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Quaternary Science Reviews 25 (2006) 846–854

Detection of Plio-Quaternary large mammal communities of Italy. Anintegration of fossil faunas biochronology and similarity

Pasquale Raiaa,�, Paolo Pirasb,�, Tassos Kotsakisb

aDipartimento STAT, Universita del Molise, Via Mazzini 10, 86170, Isernia, ItalybDipartimento di Scienze Geologiche, Universita Roma Tre, Largo San Leonardo Murialdo 1, 00146, Roma, Italy

Received 27 July 2004; accepted 14 July 2005

Abstract

The application of a new method for the analysis of the large mammal Plio-Quaternary communities of mainland Italy is

presented. This method, named ‘‘bootstrapped cluster analysis’’, avoids, by mean of successive, ever more inclusive cluster analyses,

the subjectivity in positioning boundaries of paleocommunities subdivisions. The results of this procedure indicate a good

correspondence with previous qualitative analyses, albeit with some differences. Nevertheless, some local assemblages (e.g. Slivia)

are grouped in significantly different positions than previously. The principal cause of these differences (as for unbalanced

carnivores/herbivores ratio) is taphonomic bias. The bootstrapped cluster analysis approach is not affected by single species

positioning as biochronological markers, grouping local assemblages upon their entire record with a statistical treatment. Moreover

in this way any single paleocommunity is recognizable as an ecological/evolutionary unit.

r 2005 Elsevier Ltd. All rights reserved.

1. Introduction

In 1865, under the name Villafranchian, Pareto(Pareto, 1865) drew together (biochronologically) ahandful of recurrent Middle Pliocene fossil speciesassemblages coming from the neighborhoods ofVillafranca d’Asti (Northern Italy). In 1977, Azzaroli(Azzaroli, 1983; Azzaroli et al., 1988) formalized theVillafranchian mammal age (heretofore MA) in Italyand subsequently described the bioevents (moment ofparoxysm in either first or last appearances of species)setting its boundaries. The same occurred when thesuccessive MA was established, the Galerian (Ambro-setti et al., 1972). Post-Galerian faunas have beenassigned to different units discordantly among authors.They have been generally referred to the Aurelian MA(Gliozzi et al., 1997). MAs have been successivelysubdivided into discrete units, named ‘‘Faunal Units’’

e front matter r 2005 Elsevier Ltd. All rights reserved.

ascirev.2005.07.010

ing authors. Fax: +3906 5488 8201.

esses: pasquale.raia@libero.it (P. Raia),

ero.it (P. Piras).

(heretofore FU), of biochronological and possiblyecological meaning (Azzaroli, 1977a) including severallocal faunas assemblages (LFAs) of similar age andfaunal composition.

The use of FUs for fossil assemblages on the Italianpeninsula traces back to Azzaroli (1977b) who discerned6 successive FUs within Villafranchian MA. Later, threefurther units were added to include Early MiddlePleistocene faunas (Azzaroli et al., 1988). In 1997, Gliozziet al. formalized a scheme of successive FUs spanningacross the Plio–Pleistocene. Apart from Villafranchian,the Gliozzi et al. (1997) scheme included Galerian andEarly Aurelian MAs. Gliozzi et al.’s model compriseseight Villafranchian FUs (Triversa, Montopoli, SaintVallier, Costa San Giacomo, Olivola, Tasso, Farneta andPirro), four Galerian FUs (Colle Curti, Slivia, Isernia,Fontana Ranuccio) and two Aurelian FUs (Torre inPietra and Vitinia). Successively, Petronio and Sardella(1999), proposed to institute another FU, Ponte Galeria,intervening between Slivia and Isernia FUs (Fig. 1).

