Modelling correlates of microhabitat use of two sympatric lizards: a model selection approach

© Koninklijke Brill NV, Leiden, 2009 DOI 10.1163/157075609X417134

Animal Biology 59 (2009) 109–126 www.brill.nl/ab

Modelling correlates of microhabitat use of two sympatric lizards: a model selection approach

Pierluigi Bombi 1,* , Daniele Salvi 1 , Luca Luiselli 2 , Marco A. Bologna1

1 Department of Biology, University ‘Roma Tre’, viale Guglielmo Marconi 446, I-00146 Rome, Italy 2 Demetra s.r.l. Environmental Studies Centre, via Olona 7, I-00198 Rome, Italy

Abstract Studies of the ecological characteristics of sympatric species are important for developing and testing ecological theory, but may be of interest also for conservation biology research when the study species are threatened, endemic and with narrow distribution. Here, we studied a particular aspect of the ecology (i.e. microhabitat use) of two sympatric lizards ( Archaeolacerta bedriagae, Podarcis tiliguerta ) endemic to Sardinia and Corsica (Tyrrhenian islands). We studied this issue by modelling procedures, using fi eld data collected at six study areas in both Sardinia and Corsica. We recorded 18 microhabitat variables for each lizard spot. Th e variables were entered as independent variables in logistic regression analysis with the presence/absence data for the lizards as the dependent variable, and Akaike Information Criterion was applied to select the best models describing the ecological equation of each study species. In total, we based our modelling approach on 296 individuals of A. bedriagae and 182 of P. tiliguerta . Th e general logistic regression models revealed that fi ve distinct variables were signifi cantly correlated to the presence/absence of A. bedriagae, and six to that of P. tiliguerta . We found that three variables were important for only P. tiliguerta , two for only A. bedriagae , and three for both species and with an identical sign. We also found some similarities in microhabitat choice between species. Indeed, some variables were always present in the best models of both A. bedriagae and P. tiliguerta . In general, A. bedriagae was more related to spots with large stones and low vegetation than P. tiliguerta , which, on the contrary, choose spots relatively closer to vegetation. Th e vari-ous reasons explaining the observed similarities and diff erences between species were examined. It is sug-gested that our modelling procedure may be widely used for studies of lizard community ecology, because it easy to use and allows a more-in-depth analysis than normal ‘count approaches’. © Koninklijke Brill NV, Leiden, 2009.

Keywords Archaeolacerta bedriagae ; Podarcis tiliguerta ; microecology; logistic regression; Akaike Information Criterion

Introduction

Th e study of the coexistence dynamics of sympatric species is one of the most venerable aims of community ecologists involved in either studies of invertebrates or vertebrates

*) Corresponding author; e-mail: [email protected]

110 P. Bombi et al. / Animal Biology 59 (2009) 109–126

(e.g., see Dale, 1984; Pianka, 1986 ; Ricklefs, 1990 ; Leibold and Mikkelson, 2002 ; Mackenzie et al., 2002 , 2004; Luiselli, 2006a ; Hausdorf and Hennig, 2007 ; etc.). Th us, a great deal of eff ort have been spent in developing novel methods, both empiri-cal and theoretical, in order to analyse and compare the ecological traits of potentially competing species (e.g., see the null models controversy: Ord, 1975 ; Stone and Roberts, 1992 ; Gotelli and Graves, 1996 ; Gotelli et al., 1997 ; Gotelli, 2000 ; Sanderson, 2004 ; Hausdorf and Hennig, 2007 ).

During the development process of novel descriptive methodologies for community ecology studies, lizards have traditionally been popular subjects (for instance, see Pianka, 1986 ; Vitt and Pianka, 1994 ). In general, the coexistence of lizard species is especially interesting to study when the coexisting species under study are not only ‘ecologically similar’ (and therefore potential competitors), but also threatened (e.g., Pianka, 1986 ). However, it is still hard to fi nd in the international literature studies dealing with aspects of coexistence dynamics between endemic and threatened lizard species, despite their potential interest in terms of both ecological theory and conserva-tion biology.

In this paper, we report one such case study, where a threatened endemic lizard [ Archaeolacerta bedriagae (Camerano, 1885)] is studied against its potential, ecologi-cally generalist, competitor species [ Podarcis tiliguerta (Gmelin, 1789)] ( fi g. 2 ). Th is latter is also an endemic island species (Bruno and Maugeri, 1977 ). Th e fact that both species are endemic (being found only in the islands of Sardinia and Corsica in the Th yrrenian Sea; see Bruno and Maugeri, 1977 ; Sindaco et al., 2006 ), is another ele-ment of special interest for the case under study, because of the higher extinction proneness of restricted-range taxa/populations.

