Identifying Endemism Areas: An Example Using Neotropical Lizards

Abstract

Areas of endemism are central to biogeography. They are used as study units by analytical biogeographic methods and as a criterion to identify areas for conservation. Liolaemidae is one of the most diverse groups of lizards in terms of species richness and environmental diversity. Over the last decade, the number of new species recorded for the genera Liolaemus and Phymaturus has increased exponentially. Most of them have restricted distributions, low population density, and high extinction risk. These features make this family one of the main environmental components of the ecosystems they inhabit. Furthermore, it has been long recognized that Liolaemidae species, especially within Phymaturus, are endemic, but that recognition was made intuitively and mostly equating endemic with "having a restricted distribution." In this study, we provide methodological confirmation of the high degree of endemism of the species of Liolaemus and Phymaturus, with endemic species defined as those "having congruent distributions." Our goals are to analyze the distribution data of 289 species of Liolaemidae and identify areas of endemism using the software NDM/VNDM. With cells of 0.5° × 0.5°, we identified 27 consensus areas and recovered 118 endemic species (41.11%). These endemic areas presented patterns of repeated taxonomic groups. We also found that some areas of endemism were recovered with different cell sizes, defined by almost the same endemic species. According to the hypothesis of vicariance biogeography, barriers (physical or ecological) fragmented ancestral distributions of taxa. Therefore, the areas of endemism proposed in this study might have been the result of historical events that fragmented the ancestral distribution of the family, giving rise to present day distribution patterns. The identification of biogeographic patterns enables us to understand ecosystems from a historical perspective and generate important information for their conservation. As such, the areas of endemism of a family can be an important and relevant tool to assess priorities for conservation of biodiversity.

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Identifying Endemism Areas: An Example Using Neotropical Lizards
Author(s): María Soledad Andrade-Díaz, Thomas Nathaniel Hibbard, Juan Manuel Díaz-Gómez
Source: South American Journal of Herpetology, 12(1):61-75.
Published By: Brazilian Society of Herpetology
DOI: http://dx.doi.org/10.2994/SAJH-D-16-00038.1
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Identifying Endemism Areas:
AnExampleusing Neotropical Lizards
María Soledad Andrade-Díaz¹, Thomas Nathaniel Hibbard¹, Juan Manuel Díaz-Gómez¹*
¹ Instituto de Bio y Geociencias del Noroeste Argentino, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Salta,
Avenida 9 de julio 14, CP 4405, Rosario de Lerma, Salta, A rgentina.
* Corresponding author. Email: jmdiaz@unsa.edu.ar
Abstract. Areas of endemism are central to biogeography. They are used as study units by analytical biogeographic methods and as a crite-
rion to identify areas for conservation. Liolaemidae is one of the most diverse groups of lizards in terms of species richness and environmen-
tal diversity. Over the last decade, the number of new species recorded for the genera Liolaemus and Phymaturus has increased exponentially.
Most of them have restricted distributions, low population density, and high extinction risk. These features make this family one of the main
environmental components of the ecosystems they inhabit. Furthermore, it has been long recognized that Liolaemidae species, especially
within Phymaturus, are endemic, but that recognition was made intuitively and mostly equating endemic with “having a restricted distribu-
tion.” In this study, we provide methodological confirmation of the high degree of endemism of the species of Liolaemus and Phymaturus,
with endemic species defined as those “having congruent distributions.” Our goals are to analyze the distribution data of 289 species of
Liolaemidae and identify areas of endemism using the software NDM/VNDM. With cells of 0.5°×0.5°, we identified 27 consensus areas
and recovered 118 endemic species (41.11%). These endemic areas presented patterns of repeated taxonomic groups. We also found that
some areas of endemism were recovered with different cell sizes, defined by almost the same endemic species. According to the hypothesis
of vicariance biogeography, barriers (physical or ecological) fragmented ancestral distributions of taxa. Therefore, the areas of endemism
proposed in this study might have been the result of historical events that fragmented the ancestral distribution of the family, giving rise
to present day distribution patterns. The identification of biogeographic patterns enables us to understand ecosystems from a historical
perspective and generate important information for their conservation. As such, the areas of endemism of a family can be an important and
relevant tool to assess priorities for conservation of biodiversity.
Keywords. Biogeography; Liolaemidae; Patterns; Reptiles.
