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

doi:10.21203/rs.3.rs-1557572/v1

PDF

Research Article

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

https://doi.org/10.21203/rs.3.rs-1557572/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

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 on a range of temporal scales, from diurnal to seasonal, responding to multiple drivers, such as access to food, weather constraints, risk avoidance, human disturbance. Within this framework, we hypothesised that large herbivores may 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.

Methods

We considered day-night alternation between food-rich open and closed refuge habitats as a proxy for complementary habitat use. We first identified day-night alternation by using IM-SAM, 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 the alternate use of habitats in response to changes in resource availability and individual needs. By repeatedly crossing habitat boundaries large herbivores may “engineer” their landscape by transporting seeds, parasites and nutrients between those habitats.

Animal trajectories

Habitat use

IM-SAM

Resource composites

Roe deer

Sequential Analysis Methods

Spatio-temporal patterns

Vegetation green up

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 animal 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. Mandelik et al. (2012) defined ‘complementary habitat use’ as the use of different habitats at different times by individuals during the course of their daily and seasonal activity (Complementary Habitat Hypothesis, CHH). For many ungulates, for example, accessibility of forage acquisition 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 often spatially distinct (Benhaiem et al. 2008) and temporally variant, driving complementary use (sensu Mandelik et al. 2012) of ‘open’ vs ‘cover’ habitats (Mysterud and Ostbye 1999, Berryman and Hawkins 2006). For example, forest canopy can provide cover from adverse conditions (e.g., deep snow: Mysterud and Ostbye 1995, Ewald et al. 2014, Ossi et al. 2015) and protection from predators or human disturbance (Gehr et al. 2020), 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, but mainly at night to avoid human disturbance (Godvik et al. 2009, Bonnot et al. 2013, Dupke et al. 2017, Bonnot et al. 2020), or to diminish the risk of ambush predation (Lone et al. 2014; Gehr et al. 2020). Hiding cover may also be seasonally available in open habitats, for example, in summer, when crops are high (Mysterud et al. 1997, Bjørneraas et al. 2011, Bonnot et al. 2013, Dupke et al. 2017).

In temperate ecosystems, open and closed habitats, hence, may represent composites of different resource types, and animals have to 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 used 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 may be linked to activity cycles e.g., foraging vs. resting and ruminating, or forage acquisition-predation risk avoidance trade-off (Hebblewhite 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; Gehr et al. 2017, Bonnot et al. 2020), vegetation productivity (i.e., green up and senescence, or cultivation / harvesting; Peters et al. 2019), and physiological cycles (e.g., growth, reproduction, dispersal; Bonnot et al. 2018). 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. For example, 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 and well established in ecological theory, but rarely tested for wild populations.

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 (Hewison et al. 2009), including open agricultural areas (Andersen et al. 1998). 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 Dunnings et al. 1992), from completely forested areas to wide open agroecosystems, exhibiting marked behavioural and ecological plasticity (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-based 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 Dunnings 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, such as reproduction, and other physiological constraints that are linked to sex and age (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 reproductive season, when adult females should alternate less due to the constraints of maternal care (Andersen et al. 2000), while adult males should alternate more to patrol and defend their mating territory (Johannson et al. 1996). Therefore, we expected these seasonal patterns to be more evident in adult males than juveniles linked to reproductive status (Sempéré et al. 1998).

Table 1

Summary of hypotheses and analyses (see footnote for abbreviations).
Hypotheses	Predictions	Analysis	Input data
H1: Landscape Composition and Structure Hypothesis (LCSH) “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”	P1 Day-night alternation mainly occurs in heterogeneous landscapes	Exploration with mosaic plot and non-parametric statistics based on overall frequencies of habitat use sequences in each population, related to the open/closed composition and structure.	Habitat use sequences, classified according to the following patterns: Homogeneous open (o) Homogeneous closed (c) Random (u) Alternation (a)
H2: Complementary Habitats Hypothesis (CHH) “Roe deer alternation between habitats varies seasonally, in relation to environmental and roe deer intrinsic cycles”	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.	1. GAMMs – Alternation (0/1) M1: a ~ s(bw * pop) + pop + 1\|ind 2. ANODEV NDVI \(ANODEV=1-\frac{L\left({M}_{NDVI}\right)-L\left({M}_{FULL}\right)}{L\left({M}_{NULL}\right)-L\left({M}_{FULL}\right)}\) Where: M_FULL: a ~ bw * pop + 1\|ind M_NULL: a ~ 1\|ind + pop M_NDVI: a ~ s(NDVI * pop) + pop + 1\|ind	Binomial variable based on the habitat use sequences, defined as follows: c, o, u = 0 a = 1
	P2.2 Day-night alternation between habitats varies seasonally in relation to intrinsic cycles linked to life-history.	GAMMs – Alternation (0/1) M2: a ~ s( bw * age:sex) + age*sex + 1\|ind

