Which population density affects home ranges of co-occurring rodents?

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Which population density affects home ranges of co-occurring rodents? Paolo Casulaa,∗ , Luca Luisellib,c , Giovanni Amorid a

Agenzia Forestas, Servizio Tecnico, viale Merello 86, 09124 Cagliari, Italy Institute for Development, Ecology, Conservation and Cooperation, via G. Tomasi di Lampedusa 33, I-00144 Rome, Italy c Niger Delta Ecology and Biodiversity Conservation Unit, Department of Applied and Environmental Biology, Rivers State University of Science and Technology, PMB 5080, Port Harcourt, Rivers State, Nigeria d CNR — Institute of Research on Terrestrial Ecosystems, viale dell’Università 32, 00185 Rome, Italy b

Received 5 March 2018; accepted 10 November 2018

Abstract Animal space use patterns can be affected by the intra- and interspecific density of individuals competing for resources, with home ranges generally decreasing with increasing population density. By applying spatially explicit capture–recapture models implemented in the R package secr, we study whether home ranges of co-occurring yellow-necked mice, Apodemus flavicollis, and bank voles, Myodes glareolus, are related to population density of (a) conspecifics (intraspecific density), (b) the other sympatric species, A. flavicollis or M. glareolus (interspecific density), or (c) total rodent density (A. flavicollis plus M. glareolus). Home ranges of both species were negatively related to intraspecific population density, and were not related to interspecific density or total rodent density. Given that rodents tend to reduce home ranges if resources are abundant, this pattern may merely result from the higher abundance of resources generally associated with high density populations, if the two species were responding to different subsets of resources. However, intraspecific density could directly reduce home ranges, because conspecifics are more likely to interfere with each other due to the overlapping of space use patterns. Therefore, results suggest complementary space or resource use patterns between species, with consequent weak competition and niche differentiation. Across several years and population densities, home ranges of the two co-occurring rodents thus appear to be affected by conspecifics only, suggesting that the two species may coexist in the study area owing to limited space or resource use overlap. ¨ © 2018 Gesellschaft f¨ur Okologie. Published by Elsevier GmbH. All rights reserved. Keywords: Coexistence; Intraspecific competition; Movement; Species interaction

Introduction Spatial distribution and abundance of resources determine animal spacing behavior (Ostfeld, 1990). Given that

∗ Corresponding

author. E-mail addresses: [email protected], [email protected] (P. Casula).

ecologically similar species may share available resources (Leibold & McPeek, 2006), both intra- and interspecific density can affect space use patterns, with home ranges generally decreasing with increasing density of resources (Mazurkiewicz & Rajska-Jurgiel, 1998; Stradiotto et al., 2009) and animals (Sanderson, 1966; Bogdziewicz, Zwolak, Redosh, Rychlik, & Crone, 2016; Efford, Dawson, Jhala, & Qureshi, 2016). However, interspecific competition should

https://doi.org/10.1016/j.baae.2018.11.002 ¨ 1439-1791/© 2018 Gesellschaft f¨ur Okologie. Published by Elsevier GmbH. All rights reserved.