Resumen. Las áreas de endemismo son centrales en biogeografía. Estas áreas son las unidades de estudio de los métodos biogeográficos ana-
líticos y también son un criterio relevante para identificar áreas para conservación. La familia Liolaemidae es uno de los grupos más diversos
en cuanto a riqueza de especies y ambientes. Durante la última década, el número de nuevas especies registradas para los géneros Liolaemus
y Phymaturus aumentó exponencialmente. La mayoría de estas especies presentan distribuciones restringidas, baja densidad poblacional y
alto riesgo de extinción. Estas características hacen de la familia Liolaemidae, especialmente el género Phymaturus, un constituyente funda-
mental de los ecosistemas que habita. Además, las especies de Liolaemidae fueron reconocidas como endémicas, pero este reconocimiento
fue mayormente intuitivo y considerando el término “endémicas” como “con distribuciones restringidas”. En este trabajo presentamos infor-
mación metodológica confirmando el alto grado de endemismo de las especies de Lioelaemus y Phymaturus, entendiendo endémico como “con
distribuciones congruentes”. Los objetivos de este trabajo son: analizar los datos de distribución de 289 especies de la familia Liolaemidae e
identificar áreas de endemismo utilizando el programa NDM/VNDM. Con un tamaño de grilla 0.5°×0.5°; identificamos 27 áreas de consenso
y 118 especies endémicas (41,11%). Las áreas de endemismo presentaron patrones de grupos taxonómicos de especies repetidos. También
recuperamos áreas de endemismo con diferentes tamaños de grilla, definidas por las mismas especies endémicas. Según la hipótesis de la
biogeografía de la vicarianza, barreras (físicas o ecológicas) fragmentaron la distribución ancestral de los taxa. Por lo tanto, las áreas de ende-
mismo propuestas en este estudio pueden ser resultado de eventos históricos que fragmentaron la distribución ancestral de la familia dando
lugar a los patrones de distribución actuales. La identificación de patrones biogeográficos de las especies, nos permite entender el ecosistema
desde una perspectiva histórica y generar información importante para su conservación. En este sentido, las áreas de endemismo de una
familia pueden ser una herramienta relevante a la hora de identificar prioridades para la conservación de la biodiversidad.
relative sympatry (Morrone and Crisci, 1995) or at least
relative congruence (Wiley, 1981) is expected. Several
methods have been proposed for the identification of
areas of endemism (Harold and Mooi, 1994; Morrone,
1994; Linder, 2001; Hausdorf, 2002; Szumik etal., 2002;
Hausdorf and Hennig, 2003; Szumik and Goloboff,
2004; Dos Santos etal., 2008). Szumik etal. (2002) and
Szumik and Goloboff (2004) proposed a method for
identifying areas of endemism that evaluates how well the
distribution of taxa fits a given set of cells (areas), coded
in the program NDM/VNDM (Goloboff, 2004). NDM/
INTRODUCTION
Areas of endemism are central to biogeography.
They are used as study units by analytical biogeographic
methods (Morrone, 2008) and as a criterion to identify
areas for conservation (Vane-Wright etal., 1991; Faith,
1992; Pressey et al., 1993; Rodrigues et al., 2000; Díaz
Gómez, 2011). An area of endemism can be defined by the
congruent distribution of at least two species of restricted
range (Platnick, 1991). Exact congruence of the species
distributions is not required by the definitions, but a
South American Journal of Herpetology, 12(1), 2017, 61–75
© 2017 Brazilian Society of Herpetology
Submitted: 06 August 2016
Accepted: 06 March 2017
Handling Editor: Gabriel Costa
doi: 10.2994/SAJH-D-16-00038.1
12(1), 2017, 61
06 August 2016
06 March 2017
Gabriel Costa
10.2994/SAJH-D-16-00038.1

VNDM has been shown to outperform other methods for
identifying areas of endemism (Carine etal., 2008).
According to Platnick (1991), “in biogeography, we
can always prefer to initiate our studies with those taxa
that are maximally endemic, those which include the
largest number of species, with the smallest ranges, in
the area of interest.” Liolaemidae Frost and Etheridge,
1989 is one of the most diverse group of lizards in terms
of both species richness and environment diversity
(Abdala etal., 2010; Breitman etal., 2011; Quinteros and
Abdala, 2011; Abdala and Quinteros, 2014). This family
is widely distributed in Argentina, Bolivia, Chile, Peru,
the sandy coasts of Uruguay, and southeastern Brazil
(Cei, 1993; Lobo etal., 2010a; Avila etal., 2013; Abdala
and Quinteros, 2014). It encompasses three genera:
Ctenoblepharys Tschudi, 1845, Liolaemus Wiegmann,1834,
and Phymaturus Gravenhorst, 1837 (Etheridge, 1995;
Pyron etal., 2013; Zheng and Wiens, 2016).