It has been assumed that MAs, and more compel-lingly their subdivision, the FUs, provide a foundation

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Fig 1. Biochronological scheme for the historical subdivision of the Plio–Pleistocene of Italy in MA and FU (from Kotsakis et al., 2003, modified;

LFAs assigned to Torre in Pietra and Vitinia FUs are considered together because very close geochronologically and very similar as

assemblages).*Other LFAs belonging to Torre in Pietra (lower beds) and Vitinia (upper beds) FUs analyzed in the present paper are: Bucine, Campo

Verde, Casal de’ Pazzi, Castel di Guido, Cerveteri, Contrada Monticelli, Fara Sabina, Grotta Romanelli, La Polledrara di Cecanibbio, Malagrotta,

Monte delle Gioie, Grotta Paglicci (external beds), Ponte Corvo, Prati Fiscali, Quisisana (Capri), Riano, Sedia del Diavolo, Torre in Pietra (upper

beds), Torrente Conca.

P. Raia et al. / Quaternary Science Reviews 25 (2006) 846–854 847

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for studying the evolution of communities in terms ofdiversity of species, rate of change, and guild’s responseto Quaternary environmental changes (Caloi andPalombo, 1998; Sardella et al., 1998; Petronio andSardella, 1999; Palombo et al., 2003; Rodriguez et al.,2004).

The method of recognizing mammal communities iscritical for investigating their evolution. Unfortunately,the concept of community is ill-defined for living species(McIntosh, 1995), and it has not been applied effectivelyto the study of past communities. Palaeontologists dealwith 4-dimensional (space plus time) instead of 3-dimensional assemblages. Furthermore, sampling cre-ates much more difficulties than to ecologists, andtaxonomy may vary considerably. Palaeontologistsconcerned with marine fossil records have a simplertask because stratigraphic continuity is common inmarine sediments, thus allowing the description ofpalaeocommunities as assemblages of species occurringat the same stratigraphic horizon. Conversely, theterrestrial record is discontinuous and species are oftenrare (in terms of density of remains at fossil sites),making the absence of species due to taphonomic effectsan important problem to be overcome. Furthermore,fossilization is much lower in certain habitats (e.g.closed forests, mountaintops), thus resulting in probableunder-representation of species which preferred thosehabitats. Many assemblages received some form ofselective transport and many others come from dens ofscavenging carnivores that intentionally avoided unap-petizing species. Therefore, as reviving past communitiesis of concern, it is important to utilize robust tools forcorrelating different fossil sites of similar age andcomposition. This is particularly true because the moresites make up a unit (here ‘‘unit’’ means a discreteassociation of species once living together), the moreunlikely it is that preservation biases of any sort areconstant throughout the unit. If different sites haddifferent taphonomic histories, they preserved speciesdifferently. Therefore, any single species of theonce-living community has a higher probability to berecorded (as a fossil) at least once at the unit level.The effect is the same as that of investing one’smoney in different, unrelated businesses. The morediversified the investments, the more unlikely it is thatthey all fail. In economic research, this is called thePortfolio effect.

Given these many advantages of clustering sites intodiscrete units, the question becomes how to put themtogether. Biochronology affords the equipment neces-sary to arrange sites in precise temporal frameworks.Unfortunately, biochronology necessitates indicativespecies, and does not say how many units are to berecognized. Classic cluster analysis (Alberdi et al., 1997;Palombo et al., 2003) helps to resolve the formerproblem (the need for indicative species), but not the

latter (the number of communities to be recognized). Arecently developed type of cluster analysis matches therequirements for resolving the dilemma on how manygroups to recognize by resorting to a resamplingtechnique, the bootstrap (Pillar, 1999). We used boot-srapped cluster analysis (BCA) to divide the fossilrecord of Plio–Pleistocene large mammals from Italyinto discrete, both in time and composition, ‘‘palaeo-communities’’ (PCOMs). The goal was to attain themost objective division possible of past large mammalfaunas, thus providing a tool for studying communities’evolution through time (Fig. 2).