Specifi cally, we analyse the correlates of microhabitat use by these two sympatric species by applying novel statistical procedures. For this issue, we created appropriate statistical models for combinations of microhabitats available at the study area, and we correlated these models to the presence/absence of each species by using a general logis-tic regression approach (Hosmer and Lemeshow, 1989 ). Th is procedure allowed build-ing the ecological modelling equations for the two species ( sensu Luiselli, 2006b ). We then used a model selection approach, where several competing models are contrasted with Akaike Information Criterion (Akaike, 1974 ), to select the best models describing the microhabitat ecology of our target species (e.g., Mazerolle, 2006 ). Th e model selec-tion approach will allow understanding the microhabitat relationships between the two target species in a deeper way than was previously possible to scientists using more traditional count approaches (Vanhooydonck et al., 2000 ).

Materials and methods

Study species

Archaeolacerta bedriagae is a lacertid lizard endemic to two large Mediterranean islands (Sardinia and Corsica) and several satellite islands and islets (Archipelago of La

P. Bombi et al. / Animal Biology 59 (2009) 109–126 111

Maddalena and Isola Rossa close to Sardinia, and Folaca islet close to Corsica) (Delaguerre and Cheylan, 1992 ; Bombi and Vignoli, 2004 ; Sindaco et al., 2006 ). Th is species, despite having a wide altitudinal distribution, is a strictly rock-dwelling lizard (Bombi, 2005 ) that is confi ned to large rocky outcrops of both Sardinia and Corsica. Th is species shows some specialized characters probably associated with its rupicolous habits, including the possibility to climb vertical surfaces and hide into small refuges (Arnold, 1973 , 1988 ; Vanhooydonck and Van Damme, 1999 ). However, in spite of the interesting relationships between some morpho-functional traits and the peculiar autoecology of this species, only few papers examined in detail the habitat use of A. bedriagae (Castilla et al., 1989 ; Vanhooydonck et al., 2000 ).

Podarcis tiliguerta is also endemic to Corsica and Sardinia but, diff erently from the previous species, is a very common lizard, widespread within the two main islands and near all the satellite islands and islets (Sindaco et al., 2006 ), and able to inhabit a great number of diff erent habitats (Van Damme et al., 1989 ; Sindaco et al., 2006 ). Populations from Corsica and Sardinia may however be quite distinct under a genetic and morphological point of view (Harris et al., 2005 ; Bruschi et al., 2006 ; Vasconcelos et al., 2006 ). Both species are approximately the same in size (Bruno and Maugeri, 1977 ), and are apparently similar in most ecological aspects, including diet and activity patterns (Arnold, 1988 ; Sindaco et al., 2006 ).

Study areas

Microhabitat use of lizards was studied in six sympatric populations of A. bedriagae and P. tiliguerta (two from Corsica and four from Sardinia) inhabiting insular, coastal and mountain sites. Th e six populations were situated as follows: Restonica (Corsica) (1200 m a.s.l.; 42°14’N 9°3’E); Roccapina (Corsica) (30 m a.s.l.; 41°29’N 8°56’E); Punta Falcone (Sardinia) (5 m a.s.l.; 41°15’N 9°14’E); Petraiaccio (Caprera Island, Sardinia) (50 m a.s.l.; 41°12’N 9°27’E); Monte Moro (Sardinia) (420 m a.s.l.; 41°6’N 9°30’E); Punta Balistreri (Limbara Mt, Sardinia) (1300 m a.s.l.; 40°50’N 9°10’E). All these sites are characterized by large granitic rocky outcrops, with the exception of Restonica that is a scree of metamorphic stones with many crevices. Rocks are sur-rounded by diff erent vegetation types according to the site elevation, in particular the low elevation sites are surrounded by Mediterranean maquis, Punta Balistreri is sur-rounded by high elevation garrigue dominated by Erica arborea , and Restonica is close to Pinus laricio pinewood.

