# Back and forth: day-night alternation between cover types reveals complementary use of habitats in a large herbivore

Johannes de Groeve, Nico van de Weghe, Nathan Ranc, Nicolas Morellet, Nadège Bonnot, Benedikt Gehr, Marco Heurich, A.J. Mark Hewison, Max Kröschel, John Linnell, et al.

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## To cite this version:

Johannes de Groeve, Nico van de Weghe, Nathan Ranc, Nicolas Morellet, Nadège Bonnot, et al. Back and forth: day-night alternation between cover types reveals complementary use of habitats in a large herbivore. Landscape Ecology, 2023, 17 p. 10.1007/s10980-023-01594-1. hal-03998438

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Submitted on 21 Feb 2023

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# Back and forth: day-night alternation between cover types reveals complementary use of habitats in a large herbivore

Johannes De Groeve · Nico Van de Weghe · Nathan Ranc · Nicolas Morellet · Nadège C. Bonnot · Benedikt Gehr · Marco Heurich · A. J. Mark Hewison · Max Kröschel · John D. C. Linnell · Atle Mysterud · Robin Sandfort · Francesca Cagnacci

Received: 14 April 2022 / Accepted: 9 January 2023
© The Author(s) 2023

## Abstract

Context The Complementary Habitat Hypothesis posits that animals access resources for different needs by moving between complementary habitats that can be seen as 'resource composites'. These movements can occur over a range of temporal scales, from diurnal to seasonal, in response to multiple drivers such as access to food, weather constraints, risk avoidance and human disturbance. Within this

framework, we hypothesised that large herbivores cope with human-altered landscapes through the alternate use of complementary habitats at both daily and seasonal scales.

Objectives We tested the Complementary Habitat Hypothesis in European roe deer (*Capreolus capreolus*) by classifying 3900 habitat-annotated movement trajectories of 154 GPS-monitored individuals across contrasting landscapes.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10980-023-01594-1.

J. De Groeve (✉) · N. Van de Weghe
Department of Geography, Ghent University, Krijgslaan 281, 9000 Ghent, Belgium

J. De Groeve · N. Ranc · F. Cagnacci
Research and Innovation Centre Fondazione Edmund Mach, Via E. Mach 1, San Michele all' Adige, 38010 Trentino, Italy

J. De Groeve
Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1090 GE Amsterdam, The Netherlands

N. Ranc
Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, USA

N. Ranc · N. Morellet · A. J. M. Hewison
Université de Toulouse, INRAE, CEFS, F-31326 Castanet-Tolosan, France

N. Ranc · N. Morellet · A. J. M. Hewison
LTSER ZA PYRénées GARonne, F-31320 Auzeville-Tolosane, France

N. C. Bonnot
INRAE, EFNO, F-45290 Nogent-sur-Vernisson, France

B. Gehr
Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland

B. Gehr
Wildtier Schweiz, Winterthurerstrasse 92, CH-8006 Zurich, Switzerland

M. Heurich
Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, Freyunger Str. 2, D-94481 Grafenau, Germany
*Methods* We considered day-night alternation between open food-rich and closed refuge habitats as a measure of complementary habitat use. We first identified day-night alternation using the Individual Movement - Sequence Analysis Method, then we modelled the proportion of day-night alternation over the year in relation to population and individual characteristics.

*Results* We found that day-night alternation is a widespread behaviour in roe deer, even across markedly different landscapes. Day-night alternation followed seasonal trends in all populations, partly linked to vegetation phenology. Within populations, seasonal patterns of open/closed habitat alternation differed between male and female adults, but not in juveniles.

*Conclusion* Our results support the Complementary Habitat Hypothesis by showing that roe deer

adjust their access to the varied resources available in complex landscapes by including different habitats within their home range, and sequentially alternating between them in response to seasonal changes and individual life history.

**Keywords** Animal trajectories · Habitat use · IM-SAM · Resource composites · Roe deer · Sequential analysis methods · Spatio-temporal patterns · Vegetation green up

Introduction

Fitness is influenced by spatial and temporal heterogeneity in the distribution of multiple, often complementary, resources (Gaillard et al. 2010). A given habitat may not fulfil all the needs of an individual simultaneously, while the functional role of a habitat may fluctuate over time in relation to modifications in an individual's requirements, or in the value of the habitat per se (Peters et al. 2017; Couriot et al. 2018), for example, driven by vegetation phenology. In response, animals may adjust their behaviour through 'complementary habitat use', defined as the use of different habitats at different times by individuals during the course of their daily and seasonal activities (Complementary habitat hypothesis, CHH) (Dunning et al. 1992; Mandelik et al. 2012). For many ungulates, for example, accessibility to forage and protection from predation risk are two key resources that are selected (Hebblewhite and Merril 2009). Indeed, these two types of resource are both positively related to fitness, but are often spatially distinct (Benhaiem et al. 2008) and fluctuate seasonally, driving complementary use (sensu Mandelik et al. 2012) of 'open' vs. 'cover' habitats (Mysterud and Østbye 1999; Berryman and Hawkins 2006). For example, forest canopy can provide cover from adverse conditions (e.g., deep snow: Mysterud and Østbye 1995, Ewald et al. 2014, Ossi et al. 2015) and protection from predators or human disturbance (Bonnot et al. 2013), while the understorey provides seasonally rich foraging (i.e., during the vegetation green up and re-growth, Mancinelli et al. 2015). In contrast, open areas may be used by ungulates as a seasonal source of forage (Abbas et al. 2011), but mainly at night to avoid human disturbance (Godvik et al. 2009; Bonnot et al. 2013; Dupke et al. 2017; Salvatori et al. 2022), or to

M. Heurich
Chair of Wildlife Ecology and Wildlife Management,
Faculty of Environment and Natural Resources, University
of Freiburg, Tennenbacher Straße 4, D-79106 Freiburg,
Germany

M. Heurich
Institute for Forest and Wildlife Management, Inland
Norway University of Applied Science, Campus Evenstad,
NO-2480 Koppang, Norway