Only large mammal faunas from mainland Italy havebeen considered because the strongly endemic characterof insular faunas (Sicily/Malta and Sardinia/Corsica)excludes any comparison with the mainland assemblages(Abbazzi et al., 2004). Our dataset comprises LFAsranging in time from Triversa FU to Vitinia FU. Thenumerous LFAs of Late Pleistocene have not beenconsidered to avoid compositional bias as a consequenceof human hunting. At least one LFA (Isernia La Pineta)was influenced by hominids to some extent. Yet thisinfluence has been defined as ‘‘opportunistic and rapid’’by Coltorti et al. (2005). The methodology and theresults obtained should not be viewed as competing withany biochronological scheme. Instead, they are meant toobtain an objective division –in time- of the fossil recordwhere the principal aim is to study communitycomposition and their evolution.

2. Materials and methods

We computed presence–absence matrices of speciesacross LFAs from data in Palombo et al. (2003),partially reviewed to accommodate for more recentfindings and our personal conviction about the systema-tic status of some remains. We posted our dataset at thisweb address: http://host.uniroma3.it/laboratori/paleon-tologia/publications.htm. As our concern was with largemammals, our analysis included proboscideans, artio-dactyls, perissodactyls and carnivores down to 5 kg inbody size as they represent the vast majority of largemammal species in ecological communities. Body sizedata have been calculated with ordinary regressiontechniques as in Damuth and MacFadden (1990) andAlberdi et al. (1995). Both body size data and methodsare explained in Raia (2003). To maintain taxonomichomogeneity for among–groups analyses, we excludedany mammal not belonging to one of the groupsconcerned, even if weighing more than 5 kg. This sizelimit is perhaps arbitrary but reflects the fact that verysmall mammals (e.g. rodents, insectivores, most viver-rids and mustelids) interact significantly with each otherwhile having little interaction with larger mammals.Furthermore, very small species have disproportionately

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smaller chances to fossilize (Damuth, 1982). The Italianfossil record of Late Pliocene–Early Pleistocene smallmammals is scarce and does not allow a safe assessmentof PCOMs for that period if small mammals are to beincluded. It is worth noting that similar size limits wereadopted elsewhere (e.g. 7 kg in Van Valkenburg, 1988).The presence–absence matrix comprises 69 LFAs and 89species. We calculated resemblance between samplingunits (here LFA species lists) utilizing the Jaccard indexof similarity. Cluster analyses were performed with theUPGMA algorithm. Presence–absence were thenshuffled (with replacement) among sampling units andthe clustering procedure repeated each time. This wasrun n times (here n is 1000). At each repetition, adistance statistic G* between the bootstrap sample andthe reference sample for a given partition level k

(number of clusters) was computed. G* was comparedwith the distance statistics G1 generated under theassumption that the k-groups partition is in fact sharp.The probability P(G1pG*) calculated over the n

pairwise comparison should be higher than the sig-nificance level for the k-groups partition to be sharp(Pillar, 1999). We chose the higher k-partition level at P

higher and closer to 0.05 to attain the finest resolution(see Figs. 3 and 4 captions). Of course, it is preferable to

PCOM

PCOM

PCOM

PCOM

PCOM

Ord

ered

in ti

me

by F

AD

/LA

D Significant

community

changes

separate

PCOMs

F

s

F

U

Fig 2. Graphical depiction of the basic differe

select the highest partition given a stable statisticalsignificance (see Figs. 3–5 captions). Therefore, BCAgives the fundamental information about how manygroups (here PCOMs) one should recognize. We discloseat this point that the analysis of the entire data matrixgave large clusters of diverse genera mainly separated byextinction events did appear. This is because eitherextinction or origination events tend to cluster in time(either because biological interactions have a fluctuatingeffect or, we believe, abiotic factors influence thedynamics of extinction/origination (Raia et al., 2005).Therefore, we carried out new sets of BCA on thelarge clusters resulting from the whole-matrix trialseparately (see Appendix A). This aims to eliminatethe likelihood of species appearing at LFAs chronolo-gically far away from their period of existence in thebootstrap samples. These ‘‘nested’’ BCA (BCAn) wereperformed taking subspecies into account. Some sub-species of high chronological relevance were included, tothe extent that the division of Italian large mammalfaunas into FUs was partially based on the appearanceof different chronosubspecies (e.g. subspecies of reddeer, Cervus elaphus). Only clear-cut subspecies, notknown to occur simultaneously (chronosubspecies),were built in. Chronosubspecies were not allowed to

Faunal Unit

Faunal Unit

Faunal Unit

Faunal Unit

Faunal Unit

Faunal Unit

Ord

ered

in ti

me

by F

AD

/LA

DAD/LAD

eparate

aunal

nits

nce between Faunal Units and PCOMs.

