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Aspronema cochabambae (Squamata: Lacertilia: Scincidae): its discovery in Argentina, morphological variation and extent of suitable habitat

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© 2019 Deutsche Gesellscha für Herpetologie und Terrarienkunde e.V. (DGHT), Mannheim, Germany
SALAMANDRA 55(2) 135–139 15 May 2019 ISSN 0036–3375
Aspronema cochabambae (Squamata: Lacertilia: Scincidae):
its discovery in Argentina, morphological variation
and extent of suitable habitat
F B-GM H
1) Instituto de Ecorregiones Andinas (INECOA), Universidad Nacional de Jujuy – Consejo Nacional de Investigaciones
Cientícas y Técnicas (UNJU-CONICET), Facultad de Ciencias Agrarias, Catedra de Evolución, Alberdi 47,
CP 4600, San Salvador de Jujuy, Jujuy, Argentina
2) Department of Biological Sciences, Broward College, 3501 SW Davie Road, Davie, FL 33314, USA
Corresponding author: F B-G, e-mail:
Manuscript received: 19 September 2018
Accepted: 8 March 2019 by S L
As currently dened, the genus Aspronema H 
C,  contains two species, A. cochabambae (D,
) and A. dorsivittatum (C, ) (H  C
). e latter species is widely distributed in Bolivia,
Brazil, Paraguay Uruguay, and Argentina. In Argentina,
A.dorsivittatum occurs in Chacoan grasslands and forests
below , m a.s.l., yet has been poorly sampled (C ,
A et al. ).
Prior to its rediscovery by M  L ()
and H et al. (), A. cochabambae was considered
a subspecies of Mabuya frenata (currently Notomabuya).
H et al. () redescribed A. cochabambae based
on the holotype, available museum specimens, and new
material from the department of Cochabamba (H
et al. ). Based on comparisons with A. dorsivittatum,
these authors identied a suite of diagnostic characters
distinguishing A. cochabambae from all congeners. Until
now, A. cochabambae seemed to be endemic to the An-
des in the departments of Cochabamba and Santa Cruz,
Bolivia, and has been classied as “Vulnerable” due to its
known occurrence at only ten localities (A  H-
 ).
During eldwork on  and  December , we ob-
tained two specimens of A. cochabambae at Las Cuevas,
Cerro Bravo, , m a.s.l., near Los Toldos, Santa Victoria
Department, Salta Province, Argentina. Additionally, we
observed ve specimens of this skink in the same area. At
this site, humid pastures consisting primarily of the grasses
Festuca spp. and Stipa spp. border streams and are populat-
ed by a mosaic of patches of the tree Polylepis australis and
mixed brush, including the shrubs Berberis commutata,
Schinus sp., Baccharis spp., Hieracium argentinense, Ono-
seris hastata, Mutisia spp., Bomarea edulis, and Chuquiraga
longiora (Fig. ).
e two collected specimens were deposited in the
herpetological collection of the Laboratorio de Genética
Evolutiva (LGE), Instituto de Biología Subtropical, UN-
aM-CONICET, Posadas, Misiones, Argentina. In Table
, we compare the new specimens of A. cochabambae to
those of Aspronema examined previously by H et
al. (). Both new specimens are females: LGE ,
snout–vent length (SVL) . mm, tail length . mm,
from -.° -.°, , m a.s.l.; and LGE
, SVL . mm, tail length . mm, from -.°,
Figure 1. Habitat of Aspronema cochabambae in Argentina: Las
Cuevas, Santa Victoria, Salta, Argentina, 2,700 m as.l.
Table 1. Comparison of selected characters between two populations of Aspronema cochabambae and A. dorsivittatum.