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be less intense than intraspecific competition, as a consequence of higher niche differentiation between competing species (Chesson, 2000). Indeed, coexisting individuals can reduce their competition by niche partitioning (Rosenzweig, 1995; Svanbäck & Bolnick, 2007; Luiselli, 2006, 2008; Tarjuelo et al., 2017), which reduces the likelihood of negative interactions (Grether, Peiman, Tobias, & Robinson, 2017). Therefore, the relative effect of intra- and interspecific density on home ranges should provide information about actual levels of competition experienced by co-occurring individuals (Leibold & McPeek, 2006) of same or different species. Capture–recapture methods, using trap grids or trap line transects, have widely been used for studying density patterns of rodents (Geuse & Bauchau, 1985; Krebs, 1999; Gurnell & Flowerdew, 2006; Stradiotto et al., 2009; Hille & Mortelliti, 2010; Bogdziewicz et al., 2016; Gasperini et al., 2016). Spatial capture–recapture (SCR) models (Borchers & Fewster, 2016) implemented in well developed software such as the R package secr (Efford, 2016) can be fruitfully applied to this type of data to study home ranges, as also shown in previous studies of rodents (Bogdziewicz et al., 2016). These models use the spatial information contained in grids of detectors to describe capture probability as a function of the distance of animals’ home range centers from the traps (Borchers & Efford, 2008; Efford & Fewster, 2013). Several models have been developed to describe the detection function, generally specifying an intercept (capture probability at distance = zero), and a “range” parameter describing the relation between capture probability and distance from traps (Borchers & Fewster, 2016). Such range parameter may represent a measure of home ranges of animals that can be related to environmental variables thought to affect the animals’ spacing behavior (Bogdziewicz et al., 2016; Efford et al., 2016). As well as many statistical methods, SCR models make some restrictive assumptions and have drawbacks. For example, home ranges are assumed to be stationary, potentially biasing home range estimates in the presence of dispersal (Royle, Fuller, & Sutherland, 2016), which can be present in capture–recapture rodent studies (Mazurkiewicz & Rajska-Jurgiel, 1998; Rajska-Jurgiel & Mazurkiewicz, 2000; Stradiotto et al., 2009; Andreassen, Glorvigen, Rémy, & Ims, 2013). Nevertheless, SCR is a fast developing field that is progressively relaxing restrictive assumptions (Borchers & Fewster, 2016; Royle, Fuller, & Sutherland, 2018), offering interesting opportunities to reanalyze available data sets, in the attempt to capture animal space use patterns. Here, we aimed at extracting information about rodent home ranges from a relatively well-explored data set (Amori, Castigliani, Locasciulli, & Luiselli, 2015; Amori, Locasciulli, Tuccinardi, & Riga, 2000; Amori & Luiselli, 2011a, 2011b) with standard SCR models implemented in the software secr (Borchers & Efford, 2008; Efford, 2016). Specifically, we study the relation between home ranges of two co-occurring rodent species, the yellow-necked mouse, Apodemus flavicollis (Melchior) and the bank vole, Myo-

des glareolus (Schreber), and intra- and interspecific density. The two species represent the guild of forest- and grounddwelling rodents in the study area, where they systematically co-occur, although with variable densities (Amori & Luiselli, 2011a, 2011b; Amori et al., 2015). Both species are sympatric in most areas of central Europe (Mitchell-Jones et al., 1999), and show some level of niche partitioning that likely allows the two species to co-occur with moderate competition (Hille & Mortelliti, 2010; Amori et al., 2015; Leˇso, Leˇsová, Kropil, & Kaˇnuch, 2016). In fact, they have different resource requirements (Mazurkiewicz & Rajska-Jurgiel, 1998; Butet & Delettre, 2011) and are also known to reduce interspecific competition at the landscape level by partitioning habitat use (Hille & Mortelliti, 2010; Amori et al., 2015). Nevertheless, it is not clear whether they present substantial ecological differences to allow coexistence (Grum & Bujalska, 2000). Given the long term nature of the data set (1988–1995; 2000–2005), we took the opportunity to evaluate whether and how home ranges were related to changes in population density. To do that, we estimated the range parameter and density, considering possible effects of species identity, behavioral response to capture (i.e. animals that become either trap happy or trap shy, see Otis, Burnham, White, & Anderson, 1978), sex, and sampling session (month). Then, by means of generalized linear models (McCullagh & Nelder, 1989), we evaluated the relationship between estimated home ranges and intraspecific density, interspecific density (the density of the other sympatric rodent present in the area), and total density of the two species combined. In this way we estimated home ranges by taking into account possible sources of variation due to individual covariates and time, and subsequently attempted to capture the relative importance of intra- versus interspecific interactions on space use patterns.