M. Kröschel
Wildlife Institute, Forest Research Institute of Baden-
Württemberg, Wonnhaldestraße 4, D-79100 Freiburg,
Germany

J. D. C. Linnell
Norwegian Institute for Nature Research, Vormstuguveien
40, NO-2624 Lillehammer, Norway

J. D. C. Linnell
Department of Forestry and Wildlife Management, Inland
Norway University of Applied Sciences, Anne Evenstads
vei 80, NO-2480 Koppang, Norway

A. Mysterud
Centre for Ecological and Evolutionary Synthesis,
Department of Biosciences, University of Oslo, P.O.
Box 1066, Blindern, NO-0316 Oslo, Norway

R. Sandfort
Institute of Wildlife Biology and Game Management,
University of Natural Resources and Life Sciences,
Gregor-Mendel-Str. 33, 1180 Vienna, Austria

F. Cagnacci
NBFC, National Biodiversity Future
Center, 90133 Palermo, Italy
diminish the risk of ambush predation (Lone et al. 2014; Gehr et al. 2017). Hiding cover may also be seasonally available in open habitats, for example, in summer, when row crops are mature and provide concealing (Mysterud et al. 1997; Bjørneraas et al. 2011; Bonnot et al. 2013; Dupke et al. 2017).

In heterogeneous landscapes, open and closed habitats, hence, may represent composites of different resource types, and animals must alternate between them to satisfy their requirements (see Dunning et al. 1992). Large herbivores use these composites at different spatio-temporal scales, so that the open and closed habitats visited along the movement trajectories generate specific patterns of sequential habitat use (De Groeve et al. 2016). For example, at the daily scale, the day-night alternation between open and closed habitats may be linked to activity cycles e.g., foraging vs. resting and ruminating, or the forage acquisition-predation risk avoidance trade-off (Heblewhite and Merril 2009). In turn, daily alternating use of open and closed habitats may vary in relation to seasonal changes in perceived risk (e.g., anthropogenic disturbance, hunting activity; Bonnot et al. 2013, Gehr et al. 2017, 2020), vegetation productivity (i.e., green up and senescence, or cultivation / harvesting; Peters et al. 2019), and physiological cycles (e.g., growth, reproduction, dispersal). The understanding of the complementary use of resource composites across spatio-temporal scales by ungulates could inform managers regarding the functional role of different habitat types in human-modified environments. In particular, the spatial association between open agricultural habitats and forest patches could support thriving populations of wild ungulates in matrix landscapes (Hewison et al. 2009; Linnell et al. 2020). These concepts are well-addressed within the Complementary Habitat Hypothesis framework (Dunning et al. 1992) and well established in ecological theory, but rarely tested for wild populations, especially vertebrates, as most studies concern insects (Mandelik et al. 2012).

Here, we considered the patterns of alternation between open and closed habitats in European roe deer (*Capreolus capreolus*) to test the Complementary Habitat Hypothesis in a highly managed large herbivore (Apollonio et al. 2010) that has successfully adapted to human-modified landscapes (Andersen et al. 1998), including open agricultural areas (Hewison et al. 2009). Roe deer is an ideal

model species to test the Complementary Habitat Hypothesis because it occupies a wide range of landscapes (i.e., different arrangements of complementary habitats, *sensu* Dunning et al. 1992), from completely forested areas to wide open agroecosystems, exhibiting marked behavioural and ecological plasticity (Hewison et al. 1998; Morellet et al. 2013). Moreover, roe deer are known to prefer ecotonal and forest habitats, complemented by the use of open habitats (meadows and crops), typically at night (Bonnot et al. 2013; Dupke et al. 2017), in relation to their bimodal crepuscular activity pattern (Pagon et al. 2013; Krop-Benesch et al. 2013; Bonnot et al. 2020). Finally, roe deer are characterized by sex-dependent space use patterns (Malagnino et al. 2021), especially in spring and summer, when adult males display territorial behaviour (Hewison et al. 1998), and females give birth and care for their young.

We applied the Individual Movement - Sequence Analysis Method (IM-SAM, De Groeve et al. 2020a) to six populations of European roe deer living in contrasting landscapes to evaluate how day-night alternation between closed and open habitats varied across the seasons and environmental contexts. We first hypothesized that day-night alternation between closed and open habitats would mirror the landscape composition (*sensu* Dunning et al. 1992; Table 1: Landscape Composition and Structure Hypothesis, LCSH, H1). In particular, we expected that day-night alternation would occur mainly in heterogeneous landscapes (Table 1: H1, P1). Second, we hypothesized that day-night alternation would also vary seasonally, in relation to the phenology of vegetation (Pettorelli et al. 2006), according to the Complementary Habitat Hypothesis (Mandelik et al. 2012). Specifically, we predicted frequent alternation during vegetation green-up to maximize access to high quality food in rich-open habitats, but less alternation in winter, when meadows and crops provide less food resources and are more exposed to extreme weather conditions (e.g., snow cover, wind exposure; Table 1: CHH, H2, P2.1). Also, we hypothesized that day-night alternation between open and closed habitats would vary over seasons according to key life history events that are linked to sex and age, such as births, or the rut (Table 1: H2, P2.2; Andersen et al. 2000, Bongi et al. 2008). In particular, we predicted that the day-night alternation of females and males should differ the most during the spring-summer season,
<table><caption>**Table 1** Summary of hypotheses and analyses (see footnote for abbreviations)</caption><thead><tr><th>Hypotheses</th><th>Predictions</th><th>Analysis</th><th>Input data</th></tr></thead><tbody><tr><td>H1: Landscape Composition and Structure Hypothesis (LCSH)<br>"Proportion of alternation between closed and open habitats in roe deer populations depends on the proportion and spatial structure of these habitats in the landscape"</td><td>P1 Day-night alternation mainly occurs in heterogeneous landscapes</td><td>1. Exploration with mosaic plot<br>2. GLM—Habitat composition (proportion data; beta distribution); Kruskal Wallis non-parametric comparison<br>M_P: p(closed) ~ pop<br>3. GLM—Habitat structure (continuous skewed data; gamma distribution); Kruskal Wallis non-parametric comparison<br>M EDUC: edge_density ~ pop<br>4. GAMMs – Alternation (0/1) (see below)<br>M1: a ~ s(bw * pop) + pop + 1lind<br>1. GAMMs – Alternation (0/1)<br>M1: a ~ s(bw * pop) + pop + 1lind<br>2. ANODEV NDVI<br>ANODEV = 1 - <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">L(M<sub>NDVI</sub>) - L(M<sub>FULL</sub>)</div><div style="padding: 0 5px;">L(M<sub>FULL</sub>) - L(M<sub>FULL</sub>)</div></div><br>Where:<br>M<sub>FULL</sub>: a ~ bw * pop + 1lind<br>M<sub>NULL</sub>: a ~ 1lind + pop<br>M<sub>NDVI</sub>: a ~ s(NDVI * pop) + pop + 1lind<br>GAMMs – Alternation (0/1)<br>M2: a ~ s(bw * age:sex) + age*sex + 1lind</td><td>Habitat use sequences, classified according to the following patterns: Homogeneous open (o) Homogeneous closed (c) Random (u) Alternation (a)<br>Binomial variable based on the habitat use sequences, defined as follows:<br>c, o, u = 0<br>a = 1</td></tr><tr><td>H2: Complementary Habitats Hypothesis (CHH)<br>"Roe deer alternation between habitats varies seasonally, in relation to environmental and roe deer intrinsic cycles"</td><td>P2.1. Within a given landscape, alternation between habitats varies seasonally following the phenology of the vegetation, with more day-night alternation during the vegetation green-up in spring<br>P2.2 Day-night alternation between habitats varies seasonally in relation to intrinsic cycles linked to life-history</td><td></td><td></td></tr></tbody></table>