A. cochabambae Bolivia
(H et al. 2008)
A. cochabambae Argentina
is work
A. dorsivittatum
is work
Internasals Contact n = 15 (94%)
Separate n = 1 (6%)
Contact n = 0
Separate n = 2 (100%)
Contact n = 12 (92.3%)
Separate n = 1 (7.7%)
Prefrontals Contact n = 0
Separate n = 16 (100%)
Contact n =0
Separate n = 2
Contact n = 3 (21.5%)
Separate n = 11 (78.5%)
Frontoparietals Fused Fused Paired
Supraoculars 3 n = 32 (100%) 3 n = 2/2 (100%) 3 n = 25 (92.5%)
4 n = 2 (7.5%)
Supraciliaries 3 n = 26 (93%)
4 n = 2 (7%)
3 n = 2/2 (100%) 3 n = 3 (12%)
4 n = 4 (88%)
Supralabial below eye 4 n = 0
5 n = 19 (68%)
6 n = 9 (32%)
4 n = 0
5 n = 1/1 (50%)
6 n = 1/1 (50%)
4 n = 9 (24.4%)
5 n = 28 (75.6%)
Lamellae under fourth nger 11.9 ± 1.0, n = 15
10 n = 2 (15%)
11 n = 2 (15%)
12 n = 5 (39%)
13 n = 4 (31%)
11 (n =2) 12.4 ± 0.7, n = 13
10 n = 0
11 n = 1 (7.6%)
12 n = 5 (38.4%)
13 n = 7 (53.8%)
Lamellae under fourth toe 14.9 ± 1.0, n = 14
12 n = 0
13 n = 1 (7%)
14 n = 3 (21%)
15 n = 7 (50%)
16 n = 2 (14%)
17 n = 1 (7%)
14 or 15
14 n =1
15 n =1
17.0 ± 1.0, n = 14
12 n = 0
13 n = 0
14 n = 1
15 n = 1 (7.14%)
16 n = 3 (21.4%)
17 n = 4 (28.5%)
18 n = 5 (35.7%)
Dorsals 57–62 (59.7 ± 1.9, 14) 58–62 (n = 2) 53–60 (57.3 ± 2.3, 13)
Scales around midbody 28–32 (30.9 ± 1.1, 16) 29–32 26–32 (28.6 ± 1.8, 13)
Ventrals 34–43 (38.5 ± 2.6, 13) 37–39 29–38 (35.2 ± 2.9, 13)
Dorsolateral white stripe Present Present Present
Ventrolateral white stripe Present Present Present
Palms and soles Usually darker than venter Darker than venter Pale
Figure 2. Adult specimen of Aspronema cochabambae (LGE 18998) from Las Cuevas, Argentina, illustrating fused frontoparietals and
black palms that are diagnostic of this species.
-,°, , m a.s.l. e two specimens possess each
of the characters used to diagnose this species by H
et al. () (Table , Fig. ): () prefrontals paired, usu-
ally separated medially; () frontoparietals fused; () pari-
etals usually in contact with each other behind interpari-
etal; () secondary nuchals absent; () supraciliaries usu-
ally three, rst longer than combined second and third;
()palm and sole usually darkly pigmented (rarely pale);
() narrow vertebral and paravertebral brown stripes
present dorsally; lateral black band edged dorsally and ven-
trally by prominent pale stripes; () lamellae under fourth
nger –; () lamellae under fourth toe –; () limbs
relatively short; fourth toe just reaching wrist when legs are
adpressed against anks; () supraoculars three, the rst
Figure 3. Known distribution of Aspronema cochabambae in Bolivia (green circles) and Argentina (green square) and extent of suitable
habitat identied by bioclimatic modelling.
larger than remaining two combined; () supralabials –,
the h or sixth largest and positioned under eye; () in-
ternasals (= supranasals) usually in contact; () postmen-
tal entire. is combination of characters immediately dis-
tinguishes the new specimens from their only known con-
gener, A. dorsivittatum.
H et al. () noted that a surprisingly low ge-
netic distance separates A. cochabambae from A. dorsivitta-
tum. It is therefore noteworthy that this distant population
has retained the same distinctive combination of diag-
nostic characters. ough genetically close and distribut-
ed parapatrically, these two species appear to be retaining
their specic cohesiveness.
To further investigate the potential distribution of
A. cochabambae, we used Maxent .. (P et al.
) to identify suitable habitat of this species based on
its known localities (Appendix ). We extracted  biocli-
matic variables from the WorldClim Global Climate data-
base ( with a resolution of 
arc sec. (F H ). Denition of the area of
study is crucial for precise models of ecological niche and
should be informed by dispersal capacity of the study spe-
cies (B et al. ). erefore, we dened the acces-
sible area (,, hectares) by considering the known
range of A. cochabambae, the potential for habitat in the
eastern Yungas ecoregion (O et al. ), and the low
vagility of reptiles compared to other vertebrates (V 
C ). We modelled  interactions with the
following parameters: maximum training sensitivity plus
specicity, do jackknife to measure variable importance,
random seed, and cross-validity as the replicated run
type. e background was dened as the area of interest
and , random points were set. We used the default
CloLog to represent the potential suitability of the habi-
tat of the species as a probability, with the highest values
representing conditions favourable for the species’ pres-
ence (P et al. ). We evaluated performance of
the model by using area under the AUC curve (F
B ), where AUC =  indicates perfect t of the
model and AUC ≤ . indicates that the model performed
no better than random (E et al. ). We then divided
habitat suitability values from our model into two classes:
high and moderate.