1 a = daily habitat alternation (0/1); pop = population; 1|ind = 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

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 fawns (< 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, 20m 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 modelling 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 fawns/yearlings and 132 adults; 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 extracted the weekly NDVI values of 1000 random points sampled within that area. 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. The input for this analysis consisted of an abundance table of patterns of sequential habitat use (a, c, o, u) per population. The results were visualized using a mosaic plot, along with a chart indicating the proportion of closed habitat and 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). Second, we performed a non-parametric Friedman test (Hollander and Wolfe 1973) to compare the proportion of patterns of sequential habitat use across populations.

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

For this set of analyses, 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 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; 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., a population‐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 3.4.4 (2018-03-15) with the packages mgcv (Wood 2017) and visreg (Breheny and Burchett 2017).

Sequential habitat use through space: Landscape Composition and Structure Hypothesis, H1

The proportion of the four sequential habitat use patterns was only marginally different across populations (Friedman-test, Friedman chi-squared = 6.3; df = 3; p = 0.10). Indeed, the homogeneous closed (c) and homogeneous open (o) sequential habitat use patterns were specific to certain populations (Fig. 2), but day-night alternation (a) was present in all populations, with approximately 40% of all habitat use sequences classified as such, except when closed habitats were widely prevalent (in population IT24; Fig. 2). In contrast, random sequential habitat use patterns (u) were rarely observed across all populations. Hence, our results do not fully support the hypothesis that day-night alternation simply mirrors the landscape composition and structure (Table 1: P1).

Sequential habitat use through time: Complementary Habitats Hypothesis, H2

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

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 autumn (between mid-August and mid-November, from 18th to 20th biweek). However, while a decrease in day-night alternation between mid-January 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 vegetation green up in each study site (Fig. 3, overlaying NDVI curves). Our results, thus, support the prediction that day-night alternation is not a fixed property at the landscape level, but varies in time to track seasonal 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 female and male adults (Fig. 4a 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. 4a: probability of alternation for females = 0.29 [C.I.: 0.20–0.42] vs males = 0.59 [C.I.: 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 [C.I.: 0.34–0.63] vs males = 0.18 [C.I.: 0.09–0.34] during the 3rd biweek). During other parts of the year, the confidence intervals of the predictions overlapped strongly, indicating little difference in habitat alternation between sexes. Our model also showed that the seasonal pattern in the probability of day-night alternation was not significantly different between female and male fawns (Fig. 4b, Table 2b; Table S2.1). Specifically, the temporal pattern of alternation in fawns of both sexes followed the same trend as that of female adults. Overall, we thus found some support for the hypothesis that temporal variation in alternation between habitats is also linked to life-history constraints (Table 1: H2.2, P2.2).

In all models, the random effect of ‘individual’ (i.e., including individual identity as a random effect on the intercept) contributed considerably towards explaining the variance (R² (adj.) _{NULL model} = 0.31; see Table S2.1), indicating marked inter-individual variability in habitat alternation within a given population. Finally, the fixed effects for population or the interaction between age and sex were not significant, indicating that on average alternation did not vary between respective factor-levels.

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 biweek per modality of the age and sex interaction term, added as a fixed factor, and the spline of the biweek, AIC = 3837.9, adj R² = 35.2). 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.
(a)	edf	Ref df	Chi.sq	p
s(biweek) : population CH25	6.965	8	1878.5	< 0.0001	***
s(biweek) : population DE15	6.288	8	2935.4	< 0.0001	***
s(biweek) : population DE2	6.582	8	2182.9	< 0.0001	***
s(biweek) : population FR8	6.015	8	1660.0	< 0.0001	***
s(biweek) : population IT1	4.733	8	238,0	0.0499	*
s(biweek) : population IT24	6.242	8	768.3	< 0.0001	***
animal	144.473	148	663.7	< 0.0001	***
	estimate	sd	z	P
Intercept	-1.174	1.715	-0.102	0.919
DE15	-1.270	2.860	-0.444	0.657
DE2	-2.012	3.833	-0.525	0.600
FR8	-0.321	2.906	-0.111	0.912
IT1	-1.182	3.137	-0.377	0.706
IT24	-2.860	4.093	-0.699	0.485