Methods

Eighteen microhabitat variables were selected in order to describe the microhabitat use of the two study species at the six study areas. Th e exact site of the fi rst observation of a given lizard individual is defi ned below the First Sight Point of specimens (FSP). Th e values of the following variables regarding FSP and four surfaces around and near the FSP, according to the scheme proposed by Vanhooydonck et al. ( 2000 ), were measured ( fi g. 1 ):


• substratum type (SUBSTR) (1 = bush, 2 = grass, 3 = rock, 4 = moss, 5 = stone (< 20 cm), 6 = ground);

• height of FSP from the ground level (HGR) (1 = ground level, 2 = lower than 1 m, 3 = between 1 and 4 m, 4 = higher than 4 m);

• inclination of the FSP (INCL) (1 = less than 20°, 2 = between 20° and 45°, 3 = between 45° and 70°, 4 = more than 70°);

• exposure of the FSP (EXP) (1 = north, 2 = east, 3 = south, 4 = west); • distance of FSP from the nearest plant (DISTP) (1 = closer than 1 dm, 2 = between

1 and 4 dm, 3 = between 4 and 7 dm, 4 = between 7 and 10 dm, 5 = farer than 10 dm);

• distance of FSP from the nearest hold (DISTR) (1 = closer than 1 dm, 2 = between 1 and 4 dm, 3 = between 4 and 7 dm, 4 = between 7 and 10 dm, 5 = farer than 10 dm); hold is here defi ned as a rock crevice.

• dimensional class (length) of the nearest hold (DIMR) (1 = smaller than 3 cm, 2 = between 3 and 20 cm, 3 = between 20 and 50 cm, 4 = larger than 50 cm);

• percentage of rock coverage in the model surfaces (ROCK) (1 = less than 25%, 2 = between 25% and 50%, 3 = between 50% and 80%, 4 = between 80% and 99%, 5 = 100%);

• percentage of ground coverage in the model surfaces (GROUND) (1 = less than 25%, 2 = between 25% and 50%, 3 = between 50% and 80%, 4 = between 80% and 99%, 5 = 100%);

• percentage of grass coverage in the model surfaces (GRASS) (1 = less than 25%, 2 = between 25% and 50%, 3 = between 50% and 80%, 4 = between 80% and 99%, 5 = 100%);

1 m

0.5 m

2 m

FSP

Figure 1. Schematic representation of the First Sight Point of specimens (FSP) and the four model surfaces considered to collect the microecological data in the fi eld.


Figure 2. Morphological aspects of Podarcis tiliguerta (upper photo) and Archaeolacerta bedriagae (lower photo) from a sympatric study area in northern Sardinia.


• percentage of shrubs coverage in the model surfaces (SHRUB) (1 = less than 25%, 2 = between 25% and 50%, 3 = between 50% and 80%, 4 = between 80% and 99%, 5 = 100%);

• percentage of trees coverage in the model surfaces (TREE) (1 = less than 25%, 2 = between 25% and 50%, 3 = between 50% and 80%, 4 = between 80% and 99%, 5 = 100%);

• maximum vegetation height (VEGH) (1 = no vegetation, 2 = less than 6 dm, 3 = between 6 and 10 dm, 4 = taller than 10 dm);

• number of small holds (size < 3 cm) in the model surfaces (NSR) (1 = no refuges, 2 = less than 5, 3 = more than 5);

• number of medium holds (3 cm <size< 20 cm) in the model surfaces (NMR) (1 = no refuges, 2 = less than 5, 3 = more than 5);

• number of large holds (20 cm <size< 50 cm) in the model surfaces (NLR) (1 = no refuges, 2 = less than 5, 3 = more than 5);

• number of extra large holds (size > 50 cm) in the model surfaces (NXLR) (1 = no refuges, 2 = less than 5, 3 = more than 5);

• presence of covering vegetation (COVEG) (1 = no, 2 = yes).

Th e same measurements were also carried out on 175 descriptive points, selected at regular intervals (10 m) along random linear transects throughout the same sites where the observation points were taken, in order to investigate microhabitat availability in the studied sites. Th ese random points were always diff erent from the spots where lizard individuals were seen, although in exactly the same study areas.

Th e data collection in each site was repeated, in the months of April and May, at diff erent time during the day, in order to take into account the seasonal and daily vari-ation of the lizards’ activity pattern and habitat use.