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P(G0≤G*) -

0.147

0.087

0.0510.00

0.05

0.10

5 10 15 20 25 30sample size

P

4 subgroups

5 subgroups

Partition level

3 subgroups

4 subgroups

5 subgroups

Fig. 4. Course of sharpness probability as sample size increase run on the G2 clustering unit (see Table 1) for 4 and 5 partition levels. We opted for

the 4 subgroups solution instead 5 because the p value at 4 subgroups is constantly higher than 0.05 while the p value for the 5 subgroups partition

level reach 0.051 only when the last LFA was added during the bootstrap analysis. This course of the p value is claimed to be statistically unstable

(Pillar 1999).

Partition level P(G0≤G*)

0.088

0.043

0.00

0.05

0.10

7 9 11 13sample size

P

3 subgroups

4 subgroups

3 subgroups

4 subgroups

Fig 5. Course of sharpness probability as sample size increase run on the G3.1 clustering unit (see Table 1). The 3 subgroups partition level is

statistically significant.

Partition level P(G0≤G*) -

0.235

0.238

0.024

0.035

0.00

0.05

0.10

0.15

0.20

0.25

0 35 70

sample size

P4 groups

2 groups

3 groups

4 groups

5 groups3 groups

Fig. 3. Course of sharpness probability as sample size increase (significance level at p40:05). All LFAs together. Only the 3 and 4 group’s partition

levels are shown. We preferred the 3 groups partition level instead of the 2 groups partition level because of a finer partition in spite of a minimal loss

(0.003) in p value.

P. Raia et al. / Quaternary Science Reviews 25 (2006) 846–854850

fall in the same group. At each level of the experiment,outlying sampling units were removed and the analysisrepeated.

As BCA does not include any information on LFAsage per se, biochronologic and geochronologic informa-tion (directional evolution of some lineages, absolute

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dating, etc.) was used to place PCOMs in time. Themajor problem with BCA is that almost contemporaryLFAs could fall apart because of taphonomic, habitat ordiversity differences. In the nested experiment, habitator taphonomy heterogeneities might allow some LFAsto gather together, apart of larger clusters, if LFAs arenot perfectly nested. As we were seeking the average (inspace and time) composition of the Italian fauna, theinclusion of species encompassing different habitats wastheoretically permitted. We therefore refuted the split-ting of large clusters if, for the resulting smaller subsets,there was no biochronological indication that they couldactually be separated in time, or if they were just anested subset of the larger one. Importantly, theinformation concerning fuzziness of group partitionthat BCA gives (Pillar, 1999), should profit frombiochronological information, as taphonomy adds itsown ‘‘fuzziness’’ to palaeontological data. BCA wasperformed by Multiv 2.1.1 (Pillar, 2001). We namedPCOMs after the mammal Ages to which theytemporally refer relying on their similarity (as) withthe biochronological approach. While this choice mayseem debatable, its purpose was to avoid confusion.

3. Results and discussion

BCA performed with the entire data matrix points toa three-group partition (see Table 1). The interpretationof these large clusters is straightforward. The first group

Table 1

Scheme of palaeocommunities recognized, with their constituent LFAs and

Clustering units PCOMs

G3 G3.2 Aurelian

G3.1 G3.1.3 Galerian 3

G3.1.2 Galerian 2

G3.1.1 Galerian 1

G2 G2.4 Villafranchian 5

G2.3 Villafranchian 4

G2.2 Villafranchian 3

G2.1 Villafranchian 2

G1 Villafranchian 1

G1 comprises Triversa and Gaville LFAs, and it is likethe Early Villafranchian in Gliozzi et al. (1997). Wenamed this clustering unit Villafranchian 1, the oldest(here) distinct ‘‘palaeocommunity’’ (PCOM).