(b)	edf	Ref df	Chi.sq	p
s(biweek)	6.753	8	2006.048	< 0.001	***
s(biweek) : ordered(sex*age) female fawn	1.115	8	23.331	0.169
s(biweek) : ordered(sex*age) male adult	3.228	8	2639.053	< 0.001	***
s(biweek) : ordered(sex*age) male fawn	0.002	8	0.001	0.513
animal	148.501	152	732.484	< 0.001	***
	estimate	sd	z	P
intercept	-1.0681	1.2351	-0.865	0.387
female fawn	0.2464	0.3259	0.756	0.450
male	0.3487	1.9989	0.174	0.862
Male fawn	-0.0238	2.0079	-0.012	0.991

In spatially heterogeneous environments, wild animals must access multiple resources with different spatial distributions and, often, asynchronous phenology. Our analysis has shown that the day-night alternation between open and closed habitats is a prevalent characteristic of roe deer movement tactics across a wide range of landscapes with contrasting composition and spatial arrangement (Dunnings et al. 1992; H1 not supported). The most likely explanation of this behaviour is that animals use day-night alternation to access food and cover in these resource composites (Padié et al. 2015). The observed seasonal variation in day-night alternation within a given landscape supports this interpretation. 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 clear cycles of day-night alternation over the seasons (H2.1), with clear differences between the adult sexes (H2.2).

In the highly anthropogenic European landscape, the availability of essential resources for free-ranging large herbivores – food and cover – vary naturally (i.e., due to the phenological cycles of vegetation, Pettorelli et al. 2006), or as a consequence of human activities (e.g., agriculture and forest management practices; Lande et al. 2014). Hence, large herbivores must continually adjust their use of habitat to these changes. Although relatively overlooked in analytical approaches so far, the order in which habitats are used can be considered as a behavioural tactic to meet different resource needs and cope with other constraints, such as the avoidance of human disturbance and predators, at various spatio-temporal scales (De Groeve et al. 2016, 2020a).

Temporal variation in alternation between open and closed habitats might be due to a complementary functional use of habitats encompassed within the home range (Dunnings et al. 1992, Mandelik et al 2012; Couriot et al. 2018). In our study, individuals in most populations alternated between open and closed habitats 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 availability 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 alternation 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 (Aikens et al. 2020), instead, they maximized the ‘greenness’ quality of their ranges. Alternating between habitats could be a tactic to achieve such maximization locally, accessing complementary resources in different habitats (see also Peters et al. 2016).

In winter, individuals in study areas with substantial 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 provides thermal protection, shallower snow (Mysterud et al. 1997, Mysterud and Østbye 2006, Ratikainen et al. 2007; Ossi et al. 2015), and potentially better food availability compared to open habitats (Ewald et al. 2014). Interestingly, the population in the agricultural landscape of Southern Germany (DE15; Fig. 3) exhibited the opposite pattern to all others, with more pronounced alternation in winter, 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 in winter for winter grain (no cover but valuable food resource), while using open habitat both as a food and cover source in summer, when crops are abundant in the fields and can also provide hiding cover. This pattern has not been observed in Southern France (FR8), a landscape also consisting of patches of forest within an agricultural matrix. This could be due to the use by deer of small shrubby vegetation patches, typical of Mediterranean environments, that are not defined as forest by TCD (https://land.copernicus.eu/user-corner/technical-library/hrl-forest).

To better evaluate these explanations, future studies should look into fine-scale alternation of individuals between habitat types with different vegetation phenology and productivity (see Couriot et al. 2018), while explicitly accounting for the availability of anthropogenic subsidies and resources (Ossi et al. 2016, Ranc et al. 2020a). In this paper, we used a high resolution (20m), but static and simplified, classification of habitats. As temporally dynamic remote sensing products indexing the composition and structure of habitats become increasingly available (Pettorelli et al. 2014; Neumann et al. 2015; Oeser et al. 2020), alternation between habitats could be linked to the resources they offer, revealing their functional role at different spatio-temporal scales (Mandelik et al. 2012; Couriot et al. 2018).