Statistical analyses

We analysed the 18 variables aff ecting the presence of the two lizard species by logistic regression because this technique is a powerful analytical tool that can analyse the eff ects of one or several independent variables, both discrete and continuous, over a dichotomous dependent variable (Hosmer and Lemeshow, 1989 ; Teixeira et al., 2001 ). In addition, logistic regression models rely on fewer statistical assumptions than their alternatives and generally produce robust results (e.g. Teixeira et al., 2001 ; Arntzen and Alexandrino, 2004 ). Variables were entered as independent variables in logistic regres-sion analyses, with the presence/absence data for the lizards as the dependent variable, after deleting autocorrelated variables. More details of the model procedures are given in Luiselli ( 2006b ). Th e form of the logistic regression is as follows:

π ( x ) = C g ( x ) / ( 1 + C g ( x ) ) (1)

where π(x) is defi ned as the probability of occurrence of the target species, and:


g ( x ) = β o +β 1 × V A R 1 +β 2 × V A R 2 + … + β p × V A R p (2)

In this formula, βo is a constant, and β1 , β2 ,…, βp , are the coeffi cients of the variables x1 , x2 ,…, xp , included in the equation (Hosmer and Lemeshow, 1989 ). We performed logistic regression analyses by an SPSS version 14.0.1 PC software by a forward step-wise addition model. Th e variables were presented in the fi nal equation by order of entrance in the models. Presence data were contrasted with random data collected in the same study areas as absence data. We equilibrated the impact of presences and random (= absences) data through a weighting procedure (Teixeira et al., 2001 ), and we used a correct classifi cation score (CCS) to show the percentage of correctly described presences and absences at π(x) = 0.5 . Th e various models, each associating a diff erent set of variables into the logistic regression, were compared by model selection theory (see Mazerolle, 2006 ). Th e number of parameters in each model was denoted K , where K is the number of variables entered in the logistic regression model + 1. Model selection was based on information-theoretic methods (Akaike Information Criterion, AIC), and more precisely on the small-sample-size correction (AICc). Since no overdispersion was detected in the most-parameterized model, the small-sized quasi-AIC (QAICc) was not used (Burnham and Anderson, 2002 ). Th e formula for AICc used in this paper is as follows:

AICc = − 2 log Likelihood + 2 K + 2 K ( K + 1 ) / ( n - e s s − K − 1 )

In the above formula, n-ess is the sample size. Th e best model was that with lowest AICc (Burnham and Anderson, 2002 ). AIC is a term that measures the fi t of the model to the data (based on the likelihood) and a penalty term for the number of parameters in the model (Burnham and Anderson, 2002 ), and ΔAIC j is the diff erence in AIC between the minimum value and the value for model j (Burnham and Anderson, 2002 ).

Results

Microhabitat choice of 296 A. bedriagae individuals and of 182 P. tiliguerta individuals was examined. Th ese data were compared with 175 random points for both A. bedriagae and P. tiliguerta . Descriptive graphics are given in appendix 1 .

Th e general logistic regression for A. bedriagae revealed that fi ve distinct variables were signifi cantly correlated to the presence/absence of the species, four of them infl u-encing signifi cantly the absence and just one (i.e. INCL) infl uencing signifi cantly its presence ( table 1 ). Th e general modelling equation for A. bedriagae is given as follows:

g ( x ) = 5 . 9 5 5 - 1 . 6 0 7 N S R - 0 . 6 0 4 D I S T R + 0 . 3 6 9 I N C L - 0 . 9 2 5 V E G H - 0 . 2 8 3 D I S T P

Th e various competing models, ordered by their AICc value, are given in table 2 . Th e variables NSR and DISTR are found in all the eight best models, thus showing that these are certainly the crucial factors determining the presence/absence of this


Table 1. Summary of the logistic regression scores (forward stepwise conditional model) for the various variables signifi cantly infl uencing the presence/absence of Archaeolacerta bedriagae (see methods section for abbreviations).

B E.S. Wald df Sig. Exp(B)

NSR -1.606681901 0.235802595 46.42614689 1 9.51375E-12 0.200551963Constant 5.955297311 1.080584409 30.37312669 1 3.56432E-08 385.7915937DISTR -0.604042484 0.11797514 26.21524783 1 3.05402E-07 0.546597552INCL 0.368745938 0.135646564 7.389871648 1 0.006559217 1.445920204VEGH -0.924984698 0.350196493 6.976627117 1 0.00825811 0.396537487DISTP -0.283060789 0.137833698 4.217437027 1 0.040010553 0.753473984

species. Th e variable INCL was also found in the four best models, and VEGH in four of ten best models.