Over the last decade, the number of new species
registered for the genera Liolaemus and Phymaturus has
increased exponentially (Quinteros and Abdala, 2011;
Quinteros, 2012; Abdala and Quinteros, 2014). Currently,
Liolaemus includes approximately 262 species (Avila etal.,
2015; Huerta etal., 2015; Troncoso-Palacios etal., 2015)
and Phymaturus has 46 species (Lobo etal., 2012a, 2013,
2016; Scolaro et al., 2012; Scolaro et al., 2013 , 2016;
Lobo and Nenda, 2015; Marín etal., 2016). Liolaemus is
generally characterized by (1)a great ecologic diversity:
species with morphologic, anatomical, physiological,
ethological, and reproductive specializations (Cei, 1993;
Morando et al., 2004; Abdala et al., 2012a, b; Abdala
and Quinteros, 2014); (2) high ecosystem diversity, as
they inhabit different environments from sea level to >
5,000 m above sea level (asl; Aparicio and Ocampo, 2010),
and (3) marked endemism and restricted distributions
(Díaz Gómez, 2011). Phymaturus also possesses unique
anatomical, physiological, and ecological characteristics,
but, in contrast to Liolaemus, these species of Phymaturus
have highly conserved characteristics (Cruz et al., 2009,
2011; Espinoza etal., 2004; Ibargüengoytía, 2004, 2005;
Ibargüengoytía et al., 2008). Species of Phymaturus are
also characterized by their restricted distributions, with
marked endemism in the arid region of southwestern
South America: Andes, Patagonian highlands, and Puna
(Cruz et al., 2009; Díaz Gómez, 2011). Ctenoblepharys
is monotypic, including only the species Ctenoblepharys
adspersa Tschudi, 1845 from coastal Peru (Etheridge,
1995).
Liolaemidae is noteworthy because of its restricted
distributions, the high specific diversity and the low
population density that many of the species have (Abdala
et al., 2012c; Ávila et al., 2013). On the other hand,
Liolaemidae also presents a high extinction risk related
mainly to the alterations of the different ecosystems,
which imply an alteration in the reproductive behavior
and diet (Abdala etal., 2012c). It is important to note that
in the last categorization of reptiles and amphibians of
Argentina (Abdala etal., 2012c), 24 species of Liolaemus
were classified as Vulnerable, 3 as Threatened, and 1
as Endangered, while species of Phymaturus were all
classified as Vulnerable (Abdala etal., 2012c). Therefore,
it is necessary to implement conservation criteria for the
group in particular as for the environments they inhabit.
There have been two previous studies of endemism
in Liolaemidae (Díaz Gómez, 2007, 2011): one of those
studies was restricted to a few species of Liolaemus
inhabiting the Puna in Argentina (Díaz Gómez, 2007),
while the other study (Díaz Gómez, 2011) was made in the
context of a larger analysis of the historical biogeography
of Liolaemidae and the areas of endemism were not main
the focus of the article and were not discussed extensively.
Although the first study included only 29 species, Díaz
Gómez (2007) identified four endemism areas with
a particular pattern. Those patterns consisted of the
repetition of taxonomic groups, but with different species
in each area. Thus, he concluded that several vicariant
events fragmented the ranges of the ancestors of those
species (Díaz Gómez, 2007).
In this paper, we have two goals: (1)to analyze the
distribution data of 289 species of Liolaemidae and to
identify areas of endemism using the software NDM/
VNDM (Goloboff, 2004) and (2)to identify patterns in
the taxonomic composition of these endemic areas.
MATERIALS AND METHODS
Area of study
The area of study corresponds to the entire
distribution of Liolaemidae, encompassing southern
South America from coastal Peru to Tierra del Fuego
and including Argentina, Bolivia, Paraguay, Chile,
southeastern Brazil, and sandy coasts of Uruguay.
Distributional data
We collected distributional data for 289 species of
Liolaemidae, totaling 2,897 individual records. In this
work we updated the original matrix from Díaz Gómez
(2011) by adding almost 100 species, including all species
described to date. It should be noted that the actual
number of individual records is higher, given that in many
cases there were multiple records for the same species at
the same locality. For these species, we only listed one
or two of those duplicate records. Furthermore, due to
the amount of new Liolaemus species discovered each
year, when we compiled our data set only 262 species
of Liolaemus had been recorded (Avila etal., 2015). We
Identifying Endemism Areas: AnExampleusing Neotropical Lizards
María Soledad Andrade-Díaz, Thomas Nathaniel Hibbard, Juan Manuel Díaz-Gómez
62
South American Journal of Herpetology, 12(1), 2017, 61–75

obtained data from herpetological collections, relevant
literature, and online databases. The type locality was
included for almost every species. Some type localities
could not be included, as they were listed as an area,
region, province, or country (e.g., Chile), in which case
a single point locality could not be assigned. The list
of species included and data matrix can be found in
AppendixS1.
Analysis
We used NDM/VNDM v3.0 (Goloboff, 2004). The
optimality criterion used by NDM/VNDM checks the
distribution of all taxa in a data matrix and compares
them with given areas (set of cells) that are evaluated
heuristically. Then, it weighs and assigns a score to
each species according to the ‘fit’ or congruence of its
distribution with the area. The score for any given area
will be the sum of the individual scores of the species
included in that area.