The second group G2 embraces all localitiesspanning in age from the Middle to the LateVillafranchian. Nested analysis of this groupindicates the presence of four subgroups. The firstsubgroup G2.1 (the oldest) comprises Costa SanGiacomo, Colle Pardo, Valle Catenaccio, Cava Toppettiand Montopoli LFAs. We named this subgroup theVillafranchian 2 PCOM.

A subgroup embracing mostly Upper Valdarno(G2.2) localities follows in time. We indicate this LFAsrich and very distinctive subgroup as the Villafranchian3 PCOM. Villafranchian 3 PCOM contains, amongothers, Olivola, Matassino, Tasso, Poggio Rosso andCasa Frata LFAs. This argues against the conventionalbelief that two distinct evolutionary faunal stages(Olivola and Il Tasso FUs even if slightly different inage) were actually in existence at the time of thePlio–Pleistocene boundary.

The third subgroup (G2.3) was named Villafranchian4 PCOM. It mostly comprises LFAs traditionallyascribed to the Farneta FU. Subgroup 4 includes endVillafranchian localities spanning in age from PirroNord LFA to Colle Curti LFA (G2.4). As with Olivolaand Il Tasso FUs, this subgroup includes Pirro andColle Curti FUs within a single PCOM. We named itVillafranchian 5 PCOM.

the clustering units to which they are ascribed

Local faunal assemblages LFAs

Bucine, Campo Verde, Casal de’ Pazzi, Castel di Guido, Cerveteri,

Contrada Monticelli, Fara Sabina, Grotta Romanelli, La Polledrara

di Cecanibbio, Malagrotta, Monte delle Gioie, Grotta Paglicci,

Pontecorvo, Prati Fiscali, Quisisana (Capri), Riano, Sedia del

Diavolo, Torre In Pietra (lower beds), Torre In Pietra (upper beds),

Torrente Conca, Vitinia (upper beds)

Cava Campani, Cava Nera Molinario, Fontana Ranuccio,

Notarchirico, San Romano, Spessa 2, Venosa-Loreto, Visogliano

Cesi, GRA, Isernia La Pineta, Valdemino

Borgonuovo, Monte Oliveto, Monte Tenda, Pitigliano, Ponte

Galeria 2, Slivia

Capena, Colle Curti, Imola, Madonna della Strada, Pirro Nord,

Redicicoli

Il Crostolo, Mugello (fluviolacustrine), Pietrafitta, Selvella, Val di

Chiana (Farneta)

Bacino Tiberino (various sites), Casa Frata, Casa Sgherri

(Massarella), Faella, Fontana Acetosa, Matassino, Monte Riccio,

Olivola, Pantalla, Poggio Rosso, Torre di Picchio, Upper Valdarno

(various sites)

Cava Toppetti, Colle Pardo, Costa San Giacomo, Montopoli, Valle

Catenaccio

Gaville, Triversa

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Group three (G3) comprises Galerian and EarlyAurelian LFAs. It splits in two under BCAn: roughly,Galerian as a whole, and Aurelian. A further nestedanalysis results in fuzzy groups for the Aurelian, hencewe pinpoint it as the Aurelian PCOM. BCAn carried outon the Galerian subgroup proved to be particularlypuzzling. Indeed, BCAn gave a subgroup, which isalmost barren of carnivores, including Borgonuovo,Monte Oliveto, Monte Tenda, Pitigliano and PonteGaleria 2 LFAs. The successive, in time, subgroupincorporates Slivia, Cesi, GRA, Isernia La Pineta,Valdemino LFAs. It includes carnivores of plainlydifferent age; late Villafranchian holdovers (e.g. Pachy-