Th e general logistic regression for P. tiliguerta included six variables signifi cantly infl uencing the presence/absence of the species ( table 3 ), out of which fi ve were signifi -cantly infl uencing its absence and only one (NMR) its presence. Th e general equation is given below:

g ( x ) = 1 2 . 0 1 3 − 2 . 1 8 5 N S R - 0 . 6 5 3 D I S T R - 0 . 5 7 2 D I S T P - 1 . 3 4 3 N L R + 0 . 8 5 4 N M R - 0 . 7 8 4 R O C K

Th e various competing models, ordered by their AICc value, are given in table 4 . Th e variable NSR is found in all the seven best models, thus showing that this is cer-tainly the crucial factor determining the presence/absence of this species. Th is evidence mirrors evidence already presented for A. bedriagae (see above) . However, in P. til-iguerta the variables NLR and ROCK were also found in all the seven best models, this fact being in contrast with the best models selected for A. bedriagae . DISTR, NMR and DISTP were also important variables because they were present, respectively, in six out of seven (DISTR), and in fi ve out of seven (NMR and DISTP), best models.

Table 2. List of competing models, ordered by their AICc values, for the general logistic regression equation of Archaeolacerta bedriagae . In bold are highlighted the most signifi cant models. Note that the variable NSR is present in all the best models (see methods section for abbreviations).

model K n -2(loglikelihood) AICc ΔAICc

Ψ (NSR)(DISTR)(INCL)(VEGH) 5 388 369.341 379.498 0.000Ψ (NSR)(DISTR)(INCL) 4 394 371.797 379.900 0.402Ψ (NSR)(DISTR)(INCL)(VEGH)(DISTP)

6 388 369.341 381.561 2.063

Ψ (NSR)(DISTR)(INCL)(DISTP) 5 394 371.797 381.952 2.454Ψ (NSR)(DISTR)(VEGH) 4 392 377.388 385.491 5.993Ψ (NSR)(DISTR) 3 398 380.107 386.168 6.670Ψ (NSR)(DISTR)(VEGH)(DISTP) 5 392 377.388 387.543 8.045Ψ (NSR)(DISTR)(DISTP) 4 398 380.107 388.209 8.711


Discussion

Eco-ethological and habitat characteristics of A. bedriagae and P. tiliguerta are generally considered quite diff erent each another, being the fi rst a strictly rupicoluos species and the latter a generalist species occurring in a wide spectrum of habitats (Castilla et al., 1989 ; Van Damme et al., 1989 ; Vanhooydonck et al., 2000 ). Th is is in part confi rmed also by the present study, as the logistic regression equations revealed that three variables were important for only P. tiliguerta (NLR, NMR, and ROCK), two variables only for A. bedriagae (INCL and VEGH), whereas three variables had signifi cant infl uence for both species and with an identical sign (all negative: NSR, DISTR, and DISTP). Overall, A. bedriagae uses spots with higher slope and with less vegetation

Table 3. Summary of the logistic regression scores (forward stepwise conditional model) for the various variables signifi cantly infl uencing the presence/absence of Podarcis tiliguerta (see methods section for abbreviations).

B E.S. Wald df Sig. Exp(B)

Constant 12.01292179 2.178435252 30.40940949 1 3.49827E-08 164871.5217

NSR -2.18482976 0.375770379 33.80571446 1 6.08997E-09 0.112496883

DISTR -0.652549607 0.170114757 14.71441795 1 0.000125086 0.520716461

DISTP -0.572260324 0.167724011 11.64117439 1 0.000645079 0.564248611

NLR -1.343122588 0.395260583 11.5468704 1 0.000678634 0.261029308

NMR 0.854120303 0.327920005 6.784256603 1 0.009196532 2.349306792

ROCK -0.783983349 0.329811322 5.65044377 1 0.017450955 0.456583652

Table 4. List of competing models, ordered by their AICc values, for the general logistic regression equation of Podarcis tiliguerta . In bold are highlighted the most signifi cant models. Note that the variable NSR is present in all the best models (see methods section for abbreviations).