Materials and methods Study system and sampling design The study area was located in central Italy (Majella National Park, 42◦ 08 N, 14◦ 05 E, 1000 m a.s.l.), within a heterogeneous thermophilous beech forest (Fagus sylvatica) that was characterized by different habitat types depending on tree age and percent cover of woodland and open habitats (see Amori et al., 2015 for further details). The yellow-necked mouse, A. flavicollis and the bank vole, M. glareolus, are the only forest- and ground-dwelling rodents found in the area (Amori & Luiselli, 2011a, 2011b; Amori et al., 2015): during 14 years of live trapping, 3680 small mammals were recorded, among which 58% were M. glareolus, 40% A. flavicollis, and 2% other species (Sorex spp., Microtus spp., Erinaceus europaeus, Eliomys quercinus, and Glis glis). By means of a square grid of 100 home-made singlecapture live-traps spaced 12 m apart, rodent were trapped monthly every year from May to October. During some years sampling was extended up to November (see Appendix A in

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Supplementary material). Each trapping session lasted 3 (in 1988, 1989 and 2000–2005) or 5 days (1990–1995), for a total of 1800–3500 trap-days × year depending on the year. Traps were placed in the morning, provided with a cotton nest, baited with chocolate cream and seeds, and always checked approximately 24 h after placement. Captured animals were sedated with ether, ear-tagged, identified, measured (species, sex, body length, weight), and released at the site of capture (see Amori & Luiselli, 2011a; Amori et al., 2015 for further details).

SCR models for home range and density estimation SCR models implemented in the R package secr (Efford, 2016) were used to estimate capture probability at distance from traps equal to zero (g), the range parameter (σ), and population density (D). SCR models also have advantages over non-spatial methods when the goal is to estimate D, as they model heterogeneity of capture probability due to distance of individual home range centers from traps, and also overcome edge effects that are problematic in conventional capture–recapture (Efford & Fewster, 2013). Considering that secr models are computationally intensive (Efford, 2016), and that no recapture was found among different years (Amori et al., 2015), the analysis was done separately for each year. Additionally, maximum likelihood estimators for singlecatch data have not been developed (Efford, 2016), and the multi-catch likelihood was therefore used. That is, the model assumes that multiple captures are possible with a single live trap/day, which is rather unlikely with the trap system used in the study. However, this approximation has been already applied to small mammal capture–recapture data, as estimates of density and range appear only slightly biased with this kind of model misspecification (Bogdziewicz et al., 2016; Efford, 2016). Possible species and sex effects were treated as covariates (factors), and conditional likelihood was thus used. The function suggest.buffer(data) provided suggestions for buffers 100 in all data sets, and buffers were thus set to 100 (Efford, 2016). By means of model selection based on AICc , and likelihood ratio test (LRT) when AICc was below 2 (Burnham & Anderson, 2002), we evaluated the more appropriate detection function (exponential vs. half normal), and the effect of species identity (Sp), behavioral response of animals to capture (b or bk), sex, and session on the detection function. Given the computational complexity of secr models (Efford, 2016) effects were evaluated with forward model selection (Mac Nally, 2000), as follows. To evaluate whether the parameters of the detection function g and σ differed between species, we analyzed merged data of both A. flavicollis and M. glareolus. Four models were specified: a null model with g and σ that did not differ between species (M0: g0 ∼ 1, sigma ∼ 1); a full model with both g and

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σ that differed between species (MSp: g0 ∼ Sp, sigma ∼ Sp); a model with only g differing between species (MSpg: g0 ∼ Sp, sigma ∼ 1); a model with only ␴ differing between species (MSps: g0 ∼ 1, sigma ∼ Sp). To reduce computational time, data sets were subsequently analyzed separately for each species. The presence of behavioral response to capture of animals, considering both potential effects on g and σ, was evaluated by using either the b or bk parameterization specified in secr (Efford, 2016). Model MB (g0 ∼ b, sigma ∼ b), MBg (g0 ∼ b, sigma ∼ 1), MBs (g0 ∼ 1, sigma ∼ b), MBk (g0 ∼ bk, sigma ∼ bk), MBkg (g0 ∼ bk, sigma ∼ 1), MBks (g0 ∼ 1, sigma ∼ bk), and M0 were thus confronted. The variation of estimates of g and σ among sessions (month) was evaluated by confronting model Ms (g0 ∼ session, sigma ∼ session), Msg (g0 ∼ session, sigma ∼ 1), Mss (g0 ∼ 1, sigma ∼ session), and M0. The variation of estimates of g and σ between sexes was evaluated by confronting model MSex (g0 ∼ Sex, sigma ∼ Sex), MSexg (g0 ∼ Sex, sigma ∼ 1), MSexs (g0 ∼ 1, sigma ∼ Sex), and M0.