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 the reproductive season (including the birth and territorial seasons). In mammals, males and females have very different schedules of resource allocation to reproduction linked to the species’ life history. In roe deer, males are territorial from March until the rut in July-August (Sempéré et al. 1998), while females mostly give birth in May or June, allocating heavily to late gestation and early lactation during summer. Alternation between open and closed habitats decreased in females during the birth season and the period of intensive maternal care, possibly because of spatial constraints imposed by lactating and avoiding revealing the location of their vulnerable hider offspring. In contrast, males maintained a higher degree of habitat alternation throughout the summer, possibly linked to territory patrolling and defence (Linnell and Andersen 1998). The fact that these sex-specific patterns were observed in adults, but not fawns, provides further support for these interpretations linked to sex-specific schedules of allocation to reproduction.

Large herbivores’ fitness shall depend on the use, or, as we suggest here, alternation, between habitats that are rich enough to ensure the necessary energy intake, but are also safe to provide protection against predators and human disturbance (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), including thermal cover (Mysterud and Østbye 1999), to rumination cycles, where the use of cover is higher during rumination than during feeding bouts (Cederlund 1981), and to repeated visits to the same resource patch, as a result of perception and memory of food availability (Owen-Smith and Martin 2015; Ranc et al. 2021). In this study we showed how investigating daily alternation between habitat types may provide insights into the complementation in resource needs at different spatio-temporal scales and in relation to life-history traits.

Understanding the complexity of animal habitat use and resource requirements is important as it affects a range of ecosystem processes. The frequent alternation between habitats documented here likely affects transportation of seeds, parasites and nutrients between cultivated and more natural habitats. Further, such habitat alternation may affect various other aspects relevant for management, from harvesting efficiency and forest damage, to risk of deer-vehicle collisions. Europeans ecosystems hold fast dynamics due to land use and climate change, and understanding mechanisms and patterns of animal habitat use may help anticipate and mitigate the effects of such changes.

ACKNOWLEDGEMENTS

This paper was conceived and written within the EUROMAMMALS/EURODEER collaborative project (paper no. 017 of the EURODEER series; 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.

Funding: JDG was supported by Fondazione Edmund Mach – FIRST PhD School, Special Research Fund (BOF; grant number 01sf2313) of Ghent University. Research Foundation – Flanders (FWO), grant number V417616N supported JDG as visiting student at Harvard University. FC was supported by the Sarah and Daniel Hrdy Fellowship 2015-2016 at Harvard University/OEB during part of the development of this ms. The GPS data collection of the Fondazione Edmund Mach (IT1, IT24) was supported by the Autonomous Province of Trento under grant number 3479 to FC (BECOCERWI—Behavioral Ecology of Cervids in Relation to Wildlife Infections), and by the help of the Wildlife and Forest Service of the Autonomous Province of Trento and the Hunting Association of Trento Province (ACT). The GPS data collection of the CEFS-INRAE (FR8) was supported by the “Move-It” ANR grant ANR-16-CE02-0010-02. The GPS data collection of DE15 was funded by the federal state of Baden-Wuerttemberg. BG was supported by the Federal Office for the Environment. The Norwegian participation was funded by the Research Council of Norway (grant number 251112).

Conflicts of interest/Competing interests: The authors have no conflicts of interest to declare that are relevant to the content of this article.

Ethics approval: roe deer captures and collaring were compliant to national and international welfare regulations, and approved as follows. For 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. Animal 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 animals (Reference number 55.2-1-54-2531-82-10); IT1 and 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 Autonomous Province of Trento, 09/2004S; Switzerland (3B): The animal capture and handling protocols were authorized by the cantonal veterinary and animal welfare services with permit number BE75/11; FR8: prefectural order from the Toulouse Administrative Authority to capture and monitor wild roe deer and agreement no. A31113001 approved by the Departmental Authority of Population Protection; DE15: The animal capture and handling protocols were authorized by the animal welfare and hunting administration of the federal state of Baden-Wuerttemberg, Germany (RP Freiburg; G-09/53).

Consent to participate: All authors consented to participate in this work.

Consent for publication: All authors consented to publish this work.

Availability of data and material: Unfiltered classified sequences are available on Zenodo at https://doi.org/10.5281/zenodo.1254230. Additional associated variables (sex, age, NDVI) were retrieved from the EURODEER spatial data base hosted by the Fondazione Edmund Mach (https://euromammals.org) and can be accessed upon login. The sub-set of the data and scripts used in the current analysis will be made available on Zenodo upon acceptance.