Model K n -2(loglikelihood) AICc ΔAICc

Ψ (NSR)(DISTR)(NLR)(NMR)(ROCK)

6 267 243.538 255.861 0.000

Ψ (NSR)(DISTR)(DISTP)(NLR)(NMR)(ROCK)

7 267 243.606 258.038 2.177

Ψ (NSR)(DISTR)(DISTP)(NLR)(NMR)(ROCK)(NXLR)

8 267 243.606 260.164 4.303

Ψ (NSR)(DISTR)(DISTP)(NLR)(ROCK)

6 267 249.550 261.873 6.012

Ψ (NSR)(DISTR)(DISTP)(NLR)(ROCK)(NXLR)

7 267 249.550 263.982 8.121

Ψ (NSR)(DISTR)(NLR)(NMR)(ROCK)(NXLR)

7 267 250.516 264.948 9.087

Ψ (NSR)(DISTP)(NLR)(NMR)(ROCK) 6 268 253.468 265.790 9.929


than P. tiliguerta , which inhabits spots with less rocky surface and with lots of medium-sized refuges. Nonetheless, our data also showed that the microhabitat choices of the two species in syntopic conditions are more similar than supposed earlier (Bruno and Maugeri, 1984 ; Delaugerre and Cheylan, 1992; Vanhooydonck et al., 2000 ). Th is is showed by the fact that some variables are always present in the best models of both A. bedriagae and P. tiliguerta , even if with diff erent coeffi cients. In particular, NSR is negatively correlated with the presence of the two lizard species, that is: too many small-sized refuges are not favourable to the presence of P. tiliguerta and, second-arily, A. bedriagae . Th is fact seems to be more dependent on the structural characteris-tics of the areas with many small refuges than to an active avoidance of these areas by lizards. Indeed, the presence of many small refuges at our study areas is linked to the relative scarcity of large stones in the habitat, with large stones being the most peculiar habitat type for these two species at the six study areas (Bombi, 2005 ). Th e variable DISTR was also negatively correlated to the presence of either species, thus showing that both lizards ( P. tiliguerta more than A. bedriagae ) use, for resting/basking, spots that are close to a refuge in order to avoiding predation attempts. At the study areas, snakes (e.g., Hierophis viridifl avus ) and birds of prey actively prey upon these lizards (Bombi, 2005 ). Th e same antipredatory reason can explain the signifi -cantly negative correlation of either species’ presences with the variable DISTP. In this regard, it is important to highlight that the distance from the nearest plant was more important for P. tiliguerta than for A. bedriagae . Th is is in agreement with the fact that the latter species is more rupicolous and less dependent on ground vegetation than P. tiliguerta (Bombi and Vignoli, 2004 ; Bombi, 2005 ). All the other variables signifi cantly infl uencing the presence/absence of the two species were species-specifi c, thus characterizing in a diff erent way the respective ecological equations of each species. Firstly, the signifi cant positive correlation of the variable INCL with A. bedriagae is a clear demonstration that this species has a better capacity of using sloped microhabitats than P. tiliguerta, this fact being in agreement with its own rupicolous ecology. Secondly, A. bedriagae is linked mainly to spots with low vegeta-tion, as demonstrated by its negative correlation with the variable VEGH. Th irdly, P. tiliguerta is clearly less rupicolous than the former species as evidenced by its nega-tive correlation with ROCK. Fourthly, NMR and NLR are linked probably to the larger-scale morphological features of the sites inhabited by this species. Notably, P. tiliguerta seems positively infl uenced by the presence of medium-sized refuges, but negatively by large-sized refuges (that are in turn present only in spots characterized by very large stones). In general, although at the study areas both the species tend to exhibit a generic use of stony and rocky areas, A. bedriagae is selecting spots that are more rocky and sloped, and P. tiliguerta is found in areas that are more vegetated and less uniformly rocky in appearance. Concerning the observed similarities in micro-habitat use by sympatric lizard species, it should be emphasized that the scale of our study design probably underestimated the variety of microhabitats that P. tiliguerta can occupy in its whole distribution (Bombi, 2005 ), and this may have in turn pro-duced an overestimation of the apparent similarities between syntopic populations of these species.


Overall, our study has allowed modelling the ecological characteristics of the two lizard species at the scale of their microhabitat in a much clearer way than previous studied did. Given that the modelling procedure used by us is easy to implement, we strongly advice further researchers to apply a statistical procedure similar to ours when studying the ecological relationships of their study species in the wild.

For the next future, it would be interesting to continue this research line by per-forming the same sampling in a number of 1) rocky sites without A. bedriagae and 2) non-rocky sites, presumably with P. tiliguerta alone. Th is would allow the experi-menters to provide null models for competition assessment for these two species.