Model averaging and derived densities This first part of the analysis aimed at finding relevant sources of variation of the detection function. To avoid bias of estimates due to different model structure, the same subset of models was subsequently used to obtain estimates of the range parameter and density across years and species. For simplicity, and to reduce computational time, we focussed on the most common effects found in the data sets (see Table 1 and ‘Results’), which were bk (behavior) on g and session (month) on σ. The following models were thus compared and used to obtain parameter estimates: M0, MBkg, Mss, and the full MBkgSs (g0 ∼ bk, sigma ∼ session), where both effects were specified. Parameter estimates were then taken with model averaging (Burnham & Anderson, 2002), considering the relative weight of each model in this subset, as follows: fitsbest <- secrlist(M0 = M0, MBkg = MBkg, Mss = Mss, MBkgSs = MBkgSs); sigmaMa <- model.average(fitsbest). Given that we used conditional likelihood, the density is not a parameter of the likelihood, and its value is derived from the number of animals caught in each session (Efford, 2016). Estimates of density were thus taken from model MBkg with the secr function derived: D <- derived(MBkg). In this way, we attempted to find the best estimates of the range parameters and density across sessions, considering effects of behavior on capture probability (g), generally present in the data set (Table 1).

Relation between home range and density The relationships between session-specific estimates of the range parameter (σ) and: (a) the density of conspecifics (DIntra ), (b) the density of the other species (DInter ), and

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Table 1. Support for species, behavioral, session, and sex effects found across years. Each cell presents the best model supported by AICc and LRT for each data set (both Species, only Apodemus flavicollis, and only Myodes glareolus), year and effect evaluated. DF = detection function, exponential (EX) vs. half normal (HN). See methods for details about model structure. Year

1988 1989 1990 1991 1992 1993 1995 2000 2001 2003 2004 2005

n Af

68 61 45 64 67 50 100 12 37 26 74 35

n Mg

17 54 65 38 61 30 303 29 76 37 37 54

Species

MSp M0 M0 M0 M0 M0 MSp M0 M0 MSps M0 M0

Apodemus flavicollis

Myodes glareolus

DF

Behavior

Session

Sex

DF

Behavior

Session

Sex

EX EX EX EX EX EX EX EX/HN EX EX EX EX

M0 MBg MBks MBg MBkg MBk MBkg MBs MBkg/MBks MBkg MB MBkg

Msg Msg/Mss Mss Ms Ms Ms Ms Ms M0 M0 Ms Mss

M0 M0 MSexs M0 MSexs MSexs M0 M0 MSex MSexg MSex M0

EX EX EX EX EX EX EX EX EX EX EX EX

M0 MBkg MBks/MBkg MBs/MBkg MBkg MBkg MBk MBs MBkg MBkg MBkg MBkg

M0 Mss/Msg Msg M0 Mss Ms Ms Mss M0 Mss M0 Ms

M0 M0 M0 MSexg MSex MSex M0 M0 M0 MSexs/MSexg M0 MSex

(c) total density of the two species (DTot ), were evaluated by means of generalized linear models (GLM) and forward model selection based on AICc and LRT (Mac Nally, 2000). The glm function of software R 3.2.4 (R Core Team, 2016) was used for the analysis. The effect on σ of species identity (Sp) and sessions across years was also evaluated. Year of sampling (Year) was also modeled as a fixed effect, and mixed models (Bolker et al., 2009) were not considered necessary for the analysis. Data were not transformed and, considering that the range parameter is measured in meters, a gamma error structure was assumed (Gaussian distribution resulted in very high dispersion parameter). The following models were thus confronted: no variation among range parameters: MNull (Sigma ∼ 1); species effects: MSp (Sigma ∼ Sp); session effects: MSession (Sigma ∼ Session); year effects: MYear (Sigma ∼ Year); effect of conspecifics density: MDIntra (Sigma ∼ DIntra ); effect of the density of the other species: MDInter (Sigma ∼ DInter ); effect of total rodent density: MDTot (Sigma ∼ Dtot ); full model with all considered effects: MFull (Sigma ∼ Sp + Session + Year + DIntra + DInter ). Given that collinearity (Dormann et al., 2013) between DTot and DIntra or DInter was high (Pearson correlation = 0.78 or 0.79), the full model did not consider DTot .