As there is no formal argument to select a ‘better’ cell
size, four different cell sizes were evaluated: 0.35°×0.35°,
0.5°×0.5°, 0.75°× 0.75° and 1° ×1° in order to be able
to compare the results. Díaz Gómez (2011) previously
used a grid size of 0.75°×0.75°, which yielded the most
endemism areas, with the highest endemicity index. We
decided to evaluate slightly larger and slightly smaller
grid sizes, although the exact size of the grid is somewhat
arbitrary.
In this work, we report in detail the results of the
analysis using a cell size of 0.50°× 0.50°. Grid origins
were fixed at x=80, y=5. The radius sizes used were: to
fill: x=40, y=40; to assume x=80, y=80. Searches for
areas of endemism were conducted using the following
options: save sets with two or more endemic species,
with score of 1.5 or higher; swap one cell at a time;
discard superfluous sets; keep overlapping subsets only
if 50% of species unique; use edge proportions. In order
Table1. Number of endemic species, endemism areas and consensus
areas obtained in the analysis of cells with cells of different sizes
(expressed in degrees).
Cell size Endemic
species
Endemism
areas
Consensus
areas
0.35×0.35 67 29 17
0.5×0.5 118 47 27
0.75×0.75 174 101 28
Table2. Endemic species and areas included in consensus areas (Cell size: 0.5°×0.5°).
Consensus
areas
Areas
included
Species
0 0 8 26 42 Liolaemus cinereus; L.gracielae; L.montanezi; L.parvus; L.vallecurensis; Phymaturus punae
1 1 2 3 4 5 9 10 39 L.carlosgarini; L.cristiani; L.curis; L.flavipiceus; L.riodamas; L.septentrionalis; L.smaug; P.damasense; P.maulense; P.verdugo
2 6 27 L.dicktracyi; L.famatinae; L.pseudoanomalus; P.mallimaccii
3 7 22 38 L.calchaqui; L.diaguita; L .griseus; L.heliodermis; L.pacha; L.pagaburoi
4 11 16 37 L.erguetae; L.foxi; L.hajeki; L.paulinae; L.torresi
5 12 24 L.chaltin; L.orientalis; L.pulcherrimus; L.pyriphlogos
6 13 44 L.abaucan; L.orko; L.salinicola; L.tulkas; P.antofagastensis
7 14 L.baguali; L.caparensis; L.tari
8 15 28 L.coeruleus; L.hermannunezi; L .tregenzai; P.vociferator
9 17 L.gununakuna; L.mapuche; L.puelche:
10 18 L.chacabucoense; L.kolengh; L .scolaroi; L.zullyae
11 19 L.morenoi; L.purul; P.zapalensis
12 20 L.avilai; L.exploratorum; L.silvanae
13 21 L.albiceps; L.irregularis; L.lavillai; L.scrocchii; L.yanalcu
14 23 P.desuetus; P.etheridgei; P.excelsus; P.manuelae; P.sinervoi; P.spectabilis; P.spurcus
15 25 L.curicensis; L.fitzgeraldi; L.moradoensis; L.ramonensis; L.ubaghsi; L.valdesianus; P.darwini
16 29 L.antumalguen; L.carlosgarini; L.choique; L.f lavipiceus; L.punmahuida; L.tromen; P.delheyi
17 30 P.castillensis; P.felixi; P.indistinctus; P.videlai
18 31 L.audituvelatus; L.erguetae; L.fabiani; L.foxi; L.puritamensis; L.schmidti
19 32 L.cazianiae; L.halonastes; L.porosus
20 33 P.aguanegra; P.extrilidus; P.williamsi
21 34 L.shitan; L.telsen; P.ceii
22 35 L.cuyumhue; L.cyaneinotatus; L.sitesi; P.sitesi
23 36 L.loboi; L.tehuelche; P.manuelae; P.siner voi; P.tenebrosus
24 40 L.chehuachekenk; L.uptoni; P.camilae
25 41 L.isabelae; L.manueli; L .patriciaiturrae; L.velosoi
26 43 L.abdalai; L.coeruleus; P.querque; P.zapalensis
Identifying Endemism Areas: AnExampleusing Neotropical Lizards
María Soledad Andrade-Díaz, Thomas Nathaniel Hibbard, Juan Manuel Díaz-Gómez 63
South American Journal of Herpetology, 12(1), 2017, 61–75

to improve the support of the areas found, one hundred
replicates of the analysis were made. The results are
shown through consensus areas that merge areas (sets
of cells), which share a user defined percentage of
their defining species (i.e., areas which differ little in
their composition will be merged). Thus, the resulting
consensus area shows cells with maximum, low, and
minimal values of endemicity reflecting the different
scores of the added areas (Aagesen et al., 2009).
The consensus was calculated using a cut-off of 40%
(percent of species similarity), and including areas in the
consensus only if it shared that percentage of similarity
with all other areas in the consensus.
RESULTS
We evaluated four different cell sizes (Table 1).