a = daily habitat alternation (0/1); pop = population; 1lind = individual identity as a random effect on intercept; s(bw) = cyclic spline smooth of the biweek; s(NDVI) = cubic spline smooth of the Normalized Difference Vegetation Index; age = fawns & yearlings (<2 yrs); adults (>2 yrs); sex = male or female; L = log Likelihood; GAMM: ANODEV

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when adult females should alternate less due to the constraints of provisioning for their young (Andersen et al. 2000), while adult males should alternate more to patrol and defend their mating territory, especially during rut (Johansson 1996). Therefore, we expected these sex-specific seasonal patterns to be more evident in adults than juveniles linked to reproductive status and behaviour (Sempéré et al. 1998).

## Materials and methods

The habitat use sequences were obtained from roe deer movement trajectories of the Eurodeer database (Urbano and Cagnacci, 2021), as processed in De Groeve et al. (2020a, b), with some simplifications (see below). In the Eurodeer database, roe deer trajectories obtained from GPS collars deployed across European populations are stored and curated together with individual-based information obtained during capture (Urbano and Cagnacci, 2021). In particular, individuals sexed and aged at capture as juveniles (<1 year old) are considered to become yearlings from 1st April (just before the birth period of roe deer over most of its range) of the year of first monitoring and for the subsequent 12 months. All other individuals are considered adults.

### IM-SAM roe deer habitat use sequences and classification as open/closed day-night alternation

To prepare the obtained habitat use sequences, De Groeve et al. (2020a) regularized the roe deer GPS trajectories using a fixed four-hour relocation interval (0, 4, 8, 12, 16 and 20 h) and segmented them into 16-day periods (i.e., referred to as “biweekly” sequences) starting on January 1st (i.e., 01/01–16/01, 17/01–01/02, etc.; 23 biweekly sequences over a year). Then, GPS locations were intersected with the reclassified High-Resolution Raster Layer Tree Cover Density 2012 (TCD, EEA 2012, 20 m spatial resolution), distinguishing closed (C, TCD ≥ 50%) and open (O, TCD < 50%) habitats (De Groeve et al. 2020a). These sequences were then classified following the IM-SAM procedure, where sequences of observed habitat use were clustered together (i.e., classified or ‘tagged’) with sequences of simulated habitat use, reflecting specific patterns of sequential habitat use. The final data contained the following

patterns of sequential habitat use: homogeneous closed (c), homogeneous open (o), day-night alternation between closed and open habitats (a) and random (u). Seasonal and latitudinal changes in day length were accounted for when classifying day-night alternation (De Groeve et al. 2020a). The final classification of sequences into these four patterns of sequential habitat use is illustrated for two representative populations in Fig. 1 (see also Appendix S1, Figure S1.2 for the final classification of all populations). From the resulting dataset, we extracted the data for six roe deer populations with a representative number of individuals (≥ 10 individuals): Southern France (Eurodeer database study area identifier: FR8), Switzerland (CH25), Southern Germany (DE15), Southeast Germany (DE2), and Northern Italy (IT1, IT24; see Figure S1.1, Table S1.2 for a description of each population). Because we were interested in modeling seasonal variations in day-night alternation, we removed individuals which had less than 10 biweekly habitat use sequences. Our final dataset consisted of 3900 habitat use sequences from 154 animals (95 females and 59 males, made up of 44 juveniles/yearlings and 132 adults, with 22 animals switching from fawns to adults during monitoring; Table S1.2).

### Covariates linked to roe deer habitat use sequences

To test whether day-night alternation is linked to vegetation phenology, we computed the landscape level vegetation productivity profile for each biweekly period estimated by the Normalized Difference Vegetation Index (NDVI; smoothed and pre-processed as in Vuolo et al. 2012) using the following workflow. First, we defined the study area as the Minimum Convex Polygon (MCP) of all locations from all individuals within a population. For each population's MCP, we sampled 1000 random points within that area (corresponding to a survey density of 1.06 ± 0.06 points per hectare, average ± standard deviation) and extracted the weekly NDVI values. Next, we matched each biweekly period with the corresponding averaged weekly NDVI values.