Results SCR models for home range and density estimation A total of 690 yellow necked mice and 831 bank voles were captured during the study (Table 1). The exponential (EX) detection function clearly fitted the data better than the half normal (HN) in 23 out of 24 data sets (see Table 1). The analysis of the effect of species, behavior, session and

Table 2. Model selection results of generalized linear modeling. Models specified the effect of intraspecific density (MDIntra ), interspecific density (MDInter ), total density (MDTot ), year (MYear), session (MSession), and species (MSp) on home ranges. MNull = null model (constant home ranges); MFull = full model (all effects considered, apart from DTot ); Log-L = model log-likelihood; K = number of parameters; AICc = small-sample correction of Akaike’s information criterion; wi = Akaike’s weight. See methods for further details. Rank

Model

Log-L

K

AICc

1 2 3 4 5 6 7 8

MDIntra MDTot MNull MYear MSp MDInter MSession MFull

−353.49 −357.68 −358.95 −346.39 −358.74 −358.87 −358.52 −340.28

3 3 2 13 3 3 8 22

713.20 721.57 722.00 722.35 723.70 723.96 734.39 735.45

AICc 0.00 8.37 8.80 9.15 10.50 10.76 21.19 22.25

wi 0.95 0.01 0.01 0.01 0.01 0.00 0.00 0.00

sex on the detection function was thus performed using the exponential model. Data coming from 1994 and 2002 showed very large confidence intervals of parameter estimates and were thus excluded from the analysis. The detection function was generally not affected by species identity (see Table 1, column Species): the effect was present on both parameters of the detection function in the 1988 and 1995 data sets (MSp), and on the range parameter only in the 2003 data set (MSps). Therefore, the two species appear to have very similar home ranges (see also Table 2, where the effect was evaluated with GLM). The behavioral effect on captured animals appeared in all data sets except 1988 in both A. flavicollis and M. glareolus (See Table 1, column Behavior). Behavior generally affected the parameter g (models MBk, MB, MBkg, and MBg; 19 out of 24), whereas the effect on σ emerged in 9 out of 24 cases

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(models MBk, MB, MBks, and MBs). In 3 cases (M. glareolus 1990 and 1991; A. flavicollis 2001) the effect on the two parameters could not be disentangled (e.g. MBks/MBkg, i.e. AICc was lower than 2 and LRT could not be applied as models were non-nested (Burnham & Anderson, 2002)). Additionally, the most frequent parameterization was bk, with a site-specific step change in capture probability of animals (Efford, 2016). Parameter estimates of the detection function, not shown in the table, evidence a trap-happy type of response (Otis et al., 1978): there was an increase in capture probability after first capture, likely due to increased willingness of animals to enter a trap once discovered that there was food and shelter inside. In 18 out of 24 data sets, session effects on the detection function emerged (See Table 1, column Session). The effect was shown on the parameter σ (Ms + Mss models) in 16 cases, and on the parameter g in 13 cases (Ms + Msg models). Therefore, session effects were rather well supported on both parameters of the detection function. However, to reduce model complexity and considering the focus of this study, only the effect on σ was considered in the subset of models used for the estimation of the range parameter (Mss). Sex effects were shown on the parameter σ in 9 out of 24 data sets (see Table 1, column Sex, MSex + MSexs models), while on the parameter g were shown in 8 out of 24 data sets (MSex + MSexg models). However, in the majority of data sets there were no differences between sexes in both parameters (model M0). Additionally, mean range parameters across annual estimates taken from model MSex (A. flavicollis: σ F = 10.78 ± 7.09 (SD), σ M = 10.41 ± 2.82; M. glareolus: σ F = 8.56 ± 3.09, σ M = 10.15 ± 4.18) did not differ significantly (LRT on GLM, family gamma, models σ vs. σ Sex , p = 0.83 for A. flavicollis and p = 0.27 for M. glareolus). Similarly, average values of g (A. flavicollis: gF = 0.35 ± 0.22, gM = 0.26 ± 0.10; M. glareolus: gF = 0.43 ± 0.25, gM = 0.31 ± 0.27) did not significantly differ between sexes (LRT on betareg models (Cribari-Neto & Zeileis, 2010), g vs. gSex , p = 0.38 for A. flavicollis and p = 0.43 for M. glareolus). Therefore, sex effects were not considered in the subset of models used for the estimation of the range parameter. Estimates of the range parameter (σ) and rodent density (D) used to evaluate the relation between home ranges and density were thus obtained by model averaging (Burnham & Anderson, 2002) parameter estimates taken from models MBkg, Mss, MBkgSs, and M0, as described in the method section (see Appendix A in Supplementary material for parameter estimates and best model selected within this subset).

