wild boar sex was not included
in our model, as it was possible to recognise amongst males
and females only in few records. We ranked and weighed the
six alternative GAMs by using the minimum AIC criterion
(Symonds and Moussalli 2011), to find which model was
best supported by the empirical data, thus identifying the
measure of night brightness, which best explain wild boar
activity pattern variations.
Table 2 Alternative Generalised
Additive Models predicting the
nocturnal activity of the wild
boar in Central Italy
Model # Variables in the model AIC ΔAIC Log Lik
Model 6 activity ~ j. night + time-int. + sky brightness index 8834.1 0.0 − 4384.2
Model 4 activity ~ j. night + time-int. + lightening 8869.3 35.2 − 4401.2
Model 5 activity ~ j. night + time-int. + sky cover 8890.2 56.1 − 4411.9
Model 3 activity ~ j. night + time-int. + moon visibility 8908.2 74.0 − 4426.1
Model 2 activity ~ j. night + time-int. + lunar age 8922.6 88.5 − 4434.8
Model 1 activity ~ j. night + time-int. + moon phase 8927.1 93.0 − 4434.8
Null model activity ~ 1 9088.0 253.8 − 4543.0

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Results
Analyses on activity rhythms were carried out on a total
of 1048 independent records (first year, 507; second year,
541). Throughout the year, wild boars were mostly noc-
turnal, particularly in the warmest months, with a peak
around midnight (Fig.2). Annual and seasonal activity
patterns were significantly different from random accord-
ing to the Hermans–Rasson test (r = 70.16–81.34, all
P < 0.001) and activity peaked in the first part of the night
(i.e. after sunset) in all seasons. However, interseasonal
temporal overlaps were very high and patterns of tempo-
ral overlap were similar across seasons (∆4= 0.78–0.94,
95% CIs = 0.75–0.97, all bootstrap P > 0.05). We observed
a very high temporal overlap between sampling years
(∆4= 0.96, 95% CIs = 0.91–0.98). We did not detect any
significant difference in the comparison of temporal over-
laps of each season pair (Mardia–Watson–Wheeler tests,
W = 0.052–0.085, all P > 0.10).
Nocturnal activity
According to the minimum AIC criterion, the best model
explaining wild boar activity during night includes the sky
brightness index (Tables2 and 3). Results of the model
showed that throughout the year, nocturnal activity peaked
Fig. 2 Patterns of activity rhythms of the wild boar in Central Italy
assessed through kernel density estimate of activity throughout
the year (annual, N = 1048 camera-trap records), and in each sea-
son (autumn, N = 255; winter, N = 272; spr ing, N = 291; summer,
N = 230). Coloured lines represent bootstrap estimates. In each graph,
the black line is the mean activity pattern and dashed lines represent
95% confidence intervals
Table 3 Effect of predictor
variables estimated by the best
Generalised Additive Model
(see the text for more details)
fitted to predict the nocturnal
activity of the wild boar in
Central Italy
Parametric coefficients:
Estimate Std. error z value Pr( >|z|)
 (Intercept) − 3.361 0.124 − 27.08 < 0.001 ***
Approximate significance of smooth terms:
edf Ref.df Chi.sq p-value
 s (Julian night) 1.785 2 22.382 < 0.001 ***
 s (time interval) 3.883 4 77.809 < 0.001 ***
 s (sky brightness index) 2.967 3 33.969 < 0.001 ***
 s (site) 17.727 24 74.157 < 0.001 ***
 s (year) 0.896 1 9.115 0.001 **

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around 3rd November (i.e. 307th Julian night, Fig.3A),
while minimum values were recorded around 3rd May
(i.e., 123rd Julian night, Fig.3A). Activity peaked
around midnight, after which decreased towards dawn.
Interestingly, activity around dusk was higher than that
around dawn (Fig.3B). The maximum nocturnal activ-
ity was reported in conditions of low sky brightness
index values (range 0–2.5). For nights with a sky bright-
ness index above 2.5, the activity of wild boar deeply
decreased till becoming null for sky brightness index
around 4 (Fig.3C).
Discussion
Wild boar resulted in being mostly nocturnal, with an
acrophase of activity concentrated around midnight, in line
with previous literature (Caruso etal. 2018; Mori etal.
2020). A few diurnal activity was observed only in spring,
when nights are shorter and likely insufficient to fulfil
nutritional requirements of wild boar (Corsini etal. 1995,
for the crested porcupine Hystrix cristata). Conversely,
diurnal hot temperatures of summer may force wild boar
to travel at night, requiring the presence of water for mud
baths to thermoregulate.
Activity patterns of wild species are shaped by intrin-
sic (biological clocks and nutritional requirements) and
extrinsic factors (photoperiod, moon cycle, and tempera-
ture fluctuations: Daan and Aschoff 1982; Refinetti 2016).
Our findings provided the first strong evidence that wild
boars limit their activity in nights with high light intensity,
i.e. those with bright full moon and clear sky. However,
our analysis failed to show any clear lunar synodic pattern
defined by environmental stimuli known as “Zeitgebers”
(Daan and Aschoff 1982; Kronfeld-Schor etal. 2013), as
wild boar activity was explained by the variation in sky
brightness (i.e. in light intensity) better than by the varia-
tion in lunar day (Youthed and Moran 1969). Accordingly,
in our study, the wild boar was mostly active in the darkest
nights, i.e. when the sky was particularly cloudy or around
new moon nights. The limited visual abilities of the wild
boar and the lack of the tapetum lucidum suggest that this
ungulate has evolved as a mostly diurnal species (Boitani
etal. 2003). In line with its perceptive capabilities, previ-
ous studies highlighted that wild boar nocturnal move-
ments are mostly concentrated during brightest nights or
crepuscular hours, when environmental visibility is the
highest (Brivio etal. 2017; Colino-Rabanal etal. 2018).
However, wild boar food search is mostly based on the
sense of smell (Ollivier etal. 2004; Morelle etal. 2015;
Mori etal. 2021b), allowing this species to range also when
environmental visibility is at its lowest (Schlageter and
Haag-Wackernagel 2012). Finally, as the model including
the actual environmental brightness works better than the
models describing moon cycle, we can argue that noctur-
nal activity cycle is only weakly related to moon cycle.
Hence, this result seems to suggest that nocturnal activity
has not been evolutionary selected, i.e. a lunar synodic
endogenous clock — driving nocturnal activity rhythms
— is not present in wild boar. Instead, our results revealed
that the activity pattern is a plastic behavioural response of
this species which can select the best environmental condi-
tion night by night. Human activities are known to shape
the spatiotemporal behaviour of the wild boar, which, in
turn, shows great ecological plasticity (Podgórski etal.
Fig. 3 Predicted wild boar nocturnal activity in Central Italy follow-
ing the best Generalised Additive Model. The figure shows the effects
exerted by Julian night (A), time (B), and the sky brightness index
(C). The predictions are given according to the mean of all other
covariates in the model. In the graphs, the gray-shaded areas are the
estimated standard errors