Authors' contributions: JDG conducted the research with an equal contribution and conceived the study idea and the study design together with FC and NVdW. NM, NCB, MAJH, 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.

Abbas F, Morellet N, Hewison AM, Merlet J, Cargnelutti B, Lourtet B, Angihault JM, Daufresne T, Aulagnier S, Verheyden H (2011) Landscape fragmentation generates spatial variation of diet composition and quality in a generalist herbivore. Oecologia 167: 401–411 https://doi.org/10.1007/s00442-011-1994-0
Aikens EO, Mysterud A, Merkle JA, Cagnacci F, Rivrud IM, Hebblewhite M, Hurley MA, Peters W, Bergen S, De Groeve J, Dwinnell SPH, Gehr B, Heurich M, Hewison AJM, Jarnemo A, Kjellander P, Kröschel M, Licoppe A, Linnell JDC, Merrill EH, Middleton AD, Morellet N, Neufeld L, Ortega AC, Parker, KL, Pedrotti, L, Proffitt, KM, Saïd, S, Sawyer, H, Scurlock, BM, Signer, J, Stent, P, Šustr, P, Szkorupa, T, Monteith, KL Kauffman, MJ (2020) Wave-like patterns of plant phenology determine ungulate movement tactics. Current Biology, 30: 3444–3449
Andersen R, Duncan P, Linnell JD (Eds) (1998) The European roe deer: the biology of success, 376. Oslo: Scandinavian university press.
Andersen R, Gaillard JM, Linnell JD, Duncan P (2000) Factors affecting maternal care in an income breeder, the European roe deer. Journal of Animal Ecology, 69: 672–682 doi: 10.1046/j.1365-2656.2000.00425.x
Apollonio M, Andersen R, Putman R eds (2010) European ungulates and their management in the 21st century. Cambridge University Press
Benhaiem S, Delon M, Lourtet B, Cargnelutti B, Aulagnier S, Hewison AJM, Morellet N, Verheyden H (2008) Hunting increases vigilance levels in roe deer and modifies feeding site selection. Animal Behaviour, 76: 611–618
Berryman AA, Hawkins BA (2006) The refuge as an integrating concept in ecology and evolution. Oikos, 115: 192–196 (ISI:000240998300020)
Bjørneraas K, Solberg EJ, Herfindal I, Moorter BV, Rolandsen CM, Tremblay JP, Skarpe C, Sæther BE, Eriksen R, Astrup R (2011) Moose Alces alces habitat use at multiple temporal scales in a human-altered landscape. Wildlife Biology, 17: 44–54
Bongi P, Ciuti S, Grignolio S, Del Frate M, Simi S, Gandelli D, Apollonio M (2008) Anti-predator behaviour, space use and habitat selection in female roe deer during the fawning season in a wolf area. Journal of Zoology, 276: 242–251
Bonnot N, Morellet N, Verheyden H, Cargnelutti B, Lourtet B, Klein F, Hewison AJM (2013) Habitat use under predation risk: hunting, roads and human dwellings influence the spatial behaviour of roe deer. European Journal of Wildlife Research 59: 185–193
Bonnot N, Verheyden H, Blanchard P, Cote J, Debeffe L, Cargnelutti B, Klein F, Hewison AJM, Morellet N (2015) Interindividual variability in habitat use: evidence for a risk management syndrome in roe deer? Behavioral Ecology, 26: 105–114
Bonnot NC, Goulard M, Hewison AJM, Cargnelutti B, Lourtet B, Chaval Y, Morellet N (2018) Boldness-mediated habitat use tactics and reproductive success in a wild large herbivore. Animal Behaviour. 145: 107–115
Bonnot NC, Couriot O, Berger A, Cagnacci F, Ciuti S, De Groeve JE, Gehr B, Heurich M, Kjellander P, Kroeschel M, Morellet N, Sonnichsen L, Hewison AJM (2020) Fear of the dark? Contrasting impacts of humans versus lynx on diel activity of roe deer across Europe. Journal of Animal Ecology 89: 132–145
Breheny P, Burchett W (2017) Visualization of Regression Models using visreg. The R Journal, 9: 56–71
Burnham KP, Anderson DR (2002) Model selection and Multi-Model inference. A practical Information-Theoretic Approach, 2nd ed. Springer, New York
Cederlund G (1981) Daily and seasonal activity pattern of roe deer in a boreal habitat [Capreolus capreolus, Sweden]. Swedish Wildlife Research, 11: 315–353