Relation between home range and density Model MDIntra was very well supported by AICc values (see Table 2). The model specifies a relationship between the range parameter and intraspecific density. Model MDTot ,

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Table 3. Parameter estimates taken from model MDIntra Year. Only significant estimates of the year factor are shown. Coefficients

Estimate

SE

t-value

Pr(>|t|)

Intercept 1992 1993 2003 2005 DIntra

0.0958 −0.0348 −0.0285 −0.0534 −0.0328 0.0024

0.0105 0.0120 0.0125 0.0117 0.0131 0.0007

9.127 −2.909 −2.278 −4.550 −2.494 3.589

<0.001 0.004 0.025 <0.001 0.014 <0.001

which has an AICc value very similar to the null model (3rd ) and a likelihood that is not significantly different from it (LRT: MNull vs. MDTot , p = 0.111), must be rejected. The model specifying the effect of year (MYear, 4th ), instead, has an AICc value very similar to the null model, but it is significantly different from it (LRT: MNull vs. MYear, p < 0.001). That is, in some years the range parameters were significantly different from the average. Home ranges were not related to the density of the other species co-occurring in the area (see ranking and AICc value of MDInter , 6th in Table 2): home ranges of A. flavicollis were not related to M. glareolus density, and home ranges of M. glareolus were not related to A. flavicollis density. Therefore, across several years and densities, there is no evidence of effects of interspecific density on home ranges of the two rodents. Additionally, home ranges did not vary between species (MSp) and among sessions (MSession). The session effect was also evaluated merging sessions belonging to the same season (May and June = Spring; July, August and September = Summer; October and November = Autumn), without any improvement in model fit compared to the null model (MSeason, AICc : 726.08, not shown in Table 2; LRT: MNull vs. MSeason, p = 0.91). The best model for inference should be thus MDIntra Year, which considers both DIntra and Year effects (AICc : 713.07, not shown in Table 2). The slope parameter DIntra , obtained from model MDIntra Year, was highly significant (Table 3). Note that model MDIntra provided essentially the same parameter estimates, with a slope parameter of very similar magnitude (DIntra = 0.0025 ± 0.0007(SE), t value = 3.745 Pr(>|t|) < 0.001). The relationship between the range parameter σ and intraspecific density predicted using parameter estimates taken from model MDIntra is negative, and home ranges thus appear to be reduced by increasing densities of conspecifics (Fig. 1).

Discussion Home ranges of co-occurring yellow necked mice and bank voles were both negatively related to intraspecific density. Given that the yellow-necked mouse and the bank vole tend to show reduced dispersal and home ranges with higher abundance of resources (Mazurkiewicz & Rajska-Jurgiel, 1998;

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Fig. 1. Home ranges (Sigma) decrease with increasing density of conspecifics (ind./ha). Regression line was predicted using parameter estimates taken from MDIntra .