### Sequential habitat use through space: landscape composition and structure hypothesis (H1)

First, we investigated the variation in the proportion of habitat use sequences across populations.
Population CH25

<figure><img src="image_4.png" /></figure>

Population IT1

<figure><img src="image_5.png" /></figure>

**Fig. 1** The IM-SAM classification of biweekly habitat use sequences (light green, open habitat; dark green, closed habitat) in the four sequential patterns: daily alternation (color of the y-axis: light brown), homogeneous closed (dark green), homogeneous open (turquoise) and random (blue), for ani-

mals ranging in mainly open (CH25, Switzerland) and mainly closed (IT1, Northern Italy) landscapes. Aligned on the right: *proportion* of closed habitat used for each sequence. See Figure S1.2 for all populations

The input for this analysis consisted of an abundance table of patterns of sequential habitat use (day-night alternation, a; homogeneous closed, c; homogeneous open, o; random, u) per population. The results were visualized using a mosaic plot, along with a chart indicating the proportion of closed habitat and closed and open edge density (as a proxy of landscape fragmentation) for each population, a circular map extract covering the population range and the number of individuals and sequences (Fig. 2). To test the Landscape Composition and Structure Hypothesis (H1), we first verified whether the probability of alternation varied across landscapes. We expressed habitat alternation as a binomial variable where day-night alternation (a)

is 1 and all other patterns of sequential habitat use (c, o, u) are 0. We built Generalized Additive Mixed Models (GAMMs) with a binomial distribution of residuals, with day-night alternation as the response variable, the cyclic spline smooth of the biweek to account for temporal variation, and population as a fixed (ordered) factor, or as a factor-specific temporal effect (e.g., a population-specific spline of biweek; M1, see below and Table 1: H1; H2.1). We included individual identity as a random effect on the intercept. The most parsimonious model was selected based on minimization of the Akaike Information Criterion (Burnham and Anderson 2002). Second, we compared the landscape composition between populations, measured as proportion of

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 2** Mosaic plot of the proportion of different daily sequential habitat use patterns (first panel), bar plots of the proportion of closed habitat (second panel) and the proportion of open and closed edge per hectare (third panel) in each population (codes along the x-axis of the third panel). The number of individuals (ind) and the number of sequences (seq) for each population are shown in the bottom panel. Further, a visualization of the open-closed landscape composition is presented as circles (study area codes as in main text; see also Appendix S1)</figcaption></figure>

closed habitat, closed edge and open edge densities. To do that, for each population's MCP, we sampled 1000 random points within that area, buffered them with a 200 m radius, and measured these metrics. We built a Generalized Linear Model (GLM) with proportion of closed as the response variable with beta distribution of residuals, and population as a fixed (ordered) factor; and two GLMs with open edge and closed edge density, respectively, as response variables with gamma distribution of residuals, and population as a fixed (ordered) factor. We also applied the non-parametric Kruskal-Wallis

test to each variable separately, using the measures in the buffers as repetitions, grouped by population, as a further confirmation of the robustness of the analysis. The Landscape Composition and Structure Hypothesis (H1) cannot be supported if a clear link between the probability of alternation and landscape composition and structure cannot be established, for example if the probability of alternation does not vary across populations, but the composition and structure of landscape is different. The GLM analyses were performed in R 4.0.0 (2020-04-24) with the package glmmTMB (Brooks et al. 2017).

Sequential habitat use through time: Complementary Habitats Hypothesis (H2)

For this set of analyses, we used habitat alternation as a binomial variable, defined as above. We built two Generalized Additive Mixed Models (GAMMs) with a binomial distribution of residuals, with day-night alternation as the response variable, the cyclic spline smooth of the biweek to account for temporal variation, and a given predictor (Model 1, M1: population-see also H1; Model 2, M2: age x sex) for each set of models, as a fixed (ordered) factor, or as a factor-specific temporal effect (e.g., population or sex/age class-specific spline of biweek). In all models, we included individual identity as a random effect on the intercept (see full models in Table 1: H2.1 and H2.2). The most parsimonious model was selected based on minimization of the Akaike Information Criterion (Burnham and Anderson 2002). For the population model (M1), we subsequently used the ANODEV procedure (Grosbois et al. 2008) to quantify the proportion of variation in day-night alternation that was accounted for by NDVI (see Table 1, H2.1 for the formula). For visual purposes, we also modelled the annual pattern of NDVI (see Fig. 3) using a GAMM with the exact same model structure as M1 (i.e., population-specific cyclic spline smooth of the biweek, with individual identity as a random effect on the intercept), but with the population-level NDVI value of the sequence as the response variable. All statistical analyses were performed in R 4.0.0 with the packages mgcv (Wood 2017) and visreg (Breheny and Burchett 2017).
## Results

Sequential habitat use through space: landscape composition and structure hypothesis (H1)

Visually, the proportion of the four sequential habitat use patterns was not characterised by the populations. Indeed, although the homogeneous closed (c) and homogeneous open (o) sequential habitat use patterns were specific to certain populations (Fig. 2), the day-night alternation (a) was present in all populations, with approximately 40% of all habitat use sequences (38-47%) classified as such, except when

closed habitats were widely prevalent (in population IT24, 14%; Fig. 2). This was statistically confirmed by the fact that the probability of alternation did not depend on population (i.e., as a fixed effect; Table 2a, Table S3.2), but only its temporal variation did (i.e., interaction between spline of biweek and population). Random sequential habitat use patterns (u) were rarely observed in any population.