Normally, increasing cell size yielded more areas, with
more endemic species. However, most of the larger areas
(i.e., cells of 1°× 1°; results not shown) were too large,
often encompassing completely different environments,
and were not representative of the actual distribution of
the taxa included in the study. Several areas were twice
or thrice as large as the distribution areas of the species
of Liolaemidae and included disparate grouping of areas
(e.g., Puna and Monte as one area of endemism). Although
the areas from the cell size of 0.75°×0.75° made more
biological sense than the 1°×1° cell size, some of these
areas presented many of the same problems, being too
large and encompassing dissimilar climates. On the other
hand, in the 0.35°×0.35° cell size evaluation, we found
that these areas were too small and disjunct, presented a
very low endemic species count, and therefore were not
informative enough. On account of this, we decided to
present the results and discuss in detail the areas obtained
with the 0.5°×0.5° cell size.
Using cells of 0.5°×0.5°, we identified 47 areas of
endemism. After a consensus, 27 areas remained. We
recovered 118 species as endemic (AppendixS2) (41.11%).
Species included in each area and their endemism score
can be found in Table2. Figures 1, 2,3 and 4 show the
consensus areas. We found areas of endemism on both
sides of the Andes, but none were found in Bolivia, Peru,
or the Atlantic coast. We were able to observe that the
endemism areas obtained with cell size of 0.5° × 0.5°
Figure 1. Areas of endemism found by the consensus in the NDM/
VNDM analysis. Cell size =0.5°×0.5°.
Figure 2. Areas of endemism found by the consensus in the NDM/
VNDM analysis. Cell size =0.5°×0.5°.
Identifying Endemism Areas: AnExampleusing Neotropical Lizards
María Soledad Andrade-Díaz, Thomas Nathaniel Hibbard, Juan Manuel Díaz-Gómez
64
South American Journal of Herpetology, 12(1), 2017, 61–75

(Table 2) presented patterns of repeated taxonomic
groups in different areas. We considered a pattern to be
valid if the main groups were repeated several times, but,
as seen in Table2, not all taxonomic groups are present in
all of the areas corresponding to the pattern. The groups
of species that are repeated, as well as the areas in which
they are present, are summarized in Table3. We found
three patterns, named A, B, and C. Pattern A includes
consensus areas 0, 2, 3, and 6 (occupying northwestern
San Juan and La Rioja, southwestern Catamarca,
western Tucumán, and the southern portion of Salta
in Argentina), with different species of the Liolaemus
boulengeri, L.elongatus group, L.montanus group, and the
group of Phymaturus palluma within the P. punae group
included in each area (Fig.5A). PatternB includes areas
4, 5, 13, and 25 (occupying regions I and II in Chile and
the western part of Salta and Jujuy in Argentina) with
species of the groups L.alticolor-bibronii and L.montanus
(Fig.5-B). PatternC includes areas 8, 9, 16, 21, 22, 23, 24
(occupying much of Neuquén and Rio Negro provinces in
Argentina) and species of the groups L.alticolor-bibronii,
L. boulengeri, L. elongatus, P. palluma, and P.p atagonicus
(Fig.5–C).
We also found that some areas of endemism were
recovered with different cell sizes, defined by almost
the same endemic species (Table4, Fig. 6). These areas
are largely congruent, both in location and species
composition.
DISCUSSION
The choice of using NDM in this analysis is due
to its ability to identify areas of endemism. NDM has
been shown to outperform other common methods
for identifying areas of endemism such as Parsimony
Analysis of Endemicity (PAE), which consists of scoring
on a grid presences/absences of a set of species in a matrix
and then analyzing it under parsimony using the grids as
terminals and the species as characters (Szumik et al.,
2002; Carine etal., 2009; Díaz Gómez, 2011). The use of a
criterion (parsimony) devised for the analysis of another
problem (systematics) causes some problems with the
identification of areas of endemism with PAE (i.e., areas
defined by species that are not endemic of the area). NDM
has several advantages, like using an optimality criterion
Figure 3. Areas of endemism found by the consensus in the NDM/
VNDM analysis. Cell size =0.5°×0.5°.
Figure 4. Areas of endemism found by the consensus in the NDM/
VNDM analysis. Cell size =0.5°×0.5°
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South American Journal of Herpetology, 12(1), 2017, 61–75

not borrowed from another discipline, but developed
specifically for solving this problem, and the possibility of
working with actual records (i.e., geographic coordinates).
Díaz Gómez (2007) carried out an endemism analysis
using NDM/VNDM for 29 species of Liolaemus inhabiting
the Argentine Puna. That was the first analysis made for
the genus using an explicit methodology. By comparing
the results of NDM/VNDM with those obtained with PAE,
Díaz Gómez (2007) identified four areas of endemism for
Liolaemus. Later, Díaz Gómez (2011) identified areas of
endemism for the entire family Liolaemidae, including in
that analysis 197 species. Those areas were used as units for
an ancestral area analysis but were not further discussed
in terms of endemism. The endemism analysis performed
here is the most comprehensive for Liolaemidae and one
of the most relevant for Neotropical lizards.