crocuta brevirostris and Panthera gombaszoegensis)together with an ensemble of typical Galerian flesheaters and bone consumers (e.g. the spotted hyena, thelion and Hyaena prisca). ‘‘Villafranchian’’ carnivores ofthis subgroup are present at Slivia LFA. Indeed, Slivia’sresemblance to younger faunas completely depends ontheir sharing ‘‘modern type’’, long lasting herbivoressuch as the wild boar, Sus scrofa, the deer Cervus elaphus

acoronatus, and the elephant Elephas antiquus. On theother hand, its scarcer similarity with older faunasreferred to above (e.g. Borgonuovo, Ponte Galeria 2)seems to depend upon the absence of carnivores fromthe latter. To test this, we repeated our BCAn twice,testing carnivores and herbivores separately and includ-ing older LFAs (Pirro and Colle Curti) for comparison.As suspected, we found Slivia LFA to gather with anolder group inclusive of Pirro Nord and Colle CurtiLFAs in the carnivore analysis and with Cesi, GRA,Isernia La Pineta and Valdemino LFAs in the herbivore.Plainly, Slivia LFA is younger than Late Villafranchianyet much older than Middle Galerian. Eventually, wedecided to gather Slivia local fauna together withBorgonuovo, Monte Tenda, Pitigliano and PonteGaleria 2 LFAs, which falls between Late Villafranchianand Middle Galerian. Our implicit assumption is thatcarnivores absent from Early Galerian LFAs have beenfound at Slivia LFA (see Palombo et al., in press) forfurther details). In sum, we discriminated three GalerianPCOMs, as referred in Table 1.

The results obtained partially alter the scheme ofGliozzi et al. (1997) by simplifying it (see Table 1 andSection 4). This is neither surprising nor upsetting. Aswe looked for internally homogeneous faunal assem-blages and their limits in time, we renounced the detailedtemporal information presented by biochronology. It isimportant to note that a single subspecies evolving froma former one is perhaps enough to distinguish (in time)two succeeding faunas, yet there is no way to ascertainthat we are actually dealing with dissimilar commu-nities. This is the main difference between biochronolo-gical units and PCOMs. For example: Galerianbeginning has been traditionally defined as synchronouswith the arrival, in Italy, of the megacerine deer

Praemegaceros verticornis. From our data, the localityrecording P. verticornis’ first occurrence (FO) is ColleCurti, which clusters with typically Villafranchian LFAssuch as Pirro Nord and Capena. The correct way tointerpret this apparently contrasting information is thatColle Curti LFA is probably younger than Pirro NordLFA, yet its fauna is more similar to Late Villafranchianfaunas than to younger (Galerian) faunas.

PCOMs have two overt advantages over FUs. First,they do not need indicative species, which proved to bemisleading otherwise (the case of P. verticornis isemblematic in this regard). A second advantage is muchless evident: PCOMs resemble living communities intheir body size distribution, patterns of species abun-dance and structural organization (Raia, 2003). Thoughthis latter point will not be discussed here, it hasimportant ecological and evolutionary implications.

4. Conclusions

The following points in Table 1 arouse interestbecause of the difference with the biochronologicalscheme (cf. Gliozzi et al., 1997):

1.

Differences among some Villafranchian stages (Earlyvs. Middle–Late) are not less than among differentMAs. The position of Villafranchian 2 PCOM isdifficult to explain because of the scarce remainsintervening between 3.2 and 2.6Mya. As a workinghypothesis, we placed Villafranchian 2 PCOM in acluster with later (Late Villafranchian) PCOMs,though it could have been clustered alone. As statedabove, this is a consequence of the importantdiscontinuity in the fossil record between Villafran-chian 1 and 2.

2.

The LFAs historically assigned to Costa SanGiacomo FU cluster with LFAs assigned to Mon-topoli FU and not to those ascribed to Olivola FU.This suggests that, at its arrival, the canid Canis

etruscus settled in a different PCOM than itscompanions of the ‘‘Wolf event’’.

3.