Mammal Research
1 3
2013; Fanelli etal. 2021; Zanni etal. 2021). Particu-
larly, hunting pressure is reported to bring wild boars to
increase their spatial movements towards protected areas,
which provides suitable refuges for the species (Santilli
and Varuzza 2013). In areas characterised by human pres-
sure (e.g. hunting), a shift towards more strictly nocturnal
habits is observed in wild boars in respect to protected
areas, even outside the hunting season, to limit contacts
with humans (Boitani etal. 1994; Keuling etal. 2008;
Podgórski etal. 2013; Brivio etal. 2017). In some areas,
wild boars develop mostly nocturnal habits only when the
risk of encounters with humans is the highest (e.g. hunt-
ing season, Ohashi etal. 2013; Johann etal. 2020; Zanni
etal. 2021), whereas elsewhere, this ungulate is nocturnal
throughout the year (Brivio etal. 2017). In other words, a
whole-year nocturnal behaviour may have been developed
after decades of severe hunting harassment and, moreover,
may provide wild boar with an optimal thermal balance,
limiting energetic costs (Brivio etal. 2017).
Predation risk is widely reported to affect the tempo-
ral activity patterns of prey species (Borowski and Owa-
dowska 2010; Mori etal. 2020). Thus, the intensity of
predation risk by wolf may force wild boar to use the areas
where vegetation cover limits their detectability, or roam
during darkest nights, as shown by our results. Similarly,
Mori etal. (2020) showed that the wild boar increases its
nocturnality and reduces diurnal activity in areas where a
high frequency of wolf passage was recorded. Our find-
ings showing that wild boars are less active during very
bright nights, lead us to interpret this behaviour as an
anti-predatory strategy, similar to moonlight avoidance
in small mammals (Viviano etal. 2020; Hernández etal.
2021). Thus, where hunting occurs, where predation pres-
sure is high, or in suburban and urban areas, the onset of
wild boar activity is usually recorded at sunset (Cahill
etal. 2003; Mori etal. 2020; Rossa etal. 2021), whereas
in protected areas and where predators are rare, it may
occur some hours before (Russo etal. 1997; Podgórski
etal. 2013; Zanni etal. 2021). Although many studies
have observed a sort of seasonality in wild boar activ-
ity, we showed a similar pattern of daily activity rhythms
throughout the four seasons, possibly related to climatic
conditions in our study area, which are characterised by
reduced seasonality in respect to Alpine or Mediterranean
areas (Russo etal. 1997; Keuling etal. 2008; Johann etal.
2020). In our study, regarding the Julian nights, the peak
of wild boar activity occurred in autumn, thus confirm-
ing previous findings (Podgórski etal. 2013; Brivio etal.
2017; Johann etal. 2020). Autumn corresponds to the
main hunting season, which is reported to trigger wild
boar movements, thus increasing the activity time of this
species (Brogi etal. 2020; Johann etal. 2020; Fanelli etal.
2021). Furthermore, after leaves fall, wild boar might have
to range for long distances to find suitable resting sites for
diurnal hours, often far from feeding areas (Johann etal.
2020).
The great ecological plasticity of wild boar has been
suggested to have helped this species to expand its popu-
lations throughout Europe (Podgórski etal. 2013; Massei
etal. 2015; ENETWILD Consortium etal. 2020), including
habitats where it was previously not recorded (i.e. subur-
ban areas: Stillfried etal. 2017). Such a great adaptability
requires a high number of studies in different geographical
areas to depict a clear knowledge picture. Further research
on effect of night brightness on wild boar activity should
be carried out comparing areas with and without hunting,
as well as with and without wolf predation pressure. Given
the severe problems triggered by wild boar populations
to human activity and wellness, behavioural plasticity of
this species should deserve further attention to explain the
expansion process and develop effective management pro-
jects (Caro 1998).
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s13364- 021- 00610-6.
Acknowledgements Three anonymous reviewers kindly improved our
first draft with their comments. A native English speaker (E. Basset)
kindly took the time to review our MS for language polishing.
Author contribution SG, EM and AV conceived the idea; EM, LG and
SG collected most data; LL and AV organised the dataset; SG, FB and
AM carried out model analyses; FB created the figures; LG, LL, AM
and EM wrote the first draft.
Data availability All data are available via the corresponding author.
Declarations
Conflict of interest The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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