Couriot O, Hewison AJM, Saïd S, Cagnacci F, Chamaillé-Jammes S, Linnell J D C, Mysterud A, Peters W, Urbano F, Heurich M, Kjellander P, Nicoloso S, Berger A, Sustr P, Kroeschel M, Soennichsen L, Sandfort R, Gehr B, Morellet N (2018) Truly sedentary? The multi-range tactic as a response to resource heterogeneity and unpredictability in a large herbivore Oecologia, 187: 47–60
De Groeve J, Van de Weghe N, Ranc N, Neutens T, Ometto L, Rota-Stabelli O, Cagnacci F (2016) Extracting spatio‐temporal patterns in animal trajectories: an ecological application of sequence analysis methods. Methods in Ecology and Evolution 7: 369–379
De Groeve J, Cagnacci F, Ranc N, Bonnot NC, Gehr B, Heurich M, Hewison AJM, Kroeschel M, Linnell JD, Morellet N, Mysterud A, Sandfort R, Van De Weghe, N (2020a) Individual Movement-Sequence Analysis Method (IM-SAM): characterizing spatio-temporal patterns of animal habitat use across landscapes. International Journal of Geographical Information Science 34: 1530–1551
De Groeve J, Cagnacci F, Ranc N, Bonnot NC, Gehr B, Heurich M, Hewison AJM, Kroeschel M, Linnell JD, Morellet N, Mysterud A, Sandfort R, Van De Weghe, N (2020b) Individual Movement-Sequence Analysis Method (IM-SAM): characterizing spatio-temporal patterns of animal habitat use across landscapes [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1254230
Dunning, JB, Danielson, BJ, Pulliam, HR (1992) Ecological processes that affect populations in complex landscapes. Oikos, 65: 169–175
Dupke C, Bonenfant C, Reineking B, Hable R, Zeppenfeld T, Ewald M, Heurich M (2017) Habitat selection by a large herbivore at multiple spatial and temporal scales is primarily governed by food resources. Ecography 40: 1014–1027
Fagan WF, Lewis MA, Auger-Méthé M, Avgar T, Benhamou S, Breed G, LaDage L, Schlägel UE, Tang WW, Papastamatiou YP, Forester J (2013) Spatial memory and animal movement. Ecology letters, 16: 1316–1329
Gaillard JM, Festa-Bianchet M, Yoccoz NG, Loison A, Toigo, C (2000) Temporal variation in fitness components and population dynamics of large herbivores. Annual Review of ecology and Systematics, 31: 367–393
Gehr B, Hofer EJ, Muff S, Ryser A, Vimercati E, Vogt K, Keller LF (2017) A landscape of coexistence for a large predator in a human dominated landscape. Oikos, 126: 1389–1399
Gehr B, Hofer EJ, Ryser A, Vimercati E, Vogt K, Keller LF (2018) Evidence for nonconsumptive effects from a large predator in an ungulate prey? Behavioral Ecology: 1–12
Gehr B, Bonnot NC, Heurich M, Cagnacci F, Ciuti S, Hewison AM, Gaillard JM, Ranc N, Premier J, Vogt K, Hofer E (2020) Stay home, stay safe—Site familiarity reduces predation risk in a large herbivore in two contrasting study sites. Journal of Animal Ecology, 89: 1329–1339
Godvik IMR, Loe LE, Vik JO, Veiberg V, Langvatn R, Mysterud A (2009) Temporal scales, trade-offs, and functional responses in red deer habitat selection. Ecology, 90: 699–710
Grosbois V, Gimenez O, Gaillard JM, Pradel R, Barbraud C, Clobert J, Møller AP, Weimerskirch H (2008) Assessing the impact of climate variation on survival in vertebrate populations. Biological Reviews, 83: 357–399
Hebblewhite M, Merrill EH (2009) Tradeoffs between predation risk and forage differ between migrant strategies in a migratory ungulate. Ecology, 90: 3445–3454
Hewison AJM, Vincent JP, Reby D (1998) Social organisation of European roe deer. In: Andersen, R, Duncan, P and Linnell JDC eds The European roe deer: the biology of success. Oslo: Scandinavian University Press: 189–219
Hewison AJM, Morellet N, Verheyden H, Daufresne T, Angibault JM, Cargnelutti B, Merlet J, Picot D, Rames JL, Joachim J, Lourtet B, Serrano E, Bideau E, Cebe N (2009) Landscape fragmentation influences winter body mass of roe deer. Ecography, 32: 1062–1070
Hollander M, Wolfe DA (1973) Nonparametric Statistical Methods. New York: John Wiley & Sons: 139–146
Johansson A (1996) Territory establishment and antler cycle in male roe deer. Ethology, 102: 549–559