Acknowledgements

We wish to thank Leonardo Vignoli and Manuela D’Amen for the fi eld assistance; Achille Casale and the staff of the Department of Zoology and Anthropology, University of Sassari; Cecilia Fassó, Davide Spagnesi and the “Ente Foreste della Sardegna”; all the rangers of the “Corpo Forestale della Regione Sardegna”; and the authorities of the “Arcipelago di La Maddalena” National Park for the logistic support. Th is paper is part of the project “Zoogeography of Mediterranean-southern African disjunct distribu-tions by a multimethod approach” (Ministero dell’Istruzione, dell’Università e della Ricerca, n. 2004057217), coordinated by one of us (MAB).

References

Akaike , H. ( 1974 ) A new look at the statistical model identifi cation . IEEE Trans. Automat. Contr. , 19 , 716 - 723 .

Arnold , E.N. ( 1973 ) Relationships of the palaearctic lizards assigned to the genera Lacerta , Algyroides and Psammodromus (Reptilia: Lacertidae) . Bull. Br. Mus. Nat. Hist. (Zool.) , 25 , 291 - 366 .

Arnold , E. N. ( 1988 ) Resource partition among lacertid lizards in southern Europe . J. Zool. Lond. , 1 , 739 - 782 .

Arnold , E. N. ( 1998 ) Structural niche, limb morphology and locomotion in lacertid lizards (Squamata, Lacertidae); a preliminary survey . Bull. nat. Hist. Mus. Lond (Zool.) , 64 , 63 - 89 .

Arntzen , J.W. and Alexandrino , J. ( 2004 ) Ecological modelling of genetically diff erentiated forms of the Iberian endemic golden-striped salamander, Chioglossa lusitanica . Herpetol. J. , 14 , 137 – 141 .

Bombi , P. ( 2005 ) Frammentazione dell’areale in specie del genere Archaeolacerta s.l.: cause ed eff etti . Ph.D. Th esis, University “Roma Tre”, Rome, Italy.

Bombi , P. and Vignoli , L. ( 2004 ) Distribution, ecology and conservation of Archaeolacerta bedriagae in Sardinia (Reptilia, Lacertidae) . Ital. J. Zool. , 71 , 1 , 135 - 144 .

Bruno , S. and Maugeri , S. ( 1977 ) Rettili d’Italia . Martello , Florence, Italy . Bruno , S. and Maugeri , S. ( 1984 ) Tartarughe e sauri d’Italia . Martello , Florence, Italy . Bruschi , S. , Corti , C. , Carretero , M.A. , Harris , D.J. , Lanza , B. and Leviton , A. ( 2006 ) Comments on the

Status of the Sardinian- Corsican Lacertid Lizard Podarcis tiliguerta . Proc. Calif. Acad. Sci. , 57 , 215 - 236 .

Burnham , K.P. and Anderson , D.R. ( 2002 ) Model selection and multimodel inferenc e . Springer , New York .

Castilla , A.M. , Bauwens , D. , Van Damme , R. and Verheyen , R. ( 1989 ) Notes on the biology of the high altitude lizard Lacerta bedriagae . Herpetol. J. , 1 , 400 - 403 .


Dale , M.R.T. ( 1988 ) Th e spacing and intermingling of species boundaries on an environmental gradient . Oikos , 53 , 351 - 356 .

Delaguerre , M. and Cheylan , M. ( 1992 ) Atlas de Répartition des Batraciens et Reptiles de Corse . Parc Naturel Régional de Corse , Ajaccio, France .

Gotelli , N.J. ( 2000 ) Null model analysis of species co-occurrence patterns . Ecology , 81 , 2606 - 2621 . Gotelli , N.J. , Buckley , N.J. and Wiens , J.A. ( 1997 ) Co-occurrence of Australian land-birds: Diamond’s

assembly rules revisited . Oikos , 80 , 311 - 324 . Gotelli , N.J. and Graves , G.R. ( 1996 ) Null Models in Ecology . Smithsonian Institution Press ,

Washington . Harris , D.J , Pinho , C. , Carretero , M.A. , Corti , C. and Böhme , W. ( 2005 ) Determination of genetic

diversity within the insular lizard Podarcis tiliguerta using mtDNA sequence data, with a reassess-ment of the phylogeny of Podarcis . Amphibia-Reptilia , 26 , 401 - 407 .