Stradiotto et al., 2009), this pattern could merely result from the higher abundance of resources generally associated with high density populations. Abundant resources should provide little reason for mice and voles to move and risk predation (Mazurkiewicz & Rajska-Jurgiel, 1998). Nevertheless, home ranges did not respond either to the density of the other cooccurring species nor to total rodent density. Therefore, if the effect of density were just mirroring the causal effect of abundant resources on home ranges, we could infer that the two species are responding to different subsets of resources. Indeed, the densities of the two species vary somewhat independently (Pearson correlation = 0.3021). Responding to partially different subsets of resources would be consistent with the known differences in diet of the two species, with the bank vole being able to feed on seeds and leaves, while the yellow-necked mouse is restricted to seeds (Mazurkiewicz & Rajska-Jurgiel, 1998; Butet & Delettre, 2011). At the same time, a reduction of home ranges could be also explained with a direct effect of density, due to reduced space and increased likelihood of negative interactions among individuals with higher densities (Wolff, 1985; Bogdziewicz et al., 2016). Again, the fact that the effect emerges only with intraspecific density, and not with the density of the other co-occurring species or total density, would suggest that negative interactions regard mainly conspecifics, which are more likely to encounter each other due to overlapping space or resource use, eventually enhancing aggressiveness (Chappell, 1978; Wolff, 1985; Poffenroth & Matson, 2007). A recent study

suggests also social tolerance and weak competitive interactions between these two species (Bartolommei, Gasperini, Bonacchi, Manzo, & Cozzolino, 2018). Overall, by disentangling the effect of intra- versus inter-specific density on home ranges, this study suggests that the density of conspecifics could be the main source of competition affecting population dynamics of these co-occurring rodent species in this area. Indeed, it must be underlined that the study was performed in a single site. Nevertheless, across several years and densities, the two forest- and ground-dwelling rodents appear to have mainly independent home ranges and perhaps population dynamics (Amori et al., 2015; see also discussions in Mazurkiewicz & Rajska-Jurgiel, 1998), possibly suggesting that the two species are well differentiated and adapted to coexist (Grum & Bujalska, 2000). Home ranges varied significantly in some years, perhaps further suggesting their dependence on the quantity of resources, which may greatly vary among years and seasons (Ostfeld, 1990; Mazurkiewicz & Rajska-Jurgiel, 1998; Ostfeld & Keesing, 2000; Stradiotto et al., 2009). However, data about the quantity of resources are not available, and we cannot evaluate whether home range variations are related to different quantities of resources. Home range size may also vary during seasons for reproductive behavior, e.g. it can be reduced in autumn because females give birth to young during this period (e.g. Amori et al., 2000; Verhagen, Leirs, & Verheyen, 2000), while pregnant and lactating females are more sedentary than

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non-reproductive ones (Koskela, Mappes, & Ylonen, 1997). Although secr analysis provided evidence of home range variations among sessions within the same year, the GLM analysis did not confirmed such result, and we found no general pattern of monthly or seasonal variation of home ranges across years. Therefore, the variation observed across months within a single year appears to be smaller than the variation found within the same month (or season) across years. Additionally, it should perhaps be acknowledged that this is an opportunistic study that attempts to extract spatial information contained in a study designed with a different focus. More direct methods should be used to study animal movement and home ranges in details, and deepen understanding about mechanisms underlying home range and movement variations (e.g. Stradiotto et al., 2009). In conclusion, our study confirms the utility of exploring the information contained in available data with the new analytical tools provided by spatial capture–recapture models (Borchers & Fewster, 2016; Royle et al., 2018; Romairone, Jiménez, Luque-Larena, & Mougeot, 2018). By evaluating the relationships between home ranges and population densities with different biological meaning (conspecifics vs. the density of the other co-occurring species vs. total density) we somewhat disentangled the relative importance of intraspecific vs. interspecific competition on space use patterns of two co-occurring rodent species, showing that home ranges of both species are related to conspecific population density only. At the same time, we acknowledge the limits of correlative studies done opportunistically on available data sets, and the need of focussed studies to understand mechanisms underlying space use patterns.

Author contributions GA conceived, designed and performed the field study; PC analyzed the data; PC, LL, and GA wrote the manuscript.

Funding This research study has been partially supported by the ‘Next Data Project’ of the National Research Council (CNR).

Acknowledgements We thank two anonymous reviewers for very constructive comments, many students of the University of Rome ‘la Sapienza’ who contributed to collect data in the field over the years, and the Majella National Park and Dr T. Andrisano in particular, for having allowed us to carry out this longterm study on their territory, and also partially funding the research. We are also indebted with ‘Corpo Forestale dello Stato’ (Caramanico), for having provided infrastructures used during the execution of the present study.

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Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.baae.2018.11.002.

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