Lande US, Loe LE, Skjærli OJ, Meisingset EL, Mysterud A (2014) The effect of agricultural land use practice on habitat selection of red deer. European journal of wildlife research, 60: 69–76
Linnell JD, Andersen, R (1998) Territorial fidelity and tenure in roe deer bucks. Acta Theriologica, 43: 67–75
Linnell JD, Cretois B, Nilsen EB, Rolandsen CM, Solberg EJ, Veiberg V, Kaczensky P, Van Moorter B, Panzacchi M, Rauset GR, Kaltenborn B (2020) The challenges and opportunities of coexisting with wild ungulates in the human-dominated landscapes of Europe's Anthropocene. Biological Conservation, 244: 108500
Lone K, Loe LE, Gobakken T, Linnell JD, Odden J, Remmen J, Mysterud A (2014) Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans. Oikos, 123: 641–651
Malagnino A, Marchand P, Garel M, Cargnelutti B, Itty C, Chaval Y, Hewison AJM, Loison A, Morellet N (2021) Do reproductive constraints or experience drive age-dependent space use in two large herbivores? Animal Behaviour, 172: 121–133
Mancinelli S, Peters W, Boitani L, Hebblewhite M, Cagnacci F (2015) Roe deer summer habitat selection at multiple spatio-temporal scales in an Alpine environment. Hystrix, the Italian Journal of Mammalogy. 26: 132–140
Mandelik Y, Winfree R, Neeson T, Kremen C (2012) Complementary habitat use by wild bees in agro-natural landscapes. Ecological Applications, 22: 1535–1546
Marchal C, Gerard JF, Delorme D, Boisaubert B, Bideau E (1998) Space and habitat use by field roe deer (Capreolus capreolus) in mid-winter and mid-growing season. Gibier faune sauvage, 15: 737–746
Morellet N, Bonenfant C, Börger L, Ossi F, Cagnacci F, Heurich M, Kjellander P, Linnell JDC, Nicoloso S, Sustr P, Urbano F, Mysterud A (2013) Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within E urope. Journal of Animal Ecology, 82: 1326–1339
Mysterud A, Østbye E (1995) Bed-site selection by European roe deer (Capreolus capreolus) in southern Norway during winter. Canadian Journal of Zoology, 73: 924–932
Mysterud A, Bjørnsen BH, Østbye E (1997) Effects of snow depth on food and habitat selection by roe deer Capreolus capreolus along an altitudinal gradient in south-central. Wildlife Biology, 3: 27–33
Mysterud A, Østbye E (1999) Cover as a habitat element for temperate ungulates: effects on habitat selection and demography. Wildlife Society Bulletin, 27: 385–394
Mysterud A, Østbye E (2006) Effect of climate and density on individual and population growth of roe deer Capreolus capreolus at northern latitudes: the Lier valley, Norway. Wildlife biology, 12: 321–329
Neumann W, Martinuzzi S, Estes AB, Pidgeon AM, Dettki H, Ericsson G, Radeloff VC (2015) Opportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movement. Movement ecology, 3: 8
Oeser J, Heurich M, Senf C, Pflugmacher D, Belotti E, Kuemmerle T (2020) Habitat metrics based on multi-temporal Landsat imagery for mapping large mammal habitat. Remote Sensing in Ecology and Conservation, 6: 52–69
Ossi F, Gaillard JM, Hebblewhite M, Cagnacci F (2015) Snow sinking depth and forest canopy drive winter resource selection more than supplemental feeding in an alpine population of roe deer. European journal of wildlife research, 61: 111–124
Owen-Smith N, Martin J (2015) Identifying space use at foraging arena scale within the home ranges of large herbivores. PloS one, 10: p.e0128821
Padié S, Morellet N, Hewison AJM, Martin JL, Bonnot N, Cargnelutti B, Chamaillé-Jammes S (2015) Roe deer at risk: teasing apart habitat selection and landscape constraints in risk exposure at multiple scales. Oikos, 124: 1536–1546
Pagon N, Grignolio S, Pipia A, Bongi P, Bertolucci C, Apollonio M (2013) Seasonal variation of activity patterns in roe deer in a temperate forested area. Chronobiology international, 30: 772–785
Péro G, Duparc A, Garel M, Marchand P, Morellet N, Saïd S, Loison A (2018) Circadian periodicity in space use by ungulates of temperate regions: How much, when and why? Journal of Animal Ecology, 87: 1299–1308