e Maxent model predicted the presence of A. cocha-
bambae with high performance (AUC = .) for the train-
ing and testing data set. In Fig. , we show areas of moder-
ate (AUC = .–.) and high (AUC = .–.) habi-
tat suitability. Although the largest expanse of highly suit-
able habitat surrounds previously known localities in the
departments of Cochabamba and Santa Cruz, the model
identied smaller patches of suitable habitat in the depart-
ments of Chuquisaca and Tarija, Bolivia. Interestingly, the
model also detected suitable habitat to the south of the new
localities, suggesting that this secretive species may have a
more expansive distribution in Salta and, possibly, in Jujuy
provinces. ough resembling the habitat of A. cochabam-
bae in Bolivia, as described by H et al. (), the
suitable habitat in Argentina is more localised, consist-
ing of small patches in the “Bosque montano” phytogeo-
graphic district sensu C (). Plausibly, the new
localities of A. cochabambae represent relict populations of
a previously continuous distribution. However, additional
research in intervening areas of Bolivia and biogeographic
studies are required to test this hypothesis.
We thank J B and D B for providing ac-
cess to museum specimens. Financial support was provided
by CONICET, and C M provided assistance with
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Appendix 1
Locality records of Aspronema cochabambae used to identify suit-
able habitat using Maxent (DatumWGS).
BOLIVIA: Cuenca Taquiña, -., -. (CBG
); Inernillo, -., -. (CBG ); Montepunko,
-., -. (CBG ,, ,); Pocona, -.,
-. (MCZ , UMMZ ); Serrania de Siberia,
-., -. (CBF , , UTA ); Siberia,
-., -. (CBG); Toralapa, -., -.
(CBG ); Santa Cruz, no data, (UMMZ ).
ARGENTINA: Las Cuevas, -., -. (LGE
); Las Cuevas, -., -. (LGE ); Las
Cuevas unvouchered (): -., -.; -.,
-.; -., -.; -., -.;
-., -..
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We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km 2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5–15% (0.07–0.17 ∘ C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were ≥ 0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data.
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This software note announces a new open-source release of the Maxent software for modeling species distributions from occurrence records and environmental data, and describes a new R package for fitting such models. The new release (Version 3.4.0) will be hosted online by the American Museum of Natural History, along with future versions. It contains small functional changes, most notably use of a complementary log-log (cloglog) transform to produce an estimate of occurrence probability. The cloglog transform derives from the recently-published interpretation of Maxent as an inhomogeneous Poisson process (IPP), giving it a stronger theoretical justification than the logistic transform which it replaces by default. In addition, the new R package, maxnet, fits Maxent models using the glmnet package for regularized generalized linear models. We discuss the implications of the IPP formulation in terms of model inputs and outputs, treating occurrence records as points rather than grid cells and interpreting the exponential Maxent model (raw output) as as an estimate of relative abundance. With these two open-source developments, we invite others to freely use and contribute to the software. This article is protected by copyright. All rights reserved.
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We update the list of lizards ofArgentina, reporting a total of 261 species from the country, arranged in 27 genera and 10 families. Introduced species and dubious or erroneous records are discussed. Taxonomic, nomenclatural and distributional comments are provided when required. Considering species of probable occurrence in the country (known to occur inBolivia,Brazil,ChileandParaguayat localities very close to the Argentinean border) and still undescribed taxa, we estimate that the total number of species inArgentinacould exceed 300 in the next few years.