In contrast, landscape composition (i.e., proportion of closed habitats) differed across populations (GLMM - ref. level: FR8, IT1: 2.13, SE 0.05, p<0.001, DE2: 1.31, SE 0.06, p<0.001, DE15: 0.15 SE 0.05, p<0.001, IT24 2.26, SE 0.06, p<0.001,

<table><caption>**Table 2** Approximate significance of smoothing terms and fixed effects in the selected model (a. cyclic spline of biweek per population, AIC=3777.7, adj R²=37.1; b. cyclic spline of</caption><thead><tr><th>(a)</th><th>edf</th><th>Ref df</th><th>Chi.sq</th><th>p</th><th></th></tr></thead><tbody><tr><td>s(biweek): population CH25</td><td>6.965</td><td>8</td><td>1878.5</td><td>&lt;0.0001</td><td>***</td></tr><tr><td>s(biweek): population DE15</td><td>6.288</td><td>8</td><td>2935.4</td><td>&lt;0.0001</td><td>***</td></tr><tr><td>s(biweek): population DE2</td><td>6.582</td><td>8</td><td>2182.9</td><td>&lt;0.0001</td><td>***</td></tr><tr><td>s(biweek): population FR8</td><td>6.015</td><td>8</td><td>1660.0</td><td>&lt;0.0001</td><td>***</td></tr><tr><td>s(biweek): population IT1</td><td>4.733</td><td>8</td><td>238.0</td><td>0.0499</td><td>*</td></tr><tr><td>s(biweek): population IT24</td><td>6.242</td><td>8</td><td>768.3</td><td>&lt;0.0001</td><td>***</td></tr><tr><td>individual</td><td>144.473</td><td>148</td><td>663.7</td><td>&lt;0.0001</td><td>***</td></tr><tr><td></td><td>Estimate</td><td>sd</td><td>z</td><td>P</td><td></td></tr><tr><td>intercept</td><td>-1.174</td><td>1.715</td><td>-0.102</td><td>0.919</td><td></td></tr><tr><td>DE15</td><td>-1.270</td><td>2.860</td><td>-0.444</td><td>0.657</td><td></td></tr><tr><td>DE2</td><td>-2.012</td><td>3.833</td><td>-0.525</td><td>0.600</td><td></td></tr><tr><td>FR8</td><td>-0.321</td><td>2.906</td><td>-0.111</td><td>0.912</td><td></td></tr><tr><td>IT1</td><td>-1.182</td><td>3.137</td><td>-0.377</td><td>0.706</td><td></td></tr><tr><td>IT24</td><td>-2.860</td><td>4.093</td><td>-0.699</td><td>0.485</td><td></td></tr><tr><th>(b)</th><th>edf</th><th>Ref df</th><th>Chi.sq</th><th>p</th><th></th></tr><tr><td>s(biweek)</td><td>6.753</td><td>8</td><td>2006.048</td><td>&lt;0.001</td><td>***</td></tr><tr><td>s(biweek): ordered(sex*age) juvenile female</td><td>1.115</td><td>8</td><td>23.331</td><td>0.169</td><td></td></tr><tr><td>s(biweek): ordered(sex*age) adult male</td><td>3.228</td><td>8</td><td>2639.053</td><td>&lt;0.001</td><td>***</td></tr><tr><td>s(biweek): ordered(sex*age) juvenile male</td><td>0.002</td><td>8</td><td>0.001</td><td>0.513</td><td></td></tr><tr><td>individual</td><td>148.501</td><td>152</td><td>732.484</td><td>&lt;0.001</td><td>***</td></tr><tr><td></td><td>Estimate</td><td>sd</td><td>z</td><td>P</td><td></td></tr><tr><td>intercept</td><td>-1.0681</td><td>1.2351</td><td>-0.865</td><td>0.387</td><td></td></tr><tr><td>juvenile female</td><td>0.2464</td><td>0.3259</td><td>0.756</td><td>0.450</td><td></td></tr><tr><td>male</td><td>0.3487</td><td>1.9989</td><td>0.174</td><td>0.862</td><td></td></tr><tr><td>juvenile male</td><td>-0.0238</td><td>2.0079</td><td>-0.012</td><td>0.991</td><td></td></tr></tbody></table>

To better assess age and sex differences, we provide the general time-dependent model, followed by the age and sex differences from this general model. The latter model gives the same results as the M1 model in Table 1, which was used in the predictions of Fig. 4. Both models include individual identity as a random effect on the intercept to avoid pseudo-replication
CH25: 0.34 SE 0.05, p<0.001; res dev= -10665.4; β disp par=1.11; Kruskal-Wallis test for proportion of closed habitats: chi-squared=2213.5, df=5, p<0.0001; also confirmed by pairwise post-hoc t tests between all pairs of populations), such that some populations were mainly ranked as 'closed' (IT24, IT1 and DE2; median proportion of closed: 0.94, 0.85, 0.74, respectively), while others as 'open' (CH25, DE15 and FR8; median proportion of closed: 0.30; 0.18; 0.13). Similarly, landscape structure (i.e., closed and open edge densities) was overall different across populations, however non-significant differences between paired populations were observed (open edge density, GLMM - ref. level: FR8, IT1: -573.8, SE 549.8, n.s., DE2: -2203.5 SE 501.1, p<0.001, DE15: 2641.0 SE 660.3, p<0.001, IT24 5881.1 SE 785.0, p<0.001, CH25: -3092.3 SE 477.6, p<0.001; res dev= -100044.3; σ²=2.15; Kruskal-Wallis test=1597.0, df=5, p<0.0001; pairwise post-hoc t-test n.s. for: IT24/CH25 and DE15/FR8; closed edge density, GLMM - ref. level: FR8, IT1: -13645.4 SE 830.4, p<0.001, DE2: -14136.5 SE 825.0, p<0.001, DE15: -568.9 SE 1113.4, n.s., IT24-11444.3 SE 860.1, p<0.001, CH25: -11717.0 SE 855.9, p<0.001; res dev= -98804.0; σ²=1.77;

Kruskal-Wallis = 1021.7; df=5; p<0.0001; pairwise post-hoc t-test n.s. for: DE15/IT1).

Hence, our results do not support the hypothesis that day-night alternation simply mirrors landscape composition and structure, or that alternation is limited to highly heterogeneous landscapes (Table 1: P1).

Sequential habitat use through time: Complementary Habitats Hypothesis (H2)

The probability of day-night alternation varied over the year with contrasting seasonal patterns among populations, as shown by the interaction between the cyclic spline smooth of the biweek being in interaction with population retained in all the best models to explain the temporal pattern of alternation (Fig. 3 and Table 2a, AIC=3777.70, ΔAIC=146.0 with the second best model, R² (adj.)=0.37; Table S2.1 for model selection results).