Table3. Patterns (A, B, C) of repeated taxonomic groups in different areas obtained with cell size 0.5°×0.5°. The species group included in each pattern
is represented by the letter (A, B, and C) and in the case that one or more species group are not presented in the pattern but probably should be there we
use the symbol (?).
Species group Consensus areas (Cell size 0.5×0.5)
0 2 3 4 5 6 8 9 13 16 18 19 21 22 23 24 25
L.montanus AAAB B AB B B B
L.boulengeri A A A A C C BC CCCC
L.alticolor-bibronii ? ? A B B ?B ? ? C?
L.elongatus A A A A C C C C ? ? ?
P.palluma A A ? A C
P.patagonicus ? ? C C C C C
Table4. Consensus areas obtained in different analyses using different cell sizes and showing high congruence both in areas and endemic species
recovered. The corresponding endemic species of each area are shown.
Cell size 0.35×0.35 0.5×0.5 0.75×0.75
Consensus areas (A) 4 13 22
Liolaemus albiceps L.albiceps L.albiceps
L.irregularis L.irregularis L.irregularis
L.scrocchi L.scrocchi L.scrocchi
L.yanalcu L.yanalcu L.yanalcu
L.lavillai L.lavillai
L.multicolor
Consensus areas (B) 6 15 12
L.moradoensis L.moradoensis L.moradoensis
L.ramonensis L.ramonensis L.ramonensis
Phymaturus darwinii L.curicensis L.curicensis
L.fitszgeraldi L.fitszgeraldi
L.valdesianus L.valdesianus
P.darwinii L.frassinetti
L.leopardinus
L.nigroviridis
L.ubaghsi
L.riodamas
P.damasense
Consensus areas (C) 8 22 25
L.cyaneinotatus L.cyaneinotatus L.cyaneinotatus
L.sitesi L.sitesi L.sitesi
P.sitesi P.sitesi P.sitesi
L.cuyumhue L.cuyumhue
L.goetschi
Consensus areas (D) 12 19 16
L.cazianiae L.cazianiae L.cazianiae
L.halonastes L.halonastes L.halonastes
L.porosus L.porosus L.porosus
L.vulcanus
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South American Journal of Herpetology, 12(1), 2017, 61–75

Figure5. Endemism areas obtained with cell size (0.5°×0.5°) presented patterns of repeated taxonomic groups. (A)PatternA, (B)PatternB, and
(C)PatternC.
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South American Journal of Herpetology, 12(1), 2017, 61–75

Figure6. Congruent areas of endemism recovered with different cell sizes. (A)Consensus areas: 4 (0.35°×0.35°), 13 (0.5°×0.5°) and 22 (0.75°× 0.75°);
(B)Consensus areas: 6 (0.35°×0.35°), 15 (0.5°×0.5°) and 12 (0.75°×0.75°); (C)Consensus areas: 8 (0.35°×0.35°), 22 (0.5°×0.5°) and 25 (0.75°×0.75°), and
(D)Consensus areas: 12 (0.35°×0.35°), 19 (0.5°×0.5°) and 16 (0.75°×0.75°). Grey cells =0.35°×0.35°; transparent cells =0.5°×0.5°; blue cells =0.75°×0.75°.
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Upon studying the set of areas identified in this
study, two patterns became apparent. The first is that
areas north of a certain latitude (approximately 32°30’)
are always found on only one side of the Andes ridge (i.e.,
west or east of it), while to the south of this latitude many
areas traverse the range, suggesting that at this latitude
it is a less effective biogeographic barrier. The second
is that the areas found were mainly restricted to some
biogeographical provinces with a surprising degree of
congruence (following Cabrera, 1971). These provinces
were all situated closer to the Andes, including the
altoandina, puna, prepuna, subantártica, and patagonia
provinces.
The lack of areas of endemism in Bolivia, Peru, or the
Atlantic coast could be due to several causes. First, areas
(e.g., Monte) with few or widely distributed species will
tend not to be recovered as endemism areas because of
their low percentage of overlapping distributions. Some
areas (e.g., Peru and Bolivia) have been sampled less
exhaustively than others (e.g., Argentina and Chile) and
there are comparatively fewer records for each species,
making them harder to be recovered as endemism areas.
Undoubtedly, this situation is worthy of further study,
but it is beyond the scope of this article.
It has long been recognized that species of
Liolaemidae are generally endemic (Abdala, 2003; Lobo
and Espinoza, 2004; Lobo and Quinteros, 2005; Abdala
etal., 2010; Avila etal., 2011; Avila etal., 2013), but that
recognition was made intuitively and mostly by equating
endemic with ‘having a restricted distribution.’ In this
work, we provide explicit, methodological confirmation of
the high degree of endemism of the species of Liolaemus
and Phymaturus, not only in the sense of their restricted
distributions, but also in terms of endemic species as
‘having congruent distributions’ (i.e., areas of endemism).