Hausdorf , B. and Hennig , C. ( 2007 ) Null model tests of clustering of species, negative co-occurrence patterns and nestedness in meta-communities . Oikos , 116 , 818 - 828 .

Holm , S. ( 1979 ) A simple sequential rejective multiple test procedure . Scand. J. Stat. , 6 , 65 – 70 . Hosmer , D.W. and Lemeshow , S. ( 1989 ) Applied Logistic Regression . Wiley , New York . Leibold , M.A. and Mikkelson , G.M. ( 2002 ) Coherence, species turnover, and boundary clumping:

elements of meta-community structure . Oikos , 97 , 237 - 250 . Luiselli , L. ( 2006a ) Resource partitioning and interspecifi c competition in snakes: the search for general

geographical and guild patterns . Oikos , 114 , 193 - 211 . Luiselli , L. ( 2006b ) Ecological modelling of convergence patterns between European and African ‘whip’

snakes . Acta Oecol. , 30 , 62 - 68 . Mackenzie , D.J. and Bailey , L.L. ( 2004 ) Assessing the fi t of site occupancy models . J. Agric. Biol. Environm.

Sci. , 9 , 300 – 318 . Mackenzie , D.J. , Nichols , J.D. , Lachman , G.B. , Droege , S. , Royle , J.A. and Langtimm , C.A. ( 2002 )

Estimating site occupancy rates when detection probabilities are less than one . Ecology , 83 , 2248 – 2255 .

Mazerolle , M.J. ( 2006 ) Improving data analysis in herpetology: using Akaike’s Information Criterion (AIC) to assess the strength of biological hypotheses . Amphibia-Reptilia , 27 , 169 - 180 .

Ord , J.K. ( 1975 ) Estimation methods for models of spatial interaction . J. Am. Stat. Assoc. , 70 , 120 - 126 . Pianka , E.R. ( 1986 ) Ecology and Natural History of Desert Lizards . Princeton Univ. Press , Princeton . Ricklefs , R.E. ( 1990 ) Ecology . Freeman and Co. , New York . Sanderson , J.G. ( 2004 ) Null model analysis of communities on gradients . J. Biogeogr. , 31 , 879 - 883 . Sindaco , R. , Doria , G. , Razzetti , E. and Bernini , F. (Eds.) ( 2006 ) Atlas of Italian Amphibians and Reptiles .

Societas Herpetologica Italica , Polistampa, Firenze. Stone , L. and Roberts , A. ( 1992 ) Competitive exclusion, or species aggregation? An aid in deciding .

Oecologia , 91 , 419 - 424 . Teixeira , J. , Ferrand , N. and Arntzen , J.W. ( 2001 ) Biogeography of the golden-striped salamander

Chioglossa lusitanica : a fi eld survey and spatial modelling approach . Ecography , 24 , 618 – 624 . Van Damme , R. , Bauwens , D. , Castilla , A.M. and Verheyen , R.F. ( 1989 ) Altitudinal variation of the

thermal biology and running performance in the lizard Podarcis tiliguerta . Oecologia , 80 , 516 - 524 . Vanhooydonck , B. and Van Damme , R. ( 1999 ) Evolutionary relationships between body shape and habi-

tat use in lacertid lizards . Evol. Ecol. Res. , 1 , 785 – 805 . Vanhooydonck , B. , Van Damme , R. and Aerts , P. ( 2000 ) Ecomorphological correlates of habitat parti-

tioning in Corsican lacertid lizards . Funct. Ecol. , 14 , 358 - 368 . Vasconcelos , R. , Harris , D.J. , Corti , C. , Carretero , M.A. , Capula , M. , C. Pinho , Delauguerre , M. and

Spano , G. ( 2006 ) Genetic diversity within Corsican and Sardinian specimens of the Tyrrhenian Wall Lizard, Podarcis tiliguerta , estimated using mtDNA sequences . In: Corti , C. , P. Lo Cascio , and M. Biaggini , (eds) Mainland and Insular Lacertid Lizards: A Mediterranean Perspective , pp. 199 - 207 , Firenze University Press , Florence .

Vitt , L.J. and Pianka , E.R. ( 1994 ) Lizard Ecology . Princeton University Press , Princeton .


Appendix 1. Graphics explaining the distribution of the observed variables between species.

Modelling correlates of microhabitat use of two sympatric lizards: a model selection approach

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