In all six populations, the probability of day-night alternation followed a bimodal pattern, with an increase in early spring (between late March and late May, from 6th to 9th biweek), followed by a drop in late spring (between mid-May and mid-July, from 9th to 12th biweek), and a second increase in

<figure><img src="image_8.png" /><figcaption>**Fig. 3** Predictions for the probability of habitat alternation between open and closed habitats by roe deer in the six populations over biweekly periods of the year. The smoothed averaged annual NDVI pattern is overlaid as a second y-axis. The sampling units for both curves are biweeks (dots colored</figcaption></figure>

by season; blue: winter; green: spring; red: summer; brown: autumn; a larger dot indicates every other fifth biweek; months indicated on x-axis for readability). The background of the panels represents the landscape (open habitat in light green vs. closed habitat in dark green) of the respective study area
early autumn (between mid-August and mid-Novem-
ber, from 18th to 20th biweek). However, while a
decrease in day-night alternation between mid-Janu-
ary and early-March (2nd and 4th biweek) occurred
in all populations, alternation was more frequent in
winter (Dec. – May), before falling in summer (May
– Dec.) in South-Germany (DE15: Fig. 3, top-central
plot). We found that 44% of the variation in day-night
alternation across the year was explained by NDVI
(Table S3.1; ANODEV). In particular, the first spring
peak in day-night alternation generally corresponded
to the steepest positive slope of the modelled NDVI
curves (in turn varying across populations: best
model including the spline of the interaction between
biweek and population, Table S3.2; ΔAIC = 7754.4
with the second-best model) i.e., the spring vegeta-
tion green up in each study site (Fig. 3, overlaying
NDVI curves). Our results, thus, support the predic-
tion that day-night alternation is not a fixed property
at the landscape level, but varies in time to track sea-
sonal cycles of vegetation phenology, such as green
up or harvesting (Table 1: H2.1, P2.1).

We also found that the seasonal pattern in the
probability of day-night alternation was significantly
different between adult females and males (Fig. 4,
left panel and Table 2b; AIC = 3837.9, ΔAIC > 12.0
with all other models, R² (adj.) = 0.35; Table S2.1).
While the temporal pattern followed a similar general

pattern, adult females alternated less than adult males
during the summer (Fig. 4, left panel: probability of
alternation for females = 0.29 (CI: 0.20–0.42) vs.
males = 0.59 (CI: 0.40–0.76) at the end of June-12th
biweek). On the contrary, adult females alternated
more than adult males during winter i.e., from late
December to early March (probability of alternation
for females = 0.49 (CI: 0.34–0.63) vs. males = 0.18
(CI: 0.09–0.34) during the 3rd biweek). During other
parts of the year, the confidence intervals of the pre-
dictions overlapped strongly, indicating little differ-
ence in habitat alternation between sexes. Our model
also showed that the seasonal pattern in the prob-
ability of day-night alternation was not significantly
different between female and male juveniles (Fig. 4,
right panel; Table 2b; Table S2.1). Specifically, the
temporal pattern of alternation in juveniles of both
sexes followed the same trend as that of adult females.
Overall, we thus found some support for the hypothe-
sis that temporal variation in alternation between hab-
itats is also linked to life-history constraints (Table 1:
H2.2, P2.2). Finally, the two-way interaction between
age and sex did not feature in the retained model as
a fixed factor, indicating that the average overall
amplitude in alternation across the year did not vary
between factor-levels.

In all models, the ‘individual’ random effect (i.e.,
including individual identity as a random effect on the

<figure><img src="image_9.png" /><figcaption>**Fig. 4** Predictions for the probability of day-night habitat alternation between open and closed habitats by roe deer according to sex and age (left panel: female/male adults; right panel: female/male fawns) over biweekly periods of the year. x-axis as in Fig. 3</figcaption></figure>
intercept) contributed considerably towards explain-
ing the variance (R² (adj.) NULL model = 0.31; see
Table S2.1), indicating marked inter-individual varia-
bility in habitat alternation within a given population.

Discussion

In spatially heterogeneous environments, wild ani-
mals must access various resources with different
spatial distributions and, often, asynchronous phe-
nology. Our analysis shows that day–night alterna-
tion between open and closed habitats is a prevalent
characteristic of roe deer movement tactics across a
wide range of landscapes with contrasting composi-
tion and spatial arrangement (Dunning et al. 1992; H1
overall not supported). The most likely explanation of
this behaviour is that animals use day–night alterna-
tion to access food and cover in these resource com-
posites (Padié et al. 2015; Bonnot et al. 2018). The
observed seasonal variation in day–night alternation
within a given landscape supports this interpreta-
tion. Roe deer likely exploit the diversity of resources
offered by those habitats at different times of the year,
and according to their needs (Mandelik et al. 2012).
Our results indirectly support the Complementary
Habitats Hypothesis (H2) by showing marked cycles
of day–night alternation over the seasons (H2.1), with
clear differences between the adult sexes (H2.2).

Understanding the complexity of animal resource
requirements and consequent habitat use is important
as it affects a range of ecosystem processes. The fre-
quent alternation between habitats documented here
likely affects transportation of seeds and nutrients
between cultivated and more natural habitats (Abbas
et al. 2012; Earl and Zollner 2017 for an example on
roe deer), as well as spatial dynamics of parasites,
including vectors of vector-borne zoonotic patho-
gens (Carpi et al. 2008; Mysterud et al. 2017; Chast-
agner et al. 2017). Further, such habitat alternation
may influence human-wildlife interactions relevant
for management, from harvesting efficiency and for-
est damage, to road-crossing and risk of deer-vehicle
collisions (Hothorn et al. 2015; Passoni et al. 2021).
Indeed, in highly anthropogenic European landscapes,
the availability of essential resources for free-ranging
large herbivores – food and cover – vary both natu-
rally (i.e., due to phenological cycles of vegetation,
Pettorelli et al. 2006), and as a consequence of human

activities (e.g., agriculture and forest management
practices; Lande et al. 2014). In this context, large
herbivores must continually adjust their use of habi-
tat to these changes, trading off access to resources
and protection from predators and human disturbance
(Godvik et al. 2009; Gehr et al. 2017). Several studies
have shown that ungulates in temperate environments
modulate habitat selection at different spatial (e.g.,
Boyce et al. 2003, DeCesare et al. 2014) and tempo-
ral scales, especially diel and seasonal scales (Dupke
et al. 2017; Roberts et al. 2017; Martin et al. 2018).
Yet, comparisons across contrasted environmental
contexts are rare, limiting the possibility for testing
hypotheses on the behavioural responses to land-
scape-level heterogeneity. In this work, we were able
to test two alternative hypotheses on diel habitat alter-
nation by comparing the occurrence of this behaviour
spatially (across landscapes) and temporally (across
seasons). According to our results, the order in which
habitats were used varied over the year and across life
history stages, likely in relation to different resource
needs and constraints (De Groeve et al. 2020a).