The main hypothesis of vicariance biogeography
postulates that barriers (physical or ecological)
fragmented ancestral distributions of taxa. In other
words, the areas of endemism proposed in this study
might be the result of historical events (e.g., vicariance)
that fragmented the ancestral distribution of the family
that gave rise to present-day distribution patterns
(Sandoval etal., 2010). As in Díaz Gómez (2007), we also
found areas with repeated taxonomic groups (Table 3;
Fig.5A–C). Possibly, the ancestor of each species group
was widely distributed. In PatternA, we have species from
the Liolaemus montanus group, the L.boulengeri group, and
the L. elongatus group and species from the Phymaturus
palluma group within the P. punae group. According to
our hypothesis, an ancestor pertaining to each of these
groups would have been widely distributed in the sum of
areas 0, 2, 3 and 6, which corresponds to northwestern
San Juan and La Rioja, Southwestern Catamarca, western
Tucumán and the southern portion of Salta in northern
western Argentina. A similar explanation is applicable
to PatternB, in which species of the L.montanus group
appear repeatedly, as well as species of the L. alticolor-
bibronii group. In this case, the ancestors, pertaining to
the aforementioned groups, would have inhabited an area
approximately equivalent to the sum of areas 4, 5, 13 and
25, corresponding to regions I and II in Chile, and the
western part of Salta and Jujuy in Argentina.
In comparing these first two patterns, it is interesting
to note that the general areas are both adjacent and
share two groups, the montanus group and the Liolaemus
alticolor-bibronii group, which is present in the areas of
PatternA, but not repeated. Since the L.montanus group
appears in both patterns, then as a consequence, if these
patterns are there for the reasons previously exposed,
there must have been one species widely distributed more
to the north (areas 4, 5, 13 and 25) and another more
to the south (areas 0, 2, 3 and 6); therefore, the species
forming the patterns today must form monophyletic
groups. Currently there is no complete phylogeny of the
L.montanus section, so we could not test this hypothesis.
Further research is needed on this matter.
As for PatternC, again we have repeated taxonomic
groups, in this case the Phymaturus patagonicus, Liolaemus
boulengeri, and L.elongatus groups. The L.alticolor-bibronii
and P. palluma groups are present but not repeated.
Ancestors belonging to the Phymaturus patagonicus,
L. boulengeri, and L. elongatus groups would have
been distributed in areas 8, 9, 16, 21, 22, 23, and 24,
corresponding to an area occupying much of Neuquén
and Rio Negro provinces. In contrast with Pattern A
for Phymaturus species, the species of P. patagonicus in
Pattern C are not closely related phylogenetically, so,
according to our hypothesis, it could be inferred that
it was an early ancestor of the group that was widely
distributed. Another particularity is that the L.alticolor-
bibronii group is present in the patterns A and C but only
in area 3 (PatternA) and area 22 (PatternC). Possibly, the
L.alticolor-bibronii group had a more restricted distribution
and then was dispersed to areas 3 and 22. Currently, the
L. alticolor-bibronii group has a wide distribution, from
Santa Cruz province in southern Argentina to central Peru.
Although these hypotheses are interesting, it should
be noted that NDM/VNDM analysis does not take into
account any information regarding taxonomic groupings
or make any assumption about the causes underlying
recovered patterns. It only aims to establish an explicit
link between evidence (distributions) and conclusions
(areas) (Szumik et al., 2002). In that sense, the three
patterns found in this study might be the result of
historical causes, ecological causes, or a combination of
both, and more studies are needed in order to determine
the origin of these patterns.
Additionally, we must consider some issues related
to the chosen methodology. The size of the grid chosen
for the analysis directly affects the results (Szumik and
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María Soledad Andrade-Díaz, Thomas Nathaniel Hibbard, Juan Manuel Díaz-Gómez 69
South American Journal of Herpetology, 12(1), 2017, 61–75

Goloboff, 2004). There is no formal argument to select an
‘optimal’ grid size between different taxonomic groups.
We observed that with larger grid sizes the number of
endemic species grew, as well as the size and number of
consensus areas. On the other hand, with smaller grid
sizes we obtained a large number of fragmented areas
that in some cases differ in the presence or absence
of only one or two cells (Díaz Gómez, 2011). We chose
a cell size of 0.5°×0.5° based on the fact that the areas
recovered by the aforementioned grid size made the most
sense, biologically and historically speaking. Areas of
endemism that included different ecological provinces,
with contrasting climatic conditions, or areas that
included both sides of the Andes, are difficult to justify,
given the restricted distribution of most of species of
Liolaemidae, as observed in the areas found with larger
grid sizes (Hortal etal., 2010; Sandoval etal., 2010).