LFAs historically assigned to Olivola and Tasso FUsfall together. The recent addition of Poggio RossoLFA and the ageing of Tasso faunal elementsresulting from recent studies (Napoleone et al.,2001) perhaps already suggested this outcome.

4.

As discussed above, Colle Curti LFA falls withinVillafranchian 5 PCOM and is therefore excludedfrom Galerian MA. From this point of view, thealmost contemporary arrival of the straight-tuskedelephant Elephas antiquus, the red deer Cervus

elaphus, the horse Equus ferus, the wild boar Sus

scrofa and the mammoth Mammuthus trogontherii

point to the transition between Villafranchain andGalerian mammal ages.

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5.

The Galerian–Aurelian transition was marked by thearrival of the Irish elk Megaloceros giganteus, wolfCanis lupus, primitive bison Bison priscus, ibex Capra

ibex, chamois Rupicapra rupicapra and cave bearUrsus spelaeus.

The simplifications the PCOM system introduces incomparison with Gliozzi et al.’s (1997) FUs schemedepends on the fact that PCOMs, but not FUs, areindependent of indicative species, and look at theassemblages as a whole. As a consequence, differencesamong PCOMs are more objective and statisticallybased. As pointed out above, PCOMs are less precisethan FUs as for their temporal resolution. We arguethat paying this duty to temporal resolution is accep-table given that (a) FUs could be biologically mean-ingless as there is no way to control their state ofdiscreteness and (b) there is no hindrance in seeking therelative age of faunas within the PCOMs given that theyare not attributed the status of distinct faunal stages. Inorder to detect PCOMs, it is necessary to rely extensivelyon biochronological indications, as we did. We arguethat PCOMs and any biochronological unit (e.g. FUs)respond to different answers (Fig. 2). PCOMs are moreappropriate to perceive and study the taxonomiccomposition of past communities and their evolution,while biochronological units are more appropriate if theconcern is a refined temporal framework to arrangefaunal assemblage in time.

Acknowledgements

We are greatly indebted to two anonymous reviewersfor their helpful comments. One of them suggested Fig.2. We are also grateful to Francesca Corrias who revisedthe English text.

Appendix A. Assessment of PCOMs

First step of the analysis. All LFAs pooled together(Fig. 3).

Three groups have been recognized at this level of theanalysis. The first (group 1) comprises Triversa andGaville LFAs. The second (group 2) comprises all theLFAs spanning from Montopoli (ca 2.6Mya) to ImolaLFA (ca 1Mya). The last (group 3) comprising all thelocalities but one (Colle Curti) traditionally ascribedeither to Galerian or Early Aurelian stages. ProbabilityP that the 3-groups solution is not fuzzy holds as samplesize increase, thus pledging group partition sharpness(Pillar 1999).

Second step of the analysis. Group 2 (Fig. 4).At this stage, 4 subgroups were recognized: Villafran-

chian 2, Villafranchian 3, Villafranchian 4 and Villa-

franchian 5 PCOMs. The figure below refers to thislatter stage. From the analysis performed, MontopoliLFA falls in the position evidenced in Table 1. Indeed,Montopoli LFA is far closer in time to Villafranchian 1PCOM than any other belonging to Villafranchian 2PCOM. The important discontinuity in the fossil recordbetween Villafranchian 1 and Villafranchian 2 PCOMs(some 0.6My) let unresolved if Montopoli LFA shouldbe assigned to G1 clustering unit (named as ‘‘Villafran-chian 1’’) or to G2 clustering unit. Here, we prefer tomaintain the resulting position after the cluster analysis.

Third step of the analysis. Subgroup 3.1 (Fig. 5).Group 3 comprises two different clusters. The first

nearly is similar to Galerian MA in Gliozzi et al. (1997).The second roughly represents Early Aurelian MA inGliozzi et al. (1997). We found this latter stage to behomogeneous. As with the preceding subgroup itpartitions in three different PCOMs, which we named(from oldest to youngest): Galerian 1, 2 and 3.

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