Temporal variation in alternation between open
and closed habitats is likely due to a complementary
functional use of habitats that occur within the home
range (Dunning et al. 1992; Mandelik et al. 2012;
Couriot et al. 2018). In our study, individuals in most
populations alternated between open and closed habi-
tats more frequently in spring, possibly due to earlier
green-up in open habitats, with fresh high-quality
herbaceous vegetation (Abbas et al. 2011; Dupke
et al. 2017), and in the fall, perhaps due to the avail-
ability of plants with delayed leaf loss or crop stubble
in open habitats. Indeed, we found that about half of
the temporal variation in the probability of alterna-
tion between open and closed habitats was explained
by variation in the population-level NDVI value
(Fig. 3). A recent large-scale study on several boreal
ungulate species showed that roe deer tended not to
move across the landscape to ‘surf’ the green wave,
instead, they maximized the ‘greenness’ quality of
their ranges (Aikens et al. 2020). Alternating between
habitats could be a tactic to achieve such maximiza-
tion locally, accessing complementary resources in
different habitat types (see also Peters et al. 2017).

In winter, individuals in study areas with substan-
tial forest cover (mainly IT24, DE2; Fig. 3) alternated
less, and showed, instead, a consistent and more
homogeneous use of closed habitats at a time when
forest habitat provides thermal protection, shallower
snow (Mysterud et al. 1997; Mysterud and Østbye
2006; Ratikainen et al. 2007; Ossi et al. 2015), and
potentially higher food availability, either more abun-
dant vegetation forage or supplementary food pro-
vided by hunters (Ewald et al. 2014; Ossi et al. 2017),
compared to open habitats. Interestingly, the popula-
tion in the agricultural landscape of Southern Ger-
many (DE15; Fig. 3) exhibited the opposite pattern to
all others, with more pronounced alternation in win-
ter, followed by a consistent and homogeneous use of
open habitats in summer. This behaviour could be due
to cover-food complementation: roe deer may access
open areas at night in winter to consume winter grain
(no cover but valuable food resource), while using
open habitat both as a food and cover source in sum-
mer, when crops are abundant in the fields and can
also provide hiding cover (Bonnot et al. 2013). This
pattern was not observed in Southern France (FR8),
a landscape also consisting of patches of forest within
an agricultural matrix. This could be due to different
agricultural practices and levels of disturbance, but
also because of the use by deer of small cover features
in open landscapes such as tree patches and hedge-
rows that are not defined as forest by the forest cover
layer (TCD; https://land.copernicus.eu/user-corner/
technical-library/hrl-forest). In this paper, we used
a high resolution (20 m), but static and simplified,
classification of open and cover habitats. However,
habitat alternation may also occur between different
habitat classes (such as pastures, agricultural fields),
or between forest units with different forest cover
thresholds (in very dense landscapes). This suggests
that roe deer compensate their need for cover within
a given landscape with different types of ‘functional
cover’ (Mysterud and Østbye 1999).

To better evaluate these hypotheses, future stud-
ies should look into fine-scale variation in alternation
behaviour (see Couriot et al. 2018), while explicitly
accounting for the spatio-temporal availability of
anthropogenic resources (Ossi et al. 2017; Ranc et al.
2020) and their revisitation patterns (Ranc et al. 2021,
2022). As temporally dynamic remote sensing prod-
ucts indexing the composition and structure of habi-
tats become increasingly available (Pettorelli et al.
2014; Neumann et al. 2015; Oeser et al. 2020), alter-
nation between habitats could be more mechanisti-
cally linked to the resources they offer, revealing their
functional role at different spatio-temporal scales

(Godvik et al. 2009; Mandelik et al. 2012; Couriot
et al. 2018). For instance, identification of cover vs.
non-cover could be improved by quantifying habitat
visibility explicitly through LiDAR surveys (Zong
et al. 2022).

Among-individual variation within population also
seems to be a strong driver of alternation patterns that
could be linked to intrinsic individual characteristics
like age and sex, but also personality traits (or behav-
ioural types) (Bonnot et al. 2015). We found a link
between the temporal pattern of alternation and the
sex of individual adult roe deer, with a clear contrast
in the degree of day-night alternation between open
and closed habitats during summer (more alternation
in males) and during the winter (more alternation in
females). Mammals have a timing and intensity of
resource allocation to reproduction that differs by
species, and sex (Williams et al. 2017). For example,
the separation of sexes outside of the mating season,
termed sexual segregation, is a wide-spread phenom-
enon among ungulates. Segregation in diet and habi-
tat increase with higher sexual body size dimorphism,
due to that body size markedly affect digestion and
dietary requirements of ruminants (Mysterud 2000).
Here we suggest an alternate form of adaptation to
sex-specific needs, namely the diel order in the use
of cover and open-habitat resources, that may not
emerge as spatial separation. Despite roe deer being
one of the few ungulates with a low degree of sexual
size dimorphism, there are marked sex-specific differ-
ences in behaviour in spring-summer. Males are terri-
torial from March until the rut in July-August (Sem-
péré et al. 1998), while females give birth in May or
early June, allocating heavily to late gestation and
early lactation during summer. Alternation between
open and closed habitats was twice as high in males
than females during the birth season and the period of
intensive maternal care. Movement rate has also been
shown to decrease during this period (Malagnino
et al. 2021), possibly because of spatial constraints
imposed by suckling newborn fawns. In contrast,
males maintained a higher degree of habitat alterna-
tion throughout the summer, likely linked to terri-
tory patrolling and defence (Linnell and Andersen
1998; Malagnino et al. 2021). The fact that these
sex-specific patterns were observed in adults, but
not fawns, highlights how different habitat use of the
sexes can arise from sex-specific schedules of alloca-
tion to reproduction. Sex-specific habitat alternation
is inversed during winter, such that females alternate
around three times more than males between Decem-
ber and March. This marked effect, and particularly
the switch compared to the summer pattern might
be related to a difference in the perception of risk
between males and females linked to hunting prac-
tices (Benhaiem et al. 2008). The selective hunting
pressure on male roe deer is suddenly reduced after
they drop their antlers in November, to become higher
for females, instead. This hypothesis should be tested
in future research.