Neotropical skinks are unique among lizards and other vertebrates in their degree of convergence, in reproductive traits, with eutherian mammals. They have also been famously difficult to classify into species, largely because of a conservative body plan and paucity of conventional diagnostic characters. Currently there are 26 recognized species, six of which occur only on Caribbean islands. All are placed in a single genus, Mabuya. We conducted a systematic revision of Neotropical skinks using both conventional and unconventional morphological characters, supplemented by DNA sequence analyses. We define 61 species grouped into 16 clades, recognized here as genera. They include three available generic names (Copeoglossum, Mabuya, and Spondylurus) and 13 new genera: Alinea gen. nov., Aspronema gen. nov., Brasiliscincus gen. nov., Capitellum gen. nov., Exila gen. nov., Manciola gen. nov., Maracaiba gen. nov., Marisora gen. nov., Notomabuya gen. nov., Orosaura gen. nov., Panopa gen. nov., Psychosaura gen. nov., and Varzea gen. nov. These 16 genera of skinks form a monophyletic group and are placed in the Subfamily Mabuyinae of the skink Family Mabuyidae. Six other skink families are recognized: Acontidae, Egerniidae, Eugongylidae, Lygosomidae, Scincidae, and Sphenomorphidae. We describe three new subfamilies of Mabuyidae: Chioniniinae subfam. nov., Dasiinae subfam. nov., and Trachylepidinae subfam. nov. We describe 24 new species of mabuyines: Capitellum mariagalantae sp. nov., Capitellum parvicruzae sp. nov., Copeoglossum aurae sp. nov., Copeoglossum margaritae sp. nov., Copeoglossum redondae sp. nov., Mabuya cochonae sp. nov., Mabuya desiradae sp. nov., Mabuya grandisterrae sp. nov., Mabuya guadeloupae sp. nov., Mabuya hispaniolae sp. nov., Mabuya montserratae sp. nov., Marisora aurulae sp. nov., Marisora magnacornae sp. nov., Marisora roatanae sp. nov., Spondylurus anegadae sp. nov., Spondylurus culebrae sp. nov., Spondylurus caicosae sp. nov., Spondylurus haitiae sp. nov., Spondylurus magnacruzae sp. nov., Spondylurus martinae sp. nov., Spondylurus monae sp. nov., Spondylurus monitae sp. nov., Spondylurus powelli sp. nov., and Spondylurus turksae sp. nov. We also resurrect 10 species from synonymies: Alinea lanceolata comb. nov., Alinea luciae comb. nov., Capitellum metallicum comb. nov., Mabuya dominicana, Marisora alliacea comb. nov., Marisora brachypoda comb. nov., Spondylurus fulgidus comb. nov., Spondylurus nitidus comb. nov., Spondylurus semitaeniatus comb. nov., and Spondylurus spilonotus comb. nov. Of the 61 total species of mabuyine skinks, 39 occur on Caribbean islands, 38 are endemic to those islands, and 33 of those occur in the West Indies. Most species on Caribbean islands are allopatric, single-island endemics, although three species are known from Hispaniola, three from St. Thomas, and two from Culebra, St. Croix, Salt Island, Martinique, the southern Lesser Antilles, Trinidad, and Tobago. Co-occurring species typically differ in body size and belong to different genera. Three ecomorphs are described to account for associations of ecology and morphology: terrestrial, scansorial, and cryptozoic. Parturition occurs at the transition between the dry and wet seasons, and the number of young (1-7) is correlated with body size and taxonomic group. Molecular phylogenies indicate the presence of many unnamed species in Middle and South America. A molecular timetree shows that mabuyines dispersed from Africa to South America 18 (25-9) million years ago, and that diversification occurred initially in South America but soon led to colonization of Caribbean islands and Middle America. The six genera present on Caribbean islands each represent separate dispersals, over water, from the mainland during the last 10 million years. Considerable dispersal and speciation also occurred on and among Caribbean islands, probably enhanced by Pleistocene glacial cycles and their concomitant sea level changes. Based on IUCN Redlist criteria, all of the 38 endemic Caribbean island species are threatened with extinction. Twenty-seven species (71%) are Critically Endangered, six species (16%) are Endangered, and five species (13%) are Vulnerable. Sixteen of the Critically Endangered species are extinct, or possibly extinct, because of human activities during the last two centuries. Several of the surviving species are near extinction and in need of immediate protection. Analysis of collection records indicates that the decline or loss of 14 skink species can be attributed to predation by the Small Indian Mongoose. That invasive predator was introduced as a biological control of rats in sugar cane fields in the late nineteenth century (1872-1900), immediately resulting in a mass extinction of skinks and other reptiles. The ground-dwelling and diurnal habits of skinks have made them particularly susceptible to mongoose predation.
MaxEnt is a program for modelling species distributions from presence-only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence-only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south-west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.
Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.
(1862): Catalogues of the reptiles obtained during the explorations of the Parana, Paraguay, Vermejo and Uruguay rivers
  • J M Cei
Cei, J. M. (1993) : Reptiles del Noroeste, Nordeste y Este de la Argentina. Herpetofauna de las selvas subtropicales, puna y pampas. -Museo Regionali di Scienze Naturali, Torino. Cope, E. D. (1862): Catalogues of the reptiles obtained during the explorations of the Parana, Paraguay, Vermejo and Uruguay rivers, by Capt. Thos. J. Page, U.S.N.; and of those procured by Lieut. N. Michier, U. S. Top. Eng., Commander of the expedition conducting the survey of the Atrato River. -Proceedings of the Academy of Natural Sciences of Philadelphia, 1862: 346-359.
  • Santa Cruz
  • Data
Santa Cruz, no data, (UMMZ 68098). ARGENTINA: Las Cuevas, -22.234377, -64.772725 (LGE