Peters W, Hebblewhite M, Mysterud A, Spitz D, Focardi S, Urbano F, Morellet N, Heurich, M, Kjellander, P, Linnell JDC, Cagnacci, F (2017) Migration in geographic and ecological space by a large herbivore. Ecological Monographs, 87: 297–320
Peters W, Hebblewhite M, Mysterud A, Eacker D, Hewison AJM, Linnell J D, Focardi S, Urbano F, De Groeve J, Gehr B, Heurich M, Jarnemo A, Kjellander P, Kröschel M, Morellet N, Pedrotti L, Reinecke H, Sandfort R, Sönnichsen L, Sunde P, Cagnacci F (2019) Large herbivore migration plasticity along environmental gradients in Europe: life-history traits modulate forage effects. Oikos, 128: 416–429
Pettorelli N, Gaillard JM, Mysterud A, Duncan P, Chr Stenseth N, Delorme D, Van Laere G, Toïgo, C, Klein, F (2006) Using a proxy of plant productivity (NDVI) to find key periods for animal performance: the case of roe deer. Oikos, 112: 565–572
Pettorelli N, Safi K, Turner W (2014) Satellite remote sensing, biodiversity research and conservation of the future. Philosophical Transactions of the Royal Society B, Biological Sciences, 369: 20130190. http://dx.doi.org/10.1098/rstb.2013.0190
Ranc N, Moorcroft PR, Hansen KW, Ossi F, Sforna T, Ferraro E, Brugnoli A, Cagnacci F (2020) Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability. Scientific Reports, 1: 11946 https://doi.org/10.1038/s41598-020-68046-7
Ranc N, Moorcroft PR, Ossi F, Cagnacci F (2021) Experimental evidence of memory-based foraging decisions in a large wild mammal. Proceedings of the National Academy of Sciences, 118: e2014856118
Ratikainen II, Panzacchi M, Mysterud A, Odden J, Linnell JDC, Andersen, R (2007) Use of winter habitat by roe deer at a northern latitude where Eurasian lynx are present. Journal of Zoology, 273: 192–199
Sempéré AJ, Mauget R, Mauget C (1998) Reproductive physiology of roe deer. In: Andersen, R, Duncan, P and Linnell JDC eds The European roe deer: the biology of success. Oslo: Scandinavian University Press: 161–188
Tufto J, Andersen R, Linnell JDC (1996) Habitat use and ecological correlates of home range size in a small cervid: the roe deer. Journal of Animal Ecology, 65: 715–724
Urbano F, Cagnacci F, Euromammals Collaborative Initiative (2021) Data Management and Sharing for Collaborative Science: Lessons Learnt From the Euromammals Initiative. Frontiers in Ecology and Evolution 577
Van Beest FM, Vander Wal E, Stronen AV, Paquet PC, Brook RK (2013) Temporal variation in site fidelity: scale-dependent effects of forage abundance and predation risk in a non-migratory large herbivore. Oecologia. 173: 409–420 doi:10.1007/s00442-013-2647-2
Vuolo F, Mattiuzz M, Klisch A, Atzberger C (2012) Data service platform for MODIS NDVI time series pre-processing at BOKU Vienna: current status and future perspectives. In: Earth Resources and Environmental Remote Sensing/GIS Applications III, 8538, 85380A. International Society for Optics and Photonic
Wood SN (2017) Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC

No competing interests reported.

Appendix.docx

PDF

Reviewers invited by journal
03 May, 2022
Editor assigned by journal
15 Apr, 2022
Submission checks completed at journal
15 Apr, 2022
First submitted to journal
14 Apr, 2022

You are reading this latest preprint version

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

Status:

Version 1

Abstract

Context

Objectives

Methods

Results

Conclusion

Figures

Introduction

Material And Methods

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

Covariates linked to roe deer habitat use sequences

Sequential habitat use through space: Landscape Composition and Structure Hypothesis (H1)

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

Results

Sequential habitat use through space: Landscape Composition and Structure Hypothesis, H1

Sequential habitat use through time: Complementary Habitats Hypothesis, H2

Discussion

Conclusion

Declarations

References

Competing Interests

Supplementary Files

Status:

Version 1