It has been proposed (Aagesen et al., 2009;
Casagranda et al., 2009) that using several grid sizes
provides a kind of measure of support for a particular area
of endemism. We found congruence between the areas of
endemism recovered by the different analyses. Normally,
areas with smaller cell size were included in areas with
larger cells (i.e., all areas with cell size of 0.5°× 0.5° and
0.25°×0.25° were included within the areas with cell size
of 0.75°×0.75°). These areas would have more support
(see Table4 and Fig. 6), as proposed by Aagesen et al.
(2009) and Casagranda et al. (2009), but should not be
held as more ‘correct’ than any of the other areas found
by the analysis.
We also observed that species with very few records
(only one or two localities) are not generally recovered
as being part of any area of endemism, even though
their records are inside the set of cells (area) found by
the analysis. This is a logical outcome of the analysis,
because if a species occupies one or a few cells inside
an area, the cells where it is absent will strongly weigh
against the species. Perhaps this explains why we did not
find areas of endemism in Peru and Bolivia. A possible
solution to this situation, besides obtaining more data
on the distributions, might be some kind of differential
weighting for those under-represented taxa.
In conclusion, biogeographical analyses based on
point of occurrence are informative as long as the number
of records is sufficiently high or the spatial scale is large
enough (Sandoval et al., 2010). Another point is that
the data is highly biased towards roads or places that are
easy to access, or near scientific institutes (Bojórquez-
Tapia etal., 1995; Soberón etal., 2000; Sandoval et al.,
2010). A final word might be said about the identification
of areas of endemism. Identifying areas of endemism
means recognizing species with congruent distributional
patterns. It is commonly considered that these patterns
are formed by historical processes that affected many
species at the same time (Linder, 2001). Yet, it must be
stressed that the history of an area can be very complex
and dynamic, and therefore the resulting patterns will
not always be as clear. As an example, Sersic etal. (2011)
assessed the phylogeographic breaks occurring in the
quaternary. It is possible that some of these events
might have been vicariance events, rather than just
phylogeographic breaks, thus affecting to some extent
lizards and plant areas of endemism. In the same manner,
other, less recent events might have helped shape the
patterns we see and identify today for some species.
Understanding the temporal and spatial interaction of
the events that have modeled species distribution is
crucial to identify and interpret current distributional
patterns; in other words, species distributions are the
result of both historical and ecological events. Following
what we mentioned above, it will be very interesting
to investigate in further analyses the identification of
these phylogeographic and vicariance events for the
Liolaemidae family.
Finally, understanding the distribution of species
and their biogeographic patterns is a crucial step for
conservation planning. The present extinction rate is
a product of human activity, which generated the need
to implement different conservation strategies and
management of impoverished and degraded ecosystems
(Aagesen etal., 2012). Corbalán etal. (2011) performed
an analysis using species distribution models in
combination with an analysis of the size of reserves, in
order to assess the degree of representation of Patagonian
lizards in the current protected area network. Their
results showed that the current system of protected areas
is not very effective at protecting the lizards of Patagonia,
particularly those with range-restricted distributions (i.e.,
endemic species). In this regard, our findings provide a
quantitative definition of endemism and identify several
areas of endemism that can be very important in order
to compare with areas of species richness and the current
protected area network. In conclusion, the identification
of biogeographic patterns permits us to understand the
ecosystems from a historical perspective and to generate
important information for their conservation (Arana
etal., 2013). In this way, areas of endemism of a family
can be an important and relevant tool in order to assess
priorities for the conservation of biodiversity.
ACKNOWLEDGMENTS
This study was supported by two doctoral fellowships
(Consejo Nacional de Investigaciones Científicas y
Técnicas) to M.S. Andrade Díaz and T.N. Hibbard. We
would like to thank F. Lobo and S. Quinteros for useful
commentaries and friendship. For access to collections we
are indebted to S. Kretzschmar, E. Lavilla, and G. Scrocchi
(Fundación Miguel Lillo).
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María Soledad Andrade-Díaz, Thomas Nathaniel Hibbard, Juan Manuel Díaz-Gómez
70
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Identifying Endemism Areas: AnExampleusing Neotropical Lizards
María Soledad Andrade-Díaz, Thomas Nathaniel Hibbard, Juan Manuel Díaz-Gómez
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ONLINE SUPPORTING INFORMATION
The following Supporting Information is available
for this article online:
AppendixS1. List of examined species and data matrix
used in the analysis.
AppendixS2. List of taxa mentioned in the text, with
authorship and year of publication.
Identifying Endemism Areas: AnExampleusing Neotropical Lizards
María Soledad Andrade-Díaz, Thomas Nathaniel Hibbard, Juan Manuel Díaz-Gómez 75
South American Journal of Herpetology, 12(1), 2017, 61–75