The survival and reproductive performance of
large herbivores depends strongly on the use of, or,
as we suggest here, alternation, between habitats
that are rich enough to ensure the necessary energy
intake but are also safe enough to provide protection
against predators (Gaillard et al. 2000). Daily habitat
use in large herbivores, in particular, has been linked
to circadian activity cycles (Pagon et al. 2013), to the
food-cover trade-off (van Beest et al. 2013), includ-
ing thermal cover (Mysterud and Østbye 1999), and
to rumination cycles, where the use of cover is higher
during rumination than during feeding bouts (Ceder-
lund 1981). In this study, we showed how investigat-
ing daily alternation between habitat types may pro-
vide insights into the complementation in resource
needs at different spatio-temporal scales and in rela-
tion to life-history constraints.

**Acknowledgments** This paper was conceived and written within the EUROMAMMALS/EURODEER collaborative project (paper no. 017 of the EURODEER series; https://www.eurodeer.org). The co-authors are grateful to all members for their support to the initiative. We thank all the field assistants that contributed to the collection of the data in the field. The EUROMAMMALS spatial database is hosted by Fondazione Edmund Mach. We are grateful to the Copernicus project for the use of free data (European Union, EEA - European Environment Agency). Here we confirm that all conditions are met for free, full and open access to this data set, established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013.

**Author contributions** JDG conducted the research and conceived the study idea and the study design together with FC and NVdW. NM, NCB, AJMH, BG, MH, MK, and FC provided the datasets. The progress of the study was discussed in the context of Eurodeer meetings and working groups with all co-authors. JDG, FC, NR, NM conducted the data analyses. The first draft of the manuscript was written by JDG and FC and critically commented and reviewed by all co-authors. All authors read and approved the final manuscript.

**Funding** Funding was provided by Bijzonder Onderzoeks-
fonds UGent (Grand No. 01sf2313), Fonds Wetenschap-
pelijk Onderzoek (Grand No. V417616N) and FIRST PhD
School. JDG was supported by Fondazione Edmund Mach—
FIRST PhD School, Special Research Fund (BOF; Grant
No. 01sf2313) of Ghent University and Fund for Scientific
Research Flanders Belgium (Grant No. G.0189.12N). Research
Foundation—Flanders (FWO), grant number V417616N sup-
ported JDG as visiting student at Harvard University. FC con-
tributed to this work partly under the Sarah and Daniel Hrdy
Fellowship 2015–2016 and IRD Fellowship 2021–2022 at
Fondazione IMéRA, Institute for Advanced Studies at Aix-Mar-
seille Université. The GPS data collection of the Fondazione
Edmund Mach was supported by the Autonomous Province
of Trento under grant number 3479 to FC (BECOCERWI—
Behavioral Ecology of Cervids in Relation to Wildlife Infec-
tions), and by the help of the Wildlife and Forest Service of
the Autonomous Province of Trento and the Hunting Associa-
tion of Trento Province (ACT). The GPS data collection of the
CEFS-INRAE was supported by the “Move-It” ANR grant
ANR-16-CE02-0010-02. The GPS data collection of SW-Ger-
many was funded by the federal state of Baden-Wuerttemberg.
BG was supported by the Federal Office for the Environment.

**Data availability** Unfiltered classified sequences are avail-
able on Zenodo at https://doi.org/10.5281/zenodo.12542
30. Additional associated variables (sex, age, NDVI) were
retrieved from the EUROMAMMALS spatial database hosted
by the Fondazione Edmund Mach (https://euromammals.org)
and can be accessed upon login. The subset of the data and
scripts used in the current analyses will be made available on
Zenodo upon acceptance.

Declarations

**Ethics Approval** Roe deer captures and collaring were com-
pliant to national and international welfare regulations, and
approved as follows. SE-Germany (DE2): The research program
in the Bavarian Forest is managed by the Administration of the
Bavarian Forest National Park. Game captures were conducted
in accordance with European and German animal welfare
laws. The experiment was designed to minimize animal stress
and handling time, and to ensure animal welfare, as defined in
the guidelines for the ethical use of animals in research. Ani-
mal captures and experimental procedures were approved by
the Ethics Committee of the Government of Upper Bavaria
and fulfils their ethical requirements for research on wild ani-
mals (Reference number 55.2-1-54-2531-82-10); N-Italy (IT1,
IT24): animal handling practice, such as captures and collar
marking, complied with the Italian laws on animal welfare and
has been approved by the Wildlife Committee of the Autono-
mous Province of Trento, 09/2004S; Switzerland (CH25): The
animal capture and handling protocols were authorized by the
cantonal veterinary and animal welfare services with permit
number BE75/11; SW-France (FR8): prefectural order from
the Toulouse Administrative Authority to capture and moni-
tor wild roe deer and agreement no. A31113001 approved by
the Departmental Authority of Population Protection; SW-
Germany (DE15): The animal capture and handling protocols
were authorized by the animal welfare and hunting administra-
tion of the federal state of Baden-Wuerttemberg, Germany (RP
Freiburg; G-09/53).

**Conflict of interest** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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://creativecommons.org/licenses/by/4.0/.

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